diff --git a/.github/workflows/ci-example.yml b/.github/workflows/ci-example.yml
new file mode 100644
index 0000000..5c0983c
--- /dev/null
+++ b/.github/workflows/ci-example.yml
@@ -0,0 +1,47 @@
+name: ci-examples
+
+on:
+  push:
+  pull_request:
+    types:
+      - closed
+    branches:
+      - main
+
+jobs:
+  run:
+    name: ci ${{ matrix.os }}-${{ matrix.python }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python: ['3.12']
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python }}
+
+      - name: Install requirements
+        run: python -m pip install -r requirements.txt
+
+      - name: Install requirements dev
+        run: python -m pip install -r requirements-dev.txt
+
+      - name: Cache pip
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('requirements-dev.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+            ${{ runner.os }}-
+
+      - name: pip freeze
+        run: python -m pip freeze
+
+      - name: run tests examples
+        run: |
+          pip install pytest
+          PYTHONPATH=. UNITTEST_GOING=1 pytest --durations=10 _unittests/ut_xrun_doc/test_documentation_examples.py -v
diff --git a/.github/workflows/ci-notebook.yml b/.github/workflows/ci-notebook.yml
new file mode 100644
index 0000000..3249edb
--- /dev/null
+++ b/.github/workflows/ci-notebook.yml
@@ -0,0 +1,47 @@
+name: ci-notebooks
+
+on:
+  push:
+  pull_request:
+    types:
+      - closed
+    branches:
+      - main
+
+jobs:
+  run:
+    name: ci ${{ matrix.os }}-${{ matrix.python }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python: ['3.12']
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python }}
+
+      - name: Install requirements
+        run: python -m pip install -r requirements.txt
+
+      - name: Install requirements dev
+        run: python -m pip install -r requirements-dev.txt
+
+      - name: Cache pip
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('requirements-dev.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+            ${{ runner.os }}-
+
+      - name: pip freeze
+        run: python -m pip freeze
+
+      - name: run tests notebooks
+        run: |
+          pip install pytest
+          PYTHONPATH=. UNITTEST_GOING=1 pytest --durations=10 _unittests/ut_xrun_doc/test_documentation_notebook.py -v
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 4b2604c..69d487b 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,4 +1,4 @@
-name: ci
+name: ci-tests
 
 on:
   push:
@@ -10,7 +10,7 @@ on:
 
 jobs:
   run:
-    name: to-${{ matrix.torch }}-tr-${{ matrix.transformers }}-ci ${{ matrix.os }}-${{ matrix.python }}
+    name: ci ${{ matrix.os }}-${{ matrix.python }}
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -45,9 +45,3 @@ jobs:
         run: |
           pip install pytest
           PYTHONPATH=. UNITTEST_GOING=1 pytest --durations=10 _unittests  --ignore-glob=**pygame*.py --ignore _unittests/ut_xrun_doc/test_documentation_examples.py --ignore _unittests/ut_xrun_doc/test_documentation_notebook.py
-
-      - name: run tests examples
-        run: PYTHONPATH=. UNITTEST_GOING=1 pytest --durations=10 _unittests/ut_xrun_doc/test_documentation_examples.py -v
-
-      - name: run tests notebooks
-        run: PYTHONPATH=. UNITTEST_GOING=1 pytest --durations=10 _unittests/ut_xrun_doc/test_documentation_notebook.py -v
diff --git a/_doc/api/tools/index.rst b/_doc/api/tools/index.rst
index b591d00..05b36b2 100644
--- a/_doc/api/tools/index.rst
+++ b/_doc/api/tools/index.rst
@@ -1,13 +1,10 @@
-
 teachpyx.tools
 ==============
 
-
 .. toctree::
     :maxdepth: 1
     :caption: submodules
 
-
     display/index
 
 
@@ -15,12 +12,11 @@ teachpyx.tools
     :maxdepth: 1
     :caption: modules
 
-
     data_helper
     graphviz_helper
     helpers
     profiling
-
+    pandas
 
 .. automodule:: teachpyx.tools
     :members:
diff --git a/_doc/api/tools/pandas.rst b/_doc/api/tools/pandas.rst
new file mode 100644
index 0000000..58b06a0
--- /dev/null
+++ b/_doc/api/tools/pandas.rst
@@ -0,0 +1,7 @@
+
+teachpyx.tools.pandas
+=====================
+
+.. automodule:: teachpyx.tools.pandas
+    :members:
+    :no-undoc-members:
diff --git a/_doc/articles/2024/2024-03-01-route2024.rst b/_doc/articles/2024/2024-03-01-route2024.rst
index c65fb96..af0599d 100644
--- a/_doc/articles/2024/2024-03-01-route2024.rst
+++ b/_doc/articles/2024/2024-03-01-route2024.rst
@@ -28,7 +28,7 @@ Séance 2 (2/2)
 
 * arbre de régression, arbre de classification
 * random forest, boosting trees
-  (:epkg:`xgboost`, :epkg:`lightgtbm`, :epkg:`catboost`),
+  (:epkg:`xgboost`, :epkg:`lightgbm`, :epkg:`catboost`),
   :ref:`RandomForest, Overfitting <nbl-practice-ml-ml_a_tree_overfitting>`
 * Gradient Boosting, :ref:`Gradient Boosting et Learning Rate avec les Random Forest <nbl-practice-ml-gradient_boosting>`
 * Régression Linéaire et contraintes sur les coefficients,
diff --git a/_doc/articles/2026/2026-03-15-route2026-ml.rst b/_doc/articles/2026/2026-03-15-route2026-ml.rst
new file mode 100644
index 0000000..92226d8
--- /dev/null
+++ b/_doc/articles/2026/2026-03-15-route2026-ml.rst
@@ -0,0 +1,87 @@
+.. _l-feuille-route-2026:
+
+=========================================
+2026-03-15 : feuille de route 2025 - mars
+=========================================
+
+site web : `sdpython.github.io <https://sdpython.github.io/>`_
+
+`Apprendre la programmation avec Python
+<https://sdpython.github.io/doc/teachpyx/dev/>`_
+
+Autres sites :
+
+* Python pour la data science <https://pythonds.linogaliana.fr/>`_ (Lino Galiana - ENSAE)
+
+Fonctions utiles:
+
+* :func:`teachpyx.tools.pandas.read_csv_cached`
+
+Séance 1 (6/2)
+==============
+
+* rappels sur la régression, classification, clustering, ACP, prétraitements
+* train test, validation croisée
+* rappels sur l'apprentissage d'un arbre de régression
+
+*présentation de modules*
+
+* données : :epkg:`pandas`
+* graphes : :epkg:`matplotlib`, :epkg:`seaborn`, :epkg:`bokeh`, :epkg:`altair`
+* cartes : :epkg:`geopandas`, :epkg:`folium`
+* machine learning : :epkg:`scikit-learn`, :epkg:`skrub`, :epkg:`skore`, :epkg:`imbalanced-learn`, :epkg:`hazardous`, :epkg:`fairlearn`,
+  :epkg:`xgboost`, :epkg:`lightgbm`, :epkg:`catboost`
+* deep learning : :epkg:`pytorch`, :epkg:`transformers`, :epkg:`diffusers` et aussi :epkg:`HuggingFace Hub`
+
+*demain*
+
+* utilisation de ChatGPT, Gemini (et autres...), retour d'expérience
+* et ensuite...
+
+Séance 2 (13/2)
+===============
+
+* tests unitaires
+* pipelines : pourquoi en faire
+
+Séance 3 (27/2)
+===============
+
+Séance 4 (6/3)
+==============
+
+Séance 5 (13/3)
+===============
+
+Séance 6 (20/3)
+===============
+
+Evaluation
+==========
+
+* `https://defis.data.gouv.fr/ <https://defis.data.gouv.fr/>`_
+* le projet doit inclure au moins un graphe
+  *Partial Dependence* ou *Permutation Importance* (voir liens ci-dessus)
+* soutenance ?
+
+Quelques jeux de données
+========================
+
+* `Parcoursup 2025 - vœux de poursuite d'études et de réorientation dans l'enseignement supérieur et réponses des établissements
+  <https://www.data.gouv.fr/datasets/parcoursup-2025-voeux-de-poursuite-detudes-et-de-reorientation-dans-lenseignement-superieur-et-reponses-des-etablissements>`_
+* `Patrimoine immobilier des opérateurs de l’Enseignement supérieur
+  <https://www.data.gouv.fr/datasets/patrimoine-immobilier-des-operateurs-de-lenseignement-superieur>`_
+* `Prix des carburants en France - Flux quotidien
+  <https://www.data.gouv.fr/datasets/prix-des-carburants-en-france-flux-quotidien-1>`_
+* `Prix des carburants en France - Flux instantané - v2
+  <https://www.data.gouv.fr/datasets/prix-des-carburants-en-france-flux-instantane-v2-amelioree>`_
+* `Séries sur les surfaces, rendements, production céréales
+  <https://visionet.franceagrimer.fr/Pages/SeriesChronologiques.aspx?menuurl=SeriesChronologiques/productions%20vegetales/grandes%20cultures/surfaces,productions,rendements>`_
+* `Effectifs d'étudiants inscrits dans les établissements et les formations de l'enseignement supérieur - détail par établissements 
+  <https://www.data.gouv.fr/datasets/effectifs-detudiants-inscrits-dans-les-etablissements-et-les-formations-de-lenseignement-superieur-detail-par-etablissements>`_
+* `Résultats du contrôle sanitaire de l'eau distribuée commune par commune 
+  <https://www.data.gouv.fr/datasets/resultats-du-controle-sanitaire-de-leau-distribuee-commune-par-commune>`_
+* `Résultats du contrôle sanitaire de l'eau du robinet <https://www.data.gouv.fr/datasets/resultats-du-controle-sanitaire-de-leau-du-robinet>`_
+* `Données climatologiques de base - horaires <https://www.data.gouv.fr/datasets/donnees-climatologiques-de-base-horaires>`_
+* `Données climatologiques de base - mensuelles  <https://www.data.gouv.fr/datasets/donnees-climatologiques-de-base-mensuelles>`_
+
diff --git a/_doc/articles/index.rst b/_doc/articles/index.rst
index a12fb03..9b9602c 100644
--- a/_doc/articles/index.rst
+++ b/_doc/articles/index.rst
@@ -5,6 +5,12 @@ Collections d'articles périssables
 
 Ou *blog*.
 
+.. toctree::
+    :caption: 2026
+    :maxdepth: 1
+
+    2026/2026-03-15-route2026-ml
+
 .. toctree::
     :caption: 2025
     :maxdepth: 1
diff --git a/_doc/c_data/nb_dataframe.ipynb b/_doc/c_data/nb_dataframe.ipynb
index d7b55c0..cfa928d 100644
--- a/_doc/c_data/nb_dataframe.ipynb
+++ b/_doc/c_data/nb_dataframe.ipynb
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -524,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -962,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1039,7 +1039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1069,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1099,7 +1099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1126,13 +1126,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['date', 'prix', 'devise'], dtype='object')"
+       "Index(['date', 'prix', 'devise'], dtype='str')"
       ]
      },
      "execution_count": 23,
@@ -1146,13 +1146,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "221.0"
+       "np.float64(221.0)"
       ]
      },
      "execution_count": 24,
@@ -1247,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1276,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1311,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1397,7 +1397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1497,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1578,7 +1578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1657,7 +1657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1763,7 +1763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -1794,15 +1794,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['ville', 'annee', 'temps', 'secondes'], dtype='object')\n",
-      "villes {'BOSTON', 'FUKUOKA', 'CHICAGO', 'STOCKOLM', 'NEW YORK', 'LONDON', 'PARIS', 'BERLIN', 'AMSTERDAM'}\n",
+      "Index(['ville', 'annee', 'temps', 'secondes'], dtype='str')\n",
+      "villes {'AMSTERDAM', 'FUKUOKA', 'BERLIN', 'LONDON', 'STOCKOLM', 'NEW YORK', 'PARIS', 'BOSTON', 'CHICAGO'}\n",
       "annee [1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956] ...\n"
      ]
     }
@@ -1979,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2050,7 +2050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2071,7 +2071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2103,7 +2103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2210,13 +2210,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fd034df94b0>"
+       "<pandas.api.typing.DataFrameGroupBy object at 0x78ca1676a9c0>"
       ]
      },
      "execution_count": 41,
@@ -2231,7 +2231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2323,7 +2323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2386,7 +2386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2461,7 +2461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2539,7 +2539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2623,7 +2623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -2634,7 +2634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2888,7 +2888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2918,7 +2918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3353,7 +3353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3462,7 +3462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -3481,13 +3481,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.3328334999999999"
+       "0.3328335"
       ]
      },
      "execution_count": 57,
@@ -3582,7 +3582,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "784 µs ± 33.5 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "398 μs ± 10.1 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -3606,7 +3606,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "310 µs ± 8.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "139 μs ± 4.99 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
      ]
     }
    ],
@@ -3656,23 +3656,23 @@
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
        "      <th>1</th>\n",
-       "      <td>3</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>7</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3681,11 +3681,11 @@
       "text/plain": [
        "           autre\n",
        "cle1 cle2       \n",
-       "1    1         3\n",
-       "     1         1\n",
-       "     1         7\n",
-       "     1         4\n",
-       "     1         1"
+       "1    1         8\n",
+       "     1         5\n",
+       "     1        10\n",
+       "     1         6\n",
+       "     1         7"
       ]
      },
      "execution_count": 61,
@@ -3836,7 +3836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -3899,15 +3899,18 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'dbfread'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[64], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdbfread\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DBF\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdBase2df\u001b[39m(dbase_filename):\n\u001b[1;32m      6\u001b[0m     table \u001b[38;5;241m=\u001b[39m DBF(dbase_filename, load\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcp437\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dbfread'"
-     ]
+     "data": {
+      "text/plain": [
+       "((246123, 16),\n",
+       " Index(['ANAISH', 'DEPNAISH', 'INDNATH', 'ETAMATH', 'ANAISF', 'DEPNAISF',\n",
+       "        'INDNATF', 'ETAMATF', 'AMAR', 'MMAR', 'JSEMAINE', 'DEPMAR', 'DEPDOM',\n",
+       "        'TUDOM', 'TUCOM', 'NBENFCOM'],\n",
+       "       dtype='str'))"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3935,14 +3938,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(16, 4) Index(['VARIABLE', 'LIBELLE', 'TYPE', 'LONGUEUR'], dtype='object')\n"
+      "(16, 4) Index(['VARIABLE', 'LIBELLE', 'TYPE', 'LONGUEUR'], dtype='str')\n"
      ]
     },
     {
@@ -4127,7 +4130,7 @@
        "15         1  "
       ]
      },
-     "execution_count": 281,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4149,7 +4152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4167,7 +4170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {
     "collapsed": true
    },
@@ -4188,12 +4191,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1800x600 with 1 Axes>"
       ]
@@ -4255,7 +4258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {
     "collapsed": true
    },
@@ -4266,7 +4269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {
     "collapsed": true
    },
@@ -4275,8 +4278,8 @@
     "import pandas\n",
     "\n",
     "writer = pandas.ExcelWriter(\"ton_example100.xlsx\")\n",
-    "df1000.to_excel(writer, \"Data 0\")\n",
-    "df1000.to_excel(writer, \"Data 1\")\n",
+    "df1000.to_excel(writer, sheet_name=\"Data 0\")\n",
+    "df1000.to_excel(writer, sheet_name=\"Data 1\")\n",
     "writer.close()"
    ]
   },
@@ -4328,7 +4331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -4533,7 +4536,7 @@
        "[5 rows x 32 columns]"
       ]
      },
-     "execution_count": 287,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4555,7 +4558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -4634,7 +4637,7 @@
        "4  0.732512   3925.907588         3       1.0"
       ]
      },
-     "execution_count": 288,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4646,7 +4649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -4732,7 +4735,7 @@
        "8401  0.343340   1840.132652         3       1.0"
       ]
      },
-     "execution_count": 289,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4745,7 +4748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4782,7 +4785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4792,7 +4795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4801,7 +4804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -4810,7 +4813,7 @@
        "1"
       ]
      },
-     "execution_count": 293,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4821,16 +4824,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((830, 5), (8403, 5))"
+       "((892, 5), (8403, 5))"
       ]
      },
-     "execution_count": 294,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4860,7 +4863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -4927,7 +4930,7 @@
        "4    FUKUOKA  8075.187500"
       ]
      },
-     "execution_count": 295,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4943,7 +4946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -5028,7 +5031,7 @@
        "4  PARIS   2007  02:07:17        7637  7937.028571"
       ]
      },
-     "execution_count": 296,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5041,7 +5044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -5171,7 +5174,7 @@
        "2011     8047.0  "
       ]
      },
-     "execution_count": 297,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5199,7 +5202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -5273,7 +5276,7 @@
        "moyenne     NEW YORK       PARIS     FUKUOKA    STOCKOLM  "
       ]
      },
-     "execution_count": 298,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5286,7 +5289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -5319,15 +5322,15 @@
        "      <th>NEW YORK</th>\n",
        "      <th>PARIS</th>\n",
        "      <th>STOCKOLM</th>\n",
-       "      <th>7695.1612903225805</th>\n",
-       "      <th>7815.909090909091</th>\n",
-       "      <th>7883.371428571429</th>\n",
-       "      <th>7891.061224489796</th>\n",
-       "      <th>7922.315789473684</th>\n",
-       "      <th>7928.5609756097565</th>\n",
-       "      <th>7937.028571428571</th>\n",
+       "      <th>7695.16129</th>\n",
+       "      <th>7815.909091</th>\n",
+       "      <th>7883.371429</th>\n",
+       "      <th>7891.061224</th>\n",
+       "      <th>7922.315789</th>\n",
+       "      <th>7928.560976</th>\n",
+       "      <th>7937.028571</th>\n",
        "      <th>8075.1875</th>\n",
-       "      <th>8133.393939393939</th>\n",
+       "      <th>8133.393939</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>annee</th>\n",
@@ -5470,32 +5473,24 @@
        "2011           NaN  7418.0  7382.0      NaN      NaN  7480.0       NaN   \n",
        "moyenne        NaN     NaN     NaN      NaN      NaN     NaN       NaN   \n",
        "\n",
-       "          PARIS  STOCKOLM 7695.1612903225805 7815.909090909091  \\\n",
-       "annee                                                            \n",
-       "2008     7600.0    8163.0                NaN               NaN   \n",
-       "2009     7547.0    8134.0                NaN               NaN   \n",
-       "2010     7601.0    7968.0                NaN               NaN   \n",
-       "2011     7589.0    8047.0                NaN               NaN   \n",
-       "moyenne     NaN       NaN             LONDON           CHICAGO   \n",
-       "\n",
-       "        7883.371428571429 7891.061224489796 7922.315789473684  \\\n",
-       "annee                                                           \n",
-       "2008                  NaN               NaN               NaN   \n",
-       "2009                  NaN               NaN               NaN   \n",
-       "2010                  NaN               NaN               NaN   \n",
-       "2011                  NaN               NaN               NaN   \n",
-       "moyenne         AMSTERDAM            BOSTON            BERLIN   \n",
+       "          PARIS  STOCKOLM 7695.16129 7815.909091 7883.371429 7891.061224  \\\n",
+       "annee                                                                      \n",
+       "2008     7600.0    8163.0        NaN         NaN         NaN         NaN   \n",
+       "2009     7547.0    8134.0        NaN         NaN         NaN         NaN   \n",
+       "2010     7601.0    7968.0        NaN         NaN         NaN         NaN   \n",
+       "2011     7589.0    8047.0        NaN         NaN         NaN         NaN   \n",
+       "moyenne     NaN       NaN     LONDON     CHICAGO   AMSTERDAM      BOSTON   \n",
        "\n",
-       "        7928.5609756097565 7937.028571428571 8075.1875 8133.393939393939  \n",
-       "annee                                                                     \n",
-       "2008                   NaN               NaN       NaN               NaN  \n",
-       "2009                   NaN               NaN       NaN               NaN  \n",
-       "2010                   NaN               NaN       NaN               NaN  \n",
-       "2011                   NaN               NaN       NaN               NaN  \n",
-       "moyenne           NEW YORK             PARIS   FUKUOKA          STOCKOLM  "
+       "        7922.315789 7928.560976 7937.028571 8075.1875 8133.393939  \n",
+       "annee                                                              \n",
+       "2008            NaN         NaN         NaN       NaN         NaN  \n",
+       "2009            NaN         NaN         NaN       NaN         NaN  \n",
+       "2010            NaN         NaN         NaN       NaN         NaN  \n",
+       "2011            NaN         NaN         NaN       NaN         NaN  \n",
+       "moyenne      BERLIN    NEW YORK       PARIS   FUKUOKA    STOCKOLM  "
       ]
      },
-     "execution_count": 299,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5514,7 +5509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -5644,7 +5639,7 @@
        "moyenne  7695.16129  7928.560976  7937.028571  8133.393939  "
       ]
      },
-     "execution_count": 300,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5670,7 +5665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -5680,7 +5675,7 @@
       "(246123, 17) Index(['ANAISH', 'DEPNAISH', 'INDNATH', 'ETAMATH', 'ANAISF', 'DEPNAISF',\n",
       "       'INDNATF', 'ETAMATF', 'AMAR', 'MMAR', 'JSEMAINE', 'DEPMAR', 'DEPDOM',\n",
       "       'TUDOM', 'TUCOM', 'NBENFCOM', 'nb'],\n",
-      "      dtype='object')\n"
+      "      dtype='str')\n"
      ]
     },
     {
@@ -5844,7 +5839,7 @@
        "4        6     26     99     9              N   1  "
       ]
      },
-     "execution_count": 301,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5857,7 +5852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -6033,7 +6028,7 @@
        "4        6     26     99     9              N   1    46    51  "
       ]
      },
-     "execution_count": 302,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6046,12 +6041,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -6066,12 +6061,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -6095,12 +6090,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -6129,12 +6124,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -6154,20 +6149,25 @@
     "repsem[\"nb\"].plot(ax=ax, secondary_y=True)\n",
     "ax.set_title(\"distribution des mariages par jour de la semaine\");"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/conf.py b/_doc/conf.py
index ba8e12f..d3ec302 100644
--- a/_doc/conf.py
+++ b/_doc/conf.py
@@ -4,7 +4,6 @@
 from sphinx_runpython.github_link import make_linkcode_resolve
 from teachpyx import __version__
 
-
 extensions = [
     "nbsphinx",
     "sphinx.ext.autodoc",
@@ -72,7 +71,7 @@
 """
 
 # The following is used by sphinx.ext.linkcode to provide links to github
-linkcode_resolve = make_linkcode_resolve(
+_linkcode_resolve = make_linkcode_resolve(
     "teachpyx",
     (
         "https://github.com/sdpython/teachpyx/"
@@ -81,6 +80,11 @@
     ),
 )
 
+
+def linkcode_resolve(domain, info):
+    return _linkcode_resolve(domain, info)
+
+
 latex_elements = {
     "papersize": "a4",
     "pointsize": "10pt",
@@ -185,6 +189,7 @@
     "_repr_html_": "https://ipython.readthedocs.io/en/stable/config/integrating.html#custom-methods",
     "Algorithme de Strassen": "https://fr.wikipedia.org/wiki/Algorithme_de_Strassen",
     "algorithme de Strassen": "https://fr.wikipedia.org/wiki/Algorithme_de_Strassen",
+    "altair": "https://altair-viz.github.io/",
     "ACP": "https://fr.wikipedia.org/wiki/Analyse_en_composantes_principales",
     "AESA": "https://tavianator.com/aesa/",
     "algorithme": "https://fr.wikipedia.org/wiki/Algorithme",
@@ -202,6 +207,7 @@
     "C++": "https://fr.wikipedia.org/wiki/C%2B%2B",
     "cloudpickle": "https://github.com/cloudpipe/cloudpickle",
     "Bresenham": "https://fr.wikipedia.org/wiki/Algorithme_de_trac%C3%A9_de_segment_de_Bresenham",
+    "bokeh": "https://bokeh.org/",
     "category_encoders": "https://contrib.scikit-learn.org/category_encoders/",
     "copy": "https://docs.python.org/3/library/copy.html?highlight=copy#copy.copy",
     "cProfile.Profile": "https://docs.python.org/3/library/profile.html#profile.Profile",
@@ -217,14 +223,18 @@
     "encoding": "https://fr.wikipedia.org/wiki/Codage_des_caract%C3%A8res",
     "eval": "https://docs.python.org/3/library/functions.html?highlight=id#eval",
     "Excel": "https://fr.wikipedia.org/wiki/Microsoft_Excel",
+    "fairlearn": "https://fairlearn.org/",
     "folium": "https://python-visualization.github.io/folium/latest/",
     "format": "https://pyformat.info/",
     "format style": "https://pyformat.info/",
     "garbage collector": "https://fr.wikipedia.org/wiki/Ramasse-miettes_(informatique)",
+    "geopandas": "https://geopandas.org/en/",
     "Graphviz": "https://graphviz.org/",
+    "hazardous": "https://soda-inria.github.io/hazardous/",
     "Holm-Bonferroni method": "https://en.wikipedia.org/wiki/Holm%E2%80%93Bonferroni_method",
     "HTML": "https://fr.wikipedia.org/wiki/Hypertext_Markup_Language",
     "ICML 2016": "https://icml.cc/2016/index.html",
+    "imbalanced-learn": "https://imbalanced-learn.org/",
     "indentation": "https://fr.wikipedia.org/wiki/Style_d%27indentation",
     "issubclass": "https://docs.python.org/3/library/functions.html?highlight=issubclass#issubclass",
     "joblib": "https://joblib.readthedocs.io/en/stable/",
@@ -280,6 +290,7 @@
     "PyPi": "https://pypi.org/",
     "python": "https://www.python.org/",
     "Python": "https://www.python.org/",
+    "pytorch": "https://pytorch.org/",
     "QuantileLinearRegression": "https://sdpython.github.io/doc/mlinsights/dev/api/mlmodel.html#quantilelinearregression",
     "R-tree": "https://en.wikipedia.org/wiki/R-tree",
     "R* tree": "https://en.wikipedia.org/wiki/R*_tree",
@@ -292,10 +303,12 @@
     "scikit-learn": "https://scikit-learn.org/stable/index.html",
     "scikit-survival": "https://scikit-survival.readthedocs.io/en/stable/index.html",
     "scipy": "https://scipy.org/",
+    "seaborn": "https://seaborn.pydata.org/",
     "sérialisation": "https://fr.wikipedia.org/wiki/S%C3%A9rialisation",
     "skforecast": "https://skforecast.org/",
     "sklearn": "https://scikit-learn.org/stable/index.html",
     "sklearn-onnx": "https://onnx.ai/sklearn-onnx/",
+    "skore": "https://docs.skore.probabl.ai/",
     "sktime": "https://www.sktime.net/en/stable/index.html",
     "skrub": "https://skrub-data.org/stable/",
     "SQLite": "https://www.sqlite.org/",
@@ -401,12 +414,14 @@
         "cartopy": "https://scitools.org.uk/cartopy/docs/latest/",
         "catboost": "https://catboost.ai/",
         "csv": "https://fr.wikipedia.org/wiki/Comma-separated_values",
+        "diffusers": "https://github.com/huggingface/diffusers",
         "Enedis": "https://data.enedis.fr/",
         "fonction": "https://fr.wikipedia.org/wiki/Fonction_(math%C3%A9matiques)",
         "fonction continue": "https://fr.wikipedia.org/wiki/Continuit%C3%A9_(math%C3%A9matiques)",
         "fortran": "https://en.wikipedia.org/wiki/Fortran",
         "GEOFLA": "https://www.data.gouv.fr/en/datasets/geofla-r/",
-        "lightgtbm": "https://lightgbm.readthedocs.io/en/stable/",
+        "HuggingFace Hub": "https://huggingface.co/",
+        "lightgbm": "https://lightgbm.readthedocs.io/en/stable/",
         "machine learning": "https://en.wikipedia.org/wiki/Machine_learning",
         "matrice de confusion": "https://fr.wikipedia.org/wiki/Matrice_de_confusion",
         "nuage de points": "https://fr.wikipedia.org/wiki/Nuage_de_points_(statistique)",
@@ -414,6 +429,7 @@
         "R": "https://www.r-project.org/",
         "recherche dichotomique": "https://fr.wikipedia.org/wiki/Recherche_dichotomique",
         "seaborn": "https://seaborn.pydata.org/",
+        "transformers": "https://github.com/huggingface/transformers",
         "UCI": "https://archive.ics.uci.edu/datasets",
         "variable aléatoire": "https://fr.wikipedia.org/wiki/Variable_al%C3%A9atoire",
         "voyageur de commerce": "https://fr.wikipedia.org/wiki/Probl%C3%A8me_du_voyageur_de_commerce",
diff --git a/_doc/examples/prog/plot_einstein_riddle.py b/_doc/examples/prog/plot_einstein_riddle.py
index da1de5f..5c4fd03 100644
--- a/_doc/examples/prog/plot_einstein_riddle.py
+++ b/_doc/examples/prog/plot_einstein_riddle.py
@@ -87,11 +87,11 @@
 On commence par la fonction `permutation`:
 qui énumère les permutations d'un ensemble :
 """
+
 import copy
 from io import StringIO
 import pandas
 
-
 ##########################
 # Fonction permutation
 # ====================
diff --git a/_doc/examples/prog/plot_float_and_double_rouding.py b/_doc/examples/prog/plot_float_and_double_rouding.py
index d6d09f1..f184755 100644
--- a/_doc/examples/prog/plot_float_and_double_rouding.py
+++ b/_doc/examples/prog/plot_float_and_double_rouding.py
@@ -20,7 +20,6 @@
 import pandas
 import matplotlib.pyplot as plt
 
-
 rnd = numpy.random.random(100000000)
 rnd.shape, rnd.dtype
 
diff --git a/_doc/examples/prog/plot_gil_example.py b/_doc/examples/prog/plot_gil_example.py
index 4e64c81..bd7ce23 100644
--- a/_doc/examples/prog/plot_gil_example.py
+++ b/_doc/examples/prog/plot_gil_example.py
@@ -17,6 +17,7 @@
 On mesure le temps nécessaire pour créer deux liste et comparer ce
 temps avec celui que cela prendrait en parallèle.
 """
+
 import timeit
 import time
 from concurrent.futures import ThreadPoolExecutor
diff --git a/_doc/examples/prog/plot_numpy_tricks.py b/_doc/examples/prog/plot_numpy_tricks.py
index 3c73243..92d0b67 100644
--- a/_doc/examples/prog/plot_numpy_tricks.py
+++ b/_doc/examples/prog/plot_numpy_tricks.py
@@ -9,6 +9,7 @@
 accéder à un élément en particulier
 ===================================
 """
+
 import timeit
 
 import numpy
diff --git a/_doc/examples/prog/plot_pandas_groupby.py b/_doc/examples/prog/plot_pandas_groupby.py
index 07cf758..238636a 100644
--- a/_doc/examples/prog/plot_pandas_groupby.py
+++ b/_doc/examples/prog/plot_pandas_groupby.py
@@ -14,10 +14,8 @@
 ============================
 """
 
-
 import pandas
 
-
 data = [{"a": 1, "b": 2}, {"a": 10, "b": 20}, {"b": 3}, {"b": 4}]
 df = pandas.DataFrame(data)
 df
diff --git a/_doc/examples/prog/plot_serialisation_examples.py b/_doc/examples/prog/plot_serialisation_examples.py
index 49fc35f..d943aac 100644
--- a/_doc/examples/prog/plot_serialisation_examples.py
+++ b/_doc/examples/prog/plot_serialisation_examples.py
@@ -17,6 +17,7 @@
 Ecriture (json)
 +++++++++++++++
 """
+
 from io import StringIO, BytesIO
 import timeit
 import json
@@ -25,7 +26,6 @@
 import cloudpickle
 import pickle
 
-
 data = {
     "records": [
         {
diff --git a/_doc/examples/prog/plot_serialisation_protobuf.py b/_doc/examples/prog/plot_serialisation_protobuf.py
index cd71f77..1ff3b30 100644
--- a/_doc/examples/prog/plot_serialisation_protobuf.py
+++ b/_doc/examples/prog/plot_serialisation_protobuf.py
@@ -25,6 +25,7 @@
 On récupère l'exemple du `tutorial
 <https://developers.google.com/protocol-buffers/docs/pythontutorial>`_.
 """
+
 import os
 import sys
 import timeit
diff --git a/_doc/examples/prog/plot_tarabiscote.py b/_doc/examples/prog/plot_tarabiscote.py
index a68fbab..5fc3ac4 100644
--- a/_doc/examples/prog/plot_tarabiscote.py
+++ b/_doc/examples/prog/plot_tarabiscote.py
@@ -16,6 +16,7 @@
 listes contenues dans ``ens``. Le résultat retourné est effectivement
 celui désiré mais la fonction modifie également la liste ``ens``, pourquoi ?
 """
+
 import math
 import copy
 import numpy
diff --git a/_doc/practice/algo-base/exercice_simulation_covid.ipynb b/_doc/practice/algo-base/exercice_simulation_covid.ipynb
index d88573d..53c4f27 100644
--- a/_doc/practice/algo-base/exercice_simulation_covid.ipynb
+++ b/_doc/practice/algo-base/exercice_simulation_covid.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +178,7 @@
        "806                           883.0                          106.0  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -194,12 +194,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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IzT70u9/9Lqt8/vz5WduqqnL22WfzzDPP8Pnnn3c4Tl1dXXp9x44dWfvsdjtjxozBMIwuc8B016RJkygtLeXBBx/MGnb00ksv8eWXX6bfm3A43CFIMGLECHw+X4fhSlVVVVk/r0AgwOOPP8748eMpLy/vtC0ejyfnkKZUb5XMn92SJUtYvHhxD16pqbGxsUOwKTVLVup1qKqKoihZPT42btzI888/n7PNffUZ3N3PxuOPP05LS0t6++mnn2b79u3pz3Gu990wDO69994et3VnevL+jBgxgvfff594PJ4ue+GFFzpMuS6EEELsCpm2XAghRL/00ksvpXvRZDryyCMZPnx4evu8887jvvvu4+abb+aQQw5J58ZJ+e53v8tDDz3EnDlz+PDDDxk6dChPP/00ixYtYv78+VmJWNv75je/yfXXX89ZZ53F1VdfTTgc5ve//z2jRo3qkJB24sSJvP7669xzzz0MGDCAYcOGMXXq1A7HLCkp4YYbbuDWW2/lpJNO4vTTT2fVqlU88MADTJ48OSv57O4YP348559/Pg888ADNzc0ceeSRvPHGG6xdu7ZD3f/7v//jzTffZOrUqVx22WWMGTOGhoYGPvroI15//XUaGhoAOPHEEykvL2fatGmUlZXx5Zdfcv/99zNr1qwu38fustls/PrXv+aiiy5i+vTpnH/++elpy4cOHco111wDmD1sjj/+eL7xjW8wZswYrFYrzz33HDU1Nenp6lNGjRrFJZdcwtKlSykrK+ORRx6hpqaGRx99tMu2TJw4kSeffJJrr72WyZMn4/V6Oe200zj11FN59tlnOeuss5g1axYbNmzgwQcfZMyYMelhUt315z//mQceeICzzjqLESNG0NLSwh//+Ef8fj+nnHIKYCaEvueeezjppJO44IILqK2tZcGCBRxwwAFZgcZUm/vqM7i7n43CwkKOOuooLrroImpqapg/fz4HHHAAl112GWDmUBoxYgRz585l27Zt+P1+nnnmmZ3mktoVPXl/Lr30Up5++mlOOukkvvGNb7Bu3Tr+8pe/dDllvBBCCNFte3taLSGEEGJ3dDVtOWA8+uijWfV1XTcqKysNwPjVr36V85g1NTXGRRddZBQXFxt2u9045JBDOhzHMDpOM20YhvHqq68aBx98sGG3240DDzzQ+Mtf/pJz2vKVK1caxxxzjOFyuQwgPZVx+ymRU+6//35j9OjRhs1mM8rKyozLL7/caGxszKozffr0nFNBdzadenuRSMS4+uqrjaKiIsPj8RinnXaasWXLlpyvs6amxrjyyiuNyspKw2azGeXl5cbxxx9v/OEPf0jXeeihh4xjjjnGKCoqMhwOhzFixAjjuuuuM5qbm7tsR2rK5qeeeiqrfMOGDTl/pk8++aQxYcIEw+FwGIWFhcaFF15obN26Nb2/vr7euPLKK43Ro0cbHo/HyMvLM6ZOnWr84x//yDrOkCFDjFmzZhmvvPKKMW7cOMPhcBijR4/u0I5c05YHg0HjggsuMPLz8w0g/X7rum7cfvvtxpAhQwyHw2FMmDDBeOGFF3L+TNq/z+0/Cx999JFx/vnnG4MHDzYcDodRWlpqnHrqqcayZcuyjvPwww8bI0eOTLf/0Ucf3ec+g7v72fj73/9u3HDDDUZpaanhcrmMWbNmZU1FbhiGsWLFCmPGjBmG1+s1iouLjcsuu8z45JNPOnyGZs+ebXg8ni7Pm2l33h/DMIy7777bGDhwoOFwOIxp06YZy5Yt63Ta8vafPSGEEKIrimHspYx5QgghhBD7kKFDh3LwwQfzwgsv9HVTRCfeeustjjvuOJ566inOOeecPmnDww8/zKWXXsqWLVt2eWYsIYQQYk+QHDpCCCGEEEJ0Yvv27SiKQmFhYV83RQghhMgiOXSEEEIIIYRop6amhqeffpoHH3yQI444Arfb3ddNEkIIIbJIDx0hhBBCCCHa+fLLL7nuuus44IADeOyxx/q6OUIIIUQHkkNHCCGEEEIIIYQQop+RHjpCCCGEEEIIIYQQ/YwEdIQQQgghhBBCCCH6mX6XFFnXdaqqqvD5fCiK0tfNEUIIIYQQQgghhOgVhmHQ0tLCgAEDsFi67oPT7wI6VVVVVFZW9nUzhBBCCCGEEEIIIfaILVu2MGjQoC7r9JuAzoIFC1iwYAHJZBIwX5zf7+/jVgkhhBBCCCGEEEL0jkAgQGVlJT6fb6d1+90sV4FAgLy8PJqbmyWgI4QQQgghhBBCiP1GT2IekhRZCCGEEEIIIYQQop/pNwGdBQsWMGbMGCZPntzXTRFCCCGEEEIIIYToUzLkSgghhBBCCCGEEGIfIEOuhBBCCCGEEEIIIfZj/SagI0OuhBBCCCGEEEIIIUwy5EoIIYQQQgghhBBiHyBDroQQQgghhBBCCCH2YxLQEUIIIYQQQgghhOhn+k1AR3LoCCGEEEIIIYQQQpgkh44QQgjxFabrBpGERiieJBhNsiMUp64lll5Glnk5Y/zAvm6mEEIIIcRXQk9iHta91CYhhBBC7AXRhEZDKJ5eGsNxdgTNx/bl5mMCTe/83s6scRUS0BFCCCGE2AdJQEcIIYTYhxmGQXMkwfbmKNXNUaoD0azATPsgTTiu7dJ5FAW8ditFXjslPgclPgfFXgfjK/N79wUJIYQQQoheIQEdIYQQog/Ekhq1gRjVgSjbm6PUNLc+tkQJRBIEokmawnGqm6PEknqPjm1TFQrcdgo95lLgsVPksVPgtlPktWfvc9vJc9lw2iwoirKHXq0QQgghhOhtEtARQggheoGuG4QTGoFIgtqWGDWBKLWBKDWB1vWWGE2RBLGERl1LjB2heI+OX+ixU+53Up7npNibHaRJBWdSwRufwyrBGSGEEEKI/Vy/CegsWLCABQsWoGm71pVcCCGE6AnDMIgldWIJnUhCoz4YSw95qmldqgMxalp71TRHEvR0mgG71ZIO0qQey/xO8l02fE4reS4bFXkuSv0OnDZ1z7xQIYQQQgjRL8ksV0IIIfZ74XiS+pY4NS1RdgRjRBM60YRmLkmdcCxJTSBGVXOEqqYINYEYoXiyxwEaMIc7lfqclPodlKUe/U5KfQ4K3HZsVgslXgcVeU7y3TbpSSOEEEIIIdJklishhBD7LV03CMaT7Aia02vXB2PsCMZoDCdoDMdpbn1sDCdoCMWpD8Z2OVFwimpRKPLYKfObPWjK8xyU+52U+s2eNWV+JwUeG36nDYdVctEIIYQQQog9TwI6Qggh9rpoQqM5kmhbwons7UiCQOZ6NEEwmqQlliQY27WeM6nhTSU+By6bitNmwWFVcdgsuGwqpT4nA/KdDMx3UZ7nxOc0EwU7bSo21dL7b4IQQgghhBC7QQI6QgghdlkkrrX2honTlNEzpinU+hiO0xTpGKyJ93DWplw8dpVin4MSr4Mir5kQOM9lp8BtM2ductso8tgpbt3vlUTBQgghhBBiPyIBHSGEEF0KxpKsqAqwpraFNTVBNu0IsaUxwtbGMNHErgdmLAr4XTbyMpb22+lyp5kk2Oe04nVa8TttkiRYCCGEEEJ8pUlARwghRFogmmBtbZC1NUHW1Lbw5fYWPtjQQFzrPHBjUxXy3WbPmHyXnfzWHjL5ntbHXAEbtw2v3YrFIj1mhBBCCCGE2BX9JqAj05YLIUTvSmo66+tDLFm/g8Xrd/DRpiaqA9GcdSvynBxY7mNkqZdhxV4GF7qpLHRR5HXgsasylEkIIYQQQoi9TKYtF0KIrwhNN3j1i2peW1HDZ9ua2bgjRELr+CegzO9gZKmPA0q9HFDqZeqwQg4o9UrQRgghhBBCiD1Mpi0XQgiRZdOOEHOf+oSlGxuzyl02lfGV+Rw5ooipw4s4sNxHnsvWR60UQgghhBBCdJcEdIQQYj+2tTHMgjfX8tSyrSR1A6/DygVTB3PEiCJGlnoZkOeSPDZCCCGEEEL0QxLQEUKI/YyuG7y9po4nP9jCGytr0sOqjhlVwm1nHkxlobuPWyiEEEIIIYTYXRLQEUKI/UQwluSZD7fy5/c2sr4+lC4/YngRPz5xFJOGFvZh64QQQgghhBC9SQI6QgjRz322tZmnPtzCcx9toyWWBMDntHLuxErOnTSIgyokgbwQQgghhBD7GwnoCCFEP/VFVTN3v7qa/66sTZcNL/YwZ9pQzj5sEB6H/IoXQgghhBBif9Un3/Y3bNjAxRdfTE1NDaqq8v777+PxePqiKUII0a9ousG7a+v5y/ubeG1FDQCqReGUQyo4d+IgjjqgWJIcCyGEEEII8RXQJwGdOXPm8Ktf/Yqjjz6ahoYGHA5HXzRDCCH6jbW1LfxtyRb+/WkVdS2xdPnphw7gRzNGMrzE24etE0IIIYQQQuxtez2g88UXX2Cz2Tj66KMBKCyUJJ1CCNGZQDTBb19bzeOLN6Hp5mxV+W4bZ44fyLcOH8wBpb4+bqEQQgghhBCiL1h6+oR33nmH0047jQEDBqAoCs8//3yHOgsWLGDo0KE4nU6mTp3KBx98kN63Zs0avF4vp512Gocddhi33377br0AIYTYH+m6wdMfbuVrv3mLRxdtRNMNZhxUyp++M4kPbpzBLaePlWCOEEIIIYQQX2E97qETCoU49NBDufjii/n617/eYf+TTz7Jtddey4MPPsjUqVOZP38+M2fOZNWqVZSWlpJMJvnf//7H8uXLKS0t5aSTTmLy5MmccMIJOc8Xi8WIxdqGFwQCgZ42WQgh+pUvqpqZ988v+HBTIwDDSzzcevpYjh5Z0sctE0IIIYQQQuwrehzQOfnkkzn55JM73X/PPfdw2WWXcdFFFwHw4IMP8uKLL/LII4/w05/+lIEDBzJp0iQqKysBOOWUU1i+fHmnAZ077riDW2+9tafNFEKIfsMwDLY1RXh9RQ3/+qSKjzY3AeC2q1x9/EgunjYMu7XHHSqFEEIIIYQQ+7FezaETj8f58MMPueGGG9JlFouFGTNmsHjxYgAmT55MbW0tjY2N5OXl8c477/C9732v02PecMMNXHvttentQCCQDgYJIUR/FooleeL9TTy2aCPVgWi6XFFg1iEV3DTrICryXH3YQiGEEEIIIcS+qlcDOvX19WiaRllZWVZ5WVkZK1euNE9otXL77bdzzDHHYBgGJ554Iqeeemqnx3Q4HDgcDhYsWMCCBQvQNK03myyEEHvdjmCMv3+wmUcWbaQhFAfAalE4ZFAep40bwKnjKij1O/u4lUIIIYQQQoh9WZ9MW76zYVu5XHnllVx55ZUEAgHy8vL2UMuEEGLPqW2J8tDb63ni/U3EkzoAQ4vcXPW1kZw6rgKnTe3jFgohhBBCCCH6i14N6BQXF6OqKjU1NVnlNTU1lJeX79axpYeOEKK/aQ4nWLqxgc+2NfPJ1ibeXVNPsnXq8UMH5XHRtGGcOq4Cqyr5cYQQQgghhBA906sBHbvdzsSJE3njjTc488wzAdB1nTfeeIOrrrpqt44tPXSEEP3Fx5sbufeNNfxvTT1aawAnZeKQAn54/EiOHlmMoih91EIhhBBCCCFEf9fjgE4wGGTt2rXp7Q0bNrB8+XIKCwsZPHgw1157LbNnz2bSpElMmTKF+fPnEwqF0rNeCSHE/igS13jxs+38/YPN6enGwZxyfHxlPocMzOOIEUWMLvf3YSuFEEIIIYQQ+wLDMNB1HU3Tsh6bm5u7fYweB3SWLVvGcccdl95OzUA1e/ZsHnvsMc477zzq6uqYN28e1dXVjB8/npdffrlDouSekiFXQoh9STypU90cZXVNC2+vruP55dtoiSYBUC0KZ00YyBXHjmB4ibePWyqEEEIIIcRXm67rJJNJkskkmqal13uyJBKJrGNkLqmATGdL+/26rqPres62xmKxbr8uxTAMY+fV9h2pIVfNzc34/XKnWwix97REE/x3ZS0vf17NW6vqiCSyA8yDClycP2Uw50wcRJnMUiWEEEIIIb5CUkGT9gGM7qynllwBl8zt7uzLdaz+FPZIJBLcfvvt3Yp5SEBHCCG60BiK89qXNbz8eTXvrqknrrVF0h1WC4ML3Rw2uIBTD61g2ohiLBbJiyOEEEIIIfaeVNAi1YMkkUjkXNoHOXIFPtqXdWc99difQgs2mw2r1YqqqlitVqxWa7qsu4uqqlgsFlRV7bDsrLyrx57EPPpk2vJdIUOuhBB7WlLT+WhzE0s3NlDVFGHF9gCfbm3OSmw8vNjDSQeXc/LBFRw80C+JjYUQQgghRI/ouk48Hk8HYGKxGNFoNOeS2RslFotlLfF4nFgsts9eI1sslqzARnfW2wdMcq13tS/zOJ09WiyW/eY7vPTQEUJ85W1pCPPE+5t49qOt1AfjHfaPLvdx8sEVnHxIOSNLvfvNHwAhhBBCCNF9qUBMKpCS+Zi5JBIJYrEYkUgkHZhJrUcikR7lSOkpVVWx2WzYbDbsdnt6vX3wo7PAx87Kuruuqqp8Z95F+2UPHSGE6E2abvDBhgYeX7yRV76oJtUJp8BtY9oBxQwt8jCyzMuEygIGF7n7trFCCCGEEKLHDMNIB1dyBWF6+phIJHq9jVarFYfDgdPpzLnYbLZ0kMbhcGC323E4HOnFbrdjt9vTARuLxdLrbRT7rn4T0JEhV0KI3bWlIcwnW5tYtrGR/3y2ndqWtrsjR48s5jtHDOXYA0uwqfKHUAghhBCit+Sa4WdXZxrKnHEoc2lfluotsycGpFgslnRgJfMxs1eMw+HA5XKlAzOp9cwy6cUidpcMuRJC7NfC8STPfbyNvy3ZzBdVgax9fqeVWeMqmHPkMA4s9/VRC4UQQgjRVwzD6PGi6/ouPW9fXHK9ltR0yjubcrkn231NUZR0T5b2QZidPeYqs1qtEogRe4wMuRJCfKUZhsFn25pZuHQL/1peRTCWBEC1KBwyMI+DKnycMKaMow4owW6V3jhCCCG+GnJNF7yzsswL/NTF/55c39vnEH1DUZQOSW57OrtQKjdMZ0tmMMZms0kARuyXJKAjhNgv1AdjLFpbzzur6/nfmrqs4VRDi9x8+4ihnDVhIIUeex+2UgghxK7KdVHeWVlnF/CZPRJ6+3FfPXZmgEbsPkVR0ktqppz9Ycmc9af9dMt7YlvyvAjRO/pNQEdy6Agh2qtqivDMh1t5+YvqDsOpnDYLJ44p55tTKjlieJHclRFCiAyZuSsyc09k5qfIrJNa705Zrl4dPQm8dFYm9oz2F93tpxJOLamL/lzbe2J9b56jJ4sQQuxLJIeOEKJfiSY0XltRwz+WbeHdtfVk/gYbU+Hn6FHFHDOyhIlDCnDa1L5rqBBC7CbDMDok90xNf5taMmdeSQVkMrdzJQ1NJpP7XYCkO4GHPfm4Lx+zs0BN6lEIIcS+RXLoCCH2G5pusKE+xCdbmnj9yxreXl1HON7WU+/w4YV8/bBBHHdgKSU+Rx+2VAghOkomk0QiEcLhMOFwOL0ej8fTwZdoNEokEslaUgGbvSGVx8Jms+XMU9F+vauyXD07cgVZuirvbllmuRBCCPFVJAEdIcQ+R9cNPtzcyAufVPGfz6upy8iHAzAw38XZhw3knImVDC5y91ErhRBfFfF4nFAoRCwWS/eWSQVoQqFQ1mMkEkn3hEn1nukNVqsVu92Oy+XC4XCkp7x1Op07TQqaCtS0f0wtEhARQggh+icJ6Agh9hmra1p4/uNt/HN5FduaIulyp83CgeV+po8s5oQx5Rw80C/j2IUQ3aLrOvF4nFgslh6ilHpsv2QObwqFQulld4MyiqLgcrlwu93px1SgxWaz4XQ6cblcHZbU9Lg2m02CLkIIIYTooN8EdCQpshD7F8Mw2LgjzKrqFj7d2sRbq+pYsb0tsbHXYeXEMWWcemiFTC8uxFeUYRjpIUm5AjKZeWS6euwNqqricrnSQRiXy4XH48Htdmc9pupYrVYcDgdutxuHwyEBGSGEEEL0OkmKLITYaxKazpsra3njy1r+t6aOqubsCy2bqjB9VClnTRjI8QeVSlJjIfqxVDBmVwIwmY+99TXFYrHgdDrTw5UyH+12e1ZvGJvNlg7QeL1ePB4PDodDegYKIYQQYo+TpMhCiH1GNKGxeN0OXl1Rzatf1LAj1DZ0wW61MLrcx6gyH1OGFXLCQWUUeOx92FohREoqmW/7GZV60lumN4Mx7QMxnQVnOqtjtVolICOEEEKI/YoEdIQQvSaa0PhoUyOL1+9g+ZYmNtSH2NYUyZpavNjr4LRDKzj2wFKmDiuUXjhC7GGGYRCLxbJywmQm8c2VNyYcDvfaUCVFUXYrEONwOLDZbBKMEUIIIYRoRwI6QohdltB0PtnSxKK1O3hvXT0fb2kintQ71CvzOzhhTBkzx5Zz+PAibKrkkhCipzKnv47H4x2CMMFgMOsxGo2mgzW63vHfZXcoirJbgZjUDEwSjBFCCCGE6H0S0BFCdMkwDGpbYqyoCrBie4AVVQEaQnGaIgk21AeJJrIvFMv8Do4YXsSUYUWMLPMyrNhDkccuF3RCYAZlotFoeihTJBJJB2lSZclkMl0vsydNLBbbrXPbbDY8Hk/WkpptKTUldipvTGpxOp2SzFcIIYQQYh8lAR0hBElNZ1tThC0NERrDcZrCcbY2RtIBnMy8N+0VuG0cOaKYI0YUceSIIoYVeyR4I/ZrqSFMwWAwPTQpM0DT1WMikditc6emv04l77Xb7VkBmFQCX6/Xi9PpTAdrUs8RQgghhBD7DwnoCLGf03SDhlCc+mCM+mCM2kCM2pYYVU0RNjWE2bwjxNbGCEm98+SlFgVGlHg5qMLP2AF+yvOc+J02Bhe5GVbkwWKRAI7oHzRNy+oFk0wmSSQSWYl/MxMBx+PxdL1wOEwwGCQYDKJp2m61w+Fw4HK50ovb7U6vW63WrCmvM6fF3ts9ZgzDwIjFsDide+2cQgghhBCie/pNQGfBggUsWLBgt79EC9GfGYZBIJqkIRQnFEty8MA8WqIJ/vVJFY2hODtCcRpCcXYE2wI4DaE4XcRq0hxWC5WFboo8dgrcdsr8Dg6q8DNmgJ9RZT5JXiz2KbquE4vFsoYrZS7ty8LhcK8MW8qU6h3jdDpxuVzdekwtezIoo0ci6JkJjXWd+ObNJOvqWrcNEtu3k6ytzX5eMEh86xZImn9n9VCI+MaN5J15BuXz5u2x9gohhBBCiF2jGL01p+he0pM52YXYFyQ1nXBCIxRLEopphONJgrEk4ZhGKJ4kHM/eF4qb66FY6754klAsSVM4QWM4TkIz/8n6HFY+u3UmdS0xJt/2epdtUBQocNsp9top9Tkp9Tkoz3MypMjN4EIPQ4vdlPmc0tNG9BnDMNK9ZcLhMM3NzQQCAQKBAM3NzbS0tGQFZ6LR6G5Nia2qKqqqYrVasdlsWcGWzOBLashSatiS1+tND2vam0OYDMMg+sUKop99ipGR4FgPhoitWYMejQCgNTYRWb4cevHmh+fIIxj8yCO9djwhhBBCCNG5nsQ8+k0PHSH2tHhSJ5LQiMTNwEpqPZLQCMez18OxJMF4RlAm1hZ4aQvCmEGZWI5Zn3aXx65S4LGT1HTy3TZOGFNGodtOgcdOocdGkcdBsc9BsddOiddBoceOVWaWEn0gNZwpFosRi8VoamqiqamJxsZGmpqaaGlpoaWlhXA4vEvHt9lsHYYs5RrGlNreFxL9GoZBfONG9EAAAC3QQvDtt9t60JiViG/YQGzjRjAMc9mNII21tBTbwIHQ+rqtRUXYBg1CyQjiKg4ntspB6eFVit2Bfchg7JWVu3xeIYQQQgix50hAR/Qbmm60BlSSrUEXLSMA03l5JJFs3a8RTQVk4hqR1t4xqUBNVzlkeoPVouBxWPHYVdwOa9u63YrX0VqW3rbidqh47FbcdhWPw0qey0ahx06hx95h+NMfvzNpj7ZdiK5omkYgEKCuro7a2lrq6urYsWMHTU1NBIPBHh/PbreTl5eH3+/H7/eTl5eHz+dLB2ja55vpK4amoWe+PsMgtn4DkeXLMbQkGBBbu4bwkg8wksm2atEoeijU4/MpLhfuyZOwuNxtZTYbjpEjUfPyzG27HfeUyWbwJvO5kqhcCCGEEGK/IwEdsUcZhkFc04kldRJJnYRmkNB04ppOQtOJJnSWrN/Bl9sDNIYTrK8P0hROEEvqaLqBx65isSiE4xrxPdDTJRfVouC2qbjsrUvrutuu4rJZcdlVMwBjNwMwHoc1HYwxgzRtwRhPKijjUHFYJQeN6L90Xae+vp6qqiqqqqqoqamhsbGRSCSy05mbFEXBbrfjcDjw+/0UFBSQn59PQUEBPp8Pn8+XHsJktVpR1b37b8UwDGKr16CHOw+yGJEILW+9RWLLVnM7kSDy6afpXjY9pTgcWIuLzQ1VxT1pEs6xY8zxka2sJSU4DxqDYrOZ1fLzsDgcu3Q+IYQQQgix/5GAjsgSjCVpDMXTw4eCrcOGgrFka56XZEYPl4zhSIns3jHhuEY0rhFOaGi70fMlEE12KFMUcNlaAyx2FbfNitOu4s4sSwdizB4unZW3P47LrmJTFbmbLb6yYrEYVVVVNDY2EgqFaGlpoaamhu3btxOPdz59vaqqFBUVUVpaSklJCcXFxRQUFJCXl4fb7d4r/6b0UAgtmBGUSSYIvb+E2OrVXT4v/NFHRD/7rFfaYPH78UydgsXrA0AtKMA7fTpqfn66jmJVsVdWosg04kIIIYQQYjdIQOcrIJ7UsVoULBYFXTdoiiRoCMVak+yaiXabwnEWr9vBW6vr2JNpsq0WBZtqwaamHi2MLPNy5IhiCj02hhZ5KPU7sakKVouFYMy88++yW9PBF4fVIgEXIXaTYRi0tLSwbds2qqqqqK+vp76+nrrMPC7t2Gw2KioqGDBgABUVFRQWFqZz0uzpvDSZiYABElu20PzPf6E1NZnbtTUE334HdtJbqDOK04m1tLTz/YqCc9w43BMngmr+DnIccACOMWOyfx9ZrfL7SQghhBBC7BV9EtAZOnQofr8fi8VCQUEBb775Zl80Y78UTWisrwvRGI7zvzX1vPT5djY3hLGrFvJcNnaE4jvtMeOwWvCmcrw4zPwu6fXW4UNtPVus2b1csoYoWdPrTpsFm8WyC7MoOXf9zRBCmFPdBwLU1NRQW1ubfmxoaOh0qFReXh4lJSXp2ZyKi4sZMGAAJSUlvR600ePxdFCmtcGE3ltMeNlS0M1kwLE1a4iuWEG3os3tcuo4RozAc8QR6WFLuaj5eeSdeSbWoqJdfBVCCCGEEELsfX3WQ+e9997D6/X21en3GzWBKEs3NrBsYyNLNzbw5fYAueI1saRObUssvZ3nspHvtpHvtlPgtpHvsjGowM3ZEwcxrNizF1+BEGJ3aZpGU1MTzc3NhEIhQqEQO3bsSAdvotFozucpikJpaSkDBgygrKyMwsJCKioq8Pl8vdKu9tOKaw0NBF54gcT2anO7uZmWV1/dpQTBAJ5jjsZ18CGAmQzYe9yxOA88cLfaLIQQQgghBIYBugaGZj7qydZ1PUeZBoZubmc+J2dZ6rldlAW6P6mIDLnqR3YEY3y8uYmq5gjLNzexdFMDWxoiHerlu22U+hxUFrg5d9IgDhtSQDim0RJNUuJzUOS1Y5MprIXoN5LJJC0tLQQCAZqbmwkEAgQCARobG2loaKCxsRFd7zxpuKIoFBcXU1ZWRmlpKWVlZRQXF+P3+7F10XOlJ/RolNDixRixOGAQWrSI5n/+C6OLvDsZDcxKBmyrHIT/pJNRfWbQXy0uxjNlCorLla5jsduxeCT4LIQQQgjRI3oqyJAALdEaSEiYZZnbWmtZekkFHpLZQY0uy1JBjVzBj+6UaW3t7UlApCdlerLt3Jll7NkZkLsU6/65exzQeeedd7jrrrv48MMP2b59O8899xxnnnlmVp0FCxZw1113UV1dzaGHHsp9993HlClT0vsVRWH69OlYLBZ+9KMfceGFF/a0GV8JVU0RXvx0O2+srKE+GGd9XbBD7xuLAgdV+Jk8tJBJQwuYOKSAcr+zYw6H3rnhLoToBYZhEIvFiEajRCKRrMdwOJwO2KQCOKFu9GCxWq3k5+fj9Xpxu93k5+dTVlaWDt705vTewXfeIbT4/bYCXafltddIVFXt9LnOQw7BPWWy+TvKouI54nDchx8ueWeEEEIIsX/TEpCIQDIGyQgkouZjMtZaHjWXROujFm8ti5mLFmtb77CdWT+esR3LCMwk2oIXYvdZrKCoYFFbHy05ytqtd7csogPPdqsZPf6GHwqFOPTQQ7n44ov5+te/3mH/k08+ybXXXsuDDz7I1KlTmT9/PjNnzmTVqlWUtiacfPfddxk4cCDbt29nxowZHHLIIYwbN66nTdlvVTdHuf6ZT3l7dcfkpKPKvAwudKeDOBMG5+Nz9s4ddiFE9+m6TjQaTS/tAzOpx87K2g9H2hlVVfH7/eTl5eH3+9PrRUVFFBUV4fP59lhS4vDHHxN+3wzgxDdtpvn553PWs5aUYBsy2FwvKqbw29/CPnx4er+iqqh5eXukjUIIIYQQ3aLrrcGQ6E6CKpG24EoymiMYk1ke3fkxDK2vX3nnFAtYbGZAQrWaj+23Ow1GWFvXLbtf1q0gSXcDJ1azbrfKOmtDF+3akwIBuKx7AR3F6OlVReaTFaVDD52pU6cyefJk7r//fsC86KmsrOQHP/gBP/3pTzsc47rrrmPs2LHMmTMn5zlisRixWFvul0AgQGVlJc3Nzfj9/l1t+j5H0w1WVgd49YsaHl+8kcZwAkWByUMLOXVcBQeUeBlc5GZQgbuvmyrEfkPTtJ0GXzrbl/l7aVepqorL5cLpdGY9pgI2mQGcvTX1txYMoQdb0tuh999n+403mV9+MuSdcTpqcXF621ZWTv6552DJGBYlhBBCiK8oXTd7iGitPUSSsdbtjF4jWmtZMt5WN72eWTfH8zKfkz5+oq3XSvr4qX0Z6/tCYMXqbFtsTrC6wOoAW+uj1QVWu7lfbX20OsxFdbStd9jOrG8396l2MxCh2joP2uzpAIXokUAgQF5eXrdiHr2aQycej/Phhx9yww03pMssFgszZsxg8eLFgNnDR9d1fD4fwWCQ//73v3zjG9/o9Jh33HEHt956a282c58STWg8umgjjyzaQF1G0uKxA/zcd/4EhpdI4mghcjEMA13XSSaTWUtq2FJnS2aAJt6d/C47YbPZOgRkUtN45wrWZO7rrfw1vaXp2eeovvVWjBzBKs+RR2AbOAgsFnwzZuA9+qg+aKEQQoj9imFk5K7Qe77oWtsxspbM4xk5nqN3sj9XO3ajjZ22L/NcOfbrXbS/QxuNTp6Xub+Ltuu78L7v9Hn7QMCkuxSLGTzpEFRxZgRXnNmBllx1cwZmcj2vdZGh5qKX9GpAp76+Hk3TKCsryyovKytj5cqVANTU1HDWWWcB5t3xyy67jMmTJ3d6zBtuuIFrr702vZ3qobM/+HhzIz9+6hPW15n5MbwOK5OGFnDm+IGcckgFdqtESsW+Sdd1NE1LP2qa1iGwkrnsbH9Plsxj7UYHwyx2u32nwZfO9vVmbpq9LbZhAzW/uo1kbS1gEFuz1txhtaa/aCgWC/nnnEPZTTeiyN0bIcTeYhjmQupitfURo9165j5y78t5jNQ+cl8Yt39O1nFyHauzurmO2b7N3Tl/J3W7ff6MBKVZSU7blaUTonayP9f27gRLxFeHxdraW8TW2qvE3tqTpLUs1ZMk3avE1tb7JL1ua+uNkn5Orno7O7Y9o/fLvnVzTYie2utXIsOHD+eTTz7pdn2Hw4HD4WDBggUsWLAATetHEd9OxJM6v3tjDQ+8tRbdgFKfg+tmHsgZ4wdKEEd0YBgGyWSSeDyeXhKJBIlEIivIkRlYydzujX3tl32RqqqoqorT6cTtdudcXC5XekkFZhwOB6qq9nXz9zjDMEhs2YKRNH9+RiLBtquvJr5pU1a9gu98m7IbbpAkxUL0B7qenVgz52O0NRGmlnHB3tmFfY6L9pz1O7noz5oRpZv1cwZm+nBmEbHvUiydLGrrbIk59lm62Ke05sdovz+VNyO9KDmek2Nfh+dl7s+1r3V/zud153XlaHuHc+3Oa+tOG7tYUkN80kN+5BpHiD2hVwM6xcXFqKpKTU1NVnlNTQ3l5eW7dewrr7ySK6+8Mj2erL9aXdPCjxYuZ8X2AABnjB/AraePJd9t7+OWic6keqFk9hJJJBJZgZXM9cy6mfvi8TjJZLJD75auHlPH6K2eKHuSzWbDarWiqipWq3WXl115vqqqeywh8P5Aj8fZevkVhBYt6rDPWlFBxS9+gWJVUQsLcR54YB+0UIj9nJaAWIu5xIOt60GIpx5TSwjiYUiEMtbD5r5EpC3ZZipgo+3+sNH9l9La01DJvnDN3E7VaX9h2/456bLOjmXJ3tfp89vVhdwX1h2e391jKp2cR2lL+mmxtlval+1su32Z2snFfk+CCqk6XQQN5G+8EELk1KsBHbvdzsSJE3njjTfSiZJ1XeeNN97gqquu2q1j9/ceOoZh8PjiTdz2ny+JJ3UK3DZuO+sQTjmkoq+btl/QdT3deyUWi2U9tu/Vkhlg2VlZKgCzr7DZbNhsNux2e1YAJRUESQU2Mssz93W2dLU/87ipsvbrFotFenTsY0IffEDghRcxdI34xo1Eln0IVisWjyddx1ZezoA7bsc5ZkwftlSIfZSutQVh0oGYgBmAyQrM5CprF7xJRvd8ey22jJwNzuw8D6qt4wV5VlnmBbot90W8muuivpNjqLmO0Un9XMGHDuud7ess2CF/j4QQQnw19DigEwwGWbt2bXp7w4YNLF++nMLCQgYPHsy1117L7NmzmTRpElOmTGH+/PmEQiEuuuii3Wpof+2ho+sGb6+p45F3N/C/NfUAHHdgCb8+ZxylPmcft27fYhgGoVCIaDSaHk6USCQIhUIEAgECgQDNzc3pGYYygzaJRGKvtdNqtaYDKpnBldTSvvdI+/2pYEhXj5kBE4fDkT6G9EIRueihEMm6uvR2dNVqts2dC5n/LqxWKh96EO+0aX3QQiH2EsMwe7GkgyqBjkGWzABMV/US4d5vn9UJdi84vODwgd1nrqfKbB6wu8HuaV1v3bZ5zGBNVpLNdo+W/X/oqBD7otTQ+P5601kIsfelbpr3xg3xHgd0li1bxnHHHZfeTiUsnj17No899hjnnXcedXV1zJs3j+rqasaPH8/LL7/cIVFyT/XHHjrRhMY1Ty7npc+rAbBbLdx0ykF854ghX7neDKnpoTVNIxgM0tjYSFNTU3pJbSeTyd06j6Io6QBI6jG1tA++dKes/TAgVVW/cj87sW+LfPY5my+9FL25ucM+zzFH4544yVw/8khchxy8t5snRPekhiRlDkdKB19acgRkcvSGSQVqent2FdXeGnzxgsOfEYxpfcxcdlYmyTeF2K/E43G2b99OOLwHAsBCiP2a2+2moqICu333Uq8oRn9IzpGhJ3Oy96V4UmfOox/w3rod2FULF0wdzLePGMKI/XAacl3XCYVC6Wmh6+rqqKmpYceOHQSDQUKhEJFIpNvHczgcWb1d3G43fr8fv99PXl4ebrc7Z9DG4XD0WqRTiH2VYRiE3l1EfONGMAwaHnuMRFUVitOJkjENuu9rx1Hxy1+i7OYfCSG6pCVbAymtQZZo5npzW6AlGmgXoGk3TKnXhyQpXQRa/K2POco61POas6QIIUQ7uq6zZs0aVFWlpKQEu90u30GFEDtlGAbxeJy6ujo0TWPkyJEdRmH0JObRf+fb3YfFkho3PPMZ763bgddh5Y/fmcQRI4r6ulm9IplMUl1dzZYtW9i2bRt1dXXp6eq7Q1EUPB4P+fn55OfnU1BQkF7Pz88nLy+vX08DLcSetuNPf6Lu7nuyymxDBjPsqadQ9+Egt9hHaUkz8BJpNJdoU9t6asnVUyYVuOntYUmqIyPY4m8XfMnoHZMuz+w5kxHAsbkliaoQYo+Kx+Pouk5lZSVut7uvmyOE6EdcLhc2m41NmzYRj8dxOnc9FUu/uXLuL0OulqzfwQ8XLqc6EEW1KCy48LB+F8xJJBJs376durq6dK+bcDhMY2MjVVVVnQ6LcrlcuN1uCgsLKSsro7S0FJ/Ph8fjSU8bLTlghOi+5n//mx1/+CN6LGYWtE49DuCZfgyqx4PF66Po0kskmCPMJL6RRgjVQbghIzjTBOF6CNZBqBaCtRBpgEgzxDoO1dslVpcZSHFmBGGcfnDktT76ssvtGT1kZEiSEKIfk++2Qohd0Vu/O/pNQKc/JEXeWB/iu098SHMkQUWek3mnjmH6qJK+bla3NDU18cUXX7By5Uqqqqq6DJy5XC4qKysZNGgQZWVllJSUkJ+fL3/QhNhN8a1bafrHUxixKFpLkOZnn81Zr3DOHMp+ev1ebp3oE/GwGaAJ1ZvBmFCduQTr2tZT+8I7wNjFWfkcfnDlg6vAXJyp9Xxw5uXuFZMK2Dh8YJWhfUIIIYQQe1u/Cejs63Td4Mq/fURzJMGEwfn8/bLDcdr2rRknDMOgrq6OqqoqGhsbaWlpSc8cVZcxQw6Ax+NhwIABWb1rfD4fAwcOpKioSMYIC9HLkvX1bPrWt0lWV2eVF3zrW/hnnZLeVv1+HCNG7O3mid6SjLX1okkHY9oHZ1KP9ZAI9fwcrgJwFWYEZPLBUwyeEvCWmo/u4rYAjjNPesYIIYQQQvRDEtDpJW+uquWLqgA+h5WHvjVxnwrmtLS08Omnn/Lxxx9TX1/fab2hQ4cyduxYRowYQUFBgQRthOimzNzyiS1bCC9dihGPp8u0QAstr71GYvv2zo8RiaCHw9iHDME3cyYAjgNH4T/lFPm3uC/S9dbhTA1mDppok5lXJtIAoR3mEKdQvfkYbmjLR7MrOWdUR2sgpjUo48lcLwFvSdu6u0iCM0IIIfYZc+bMoampieeff76vm9Ln/vCHP/DLX/6Sbdu2cc8996Tfl+XLl/d100Q/1m8COvt6Dp2H3l4PwAWHD6bUv+tJjXZXqhdOY2MjDQ0NbNy4kdWrV6cvOK1WKwMHDqS4uBifz4ff78fn81FeXo7P5+uzdgvRlwzDILl9O3qorTeEoevEN2wkmeq9ZujEN20mWVub9dxETQ3RFSugk9xSPaEWFTHowd/jGDZst48lesAwzKBMsAZaqs0eMqmZmaLNbetZQ5/qd316bMViBl48Ja2BmdKM9dZHd7H56C01hzdJUE8IIUQvOfbYYxk/fjzz58/fo88RbQKBAFdddRX33HMPZ599Nnl5eei6zg9+8IO+bpro5/pNQGdfzqHz9uo6PtjYgE1VuHha31yIxeNxPv74Yz744AN27NjRYf+gQYOYMGECY8eO3a0s2kLs64x4nERtHWCkkwjHN23K6kWj1e8g/NFH6BGzt4S2o4HE1q290wBVxT1hAmphYUaZBffkybgnTuzywtxeWYnF5eqddnyV6XpbQuBgDbRsh8B28zGdLLip7THSsOvTZjvyzCFLqcWV3xqsyQjKuIvactO4CsxcNJJzTAghhPjK2Lx5M4lEglmzZlFRUZEu93q9nT4nHo9jt0uOOtG1fhPQ2Vc1huL85OlPALhw6hDK9mDvHC2pU7elhUB9hEB9lGBjjMO/PoSlS5eyePFiwmHz4lRVVUpLS8nLy6O8vJwxY8ZQVFCMapMLiP2JYZgBi9aNnOtGarurOka6Zqd1M4Mh6XJNQ4/FMeIxjESSVAAltRiGAbrRsTxXWTKJHo1ixGLmxXgOeiRKbM0a9FDQbEY8TmzjRvRgRo4RXSexbVvWcKdus1o7zBRlGzAA26BBKBYzCGMtK8dWOQgl42Lc4vHiGn8oltY/yBanU4Iye0Iy3tpDpjZ7tqZgrRm0ST2G680gDcbOjtiRIw98ZWaPGWde24xNqXV3ccYQp1IzUCPJgIUQQrQyDINIYu+PJnDZ1G4Pz54zZw5vv/02b7/9Nvfeey8AGzZsYNOmTVx33XV88sknFBYWMnv2bH71q19htVo7fU5lZSXf/e53+e9//0t1dTWDBw/miiuu4Ic//OEuvQ5d1/nNb37DH/7wB7Zs2UJZWRnf+973uOmmmwC4/vrree6559i6dSvl5eVceOGFzJs3D5vNHGr8ySef8KMf/Yhly5ahKAojR47koYceYtKkSTs996JFi7jpppv44IMPcDgcTJkyhYULF1JQUEAsFuO6665j4cKFBAIBJk2axG9/+1smT54MwFtvvcVxxx3H66+/zvXXX8+KFSsYP348jz76KAceeCCPPfYYF110EQDDhw9Pv3+PPfZY1pCr1PC0yZMns2DBAhwOB2+++SbDhg3jySef5L777mPZsmUcfPDB/PWvf6W5uZnLL7+clStXcvTRR/P4449TUtI2Ic+f/vQn7r77bjZs2MDQoUO5+uqrueKKKwAzWHTttdfyzDPP0NjYSFlZGd///ve54YYbdulnJ/qOBHR2Q3M4waWPL6MmEGNEiYfrTxq9R86TiGt89uZWPv3vFkLN5oWqgUHEvY3Fm54jFjPvLOfn5zNt2jQOOeSQdC+cus0tvPvEGooGNXHMeaP2SPv2BUYyiRGLZQceMvcnEiRr6zBi0XbBDCMV9egYjDBorZejnM72mfv1UJDmF14gvmGjGaAwDAxDN5/bup31nHSdjHZkluk6hq6DpnUa8BBtFLsdrOavN2tREY5Ro1Csbb/uLE4HrsMmYm39o6c47LgOHY/q9fRJe7/StERGQKbWDNQEtkPzFghsg+ZtEKw2hz71lN1nBl98FeArB2+52WMmlSg49eguBG8Z2CQQJ4QQYtdFEhpj5r2y18+74hczcdu7d1l37733snr1ag4++GB+8YtfAKBpGqeccgpz5szh8ccfZ+XKlVx22WU4nU5uueWWnM8pKSlB13UGDRrEU089RVFREe+99x7f/e53qaio4Bvf+EaPX8cNN9zAH//4R377299y1FFHsX37dlauXJne7/P5eOyxxxgwYACfffYZl112GT6fj5/85CcAXHjhhUyYMIHf//73qKrK8uXL08Gerixfvpzjjz+eiy++mHvvvRer1cqbb76ZTvXxk5/8hGeeeYY///nPDBkyhDvvvJOZM2eydu1aCjN6ZN90003cfffdlJSU8P3vf5+LL76YRYsWcd5551FZWcmMGTP44IMPqKyszAq8ZHrjjTfw+/289tprWeU333wz8+fPZ/DgwVx88cVccMEF+Hw+7r33XtxuN9/4xjeYN28ev//97wH461//yrx587j//vuZMGECH3/8MZdddhkej4fZs2fzu9/9jn/961/84x//YPDgwWzZsoUtW7b07Acm9gn9JqCzr+XQSWo633lkCZ9sbcbvtPK78yfgsvd+IuRgY4xn7lpGsCGGgY6R1wz+FkJGHdFkCGJQXFyMsa0U5+ZSvtge49Mnl6IldLSkjq6Z0Ye6LS1MPW0YDvfuJcs0DAMjGkULtKA1NpCorsaIRtFDYbTGBrSmJjMAoelowRaz94TWMbeIFmghUVWFoSU7Bjlo7dnRIcCSHexI9T4xEoleyV8iekhRUJxOFLvdDJYoCiigoLSuty2K0rEss65is6I4XVgcDlBz/ztSVBX78OFYi4vMAouKffBg1MKCrHq20lJsQ4ZIIuF9gWGYw54C26B5a+uyBZq2tK23VNPt3jQWa0Yi4FIzCOMtNXvLeFsXd8YQJ+k9I4QQQmTJy8vDbrfjdrspLy8HzEBEZWUl999/P4qiMHr0aKqqqrj++uuZN29ezueAOSrg1ltvTW8PGzaMxYsX849//KPHAZ2Wlhbuvfde7r//fmbPng3AiBEjOOqoo9J1fvazn6XXhw4dyty5c1m4cGE6oLN582auu+46Ro82b7KPHDmyW+e+8847mTRpEg888EC6bOzYsQCEQiF+//vf89hjj3HyyScD8Mc//pHXXnuNhx9+mOuuuy79nNtuu43p06cD8NOf/pRZs2YRjUZxuVwUFZnfX0tKSrLew/Y8Hg9/+tOf0kOtNm7cCMDcuXOZ2Tppxg9/+EPOP/983njjDaZNmwbAJZdcwmOPPZY+zs0338zdd9/N17/+dcD82axYsYKHHnqI2bNns3nzZkaOHMlRRx2FoigMGTKkW++V2Pf0m4DOvpZD5+9Lt/DJ1mbyXDae/N7hjC737/xJu+DL96oINsSwFAYJ5a0lGAlAwtzndDo54YQTmDBhAg9d9TZxXSdOx6EmIyeXccRZIzoN5hiJBPEtW4lvWE9802a0pia0QDN6oAWtpaVtPRBADwTMAEo/pObno7hdOQMOQKfBCLNIaa3QeVAiK4BhseCeNBHvsce2BTsslvQxFIvSrgxzGI+imAlTU+dsLVMsFnPdoprPTQ35yWw/tAVO2u9LtT+92kmd1nWli33pAI0QYAZtwg3QsA7qV7cua6BhvRm0iQd3fgyLLSNI0xqoyauEvIHgHwj+AeZ+Z77knhFCCLHPctlUVvxiZp+cd3d8+eWXHHHEEVnf76ZNm0YwGGTr1q0MHjy40+cuWLCARx55hM2bNxOJRIjH44wfP36X2hCLxTj++OM7rfPkk0/yu9/9jnXr1hEMBkkmk/gzhstfe+21XHrppTzxxBPMmDGDc889lxEjRuz03MuXL+fcc8/NuW/dunUkEol04ATAZrMxZcoUvvzyy6y648aNS6+n8uTU1tZ2+f61d8ghh+TMm5N57LKysnTdzLLa1ok7QqEQ69at45JLLuGyyy5L10kmk+nr6Dlz5nDCCSdw4IEHctJJJ3Hqqady4okndrudYt/RbwI6+5LmSIJ7Xl0FwLUnjOrVYI6hG+ihBFpLHItdZeuaOgL+VcTsNRAxE2eNGTOG4cOHM2zYMBwOB4Zh8K1fHUEipqEldCxWBavNgmpVsTtV7C7zx6wFQ0Q//5zIZ58S/fwLElu3ogVbSGyr6nkPF1VFzcvDWl6G6vaguFxYCwtQ8wvSgQrV78Pi9WUNdUmxuF3YBg40h8ZkBjDaBzkyghodAx+twRGrFcXh6Lp3h8WC0o0ul0J8FRm6QXxTgPiWFjAMFKcVPZIksaWF+PYQFpcVW7EDqzuIVd2OTfsSa/gzlKaN0Lhx50Ebd1FrgGZQ22N+anuwOQxKgoRCCCH6OUVRuj30aX+wcOFC5s6dy913380RRxyBz+fjrrvuYsmSJT0+lmsn+QcXL17MhRdeyK233srMmTPJy8tj4cKF3H333ek6t9xyCxdccAEvvvgiL730EjfffDMLFy7krLPO2q1zd1fm8K5UcCwRipFsjpFsMW+6JwMxkg1RUECPJDE0nUS1mQ9SjyZx251ooTiKTUXJyH+a69jty/TWtAzBoPm97I9//CNTp07NaqPaeq102GGHsWHDBl566SVef/11vvGNbzBjxgyefvrpXnkvxN7z1fmN04t+98YaGsMJDij1csHU7kdcM+nhBInaMIntIeLbghhxjWRthERtuDWRLDQqIdbbPiHmjgBwaMVojps2HZfbDRZQW3QMu4GiKPgKcydj1pqaCLz5Ho1/X0h42bK2hLbtKC4XjmHDsA8dilpchOrzmwEZfx6q34fq92Px+81Hnx+Lxy29NITopwzNILqqgejKBhLVIRLVIYx457mZNCCxLRW0KQSmYeEQ7JbP0Y08QMVij+Aq2o5rqIKlfBgUjTCDNXkDJTeN6FeMpE58c4BkQwzFqqAWOrEWOLE4VZI7osQ2BUhsC2JoBhaXFWuBA7XAieqzY3GbX6sS20PEq4LokSQWpxWLU8Xid2AtcGAtdGLx2kE30AJx9HDCLNvNIdFCCNFddrs9K43FQQcdxDPPPINhGOnv94sWLcLn8zFo0KCcz0nVOfLII9OJdsHs0bIrRo4cicvl4o033uDSSy/tsP+9995jyJAh6QTJAJs2bepQb9SoUYwaNYprrrmG888/n0cffXSnAZ1x48bxxhtvZA0fSxkxYgR2u51FixalhyUlEgmWLl3Kj370I/NmfGsi7GRzjKRu5utMNJjXb1pTDN0XxwibIxz0UAK9dd1IaKCbf3fMygZG0kBrjKXPn6gzJ71JNERI1IVRbCpatOsb8WVlZQwYMID169dz4YUXdlrP7/dz3nnncd5553HOOedw0kkn0dDQkJUXSOz7JKDTQ+vrgvz5vY0A/PzUMdjU7nX/T9ZHCH1cS2x9E8naCHqoi2FLCjS5o7yY/JCoksBjOPha/GDKNuQT3LCWzHvhFrcV+2A/9sE+7IP92AZ5iC7/kOD//kd48ftEv/wyK4hjHVCBa9yhuA4ei334CCxeD/bKSqxlZVkz9wgh9i9aME5sTZMZyFnTiB7K/jKgWBM43RtQYrXoCSsKceyWNdiUdRi4SBiDSSrDSVgOIJEoRdf9RPUj2w4Qg2jVBBprFZwHFOA8sABnfgFWCeaIfZih6ejhJFpzjNjGAPGNzUTXNGHE9nC+PlUBLfsGi8Vnw1bmwVbmxjEsD/tgHxaPDT2cRA8lsHhtqF7JCyWE2H1Dhw5lyZIlbNy4Ea/XyxVXXMH8+fP5wQ9+wFVXXcWqVau4+eabufbaa7G0Xh+0f05hYSEjR47k8ccf55VXXmHYsGE88cQTLF26lGHDhvW4TU6nk+uvv56f/OQn2O12pk2bRl1dHV988QWXXHIJI0eOZPPmzSxcuJDJkyfz4osv8txzz6WfH4lEuO666zjnnHMYNmwYW7duZenSpZx99tk7PfcNN9zAIYccwhVXXMH3v/997HY7b775Jueeey7FxcVcfvnlXHfddRQWFlJZWcmdv76TcCjMd077JomqIFqTGYDRg3F0tfUaL2EGaRSrBYvHhtIa8Ld4bKh+83e54lBRVAVrscscheBQUawWFIeKkdBBN9qCPQkdI6ZhxDT01t4+ibowmupGcaodJoa59dZbufrqq8nLy+Okk04iFouxbNkyGhsbufbaa7nnnnuoqKhgwoQJWCwWnnrqKcrLy8nPz+/xz25/0v59NDrJ89jpRDyd1U/PKNxWltASaLpGbagWa9KKgZk31sCgpaWl223uNwGdfSUp8p0vryKpGxx3YAnTR+XOTp6iR5OEP6kj/FEt8U2BDvvVfAfWUjf2Sp95ly/fQdCbZMXGVby3eBkxLYEj4WWyZyojjx1CojpsdsnTzaTByeYYejhJdKV5px3AMHT0wDaMiBvc03AcehiK2oL7sEoKzz8L24ABe+R9EULsewzdIPJ5PcH3qszfQRl/RCxqGLf6NnbjU2zKBqxKFUpcNxMo2RUoGAqFw6HieKgYj2vAeMgfAoqCobUN0VLz7Cg2lXhVkMindSTrIlm/k6zFLlS/HT2ho1gtOIb6cY0pwjbQm54OXnTNMAxIGuhxzfyCh4HWECNRFya5I4oeTqDYLFjcNlS3te3LoqKQqAmTrAujOK3YKzzYKjyoBc6vxHtvGAZGTCO5I0p8awvxLS0ktgXRmmMYmvl31Ejk7plm8dmxlbsxkjpaQxQtEAcDFLsFe6V5A0Wxq+YQ6cYoyaaYedc1lMDQDGxlbmwDvag+u/nlO2IGjZINUbTmWFswx6pgcVrRgwn0lgSxliZia5sILqrK2S5buRvXuBLch5ZgLZJgqRBi18ydO5fZs2czZswYIpEIGzZs4D//+Q/XXXcdhx56KIWFhVxyySVZSYhzPed73/seH3/8Meeddx6KonD++edzxRVX8NJLL+1Su37+859jtVqZN28eVVVVVFRU8P3vfx+A008/nWuuuYarrrqKWCzGrFmz+PnPf84tt9wCmEOJduzYwXe+8x1qamooLi7m61//errXTeYFeOriOlU24oARvPTyS/zsZz9jypQpuFwupkyZwjnfOIdENM4vfnoziWCMb1/4LVpCQSaOm8ALTzxLvqc1r2vrn1TDbcHwWTBQ0L1mIEwvsJD0gdb6K1tzQ8Jtnle3gaFAzJogoSWIKwkSliQBbwQDA4uuEG0wb8BF3AnCnjhq0oJuMf92GXHN/JvSjNmrx4BIVTMJq8bp55xKVIny4O8e5LrrrsPldjF67GguueIStgS2kLAm+NUdv2Lj+o2oqsq4CeP40z/+xJbgli6DGrkCE+2DGEb2zpx1uqrb7foZz+us/q4+Z2/QEzq14VquffVatse3Z+3TIt2PeShGZ+GlfVQqKXJzc3NWEqy94YuqZmb97l0UBV750TGMKvPlrGcYBuGPamn+z/q2u+AKOEYW4B5XjK3cg7XEjcWhput/+umnLF68mOrq6vRx7JofX/1YjjhtFJNO6RjpNpI6ie0homvqCLy2DD1kw+Iu7rT9tnI3jlEFOEcWYC1xmXllrAqKyyrDp4TYnyRjxFd8RtPLDcQb2i76bMp6nJYPcaofYle+RFE0c2rvikOhbAyUjoHycVB6ENjdPT6tYRgka8JEVjYQXdVIfFMzdDKSy+K1YSv3YCt1Yy1zo+Y5MKJJtFACi13FXunDWur+agQeNAMjYd51SzZGia4xL+q1pih6TMeIJzt9H3eFYlextQZ3bKVm0EIPJzB0A2uB0xxi1DrMSLFm99zUo0nz7qF13+jRqYUSxNY0Et/cgtYcQ2uJm4GVSBItkkTpxjccA9BdkCi1EBuoEBsEsTLzS3bqC5+h6xAH3WaAhfQdtPSjWbFDWXo7s0wzsIcs6FaDhFM3v7THwdmk4myw4N5hxb/dhrNZRTEUDAySDgNbLPs9bymJUzcsQuPAKDFPEk01QGlrW/r1tX7NS7UltZ65P31xk6NO5mtN18l4ve2f1/74mW3I/f53XTfnhURbQed1d3KXtau6XbVxp8ft4n3ocNyu3rNebF+uz0Tm5zN9DIPc5ZnP6eRz0KFe+89IZ+WZ/2bavR/tn5OzvJP3c1fepy63cxy7yFrEt8u/TfmgctQuZrrdkxeNPTp2D5vR2+3e6fG6eYHdZZ1eukhXDZU8zYtf82AzOvZ/MDAIqhFa1BAxJU5S2fsdDqyGikd34dXcuHSHOUlLOzElQUAN0qKG0JRe/CIhdpuCYgZ0ttZy9/q7qUnUoNA28Ywe1vnwux92K+bRb3ro7At+98YaAE4dN6DzYI5m0PTvdYTeN6Ns1mIXninluMeXoPodHerH43H+9a9/8fnnn7ceQMGW8OMMl+OIluJ02xg+vjTnuRSrhWTdGmpv+wmJbdsA8J12DnlnfAeLw4eR0NECcaJrG0lsC7b28AkTfGdb9nHsKo5hfjxTynGOLkJR9/8LKCH6PV2HwFbYsRZ2rEs/6nWbCew4kmDyTMCFQhiv+k881pexOhNm0KZoPJSeD0OnmQEcy+7NjpGiKIoZpCn34D+2Ej2aJLauGSOhodhU9EiC6KpGoqsa0YMJYmvNwEWnx7Or2MrdWEvcWIucoIDWHCfZEEVviZu9UFpnjFO9NiweGxavDcWioAXiaIE4FoeKWuDA4rCavSKLzPwlRlxDjybN3hOtXZgtTisWnw3VZze7O0eSJBuiJBujaI0xs9u024rFbZ7HzJlia21XjGRtGC2cRLEoKKqSDi6AOfI1VSe5w0yGiG72uiHZgy+frb+f1TwHthIX1mIXFo8NI2EGZVLDc5LNMdANM2BW4jaTXFeHSNSEMOKa2cMqR8/R7B8AKH4b5FvNQEFDAgLmTQrDqWB4VHSPQsQeI+SIkbTp2EIKjqCKokHcqRFzJok7kgTsYeqVBkL2KNakSn7QTUGLF0U370yGHFFC9ihRNU5MTWDRFUoDeVQ2luKJOdAUnaAtgmKAXbdh06w4k3by496umg9AiyXEGtdmVjs3sca5ia2OGhKKho5O2BIlpIbRU5Gfxtbls+7/SPaICrCVWXHpDoKt7fMnPUwJHsJxgckcGjoQX50dX50dyEs/rVlt4V3fcl7PX8xK50ZyfL8XQvSSkD1EsjRJXI9j0faNILfYNQoKNsOKzbDi1dz4NHc6QKJjEFLDhCwR4pYkOroZwEnNdAuoipozoNITVosVm8WWvqBPHU9JTQqTcfzMsigaMSIohoJigKpbsMes2OIqDsNGSbKAkmQBmtXAsLQGUhXQrQaaw8DI+OhmvYZ25+ywHzp0CMj1HqTamrM8h3S50kn5zo6TerqBmQRSS0eD24KaRluQOF0nPaFxa3st2e1WVAtYlawbWp11iNjZ+5QSjUZRm1SeOeMZnM7sfLiBQIC87+blfF6H80kPne5ZvG4H5//xfRQFXrvmGA4o7RjQMXSDxqdWE/64FhTwzxiC79hB5gcgh1gsxt/+9jc2bdqExWKhQB8ONSX4870cOLWcyoMKKR+eh2prd4c0FiP43/8SXfElzkMOZtvVP8Q2cCADfv1/uCdNynkuLZQgtraR6OomYmsb0UIJ88Pb7qdv8dvxTC7HM7kca37HAJQQYi9ITQXeUgWBKghsa33c3rbeuBG0WNZTovoRNCUvQTPKAXD51pA3vh7r8DFQMc6cYWof6I1nJHTi24Mka8MkasMka8LmzH5uGxa3OfwkvrWly0TNe5xFSSeo36kc+VB27Zzm2HrHsDycBxRgq/CgOFUsdtUcV29Xd9pjKa7FWde0jnAynC7TDZ2qYBX1kXoUHdwBG/HtQRw7LBSFfSRUjbAtRkJP4ApZKYkVUBorwKn3j78BGxzb+MjzJdW2ehqsAZqtLbSoYeK2JC6Pm7gliaIoWV+SFUVp+7Lcup55Z6x9WWd1M79U56prUSzpfZ3Va3voeJ5Uefs6npiD0TWDOaBmAAObilCNjt8zGjwtrBqwjTp/MzV5TUSc8azXn+ucKR1e707er/bHzTxGersbX/y7U7dHx93JBUHWcXupfe337+yCKLu5u96+rnpad/gZtv8MdGe93b+P9uXt29pleY5/Azn/DeX4d5OrbV29Dz15/3t8vAQ4Ag4GDx6Mw+nIebzd0dVncK8cfzdP3/74mzdv5rBxh3Va/+NPP86e3jvj915Pz9X+GKedchrvvvtux/0GXH/1j7n+yrkdn2pXzRtFLmu/7C1s6Eb6Ro8R77wHkeJQzZtVTmun16z7MsMwzGTSWmvOId38nmlEk235h3qZYrWY388c1vSNNsWigKp0+bu4vWg0yoYNGxg2bFjugE43Yx4S0OmGhKZzyr3/Y01tkG8dPphfnXlIznpN/1lv9n6xQOH5o3Ef0nmOHU3T+Otf/8r69eux2+1ccMEF0OKlsTrMIdMHYs3RdVOPRml4/Al2PPwwenMzACNef43wkiX4Zs5E9XZ+p7IzRlInWR8h/HEtoWU1bcmaFXCMyDdn3vDYUL021EInzpEF+0xXeyH2aVoCYi0QC7Q+BtttZyzx1sdQfWvgpiorWNMpiw0KhxNzHklz3deINxcAoPqt5J85EteYzodg7usM3SBZFzZzwNSGSbbO+KD67K29bGxoLfHWOy4GejCBlsphkjRQ/XYzd09UQwvEMKKa2dumIQpJM1eQ4jD/GJvBEgtGJIkWTGQlxLX4bOYwpAKn2aMmYvaA0YJx9GCiLSiuKliLzHxBqfwsFrcV1WdPf6G0eO3YytxYi13mH36LYn5htFuIWRKE9HD6i4CBQV24jppwTXp4warGVazcsRI9Y/xVOBFmbdNaIklzNo2EliBpdD37Rfd+AJCnealIFDMgUYoFCzvszWxx1WDHRoGWR6HmJz/pYyDllBpFODU7QWeUFlcEXTXwxl244048cQfehBtfwo0jakW3GkS8SYKFcQyrgTWh4oio2KNWrAkLasJ8D6IFGi1lSRIFYMOKPWZ+qTasoFvN4U+JfMgVd3JYHYwvGY/b1vOhg/2NoRkYSTO/UqI6TPijGiKf1XfID2Txmb3X1HwnzpH5eCaXo+b1j6CdEPuiri7GREfJZJKNGzd2un/o0KFYrXtm8Mi2bdsIt4TQIklzhqmMGzaF+QUUFhSCopgX6jYLFq8NSxfD6PobI6mbQQ6t9e+CbqBHtexAjwKKw5yVMasXitXS40BFbzEM8/uUoRnmnUvdQI+Y7TZ0I/0dsFNKa/uVdHTQXE/dJ1Fav4tZlfSxjNQx9exjG0nDDBDt7Hw2FYvLagYCd3LNLAGdvRjQeejtddzx0koKPXbe/PGx5OWYWrTlf1tpfnEDAAXnjsIzsazLY7766qu899572Gw2Zs+enZ4SsDNGIsHm736X8OL3AXO2qrzTTqfgwguwleYektVTRlIn8sUOQku2E1vfnLOOxWfDM7Ec98RSbCX7/xdlsZ/RkpCMghY3H5NRSEQhGYFkDBKRjuWJaEZZa70O5dG2oExqSUZ3v72eEvAPAP9A8FW0rg8w1wuHoTsqaH51S3qIp2Kz4D16IL7plekcXSKb0TpjhGKzdPrlxEhoaKGkeceqq7wIrXe/jLhuJodWLYQSIVY3riapm0GVmnANqxpWpbfB7EWzvnk9oUQIgJgWY1NgE5rRO2Pw/XY/hc7sKUdL3aWUe8qxKOaXixJXCYP9g1GVttdnV+1UeCpwqI709gDvgPS26D/0WJLIp/VEVzeme8F1YAHn6CK8h1fgOCC/X96BFqIvSUBn32VoujmcOpo0Axm6kd2T1mImpE/PKmVtvbDfB3ox701GUjdvVIUTnU4SAJjvjc2CYjNvgClWS/pvRmYoIX2zqrPvV611c+1PTWRgJHWMuG4GbTS9e7mfFMVMGZI6v6qYN+2cvdu7Kt0bKKGZgaWEZgabFDoEgCAV3DF7WGNR2t6f1jb1VkBHcujsxPbmCPe25s756cmjcwZzwh/XpoM5eScP3Wkw5/PPP+e9994D4Mwzz9x5MMcwqPm/XxNe/D6K203FzfPwn3Zar08zrlgtuA81Z89I1IWJrW8273q33omObQqgB+K0vLWFlre2YB/swzOlAtchxXLxuDcYrdFpQ9/J0lUdrReO0UkdXctdL12udayT2taT5rqeMNe1ROt2MqMs2cW21vqcZPaiJczASiwI8aDZhr3N5gaHD+xe89HhA4c/Yz1jcRWYwRv/APCVg7XzC+nIqgaanlluzr4DuA8rJW/mULnjvhNKa6+Y9uoj9eleLgCoQKx1ASLJCG9teYutLVsB0AyNNY1rqI/Up59iYNAYbey1wEyeI49B3kGorTmOytxlTCybmBVgsVlsHJB/AH6HP71d5i77yn0pFdksDmt6+DSAFjRzShlJnWRdhNCyGuIbmomu2EF0xQ7UIieeSeW4Dio080KpChaHKj1yhRD9hmEYrZMrJDGiuXuqKg4V1WtHcarydxLz2k/12VF9dvSEhhFJose0th5Mhhn0QTfSU6bv/KC0BS0Uxez1kgp2pI6bylOTqaveNpnBEJsFi8sc6qSggIW9FoxTFCWdR8fiyo4JpGckjSXRI2aORiOhoSU6vmepqek1zAkpdpcEdHbirldWEY5rTBxSwDmHdQy8RNc20vDUagC80wbgPabr4ExtbS3//Oc/AZg2bRpjx47tsr6h69T86lc0/u3vAAz49f/hP+GEXXkpPWIrcXfogWNoZg+e8Ee1RFc3EN/cQnxzC43Pr8FW7kH12LAP9uMaW4St3NM7DTGMth4RybjZsyK1JGPmBbsWa91ut1+LtwsMJNuCClkX/jm2M4MMmYGIVN32ZentVJneLojRWf2M4AgGOQMlqWUPzpLwlaSoYHOB1WkuNidYXWYQJV2esW5r3Wd1tdZ1ZpfnCtLYfaD2/q/Z4OIqmv61DgywFjnJ//pInCPye/08/U1tuJaWeEt6O6knWV67nHXN67p83qqGVXxU+1GvtaPMXZYe6uOz+xhbNBa3te33qUWxMDRvKEXOIsD8gjAibwRlnq5vBgixq1SvHdVrB8Ax2I9nYhmJmhChJdWEPqpB2xEl8MpGAq9sbHuSBWxlHrzTBuKeWCoXP0KIfYqhtfa8SV1Eh5NZQ6kUmwXF2drTVlWyepWIjiw2FWwq7W95pXo2Gwnd7JESbx26lerQk3pLUwEZg9acNl3NRtYxjytg/pxsqhm0Sd1U6KPhXj2lKArYFFSb+ffW0PSMwI6eNUTMSOoYSR0tGUdrjlH/+Bf4hhVjH+AFBdQCJ4az+9d9/Sags2DBAhYsWICm7b077PXBGP/+pAqAn806CEu7XwLJxigNf1sJuoFrXDF5s4Z3+YHTdZ3nn3+eRCLB8OHDOf7443fahsBLL5nBHEWhfN7P90owpzOKasE9rgT3uBK0ljihD2sILa1G2xElsTVIAoiuaiTw2ibsg1x4xlpxDoygKq25Q+Ihs5dEPGiux4Idy9LbYXN4SyLUGswQPaZYdmNRdm+/JbWuZpSp7eqobXUsKlis5qLa2tbT26qZL2ZXtq2O1uCKpy0IY3X02sxOe5OhGzS/vJHgO2YvEc/kcvJPH45i63+vpSe2tmxldePqrG691eFq/rv5vwTi5kxNLfEWtgW3dXaInVJQcFo77zJvUSwcUnwIk8snpxPdVvoqGewfnJWMMc+RR7mnfJfbIcTeYivzkH/6CPwnDSXySR3hz+qJb2w2u90bgA6J7SEan15NcMl28k4YgmNkfr/4Yi2E2D+lhwhFOkn0qyrpCRYs+/l3o70l3bPZrgIdR6pkMvTWnDcZj+meOhbaAmpG9lAtDNL5bvaXvzGKajFvpLRLcWto5pAyPaahhMyeZMm6CC3rN2fVC6ndyKXZqt8EdK688kquvPLK9HiyveHJpVtIaAaHVuYzYXBB1j5DN2j4+0r0cBLbQC+F5x6406jv4sWLqaqqwuFwcNZZZ2HpxpCp4NtvA1A4Zw4F55+/6y9mdxiGmci1pcacJrl5G2qwGn9kB75h9STzdZItVrSojWh4BNHkBOJbI8S3AuhYlXqsyhasShUKSWyWLTgtS7EooZ63RXWYF+OqzVxXba3b9rbFmrGeGRiwWLMDB+nt1jIltc+Ssa62BRwUSydlmdvtyy3t9rXbTtWj9ZddKlCC0jEA0t1gCop5HrFfMQyDpufWElpaDYB/5hB8x1b2uz984USY5piZoyuiRfiw5sPsYUuGwYc1H7K8drmZ/Neg20l+VUXFZ8+egXCofygTyyZitXT+585n93HS0JOkh4z4SrLY1azhWWB+x9Fa4kSW1xJ4YzOJLS3UP/I59iF+vEdUYB/iNy+aZLi1EGIPMzImJOgQxEnlS2lNRCtDqfpWelhUd+ru8dbsmxTVguIyh43ZnAZqkwP/SSUYXwZINsXM7731EfRA9ye46DcBnb0toen89f1NAHzn8CEd9oeWbCe+uQXFqVJ04UEots4voA3D4L333uO1114D4MQTT8Tn6zjtea7npZIge6cfsysvo/taqqH6M6j5Apq3mrPstGyHUB0EazudcUfBjNWm4rVeK2hqPiHtBML610jqlSSNQSSNjKFoGqBouCtq8Y5owF6ituYX8Zr5RuxesLtb1z3mkBabuzXo8lX95y++yoykTtO/15nBHAUKztl54vW+oLf2pjMMgyXVS/jP+v8Qy/jd0RJv4YPqD0joiR4d16pYGVkwMit3jEN1cMygYzgg/wCzjsXKmKIxeO09n+1PCJFNsShY8xz4plfiPqyMlre3Enx/O/FNARo2BdL1LH47rjFmYuVeG2othPjKSA3nQTMwDANFUcyeG61Dcoyk3nEolSNjFqF+OM22EJkUi4J7bDHOiW3XyoZmYPt0M8zv3jEkoNOJfyzbQlVzlGKvnVnjKrL2aS1xmlvHmefNHIq1sPNu+i0tLTz33HOsX78egClTpnDYYYd1qw3x9etJ1tWhOBy4JkzYtReSouvQvAUa1kHDejNgE6yB5m1mECdUu/Nj2H2QN7Btxh1Psbm4i8FdCK5CcOWjugrxu/LxW1S0ljiJ6pA57XBDFEMziK1vJlkbJlxVQbiqAvtQP94jB+AaVWxmKBdCpMU2BWh6bi2JarNH274QzNnSsoXP6j5LbxsYvLvtXV7e+HLWbE6dsVlsKChYFAtjisYwIn9E1rClAd4BHDf4ODxW8wLR7/Djsrp6/4UIIXZK9dnJP3U4vmMGEnx/O5HP6kk2Rs28FYE4ofe3E3p/O/YhflyHFOMeX5LO1yOEEHPmzKGpsZFn//Gs2cOmdWpoI6FjxJI7TRF5wrmnMO7gccy/57dY3PtuEGfjxo0MGzaMjz/+mPHjx+/RcymKwnPPPceZZ565TxxH9C5FVXCOKNh5xVYS0MkhmtC47421AFx53AE4243BbH5xPUZUwzbIi2dqRa5DAKBpGk8++SRbt27FarVywgknMGXKlG53BQy9txgA98TDsDh6OHONloTaFbD1A1j/Nmz8H0QaO6+vWKBoJJQfDAVD26ZG9pa1Bm5KzV4zPZTKnM7Itg+lYRjEN7cQfK+KyGf1xDcGaNgYQM2z4zl8AM5RBWb03aFi8dqk66T4SjIMg8Arm2h5awsAFo+VgrNH4RpTtNfbsqphFZ/UfQJAdaiax754bKe9bJyqk7NGnsUQf1sPR1VRmVg2kZEFI/doe4UQvU/1O8g7cSh5Jw4FQI8kiW9pIfTBdiIrdhDfFCC+KUDzyxtwjSnCPtiPYrNgLXJhH+TF4pSvnELsK4499ljGjx/P/Pnz98hz0rluYhp6OIEe1UjWhXNXtihtM+q15lKhNY2FYm2d1chtNa8nBADbt2+noMC8ttqbgSSxb5K/rjn85f1NVAeiDMhzcsHUwVn7ouuaCC+vM4c9nHlAl3lz/vvf/7J161YcDgeXXnopJSUl3W6DoWkEXngBAPfhR+z8CVoCNrwDa1+HbR/C9k/NmaEyqXYoGAaFwyCvEnxl4C2H0oOgdMwuBWx2haIoOIb4cQzxo82KEXx/O6El1WjN8Q6zbKS6c/uOHoi1SO7Qi68GI6nT+PxawstqAHBPKiPvpKF7/K53Qkvw7rZ3aUm0zRS1sXkjD3/+cHo4VcpBhQelp8oGKHWV8s3R36TSV2m22ebOGiIlhNi/WFxWnKMKcI4qQAvECH9aT3h5LYmtQSKf1hP5tC03FqqCc2QBnsMrzJs2MtOMEPsVQzcw4hpGXEOP69nThqd64KRmMLIobQlwnerOkxfvpSmp+5Pycpl8QbSRgE47wViSB94yp7j94YyROKzZv2QCr5sZqD1TK7AP6jwPTk1NDe+99x4AZ5xxRs+COYZB/YIFRD75BIvbjf+UU3JX1BKw7k1Y8U9Y+QJEm7L3O/JgwHgYejQMnw4DJphJhPchqTt+/uMGE/60jtAH1SQbo+YUb3GtrTv3ku24xpXgO2YQ9oGSI0Psv7RgnB1//ZL4hoAZOD5rJJ4pe+YP96bAJn7/ye9pijUBsLZxLTXhmpx1J5VNIt9hznAzfdB0Th9xunzBEkIA5t9y31ED8U4bYAZ0VjaQrAlhJA0SNSG0xhjRlQ1EVzZgLXHhnTYA92Fl5nTCQuxPDAMSnfRE2ZNs7m7nmZwzZw5vv/02b7/9Nvfeey8AGzZsYNOmTVx33XV88sknFBYWMnv2bH71q19htVpzPmf92nUMLBnA9y7/Hm/+721qamuoHDiI733nUn5wyRUo9tZExS4rloQVe8XOv7+HQiEuv/xynn32WXw+H3Pnzu1QJxaLcdNNN/H3v/+dpqYmDj74YH79619z7LHHpussWrSIm266iQ8++ACHw8GUKVNYuHAhBQUF6LrOr3/9a/7whz9QXV3NqFGj+PnPf84555wDQGNjI1dddRWvvvoqwWCQQYMGceONN3LRRRd16/1dv34911xzDUuWLGHkyJE8+OCDHHFE2835Z555hnnz5rF27VoqKir4wQ9+wI9//OP0/qFDh3LJJZewYsUK/vWvf5Gfn8+NN97IlVdema6TOVRq2LBhAExoTc8xffp03nrrLZYuXcqNN97Ixx9/TCKRYPz48fz2t7/tduoP0X/0WUAnHA5z0EEHce655/Kb3/ymr5rRwSPvbqAhFGdYsYezDxuUtS9RHyG+oRkU8B1X2ekxDMPgpZdewjAMxowZw5gxY7p9/vjmzVTfdhuht98BoPzWW7APGphdKdYCHz0O7//ezIuT4i6G0bNgyJEwcCIUjug3sx0pNgueiWVZuUGMhEZsQ4Dgom1EVzUS+aSOyCd12Co82Ct9KHYVNd+BfbDP3JaLS9HPJapD1P/5C7TGGIpDpfD80bhGF+7+cfUEL214iaXVS7N62ry5+c2s3jgAJa4SRhWOSm+risqJQ06UAI4QYqcURcFeaf5NzpSoCRFaWkNoaTXJughNz6+j+aWNOIbnofrsKE4Va6EL96ElWFxyr1H0Y4kw3D5g75/3xipzIpFuuPfee1m9ejUHH3wwv/jFLwAzTcQpp5zCnDlzePzxx1m5ciWXXXYZTqeTW265Jes5t95yK1pLjCJbPokdYQaWVvD33/+ZwuIilny8lMt/fBUDRw7hmxd8E6BHPfKuu+463n77bf75z39SWlrKjTfeyEcffZQ1lOiqq65ixYoVLFy4kAEDBvDcc89x0kkn8dlnnzFy5EiWL1/O8ccfz8UXX8y9996L1WrlzTffRNPMGbLuuOMO/vKXv/Dggw8ycuRI3nnnHb71rW9RUlLC9OnT+fnPf86KFSt46aWXKC4uZu3atUQikU5a3NFNN93Eb37zG0aOHMlNN93E+eefz9q1a7FarXz44Yd84xvf4JZbbuG8887jvffe44orrqCoqIg5c+akj3HXXXdx4403cuutt/LKK6/wwx/+kFGjRnHCCSd0ON8HH3zAlClTeP311xk7dix2u9mbu6WlhdmzZ3PfffdhGAZ33303p5xyCmvWrOnW5Dyi/+izv5q33XYbhx9+eF+dPqemcJw/vmMmL77mhFFY2yXdCi8zpwt2jirAmtf5UIIVK1awceNGrFYrJ554YrfObcTj1D/4EDv+9CeMeBxsNkquvJK8004zK2gJ+PJf8Nkz5tCqeOtFmLsYDv46jDkDBh/ROgX2/kGxqenu3PGqIMF3thL+rJ7E9hCJ7dlTnluLnLgnlOIcU4St3CPduUW/E9vQTP2fv8CIaqhFToq/MwZb2c6/nGm6xmf1n9EQbWg7lhbj2TXP8mHNhxiYM0dohpbz+eNLxnP2qLNRUPDYPBw96GgZKiWE6FW2Mg/5pw7Hf8JgwstqaHmvCm1HlOiXDVn1Aq9uxHvMILyHV0jOHSH2kLy8POx2O263Oz1056abbqKyspL7778fRVEYPXo0VVVVXH/99cybNy/9HJfDSZHFDx7z5pDVYePWW3+B4lJRrBYOnHowH3z+IU8/+3Q6oNNdwWCQhx9+mL/85S8cf/zxAPz5z39m0KC2G+ybN2/m0UcfZfPmzQwYYAbO5s6dy8svv8yjjz7K7bffzp133smkSZN44IEH0s8bO3YsYPbuuf3223n99dfTvWaGDx/Ou+++y0MPPcT06dPZvHkzEyZMYNKkSYDZY6Yn5s6dy6xZswC49dZbGTt2LGvXrmX06NHcc889HH/88fz85z8HYNSoUaxYsYK77rorK6Azbdo0fvrTn6brLFq0iN/+9rc5AzqpUSBFRUVZQ7G+9rWvZdX7wx/+QH5+Pm+//Tannnpqj16T2Lf1yV/LNWvWsHLlSk477TQ+//zzvmhCTg+9s56WWJLR5T5OPSQ72bEe1wi15rPwTO58+EM8HueVV14B4KijjiI/P797J7daCb77LkY8jufIIyn7+c9wDBtmJjf+5G/w9p3ZvXGKDoAjroJDzwdb57Ns7S/sA7wUfnM0eafGia1vJlETxkjoJOsjxNY2ktwRJfD6ZgKvb0b12/FMKcd5UBHWAgeK0yoBHrHPMAyDpJ4kaSTRdI2kniT2ZSOJp7eBZhCrUKg/Pckm/TOaNzZTHao2p/Bs1Rxv5oPqD2hpDeo2x5qzgjmdKXQW8vWRX8dvb8t7U+As4JRhp2BXJdGgEGLPsziseKcNxHPEABLbgsQ2BTBak6ZG1zSSrI0QeHkjLW9twXv4ALzTBkgi1D3IMAziW1rQGqJogTh6NInFZcVI6igWBdXvQI9rJGvDGEkdtcCJolrM3tEDPKiFTum52Z7NbfaW6Yvz7oYvv/ySI444IuvnOW3aNILBIJvXbmRQyQAzFUIkCUkdVAVrgROL08qCBQt45JFH2Lx5M5FIhHg8vkvJedetW0c8Hmfq1KnpssLCQg488MD09meffYamaYwaNSrrubFYjKIic9KI5cuXc+655+Y8x9q1awmHwx0CI/F4PD1k6fLLL+fss8/mo48+4sQTT+TMM8/kyCOP7PbrGDduXHq9osK8nqytrWX06NF8+eWXnHHGGVn1p02bxvz589E0DVU1b8xnDtFKbfckgTWY6T9+9rOf8dZbb1FbW4umaYTDYTZv3tyj44h9X48DOu+88w533XUXH374Idu3b8851dmCBQu46667qK6u5tBDD+W+++5jypQp6f1z587lrrvuSueY2Rc0heM8tmgjAHNPPBBLuwBAy9tb0YMJ1HwHzoM6HwKxePFiAoEAeXl5TJs2rdvnVywWym+eR2LLVnwzTzR/oa5+FV65AXaYM27hKYGJF8GBJ0HFhH1qONWibYt4ddOrJLQEh5UdxsyhM1EVNd07wMBID/UwDAMdPV2ektrOLE9dyBpk7BtmwFALBgrgxYi7YGUY5cswyuYYWiCeDu4AGCrohSqhco3gQI3wAB3NaYBB23kyzptuk0HH9rRup9Yz213uKefwisPZ0rKFZdXLUC1qempmVVGhk+88mq7RFGvq1nTPKUonB+vsi1Wu+j39EtaTY+Ss24M2WxQLNovNnN5aUbI+MwC6oXf4bKW325VpukbSSJLQEui0DjcyyPpZx7QYNaEa4lq84/FS24ZBQ7SBQDyAbuid1sm5jhnE0Qwta8iTR3PxrbpTOa1xOioWFns/4f/8jxB/p+sZpNrz2XwMyxuW9RkbUziGb47+Jl6bOWa90FWIzbJv5dASQnw1KZaOQ7MMzSC8vJaWt7eQrI3Q8tYWWt7dhmtsEfZBPhS7BWuxC3ulT3Lv9JChGWjBOFpDlOSOCIrVQrIxSvjjOpK1u57vRXGq2Ad6UX129JiGFoij+u3YB3pxDM/DWur+6k1hryjdHvq0rzIMAyOmkQzEAEg2RtHdiXRyY4vbhprvQLEoLFy4kLlz53L33XdzxBFH4PP5uOuuu1iyZMkeaVswGERVVT788MN08CPF6zW/77hcnU+iEgwGAXjxxRcZODA7pYWjdUbhk08+mU2bNvGf//yH1157jeOPP54rr7yy2ylCbLa271qp77i6rndWfY+ZPXs2O3bs4N5772XIkCE4HA6OOOII4vH4Xm+L2LN6HNAJhUIceuihXHzxxXz961/vsP/JJ5/k2muv5cEHH2Tq1KnMnz+fmTNnsmrVKkpLS/nnP//JqFGjGDVq1D4V0Hn+421EEhqjy30cf1Bp1r5kU4yWt7cCkDdrGIqaO5ASjUZZvNicanzGjBlZ/6C7wzV2LK6xYyERhdfmwQcPte4ohKN/DJMvAVvfzPQUjAfZ0LyBL3Z8QVyLs6pxFZ/Xf45maCS0BFWhtrsR/17/b25dfOveb6QdbMOtTGsZz7GByRwSHolbd6JooNZp+OvA/5mKhsI65xY+8axihWs9VfY6YkqcOlsDumLs/DydmD5oOodXHM5ndZ8x7715vfjCxP5GMRRmNE/l4tqzyNfMC5r/5L/LY5X/ZqBnkBkABDw2DwM8A7Ba2n5V21QbE0onMMhrdkG2WqyMKRojvWyEEP2aoip4JpbhnlBK9MsdBN7aSmJLSzp/Xrqe04r3iAq8R0rvnc5ooYSZf3BNE1pzDL0l3jbTUDuKXcU20Ivqt2NxWdEjSRSbxZx2uiWOYlOxFjlRHCpaYwxD00nuiJKoDmFENWLrmrOOl9hG1lA6a6kL+2A/qt9uzmqkmoE556gCFNu+c2Pyq8Zut6dzygAcdNBBPPPMM2jhBHpLHCOhs+idd/F5fQwaOAiL14bd48RwWrAWto0MWLRoEUceeSRXXHFFumzdunW71KYRI0Zgs9lYsmQJgwebsww3NjayevVqpk+fDpiJfzVNo7a2lqOPPjrnccaNG8cbb7zBrbd2vBYZM2YMDoeDzZs3p4+ZS0lJCbNnz2b27NkcffTRXHfddb2S8/Wggw5i0aJFWWWLFi1i1KhRWQGq999/P6vO+++/z0EHHZTzmKmcOZk/z9RxH3jgAU5pnVxny5Yt1NfXd3i+6P96HNA5+eSTOfnkkzvdf88993DZZZelM4E/+OCDvPjiizzyyCP89Kc/5f3332fhwoU89dRTBINBEokEfr+fefNyXwDHYjFisVh6OxAI9LTJO2UYBguXmsOZzp8yuEOPgeA7WyGpYx/mx3VwcafHWbJkCdFolJKSkvRYzR42BNa/Ba/cBLVfmGVTvw/H3QROf5dP7alwIsyO6A52RHakH6tD1WwNbiWUCLE5sJmqYJXZo8GApNF17xGrxco5I8+hwFnAv9b9i23Bbd1ui0LbdIRK63/m/0rW/sx6HcpTXRMU+Mi5mo9L16AYCjbDSkHCz/DoIA6LHMSBzYMpjRQwKjqEUdEhWe1IWJJs8deyqmgLq4u3ssMd2Gl72k6rMLbI/JmXuEs4auBRZq+S1p4cneUvAbM3Sp4jD6faceic0ck3sFzlmcNydnqMTr7Y7bHzdaKzYySNJAk9QUJLYGBgwQIKWLCY733r+29RLFk/j/bbiqKgKipWizXd2wfaPmepbavFSrm7HJfVlfP4YN5lKXAUUOAsMMsVpa09GXVT+9q3x2qxoioqyvYEiZe2o20374qqJS7yTxvOd0cdzXe5odvvnRBC7I8Ui4JrbDHOMUXEN7cQXdVAsj6CEddJVAXRAnFa3txCy/+2mpMpTK3AVuH5yg790VriJKqCxKtCJLYHSVSFSO6IdPw7b1FQ8+xYi12gGVg8NhzD83BPKN2lfEVGUidRGyaxLYgeNYNAqt+B1hwjtr7ZHMrVHCNZGyFZ2zGhrGJXcR5UiPuQYpwHFqDsbOpq0auGDh3KkiVL2LhxIx63h+9f+j3mz5/PVVdcyeVzvsfq9Wv45W/v4Jof/gjHQHPikWHDh/HBBx+wceNGvF4vhYWFjBw5kscff5xXXnmFYcOG8cQTT7B06dL0zEs94fV6ueSSS7juuusoKiqitLSUm266CUvGiIRRo0Zx4YUX8p3vfIe7776bCRMmUFdXxxtvvMG4ceOYNWsWN9xwA4cccghXXHEF3//+97Hb7bz55puce+65FBcXM3fuXK655hp0Xeeoo46iubmZRYsW4ff7mT17NvPmzWPixImMHTuWWCzGCy+80Gkwpad+/OMfM3nyZH75y19y3nnnsXjxYu6///6sfD9gBmPuvPNOzjzzTF577TWeeuopXnzxxZzHLC0txeVy8fLLLzNo0CCcTid5eXmMHDmSJ554gkmTJhEIBLjuuuu67L0k+q9ezaETj8f58MMPueGGtosSi8XCjBkz0j1X7rjjDu644w4AHnvsMT7//PNOgzmp+rkirL3p063NrKxuwWG1cOb47O53ejhBaKmZDNn/tY7BnpREIpGOph5zzDFZv3x2yjBgw9vw5u2wpbWLoqcEzvw9jOyY/KonYlqMjc0beX/7+3yx4ws2BzazObC5w8w23VHgKODg4oPx2ryUuks5fMDheGweFBQGegdS4jaTcl1+6OVEkpGsi+rUBXn7i9y+oDXHiK5rIrau2fxy2BxDj2vYklaGNw1geNMATl431byDNLoQx/A8FNVsq1roxFrs6rLtk8snM7l88t56OaIfMHSD6OpGwkuriazYAYb5ZdY/YzDeIwegWOUupRBCZFIUBccQP44hbTe0DN0gumIHLW9vJb6lhdCSakJLqrH4bDgPKEDNd2AtcGIf6sda0vXf6v5KCyUIvb+d2KYAie1B9JbcQ3RtA714pw3AVupGzXNg8dh6NZ+gYrVgH+DFPqDjVNTeI8xktXo4QXRdM8naMFpLHDQDI6kT29CM1hRL975S7BacBxbiGJGHmu/EYlex+Gw7/b4ldo1hGFx71Y+Yc8lFjDloDJFohFXvfcY/H3uaG277GZNnHklhYSGXXHoJP79lXvpnMHfuXGbPns2YMWOIRCJs2LCB733ve3z88cecd955KIrC+eefzxVXXMFLL720S2276667CAaDnHbaafh8Pn784x/T3JzdC+zRRx/lV7/6FT/+8Y/Ztm0bxcXFHH744elEv6NGjeLVV1/lxhtvZMqUKbhcLqZOncr5558PwC9/+UtKSkq44447WL9+Pfn5+Rx22GHceOONgNnj5YYbbmDjxo24XC6OPvpoFi5cuKtvd5bDDjuMf/zjH8ybN49f/vKXVFRU8Itf/CIrITKYgZ9ly5Zx66234vf7ueeee5g5c2bOY1qtVn73u9/xi1/8gnnz5nH00Ufz1ltv8fDDD/Pd736Xww47jMrKSm6//fac08CL/k8xOrtF3p0nK0pWDp2qqioGDhzIe++9l5XM6Sc/+Qlvv/12h/GUqYBOV13YcvXQqayspLm5Gb+/d3qtXPPkcp77eBtnTRjIb88bn7Uv8OZmAq9swlbhofTqCZ3+YVm+fDnPP/88eXl5/PCHP+x+QGfHOvjXD2BTa/c71QGTLoKj54K3pNuvwTAMasO1rG5czZLtS1jbtJaNgY1UBas67THhVJ0UuYoochZR6Cqk1FVKpa8Sv8NPmbuMoXlDzR4FKHjtXjy2/j0muCuGbpCsjxBd3Uh0ZQOxDc2g5X7frEVOnAcVmcGeYf5Oh+AJARD5op6mf61Da24bs+yeUEreycNQ/TJcQAghesowDOIbA7T8bxuxNY0YiY75KazFLlz/z955h8dRnd//M7O9qax6tSRbcu8NGxsbMJgWeieE3kkogZBQQ0Iv+RqSAEl+IYROgFBCx8a9927LVu99tdpeZn5/zO5Ia62MAQO22fM8erQ7c+fOnbIz95573vOOSsc0Oh1d7uGr4JFlmbDDT7jLj3dXJ+7VTcj+PqpfQTlWXa4Vfa4FXY4VXa7lkPauiZoxe7e2493aTtjhj1tOl23GNDoDQ2mK4qN0iCW48Pl8VFVVUVxcjNF4aCcoifriSJ6QYmy87/BPFBD0GiX0LuFR9aOiqKiIW2+9lVtvvfXHbkoC3zP29wyJevIeCOfxo+aE3JeNjAeDwYDBYOCvf/0rf/3rX/vFB35XNDq8/G+z4v9y5dGx8kBZknGvUtQ51hl5++0MrF27FoBJkyYdOJnTuBFeOw/cbQqRM/FymHEbJOV87aagPJx3du7k06pP+bTqU1o8LXHL2XQ2RqWPYkrOFIqTiym0FZJjyVHUNYdpB+dgQxAFdJlmdJlmbDPykHwhhdzZ2Umw1aO8+GQItnoIdfhwLWvAtawBwaCkVjeUJCsGgBnmQ67DkcAPDykQJlDjxL2qCe/2DgBEsxbz+Ewsk7PRZR+55GgCCSSQwPcNQRAwFCdjKE5WFB/VTgLV3YTdQUItHvy1TkLtEWPlRXVo7EZMo9IxlqagTTMhaEVEm+6Q7APJwTCBBheBGif+mh4CtU4kV6wKR5djwTIlG12eFV225bAbgAuCgKEwCUNhEsmnFBOsd+Hd1UmgrgfJHVQMeR0+gs0egs018GUNglGLYbByzbXpJqJR79psC9pkw499SIcc5LCEHJJAkpFDMmFXEMJ9iE+NgGjUIpq0CDoxMTmZQAKHMQ4qoZOeno5Go6GlJZZYaGlpITt74FTfB4KbbrqJm266SWWrDhZeWlFNSJKZVpLG6PzYen27Owl3+5WB2JiB1TLV1dU0NDQgiqKa8u5rseYfildO2A/Zo+HCNyClYL+byLJMeVc5S+qXsKltE1vatuDwO9T1GkFDnjWPydmTGZ0+mqLkIgYlDSLNmHZIdloOZYhG5Zrve90lfwj/HgfenZ34dnUiuYPqDFMMNAKGwSnYZuVjHJzywzU8gR8FsiwTdgYI1vfgid4PfRRe1mPyST5hUMIAMoEEEkjgIEPQihiHpGAckqIuk/whfLs68W5tx7e7i3CnD9eSesUTMbqdSYuhJBljaQq6HCv6POuPFv4qS4pixbWiEe+29v4KYY2ANsWANtOMZXI2xmH2I2bySBD6ZzwDJVzLs7Udf3kXvopuZF8I3/YOfJFJkr7QZprRZpiUEL0hKeiyLUfM+TlQyGEJyR9Gjv6F4mRVEgVEkxbRrEXQa37wsUFtbS0jRowYcP2OHTtUM+RDEY888giPPPJI3HUzZ8781mFmCSTwXXFQCR29Xs/EiRNZsGCBGoYlSRILFizg5ptv/k51fx8KHacvyOurldTW1x5T0m+9e42izjFPyBpwIOb3+3n//fcBxXk9mjJvIPg9Hro/eZTgmpcwakSSR8xFe94/wLh/kmpd8zrmbZjH5rbNMcsNGgPH5B/DqcWnMiN/BgZNYpbi+4Ro0CoS7lHpSgesvgd/eZcyO1jj7JV9h2VleXkXhtIUTKPSMQxKQpuZUPAcKZAlJcWud0s7gYaefj4GmmQ9xqF2rNNzE4qcBBJIIIEfEKJBi3lsJuaxmUiBML7dnfh2dOKvcSoZfMISsncfgkArYii0oS9KQpdh7lUwWHVorHpEi/Y7qRjksEzY6SfsDBB2BpCcftXIONztR3L3Jp8QbToMhUnoB0X+cq0/uQkB0azDOjUH69QcZEkm2ODCt9dBoMap+PGgGDOHWj3qX/RaihadEhIfUS6JZh3msRlK6N0RokSRI8px2R8i7ArGhuFFoREVD0hRQDRoDrqP0jdFbm4umzZt2u/6QxnXX389559/ftx1B9NsuLq6+qDVlcBPA9+Y0HG5XOzdu1f9XlVVxaZNm7Db7RQWFnL77bdz2WWXMWnSJKZMmcK8efNwu91q1qtvi+9DofPWmjpc/hClmVZmlcUqMUKdPny7lLSLlinx1UXhcJj33nsPh8NBcnIyJ5zQ38C4p7OdvWtXUbFuNW01VXi6HZE1YwEQqnwkrf01qTl5pObkkpyRjcFiITU7l8ySwQSFMP+3/v94c7dixqUX9UzPnc7UnKmMyRjDMPuwRLriHwmC2CsZhsjsiC8MkozkCeJa2YR7TTP+PQ78exzKRloRbYoBNJFMSkYN2lQjiALaVIPScSu0IRp+1GjIBOJAlmWkHqUjHnb46VlST6C2j7m4CLosC/pCG5bJ2ejzbQNXlkACCSSQwA8CUa/BPDoD8+jefp4ckgg2ufHtVsJ8AvUuJHcQf2U3/srugSsTFEWQJtWI1m5E0ImEnQFC7R60mRZ02WZEsw7RrEVj1iEYNCDJBBpduFc3I7njGxgDCAYNphFpWGfkHdaeP98HBDG+igcg7AoQbHARbPHgr3Dgr+pW1NPbYpU8rmUNqt+QflAShqIk9AU2NDY9clgGSUa06X9wwkOWFKPomP1qhAhZE0YOhkEQlDaGJWRJVsylw3I/HxxBp0EwaBANoqLAOcTIK61Wy5AhQ37sZnxr2O127Hb7j92MBBLoh288aly3bh3HHnus+v32228H4LLLLuOll17iggsuoK2tjfvvv5/m5mbGjRvHZ599RlZW1ndq6MFW6ATDEi8urwLgmpkliPs8wF3LG0AGQ2kKukxz3Do++OADdu3ahUaj4eyzz44xM2qrrWbpa/+iatP6ftuZNAEMliTcAYGgz0t3SzPdLc1U71NWb7WwdaiL1Rm1IMI5pedw07ib1GxSCRxaEDQiGovy8tTY9KSeOQTbjDzcG1oI1Cpx8HJAItQem74zUO3cpyLQpBqVmRSNYlKnz7GgL0rCUJSMaEqQPd83ZFlWYvfrewg2uwk2uQk2u5E8oZhygl7Edkw+htJU9LmWRNrVBBJIIIHDAIJWjCEIZFkm1ObFX9lNsMFFqMOrDLR9ivpBcgeVNOAyyMFeVUhfBKq6CVTthwwC0AhokvRokgxokvRo00zoC21okg3ossyJjIffAhqrHs1QO8ahdmzH5COHJIWkq+tBlmQEAQKNbsXPLiQRavMSavPiWdffd1LQi2jtJoWQsxvRppnQJOuVSTZZVsgUWUaWUJTYYghZG0YKhJF1ikJbJVwkhSRCipAv0WGGTC85EyGSvhNEAdGiQ2PRJe6fBBL4ieI7Zbn6MfBNHJ/3h/k7Wrj65XWkW/Us/+1xGLS9AzHJG6Lp0TXIgTDpV47CWJbab/va2lpefPFFRFHkwgsvpKysTF23+ctPWfDi88iS8nDPKRtG6djRFGx7itRwA4axZ8I5/0QGPN0OOhvr6WpqoKupkZ72NnxuF201lXgiafq8JplhJ57A6Wdfh9548CR9CfywkMMyYYePcHdA7RRI7iAhhx9kmWCLh0C1c8BsDwCCTsR+wVBMo9J/wJYf2ZBlmXCnj0BdD8E2L+EOL4F6Vz/iDQABZQZPJ2IalY7t6Fw0SYkwxwQSSCCBIxlyRHmLpJjeh7t8hDp9EJYRzVq0aSYCTS7CXX4kbwjJE0TyhNQwGE2KAdPINEyjM5QQmAR+cMiSjOQKEqjviRhOOwk2upADUgzZ8k0Qsgn0HGthUG4hRu13UMuLQmTfcmwbNEKvYlsUlHtHo/wXNKKyLBHGn0AChy2OiCxXPybeWa8Y4501Pi+GzAFwr2lCDoTRZZsxlKbE3X7RokUAjBs3LobMWfXft1j+1isADJ40lVmXXkWqPQVePQeohezh8LNnQRAQAEtKKpaUVApGjI6p/8vKz/n7Kw8ytiIFk1ek5oP5/GP+KsaccDIFw0eRPqgYS0pqQpJ7GEHQCGjTTGjT9k/KhV0BQq1eJF9ICePyhgjU9uCv6ibc4aPjtZ3YjivEdkw+oiGhCImHkMOPv7yLsDsAKOFsptHpqvxYDkl4d3Tg3daOv7K7XwYRUMgzfaENXbYFXY5F+Z9lTqhwEkjgJwKfy0UooBDsPZ3tBH1+3F0ddDU30tXUiKOlCWQZc3IKAFI4jFavx2C2EvT76Gqsx9XViSxJ6IwmwqEgUjiMRqdDo9URDgUJeL1IoSBJGZmk5uZjz83HnpdP/rCRpGTnJt7xhwgEUVDTgGsAXXr/93i8cKAEDh0IoqKOMo1IwzQiDYioaSLrZEkm1OYh3B0g7A4S7vAS6vAR7gkg+cMKcSIS+a8QKQGdhKCRoa8wJkqyRMtFPGxi2hIlZPqSMxHIsqLwQVTSeCWeAQkkkMDX4bAhdA5myFWnO8CCXYrU8pyJ+THr5JCEa7mSxtw6Iz/ug7Suro7KykpEUWTmzJnq8m2L5qtkzrRzL2bauRcheDrg36dBw3rQ2+CCV8Gwf+PkNk8bL2z7O+VFPcw85XxmO4ex7n//paupkTXvv82a998GIDU3n9mXXkXJhMnf/mQkcMhBY9WrHUcVU3KQwzKO9/fiXttMz4JaehbVKf47spKSUrTpMAxKUkKzBiWjSfrpeSv59nbRs7i+17OoD/QrGkk5fTCBGifORXWxJsYaAX0k/avGbkSXbsJQmpLwMkoggZ8YAj4vO5YsZMuXn9BWW33Q6vW5Xftd39WkkESV69eoy5IyMskuKUWWZdyOLmxp6ZRNm0HxuInoDMb91JZAAgkcCPqqWwRRQJdlQZd14EkMfD4f3VVV6DMt6AyKWve7EjCCIIA2QeIkkEACB46fZMjVS8ur+P3/djAqL4mPfjkzZp17Yytdb+1GtOnIuWtK3HjUV155hYqKCsaPH88ZZ5wBQPWm9bz3xB+QwmGmnHEuMy++HAJueOlUaNwIplS48HUYNH3AdoWlMG/seoNnNz6LN+TFrDXz6TmfYjfakaQwe9esZPeq5bTXVNHV1IgsKyFdJRMmc8wlV5CWf+im+kvg4ECWZbxb23F+Xk2ow7ffspoUA/oCG8ahdjQ2HXJICfMSjFpEkxZNigGNRfcDtfzgQzEpDhLqVGbRfLs78W6JpI8XQF+YhDbDBDJ4t7cj+2LJYDFJj2V8JsbhdvR5tp9cBpEEEvipIxQIsGftSloq9+LrcdJSuZeO+jr13QogCMpzwWK3ozeaMCcnk5qdS0p2LqnZuQiiiMfZjSAKiKKGUMCPz+1Gp9eTnJVDUkYmoqgh6PMiarVotFrCoRDhYBCNTofeaEIQRRwtTXQ1NtDZVE9bTRWNu3chhUNx260zGBk8aSplU49m0NjxiVDsBBL4kbC/cIkEEkggga9DIuTqO+CdDUq41bkT9lHnyDKuJco66/TcuGROXV0dFRUVCIKgqnPa62r48P8eQwqHGT7zWGZcdJlS+P0bImSOHa78HDLK+tUXRVV3Ffcvv59NbZsAGJM+hrun3o3dqLipi6KGsqNmUHbUDEBJf776vbdY//EHVG5YS+WGtZRMnMLk084mb/jIhETzCIUgCJjHZGAanU7Y4SfU6VPluqFOr5I+vdpJsNlN2OHH6/Dj3do+YH3adBMauxFNkh7RqEX2hwl1+ZSsW2lGTCPS0KYpBoGCQYMgCITdQULtXuRgGH1hEqL++wtBkiWZUIeXsDOgGlEGWzwEm92EWj29aeLVEwSWo3KwzcxHa+99MIaOL6T70yq8OzsQdBqS5w7CMjn7kMsA8WPC4+zG0dyIFA5jMFsQNVp8bhcBjxtbegb2vHxEMRFulsDhBykcprliD3Xbt1CzdRM9HW2IGi3O1hZCwUC/8slZ2Uw4+QyGTZ+JKSkZWZa+93s/OTOLQaPHqd+DPh/1O7fR1dSAoNFgTkqmuWIP5auW4WxrZdfyxexavhiNTkfByDGk5ReSXjCIwROnYLJ9e3/BBBJIIIGDjcsvvxyHw8H777//rbafPXs248aNY968eQe1XT8Gvuu5+C546aWXuPXWW3E4HD/4vhP4fnHYEDoHK+RqV7OTbQ1OdBqB08flxazzV3QTbHIj6ESsU3Pibr9q1SpA8c6x2+0E/T4+mvc4QZ+XgpFjmHv9rxQypfxz2PEBiDq46M39kjnza+bzu6W/wxf2YdFZuH3i7Zxbdi6iMPBg02A2c8wlVzDq2BNY9sbL7Fm7ksr1a6hcv4bsIWUMmXQUqTm55JQNw2ZPGOgeaRAEAW2qUQm5ikBfYMM8NhMAyRci0OBSUrCWdykpMbUiCCD5woppY0+AULs3vvkv4AfcK5t6F4ggaDXIgT6/QU0ku0KSXknhqhERLTpEkxZBKyLoRLRZZvQFtv0SP1HDSckbUv+C9S5cKxqQ3PFnqZUToSiRFG8i44DpwrV2I2mXDFcIIE3CRBCgo76OPWtW0FK5l5bKvfR0tO23vNZgILuklCGTp1EycTIpWTkJ4jiBQwayJNHZ1IC3uxtnRxsddTV0NNTj6e6io76WgDf+c86WnsGQyUdhTkohvbCI7JIhWO1pMWUE4YcnMnVGI8XjJ1E8fpK6bOi0mRxzyRU07y1n98ql7F23Ss2QGc2SKYgiBSNGM+q4EymbejQa7WHTzUsggQR+QHwbkuRIIlYSSOBIwmHzpr/pppu46aabVPnRt8W7ETPk44ZlYrfEeoy4lirrzJOyEM39Q1FCoRB79uwBYOLEiciSxOfPP0NHfS2WlFROu+U3aLQ6CPnhs98qGx11AxROHbA9n1R+wl1L7wJgavZU/nj0H8mxxieT4sGem8/pv76bzsYG1n/8HtsXL6B5bznNe8vVMqOOPZHZv7gagzl++vUEjjyIRi3GwSkYB6fACYPilpE8QQINLsLdfsLdAaRAGFEnoon48gRqevDt6UJyBxUiREIlczTJBkBWtnMqf8H6/XhEiAK6TBPsq3qTQXIFCfcEBk7dqRXR2g0IGhFtugldlhldtgVtllklkQ4UP/WwKo+zm71rV1K+ajk1Wzb2W5+UkYlGq8Pn6kGSwhgtVvRGE46WZoJ+RTFQv3Mbi17+B0ZbElnFg9HqDVhTU8kpHcbQ6ceg1R2+YXwJHF4Ih4K019awbdF8dq9YgrfHOWBZo8VKwcgxFI4aS3rBIMLhEMkZWSRnZiGIh89zQRAEckqHklM6lFmXXkVnQx01WzbibG+ldtsW2mqqqN22mdptm1ma9hLjT/4ZQyYflSBgE0gggQQSSOAIxWFD6BwMhCWZ9zcphsfn7BNuFer04dvdBQLYjs6LtznV1dUEAgGsViu5ubksfu1f7F65FFGj5dRf3YnBaqSh4Q26dr1IsbcGizUbZv1mwPbUOGt4cOWDAJxXdh53T70brfjtLok9N48TrrmZo8//OdsWzaejvpb22hpaayrZtvALqjatY8YFlzJy1vGHVec1ge8PolmHsTR1wPWWSdnqZzmoZNuS/CE0yQZEvUZJ9+3wI3lCSgpXhx/CMmF3ANkXRg5JSL4wwYYewt0Bgs2er22TYNQgmhSPH9GqeNyYxiTSvH4XBAN+di5ZyK7li6nfub3XH0QQKBk/iYKRY8gqGUJm0eABSV9JCtPV2EDN1s2Ur1pK895yfD3OGFJo85efsuyNfzP5jHMZffxcdPpEOvcEvhs8zm6qNq6ju7WFcDCAzmjC2+PE1dGOo6WZjvoawqFeBZ/WYMBmT8OcnEp6QSFp+YVY09JJSs8kY1DRERcyKAgCafmFMf55juYmdi5bxKYvPqano40lr77IkldfJCkji+ySIZiSksgqKWXwpKmYk7795FgCCSQQH7Is4w3FVwR+nzBpTQdM2l5++eUsXryYxYsX88wzzwBQVVVFTU0Nd955J5s3b8Zut3PZZZfx0EMPodVqB9ymoKCAa6+9lq+++orm5mYKCwu58cYbueWWW77Vcbjdbm644Qb++9//YrPZuOOOO/qV8fv93HPPPbzxxhs4HA5GjRrF448/zuzZs9Uyy5cv55577mHNmjUYDAamTJnCm2++SWpqKpIk8fjjj/P3v/+d5uZmysrKuO+++zj33HMB6Orq4uabb+aLL77A5XKRn5/P3XffzRVXXPG17a+rq+PXv/41X3zxhZo855lnnqGoqCim3FNPPcXTTz9NIBDgwgsvZN68eegiE2JFRUVcddVV7Nixgw8//JCUlBTuvvtubrrpJnX7P/3pT/zrX/+isrISu93Oz372M5544gms1t7EOy+99BL3338/7e3tzJ07lxkzZnyTS5HAYYSfFKGzqrKDth4/ySYds4dmxqzzbFbCDQyDU9DGSUcJsHv3bgDKyspo2LWd9R+9B8BJN95KwcgxbNx4GZ1dy0AA93Abkwc/imgYOI3lI6sfwRPyMClrEvdMvQfNQehsmpNTmHLGuer3+h3b+PyFZ3C0NPH5C8+w8bOPmH3Z1f3SpCeQwP4g6EQ0Oj0aelVt0bAvUoG8/WduCzl8hFo8xLNgF81aNMkGNFZ9grg5yNi9cilf/etveLod6rLM4sGUTT2aodNmkpJ9YGpAUdSoA8cJJ/+MUDBIW00l7XU1SKEw3W0t7Fy2CFdHOwtf+jsr332TMcfPZdyJp2JLS4R8JjAwggE/QZ+PnvY2GnbvoLOhjs7GBjXl99fBYLGQP3wU4044hYJRYxSV7E8YKdk5TDv3Iiaffg47li5k59KFNJbvwtnWgrOtJVLqU/Qvm5hyxnlMOPWMBPmaQAIHEd6Ql6mvD6zM/76w+uLVmHUHpsR/5plnKC8vZ9SoUfzhD38AIBwOc8opp3D55Zfz8ssvs2vXLq655hqMRiO///3v426TkZGBJEnk5+fz9ttvk5aWxooVK7j22mvJycnh/PPP/8bHceedd7J48WI++OADMjMzufvuu9mwYQPjxo1Ty9x8883s2LGDN998k9zcXN577z1OOukktm7dSmlpKZs2beL444/nyiuv5JlnnkGr1bJw4ULC4TCyLPPwQw/x2muv8czTT1NaVsqKVav5+c9/TkZGBrNmzeK+++5jx44dfPLJJ2RkZLB37168A4Tt9kUwGGTu3LlMmzaNpUuXotVqeeihhzjppJPYsmULer3Sh164cCGZmSl8+eUnVFc3cMEFFzBu3DiuueYata4nn3ySu+++mwcffJDPP/+cW265hbKyMk444QQARFHk2Wefpbi4mMrKSm688UZ+85vf8NxzzwGwevVqrrrqKh599FHOPPNMPvvsMx544IFvfD0SODzwk8py9Zt3NvOfdfVcNKWQR8+OJTSa/289oRYPqeeUYpmc3W9bSZKYN28eTqeTCy44n1X/+DOdDXWMmXMSJ1xzMz5fE8tXzAAZdCGJoE6kIP9ySkvvjcuYd/u7mfXWLMJymI/O+ohBSfHDYg4GQsEgGz/7H6vefZOAV1FJZBYPxp6bj8FsZsikoxg0dkJCjp1AAkcIulubWfbmK+xavhhQQqnGnXgqZUcdTXJm/+fbwUAoGGTH4gWsfv9tdeAoiCKlU4+meOwE9CYTBrOV3GHDEwPInzBkWcbZ1sretSvZvWIpzZV7kCVpwPKZRYPJHlyKVq8n4PNitNqwpWWQlJ5BxqBikjIyE++ur0HA56V+xzYcLc24ujqo3rhOTcluTUtn1KzjGTn7BFKyvp9nQwIJHKmIl6HGE/Qc8oQO9PfDueeee3j33XfZuXOn+kx97rnnuOuuu+ju7kYUxQP20Ln55ptpbm7mnXfeAQ7cCNjlcpGWlsarr77KeeedB0BnZyf5+flce+21zJs3j9raWkpKSqitrSU3N1fdds6cOUyZMoVHHnmEiy++mNqaGhZ8+SVBv49wMIgsSUhSGHdPD0PHT+Q//36JSRPGq9v/+u578Pn9/P2vf+WSyy8nNSWFeY8/hkarRdRoEUQBjU6HVm9Ao9UiyzKyJCHjQxa8GAxZvPHm2zz88MMx5zAQCJCSksJrr81j7omncONN97Jw4Vds2vQhGo2GpKTRnH/++YiiyJtvvgkoCp3hw4fz6aefqu278MILcTqdfPLJJ3HP3TvvvMP1119Pe7uSCOXiiy+mu7ubjz/+OKaOzz77LGGKfAjhJ5fl6ruaIvtDYT7d1gzAmeNyY9YFm92EWjygETCNTIu3ORUVFTidTgwGA107t9LZUIcpKZmZF10OQJdjNQA2V5Ci+gBbh1uoq3+JQKCdkSP/T019GsXKppWE5TAlySXfK5kDoNXpmPyzsxk563hW/Oc1tsz/jNaqClqrKgAlVKJg5BjO/M19ifSnCSRwGMPT7WDVf99i85efIoVDWHI8lJ3iYcSYG8nOPuV73bdWp2PMnJMYddwJVKxbzcZP/0fdjq2Ur1xK+cqlajlLSipTzjiX0XNOShA7RyBkWcbV1UFbdRWt1ZW011YTCgbw9vTQ1dRA0OcjFPD3205vMpM3bAQZg4qx5+Zjz80nNTcPo2X/6r8Evh56o4mSCZPV7zMv/AW7Vixh6Rv/pqe9jVX/fYvV77/NiJnHMuzoWaQXFmFJSU0QZQkk8C1g0ppYffHqH2W/3wU7d+5k2rRpMb/7o48+GpfLRX19PYWFhQNu+9e//pUXX3yR2tpavF4vgUAgRlFzoKioqCAQCDB1ai8hZrfbGTp0qPp969athMNhyspik834/X5sZhPttdWsX7uG0046ia6mhn77qKyswuv1cuHlveFTMoq6ZtSI4QR9Xi696EKuvumXbN2+nVkzZnDSCXOYPGFCv7oEQcZo9yOI0N3pZuWSxezduxdbn7AnmeigvZZQuAuvy8mwocVoNEpUhqurk3S7nW3bt+Pq6lTHYdOmTYvZ17Rp02KItPnz5/Poo4+ya9cunE4noVAIn8+Hx+PBbDazc+dOzjrrrH51fPbZZwOc/QQOZxw2hM53NUVeUdFBjy9EVpKByUX2mHXeXYqs21iWGtcMGWDt2rUAjBhaxtr3Xgdg5iWXoDEoM4uOLuXhneoIkll4AcOGHsXu8gdpaf2InNzzSLPHxi0uq1+m1JE38xsfy7eFOSmZOVffyJQzz6N5726cba04WlvYvng+ddu38N7jD3L27x5MDLISSOAwQ8PunexavpjtixcQ9Cmy4KLxI0mftohQuJ2a2ue+d0InClHUUDplOqVTptNWU8WWBZ8ppso+L46WZtxdnSz89z9Y9d+3GDRmPGOOn0vByDE/SNt+ipBlmc6GOkLBIJaUVIxWGwGvB53RqD7rZVmmva6G7pZmbGnpiFotPe1tONtalUoEAUFQ0n/7PR78HjcBj4dgwI8oivg9bjxOJ15nN55uB36Pe79tEjUaskqGMGLmcZRMmIwtPSOymwSB8ENAEEWGz5hN6ZTp7Fmzgu2LF1CzZSPbFy9g++IFAGh1emwZmaRm5zB85rGUHXX0EedBlEAC3wcEQfhGSpnDHW+++SZ33HEHTz/9NNOmTcNms/Hkk0+yevXBJbXCoRA9He0019Wi0Wj47L13+1lVWCxmQsEgBoOidNDodOgMRrR6PaIoIogimgYle+vHn3xCXp7imSrLMlIohEYjkpKVw7kXXsxxJ5/MBx+8wqKFKzn/F5dz7dVX89DvHyAU8BMOhRXFjkEhcwC0xjBuj5sxo0by16efjmmXMTVAekYKAIIoo9P1ttvV2UHQ7yMUCODq7FCP1dvjxNXZgc5kQqc30Degprq6mtNOO40bbriBhx9+GLvdzrJly7jqqqsIBAKYE0lwfnI4bAid74r5O5QQgDnDsxD3SVkcqO0BwFAcnyjq6uqivLwcQZAI1e0mFPBTMHYITv3jrFh5H9OnzaerbREAqU4Jzvg1eSmFOJ1baGz6Dx0di1RCp8WxjZXb7qK9rRmQmZE/A0nyI4o/HImSlK7I1aMYOes43nnoXsVv5/lnOPVXdyY61gkkcJhg3f/+y+JXX1S/Z5WUMvPiy/Do3qaxUZHeulw7cbn3YLWU9ts+FHKj0Zj6qQgPBjIGFXP8lTeo38OhINsXLWDVe2/R097GruWL2bV8MbllwykcNQZzcgpGq42MQcWkF3y/ysXDHeFQkKa95dRu3UzDru0E/T70JjNWexoGswW9yYzf46Ju2xba62ri1qE3KTOBkiQR8vdXzXxbCKJIWl4BGYOKyRhUjMFiQWcwYs8rwGC2YE21o9Xrv76iBL5XaPV6hs+YzfAZs2ks38m2RfOp2bKJnvY2QsEAXY31dDXWU7lhLSv+k8eUM89j+IzZiVToCSRwBECv18dEPQwfPpx3330XWZbVMcDy5cux2Wzk5+fH3SZaZvr06dx4443qsoqKim/VpuLiInQ6HYsXzOdnJ59MOByis6OD3bt3M2ncWNyOLoYNGUw4HKa9o4NpU6cqIVGiBlEjojdZ0Or1jJ84gdUbNpJRWNRvH2PHj8dgMFBbW8usWbMGbEua1sfFF5/KxRefyoyZ07n77od55s9/VtfLsoTLtRNZJtJ/kpgyfTz/+/RzBo8YgdViQUAATYhguFcppNHJ0GeIZUqyotXq0Gi1GK1WAhGvnjVr1yo+cl1KucUL5jO4aBDtdTUs/eorJEni6aefRowkuvnPf/4T0/7hw4f3I9VWrVp1IJchgcMQP4m3sizLLNipzDTOGZHVb32gXiF09AXxDYy3b98OSEyZ+gU6bRuD7WYyivz4fEqa87q6l/GGWhFkmZRBZ0OKIktMS5utEjqU3gvAiq23kxSs4LxkGGvUkez8jEW7LqN0yN0UFFx2kI/8wJAzZChn3nkf7zx8H7tXLMFkS+LYy69JzMYlkMAhClmWcDQ3seyNlylfvRyAsmkzGTnrOIrHTUKWQyxZqjxPjMYCfL46Vq8+CaOxgAnjX8VkUjpnnV0r2bTpcgoKrqB0yG+/93ZrtEpY1sjZc2jcvYPdK5ey9asvaCzfSWP5zpiyRWMnMGjMeEomTMaemz9AjUcmwqEQdTu20lKxh66mRrpbmwkFA8iSjCxLhINBHM2NMRme9geNTofBbMHrdPZmOQO14wiKIiM1Lx93VyeyLGNJTiElOxchOgEiKzPPerMFg9mMwWxBZzAgSRJ6kxlzUhKmpGTMSckkZ2Yf9oRNOOxn67absFiG/CC/jR8buWXDyS0bDvTOhHe3NlO/czubPvsfXU0NfP78PFa+8zqjZp9A8fhJpBcMOuyvcwIJ/FRRVFTE6tWrqa6uxmq1cuONNzJv3jx++ctfcvPNN7N7924eeOABbr/9dpU02Hcbu91OaWkpL7/8Mp9//jnFxcW88sorrF27luLi4q9tgyzLBP1+/G4Xfq+HkN/PReedy9333Y9JpyU9LY3Hnv4/RV2DgNFqY/yUqVx04YXc9rt7ePrppxk/fjxtbS0sWLCAMWPGcOqpp3LPPfcyevRobrzxRq6//nr0ej0LFy7kvPPOIz09ndtuu5lbb/0VgYCL2bNPpLu7m+XLl5OUlMRll13G/fffz6hR+ZSW5hEIBPj4408YOrQEn68JjcaCTpeELIeU96kgYDTm4/XWcs45s5k373nOu/Bc/vCHRygsHMSePRv573/f5fZf30h2VjJKEFYvrPYUDBYLGp0Oa3oysmRCo9WybuMm/vbSy8w9/lgWLlrM/z79jFf+8XdCgQC5mekEg0Ee++MfOP2MM1i3cRMvvPBCTL2/+tWvOProo3nqqac444wz+PzzzxPhVkcwfhKEzvZGJ81OH2a9hmklsR45oW4/kjMAIugGyNRTXl6O3d6IXq9kwrLlefAFK9X11dV/BSCpJ4x2Vm/Hz26fjiBo8Xiq8HiqEUU91kAFCBBGpMwQpLHhFQBaWz/90QgdgIKRY5hzzU188cKzbPr8I9pqKpl8+jkUjh6XCMFKIIFDCD2OGlavPQ13C1RvzQVBz8yLLovJbudwbCQcdqHT2SkpuZUdO34NgM9XR3Pz+xQX3wxATfXzyHKIhoY3KCm+FY3GGHefBxsarZaCkWMoGDmGKWeeR9XGdbRUVeBz9eDp7qZx906qN2+gevMGFr/yT7KHlDFs+izsefkgy/R0tNPT2Y41NY3krGzsuXnY0jIOK2Wh3+PB1dmOp9uBq6uTzoY6VWa9d+0qfK6er63DZEuiYNRYCkeOwZySgt/txt3VScDrwe/1otVpySoeQvGEyRgtViQpTMDjRWc0EvT58PZ0Q+ScWVPt6Aw/zPU/HNDRuYiOjoV0dCympPgWNJqfjr+cRqslJSublKxsBo0ex+SfncXmLz9l3Ufv4WxrZcXbr7Hi7dcQNUr2uzHHn8SYOSchahKTQAkkcLjgjjvu4LLLLmPEiBF4vV6qqqr45JNPuPPOOxk7dix2u52rrrqKe++9d7/bXHfddWzcuJELLrgAQRC46KKLuPHGG2MMfWVZmYwIBQL4PR5CAT9SOEQ4FCIUCMS06/f33oMvEOCy627AZrVy6y234A0GMSUlqcbt/375ZR566CF+/etf09DQQHp6OkcddRSnnXYa4bCfwYML+eKLL7j77ruZMmUKJpOJqVOnctFFFwFwzz3XkpKi4fHHn+SGG24hJSWFCRMmcPfddwOg02m5//7Hqa1txGQyMm3aeP75z0cJBNpB6ECrHYEsK0olAQ06XTKynAs08sknL/LAA//HueeeS0+Pi9zcLI45ZjL21DxMphRi5DmALIci/8N43JUohI/Mr3/9a7Zt28YTf/oTSUlJPPXkk5x7ySWEg0Em2Wz8/p7f8exzz/PHxx7nqMmTufvOO7jp1tvobmtFEw4xbtQo/va3v/Hggw9y//33M2fOHO69917++Mc/HrybKIFDBodNlqu+psjl5eXfKMvVvPnlzJu/h7kjs/jbpZNi1nm3tdPx6k50ORaybulveOXxeHjyyScZNnwh6el1dNdYyR90BqkFRsyWwezadbdadrR3Apmnvh2z/YYNl9DlWEVZ6f24fU001P2Dcp/ISZNfwVH/HJ1dyuy6QZ/FjBkrvulpOegoX7WMT//6f6pppVanJ31QEbmlwyg7agZ5w0b8yC1MIIGfLlqrK1n43o2kj90DgBQwMWLI8+QNifXiqqh4muqa58jK+hnDhz3Cho0/x+ncDEBS0jgmT3oXt3svq1bPVbcZNfJZLJYhbNj4c/Lzf0FJ8S9/uAPbB11NDZSvWk79ru3UbNm43yxIUZhsSWQNLiV7cCnm5BTcXZ3oDEYMZgtGmw1rip2swUNiSAtZlnE7uvC7XVjt6RgOQtx5MODH73Lhcyt/frcLn0v5c7Q0KWm5G+q+NiW3OTmFwlFjsefmk5Kdg96khMUJooggCKRk55KcmXVYkViHE3bsvIumJiVDy6SJ75KcPO7HbdAhgGDAT/nKZZSvWkbjnt34epzquuTMLEYccxwjjjk+kS0rgZ8M9peh5qeOUDCAp7tbNcIfaLgpCAIGixWDxYLeaPpOIZ1KGNQuZCQs5lI0mv4T0uGwH7e7XP2u1SVjNsUaPgeCXfi89YiiAau1jHDYSzDYRSCgeNxYLKXIcgiPp0oto9TtxeOpRpZD6PVpGI25uN0VhMMeTKZCdLpkJCmIJPkIBNoJhVwYjflotTbc7t2qgnb06JO57bY7uPXWWwc81nAoiM/txu92EfB59xX+AEr4szkpGXNySiJU9hDFTy7L1XcxRZ6/U/HPOX54/3Arf93+w6327t2LVushza6EV3XtGMzPLr5bHRTUVT6HO1CP1R0m4+gn+m2fap9Ol2MV3c6NtHYqsYw7Q5nckHEUZBxFKNTD4iXj8AdaCAad6HTfLBX7wUbZUTPIHlzGhk8/ZPeKJbi6OmneW07z3nI2fPohE087i1mXXIEgHny/jQQSSGBg1O/cxvtP/JHcY5rUZaLeS3XrPeiS7yHNfoyqIujoVLJKpdlnotGYmTzpv/j9LSxbPh2nczOBQDv1Da8BIAhaZDlEc8sHaLU2gsFO6utfprjopu/FV+dAkJqTx9SzzmcqSuauXcsXU71lI67ODgRBxJycjC09A3dXJ47mJhwtTXh7nFRvWk/1pvUD1qvV6cksGYLRYqG7tYXu1paYjEsGi4WktAxs6RkYLVZcXZ10NTfi7upCq9djz83DnpuPwWrFnpOPt8eJ1+VEEERcnR007dlNT0fbAR+nwWLBnJyKJSWF1Jw8dAYjggAlE6aQP2JUIuz1e4Df30oo1IPFMnjAMrIs0d6+UP3e07M9QegAOr2BkbOOZ+Ss45EjSrmKdatY8fbrdLe2sPKdN1j5zhuUTp3OtHMuIr2wKEE4JpDATwySJOFz9dDT0R47GSMAsuLdpjeZ0Gh1ShivyXzQ1H3hsEdVzgQCbWp4eV+EQt0AiKIBSfITCnYT1MaOv6SwDwCtVonc0GhMaDQmwmEP4bAXSertNwhCb9s1GhMGQxY+X4NaRlXyRMqJog5R1BEMOiLrQ0iSF1mWIv2xMCAjSbHKpX2h0eqwJKdgSU4hHA4R8HiQwsq24VAIv8dDOBjE7ejC0+3AlJSMNdX+k1NSKudZQKf75gmVDiccNoTOt0VTt5dtDU4EAY4bltlvfdQQeSBCZ+fOnaSn1yKIMp5WI9PPvKV3hleSGFTvZ0+KRKk4DcHeP17UZhsJQFfXauSg4uNjTz1aXa/V2jAYsvH7m3F79pCSPFFdp0gUQ4hi/Mxb3xeSMjKZ/YurmXXpVXQ1NdJaXUHl+jXsXLaI9R+9R0d9LXOuupHkzP4EWQIJJHDwEAz42bVsPrVNf6Zxs5eA30JSvuJ7Mn7cy+zafT9ebzVbt96IzTqSSZPeIRRy0dOzDQC7vVe5YzBkYbONpKdnOx0di+nqWglASfGtVFQ+pXh9obzog8FOenq2kZSkZJ+qqfk7LtcucnLOxW6f/sOdABSlyoRTzmDCKWcMWCYUCNBWW0VLxV6aK/dEFDdphAKBiDqmB0dLE67ODhp374jZVhBE9CYTfo8bv9tNm9tNW211v30EvCGaK/bQXLHna9ssCCIGqxWj2YLBYsVotWKwWElKz8Cel09aXiH2vPxESu4fGLIcZv2GC/D5mpky+QN1VnVfOJ2bCQY71O/R31NsXTKVlU+j0dooGnTd99bmQxWCIJCUnsH4k37GqGNPYO+alWxf8hU1WzexZ/UK9qxegc5oQqPRkFk8mLKjjmbEzOPQJVQMCSRwRCEUCBD0+wj6fAT9/hg1js5owpycjN5gRNRqqa2tZUjZsAHr2rFjx37To38dwuHeDIvBoAO93o5GE6u8DUYIHb0+HUnyEwi04/PVEwrZMBiyEEV9HxImdpj81FP/4Mkn/4zCTgmApH6eOXMmn376qZrkZiBCJ4po3aoXDyCKekRRr7ZfkoL9xoCyLBMK9aDVWtQ6NRotJltSv3J+jxu3o4ugz4en24G3x4nBZAJBQKPVoTeZ0BmMRyzJEw778XrrANBohu13PK2UrUKnS8VgOPzGt0c8oRM1Qx5fkEK6tb/0TpdtRnIH4hI6HR0d7Nq1ixEjFXUOnjKGTT+mt8DGV8jZtZMcvRVueiru/m1WhdAJBJR2tAYFxhXGDogsllKF0HHFEjp7Kx6jvv5VJox/leTk8Qd+0AcJgiBEZqTzGDb9GIrGTuCLvz1L9ab1/PvOmznxul/Gno8EEkjgoKGxfBefPT8Pc9E6MkZ3UZIDwZrTEDS7MeizSE2dzoQJr1Fd/TwtLR/S49pObd2/MJuLABmLpRSDIZbETk87jp6e7bS2fYHbrWShyMk5G5d7Ny0t/wN6s1e0dywmKWkMXm8deyseB6C55QMGl9xJUdH1P9BZODBo9XpyhgwlZ8jQAcvIskxHfS3ttdX4PR6SMzJJzs4hKT0DjVZHwOuhp6MdZ3sbPR1t+FwuzMkppGbnYktPJ+jz01ZTibO9DW+Pk86GOky2JCypdmRJwpKSSnphEVklQzCYLQllwo+AeJ3fvujqWoXXWwtATe3fGDni6bjlOruU8GetNolQyEmPa3u/Mm3tX1Bd8zwA6enHxc0gB1BX/zJVVX9m9OjnSE2Z/I2O53CBzmBk+MxjGT7zWNpqq1n+1qvUbN5A0OclCNRu20ztts0sfePfjD5uLuNPOo2k9P4TbAkcmpBlGb/bTcDrQZIkZFnCYLZgsiUlnnM/YQR8Plwd7Uq4zz7Q6HTK+zElNeYeycvLY9OmTQPWmZub+53aFApFCB1BAFnG7a5Aq03CYMhSPQKjRItCiKQQCvUgSX6CQQeCoMFozB2QhLn22qs4/fRj0OpsaEQDfn87Ol0SRmMOpkjWyF5CJ4gsS8gcCKHTW8ZgyGb79q+QpAAu1260WismU6GqmPb5GgkGO9HpUuMqkHrrFzBarBgtVvweNz0d7cokl7uX9HI7lBRaRqsVS4odneHI8kyNqrGUzz3o9fYBy3p9tUhSEL+/Fb0+87B7tv0ECJ1IuvI42a0AUs8YAkAg0E55+Z8IhZyMGPEkACtWrEAQAqQkNwNQOvrS3gsccMOCB5XPs38HyXlx6zcYMtDrMwgEFBl+U0jPZXmxfhcWSymdnUtxe/aqy8JhDw0NryNJPvZWPMnECa9/i6M/uBhxzHFkDynj8xeepXH3Dj5+5gl2r1jKMZdcTmpO/ONPIIEEDhyyLFO5YQ1rP3yXhl07AJnCU7vU9YNn5lBbC/a0mcrL2pDNsKEPkpw0jh0776Cq6s+kpx8HQEqcwWNq6nSqqv9Me/tXgIROZ0evz2T4sEdwuytwuXaQnDyJ7u51tLV9RmrKZDojSp4oKiqfwmYbSVrazH71H8oQBIH0gkEDpkPXm8yk5ReSlj/w7GBafsH31bzvBJ+vEZ0uLa5fwE8Fzp5trF9/PulpxzFy5NNqp7ovmlv+p35uafmIkuLbMZn6v7t8PiXFbEb6HJqa/4vLtQdJCqgzp7IsUVk5Ty3f1Pg2paV396snFOqhsvJPhEI9lJf/gSmTP/jRwhh/KGQUFnHmnfcSCgZxtrUSCvip2bKRLfM/w9HSxLr//Zf1H79P6ZTpTDjlDHLLhh12HecjCd4eJ9sXzcfZ0YavpwdvjxO3owu3owtJktDqdHid3XEz6ml0Omxp6WQWDyEpPQO/x01HXS0+twtzUjJZJUPIGlxKVvEQUrNzEqH6RxD8Xg+O5kZkSUlxrjMa0RmM6AwGtAYjGq027u9aq9UyZMiQ76VNsiwRljwAmE1FBIOdBIPdhEJOQuEerJZhyvNX9fLRIggiFstg/P42AoE2wmFvpK74JEx6ejYWix9RNKDVJRHwm1WvnCgEQYMgaJDlMGHJp+6vH6EjKkNwSQ4i9tmfKOowm4vxeKqVkLBQD+GwOxIS7yQYVPz3QqFuZDn3gN4pBrMFvcmsKqlAIBTwK/5GwYDq82dOTsaSYj9i/HaCB0johMM+NcwOQJYDCMLh1Z86Mq7YAPAEQiyvUGTTc+L458RCpK7+JQCGDn0Ivz/Mpk2bSElpRtRIBN0GSmed3lt8wyvg6YDUIpi6f7m1V5OBBoXQGZV3Gmmm2ExbFovycHO7eqX8be0LCIeVB5PDsZolS6dgMuYxceLbiOKPd9nsuflc8MCjrHj7ddZ88DZ7165k77pVFI+dwJDJ0xh+zLGJrFgJJPAtULF+NcvefIX2SLiPqNEw4sQhCOIutYzTuQUAm21UzLbZ2WdS3/AaTudGWls/BiAluT+hk5Q0FkHQI8tKbLbNOgJBENBozEyc8Drd3ZuwWAazfMVMXK5dbNh4ibrtyBF/osuxmsbGt9ix8zdMO+oLtNr4oaoJ/HDo7t7EuvXnkZ42m7Fj//FjN+dHQ13ti0iSn9a2Twlv9TF2zN8BQR1UhMN+WluVrCvRMOcdO++kuOgmAOz23lBov1+ZCEpOnkhb+3xCISebNl1Baend2GwjaW9fEDHVFAGJpub3yM29AIMhE43Gqu6zvv4VQiElrNvl2kFr6ydkZZ3Wr+2SFKSu/t8k2caQmjrlezpDPyy0Oh32XIUsyywqYdJpZ1G5cR0bPvmA2m2bKV+lmCtnDy4le8hQLCmpFI4aQ05pguD5PqB4arjxOp3U79yKo6UZZ1srNVs24ve4v74CFA8yQRQRRIGA10s4GFQ8zJqb+pXtbKijfmdvqKLeZMael49Wpyc5M5uSiZMpnTI9ca0PE8iyjCxJCKKAt6eHnvY2ZFlGbzaTnJH1gxAAshwmEGhHq02Om5EzHPaALCMIWjQaC1qtFb3eh8dTFfGp8cUQ/VEiRBA06HQpBAJtSJIvYncRP+SqV+UTQJYiiuZ9CBVBEBBFA+GwBykyjlMQW06MKnSkELK4r8+OHoulFK+3JqIgUvpsUXGAcj4kQiHXAXuvCoKA3mhCb4zN2Bj0+3F3deJzu/B0d+N1OjElJaM3mRQPJBl0JhNa3Q9r/3EgkGV5wGdIOOyPIWlC4Z6IT1F/AizQJ8RaKetBH2dS6FDGEU3oLN3TTiAkUWg3U5q5f68CnS4VjcZMOOzB729k27Y2wuEwGWlKuJVRHIdGEzld4SCs/IvyefqvQDPwTb62eS0LW/YyJ/J7m1x0Xr8yUam2y12u3pwtkZlEjcZKOOwiGOwgGOzA7d6t+vL8WBA1GmZceCnDps9k6Rv/pnLDWqo2radq03rWfPgOc66+iaIxP3yIWAIJHK7YtvBLPn/hGQD0ZiNj55xK4VQ91Q1PEO6NglIzVZmMsTJbQRDIz7uYHc6N6rJ4Ch2NxkBS0mi6uxXjYKttuLpOq7Wpqpvi4lvp7FyqlgORjIwTycg4KRK2UkNV1Z/jqhISODC0dyxCFPSkpk4bsEPidG6hte0LcrLPVIn/fdHU/C4g0d7xFd3dG0hO7p+t8XBHl2MtnR2LKSq6OW5HPhh00NrWmyK3o2Mheysep6npPXJzzmXIkN/Q3b2OcNiFQZ/F2DH/j/UbLsThWM3GTUqygkkT31FDm/1+JUTaYMgiI+NEmpreocuxiorKPzFu7D/V32FOzjl0dCwiEGhj1eoTAYUEmjjhTUCgtu4lQCFSnc7N7Nn7GGlps2KIUFmW2bHzTlpa/odOZ2fG0St/1Emb7wuCKDJ44hQGT5xCW201Gz75kJ3LFsb4Ui1/C4ZOm8nc629JeO0cJMiyTPmq5Sz453N4+2Ql64uMwiKKxk/CZLWpYTKWiHlqyO/HnJyCOTkFrV6vbhMKBhVT+pYmmiv24O1xotXpyRhUhMmWjLO9lZbKvbRU7qGtuoqA10PzXiWzUP3ObWxfPJ8hk49i1LEnkpSRSXJmVr+BZgKHBgJeL46Wpojhbi+MFquSafEHUl4FAh34/a34/a1YLKX93gVR0kOjMarvVI3GiEZjUsOqogSNoqLpfe+KogEEAVmWFLImkkq8f5iUDkEQI+UUskagv/+MKOojBsqeuPtTlvUJuYoTlqUQQ/rIsQUjZYOR4zIRDnsJhbq/czIdncFASnYOfq8HV2eH6rfj6XbElNObzVhT7OiMxh+diA0EOvAH2pClIDpdCkZjbr9rFQ67ANBoLMhyAEkKEgx2q+bIfYkdWQqpy2RZUq6bLvUHOpqDg8Om19A3bfmBYv6OaHarr4+FEwQBozEft7scr7eeTZt2ApBkUwidkuF9iJht70J3HVgyYdwl8apT8f7e96lTjcpFbNb+ab+t1mGIopFAoBWnczMWSykdHUsAGDvmHzQ3v0dj038A6O7eqBI6fn8r7R0Lyck+S/3R/5BILyzirLseoLOxnj2rV7Dpi4/pbmnm3YfvY8oZ53L0BZcesUZbCSRwMODq7GDF26+xdeGXAIw5OxNt1moGFZ9AReWjyHIYm210JGa6A0lSZhuMxv5hIpmZJ7G7/EHCYRdGYwFGY07cfaakTFaJmnjPI4CS4l9SUvxLurs3srv892Rnn6lm0Bpa9gCbNl9JXf1LFBRcMeB+EhgY3c7NbN58FaAYV48a+QyyHFRk3JHBfijkYvOW6wgEWqmpeYHSIb+jsPCqmHpkOUxb2xfq96rq5xg39v/tU0airu4lWts+RyMaGT36ObRay4BtCwTa0enSfvQOWxSSFGL79tvw+5sQBB0lJbf0K9Pc/D6SFMBqHU5mxlwqq+ZRW6uch5rav1FScjs+v6IisFjLsNmGM3r0X9my5VrVT6Gx8T99CB2l72AwZDN82GNkZZ7Gps2X09W1gnDYg8dbA4DVUoY9dRpV1X/B728lHHbR3b0et3sver09YqwsMG7sv1iz9gx8vjrWrjsbWQ4zatSzJNlG0dj0H3UCJxjspMuxijT7jO/1nP7YyCgsYu71v2LmxZexa/kSvE4HnQ317F23it0rl9JcUc7Mi6+g7KijD5n78HCBLEn4PG7cXZ3sXrGEncsW0d3aoq7XaLXkDRtJemERSemZ2PPyGTRm3DfOqKfV6UjOzCI5M4tBo8fFLTNq9hwApHCYjvpaHK3NhAIBWirK2fjZR+xdu4q9a1cBUR+m2Yw78dREdrRDALIsE/B68fZ043e7Y1KOixoxkp0x9Qe9TqFQLyHpdu9BiPilGfRZ6PWpqrHwvgN7UTQCPYQlX+RzPKJGQCMaIxmsfPvxvYmqb7yEI+qPfcso+1TUHdEQrnhlegmdsKoIgv4EklImEPmvHKNOZyccboj4/mgxGLK+czivwWRGn2si4PXi6XYgSWGFrJNkAj4vAY+HTo8HQRQxWqwYzGZEjRZBFNDo9IgHkdgLBp0RQkZEr0/r540XCLQjR0iuYNBBWPJhMhZEvIXsaDRG9ZwqRtNW/P4W/P7mSF9Cg8VS1uf+Vc6rRmMlFHKqRFxfhEI9+P3NaDQ2jMbsg3asBwuHDaHzTdOWhyWZr3Yps2wnfG24lQJThNBpbdtBY2MTBoMbo9WHLAvkFiovJiQJls1TPh91Pej2P4u0o2MHTX4NaGykp06N25HWaMxkZpxEc8v7NDW9Q07uechyEL0+g9TUKaSmTsFgyKaq+lm6uzeSn/9zAMrL/0Br26d4vbUMGXznAR3j9wF7bj5Tzzqf8Sf/jCWvvsjmLz9lzQfvUL9zO0df8HMKRoxOxE4nkEAfdDU3smX+Z2z6/GM1bfb4k3+GkPUMkuRnb8UTAKSlzWbsmL+zect1dHT0plGOZ4Sn0ZjJyjqNxsY3SU2dOuC+U5InURP53FehEw/JyeOZMvmDmGVpabNIShqH07mJjs4l5OVeACgERFvbl6SnH3dIpYf0+Rrx+ZtiDOd/bDQ09HqidXYuZf2GC/F4qjGZ8jhq6pcIgkB1zfMEAq2qcnRvxZMEAu10OzcxYvhTmEx5OBzrCQTa0WgshMNeOjoWUlv3LwoLrlDr7+nZxp69D6vf29vnk50dP2NYW9sXbNl6A8XFt1JS/MsB29/dvRFZDpOSMuk7nQdJ8lO+5xFs1mHk5V0Ut0xH52L8ETKmtu6f5Odf2i8OvqNzKQA52WeTm3sB9Q2vEgi0q+t7erYS8CtSdYNeMeNNs89gxtErcfZsZdOmy2hp/ZjS0nsj6WQV+bXBoEwG2e0zMBrz8fnq6excgdej/IJM5kFkpB+vns/1Gy7G4VhNt3MjNutwdX86XTLDhz/Kxo0/x+OpBKCp6R2SbKNwONbEHEtLy0dHPKEThTkpmQkn/0z9Xr9rOx8/8wTdrS18NO8xcoeOYPp5F1M4amxigL8ftFZXsv7j92ks30l3a0tsqmhAqzcw6bQzmXrWBYgazQ8+0SZqNGQMKiZjkJIJdvjRsxhxzPFsmf8pddu34nF243P1sGX+Z2yZ/xkIAtZUOzmlQxl7wimJ6/8DI+D10tPZHvFaUWC0WLFlZIAsK4P4H/h6SFKoDzmiVVQt6oC+E70+FTWpQz9CJ2JSHPYja6JETf8xiRghdMJh94C+NwCCqIdwrxH0/gidqGpof4SOUs4ft119FTp9Q8G0Whs6XSrBYBeBQDuhUA8m0yDVR0+WJTzeapDBbC46YLJHEAQMZjMGc2xmsKgiz+92IUkS3h5nP8WfzmjEaLFisiV9p2dMOOyLJC9QrkEo1I3ZXBwjXIgSW0ZjDn5/G1LYh9u9J7IujMlUgEyU4BPR69MJBrvU66GQaKE+hJlSVqu1EAo5kcI+enp2YjYPQqMxEww68UYmcsJhX1yS6cfGYUPofFNsqnPQ4Q5gM2qZXDywq3VfGCPmiBUVa4AC0kzKxTNoinsl0nu+gLadoLfBpKsGqEmBJ+ihsrsSSRYYNfETMi0DE0s5OefQ3PI+La0fYY10BPtK7KMzh93ODYDy4+/oVFQ89fUvU1hw1X7du38I6I0m5lx9EwUjx/LF356lsXwnb//xHmxpGQw7+hiKx08iOTMLmz09QfAk8JOEs72NNe//h83zP1M7DLllwznmkivIGlLA4iVPqGUFQUPpkLuVrAuG3tkAZfbB3K9ugCGDf4NBn0lu3gUDtiElZRI6XSqiaMRsKv5Wx5GWNguncxOdHUtVQqe6+q/U1P4dozGP8eP+jdn87er+ppCkELIcVBVE+2LL1hvp6dnKpIlvf6dwJFmW2bbtlwRDDsaNffGAVJEuVzlebzVpabP7pCJ10tLyEQBlZQ9QUfF0xI8FPJ4qgsEONBozdXUvAop3UWPT27S3L6Cm9u8AtLZ+zKBB19LWrii7MjJOxGQaRFXVPPbseYiGhtcpLvol2dmn4/PF+lu07YfQaWp+D4C6un8yqPCquPeZ11vP+g0XIcthxox5gYz04+PW5fU20ND4BjnZZ2GxDI5bpq7+ZRoaXkUQNKSmHhX3nmloeEP9HA672b7jdrIyTyMY7KSg4ApEUacqaiyWwWi1FoYNe4S62hfxB1rxeCrp7Fymxsjr+2R+0+mSsacejck0CK+3hta2T7GnKlkoBUGHTmePfBZITz+W+vpXaG9foHSUAbMp1mA7OXkCDsdqnN2b0GlTADBEzDLtqdMYPvxxqqr+jM9Xr/phBfwK8ZSTcy5NTe/Q2vox4bCbkuJbBgyzO1KRP2wkV/7f31j7v/+y9n/v0rh7B+88dC8pWTnkDh2OzmCgaNwkSsZP+smrfzvqa9m2aD4V69fQ1Vjfb73eZCJ7cCmjj5vL4IlTD7kQtsyiEuZcrfhXybJM/c5tbPr8Y/auXYkUDuPq7GDP6hXsWb2ConETOemGW7GkHF4hEIcbZFnG7ejC1ak8KwVBwGRLwmhLQmcw9CNxFIIh9LUDW0kKIQjifkkFZUAtxyU+oDcduSgasFrLkKQgoZATn68RSY6GI0XImn1ULlGSQyFNImRnnP1oNCaCwS5CaupzgX19bwBEIfZ44xM6X19GEASVnOoldPYlo3SRtgd62x4pZzLlo9Um4fM1RNKvt2AyKUkdgqFuwpGMX8FgF3p9rHfrN0VUkSfLmQR9PnyuHoIBP1I4jCxJSOGwkrbe58PV2YHebEZvNKLR6tHodGh0ugNS8MiyjM9XD8hoNObIuQngdu/FaMxFp0uJlIwqamyYTAY8nmq1DlVdo6rKlHvPaMzD661V7xMlrC5K6CjLQn4JARMyXmQ5RDDoRKMxx6jDAIIBBwZjxrc4k98fjlhC54vtSmaq44ZlotMcGHlgMioZTHp6qoECko3VAGTlHtdbaP2/lP+TrgBTyn7r2921G0mWyDRlkmXdfyq+1NSj1BnAunplH307c0lJ4wDwemsJBNpxuXarD7hw2ENt3T9/VJVOXwydNoPswUNY8/477F61lJ6ONtZ++C5rP3wXgKSMLMbNPZXRx56I0bp/b6MEEjgS0N3azMfPPknTnt3qsqJxExl34imUTJiCIAh0dCxV19lsI8nMOEUdCBv6hDXt65/TFzpdctyQlL7Qam1Mmfw/BEH7rb060uwzqap6hs6u5UhSCFHU0tr2OaBkCNq46QqmHfV53ExDSsdE3O++w2EPHk/V1/qFud2VbNl6LYFAF9OO+hy9Pj1mfTDooKdnK6BkOBqI0AkE2inf8zD5eZcMqDrx+epVn5b29oVkZs6NW66l5WN27votdvtMOjoWIUl+DIZsxo17CaullNbWj5EkHxZLKfl5l2IxD2bP3sdwuXaox6TTJSNJAbTaZNLT52CzjcThWKMa7LrdSkZET+R/asoUcnLOA1miqvoveDyVVFb+H9nZp/cSGZFsi+3t86mt+xc226iYNNqSFKSzU0nXHQr10NLyEbm55/c7vtra/6fG8W/bdguTJ71LQ+MbSFKQYUMfUjv9u3ffS0fnEurq/kVp6T3k5V4UMyAIBruprlbSfstymN27HyA5eSK5ueerYXzBYBcdHYsAGDbsEXbvfoDOzqV0RhQ5Gq2V/LyLVUJHb1AmTTLSjycj/XgaGt5g1+576ehcpnZoDYbYdNmCIJCddTpV1X+ms3M5FnOJWq5ve9PTjqe+/hVaWj+JvHsFTKbYrGfJkfd0t3MjFmsZQExIYm7OuaSmTGHFymPp6dmpdMKDCqGTmXESXZ0r8PkbaW39BEnyR4ydexEItOP11pOcPK7fdTlSoDMamX7exYw+/kTWfvguW7/6AkdLE44WhZjc/OWnZAwq5ow77iU588DU10cati38ki/+9uc+ISYiQ6fPZOSs40nLL8SUlHxIGpgOBEEQKBgxmoIRowkG/PjdbhzNjexeuYytX31O9ab1/POWaxk6bSY5pWWk5Q/CnJREKBBAZzCSlJn5jUPGEuhFKBjE2+0g4PcTjKQgN9mSsNrT9mt27PM3Egx0YjYXo9XG78uHQi483mpuvOH3uFwh3n///X5lZFnG461GCnsxm4fEzdYYffdFJ9dFUYdWmww0IkfTg/f5PfRFtB8SQ5zEGf5Gw7GkPqFU8ZRI+07mxCdrBi7z97//nT/+8Y80NDTw2GO/o6urk48/XsiyZW/HCfGKZlYMIUV8XhAE9RgV/xwZr7e2j/pEVicKAPyBNnS61IOSYVEQBPQmE3pT7ARaOBjE73Hj6XES8iu/4YcfeZTP5s9n/v8+BEDUatBotOgMRgwWC3qTqV+bFN8hL4IgquSU11sdUe3UIYomRFEfc61F0YpBn00g2K6mgF+4cCHHHXccNTXLsBkNdDe6ImGDOoypEqJWpru1ESmkRZZldLYAogZcXV1IQRGdWYvOEsLb04WzxYvW7EVrADksIGhkvJ4WXB0+JUzNbEZn+PF9hY5IQkeWZT6PEDpzRx54nJsxMlCy2doZWfYl9mylI2y3H6UUCHigcpHyeeyFX1vf9vbtAIxIi+9T0ReCIJKediz1Da/g8VQBYDH3Ejo6XRIWSylu9x66uzficKwFwGQqxOutPWRUOlEkZ2ZzwrU3c+zl11K5YQ171qykftd2PA4HzrYWlrz6Iivefo0xx81l8hnnYk09NNqdQAIHE0G/jx1LFrLsrVfw9TgRBJGcsmHMuPBSCkaMjinr6F4HKBmrRo54OmZdX4WOMU641TfFd/W9sdlGo9UmEQo56enZgl6fpcpRRdGIz1dHQ8ObFBRcFrNdKNTDqtUnYzBkMWniOwO+AHeX/4GmprcZPvxxcnPOjVvG621g3fpzCUXSUjY3f0hh4ZUxZbq7e02i29q+oKz0vridmrr6V2hp+RCncxNHTf0yLtnU3b1B/dzU9M6AhE5d3b8Ihz20RQguUTTg9zdTU/MCI0c8jTsScpOWNisSznM0U6f8j02br6SjYzEeT4WaAtVozIn4u+UydepntLV+RvmeP+L2KESOPxJWpI+QDyUlt5CTcw4rVs7C66slHPYQDHRG9jeb9vavCAY72LPnIUTRxPTpizBESDCnc7NqIghQXfM3BEGLo3sd+XmXYLONxO9vo7HpLQDM5sF4PBVs2PhzNY3qoMJrMJuL8Afa6ehcBoAk+di9+z46O5cyauQzame4ofFNQqFuDIYc/P4mOruW09m1nPaOBUya+C6iqMPrrQNkDPos8nIvIMk2hr17H6Wza7l6TXNzzlX3Hw2nisIeCV1yOjdhjhI1+v4kQJToczq3xBgi90Vq6pRIaJsrcm1y+xGWUaLF7d6Dy7VLLdcXRmOBKpV3uXbjj4aCGbIYP/5l2tu/Ys/eR2hvX4jP16hu73CsY8vW6wkGuygtvTcmrO5IhM2eznGXX8eMCy6lZusmOhsbcDs62blkIW01Vbz621sYf/LpTDz1DAzmgT2hjiS019Ww7qP32L5oPqBMCow+9gQKR407YibHdHoDOr0Ba6qd/OGjGHfiKXzyl6dprapg28Iv2Lbwi37bWNPSGXfCKYw/+WcJY+UDhCxLzJ49m5HDhnH/XXdGo1sAsKWlD6iGmj17NuPGjeNPf3pCfbcEg464hI4khZRnuCwjy0EkKb4HajDoUNUkgUA7JlN/j8Cof2Bf1ahCuEQNigMDphqPpgKXpGAfk+J4IVeGftvFg3AACp2+bVO+K30Kp9PJzTffzJ/+9CfOOeccdDoHgYCT6667eMC29x5jhGhiXxVPrHFyOOxGknzKMQoaZCl4UFQ6+4NGp0NrCmHSBtFpcgj6Amj1hgjhIiJJElIoDEIQra4bl1NLuFWPwWLFaLGiMxhBEAgGFEJRCos4mlqQZAlZ0qFPCiCIEm5HK+GgSPQ26KivQwpJkYRCGkzpCqHjaGlU2xYOhZHl3r6mLAmArKi8Asr10UdW6/RG0GmRBQ8QAkFCCoUQBOUHEg7o0JoCiFqZQMiDq8sHXcrvQBBFDGYLRosFjU6PVq//QUmeI5LQ2dPqorrDg14rMqvswCVRkqQ8wEwmFyaT0mkTRVPvjG3VYgj5ILkQMr+epNnRocy4jkj/+rKgmJXWN7yift9Xbm2zjsTt3oPbU0l7x2IABpf8mprav9PTs/2QUulEodXrKTtqBmVHKR3rYMDPrmWL2fjph0qmi08/ZNui+Rx7+bWMnHX8j85wJpDAN0EoGKS1qgJvTzcGs4VwMERXcyPNFeW0VO6lo65WfaFnlZRy+q/vJik9/jMpalScktxfIWLoS+jEMUT+oSGKWuypR9Pa9imdXSvRR0JTkpMnkp19Jrt330dF5VM0Nr1DWem9qqdPe/tX+P1N+P1NdHWtxOFYS1LSaNLSjlV/+7Is0d6uDFiqqp5Fp00GBDIy5sS0oa7+JZXMASVcqB+h0yfrl9/fzPoNFzCo8BoyMk6MKefoUjIdeb21tLZ+HDckqS851NG5mJqavyHLYSzWoWrYUTDYRXckA5LZXExG+omkpExm85arcTiU6xsMRBUzsWois3kwHR2LFcIn0tmMue6GbFIjqbXd7r3KLFwgQj7oe+8pkykfnS4tkhVxL4EI2aHXp5GZcSINjUoIkyR5qal+nrKy+yLHpITw2lNn0OPagddbzY6dd0bOXSvjxv4/OjoWIkkBbLbRjBn9PKtWz1XJFFBCxszmoojJr0RS0liyMk9lb8VTtLV9wd6KJygrvVctC5CXe6FCgLXPJxBopadnO9XVf6Wk5FaVXImGSdlswxk//mU8nipWrppDV9dKlSBTQqRiByEmU4Gaojwa1mYw9P/9JSUp5KrXW40rWm4f4kcUDdjtM1Sizmwq6lePXp+OyViI11dLa+tnkesWS+gIgkBS0hg6OhbT3b2BYLBL3dZgyKSw8CraOxbS1bWShsY3GVxyO8FgN5s2X6mqcvfseUQxZO6Tav1Ihd5kpnTKdPX7pNPO5sOnH6Glcg8r33mdrV99zuxfXEPp1GlHpEpDliSqNq9n02cfUbVpvbp84qlnMuvSq474PlNafiE/f3Qe9Tu2UrF+NR31dbTX1xLwuNHqDQQ8Hlwd7Sx782U2fPoh0869mNHHnfiDpNE+3CDLMn63C0+Pk6DXS9DnJxgIgAwGsxmj1YbWoBBqX4e+HmWhUE/c9NGBQFskrEVZrqQEDxMIKGHFWq0VWZbw+5vVbYKhLgxSVr9JlXhpxAVBQBD1yGHffgkdUCaaJClIqE/Wqf5ltAiCZr/1KOUORKHT2zZlgfJOr62tJRgMcuqpp5KTk4PXK6HXhwBz3LoEQSAYlNFqBzZY7vWBCcUQPxqNFa3Wgs/XFEl2YP/enheSFMLnbwZZRqNxYknJxWi1otHpyCgqQQqHCYeC+AM1yIDOHCLs1+D3OkDfirtHQ8ClQ2sMo7eBFJII+Hp9isJBAa0BAgEXYa8GU4TQCYfCKhkpywKyFD3VveSh0WLDlJEFggAC+P1NhEIOzMk2NPZUBAT8oSpkOUxSRhYajZFQqAePpxqtQYs1v5BAqJ5w2IPNnkUo1EMw6MCUCmGPhYDPp4aehUIOvN4OZC+EPSZ0BiM6owmj1RYTJvx9XIcj0sjk823Kw2HmkHQshgN/qFdWxsbIWTQnMnHCG73+OeVKB42yucqN8TWIEjoj0w4szXhKypSY7+Z9CJ3ozLzLtQtPZIbWbj+a4mIlxKKu7iXqG16PcaM/1KDTGxh93Ilc+sSfOefuP5BVUkrA6+Hz5+fx/hN/oL22+sduYgIJHBCa95bz0q9v4I377uD9J/7IW7//Le88fC8L/vkc2xfNp722GlmWSMrI5LgrruPCPzwxIJkjy2G6uzcBCimyL/rO8u8v5OqHRDQcyuOpVMmANPsx5Oach9lcTDjsweXaQX3Da+o20bAsgC1bb6Cq+lk2b7mGrVtvUJ9bLtcudZDr8zWwZev1bNl6HbW1L6rbhkJumpreBhSPGUHQ43LtoCeiiogiqqoRRZP6fcfO38aUCYd9KgkDUFn1TExnVa0r4l8mCHpkOczeiieoqHyaLVuuU31qlLA5CatlKNOOms+QIb+JTAiI+Hx1+PzNvQSLLlaVGA318Xgq8EfqMxpilVRmUxGCoCUcduPz1RMIRMmaWGWKNRLu43KV9xJIOjvFxb+iuPgWhg1VTJLrG15XOmFAV5eSbSYr+2dMnvQ+6em9BJrLpWR9DETqslqHYjTmUFJ8a8x+o94yLc2KxDo7+0wKC69i9Kg/A4p6qSMyGREIRJUpmQwZ8humHfUFw4Y+BEBN7f9DkgL4A/HVMmZzMWbzEGQ5RGOjohgy6DPidpIsltKY7/o4Ch2dLlWVd7e3zY+7T4D09N7wa5N5UL/1AMkpitqnr5JnXyTZxir76liE4gUgqH49AHl5ymxtQ8MbhEJuNcTaoM8iO/tMQKKm5m9x93+kIyk9g4sfeopTb/kNKdk5uDo7+GjeY7x463Vs+OQDardtxu3o+rGb+Z3hdfWw9n//5Z+3Xst7jz2okDmCQOmU6Vz4hyeZ/Yurj3gyJwpBECgYOYbZv7iGc+7+A9c99xK/fOltbvj7q9z0r7c46cbbSMnKwdPtYME/n+OlX9/AzqULY0x9f2qQJImA14vf4yYcDBLweeloqMPR0kzA4+FXd/6GlWvW8P9e+jc5Q8qw5+bT0tHJipWrmDJlCgaDgZycHH77298SCimhPpdffjmLFy/mmWeewWTKIzl5DDU1DYRCfq666nKKi4sxmUwMHTqUZ555RlWMKCS6AEj09OzA72/B52sAlPevYk6rUUKeZBmPp5aHHvotgwcPwmAwUFhYyBNPPAcoZMZdd91FWVkZZrOZ0aOO56GH/kIg4CY6iN+8ZQfHHnssNpuNpKQkJk6cyMaNyjuMATJhRbF69VZOPfVKsrOnkJ8/gblz59LVpTxP/H4/v/rVr8jJKSQzcxJz517G+vXbiA6lFy1ahCAILFiwgEmTJpGZMYYTTriUPXuqENDy0ksvMXq0MnlQUlKCIAjU1jby6KPPMWNGNJOyhssvv5wzzzyThx9+mNzcXCZMOJmamgZMphz++9/POfHESzCZTEyePJny8nLWrdvA7NkXkps7lZNPPpnWSFY7QdCg06Xy8sv/ZeLEkzGZTAwbNoznnntOPd5AIMBNN91ETk4ORqORQYMG8eijjx7QPeZwOLj66qvJyMggJSWV0069kq1bdxMMdvXJ2gWvvvoqg4cMISMri8uvuI2eHmViwmwXOOeSn3PnnY/w2/v+SNm4CQwdPY2HHvoLGp2elKxsUnNy+XTxEubMvZC8vKMYOe4YfnnX72hrU/oiaXkFZAwqYv3O3Rxz8qlk50zmtNOuor27d7LPYFaIJY1Wi0ajVX2JRK2ghEyZTDEGysr/qAorjM5goFfCJmIwZCuqKQIYUwQyBhWRMaiY1Nxs9LYgGoOE1iCBJoDP7aano522mipaqypoqdxLW00VjuYmPN0OggH/QRuzH5EU9uc7lA7qiSO/WXz1li17KC3r/T5x2lPo9BEpryRBeWQwMvSkr60rKAWpcSohCGWpZV9TWoHBkIHZXILHU4lWm4xeFyuPiw7kOiNSdp0uDZ0ulfS040izH0NH5xJ2774PrcaizjCHQi62br2JlNQpFBfddEDt+CEgCAJFYydQOGosa//3X1a+/RqVG9ZSuWEtRqsNc3IK6YVFjJx1HMXjJv1kOi4JHLoIeD10NTfR2VBHxfo1lK9chixLGK02kjOz8LldaLQ6kjMyySoZQlZJKdmDS7Hav17m6ve3IEleBEEb10C27+DyUFDoQO+A1uOpUdUWdvsMRFHHhPGvU1X9ZxoaXleVEeGwj46OJer2fcN72tq/xOerx2QqoKtrJaAoIpR4dxGQ2LP3YSyWIaSlHUNzyweRrA5FZGX9jNa2L2hr+4xt225m7Jh/YDYXI8th1Xh2+LBHqKv7F86eLYRC3YRCPSpR73RuQpYDkVTdIl5vDevWn8/kSe+p2boUckohi0aPepaW1k8QEOjoXEYw2IHLtROjMUclK9LSZqvHptXasFmH0+PaTrdjvSpT1+0jf44S+G53pUrQ9FXoKOdEh9lcjNu9hy7HahQyQOwXamuxlCnqFXe5SiDp9GkYDJmUFP8KgMamt3E6N9He/hX5eRerHWyrpQyTKY+xY/5GKNTD4iXj8PubCQYdKtEWVcIUFFyB2VxEW9uXNDb9B4+nEkkK4oz4FmWkn6D8z5hDXt4lNDS8RmPj26SlzVJJs75KpczMU9GVP0gw2BkJf4qmD48lrAAyM06gumYvjY1vKvXEIWCUc1Gqeu4odaXHLZeUNBavt5Ye1/YB95meNhtlYCJjMsUndFJTptHc/L76PS6hkzQGgK6IMkwxKe/tjmWkn6AaNdfXv6zWYTYXU1x0M83N79PlWB1zH/+UIGo0DJt+DIMnTmHNB++w6fOP6W5pZuG//6GuL516NNPPuxh77qFBgH8T1G7bwvtP/lH1MzFYLIyaPYdxJ55GSvZ3C5c90qDV6Rg563iGHX0MWxZ8zsp33sDR3MQnf3laMeIVRSQpjDkpmUmnncXYE0/53sOyZFlG9nq/vuBB3mdYoyHk9+H3egh4PHEHiqJGxJyUwnMv/I26pmZGjRrFH/7wBwDC4TCnnHIKl19+OS+//DK7du3immuuwWg08vvf/55nnnmG8vJyRo4cwW9+cwkA2dkFBAJOcnLSefPNV0hJsbB27Xauu+467HYNZ5wxC1E0oNFYgN5Jc0VRI6vpwUVRh16fiddby733PsS///0ujz56J7Nnn0ZbWw+bNn0FKANum83GSy+9RG5uLuvXL+bGG39NcnIGt9xyKQCXX3Y148dP5Pnnn0ej0bBp0yb0hthrHo/Q2bRpE6eddhk///kZPPbYXRiNqaxcuZNwWGnjb37zG959913+/e9/k5bm55ln/sXZZ19PRcVc7Pbed/A999zD008/TVKSzM03/5qbbnqAJUsWcMEFF1BQUMCcOXNYs2YNBQUFJCfvq7ZRCIUFCxaQlJTEl19+GVGqKt4/jz76HE888QBlZVO48sorufjii7HZbDz++L0YjRquvPJ3/OEPT/DUU3cgCBpef/1NHn74eZ588i7Gjh3Jzl1t3HD9r7BYLFx22WX86U+P8uGH/+W11/7O4MFjqKuro66uLua+CoV6CIYcaDXmmPf1eeedh8lk4pNPPkGrbefFF9/k9NOvYf36/6E3KH2FiooK3n//fT766COamrZz6aU38+yzb3DPPdcgyQrh+sYbH3LppWezes0aVq9ezE033cagQSXcdNNdShsQ+P3v72XQIBtdXT5+97snuOGG+3j33RfQGYzU1dVxzrnnctNNN3HpL05j3drV3HvvH/ue1X3OcTRdfEg9xl4DZUG9H5V14X7+TKKow2DIxudrVNKny2El86ygxR/s3Y85xQIhKz63i6DPp/4epXAYn9uFz630gSUE/G43Aa8H43cwr//BCR2Hw8GcOXMIhUKEQiFuueUWrrnmmoNWf32Xh20NTkQB5hxgunKAzs5OmptbVEJHkFJ6yRxQwq16msCYDEUzv74dPfWE5BAmrYlMc/+O4UBISZmCx1OJxTKkH4kRVehEJe7RjCCCIDB27P9j1+77aWx8k7a2L1VCp71jIZ1dy+hyrCY/7+eHVDphUDpeU888j8ETJrP8Pwqp43P14HP10NlQR/nKpeSWDVc8R0aO+bGbm8BPDH6Ph71rV7J7xRJqtm5CCsfGgA+dNpM519yE0fLN/At6enbQ3b0Bs7kYu/1oPBH/GaMxL25HQ6MxY9Bn4Q+0qF4gPzaiGX5cru2q0bHVOgxQBsNFRTfR0PB6ZJDvp7NzKZLkxaDPIhDsQpYDpKRMRZL8OJ2b6O7egMlUQGeE0CkpvhWDMQebdTi1tf+ksek/1De8SlraMWqIVE7O2QiCyJDBd9Dj3ILHU8W2bbcwZcqHuN0VhMNuNBorWVmnkp19OouXjCcUcuL3t6gD4a5I2mh76jRKSm5lw8af4/XW0Nb+perf43RuQZbDGAw5ZGScQEaGQlRs234bLS0f4nKVk55+HJ1diqlwWtqsmHOVnDKJHtd2HN1re02KB1Do+Hz1eCMZG+J5HUW91Lo6lfOk16f1u2esEVWKy12uvi/23V96+nFK6vmOxeTlXqSqb/p22LRam2rW73Lt6iWHIoSOkv3pOIJBR4TQqYp0PmUEQR9DRObmnEtDw2t0dC5FkgJxCR1BEEhNPYrW1k/o7FpJIOpno+//Dk1NnUZ1zfOqEWQ8AqbvuYi2O55RN0CSbUwkVIxIff399/T6dFJTptLlWEWSbXS/9dF29YXB0P8aWq1DAZDlSNv1sco9UdRRUnwL23fcTk3t31XFjsGYE1EnKf5F7R2LyM76Wcy2fn8rlVXzyM4+K8b0+kiEzmDk6PN/zpTTz2X7kq/YtXwRnm4HXU2N7F6xhPJVyxhz/ElMP+9izMkpP3ZzDwgtlXv54CmFzEkvGMT4k09n+IxZisdEAgNCo9Uxfu5pjDzmONZ//AHbF89XUriHlYGYp9vBktf+xer3/sOo405k/NxTSc48cI/NbwLZ62X3hP5K2+8bKe/+B6HPgFCj1SKIIqFAIOLvYcaWloFGq8UK6PV6zGYz2dnKebjnnnsoKCjgL3/5C4IgMGzYMBobG7nrrru4//77SU5OjmxjIisrHUEQMRhSkWUvd911ubrf888/hVWrruDddz/ijDNmoSRB0CGKJjQakxo6JMtBUJUcGnS6ZNzuLF544TWefPJ3XHzxGRgM2QwdmsbYsSmAQsTce++96r5yc23s3LmFd9/9kF/9SnlO1tbWc+eddzFsmNIfKS0tjUk7Ha1nXzzxxBNMmDCWP/1JqV+vT2fiRCWc2u128/zzz/PSSy9x8skn43Ru5dlnH2DhwlX885//5M47e+0uHn74YWbNUiYtbrvtKs477yb8/hA2m4m0NGUiJyMjg+zsbNXsOdqm6LjPYrHw//7f/0Ov1+P3Z1JeroRb/vKXl3HiicdiMhVyyy23cNFFF7FgwQKOOqqEUKiHyy+/hJdffh1QCJ0HHniAp556ijPOnIoU9lFUlMett97C3/72Ny655Gyqq8spKSlk4sRBEYXOjJhzEgx2qArkULAbjcaKRmNk2bJlrFmzhtbWVnQ6RQ388CN38sknS/jggy+58kp7JPxL4qWXXsJms1FcbOKCC05j0aIVPPzwI6pKNy8vm0cfvZOkpGGUlKSxefNa/vKXf6qEzpVXXqmYa3uqGDzYwLx5T3LUUTNxubzYbPD8888zePBgnn76aXy+ZkqK09m1q56nn/5r5Lzua5KtUB+qyXQfA6nesmIf76IgvdnRxMi9kQYI+HwNagasKOmj1iWGMaekYklJRZLCBPwdaLU2pBAEfF4CPi9Bn5dwMITf4+a9x//AsKnTSc3JRdRoSc3NQ2s58AmbH5zQsdlsLFmyBLPZjNvtZtSoUZx99tnqTf5d8eUOZUZvUpGdNOvXx4FGsXevEsJUv7eM3KK9TJiwj5x5UyRsYNS5oP36equ7qwEoSipC/AbO4jnZZymGmxn9VUDRLFxRmM1F6mdB0JCTfRaNjW/S5VijxrNGfTlkOUhb25fk5sY3GP2xkV5YxBl33IPf48bV2UFPRzvVmzew+YtPaCzfyX/+cDeFo8dx3OXXkZZf8PUVJpDAd0DA62HDJx+y7uP38Lvd6nJzcgrJWdlkl5QycvYcsorjp2OOh6am/xKIvBzr6/8NKDMFR09fis+rzIhEwz7iYfTov+DzNWEeINTjh0ZUoRAdUJvNg9BoejuTBn0WWm0yoVA3bneFqtpIS5uFJAdpafmYwSW309r2OU7nJhzdG8jKOg1HlGCxH62GdRUWXkVj03/o6FhMINCuhglFfUzM5mImTXqH5Stm0uPajtdbq8blm0wFaufNYMhWUp36m1WPsm6HYkadkjoVs7mYNPsxNDb9Jybdd9SnxWaL9UOzWkppQTHBDYXcqqfNvuVSUiZRX/9vHI71fcKkYpUiOp0drTaFUMiBo3ut2t59ETXLjyqZ9Pr+YXzRDEtu9x61k6HbR8WTZj+Gyso/0dW1KjLLFIzUF/sutlqHqYROMOhQyuzjVROdXFAInaZI27NiOlI22yg101ZX1+q4BBIohEhr6yd0da1U76d44U/7nuOBCJ2+IVfxiKEo9s0cFY+IARg56hncrt2qL9S+2NfQc19fn2jdisGy8mzZ9xwAZGWdRmXlPLy+WjWELarUyUg/npraCpqbPyA1ZYp6fiQpxPr1F+D11dLdvYGjpn424PEeSdAZjYw78RTGnXgKAC1VFax4+zUq169h85efsH3JAsYcfxKTfnYWNnt8hdahgM7Get595H4CXi8FI8dw9m9/j1av//oNDwKU36NwyCTW+LbQm8xMO/cijjrnQlxdHSArZqXVmzew+r23cDQ3sf6j99jw8QcMnjSFiaecSd6wEXic3fR0tJOWX3DYkmeCKKrpog0Wq+qFE8/fJh527tzJtGnTABmPp0bpnxx9NC6Xi5qavRQWRvseveEnOl0q4bCH5557gVdeeY/6+mZ8Pj+BQJAxYxRCJfr+FUUdFssQXK7dSFIgru9NeXk1fn+AE044JdL2YEz4Doi89dZbPPvss1RUVOByuQiFgthsVrXcbbfdwtVXX80rr7zCnDlzOO+88ygqin0uD6TQOfvsn8UtU1FRQTAY5Oijo75lAjqdjokTR7Fz586YesaMGRPZXk9WlvK8aWvrxGbr//6J9QTqfV+OHj0afeS33zcF+siRZWrK9aysLLWsICj9sIwMO21tymSJx+OjoqKCa665luuuU5TOAKGQRHJyMj5fIxdffDpnnXU9Eyeezpw5R3PWWT9n7tyT1f1F+3dR+P3NmEyD2Lx5My6Xq8/YXanb6/VTXd2ILIcJBrsYNCgPi0VRR8lIZGdn0NbWjlZrQau1oNFYmDw5H0EQVDXMlClj+ctfXiYcDqPRaFi/fj0PPHA/mzdvwOFwIkV4k4aGFnJylPt26tSpkXOlnLMpU/oKAGLH4P0VOlK/sr0p5QORezCi0OlTl1abBDSo7e6bVh56PY+U89ZEMNhFWHJhsZSgM+rRBYJoNHm4uj2Ibe14uh0se/Pl2LaaDtzw/wcndDQaDWaz4mbk9yuxYwfT8+WziH/OiSO+WbhVlNBp2JTHsKzLSUvvY0zqdcDOyMzd+J8fUH1VTiUEoSi56Bu1IyVlEsfO3hX34avE7fUadu1rypiUNBpRNBAMdkRUPoPpdvRmZmlp/eiQJXSiMJgtGMwW0vILKRo7gUk/O5vV773FlvmfU7t1E6/fezsn3XgbQyZPS4RhJXDQEAoE2L54Ae11NTjbWmgs34XPpcycpObkMXzGbIZOn/mt5fseT5VqMNsXshyite0z1fx1oDAOUDLxJB9CAjut1open64qLSyWoTHrBUHAah2Gw7Eal2sXfp/ybDaa8hlUeC1lpfei06XgD7RRV/ci3d0b8PtbCYfdCIJWVfsodQ8hyTYGZ88Wmlv+p5I1BmMv4WEwZJGcPAmHYzXt7QvRaJUXYd/BssGQhdtdrrYFwOurV/YRIUqiKeKjxATQx9MmNnzGYol41bjL8UXq0WqT+4XB2KwKMeV271af37p9FDOCIGCxDKE7ku1MaW8chU6ErPEHIuFIccgAa6Rdfc0m91Xo2Gwj0ensBIOdtLV/qbZ9XwWL1TqM9vb59PTxNtLpUmLKRAkdv78Zt6cC6K8uimZybGz6D83N7w1IIKWmKFklnc6NanihPg5Zo9OlYjTk4vM3Rs7DQIROrxddvHqiSEoaz5DBd+Fy7cZgyBwwfb1Bn47ha0iBaPZJiG9+KAgCFvNgnD1KSGA8Uk4QNNiSRuP11arHGPVUSs+YQ03t3+noWMiy5TOYMvl9bLaR1NS8gNen7Nft3oM/0B73/jjSkVU8mLN+cz9127ew5PWXaN5bzoZPPmDzFx9TOHoc9tw8NFodxeMnkTd0BIL449pJdjU3smPxAjZ9/jE+t4uskiGccce9B43MkWUpblafKEIhN6vXnIaAwLRpC9BoDv9MUYIgxJB3o2bPYeQxx1G1aT0bPv2Qmi0b2bt2FXvXrlI8MSPjEIPFwtBpMykcNY6CkaMxJ33zl65gMjF0w/qvLSfLMqFAkFDQjywpagZRVMLEwqEQsiThd7sIh0ID1hFNBW1KSsaUnoEY517+pn3lQKBdVY5EB6UeTzUeT58QFXozMH3wwXLuvfdPPP747xk/vpiUlAz+8pc3WblysVoups2ifkBCxxRJhx0ddEtSMKbMqlWruOSSS3jwwQeZO3cuVquJV199nr/8pXcg/Pvf/55LLrmUjz/+mE8//ZQHHniAN954nTlz+thfxCF0TCZTTFsH8tlR1on7EE290Ol06nH2JnuIfw36Zszquz+LxRK3jE6nVbNcRevW6XSIYnQcrdxHAG63EtL0j3/8g6lTp+Lx1BAO+zAaczEYkpEkN+PGjaCiYi8ffPAKX321lAsuuJA5c07gnXfeibQ7MiEUycwYCvXgcu2kq6uenJxsFi1aTCjkiaQU12GxlGC1aoEeZDmEVisSCjkVoliWEATU9u0LhWCJXed2u5k7dy5z587lH/94lPR0Oy0tQU477VwCwf6/i96MX73r9n327Uvo9KpvhJjfinLeA4rZ9D4eO8rn3uulkDpypA2KXYAsh/B4a9GIRrXvFJ3E8fmbCQY6EcVuDOYCLKl2pp59AQ1bN+Hu7iIUCNLRUIvL6Yh7ruLhGxM6S5Ys4cknn2T9+vU0NTXx3nvvceaZZ8aU+etf/8qTTz5Jc3MzY8eO5c9//jNTpvQa/jocDmbNmsWePXt48sknSU8/OB2OijYXq6s6EQQ4adSBSynD4TCVFUon1BwOMn7u6bEFdv5PyW6VOQJyxx9QnVGFTnFS8QG3I4qBHr6iqMVgyMXni8zm91HoKOsNJCWNw+FYjcOxBoMhix5XL3Pc2bmc7dtvZ/DgO+LG9R+KsKbaOf7KG5h02tl89vz/Ub9jGx8+/QipOXmk5uRSMHIMI4457lu9dBNIAKB+13a+eOFZupoaYpan5uQx7byLGTptxnfOntLe/pX6WaOxMmzYQwT8bezZ+zCtLZ+gj2TeMe9HoXMowmQapBI6URKhL6zWMoXQce/G5+81+lXk1ykApERSRrtcu3C7FWJdUXfEnvPsnLNw9myhtbWXADPoY5/z6enHKYROx0KVGOgbzhIdEEfJGlmW+6SpzoyUyY6U6SVDfKrqJHZ/UfNhj2evKueOZ1ptNObGkPEajTlGzRSF3T4jhtAxGvu/x2zWWGVKPJJCq7XFkB2geK71hSCIpNln0tzyAc3N7yl1xSOHIsSay7VLlRbvS0bpdCkqORRVDu1r6AyQnn68Qui0fBBpZ38CyWwuVsMLo95M8VKNA1htI9RjHIis0Wptamr0/Sl0BEFg0KBrB1z/TVBcdDM7dv6mX/hVX1gsQ/oQOvEVyvsaOkff28lJ4ynIv5yW1o8IBNppa/8Km20kHR2LYsp3diwmJ+ec73AkhzcKRo7h4oeepmbzBla99x8adm2nauM6qjYqv7E1H7xDVskQjr3sWvKGHVg20oOJgNfD/P/3HDuXLVKXZRSVcPbvHsRgNg+84TdAW9sXbN12Cykpkxgy+E7Vv6kvWts+VdWFDseafiGjRwoEUaRkwmRKJkymo76WDZ9+yI7FXxEKBkBQDFL9bjdb5n/GlvmKui29YBBp+YWIWi2iRqP8iRp0RiM5Q8rIGNz73otOUMuSRFgQ8HuUwZsYzbgVGfDJsqwYFXs9+yVrANBqESPpjzVaHYIoIMsyRqsNo8X6nbJ56fV61R8GYPjw4bz77jvqOxFgyZIF2GxW8vKykOUwOp2WcFhpc3Rwu3z5cqZPn86NN96Mx1OJKOqpiIyplHIDZWXqo3yIlCktLcVkMrFo0XIuumg2khxL+qxYsYJBgwZxzz33ROqQqK1tJBYiZWVllJWVcdttt3HRRRfx0kv/5oQTHuuti/59ujFjxrBw4WLuvPOiaEvVdYMHD0av17N8+XIGDRqERmPF621nw4Zt3Hbbyf3qglhlzUDjOuW4o+sGyqr19WnSo0RGL0kBWdk55ObmUllZySWXXILHoycU6sZozEGrTVGTHSQnp3LhhZdw1lnHc/bZp3LmmVfR2dmJ3W5XiQyNxohGk4vf34Ishxk1qpjm5ha0Wi1FRcV4PAKixog1MoESDPaaEkcnbwYiwNat26aul2WJtWu3MGRICRqNhl27dtHR0cFjjz1GaqoHSQqwdWuEKIyoZYYPH86HH34Ycx7WrN3QZw8DETpRfxw5pr6+5z0cjqiU5F5VWm89gtqv60tGCYIWUZSRpAChYDchuvtsoyUc9qleiopPpAdBEBgx81gmnNB7L4VDIXatW829730R97zti2/8JHC73YwdO5Yrr7ySs88+u9/6t956i9tvv50XXniBqVOnMm/ePObOncvu3bvJzFQ6UykpKWzevJmWlhbOPvtszj33XFU6ti/8fj9+v1/97nQ645YDeGWl0qE+flgm+akH/jKsq6sjFA4jhIKMPmpaf8O0aHarEWccUHYrgKrub6fQ+TqYTPkqoROdFe2L1JQpEUJnbSR8Q8JoyMVsGUxn51KaWz5Ap0+jrPSemO3c7kpcrp1kZp5ySCpfkjOzOPeeP7LszVfY9MXHdDU10NXUQOWGtax+/23OvfsPZJUM+fqKEkgggqDfx7I3X2HDpx+CLGNJtTPimONIzsgiJTuHghGjY9IMfhdECZ3S0nspyL8cQRDw+RrZs/dhHN3r1IHm/kKuDkWYTUVqWGdUOdIXVkuUDNjdq6rZhxQxGLIwGvPw+RpobfssbhmAlEj2r56eLb3eI/ukoE5PO469ex+lq2u1Wkffgb5BJWsUdUs47EKSIsajhlgj4r4qHlWhs4/qxGjMRxRNSJKXzk7FPyfqddYXoqjDaMhT1RP7kitRZKQfR1XVvMg2RjSa/u8xk6kQrTZJJVfiqTsAbEmj8bUpHV2NxoJG0z9UOCl5HM0tH+CMZPmKR+jYIoSO212udsTjhRGZzUV0d3eq5yHeNUzaJ6wpvjJF8dGJkj5KXfGJGJttlJrifiDSBxTyxO9vGrCeg42cnHMwGvP6ETL7tikKfZxU6vuWgV7FliCIlJXdh9lcwu7y+9UwRX/EkyA1dRpdXStp71g0IKHT1Pw+wWAXhQVXHPiBHYYQBIGicRMpGjeRtpoqarZuwu3owuPoYs/aVbRU7uXNB35DwcgxjDl+LiUTp3yvprmuzg6qN2+gac9u9q5bhafbgSCIFI4ey+jj5h7U9OuhkItdux9AlgN0da1g3frzKCt7gPyIJ1MUTU3/VT93dC49YgmdvkjLL+SEa25m1qVXEfB6I2mFRWq2bKJq0zrqtm+lvbaa9roa2utqBqzHbE9n4iVX0q4V0X4LpZcgCOiMRkRRgyCKqlJHo0jm1Q8AAQAASURBVFW+a/UGTFbb96IiKyoqYvXq1VRXV2O1WrnxxhuZN28ed9zxMNdd93PKyyt46KGnuemmX6jKn0GDClizZh01NQ0kJadjMkmUlpby8ssv8+WXi8jKknjzzY9Yu3Ytgwb1Pq/6oldFESBKZkRJCqPRyF133cXvfvcA8GuOOmoiPT21bNq0jCuuuJjS0lJqa2t58803mTx5Mh9//DEffdQ7YebzBbn77l9y7rnnUlxcTH19PWvXruWcc85BEHXI4YFTkv/ud79j9OjR3H77Q1x55flYrR6WL9/AeeedR3p6OjfccAN33nkndrud/PxcHn/8MbzeIFdddVXc8ysIGpWs3zfVeW8ZoU9mpfi/+75hWfHOp7Ks18RXXYbIgw8+yK9+9SuSk5M59tgJuFwtbN06H6czyLXXnsxf//oKRUXjGTt2ND5fDe+++xFZWekRlQ1qVrBo8gWdzk447ObYY2WmTBnDmWeeySOPPEBBgYnW1h6++uolzjrrLCZNmoRGo3hLRrOdyar6JnaMWV/fxN13P8n11/+K9etX8fe/v85jjylG3YWFhej1ev785z/zi8tOYdvWLTz66LyYeq6//nqefvpp7rzzTq666gqWL/+Y11/r7T/0V+hESTQ5VhU0gJKnb9hZPLWPQuiE6WucDIq6Z1+Iok71DooqA6PqnX2h0WopGBHfqy8evjGhc/LJJ3PyyfHZSIA//elPXHPNNVxxhdJJeOGFF/j444958cUX+e1vY9PFZmVlMXbsWJYuXcq558YPBXr00Ud58MEHv7ZdLn+Id9YrkvdfTCs6wKNRULV3DwAaTw9jjpsbuzLkh8pFyueyfdbtB9XOagCKk7+5Qmd/MBkL6EKZATXHCc9ITlYURM6e7ZgihE9yykRGDH+Kmtq/UVn5J9yu8n7bbdl6LR5PFcPCLvJyLziobT5Y0Gh1zPr5lUw58zwad++gu6WZzfM/o7Ohjv/84XeMmn0Cw2ceS/bggTvRCSQgSWF2r1jKyndep6tJGfCOnD2H2b+4+hubGx8IgkEnjojqIiP9eJUwNRpzSU4aT7dzoxo+c7gROn1TN8dX6ChhWC7XLlVqGs/o12Ybjc/XoGaJiqfuMEVCTKMvV70+vV8nyWwuVpUpUbXCviFXgOrBE52J1GptKnkSJSJ8MQqd+GSUIIhYLEPo6dlKe4fSqRworbzJVKASOgP5VFj7qG8kKX7KXUEQsNlGqkqYfQ11o0iyjaYtkiZ+X0VNFNGw3WgnMB6hYzQqHkTKTJI/Ul9Kv3IW8xC6uzcQjJg+G+JcZ70uTVXyKPuLT2z1JXQEQReXQIJYH539kTVZmafhdG7Cbp8xYJmDjdTUo/a7vi/Zo9fFVylbzLEeXfv+dlIipsfd3RuRpKDaUczNOZ+urpV0di5FlsP9Bgo9PTvZsePXgKIKs+6HeDqSkDGomIxBvX2yWd0Olr31CtsXzadu+xbqtm9B1GhIyyvAlJQMgkBPRzsarZbBE6dgSbWTmp1Lbtkw9KZvpqAJ+nx89vw89qxeEePZYEvP4NRf/Ya8ocO/8fG0tn6Gy72H4qKb4g7yampeIBBoxWQsxGYbSWvbp+zefR+ioCM3V0mT7PXW4nCsVrfp6FgCA9wOXm8DTU3vUFh45RGTXU1vNMUQeMXjJlI8Tpk88Di7qd+xFVdnB1I4jCRJyv9wGI+zm4Zd2/G4XMhSf+sIUaPBYDYrWbbCYfoOYgVBQNRq1H3/WCF/d9xxB5dddhkjRozA6/VSVVXF+++/wm9/+wDTp59Namoyl156FnfeebW6za23Xs/VV9/M1Kln4fX6qKqq4rrrrmPjxo1cdNElCILMOeecxPXXX82nn36MctwDETpB9dnU9xl13333odGIPPLIczQ1tZKTk83ll58NgobTTz+d2267jZtvvhm/38+pp57Kb3/7Sx555BkAtFodHR0d/OIXv6ClpYX09HTOPvtsHnzwQcLhXiVPPPKkrKyML774gt/97jccd9zFmExmpk6dykUXKYqdxx57DEmSuPTSS+np6WHSpEl8/vnnpKbGfz/BwO/6WHwdoSPu47UTT6ETh9ARNFx99dWYzeb/z95Zh0lSnV38V+067js7s7Puy/riLB7cCSRYCASH4JAPT0iCS5CEECx48ODOurDuOjM7s+PablXfHyVd3V09OwsLLITzPPvsdPftW9XV1VX3nnvOebn77ru55pprcLkcjB07kiuu+D0AHo+Hu+66i40bN2I2m5g0aQyvvvoIougHcjIUVIIgYLF4MJnMvPrqo/zlL89x3nkX09bWRmlpMfvvf6Am0FCvSbISS19JKhWnn34CoVCYvfc+ELPZxAUX/IrzzjsbkMOjn376aW688UYeeughJkwYyR//eDWnnnqxdtyqqqp47bXX+P3vf8/DDz/M5MljuPnmy7j44ptT9iN5XAQEkwVJjMl2Kk2hk0o0qcdUHvto707rywJEFGIoqeKxWJIZeXpISMlCDrZSIpFmEokgWVxoO4VdmqETjUb5+uuvueGGG7TnTCYTBx98MPPmyYPPlpYWXC4XXq+Xnp4evvrqKy688MKsfd5www1ceeWV2uPe3l4GDswMxV20tRN/JM7AAif7DN05C9fWDesByHG7Um72ANTNgagfPGVQNqFf/XWFu+iOdANQ5d21EzR19dduLzP0OauZA9FoKyFFru7xjMJkslCQvydbgEBwU8p7EomQJm2vrX1ktyV0VDg9XoZMlgOwxs48hDfuup2GNatY8v7bLHn/bUoHD2P8QYdRPKgGu8uDJz9/pwdfP+OnCUkU+e/9f2XjQllF4Mkv4JDfXcrgid9dNZjOrtlIUhy3e1gGYVNRcSo9vUu1x07njyvwWyWVTSabYf6PmjGjrUhgrNxIZrAo1iYDq5HF4sZmK9GsAUZBuYIg4HIPIRzZrm1TT3io/UY0Qkcm0mw6K446aY7He0gkgphMTm2/jMgoj2cEPt9Krey3kUJHfn4gKAsxtiwKHUEQNLVSX/B6xyZDkbOoO7w5yZWdbIPK9O/MiBwymSwp6iIQsFgyLa65uRPZ3vSK9thh8D3LOUHDtAlkNjJKb1Wy24qz5n94PclJcF+ETkXFSZSXn7hbqU9TCJ0s36HLNUiTdFss3oxJtNs9VMs26Oqapw08i4sPxmRyEI/7CIXqM9S8mzffpf3d27P0f4bQSYcrN49Dz7+UGSecyopPPmTd3C/paWmmrb42o2277jnBZKJk0GAKBwzE6nQR8vXi62ijaswERu83k/zyAcTCIfxdnfg62vF3drDysw9pXLcGgPJhI6gYPorq8ROpGjses8Wasb0dIRjcyqrVVyBJMTzuYYRC9TiclZSWHKG1aVPUa4MH/57S0qPZvOUe6uoeZ/2GW8jJGY/HM4KOztmAnKnl968jGNxMKNTIli33kRCDjBv7iPb727Tpz7S2vY8oRRk6JDMT7qcGV04uw2f0TQJ3tbfR2NRMfvkAOYPFZFJUF7vPtUYPfUDy8OHDtbmZirIyG59//gI2WzFWax7h8DYSieTiwtChA/nyy/eIRJqxWvO0MctTTz3FU089hT+wETERxm4v4w9/OFshIwSefvpprY+kmiQK2JTnkiSFyWTi//7vJi6//EQkKYHNVkg02qHZpO666y7uuit5DQuFGrjgglMAsNsdvPjii4afPRjU5+MYfz/7778/c+cuMHzN4XDw0EMP8dBDDxm+fsABB2QQe3vssUfKc+mPAf7vpqu5/vrztGOgP1YqamoG0dOzQtl3c8b21P9/9atj+dWvjk1pd/rpp3P66adrlb7MZid2exnB4FbOOeeXXHrpzdp2otF2wuEmTVWDUlY+U71ixet1c//9f+Xee28nHN6OxZKLy5Uc495yy01cc82vU3KQLrroDK677i8pfVmtNu6++xoeffRhEokg8bg/5Xw47bTTOO2004hEWrRFuJ6eFSlVm4866iiOOuooQF6wkKQ4v/71ccqrmeMHk2AhQQxRjCfPBYPPCElCRz2XU9qYLJAAUYprhJUgCEr1UXmsFAxuTZJCkogap2Q2O7BYvESjvSQSwZR+E4kw69bdSCDY/4zhXUrotLe3k0gkMuxTpaWlrFu3DoC6ujrOP/98zWt66aWXMm5cdkmR3W7HbrfzyCOP8Mgjj6T4PfVY0yTLzycOzMdk2rkLaWeHvKJYaVSxZoO8wsmwQ6CfLHq9Tx74lrpKcVl3LZGgBncarYZDcmISj/cSVEIZ1YG1S3lvJNJMPO7TBoc+/xrt/eFwI729Kwx91rsjbE4XJ/3hj2xdupj182axccEcWrZs5OMtG7U2ZouFYdP3ZsLBv2DAqDG77Y32Z3w3aGv9irotL+CMHs22VRvYuHAuZouF6SecyqRfHIPd1f8U+WwIBrficAzEZLIQjbazdevfGFD5KzzuYQT88rmYq2TF6FFaegxr1yWVi0YWm90ZOTl7IAgW8nKnaqUg9bBY3DgdVRoZYLHkGRLR7rQJp5FCB2TiJ0noGOekuZyD6GSW9jiFrNEydGRCRyN9dGSArNbxkEj4iURasFrztJuxEYlUkL8XTU3/0R6nVyNM7ldyoJNecUqPPSb8i1Wrf0/NoIuztsnxjtV9vmwKnWSbdMm2CodjgCIZjvfZl15dJOfeZPaXk5uaL2ek0IFkrpK8PePFF6dzoEZs2QyOeXL/KxhUfRESUlYVj4rd7brvcAzAbHaRSASzZvuoRGkwuCUjkBvkAWZe7hTa2j+mpfU9IKk2c7uG4POvJhDYlELo9PauoKPzK+1xT+8yKipO2cWf7seFnKIS9vnlGezzyzPoaW2mc3sjYV8vEuDOzSfQ3Un9quVEgkFaa7fQ29ZCy5ZNtGxJXRxr2rCOBW+8jMVqk3NZ0mBzujjh+lt3Oq8nEmlh2fJzKSk+jJqaSwHYuPFOLZti3fqbNNVbYNAGBg++AkCrJOfxjEAQBIYMvgqfbzWdnbNYuepSpk55Qwvvlq/hDnp6vmbLlns1hVwotA2XqxpRjNHRKV9X29o++Z8gdPoDp8eL2dKOxWr9Vnk23wdCoQYSiQAu1+CMXBYVetuI2ezA5RqKKIZJJALKRD+KyZxdUWIS7IiEtQlqX3kvohjTrFxG7VSLlEooZVOwpCp1+woy3jVWxl0Ns8lJnJ6MPDk9ZHJBqZpkdKy0qkz6MOD0MGqVoEgNmk7tR83iUXNvMqs7qX2JYkQJts4MDE7us9pX0m6VuUCjBken25bSP2P65zaek8uLIHH1geG9PzUY2WL4GfVZO9m2Z1Lb6IkhpeS5Or5xOquJxTqIRjvkgHrd/svqaZnQSSQigJytqEakBAL9l+5871efadOmsWzZsp1+38UXX8zFF19Mb28vuQalXtY1y4nsI8t3TgYqiSLBSBQsVgaPTJO8SlIyP2d4ZhnxbGgPySGhpa6dq7TVHxQVHcSIEXdQkCVs0WLxagnbfoWoUSchVmuOVjY2ENxCbo6sOPL1rkrpo67+CcaNfXiX7/t3BbPFwtCpMxg6dQbB3vNZ9fnHbFmykJ62VqLBINFQkHVzvmTdnC+pGrcHR11xHU7PT0Mu/DOM0dPazKK3XycaDiGWPoOzMMDKz1fRuSEPgEPOv5Rhe07AYsl+A+0v6rc9xcaNf8TrHcPo0feybdtTbN/+MoHgZiZNfE5TWxhZccxmOyUlR9La+q6h6mF3h9NZyV57fYnVkpe1jcczQiMDjBQukJkHlq1ktMtZnVR3ZCF0nGll3fU5O+p7YrEuEomwptBJz1+RV682EQ43aVU+rNZCwwGXbOOR/djQl0InSeikV5zSw+0eyvRp72R9HWSFTrIvY7WP3hYV0ZVg18NksuBwVBIK1cp9ZSFYHM5KTV1kZLcC2R6k5glBdlLOrVuMyEYggazSaWr6jyGJpseQIVf1+fruCkEwMWL4bQSCm3eYtRMMbslKkOXmyYSOmiWkEpgut0robKa4+BCtvT+Qarnu7VmWddvxuI+W1vcoKz36R0c2f1PklpSRW5J5bRm934Ha373trTRt3EB383YS8Rg2hxOHx8v6ebPYtnqFRubYnC48BYV4C4vIKS5h4mFHZarADRAM1rF8xbl4veMYOeIO1m+4Fb9/LX7/WmpqLsXnX6dYPOVSxCqZA7C19mGKig7E6x2jq0onX28EwcSY0feycNExBIObWb/hFu365nBW4nLV0NPzdUp+VSTSgstVTW/vchIJv7J/mwgGtxrmOP6M3Rfq+RAK1eF2D9VVq9JPeFPzRARBwGx2apNaUYzoCBSDCa5G1oSU96dOwuvr6xk9ejTp1YzUvtasWUNVlXyvNAlWRMJZ+0rfZl9tAI47/mzmzJ5ruO833ngjN954Y9b3fpew2Yq0uVs2pAQs9xGerAZWy2OSdHuQSrDENcImG+nz4ouvc8UVtyOPayT0x6u6uprFiz9U+oohoZ5HmaSP3EbUFD/GRI1qzTKuJpVsmE4+ZSd0tL/7IH3kbSaS/aSrb/qVXaT2oyOG0trJ+YWFCsEupZGmLmU7Is3NrzNkiBxXE4/7Dfe7L+xSQqeoqAiz2UxLS0vK8y0tLZSV9b/qlBF2pNBZpyh0RpXn7FS/2zdvRFTkrkPHpVmq2jdCVy2YbTD4gD77kSSJh+paebWlE1/US9i9L/mOXe+LNZksGYF2egiCgN1WSihcb7iq7HYNIRptIxjYpBE6vb6VABQXH0Zb20e0tr5Hb+/55OT0P4xpd4ErJ5dpx57EtGPlTCZJkmjZsokVn7zP2tlfUr9yGc9eeynjZh7C+IMOx1NgPBn6GT9eNKxbzdv3/ImQrxfBJDLuN7KPtWhwPmVlM6nao5qY5wVmzT6fivJTGDXqz994W4lEmNraRwHw+VazZMnp2gC5q2se4XCTVhZbtUOmY9TIO7HbSygp7j9pvDvByFqjh8czUiuLnVVVk67Q6Qfxk22in54tpicpLJYcTCYHoiiTORFN7ZOqkHAohE4k0qRl2RhVnJL7L1TKVKtVroy/Z72dzpolO6a/cDqr8XhGEo/7cWYhkPToa3suV3WS0LFnUcw49GSUsRJGEEw4HBUElbLl2XJ7PCmETnZ79IABp9PZOTvFQvJTQ3l5ZmGJdHg8o2hr+0jLO8p4XTme6mRNPaZqoHIguDGlfTQiq9IKCvals3MW/sBG4vEAFkumUrG27u/U1T1GY+OLTJv6Zr8+0/8CcopKyCnKVFWNnXkIsUiYQFcXrry8bxyuXF//BMHgVoLBrfj9awkEkhWDRDFOwC/HBOTlTSUW6yIQ2IDVWoDXM4rOrjl0dc1XKqLJEy29es1mK2TsmAf5esmpNDe/odmAnc4q8vOmsWnzXSnZD6qaUa/qAmhr/5Tqqt+SDlGMU1f3GB7PCIqLD/1Gn/9nfLdIJEIEAhtJiBEEBGy2Emy2IgRB6EORkVTVZFN3pLczalNRUcGyZcsIBLamhM26XDWYzTYqKioy+upLtaFvl22fVDz5z6fp7W3CbHZmVJksKOhP1s13A5k0y6x6mdpGXzErG0mRVPHIJeXTCQq5zLwkiVlVT+p2fvGL/dh//yUEApuRJFH7fkAtk55U+yT3J13hYtIsw3rbkh5ffPEF0VgX4VCDYlsSDfsCIyIr23HQ5w3tmNCRJCUfKGP/d0wg6VU8ydeNFEGqCklMU/IIylhpO/Xbnia/YCQF+XuSUEjMosKZwFOGnyEdu5TQsdlsTJ48mU8//VQrZS6KIp9++imXXHLJt+q7L4VOOJZgS7t8AxpVtnOEzoZlcn6FGQmPN021oapzBu0L9r7DUl9t6eLPW9VVUBsUns9m07qd2pddBZu9WJd3kEbouIfR1T1fKw8M4PPJCp2K8pMwm5w0t7zJ5s33MHHiM9/fTn9HEASBsiHDKBsyjEm/OIY3776DntYW5v3nRRa++SqD9phM0cBqzFYr5UNHUDlqLBabcRr9z9j9sfrLT/n4Hw+TiMcpqRnCkL2qCZnlwe+Q6SMZPfpKVq66lI7WLwDY3vQKQ4ZclXVimb6C1dr2IYHAJgZVX4QgCDQ1vUYs1onDXoHFmoPfr//NS7S0vEM4LAfxZSN0LBYPw4f93y749Lsn3EowMmQnf6zWfCyWPOLxbqAv4meQ9nc2QkefC2MyObVKC6AQ3vYyQqFawpHtmh87veS1mrUTjjRrA59sqiGQiXKV0MmmZNATTX0pdPoDQRCYOuUtIJG1egbA5Ekvs3nLfQwfdlPWNvrj1ZcFSkVf1iaHvVwjdLJZnPQKHZNB5S0VuTkT2GfvOVlf/1/BwMqzsJjdlJYebfi6Ky04WSUnVXt2W9snzJt/KAMGnEbVwHO0AHavdyyBgExa1m/7FwMqTs0gNlXVj8+3klisa4e2tp8BVruDvLLs14odIRbrpan5Te1xIJBKyEUizQSVa43LWU1u+QmsXXsdQwZfSTzhp7NrDj09SygsPACQba7pFsm8vCnY7WVEIs2a5crpqMRi8VJWdhyNjc+nbA+gs0O2W3m94/D5VtLS8l+qBp6b8Ttvbn6dLVsfwGx2s9++i/pUHfyM7w/puS3qfU1Ckr9jQcBuK+rbPqNU5UmIYcM2gMH3nToptlgsDB06lGDQQjzu0573eIYZlOfecblu+Xk9oZN9Ib2yshLY8QLI7ojUEug7Lm9uTD4ICIIVSYpoC1WZpIUZBBNer5uysmoCgSggZXw/UaXstqz0sWbfpmKbSwYL92FbkhJZ1T7ycztWzGR+ph0TOtmrXO2YQNJbtzRiyHC/1Ock3bxCfs5qzVXGjSJ1dX+nIH9PROX3adoB0df33u0Afr+fZcuWabaprVu3smzZMurr5ZvClVdeyRNPPMEzzzzD2rVrufDCCwkEAlrVq+8Cm1r9JESJPJeV0pydu3nUbZLlxx6XwSBczc8xqG4lShLnrtrKsUs2UhuKcMMGeRX+4qoSZlhlsmSpOIJVvmDGe79r6Cc6+uotIMuwAQLKoDseD2irP17vWAYPvhyQg1z1F1sVPt/alIC0HxOKqgZx1j2PcMQlV1ExfBSJeJzNixew4I1XmPvK87x25808fsEZfPLkY/S0Nu+4w5+xW0CSJHpam/nqhaf54NH7ScTjDJu+F7+87a9UTUpOUEKK9Sm9RKA6eNZXHpEfJ1i+4lzmzjsAn2814XATK1dexJYt99HVJct2G7e/BEBV1W8ZM/p+bXKd4x2v9P26LlA3MwPjfwFq2WvInqsCSfWNIFiyEws6hU42S4+sWJEnGXKgbuqEw61eA/0biCqETnqGSbJ0eVPy++uD0JEzK0yU9KEmkUNt84C+M3T6C5PJssPJUl7eFCZPegGvN3sFHT3RlC2kuL+ETs3gywD6PA5Wa3LRJb2K0+6Ghd1+tgQjO274HcJqzaWq6tysoc8ORzkmU3LQp9rYVIVOIuEnGNxMbe1jSFKCSCQZFq5Wxdy69QGWLP11yoo5pJJFjdtf4Wd892hufh1RDOF2D2PG9I8yfm/hcAOhoKIGdA2iovwkDth/JQMGnEZujvx99vQuJapUm8tWSc7jSb0mqL/x6qrzycmZgMUi/07DkWYkSdSyFkcMvwWTySFX9mv/NKUPUYywdats108kAnR3L/7Gx+Fn7GokCR27vQynswq3e7h2fiTi6lzFWCEhCAImhThRJ5vpE2DILM/dH1WN3M5A7ZMxmc1G6Ji17eyuOTnfFhq5JRhl0Kgv9V0JC5LHXSXlMm1MAiY1DFiSyRzlnWn96PJ4+rBJmTKChY3yf5I5NEmFjpHKJf29xotGKZarfil01CpXBtWwdnBMU7N4jI9V9v3Q2xrleXpYUfSrCh2zqf+Ezk4rdBYvXszMmTO1x2oFqrPOOounn36aU089lba2Nm6++Waam5vZY489+OCDDzKCkncWfVmu1qp2q7KcrKuC2eAPhcFko6Qkbf/8rVCvpL8Py5SNvtXazbttPQBcu34bgYTIBK+TGweXc2PjIpYG2om4Z3Drpu28useQnd6vbwN9LkH6Src6gFZXfWSpvYjVWqANGNUwSp9vdUrp1ba2T1ix8neUlR3PmNH3fLcf4juC1e5g1L4zGbXvTFq2bKJ+9Qp621qIBINsW7Ucf1cnyz96lxWfvE9++QAqho9iyORpmmonr6yCvNJvZx/8GbsGkiRRv2o5X/37KVprk5L0qSfNpGxCiLUbrsKn2AkBLctGtUQVFh5AR8cXbN/+Mi5nNWvWXk9FxUkMGypX6WtofEEro71k6a/J8SYtiN09X5OfvydBpWJcUdFMnM4qxo79G91dC6iq+i2z5+yl/c4EwbLDLJCfKpzOKs3m1Jc9y+UaRG/vUuz20qw3YTlYWM6NyHY8TSY7DkeFEqibSQx5PWNob/8Un2+1plZI70sNMN7e9JpWIaovMsrrHcM+e8/WJkHZUFJyOO3tn6ScSz80nDrVUzabVKpdLDuhk5c7mT1nfJbVnqZi+vQPCIcaUsqO704QJYk7tzTxt/pWKuxWvt5z9Pd6D98ZCIIJl2uQpg5USbn0inqxWAc9vcu0UHGbvYSamsswCTbaOz4nGNxMQ8O/qar6jfaeeLxX+3v79pcZVP277/rj/M8hGu2gq3shJcWHIQgmupSMsPLyE3G7hzBxj+fY3vQy3d2L8fvXEg43pih0IKkK9HrHIQhWotF2enuWA9nVgF7PSDo6Ppfb2Iq0PpzOSqZOeZ1tDc+yYcNtRCItxOO9Wsio1zuGgQPPpq7ucTZtvguPZxROp6w+bW5+i3AkWRq6veNzCgr23qXH62cYI5EIE4/3gGDGZi3MuF7pF6xUexWAKLqBDqXiVN/2JpPJllLC2SjLRRAsmq1HftyPIGPBZLi99MlsdoWOgMlkkxebf6KEjtnswGSypZD36Ui1ZWU5VqrKJguJAbrAY93ifaZ9Sxd4LGXPVBJM6YROXzk0+pLrOw5F/nYKHYWIIU5SoWNMIiWDpvve9+Q5bzRWyG7DkiH3Ew43I0kSojJP2RmFzk4TOkZl2dJxySWXfGuLVTr6slx900BkgIGTp9O+dCnlVWnlxZf+G6QEVE6FAnlVeHaXjy86fXTHEnzWmRzofNUlhxedWJqPWRDoDHfi7l5Iwj2d2d1+lvqCTMr59pV0+gv9anP6yrNHsT+EQtuIx/1aKKg+syLHO16rdqUndJpb3gagtfVdRgy/JaOE6o8NpYOHUjp4qPZYEkXqV61g0TuvUbdiKZ2N2+hs3Maqzz9KeV/50BGM3OcARu87E4enbyvez9j1iAQDrP7yM1Z88j4dDbIy0GyxkF8+gKnHnUSP5U62NWzJfF+kGVGMa9UXKspPobt7McHgFlasvACA+vp/MnTItcTjPrZsuRdACxLv7EraP7q7FxGJtCCKUYWskdU3xUUHUVx0ECDbGwNKAKndXvaTXTnaEQTBTG7uRLq65qXYr9KhKnT6sjaZTHYGDbqQcKgBl2tw1nZOZ7VM6BhUD/J6xwCy1VS1XKWrH0pLj6K17UPa2z9VLAfCDiv/9YewGzXyT0jSHX3Kwr9veDwjEQQzDkdl1soncnUyufKX1dK37caVFkptuE33sN26VPZrLV38rV4+N7ZHYmwIRhjh7v/ASo+oKLIpGGG055vlqfQHLtdgjdBRF3SMvsu2to+TCh17CR73MMaMuZfG7S+zbt2NbK19iIqKk7V7ezzeo703FKojHN5uqDRMJCIIgtmw+tnPSKKraz6CyUpe7mTtubXrbqS9/RNGj76X8rLjtIwjlZDzekcxwnsra9fdiN+/llC4Ucu8cqblhZnNdrzesfT2LtVKlmdTA+oVOg6Dynwq+R6JNGvVsuQMMhvVVb+jsfElgsHNzJt/MJMmPkde3hR8PlnF43INJRjcRHv7Zz9pO/HuAjkTJxmjYDa5sFjSXQfJSat+IqkpLZRJa59qi36ob1LIFfoidOy692QjaixaBktf7eT+XCQS4Z+sxU8QzCl2ZSOkXPOzHfd+2NjSy3VjUJlKH3icDFjuS6Gjqj/7slLpuYVvV+Uq+Xd/FDrGuVHJfeuLjNLZxfqohiUIgqxo0ymQ9P2p+yOKIeLxXk1BZTL1f9zwk7j76hU6O4vOTtkHWFSkW8kVRVii5MdMPgdJkrivtoW7a1NtOBYB4rrz7+BCmWjqCndhTrQz1RNnnt/Kx+293y+ho5ucpE8ybLZC7LZSItEW/IH1hBV/tF7J480ZR2vb+1pYMshBd51KyUpRjNLW9nG/Ah1/TBBMJqrH70H1+D3obW+jY1sdGxfOpWXLZiQkJFGkY1s9TZvW07RpPbNfepYRe+7LgJGjGTxxCq7cvB/6I/ykIUkSq774mFnPP03IJ//mLXY7Yw84mD1POh1XTi7t7Z/RtGILFosXj2cU3d0LlXcLSFKcaLRVU+jY7cVMGP8PVq66JKVKSK9vFbFoB/G4D6ezimlT36F+279oaHgOh70cn381PT1LCQZl0sjhqDCcyOTmTtIInWz5Of8rGDvmQUKhupRS2ukoLNiP2trHKFKyH7JhyOArd7g9l7Oarq65hhYildDxB9Zrz6UTPyaTnfHj/k5n51eEI83k503bZRVddicyB+SJ2+RJr2hKJCMIgoDTWYXfv6ZPhc6PAZ2xOMGESKUje/bQst5Uq/SCbv83JnT+b2Mjz27v4PHR1RxX+t0cOz25qb//Vw44g4bG5yguOkSrhKUqdPSLPRXlJ1FX9zihUD1dXfO1qljxWKrtuqt7IeVlx6U8F4m0sHDRcdjtxUyd8uZud37vLojHAyxddg6SFGWPCf+isHB/EokgnUrYcFfnHMrLjiMSlaukpttO1SqJPt9qzTacrsICyM2dSG/vUnp7lyr97NhypVfgqbAbEDpqX1ZrDpMmPc+6tTfQ61tBU9Nr5OVNIaYQgKWlR1Jb+yihUB3tHV8YXtMDgS309HxNeflJu6367ccCvWoGIB7vxmJxpWQAZpu0akoLMSa31yac2Sf6ycfZVCB22AGhk5J7k3ViLmAyObSQ7r6uLQ5HOTZbQZ8Klh87dvQ7SVHoZK2E1Q+rWz9VNSrZlugjHyfznDG2I+lVXdnbCWnqrx1bz4xsgfLzyueWEn2SmDsKWNYfv76ILQABQaOsMjOqBCyWHKLR7UQizdo8xbwTBOWPhtDJZrmSJEkjdL6JQqe4uJhwOJxK6NR+JVe3sufCmOP5uKNXI3OOK8ljqMuBL5HgkMIcfr1iC2FRYojTzmCXfOA7w/LkcL88B/P8CT7t6OW6wd88JG9nYeuD0AHweEcR6WjB71unq/KSbKdOuvTlzHt6l6bIrxsan8dizaWocOZPcgCXU1RMTlExNROnpDwf6O5i/dyvWPnZR7Rvq2PV5x+x6vOPEAQT048/mb1PPYOQ38eqzz7C5nRSOWosBQMG/jxg+ZaQRJHPn3mCpR/I5ZzzKyqZePhRjN53JnZXkiytr38SgIqKX1Iz6FLWrrsBm62QjvYvCIXrCYUbNYWOyewiN3ck06e9R3fPIpqaXqOj4wu6OudovyGnsxqLxcPgmssYXHMZkiTy1awpxOM9tLa+r7UxQm7uRLYrGTs/VH6OJEks94UY7XFgM/1wv1ObrTDrxEJFTs449t9v2S5Z5S8vPwl/YD1laZNPkCcqVmuBRuI5HVWGFSYEQaCwcP9vvS8/BuTm7rHDNiXFhxION6aoC35siIkSR3+9ke2RGF9MG0G103iw1BaTV6tzLCZ64yLzewKcOSB1gp2QJP6wsZE8i5nrs9zfu2JxXm6Wz7NXm7u+M0LHrSN09ETA0KHXU1n5a+z2Ur6aNZVgcKuuXXKcIAhmCgr2obHxBbq6F2iEjjpBLyo8kPaOz+juWoDXOwaXs0b7nW7ceCfRaCvRaCtdXfMpKNjrO/mMP3bE4z2arWXlqsuYNvUtAoFN2sp1d8/XSJJEVCF00sloh0LodHXJUQA2W7FhZTKvJ9XGmM1G6XJVa1ZYlSzSQyV0otE2zZpqtSav4V7PSKqrf8fKVRfj86+VP2OsW9nXCspKj6Gp+TVWrryQCeOfzDgvVq+5Ep9vJWazk9LSowz38Wf0D+n5f7FYDzZbCaFQHZIUw+0eBlkmrfKEVUAOa43twHKVrvr75lWnUvvK7vowm/WETnaFjlwC+rtTQf4YoB879V0JS/94xwqdvvqSpETSvtUHOdTX9pJ96YnJbPk4Fu06+q3KlqOzeUl9ZN+Y+rZvqTk7khTXKrtl33dTUsVjsO+yG2Cd7CZQCZ0sRTaM8KOZiV988cWsWbOGRYsWpTzf5ovQFYxhEmB46c4TOkcddRQXXnhhSqk8Vr0m/z/2eLC5eKetG4AzKwp5fMwgrq4p47ahA9gn38s++fI2Dy6U1UGSJNEVkVdPDisuQABW+EO0ROQvuisW518NbTRFUsMHdyX0K282A0LHq6zM+PxrNMuV3ZZs51UInVC4nphyg+5ol73WOTl7ANDbu4wVK86nWVeNQQ+9F/KnBHdePpOOOJYz7/4bJ954O1OPOZHSwUORJJH5r7/Mfx/4K89cfTFfPf8Un/zzUZ6+6iIe/90ZzH7pWfydHT/07v9oMeulZzUyZ59fnslZd/+NiYcdlULmRKLtdHXPBwQGVp6JxeJm3NiHGDH8FhyKxz8cbkTUwsbkm7/dXkxpyRFKeUDo7Jyjnffp+QOCYCIvTyb5tjfJ1wmjVVIgZeL7Qyl0/r6tjcO/3sAdm7fvuPFugF1l2cjN3YMpk181JCoEQUgJAh406KJdss2fOmpqLmW/fRdrYbs/RrzT1s3mUISQKPJiU1KVl0izkbdF5fv10cV5AMzv9mdYzZ/b3sHTje08UNfC1izBya80dxIR5ffN6vLhj38398VsCh2z2YHbPRSLxavd90G2zpjTKozl58n26q4uOcNFkkStMEJR8cGAXBVwwYLDqa9/AoDu7sW0tP5X66Op+bVd+bF+UtAXk0gk/GzcdCftHZ9pz4VCdYRC9dr9KV2ho97D1MlttoUET5qtNRuRLghmPEpgvVFfNluhMlFJaGXS0/tSVT6BwHpEMU5MWfSzWnIZOfIOiooORhSjrFx1KaFQg/a+SKRVy7dTrWE/45tDVRhYLDla5kcgsJFEIogoxkgkIjrSxyDsWLnvyuRiX1WGdqy2ADAJ+muL3Obss8/Wqh+nvzeZUWLQVz9Lkv8UcMABB3DFFVdojwcNGsQDDzzQ53sEQeDNN99U/t5xOHR/CJZkufjsFiK5Xep4zWr1aPuS7L9/JGC6EibbIni2fBz9sds5y5Wos/RlbtO0EyXQ+/rtpO+vEdGkXl/DkWYtFPk7rXK1u2GNos6pKXLjsO6CH3siDuvelf8eczwJSeLTDnkbx5Vkrq7dPKSCswcUcWm1TIj4Yj7ionxxGuItYg+vzK592tGLP57g1GWbuXFjI0d9vZG60HdTPUM/oHMYKnTkFRy/f61WklKv0LFac7UbfK9S0rxHke9WDjidoUNvwOuRbQtqro4KSZL4esnpzJ13ELFYDz9VCILAoAmT2O9X5/DrPz/Afr+WgyTXz5tFoKuT/PIBVI0dj8VmJ9jTzYI3XuGJS87lw8cfIhIMIIoJ1s76nCXvv0MiHsff2UF7fS297a0k4tlvbv9LkCSJ7pZmVn72EYve+g8gsf8F+zDq4NGYLZkT/7hyvlksORmKGJVQCYcaklLGNOZbDW/s7llCJCxXNjKq6KNOfNRVgmyEjtM5SHu/8wcgdHrjCR6okwnbF5s6CaSpG0MJkbX+EHGx70y0nyI8uqpPZWXH/4B78uPC7qzGDCQSfNDWo5Ex6ZAkiceVXByAl5o6WeMPcfjiDUyau5r2aPK6q/59WFEuFkHO0Tl6yUbmdPm01/+8pUlr/2BdCzMXrkvpH+Df25MkflSS+KSj1zCDUJIktgQj+L4h4eN2D8Vqzcdhr8BiyTVsk6MjN42ypfLypwPyuCAW61aIA3kSWFR4YErb9o4vAOhU1CJuJQ+ptfVDw+qYPwONqAF58tLe/inNzW8rj+VJT1ubXFnVbHZn3J/SVTSuLISOyzUkZUKTLRQZYMjgqygvP5mSksxKroJg1vKYfL7Vcl9phI7TORCz2YMoRgkGt2iZSxZrHiaTnbFjHsLrHUc83s2atddq71Pt+wAdHV8iisZjnlCogXnzD2HbtqezfoafOiRJ1K38Z2uUDCC2WvOU9yWPaTbljToJzsxMAcPy0iYrRx75G66//q/a9oywsyRMusJID701a1dOWWOiSEsklkHm/5B4/fXXueOOO75VH+p32ZcSRq8g6atyU/Jx/9Q+RsgkkPpDDmX/nlPJmp0jfbK1Ua1SO87t6YfFq892+syczH1XSfxIWG+5+g5DkX8oZLNcJQORdz4/xxB1cyDYAc4CqN6HJb1BOmMJci1mpuZmyluHux38ZXjyRtsZklf93FY3drOdgwtzWOoL8klHL593+ljhl7+kxkiMc1Zu5dOpI3a5HcdiyVXS6KMZVa4gqdDx+9drMt6MKi+uwXIIYmgbQDI82VlFefmJFBcdxLz5B9PVNZdYrEubuIbDDXQrVRoaG5//n1n9nnr0Cdhdbjq3N5BfVsHo/WZitTtIxGNsWbKIr999i8Z1q1n1+UfULluMyWKht00e/C9570162lo12Z/ZamXG8acy/fhTaN9Wx4YFcyisrGLwpKnYHD9tSakkSdQuX8L6ebOoX7kcX0eb9tqEk4vokZ5g+fI32WfvORk3ItVKZSS7VQkd2XIgGbZzOgdhseQQj/fS2ytXCFEHR3oUFc1k46Y/aY9dWQgdQRAoLzuBxu0vpoSLf194rL6VbmWC6E+IvNvWwyllycH9HzY28EJTJ/kWM38eXslxpfl0x+LcvKmRE0rzOaBgF11Td0PUDLqERDzAwIFn/xzk+hPApx29XLq2js5YgpkFXl6cMIT6UIRzVm3lpNICLqwqYbkvxAp/CIdJwGky0RyNceCiZI7Se23dmq2qTSF0qp129s7z8mWXj8W9Qe6vbWHvfC9vtnbRE09oOXovKbaqWzdvx2k2cdaAIgLxBBsV5c5p5QW82NTJBWvquHNLE29PGkaZXR7orvaHuHRNHWsCYfbN9/DqHjuvgDKbncyY/gFqhRkj5OZMpIFnAVmVmA67rUgLs+3uXqSpL0wmO3Z7MXl507V7e0wpia3aFouKDkaSEgSDW1i58hLGj3/8f97+kA51gO50DqKwcD8aGp5FFEPYbCUUFuxLU/NrtLbJBRiMVDU2W7FiS5AnH2oWWDrMZjtO5yCCQbnyo7UPq2tBwV59WuQc9jIikSZ6VULHmtqXIJjweEbS07NYIQJlQseqVPszm+2MHfMA8+YfRHf3QhKJMGazg46Or7Q+4vFeenqWkJ8/LWP7zc1vEgxuYVvDswwceHbW/fwpQ80wcjqrsVqN78l6ssZuL8Ns9iBJcWKxThKJEJIU142XjImaREJH6KQFJ6vInLgaz136E3isviZJiYxcFz0sFg9msxPBZOv3XCkqikiAvQ+beVMkRlcsQUKSqOgjT+37REFBdvK1v5BtlJGsx1RWZFl3EFLcT1VNliIKqX2ZtO9ZfWzczprynuwd7hxZk317grZfKmH6TTJ00ren9m0M/fOZfak2W9lypYQi78R9dPddbktDNsvVOkWhM/pbEjq+eIJXmjt5YoMywBt5JJgtfNQu36AOLPBiNe34YqLarfLtMsFxcJG8X593+vivYt16auwgrILAmkCY2lCU2C5eIRcEgYqK08jLnZohvwVZUWA2uxDFsFZ2OZ3QUatehSNyCTWtGoxywrlcNXg8I5GkBG1tH2vv6+5Ofj/bGp4hkfhuVEi7I8YfdBgHnHEuEw75BVa7zKqaLVaGTduLX972V0697a/kFJfi7+qkt60Vh9uDzemkp7UFJAmnNweT2UIiFmPOK//mHxedzXPXXc78117i3Qfv4rHf/oq37vkTKz/7iFg4vIO9+fGhu7mJV+/4A6//+RZWf/EJvo42zBYLRQOrmXDUeISC2YDs6e/pXYYoxlm/4XYaGp4HyKq8AXAolZOCoWSGRPqEQxAE7XfgV8qNG+UPuFw1KRaHbNJ3gKFDb2D//VZkVfF8U0RFsc+VpVmdPh6ql0nYyTny8XikvpWvewJamy87ZTK8K57g0W3y7/ufDe280tzFnZub+CnDbi9lzJj7dli56mfsHggkEqzxh7K+/mRDG50xecD4eaeP7licy9bWs9of5jbFbrhFUcROzHFl5OEAfKDc6yOiqBGhxTYLT4wdxCOj5N/vgp4AvnhCs1jpVbvq6OD/NjbSFYvTqpBCTpOJ8yuLsSmDvPpwlN+u2kpUlCdhzzS2syYgX89ndfm/sS3LZisyJKBV6O2HVotxO3VS3d2zWMvMsyiT8wnj/8GkiS8CcpVMUYxr1lSrNY+RI+/EbHbR2TWbtWuv/0af4acMdbJsNjsYMvhqamouZ9TIvzJj+vvk5+8JoAsyziTcBMFEZeUZ5ORMZNSov1JZeUbWbXl01XD6UujsCHZlHKgSeEbkkFdRO/p8q3UKnaRKTM6hywMkgsEtSFKCjk75Xq7eR9uz2K7U8WQoVEc02mnY5qeARCJIMFhLPB7IeE0NpA4ppeqNkbRTCYIJqzVHDghWxjiigULn7LPP5ssvv+TBBx/E5aomN3c8W7duZvbsxcw84DTsdjvl5eVcf/31xBXV+Dnn/IbZsxfz2GPPk5s7HpPJRG1tLYlEgnPPPZeamhqcTiejRo3h8cdfAoU8yAaXazAWS07G+EgURe666y6GDh2Kw+Fk1KgDuf++57TXr7vuOoYPH47L5WLw4MHcdNNNxGIx5bNKvLNgEfscMBOv10tOTg6TJ09m8eLF2vslScKXkI9Hr+56+/TTT5OXl8ebb77JsGHDcDgcHHbYYWzbti1l/x577DGGDBmCzWZjxIgRPPfccyl933rrrVRVVWG326moqOCyyy4jkhBZ5Qty54MPaX2XlpZy0kknae9Nt1wB+Hw+TjvtNNxuNwMGDOCRRx7JejwB2tri/OY3N1NUNJCCggKOPfZYamtrtde/+OILDjjgFMrLp1FVtTf77rs/dXWp55YgmEAQUh8Db731FpMmTcLhcDB48GDu/NN92rmhor29neOPPx6Xy8WwYcN4++23tXNg1qxF2O3FvPvuu4wfPx6Hw8GMGTNYtWqVRpx0dnZzzjlXMWDAAFwuF+PGjePFF1/U7YuZQCDI7353I7m5RZSXl3PvvfemHQUT2h25D3Ioabvqqyz5zhM6/VPoGGfogEzoJLRoiP6HIv9oCJ1s0BQ6Zd+uhLY/keCytfXcaptMHDOMOQ6A+d3yBXZmYf8II1WhU+CQb6LjPE5KbBZCCmM8NcfNL4rzNLXPTZsaqflqOU81tn+r/U/HiOE3M3nyS4Yl/ATBjNc7LuW5dCWPvsJBIuHX5MJ6O1dJ8S8AtHBYSCV0otF2Wlvf/Zaf5KeDypFjOPOuhznm6j9w4o2389u/Pcmv//wAe550Omfe/Tcu+ucLXPHv1zn0gsswW634uzqRJJHq8RPJKy0nHouyadE8Pvr7Q7xw09Wawmd3R9jvJxLMHKioiAQDLHjjFZ699lK2rV6B2Wplj8OO4sQbb+fif73EWfc8QvWM1PLw7W2f0tr6Hg0Nz7B+w81IkkRCzK7QsSgrW6rSzGSyG64cqflTiYQfIGtFn7zcZFi2UYUQFUKWla5vg/pQhHFzVvObVVuRJImeWJzrNzTwspIJ4osnOH91LQkJTizN54kxg3CbTawPhDlyyUbeaOmiJxanMZKUca/yheiNJ/hPi9zH2kBYm3D+jJ/xXSEqivxlSxNvt3ZnbdMdi3PE1xs5cNF6ZncZ23n0dimQrU7zdeRlayRGs3K+l9ttXD2ojPcmDWP13mP5cpqcIzJbIVPUviwC5FnM5FjMnFhWQI3TRkyS+KrLR21IXt2ckefRCNMrqksZ4XYQkyS+7PTRqli/SmwWRnmcrN5nLB9PGU6OxcTi3iDPNMoTteZIqp1iaVqFrV0FfWlqNXg3HTlemeD09a7SApFVC5fF4iEvbwomkx1JihMON2rVlqzWPPLzpjJhwr8AEy2t/9VsWT9DhrrgYDI5sVjcDK65jIqKk7Ba8/B6U4OM0/NzVAwf9gemTvkPFeUn9bmKrc+56kuhsyOoVjptvwzIITWEWQ51lifHesJQEATc7iEABAKbCIUaiMe7MZnsDFYqFjY1v5Gx+CeKMXp6l2iPVdXsjwGSJBGLJPr1Lxz00d21iVCwl4CvOeP1eEwgFpWIRSXCQb9hH9FwnFhUIl0xo+Z/SGIcPekD8OCDD7Lnnnty3nnnUVe3ig0bPsNkFjn55IuYPHk8y5cv57HHHuPJJ5/kj3/8o/aeadMmcNZZJ7Jhw2ds376dyspKRFGksrKSV199lTVr1nDzzTdz220P8N67K/pU6JjNDlyu6owx2w033MBf/vIXbrrpJtasWcMLL7xAaWkpoiTRHo3h8nh4+umnWbNmDQ8++CBPPPEE999/PwCBhMh1555DaUUF78yew/xFi7j2uuuwWpPEUlgUNat5RJSI6MY6wWCQP/3pTzz77LPMmTOH7u5ufvnLX2qvv/HGG1x++eVcddVVrFq1it/97necc845fP65nDP62muvcf/99/P3v/+djRs38uabbzJu3Di64wlWfL2Em6+6kltvu43169fzwQcfsN9++/V5Lt19991MmDCBpUuXcv3113P55Zfz8ccfG7aNxWL84hdHkZtbwKxZs5gzZw4ej4fDDz+caDRKPB7nuOOOY99992TOnNf45JN/c/7552eMUQVBSMmOQTAxa9YszjzzTC6//HLWrFnD3//+d5577iXuueeJlPfedtttnHLKKaxYsYIjjjiCX/3qV3R1pY7/r7nmGu69914WLVpEcXExRx99NHGlZHQ4HGHixLG8++67rFq1ivPPP58zzjiDhQsXKvtm5qab7mPOnK95443X+eijj/jiiy9YsiR5rVDVN5A9FFntK/1z992mf5ar/mToGPWlhs6HI81a5tr/TNnySDzBplZ54vVtLVelNitWJGKCmeaC0VQOnklCklilrAqqWTg7QmckldAxCQIHFeZoAYwnlMkTxAMKvMzt9vOJks9z66ZGzjFYNfyuUJC/lyafNpkc2iqcCodK6ISbNXWO2exJUT+UlBzBlq3309k1l1isG6s1jy6lTLTbPYxAYCO9vhU/ufLm3wZ2l4thU/fUPXaz18mna48Fk4lxMw9l6JQZdLc0YXe5KaioRJIkWmu3sOXrhSz76F3a62t56dbrOOvuv2Fzuljy3tvEY1HGHXgorhzjDAVAyetpx+7yICbi9LS2UDiwSrNyiYkEyz56D0kUKR82nPJhI78xIdGyZRPzXnuJrUsX4fB4+dWf7sNbVEz7tjo2LphL4/o1BLu76GjchqTcUCtHj+XwC68gtySVYFTtVC7XEILBzbS1f6pNJgBNWgxgNmX+Vi1mmfCNRmULV7bkeFuaFSHbSnZxyWFsb3qlz76+Kzy3vYOeeIIP23t5pbmLR+pb2RAM85JJ4KjiXJb0BumKJxhgt3LviIE4zCb+O2kY99Q2825bD1ev38btQ2UL2gC7FatJoDYU5bH6Vm2iGpMk1gXCjO/nde9n/Ixvgr9saebRba04TSYOKvDitqQOsCRJ4jeralmvKFie296hFSLQo0OpSrVvvodZXX7+uCVVYbbMF9SIkzKbfM5PUhZVCqxmapw2toaifN7pY6Aivy+yWjHprn0HF+bwREM7n3b0Uh+WJ5/VDhsPjqriy04fZ1QUEhZF1gfCfNrZy6GF8nW4VLFWeS1mxnldXFtTzv9tbOSt1i7OG1icouQJiSLze/xIwIw8d0plOlGSEOhLzt039NU4PGkEggq1IILPv1rLJNPbPATBhNNZRSCwkVCoVqfQkcc1+XlTqRp4DvXbnmTD+tso3HN/BEFAFCNs3PRX2to+pLzsBKqrz8di+XaLcD82aCuuBiGXLtfgFDuVkUJnZ6CvdJrtHtYf9Cdg2a2ogdSQY5PJlvEZ3a4h9PR8TSC4GaezUumrmOKiQ3DYKwhHttPS+g4V5Um1gs+3Wrvvg5zjWFQ08xt/lu8T8ajIPy7/8hu8sx3oS4nT98LvWXeXge7QayXJpXhKCXOA3NxcbDYbLpeL8vJKQiGR229/iAEDyrj33lvweoczcuRItm/fznXXXcfNN9+se4+D0tIiQvYi1oSiDHXZue2227Tt1tTUMG/ePP7zn9f55S+T49v+wOfz8eCDD/K3v/2Ns846C4AhQ4awzz770BqJ0RSJcfqV1zDUZZezLAcN4uqrr+all17i2muvxR9P0NzQwFmXXUF+zVBiwPSBgxjkSi5u98ZTF6t88QR2m3ytjcVi/O1vf2P6dDlT7JlnnmHUqFEsXLiQadOmcc8993D22Wdz0UVynMSVV17J/Pnzueeee5g5cyb19fWUlZVx8MEHY7VaqaqqYtq0aWwJRmhu2IbT7Wbfw3/BgPw8KCnj12NTF9fTsffee3P99bLicfjw4cyZM4f777+fQw45JKPtyy+/jCiK/POf/9S+56eeeoq8vDy++OILpkyZQk9PD0cccRiDBw/EZLIyZcpIw+3K545iRcLEbbfdxvXXX699J4MHD+a2227muutu4PrrL9SUMGeffTannXYaAHfeeScPPfQQXy9ZxcwDJmh933LLLdr+P/PMM1RWVvL22x9w5JETqago5fLLz8ftltV7l156KR9++CGvvPIK06ZNI+AP89xzb/CPf/yZgw8+GEEwaX2k7r9ZsRr2n9DZdQqd7FWu+urLZlcydCLNWMzyIvbOWJd/NISOUYbO5tYAcVEix2GhIrf/wUFGMAkCA+Jd1FoK2Db6l1SazGwOhAmJIk6TiSGu/smeusKK5cqRXNk/WCF0LAIco1TN2C/fy50kB505luws9neBgoK92bJVZrRlb3bqCWi3q5arJsPS5gBu92A87hH4A+tpa/uEwsL9CIVqAYGK8lPYuOlPBANbvvPP8lOE05uD06sfSAuU1gyhtGYIYw44iFduu4Ge1hY+e+rvuPMLlNBgmPvKv8kvH4DJYsFbUMjo/Q5i+PS9EEwmln/8Hp8/8wSJWOqKsMlsZviMfZhxwqnMf/1l1s1JDkSKBlZTM2kq5cNGMGDEaBxuD1+/+ybr582ip62VwgGVFFfXEA0G6W5pRkICSSISCNDZ1KjlAgV7uvnPn25CkkS6mzPtPIWVVUw79iRG7rM/JlPmbyEhyhO6kuJDqat/gmBwk2YXBKV6VR8ZOurkQV1BNGdhvfXV3gCsNmO5elHhAYwd8yCOPtQ53wXiosQrzUnp+eXr6rW/w6LExx29bFcmrhNzXDjM8k1jlMfJP8YM4uRlm5nb7eeq9bKEeLTHSZHNQm2ok/uVAGUVK30hxntdbA1GuHNLEyFR5P6RAym27dg3/TO+X3TF4njNZiz9sAV/n0hIEuYsBMTcLr9m9QuJItdsaODzjl4tzwmgNhRlbrdfe89brd10xjaxMRDBazHx3PjBVDtsGqFzWnkhs7oUdZ0gUOmwsjUUZWlvkGZFMVNmT19NEzikMJd/NLQxq8vHIYoat8SW2u4gHaGjWrKqnDaqnXaGuhxam8e2tfF5h48JChlanNbPkcW5/N/GRhb3yiSTGuJ8RHEur7V0cW9tC9DC5dWl3KCUQw8kEsxcuJ5yu5Xnxw/G8w3HC9OmvkNz85tUV59v+LrbPRSTyUY87sOnFERIX+xxuWoIBDYSDG5NsVypqKm5nG0NzxAK1xOJNGG3l7Ni5YV0dMj3ldq6RwmFtzF2zAPf6DP8WKFmIhjde0wmK273UPxK+e9sCp3+Qg3ul/v+5sN8jzt1smek9lHz6dR7q1Eot6oYCgQ2kaOQhjZrASaThcrKX7Np811s3fowFrOX4uJDEQSB7m51RV4mIXt7ln3jz/G/AoF0pYVSvUqKYVIVOn2UI1+/fivTpk1IGYPtvffe+P1+GhoaqKpKtUZ1xOJIEvTEEzz1+OP861//or6+nlAoRDQaZfyECdSFIhRa+38Orl27lkgkwkEHHZTxmmqPeuOVV3j9icep3bIFv99PPB4nJ0e+TvkSIr+++FJuv/Ri3n3pRaYfMJNDjjuBwrGj8CrXTZ8yj7SZBKKihC8uUqREzlgsFqZOnaptc/iIEeTl5bF27VqmTZvG2rVrOf/81OvnpBkzePThh4mJIieffDIPPPAAgwcP5vDDD+eII47gqKOOIphIMGPmgZQPHMjEEcM54JBDmXzgQRx41DHMKMv+e99zzz0zHmerfLV8+XI2bdqE15tKlofDYTZv3syhhx7K2WefzdFHn8bMmdOZOXNfzjjjYsrLyzP6Sicyli9fzpw5c/jTn5L5kYlEgnA4TDAYwu2WCYjx45M2drfbTU5ODu1K3IjRZyooKGDEiBGsW7eRI4+cSCKR4J57/sYbb3xMY2Mj0WiUSCSCyyXfT7du3UY0GmPKlHGoxInahx5qlpDQRz5TpkLnm4Yi77gf5RXdn9kzdOLxXi3jyPRTDEW++OKLufjii+nt7SU3V75hrGuW1S0jy3O+va0h2Emlr5ba/AIaquULyUqfPEEc63FmHZSmozOcqtABmdA5qTSfMR4nhcrgbpzXSYHVrPn+u5Vwrv5u59tCb7lS/dF6qBk6kUgTUTU/x55ZGaOk5Bf4t66nte09zZ7icQ8nJ1dmYwNKMN/P2HXIKSrh8It+z8u33cCar5JlTwsqKunc3kBHgzzJb6vdwpYli5hwyBGMO+gwPnvq74iJBGaLRa6kJQg4PV5Cvl7WzflSI3JMZjODJkyifvUK2rfV0b6tTnneQmHlQNrqkjk0jevW0LhuTdZ9Hbn3/ow94BD++8Bf6GpqBOTQ5+rxExkyaRreomKKBlbjLex7AKsOhm32EoqLD6W19b2U18PhpqREsQ9CR4Wpnwodm0GVKxWlpUf1uc/fBT7r7KUlGifPYiaQEIlJEqPcDsZ6nbza3MV/27pxKKv6Yzypx8EsCNw5fAAHLEwGwY72OBnstGsKQgFZGTCvO8ByX5A9gx4OWrSekKKgOm7JJl6fOFRTHfyMHx4Lu/2ctGwzx5Xm8dCozDwnSZJ4pbmL4W4HE3O+H8WVL57g+g0NvNfWw6Ojq/iFspChh5oppypTXm+RF0P+vq1NI3RaFLKjxmnDIghsDEY0wqY5Ck9sa+PGweWEFfn8IYU5jHQ76IjF+eeYQawNhLl+QwPLfEGCSmaC0bmrHpc1/hB7KH8XpRExM3I92ASBFkVRYxZggD11sDgt143bbKI9FteqY5akEaDldhuTc1x83Rvk3bZuTaHziyKZ0FHxQlMH1wwqw2IS2BCIUB+OUh+O8rvVdTw7vuYbjRU8nuEMHXpt1tdNJise90h6fSu0ClbWtAm6yzkIgGCoNmm5siSvkxaLWyN9/IENmM1OjcwZVH0RtXWP0t7+KaIY7TMM9ceEYLCWzZvvoarqN+TmTjJss6MytB73CB2h881tUiAvtk2a9FJGiPHOwukciCDYtIqORv3J5c2TbYxynFRCJxjcTFQJ0lbJoYqKU6mrf5JwuIGVqy5iwvh/UlQ0kx7FYlVaciTNLW/R07scSUr0q2rSDw2LzcT5D+7fr7Y+/zo5GFiwIkox7LZS7PYi3etrkSQRt2sIwdBWJEnE5awiEmnDas3HZssnENhKQgxiTSOrVaJGEuPJSliGKgRb2uO+VA3ya3EsWv7nqy+9zHVXX829997Lnnvuidfr5e6772b2vPl0xxJ0xxKEE9kt3JIk0RNPEBEl7A7j30dclAgkRJYvXMCN5/2GS/9wEw8fdQR5eXm89NJL3HvvvcRFiVBC5MIb/sAFZ/6at//7Lp98+AGP/flP3P/0s1z0y1MQBIGQsi9ldiv1oSi98YT2nH57TdEYXbE4IrJC0ghRUaQrlkCUYFs4Sk1lJevXr+eTTz7h448/5qKLLuKvd93N3/77Pu4cLy9+NZevZ3/Fgs8+47E//ZHH/3wnyxcvIj8/+1izv/D7/UyePJnnn38+47XiYnls+9RTT3HxxRfwzn9f5PXXP+COOx7k448/ZsaM1MIdqdlHJvx+P7fddhsnnJB0XEiShN+/AYfDjqQQhnprG8gLJlIWxUpqO9UK+DSPPvosDzzwEOPGjcPtdnPFFVcQjaaGOPdV2hzA6RyAKBb3qXDpT3Uqfahz9sph30ChY7Ati8WNyWRHFCOIyiK2kaIzG340hI4R1iqByKO+ZX4OAI1LGKiUKm5QJnFqRapx3v5LngZ4BjCpZBI1uTXac3aTib+NTh1omwWB62vK+aC9h886fcQkicZwlCpn/wOQvg30qzZ6WasKNUMnkQhqpIzdoNSpbLt6gM7OORQU7AvIKzZuJewuEmkmHg9gsSQrhG3b9jTd3YsZMeK2bz1w+V9F5aix7HPqGSx5Xy57Ou3Yk5h0xLH4OtroaNgGkkTD2lUsfPs1ln/8His+/QBJFBk6dU+OufIGYtEISBJWh5OWzRv57Jl/0LJ5E97CQg448zyGTp1B2O9n06J5bN+4ju3r19LRUE9b3VYsVhv7n3Eu5cNG0NG4jfb6WsxWG8VV1ZjM8nllczrJK6sgp0i+iRx//S2s+eozBo4ZT80ek7E5d25imQwIczJyxB0GhE4jEor6xpDQSV1lznaRTz/H+woZ/SGgWjRPKstnWq6HFb4gl1WXsjUU4dXmLj7t6NUmrOmEDsBIt5PxHqd2bRuVNsm/alAZQ1125nUHWOkL8W5bNyFRZJTbQW88weZQhNs3b+eR0ZnEwc/Y9fj39g5eaurgryMGGn6fAH/e2kRUkni3rYd7R0gZ4f0r/SFNyfXgyCpOLf92lTQSksTpy7cQEkWeHldDgcHq61krt2rKmj9sbGT/ghxc5tQBjErW/KqigH82JK0ES31BmiJRyu02rU2pzcohRbncsXk7xTYLv6ss5o9bmnitpYuzFauywyTgMZv4ZMoIEkjYTSZNobasN6itzpYZKMxGK8d2TSBMi6JwS1eiOcwm9shxsVDJ5qm02zIUUTaTib3yPHzc0auFjqcrfQCOLM7j694gLzV1ElMmCjMLUscxbdE4n3f2ckhRbkop9k87e/n39g7O+o4s2l7vGHp9K+jtXQYk88dUOF2DAAgENiezxtKuk273cAKBjQT8G3A65FV9i8XL4MG/p3H7S8RinfT0Lic/byo/BTRuf4nWtvdpbXufvfeeo1nWE4kggmDDZLIgJpL3MCN4PCNAEUnav6XlCtglx1YQTNhtRYQjcrC40f1QEEw4HGWEQvI1Jv1eC+ByqYROrbZAqObxWK15TJv2NmtWX0VX93x6e1dQVDRTIwsLCw+gveNz4vFeOjvnUli4r9ZvIhFkxYoLKCw8gKqq33zrz7urIAgCVnv/iCdLVAJJwGbLJRrtQDD7EMwuzIrlwmKVs3FsDhtxyY6YCCMJPZgsYTB1YbUXYY2DKSEYKAWUDB0pblhlyGazkUjIE1VBMDFiRA1vv/0JkpS8rs2ZMwev16tZWux2D6JoImGrBGWOvWDeXPbaay/NhgSwefNm9BRISBQNp7mSJFEfjtKtLG7nVA/C6XTy6aef8tvf/lZr16uoalYtXED5wCrOvkq2XbktZi3UN6C0sZsERo4axR6jRnHDVVdyzCmn8upzz3LuKSdjM4Fah8ZrNpNrNdMTS9CkXPfj8TiLFy+mesJEOqNxajduoLe7m5Ihcp7UqFGjmD17tmY9aorEWDp/PoNHjsQXF+mIxSlyOjn66KM5+uijufjiixk5ciSbVq9i6pTJxE1Wph9wINMPOJDzrruBfasq+PjTTzlFF46sx/z58zMejxo1yrDtpEmTePnllykpKdEUS0aYMmU6kydP5dZbBPbaay9eeOGFDEInXaEzadIk1q9fz9ChqVUYe3uVAi19FOkQSD0v58+fr6m9urq62LBhA6NGyTbgBQuWcuSRB/PrX/8akAOyN2zYwOjR8utDhw7FarWwePFKRo8+OKWP/fdPkqiCYN6xXamf1ans9vIdVA5LzRvKTjT1bbkC2b4ciTQn3/FTtFwZYZeWLG/8mkolMHVbWL5KrfTtPKFzxugzOGN09uoDepw5oIgzBxSxz4K1bApGqAt9f4QOwITx/2TFygsYNeqvGa+ZzU4sljzi8W56e1cAqb5sFW73EMxmD4mEH79PXl2y2gqwWvOxWguIxToJhrZqMluADRv/CEh0ds1mzxmfYctia/kZfWP68acw/fhTUp7LKSohp0j+nmomTiGvvIJPnngUMRHHU1DIIeddjGAypZQ/Lxs6nNPvuAdJklIuRA6Ph7EzD2HsTNnrWrdyGevnfsW4gw6jfKgsbywd3L8SuxXDR1Ex3PgmpEKSRNrbP8XhqNQqZ6hIlvBzYLXmMWP6h9TWPko01kln5yzCke3aQNIo0yZdoZM9Q6dE18ZtGCr+Q6JBuTaNdjs5piSPY0ryABjvcVLtsFEXjmo5OKOyEAAnluWzYpN8bRvtcVLttHNFdSkhUeT3g0qpU96/JhDSrKCnlRcwI8/DoYs38HpLF5dUlWTt/2f0Dz2xeArpkI62aIybNzUSTIicsmwzb04cyjB36mrNnC4f85Tg/kBCZKUvqGXDqFC/T4Ar1tUzyuPImo203BfkyYY2Di7M5ejiXMOBydc9Ab5Uwol/tXwLL+8xJMUyHBFFjcwptlnYHonxSH0Le+d5eaiuhVuHVTDS7dQybfbK87A+EGZhT4Aiq4XGSIz323r4TWUxrRFZvVJit3JeZRH5VjMzC7yU2Kz8q7Gd7ZEYzzfJCtNCqwVBELAIYFGmDqPcDmyCQFc8QZci1y8zUOgMcdpxmASCCZFFCmGTbpUCmJ7r1gidQVnu1WM9Tj7u6NUiSNMVOgD75MuTtZUKsZpvMeO2mLm2poxFPQGKbRZeae7i8W1tTMt1Z4Q+3721mRNL87+x9aovpJfDzrBcKQoddVwAAlZrqorH4x5GK+APbNAUK1ZrAYJgIj9/T1pb36Wrcw5uV41mL+ronM3WrQ8yZPA1hiWsd2eo+W0Aa1ZfxcSJ/yYSbWHRouNw2MuZOvUNnYI0i0LHk7Q3fVvL1a6EzV6sETrZJiF2e7lG6BiRPg5HOSaTE1EM0aNV8kou5jnsZRQVHURX93z8gQ0AWpU1qzWPsrLjaGh4lsbtL6UQOp2dc+nsmkNn1xzy8qaSk9N3Hsn3jXgiiEmwZq30JEmSNhG2WHKIRjtIJEIEg7W4XDUpE1JBMMkqHsIkEvI1KCFGkCQpo4JV8j3Ja5goZZapHjRoEAsWLJAJEcHPb397Ko899m+uuupWrrjietavX88tt9zClVdeiUlR/tbUDGbJkmVsqN1OzO4kN7+AysFD+O+LL/Dhhx9SU1PDc889x6JFi6ioHpT8rBirXMKiqJE5AL2ChfOuvIqrr72WhNnCwfvtS1tbG18uXcbhvzqT0SOG0dywjQ/+8yozpk9l9ocf8sYbb8jHW5IIh0Lce8v/8ZtfnkpNTQ0NDQ2sWbqEA48+lpAoYlaPkSCrLMttVnriCXzxBL2xBFarlUsvvZT/u/te/JLAXddeyfip06icMJGOaIxfXnIZV511BpMmTeKAAw/iydde57N33uKFd+VFxkef/BcuASZNncbAvBz+/e9/43A6Ka+qYu6H79OwtZbB02aQk5fP7I8+RBRFaoYNJy5KBBMinbE4zZEYpcr9Z86cOdx1110cd9xxfPzxx7z66qv897//NTyffvWrX3H33Xdz7LHHcvvtt1NZWUldXR2vv/461157LbFYjH/84x8cc8wxVFRUsH79ejZu3MiZZ56Z0Vc6oXPzzTdz1FFHUVVVxUknnYTJJNuwvv76M2666VLD/Um+P/U+dfvtt1NYWEhpaSl/+MMfKCoq4vjjjyccXs+QIdW89dYnzJ07l/z8fO677z5aWlo0Qic3t4gzzzyJm266j8rKCZSUlPCHP/xBOz93BnqiSVbiGFMitizFUYw+X7rtMbVd36HIIF/v9ITO/0yVq7VN8qBy1K4idMLyQWwIR/movYdliuXquw4GrXbIX1ht+Pst8V1UNJOZB6ylvOw4w9cdDnmVSa0uYKTQgeSNOaCVepZPfrdLrmygz9GRg9nki3o87mPT5ru+3Yf4GX1i3MxDueAfz/Hbh5/k3If+iSs3L2vbHdkWq8ftwaG/u0wjc3YlYrEelq84jxUrL2DhoqNYuvQsgsFkOKCaoaOubrrdQxkz5j6Kig4EZIWOqjTLllGg96L2R6Fj7cNu9X0hKoos6PZrAyG1MtUAR7pEWkgJVfeaTVRmsUUdX5KPgKxUGKxMSq8fXM5tQwdgFgQGOW0U2yxEREmbuE/P8zDe6+Lo4jwk4K6tzYZ9/4z+oTUSY+r8Ney3cB21IePr/t/qWzWbUEcsztXrt2W0Ua1yKubo8mZU6NUdEvCPbW2G23ujpYsjv97AK81dnL+6lt+urmVTMMxxSzbykVLSG+A93d9LfUGO+HoDf9q8XbNMqRYimyBwhxK+/WBdCycs28QXXT4erpNX6PUhxc+OG8ziPUfzm0pZmfC+sg19pSibycTp5YWU222YBYFTy+SFgH9vVwgdAwLGZjIxNm1BptSAYLGYBEYoZJl6zhspa6bnJavtVTuNV+tGpJFuRv2kZ/KpaqArB5Xx4oQhXDiwBLMgf58HLlrPJqVM+kml+Qx22mmPxXmk/rupcpiTs0fK4wzLlUtWH6vqHIslJ2PA7vbIQbmBwEZiqsVGUWQUFOwNwNbah5k1ezrt7XJ1mIZtz9DTs4QlS0/D58tu4909kbRrdHXPp6t7PuvX3UQ02kavbwXxuD8poc+SiaAndL6Le8/WYISmSHTHDdPgcFT2o02F9reRQkcQTFrIaXf310BmPl3ynFEJHZ/W34AKucpQe/sndHUt0AgMvbp8/YZbted3B8TjAYKBzQSDmzV1TDr0z5vNrhTLtyiG0z6PSVMIaO+TJCVrwzgfRw1CB5CUTA79pPLqq6/GbDYzevRoagbNIBaL8+qrj7J48TImTJjABRdcwLnnnsv//d//Zbzn4EkTmTm4mqZt2zjpnHM5+rjjOfXUU5k+fTodHR0pah11jhtXxjG+eIKIcm9TihvhMAvkW+XryNlXX8evL76M2269hVGjRnHqqafS2irft0449jguvvxy/nLNVRwxYzqz5szloutuACAhgdlspqezgzPPPJPhw4dzyimncOChh3Hhjf9HKCFq27MoFUjtZhPlyvW3Ox7H6XJx3XXXccnZZ3L2YQeR4/Hy93/LFqaGcIz9jjyaa/5yN/fccw8Txo3lP0/9izsefZyTDz2EfKsZb24ezz75JMceeAATJ0zgk08+4R+vvkZeQSGFefl8+PZbnH/0kZwwbRL/eeqf/PnJpxk6ejQhUUREVsC2RGKasOCqq65i8eLFTJw4kT/+8Y/c/Ne7qNx7v5Ry6ypcLhdfffUVVVVVnHDCCYwaNYpzzz2XcDhMTk4OLpeLdevWceKJJzJ8+HDOP/98Lr74Yn73u99l9JVKbpg47LDD+O9//8tHH33E1KlTmTFjBvfffz8DB2bm72T2ZdZUYAB/+ctfuPzyy5k8eTLNzc2888472Gw25fw6nwkTRnPYYYdxwAEHUFZWxnHHHZfS1wMPPMF++x3A0UcfzcEHH8w+++zD5MmTd7gfRvulwuEY0KfVsL/99FUmXW/FyqrQSQux/0lm6KSj3Reh3R9BEGB4qWfHb+gLkgSNXzPQKg8+v+ry85Xi0x/msjPc9e0Cl3eEGpcNOmFrMPVmWx+KsCEYYWaB9zvL1unrBLbby/D712krJUYZOiATOqFQHQElpFaV0rrcg+nuWZSSo6OuLKhobn6LIYOvzNr3z/j2cHq8OD27d0WRDRtup6PjC0wmG5KUoLNrNgsXHcXkSS/h9Y7R5Orpq5sOuzyQDIe3Y1PImGzqG4vFSzQa7rON/jz8oQidrlicRT0B9vC6uGPLdl5t7uKu4ZWcOaCIRuUmX2FA1vyqopBbN8srqb6EmJWgK7FbmT9jFBZBMAzRNQkCJ5Xm85gy8XebTYxxy5Pia2rKeKetmw/ae6gNRbKqFH5G33iztYveuEhvPMqxSzby8ZQRlOi+00AiwTONsg3pvpEDuW59Awt6AiztDaZY5BqVCdqkHBdLeoPM6/ZzaXVqsHebQrBM8DpZ7gvxVms3Nw2pyMiSebiuhbgEU3PcLPMFebeth3fbZGJl5Zo6Nu83HkmS+EAhW66oLuWV5k42BSM8XN+KgKy2adUsSxaOLcnjg/Ye3tSVJV/hCyJKkkb8lNqtOM0mnGYTRxbLtqrZXX42BsIplqt0zMjzQF0LfmVikC14cw+vfGxAVsJkU0SN9cjHJ6EM+I3Cv6fmuBCQibEqRz8JHYPfqttsZoDdqhG06aTPKI+TF8YP4aI1dTRGYryj5A1V2K38Ykg5566q5fFtrZw5QCa49IiKIh+09zIj12247R3B4xmJw1FJONwAZE7QbbZizGa3di83UmR43OrkfBMRpUS6mkdWkL9XStuu7vkUFc3UbLUgT86nTH5lp/f9h4JeoQOwbu2NhMK6wPpwoy5Dx3gxwWYrprj4MOKxHpy7OGx/WzjKngvWUuWwsWDGqJ3KnBw65Fq6uuYxYMBpWdvoCZ10AlCF01mtVK6Sx9bpJdDVcyYYrCWRiGjjTovFi9s9hNycifT0LmXJ0tMpLz+Z0aP+kjKe7O1dRmfnLAoLM7NrotEOtjf9B1/vSmpqLs2o3rWz6O1dQW+vWsHV+HqiVtQUxRjhSDNOxwASiRCCYNEpdlQiRpCta/YykCSi0fYUokbNC1GrVukhipGkQsdgX0wmC4lEHFFUif1km+HDhzNvnpyVFQ43EY22U109gNmz39dyNFUkFDJm+PDhzJ47l1WKg8FjMeGPi9z/xBP8+5mnU95z5h9uJipK5FrM3PHYP/BaTPTE4tSGolhNAqM9Tm2xyoRApcOG1yITFZdcdx3nXXMtA502CqwW1vpDREUJM3D/3Xdz/q13aBk+JgHuuPZqmiJRrDYbjzz7byp11+eOaIyGcIyQKOJVtmfW/QRK7FaiyvMScMIJJ7DH4UfiiyeodNjIt5rZFIxoOTun/PY8rr/sEgC2BCPYTTI5NNBh45cnHM+BRx0NyDlsAxw2NgTChBIie++zD1988QVt0RjbIzGsgkBMlIiKEoIJnnz3A22fumIJPli1lnK7lVzdvW2FL0hCkgnaGpddq16moqysjGeeeSbjPADIycnR1Ew7gl5Vps4TDzvsMA477LCUdr29K7W/0/cFoLu7G5DPU7tdvqfss88+rFq1ynC7BQW5vPjiwxkqfT08Hg/PPfcczz33nPbcNddcs4NPlAmLxYvFkoPVmvut4hVSCJ0+SOX+KXSS8w7Vrttf/GgUOo888gijR4/W0sfXKoHIgwrduAxWwHYK3fUQbKcymhoOfExJHm9NGpaRSbCroU6K6hSFjiRJ3LChgRnz1/LrFVs4YekmbSL3fcJhT72g27IpdJSgPHW1RF2Jcyk5Oj7fau2Go96kTSYbuTkTkaQo2xqe3fU7/zN2G0hSwvBCryIcaaalVZaQ7jHhGfac8Qle7zgSiSBNzfLNJ5FldVOtsBEOb9eqXGUbMOttV9lyDMxmp+Zd/77zc1b5gpy7aitj56zizJVbOXjxel5tllUPb7d20xtPaJPXCkfmwM5rMWt5IpenTerTUe20Z6h89DitPCmHn5rj1oif4W4HBxZ4kYAnG1KVHjFR4ovOXgIGK0c/IxVvtHRrf7dE4zxc38IKX5CtihJjazBCWJQosJo5rayAYxVr3a2bGnmyoY2oElKtkjXHKa/P7vLz6xVbWONPTjDVNocU5jI1x01Mkjh75Vbmdvm1ATFAu1Ip6s7hAzipLJXMDCjn3bpAmNpQFLtJ4NKqEj6cMpyLq+T7goQ82FRVNaV2K4IgcO+IgeyhU7luDkZoCEeJKWW49ZakQU47hxflICErlNR9N7I/patcshE6egKsrzDv0Z4dK3lyrRbNgj3cbbzQM9hlT50sZBmfDNUtFBkRL/sXeJmRJ9vn1NXaYpuVI4pymZbrJiRKXLa2no/ae7Tra1QUOWvlVs5fXct1GxqyfdQ+IQgCJSW/0B6nZ+gIgoDTmczQMiK+nc4qJdwxrFmzVEWG0zmQivKkVTgQkBd8VEsSgM+3Kquq4ftGIhEiGu27XLQoyr/bqqrfIgiWFDIH5PtToo8qVyAf1/HjHmXSpOd3efDvy4qSrz4c1UK9+wuns5J991nIkMFXZm2jHytastw3nY5Ukiq9YpbNVozFkgeIBIIbiceTCjCAESNu13Ia1ZDteCJVkdjY+KLhtleuvJjNm++ite19ausey/o5+oOVqy5l0eLjWb/hFlauvJimpjeIx1MXKhOJsKYwAohFO/H7NxAIbFIqwcowImJUFY4oxnQKHZPymhGhE9YCaY2r9KSH1BpP+9In8N2xOH7lXp6QJNb6w2wMhJEkSSN3EOQFHyAjVFh9H0CuYg31J0SNyI+JEqIkaQS6WZAXk/KtFvKtFi1zTb1HqX2ZFGXNALsVt0VuI0pk9KWHQ9tHSVMJWdJIzdw0+2pCR/yYBIEap40SuwW7MhYKi6LWRu1LEATKHTYGKOOzaNq+qwvzxTYr4z1OLX8uKiaPaY7FzECnDUGAiChRF44S192n9d9euhV3VyJdoZMN6rg6m11J68Fkx2zuOwTfpmSHpZOJ3xahhGgYym0yWXC5qr/1WD/1N5V9rtO/DJ3kvuxMyfLU3ndzXHzxxaxZs4ZFixYB8HWtPNGZVLULVtEbZQloeX7qJOjqQWWGgY+7Giqho0rvF/QEeKqxHRE53GtBT4AzVmzRBvHfF9Rg5ORjY0In88YsD9w8bjlErKPjcxYuOoZYrFcrc2qx5FJVfR4A9fVPUL/tKVpa389Y6foZP24kEkEWLDySBQuP0MrwpaOx4XkkKU5e7lTy86fhdFZRUS4HxIVCss1EK/madoFTVwZjsU6iSohitougfqU5m0IHkud5+grid4mEJHHiss2829ZDQpIr/7TqbtYLegKsUybpBVYzbrPxgP9Pwwbw/PjBXFb97RRvw90OpigTYXVSqeI8xRbzYlOnNuCTJInL1tbxy+Vb2G/hOuYZWH9+KNSGIlpAbV/QD5q+Dbpjcf7V0KYFNBrtz1JfEBPwt1FyMOCTDe0cungDxy/dhCRJWg7SIKcdQRD43UD5mC/oCfCHjY38R7M3yeTJ/gU5VCirjJ909HLKss0aOdQWSypmbhpSjtdsYqkvyAnLNjF53mraojEkSaJLyTHIt1r4bWVmKGsgnmCBkh+zV54Ht8VMsc3KTUMqOEmpSrXSF9ImjCoh4raYeW/yMGr3G88AuxUR+LBdJvaLbJaMBZPLFDLytZZOlirKGiNypcJuxal7r5HlClIJHaNAZBV6a/VBBTlMTzvvVTw4soo7hg7g4EJjq7fdZKJGp1wrshpvc6iOkMpG+lSmka7FNjkn6NYh8nVvVpefM1du1Sp//XlLE58r5/r77T3feMygJ3SMcsRcSjAyZAvKNWuVjbq7FijtkmO1UaP+zMSJ/wbkykcA8Xi39rooRrRMlh8aS5b+mtlz9qGjY1bWNuq4xe0ayuCa3+P1jmPUqL9SVCRXTA2FG5KhyDtRtWRXQJIk3mpNVk/bFAz30doYO1L0pCp0jH8X6aqj9PurIAh4FNuVXKJcPnfVCaPXO5pxY/8GQDTaSizWpZE+eXnTAWjv+IyIkoWpRyictKv6fMbKAK1tqIGwUiAlHaIY1QoyCIKNjs6vqK17lHi8m0QiaZ1NjnNzsNtLlffKrycSSStVMqhYn+UhXy9EKZqRjWOs0AnrAmmNCB1L2uNspZeT15rWuIO6UJStoQgJSc52SUgSEVEmTdRbpRm0sUh3PJFyvZEktUyFTPpYTAKSBJtr69izooQ9K0rI8XoZWJDHnhUljCspwuPxUF+vZDEp13Y1NF7tWSVrcq0WhjjtqLeAqEIQyfuVer6qlT8vOOFYKvPl7U1Qtqf+e/Cvf5H3G3kskE7CWE0myu02chVbWDghafuWrnS2K9uLKMcjvS/5eAvYlMdRSUwhowqsFsZ4nDjM8jHriCXHgvorejAh9rlYujMYM2ZMyvHIycmnomI6FRXTeeGFl7K+z+EYgM1WpFlxvw3s9lI8npG7dDE1IUlsDIbZFAxnrVSmb1sXitAd++ZEWV+2z5QqV1l+h5b/BUInHV/XyTeo6TW7YNLVLEvGrOXjU57OtgK3q1GjePE3ByP0xOK8pKym/LKsgK+mjaTAamZNIMxDdd+NZz4biosPQe/5y265Sg3wUwdu+fl7UV11PhaLF79/LVu23qfd6KzWPIqLDqGs9DgkKc7GjX9k1apLWLM2taSqJEms33Ars+fsrU3uf8aPB3V1T8jVTgIb6OycA0Ao1EgwmCx9vr3pPwAMHHi29pw6+Asr37kmV09T6FgsOZqiRp0YmE3ZLVcq+rpQqjk636flqiUSoyeewCzAF9NG8OW0EZTZrHjNJspsVmKSxEvN8nWhwp59lcMsCBxUmJOV8NkZ3DuyigsGFnNu2uT+gAIvQ112/AmRN5SJwqstXbyh2GoaIzEuXF23ywYa3xa/XVXLqcs3s7APkunt1m6GzFrBQ3WZkwGQV3j+tHk7163fxjON7drA9cP2Hl7YnqrsvGXTdm7c2MjdSs7Q3C4/+y1Yx1xl0v2+YmPaN9/LiaX5TM5xaYO0ZkWKrZL7Ktk/zuvizmEDtG0s6gkQSoj0xuV3ltut/HfSMJ4aO4ixHiftsTjXKJk7qsqlxGZhWp6Hr6aP5JSyfNxmmTT8vNNHMCFqg9N8q5kxHiczC7wpw+KNwYhWCaQmzWo3XlGtrPQHtSpRepLCJAg4zCYm5cgkybuKhciIYJmU42ZKjou4JE8SwFjBIq+a6okTY1JksNOOV1mhNQpEVjElx8VVg0q5b8RAnuujLPgoj5PzBhZj6mOSq9quCqzmrArfISmEjvF+DUwjdNRS6pNy3fxnjyFMUI67SqCqAdkqVKuZER6tb+XWTY2Gv9Mcb3IspFph9FCDkSG7ktHllAf4qlolfQLvViofhUINJBIRYjH5d6EqFPz+9Vn3/ftEb+8yJCnGsuVnp9y39NBbggcNuoBpU9+kovwkLX9Gtlwpocg7kYmwK7DCH2JjMEk2bNb9vatg12fopAVkq3A6q1IeGwU/u5VzrbtHXmSVLQfJ34nF4tGOqd+/XrNc5eZOJC93KpKUYM3a6zIUMyrxAxAMbk1Rz+gRDG5l/oLDWbjoaO18zNbP2DEPpJAg+jwfSZKvuWazE7u9BLd7mKY+AHSLW5nZN1pOjkE2jpFCR78IajRJzKzMk02hI7cL4KYzoZJK4I8nNGIC5BwckaRaxmM24TKbkCT5HrHGHyKUSMh7rpEUsqIGoLi8nJdnzePlWfP4atFiPl24iJdnzeOD+QtZtmwZFRXyuWRVrq+qkkfS9ZX8vEIKeaKSIumXXLMgYDcJ3Pzwo7w1d4G8vQXy9tR/F114ISefcSaz67cTFEWNjLKkq32U7YVFMavax2ZSiRophdhK70trZ0AgmQVBs/22x+KIUupxAJmAiO6icdZ7772XcjyWLVvGrFmvMmvWqxxzzNFZ32cyWXE4yvtFVB9wwAFIkkReXp7h64IgZA0Q/6YIJ0QkSc5Xiuxg0a4zFqc7ltDy/XY99CfAjhU6O3uv+NESOqu2yxfb6YN3AaHTqgTwlY7BqfxYf1n2/a3OD3baGel2EBYlHqxr5W1lsHtaeQHVTjt3DpNvYA/WtXyjULtvCo9nBDOmf0RR0cFUDjgje2WgNIWOarkymawMHXod48Y+AkBDw/N0dcsl+CyWXATBxOjRdzNo0MV4PKMQBDOtre9pIYkA9fX/oKHhOSKRZs1+8zN+eDQ3v82qVZdnDJz0iERaqKv/h/a4peUdOjq+ZO68/Vi46FjtvaqcPTd3otZWHfyFwtuQJCkZKJlGxAiCoMkz1RXd7AqdJKFj6lOhIyvT0kMbvy3iopR1xVytXlVutzLS7aTKaWfW9JHMmzGao0vkAfILCtE7wMBu9V1ghNvBrUMHaOWeVQiCwK8US5YaSPvXLfKK5mVVJThNAs3RGBsMJg4JSeKWTY38Y9v3Q073xhOsUpRN+jBfPdb4Q1y+to6IKPFofauhdPzD9h4erm/lme0dXLehgZkL17M1GOH81bVcuX4bdQoBE06IGlnxZks3oiTx161NbAiGebJRtqhtUdpOyXUhCAJ/HFbJOJ3dZ2MwTJ1yPlTrJvS/qSzmmXHyJHlpb9La5DAJeM0mKhw2flGcx8OK6meJllWTWoa73G7joVHVnFEhf4eLewJ0KsSJTRBwKffAf44ZxJfTRrKnolTZFAyzPZI8T/UY65F/Tyt9IV2QceZ5OllRy8xXlD7ZLFAz8lJz8bIpWIbobEvZLFcmQWAPZbt9ETqCIHBNTTmnVxT2Sdb0B2runpGySMUwveUqy+dLz+nR5/rsk+/VrJGqkkk99up581WX8cS1LhTh9s3beXxbm/b70EMQBPbZex7Tp72Xor5QkarQMSa+0xUZ1jRCx2YrUq7JIsHQVs2Oraot/P71uw0prKKp6XXD59ND+1U4dYROUqGza6oDftDW0y+1zQdtqde9dEKnKRLlhKWbeLU5NWB9Z6C3XGXP0On7fICkqrunR66EZbF4MtRBav6N378+acsyexg85CpMJgednbNYseJ8Tf0iSaKW26NOkHy+1RnbliSJdetvRhRDxGJdbGvIzCFJ9uOkpOQw9tt3IZWVcvlqfQZUuvLGbHbgcJRp372q1unLciVJopZ7o76uV+ioyptU5bNBJl4aoZNNbaW2i5LavieeSLknJnTWJpPSnzomiYsSMVFWe+ptWSYgz2qh0GbBYrFQNWQIVUOGMGDwEKoGy38PHjqUoUOHYrHIn0sjdPQWLzInraoFKiJKJMhUwqhwmk2UVlRQPKiGqiFDGKpsT/1XUFCg2bxCCdFQVQPphI78XAZRIwgIgiycCotSCrGV0k6nQlI1IXq7WJ7FjMUkEBclAgkRPR/hVPbVHxcNxyw7i+rq6oxjMnbsvowePZW8vLIdd7CbIqw7aOEdKFZ9yjgoIqaec7sK/VHo/E9YrtIRS0iU5TioKtgFFahalIt76Rj+s8cQLhxYzJ3Dd5zsv6sgCAJXKDLzR7fJlU1qnDamKeVnjy3JY0aunH3wZEPfXu5dDbd7MBPG/50RI27N2kbN0NEep02ECwr2pqjoYECksfEFIHnSCoKJIYOvZPq0/zKw8mwArfJVLNbN5i33af10dHyx2w3w/lexes3vaWn9L5u33J21TUfHV4hiWBu4Nbe8xbLlvwHkcOxQqA5RjKOuQulX4uRJhEAiEdQFBBoz1unnX9ZQZLNH1yb7hbJy4JmUlBxBWemxWdvsLEIJkb0XrOXQxRvojSf4+7ZWvur0aeezGo5aqVPfeC1mimwWDilMHSAP6EOh833hlLICbILAcl+Ij9t7aIzEMAGXDyplinLdmmugiHm/rYe/b2vj5k3bs1qSQB5c9/W6ioQk8U5rN+/oQnf1WOFLrpp+2tFr2OaPm7cTUm763fEEbxv01aKrFFVktbA5FOEShQQCORgR4LPOXi3nqDka48WmTs2mNKfLT0KStDw0NcNoYo6Lj6eO4Mhi+XveEAhrCp30SkpqFs0GJcsG5Em+fpA+zOXArpTgrg9HNZ99eg7NFEUts7gnQJciMc63mrW+3BYzw90OjXjYGIzQFJaPQ3oot1pJqjESY31AnmQakTVT00qqZ7NATdLZpMxCdrJGr3Ip6iNL75dlBZTaLBxY8P2Ew09WPmd6zo8eQ907VugYWa70UO1kSxXyTlVjqRlIszqNVWkv6ybva/zGpIDdXpI1PNapJ3TSqnJobdIUGda00q+CIOBSVDq+3pWaqiEvdwoAW2sf4osvx9He8UXK+0KhBk3p+10jXT4fibQgSSLtHV+wYeMfCYUaAbQFh4zQfl3GWzIUecerrpIk8X5bt5adlI7V/hBnr9rKPgvWsSHQN6mzSLn+qCq6TcGwsmotX7veae1mbrefS9fWM38HVtmuWJz/NHdmLExYLB7N0pxewl6F3V6hIzhchiv6dmVxJlsgNyQVY/7AehIKoWO2eMjPm8qkif/GbHbR1T2fbdueBlILceTnzwCg17cKUYzQ3Pw2McWq3dHxBV1dc7W227Y9pcQE9GhqHXURymLxKP97ycmZAMgqreT4NDX7RoU6xkkSOkaWK5OOrAlrz8n/CwiKgkHeByHtfTsmdLJN+9RtSEqfKmHQG08QTFfo6PJsAFxmM1VOm0auBEUxxZal7tcAu5URbodGrEeUyk6QSXboLVd65U36Z9QrdMQsxIm+nYp0VY38ORSSJJEwVATJ/chkjSglc4PS+9LbqdRKlYJAxkKBVRBAIX7Uql/67Zl0CyxhUdQIK5OQ3NeGcJQNgbBh1av+IC5KdCkKoHRYrTnY7SU7FaK+u0GvLjPK0VGRkCRt7AbGmVDfGjsZipytImI2/GgJHZDVOd/6RAv3QI9i5SkZxeRcN7cMHaD9WL4vHF2SxzBl8Ocym7h5SIX22QRB4EIlePLZ7e344wneb+vmxaaO3YLg0EtnBcGqWWD0yMudBKDdPI1WcSorzwTQQuPC4e3aIA/k8umz5+zJkqVn7Baf+2fISh0V4XATK1ZeRJeSmaAOpPLzZxiWPg2HG7SBDaQSOiaTXfOeq2VMwTh/IF1Jk23ArA/37CtDJzdnAuPGPozTuetI3YU9AerCUdYFwhz19UZu2bSdU5Zv5vQVW0hIkqbQSZ/AgZxXop/IGVW4+r5RaLPwC4WAuHWTXFlrhNuB22xmT0Vd8VlHL++1dWs5O5BaZnttlolkZyzO6Su2MOyrlVknGGv9Ic5ZuZVp89Zw3upazltdazixWe5LrppuDEY0JY0eW5XnZiqT/We3Z5LmHcpE+dwBRdw2VFYsfK2zs9Qq359azUldsfvDxmQwbXc8wWp/KFl6Po2YG64jTup0GTp6lNqtVCg5NCpBla7usJgEra+FPQFthSqd8FCJt3WBsHb+5RsQJyqhsykY1ixX6ZWVcixmBink02LluBipTvbIcaU8X2o3JmFUaxaAy2TKqpjREybZSB+AE8sKWL73WKblZd6bvgscVODlhfGD+cvw7NWKymxWLYjTKOQcUq8HVkEgL00tN9rtxG4S6IknWNIb1GxzJ5bK18QlvoA2oVAhShKv6Aid1QYKHRUxUWJJTyBjsJ9quTJW6KRf840yydzuIQD09CwB5PFDTu4eyX0VQ2zRLepEIm3Mm38Ic+cd9L2UqE7PfYtG21i1+gqWLz+XbdueokFRcai2l8zQfrUKYyOikrGSLRRZj486ejlnVS2XrzXOEdqou9adsWJLVvI7LkosUUhtVXX+WaePobNWaBZ+NTsL4ILVdX1OZP60uYlL1tZz44bGjNeqq86jsGA/vN4xhu81mSw47DLBZU1bhFFh12xJipXFkknAujWFzgYtFNlilq8XubkTGTb0RgA2b7mHSKRNs1cJgo28XLm0sa93JStXXsLqNb9ny5YHlf7WAVBaegwu1xDi8V4WLT6W2XP2YsGCXyCKMW17ZnPy+uRxjwAEJCmhnS/p2TfJY5BK6GQrN66SMKrySz9VMykqHZPJnmZP2XE2jtE+6WG15iHXkQKv2YRZkK0q4UTy9x+TpIw8G5DvHdXK/SqbwkVQrLcOLVhYSgkfTtkX5X1GeTZ6pCh0tAydTDjSfFiGKh6FPFGLAKjqIj1Min1L/ZxgTA7ZlL5CYiZRo0IQBO1zquqRzPyfzM9nEoSMOWpzJKbNi+KinAXTEI4S3MHCWFMkRn0ompLT81OCXqETEmULXFs0Rns01VYViCdS7GyhLGqebzP3FHYyFDlbgZds+FETOgeO3AWlrlvXyv/nDADn95eZkQ6zIPDChCE8MqqKpXuO5hfFeSmvH1KYwxCnnd64yGstXfxmVS2/X7eNJxu/X8WOEfSWK5vVmGSzp1XMMvLdqys7ohglkQhr5I/bPQy3IsWNRtvo6ppLJNK8q3b/Z2SBJEnE4wEWf30y6zfcatgmHu/RVq22N71KW9uH1G/7FwAJMTmAHTb0BnJzJzNm9P2UFMuBm6FwYxqhkzrwUKtiBAIbdW0MCJ20iUJ2hc6Oq1x9V5itsz5sUGTyVkHg804fc7r82kpsemYGyBP0E0qS1yYj0ueHwFHKNWqzQohMUNQjeymT5o87evnNqloeVHJp6kIRPutMqmRWGkwkRUni1GWb+bzTh0gqAaTH49vaeF9RBqlY0ptp/1uuU+iAbINKhxrie0V1KRZBJmpWpb1PHewU2izsZ6DyUIkiNXz5qkGyRFkdTKgT91ldfo08SbfOqZltMuljTOhAUpWhlg83svWM9Mh9faXsj9tsyshVKrNbqXTI5NBnHXK7dMIAkuG9GwJhtmuETuY2x3lSf3dGCh2zIHCk7t6WzQKlf97XxwRziO74ZAtF/iEgCAIHFub0qRoSBIGHRlVx65CKlIpXeuRYzNp3UmSzZK7wmgTNrvehcj4UWM0MdtkpsJpJSLA5zZazqCdAQzj5u3mvvZtJc1dzy8bMSfoz29s5YslGLk0jFqzWAp0iI89w3zMUOgbEj1uphNnTu1Rpk6tMkJMIheo1JUMwuBlJihKPd9PZmT2keFchndCJRNvo6EhawkNhRaGTNbRfJjCi0TZiSuhzfwbp7yk2qUU9AcNVZX3GQ104mlWJtTYQIpgQ8ZpNHKH73cUleLFZtsvqJ3LN0RivKgqc5b6gdq1S8e+mDu3/9ODQQYMuYo89nqIpKnDOyq08WJuZR6bartJV3Coy8hgNFTry+REIbNDIGlUxA1BR8Uvc7mGIYpTe3qW6alkevN6xALR3fEF7x2cANDW/BkA0Jt9rHPYyxoy5D5utiFCoHlEME4m2EIm0aoog/fbMZps2donH5e/NSHkDRgod43LjWqWrRKpCR96eW/s/1YKVLRvHklEuOVs4rMNRiVn5nZoEwZAkT0i6ilMG5INJUa+oqh4jMt4w9yatL4tiW5LbGZM+cl/GhEe2ber7z9aXps7B2KLm2Im+VEI9W6qh+t5sFbrsZp1CRx9GbdKTAzK5pCpMeuIJumMJOqJxNgUjWa3+kiThUwifwHehSPmOEU6I1AYjfZJWKQodUaQzlmB7OEZjOJZiq/LplFRAxkJIOCGyJRhmpT9EvcHCoJodpid709GvsuUWnULnp0ropJct32toIcdMyPR27zRUu1XJ6G/f17fEQIeNE8sKyDW4iJoEQauq8XpLl1YY7dZNjTxc1/KDlgrWEzrpsmoV9rQydEZSWvmHIJ+S8XhvUs1jzae46JCUtupqys/Y9YhG21m67GzmzT+I1tb36elZQkPDc1r5WUj9/jo7ZwOyggqScml1MGIyOygpOZwpk1+hrOwYHIryJRxKKnQEwZIx+FEHf36F0DGZ7IaDlvSV32xkTWoo8i6wau4E1Co0Kg4qyOFXSobJS82dukm+MVlzsq6MdF+ll79P7F/gTfGOqzkl+qpCIKtEQJ6k6IeRq3xBFqdNWNqi8RSi56P2Hk5dtplDF61nre55NSvksqoSflUuf/8rfZkE0XJFLaLamf66tSml4ksgntBu3GM8Tm3S82xa0LFG6FgtFNusKZk3AHWhKN2xOD3Kdfh3A0u4b+RAzqwo5PLqUq1y0/tt3drAKT3cWlVoLukNKpXOBEqNVC4KcVavlbLObDPKLe/fLIVINGoDckl6gI87VDLAQKHjTiqH1FUrIyJmZhrRlS0X5qjipDqzL1VNfzBEKRFuFqD4e6hIuatxWFEuF1T1vTClkrzZPl86wafm7KgqrXTlmpqhop5bDWE5iPvvDW0Zq49qHtRrLV0pvz9BEDSriUtR2aTDbi9LqbBjlJnicsnvVUl7iyUXm60Ih67EdTzu0zJPkooFaFDs298l9AsOIIfm6sNv1YWlZGh/6nXBas3X7jVhhfzZkYxelCRNfReTJMOMo6a00M7/tnWzz4K1TJy7mivX1WsTONVuNSXXnXEtqQ1F2RqMaDY9lRy9u7aZ0bNXcdjiDRy8aL02tmxLW9F+ymAxsSEc5ZglG3m/vYc/b21KsbwCOJR7utG5AJl5jEbjRJerBkGwkkgECAQ2AaSowgVB0IgbOTg5mbOTlzcFp7NKe05+Xr5uxRRCx2rNJ8c7lqlT3mDAgF9p7SLRZp0iKFXpp5J0kUirotbaseVKkqSsxI9K1Kiv6xU8dnspXu9oLBZ3qkKnD+WN+jv04WV9MMZKX0iz2aa2E7R7tEkwHmvIlqtkm/T3qyoXX1xVpmTuj80kW41ESadMMejLkqZeSSd9IEnUxPsoW65tU4f03Bv5uSSJJPdjrAzdGUJHvWcatYGkEinbNvUKHVGnVLKbTQx02BjktGn30U7lO43rruOSlL28eVSStLLw6QTG7g5JkqgLR+iJJzKuhypESdJKx4OsONWT1HpCRz0OXmXhK12p2BGL44vLActdsdSgcACnYwAOR0XGQkYqUtVqRtAvkBhVtesLPxpCJ71s+R+PHbtrfH26QOTdHeqqq5rJAPJKy5+2NDF+7mpu37R9h2XZvgvIAcfyjyDbjdrRD4WOIJi0G3g83quVobZa8xk48BwqKk7VBnp+/9pdtfs/Q4dEIszixSfT2TmLUKiO2rpHtde21j6i/S3qBtbbm15FkiSN8AmHtytt5MFwetlbNSgyFG7QZeNkKhHUwV9AI3SMB8LpJGJWhU4/q1ztavTE4trA9upBZUzNcXPn8AGaBP69tm7WKpOuyizWizEeJ4cU5jDIaWO85/tVF2VDjsXM9NzkwFYlGuwmk1ZmG5JVllQCYrAycXi+qZOjlmzkwjV1WtsmXYWkAquZrniCL7t8rPCHOGrJRtYF5MmNOkCZlufRLF4rfCHea+vW2nTF4lq48D0jBnJ6eQEi8IcNyco+qjrHZTbhsZg5SyHZ/tPSpQXkAXRE5b/VgdMBaeRFXSiiqayKrBZcZhOnlxdy14iB3DC4nP3y5X1U7UgFVnOGZHqIy5EyXK1SSpanY3paDo1R/soohYRRP19xltLZaqlutV2+NXMdcYDdSoHu+QKrWctX0OOQotTJV7Zt6gOPsylTAH6vkGCn9lGgINdq4W+jqnloZBVuA3XRTwEqoZNN7aN+hxvTiBpV8ZUeTq7+xvY3UJqlZ7boz63bN29PeW3smAeZNvVtvJ6RhvtlMlk0y5EgWAztMxlBuZYcBEFg4h5PMWXyq0r2HnR2ytkmCV0If3v7Z/h8xuOAYHAr27f/51vbstIJHf19D5KETjK0P/V8lkP7B6Q8t6MMneW+EO26yfZi3XhPRZNCrkxRyLz/tHSxSalC90JTp5Zfpl5vpuS4EYSkFVPF55292rX00uoSciwm2qJxbaW/O57QzqtFafvxQlNnBgH43PYOGiMx7Tr2ly2p5b/V/JtsdmaTyY5Fl8lkdM6YTFZNyaORNZZUgiWZs7NBU+iYLR7MZhfTpr7NoOqLKCjYF4BItEVWhEcVQkdRDzkcFYwccTu5SqZTJNycktmjh8XsVhYkJcKRpj4sV8nAY0mKo5qXsrVTkWrVELTxdopCp4/pnErodFGgTXC7FaudKEl0ROPElec1sgYBkyAw3G3HZTbhNieJE3EH4cMg59BApoUI5AVqNWMmJmbvK2lHyq7QMQtCxvNGfZkFIaXioBHBordAZesHMu1bfVmusgUiq0ivgpiZ2aMcdzFZ0Ur9vAU2C7lWi1a4QiUhVKJCJZU6YnHDkF9/PHl9jIkSsR2EBn9fiOgse9nQFo1rVkB/XDRUIUV0ZKFRsUmdk1DLJ1LP8/Rg5PT96UwjyQTBjM1WiMnUlyrXgtWah9VW0IflKrngJaUpRHeEHw2hk46SnF1Q+lGSoF7O+/gxEDrqqquKcwYU8fCoKoY47QQSIo9ua+UdZUXt+4QgmDRPdDY/fXrJ82ylLa06Qkdf4txmK2DUyDupHHAaAD6dQqerawGr11z9vQUl7q6orX2Ur2ZNIRDY8o376O1doZWZBQiFkpPt9vZPCAZrU7ziAB0dn9Pc/Lq2whWP9xKP+3SVP1LJmmRQZGNW0gcyLVfZSJgMhU7WKlf9y9DRoz0aN8xd0aMpEuW2TY1Z283vCSAir4BeXVPGO5OHUe20M8Hr1KrbqZOsbHYqQRB4dlwN82eM3q0mrqpq0CYIjPYkr8m3DR3A13vKqsdt4ShxUdIq9KVP/N9v7+EjRV3QrMuX0YdBl9utBBIirzcrAZbKhKfIamGcMqFd1BvgN6tqOXPFViCZM1HpsJJvtfAnpVpgeyxOpzKYTVZkkm/Ce+V5GOqyE0yIKSHKessVwPGl+dhNAjMUcqU2HNUIKyPb3GiPUxsoqJ8vHU6zKaWqUY3T+FyYlOMmx5Lsy2gVdVQa6VeSJatmUNo28gxUIIIgpIQZG9mtQFaGOPUD5izlus2CwFfTRvLi+MEa6WCEKweV8eL4wSnl2o1wfGk+J36PVSm/b2gKnSzBySPSjqFKwmiETppCp1k5543O03QbpD5oc1aXL0VNZ7XmZs1LUaFew63WfGPrQloFLXVc4HLVkJs7iYKCvQG0sFp9wC2ILFv+Gy2YWI8lS89g7brraGh4ts/92xHU+5ysANGrJGRLZSTSiihG+wztV9uqSFeQhhIiq3xBjRxRrzvq1hYrVtL1gTA9ynVIDSc/vtR4vLVJIWHWKN+nqp58fEw1fxw2gOtq5H36otNHW0zuq8Zp5/8GV1DtsPHn4ZVaUQ41Y0xVWp5Umo9VENgWjmrB7CpUFc+pZQWYBTmvR6/Sqag4hdGj7qJm0KWG+w1gtycXA4wIHdBn7chIz230GOXs6IKMhwy5ij0mPKW9LxTepi0gpo8n1Cy/SKSFuHL+pSt0IDn2lcRYVkJHEEyaqkYUoyBlU/JYM95nBH27vrJxVEJH0pErQVEOxt4WjtIQjmoVDEVd8C6A02xmmNuh3ftSFDoG23KZTZx75OH89bprAGMSBjKVKUZ9WTV1SvYcGsi812Q7Ev0ha/RKnmz7nr6g0ZcVzKjN2WefzXHHHQdkkkFG5JQ1zb6VbinTVDxKYLV6VuVZLdjNAqKUzAGsra1FEASWLVuWkb31nQQB7yTCCZF1gTAbAuE+82pUNZJ6mPVZYFpfokpsmTTbe57VrJ0viTQlE8jfv/rd6VVs6pFxK2OvznhipwUUgiDgdA7E6cg+ptGr9RJpCwg7wo+W0Nkl2LYAWlaCxQFDDvqh92aHGO52pHxhYzxOTi4rYPb0kVymyLbv2tKsMe3fJ1SpbDZvtMlkS/FHZ6uMYbEq8td4r04Cm+zTo6wGqpYrUYyxes1VNDe/QUPj89/uQ/zIsb3pNWKxLtraPvrGfaSvSKpQBwSBwMaUNmVlxwOwZu21Ke3D4e3Jyh/pQZGq5UoXipxZiSHZTi1nm1Whk0Yi9qdseX8UOnFR4pglGzlg4bqsZE1UFDlnZS2PbWvjuvUNhm1UYiHdiiQIAr+uSJWY91XBanesNHBMSR55FjO/KM5NrkgpKLdbsZsEYpJEYySq5a/sZRBMe8umRkRJ0laey+xWTlFsZgcV5HDlIHlQvUyZ+KiryoU2C0Nd9hQioT4s25/UvtRj6jSbtEBpdZLSqvSj5tAIgqBVWKrTTVb0liuQCZqVe4/l+fGDEZAHWmrp6CoDIsas6xeyl54/pawAr9nEjFw3l1WVGraxmAT2zU+ey0bWplKbJSU8uyiLXSc9oyc/C1k4JUdP6GQ/Rw8szLRIGGG428HMHbS1mgRmFubsVgTmD4FjS/IY5XZwfGme4etD05RdKqGTrE6WOijUB1ufVp56v16VZlvs1g2S4xKsCYQypOZ9IWmxMSYeLBZviiIjfVyghdgqilx1Ql1YsB9u93Ci0VY2K1Ux9YhEZGXItm3P0Nk5l+6er/u9z3qo9yez2ZliB8rxjlPuiSKhUHIBxOi+kkHopCl0btnUyMGLN/Cpkne1yi9fR9TQ+SW9Qd5o6WL/hes4a6VMVjdF5WvTBK9Ls2o6TAJnKvcTldBRr5OqRXK0x8lvK4s1In52t1+7BhbZLJw5oIgFe47mnAFFmpJSreCnKoX2L/AyJVe+lj3f1MFftjRp21EtpxNyXPyiSN5/fQVCs9lJefmJGdYqPWy2HRM66Vk76Qodt6LQCYW2asqbdBJGEARczmqlXX2K5UoPh0beZVfoyP3J1ylRikPSuJTRLtV2lY34Sb8/ZAs83nEosrxNxcKlaxMXJWKSpP3GuzTFjnFvKvEQlyTUq8L8r75CEAS6u7u1dg6Tifuee4GL/3Cz3E8/SZi+FDpRUeKmC8/n/FNONuzLpnuvUSUso21kO1r6vvqySam7L9u0DBQ6SgUro20/+OCDPP3008r7s++jCpVgCGh5PKlttP2R5GOlEhVXnHcuV572SwDalEpWAwcOpKmpibFjx2pKPI0w+gHmjulQ7XVRUaIjFicqijSEoxmKGPUcLNDZzWKiyLZQVCOmVPWXzSQwwGFjuNtBlcOmnVd6QkejVgVBIy/bo3GNVFLJm3yLRSsl3xCOfqcFetIVoTvC/zahM/8x+f9xJ4M7+w1md4HLbKJGNwAfqazACYLAZdWlFFjNbA5FeEOXEdEbT3Da8s08Ut/6ne6bTVPoZD+O+mDkbKUtNctVLKnQselusB7PKED1sodpaXlbG7x1dnz3IYkA0Wgnq1b/XqvMsTsgHvdp1cF8fjlvoKtrIatXX0ksZlyu2QiiJA8UXa4a1LuRyeSgsGA/QF6lUit6AAwdcq2hz11f1SNdYq6y0/G4j2i0XdlG5iQxsxx5NstVartsxM/OZuh83tnLllCEkCjxanOXYZv7altYpqxAftHlY61fnvA83diurVaq8nmjHJOT01ZYjawsuzMGOGys2HsMj42uznjNJAia4qQuFGV7WCVYrByiTCjuHCZXFNwairI2ENYUOmV2K3vne5k/YxRPjRuk2bmW+0L0xhOa9LjQasEsCFrZcRUrfCFtFVuvKFGvn0lCR26j/26qHHKb+rDcJiZK2kRFn/mSYzHjtpi1/tW8GiPlA6SSIun5OSquqilj437jeXPSMCalWav0mFmQ/M0ZKTcEQeDx0dVaoG42a1N1GqFjlKEDMDlnxwodgL8Mr2TPPDf3jshe3eln7Bwm5br5fNpIDigwJsBcZlNKefsSzXIlf7ebghHOWrlFy8NRf2OlNgs3D6ng0dHV3DFUviav8BkrdFR12V+2NDHoyxW80JSaMZUNap6AUYWrZJvkaqW+EqH82iBAzjeJxXq1CbXdXsaY0fcC0Nr2IcFgraEyNRSuZ+myM/j661NIJIwHx37/hpR8OFGMa4ob9X5oMtmw25IqY4ezUlNuBINbtecNrcNphE56zo4a3K4qWVT14KGFuZgF2B6JcelaWSk7vyfAWn+IlkiSqNlHIXePLM7TSOPNwTCiJNEVTyWiVYz2yNXRgglRW/VPv44MdiWvlZIuy2dyjpv9lW3+rb6VB+paeKBOtp71KPueZzFrofnvtvXs1KTHnkLoGJ/zOyJ07PYyLJYcJCmhZfsZkTDq+SkTOqrFP12howTcR5ozqmrpoZEyOpufkWpGUAOPxWgf4cn9VejYdtgGkuMdVaFj1RQIScJWJS+SCh3jzJm4lJzcGtEddpNAbkEBbq98jvRboWPQLr1NtiWt/ihvIJU8SSdhYopSzapbmMrWlyAIWo6OJcth15cuT+8rNzeXvLy8jH03qqoFycyeWBbrmX5/5PDk5PdjNQkaAdEdS2A2mykrK8NsNmv9qVZroxydYCLBxkB4l+a0JiQp6zVB/+z2SIz1gTAd0TjbwtGUylTqOVhgtWASZAJojT9MZyyu5eQkdNZAsyDgNJsQdBY9/adV+zPp+oyIyVLm6hDTLChjPEH+/bRkySfaFVDnUP3Fj2v2sCvRXQ9r35H/nn7BD7svO4FROluDXmrtsZg5d4B8I3y9JTn5fLW5k887fdxf27xDT+K3gdsjV6FyZwlIhNSBTbbKGJYUy1UyQ0eFzVas3HBFAoEN1NU/ob3W07uUbduepq7+nyTSfgiSlGBr7SN0dS3Mun9tbR9n9eTr0bj9RVpa3tbKXe4O0FvQ1ADJJUtPo7nlLbbWPgzIZExDw/P09CxBFOOEI810dHylVamC5IqkzVqE1ytbZrzesZpNKhJpTlHV2O0ljBubzNZRV4xC4Uad5Sp1Mmk2u7RBUzC4RekrcyBsS8vGSR8Ia+1054fJ5Mg6sFEJnXqqua8xbhgKqMcLugpLLzZ18Fpzp5bPouJlpfzvAGWS+/i2Nh6vb+P6DQ38cbNMNLZrK6CZE+Fcq0XLO/mxwtZHWWlVAbIhGNaIrXK7jQdGVvHOpGH8prJYU+x80enTJpsqaTDIacdmMjFSV55ZzYVwm01aDs1FA1Mtnct9wRS1jwp1kqKuOrdqmT3JNioho+aJaNJejDNm1Mm0WiI9K6GjI2iyhV/3F/oMn2zhw9PyPCzeczQvTRjMWQOMiXaX2ZQSlppn8PkAJuQkf3t93UaKbVbemDhMC/z+Gd8P9GMB1YJXZrNqk4UP23u5Ym09bdFYym8s32rhhNJ8TT240p8aYtutDOL3UybwX3X5kYDrFTWiJEm819bNSUs3ceumTOtTQcHemExOCgr2ybrv+vLmVkvqQo/F4tYUG6FQnabQMVs8eL2jyc2ZiCTFmDf/IOYvOAS/fz2QWaoZwB9Yn/FcV9dCFi46lkWLj6e29lEkSWLJ0tOYO+9A4vFAclHCZMdmT5IIDscAbaKvEjrZ7j0q8aMifWFCrTimTkJUa8RAh42bBldgFuRJtHqFfXRbKzFJnpqX2qxcNaiMKweVctvQARpxuykoh4WqGRHp1y2zIKQsDtoEAW/aYoL6+paQ3JdqX6iwW9k/jVx8WcnTUQnAHIuZgwtzsJsEtoQirAv0f6VZT9b0T6FjyhgbCIKgqXRUdVY66QPJDKeAf4MWdp2uMtdbrtQMJ6O+0kkZBMHwfDAp56ZMFhpbrgTBnPLejL7Vd6n2LQSaEh7t3EmHxZKLVTf+9ijf9ZEHH8Sfr76SP199JTMqyygqKuKB229DkmQtz3PPPceUKVPwer3UDBjA9eeeTWtrCwkJGuvqOOEwuVhJfr5sqTz77LMxCQLnHXk4d12vWK4QiEQiXH311QwYMAC328306dNZ+NVX2v689fxzlBQU8OGHHzJq1Cg8Hg+HH344XS0yUfjYn//EOy88z4f/fUfJEBL44osviEajXHLJJUwZPIhpJQX8YuxI/nnv3YbHAJLVJlUVz2OPPcYxxxyD2+3mT3/6EwCfvPMOv9x3L6aVFLD3qBHcdtttxOPJ43rfffcxbtw4JpQUctjo4dz++yvw+5Mh208//TR5eXl8+OGHHD1lIntWlHDRCcfS3pzMk9JbrqwmgXOPPJy/XHMV91x3DQUFBZSWlvLEE08QCAQ455xzGFFcyNF7jGP2xx/Kx1QQSCQSnHvuudTU1OB0Ojl84nief+wRwqJIXJKP2UvPPcfbb73FWK+LPXLdvP/pZ5rl6uuly7T9WT5nNr+auR8j83MoLy/n+uuv1z5zYzjGaYcfynmXXMq1115LQUEBZWVl3HrrrRnHNy5K+HdA/ATiCVb7Q5piOx36tTlJkh+raq7GcIyoYhVU21mE5PeqQlWRaiRM2jZUcs1YoSO/rvap2tL0v9Qci5lKZVGuJRJLsSYnJInuWJzOWLxPIluS5GPVl6MmkcUxkQ3/E4SO4UGd+zBICajZH8rGfv879Q2h5uhUOqxaEJaKY0ryAJjd5de81q8p5I4/IWZ46Xclhgy+mimTX6Ok+PCsbfSVrixpA7fk83pCJ1MCK9+oZfKot3ellq9it5cjSXE2bLyDTZv+zKLFx6Vk6rS0vMuWLfexavXl+P3rWbP2OkKhpEUmGNzKipUXsHTZmVltRyr8CunT61u1UytPjY0vZSWBgsGtWglOPQKBLSxdehY9PcuQpIRhG3mfVmt/h0J1KZ9NJca2bn2Y9RtuZvHXJ/PlV+OZO3c/li0/h7nzDqCl5V0AXRaAjcKC/QHIz5+eOqhJqBU95MFjQcFeTBj/Tyorz6C8/ERAzccxtlxBMhg5ENystMkkdORzQV+uc8eWK0nKTtKo59ar/JK/NQTYb+G6rB7YtmiMj5TKPxYBGiMxLl5bz6nLNmvha8GEqNkX/jJc/jxvtXZpK+FzuuXvSp/3YoSnx9Uw3uvknp+gqkHNaJmnhHQ6TAIFVjOFNouWy6KSE1909qYodPSwmgTGKLkwnygZE/rj+ftBpfx9TDU31MjXmGW+oPbd6K1Hg9IUOi1plivIJHTU7y/fmlk2GmCIM/W8rMpC1kzWWa6cRsuRO4FKh41rBpVxcVVJn+SQx2LmgIKcDDucHvqJXX6Wc1Rf8jxbqfGf8cNhhE6BparNBEFIIeh8CZE7Nm/XiNUynUpslEe2c7dG42xXznv9BH2/tADlXKXfN1q7+c2qWmZ3+3l8W1tGNZUc71j2328ZgwZdmHXfnTpCxyhbL6miqNMm3apCQl+FCMDnW6X8lXld9+nukSDn36xYeQGSosLZvOVeGhqfo6dnCZFIE72+FTqFjh2bXqFjr0gSOooyNpsyVG+5WiTsw8cdyclfIJHQri/pBHKBzcwFVSV8NGUEfxhczn0j5fuDqhYttlmwmgSKbBaurSmnyGZhiEJYN0ViWqaX12zKKNkMMNSV/N0X2ywZigVNoaOrhJVjMeEwmxjvdaYQyb6EyGaF+AF5guWxmDUlz+9W1/GhkpOm4oWmDk5fvlkj1VXYdHmLESGXmzY2sKDbn9ZGr+LxGFpe1BwdNQdQrWalh3puqSoeQbBm5PGkZuj0ZbkyaSSMJEnEI3Fi4XDGv0RUIhaJEg31Eg0HiUWixCOxjHbxqEgsEpX/haOGfcUjMWKRKL6Ime6QxLZQhKa0YHN53wQs1iQJ5tFdz9958XnMFjP//uxL7r3/fp7528O8/szTCIKsWrnjjjtYvnw5r7/xBtvr67npgt8Rk0TKKit55uVXAFi/fj1NTU08+KA8vtXf3syCwCWXXMK8efN46aWXWLFiBSeffDKnHXMUdZs3ae2CwSD33HMPzz33HF999RX19fXccv11AJx16eUcevyJHPj/7F13eBTV3n7PzPaS3jslCYTemyAqCvKJYr+IYtQLKmBDbPeKgtiuBRXE66efgoVr74r9iigiTekQWkJCSEgvm2zf/f6YObMzszObjaC0eZ8nT5KdM2fOlJ1zznve3/s7bzyqqqpQVVWFkSNHYvHixfj000/xyoq38PHGzXj05VeRnROuFqaI0bHIMxsEf7H58+fj4osvxrZt23D99dfjp59+wqwbrsNVN8/Eh+s24annX8Dy5csFsgcAGIbB4sWLseb3LVj475ew7sdVuPtuqeUAPZfFryzDqyu/RvWhQ1jAn4scVBn12VsrkJCUhPXr1+OWW27BzTffjMsvvxwjR47ET+s3YPjZ5+D+GdPhbG/nUsMHAsjKysJ7772HnTt34s5//BNLHpqP9997D4FgENfechsuufxyTJgwAWWHKvHdnv3oMXSoMOalv2urDuPSSZPQa+AgvLvmVzy/9AW88sorePjhhyX38rO3VsBksWDdunV44okn8NBDD+Hbb7+VnMu+dhf2t7sjkjo1Hh+CQS6cV2n+RBVisXoWhVYTulmM6Gk1CWFnrkBQ8nZnCAlTFlMFmloae/r0i02RQwodriz1UvIFZdv5uhINOiE0i/pP+YNB7HK4cNDpQYXTI6h7lFDv9WF/u1vYV4ysrGkAgO7d7lTdXwknX57PP4CDW37DT2+9jn7nnY++50wAHDXAb7xZ3ujOXbDjjbEJdjxVVo2zFeTX+VYTCiwm7Gl34Zv6FgyKseK3ltCK228t7WFmmccKLGtCbGz/iGVCCh2iuvJCTZG9ElNkqVKDqntcfKgVIQYkJZ2NSt5DhxAD2tr2oKrqQ+TkXA+Ak2UDgMdTg983T4PHUweftxl9+77I1cVnZvJ6G1Bb9z1SUyaqnoejjVPD+HxNcLkOwWzOhs/XCpaVDixaW3chGPQiJqYvAgEfSvYsQDDoQXr6pZIsD05nOX5dNx4GQzJGDP9OEoe/d9/DaGj8GQ2bfkZy8gTU1f0XRUVPwGYthMGQIKxUtbbulLSxvOKV0D/8i8jtPiJ8REkrvT4eXm8DDpQuRmrq/wiu6oQxIC9vNqzWfCQnn4uami+FOoSMHqJVsaSks5CUdBbKy1/hr2elKG15OFljNGUArVvhbOcGW0qEDiEs9PpYgZBSS0culh13ROgkJp6FA429gSDnkn/vnkOY3z0Te9pc+L9DtbgwJQ7nJcViM586utBqQl+7WRhEH/H48FlNEy5NSxB8deL41cg8swFlTg+28tL0Qy4vKl0ekUJH+XWbazbim8GFqu0+mUFDen7h07anG/Vhg2866F/X1CaoC9IV1Ez97Rb81tIumIaKr6ddx+KilHj8zIc9bWltF8KaxBPXrjzBFBZyJTINph44h1xe+PkMIIB6iu0JybF4UxSCkq1iZhyn18HMcOFhgyOEU0WLO7ukdVwoCuSajfiV98dQUiBRvN+/Gz4+0oQZogxmGk4MSBQ6ou/O3V3S8PzBGlydkYhHDlThXf49ZiAEiaJ7bWVZDIixYFNLOz6pacLNOSlw8h4bQOg7SuHwcYPxvbJFoo3NbZiQLCVlImX9AEJeaUC4QgcALOZcNDdvQrvzoKCQYHXc9yc1dSIaG39BVfWHAACn6zDvTRK++hsiezg0NPwMn68ZFks3xMYOQFXV+yg/+LKo/A4+9JiGXIkVOunCeIYqdNQWHITQLFjwLO4AtpXi4wHdMTzOhkpXqJ2HePP4Rll4Zy+bGb1sZngDQTy8v0oggJRCH+P1OiTqdaj3+oSsVIkq/Q6n5uFIFqW+iZLxjT4/9vE+TDRzHUsI3unXDTUeH5aWH8HqRge+q2uREDoAMCsnBT81tmJPuwu37DqI3Wf0AUMIWn1+PLC3Eg5/AE+VVeMJ0WKGOOTqwcNx+Lq5Du9UN2DP6L7C52KfHVYh/AlAWPpgZYUOV4aqt5TMu6lVgNt9BD5fHleXyjFBWCAYgM/jxfLZDyqX+RNx1vOvo4YQrq9hGdR5vPAEgkg36kGnwYQANh0jpOdOy8zCPY8/gSAIuvXrg9W/b8aKF57HP2bdjOuvv16ou2vXrvjHE0/hb2NHo6mlFSarDQkJnJopJSVFCCECpOnFD1eUY9myZSgvL0dGBmeCPnfuXHzx5Zf45M03cOuDC8Dw5NGLL76Ibt04lf/s2bPx0EMP4T4AFpsNRrMJrM+LtLRQv1deXo78/HyMHn0GypweZOTkhC10i0EIQayoH7/qqqtw3XXXCf9ff/31mHvPPbjwqqsBAFk9C7Fw4ULcfffdePBB7n7efvvtALgJfl6XPNidCzF75ky88EIoIyw9l5jsHBx2eXHl9BvxypOPK7aJEjoFvftg1j33Id9qwn333YfHH38cSUlJmD59Olz+AG68516898rL2LtjOwrGnAG9XocFCxYI9VydnYPVv6zFZ++/j7MuugQWmw0Wsxl+jwc5GelwtLngCwTDMmG9938vITs7Gw8sega+INC9f18sqK7CPffcgwceeEBoX36v3ph93z+RYtQjPz8fzz//PL7//nucey6n0mr3++Hm1SbNPj9sCvfBHQgIiwS+YBDuQBAmWfyYWFVjEqkGDQwDd8APbyAIv4ifZsCptQ0METK4CSQMX0YeosZ0oNAR3xcalibfDnCLivUen1BG7F/EXZOA6vNI3/2NXj9SDFLCvSD/AeTlzYLRkISWlugtM/5yhU5FRQXGjh2LoqIi9O3bF++9996ffsxdP69CTdl+1JTyMdZb3gZ8LiBzENBlzJ9+/GOJQbFWbBpRhIe6K7tkX5DCDYhW1jbjE5GXDgB8W9+MlytqVWWZfzZox8ilOVcLi4kccgWEOlPqnaPT2ZCWeiEAICF+FLKyuJcxJXz8fifq638U9qe+LbV13wmDMa8vtHpUVfU+/P52bNx4GXbtuk9ybL+/He3tZcL/La3b0dS0ET+uHoADB56WlNv029+w6bcp8Hqb4HZXCSuBHk+tpE6HYzeCQT/c7mrs3fcoWh27BeZanHa1tvYrBIMe7NhxO9atPx8bN10hlKO+OTRWWpzdg6ZW9Qc4oqFX0SKMGP4dRo5YhcGD3ueulYtL5SxOI86yRqSlXQiWNQuDUpf7iBBKpUTUhDJYVQlyQXnIFRAaXNF7rOShA0jvfUfpXjsCIQT9+/0futpDA/PXD9ej6+qtmLBpD94/0ohH+DSrNNQgzaDHI/lZ+FdBFoozuf1ePlSHYDAokAJ5fHrpiUlxYcfc0NwmrIirETqnMqj6g05SlAx1u1uMyDTq4eEzbgDKmZv68T46dOVZiWChKZwPubyC54OaQicYDAqEjngSnGbQQ0cAbzCII26vKMOVcsd8Zrxd0pFmRTAN/nlYT3zYv7vQzhMBYv+VeJ36M3pGvB1P9chGzGluUnwiQkzoiJUT12RwJre35Kaij2gxJ1WBWP0bb5D8Fh8+08xL7lnCZVzLFH2PnIEgjnh8Eqk5EMqE1Bl0rNDhjWvbD4YUEvwYgGGMKCp6El263A6A68fEIcRZWdPQJe9WANyiB/UsAUJm+zZbIZKSzub2d4dSsztad0qyMIpJBJMpQ1Achwgd5QUHMaFDccfucrT7A0KYFcAN8Kk6hyD8u6hnCKZnhfouNaUcVd7Qe6FGRIsVOkn68LqsLIs0/r1I6xJ7jfW0mXFmgl0wWP6uvkV4HiihMyzOhk0je8HIELT4AsK7++2qBmH1+q2qBpSLkg7QRSofWHzdzNXT4pOudEvDssKJGiA8g5qyh06etF4F826adSsY9MDprFCtC1APjfqr0ezzo8btRaXLi1qPD+6ANDMVF8ZsgoVlMGbEcMF43h0Iou/QYSjfvw/BgB+bNm3CpEmTkJOTA7vdjusmjgcAVFbw10FFaCp+tezesR1+vx8FBQWw2WzCz8+rV+NQKffdISCwWCwCmQMA6enpqKmpkXjPyA9XXFyMzZs3Y3CvIvzr7rn45fvvVNukhMGDB0v+37JlCx5duBAjMlIwIiMFuQlxmD59OqqqqtDezi2Of/fddzjnnHOQnZWFnIR4XHfttaivrxe2AxDOhSrjktPSUFuj7GPKEAICjjChbWdZFomJiejTpw8ATi2SmMK9RxpqawWFydKlSzFo0CAkJycjIy4WHyx/FZUVFYJ3DL0UhBDBB4365NA3YWlJCUaMGCH4N7oCAYwaNQoOhwOHDh0Snpv8Xr1R4/HhiNuLarcHSalpqD4SWiSu94TerZRYCcq8cuRzT3mWLUDdw4kqZjzBgFCG8KFzhBDkmY3CO9HHH5eSK3IvJCHkiv+fayf3NyUjxSbgQLiCR1xPIAg+DEyqOFLLGiZPsS63QyGESBYQosVfPsPQ6XR49tln0b9/f1RXV2PQoEGYOHEirNajX7FUgtflwt71awEAPUefxX1YwacqL5osffOcJMiIILE/JyEGi8qO4LeWNoGRHxNvw+pGB76qa8FXdS2o8Xjxz24ZqnX8WTBbuIGZPIW5GNQU0eOuFeTVYVmM+M6UKk5Y1oq4uMEYOWI1jMZUHKp8Q7K9oeEnBAJOEGIQSBXuVRdEefkr6NHjYfi8IUKnvv4nVB5+B80tv6O5ZTPy8/8hKIocbXshlnO3tmxDSdX7AIIoO/hvdOs2FwCXSt3PDz4pYUPh8UgNJd08wQQAlZX/QWXlf1BQMB/ZWdeEmf+J4XQehMtVCaMxGW1tnHQ1JeV/UFUlJUndHu460FAplrUIK4/UaygQcMLna1LNOmUUZXqgyhslxQwdRLlclUImCyUpOh38UiJNSaEDSO+9EjGkhBfKa/BJTSNe7d1FMRyFvmSnZyVhZW0zKkWS75I2F9r9gdBKo55FjI7FtZlJqPP48FZVPTa3tmN9cxtK+SxINL30/yTH4oUKaaf9a3NbSKGjMrA+lZFvkd7XDIVJCCEE4xJj8Nrh0PdCafW5vyxLmBJBFqNjkW8xYm+7Wxi4pCkQOi2+ABq8fiHDi3gSrGMIMowGlLs8qHB5wjJcyaFnuLTe6/hJjymCsXWmyXDU/jnHGmK5ciSFjoYTFwVWE7qajbDrGFXCbUyCXUhLrvT9uiglHg/srcSedhd+b2mHhXf7jNWxIITgmR45+L2lHW9U1eGQy4sD7aEQmx5WE3a3uQRVSGdgEqVwVVLo0HFDu/Og4Bkiz1hk5vsdt+uwkNqcYYwoLHgQTmc5SssWo7V1G1b92Btd8m5BXt5MIXxZp7MjNqZ/2HFbWncgIZFb8GOIQQgFYhgD9PpEmPgFKrpAo+bxRv3iPAh970udHnxS0yis7AKAJxgUvGbidGxYFiAAuC4zCY+XSg2I5ehmMWJdc5sQpqT23uqmEKYnRxeLAdUer0DoKL1zh/EeaL+1tgur2OJnMEGvQ6HFhK0OJ3Y4nMgxGfBKJXfN7CyDVn8Azx48gkU9eANtnjj7DUOEOhhwEyZKQoonO0opxAEuLE4MpXImUwavUuYXDxWytNL77fXWC4kn1I5J+Km2zqDHDf9+AmZzFxCEG/C2tpUgGOD6lYPgxmJWHYOuovBdt6cGbnctDiMDXhiRaNApmul7vU2ocTvREIwFY+D6tyOyMDZfMCgoC+hE2cAwYPi2GRkGDgTgDgSEia3H5cb48eMxfvx4rFixAsnJyVi7Zx+uv2gSvHyWNbXZk7gHbHe0gWVZbNq0Cawo1MsXCKKSJ8AYAHoZqUgIQTAY5M27QxN4MQYOHIjS0lJ8/sVKvP/V17j7umn47KyzsfKjD1VaJoV83ulwOLBgwQL0GT8RvkAQ2WaDoDQxmUwoKyvDBRdcgJtvvhmPPPIIEhIS8PPPP+OGG26Ax+OBxcKNUei5GGmD+XNRAwGg4xM8iM+f1sMQAgM/rggEAmAJwdtvv425c+fi6aefxogRI2Cz2XDfY49j28YNwj0UP3c2lkGz14/2gFShQ4uYGAatCAheWRSUPNHpdfAHg0JYvDMYFKw9/MEgmkQ+Q07+GNUeL+o8PuRbTDCxjJDBSs8QeHnDYXsggINOD+L1LJIMetUsa9QPzisiJ8XZvswsAyNDuPYFuXAqv0pdlDij10BMr9DroZcTOrLt4nq4usIDfdsCARxod0NPuEWUarcXiXqdkK2Mwn2M0sX/5Qqd9PR09O/fHwCQlsaZcDU0NETe6Siwb9M6eN0uxKakIqOgBxd+cmgDtzFrSOSdT0LQ2NAjHp+QRndKutSg8udGR9h+L5TXYNKmvWjqwCj2aBBj74vCgofQs8djqmWoQqedTwVKCBsWnkU7U0rY0BUaszkTDKMTslF43Nzkur7hZwBAZubfYDJlgxA9Cgo4+eSRms8RCPjg9YllbQHs3/8U/3dQEs7kkJkmt7Zuh9cbnvGjviFk+OZo2yOs7AAQvIEo5AQPALQ0/w4AQlYPiqKeT2HY0JWwWLpyx3fsgMdTj2DQB0L0yM66JiwG3O0+wrHVPEEmzvDEskYhM5nLVSXx0BGDrjL6/Q6hvUqKGSM/sPZ4akIDa4VylAyiYXXREDpMFKnGS9EFD+8/jC2tTjx38IhiGTrRvzAlHutHFGHryF7YO7qPMKnf5XAKqTzjRAPTJIMOl6dyA77FB2tQJlLoAFxacrqiOYnP8PF9fYsQtqA2sD6VkWM2YrCIiFEidICQ/xfAG3QqTEq7W4zCKhOgTpANFYUzUeNQCjPLCEqD31rahBWjFFmIF/XBKY+C0AGAfxVmwcIyuEyWtexkgFh5EclrR8OJCwPDYPXQHlg5qEA1Ze8YUdiUkrojRsfifP699VVds0AYULXFmAQ7bstLFdKhlzndgiLjHF6lsaW1Ha4Ig1OvggGkmNBRUjiEUkuHh1zJ63C6KkX9nJXfFgrnCQQ8OFj+fwAgInRiYDSmhqUXb2/fDx/tn1ijYKBrsXTlJsHydOQqfRi9H15Ir/maRoegSKSgGa/UwqRi9TrM4xfjrstSXuyhRA31B1MPuRIpdFTK0NTlNPuZUkY9Gh5L+1UTQ8JI7SJeHbbD4USFy4MypwcGQvBKb47MeKe6QehP6YLfrxgp7B9ASOUJSBU6amoZuUJH2ciYICYmFMolXzwU6pLf6w4UOoQQMEYr9vqCqAIDvckk+TGa7NAbDdAbDWCNJrBGEzw6g6SMwWSD3mhAwBgL1mhCE9GF1aM3mWCxp8FkzwJrNCFOpY/yixQI8rfDunXrhGxMrkAQ2zasR0637thTshv19fV4/PHHMXr0aPTo0QPNdVJ1uZEnkPwypYVYXdF/QH/4/X7U1NSge/fuwk9hfnck8+FTakkVgJAyQ683IKCg6IiJicGUv12JB5csxROvvoYvP/7oD88pBw4ciJKSEgzuWYhehfnoW1ggtJdhGGzatAmBQABPP/00hg8fjoKCAhw+fFi1Pj3DpxPvQDdAT1+ejlwMeWr2NWvWYOTIkZg5cyYGDBiA/Px8VPKKJ65SwKDXC/eG+uAJIVc8/ZBf2ANr164VyCdXIIA1a9bAbrcjKytLIE/sfEbPWD0rLPzQbFU+EckCcO95pz+AGrcPgSAE4p8SLDGC4XAApU5u8Y2GIMm9asTXEuCU06Ey0mvEiDJY+UTl1BQ6cj8hIESK6ETHU1Lw0OPRqv0ICqSP4L8TCKLV50eD149dDhcavX5Uur1olT3H7oB6n1nicKpuk6PTo7fVq1dj0qRJyMjIACEEH3/8cViZpUuXIi8vDyaTiXMzX6+cWWjTpk3w+/3Izv7zzEB3/fQDAKDnGWO5jrX5EOA4AjA6IL3fn3bc4wX6pQNCHfrwOKtEbu2QPUz+YBDPHqzGhpY2/NCgbLp7LEAIQVbW1IheO9RDhxrZKcU06wSFDrdSJV8tEUzseGUKJSAslq4YNOhtDB36GbIyr4JeHw+frxXNLb8LCh1KblCfGABoad0m/O3gs0nFx48AADQ0rhEdOfR1qq8PETptbXvhdIUIHTmBQ1f4uuTdgt69uYxUtDyVmHPnHYOUlAmw2QoRGzsQACcjF6802u29MPqMdRg08B0MG7oSABf+5fO1hpQ1MmIkpKo5LAm5EkOnswlEEb03SsobvYh883i4TlVpoMvwpFLIOFl5MCxOd9uRQicIYBlmCC/Vd6obhNThYtDVAwvLgCUEKUbOYLyPjWvTVodTWG2IkxELs3JSwAD4vqEFn9c0AQiZRzKE4PmiHNyZl4r53blrSgfsNpaJqNw4lXFNRmjwrRRKBQDD40LfYY/KShZLCPraRb5NKpMQcV3UOFQMmvL79t0VCID71srJGuqDs7mlXSDA1SZGANDDasbmkb3wXM8c1TInKs5OjMGlqfH4Z9f0jgtrOGGhY0jElL1iorPNpzyApGmv94rUN3LFjzhskRI6fe1mJBt08ASDglJNjGAwiHtKKtBt9Vaslo0xdDobbLYe0OsTYbP1CNuXhlx5PLVwe7hFGrmHSSgL4+FQf8j3V4QQJCaEQuv9fgcCAa+k3wSA2JgBQhmurwsK6cwZxgi7rRd6FT2DIj5dulxp3NGCg1ihAwBrmhySkCuAM3MHIpPHs3JSsOuM3rgoRZl8kKsi5YahFHYdK2S4U1Xo8PeaLkoolUs06CQr1UoKMWpov9PhFJ6ZeD2LMQl2nJVghz8ILCrjxnN6fRxysm+A2yh9FsSqE5a1CeOPSJmwaNZNuo8SYkTqLHnKcgpxQg8ggkJHREg6YYQ/CLQqGMQqhZgHgtLQC6pGYxDaX20ln05I9QwRzGMBCFkg/VD2AAE4H5oF99yFsr178ME7b+Otl17EVTfNRG5ODgwGA5YsWYIDBw7g008/xZLHpYuxXfJyQQjB559/jtraWiHbEwEXsqJjCHr36IGpU6di2rRp+PDDD1FaWor169fj8ccfx5qvvxLKq4FOjjNycrF92zaUlJSgrq4OXq8XixYtwltvvYWSkhIc3LcX3378EZJT0yR+Pp3BAw88gNdffx0vPPYonKX7UbJ7N95++23cf//9AIDu3bvD6/UK1+SNN97Aiy++qFofw2eTS1UgQiXl+N+RwsUMohvHEoL8/Hxs3LgRX3/9Nfbs2YN58+Zh+2+bQmUAdOnSBVu3bkVJSQkcDfXw+bwC8UJ/X3vjTaioqMA/59yO0j0l+OyTTzHvwQdx6+13gGGYUAgUuLFyntkoyeTpCwYFckjPEKGd8ncbEFL72FnOw8kbCMLlD8rK8NdEdi0kCh2VMoA0VIp+c+TXVW6KLFbf0PkmrScYDKl0lI5Jy/lFBJKOEMn9EsMfDArHpd9Vt8qY1xMI4Nbd5YrblNDpGUZbWxv69euHpUuXKm5/5513MGfOHDz44IP47bff0K9fP4wfPx41svjBhoYGTJs2DS+99FLE47ndbrS0tEh+okV7SzPKtvwGAOhxxljuQ6rOSe0NGE4cH4NjCXGHbmUZpBn0+E+/rnizL6fqOOj0SDqOra1OIUZ5b7sLd5dU4LEDVZ3K4HSsQBU61NRQacWEdszCSpxstY4OtARlimjgZjKmwWbNByEsEhJGAwDq61cJoT/p6RdLFCwAhEEdwKltACAt9aKwFTqG0SEYDMLpLBekuQDgcOyB0xn6UnrCFDocoWMwJAl+AjRLlY9X6GRmTsWA/q8LZIzdVsTXvUuy0shdHxPi4gbDZisUPnO7q+EPtPPtVCF03IdFWT3CBxyCFwB/bkomkAxjFFLG0pAzJbJGvu/ReugwjBH1SMJ+UgAdAXpaTXAHgnj1UB3+XV6DoWt3CiuAdCXRLFMjULJgW2u7oNCJlQ2Gu1iMgpqErhiKswSdEW/HXV3SkWHUI0EvVfecrhCrb9SuA0tIVOnb+4m8Z9QmPWJCR57OEgDu78bdH6q8uTYzKSy8gQ5YXqms69CLgiJGx0acUJ+oYAnB0qJc3JKb2nFhDSctzCJCOcukPMEo4NUde9tdwsQ7Tubl0kVkLC72TJmQxIVLPbL/cJgnwMuHavHa4Xp4gkHcvaciTMUzZPDHGDVylaIPjV4fC50uDkBoEUfeR3N9MYNAwCP0teJxQWHhQ+jZ419gGCOCQT/a2w+EEToxsdwCn9GQirg4zlujuYVTyjLEAEII0tIuhJ0nneTh0GoeOhQecP1EjskAAyGocnvxE08W04k3VcKokTAUatnoAAiZrigivbdo1tRMFd+vrrK6lAgdlhDBLBlQfucW2bjnaofDJUltDgB385kJPzjSiEb+nZyf/w/4DFmSOqpEhA4hRLj+aqbIhDBCWByg7rUjVugYVAgdq6Wb5H/5mDN0zNC5e3hFlj8Y7pEhbbPURFVeFyMKCKkThbhUuT1ol6dUJlxYr03HoMBqlGT7UfIAAYBp06bB43Lh6rPPxMNz7sBVN83E5ddfj5SUFCxfvhzvvfceioqK8Pjjj+Ohx5+Q7JudlYUFCxbg3nvvRWpqKmbPni1si9Ox6GE1gSUEy5Ytw7Rp03DnnXeisLAQkydPxoYNG5CdnSO0Ww0G3m/zkuJiFBYUYvDgwUhOThZUJE888QQGDx6MqWeNweHyg1j20cdg/qDSdPz48fj888/xzTffYMiQIRg+fDieeeYZ5OZypHK/fv2waNEi/Otf/0Lv3r2xYsUKPPaYesQBwGWatHSwmMcSAgbKymQK8cIUC4Ibb7wRl1xyCa688koMGzYM9fX1uGbGDEmd06dPR2Ehd81SUlKwY92vwnZKKmRlZWLlypXYtGEDrhg1HI/ccSsuunoabryby8pFiR/xLSKECOSBNyj1ZzLLvHqAECFCj6kjRPGdE5TVJQZVKHkjPMu0bnpM1SxXMlNkpWOyIvWNRyQ/kreL3hZ/UPo9JAplAI6YosQWfS7cfuW59ksVtdjfHn3q8k7PMs4//3ycf/75qtsXLVqE6dOnC87hL774Ir744gu8+uqruPfeewFwJM3kyZNx7733YuTIkap1AcBjjz0mcfLuDEp+WY1gIIDUrvlIzORVQJU8g3kKhltR5FtMWM0PFLpbOMPWZIMeZyXoYCAEnmAQh1weIQMNzQwDAJ/WNGEf/wB1sxhxRVqog/MHg/i8tgnDY22qK+1HC0pAUCgROvKOWb5aYjBwE5NAwA2fryVs4EaRmHgmjhz5FPX1PwqeMiZjBlKSz0dV9QcwGlLh9hxBq0ih4+FXCM3mXHTtcgd27Q6lIgwEPAgEnGjmU2DqdLHw+ZrR1rZXovjxemSEjpsSOsmCrJsLWXIJpEhqygWIiekj7GOzc4ROa+sOwdxRaaXKaEyDz9ciSTceptDhBz0u12EhSxRDlAmd9vb9osxUyhNwnc4KrzfEzit66DDSAblqyJVB7KGjPmDOSL8C5ZWrAHADxdtyU3HTzoN4t7oBbf4Amnx+fHSkEbfnpkoUOmL0EQgdp/B8xyt0sHd3ScfHvDoHCK1Yi0EIQU+rGWt4H4PT0T+HwswyeL1PF6xubFU0jqZY3qcLZu8sx/QIWZT6iwidJJVVL/Hq0WF3uEIrTq/D//bKw7y9lZicEocbFY43JCY04E426JCk1wnmnxo0nKz4bnABllfW4z4VNRYNwyl1ugXvL7niootIoSNW8dzdJQ2f1DRiq8OJuSUVuCsvDRkmA5z+AB7lzeZNDEGZ04N/V9TgjrzQgginRlAfU5hMGXA4moT/5WoLhtHDaEyB210tLLqIJ81mczbM5mwcrnoXzc2b4HCUiPpN7nudnHQeyspeQGraJKGvdLmq+PrD+0POWyVeZOyvTkjrdDHw+rjzi9OxyDTpsbYpZJg/JMaKHxtbBUXn0YTn5piM0JFQppdIdS3Mz8SPja04L0n53dZF1rclK5gnA0CqUYdqXg2rROhQhU6Fy4NDfGgFnbwOiLEIHkz/rW/BpfyYM0Qosmjy+QXvDgqDIRktrhrMaxqLiyrrMC0zPATNaEqH08URfGpKntiYkFJfLVmH1Zov+T8ahY47GLru3kAQrEgmYDDEg2WN8PraAXfo81afX7guDGPiCUhG4HzqPD4whJvQ17h9qHH70C/GIpng2nWsUAcL7vkKBIFAyM5FAr1ej2eXLMHsJxYJn7H8DHTKlCmYMmWK8LnD58dmkQKPATBv3jzMmzdPUueqVavCjrFgwYKwuVyZ041mrx9XXDMNc2dMl2ybPHkyZ87Of0cSkpLx9TdfSybnY8eOxfTp3H6NXh9qPT6JyX8kqC1YU98gNdxxxx244447JJ9dc801wt/FxcUoLi5WPBeK5cuXS7b//OOPEo8oACgrK5OU0REiXHuGADqjEcuWLcOyZcuEModdHtw4j7vGLCFITk7GN998I2w/0O5Cqy+AOrcXDr8fTV4/WBCceeaZ2LB+Pfa3u+DgF/cDfIhWAMArX3yFAqv0XfDvd96D0x+ANxAUyAuGEFgYBs2QqtK8AnESCoFKNLAIIog63rQb4MOkVEyR6aJbIBhScSsqdBgC+GmoFPeZPJRN8NDh/1c7pp6fL1MFjVjBI9TF+7HS8DOA+x4m6FlUub2wsAxSDDpUur1chi6ECCQry6LR64c7GIBfFnZV4/ZikYpthBqOaQyAx+PBpk2bMG7cuNABGAbjxo3D2rWcMXEwGERxcTHOPvtsyZdADffddx+am5uFn4qKig73odj18yoAXLgVf3DgAPcZsgYr7XJKIF+00p0vMr5jCRFedmXO0IRb7KmzT8QG/nPPIUnIykP7DuPGHQdx755Df0q7gZApMoWiQkdG6MhXS1jWKFrROwKfn0qwZYROwmgABA7HLrS17eWPH4tu3e9Gdvb16NePS2HqdJbDy4dk0XApgyER6ekXS+L/Ac4ThvrecCs/DHy+ZknKVI/Mc4eaIhuMSdDp4oTBqst1SFDoyEksukrodlejnYan6cIHZUL4mbtaYooshkkwlFT30BHXRY+nRrDIB9tKSp5whY4KoaOTKnQavT5F5/ju3e9FZpe7AXDKm/OSYmFlGVS6vULGqvXNbfCK5I5mWW/QhycLdre5hOc+VsEktqvFiCvSQu1KVDGS7WkLnePprNABgPOSYvFwflZY+JMYuWYjPhuUL1H0yDEgRkzoqF9Tupqj5tkzJNaKrwYX4KacFEXPkdEJdvw4tAe2jeqFbaN644ehPQQCXIOGkxW97RY81SNb1Wsj3aiHjWXgD4bCf+Jk7zf6PSh3eiQKnWSDXvB3eauqARM27YE/GESl2wNXIAgry+ApPj31iqr6TimAad9DoVNQSNC+WOjLFcrQkC6pspUbF1gsuThzzO/I736f0EdSpbBqSLA4fXYEQicx8Ux4+ZArE8tgVJx0LDIiTtrWhKMwJ9czRELERAoVzbea8PesZFXvLPlihVpoljikRCnkKk6vE7y6qHF2jMgg9zyeLP+mPqTAp6FKdAxbFUboJGITBmONKw137zmk6P8oVuiohVyJx5hqWaqsNjGhQ8LGUEr7u4MitY7Cs86yFrAyRVCD1ycyq2VgteYjyAeIUN+SGrdPWJQCeFUD/7e8exUrEUIqhPD+Tq4sVZsYisNIlCa3nQVVU0RStoqVhZG8duL1OhRYTUJmqZMNHV1LnWi72nWQhmUpbeeujScYEJ4zcb1dzUYh3FxI162ihgkpYULECEO4902yQYdsk0FYXJMbC9NmJhn06GEzC2SNL4JChxX541ACSMlziLZLoqqRFZOnLQ+qHFNeV6Tvjlyhk2TQIdtsQFeLEbF6neDLGBApjCwsd06BIFDr8WFVQ+j9t6GlDe3+QJjiMhKO6ZNfV1cHv9+P1FRp55uamorqak4qu2bNGrzzzjv4+OOP0b9/f/Tv3x/btm1Tqg4AYDQaERMTgzfeeAPDhw/HOeecE1VbmmuqUbW3BIQw6DGKj5/e8xVwZDugtwDdx0Wu4CSGOORKTOgAoRWXA3z4yWGXB+ubw02SAaDVH8AanuzZ5XDifw9xSpIv65oVzQ2PBeSkhNzYDghfHVFaLRHCrjw1qgodgyFRMBemg0C9LhZGQxIK8v8Ju70XzGZOEtrauh2BgEdY1TMYEkEIi8GDP0Sf3i8IAzuvt1EIBTPoE2Cx5IW1TazQCQaDQsiV0ZAMQoig0nE6KwRj4TBVks4utK2pcR3/mRKhk8bXVQ76upErdIzikCsVDx1xXdTUWSltOdcO6eBUaeVS7jfQkUKnGTGYd6Qrin7ejiu37A+bDLCsCeY4LoTOwjKwsIwQAkCxqaVNSJXKlZMO3rKMethZBt5gEDsdnKJKabURAB7Nz8KFKXF4qHuGakdcZBX5vaisbGroHHJMBnQxG2BhGaGDVMLXgwswNt6O/+2V94ePVWg1KZqAatBwqoIQgu78mGFjCz/xlr0DqaFyqz+AVv59Sstck5GEt/p2hZkhqPH4cKDdjcO8IiPTaMDE5DiYGIJDLi92tbkQLeR+NUphNnSs0OYIV+hQhAid3UIiBPlCD/eZvA9TftfQBAxAZA+dwoIFiEmcwLWTIZiSnoChsVbkW4wozkzC2ARp3x2JhIkG4knA0ah9zCwjIcXVSHSxybZan5nFv6/3tXP3XRxeMp7vq39oaIEnEEAwGEQLH05ElQHVMj88u72XQJIBXCp0OfT6OOFvtZArACjq+QQSEkYjM/Mqxe1WS3fRf0FVJQ8ldAJg4AuGysjTFFOIvTv0DIE/CNR4vEIoFSFECMhKM+oVk/J6I5A1YkInKKgQwusQm8nS9ihBL9pwLBwZEvQsrDomYnZFA8Ogm8UYphCJhBUrVkhSpIt/evXqdfQNPw6I0bEwMERxkZFCfH+USDIhbCkQFBR80vtOJKFN4hCoMGUX/7/4+WPB+bhlmAxIEPkXcqbJysbCAKAT6lJXywChsDNqIqzsoQNJGUIUQq4Q8scJSAhRZdLKE8XxxKQp4etKEGUuo9c5gND3niUEBVaT8C5883BooYMuXKd3Ygz6ly8bn3HGGQhEcHRWw6xZszBr1iy0tLQgNja2w/Klv3OhVZk9imCNiwcCAeCHR7iNQ2cA1s7neD9ZUCBS6HSXvQS7WIxAPVDa7saW1nZcvnkfnIEgck0GVLo9wpecgkqCF5VJpV9bW9sxKFY5jvhoIPd3SU4Olz6GKXIUQ41S0da2Bx73EVVCB+AGge3t+4X/5Qohq7UATmc52tr2wWLl4qgJ0QnkidGQhJSU8ThQ+gw8nlp4vU3wibx97PZeaG8/IKlTrNDx+9uEcCwaE242ZcHh2MVl9RAydoQPRszmXDid5XwqdZXz40mYdpGnj6qHjuswzHxGEKUBrDy+XF2h0zGhI99XbcBs4FfPvsFEfN3Kfe/XN7fh99Z2DIyRHkfwxuFXdC5KicMHRzg5PAGXqnozn/lNRxCmFiGEINNkwO42lyARVfMqsOlYvNQBWdBDpNA52gG6Bg6EEKwcVABXIBAx3rynzYy3+3dT3a5BgwZl5FuN2NzaLoTGyCfodpaBiSGS9LZi0uesxBj0spmxsaUd2x1OQU2QadLDwjIYE2/HN/Ut+KauWch+1BHCMkopKCSoQsfl5rLOKC302AVCpwQBPrxY3udz9UdH6BiMIoVOBI83vT4Wscn/AzRUwMgwyDQZ8OnAkOrDHwwKoUXA0WdE5DJdtfB1/XG1D8AtAtLQVTWCW6zQkfvOUdBJezkfVhajCxEeA2IsSNLrUOf1YW1TGwbHWgQlLR3PykOuuuTNQmJgL1DOjZ9eO1yHGdnJkkmZeJErUkhcevqlSE+/VHV7pHsrBiV0PLLwQbUFUGEiTAiSDTocdnmFUKruFiPMLCOZBOsIgTcYlKgPXAFxmIq0fsHfA8o+IeLQKB0hIc8RFZvio1XkyGFhWXS3dPx82iL09Uq48MILMWzYMMVt8vToJwtYQtCjA59Bqc+O+nZPIKio0BGX8QaDkjTcYSSMSFVDyQo1w2Cx8oY7F1m7CIEL0mxZSpSpnjBwwS/0PUqkDz2moOJRKCNPNx5SIcmOJ7peam0S1D6iq6XULnr9/DJiS8cQpBj0OAAuOmBTSzsGx1qFftMYySk77BjHEElJSWBZFkeOSCf/R44cQVpamspe0WHp0qUoKirCkCHRed+UbeXM7PL6cdmAULkRqN4GGGzAqNuOqi0nOpL0OiF9cm/ZgKmrKP59aXkNWnwB9LWZsaJfV+SaQuTP2QkcOUDT+h5wcp0mdeWm3iDHGvIOIy52UFiZcIVOOLFEV86czkMIBDg1kjLhIfUS0OukZCE1w2trPwAvH26l1yeErdBQ2S6n0OFTq7IWdOs6ByYjR5hQRY3H0yCwsFSdw7I2YZBq4hU6DkdJ6BwV5OOUYHG5DqmenxAm1V4KACDEAIaRDrho+9zuGvj4thOFAax4tQtQNymWroQRxcGwXCXUUdrydkjP/x2F1Tgn/9K18LLSsQl2jIizYlxiDEbxRrk/8n5RckNkCnmIjtpqYzQoFHW+x3YYdHojXq9DuoqJpwYNGo4OBTJVr/wdSD35KEwMCQtzoJ4p2x1OVPITeKrQOI9XY3xdF32CC3HIFctaFRUStB8Tyin0mVZrAQAuA6aXX1hRUujI91Xrn+g4oxy5uPHIKKwR+RHK4QrQtN7hbWcJwaj4UL/ZkSlyRzhWCh0gZIxs5ZWvSkiNQqFDF0do6JSYkGcIwcRk7rl483A9WnkfD5aEVOVyQocQFm0kNF4rdXqwU5biV0zW7W5z4Z97DuFAJ0xGJXUpKKDloISOF9LnRS1zo3gimaDXScKLHP6AdEJNIAo5CS2Ku/yBiGEqAK8eEOpRHo2IJ76RTIpPBt9/u90uSZEu/qEGxycjCCERSTWD6L0YSaHjERkGy8tRlU8wGDlsSZx1ipKv4QoX7rfYGFwpVI+SMN4OnlNxOnBAmcAID5MKByFEOJ+AWOEWRkhxv0OePerkkD+oHrolrlv8KqCf6RkivFuXV3IWHDT7l1J/oYZjSugYDAYMGjQI33//vfBZIBDA999/jxEjRhxV3bNmzcLOnTuxYcOGDsv6fT5U7NgCAMjtO4D7cM/X3O/88wCLspP9qQJCCN7o2wWv9+kS5vtACZ2dDie+4+OVHy/MQneLSRgExOlYwauCZoKp4Ymdi/lUmWsa1QkdfzCoKjHtDGLsfRUHbuHhR+ohV23t+yKWk4d06eSEDq/KaW/bJ/HPkYOSHV5vk0hVY4XZnIMRI75DUdHT6NuHS28YDHoEs2PBP0dUJ8101erYCYBbHVQaUOr5faiRsRKhQ+t1uSr5NoWviHLhYwYAAbicFcIxw88xXvK/mmeAeHWTy3ql8BJk5abIalmuuO8qXfGixMzHNU1hmVKoTJkOigwMg48G5OPNvl0xlPcooClz1QammbIwHnna8s7Ayh7dyqgGDRo0/NWQh2krTdBTRIpDJc+U3rzB/PZWJyqFkCvuHX4u75fye2u7oJgEgD38hPubuuawrEByQkcJJllqaaVyOp1N8Nej0CsqdKIz7acKnf9gGnZ6YnDp5v2S7d5AyCiTriqbVFZcz4gP9d9Hq+ik99Akmij8UVBCRc0/B4CQ/hxQfh6AUF9K76z8ubqWNzX+sq4pFJbFssIiyzaHE9dvK8WYdbvxKh/+3yjzzZGH8aWmTOQ+t0zBWRtK8EplHV6skGbcjRbyxT9lSBU6gtpBRaETSqtMhPALGr7mCgQEwgfgFoXEfh0U4nLhoSUQytMyanSAxKMlwhKUXNGh4cQBS0KEmyKhI3oe6TMkfx0xhAjqm0hhS3oxCUPJIdnxxM+KRyijEEolUfKoEzF6WRsiKXQo1PyZhO8SIhgxE+n3V4noZEXKG3EIpRxhbZVdU7oQTUlpZ4QFADV0+k3vcDiwefNmbN68GQBQWlqKzZs3o7ycc5KfM2cOXn75Zbz22mvYtWsXbr75ZrS1tQlZr/4KVO0rgcfphMkeg9QuvOx+L+/0nX/eX9aO44k+douwEiZG/xgLYnUsKt1etPsDyDTqMYA3g6WETl+7WViBq/NwJm1UqXNxKjepX9/cpkraXPL7Pgz/dZckbV1nkJY2GQxjQo8ejyhuZ2UrakoDNyrRbm/bL5RRMrwzygaB8sGdxcrFTre1HwgROnoFQocfJMoVOgA3GExPmwybrVAId/J46lF28H/x++9TuTpF5opmQaGzm69HOfZbHgKltIJEP6PeP0qEDiEM9HruWaEEkyKBFK1CR9ReNZmz/HO1ATMlqWis/NmJMcg06tHs82O1bDVUUOgoDGDpALeU944yqwxyxQodHeFWJY8GC7tnopfNhOsVMnBo0KBBw4mGYXFWCZGtTOhEVmT0tnF933aHE4d4hU4GT5anGvW4hB9HzC2pEAbLL1bU4JXKOkzbVorbdpVL6hOHXCmpVeVlAGXlLgCYTOJyRJn4CcuiFdlDx4IQMXWEV5I0en0YtW4XLtnMLSrRBQi1AfpokUJHKbtiZ9DXbsaIOCuuyUg86jAZqvKWZ7wSQ6zQUVsEkauO5CGzvWxmDI21whcEXiivEcqI615Z14w97S4sPliDYDCIRq80m85uGaFjNKZizOjf8B6ZKnwWSaHjDwZx5+5yPLC3MsynLyPjSgDAdsNEvFPVoGjqHfLQ4X7TsYiH9yOp9/jQ5gu1WWnySscmTn9AmgaZEEUyhQu5Qlg9gNxDJ1SXEnSizyMpdGxHOSbS8OeBECKQHkq8sY6QMLJBifCgdbijUML4IphyE56o5OqK4EMjCvNSU8uIj0kRqV0UahFLYmWNmsItjEDqyBQ5Qshi2HdTpZ5Dbq6/pB46pk583zq9DLBx40acddZZwv9z5swBAFx77bVYvnw5rrzyStTW1uKBBx5AdXU1+vfvj6+++irMKLmzWLp0KZYuXQq/399h2YN8uFVun/4gDAO0VAHVWwGQU9oMORrY+ZTOD+3n4swnJscKHf5laQn4qq4ZxZlJwgNe5/Wh3uNDANwDOTLOhkS9DvVeH35udODb+hZMSo7DSH4w0ub3Yx2fxWCXw/mHfHaKej6BgvwHFVfOAG5wRYhOpExRV+i0O0v5MsopK8UybZomUgwrb5rs8dQIPjTKCh0actUEv09K6IhhMCTC5ToEr7cB+/c/EWqviNAx8QqdSOen1A5lQkdK6ikROuLPfb4mAGoKnTjpPmoeOqL2qsWeR6vQoQotH7/iZWYIzkmMweuH67GqoRW/t7Qjz2zElekJAoGoROjQSYdLFpYlR4YolCdWpzvqwfD07OSIabg1aNCg4URCvF6HRT2ycf32MgDKmf7Eag0lL6seVhNYwil8f+OzZWWKJuYLumfgv/Ut2O5w4qOaRlyRloBaT0ht8bNMAWwSKXQIUR62hvnsqPSbRmOaaLFEOXwraoUO32/bEQofu2LLfgyPtcIXDKLc5UG5ywOnPyBMaEwqs+VuZiMmJMWgyesXwtP+KIy8OvVYYHS8DS/1ypNkGJQjrYMsV0C4H51SuSvSErC+uU0IjY7RMUjkM2RVe7y4Kj0RbxyuR7XHiwNOt6DQGRZrxbrmNuyShVwBnH9RvTeUHfeIzFxZjK/rmrGCD+c+PzkWI+JCz1BW1jVg9Ym4oSQLrt3l+LquGS/2kobviE2RAY68a4Yf3kAQDV4/Drk8IAToyy+iKk1e6fPhDgaF7EB0gqhE6LgDAUnaaDGinWzK6440hcwwGkDgOeqwQA1/DowMA0/AH0ZGACHTY3cwpDhRDG0iBE6ISRgF0kfkoRPJe0nHAH5/KARKiUBSzkylfkwKJbJGXkbteWdE6cY7MkUOtSm8nhAxFBTOTakc4ck0ISxLhXhq8QXQ4vOHQnQ7MQfp9Ddy7NixHaabnD17NmbPnt3ZqiOiM6bIZnsMEjKzkUfDrfZ9y/3OHAjYtMnV9ZlJeK2yDgddHiGECuBWSNYOLwIA/Mp75NR5fKjhO8BE3rV8ZJwNn9U24fbd5ajx+LCpuQ3fDCkEAMFIEeBSoP8RQocQVpXM4bYTsKxNIB+UBm4GfgBIszapEjoihY7cP4fuZzSkwu05gqam9Vy5KEOulFYIDfoEuFyH4HJXSz4X24/JpeNKMf7cMeUKHYVsHTKSh2WUB2V0lTIY5AhThigROtLjqalvpAod5YFptGnLAS5DCHMwFvBwA6TR8Xa8frgebx6uhycYhJ4QnJ8cKzDaSv448lVkNYVOpqnjlUYNGjRoOJUxMTkO87tlYEebE/3s4X1GagcKHTPLoLvFhJI2l0C0i0mKZIMe0zISsbi8BmsaHbgiLUESPlPt8cIdCAjePOIwKdq/yqHXx4NhDKE+X0WhIyZ+5Fk1KcI9dFQUOnzIlVdkglvS5kKJTClS7faGQq5UFhMIIVjep6vituMJQgguTImLWCbRoAMDLnuLuoeO9PMYhZDkXP4ZoeEgdpYFQwi+GVwIbzCINKMe+9pdWNvUhjWNDjT6uGdmRJwN65rbwq47hUO0EHzY7UUwGFRcrKHKIPq3mNAhhIE1cQJcge0AOLXQu9WNmBhrgo9X4HCTNhYBPsOVkQlN4g7zK+/BIBfCoWeIYKQqntzpCZdxyh8MrdLTCaLSBFYczRWetpwvA1E4iOIVCqkk5O1RKpcdQa2l4fgi06hHq45RTRphYIigvCEqTwMlRVxRKHTo8wyoq2rcCEZU++gFQicU1aFcTvq/sqcNgVF0juoKHVGolEo4Yjg51FE9RLUcwIVn+aB8HRhCuHdiIIBKl0eIODhuHjonCgaefyGuW/Rv9BrLq3EE/5zwjEmnI0wsg08G5uPzgfkYqEK40PSUdV6v4J9DB3HUvI9+vqPNKQzaqLwaCKWn/DMgVq0opi0XpRNVKwMARlFctE6vTBRarNwgixI6iiFXVKHjawxluVJQ6OgNHCnS1iaNtU9KDKnedDq7JMxKydwRAAyGjkOu5MSYWphUuElx+ACWI4xEpmtqdenEHjrRKXRYxog2vx/PHzyClbVNktjxrKyrYbByRKOR4cwjCUIxud5gEN/Vt0RW6MgGk+qmyKHzjjvK7CAaNGjQcLLippwULOmZq7iimmLs2DPlDPFkGNLU1gAwlN++gVf0Nvmk6mvxWEI8+fbxClg5CCESskbVa0fc56ss9ISnLVdT6HDjDI/IBNfGMmFhKVVur7DiKjeQPhXAEiIQduJnQ4x4nTzkKvw6pMqeEfpsJRp0wvMzkn9ufmlyCCFXlHipdHsxfmMJnikLLZh5AgFJRrY2fwDNvnCl/6bmNmxsaYeBcNPcb+tbsEdGELXKbAR+bXLgph0HUeX2Yn+7G+1+P0ymTAT58RNLiKAGFhMv9FkIpXsOgRAihFnQMY3gi9JBegW1kKugyJRWLZxKYooc8SgaTmQYWQZJBr0qKSdWyulVbnQo5EpdoSNOdU8VP5HCtzxB9bp0AinC/a9knCwuJ7RBufmSBVs1Dx3qJ+QJqIeMhSt0lAgk7rcfoZTrasp+cf1KddF33CGXRwjRVVt8Vqw/6pLHGZ3NcgXwF9XnAQ6s4j7IP/fPadxJiDSjHoMjqGeS+C99iy8gDKyozHpUnJQc8Qe5NOaAdBC2/w9mE4gGOklYj4JCx5AEMd+qNnBjWZOgPFFS6ACA1dJdVndHCp0IIVc8GdTGpxrX6xMwfNjXSE+/RFJOrNJRI6P0ennIlUK2DtYKKQmjotBhOpaYc147caIyHSt01IyT5ftWek04f+NePHygCtdvL8OFv+2VeDC5RYPhBL0OfexSAuqL2ia0B6JX6KgZRaZHka1DgwYNGk5npEQRYnNNZqh/CiKcyBjMh/AccLpR5/GhQWZwW8GPJWp5tQ4FTSigBPECjdpCiFHkocOqjgtC+76NqzGzPEXSBgrq5+PhPd7uzEvFphFFeL1PV/QT9VHVHq8oy9XRhfGeqHiuZw4eK8hCoUW5zw9T6Cg8N6mGyD47ADAqjrtna5ocgqorz2wQ+u4trU48XVaNOn7B0SEaR9A+/bA7POxqPU8sjkuMwTm8cffK2iZJmVYZEfRNfTMO8L58Pt7TR6+PRZAPfmAB5JoNsMnIqxCho2zGSscwbVShoxJyZZArCJQmvPxH3giZeuR1n6KPqAZwhE5XixEWllHNgEeVKUL2KpW6hLCrKJQ8kRQz0ShhhLo6IEUA6RxA7Vk2ikIb1cIRxQbRau2S+FR1cL2khE749lSeDD/k9kbMiqiGk4bQ6UyWKwnKfwE8DsCaAqT3/1PadioiVscKJmk0cwAdxHW3GCVZLgBgU0s4obPvTyR0xAMuJZNEhtHzpA5fXmXgBoQyXakpdGz2npL/I3voNEqyXMlB29Tetk/Yz2rtHhbHL86+peYFEI1ChxBG8nlHHjoU6lmn4jquS6zQUVHxEMJISJ3HqqzY0+5CskEHK8tgY0s7Pj7SKGynK2z0JXx2AndOPfjU4P+tbxUmBJE8dCjUWG8zyyCBH3jKY/41aNCgQYPUQ0eN0OlhVe4fKOL0OiFF+oZmB5p4tUVf3oS33OnB5pZ2DF27E3/n/XyAkLecEiTqmygUOqohV6KFj8/IxVjjMOCTmibV49KQq1yzEbF6HUbG2/D14EJcxps/H3Z5RFmuTpphd6cwIs6G6zKTVFen5Z4rSs9NjI6FWTTTUSJ0BsVaYGQIaj0+ISwhXq+TEGW+IPDBEc4Lh5IwZoZBDq8iOuTyYEtrOy7+fS9+4jNftvDlUox6nJ3AjRd/knk5OWRh3S0+KclHCRi/iDzRMwy6mo0ospkE9RJ9FtSUAUaRekC8XTwZJqTjiTAhRFD/eCNMugGZKXIHSqCOMHbsWNx+++1HVcefDUIIPv744+PahmN1neT15OXl4dlnn1Utb9exyLeakGTQK24PzyalXE80ChZxaBagbhhMJGSH8gHlxuBRKXRUnmWjSIUUyTBcLyE6FRQ6EKngVLJlhdpLFP+moFEwh1yeUJYrtZgxxfpPdezl/XPyzwVOQanrnwVCiMDeUqM5SuIQQnARH1NNMzP81sKtbogJnTKnW2BujzWkCh21WPlQ2JWaQgcIDfDUBndJSedATAtH66GjqNDhY+7b2kv5/eLDynBtF8vClQkdlrXx6cZpG5TbL/68Iw8dimgIHdXMVKw47tyIp0qrsfjgEYVjcgP3OiThpxbuu/lh/+6Yk8etoL52uE4o65bJ1W/JScHD+Zn4cEB3ZBr1cAYC+LlRPSW5gWEkrH2kVK6ZfNiVptDRoEGDhnB0lOWK4rU+XUAA3KhiDD+UVwn/t6FVmNxSw9gKlwc3bC+FMxDEt/UtiInpDwCos1+IDc1tYanNAZn6RjXMWpwxS02hE95P7lQw3AWAIUM+gc7CeQjKV1OphL7a4+0wy9WpjmgUOoQQqfpLoZ82MoyQtRLgSA47y+DaDG6xrC+vjHqbz0RFSRi7jhE88kraXBi/cQ/WNrXhaT48q5X32YlhOZ8+ANjY0ib42AAhcqi7xSgxgqbZMJ2BgMRklc7FCE/s0HvvkhM/ssldeGhJuEJHJ5vcqoWpyFOdR1Q/iOqKBqtWrQIhBE1NTZLPP/zwQyxcuDC6SqJEcXExJk+efEzrPN6QX6eOiJhosWHDBsyYMeMP7x9GFKo8EHLiRy2zlrSu8DLUrFkoE6ltHRAsQHQqSAP/XXSLUrgrkSz6DtolPmev4E2kDHGzlK4VfadUujxw+aWL2NHgpOlZogm52rthLbwumW/Lvu+531q4VadBfXR2tlFCJ9SBPdAtE7+NKMKd/OR7Y3MbgsGgxBTZEwwKsmkxfIFgmHS1s6CDNS4zlTLLbDSEMmNEJHTMXFYpVXLFkIS4uNBzp+yhEweASw8eShGurtAJBj2S/cLaZOw45IoQIlLpKKdfBaTKnWg9dIgqoRO6Rqp1iQioX3098FRZNR49UIUmmaSeZsn6EWcjAIJRcTbkW024Mi0BBkKwpdWJzbzyyy1T6Fh1LP6elYwEvQ65vEFfA7/Cq6a+EXviqHnoAEAGP+jTCB0NGjRoCEc0Ch0AGJ8Ui99GFuH+rhmK2wfHcsTJf+u5PtPMMOhu4d7nH9U0olIUGtOz1wuwZN+DOY5rMem3vRjx666w8UWn05urrgQzYFkLxJSRmuI4xt4bAR3Xr8snEjQMqMrtFfqwUzXkqiMYGEYgPgDO8FgJYq8lNWPXAmto7BHHZ6OckZ2M9cN74r1+3WBiCHa1ubC7zSWMNe0sK3jkPXKgStifKnPob7uOFQgbdyAoeDwBIdLHpmPQLyY0ZrKyDDdxDXIqHco1yv07BEInEEAwQrpn+X4hhQ4RlYkuM5W8LjUFlVIGrT+KhIQE2O3qY24NHP6s65ScnAyLRT0rXUcIT9etDDXiUQx5WKCa4MQoCZNSfxb1su+AYhlRXT4F4l/crmAwZMasqNCRmIWHbyeECJ93RJqyiPx9TRV76ESwkFDDSUPodBRyVXuwFJ8+9QheuW063O38C9jTBtRy6SmRM+IvaumpgyQ993BRWal4EKdnCDJMBvS1W8AAOOLxoc7rwyHeyZ9+6ZSMkadtO4Aha3cqkj3RgqpW1EgMADBEqdDJypyK9PTLkJk5RbVMQsIZoXploU5c/XGAIL2j6caV0pYnSf5XI5HEHjpqIVdAiFzS6WyK6VcBqTeQqoeOgkmxcl1xojLKknpKQPnA4lVn6HtXJyN0GJ5E+gljAQDXZHDnkmTQ4fxkrs1f1zUDCFfoiCFf+VNLSS6eeERS6IxNiIGeEAyL63yGNg0aNGg41WFkGCELYCRCBwDSjYawFV8KqrSgxE2CnkW2mYbFSH1OHEwCAklTQJeCyl0e3F1SIcm6ahD1p+oLHKHP1TJm0f39Irva/RGSPLhV/A7EhM4f8UQ41UCfGQvLSLwpxBAbI6s9W2KfHhoizRCCHD7kbWAMd483t7YLRI1NxyDDGL74R42OW/lxboyOBSEEoxO4ccy/K2qE7FkOvoydZYXsbwZeXUAXklp9fn6W6Ac8fgREP3pfAPD6EXD74XH7EHD7AI8fjCcgKcd6/cL+3Hbuc8YbED5jvQEwonKMN6CYgVg+6RU/fWPHjhWyEsfHxWFslxwsffgh+Hny8Y033sDgwYNht9uRlpaGq666CjU1XCawsrIynHXWWQCA+Ph4EEJQXFws1CsOAXK73Zg7dy4yMzNhtVoxbNgwrFq1Sti+fPlyxMXF4euvv0bPnj1hs9kwYcIEVFVxxNv8+fPx2muv4ZNPPuEziRGsWrUKHo8Hs2fPRnp6OkwmE3Jzc/HYY4+FXQM11NXV4eKLL4bFYkF+fj4+/fRTyfYff/wRQ4cOhdFoRHp6Ou699174fKFx7Pvvv48+ffrAbDYjMTER48aNQ1sbN/+kiqIFCxYgOTkZMTExuOmmm+DxhOY94us0duxYHDx4EHfccYdwjgBQX1+PKVOmIDMzExaLBX369MFbb70V8bzESp9gMIj58+cjJycHRqMRGRkZuPXWWyPem9U//ih8Pw+Xl+OGyy5BfHw8rFYrevXqhZUrVwJQUvKEtyXM50lFvyIuF+kNGW1oYJJBB0LCQz1DbSXCMaPJwBXpeOEErErIVQdhZfTdV+n2CspAYydCdE8Zk4hf3vsPACCrZ28YLXyHfWQHgCBgSwXsaeo7a1BEkswnR56BAOA65hSDHtUeL0rb3ajmB2ZDYq34pcmBfe1uiLVRwWAQ/+Vjlv91oArPF+X+obZR0kAtHAkAjMboFDoWSxcU9fxXxOOlpU7GgQOLYDJlKZIiDKODXh8Pr7dB+EypnNEglZ4bogm5UlHoAKGsWUr+OcL+UXnoSNuqqtAxiBQ6aqbI/ID5dwzGIX+ITKrz+NBddBjalnpwJNdwkdl2kdWMT9AkmBfKPXTEkKcXVyNrxOUisd7XZSbhqvSEUzIbiQYNGjQcC6Qa9Wjy+cPev51Bpknaz8TpWcHnRI5aj1eYnMfpWDgDAfzQ0IpPapowmfeqof18LZLxRb0HF6Yop6em8PnUDZZZ1gofWoX/S50e+INBxawpaobHQsiV2yuMp05VD51okKDXodLtVUxZTpEahfqrUKTQUfK662M345cmB7a3OjGID+uzs6yQiQsAupmN2O904wifxpxmvqLHHJcYg/eqG/FDQys2bNqD7aN6h9Q+OhZj4u14orQaQ+OsIMQHE0PgAE/oeANIfGozqhAOqu+uBWDg/3fyP0rlKA4rfB6Q/R98aCSIQXrNOppsvvbaa7jhhhuwfv16fPDTGiy87RYU5OVi7syb4fV6sXDhQhQWFqKmpgZz5sxBcXExVq5ciezsbHzwwQe49NJLUVJSgpiYGJjNyuPL2bNnY+fOnXj77beRkZGBjz76CBMmTMC2bduQn58PAGhvb8dTTz2FN954AwzD4Oqrr8bcuXOxYsUKzJ07F7t27UJLSwuWLVsGgFO3LF68GJ9++ineffdd5OTkoKKiAhUVFYptUMKCBQvwxBNP4Mknn8SSJUswdepUHDx4EAkJCaisrMTEiRNRXFyM119/Hbt378b06dNhMpkwf/58VFVVYcqUKXjiiSdw8cUXo7W1FT/99JOEVPv+++9hMpmwatUqlJWV4brrrkNiYiIeeeSRsLZ8+OGH6NevH2bMmIHp06cLn7tcLgwaNAj33HMPYmJi8MUXX+Caa65Bt27dMHTo0A7P8YMPPsAzzzyDt99+G7169UJ1dTW2bNnS4b35dN1GpHbpisfm3gHi82L16tWwWq3YuXMnbDZurB4ecqWg0KFGxoJqTbmdxg6UMKFt0ZXLNHFG6ZHUPkaGCD5V8ropOlLoAJxSySvSc6qbIkeuK41/91W7vcL1MHdCOXdKEDpHDuzDvg1rAUIw4rKrQhuq+Ic2re/xadhJjiRZRyk3QqbIMHGEzsaWdgQBmBmC4XEcobNHtqpV7w2FWv3c6EAgGIz4hVMDVa1EIjskhE6EctHAbM7EiOH/VU3VDXBmyZTQIUQn8bcJlZESOqohVxKFTgQVkqDQUSesdBIPnShNkRXaDsjVPmrhW9y1boSUrKJZJ8Rt8YFFgHADEPFgmA6Ej/CEjtoKKADEytKhqhE64hCqjlIBamSOBg0aNKhjbl4avq1vFtJI/xGkGHTQEyJk4InX6SSEzsg4G5p9PuxwuFDj8QmT7gExFvSxmbG4vAZf1TWj0GrCppZ2XJE8AADwKPMIanaWo80fxFUZ4SHSFHZbT9VtOtYKr2yIfKDdjXxreL+nZnhMFTpHPF6hXzpdQ66AkJpWKWU5Raqh45ArMaGjRPr04Y21tzmcQniWXcdKslg+2D0D07aVwhMMosHrl5A1AHBhchyMvRncvPMgHP4AKlwetNCQK5bB4Fgr/jukEMlBP+oOVXBjk0Botf9EgfzqyK98dnY2nnnmGRBCcGV2Lvbt2IFXnl+CuTNvxvXXXy+U69q1KxYvXowhQ4bA4XDAZrMhIYFbUExJSUFcXJzi8cvLy7Fs2TKUl5cjI4MLvZw7dy6++uorLFu2DI8++igAwOv14sUXX0S3bt0AcETDQw89BACw2Wwwm81wu91IS0uT1J2fn48zzjgDhBDk5nZugbi4uBhTpnDK/EcffRSLFy/G+vXrMWHCBLzwwgvIzs7G888/D0IIevTogcOHD+Oee+7BAw88gKqqKvh8PlxyySXCcfv06SOp32Aw4NVXX4XFYkGvXr3w0EMP4a677sLChQvByMaYCQkJYFlWUENRZGZmYu7cucL/t9xyC77++mu8++67URE65eXlSEtLw7hx46DX65GTkyPsF+nefPzm67hx3nxUH6rAhRdfIpxb165dhbqjSSNO/XE8HWRZM0QZciUxBlc/7Q7rCR0z5JHVoUJHNVxR+r/aYRnJ3+GF4vU6sIQL3arkFaqdUXSeNITO0qVLsXTpUvj94d4rO37kfHJ6jByDxKzs0AZK6KT3+yuaeMoh0SAndJS9amgnSWONM00GIcPFboeU0Kl0h+SG1R4v1jW3YcQfGBBSgiZSOJLREF3IVbSwWCJ3FgZDkpCOnGUtiiuDOl0MCDGIPHQ6VuggGJ4uleJYKXQYmUJHzfCYEe0vV+i0+wNYXlmHiYlcGS+kpJA85IplzUJ2EED64hKk6h5K6KgrdOQhV2rqm2hDrjRo0KBBQ2RMSonDJD45wh8FQwgyjHoc5MOv4/QsYkULSdOzkvD64XrsgEui0InVsRjCKy/2tLlw1oYSAECyoQvOGbMZNT/tBwC8cbhekdAZOuQzHDnyOfLyblZtG6uzwgfpmGeHw4l8qwnBoFT5Q01u5QsBKQa9MECnCSNO55ArqqaJFKYn9tBRMkUGgBxzaGyh5MfYhw+H2u5w4lw+BblNx6C3zYxMox7dLEacmxiDRL0O9V4fqkXPFj0mIQQTkmPRzWLEdocTZU53KOSKb3+RzQyXy4U68Cv5dKimZ9B2zwDkK2R6q3C60eT1I82oR4PXB08giG4WIyyiaxIMBrHD4RS8eFKMOqTy/j8lDic8gSCSDDrYdSxKeW8nq44B0YdfL3lom3xYOnz4cOFZ7mo24oIxo/HG84vh9/uxefNmzJ8/H1u2bEFjYyMC/MJaeXk5ioqKwo6lhG3btsHv96OgoEDyudvtRmJi6LtpsVgEMgcA0tPThfAuNRQXF+Pcc89FYWEhJkyYgAsuuADnnXdeVO0CgL59Q4v9VqsVMTExwjF37dqFESNGSL7no0aNgsPhwKFDh9CvXz+cc8456NOnD8aPH4/zzjsPl112GeLjQ2P6fv36SbxsRowYAYfDgYqKiqjJJ7/fj0cffRTvvvsuKisr4fF44Ha7o/bIufzyy/Hss8+ia9eumDBhAiZOnIhJkyZBp9NFvDfmOO48ptw4E4/OuQ1r//s9xo0bh0svvVS4bmKyQ82UG5AqYdTefkYxcRLhfMRESCT1ZTSQzyc6zHKlUk80XkJc/R378cTqWDR4/cIiR2cUnSdNzxLJQ6fh8CEAQE4fGXGjETpHhTPibMLEd0ScFTaVB4tmBVrbxMmXc0wGIZ30nnYXpm8vw5h1u9Hk9eGwzDfn/w7VYvbOgxj0yw5BjRENqBeNUgpximhDro4VJGnSVbxqOCPjUJvVFDpi9Ys/oB67b9BTQkf9/KLz0IkubblY4SNX6Ny35xAe2n8YN+2qAiEGeOSEjifcQ0dcRvxyTRVJ1f3BoPByU1LOxMk9dI7SFFmDBg0aNPw1EIddUb+DjwZ0x/M9c3B+cpwQqlQrUujE6FhBeSE2Kz7Q7pb0haVOZSNju70I3bvfHbHfZFmLZMEB4LIj3bG7HEN/3YW9baF+2aVieMyKsjaplTmdEA2hE41CRxzaUSsbVwBcFiozQ9DuD2BLK+eTZGdZWHUsNo4owlv9uoEQIvE4ErJcycYTuTx5dNDlEcooGTpLJnWEgDHowBjYsB+dUQcYWPj1DAIGFjCwYI3SsqxRB5YvBwMrqYvurzfqoDeykjJKE1z5WCeS7wghBCz/fLpcLowfPx4xMTFYsWIFNmzYgI8++ggAJD4wHcHhcIBlWWzatAmbN28Wfnbt2oXnnntOKKfXS79rhBBFTyAxBg4ciNLSUixcuBBOpxNXXHEFLrvssqjbpnRMSlp1BJZl8e233+LLL79EUVERlixZgsLCQpSWlkZ9/Gjw5JNP4rnnnsM999yDH374AZs3b8b48eOjvgfZ2dkoKSnBCy+8ALPZjJkzZ2LMmDHwer0R782jTy8CAFxybTE27C7BNddcg23btmHw4MFYsmQJAClZqJYeHJCqb5TCsgBpaJM/wm0/loux8jmtYpYrUbvUmhVNJi+u/hDUrpf83diZBYBTYlbTfIRLOxiXKlI1+DxAzS7u73Qt5OqPYGCsFbvP6I39o/vgw/7dVdlQ2ik28YOtfKsJXcxG6AlBmz+Az2qbsKfdhfeqQ1kr8vlMFl/UNuP9I9zn7x9pDKt7c0u7ZOBEkZx8HvLz70e3rneqtj/atOXHCuJwqkhmzWIfHb0+3GCZIiPjbzAYkpGacoFqmfj4EWBZGxISRquWkWa5Ugu5EreXgBAVIzGexPkMk3Hexn2CZxIAvFPNhZttbm2HTmeFG1KVT72iQocbLJkYInm+6DPV7POjSRSmpzQYjvsDIVeaQkeDBg0ajj+yTKFJFfXjGRFnw2VpfDgHP8Gv9fgE49pYHYtskwFmJiTlB8INOJt8fgQ6mBSqgfPQkU74Dru9eKuqARUuD87ZUIJGrw/BYDBiRpI0mbL5dA7njdfRkKsIhE4UpsgAkMmXUwr5YwlBTz7sam1Tm+SYhBBhUknJo2q3WKEjPSYNASx3ekRhWeH3kCFEEg6i5hVCJ3++YDCUtlyhrLgV4u10gqljiGRyrDaJDCN0ZOXWrVsn+f/XX39Ffn4+du/ejfr6ejz++OMYPXo0evToEaaYMRi4a6MUOUExYMAA+P1+1NTUoHv37pIfcWhRRzAYDIrHiYmJwZVXXomXX34Z77zzDj744AM0NDQo1NA59OzZE2vXrpWQSmvWrIHdbkdWFpcZlxCCUaNGYcGCBfj9999hMBgE0gsAtmzZAqcz5I7066+/wmazITtbFEnSwTmuWbMGF110Ea6++mr069cPXbt2xZ49ezp1LmazGZMmTcLixYuxatUqrF27Ftu2bYt4b7IyQvPp7Owc3HTTTfjwww9x55134uWXXwbAfc/o8xSJpzZE8ZyK1StqmakAwKbjvLBoNsSjgZllJZEoSlNc8XdMLRNcWCY5leNJTZGVy4QROmovEqX6oy55gsLv86G59ggAIC5NROgc2Q4EvIApFoj7Y8a7GvhUk7zrvxoyTNIBS77FBD1D0E32hXvjcL0gOz47IQaTkuMk2z+tkRI6DV4fJmzag9HrdwuSZgqWNSEn+zpYLHmq7dLrE4TQIbXQpmOJaBQ68nJqCh0A6NnjEZwx6hfFrFoUsbH9ceaY35GddY1qmc566DCMQfV+m01cR/Y2uQbbHE7M3nkQlS4PqkShdDaWAcvaBLKGsuC7HE5c9NtejPp1F27aUYYgCa2Ayge5dpYRSJeDrtAqazRZrtTUN53x0NGgQYMGDX8+qMIXUDa4TdZThY5XotBhCBGyZFE0+/xCClqKNY0OSf8ULXSsFR4ZobO7LTQ58wSDeKmiFp5gUFi5VQoJTjVKz+l0NkUeFmeFjgDDY9UXvNKNehBwk5NIhtsfDeiO+7qk4/5u6Yrbe/OEDl1IUlKY04WjMqcbPv4myidUuWZuDHnQFR5yJYd4NV9VicB/7g0GhZAqJWWANCW5yF/QoEeaUY9YHSspozYHlpOc8iOVl5djzpw5KCkpwVtvvYUlS5bgtttuQ05ODgwGA5YsWYIDBw7g008/xcKFCyX75ubmghCCzz//HLW1tXA4wk3GCwoKMHXqVEybNg0ffvghSktLsX79ejz22GP44osvlButgLy8PGzduhUlJSWoq6uD1+vFokWL8NZbb2H37t3Ys2cP3nvvPaSlpan6+XQGM2fOREVFBW655Rbs3r0bn3zyCR588EHMmTMHDMNg3bp1ePTRR7Fx40aUl5fjww8/RG1tLXr2DPlyeTwe3HDDDdi5cydWrlyJBx98ELNnzw7zzxGf4+rVq1FZWYm6ujoAQH5+Pr799lv88ssv2LVrF2688UYcOXIk6vNYvnw5XnnlFWzfvh0HDhzAm2++CbPZjNzc3Ij35vsvuUxWT9x7F1Z9+w1KS0vx22+/4YcffpCcI32eIxE6xiiVPBTeDkj4RIMO1qMw4xcj3aiHiSUwMkSVsOlqMSLLpFedN4j3ixR6JiVglcvEys6rMwsAJ42Hjhpa62oRDASg0xtgixNNfEtXc79zRqo7FGk4JsgwSkNrqPqmh9WE3SJ1zZ52lyBZzTTpMSUjAd/WNyPDaECZ040trU4caHejK7+/OATrlyYHzk5U94lRAiEMevR4FG73EZhMGX/o3DoDcSjVsSB0AKimIu9MGb1YoaNG6DAWURl15js2dhC6dp0D8KrSn5scOGPdLjhFRoAZRgN0JKTQyTIZsLvNhV95jyUA2O90oyA+H3FYAyB8IEwIQbpBj/1ON8qd3GCcJeHx4ED4C1BdoRN63ZlPY9m7Bg0aNJwoEGcekofPAkAyv4Ja4/EJk236zi+wmrDVESJZmrx+tPikhM7lW/aDAXBhShyeLMwWJuPt/gAeP1CFWt6nzaZjcU+XdPzc2IoGrw9nqHjoiLHd4ZSY4CqRNXLvwdM55GpsQgz2ju4bcUHFrmPxeEEWAkDESVuO2Yjb8lJVt8tX8JXUPtSvZw8/TmUAWGVty6MhV06PsE0p5ArgJrcunt5TI3ToGEacXUdpEV5NfWNiGcXnLKASEEIIgY4h8PHHk082p02bBqfTiaFDh4JlWdx2222YMWMGCCFYvnw5/vGPf2Dx4sUYOHAgnnrqKVx44YXCvpmZmViwYAHuvfdeXHfddZg2bRqWL18e1oZly5bh4Ycfxp133onKykokJSVh+PDhuOACdfW5HNOnT8eqVaswePBgOBwO/PDDD7Db7XjiiSewd+9esCyLIUOGYOXKlaqESWeQmZmJlStX4q677kK/fv2QkJCAG264Affffz8AThm0evVqPPvss2hpaUFubi6efvppnH/++UId55xzDvLz8zFmzBi43W5MmTIF8+fPVz3mQw89hBtvvBHdunWD2+1GMBjE/fffjwMHDmD8+PGwWCyYMWMGJk+ejObm5qjOIy4uDo8//jjmzJkDv9+PPn364LPPPhP8i9TuzXkTJwIAAv4A7rrtVhw+dAgxMTGYMGECnnnmGaF+HUPgDgSjDrmK9PpLNOhQ7/FJfLT+bLD8wgCBOhHD9Rnq7yK9WHkT4ViMyt9iyN9TnbGHOGkIHTVT5KYjXGLA2NQ0EPGJH1jF/e565l/UwtMXGbIvX3d+1UyciaCv3YytrU5U8SRNhpEzTv5lWE/YdSyu316KnxodGLluFy5KicP/9spDm0iV83Vdc6cJHQBIT5v8B87oj0FM1OgihFwZjKGQK50u7s9sEn8MzkPnB5yDmTsseKtfGwbKVsjECp096Il8lwcZCuljCSHokjcLSYe2CybHTllWhza/H6zRKvjjUEJHjldaCnEjuFA4pTjRNCNH6JTxPghqTHWcbFVXbbAonixYIqRN1aBBgwYNfw0yRQrfBAWFjjjkivY0MSJCR4xGn08InREjAODjmiacnRiDK/hQrm/qmvHSoVpJuTKnGz81ciqDjzLjwzx0fLI58952l6AeJpCGFlAkyZJLnM6myEB06thrM5M6LNMRqLKGQklVQxU6NBurXUGNnmviFTpOjxAeaFPJ0hWdgSr3W0zoKE0jdVFOEikiiRqMhMCnQvjo9Xo8++yz+Pe//x22bcqUKUIWqNBxpPXMmzcP8+bNk3y2atWqsGMsWLAACxYsUGxDcXExiouLJZ9NnjxZcqzk5GR88803kjJjx46VpPjuDJT8eZqamiT/n3nmmVi/fr3i/j179sRXX33V4XEinbf8Og0fPlySUhzgsl99/PHHEY8hr6esrEz4e/LkyZg8ebLqvmr3JhAMYlurE/c++TS6WYywqanSolDoGKJQrgFcGGWyQfeXh6X+kUzLYrCMmHxVryualOtiQodAWfWpWn/UJY8z1EyRm3j/nNhUUSymzw2U/8r93UUjdP5spBr0woOUoA/FJPbkHf5TDDrc10Uqi6VGiBkmA0foiDrwz2qa4A4EJAOzb+pbOjRIO97obMiVThcDhvnzOVXqobMFA9DsJ/i4pimsDPXQOYRszPPfjYFrd0asU7yiJE9v3+r3IzFhNLyEuwbZMmJoUY9sZJsMqPcb8RPGAlB+aaUJcmiaHUT5xRYvegEaZXHlYsRIQq5O31VSDRo0aDhRIFHoKEwaQqbI0ixXAFAgC7lq8vqFsCyAW928Oj0RV6RxIdcHRSbJR3hlTj+7GXfySg9K5gBAK2Lg49c8c2V92AA+i1K50yN4B8p94CjCFTonzbD7pEaO7J4phVzJxxhK3jiZJm586wwEBJNttZArXSdCrigYlRCNaMIzxIhk5duZSaEGDQD3zNHnOdLjoxMIHfVCLCHobjGiu8UYsRwh5KT0GJOEXEUo11lTZBPDdCqT18l35WSgCh2JIXLFesDnBKwpQEpPlT01HCvoGCIY2Ylj2sclxuDG7GQs6pGDM+LtEilrlsx35/zkOBwYw5lXB8B1sK0iNVaV24vfWtojtsMbCGLKlv245Pd9grz0r0T0hA6n0Oko3KozqPV4w7JIUeh5Dx262rixpS2sDFXoVCJL+IyGvFW6PGiWGRrT1aX+dgte79sFr/fpgvFJ3HFafQHk5s2GLeEsANIBOwD0s1swIo4jkOrAXQtFM0lRfDugrtCxsoywmmWJ0BmIJwuRymnQoEGDhr8GGR1PiiHFAABfm0lEQVR46FBCpNHnF/o4Suj0tZslqhiO0OHKFFlN2DemD57qkS2ohstFWTYbeLP9wTFW3NUlHWclSBMntMMm9Jnxep0ktLdfjAWxOhYBADv5MCw1oiZFpNBhidRnRcOfBzmhE0mhQyE3RAa4cBHqE0kVWpFCrijUbrPcp0PtuWGjUPuIoRZyBYS+V6fLOtaKFStgs9kUf3r16nW8m3fSIMOoR5JBFzHshz6/SupEMaw69pj53pxoUPPekSMahU7sUSw8nzQhV2poFggdkUKn9Efud9czNf+cvwjpRj2q3F5J3LKOIVjQPVP4v7/dgjV8avNEhYGbhWXQz27GllYn9re7BAM6iocPHI6YbWtZZS1+aGgFAOxqc6KPXZ1U+TNg0Ic8dAJBdRPG2Jh+XGaq+FHH5LjuQABj15egyefD1pG9Ja7tQEihQ02Kt7c64Q4EJAQJJaB0CPkWrW1yYEisFWes24UCqwlfDSoQrr2bV0v9b69cQdp8RrwdXVdvRRCcPwENxQoPyTMKIU8OPuRKiayhgy06CFfrMAghiNPpUOf1RZRza6bIGjRo0HBiwcIyGBlnQ4XLEzYJBzjTe5ZwqWyreVUNTS2dYTLg80H52OFw4o7dFWj0SVOb00kxrbfCKSZ0OOKHhnn9o2s61je3CaHe7bAIHjpGhktxTevONOqRbzFiY0s7tlNCR6VPESt0TsbV55MVVh2LJL1OCA23K9wf+WKTWlatPJMRh1yhsZGSkgeQEjpqCh2aZYtmuFLz/JOEXHWQlKTK7UWWMfy7Q2HTsehmMYYZJMtDdU4VXHjhhRg2bJjiNnmq8j8LSl5CJxvi9TrEd3C54vUs9MRwypI10UD8XY+kJWCi+E7LFTqdwUnfuzQppSyn/jlauNVfhjx+Uk9TRSqBxq4D6g8zXUnb3+4W5NXDYq0wMQRrm9rwiSxc6PmDR7C0vAa1Hi+eLK0WPt/WKjUv/CvAMKE3n9+vfnyTKQNjRm9Ajx4PSz7/tKYJjx2o6nSa1SavH/VeH/xB4Omy6rDtDGNAXu7NIEbOGNoTDGK77PpQhQ71vQGANU0OrGtugzMQxJZWJ3aJfHC8/FtLPIAxM6G0na1+P1x+rozcZNDIMIJCppV66Cgw0TTd62FeKRRpMEz9cSKlI4/T66AnBHpCYNM8dDRo0KDhhMAH/bvhl2E9FUkRhhAk69WVFH3tFvTnF28avX5h3CD2TKNhv1KFDk/o8AsgfewWlJzRB2PjuT6pDSZ4+TVPA0/oUGQY9cjn/XvoWEMtrCVZtMByOhsiHw/kmkPjGSWFToyOFdKf0/+VUCQb16oqdCTZfNShFz3mamOWaBU6yQY9etvMqh4nFDYdKzGnPZVht9vD0nDTn9xcLevxsQRDCGL0uojeOKcT/BGUclTJQ4j6d1qy8NzJ7+tJrdAJBPxoquYVOjRluasFqPyN+1szRP7LcFdeGgosRvxNRNrIcUVaPHzBIHrKjAzFoKnO97W7BbPEnjYzRsfb8VRZNV4or8HkVC4evs3vx8MHuPvv9AfQKjJR3tLajquQiOMFvz9yeBjDSFdTAsEg7iqpQLPPj5FxNpwpk39HgkdEAL12uA43ZCWhm8xboFu3uWAaSgAPN/jc1NKGQbFWBINBEEL4VOV6eIIhhdUvjQ7JwOXz2iYU2cwIBINCWkHxAIEQAjvLosnnR6svABefPlb8UqJ+N1RKGEmhk2qUew+odxg0nCrSC9DCMnihKBcBBDWFjgYNGjScICCESDKFyJFnNgjqHCB84k3JmyafD01ef1iZHH5iX+X2whMIwMAwqOfDt8RqYR1DBPWPm0kQFDoGwkjSj2eYDIJ/zzYH19erraaKTZF9J7gP4KmGDKMBm8DdH7VFnAKrCZX8opEaoTMwJqT21hOiSt5Fo9ABqH9GZIWOWpYrJRytqasGDRqODSK94llCkGM2gIF6Ri0xoaO00B0JJ/WsprnmCHweN3R6Q8gU+eAaIOgHEroCcTnHt4GnEbpYjLg9Ly3iKgEhBFMzEsMyLIlBCR1xyJWdZXBdZhL0hGCrwynErLeJQrI+r20CEFJ1bD0OCh0xAgE/rt56ABM27kG7P5JdHYcyp0eQc//a5OigtBRivyB/EFjFh53J4RKV29jSjv9U1SP/p214dP9huAMBsKxZotDZ73Tj+4YW4f8vark0iV7RG0su4aXZHxw+P5w8oSNedaVm2DTkyke4+6VE1sgNMiMrdHR8vZFfaZNS4nBRSnzEMho0aNCg4cRBvmgRyMiQMCVPnI57//uDIUWneGCcpNfBzBAEAVTyoTP1QsiVtJ+h6h83m4yMnJuFY6opdKgXjxqhYxURCfKU6hr+XIjDz9UyU/UQPVtqZseDRWNWHVE2v6bbqNdpJBJGMoaKVBcPEtFqVYMGDScL4vU6xCpYjlDYT9eQq/qKcgBAQmY2GIa/CAd4/xwt3OqkhDjkipoi23Vc5qzzeNPdt6saAEAgDAAIabEv57NZ7GxzCmFBfyW6d78PLGtDepd78F19Cza3tmN5ZV2H+21tDSl6fm3uHKHjkVHCe9vdiuXcouu1qbkN71Q1wOEPYHF5De4uOQSWtcADaarPElGYVUmbC/vbXZJ0m/LBCB0Mt4hCrkwMwQPdMmBlGSzqkQ0gnHhRenHF6eWETscKnY4IHQ0aNGjQcHKhmygFtZKKwswywqIAzWQlSf9KiOCXQsOuKBEj9/Oj+7X4/WCN3KKggSGCeTMB5+8mz3ylhVOdeBCTdWoLQj2soXAqNYWOOCxLPO6UgxCCWB0LPRM5Ww+R7aME8eK89mhp0HB64GhCrk6a2c/SpUtRVFSEIUOGCJ/VVRwEACRm80qcYBDY9x33txZudVIij5dGN/r8OMgbGNJ0kzSc6/0jDfAFgorKlwlJsbCxDNyBIPa2u8K2/9nIzfk7zhzzOxyG7sJnrxyq7ZBc2iIidH5vaZeQLw1eX8SU7V7ZAGNvm/J5u0VtqHR78bsoa9h/G1p4QifcWI8BMCSGW6Fa3eiQEjqykQZll1t9AWHgY2YZzMxJwZ7RfdCX9zqQZ5lSJHR00oF2pAFSvL7jkCsNGjRo0HDyobtIRRGrMummmXzouEFeLsfEkUIVLg8CwSAaZabIFDG8kqPF5xf6YbGHTrJBBwPDSEKpAC0d+YmIzAhGwRSFYoWOyoIQISTMC1ANeWYjelpNEUOuskwGGBgijHeVoCcENh2DOD2rhVRp0HCC41iRrhJT5E4uUJ80PdCsWbOwc+dObNiwQfis/hCn0EnK5k2uDv4C1O8F9Bag61nHo5kajhJWNmRSRwkH+oCflRCDBD2LBq8f65vb4FQgdHpYTehj51ZcNrdG9rE5WngCASFTgRiEMELKb4AjT76qa45YlzhEzBUIYgt/7p/UNKLo5+1YWl4DAIqGyeEKHWVCR+5pI96v1uODm4mFm1foTEqOE1aRultMOCeR87pZ09gKT5CrR0fCY7dpnHqr3y8cjw50xQMcuYeNkvpGz0gHUZHiSSn5oyl0NGjQoOHUgjh7ptrEmqo0D7o4hU6sbEGA+uisa3ag3usDHT2EEzq8QsfnFxZBjAyDYbFWDI6x4LrMJL5+VpKJqLN+Bxr+fFySGo8RcVbckZuqWibfGnq2vBEWzoZGsAqQQ011Q2HTsehpM0cMvSCEoJvFJGQR1aBBw4mLbhYjTCxBV8vRfV9PC4WOEgSFThav0Fn/Eve77xWAOe74NErDUYNmpKAKD6r60DEE4xK5sKuv65rRLlOmZJu41HlDY20AgNUqXjLHAt5AEGPW78Z5G0sU1TNHRAaOgDrJAnAkDTVW7Mp33uua2wAA9+05BAB4+EAV7iqpQI+ftwmScgrqoZPEDw5qPD408auPYtDVxmGigckZcTZBllyDTCG1eRezAcPjuHK97WaMjOOu6S9NIYWOnoS/Pmg6z2ZvaDCs9FKSEy9q6huxj04khc6E5FgUWU24MCVOtYwGDRo0aDj5kC0Kb3Ko+NDQEF0f3x2HK3S4Ot6rbkTfNTsAcGocvWwxIUZQmfqFRQ8DIbDqWHw+qAB35HF+jQwhknCtSAqdaNUdGo4tzCyDjwbk456u6aplxB5HSouEFE8VZqOXzYSnC7OPaRtPFxQXF2Py5MnHuxkYO3Ysbr/99uPdjBMWZWVlIIRg8+bNx7spJxUsLItCq1nVhytaWFlGIGZOG1Nkv8+HxsPcZDcpOxdoPQLs+ozbOGT6cWyZhqNFuiy7kU00GBqfFAsA+Lq+OSzkikpnz+EzRK1qaJUYBkdCm9+Ps9bvxnXbStHKmxNHQoXLgzKnBzscLpQ6PWHbq91SQqfWE06wUBx0etDiC8DIEFyQzJ3fIT7OP0MkGf7oSCNafAG8V92IS37fh9t2cQo1OuhM0OuEa7e33Y3/O1SLwWt3YG+bC8FgUDBFHhVvE+ocGGMRSKRqpAgKHTPL4I7cNGSZ9PhbWgL6x1hgZhg0eP0R07TSl1mdiFBSeimFhVypvLjiJDHw6i+3XjYz/ju0B87jnw8NGjRo0HBqQKzurJEtllDE65SVNhTnJMYglQ+ToqMCuTpHvF+zLORKCeKwq0gLDmphYhpODPyjazryzAYU8+orJWSaDPh+SA9MzTh+2VOPNf4IuXGyECKrVq0CIQRNTU2Szz/88EMsXLjwmB7rRCGrjgWys7NRVVWF3r17H++mnJZgCBH6oNNGodN8pBp+nw96owkxScnA4d+57FYpRUCa9iCezEiTETpixnNsvB1GhqDM6ZF4wAChbAUDY6yI03Hps39XCLta1dCCa7cdwB6R18xOhwu72lz4sq4Zl/y+Dy5/AHfuLsf5G/fghu2lAnn0RW0T/r69FKUilYxSaBdV6NAvZK3KIBQAyvi6upqNwgCxiSeVskyha+Hg2/BUWTV+aXLgneoGuAMBwZ/HwBAhleqqhhbcv7cSh1xevH64Dt5gUBjEUrUNvVZ5lNAJpggeOiaGwZgEOzaO6IUxCXYYGEaQHNMsWvKVTSBEvonPNxqFjtrqpthHJ9KAWYMGDRo0nPpoVVFRyE30Y2X/F1hN2DKqN84Q9X9yQ2RArNAJCGpUtb4nSR/qnyOZIi8tyoWeEDzYLUO1jIbjh1tzU/Hr8CJkmDr23NFwciMhIQF2u/14N+OEhMfjAcuySEtLg06nHg6o4c+F/XQjdBoOVwAAErOyQRgGaOLUCkjoehxbpeFYQK7QEa+0WXUsBvEGvTQsiYISDjqGYCyv0vm+nku7HQgG8cbhOszfV4m/bTmAr+ta8PrhUPapFpEqZ5vDiVcq67CiqgG/t7bji9pm/MCn775hexk+r23GPXsqhPK/t0jbAQDVbk6hQv186iIodKiaJdmgE9JvN/EZOJgO0lXWeHxC3LeOECEe/OmyI0KZcpdHYojc02ZGF7MBMToGQ2Kt6MITOocDCUKWK7nHDcCpeQBgKx8eppRuk94rqkgyMkTR0E9ev9pgOFqFjgYNGjRoOHVxc3YyAODOPGU/FLmJvlrGov58PwZ0rNAJETrKfY84LXYkA8sRcTbsG9MHN+ekqJbRcOogGAzC4/H85T+RkmfIUVxcjB9//BHPPfccCJ+KvaysDD/++COGDh0Ko9GI9PR03HvvvfD5fBH38fv9uOGGG9ClSxeYzWYUFhbiueee+8PXb+zYsZg9ezZmz56N2NhYJCUlYd68eZLze+ONNzB48GDY7XakpaXhqquuQk0N5zVZVlaGs87ifFTj4+NBCEFxcbFQt1hh5Ha7MXfuXGRmZsJqtWLYsGFYtWqVsH358uWIi4vD119/jZ49e8Jms2HChAmoqqoCAMyfPx+vvfYaPvnkE+GarFq1Ch6PB7Nnz0Z6ejpMJhNyc3Px2GOPRXX+u3fvxhlnnAGTyYSioiJ89913IITg448/BqCsPtq8ebNwPyh+/vlnjB49GmazGdnZ2bj11lvR1haar+Tl5WHhwoWYNm0aYmJiMGPGjLCQq8bGRkydOhXJyckwm83Iz8/HsmXLojoPDX8MVNHZWVPkk5aCyyrqg8v++TCCvEErmjg/HcTlHr9GaTgmSA0LuZIOzATPF14FMi4xBnPz0tDPHko/OTbBjo9rmvBLE5cC/IMjjbir5JCknm0iI+IWWZjVuiZp6vBSWSrwQ66QAmVzixNyUIVOL5sZ65vbIoZc0W3JBr3gGdPEd6BtEWK6AS60ixI6BoagSJSCk2J7q1MwKAYAM0Pw6cB8uAJBJBp0golXVSAeAGferESwUB8Aeq2UVi0ps0zvjZry5o946GhZRDRo0KDh9MQ/u2bgwpR49LGF93EAkGqUDmfVwpz626MjdFp9friDNORKTaETnYcOoClMTyd4vV48+uijf/lx//GPf8BgiE5l9Nxzz2HPnj3o3bs3HnroIQCA3+/HxIkTUVxcjNdffx27d+/G9OnTYTKZMH/+fMV9kpOTEQgEkJWVhffeew+JiYn45ZdfMGPGDKSnp+OKK674Q+fy2muv4YYbbsD69euxceNGzJgxAzk5OZg+nbPU8Hq9WLhwIQoLC1FTU4M5c+aguLgYK1euRHZ2Nj744ANceumlKCkpQUxMDMxm5ffG7NmzsXPnTrz99tvIyMjARx99hAkTJmDbtm3Iz88HALS3t+Opp57CG2+8AYZhcPXVV2Pu3LlYsWIF5s6di127dqGlpUUgOhISErB48WJ8+umnePfdd5GTk4OKigpUVFQotkEMv9+PyZMnIycnB+vWrUNrayvuvPPOTl+//fv3Y8KECXj44Yfx6quvora2ViDJxITMU089hQceeAAPPvigYj3z5s3Dzp078eWXXyIpKQn79u2D0xk+59Fw7BAKuercIvZxIXQuvvhirFq1Cueccw7ef//9P1SHyWpDSrpIvkoJnXiN0DnZkW6Qh1wxsv+5h51mkkrU6ySrbgAE1ckRtxeBYBBLDnLM/dh4O9JNerxV1YC97Zy3DCEEzTJCZ6NMdVMqMyIWY7ujHb5AEDrRl4966PTmB5+1XvWQK6reSdLrhNSrVKHj8EvbRbN8UVTx5wdwaS4np8bjkNuDBq8fBRYj/rG3EpVuLw67KcHCrSAki64xDbk6HExClpkFXMoKHZOM0IkUclXDn5OaZDCatOWANFOJptDRoEGDhtMTOoZggKyfF+PytAQ8d/CI0D/aVFY3xXUoZUWkg2lPMCj46an1PWIPnUghVxo0nGiIjY2FwWCAxWJBWhpn9P3Pf/4T2dnZeP7550EIQY8ePXD48GHcc889eOCBBxT3AQCWZbFgwQLh/y5dumDt2rV49913/zChk52djWeeeQaEEBQWFmLbtm145plnBELn+uuvF8p27doVixcvxpAhQ+BwOGCz2ZCQkAAASElJQVxcnOIxysvLsWzZMpSXlyMjg5tPzp07F1999RWWLVsmkHJerxcvvvgiunXrBoAjgSihZbPZYDab4Xa7JdekvLwc+fn5OOOMM0AIQW5udHPTb7/9Fvv378eqVauE+h555BGce+650V46AMBjjz2GqVOnCmqk/Px8LF68GGeeeSb+/e9/w2Ti7BnOPvtsCWEkVvjQ8xgwYAAGDx4MgFP1aPhzISh0OrkIcFwIndtuuw3XX389XnvttWNXKQ25iss5dnVqOC4Qe+gYCAlb2bILqbH5NNwKgzI60Kr3+vBtfQv2tLtgZxm81DsPBkLwTlUDGrx+1Hh8SDXqwxQ6dFBICRQl42MKZyCImbsO4pacFPSxWxAMBgWFTm9eNdTiC8DlDyhK6CjZk2TQiRQ63PHlCp3HC7Lx2IHDKHd54A8C1W6PEKZlYAgsLIO7u4QyOrx8qBalTg828uFpSquEXfh0rrXeAOL1qQBcyr43jEyhoxBKZZeFXKmZHYcrdJTLxYtCrpRCvDRo0KBBg4YEvQ7fDynEjO0H0c1iVAz1BYAM0fiiyh3er9tYLtA5iNBii1rf0xmFjobTB3q9Hv/4xz+Oy3GPBrt27cKIESMkaddHjRoFh8OBQ4cOISdHfX61dOlSvPrqqygvL4fT6YTH40H//v3/cFuGDx8uaceIESPw9NNPw+/3g2VZbNq0CfPnz8eWLVvQ2NiIAK9CLy8vR1FRUVTH2LZtG/x+PwoKCiSfu91uJCaGzK8tFotA5gBAenq6EN6lhuLiYpx77rkoLCzEhAkTcMEFF+C8887rsE0lJSXIzs6WkENDhw6N6nzE2LJlC7Zu3YoVK1YInwWDQQQCAZSWlqJnz54AIBA1arj55ptx6aWX4rfffsN5552HyZMnY+TIkZ1uj4bocWFKHEraXDgzwQ4E1MUAchwXQmfs2LGSGMVjAo3QOWUgJnS8CjHBcsWOXO0BhAZaDn8AHxxpBABclZEorL51sxixt92NnQ4nUo16QaEjV8CMirPjs9omlDnd8EeIT/60pgneQBDL+nThs2NwZQssJugJgTcYRJ3Xhyw2XA4rKHQMOsEzptnnhz8YlBA6GUY9JiXH4sKUODy4txL/e6gWVW6vkHZTpzDo7GO3oNTpwSbeQFqJOInT6wQT6QNOzihaidChZBRNC6uk0LHLwuPUFDomhgiDZkCZlOPaJgq50lK/atCgQYMGFaQbDfhsUH7EMoQQ5JoMOOjy4OyEmLDtDCGw6xi0+ALCwkR0Wa60BQcNHAghUYc+nQp4++23MXfuXDz99NMYMWIE7HY7nnzySaxbt+5POV5bWxvGjx+P8ePHY8WKFUhOTkZ5eTnGjx8Pj0d98VUOh8MhkEOsbOxqs4XM0+VEGSGkQ7+igQMHorS0FF9++SW+++47XHHFFRg3btwfjkoRg+HH1eI2eGVRAA6HAzfeeCNuvfXWsP3FxJzVao14rPPPPx8HDx7EypUr8e233+Kcc87BrFmz8NRTTx3NKWiIgItT43FxajwAoKUlekKn0zOk1atXY9KkScjIyJCYNImxdOlS5OXlwWQyYdiwYVi/fn1nD9M5uFoAJzdp1widkx/ieHWlV2YYaaCgAonRsdDzBMc2PguVOPa+J//3Tj7TFVWd9JR50JzBp/g+7PYq+uA8XZiNUXzWDJrZqZr/Ha9jYWYZYdCn5qMjDrkSGzs2+/xo40OunijIwtv9ugkrFpT0knvoyEHPeUMEhQ4QUsJQskYx5EpWv9Lx7DLfArVVS0KI5Bhqg2Et5EqDBg0aNBxLfDYwHy8W5WJKunIKarrwQxMWqGe5is4UWYOGExEGgwF+UVh/z549sXbtWglRsGbNGtjtdmRlZSnuQ8uMHDkSM2fOxIABA9C9e3fs37//qNomJ4N+/fVX5Ofng2VZ7N69G/X19Xj88ccxevRo9OjRI0wxQwk1eVvFGDBgAPx+P2pqatC9e3fJj1gh0xGUrgkAxMTE4Morr8TLL7+Md955Bx988AEaGhoi1lVYWIiKigocORJKbLJhwwZJmeRkziCeGjMDEEyMKQYOHIidO3eGnVf37t07TTYmJyfj2muvxZtvvolnn30WL730Uqf21/DXoNM9UFtbG/r164elS5cqbn/nnXcwZ84cPPjgg/jtt9/Qr18/jB8/vkN52lGBqnPMCYBRS0d3qkNOGljYcPNDQohApNBwKbHyp4hPcb7LwZl7UYVOT5tJUk9vm1mIZ9zuCDcC62c34w4+6wat4wif4YqaOyfrKaEjZVqbvT5UuDyiLFd66BkimA83e/2CQufsxBgUWENto5nAqj1eIW25XkGhQ72EKlzcNVCL84+VZQhRKicnZwxEyRRZ5o2jEnIFSJVVqkST6F5rppIaNGjQoOFokWLUY3JqvKLKFABi+DEF7X/VFDqSLFda/6ThJENeXh7WrVuHsrIy1NXVYebMmaioqMAtt9yC3bt345NPPsGDDz6IOXPmCKoQ+T6BQAD5+fnYuHEjvv76a+zZswfz5s0LIyE6i/LycsyZMwclJSV46623sGTJEtx2220AOIWJwWDAkiVLcODAAXz66adYuHChZP/c3FwQQvD555+jtrYWDocj7BgFBQWYOnUqpk2bhg8//BClpaVYv349HnvsMXzxxRdRtzUvLw9bt25FSUkJ6urq4PV6sWjRIrz11lvYvXs39uzZg/feew9paWmqfj4U5557Lrp164Zrr70WW7duxZo1a3D//fcDgLCg2717d2RnZ2P+/PnYu3cvvvjiCzz99NOSeu655x788ssvmD17NjZv3oy9e/fik08+wezZs6M+LwB44IEH8Mknn2Dfvn3YsWMHPv/8cyFcS8OJhU73QOeffz4efvhhXHzxxYrbFy1ahOnTp+O6665DUVERXnzxRVgsFrz66qt/qIFutxstLS2SnzBo4VanFWzykCuVlbEkWQYLcTr0Il65soMnaVr4MKseMoVOpsmAPN5jZiuv9BEj1agP872hhshpvPEwJZbkqcsv+G0vhqzdiSq3V1IuTvCh8QqhW1bZOaaKFDoeaoqsMOiUp25VG3TG6eSqp/By8s8UFTpRhlwB0vumaoqspS3XoEGDBg1/IeT9pqqHjojQiRSSrUHDiYi5c+eCZVkUFRUhOTkZXq8XK1euxPr169GvXz/cdNNNuOGGGwRCQWmf8vJy3Hjjjbjkkktw5ZVXYtiwYaivr8fMmTOPqm3Tpk2D0+nE0KFDMWvWLNx2222YMWMGAE4xsnz5crz33nsoKirC448/HhYClJmZiQULFuDee+9FamqqKpGxbNkyTJs2DXfeeScKCwsxefJkbNiwIaJfkBzTp09HYWEhBg8ejOTkZEHV9MQTT2Dw4MEYMmQIysrKsHLlSoEYUwPLsvj444/hcDgwZMgQ/P3vf8c///lPABCMjPV6vUAW9e3bF//617/w8MMPS+rp27cvfvzxR+zZswejR4/GgAED8MADDwjmz9HCYDDgvvvuQ9++fTFmzBiwLIu33367U3Vo+GtwTD10PB4PNm3ahPvuu0/4jGEYjBs3DmvXrv1DdT722GMS93RFaITOaYVw0qDjwRYQIlgAoA9vVryn3YV2f0BQ1yQbdEgx6FDj8UFPCFIMOnQ1G7Gl1SlJcw5wbGiCXgcXT7rQsC1qiJzCp1GlGaV2OJwoc7qRZzbCGwhirywVOiWg4vU6VLq9qHSHFD1yQiddHHLFm8EpDTrl5JdqaJO+Y+InTKGjUFeiXid4BgHq3jiAnNBRbpeWtlyDBg0aNPyVkBM6aupQq2gs4pAlMNCg4URHQUFB2NwsLy8vok2G0j4AR4yI02ED3PyNYvny5Z1qm16vx7PPPot///vfitunTJmCKVOmSD6T+9rMmzcP8+bNk3wm92/V6/VYsGCB6jyzuLgYxcXFks8mT54sOVZycjK++eYbSZmxY8cKGbk6ix49euDnn38W/l+zZg0ATplDMWrUKGzdulWyn/z8hwwZEtYuMeQZrQDu/ovruf/++yWEnoYTF8d0hlRXVwe/34/U1FTJ56mpqaiurhb+HzduHC6//HKsXLkSWVlZEcme++67D83NzcJPRUVFeKEGPlZTI3ROGfSzm1W3RRNyBXDkgrAPy8Aq2i/daECqQQd/ENje2i6QMTE6FhlGTpGTZtSDIURI6y1X6CQbdGAJEUgHdyAIpz+gqtB5pbIOZ28oQY3bixpZ+JWdZYQYfFrfIT5MykCIxFcIAFL5up2BoBCypWSKLCe/1AamsVEodMI8dBSOp2MIckyh+NxICh3xNlXlkOgeagugGjRo0KDhz0aYQicKdahScgYNGjRo6Cw++ugjfPvttygrK8N3332HGTNmYNSoUZJMWxo0yHFcslx99913UZc1Go0wGo1YunQpli5dqmxwdWAV9ztryLFpoIbjjpd65WH+vsOYlZMSts0mIxuiUeiI/XMoBsRY8FVdC35vbRcUOrE6FpkmPTa3Apn8Phkm7rdYMQOESBUby4AlgD/I+ehQhY7cQwcA2v0BfNfQgh4WqVdPskg9RDM7HeaPJ1fnABzhEq9j0ejzo5z3CFIadIavNKp56HSs0JGTPGr+A10sRux3ulXrobBEYYpsF5VxBbQVUA0aNGjQ8OciURauHYnQea5HDlY3tuLi1Lg/uVUaNJz86Cit+M6dO//C1vz1WLFiBW688UbFbbm5udixYwdaW1txzz33oLy8HElJSRg3blyYR44GDXIcU0InKSkJLMtK3LkB4MiRI51yDFfCrFmzMGvWLLS0tCA2Nja0oaEUqNsDEBbodtZRHUPDiYNcsxHL+nRR3Bau0OnYQyddidCxWzlCp6Udrf6QQieTV+hk8kqTZH34vkCIhCGEIFbHpTtv8vlCCh3+mPJwpu/rW8IIFPEAkma6quQVOmrnl2bUc4QOX06vYFIsD7lS9aoJI3Q6NkVWU/t0MYsUOpFMkcUhVyrnSEQqIM2jQIMGDRo0/NmQh2tHWpi4Mj0BV6Yn/NlN0qDhlEBGRkZYRib5dnlY1KmECy+8EMOGDVPcRtOjT5s2DdOmTfsrm6XhFMAxJXQMBgMGDRqE77//HpMnTwYABAIBfP/995121pZDVaGzj1f75IwATLHhO2o45WCXTf7VQq46Uuj0j7EAAH5tahPMh2N1LM5PjsXKuiZcmBIHgAutEqOH1YR2fwAXiVbkKKHT7A0pdGjI1bjEGORbjMgxGfF9Qwt+bGjFkBirpM72QOi5pgodGnJl0ymfHy3XwIdcKa0iWhgGDACqbVEjTsShTSaGgFEIp5IroZSyagEQQtS4uo5OoQMAN2YlY02TA+cnx6mW0aBBgwYNGo4F5H1+NCFXGjRo6Bg6nU7iBXO6wW63w27XsjFrOPboNKHjcDiwb98+4f/S0lJs3rwZCQkJyMnJwZw5c3Dttddi8ODBGDp0KJ599lm0tbXhuuuuO6qGqip09vKGT/nnHlX9Gk4eyAkONRVIkiiMSWyITEF9eqp5AoYBF940Is6GjSN6ieqRfk2GxVrxr8JsyWdc2m8Pmnz+8LTlBj1+GtYTgWAQfdbsQL3Xh09rmyT7V7pC4VzUQ6cyQsgVECJLWvlwMSUPHUII7DpWCCmLJuRKzfdGTgapDXK7/gFCJ1K5BfmZqts0aNCgQYOGY4kk2XhBLcuVBg0aNGjQcCKg04TOxo0bcdZZodCmOXPmAACuvfZaLF++HFdeeSVqa2vxwAMPoLq6Gv3798dXX30VZpTcWSgqdIJB4CBvqNzt7KOqX8PJA5YQWFgG7XxWCTUzQnHIlZJCJ07PZbA6wPu9xOhYRWWKnNCRh3wBIRKmtN0tZHhKke3HEIKxCXZ8cKQRv7VIDZbFku14vt2UhJF7BlFQEsTJq4vUCBa7jhEROh2nLVfLTGUgBAQADXxSDbmyhAidSMobShzpCQGrDZg1aNCgQcMJALlCR62v06BBgwYNGk4EdJrQGTt2bFhqNDlmz5591CFWcigqdNobAE8r93dSwTE9noYTG3YRoaNGQIiJGCUPHYAzRhYTOkqwsqyEQJJnjgJCPjkl7S4AnCeOPDMVAAyNteKDI43C/4/kZyJOx2JCckh1Fifz3LGqhJTJfW7UQqC49nJqn2jSlqupZQghMDEMnLw5sdrxsowhDx1KJCmBKnQikT4aNGjQoEHDX4nkTpgia9CgQYMGDccbJ/eyQ1MZ99ueDuhNEYtqOLUgVsmomQYnShQ6BsUyA3gfHSDcGFgM8QBPbjQs3rekjSN0Ug3KXOlA0fEAoNBqwqVpCRLSRt4O1ZCrKLNOia9VNKbIkYyMxdvUiBid6PMqWWYwMeh9ixRupUGDBg0aNPyVkKtyNUJHgwYNGjScyDhpZlJLly5FUVERhgwRpSZvPMj9jss9Po3ScNxgYzv2fDGzDHJNBpgZIsm8JEZ/e4hgUVPoAFIJdqSQK4HQUVEE9bSaJebCSsqhFFn8fkceOhRqcf42iVeNcpm4KEgfQHqt1RQ6QOi6XsQbSyuBhsppCh0NGjRo0HCiQK6uNSpkkNSgQYMGDRpOFJw0vdSsWbOwc+dObNiwIfRhE0/oxGuEzukGO6+SMatkZKL4dGA+vhtSKMniJEYvmxk6fveICh0RySLPsgWEyKA2PixLybMH4NQrXUUeM0pmzV3MRgk5E23IlU6FGBETVWpeAHYdC7q3GkHGHTO0LdKq5Qf9u+HbwQUYlxijWoaGyqmFzGnQoEGDBg3HAzEiJa5a36pBgwYNR4Ply5cjLi7ueDcjIoqLi4XM1ccLx+o6yeuZP38++vfvf9T1ngg4uWdSjWXcb02hc9qB+th0RAakGvXoZlEPxzOzDHpauWxXR6XQkRFGSkQNRYYo/MuqUJeeIci3hkifo1Xo2CWEjnIZhhDh/NVSm3PbQvsreQRRWHUs+tgtIBHINs1DR4MGDRo0nIiINB7QoEGDhs4iLy8Pzz77rOSzK6+8Env27Dmmx1m1ahUIIWhqajqm9R5PyK/TsSJi5s6di++///6o6zkRcJITOppC53SFTVDoHP0jTH104vXqAzhxTL1NyRRZNvhTC7kCgJFxtg7bREkmQNmzBwgns9Q8dGxRpgen53AsFDrRgB5P6Xpq0KBBgwYNxwuRFLsaNGjQcCxgNpuRkpJyvJtxwuPPuk42mw2JiYnHvN7jgZOG0FH00GnSPHROV1CFjpohcmcwMycF12QkYlpGkmoZSciVAsESJxv8DY+zqtZ1Q1YS/p6VhDf6dFEtU2QLETrRhlwdjUIHCJ2DKZIpspjQOcpU46Pj7bghMwlz89KOqh4NGjRo0KDhWEIpm6UGDdEgGAzC72//y386ykAsx9ixY3Hrrbfi7rvvRkJCAtLS0jB//nxhe3l5OS666CLYbDbExMTgiiuuwJEjR4TtSqE4t99+O8aOHQsAeOmll5CRkYEAnxmV4qKLLsL1118v/P/JJ59g4MCBMJlM6Nq1KxYsWACfzydsX7RoEfr06QOr1Yrs7GzMnDkTDodD2K6k1nj22WeRl5cHAHC5XOjVqxdmzJghbN+/fz/sdjteffXVDq8TDdP5+OOPkZ+fD5PJhPHjx6OiokJS30UXXYTU1FTYbDYMGTIE3333nbB97NixOHjwIO644w4QQgT1ulIoUUfXgxCC//u//8PFF18Mi8WC/Px8fPrppwCAsrIynHXWWQCA+Ph4EEJQXFwMAHj//ffRp08fmM1mJCYmYty4cWhra+vw/CmeeuoppKenIzExEbNmzYLXG0p60tjYiGnTpiE+Ph4WiwXnn38+9u7dK2w/ePAgJk2ahPj4eFitVvTq1QsrV64EEFIUffHFF+jbty9MJhOGDx+O7du3h90D+veCBQuwZcsW4VouX74cQMfPihzyZ2fVqlUYOnQorFYr4uLiMGrUKBw8eDCqexMMBjF//nzk5OTAaDQiIyMDt956a9TX92jR6bTlxwthacsDfqCJ/zLF5x3Xtmn460FJimPhv5JnNuLJwuyIZZJEIVWR0pYDQHeLEYURwrwMDIOH87MiHq/IFto/2pArnQrBEhOl4TE9h4gKHfbYKXTMLINHCiJfBw0aNGjQoOGvRmwExa4GDZEQCDix6sc+f/lxx565DSxr6bigCK+99hrmzJmDdevWYe3atSguLsaoUaNwzjnnCGTOjz/+CJ/Ph1mzZuHKK6/EqlWroqr78ssvxy233IIffvgB55xzDgCgoaEBX331lTCZ/+mnnzBt2jQsXrwYo0ePxv79+wXi5cEHHwQAMAyDxYsXo0uXLjhw4ABmzpyJu+++Gy+88EJU7TCZTFixYgWGDRuG//mf/8EFF1yAq6++Gueee66EWIqE9vZ2PPLII3j99ddhMBgwc+ZM/O1vf8OaNWsAAA6HAxMnTsQjjzwCo9GI119/HZMmTUJJSQlycnLw4Ycfol+/fpgxYwamT5+uepxorgcALFiwAE888QSefPJJLFmyBFOnTsXBgweRnZ2NDz74AJdeeilKSkoQExMDs9mMqqoqTJkyBU888QQuvvhitLa24qeffoqaBPzhhx+Qnp6OH374Afv27cOVV16J/v37C+dSXFyMvXv34tNPP0VMTAzuueceTJw4ETt37oRer8esWbPg8XiwevVqWK1W7Ny5EzabNFrhrrvuwnPPPYe0tDT84x//wKRJk7Bnzx7o9dKIhyuvvBLbt2/HV199JZBmsbGxAI7uWfH5fJg8eTKmT5+Ot956Cx6PB+vXrxfIt47uzQcffIBnnnkGb7/9Nnr16oXq6mps2bIlqut7LHDSEDph2P0FEPACjB6IyTjerdHwF4MSOpZjEHIVDTry0BHLsy9IjovoHRMNikQhV2q8idzrRs3TRhxyFUmhExsFSSZWBUXKcqVBgwYNGjScrNA8dDScDujbt69AFOTn5+P5558XPEW2bduG0tJSZGdzC56vv/46evXqhQ0bNkijJVQQHx+P888/H//5z38EQuf9999HUlKSoCJZsGAB7r33Xlx77bUAgK5du2LhwoW4++67hXbdfvvtQp15eXl4+OGHcdNNN0VN6ABA//798fDDD+Pvf/87/va3v+HgwYP4/PPPo97f6/Xi+eefx7BhwwBwRFjPnj2xfv16DB06FP369UO/fv2E8gsXLsRHH32ETz/9FLNnz0ZCQgJYloXdbkdamroqPZrrAXAEypQpUwAAjz76KBYvXoz169djwoQJSEhIAACkpKQIqpb9+/fD5/PhkksuQW4uF9XSp0/0pGN8fDyef/55sCyLHj164H/+53/w/fffY/r06QKRs2bNGowcORIAsGLFCmRnZ+Pjjz/G5ZdfjvLyclx66aXCMbt27Rp2jAcffBDnnnuucH2zsrLw0Ucf4YorrpCUM5vNsNls0Ol0YdfyaJ6VlpYWNDc344ILLkC3bt0AAD179hS2d3RvysvLkZaWhnHjxkGv1yMnJwdDhw7t8LjHCicvofPJTMBIgNgsgNE63tMNNAOFWjjSsQZNJa4jyqm/40UKnglJsUd9PDGB5Fch0OVqGzWCxR6lQoeeQySSTLy/WsYsDRo0aNCg4WRGrsnYcSENGhTAMGaMPXPbcTluZ9G3b1/J/+np6aipqcGuXbuQnZ0tkDkAUFRUhLi4OOzatSsqQgcApk6diunTp+OFF16A0WjEihUr8Le//Q0MP37csmUL1qxZg0ceeUTYx+/3w+Vyob29HRaLBd999x0ee+wx7N69Gy0tLfD5fJLt0eLOO+/Exx9/jOeffx5ffvllp7xTdDqd5Jx79OghXIuhQ4fC4XBg/vz5+OKLL1BVVQWfzwen04ny8vKojwFEdz0A6X2zWq2IiYlBTU2Nar39+vXDOeecgz59+mD8+PE477zzcNlllyE+Pj6qdvXq1QusaL6Vnp6Obdu4Z3zXrl3Q6XQC2QUAiYmJKCwsxK5duwAAt956K26++WZ88803GDduHC699NKwZ2/EiBHC3wkJCZL9o8XRPCsJCQkoLi7G+PHjce6552LcuHG44oorkJ6eDqDje3P55Zfj2WefRdeuXTFhwgRMnDgRkyZNgk7311AtJ82MTNFDJ74LcMbtx61NGo4fxiXGYGy8Hddm/jVmVl3MBlyRFo9bclIV1TcxOha35KTgxuxk9LN3vlOVgxCCR/MzcV5iDCaqEERhHjoq6htxiFgkhc6VaQkYHW/DhSlxqmUsxzDkSoMGDRo0aDgRcXNOMsYlxmBRB+HYGjTIQQgBy1r+8p8/ogyXh7MQQsI8b9TAMExYyI7YVwUAJk2ahGAwiC+++AIVFRX46aefMHXqVGG7w+HAggULsHnzZuFn27Zt2Lt3L0wmE8rKynDBBRegb9+++OCDD7Bp0yYsXboUAODxeKJuBwDU1NRgz549YFlW4u9yLDB37lx89NFHePTRR/HTTz9h8+bN6NOnj9DGaNHR9aDo7H1jWRbffvstvvzySxQVFWHJkiUoLCxEaWlpVO06mucEAP7+97/jwIEDuOaaa7Bt2zYMHjwYS5YsiXr/aBDNs9IRli1bhrVr12LkyJF45513UFBQgF9//RVAx/cmOzsbJSUleOGFF2A2mzFz5kyMGTNG8Vn8M3DSKHTCPHQA4O/fA9ZTw51aQ+eQbjTg7f7d/rLjEUKwuGdk8+1/dju2oX/XZyXj+qxk1e1hCh01QkdsihwhnGpwrBXv9e8esU2mY2iKrEGDBg0aNJyIsLIs3uwbHhagQcPpgJ49e6KiogIVFRWCSmfnzp1oampCUVERACA5OVliXAsAmzdvlkz+TSYTLrnkEqxYsQL79u1DYWEhBg4cKGwfOHAgSkpK0L278thz06ZNCAQCePrppwVVz7vvvispk5ycjOrqagSDQYHU2rx5c1hd119/Pfr06YMbbrgB06dPx7hx4yQhNZHg8/mwceNGIYSmpKQETU1Nwv5r1qxBcXExLr74YgDc5L+srExSh8FggN/vj3icjq5HNDAYDAAQdixCCEaNGoVRo0bhgQceQG5uLj766CPMmTPnDx8L4J4Vn8+HdevWCSFX9fX1KCkpEZ4VAMjOzsZNN92Em266Cffddx9efvll3HLLLcL2X3/9FTk5OQA4k+U9e/ao3h+laxnNsxINBgwYgAEDBuC+++7DiBEj8J///AfDhw+P6t6YzWZMmjQJkyZNwqxZs9CjRw9s27ZN8sz/WThpCJ0wJPfUyBwNpzXkCh31kKvo0pZ39piaQkeDBg0aNGjQoOHUwrhx49CnTx9MnToVzz77LHw+H2bOnIkzzzwTgwcPBgCcffbZePLJJ/H6669jxIgRePPNN7F9+3YMGDBAUtfUqVNxwQUXYMeOHbj66qsl2x544AFccMEFyMnJwWWXXQaGYbBlyxZs374dDz/8MLp37w6v14slS5Zg0qRJWLNmDV588UVJHWPHjkVtbS2eeOIJXHbZZfjqq6/w5ZdfIiYmRiizdOlSrF27Flu3bkV2dja++OILTJ06Fb/++qtAgESCXq/HLbfcgsWLF0On02H27NkYPny4QPDk5+fjww8/xKRJk0AIwbx588IULHl5eVi9ejX+9re/wWg0IikpPLNuR9cjGuTm5oIQgs8//xwTJ06E2WzGjh078P333+O8885DSkoK1q1bh9ra2qgJrUjIz8/HRRddhOnTp+N///d/Ybfbce+99yIzMxMXXXQRAM7b5vzzz0dBQQEaGxvxww8/hB37oYceQmJiIlJTU/HPf/4TSUlJYVnUKPLy8lBaWorNmzcjKysLdrs9qmclEkpLS/HSSy/hwgsvREZGBkpKSrB3715MmzYNQMf3Zvny5fD7/Rg2bBgsFgvefPNNmM1mwbPoz8ZJE3IVhtwRHZfRoOEUhtwUWZXQYcUeOkdHwoiPqdc8dDRo0KBBgwYNGk4pEELwySefID4+HmPGjMG4cePQtWtXvPPOO0KZ8ePHY968ebj77rsxZMgQtLa2CpNfMc4++2wkJCSgpKQEV111lWTb+PHj8fnnn+Obb77BkCFDMHz4cDzzzDPCJLhfv35YtGgR/vWvf6F3795YsWIFHnvsMUkdPXv2xAsvvIClS5eiX79+WL9+PebOnSts3717N+666y688MILgtrohRdeQF1dHebNmxfV9bBYLLjnnntw1VVXYdSoUbDZbJJrsWjRIsTHx2PkyJGYNGkSxo8fH6bKeOihh1BWVoZu3bohOVlZfd/R9YgGmZmZgoFvamoqZs+ejZiYGKxevRoTJ05EQUEB7r//fjz99NM4//zzo643EpYtW4ZBgwbhggsuwIgRIxAMBrFy5UpBreX3+zFr1iz07NkTEyZMQEFBQZhR8eOPP47bbrsNgwYNQnV1NT777DNVsu3SSy/FhAkTcNZZZyE5ORlvvfVWVM9KJFgsFuzevRuXXnopCgoKMGPGDMyaNQs33ngjgI7vTVxcHF5++WWMGjUKffv2xXfffYfPPvusU15NRwMSjDZn2QkCGnLVvP4dxAy5ouMdNGg4RbGnzYUx63cL/68b3hO55nAjx3Z/AF1XbwUAbB7ZC2lGfViZaPHv8hos2H8YAPDr8J7IUziehv9v787Dqqr2PoB/DygIMR5kEGJwCkENr4pKWoBCx4lwSlO8Qpp2EwdETXu0lLwOOeQIt7pqqDmXUzhrgmamgBe6KiKohK+JKAIKKON+//Blvx4BBYFzzj7n+3kenqez9z57/9Zablb+XAMRERFpuydPnuDmzZto2bKl0honpD2io6MRFhaGvLw8dYeilWJjY+Hr64vc3FxxVy5d8qLfIWLOIz9facRZdSTzT+xVFkV2VN1WYESaqLZTroz0ZHjDuBnsDJpC3rR+u4IpjdDhGjpERERERERqI5mETmhoKK5cuYL4+PinB4zl6g2ISM2Mnp9yVcN0KplMhmNd38DZ7u1gwDV0iIiIiIjQr18/mJiYVPuzaNEidYfX6Goqu4mJCc6cOaPu8KiWpLsoMpGOq7LL1QtGzDy/3s6rMuIuV0RERESkBdavX4/Hjx9Xe04ul0MulyMkJES1QalQdTuCVXJwcGj05/v4+FTZdp7qjgkdIomq7bbljfXM+o72ISIiIiJSF1UkLTRZfbZIJ83Bv5ERSVRTPRn0n8nhGMga/3V+dpoXp1wRERERERGpDxM6RBL27IiZJirIr1SuoaMvA/Q55YqIiIiIiEhtmNAhkrDKhE5TmQwyFSRYKtfi4fo5RERERERE6sWEDpGEVY6YUcX6OQBgqv9023Nj/fptf05ERERERET1I5lFkSMjIxEZGYny8nJ1h0KkMSpH6KhqxExLIwOEOduitbGhSp5HRERERERE1ZPMCJ3Q0FBcuXIF8fHx6g6FSGM001ftCB2ZTIbZrVrgfTu5Sp5HRERERKRtoqOjYWFhoe4wNFZsbCxkMhny8vLU8nwfHx+EhYWp5dl1JZmEDhFV9ewaOkREREREpFlcXFywatUqpWMjRozAtWvXGvQ56k6CNKS33noLd+7cgbm5eaM+p6Y627NnDxYsWNCoz24okplyRURVMaFDRERERCQtRkZGMDIyUncYdVJSUgIDAwOVPMvAwAB2dnYqeVZ15HLpzEbgCB0iCRPX0FHRlCsiIiIiqp4gCCgsL1f5jyAIdYrTx8cHU6ZMwaeffgq5XA47OzvMnz9fPJ+ZmYnAwECYmJjAzMwMw4cPx927d8XzISEhGDRokNI9w8LC4OPjAwD47rvvYG9vj4qKCqVrAgMDMXbsWPHz/v370blzZzRr1gytWrVCREQEysrKxPNff/01OnbsiNdeew2Ojo6YOHEiCgoKxPPz589Hp06dlJ6xatUquLi4AACePHmC9u3bY8KECeL569evw9TUFBs3bnxpPVVOi9q3bx/atm2LZs2aQaFQ4NatW0r3CwwMhK2tLUxMTODp6YkTJ06I5318fPDnn39i2rRpkD2zK211U65eVh8ymQzr16/H4MGDYWxsjLZt2+LAgQMAgIyMDPj6+gIALC0tIZPJEBISAgD48ccf0bFjRxgZGcHKygp+fn4oLCx8afkr23nhwoWwt7eHq6srAODWrVsYPnw4LCwsIJfLERgYiIyMDPF78fHx8Pf3R/PmzWFubg5vb29cvHhR6d4vKgtQdeRMZX3FxMTA1dUVxsbGGDZsGIqKirBp0ya4uLjA0tISU6ZMUVpzd8uWLejatStMTU1hZ2eHUaNGITs7+6V19vyUq9zcXIwZMwaWlpYwNjZGv379kJaWJp6vjO/o0aNwc3ODiYkJ+vbtizt37ry0nuuLI3SIJExcQ4cjdIiIiIjUqqiiAq1P/1flz73+Tke8VscdSDdt2oTw8HCcP38e586dQ0hICHr27Ik+ffqIyZy4uDiUlZUhNDQUI0aMQGxsbK3u/f7772Py5Mk4deoU+vTpAwB48OABjhw5gkOHDgEAzpw5gzFjxmDNmjV4++23cf36dTHxMm/ePACAnp4e1qxZg5YtW+LGjRuYOHEiPv30U0RFRdUqjmbNmmHr1q3o3r07BgwYgIEDB2L06NHw9/dXSiy9SFFRERYuXIjNmzfDwMAAEydOxAcffICzZ88CAAoKCtC/f38sXLgQhoaG2Lx5MwICApCamgonJyfs2bMHHh4emDBhAsaPH1/jc2pTHwAQERGBpUuXYtmyZVi7di2CgoLw559/wtHRET/99BOGDh2K1NRUmJmZwcjICHfu3MHIkSOxdOlSDB48GI8ePcKZM2dqnQQ8efIkzMzMcPz4cQBAaWkpFAoFvLy8cObMGTRp0gT//Oc/0bdvX/zxxx8wMDDAo0ePEBwcjLVr10IQBKxYsQL9+/dHWloaTE1NX1qWmkbHFBUVYc2aNdixYwcePXqEIUOGYPDgwbCwsMChQ4dw48YNDB06FD179sSIESPEeBcsWABXV1dkZ2cjPDwcISEhOHToUI11Vp2QkBCkpaXhwIEDMDMzw6xZs9C/f39cuXIFTZs2FeNbvnw5tmzZAj09PYwePRozZszA1q1ba1XXr0yQmPz8fAGAkJ+fr+5QiNQu9HKGYPvLfwT/+KvqDoWIiIhIZzx+/Fi4cuWK8PjxY/FYQVmZYPvLf1T+U1BWVqfYvb29hV69eikd8/T0FGbNmiUcO3ZM0NfXFzIzM8Vzly9fFgAIFy5cEARBEIKDg4XAwECl70+dOlXw9vYWPwcGBgpjx44VP3/77beCvb29UF5eLgiCIPTp00dYtGiR0j22bNkitGjRosa4d+/eLVhZWYmf582bJ3h4eChds3LlSsHZ2Vnp2NKlS4XmzZsLkyZNElq0aCHcv3+/xmc86/vvvxcACL///rt4LCUlRQAgnD9/vsbvtW/fXli7dq342dnZWVi5cmWVe5ubm4ufa1MfAIS5c+eKnwsKCgQAwuHDhwVBEIRTp04JAITc3FzxmsTERAGAkJGRUasyPys4OFiwtbUViouLlWJydXUVKioqxGPFxcWCkZGRcPTo0WrvU15eLpiamgo///zzK5elsi3S09PF73z88ceCsbGx8OjRI/GYQqEQPv744xrLFB8fLwAQv1NdnQnC03dk6tSpgiAIwrVr1wQAwtmzZ8Xz9+/fF4yMjIRdu3bVGF9kZKRga2tbYyzV/Q6pVJech1pG6MTExGD69OmoqKjArFmz8NFHH6kjDCLJU/W25URERERUPWM9PVx/p6NanltXb775ptLnFi1aIDs7GykpKXB0dISjo6N4zt3dHRYWFkhJSYGnp2et7h8UFITx48cjKioKhoaG2Lp1Kz744APo/V+sycnJOHv2LBYuXCh+p7y8HE+ePEFRURGMjY1x4sQJLF68GFevXsXDhw9RVlamdL62pk+fjn379mHdunU4fPgwrKysav3dJk2aKJW5Xbt2Yl1069YNBQUFmD9/Pg4ePIg7d+6grKwMjx8/RmZmZq2fAdSuPgDldnvttddgZmYmTiGqjoeHB/r06YOOHTtCoVDg3XffxbBhw2BpaVmruDp27Ki0bk5ycjLS09OVRtoAT6e3Xb9+HQBw9+5dzJ07F7GxscjOzkZ5eTmKioqq1Eldy2JsbIzWrVuLn21tbeHi4gITExOlY8/eIzExEfPnz0dycjJyc3PFaYCZmZlwd3evVR2kpKSgSZMm6N69u3jMysoKrq6uSElJqTG+yneqsak8oVNWVobw8HCcOnUK5ubm6NKlCwYPHlynF4uInlL1tuVEREREVD2ZTFbnqU/qUjlNpJJMJquy5k1N9PT0qkzZKS0tVfocEBAAQRBw8OBBeHp64syZM1i5cqV4vqCgABERERgyZEiV+zdr1gwZGRkYOHAgPvnkEyxcuBByuRy//vorxo0bh5KSEhgbG9cqDgDIzs7GtWvXoK+vj7S0NPTt27dW5ayNGTNm4Pjx41i+fDnatGkDIyMjDBs2DCUlJXW6z8vqo1Jd201fXx/Hjx/Hb7/9hmPHjmHt2rWYM2cOzp8/j5YtW740rtdee61KnF26dKl2GpG1tTUAIDg4GDk5OVi9ejWcnZ1haGgILy+vKnVS17JUd/2L7lFYWAiFQgGFQoGtW7fC2toamZmZUCgUdW6f2qguluf/fDYGlSd0Lly4gPbt28PBwQEA0K9fPxw7dgwjR45UdShEkvf/I3S4vjkRERER1Y+bmxtu3bqFW7duiaN0rly5gry8PHFEg7W1NS5duqT0vaSkJKW/0DZr1gxDhgzB1q1bkZ6eDldXV3Tu3Fk837lzZ6SmpqJNmzbVxpGYmIiKigqsWLFCHNWza9cupWusra2RlZUFQRDExYaTkpKq3Gvs2LHo2LEjxo0bh/Hjx8PPzw9ubm61qo+ysjIkJCSgW7duAIDU1FTk5eWJ3z979ixCQkIwePBgAE8THs8uEAw83bHp2YV6q/Oy+qiNypE0zz9LJpOhZ8+e6NmzJ7744gs4Oztj7969CA8Pr/MzOnfujJ07d8LGxgZmZmbVXnP27FlERUWhf//+AJ4uonz//v06P6u+rl69ipycHCxZskT8s5yQkKB0TU119iw3NzeUlZXh/PnzeOuttwAAOTk5SE1NrfUon8ZU578Fnj59GgEBAbC3t4dMJsO+ffuqXBMZGQkXFxc0a9YM3bt3x4ULF8Rzf/31l5jMAQAHBwfcvn371aIn0nHituUcoUNERERE9eTn54eOHTsiKCgIFy9exIULFzBmzBh4e3uja9euAIDevXsjISEBmzdvRlpaGubNm1clwQM8nXZ18OBBbNy4EUFBQUrnvvjiC2zevBkRERG4fPkyUlJSsGPHDsydOxcA0KZNG5SWlmLt2rW4ceMGtmzZgm+++UbpHj4+Prh37x6WLl2K69evIzIyEocPH1a6JjIyEufOncOmTZsQFBSEQYMGISgoqNYjNJo2bYrJkyfj/PnzSExMREhICHr06CEmeNq2bYs9e/YgKSkJycnJGDVqVJVRJi4uLjh9+jRu375dY2LjZfVRG87OzpDJZIiJicG9e/dQUFCA8+fPY9GiRUhISEBmZib27NmDe/fu1Tqh9bygoCA0b94cgYGBOHPmDG7evInY2FhMmTIF//M//yPWyZYtW5CSkoLz588jKChILVu0Ozk5wcDAQPwzdODAASxYsEDpmurq7Hlt27ZFYGAgxo8fj19//RXJyckYPXo0HBwcEBgYqKri1KjOCZ3CwkJ4eHggMjKy2vM7d+5EeHg45s2bh4sXL8LDwwMKhUIl88eIdE0zPe5yRUREREQNQyaTYf/+/bC0tMQ777wDPz8/tGrVCjt37hSvUSgU+Pzzz/Hpp5/C09MTjx49wpgxY6rcq3fv3pDL5UhNTcWoUaOUzikUCsTExODYsWPw9PREjx49sHLlSjg7OwN4uvbL119/ja+++godOnTA1q1bsXjxYqV7uLm5ISoqCpGRkfDw8MCFCxcwY8YM8fzVq1cxc+ZMREVFiSM0oqKicP/+fXz++ee1qg9jY2PMmjULo0aNQs+ePWFiYqJUF19//TUsLS3x1ltvISAgAAqFQmkkEgB8+eWXyMjIQOvWrcVpSc97WX3UhoODAyIiIjB79mzY2tpi0qRJMDMzw+nTp9G/f3+88cYbmDt3LlasWIF+/frV+r7P18fp06fh5OSEIUOGwM3NDePGjcOTJ0/EETsbNmxAbm4uOnfujL///e+YMmUKbGxsXul59WFtbY3o6Gjs3r0b7u7uWLJkCZYvX650TXV1Vp3vv/8eXbp0wcCBA+Hl5QVBEHDo0KEq06zUQSbUY2KXTCbD3r17MWjQIPFY9+7d4enpiXXr1gEAKioq4OjoiMmTJ2P27Nn47bffsGzZMuzduxcAEBYWhm7dulV5ySsVFxejuLhY/Pzw4UM4OjoiPz+/xmFeRLpi2185CE+9hRF2cqx2c1J3OEREREQ64cmTJ7h58yZatmyptMYJaY/o6GiEhYUhLy9P3aGQFnrR75CHDx/C3Ny8VjmPBl14o6SkBImJifDz8/v/B+jpwc/PD+fOnQMAdOvWDZcuXcLt27dRUFCAw4cPQ6FQ1HjPxYsXw9zcXPx5dsV1Il0XaGOBz1vbI9zFVt2hEBERERERkQo1aELn/v37KC8vh62t8l8ubW1tkZWVBeDp1m8rVqyAr68vOnXqhOnTp79wh6vPPvsM+fn5WL58OVxdXeu1UBSRtnmtiT5CnWzgbGSo7lCIiIiIiCSjX79+MDExqfZn0aJF6g6v0dVUdhMTE5w5c0bd4VEtqXyXKwB477338N5779XqWkNDQxgaGmL69OmYPn26OPyIiIiIiIiI6FWsX78ejx8/rvacXC6HXC5HSEiIaoNSoep2BKv07CZGpNkaNKHTvHlz6Ovr4+7du0rH7969Czs7u4Z8FBEREREREdEr0fWkBWe+aIcGnXJlYGCALl264OTJk+KxiooKnDx5El5eXvW6d2RkJNzd3eHp6VnfMImIiIiIiOqtHvvLEJEOa6jfHXUeoVNQUID09HTx882bN5GUlAS5XA4nJyeEh4cjODgYXbt2Rbdu3bBq1SoUFhbiww8/rFegoaGhCA0N5ZQrIiIiIiJSq8rtiouKimBkZKTmaIhIaoqKigCg3luf1zmhk5CQAF9fX/FzeHg4ACA4OBjR0dEYMWIE7t27hy+++AJZWVno1KkTjhw5UmWh5LqKjIxEZGQkysvL63UfIiIiIiKi+tDX14eFhQWys7MBAMbGxpDJZGqOiog0nSAIKCoqQnZ2NiwsLKCvr1+v+8kEiY0TrMue7ERERERERI1BEARkZWUhLy9P3aEQkcRYWFjAzs6u2kRwXXIeatnlioiIiIiISMpkMhlatGgBGxsblJaWqjscIpKIpk2b1ntkTiXJJHQ45YqIiIiIiDSNvr5+g/3ljIioLjjlioiIiIiIiIhIA9Ql59Gg25YTEREREREREVHjk0xCJzIyEu7u7vD09FR3KEREREREREREaiW5KVf5+fmwsLDArVu3OOWKiIiIiIiIiLTGw4cP4ejoiLy8PJibm7/wWsksilwpJycHAODo6KjmSIiIiIiIiIiIGl5OTo72JXTkcjkAIDMz86WFU4fKbJqmjyDy9PREfHy8usN4KU2OUyptDWh2PVbS9BjZ3g1H0+MDpNPemlqXmhrXs6TQxlKoR0Cz45RCO1fS5Hp8libHyfZuOJoeH8D2ri9NjOl5Umnjhq7L/Px8ODk5ibmPF5FcQkdP7+myP+bm5hrdqGZmZhodn76+vkbHV0kKcWp6WwPSqEcpxAiwvRuCpsf3LE1vb02tS02Nqzqa3MZSqUcpxKnJ7VxJCvUISCNOtnf9aXp8z2J7vxpNjKkmmt7GjVWXlbmPF17T4E8lSQgNDVV3CLUilTg1nRTqUQoxSoWm16WmxyclmlqXmhqX1EilHqUSp6aTSj1KJU5Np+n1qOnxSY0m1qcmxiRV6qxLyS2KXJc92dVB0+OjhsO21i1sb93C9tZ+bGPdwHbWLWxv3cL21n662sZ1KbfkRugYGhpi3rx5MDQ0VHco1dL0+KjhsK11C9tbt7C9tR/bWDewnXUL21u3sL21n662cV3KLbkROkREREREREREuk5yI3SIiIiIiIiIiHQdEzpERERERERERBLDhA4RERERERERkcQwoUPUwGQyGfbt26fuMIiIiKgG7KuJiEgbMKHzCs6dOwd9fX0MGDBA3aGQCoSEhGDQoEHqDoMa2a1btzB27FjY29vDwMAAzs7OmDp1KnJycmr1/djYWMhkMuTl5TVuoFRvfKd1A/tq3cL3Wjewr9YtfK+1H/vq+mNC5xVs2LABkydPxunTp/HXX3/V617l5eWoqKhooMiI6FXcuHEDXbt2RVpaGrZv34709HR88803OHnyJLy8vPDgwQN1h0hEdcS+mki7sK8m0j7sq+uPCZ06KigowM6dO/HJJ59gwIABiI6OFs9VZv0PHjyIN998E82aNUOPHj1w6dIl8Zro6GhYWFjgwIEDcHd3h6GhITIzM9VQEnoVLi4uWLVqldKxTp06Yf78+WqJhxpGaGgoDAwMcOzYMXh7e8PJyQn9+vXDiRMncPv2bcyZMwcAUFxcjFmzZsHR0RGGhoZo06YNNmzYgIyMDPj6+gIALC0tIZPJEBISosYSUW0dOXIEvXr1goWFBaysrDBw4EBcv35dPJ+RkQGZTIY9e/bA19cXxsbG8PDwwLlz59QYNb0M+2rdxr5aO7Gv1m3sr7UP++qGwYROHe3atQvt2rWDq6srRo8ejY0bN0IQBKVrZs6ciRUrViA+Ph7W1tYICAhAaWmpeL6oqAhfffUV1q9fj8uXL8PGxkbVxSCi//PgwQMcPXoUEydOhJGRkdI5Ozs7BAUFYefOnRAEAWPGjMH27duxZs0apKSk4Ntvv4WJiQkcHR3x008/AQBSU1Nx584drF69Wh3FoToqLCxEeHg4EhIScPLkSejp6WHw4MFV/oVnzpw5mDFjBpKSkvDGG29g5MiRKCsrU1PU9DLsq4m0C/tqYn+tfdhXN4wm6g5AajZs2IDRo0cDAPr27Yv8/HzExcXBx8dHvGbevHnw9/cHAGzatAmvv/469u7di+HDhwMASktLERUVBQ8PD5XHT0TK0tLSIAgC3Nzcqj3v5uaG3NxcxMfHY9euXTh+/Dj8/PwAAK1atRKvk8vlAAAbGxtYWFg0etzUMIYOHar0eePGjbC2tsaVK1fQoUMH8fiMGTPE+d0RERFo37490tPT0a5dO5XGS7XDvppIu7CvJvbX2od9dcPgCJ06SE1NxYULFzBy5EgAQJMmTTBixAhs2LBB6TovLy/xv+VyOVxdXZGSkiIeMzAwwJtvvqmaoImoVp7/F4HnZWRkQF9fH97e3iqKiFQhLS0NI0eORKtWrWBmZgYXFxcAqDJk99nf2S1atAAAZGdnqyxOqj321UTai3217mJ/rV3YVzccjtCpgw0bNqCsrAz29vbiMUEQYGhoiHXr1tX6PkZGRpDJZI0RIjUyPT29Kv8z8eywP5KeNm3aQCaTISUlBYMHD65yPiUlBZaWllWGeJN2CAgIgLOzM/7973/D3t4eFRUV6NChA0pKSpSua9q0qfjflb+/dXHhPSlgX03sq7UP+2pif61d2Fc3HI7QqaWysjJs3rwZK1asQFJSkviTnJwMe3t7bN++Xbz2999/F/87NzcX165dq3GIKEmLtbU17ty5I35++PAhbt68qcaIqL6srKzg7++PqKgoPH78WOlcVlYWtm7dihEjRqBjx46oqKhAXFxctfcxMDAA8HSFfZKGnJwcpKamYu7cuejTp484ZJ+ki301AeyrtRH7at3G/lq7sK9uWEzo1FJMTAxyc3Mxbtw4dOjQQeln6NChSsPDvvzyS5w8eRKXLl1CSEgImjdvjkGDBqkveGowvXv3xpYtW3DmzBn897//RXBwMPT19dUdFtXTunXrUFxcDIVCgdOnT+PWrVs4cuQI/P394eDggIULF8LFxQXBwcEYO3Ys9u3bh5s3byI2Nha7du0CADg7O0MmkyEmJgb37t1DQUGBmktFL2NpaQkrKyt89913SE9Pxy+//ILw8HB1h0X1wL6aAPbV2op9te5if61d2Fc3LCZ0amnDhg3w8/ODubl5lXNDhw5FQkIC/vjjDwDAkiVLMHXqVHTp0gVZWVn4+eefxX8RIOmpqKhAkyZPZyd+9tln8Pb2xsCBAzFgwAAMGjQIrVu3VnOEVF9t27ZFQkICWrVqheHDh6N169aYMGECfH19ce7cOXERxX/9618YNmwYJk6ciHbt2mH8+PEoLCwEADg4OCAiIgKzZ8+Gra0tJk2apM4i0QtUvtN6enrYsWMHEhMT0aFDB0ybNg3Lli1Td3hUD+yrdRf7au3Hvlr3sL/WTuyrG5ZMeNnqYlRrsbGx8PX1RW5uLlfO1yJ9+/ZFmzZt6jSfk4g0F99p3ca+WjvxvSbSPnyvdRf76trjCB2iGuTm5iImJgaxsbHi1pdEJF18p4m0D99rIu3D95qo9rjLFVENxo4di/j4eEyfPh2BgYHqDoeI6onvNJH24XtNpH34XhPVHqdcERERERERERFJDKdcERERERERERFJDBM6REREREREREQSw4QOERFpncWLF8PT0xOmpqawsbHBoEGDkJqaqnTNkydPEBoaCisrK5iYmGDo0KG4e/eueD45ORkjR46Eo6MjjIyM4ObmhtWrVyvd486dOxg1ahTeeOMN6OnpISwsTBXFIyIi0gqq6q/37NkDf39/WFtbw8zMDF5eXjh69KhKykjUmJjQISIirRMXF4fQ0FD8/vvvOH78OEpLS/Huu++isLBQvGbatGn4+eefsXv3bsTFxeGvv/7CkCFDxPOJiYmwsbHBDz/8gMuXL2POnDn47LPPlLZPLS4uhrW1NebOnQsPDw+VlpGIiEjqVNVfnz59Gv7+/jh06BASExPh6+uLgIAA/Oc//1FpeYkaGhdFJiIirXfv3j3Y2NggLi4O77zzDvLz82FtbY1t27Zh2LBhAICrV6/Czc0N586dQ48ePaq9T2hoKFJSUvDLL79UOefj44NOnTph1apVjVkUIiIiraWK/rpS+/btMWLECHzxxReNUhYiVeAIHSIi0nr5+fkAALlcDuDpv+aVlpbCz89PvKZdu3ZwcnLCuXPnXnifynsQERFRw1JVf11RUYFHjx6xTyfJa6LuAIiIiBpTRUUFwsLC0LNnT3To0AEAkJWVBQMDA1hYWChda2tri6ysrGrv89tvv2Hnzp04ePBgY4dMRESkc1TZXy9fvhwFBQUYPnx4g8VPpA5M6BARkVYLDQ3FpUuX8Ouvv77yPS5duoTAwEDMmzcP7777bgNGR0RERIDq+utt27YhIiIC+/fvh42NzSs/i0gTcMoVERFprUmTJiEmJganTp3C66+/Lh63s7NDSUkJ8vLylK6/e/cu7OzslI5duXIFffr0wYQJEzB37lxVhE1ERKRTVNVf79ixAx999BF27dqlNI2LSKqY0CEiIq0jCAImTZqEvXv34pdffkHLli2Vznfp0gVNmzbFyZMnxWOpqanIzMyEl5eXeOzy5cvw9fVFcHAwFi5cqLL4iYiIdIEq++vt27fjww8/xPbt2zFgwIDGKRCRinHKFRERaZ3Q0FBs27YN+/fvh6mpqTjP3tzcHEZGRjA3N8e4ceMQHh4OuVwOMzMzTJ48GV5eXuKOGZcuXULv3r2hUCgQHh4u3kNfXx/W1tbis5KSkgAABQUFuHfvHpKSkmBgYAB3d3fVFpqIiEhiVNVfb9u2DcHBwVi9ejW6d+8uXlP5DCKp4rblRESkdWQyWbXHv//+e4SEhAAAnjx5gunTp2P79u0oLi6GQqFAVFSUOIR7/vz5iIiIqHIPZ2dnZGRkvPBZz19DREREVamqv/bx8UFcXFyVa4KDgxEdHd0gZSFSByZ0iIiIiIiIiIgkhmvoEBERERERERFJDBM6REREREREREQSw4QOEREREREREZHEMKFDRERERERERCQxTOgQEREREREREUkMEzpERERERERERBLDhA4RERERERERkcQwoUNEREREREREJDFM6BAREZFO8/HxQVhYmLrDICIiIqoTJnSIiIiIaik2NhYymQx5eXnqDoWIiIh0HBM6REREREREREQSw4QOERER6YzCwkKMGTMGJiYmaNGiBVasWKF0fsuWLejatStMTU1hZ2eHUaNGITs7GwCQkZEBX19fAIClpSVkMhlCQkIAABUVFVi8eDFatmwJIyMjeHh44Mcff1Rp2YiIiEi3MKFDREREOmPmzJmIi4vD/v37cezYMcTGxuLixYvi+dLSUixYsADJycnYt28fMjIyxKSNo6MjfvrpJwBAamoq7ty5g9WrVwMAFi9ejM2bN+Obb77B5cuXMW3aNIwePRpxcXEqLyMRERHpBpkgCIK6gyAiIiJqbAUFBbCyssIPP/yA999/HwDw4MEDvP7665gwYQJWrVpV5TsJCQnw9PTEo0ePYGJigtjYWPj6+iI3NxcWFhYAgOLiYsjlcpw4cQJeXl7idz/66CMUFRVh27ZtqigeERER6Zgm6g6AiIiISBWuX7+OkpISdO/eXTwml8vh6uoqfk5MTMT8+fORnJyM3NxcVFRUAAAyMzPh7u5e7X3T09NRVFQEf39/peMlJSX429/+1gglISIiImJCh4iIiAjA0/V1FAoFFAoFtm7dCmtra2RmZkKhUKCkpKTG7xUUFAAADh48CAcHB6VzhoaGjRozERER6S4mdIiIiEgntG7dGk2bNsX58+fh5OQEAMjNzcW1a9fg7e2Nq1evIicnB0uWLIGjoyOAp1OunmVgYAAAKC8vF4+5u7vD0NAQmZmZ8Pb2VlFpiIiISNcxoUNEREQ6wcTEBOPGjcPMmTNhZWUFGxsbzJkzB3p6T/eIcHJygoGBAdauXYt//OMfuHTpEhYsWKB0D2dnZ8hkMsTExKB///4wMjKCqakpZsyYgWnTpqGiogK9evVCfn4+zp49CzMzMwQHB6ujuERERKTluMsVERER6Yxly5bh7bffRkBAAPz8/NCrVy906dIFAGBtbY3o6Gjs3r0b7u7uWLJkCZYvX670fQcHB0RERGD27NmwtbXFpEmTAAALFizA559/jsWLF8PNzQ19+/bFwYMH0bJlS5WXkYiIiHQDd7kiIiIiIiIiIpIYjtAhIiIiIiIiIpIYJnSIiIiIiIiIiCSGCR0iIiIiIiIiIolhQoeIiIiIiIiISGKY0CEiIiIiIiIikhgmdIiIiIiIiIiIJIYJHSIiIiIiIiIiiWFCh4iIiIiIiIhIYpjQISIiIiIiIiKSGCZ0iIiIiIiIiIgkhgkdIiIiIiIiIiKJ+V/JvFj6+dZ9gwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1400x400 with 1 Axes>"
       ]
@@ -224,14 +224,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_1222987/102400080.py:30: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "/tmp/ipykernel_23334/102400080.py:30: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
       "  conc.index = to_datetime(conc.index)\n"
      ]
     },
@@ -317,7 +317,7 @@
        "2023-03-09  161512  38618509         0  38456997  28381491.0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -363,12 +363,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {
     "scrolled": false
    },
@@ -405,7 +405,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 5/5 [00:00<00:00, 83.39it/s]\n"
+      "100%|██████████| 5/5 [00:00<00:00, 86.39it/s]\n"
      ]
     },
     {
@@ -490,7 +490,7 @@
        "2023-03-09  161512  38618509    342253  38456997  28381491.0"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -513,7 +513,7 @@
     "\n",
     "covid_modified = covid.copy()\n",
     "for c in tqdm(covid.columns):\n",
-    "    values = covid_modified[c].values\n",
+    "    values = covid_modified[c].values.copy()\n",
     "    correct_series(values)\n",
     "    covid_modified[c] = values\n",
     "\n",
@@ -522,12 +522,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -571,12 +571,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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ceuutbohWlixZ4vyW599uueUWl0xzz549ee+99xg9ejS1a9d2Ppt90YABA5gxYwYPPfQQW7dupVy5cnz99desW7eOSZMmuXRk9l+9evVixIgR3H333Tz55JPOoRKrVKlyScd0DRs25Oeff+add96hZMmSlC9fPsvniYsXL87IkSMZO3YsHTt25M4772T//v1MnTqVxo0bu3S4dz3q1avHfffdx9SpU4mPj+eWW25hxYoVHDx48JKyb7zxBqtWraJp06Y8+uij1KhRg3PnzrFt2zZ+/vlnzp07d12xmM1mPvroIzp16kTNmjV5+OGHKVWqFCdOnGDVqlUEBgbyww8/uOxz5MgR7rzzTjp27MiGDRucw3leqXn3s88+y2effUbHjh0ZMmSIcxjMi98iXhQYGMi0adN48MEHadCgAb169aJ48eIcPXqUxYsX07x5c6ZMmXJd15yb70VWQkJCaNGiBQ8//DCnT59m0qRJVKpUiUcffRSAatWqUbFiRZ5++mlOnDhBYGAg33zzzRWfL79WOfmZ7t+/P19//TUdO3bk3nvv5dChQ3z++edXHDZWRESyT/Wn66P6k+pPqj9JvnZzB73KPYCxYMEC5/Lff/9tWCwW48SJEy7l2rVrZ4wcOfKS/VNSUowiRYoYb7755o0OVf7jSsNgAsbMmTNdytvtdiMiIsIAjFdffTXLY54+fdp4+OGHjWLFihlWq9WoXbv2JccxjEuHwTQMw/jpp5+MWrVqGVar1ahatarx+eefZzkM5r59+4yWLVsaPj4+BuAc6u+/QwZeNGXKFKNatWqGp6enUaJECWPgwIHG+fPnXcq0atXKqFmz5iVxXm54zv9KSUkxnnzySaNo0aKGn5+fcccddxjHjh3L8jpPnz5tDBo0yIiIiDA8PT2NsLAwo127dsYHH3xw1fNcbRjMi7Zv325069bNKFq0qOHl5WWULVvWuPfee40VK1Y4y1x8b/fu3Wv06NHDCAgIMIoUKWIMHjzYSElJuep5d+7cabRq1crw9vY2SpUqZbzyyivGxx9/nOVnsGrVKiMqKsoICgoyvL29jYoVKxoPPfSQsWXLFmeZvn37Gn5+fpdcy+U+m7JlyxpdunRxOQf/GgYzJ+9FVi4e74svvjBGjhxphIaGGj4+PkaXLl1chrY0DMPYu3ev0b59e8Pf398oVqyY8eijjxq///77Jb9Hl7vGy7men2nDMIwJEyYYpUqVMry8vIzmzZsbW7ZsuewwmPPnz892XCIihZnqT/+j+pPqT/+l+pMUFibDyKWevm4yk8nk0onx4sWLuf322/Hz83Mpl5aWRrdu3VyahwJ88cUX9OnTh+PHj1OiRImbFbZIoXexM/HY2FiKFSvm7nDynNWrV9OmTRvmz59Pjx493BLDxx9/TP/+/Tl27Ng1j7wgIiIiuUf1pytT/UkKiwLzCFViYiIWi4WtW7de0lN3Vj2Wf/TRR9x+++1K3oiI/MepU6cwmUyEhIS4OxQRERGRfEH1J7kZCkwCp379+thsNmJiYq7ap82RI0dYtWoV33///U2KTkQk7zt9+jRff/0106dPJzIyEl9fX3eHJCIiIpKnqf4kN1O+SuAkJia6dDB25MgRduzYQUhICFWqVKF379706dOHCRMmUL9+fWJjY1mxYgV16tRx6eX7k08+ITw8/Iq9tYuIFDZ//PEHzzzzDE2aNOHDDz90dzgiIiIieZ7qT3Iz5as+cC4+2/hfffv2ZdasWWRkZPDqq6/y6aefcuLECYoVK0azZs0YO3YstWvXBsBut1O2bFn69OnDa6+9drMvQUREREREREQkx/JVAkdEREREREREpDAyuzsAERERERERERG5snzRB47dbufkyZMEBARgMpncHY6IiIhchmEYXLhwgZIlS2I263sid1L9SUREJH/Idv3JuA7jxo0zAGPIkCFXLPfVV18ZVatWNby8vIxatWoZixcvztF5jh07ZgCaNGnSpEmTpnwyHTt27DpqGJIbVH/SpEmTJk2a8td0tfrTNbfA2bx5MzNmzKBOnTpXLLd+/Xruu+8+xo0bx+23387cuXPp2rUr27Zto1atWtk6V0BAAADHjh0jMDDwWkMWERGRGywhIYGIiAjnvVvcR/UnERGR/CG79adr6sQ4MTGRBg0aMHXqVF599VXq1avHpEmTsizbs2dPkpKSWLRokXNds2bNqFevHtOnT8/W+RISEggKCiI+Pl4VEBERkTxM9+y8Q5+FiIhI/pDde/Y1PZw+aNAgunTpQvv27a9adsOGDZeUi4qKYsOGDZfdJy0tjYSEBJdJRERERERERKSwynECZ968eWzbto1x48Zlq3x0dDQlSpRwWVeiRAmio6Mvu8+4ceMICgpyThERETkNU0RERMRp2rRp1KlTh8DAQAIDA4mMjGTJkiXO7a1bt8ZkMrlMjz32mMsxjh49SpcuXfD19SU0NJRnnnmGzMxMlzKrV6+mQYMGeHl5UalSJWbNmnVJLO+//z7lypXD29ubpk2b8ttvv7lsT01NZdCgQRQtWhR/f3+6d+/O6dOnc+/NEBERkXwpRwmcY8eOMWTIEObMmYO3t/eNiomRI0cSHx/vnI4dO3bDziUiIiIFX+nSpXnjjTfYunUrW7ZsoW3bttx1113s2bPHWebRRx/l1KlTzmn8+PHObTabjS5dupCens769euZPXs2s2bNYtSoUc4yR44coUuXLrRp04YdO3YwdOhQ+vfvz7Jly5xlvvzyS4YPH87o0aPZtm0bdevWJSoqipiYGGeZYcOG8cMPPzB//nzWrFnDyZMn6dat2w1+h0RERCSvy1EfOAsXLuTuu+/GYrE419lsNkwmE2azmbS0NJdtAGXKlGH48OEMHTrUuW706NEsXLiQ33//PVvnzc7zYHa7nfT09OxeiuQDnp6el/w8iYhI3paf+l0JCQnhrbfeol+/frRu3fqKffotWbKE22+/nZMnTzpbFk+fPp0RI0YQGxuL1WplxIgRLF68mN27dzv369WrF3FxcSxduhSApk2b0rhxY6ZMmQI46i8RERE88cQTPPfcc8THx1O8eHHmzp1Ljx49ANi3bx/Vq1dnw4YNNGvWLNvXl53PwmazkZGRke1jSt6mupOISP6U3fpTjkahateuHbt27XJZ9/DDD1OtWjVGjBiR5Q0jMjKSFStWuCRwli9fTmRkZE5OfUXp6ekcOXIEu92ea8eUvCE4OJiwsDBMJpO7QxERkQLCZrMxf/58kpKSXOojc+bM4fPPPycsLIw77riDl156CV9fX8DRp1/t2rVdHguPiopi4MCB7Nmzh/r161+237+LdaD09HS2bt3KyJEjndvNZjPt27d39g24detWMjIyXI5TrVo1ypQpc9UETlpaGmlpac7lK/UhaBgG0dHRxMXFXeGdkvxIdScRkYIrRwmcgICAS4b+9vPzo2jRos71ffr0oVSpUs4+coYMGUKrVq2YMGECXbp0Yd68eWzZsoUPPvggVy7AMAxOnTqFxWIhIiICs/ma+mWWPMYwDJKTk51NysPDw90ckYiI5He7du0iMjKS1NRU/P39WbBgATVq1ADg/vvvp2zZspQsWZKdO3cyYsQI9u/fz7fffgtcvk+/i9uuVCYhIYGUlBTOnz+PzWbLssy+ffucx7BarQQHB19S5kr9B4KjD8GxY8dm6724mLwJDQ3F19dX/+wXAKo7iYgUfDlK4GTH0aNHXZIot9xyC3PnzuXFF1/k+eefp3LlyixcuPCSRNC1yszMJDk5mZIlSzq/JZOCwcfHB4CYmBhCQ0PVJFhERK5L1apV2bFjB/Hx8Xz99df07duXNWvWUKNGDQYMGOAsV7t2bcLDw2nXrh2HDh2iYsWKbow6+0aOHMnw4cOdywkJCVkOBGGz2ZzJm6JFi97MEOUGU91JRKRgu+4EzurVq6+4DHDPPfdwzz33XO+psmSz2QCwWq035PjiXheTchkZGaqEiIjIdbFarVSqVAmAhg0bsnnzZiZPnsyMGTMuKdu0aVMADh48SMWKFQkLC7tktKiLI0OFhYU5X/87WtTp06cJDAzEx8cHi8WCxWLJssy/j5Genk5cXJxLK5x/l7kcLy8vvLy8rvY2OPu80RdfBZPqTiIiBVeBed5ITX8LJn2uIiJyo9jtdpc+Y/5tx44dwP8eQ4mMjGTXrl0uo0UtX76cwMBA52NYF/v9+7d/9/tntVpp2LChSxm73c6KFSucZRo2bIinp6dLmf3793P06NFc7T8QdI8tqPS5iogUXLn+CJWIiIhIXjNy5Eg6depEmTJluHDhAnPnzmX16tUsW7aMQ4cOMXfuXDp37kzRokXZuXMnw4YNo2XLltSpUweADh06UKNGDR588EHGjx9PdHQ0L774IoMGDXK2ennssceYMmUKzz77LI888ggrV67kq6++YvHixc44hg8fTt++fWnUqBFNmjRh0qRJJCUl8fDDDwMQFBREv379GD58OCEhIQQGBvLEE08QGRmZoxGoREREpOBRAqcAS05O5sEHH2T58uVcuHCB8+fPU69ePYYOHeoyKpg7jRkzhoULFzq/6RQREfcyDIOUDBtxyRlcSM0kITWDC6kX5zNJSHHMX1x3ITWDhH8tx8XHu/sSshQTE0OfPn04deoUQUFB1KlTh2XLlnHbbbdx7Ngxfv75Z2cyJSIigu7du/Piiy8697dYLCxatIiBAwcSGRmJn58fffv25eWXX3aWKV++PIsXL2bYsGFMnjyZ0qVL89FHHxEVFeUs07NnT2JjYxk1ahTR0dHUq1ePpUuXunRsPHHiRMxmM927dyctLY2oqCimTp16c96ofOpqw8DnhtWrV9OmTRvOnz9/SSfTIjdE0lnY8jEcWgUJxyExFjDcHZWI3Aip2RtRWwmcAmz27Nn88ssvrF+/nmLFihEUFMTmzZvx8/Nzd2giInKTpWfaOXwmkVNxqZyKTyU6PoXYxHTiktM5n5xOXHIG55PTOZ+cQXqmoxJhxo4/yQSaUggkiQBSCDAlE0AyAaZkIv5ZDvxnOYAUTMYFWrv3UrP08ccfX3ZbREQEa9asueoxypYty48//njFMq1bt2b79u1XLDN48GAGDx582e3e3t68//77vP/++1eNSW6Mm5EQErmsM3/Chvfh9y8gM9Xd0YjIzWDLXnJWCZwC7NChQ1SvXt1lxK/ixYtfcZ+MjAw8PT1vdGgiInITnIpP4dttJ1i5L4ZdJ+IxZaZSwnSeMM4RZjpHMVMC4aYLFCGRIqZEgrlAEXMiwV6JBJGEvynn/zgkmPXtsIhIjmWmw6GVsPkjOLj8f+vD60GjR6B4VfAvAWZ1TC1SICVcgDeuPlK3EjhuZLfbefvtt/nggw84duwYJUqU4P/+7/944YUX2LVrF0OGDGHDhg34+vrSvXt33nnnHfz9/QF46KGHiIuLo0WLFkyYMIH09HR69erFpEmT8PT0pHXr1s5vE00mE61atWL16tWUK1fO5REqk8nE1KlTWbJkCStWrOCZZ54BYOHChTz55JOMGTOGc+fO0adPH9577z0mTJjAO++8g91uZ8iQIbzwwgvO64mLi+Ppp5/mu+++Iy0tjUaNGjFx4kTq1q3rLPPGG28wceJEkpOTuffee6+aUBIRkZzbcSyOyT8fYMOBk7QxbaOXeTtvmg9SyfvktR3Qwxu8AsE78H+v3kH/zAe5bsv0gDd65e4FifxLUlISAwcO5NtvvyUgIICnn37aZXtaWhovvPACX3zxBXFxcdSqVYs333yT1q1bA3D27FkGDx7M2rVrOX/+PBUrVuT555/nvvvuAxx1rDVr1rBmzRomT54MwJEjR5zH37p1KyNGjGDv3r3Uq1ePmTNnUrVqVQB+//13hg4dypYtWzCZTFSuXJkZM2bQqFGjm/DOSL6TGAt//gQHljoek0q/8M8GE1TtDJGDoOwtoI6pRQo+c0K2ihW4BM7FZ/fdwcfTkqOe/0eOHMmHH37IxIkTadGiBadOnWLfvn0kJSURFRVFZGQkmzdvJiYmhv79+zN48GBmzZrl3H/VqlWEh4ezatUqDh48SM+ePalXrx6PPvoo3377Lc899xy7d+/m22+/veIw62PGjOGNN95g0qRJeHh48Mknn3Do0CGWLFnC0qVLOXToED169ODw4cNUqVKFNWvWsH79eh555BHat2/vHGr1nnvuwcfHhyVLlhAUFMSMGTNo164dBw4cICQkhK+++ooxY8bw/vvv06JFCz777DPeffddKlSocM3vuYiI/E9CagajFu5mw47d/J/HIiZYfyXElOhayMMHAsMhoCT4h4JvCPiE/PNaxHX+YmLG4+pDU/8viOxVQCRvyU/1p2eeeYY1a9bw3XffERoayvPPP8+2bduoV68e4HhEbe/evcybN4+SJUuyYMECOnbsyK5du6hcuTKpqak0bNiQESNGEBgYyOLFi3nwwQepWLEiTZo0YfLkyRw4cIBatWo5+zgqXrw4f/31FwAvvPACEyZMoHjx4jz22GM88sgjrFu3DoDevXtTv359pk2bhsViYceOHWrZLP9jGHB6tyNhc2AZHN+CS582/iWg9j2OFjdFK7otTBHJuwpcAiclw0aNUcvccu69L0fha83eW3rhwgUmT57MlClT6Nu3LwAVK1akRYsWfPjhh6SmpvLpp586+6uZMmUKd9xxB2+++aazo8MiRYowZcoULBYL1apVo0uXLqxYsYJHH32UkJAQfH19sVqthIWFXTGW+++/3zn6xUV2u51PPvmEgIAAatSoQZs2bdi/fz8//vgjZrOZqlWr8uabb7Jq1SqaNm3Kr7/+ym+//UZMTIxzNI63336bhQsX8vXXXzNgwAAmTZpEv3796NevHwCvvvoqP//8M6mperZXROR6/XEqgcGfbeKOhLmM81qEjyndsSEg3PEPQdnmUKoB+BXXt7lyifxSf0pMTOTjjz/m888/p127doCjz7/SpUsDcPToUWbOnMnRo0cpWbIkAE8//TRLly5l5syZvP7665QqVcql1c4TTzzBsmXL+Oqrr2jSpAlBQUFYrVZ8fX2zrEO99tprtGrVCoDnnnuOLl26kJqaire3N0ePHuWZZ56hWrVqAFSuXPna3xgpOGL2wbbZsG8RxB113RZeD6p0hCpRjnmz2R0Rikg+UeASOPnFH3/8QVpamrPy8d9tdevWdelsuHnz5tjtdvbv3+9M4NSsWROL5X/PwYaHh7Nr164cx5JVs95y5coREBDgXC5RogQWiwXzv24qJUqUICYmBnA0GU5MTKRo0aIux0lJSeHQoUPO63rsscdctkdGRrJq1aocxywiIv+z9kAsL3y2nLdNk2nqsc+xMqIZ3PoUVGqnPhOkwDh06BDp6enO1r8AISEhzkeYdu3ahc1mo0qVKi77paWlOesoNpuN119/na+++ooTJ06Qnp5OWloavr6+2Yrh4tDy4Kh7gWOUszJlyjB8+HD69+/PZ599Rvv27bnnnnuoWFEtKQolw4DDqxydER/8+X/rPXygQmuo2hEqd4DAkm4LUUTynwKXwPHxtLD35airF7xB5852WR+f6z7ff5vkmkwm7PbsDT/2b1mNSpXVsa90vsTERMLDw1m9evUlx9JQmyIiN86qfTFM/OxbvvEYR6gpDsPqj+mOyVCru1raSLbll/rT1SQmJmKxWNi6davLl1yAsx/Bt956i8mTJzNp0iRq166Nn58fQ4cOJT09PVvn+Hd96OKjXxfrQ2PGjOH+++9n8eLFLFmyhNGjRzNv3jzuvvvu3Lg8yQ8Mw/F41IqXIWbPPytNUK0L1L0PKrYFa/aShSIi/1XgEjgmkynbzXDdqXLlyvj4+LBixQr69+/vsq169erMmjWLpKQkZ3Jl3bp1zkeX8qIGDRoQHR2Nh4cH5cqVy7JM9erV2bRpE3369HGu27hx402KUESk4PlpTzTvz/2G2R6vU8SUiL14Ncw950CxSu4OTfKZ/FJ/qlixIp6enmzatIkyZcoAcP78eQ4cOECrVq2oX78+NpuNmJgYbr311iyPsW7dOu666y4eeOABwJF8OXDgADVq1HCWsVqt2GzX1idQlSpVqFKlCsOGDeO+++5j5syZSuAUFjF/wNKRjpY3AFZ/qP8ANP0/CFGfjyJy/fL+nbqA8vb2ZsSIETz77LNYrVaaN29ObGwse/bsoXfv3owePZq+ffsyZswYYmNjeeKJJ3jwwQedj0/lNe3btycyMpKuXbsyfvx4qlSpwsmTJ1m8eDF33303jRo1YsiQITz00EM0atSI5s2bM2fOHPbs2aNOjEVErsGSXaf4cN58PvUYR5ApGXupRpgf+AZ8gt0dmsgN4+/vT79+/XjmmWcoWrQooaGhvPDCC85HvKtUqULv3r3p06cPEyZMoH79+sTGxrJixQrq1KlDly5dqFy5Ml9//TXr16+nSJEivPPOO5w+fdolgVOuXDk2bdrEX3/9hb+/PyEhIVeNLSUlhWeeeYYePXpQvnx5jh8/zubNm+nevfsNez8kj7DbYP27sPI1sGeAxQrNBkKL4fqbLCK5SgkcN3rppZfw8PBg1KhRnDx5kvDwcB577DF8fX1ZtmwZQ4YMoXHjxi7DiOdVJpOJH3/8kRdeeIGHH36Y2NhYwsLCaNmypTPp1LNnTw4dOsSzzz5Lamoq3bt3Z+DAgSxb5p5OE0VE8qsffj/Jp199xWyPNwgwpWCUbuJI3ngHujs0kRvurbfeIjExkTvuuIOAgACeeuop4uPjndtnzpzJq6++ylNPPcWJEycoVqwYzZo14/bbbwfgxRdf5PDhw0RFReHr68uAAQPo2rWryzGefvpp+vbtS40aNUhJSXEZRvxyLBYLZ8+epU+fPpw+fZpixYrRrVs3xo4dm/tvguQdyefg64fh8GrHcpVO0HEchJR3a1giUjCZDMMwrl7MvRISEggKCiI+Pp7AQNfKaWpqKkeOHKF8+fJ4e3u7KUK5UfT5ioi4WrD9OPPmz+Njz7fwN6VilLkFU+/54OXv7tCAK9+z5ea63Gehe2vBps/3Jjq9F77oBXF/g6cfdB4P9Xqr/zERybHs1p/UAkdERCSf+HTDXyz94Stmer6NrykNo3wrTPd9AdZLO6MXEZEb6NxhmH07JJ+FIuWg11woUdPdUYlIAacEjoiISB5nGAbvrTzIzhVfMNPzXbxMGRgV22HqNQc8r39UQxERyYHkczDnHkfyJrwePLgAfK/eT5KIyPVSAkdERCQPy7DZeXXRXi5s+ozpnh/gYbJjVO2MqcdM8NTjESIiN1VmGszrDWcPQlAE3P+VkjcictMogSMiIpJHHTuXzFNf7aDOsc95xzrHsbJeb0x3vAsW3cJFRG4qw4DvBsHR9eAVCL3nQ0DeHCFWRAom1f5ERETymPRMO3M2/c30ZdsYaXxEV8/1jg3NBkGHV+GfIZNFROQm2v4Z7JoPZg/o+RmEVnd3RCJSyCiBIyIikkfEJacz97ejzF53hDpJ6/nOcyZh5vMYJgumqNeg6WMa3URExB1S42HFy475dqOgQmu3hiMihZMSOCIiIm525EwSM9cdYcGWv2hjW8+HHj9Sx3oEACOkIqauU6FMMzdHKSJSiK0ZD0mxULQyNB3o7mhEpJBSAkdERMQN0jPtrNx3mq+2HGfr/sP0Mq/iJ49lhFvOAWB4eGNq+him1s9ppCkREXc68ydsmu6Y7zgOPKzujUdECi0lcERERG6iw7GJfL7xKAu3H6d0yj7utazmPeuv+JnSADD8QjE1eRRTo0fAr5h7gxURKewy0+HbAWDPhModoPJt7o5IRAox9YLoJq1bt2bo0KHZLr9v3z6aNWuGt7c39erVu2FxXSuTycTChQvdHYaISJ6VkJrB6O92c+/ERZg3TuGLzGF87/USD3iscCRvQmvCXVMxDdsNrZ5V8kZEslSuXDkmTZrk7jAKj9Xj4OQ28A6G2ye6OxoRKeTUAsdNvv32Wzw9PbNdfvTo0fj5+bF//378/f1zJQaTycSCBQvo2rVrrhxPRESytv7gGZ6at4W7UhawyvM7AkwpwD+PSVW7HRo8COVbqYNiEZG8ZNun8Os/SZs7JkNQaffGIyKFXo5a4EybNo06deoQGBhIYGAgkZGRLFmy5LLlZ82ahclkcpm8vb2vO+iCICQkhICAgGyXP3ToEC1atKBs2bIULVr0BkYmIiK56dMNf/HqzPnMSn+K5zznOZI3JWpDl3cwPbUfenzsGM1EyRuRHEtPT3d3CNckv8ZdqBzbDD8MAQzHCIA1u7o7IhGRnCVwSpcuzRtvvMHWrVvZsmULbdu25a677mLPnj2X3ScwMJBTp045p7///vu6gy4I/v0IVbly5Xj99dd55JFHCAgIoEyZMnzwwQfOsiaTia1bt/Lyyy9jMpkYM2YMAMeOHePee+8lODiYkJAQ7rrrLv766y+X83zyySfUrFkTLy8vwsPDGTx4sPOcAHfffTcmk8m5DPDdd9/RoEEDvL29qVChAmPHjiUzM9O5/c8//6Rly5Z4e3tTo0YNli9fnuvvj4hIfmcYBu/8tJ91P8zka4/RVDUfx/AtCl2nwf+thcb9wCfY3WGK5CutW7dm8ODBDB06lGLFihEVFcXu3bvp1KkT/v7+lChRggcffJAzZ84497Hb7YwfP55KlSrh5eVFmTJleO2115zbd+3aRdu2bfHx8aFo0aIMGDCAxMREAH766Se8vb2Ji4tziWPIkCG0bdvWufzrr79y66234uPjQ0REBE8++SRJSUnO7eXKleOVV16hT58+BAYGMmDAgGztFxMTwx133IGPjw/ly5dnzpw5ufp+ymVkpMDCgWDYoVYP6PiGuyMSEQFymMC544476Ny5M5UrV6ZKlSq89tpr+Pv7s3HjxsvuYzKZCAsLc04lSpS47qCvyDAgPck9k2Fcc9gTJkygUaNGbN++nccff5yBAweyf/9+AE6dOkXNmjV56qmnOHXqFE8//TQZGRlERUUREBDAL7/8wrp16/D396djx47Ob3WmTZvGoEGDGDBgALt27eL777+nUqVKAGzevBmAmTNncurUKefyL7/8Qp8+fRgyZAh79+5lxowZzJo1y1nRsdvtdOvWDavVyqZNm5g+fTojRoy45usWESmIbHaDFxfu5syaGUzznIyvKQ2jQhtMgzZDvfvBrC7oJI/JR/Wn2bNnY7VaWbduHW+88QZt27alfv36bNmyhaVLl3L69GnuvfdeZ/mRI0fyxhtv8NJLL7F3717mzp3rrI8mJSURFRVFkSJF2Lx5M/Pnz+fnn392fuHVrl07goOD+eabb5zHs9lsfPnll/Tu3RtwtJLu2LEj3bt3Z+fOnXz55Zf8+uuvzmNc9Pbbb1O3bl22b9/OSy+9lK39HnroIY4dO8aqVav4+uuvmTp1KjExMTn7bCXnVo+Ds3+Cfwno/JZaSIpInnHNfeDYbDbmz59PUlISkZGRly2XmJhI2bJlsdvtNGjQgNdff52aNWte8dhpaWmkpaU5lxMSErIfWEYyvF4y++Vz0/Mnwep3Tbt27tyZxx9/HIARI0YwceJEVq1aRdWqVQkLC8PDwwN/f3/CwsIA+Pzzz7Hb7Xz00UeY/rmpzJw5k+DgYFavXk2HDh149dVXeeqppxgyZIjzPI0bNwagePHiAAQHBzuPCTB27Fiee+45+vbtC0CFChV45ZVXePbZZxk9ejQ///wz+/btY9myZZQs6XifX3/9dTp16nRN1y0iUtCkZdoY/uUOyu+dxtOe8x0rGz6EqfMEsKjrOcmj8lH9qXLlyowfPx6AV199lfr16/P66687t3/yySdERERw4MABwsPDmTx5MlOmTHHWbSpWrEiLFi0AmDt3LqmpqXz66af4+TlimDJlCnfccQdvvvkmJUqUoFevXsydO5d+/foBsGLFCuLi4ujevTsA48aNo3fv3s6W1ZUrV+bdd9+lVatWTJs2zdl9QNu2bXnqqaeccfbv3/+K+x09epQlS5bw22+/OetvH3/8MdWrV8/R2ys5dGwzrH/PMX/HZPANcW88IiL/kuOa5K5du4iMjCQ1NRV/f38WLFhAjRo1sixbtWpVPvnkE+rUqUN8fDxvv/02t9xyC3v27KF06ct3AjZu3DjGjh2b09DytTp16jjnL7ZautI3LL///jsHDx68pB+d1NRUDh06RExMDCdPnqRdu3Y5iuP3339n3bp1Lk2LbTYbqampJCcn88cffxAREeFM3gBXTOCJiBQmiWmZPP7ZZtr8NZGHPZc5VrZ8Btq8oG9wRXJJw4YNnfO///47q1atynKAh0OHDhEXF0daWtpl60N//PEHdevWdSZvAJo3b47dbmf//v2UKFGC3r1706xZM06ePEnJkiWZM2cOXbp0ITg42BnDzp07XR5vMgwDu93OkSNHnAmXRo0auZz7avsdOHAADw8Pl+utVq2a87xyA2SkwnePOx6dqtMTquoLShHJW3KcwKlatSo7duwgPj6er7/+mr59+7JmzZoskziRkZEu/9zfcsstVK9enRkzZvDKK69c9hwjR45k+PDhzuWEhAQiIiKyF6Cnr+ObHHfw9L32Xf8zIpXJZMJut1+2fGJiIg0bNszyWejixYtjvsbm+YmJiYwdO5Zu3bpdsk0dUIuIXN7ZxDQenbmePjHj6eqx3rGy45vQ7DH3BiaSHfmo/vTvZEtiYqKztcx/hYeHc/jw4esOr3HjxlSsWJF58+YxcOBAFixYwKxZs1xi+L//+z+efPLJS/YtU6ZMlnFnZ78DBw5cd+ySQ+smwZkDjken1O+NiORBOU7gWK1WZz8qDRs2ZPPmzUyePJkZM2ZcdV9PT0/q16/PwYMHr1jOy8sLLy+vnIbmYDJd82NM+UmDBg348ssvCQ0NJTAwMMsy5cqVY8WKFbRp0ybL7Z6enthstkuOu3//fudn/F/Vq1fn2LFjnDp1ivDwcIAr9oEkIlIYHDuXzP99vIYRCa/TyrITw+SB6e5pUOfeq+8skhfk0/pTgwYN+OabbyhXrhweHpdWaytXroyPjw8rVqygf//+l2yvXr06s2bNIikpyZlgWbduHWazmapVqzrL9e7dmzlz5lC6dGnMZjNdunRxiWHv3r2XrTtdKfYr7VetWjUyMzPZunWr8xGq/fv3X9KhsuSSxBhY965jvuMbenRKRPKk6+5F0W63u/RXcyU2m41du3Y5//GXa9e7d2+KFSvGXXfdxS+//MKRI0dYvXo1Tz75JMePHwdgzJgxTJgwgXfffZc///yTbdu28d577zmPcTHBEx0dzfnz5wEYNWoUn376KWPHjmXPnj388ccfzJs3jxdffBGA9u3bU6VKFfr27cvvv//OL7/8wgsvvHDz3wARkTxiy1/neHjqUl6/8AKtLDuxe/hgun+ekjciN8GgQYM4d+4c9913H5s3b+bQoUMsW7aMhx9+GJvNhre3NyNGjODZZ5/l008/5dChQ2zcuJGPP/4YcNSnvL296du3L7t372bVqlU88cQTPPjggy4Db/Tu3Ztt27bx2muv0aNHD5cvGkeMGMH69esZPHgwO3bs4M8//+S77767pBPj/7raflWrVqVjx4783//9H5s2bWLr1q30798fHx+fG/BOCmvfhowkKNkAat7t7mhERLKUowTOyJEjWbt2LX/99Re7du1i5MiRrF692tkLf58+fRg5cqSz/Msvv8xPP/3E4cOH2bZtGw888AB///13lt+ASM74+vqydu1aypQpQ7du3ahevTr9+vUjNTXV2SKnb9++TJo0ialTp1KzZk1uv/12/vzzT+cxJkyYwPLly4mIiKB+/foAREVFsWjRIn766ScaN25Ms2bNmDhxImXLlgXAbDazYMECUlJSaNKkCf3793fpL0dEpLCw2w0+XHuYFz74hhnpz1PPfAi7dxHMfX+Ayre5OzyRQqFkyZKsW7cOm81Ghw4dqF27NkOHDiU4ONj5OPlLL73EU089xahRo6hevTo9e/Z09jPo6+vLsmXLOHfuHI0bN6ZHjx60a9eOKVOmuJynUqVKNGnShJ07dzrrvRfVqVOHNWvWcODAAW699Vbq16/PqFGjXPoLzEp29ps5cyYlS5akVatWdOvWjQEDBhAaGpobb538W9IZ2DbbMd9ulPosE5G8y8iBRx55xChbtqxhtVqN4sWLG+3atTN++ukn5/ZWrVoZffv2dS4PHTrUKFOmjGG1Wo0SJUoYnTt3NrZt25aTUxqGYRjx8fEGYMTHx1+yLSUlxdi7d6+RkpKS4+NK3qfPV0TyoqNnk4x7p683nhj5vHFhVKhhjA40bBOqG0bMPneH5nZXume709SpU43atWsbAQEBRkBAgNGsWTPjxx9/dG5PSUkxHn/8cSMkJMTw8/MzunXrZkRHR7sc4++//zY6d+5s+Pj4GMWLFzeefvppIyMjw6XMqlWrjPr16xtWq9WoWLGiMXPmzEtimTJlilG2bFnDy8vLaNKkibFp0yaX7dmJJTsu91no3lqw6fO9BqvGGcboQMOY0cow7HZ3RyMihVB260856gPnYnPTy1m9erXL8sSJE5k4cWKOEkoiIiJ5VXxyBlPXHGThul28ZPqY262OPsCMcrdi7jET/Iu7OUK5nNKlS/PGG29QuXJlDMNg9uzZ3HXXXWzfvp2aNWsybNgwFi9ezPz58wkKCmLw4MF069aNdevWAY7HwLt06UJYWBjr16/n1KlT9OnTB09PT+cQ1keOHKFLly489thjzJkzx9nvSnh4OFFRUQB8+eWXDB8+nOnTp9O0aVMmTZpEVFQU+/fvd7asuFosIpKLMlLgtw8d85GD1fpGRPI0k2EYhruDuJqEhASCgoKIj4+/pMPe1NRUjhw5Qvny5TVKUgGkz1dE8oKTcSnM2fQ3X244xJ0ZSxjssYAQUyKGyYKp5TOOocItOR4XoEC60j07rwkJCeGtt96iR48eFC9enLlz59KjRw8A9u3bR/Xq1dmwYQPNmjVjyZIl3H777Zw8edLZN8r06dMZMWIEsbGxWK1WRowYweLFi9m9e7fzHL169SIuLo6lS5cC0LRpUxo3bux8RMdutxMREcETTzzBc889R3x8/FVjya7LfRa6txZs+nxzaMtMWDQUgiLgyR36Wy4ibpHd+tN1d2IsIiJSEJ2MS2HupqP0n72Zjm8u4sLaqSywD2GU52eO5E3xapj6/wxtRqrCn8/YbDbmzZtHUlISkZGRbN26lYyMDNq3b+8sU61aNcqUKcOGDRsA2LBhA7Vr13bp2DYqKoqEhAT27NnjLPPvY1wsc/EY6enpbN261aWM2Wymffv2zjLZieVy0tLSSEhIcJlE5ArsdtjwvmO+2eP6Wy4ieZ7+SomIiABpmTa2/HWe1ftjWL0/lhMxZ7jFvIe25m1Msm7A35QKgOEfhqnNSEz1HlBlP5/ZtWsXkZGRpKam4u/vz4IFC6hRowY7duzAarUSHBzsUr5EiRJER0cDEB0d7ZK8ubj94rYrlUlISCAlJYXz589js9myLLNv3z7nMa4Wy+WMGzeOsWPHXv2NEBGHP3+Cs3+CVxA0eNDd0YiIXFWBqXnmgyfB5BrocxWRG+n4+WRW749l9f5Y1h+KpVTGUVqbdzDG/DuNvfZhNdn+V7hYFWj8KKb6D4DV131ByzWrWrUqO3bsID4+nq+//pq+ffuyZs0ad4eVa0aOHMnw4cOdywkJCURERFy2vO6xBZM+1xz47QPHa4MHwSvAvbGIiGRDvk/gWCwWwNEs2cfHx83RSG5LTk4GwNPT082RiEhBkJ5p57cj5xytbA7EEh0TQ3PzHtqadzDWspNSXmdddwgu6xgSvPqdUL6lOrfM56xWK5UqVQKgYcOGbN68mcmTJ9OzZ0/S09OJi4tzafly+vRpwsLCAAgLC+O3335zOd7p06ed2y6+Xlz37zKBgYH4+PhgsViwWCxZlvn3Ma4Wy+V4eXnh5eV11ffh4j01OTlZdacCSHWnbDp7CA6tAEzQuL+7oxERyZZ8n8Dx8PDA19eX2NhYPD09MZvVrU9BYBgGycnJxMTEEBwc7EzUiYjklGEY/HbkHF/8dpQVf5ymVPoRWpt38Ip5J4289uP5r1Y2hoc3pnItoFJ7qHQbFK2opE0BZrfbSUtLo2HDhnh6erJixQq6d+8OwP79+zl69CiRkZEAREZG8tprrxETE+McLWr58uUEBgZSo0YNZ5kff/zR5RzLly93HsNqtdKwYUNWrFhB165dnTGsWLGCwYMHA2QrlutlsVgIDg4mJiYGAF9fX0z6Oc/3VHfKoS2fOF4r3wYh5d0bi4hINuX7BI7JZCI8PJwjR47w999/uzscyWXBwcFX/cZRRCQrhmHw8x8xTFx+gPTovdxnWcXTls2U9jrjWjCkoiNhU/k2TGWb6/GoAmrkyJF06tSJMmXKcOHCBebOncvq1atZtmwZQUFB9OvXj+HDhxMSEkJgYCBPPPEEkZGRzlGfOnToQI0aNXjwwQcZP3480dHRvPjiiwwaNMjZ6uWxxx5jypQpPPvsszzyyCOsXLmSr776isWLFzvjGD58OH379qVRo0Y0adKESZMmkZSUxMMPPwyQrVhyw8V768UkjhQcqjtlQ3oybP/MMd/4UffGIiKSA/k+gQOOb7QqV65Menq6u0ORXOTp6alvj0TkmhyMucBLC/eQfmQ9z3l8S0uvXc5thocPpvK3OlrYVG4PIRXcGKncLDExMfTp04dTp04RFBREnTp1WLZsGbfddhsAEydOxGw20717d9LS0oiKimLq1KnO/S0WC4sWLWLgwIFERkbi5+dH3759efnll51lypcvz+LFixk2bBiTJ0+mdOnSfPTRR0RFRTnL9OzZk9jYWEaNGkV0dDT16tVj6dKlLh0bXy2W3HDxC7DQ0FAyMjJy9djiPqo7ZdPuryE13vGYbKV27o5GRCTbTEY+6Oksu2Oii4iI/LjrFG/PX8Fw41Nut2wCwDCZMVXtDHXvg4pt1crmBtI9O+/QZyGSBcOAGS0heifc9jI0H+LuiEREsn3PLhAtcERERAzDYNLPf7J51QK+9nyXEHOiI3FT/wFMtz4NRcq6O0QREXG341scyRsPb6ivocNFJH9RAkdERPI9wzCY8NMBLqx9n888P8ViMjDC62G6630Iq+Xu8EREJK/YOsvxWrMb+Ia4NRQRkZxSAkdERPI1wzB4c+l+Mn59l7Gecxwr696P6fZ3wFNDJIuIyD9SE2DPt475hn3dG4uIyDVQAkdERPKtxLRMXl20l6BtU3nJ8wvHypbPQpvnNfy3iIi42vMtZCRD0coQ0dTd0YiI5JgSOCIiki/9duQcI77cTM/Ez3jM8wfHSiVvRETkcrb9M3R4gz66T4hIvqQEjoiI5Dsf/XKYb5csZZrHVKp5HHOsbPsStHzavYGJiEjedHovnNgCZg/HiIQiIvmQEjgiIpKvfLvtOPuWTmeB58d4mTKx+xbH3Hk81Orm7tBERCSv2v5P65uqncC/uHtjERG5RkrgiIhIvhF7IY1fvp/JRM8ZABhVO2G+cwr4FXNzZCIikmdlpsHv8xzz9fu4NxYRkeugBI6IiOQb4xduZKTxEZjA1qg/li5vqx8DERG5sn2LIeUcBJSESu3cHY2IyDUzuzsAERGR7Ph572ka7X+HUFMcqcGVsHR8XckbERG5uu2fO17r9wazxb2xiIhcByVwREQkz4tPyWDBt3Po6bEaAO+7p4CHl3uDEhGRvC/pDBxe7Zivd79bQxERuV5K4IiISJ739vdbGZE+FYDMhv2gbKSbIxIRkXxh32IwbBBeF0IquDsaEZHrogSOiIjkab/+eYYKu96hjDmWNL+SeHQY6+6QREQkv/jje8dr9TvdG4eISC5QAkdERPKslHQbc7+eT1/LTwB43T0FvALcHJWIiOQLKef/9/hUja7ujEREJFcogSMiInnWlJ/3MjTlPcwmg4w692v0EBERyb79S8GeCaE1oFgld0cjInLdlMAREZE8acexOCzrJ1PFfIJ0rxA8O77m7pBERCQ/2fud41WPT4lIAaEEjoiI5DlJaZlMmbuQQZYFAFhvfwt8Q9wclYiI5BupCXBohWO+xl3ujUVEJJfkKIEzbdo06tSpQ2BgIIGBgURGRrJkyZIr7jN//nyqVauGt7c3tWvX5scff7yugEVEpGBLy7Tx5OcbeSppAl6mTDIqRkGt7u4OS0RE8pMDy8CWDsWqQGh1d0cjIpIrcpTAKV26NG+88QZbt25ly5YttG3blrvuuos9e/ZkWX79+vXcd9999OvXj+3bt9O1a1e6du3K7t27cyV4EREpWFIzbAz89De6/zWW6uajZHgXxfPuKWAyuTs0ERHJT/YudLzWuEv3EBEpMEyGYRjXc4CQkBDeeust+vXrd8m2nj17kpSUxKJFi5zrmjVrRr169Zg+ffplj5mWlkZaWppzOSEhgYiICOLj4wkMDLyecEVEJI9KSM1g8GebuefoWO6wbMRutmJ+8Fsof6u7Q5McSEhIICgoSPfsPECfhRRaSWfgneqOFjiP/Qphtd0dkYjIFWX3nn3NfeDYbDbmzZtHUlISkZGRWZbZsGED7du3d1kXFRXFhg0brnjscePGERQU5JwiIiKuNUwREckHjp1LpvfU1dx/dNQ/yRtPzPfOUvJGRERybsccR/KmZH0lb0SkQPHI6Q67du0iMjKS1NRU/P39WbBgATVq1MiybHR0NCVKlHBZV6JECaKjo694jpEjRzJ8+HDn8sUWOCIiUrBcSM3gm63HmblsE5N4i/qWg46WNz0/g6od3R2eiIjkN+lJsGGqY77RI+6NRUQkl+U4gVO1alV27NhBfHw8X3/9NX379mXNmjWXTeJcCy8vL7y8vHLteCIiknekZthYuS+G73ec5Jf9J+hgX88Cz88IMSVi9y6C+b65UPYWd4cpIiL50eaPIDEagstCnZ7ujkZEJFflOIFjtVqpVKkSAA0bNmTz5s1MnjyZGTNmXFI2LCyM06dPu6w7ffo0YWFh1xiuiIjkR4lpmfz6Zyw/7TnNir0nqZ2xkzvN6xlv2UygRzIARlhtzD1mQbFK7g1WRETyp+RzsG6yY77VCPDQF8IiUrDkOIHzX3a73aXD4X+LjIxkxYoVDB061Llu+fLll+0zR0RECo4Mm52F20/w/e8n2XY4hrrGXjqYtzDSspHi1gRnOcMvFFPj/phaDAMPqxsjFhGRfO2XCZB8FopVVesbESmQcpTAGTlyJJ06daJMmTJcuHCBuXPnsnr1apYtWwZAnz59KFWqFOPGjQNgyJAhtGrVigkTJtClSxfmzZvHli1b+OCDD3L/SkREJE9Iz7TzzbbjTF15gBoJv9LTsp73PXYSaEpxljF8QjDVuAtq98BUJhLMFjdGLCIi+V7aBdj2qWM+6jWwXPf31CIieU6O/rLFxMTQp08fTp06RVBQEHXq1GHZsmXcdtttABw9ehSz+X8DW91yyy3MnTuXF198keeff57KlSuzcOFCatWqlbtXISIibmezG3yz9Tjv/byPuhfW8JHHAqpajzu3G37FMVWOghp3YarYBiyeboxWREQKlO1zIC0BilWBiu3cHY2IyA1hMgzDcHcQV5PdMdFFRMQ9Nhw6yys/7KFszM8M9/iayuYTABheAZgaPgw17oKSDeBfSX4pmHTPzjv0WUihYbfBew3g/F/Q5R1o3M/dEYmI5Eh279lqWygiItcsPiWDVxbtZf+2tYzx/Iwm1v0AGN5BmJo9jqnpY+AT7N4gRUSkYDuw1JG88Q6Gur3cHY2IyA2jBI6IiFyT9YfO8Py8DTyU8ilve/0EgOHhg6n5k5giB4F3kJsjFBGRQmHDVMdro4fB6ufeWEREbiC1ZRcRkRz7bMNfTPhkDp+mDeMhD0fyhjo9MT2xFdo8r+SN5Dnjxo2jcePGBAQEEBoaSteuXdm/f79LmdatW2MymVymxx57zKXM0aNH6dKlC76+voSGhvLMM8+QmZnpUmb16tU0aNAALy8vKlWqxKxZsy6J5/3336dcuXJ4e3vTtGlTfvvtN5ftqampDBo0iKJFi+Lv70/37t05ffp07rwZIgXJqd/h71/B7AGNH3V3NCIiN5QSOCIikiPvrviTHT9MY67Hy5Qxx2IPLAUPLoBuH0BQKXeHJ5KlNWvWMGjQIDZu3Mjy5cvJyMigQ4cOJCUluZR79NFHOXXqlHMaP368c5vNZqNLly6kp6ezfv16Zs+ezaxZsxg1apSzzJEjR+jSpQtt2rRhx44dDB06lP79+ztH7AT48ssvGT58OKNHj2bbtm3UrVuXqKgoYmJinGWGDRvGDz/8wPz581mzZg0nT56kW7duN/AdEsmnNk5zvNboqnuQiBR46sRYRESy7ZNfj3BqyXhe8JwLgFGtC6au08Fbf5vFIb/cs2NjYwkNDWXNmjW0bNkScLTAqVevHpMmTcpynyVLlnD77bdz8uRJSpQoAcD06dMZMWIEsbGxWK1WRowYweLFi9m9e7dzv169ehEXF8fSpUsBaNq0KY0bN2bKlCkA2O12IiIieOKJJ3juueeIj4+nePHizJ07lx49egCwb98+qlevzoYNG2jWrFm2rjG/fBYi1+zCaZhYE+wZ0H8llG7o7ohERK5Jdu/ZaoEjIiLZsunwWQ4vfdeZvKHFMEz3fq7kjeRL8fHxAISEhLisnzNnDsWKFaNWrVqMHDmS5ORk57YNGzZQu3ZtZ/IGICoqioSEBPbs2eMs0759e5djRkVFsWHDBgDS09PZunWrSxmz2Uz79u2dZbZu3UpGRoZLmWrVqlGmTBlnmaykpaWRkJDgMokUaJs/ciRvIpoqeSMihYI6MRYRkauKSUhlxpx5TLfMBsC49WlM7V5yc1Qi18ZutzN06FCaN29OrVq1nOvvv/9+ypYtS8mSJdm5cycjRoxg//79fPvttwBER0e7JG8A53J0dPQVyyQkJJCSksL58+ex2WxZltm3b5/zGFarleDg4EvKXDxPVsaNG8fYsWNz8E6I5GMZqbDlY8d8s4HujUVE5CZRAkdERK7IMAxGf7Ga1zLfxmqykVntTjzavujusESu2aBBg9i9eze//vqry/oBAwY452vXrk14eDjt2rXj0KFDVKxY8WaHmWMjR45k+PDhzuWEhAQiIiLcGJHIDbT5I0g+C0ERUO0Od0cjInJT6BEqERG5oi82HaXn8dcJN50jPbgiHndPBZPJ3WGJXJPBgwezaNEiVq1aRenSpa9YtmnTpgAcPHgQgLCwsEtGgrq4HBYWdsUygYGB+Pj4UKxYMSwWS5Zl/n2M9PR04uLiLlsmK15eXgQGBrpMIgVSynlY+5ZjvtUIsOg7aREpHJTAERGRyzqbmMbeJdNpbfmdTLMV6/1zwCvA3WGJ5JhhGAwePJgFCxawcuVKypcvf9V9duzYAUB4eDgAkZGR7Nq1y2W0qOXLlxMYGEiNGjWcZVasWOFynOXLlxMZGQmA1WqlYcOGLmXsdjsrVqxwlmnYsCGenp4uZfbv38/Ro0edZUQKtV/egdQ4CK0B9e53dzQiIjeN0tUiInJZM3/exjPMAsDc9kUIre7egESu0aBBg5g7dy7fffcdAQEBzr5kgoKC8PHx4dChQ8ydO5fOnTtTtGhRdu7cybBhw2jZsiV16tQBoEOHDtSoUYMHH3yQ8ePHEx0dzYsvvsigQYPw8vIC4LHHHmPKlCk8++yzPPLII6xcuZKvvvqKxYsXO2MZPnw4ffv2pVGjRjRp0oRJkyaRlJTEww8/7IypX79+DB8+nJCQEAIDA3niiSeIjIzM9ghUIgVW/HHYNMMxf9vLYLa4Nx4RkZtICRwREcnSibgUgra+T5AlmcTgavhHDnJ3SCLXbNq0aYBjqPB/mzlzJg899BBWq5Wff/7ZmUyJiIige/fuvPji//p7slgsLFq0iIEDBxIZGYmfnx99+/bl5ZdfdpYpX748ixcvZtiwYUyePJnSpUvz0UcfERUV5SzTs2dPYmNjGTVqFNHR0dSrV4+lS5e6dGw8ceJEzGYz3bt3Jy0tjaioKKZOnXqD3h2RfGTVOLClQblboVL7q5cXESlATIZhGO4O4mqyOya6iIjknte+WMFT+3ribcrAuP8rTFWirr6TFHq6Z+cd+iykwInZB9MiwbBD/xVQupG7IxIRyRXZvWerDxwREblETEIqFfa+j7cpgwslGmGq3MHdIYmISGG34mVH8qb6HUreiEihpASOiIhc4us12+hmXgNAQKeXNeqUiIi419FNsH8xmMzQdpS7oxERcQslcERExEVKug3L1o/xMmUSF1IXyjV3d0giIlKYGQb8PNoxX/8BKF7FvfGIiLiJEjgiIuJi4eaD9DCWARDQdpiboxERkULvwDI4ugE8vKH1SHdHIyLiNkrgiIiIk91ucHLtLIqaLnDBOxxL9TvcHZKIiBRmdhusGOuYb/p/EFjSvfGIiLiREjgiIuK08o9o7kpZCIC1+SCweLg3IBERKdx2fgUxe8E7CFqoVaiIFG5K4IiIiNOWFV9RyXySVIsfXo37ujscEREpzDLTYNXrjvkWw8CniHvjERFxMyVwREQEgN0n4mkR+yUAmfUeBO9AN0ckIiKF2uaPIf4oBIRDk/9zdzQiIm6nBI6IiACw5OefaGHZgw0z/rcOcnc4IiJSmKUnwy9vO+ZbPwdWX/fGIyKSByiBIyIiHD+fTMWDswG4UKELBJdxc0QiIlKo7foKks867kf1HnB3NCIieYISOCIiwqdLfuV283oAgjV0uIiIuJNhwMZpjvmmj6lDfRGRfyiBIyJSyB2OTaTiH1OxmmxcCI+E0g3dHZKIiBRmfy6H2H1g9Yf6an0jInJRjhI448aNo3HjxgQEBBAaGkrXrl3Zv3//FfeZNWsWJpPJZfL29r6uoEVEJPd8/uMqupvXABDQaayboxERkULNboeVrzjmGz7kGD5cRESAHCZw1qxZw6BBg9i4cSPLly8nIyODDh06kJSUdMX9AgMDOXXqlHP6+++/rytoERHJHfuiE6hzcBoeJjsXItpCmabuDklERAqzvQsheqej9U2L4e6ORkQkT8nRA6VLly51WZ41axahoaFs3bqVli1bXnY/k8lEWFjYtUUoIiI3zJeLlvHSP33fBHQa7eZoRESkULNlwqrXHPORg8GvqHvjERHJY66rD5z4+HgAQkJCrlguMTGRsmXLEhERwV133cWePXuuWD4tLY2EhASXSUREcteek/E0/vsDzCaDxAqdoWQ9d4ckIiKF2e9fwNmD4BMCkYPcHY2ISJ5zzQkcu93O0KFDad68ObVq1bpsuapVq/LJJ5/w3Xff8fnnn2O327nllls4fvz4ZfcZN24cQUFBzikiIuJawxQRkcv4ZvFSOlt+w44J/46j3B2OiIgUZhmpsPoNx/ytT4F3oHvjERHJg645gTNo0CB2797NvHnzrlguMjKSPn36UK9ePVq1asW3335L8eLFmTFjxmX3GTlyJPHx8c7p2LFj1xqmiIhkYfeJeBof/RCApEp3QGh1N0ckIiKF2paPIeE4BJaCxv3cHY2ISJ6Uoz5wLho8eDCLFi1i7dq1lC5dOkf7enp6Ur9+fQ4ePHjZMl5eXnh5eV1LaCIikg3f/riEUZbN2DER0OEFd4cjIiKFWWoC/DLBMd9qBHj6uDceEZE8KkctcAzDYPDgwSxYsICVK1dSvnz5HJ/QZrOxa9cuwsPDc7yviIhcv+1Hz9PU2frmTgit5uaIRESkUNvwPiSfhaKVoF5vd0cjIpJn5SiBM2jQID7//HPmzp1LQEAA0dHRREdHk5KS4izTp08fRo4c6Vx++eWX+emnnzh8+DDbtm3jgQce4O+//6Z///65dxUiIpJt3/zwPVGWLf+0vnne3eGIiEhhlnQGNkxxzLd9CSzX9ICAiEihkKO/kNOmTQOgdevWLutnzpzJQw89BMDRo0cxm/+XFzp//jyPPvoo0dHRFClShIYNG7J+/Xpq1KhxfZGLiEiO/bQnmqjoD8ACKdXvwU+tb0RExJ3WTYb0RAivBzXucnc0IiJ5Wo4SOIZhXLXM6tWrXZYnTpzIxIkTcxSUiIjkvvjkDJYvmMlblt3YTB74dXjR3SGJiEhhlnIetnzimG89Ekwm98YjIpLHXfMoVCIikn8YhsGr32xgWMYHjuVmg6BIWTdHJSIihdpvHzpa35SoBVWi3B2NiEiepwSOiEgh8O3W49x64HVKms6RFlAWjzbPuTskEREpzNKTYKOjewZaDFPrGxGRbFACR0SkgNv693k2fjeVOy0bsGPBq+cnYPV1d1giIlKYbZ0NKeegSHmo0dXd0YiI5Avq5l1EpADbeTyOybPm8IHZMWw4rZ6F0o3cG5SIiBRumemw/j3HfIuhGnlKRCSb9NdSRKSAWn/oDBNnf8V00xt4mzLIrBSFR6tn3B2WiIgUdttmw4WTEBAOde9zdzQiIvmGEjgiIgXQsj3RfD7vcz4xv02AKQVbeH087vkEzBZ3hyYiIoVZajyseMUx32I4eHi5Nx4RkXxECRwRkQJm/pZj/LzgYz7yeA8vUya2srdiuf8L8PJ3d2giIlLYbf4Y0uKhWFVo3M/d0YiI5CvqxFhEpAD56JfD/LbgXaZ6TMLLlIm9ahcsD3wNXgHuDk3ErcaNG0fjxo0JCAggNDSUrl27sn//fpcyqampDBo0iKJFi+Lv70/37t05ffq0S5mjR4/SpUsXfH19CQ0N5ZlnniEzM9OlzOrVq2nQoAFeXl5UqlSJWbNmXRLP+++/T7ly5fD29qZp06b89ttvOY5FJN/JSIGNUx3zLYapVaiISA4pgSMiUgAYhsE7yw9weulbvOX5ARaTgVHvAcz3fgqe3u4OT8Tt1qxZw6BBg9i4cSPLly8nIyODDh06kJSU5CwzbNgwfvjhB+bPn8+aNWs4efIk3bp1c2632Wx06dKF9PR01q9fz+zZs5k1axajRo1yljly5AhdunShTZs27Nixg6FDh9K/f3+WLVvmLPPll18yfPhwRo8ezbZt26hbty5RUVHExMRkOxaRfGn755AUC0FloHYPd0cjIpLvmAzDMNwdxNUkJCQQFBREfHw8gYGB7g5HRCRPMQyD8Uv34bvuDZ7wWOhYd8uTmG57GUwm9wYnhU5+uWfHxsYSGhrKmjVraNmyJfHx8RQvXpy5c+fSo4fjH8t9+/ZRvXp1NmzYQLNmzViyZAm33347J0+epESJEgBMnz6dESNGEBsbi9VqZcSIESxevJjdu3c7z9WrVy/i4uJYunQpAE2bNqVx48ZMmTIFALvdTkREBE888QTPPfdctmLJSlpaGmlpac7lhIQEIiIi8vxnIYWELRPeqw9xR6HTW9B0gLsjEhHJM7Jbf1ILHBGRfMwwDF75YS/B615xJm9oPwZTh1eUvBG5gvj4eABCQkIA2Lp1KxkZGbRv395Zplq1apQpU4YNGzYAsGHDBmrXru1M3gBERUWRkJDAnj17nGX+fYyLZS4eIz09na1bt7qUMZvNtG/f3lkmO7FkZdy4cQQFBTmniIiInL8xIjfKnm8dyRvfYlD/AXdHIyKSLymBIyKST9ntBi98u5Myv43h/zwWO1Z2esvRr4CIXJbdbmfo0KE0b96cWrVqARAdHY3VaiU4ONilbIkSJYiOjnaW+Xfy5uL2i9uuVCYhIYGUlBTOnDmDzWbLssy/j3G1WLIycuRI4uPjndOxY8ey8W6I3AR2O/w60THfbCBYfd0bj4hIPqVRqERE8qFMm51n52+n2e6x3OuxBgMTptvfgUaPuDs0kTxv0KBB7N69m19//dXdoeQqLy8vvLw0JLPkQQeWQMxesAZA4/7ujkZEJN9SCxwRkXwmPdPOkLmbabPn+X+SN2ZMd09X8kYkGwYPHsyiRYtYtWoVpUuXdq4PCwsjPT2duLg4l/KnT58mLCzMWea/I0FdXL5amcDAQHx8fChWrBgWiyXLMv8+xtViEck37HZY+Zpjvsmj4BPs1nBERPIzJXBERPKR1Awbgz5dT9cDz3GHZSN2syeme2dB3V7uDk0kTzMMg8GDB7NgwQJWrlxJ+fLlXbY3bNgQT09PVqxY4Vy3f/9+jh49SmRkJACRkZHs2rXLZbSo5cuXExgYSI0aNZxl/n2Mi2UuHsNqtdKwYUOXMna7nRUrVjjLZCcWkXxjz7cQswe8gqD5k+6ORkQkX9MjVCIi+cTZxDSenrOBh4+/SEvLLuxmK+Zec6BKB3eHJpLnDRo0iLlz5/Ldd98REBDg7EsmKCgIHx8fgoKC6NevH8OHDyckJITAwECeeOIJIiMjnaM+dejQgRo1avDggw8yfvx4oqOjefHFFxk0aJDz0aXHHnuMKVOm8Oyzz/LII4+wcuVKvvrqKxYvXuyMZfjw4fTt25dGjRrRpEkTJk2aRFJSEg8//LAzpqvFIpIv2DJg1T+tb5o/CT5F3BuPiEg+pwSOiEgeZhgGJ+JSWLDtBF+v2cJ4JtHUsg+bhy+W++dBhVbuDlEkX5g2bRoArVu3dlk/c+ZMHnroIQAmTpyI2Wyme/fupKWlERUVxdSpU51lLRYLixYtYuDAgURGRuLn50ffvn15+eWXnWXKly/P4sWLGTZsGJMnT6Z06dJ89NFHREVFOcv07NmT2NhYRo0aRXR0NPXq1WPp0qUuHRtfLRaRfGHHHDh3GPyKQ9PH3B2NiEi+ZzIMw3B3EFeT3THRRUQKgrRMGxsPn2P53mhW7j2Nz4XD3G35lX6WJfiY0rF5BmB58Bso09TdoYpcQvfsvEOfhbhVRiq8Wx8unISOb0IzJXBERC4nu/dstcAREckDktMz+fmPGJbtjua3A8dokLGd9uatDLXsoJhXgrOcUboJli4TILyOG6MVERG5ii0fO5I3gaWh0cPujkZEpEBQAkdExE0ybHZ+/fMMC3ecYMOew7Syb6SbeTPvmHfjZc1wljM8vDGVagSRj2Oq2hlMJjdGLSIichVpF+CXCY751s+Bh4a3FxHJDUrgiIjcZDEXUpm66hBLd/xFvdTf6GpZx3jzdrwsmc4yRpFymKp2gaqdMEU0BQ+rGyMWERHJgY3TIPksFK0Ede9zdzQiIgWGEjgiIjdJeqadWeuP8NWKjfSzzWe4ZSOB1hTndiO0BqZa3aBqF0yh1dXSRkRE8p/kc7D+Pcd8mxfAon83RERyi/6iiojcBHa7wWMzf6XmX7P5weN7fDzSATCCSmOq1QPq3IupRE03RykiInKdNk2HtAQoUQtqdHV3NCIiBYoSOCIiN8GsdYcZcOxZmnn+AYBRJhJT2xcxlbkFzGY3RyciIpILUuJg43THfKtndX8TEcllSuCIiNxgx88nE/PTBB6x/EGm2RuPu9/HVKu7HpESEZGCZcP7kBYPxatDtTvcHY2ISIGTo7T4uHHjaNy4MQEBAYSGhtK1a1f2799/1f3mz59PtWrV8Pb2pnbt2vz444/XHLCISH7zzU+reco8DwBL5zegdg8lb0REpGC5EA0bpjjm24xU6xsRkRsgR39Z16xZw6BBg9i4cSPLly8nIyODDh06kJSUdNl91q9fz3333Ue/fv3Yvn07Xbt2pWvXruzevfu6gxcRyeuS0jIJ3/MhniYb50u2wtTwIXeHJCIikvtWvwEZyVC6MVS/093RiIgUSCbDMIxr3Tk2NpbQ0FDWrFlDy5YtsyzTs2dPkpKSWLRokXNds2bNqFevHtOnT8/WeRISEggKCiI+Pp7AwMBrDVdE5Kb7cfsRWiy8hUBTMkaf7zBVaO3ukERuKN2z8w59FnLTxB6Aqc3AsMHDS6DsLe6OSEQkX8nuPfu62jbGx8cDEBISctkyGzZsoH379i7roqKi2LBhw2X3SUtLIyEhwWUSEcmPTvz2HYGmZBI8i2Mql3WiW0REJF9bMdaRvKnaWckbEZEb6JoTOHa7naFDh9K8eXNq1ap12XLR0dGUKFHCZV2JEiWIjo6+7D7jxo0jKCjIOUVERFxrmCIibpOYlknZk4sBSKnWTf0BiIhIwXN0I+xbBCYztBvt7mhERAq0a/5vYtCgQezevZt58+blZjwAjBw5kvj4eOd07NixXD+HiMiNtnzrPlqxDYDQ5g+6ORoREZFcZhiwfJRjvv4DEFrNvfGIiBRw1zSM+ODBg1m0aBFr166ldOnSVywbFhbG6dOnXdadPn2asLCwy+7j5eWFl5fXtYQmIpJnnN74FV6mTM76VaRoWG13hyMiIpK79i2GY5vAwwdaP+/uaERECrwctcAxDIPBgwezYMECVq5cSfny5a+6T2RkJCtWrHBZt3z5ciIjI3MWqYhIPnIwJpF6cT8BYK1/n5ujERERyWW2TPh5jGM+chAEhrs1HBGRwiBHCZxBgwbx+eefM3fuXAICAoiOjiY6OpqUlBRnmT59+jBy5Ejn8pAhQ1i6dCkTJkxg3759jBkzhi1btjB48ODcuwoRkTxm6a+baGb+A4CAxkrgiIhIAbP9Uzj7J/gWheZD3B2NiEihkKMEzrRp04iPj6d169aEh4c7py+//NJZ5ujRo5w6dcq5fMsttzB37lw++OAD6taty9dff83ChQuv2PGxiEh+lpJuw2fnZwCcKxEJQVd+1FRERCRfSU+C1W845luNAG8NUy8icjPkqA8cwzCuWmb16tWXrLvnnnu45557cnIqEZF8a976/dxlrAATBN060N3hiIiI5K7V4yDxNBQpDw0fdnc0IiKFhsa0FRHJRakZNs6s/ZBipgSSfEpiqd7F3SGJiIjknsOrYf17jvmO48DD6tZwREQKEyVwRERy0Zfr/+RB2wIAvFo/DZZrGuxPREQk70k6Cwsec8w3fBiqdnJvPCIihYwSOCIiuSQ1w8bpNR8SZjpPkncYHg0fcHdIIiIiucMw4Psn4MIpKFYFol53d0QiIoWOEjgiIrnkq40HecD2LQBerZ8CDy83RyQiIpJLts6E/YvB7AndPwKrr7sjEhEpdJTAERHJBYlpmZxY9SElTedI9grFo2Efd4ckIiKSO84egmUvOObbj4bwuu6NR0SkALHbDZLTM7NVVp0ziIjkglmr9/KIbT6YwKv1cPD0dndIIiIi189uh+8GQ0YylLsVmg1yd0QiInnGhdQMouNTOZ+cQWJaBhdSM0lMyyTxn9d/L19IyyAxNZOkdBsp6TaS0zNJybCRmmHHnpacrfMpgSMicp1iL6SRse59SpjjSPYtiW/jR9wdkoiISO74bQYcXQ+efnDXFDCrAb+IFC5nEtPYdTyeXSfiOXYumeiEVE7FpxIdn0piWvZazuQWJXBERK7Txz9t4XHT9wD4dBilvm9ERKRgOHsIfh7rmO/wChQp59ZwRERutPNJ6ew64UjW7Dwex67j8ZyMT73iPoHeHhTz98Lf2wN/r38mbw8C/nn19/L837KXB75eFnytHvhaLfh4WvCxWshMTSJ80tXjUwJHROQ6/H02iWI73ifQkkxSkWr41bnX3SGJiIjkjqXPQWYKlG8FjdS6VEQKlvjkDGeyZveJeHaeiOPYuZRLyplMULG4P7VLBVEp1J+wQG/Cg7wJ+2fytV5/WiXBnpatckrgiIhch48X/8IL5p8A8Ov0Cpgtbo5IREQkFxxaCX/+BGYPuH2i4z8YEZF8JtNm51R8KsfOJXP0n+mvs0nsPpHA0XNZ9ztTvpgftUsFUad0ELVLBVGzVBD+XnkjdZI3ohARyYe2/HWO2gfex8sjg6TwZvhVvs3dIYmIiFw/uw2WveiYb/woFK3o3nhERC7jYoLmRFwKJ86nuLweO5/MifMpZNqNy+4fEeJDnVLB1CoVRN3SjmRNkI/nTbyCnFECR0TkGmTa7Mz6eiHvWn4BwK/zq/p2UkRECoYdcyBmD3gHQatn3R2NiBRidrvB6QupHDuXwrFzyRw7n+yY/yc5cyo+hSvkZwCwWsyULuJDRIgvZf6ZqocHUqtUIMG+1ptzIblECRwRkWswa91h+iW8j9lskF7tbqwRjd0dkohcxdq1a3nrrbfYunUrp06dYsGCBXTt2tW5/aGHHmL27Nku+0RFRbF06VLn8rlz53jiiSf44YcfMJvNdO/encmTJ+Pv7+8ss3PnTgYNGsTmzZspXrw4TzzxBM8+6/pP8Pz583nppZf466+/qFy5Mm+++SadO3d2bjcMg9GjR/Phhx8SFxdH8+bNmTZtGpUrV87ld0XkP9ISYeWrjvmWz4JviHvjEZECKzXDRkJqBvHJGY6RneIcozudine0oDl+3tGaJt1mv+JxrBYzJYO9KVXEh1LBPpQK9qVUER9KF/GhTIgvJQK9sZgLxhetSuCIiOTQibgUjv08g/7mg2R4+GHtPM7dIYlINiQlJVG3bl0eeeQRunXrlmWZjh07MnPmTOeyl5frqHK9e/fm1KlTLF++nIyMDB5++GEGDBjA3LlzAUhISKBDhw60b9+e6dOns2vXLh555BGCg4MZMGAAAOvXr+e+++5j3Lhx3H777cydO5euXbuybds2atWqBcD48eN59913mT17NuXLl+ell14iKiqKvXv34u3tfSPeHhGHdZMh8bRjxKkmj7o7GhHJB+x2g/iUDM4mpXM2Me0/r+nEpWSQkJJBQurF10wSUjJIy7xyYuYii9lEyWBvIor4OqYQR2ua0kV8iSjiQzF/L8wFJEFzNSbDMK7S4Mj9EhISCAoKIj4+nsDAQHeHIyKFmM1uMGDaEt6OeZQipkTsHV7DfMtgd4clkmfkl3u2yWTKsgVOXFwcCxcuzHKfP/74gxo1arB582YaNWoEwNKlS+ncuTPHjx+nZMmSTJs2jRdeeIHo6GisVkez7Oeee46FCxeyb98+AHr27ElSUhKLFi1yHrtZs2bUq1eP6dOnYxgGJUuW5KmnnuLpp58GID4+nhIlSjBr1ix69eqVZXxpaWmkpf1vFIuEhAQiIiLy/GcheUj8CXivoWPkqXs/hRp3uTsiEXGjlHQbf51N4ti5ZM4mpXMuKZ0ziWmcTUznbNLFV8d629WeY7oMkwkCvT0JC3SM6HRxdKeSQT6UDvEhoogv4UHeeFjMuXx1eUt2609qgSMikgNvLvmD+6PHU8SSSHrRGlibPubukEQkF61evZrQ0FCKFClC27ZtefXVVylatCgAGzZsIDg42Jm8AWjfvj1ms5lNmzZx9913s2HDBlq2bOlM3oDjMaw333yT8+fPU6RIETZs2MDw4cNdzhsVFeVMHB05coTo6Gjat2/v3B4UFETTpk3ZsGHDZRM448aNY+zYsbn1VkhhtPJVR/KmTCRUv9Pd0YjITXI+KZ1dJ+LZH32Bw2eS+OtMEkfOJBGdkJqj4wR6e1DU34uiflaK+lud88G+VgK9PQjy8STQx5NAb08CfTwI9PHE3+pRaFrP5AYlcEREsunjX49gXj+Zdp7bsZmtWO/9CCz6MypSUHTs2JFu3bpRvnx5Dh06xPPPP0+nTp3YsGEDFouF6OhoQkNDXfbx8PAgJCSE6OhoAKKjoylfvrxLmRIlSji3FSlShOjoaOe6f5f59zH+vV9WZbIycuRIl8TQxRY4Itlycgf8/oVjvsNr6phfpACKT8lwDqd95EwSu0/Es+tEPMfPp1x2nyAfT8oW9aWYMzHjRTF/R4ImxM+xrpi/F0X8PPHysNzEqymc9J+HiEg2fPTLYXYu+Yh3rfMAsHR8HUrUdHNUIpKb/t2ypXbt2tSpU4eKFSuyevVq2rVr58bIssfLy+uSPntEssUw4KcXAQNq9YDSDd0dkYhkQ6bNTlxKBuf/eYzpfHI655IyOJ+c7lj3z2tsYhrHzqUQn5Jx2WOVK+pLzZJBVCjuR7mifpQv7kf5on4U8ctfozQVdErgiIhcQUJqBmO/30vajvm84zkdAKPpQEyN+7s5MhG50SpUqECxYsU4ePAg7dq1IywsjJiYGJcymZmZnDt3jrCwMADCwsI4ffq0S5mLy1cr8+/tF9eFh4e7lKlXr17uXaDIRQeWwV+/gMUL2o92dzQihVpSWiYn4hyjL51OSHUmYc4lZRCXnP6v5XQSUjNzfPziAV7/Gko7gFqlgqhZMoggH88bcDWS25TAERHJQqbNzg87TzJx6V7uSZrDE9aFABi178EU9bqalosUAsePH+fs2bPOJEpkZCRxcXFs3bqVhg0dLRRWrlyJ3W6nadOmzjIvvPACGRkZeHo6KsPLly+natWqFClSxFlmxYoVDB061Hmu5cuXExkZCUD58uUJCwtjxYoVzoRNQkICmzZtYuDAgTfj0qUwsdtgxT99JzUbCMFl3BuPSCFxPimdPScT2H/6An+evsCB047+Z+KSL99K5nKCfT0J8bVSxM9KEV8rIX6e/5v3dTzu5Bi1yQdfq1IA+Zk+PRGRfxiGwa4T8SzYfoIffj9JhaTfed/zM2p7/OUo0OT/MHUcB+aC3Qu+SEGVmJjIwYMHnctHjhxhx44dhISEEBISwtixY+nevTthYWEcOnSIZ599lkqVKhEVFQVA9erV6dixI48++ijTp08nIyODwYMH06tXL0qWLAnA/fffz9ixY+nXrx8jRoxg9+7dTJ48mYkTJzrPO2TIEFq1asWECRPo0qUL8+bNY8uWLXzwwQeAY4SsoUOH8uqrr1K5cmXnMOIlS5Z0GTVLJFfs/gZi9oJ3ELQY6u5oRAoku93gj+gENh0+x/Zjcew8HsffZ5MvWz7Q24NS/4y+FOJnJcTPmmWSJtjXSrCPZ4EfoUn+RwkcESm0Mm12DsYmsvN4PBsOneXXg2cwXzjFnZb1zLaso6bX3wAYPkUw3T4Rat7t5ohF5Hps2bKFNm3aOJcvdvjbt29fpk2bxs6dO5k9ezZxcXGULFmSDh068Morr7j0KzNnzhwGDx5Mu3btMJvNdO/enXfffde5PSgoiJ9++olBgwbRsGFDihUrxqhRoxgwYICzzC233MLcuXN58cUXef7556lcuTILFy6kVq1azjLPPvssSUlJDBgwgLi4OFq0aMHSpUvx9va+kW+RFDa2DFj1mmO++RDwKeLeeEQKiAybnX2nLrDpyFk2Hj7H5r/OZdn/TLmivlQNC6BKiQAqlwigUnF/IkJ8CPDW40ySNZNhGNc2YPtNlN0x0UVELicxLZNDMYn8GZPo7HF/z8l4rBkJ1DYfoan5DyLNe2lo+hOzyfFn0TB7YmrQB1qNgIASVzmDiIDu2XmJPgu5qi2fwKJh4FcchvwOVj93RySSL0XHp7L96Hm2H4tj+9Hz7DoRT2qG3aWMn9VCo3IhNC5XhLoRwdQpFUyQrxI14pDde7Za4IhIgXI+KZ0Dpy/wZ0wiB2MSORTreD0Xn0Al0wmqmY5RxXyMNqZjVDEfJ9z73KUHKRMJde7FVKMr+Ibc9GsQERG54TLTYO3bjvlbn1byRiSbElIz2HU8nh3H4vj9WBy/H4/jdELaJeUCvT1oWLYIzSoUpWmFotQqGahHneS6KYEjIvmSYRgcP5/CjmNx7DgWx77oBA6cTuTshRTKmk5T1XSMauaj3Gc6TlXTMcp5RWMxXabBYXAZR9KmbHOo2EYdOIqISMG3Yy4knICAcGj4kLujEcmT0jJt/HHqgiNRcyyOHcfjOBybdEk5swmqhQVSv0ww9csUoX6ZYMoX9cNs1qAXkruUwBGRfONMYhqr98eyct9pfjtynoTERKqYjlHbfITOpr941nyEql7H8DZdpvd+nxAoURNCq0NoDcd88WrgrUcLRESkELFlwC/vOOabDwVP9a0kApCSbmPj4bOs/TOWbX+fZ++pBDJsl34BGBHiQ93SwdSLCKZuRDA1SwZqdCe5KXL8U7Z27Vreeusttm7dyqlTp1iwYMEVR0RYvXq1S4eBF506dYqwsLCcnl5ECpm0TBtLd0czZ+NRfv/7NI1M+2hh3s3/mfdQ3etvrCbbpTt5+EBoNQj9J1lTooZj3j9Uw3+LiIjs/Arij4JfKDTs6+5oRNzqfFI63/9+kuV7T/PbkXOk21z7rgnxs1K3dBB1/0nW1CkVRFF/r8scTeTGynECJykpibp16/LII4/QrVu3bO+3f/9+l854QkNDc3pqESlEEtMy+XDtYeZuPEK1lG3cY17PR9YtBJr+M+SiTxEIrwcl60F4XQirA0XKgdnihqhFRETyOMOATdMd85GPg6ePe+MRcQPDMNhw6CxzfjvK8j2nXZI2pYJ9aFmlOJEVi1I/IpjSRXww6QtAySNynMDp1KkTnTp1yvGJQkNDCQ4OzlbZtLQ00tL+1xFUQkJCjs8nIvlTps3OvM3HeHf5HzRL+YXPPL6jmvXY/wr4h0HFtlChNZRp5uivRjdVERGR7Dm+GaJ3goc3NFDrGylcbHaDpbujmb7mELtOxDvX1wgPpFuDUrSpFkqFYn5K2EieddMe1KtXrx5paWnUqlWLMWPG0Lx588uWHTduHGPHjr1ZoYlIHvHrn2d4+ftdVDm7gi89vqK89TQAhjUAU517oXYPiGgGZvXgLyIick1++9DxWquHRlqUQsMwDJbsjmbCT/s59E8nxN6eZro3KM19TcpQq1SQmyMUyZ4bnsAJDw9n+vTpNGrUiLS0ND766CNat27Npk2baNCgQZb7jBw5kuHDhzuXExISiIiIuNGhioib2OwGk38+wLer1vOWxwdEWvcCYPiEYGr2OKYmj4JPsHuDFBERye8SY2DPAsd8k/7ujUXkJjl6NpmXvtvNmgOxAAT5eNL3lnL0jSyrvmwk37nhCZyqVatStWpV5/Itt9zCoUOHmDhxIp999lmW+3h5eeHlpV8mkcLgfFI6Q+Ztp+Thr1hq/Rx/UyqGpy+mFsMwNXscvPzdHaKIiEjBsG022DOgVCMoWd/d0YjcUNHxqby/6iDzNh8lw2ZgtZh5rFUFHm1ZgQBvT3eHJ3JN3DLWWZMmTfj111/dcWoRyUN2Ho/j2c/W8EzyRNp5bnesLBOJqetUCKng3uBEREQKEsNwjD4F0OgR98YicgPFXEhl6qpDzP3tKOmZjs6JW1Qqxti7alKxuL4YlPzNLQmcHTt2EB4e7o5Ti0geMe+3o8z57kc+sLxNGUssdosX5najoNlAjSAlIiKS26J3wZkDYPGC6ne4OxqRXHcmMY0Zaw7x2ca/Sc1wJG6alAth2G1ViKxY1M3RieSOHCdwEhMTOXjwoHP5yJEj7Nixg5CQEMqUKcPIkSM5ceIEn376KQCTJk2ifPny1KxZk9TUVD766CNWrlzJTz/9lHtXISL5RmqGjdHf7SFx23y+9JyBrykNW3A5LL3mQFgtd4cnIiJSMO3+xvFapQN4B7o3FpFcdCIuhQ/XHmbe5qPOxE2DMsEMv60qzSsV1YhSUqDkOIGzZcsW2rRp41y+2Nlw3759mTVrFqdOneLo0aPO7enp6Tz11FOcOHECX19f6tSpw88//+xyDBEpHI6dS+aJzzfSMeZjHrMuAsCo0BZLj481EoaIiMiNYhiw+1vHfK0e7o1FJBfY7AZrDsQwd9MxVu47jd1wrK9bOohht1WhVZXiStxIgWQyDMNwdxBXk5CQQFBQEPHx8QQG6hsDkfxo9f4Y3p33Ay/b3qWW+S/HyuZDoN1oPTIlUoDonp136LMQp2O/wce3gdUfnjkInj7ujkjkmpyKT+HLzcf4avMxTsanOtc3r1SUx1tX4paKanEj+VN279lu6QNHRAoPu91gyso/ObdqCl94zMXLnIHNOwTLnZOhxp3uDk9ERKTgu/j4VLUuSt5IvpNps7PmQCxf/HaUlftinK1tgn096dGgNL2alKFSqDonlsJBCRwRuWHikzN4dt4mOh55nSc91wFgq9geS9f3ISDMzdGJiIgUAnYb7FngmK/V3b2xiGSTzW6w6chZFu08xdLd0ZxLSndua1YhhPualCGqZhjenmrFLYWLEjgickNs/fs8r81dzpiU16ljOYLdZMEc9RqWpo+BmraKiIjcHH/9ComnwacIVFAflJJ3xadksO7gGVbvj2HlvljOJKY5txX1s9KtQSl6NSmjocClUFMCR0RyVVqmjWmrD7F11UJmeLxHcXMCmV5F8Oj1KZRv6e7wRERECpeLj09VvxM8rO6NReRfUtJt/H48ji1/nWPtgTNsPXoem/1/3bMG+XjSsWYYd9QtSbMKIXhYzG6MViRvUAJHRHJFdHwqP+2NZvYvB+kS/wWzPb7BbDKwhdbC4765UKSsu0MUEREpXDLT4Y/vHfO1NfqUuEdapo3YC2kcOZPE4dgkDscmsuN4PHtOxJNpdx1Pp2JxP1pXDaV11eI0LV8Uq4eSNiL/pgSOiFyWYRjEp2RwJjGds4lpjtektH8tp3E2MZ0TcSmci0+gg3kL73n8QA3Pvx0HaNAHS6fx6jBRRETEHQ6vgpTz4B8GZZu7OxrJx9IybSSn2UhMyyQpPZOktEyS0mwkpWVyIS2Tc0npnE9K52xSOuf+MyWmZV72uCUCvWhUNoRmFYvSukpxIkJ8b+JVieQ/SuCIFHJ2u8FfZ5PYeTyePSfjORGXwqn4VE7HpxKbmEaGzcCDTEK4QDFTPEVNCRQlgaKmeOr9M1/BfIqaXn/hY3J0MGd4F8HU6U2o29PNVyciIlKIXXx8qubdYFZnr4Vdps3O2aR04lMyiEvO+OfVseyy7p/l+H+2JaZlkmEzrn6CK/AwmyhT1JcKxfypWNyP6uGBNCxbhNJFfDTst0gOKIEjUgglpWWy9kAsP+09zar9MaQlX6CG6W9qmv+irukMUabzhJnOUcwST1GPBIJNSdk7cFAE1O2Fqelj4Ffsxl6EiIiIXF56Muxb7Jiv1c29schNYbMbnIxL4fj5FI6fT+b4+RROxP1v/lR8qksfM9fCy8OMv5cHfl4e+FotzvkQP6tzKupnpcg/rxfXBXp7YjYrUSNyvZTAESkkEtMyWfHHaRbtPMXaA6epYfuT9patPGr+napeR7GYrnJDN5nBtxj4FXckZ5yvxSCoDJSsB0Urg1nPKouIiLjd/h8hPRGKlIPSjd0djeSiC6kZ/H02mcNnkjgSm8TB2EQOxiRyODaRtEz7Ffc1mxydAwf5eBLkayX4n/lgX0/n+mBfq8u6AG8PfK0e+Fkt6khYxM2UwBEp4I6cSeKjXw7z3ba/aGDbRSfzb7xu2UpxjwTXgv5hULI+hFSAwJIQGA7+JRyJGt9ijuFHlZwRERHJH36f53it0xP0iIrbGYZBWqadlHQbyRk2UtIzSU63/TM5+pNJTs8kMc1GclomSf9aH5+S8U8fhGmcuZBOSobtsuexWsyUKuJD6X+mUsE+lC7i65gv4kNogDcWtYQRybeUwBEpoHYej+P9VQfZvncffS3LWGtZSYgl0bnd8ArEVKk9VO0E5Vo4kjYiIiKS/yXGwKGVjvk66o/uRrDZDWIvpHEq3vFo0qn4VM4mpnE+OYPzSemcS04nLjmduOQMZ5LmOp9eclHM30q5on6UK+ZHxeL+VA71p1KoPxEhvkrQiBRgSuCIFDAxF1IZv3Q/27dt4jHLD7xnXYfV5PimxvArjqn6HVD9DkxlW4CH1c3RioiISK7b/Q0YNijVCIpWdHc0+V6Gzc4fpxLYcSyOvScT+ONUAvuiL1z1caXLsVrM+Fgt+Fot+PzTj4yv1YKf1QNfL8ejSr5WD/y8LPj908dMcX8vigdYKe7vTbEAK75W/RsnUhjpN1+kgDAMgzmbjjJ1yRYG2ObxhvVnPEz/VCzKRELkYExVO2kUChERkYJu55eOV7W+uSZ2u8Huk/Gs3BfDpsPn2HEsLsvHljzMJkoEehMW5JiK+3tRxNdKET9Px6uvlWBfT2eHvz5WC76e6kdGRK6dEjgiBUB0fCrPzN9BiSPf8qPH5wR7/DNqVJVOcOtTEKHOC0VERAqF2ANwcjuYPTT6VA7tPhHPvM1H+WnPaWIupLlsC/T2oEHZItQqGUSNkoHUCA/U40oictMpgSOSz239+zzPf/ozI9Kn0NZzBwBGaA1MHd+ACq3cG5yIiIjcXLvmO14rtXeMFClXlJyeyQ+/n2TupqP8fjzeud7PauHWysW5tUoxGpcLoVJxfw2DLSJupwSOSD62dHc0H8/7ik8tEyhhicMwWzG1fR5T5BNg0a+3iIhIoXNgieO1xl3ujSOPS07P5NMNfzNjzSHOJ2cA4Gkx0bFWOD0alqZZhRC8PPTYuYjkLXoAUySfWrTzJN99MZ3PLWMpYYrDXqwapsd+gRbDlLwREcnC2rVrueOOOyhZsiQmk4mFCxe6bDcMg1GjRhEeHo6Pjw/t27fnzz//dClz7tw5evfuTWBgIMHBwfTr14/ExESXMjt37uTWW2/F29ubiIgIxo8ff0ks8+fPp1q1anh7e1O7dm1+/PHHHMcicomEkxC9CzBB5Q7ujiZPSsu08dEvh7n1zVW8sWQf55MzKBPiy8hO1dg4sh3v3VefVlWKK3kjInmSEjgi+dCqfTEs/XI673lMxsuUiVG1M+ZHf4bQau4OTUQkz0pKSqJu3bq8//77WW4fP3487777LtOnT2fTpk34+fkRFRVFamqqs0zv3r3Zs2cPy5cvZ9GiRaxdu5YBAwY4tyckJNChQwfKli3L1q1beeuttxgzZgwffPCBs8z69eu577776NevH9u3b6dr16507dqV3bt35ygWkUvsW+x4Ld1Ij0/9h2EYLNp5kvbvrOHVxX9wNimdskV9mXBPXVY+1Yr/a1WRov5e7g5TROSKTIZhGO4O4moSEhIICgoiPj6ewMBAd4cj4lY7j8fx3ozpTDO/iYfJjr1OT8xdp2l0KRHJE/LLPdtkMrFgwQK6du0KOP65K1myJE899RRPP/00APHx8ZQoUYJZs2bRq1cv/vjjD/6/vTsPb6pM+zj+PUmblBZati4gZd9lFRHqhiiKsrwiLiyjMiL6qogCoyKKAjoj7zijgIMOIio6igIuqIAogoBIQdmURTYFKtCWtS1dkybn/SM02BGELuQk7e9zXbmSnPOcc+6T3CQPd588p3Xr1nz//fdcfPHFACxevJhevXqxf/9+6taty7///W+efPJJ0tLScDgcADz++OPMnz+f7du3AzBgwABycnJYsGCBP56uXbvSoUMHpk+ffk6xnE5BQQEFBacmXs3KyiIxMTHo3wspRzOvhf3fwXV/hUtHWB1N0Fi39xh/W/QTG1MyAIir5mT0tc25uVM9wnVFKBEJAufaf9InlkgISTmay/Nvvs8U24u+4k2bW1W8EREpB3v27CEtLY0ePXr4l8XExNClSxeSk5MBSE5Opnr16v7iDUCPHj2w2WysXbvW3+bKK6/0F28AevbsyY4dOzh+/Li/zW+PU9Sm6DjnEsvpTJo0iZiYGP8tMTGxtC+HhKKjP/uKN4YN2t5qdTRBYd/RHB54dz23TE9mY0oGkQ47o3o0Z/mjVzHwkvoq3ohIyNFEGSIh4liOizGvf8ZLhc8RZRRQ2LAbYf1eUfFGRKQcpKWlARAfH19seXx8vH9dWloacXFxxdaHhYVRs2bNYm0aNWr0u30UratRowZpaWlnPc7ZYjmdsWPHMnr0aP/zohE4Ukmsn+W7b3I1VEuwNBSrZea6eXn5bmZ9uxeXx4vNgAGd6zPq2mbEVYuwOjwRkVJTAUckBJimyVNzknk2ewKxtkzctVsTPvAdCHOcfWMREakUnE4nTqfm8KiU3Pmw8R3f44vvtjYWCxUUevhP8j7+tWw3mXm+K0td0aw243q3pkVCNYujExEpOxVwRELAvPX7ufKXF2kadhB3VB3C7/wQIjSfgYhIeUlI8I1YSE9Pp06dOv7l6enpdOjQwd/m0KFDxbYrLCzk2LFj/u0TEhJIT08v1qbo+dna/Hb92WIRKWbLh5B3DGISoXlPq6OxxLLt6Tzz2Tb2Hs0FoHl8Vcb2akX3FnFn2VJEJHToh58iQe7XY7ms/uxNBoQtx8Qg/NbXIbqu1WGJiFQojRo1IiEhgaVLl/qXZWVlsXbtWpKSkgBISkoiIyOD9evX+9ssW7YMr9dLly5d/G1WrlyJ2+32t1myZAktWrSgRo0a/ja/PU5Rm6LjnEssIn55x2HpRN/ji4dWup9W7z2Sw9BZ3zN01jr2Hs0ltpqT/+vflkUPXaHijYhUOBqBIxLE8lwexry1hGm8CoB52UiMhpdZHJWISGjKzs5m9+7d/ud79uxh06ZN1KxZk/r16zNy5Ej++te/0qxZMxo1asRTTz1F3bp1/VeqatWqFddffz333HMP06dPx+128+CDDzJw4EDq1vUV1gcPHszEiRO5++67GTNmDFu2bGHq1KlMnjzZf9yHH36Ybt268cILL9C7d2/ef/991q1b57/UuGEYZ41FxO+bFyE7HWo3h64PWB1NwKRn5fPK17t577tfcXm8hNsNhl7eiBFXN6OqU//FEZGKqcQjcFauXEnfvn2pW7cuhmEwf/78s26zfPlyLrroIpxOJ02bNmXWrFmlCFWkcnF7vIx6dy0PH3+OmkY2rti22Lo/YXVYIiIha926dXTs2JGOHTsCMHr0aDp27MjTTz8NwGOPPcaIESO499576dy5M9nZ2SxevJiIiFOTnr777ru0bNmSa665hl69enH55Zf7Cy/gu1rUl19+yZ49e+jUqRN/+ctfePrpp7n33nv9bS699FJmz57NjBkzaN++PR988AHz58+nTZs2/jbnEosI2Yfgu9d8j6/7G4RX/Pw4dCKfZz7bxpXPf81byftwebxc2TyWxSOvZOwNrVS8EZEKzTBN0yzJBp9//jnffvstnTp1on///nz88cd/+NegPXv20KZNG+677z6GDRvG0qVLGTlyJAsXLqRnz3P7je65XhNdpKLwek0embuRq7c+Th/7WgrDqxF271KIbWF1aCIif0jf2cFD70UlsPgJWPMyXHAxDPsKDMPqiM6bo9kFvLryF95O3ku+2wtA54Y1GHVtcy5tUtvi6EREyuZcv7NLXKK+4YYbuOGGG865/fTp02nUqBEvvPAC4Bt+vGrVKiZPnnzOBRyRyubVlb9Qf8u/6BO2Fq8tnLBB76p4IyIiIqdkpMC6132Puz9RYYs3Wflu/r38Z95avZdclweADonV+ct1zbm8aW2MCnreIiKnc97HGCYnJ9OjR49iy3r27MnIkSPPuE1BQQEFBQX+51lZWecrPJGgs37fcb5d8iFvh30MgO1/XoLG3SyOSkRERIKGacLCR6AwHxpcDk2utjqiclfo8TJn3a+8+OVOjua4AGhXL4ZR1zbnquaxKtyISKV03gs4aWlpxMfHF1sWHx9PVlYWeXl5VKlS5XfbTJo0iYkTJ57v0ESCTmaum6dnf82bYS9jM0zMDrdjdBhsdVgiIiISTDbPg11fgC0c+rxY4UbfrNp1hL8u3Mb2tBMANImNYsz1Lbm2dbwKNyJSqQXlLF9jx45l9OjR/udZWVkkJiZaGJHI+WeaJo9/sIkxuS8SZ8/AU7sF9l7PWx2WiIiIBJP0rfDZw77HV/ylQv3E+pfD2Ty36Ce++ukQADFVwhnVoxl/6tqAcHuJr70iIlLhnPcCTkJCAunp6cWWpaenEx0dfdrRNwBOpxOn03m+QxMJKu+s2UfDHTO5MnwzXnsE9tveAkeU1WGJiIhIsMjcD+/eBu5caNwduj1mdUTlIrugkClLdjJr9V4KvSZ2m8EdXRswskczqkc6rA5PRCRonPcCTlJSEosWLSq2bMmSJSQlJZ3vQ4uEjB9+zWDhwvm8EzYXAFvvf0BcK4ujEhERkaCReQDe7gdZ+6FWM7jlDbDZrY6qzJZtT2fcx1s4mJkPQPcWsTzZuxVN46pZHJmISPApcQEnOzub3bt3+5/v2bOHTZs2UbNmTerXr8/YsWM5cOAAb7/9NgD33Xcf06ZN47HHHmPo0KEsW7aMuXPnsnDhwvI7C5EQdjzHxePvLGem/SXCDC9m29swOt5hdVgiIiISLI7shv/0g8xfIboe3PExRNa0OqoyyXUV8tT8rXy4YT8AiTWr8MyNbejeIs7iyEREgleJCzjr1q2je/fu/udFc9UMGTKEWbNmkZqaSkpKin99o0aNWLhwIaNGjWLq1KnUq1ePmTNn6hLiIoDb4+Xh99bzaO5kLrAfxVOjMfYKOBmhiIiIlNLur+CDoZCfCbWawh3zoXpozw25+9AJ7n9nA7sOZWMzYNgVjRnZoxmRjqCcnlNEJGgYpmmaVgdxNllZWcTExJCZmUl0dLTV4YiUC6/XZPTcTdTfPI3R4R/gtTuxDfsK6rSzOjQRkVLTd3bw0HsR4rIPwdfPwYa3wPRCvc4w8D2oGmt1ZGXyyaYDjP1oM7kuD3HVnPxrUEe6NK5ldVgiIpY61+9slblFLGCaJs8u3Ebej/MZGf4hALa+U1W8ERERqew8bvhuBiz/PyjI8i27aAj0+geEhe5FPgoKPTzz2TbeXesbqX9Z01pMHdiR2lVD95xERAJNBRyRAPN6TZ5ZsI0daxYyK3waNsOEzvdAh0FWhyYiIiJW8Xph52L4agIc2eFbVqcDXD8JGlxqZWRllp6Vz/3vrGdDSgaGASOubsbD1zTDbtNPxkVESkIFHJEAynN5eOLjzfyyaQXvOF7EaRRCq75ww9+tDk1ERESs4HHD5nnw7VQ4vN23LLI29BgPHW4Hm83a+Mpo/b7j3P/Oeg6dKCA6IoyXBnXkKk1ULCJSKirgiATIvqM53PfOBmqkr2a24wWijAJoeAX0n1khLgMqIiIiJVDogh/fhxXP+64uBeCMhouHwuWjoEp1S8MrK6/X5PVVe3j+i+24PSbN46sy446LaVg7yurQRERClgo4IgHw1bZ0Rs3dyP+4v2C8420cFEKjbjBwNoRHWB2eiIiIBIJpwv7v4Yf3YetHkHfctzwqDpIe8BVvImKsjbEcHD5RwCPzfmDFzsMA9GqbwPO3tKeqU//1EBEpC32KipxHHq/Ji0t28MbXW3ku/HVuCv/Wt6J1P+g/I6QnIxQREZFzdGwP/DjHdzv2y6nlVRPg0hHQ+W4Ir2JdfOXom12HGTXnB45kF+AMs/F039YMvqQ+hqH5bkREykoFHJHz5Gh2AQ+9v5HUnzcz3zGFFrb9mIYdo8cEX2dNHRkREZGKyZUD+1bDz1/DL1/DoW2n1oVH+ua/az/QNxq3gvyM2u3x8sKXO5m+4mcAmsdXZdrgi2geX83iyEREKg4VcETOgw0pxxn+7gY6nljODMcMoox8qBqPccub0PAyq8MTERGR8mSavpE1v3wN2xfB3m/A4zq13rD5ijXtB0LLPuCsal2s58Gvx3IZ8d5GNv2aAcCfutTnqT6tiQivGMUpEZFgoQKOSDnyeE1eX/ULU77YyiPGuwx1LPataHgF3Pw6VIu3NkAREREpm5wjkL4VDv3kG1lz6CffzXWieLuY+tDkKmjc3Ve8iaplSbjn26c/HOTJjzZzoqCQ6Igw/n5zO25oW8fqsEREKiQVcETKyd4jOTwy7wf279vNfxwv0cm2y7fi8lHQfRzY9c9NREQkKLnzfIWZ3CO++6LHJ9Lg+F44kXpy2VFwZZ9+H7ZwSOwCTbpD6xuhVtMK/XPpXFchEz7dytx1+wG4uEENpgzsQL0akRZHJiJScel/lCJl5PWavLt2H88t+onrPCuZ6Xyb6kY2pjMa46ZXoWUvq0MUERERgNxjcHADHN4JR3bCkV2++5xDJdiJATUaQlxriGsF8a19j2s2gTDH+Yo8qGw9mMlD723k58M5GAaM6N6Uh65pRpjdZnVoIiIVmgo4ImVwICOPMR/8yM7du3gp/HWudWzwrajTHuPWWVCzsaXxiYiIVFpF89KkrIFf10DKWjiy48ztbeEQFev7qVNkbd/jqnG+Yk103ZPLakO1BHBEBew0gkmuq5CpX+1i5qo9eLwm8dFOpgzoSFKTivnzMBGRYKMCjkgpfbLpAOM+3sx17q952fkfYowcTFs4RrcxcPlIsIdbHaKIiEjl4nHDnpWw9WPY+cXpR9bUauobMVO7+clbM6jZCCKqV+ifPJXVkm3pTPh0Kwcy8gC4oU0Cf7upLTWjKseoIxGRYKACjkgJZeW7eWr+FtZs2sJL4a/R3fGDb0Xdjhg3vuIbSi0iIiKB48qBdW/A6n9Bdvqp5XYH1L0I6neBxK6+OWoq6GTC58uBjDwmfLqVJdt8r2u9GlV45sYLubqlLswgIhJoKuCIlMD3e48x8v1NtM/6mi+cr1PdyMG0OzCuGguXPqSJikVERALJ64XvZ8KK//NNMAy+nzq1/h/fRMKJXSE8wtoYQ5Rpmsz+LoXnFv5EjstDmM3gnisb89DVzaji0OXBRUSsoP9tipwDt8fLS0t38fbXP/J02Fvc7PjGt6JOB99ExXEtrQ1QRESksjmyGz59EFKSfc9rNIIrH4F2A/Qz5jI6mJHHmA9/5JtdRwDfFaae69+W5vHVLI5MRKRyUwFH5Cz2Hsnh4TmbcOxfw0LHK9QzjmAaNozLR8NVj6uTKCIiEkheDyS/DF//DQrzwVEVekyATndpJGw5WLwllUc/+JET+YU4w2w8dn1L7rq0ITab5gcSEbGavuVEzsA0Teat28/fPvuBe7xzud/5KXZMqN4Ao/8MqN/V6hBFREQql6M/w0f3wIH1vueNu8P/vATV61sbVwWQ7/YwadFPvJW8D4AOidV54bb2NImtanFkIiJSRAUckdNIz8rniY82s3fHRv4T/grtwvb4VnS4Ha6fBBHR1gYoIiJS2ez8Ej4cBgWZ4IyBnn+DjrfrylHlYO+RHIbP3sDWg1kA/G+3xjxyXQvC7TaLIxMRkd9SAUfkN0zT5KMNB3j2s83c7F7ANMdcqhguzCo1MPpO9U2IKCIiIoG19lX4fAxg+q4kdcubEHOB1VFVCJ/+cJAnPtpMdkEhNaMcvHBbe7q3iLM6LBEROQ2V1UVOSsvM5+631vHKB4t4wzuOp8LfoYrhgsbdMe5PVvFGRKSCmzBhAoZhFLu1bHlqkvr8/HyGDx9OrVq1qFq1KjfffDPp6enF9pGSkkLv3r2JjIwkLi6ORx99lMLCwmJtli9fzkUXXYTT6aRp06bMmjXrd7G8/PLLNGzYkIiICLp06cJ33313Xs45JKx/Cz5/DDB989wMWaDiTTkoKPTwxMebeei9jWQXFHJJw5oseugKFW9ERIKYCjhS6fnmuvmV6ycvo+Wu1/jcMZaLbLsxHdWg71S442OIrmN1mCIiEgAXXnghqamp/tuqVav860aNGsVnn33GvHnzWLFiBQcPHqR///7+9R6Ph969e+NyuVi9ejVvvfUWs2bN4umnn/a32bNnD71796Z79+5s2rSJkSNHMmzYML744gt/mzlz5jB69GjGjx/Phg0baN++PT179uTQoUOBeRGCybZPYcFI3+PLHoY+kyHMYWlIFcGR7AIGv7aW2WtTMAwYcXVTZt/ThYQYXXJdRCSYGaZpmlYHcTZZWVnExMSQmZlJdLTmHpHys/aXo0z+aieZezbyfPirtLXt9a1odp2vkxhTz9L4RERCTSh/Z0+YMIH58+ezadOm363LzMwkNjaW2bNnc8sttwCwfft2WrVqRXJyMl27duXzzz+nT58+HDx4kPj4eACmT5/OmDFjOHz4MA6HgzFjxrBw4UK2bNni3/fAgQPJyMhg8eLFAHTp0oXOnTszbdo0ALxeL4mJiYwYMYLHH3/8nM8nlN8LAPaugv/0B08BdPoz9Jmi+W7Kwd4jOfxp5loOZORRLSKMaYMvolvzWKvDEhGp1M71O1sjcKRSWvPLUQbNWMPQGV9zVcrLfOoYR1vbXsyI6nDTqzB4roo3IiKV0K5du6hbty6NGzfmT3/6EykpKQCsX78et9tNjx49/G1btmxJ/fr1SU5OBiA5OZm2bdv6izcAPXv2JCsri61bt/rb/HYfRW2K9uFyuVi/fn2xNjabjR49evjbnElBQQFZWVnFbiHr0HZ4b7CveNOyD/R+UcWbcpCWme8v3jSqHcX84ZepeCMiEkI0ibFUKsk/H2XKVzvZumc/Q+xf8opzETWMbN/Kln0wer8I1eL/eCciIlIhdenShVmzZtGiRQtSU1OZOHEiV1xxBVu2bCEtLQ2Hw0H16tWLbRMfH09aWhoAaWlpxYo3ReuL1v1Rm6ysLPLy8jh+/Dgej+e0bbZv3/6H8U+aNImJEyeW+LyDTvZhePdW39WmErvCzTPBZrc6qpB3LMfF7a/7ijcNa0Uy93+TiK3mtDosEREpgVKNwCnJxHqzZs363YSAERH6fa0EjmmarN59hNteTeb+15bQJWUmq5wP82j4XF/xplZTGDQHBr6r4o2ISCV2ww03cOutt9KuXTt69uzJokWLyMjIYO7cuVaHdk7Gjh1LZmam//brr79aHVLJeb3w0TDITIGajWHgbAivYnVUIS+7oJC73vyO3YeySYiO4J1hXVS8EREJQSUegVM0sd706dPp0qULU6ZMoWfPnuzYsYO4uNPPWh8dHc2OHTv8zw0NgZUA8HpNVuw6zCtf7yZ13w7utn/OLOdyIo0CX4PazeHKx6BNf/1lT0REfqd69eo0b96c3bt3c+211+JyucjIyCg2Cic9PZ2EhAQAEhISfvdHraKrVP22zX9fuSo9PZ3o6GiqVKmC3W7Hbreftk3RPs7E6XTidIb4f8pXvQi/LIewKjDwPYiqZXVEIS/f7eGet9bxw/5MakSG886wS6hXI9LqsEREpBRKPALnxRdf5J577uGuu+6idevWTJ8+ncjISN54440zbmMYBgkJCf7bfw8LFilPR7MLmL7iZ67653JenDWHOw48wwrHKO4K+8JXvEloB7e8AQ+sgXa3qngjIiKnlZ2dzc8//0ydOnXo1KkT4eHhLF261L9+x44dpKSkkJSUBEBSUhKbN28udrWoJUuWEB0dTevWrf1tfruPojZF+3A4HHTq1KlYG6/Xy9KlS/1tKqx9q+Hrv/ke9/4nxLX84/ZyVoUeLyPe20jyL0eJcth5a+glNI2rZnVYIiJSSiUagVM0sd7YsWP9y85lYr3s7GwaNGiA1+vloosu4rnnnuPCCy88Y/uCggIKCgr8z0N6Ej4JCNM0WbfvOO+u2cfSzSlcZX7HP8K+oovzN/MFNLkaLn0IGl+liRBFROR3HnnkEfr27UuDBg04ePAg48ePx263M2jQIGJiYrj77rsZPXo0NWvWJDo6mhEjRpCUlETXrl0BuO6662jdujV33HEHzz//PGlpaYwbN47hw4f7R8bcd999TJs2jccee4yhQ4eybNky5s6dy8KFC/1xjB49miFDhnDxxRdzySWXMGXKFHJycrjrrrsseV0CIvcYfDgMTC+0Gwgd/mR1RCHP6zUZ8+FmlmxLxxFmY+aQzrSrV93qsEREpAxKVMA5cuRIiSfWa9GiBW+88Qbt2rUjMzOTf/7zn1x66aVs3bqVevVOf5WfCjMJn5x3J/LdzN94gHeT9xJzeB397d/wTNhaoo08AExbGEabW+DSByGhrcXRiohIMNu/fz+DBg3i6NGjxMbGcvnll7NmzRpiY31X6Zk8eTI2m42bb76ZgoICevbsySuvvOLf3m63s2DBAu6//36SkpKIiopiyJAhPPPMM/42jRo1YuHChYwaNYqpU6dSr149Zs6cSc+ePf1tBgwYwOHDh3n66adJS0ujQ4cOLF68uGKPYF74F8g64JuXrvcL+kNLGZmmybMLt/Hhhv3YbQYvD76IpCb6OZqISKgzTNM0z7XxwYMHueCCC1i9enWxYbyPPfYYK1asYO3atWfdh9vtplWrVgwaNIhnn332tG1ONwInMTHxrNdEl8oj5Wgur33zC+s2fMcN3hX0t6+innHkVIOY+tB+AHS6C2IusC5QEZFKJisri5iYGH1nB4GQeS+2fQJz7wTDDvcshbodrY4o5E39aheTv9oJwIu3taf/Raf/o6mIiASHc/3OLtEInNq1a5d6Yr0i4eHhdOzYkd27d5+xTYWYhE/Oi1xXIc8v2sLx7+dxh/1LnrXt9M/kZDqqYlx4E7QfCPUvBVupLrImIiIigeLOg8Unf5p/+SgVb8rBtGWnijcT+rZW8UZEpAIpUQHntxPr9evXDzg1sd6DDz54TvvweDxs3ryZXr16lThYqdwyc908PXMO9x35O63CUwAwDTs0vQaj/UCMFr10qVEREZFQsna676dTMYlw5aNWRxPyXlq6ixeX+Io3j/ZswZ8va2RxRCIiUp5KfBnxs02sd+edd3LBBRcwadIkAJ555hm6du1K06ZNycjI4B//+Af79u1j2LBh5XsmUqEdyS7gxemvMunEX4m0FeB21iA86T6MTn+Gauc2+ktERESCSO4x+Gay7/HV4yA8wtp4Qlihx8uzC7bxVvI+AMZc35L7r2picVQiIlLeSlzAOdvEeikpKdh+89OV48ePc88995CWlkaNGjXo1KkTq1ev9l9OU+RsUjPzeOXfLzE+73mcRiE5F1xB1KA3oGqc1aGJiIhIaa3+FxRkQnxbaHub1dGErCPZBYye+wMrdx7GMODJXq0YdkVjq8MSEZHzoESTGFslZCbhk3KXmedm6pTneCJ/CmGGl5zGvYgaPAvCNEeSiEgw0nd28Ajq96IgGya3hvxMGPAOtOprdUQhx1XoZc66X5n61U6OZLuICLcxZUAHrm9Tx+rQRESkhM7LJMYigWSaJq+9N/dU8ablLUTd+irYlbYiIiIhbdO7vuJNzSbQQvMiniuP12TrwUwW/JjKJ5sOkJ7lu2pr8/iqvDSoIy0TgqxQJyIi5Ur/E5ag9cl3O7l570TCbF6ON+xFjdte05WlREREQp2nEJJf9j1OegBsdmvjCUJ5Lg/7juWw90gu+47msPeo737LgUyy8gv97WKrORlxdVMGdE7EGabXUUSkolMBR4JSWmY+hYvG0MiWzglnPDUGvKLijYiISEWw/TPI2AdVakL7wVZHE1Aer0muq5CcAg+HTuSTmplPakYeqVn5pGXmk5qRT8qxXNKy8s+4j6rOMK5sXpt+HS6gW4tYFW5ERCoRFXAkKC1c8AF3G1/jxSBywEyoUsPqkERERKSsTNM3eTHAJfeAI/I8H84kz+0h1+Uhz+W7Lyj04Cr04vJ4cXtM3IVe3B7fc1fhyWWeU8vchSYujwe3xzy5vuhm4vJ4KfSc2ubU+uL7yHN5yC4oJN/tPefYoyPCaFQ7iga1omhYK5IGtaJoGleVC+tGE2bXH7VERCojFXAk6KRl5tNix3SwwaGmt5HQ+EqrQxIREZHykLIGDqwHuxM631OqXXi8JodPFHAgI5f9x/M4mJFPelY+x3NdHM91czzHxfFcF5m5brJdhQTj5TrsNoPaVR3UialCnZgIEmIiqBtThYSYCBJrRtKwViTVIx1WhykiIkFGBRwJOh8t+IQHbJvxYCO+11irwxEREZHyUjT6pv1AqBp7Tpu4Cr18s+swC39MZUPKcQ5k5OH2lLwqExFuI9IRRkSYDUeYjXC77+YIs+Gw2wgPM04tsxe1MYq1K/bcfvJ52KnnYSfXO+xF+/etj3TYiXKEEeUMI9JhxxlmwzCMEp+DiIhUbirgSFDZfSibNjumgQ2ON+lH7ZqNrA5JREREysOR3bBjke9x0oN/2NQ0TdbvO87HGw+wcHMqGbnuYuvtNoOE6AguqFGFC6r7Rq7UinJQPdJBjchw/321iHAiHXaqhNux2VQwERGR0KYCjgSV+R/P4RHbjxRip3bvp6wOR0RERMrLmpcBE5rfALHNT9skI9fFhxsO8N53Kew+lO1fHlvNSd92deneMpYmsVWJq+bUPDAiIlLpqIAjQeP7PUfpfuDfYIPs1oOpXrOx1SGJiIhIecg5Aptm+x5fOuJ3qzNz3by68mfe/HYveW4PAJEOO73a1qFfhwtIalILu0bQiIhIJacCjgSFgkIPCz54k4m2XbgMJ9Wvf9LqkERERKS8fP86FOZD3YugwaX+xaZp8tGGAzyzYBuZeb6fSbWqE82futTnxg51qRYRblXEIiIiQUcFHAkK//p8E0NPvAo2KLx4GI7oOlaHJCIiIuXB64ENb/keJw2Hk5P35rk8jPnwRz794SAALeKr8UjPFvRoFacJfkVERE5DBRyx3NpfjhL/3SQa2A+RV6UOkdeMsTokERERKS+7l0LWAahSE1r1BeBIdgF3vfk9mw9kEmYzGHVtc/73ysaa10ZEROQPqIAjlkrNzOPt2W/xsn0JAFVu+TdExFgclYiIiJSbotE37QdBmJPjOS5un7mW7WknqBEZzr9v70TXxrWsjVFERCQEqIAjlskuKOTRNxbzgnsqGODq+GccTbpbHZaIiIiUlxPpsHOx7/FFd+D1mjz43ga2p50gtpqTOfd2pXFsVWtjFBERCREapyqWyMx1M2zmCh47PpF4IwN3zRY4rv+b1WGJiIhIedr0DngLoV5niGvF66v28O3uo0SE23jn7i4q3oiIiJSACjgScL8ey+XOV7/mgbTxtLPtodBZg/Db54BTnTgREZEKw+uB9bN8jy8eyk+pWTz/xXYAnu5zIS0SqlkXm4iISAjST6gkoJZsS+eZuat4wft3LrHvwBsWSdjg96BmI6tDExERkfL08zLISIGI6hS2vJHHXtuI22PSo1U8gy5JtDo6ERGRkKMCjgREamYezy7Yxt4ta5gdPplE22G8jmhsd3wIiZdYHZ6IiIiUt+9f9913+BMz16ax+UAm0RFhPNe/jS4TLiIiUgoq4Mh5teVAJm8n72XRpn3cw8dMcXyCw/BgVm+IbeC7kNDG6hBFRESkvGX8Cru+AODXxrcx+e2dADzVpzVx1SKsjExERCRkqYAj5c5V6OXzLam8nbyPbftS6W9fxWf2hTSypfsatOiFcePLEFnT2kBFRETk/NjwFphezIZX8OQqFwWFXq5oVptbOtWzOjIREZGQpQKOlJu0zHxmf5fC7LUphGcf5M6wL3nduYzqRg4AZlQcRq/noXU/0NBpERGRisnjhg1vA7Cl7i2sXHaYcLvBszfqp1MiIiJloQKOlElmnpsvtqTxyQ8HWP3zETqxg/FhX3JDxHeE4fU1qtEQutyP0fFP4NQVJ0RERCq0nz6F7HTMqDhG/3AB4GLo5Y1oWDvK6shERERCmgo4UiKFHi8/7M/km12HWbXrCBt/zaC6N4Mb7N/xZPgyWtv2nWrc8Aro+gA07wk2u3VBi4iISGB4PbD87wBsiOvPrp9c1K7q5MHuTS0OTEREJPSpgCNnlOfysO9YDj8fyuGH/RlsSslg84FM8tyFNDZSudL2Iw/ZN3KZYyv2otE2YVWg7S1wyb1Qp521JyAiIiKBtXY6HNmB11mdB3/pCsBj17egWkS4xYGJiIiEPhVwKhjTNPF4TQpP3jwek0Kvl4JCL3luD3kuD/luD7kuD3lu3+PsgkKOZrs4kl3A0WwXh08UkHIsl7SsfAAMvDQy0uho7KafbQdXRWyhLoeLH7huR2h7K7QfpMmJRUREKqNDP8FXEwH4T9U/k5rpoH1idW65SBMXi4iIlIdSFXBefvll/vGPf5CWlkb79u3517/+xSWXXHLG9vPmzeOpp55i7969NGvWjL///e/06tWr1EEHk4xcF4dOFJBTUEhOga8YklNQSI7L9zzPVYjba1Lo8fqKKicLKr77k0UWrxe3x1d4cXu8vgLMyXa+ZSfXef9gXdF2XrPE52DgJYYcahuZxBqZJJBBFyOdhuFpNLOn09g4SFUzp/hGdgfU7wpNroFWfaFWk3J6RUVERKRISftclil0wUf3gKeAfTUvY/yBzjjsNv5xSztsNk1cLCIiUh5KXMCZM2cOo0ePZvr06XTp0oUpU6bQs2dPduzYQVxc3O/ar169mkGDBjFp0iT69OnD7Nmz6devHxs2bKBNmzblchKB9lNqFh+s38/nm/Zhy0nHgRsHhYRTiB0vNrwn701shte/rGh50bowPNjw4sRLpFF8vYHp2/5kW04+L9pXGF7shgd70WM8hNk82G1e/37DjJPL8eAwvFS1FRBluIg0CoikgCrkE2EW4DTzCMNz5hM2gbAI3yibCzpBo27Q8DJwaDJCERGR86WkfS5Lff03SNtMblgMtxwcDBg81bc1zeN18QIREZHyYpimWaIhG126dKFz585MmzYNAK/XS2JiIiNGjODxxx//XfsBAwaQk5PDggUL/Mu6du1Khw4dmD59+jkdMysri5iYGDIzM4mOjj5jO9M0Mc2ie+/J+9889pqYeE/em5her+/ec/K5aZKd7yYrz8WJPDfZublkn8gkNzuL/Nws8nOyOHz0GJFZv9DVto2LbTupYrhK8vIFt4jqUDUOouKgRgPfqJqaTaBWU4htAXb9fl1ERP7YuX5ny9mVtM/130rcfwK8/sdFfSrfY29R/wp+09eCrJxcPCtfpPGWqQDc73qYz71deLB7U/5yXXNdNlxEROQcnOt3dolG4LhcLtavX8/YsWP9y2w2Gz169CA5Ofm02yQnJzN69Ohiy3r27Mn8+fPPeJyCggIKCgr8z7OysgDYPPlGLvZuxjg5IsUADMyTN7AZvvuyqHEujX5TxzDtTozwCLA7fQUOmx0M+6l7w3byse03j3+z3mb7r+en2ea3Nwywh51sG3bydrrHv1128nl4pO/miDp5HwnhUeCsCpG1IcxRxldPREREykNp+lxn6j+lT2qP03HiHI986u96xskbwJmuJVkVk3DDN4r3X4X92Fnral67oRXXto4/x+OJiIjIuSpRAefIkSN4PB7i44t/KcfHx7N9+/bTbpOWlnba9mlpaWc8zqRJk5g4ceLvltu8bpyGuyQhl4kXgwKjCm57BIVhkXjDorA7o6haux7hja+ARldgxLYE/XVJREREylFp+lxn6j+Fc377T5lGNRbXeZA2V97F8OZxmvNGRETkPAnKq1CNHTu22KidrKwsEhMTSbh9BmnOMAwMsBkY2DAMA8Ow+Z4bBgYGhs2GgYHNZvMVV2wnlxs234AXbBg239+VDJuBzTi1H3vRNhjYbHaqGAZVLHslRERERM7NmfpP7jsWkFY1yjeaxgCbYfgfGxjYDHx9KXxdIF+f6OQIHMO3Hk4uO7lN0Xq7zSAmKo4BGsUrIiJy3pWogFO7dm3sdjvp6enFlqenp5OQkHDabRISEkrUHsDpdOJ0On+3vFZ8Pf2eXkRERCq80vS5ztR/iq/XWP0nERGRCsBWksYOh4NOnTqxdOlS/zKv18vSpUtJSko67TZJSUnF2gMsWbLkjO1FREREKrvS9LlERESkYivxT6hGjx7NkCFDuPjii7nkkkuYMmUKOTk53HXXXQDceeedXHDBBUyaNAmAhx9+mG7duvHCCy/Qu3dv3n//fdatW8eMGTPK90xEREREKpCz9blERESkcilxAWfAgAEcPnyYp59+mrS0NDp06MDixYv9k+ylpKT45p456dJLL2X27NmMGzeOJ554gmbNmjF//nzatGlTfmchIiIiUsGcrc8lIiIilYthmqZ59mbWOtdroouIiIi19J0dPPReiIiIhIZz/c4u0Rw4IiIiIiIiIiISeCrgiIiIiIiIiIgEORVwRERERERERESCnAo4IiIiIiIiIiJBTgUcEREREREREZEgpwKOiIiIiIiIiEiQC7M6gHNRdKXzrKwsiyMRERGRP1L0XV303S3WUf9JREQkNJxr/ykkCjhHjx4FIDEx0eJIRERE5FwcPXqUmJgYq8Oo1NR/EhERCS1n6z+FRAGnZs2aAKSkpPhPpnPnznz//fcl2o+2CeyxgnmbrKwsEhMT+fXXX4mOjj5vx9J7VPptAnmsQGxT2pwrzbGC9TUI9LGCeZtAHcuKz7rMzEzq16/v/+4W65RH/ymY8zvYtwnksdR/0vsa6G0CdSz1nwK7TSCPFczbBHP/KSQKODabb6qemJgY/wtot9tL/I9Y2wT2WMG8TZHo6OgSbVvSY+k9Kv02gTxWIM+ppDlXmmMF+2sQzPFVxNcBrPmsK/ruFuuUR/8p2PM7mLcJ5LHUf9L7GuhtAn2syt5/qojvazBvUyQY+08h27saPny4tinFNoE8VjBvU1olPZbeo9JvE8hjKe/0vgZ6m0Af63wfJ5D/HqRs9D1WMf+dB/PnSWmOFeyvQTDHVxFfh9KqSHlXEd/XYN6mtAKRc4YZArMMZmVlERMTQ2ZmZqmrZyK/pZySQFPOiRWsyDvlevDQeyHlTTklgaacEysEc/8pJEbgOJ1Oxo8fj9PptDoUqSCUUxJoyjmxghV5p1wPHnovpLwppyTQlHNihWDuP4XECBwRERERERERkcosJEbgiIiIiIiIiIhUZirgVDCGYTB//nyrw5BKRnknVlDeiUh50eeJWEF5J4GmnAt9KuAEuT//+c/069fP6jCkklHeiRWUdyJSXvR5IlZQ3kmgKecqHxVwRERERERERESCnAo4IaRhw4ZMmTKl2LIOHTowYcIES+KRykF5J1ZQ3olIedHniVhBeSeBppyrHFTAkQpHQwnFCso7sYLyTkTKiz5PxArKOwm0UM85FXBERERERERERIKcCjhSoS1evJjLL7+c6tWrU6tWLfr06cPPP//sX793714Mw+Cjjz6ie/fuREZG0r59e5KTky2MWkKd8k6soLwTkfKizxOxgvJOAi0Uc04FnBBis9kwTbPYMrfbbVE0oSEnJ4fRo0ezbt06li5dis1m46abbsLr9RZr9+STT/LII4+wadMmmjdvzqBBgygsLLQo6uCivCs55V3ZKe9KTnkncnr6PCk5fZ6UnfKu5JR3ZaOcK7lQzLkwS44qpRIbG0tqaqr/eVZWFnv27LEwouB38803F3v+xhtvEBsby7Zt22jTpo1/+SOPPELv3r0BmDhxIhdeeCG7d++mZcuWAY03GCnvSk55V3bKu5JT3omcnj5PSk6fJ2WnvCs55V3ZKOdKLhRzTiNwQsjVV1/Nf/7zH7755hs2b97MkCFDsNvtVocV1Hbt2sWgQYNo3Lgx0dHRNGzYEICUlJRi7dq1a+d/XKdOHQAOHToUsDiDmfKu5JR3Zae8Kznlncjp6fOk5PR5UnbKu5JT3pWNcq7kQjHnNAInyHm9XsLCfG/T2LFj2bNnD3369CEmJoZnn31WVdWz6Nu3Lw0aNOC1116jbt26eL1e2rRpg8vlKtYuPDzc/9gwDIDfDZ2rTJR3ZaO8Kx3lXdko70RO0edJ2ejzpHSUd2WjvCs55VzZhGLOqYAT5A4dOkTTpk0BiI6O5v333y+2fsiQIcWe//fvHiuzo0ePsmPHDl577TWuuOIKAFatWmVxVKFBeVd6yrvSU96VnvJOpDh9npSePk9KT3lXesq70lHOlV6o5pwKOEHq+PHjfPvttyxfvpz77rvP6nBCUo0aNahVqxYzZsygTp06pKSk8Pjjj1sdVlBT3pWd8q7klHdlp7wT8dHnSdnp86TklHdlp7wrGeVc2YVqzqmAE6SGDh3K999/z1/+8hduvPFGq8MJKUVDCW02G++//z4PPfQQbdq0oUWLFrz00ktcddVVVocYtJR3pae8Kz3lXekp70SK0+dJ6enzpPSUd6WnvCsd5VzphXrOGabGUUkFc/3119O0aVOmTZtmdShSiSjvxArKOxEpL/o8ESso7yTQQj3ndBUqqTCOHz/OggULWL58OT169LA6HKkklHdiBeWdiJQXfZ6IFZR3EmgVJef0EyqpMDSUUKygvBMrKO9EpLzo80SsoLyTQKsoOaefUImIiIiIiIiIBDn9hEpEREREREREJMipgCMiIiIiIiIiEuRUwJGQNGnSJDp37ky1atWIi4ujX79+7Nixo1ib/Px8hg8fTq1atahatSo333wz6enp/vU//PADgwYNIjExkSpVqtCqVSumTp1abB+pqakMHjyY5s2bY7PZGDlyZCBOT4JUoPLuo48+4tprryU2Npbo6GiSkpL44osvAnKOElwClXOrVq3isssuo1atWlSpUoWWLVsyefLkgJyjiASO+k9iBfWfJNAqcv9JBRwJSStWrGD48OGsWbOGJUuW4Ha7ue6668jJyfG3GTVqFJ999hnz5s1jxYoVHDx4kP79+/vXr1+/nri4ON555x22bt3Kk08+ydixY4tdUq6goIDY2FjGjRtH+/btA3qOEnwClXcrV67k2muvZdGiRaxfv57u3bvTt29fNm7cGNDzFesFKueioqJ48MEHWblyJT/99BPjxo1j3LhxzJgxI6DnKyLnl/pPYgX1nyTQKnT/yRSpAA4dOmQC5ooVK0zTNM2MjAwzPDzcnDdvnr/NTz/9ZAJmcnLyGffzwAMPmN27dz/tum7dupkPP/xwucYtoS0QeVekdevW5sSJE8sncAlZgcy5m266ybz99tvLJ3ARCUrqP4kV1H+SQKtI/SeNwJEKITMzE4CaNWsCvoqp2+2mR48e/jYtW7akfv36JCcn/+F+ivYhcjaByjuv18uJEyeUmxKwnNu4cSOrV6+mW7du5RS5iAQj9Z/ECuo/SaBVpP5T2Hnbs0iAeL1eRo4cyWWXXUabNm0ASEtLw+FwUL169WJt4+PjSUtLO+1+Vq9ezZw5c1i4cOH5DlkqgEDm3T//+U+ys7O57bbbyi1+CT2ByLl69epx+PBhCgsLmTBhAsOGDSv38xCR4KD+k1hB/ScJtIrWf1IBR0Le8OHD2bJlC6tWrSr1PrZs2cKNN97I+PHjue6668oxOqmoApV3s2fPZuLEiXzyySfExcWV+lgS+gKRc9988w3Z2dmsWbOGxx9/nKZNmzJo0KCyhC0iQUr9J7GC+k8SaBWt/6QCjoS0Bx98kAULFrBy5Urq1avnX56QkIDL5SIjI6NYZTU9PZ2EhIRi+9i2bRvXXHMN9957L+PGjQtU6BLCApV377//PsOGDWPevHnFhnhK5ROonGvUqBEAbdu2JT09nQkTJqiAI1IBqf8kVlD/SQKtQvafztvsOiLnkdfrNYcPH27WrVvX3Llz5+/WF01M9cEHH/iXbd++/XcTU23ZssWMi4szH3300bMeU5PwSSDzbvbs2WZERIQ5f/788j0JCSlWfNYVmThxotmgQYMyxS8iwUX9J7GC+k8SaBW5/6QCjoSk+++/34yJiTGXL19upqam+m+5ubn+Nvfdd59Zv359c9myZea6devMpKQkMykpyb9+8+bNZmxsrHn77bcX28ehQ4eKHWvjxo3mxo0bzU6dOpmDBw82N27caG7dujVg5yrBI1B59+6775phYWHmyy+/XKxNRkZGQM9XrBeonJs2bZr56aefmjt37jR37txpzpw506xWrZr55JNPBvR8ReT8Uv9JrKD+kwRaRe4/qYAjIQk47e3NN9/0t8nLyzMfeOABs0aNGmZkZKR50003mampqf7148ePP+0+/rtiei5tpHIIVN5169bttG2GDBkSuJOVoBConHvppZfMCy+80IyMjDSjo6PNjh07mq+88orp8XgCeLYicr6p/yRWUP9JAq0i95+MkycoIiIiIiIiIiJBymZ1ACIiIiIiIiIi8sdUwBERERERERERCXIq4IiIiIiIiIiIBDkVcEREREREREREgpwKOCIiIiIiIiIiQU4FHBERERERERGRIKcCjoiIiIiIiIhIkFMBR0REREREREQkyKmAIyIiIiIiIiIS5FTAEREREREREREJcirgiIiIiIiIiIgEuf8HojM5pTr4oZ8AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -729,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -738,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -760,12 +760,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x400 with 1 Axes>"
       ]
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -816,7 +816,7 @@
        "       [2.83053672e+07, 1.16408750e+00, 4.53344539e+00, 9.85762961e-01]])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -835,12 +835,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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qfnQ8g3WYVSxD6bMCAAAAAAYHn9+Qx+eXx+eX12fIZxgyjMD0IoYkw5D8oe3gMcmQIX+HelHntE0sEyht244x2czx6hkR9YwYZaGtE5wbeY3w4+FtCrbB2dgY3dBOJEygAwAAAADAUGcYhnx+Q97gy+eXx2eEApJgWeDdkMcfCEy8Pr88YfW9beXBczzB8rD9jue7g9dpu5bH217X7Y28TnhY4/b5Q/cNbvsTajbf/uN3NXe5LoEOAAAAAGDACwYZPsOQ3x/oiRHYDpT723pnBLYDdXxt+0ZbXZ+/i+cahnx+hfaDwYTXHxaUtAUfvg7hh89nRAQigeAl7HyfEQpWwsOXQCjTIUQJHg9eu60eICVQoNPVZcsBAAAAAO18fkMtHp+aXV41u31yur1qcfvk9vo7BAuBwMHToTdHV3p5hHqKRJwTfX54DxKPz4gIUULBSjC06RDKAIiUMIFOd+bQSUQ33nij6urq9Oyzz8a7KQAAAAD6gGEYoaEoHUOOYNARHLYSGN4SCD1a3N5AIOP2qaXt1ezpuO2V0xW53eLxyenyyuX1x/ujA+gDCRPonIw7nvpAuyu6PqFQb5tYkK57P3963O4PAAAADDZ+v6FWbyDcaHb51OzxhoKOZncgxHB7/aGQJLDdHpq4fX55vB32w+YN8bfNS+IPm6ekY1nEqwtlfiNwfqxJWQHgZA3qQGd3RaPeP1AX72YAAAAA6MDj8+uY063aZrdqnW7VNXt0rDnwXtcc3PeovqV92+kK9FQB0P8sZpOsZpPsFrNsVrOsZpNsFrNsluB7+7a17d0eth2rrjW4bTbJbDbJZJLMJpNMUmhbCqzubDaprTywrbZ6ZlPgvOC2wupJge2g4CLR4YtFm2SKKmuvbwqr17VzY9XTcetF3sPZ1KirV0W3JZaECXSYQwcAAAAYmHx+Q/UtgUDmmDMQ0BxrdqvWGSiraQrut783tnrj3Wygz5hNktUSCD2CwYfVYpLVHHwPlFnMbaGG2SRLx3pmUygMCW4Hzw+ea2s7P3Bu4LjNYpIlVC+wbTNHBi3W4H3Dzgm2N1Qv2Na2bbM5RuKBXtfQkNzlugkT6Az2OXQAAACA/mQYhlxef8QcLM1uX8TwpWa3V61tc7c43T41tHToRdPi0TGnWw2EM0OSySTZzMFQoj0oaA8lIgMKW4dAIxggmM0mWUyBQMNkUti2SRZzYN9sNsncVm5uKzeb2stinmsK9CoJndt2nY7nmtt6fETXbb9eeNgRCk/CAptgOBIIWQg/0D8SJtABAAAABrvgpLnhKw152+Zv8Xj9ERPjNodNlBsoaw9kWsPLO8wxE6zb4vGJhYMSm8kkJdssSrFblGy3KMVmVYojsJ9it0a8p9otSg6VBcod1ugeGx17hUQFMh2CDQDxM6gDnYkF6UP6/gAAAOhcq8enioZWNQWXcu7w3uLxRS7NHFzWuW1pZl/4Us5hSzcHVzAKP6fjUs2ByXajl4cmYOk/kXN5tPeuCL1MHfbN7T08rJ2UhY7FKDOHenoEenC0D39pC0us5tDwGVt4oGI1h0KbFLtFSbb2kMZhNUfMvwFgaBnUgQ4rTAEAAAxdrR6fKhtcOlrfooPHWnSgtlmHapt1oLZZB481q6LBFe8m4jhS7BZlJduUlWJXVopNw1LsSk+yBnqitPUwCQUdDqtSwnqqOKwW2a2RgU1gItf2niYEIQAS3aAOdAAAADD4GIahumaPyhtaVd7Qqor6wHt52HtFQ6uONXvi3VS0SbFblJ1qV3aqXcNS7KHt9v1AcDMsxa5hKTZlptjksFri3WwAGNAIdAAAADAgeHx+1TS5Vd3kanu5VdO2XdHgCoQ3bYGNy+uPd3OHJJNJykiytYUugfAlvBdNVrJNw1KjA5skG+EMAPS2hAl0Bvuy5WvWrIl3EwAAAKIYhiG/IXn9fvn9ks8ITM7r9vnl9vrl8vrk8ga2A/tt277AsfDyhlavGls9aox4D2zXtXhUR4+aXhMxUa7domRbcLttyFLoePskuZF1raHtlLDz0hxWJsIFgAEiYQIdli0HAADxElze2eNrDyvcbfvhYYbHZ8jt84UCDI/PaDvmk9cfmAjXZxjy+w352sIRf1hZsI7fiHz3+dW+HTo/7LihUFn4tdrPD79mWDgTfj0jMMmvz4iuj0iW4DLFHVYF6rgK0PGWao5ZFrxesCzGqkLtwUxkSBMeyCRZLSyZDABDQMIEOgAAAF1hGIZaPD41ubxqavUG3tu2m92+thWNvHK6Ass+O9tWNQrfb3a1121x++T2MbxnsLBbzRo1LFlF2SkaPSxFo7OTNSzFrlRH29LOwfe2kMRmMcvStiqR1dK+ehEAAPFGoAMAAAYEv99QkzsQvDS2etXkah+SEwxkGls9anR55WwLaRpb27fDwxt6lQxNVrNJ+ekOFWYmqTAzSQUZSSrMCGyPyAqEOHlpDgIZAMCgQKADAMAAEhza0+rxqdXT9t42D4nHZ8jr88vrN+Tx+eX1GfL628qD7xHbkXU9/rZzfH55/O3Da0JDfjoM2zE6DOeJGgZkqL1OW3noGsG6bUN2Og4t8huRw32C5wKdSXdYVZDZHtAUZiS172ckqSDTodxUwhoAwNBBoAMAQA+1enyqcbp1zOkO9SZxurxqDPUa8bS9+wLbbcN7AqFNW3Djbd8GBrs0h1VpDqvSk4IvmzKSbcpJtSsv3aHcNLtyUh3KbdvOTXOwShIAAB30e6Cze/duXXfddRH7jz32mK6++ur+bgoAAJ0yDEM1TreO1LW0vVpV43S1LansVo3TpVqnWzVNbjW5vPFuLgYZizk4V4tCc7a0lwXeLeYOx4Nlbe+WiHParmk2y2JSRD1z2+S+4dcJlYXqKaKe1WKWw2qWPfiymOWwWQLv1g7HrGY5rBbZreZQkMMqSQAA9Fy/BzoTJ07U1q1bJUlNTU0aM2aMLrnkkv5uBgBgiGtx+3SkviUU2Byua9XRupa2slYdrmuR20tvmaHAYTVHToTrCCzNHNxPtluUZAtMjmsPhhVtqxTZ24KKQKhhanu3tB1rr2+zBF4RgUtY0BJZRtgBAABOLK5Drp5//nldfPHFSk1NjWczAACDkNfn15G6Vu2vdWp/TbMO1DZrf41Th9t629Q63fFuInog1W5RWpJVqQ6r0h3WwLY98J7uCJRHbIfVSXVYleoILPWcarfIajHH++MAAAB0W7cDnQ0bNui+++7T5s2bdfToUT3zzDNRw6XKysp03333qby8XCUlJfrNb36j2bNnR13riSee0Fe/+tWTbjwAYGhzurxtQU2zDtY2R4Q3h4+1yMssuzGZTZLVYpbNHDmUx9Q2tCbYSyQ0JCdsiE74MB2zKXq4j8mkiJ4m5o7De0ztQ3gC9SOHCqU5AkFNepItFMJkJFmV5rC1lQdCGYbsAACAoa7bgY7T6VRJSYluuukmXXvttVHHH3/8cS1fvlyrV6/WnDlztGrVKi1YsEC7d+9Wfn5+qF5DQ4Peeust/eUvfznu/Vwul1wuV8R5AIDBzzAMNbq8qmxoVWWDS0fqW3WgtlkHapzaXxsIcKqbEqeXjckUmAg22GMkxWFVis2iJJtZSTZL2Ktt32pRsr192241y2oxyWoODPUJBjJWS6DcZm57b6sT2DbLGqxjNoXqMaQHAAAg8XU70Fm4cKEWLlzY6fEHHnhAS5cu1ZIlSyRJq1ev1osvvqiHH35Yd9xxR6jec889p0svvVRJSUnHvd/KlSt1zz33dLeZCefGG2/Un/70J0mS1WrVqFGj9MUvflE/+tGPTvgdAUAiCQ9qKhpcqmxse29wqaKxNRDgNLpU0dA6IFd8slvMykmzB16pjrZ3u3LSHMpOtSsjyab0pPYhPsFJYFPsFplMBCkAAADoHb06h47b7dbmzZu1YsWKUJnZbNb8+fO1cePGiLpPPPGEvv71r5/wmitWrNDy5ctD+w0NDRo9enTXGvT8t6XKnV2r2xfyJ0tX/abL1S+77DL98Y9/lMfj0ebNm7V48WKZTCb9/Oc/78NGAkDv8PkN1Trdqmp0qarJFXhvDAQ2lY2uAR/USIGwZnhWkkZkJmtEVrJGZiVpRFZy6JWf4VC6w0owAwAAgLjr1UCnurpaPp9PBQUFEeUFBQXatWtXaL++vl6bNm3SU089dcJrOhwOORwOlZWVqaysTD6fr+sNqtwpHXq36/XjzOFwqLCwUJI0evRozZ8/X//85z8JdADElcvrU2VET5pWVTUFetSEBzc1Trd8A3zOmsxkm4qyUzQyFNIkhW0nKyfVznAkAAAAJIS4rHKVmZmpioqKbp1TWlqq0tJSNTQ0KDMzs49aNnBs375db731lk455ZR4NwXAIOXzG6ppcqm8behTRUNraBhUoCzQoyaRVoMymaTCjCQVZafolJwUnZKT2r6dnarMFFu8mwgAAAD0il4NdHJzc2WxWKLCmoqKilDPk5N1Uj10EswLL7ygtLQ0eb1euVwumc1mPfjgg/FuFoAEYxiG6po9qmjrUVNRHwhnQvttYU1Vo0sDvENNTA6rORTSFGWnqig7WafkpGp0dopGDUtWks0S7yYCAAAAfa5XAx273a4ZM2Zo3bp1oaXM/X6/1q1bp2XLlvXo2kOhh868efP0//1//5+cTqd++ctfymq16vOf/3y8mwVggGifTLhtXprwnjTBoKYttHF7B+YcNSeSlWJTQXqS8jMcyk9PUkGGQwUZScpPdyg/w6FRw1KUn+5gDhsAAAAMed0OdJqamrRnz57Q/r59+7R161ZlZ2erqKhIy5cv1+LFizVz5kzNnj1bq1atktPpDK16dbJOqodO/uQe3bPHunn/1NRUjR8/XpL08MMPq6SkRP/zP/+jm2++uS9aB2CAaPX4oiYS7mw/UYOazGSbCjMig5r89Lawpi2wyUt30LsGAAAA6KJuBzrvvfee5s2bF9oPrkC1ePFirVmzRtddd52qqqp05513qry8XNOnT9fatWujJkrurpPqodONFaYGGrPZrP/6r//S8uXL9ZWvfEXJycnxbhKAbvD5DdU4TxzQVDW61NjqjXdzT4rDalZeWxATDGTy0pJCZcFXbppdDitBDQAAANCbuh3ozJ07V4Zx/EkXli1b1uMhVh0NhTl0OvriF7+o22+/XWVlZbrtttvi3RxgSPP7DTW0elTrdOtYs0fHnG7VNrsD7053RFhT3RRY8ekEj8oBy24xK79tqFOwV01BRvjwpySW7wYAAADiLC6rXJ2MoTCHTkdWq1XLli3Tf//3f+ub3/ymUlNT490kYFAwDENNLq+OOT2qcbp0rNmtWmdkSHOs2a1jTk/EfiJOIBzObJLy0ttDmcJMhwrSk9qGPbUHOFkpNoIaAAAAYIAzGSfqbjPABAOd+vp6ZWRkRBxrbW3Vvn37NHbsWCUlJcWphf1rKH5moCuOOd368HC99lQ26UBtsw7WNutIfWuoR43bl5hz0XQmO9UempOmINSjJiliPzfNIYuZoAYAAAAYqI6XeXSUMD10huKQKwBd4/H5tf1wvTbtq9X7B+r04eF6Ha5riXezesxqNik3zaHcdLvy0sLmpUlzKK+th01w+BNz1AAAAABDS8IEOkNxyBWAzm0/XK9/7arUps9qtXn/MTW7EyfsHZZi6xDOhIc17ZMKZyXbZKZHDQAAAIAYEibQAYAml1fPbz2ixzYd0IeH6+PdnAjJNovyMxwxetJEvnJSHbJbzfFuLgAAAIAER6ADYMCrb/Zo1bqP9cS7B+Xsh544dqtZ2Sl2DUu1KzvVpmEpdmWn2pWVYld2iq2t3B4qH5ZiV7KdIU8AAAAA+k/CBDrMoQMMPYZh6IUPjuqev+1QdZOrR9fKTXNo1LDktl4ygSAmO9WunLRAIJOT6tCwVJuyU+1KtllY5QkAAADAgJYwgQ5z6ABDy6Fjzfrhs9v1r91V3TrPajZp8vAMTR2ZqdNHZWrqiEyNy09Vij1hHncAAAAAcEL8wgEwoHh9fq156zP94h8fq8Vz4h55DqtZZxRlafbYHM0Zm60zirIIbwAAAAAMevzqATBgbD9crzue/kDbDzecsG7JqEx9ZU6Rrjx9hFIdPMoAAAAADC0J8yuIOXSAwavZ7dUv//mxHn7zM/n8xnHrXjQpX8svmaCpIxl6CQAAAGDoSphAhzl0gMHpX7sr9YNntutwXctx6+WmOXTPVVN0+bRCJiwGAAAAMOSZ490ABNx4440ymUwymUyy2WwqKCjQJZdcoocfflh+vz/ezQN6XVWjS9957H0t+eO7Jwxzrp9dpHXLL9QVpw8nzAEAAAAAJVAPnZNx91t365O6T+J2/9OyTtPd59zd5fqXXXaZ/vjHP8rn86miokJr167Vd7/7Xf31r3/V888/L6t1UP/jwhBhGIaeeO+gfvb3Xapv8Ry37ri8VK289nTNHpvdT60DAAAAgMQwqBOCT+o+0QdVH8S7GV3mcDhUWFgoSRo5cqTOPPNMnXXWWbr44ou1Zs0afe1rX4tzC4Ge2VPZpO8/86He2Vd73Hp2i1nfmjdO35w7Tg6rpZ9aBwAAAACJY1AHOoPBRRddpJKSEj399NMEOkhYFQ2tWvXKJ3rivYMnnPR49phs/ezaaRqfn9ZPrQMAAACAxEOgkwAmTZqkDz5InJ5GQFBDq0e/e22v/ueNfWr1HH8uqIwkq/7r8sn60szRMpuZJwcAAAAAjidhAp2hvGy5YRhMBIuE4vL69OeN+1X2rz061nz8eXIk6crTh+vORcXKT0/qh9YBAAAAQOJLmEDnZJYtPy3rtD5uVf/cf+fOnRo7dmyvXAvoSz6/oee3Hdb9L398wpWrJGlkVrJ+fPUUXTSpoB9aBwAAAACDR8IEOiejOytMDVSvvvqqPvzwQ91yyy3xbgrQKbfXr2ffP6zVr+3Vp9XOE9a3W8xafM4p+t78CUp1DOrHEAAAAAD0CX5JDSAul0vl5eURy5avXLlSV155pb761a/Gu3lAlGa3V49tOqg/vP6pjta3nrC+ySRdPX2kll8yQaOzU/qhhQAAAAAwOBHoDCBr167V8OHDZbVaNWzYMJWUlOjXv/61Fi9eLLPZHO/mASF1zW796a39WvPWvi7NkSNJF07I039cNlFTRnRtyCQAAAAAoHMEOgPEmjVrtGbNmng3AziuT6ua9NimA3r0nQNyurs2QfnpozJ1x2WTdM743D5uHQAAAAAMHXEJdPbt26ebbrpJFRUVslgsevvtt5WamhqPpgA4gWa3V3//sFxPvHtQmz6r7fJ5Y3JSdNuCibpi2nBWaQMAAACAXhaXQOfGG2/UT37yE51//vmqra2Vw+GIRzMAdMIwDG07VK/H3z2ov207oiaXt8vnTipM17fmjdflUwtltTBUEAAAAAD6Qr8HOh999JFsNpvOP/98SVJ2dnZ/NwFAJ+qbPXp262E9tumAdpU3duvcmacM07fmjdO8ifn0yAEAAACAPtbtP59v2LBBixYt0ogRI2QymfTss89G1SkrK9OYMWOUlJSkOXPmaNOmTaFjn3zyidLS0rRo0SKdeeaZ+tnPftajDwCg5/ZUNun2J7dp9s9e0V3Pf9StMOfCCXl64htn66/fPEcXTSogzAEAAACAftDtHjpOp1MlJSW66aabdO2110Ydf/zxx7V8+XKtXr1ac+bM0apVq7RgwQLt3r1b+fn58nq9ev3117V161bl5+frsssu06xZs3TJJZfEvJ/L5ZLL5QrtNzQ0nLCNfr+/ux8rYQ2lz4ret/1wvX67fo9e2l4uw+j6eXaLWZdNLdTXLzhVU0eyahUAAAAA9LduBzoLFy7UwoULOz3+wAMPaOnSpVqyZIkkafXq1XrxxRf18MMP64477tDIkSM1c+ZMjR49WpJ0+eWXa+vWrZ0GOitXrtQ999zTpbbZ7XaZzWYdOXJEeXl5stvtg7a3gGEYcrvdqqqqktlslt1uj3eTkEDe+bRGZev3asPHVd06b1Jhuq6bNVpXTx+pYan8OwcAAAAA8dKrc+i43W5t3rxZK1asCJWZzWbNnz9fGzdulCTNmjVLlZWVOnbsmDIzM7VhwwZ94xvf6PSaK1as0PLly0P7DQ0NoTCoI7PZrLFjx+ro0aM6cuRIL32qgS0lJUVFRUUym5l8FsdnGIbW765S2b/26L39x7p8XrrDqqumj9B1s0Zr2sjMQRuSAgAAAEAi6dVAp7q6Wj6fTwUFBRHlBQUF2rVrV+CGVqt+9rOf6YILLpBhGLr00kt15ZVXdnpNh8Mhh8OhsrIylZWVyefzHbcNdrtdRUVF8nq9J6yb6CwWi6xWKz+wcVw+v6GXth9V2b/2aufREw9ZDJo1Zpi+PKtIl08brmS7pQ9bCAAAAADorrgsW36iYVuxlJaWqrS0VA0NDcrMPP6cHSaTSTabTTabrSfNBBKa2+vXs+8f1v/32l7tq3Z26Ry71awvzRylxWeP0WkF6X3cQgAAAADAyerVQCc3N1cWi0UVFRUR5RUVFSosLOzRtbvaQwcY6lrcPv3l3QN6aMOnOlrf2qVz0hxW3XBWkW4+b6zy05P6uIUAAAAAgJ7q1UDHbrdrxowZWrduna6++mpJgVWY1q1bp2XLlvXo2t3poQMMRfUtHj3y9n79zxv7VOt0d+mcYSk23XTuWH317DHKTKFHGwAAAAAkim4HOk1NTdqzZ09of9++fdq6dauys7NVVFSk5cuXa/HixZo5c6Zmz56tVatWyel0hla9Oln00AFiq25y6eE39unPG/er0eXt0jmFGUlaesGpun72aKXY4zLyEgAAAADQAybDMIzunLB+/XrNmzcvqnzx4sVas2aNJOnBBx/Ufffdp/Lyck2fPl2//vWvNWfOnF5pcLCHTn19vTIyMnrlmkAiOlzXot9v+FSPbTogl9ffpXNOyUnRNy8cp2vOHCmHlYmOAQAAAGAg6U7m0e1AJ17Ce+h8/PHHBDoYsvZUNmn1a3v17PuH5fV37X++kwrT9a1543X51EJZLSxxDwAAAAAD0aAMdILooYOhavvhev12/R69tL1cXf1f7ZlFWSqdN14XTcpneXsAAAAAGOC6k3kweQYwgBmGodc/qdbvX/9Ur39S3eXzzj8tV6XzxmvO2GyCHAAAAAAYhBIm0GFSZAwlbq9fL3xwRA9t+FS7yhu7fN5lUwr1rXnjdPqorL5rHAAAAAAg7hhyBQwgDa0e/WXTAT38xmcqb2jt0jkWs0mfmz5C37xwnE4rSO/jFgIAAAAA+gpDroAE4vMbemtvtZ55/7DWbi9Xs7trvdDsVrOumzlaX7/gVI3OTunjVgIAAAAABhICHSBOdh5t0DPvH9ZzWw+rosHV5fPSHFbdcFaRbj5vrPLTk/qwhQAGHcOQDL/k94a9fB32O5b5Ytcx/Cd4GT2sY/TSdcKOAwAADHTN7i5XTZhAhzl0kOj8fkPbDtXplZ0V+ueOCn1c0dSt8/PTHVpy7lh9ZXaRMlNsfdRKAP3OMCS3M/Dytkietpe3NXrb2yp5miVPa1vdtv2I47HOb6vrbZWUUCOtAQAAhhZX1/9bjTl0gD7U6vHpzT3VemVnhV7ZWamqxq73xAmaUJCmpeefqs9NHym71dwHrQTQI4YhtdZLLbVSyzGp+Vhgu7ltv7VOcjVKrgbJ1dS23Si5w7YJWQAAACCpwWUo895G5tAB+pvb69eOow3asv+Y3v60Rq9/Uq0Wz8n1KjtnXI6+fsGpunBCHkuPA/3J3Sw5K6Wmqrb3Sqm5JhDOtBxrC2pqw7aPSQa9RwEAANC/CHSAk+Ty+rS30qndFQ3aebRR7x84pg8O1cvlPfl5GnLTHPrc9BH6/JmjVDyCHmhAj/j9gd4x4SFM+HtzTWRPmuAxb9dWmAMAAADiKWECHebQQbzUt3h0sLZZh441a09lk3aVN2p3eaM+rXbK5+/5MIkkm1mXFhfqmjNH6vzxubJaGFYFRPG6YocyEYFMTWRZax0T4Q41JnPnL5kkOjsCAIABzy+psUs1mUMHQ5phGKpr9uhofasOHWvWoWMtOtj2Hng1q7HV2+v3TbZZdMGEXF1SXKgFUwqUnsQkxxgiDCMwl0woeDnWeUgTHObUXCt5nPFu+cBlTZZsSe3vthTJmiTZktvfg9vWJMlik8zWDi9LN/etkskUKO80RDF1EqqcoM5xr3O8+qQ1AAAg8XUn80iYHjpAZ/x+Q80en5pavWpyedTkCm63vVo9crp9amz1qqHVo6pGlyobXapqaFVVk0seX/9kmvnpDl08uUCXFhfo7HE5SrJZ+uW+QJ+JGNJU02EYU02HUCas94y/90PSgc0kOdIDL3ta23bwPaO9zJ4aFr6EhzTJscOZ4DtBBgAAwJBEoIM+5fX5Vd3k1rFmt1xev1weX+Dd65fL65PLE7bt9bft+0Jl7mDdsHKn26emVo+aXF45XT41uQbmj0OzSZpUmKGLJ+dr/uQCTRuZKbOZH14YwIK9Z8InA3ZWhb2qA8GMs1pqrm6bDHiIDWmyp0nJ2VJylpSSHdhOyZaSh4Vth5cNk5KyJDNDKQEAANC7CHRwUtxev6qbAj1dKhtaVdHW46Wy0aWK0LtLNU6XEmtQ38nLTLbpjKIsnVk0TGcWDVPJ6EyGUiH+gr1omiojQ5rQflXYe5Xkc8W7xf3HbI0dwqRkSym5UlqBlJYnpeZLqbmBY1ZHvFsNAAAASCLQGTQMw1BD2zAjp8urxrDtplavGtvem1weubx++fyG/IYhn9+Qz6/2bcOQEaPcbxjy+Pw65vSoqsmlWqc73h85buwWs8blp2lSYbomtr0mFaarMCOJ5cXRP/y+QE+Z8B40UQFN2LGhMMQp2HMmJVZPmQ7vwW1HOsOVAAAAkLAIdBJAi9unqkaXqppaA+9tvV+O1LfoaF1r4L2+Ve4eLJeNSLlpdo0clqJRw5LbXoHt0cNSdEpOimysRIXe5PMG5pYJDmlqDg5tqoncD4Y1zTWDd6iTyRwYopSSE7vnTGchDT1nAAAAMMQkTKAzlJYtr2t268UPj2rt9nJtPVCnxgE6R0wisppNykmzKz89SfnpDuVnOJSX5lBeRpJGZQXCm5HDkpViT5j/aWCgcje395RpqpSaKtp60lS0lzW3BTatdfFubd+wONp6xOSEDWXKOU4PGuabAQAAiDfDMGTIkN/wyzAM+eWXz+8LlQXLfUYnZW3nhJf5DX/ENTuWhb86nu83/PKrk+0O1z3e8eNdW4ZktP1f+HcQ3A59NzJC++H1g+dHnRtWP7gf6/qh60hqdbZ2+Z8Vy5YPME+8d1A//tsOQhwFhjY5rGY5bObAts0ih9WsJJtF6UlWpTkCr1SHNbTfcTtYJ62tzGE1MywKPeNqkhoOS/UHpYYjYQFNRaAHTXDf3RjvlvYukyUQxqTmSak5gTlmgr1owkOa8H17KkOaAADoBR1/eIb/IO34YzX04zbsR25EnbYfmBHXC/sR3PHHcXeuF+va4ecHzw3+qA7fD3+PqN+hTFJEOzq7Vqh+2/cVbJcUo01h54Su30m7OjveWRvC//kFA4CYbex4zQ7fd8fvvdMw5AQBSVfLED++Fp92fnMny5YnEo/Przuf+0iPbToQ76b0iM1iUn56kvLSHSrIcCg/PUkFGQ7lpjmUbLfIYbXIYWsLaqzBgKZ9O3jcbjGzIhT6n9fVFtYcbns/1P5ef1hqOCS11se7lb3H4pDS2ib8Tc1vnwA4LT8Q3KTlt+/TcwYAoMCPTp/hk88f+Ou61/DK7297NwJ/xfcZYa8O9cLLT3iO39t+v7by4Hb48fB6nZ0TLOsYUhzvL/YRx7vwV/9YvRVCP447CWE6BgGdhSQAEAuBzgDx32t39VuYYzWbZDabZDGZZDGbZDJJlrb98HKzWTKbIsszk23KS3eEXoFhS4HhSwUZScpKthHEYGDz+wPBTPXHUvWetvePpZo9UuPReLeu52wpHcKYzkKaPMmRQQ8aABiADMOQ2++Wy+eS2+eW2xe5HTzm8Xnk8rkC235PdH2/O+b5Ln/7ueHHY4UzHUMXwgUAGDgIdAaAg7XN+sMb+7p9nsNq1sisZA3PStLwzGQVZiQpM9kWGl4UHGqUao8chmS38ld2DBF+fyCoObxZOvxe4L1yl+RtiXfLusHUNv9MTltPmrBQJjU3OrhxpMW7wQCQ0Lx+b8xQpLOwpMvlYUFMrPLwMo/fE++vAQCQAOIS6IwZM0YZGRkym80aNmyY/vWvf8WjGQPGI+/sV2czGU0ZkaFzx+cqP9grJi3wnpvmUFaKjflggHBNlYHQ5lBbeHN4i+QaYEOkTJa2UCYv8J4SNh9Nak6H/dxAmGO2xLvVAPpYZ0M6JJ1wqEewTmi+hvD5IoJDOWLM7xA+d0bH+SQ6zuUQc/94x8LmpYg1L0fUnBMxzo113eDwGo/fE3oP34713t1jPmPwL8ABAIgPk0yh3/Cmtv9r2wltW7rx3/5x66Hz1ltvKS2NvyS7vX498e7BmMfu+8Lp+uLM0f3cIiBBuJulo9sCPW8OvRcIb+rjNQeVKRDApBUEgpq0grCeM23baW3bydnMRYMuC/549fq98hre9m2/Vz6/Tx7DE1kWNn9EX60SETXxpTqECsf5Ad/VH/qd/ZAP3SNG/eCxrgYPXQkOjne/WGUxv/MO33+n82sAAAaM4I9us8ySSTIrsLCK2WQOHTebzKF6JpMpVCap/VjY8eA1QmWx6ne4VvAHfvhxs8ksi8kS2g5e96TK2q7bWZnZ1P65TTLJYrZEfBehV1fL2q7TsSxW3Y71wveD32fH9h3veMd7hNcPfsfh/+yD26F/J0zt4UvEdiCJaT83Vp1udsJoaGhQ5jcyu1SXIVdx9uquSh1rju5We8W04YQ5QDh3s3Rok/Tpa9K+16QjW6X++CuqySylD5cyRkqZI9veR7Xvp48I9LSx8Dgdanx+n1q8LWr2NgfePc1R282e5lCd8O0WT3RZcJhFMKwJzlsBAOie4A9ei8kS+AHatn+iH5Gd/SDs6o/E4PHgj+PgD+Bgm0LhQNh5He8Z/kO+Y1nHH/Xh2x2v1/FHbmfXC7UtxrU7nhdRR7Hb3LFex/tGhRVhYcmJjge/v6gwJey+0vHDloiApJN7MgICiaTbv0A2bNig++67T5s3b9bRo0f1zDPP6Oqrr46oU1ZWpvvuu0/l5eUqKSnRb37zG82ePTt03GQy6cILL5TZbNb3vvc93XDDDT3+IInq6S2HYpb/v7NP6eeWAAOIzyPV7pMqtksHN0kH35bKP5T83r65X8YoKfc0KWe8lDU6MrRJH05Yk+D8hl+t3tbIIOU42x1DmPCQJvy91dca748GACdkkkl2iz3wMtvlsDhC+w6LQzazLVRmNVtlNVtlNpllNbW9t+0Hw5GOQUmwXsdjHeuHXyvqnE7qhV/ParLKbDZH3yPGOfwgBzBUdPtXitPpVElJiW666SZde+21Uccff/xxLV++XKtXr9acOXO0atUqLViwQLt371Z+fr4k6Y033tDIkSN19OhRzZ8/X9OmTdPpp5/e80+TYJpcXq3fXRVVPjo7WbPHZMehRUA/M4zAkuBVu6TKHVL59kCIU/2J1BcTQqaPkEaeKRVMkXIntIc49tTevxdOitvnVpOn6bi9XSLClRihS1RvmISaBBvAYGIz20LBSTBQCe47LA7ZLG1hSlh5x/AlWKdj+NJZ/Yhj5kBIQ8ABAINTtwOdhQsXauHChZ0ef+CBB7R06VItWbJEkrR69Wq9+OKLevjhh3XHHXdIkkaOHClJGj58uC6//HJt2bKl00DH5XLJ5XKF9hsaGrrb5AHrjU+q5fZFd6e/ZvpIlv7G4OF1BUKb+kNS/cHAe91BqXp3YMUpd2Pf3NeeJo04Qxo1Uxo5I/DKGNE394J8fp+cXqecbqcaPY1yepxqdDeqyd2kJk/bq207eCz8PXjc7XfH+6MASDBWs1U2s61L7ycqs5qsoVCkYzgSClfMjuigJWw7PLgJDgEBAKAv9Oo4Arfbrc2bN2vFihWhMrPZrPnz52vjxo2SAj18/H6/0tPT1dTUpFdffVVf+tKXOr3mypUrdc899/RmMweMV3dVxCy/bOrwfm4J0EV+v9RaJzXXSs01gVdL2HZzbfux8PK+ZrIEet2MnNEe4OROYHWoHjAMQw3uBtW01qi2pVa1re2vmpaayP3WGjX2VTCHIaHjvAzhcyF0nHciaq6GTuakiDXfQ2cTUwbngug4l0P4nBjBY+H1TmbixY6fMdbxjnNXxLpP+CSWHeePCD839B3GmNMiqm745zrO/Bdd/c67NK9Gd+q2lXcMYOh9AgAYqno10KmurpbP51NBQUFEeUFBgXbt2iVJqqio0DXXXCNJ8vl8Wrp0qWbNmtXpNVesWKHly5eH9hsaGjR6dOJPFuz3G3p1V/Rwq+GZSZo8PD0OLcKQYBiS2ym5GsNeDZHbLXVSa33bqy4yoGk5Jg2ESVrt6dKY86Qx50qjZkmFp0v2lHi3asBr9bbqWOuxQEgTI5gJD2yOtR6T1+ijOYuGsCRLklJsKUq2Jre/W1OUYk2Rw+qI+JFqNVsD80a07QePBeeS6NizwGKyhMpirWrR41Uiwn5odwwDOp4bPHa8YIDJJwEAAHqm32f6PPXUU7Vt27Yu13c4HHI4HCorK1NZWZl8vn5Y1aYffHi4XtVNrqjyiybl8x+3Q41hSD53YGiS1yX52t69rdFlbqfkaZE8zW2vlrCyFsnjPE5Zs+RukmTE+xN3X2qeNHpO4FV0dmAoFRMVy+f3qd5dH9GDpqa1ptOgxulxxrvJCcNutivFFghawoOXZFtyewjTdjwYznSs23E7yZIUWu0EAAAA6Kle/UWUm5sri8WiiorIoUQVFRUqLCzszVslvFd3VcYsv3hyfj+3JAH5vO0BRzAICb27JL8v8DJ8gVWR/L5ArxJ/277h61AnVl1vh3J/2La3/VoRr7Aynyfw8ra2ta1V8ro72Y8O9oYsk0XKmxgYPpU3KfAqKJaGjZWGSNDZ7GkOBTNRQ51aayJ60dS56ob8stY2sy0iUIkIV9oCmPDyzsKWjnWsZgJDAAAADGy9+l+sdrtdM2bM0Lp160JLmfv9fq1bt07Lli3r0bVLS0tVWlqqhoYGZWZm9kJr4ytWoJNkM+uccblxaE0v87QGhuqEhu3Utw3jqQsM6/G0SN6WsB4kze3bwV4psYIarzvwPsR/wA4KZltgefC8yVL+JCm/OBDe5J4mWR3xbl2vMgxD9a561bTWqLqlWjUtNcftRTPYV2QyyaQ0W5rS7GlKtaUq3Z4eeLelK9Xe9m5LVaotNWZIExHQWFNks9ji/ZEAAACAuOh2oNPU1KQ9e/aE9vft26etW7cqOztbRUVFWr58uRYvXqyZM2dq9uzZWrVqlZxOZ2jVK0iNrR5tP1IfVX7uuFwl2QZod3y/LzCPSlOF1FgReA9/hcoq+27VIgx81iQpJUdKyZaSswPbqblS5qi2V1HgPa1AMif2yh/BSYPLneWhV0VzRSC06RDeeP2Day4ai8miYUnDlJ2UHXoNSxqmdHt6IKyxpUWEM6Fye5qSrcms+gIAAAD0gm4HOu+9957mzZsX2g9OWLx48WKtWbNG1113naqqqnTnnXeqvLxc06dP19q1a6MmSu6uwTSHzgeH6mXEmMbkvNPi3Dun5ZhUsUOq2iXV7Q8sLV13ILDMtLMqMOQIQ4c9XUrKlFKGBYKZYEATDGw6BjcpOYNqYuJmT7OONB3RUedRVTRXtAc3zeWqcFaoorliUPWmSbelKzs5OyKkCb2Ss5WTlKOcpBxlJ2Urw5FBKAMAAADEmckwYkULA1dwyFV9fb0yMjLi3ZyT8uCrn+j+f3wcVf5c6bkqGZ3V9w3wtErVuwPhTWXbq2KH1Hik7++NvmGySPZUyZbc9grbDpWnBgIXR3rYK6N9254WCHCSMgPlg3zSYcMwVNtaq711e3Wg8YAONx3W4cbDOtx0WIeaDqm2tTbeTewRq9mq7KRAEBMMZDoLarKTsmW32OPdZAAAAGDI607mkTC/2AZTD52tB+uiyuxWsyYP74OAyueRyj+QDm4KvCq2SzV76W3TG0xmyWwNe1nat00WyWqXLI7AnDDBV2g/KUZZF+rYUgKhjC2lLaRp27bYhsykwSej2dOsXbW7tPvYbu2t26s9dXu0t26v6lx18W5at2Q6MqNCmZzknOiwJjlb6bZ0VswDAAAABrGECXQGy6TIhmHo/QN1UeVTR2TIbu2FIQxel3TwHenT9dKBt6XDWwITEA9EZmtYMJEsWZMlW1JbeBEWhljsnbyH1wurHwxXTJb2kMVkDtsOlofVMVnCzjN3uIY1MN+LyRIITkLXYMjJQOTz+7Snbo+2V2/Xh9Ufanv1du2p2yPfAAwxHRZHexgTY7hTeFiTlZQlm5kJgAEAAAAEJEygM1gcrG1RjdMdVX5G0bCTv2j1J9KeV6S9r0qfvRFYNao/JWcHJrlNLwi8p+YF5lZJypSSstpebUN5kjLaQxxWp0EPGYaho86joeDmw+oPtaNmR1zntrGb7cpNzg2EMck5ge2kwHuovG0/2ZpMLxoAAAAAJyVhAp3BMuTq/YPHYpafUZTVvQvV7JW2Py199IxU+VHPGxaLPV3KKgosL50xQkorlNLypfS297QCKTU/0DsG6AcN7oZAcFPVHuDUtNb02/3tZrsKUgtUmFqogpTAe2FKYeC9rSzTkUlIAwAAAKDPJUygM1iGXMUabiVJ07syGbLPK+3+u/TuH6R9r/Veo5KypIIpUn6xlD85sJ07QUoexrwsiBu3z62Pj32sD6o+CIU3nzV81qf3TLYma2TaSI1KG6WR6SM1Mm2kRqSNCAU32UnZhDUAAAAABoSECXQGi/cPRPfQyUt3aGRWcucned3S+3+W3vilVH/w5G9uMgdCm8JpbeFNsVRQLKUPJ7hB3Hj9Xh1tOqoDjQe0v2G/9tXv00c1H2lX7S55/J4+uWdhaqHGZY7TuKxxGp81XmMzx2p0+mgCGwAAAAAJg0CnH/n8hnaWN0aVnzE6K/aPSL9Pev8RacN9Jxfk2NOk0XPaXrOlkTMCc9gAfchv+OX0ONXkblKjp1FN7iY1eZpU21qr6pZqVbdUq6q5KvDeUqWjzqPy+r191p6RaSM1LXeapuZO1bTcaZowbILS7Gl9dj8AAAAA6A8JE+gMhjl0Dh1rltvrjyovHhEjZKn4SHr+29LhzV2/gcksjThTGneRNG6eNGoWEw8PUYZhyOv3yuP3tL98nqiyVm+r3D63Wn2tcvlcavUG3jtuh++3+trOiVG3xdsip8cpQ0ZcPnemIzMU3ARDnOyk7Li0BQAAAAD6UsIEOoNhDp09lU0xy8fnh/UW8LQGeuS8uUrqSq8Fsy0Q4Ey5WppwWWB1qTjz+r1y+9yh0CAYInj93tCr033DK8Mw5Df88hk+GTLk8wfe/YY/9PIZvlA9v+GXX/6I41Ev+eX3B94Nw5DP8EUclxTaNmTIMIzQPSO2e3gsZr0Ox8Lbc7xj4cd9hi/qux7s7Ga7JudMjuh9Mzp9NEOmAAAAAAwJCRPoDAYnDHQOvis9+02p5pMTX2zYGGnmzdIZ/9YrIY7X71Vta62qmqtU2VypqpYqVbVUyelxyuV1hXpwhG+3elsD2x2Oe43BHyag/52aeaqm5k7V6bmna2reVE3ImiAbPdAAAAAADFEEOv3kw0P1WvnSrqhys0kaO8wuvfpT6fX7JSN6SFaEwmnS3BXShIWS2dylexuGoTpXnQ43HQ69jjQdUYWzQpUtlapqrlJNa02o9wcQb/nJ+ZqSO0Wn552uqblTNSVnitLt6fFuFgAAAAAMGAkT6CTiHDpOl1fPvH9YT285pC2dLFc+J6tBjjULpKNbj3+x7HHSJfdIk67sdEUql8+lz+o/0566Pdpbt1d76vboYONBHW46rBZvS88+DNCLshxZyk3OVV5ynvJS8nRKxikqyihSUXrgxaTFAAAAAHB8JsMw4jN76UkKzqFTX1+vjIyBu2LTPz4q1/ef3a6qRlendc42f6TfJ/1aaf7ola9CzFbp3O9KF9wu2dqXNjcMQ/sb9mtr1VZtrdyqbVXb9Gn9p/SyQZ+wmW1KsiTJYXXIYWl/JVmTAu9hx9Lt6UqzpYXe0+xpSrelK8ORodzkXOUk5TBUCgAAAABi6E7mkTA9dBKF32/ol698rN+8uue49f7N8k/dZf1f2fzH6XE04gzpqt8EhllJavW26u2jb2v9wfXacGiDqlqqerHlQ4dJJplN5ohXsMwkk0ymwMssc2C7K8fatiOOdagX3DebzJJJ0cdMZpnV4VjYNToeC2+TWWbZLDbZzG2v8G2zTVazNarcarZGBTMdt4Mvi9kS739sAAAAAIAwBDq9yO3163uPv6+/f1h+3HrftTylW2xPdV7BbA3Mk3Pu9+Q3m7W5/F09v/d5/eOzf6jZ29zLrT6xjr0zkixJ7T0zYvTQSLYmy26xRwQJ4e8R2yZrVFkoZDGZZDFZQqGJxWRpLzOFlckki9kSFdSE1wuGJcHrAgAAAACQyAh0etGPX9hxwjBnqeMV3WI6TpiTO1G69iG5C4r1/N5n9cftf9SBxgO93NJAL5W85Dzlp+QrL6XtPWw/uJ1hz6B3BgAAAAAAAwyBTi95avMh/fnt/Z0eLxmVqWVjDmv+5j9Jnc1aNPkq6ZrVWl/xrn76zBUqdx4/HDqRVFuqTs08VaPSR2lU2iiNSBuhkWkjNTJtpIanDmceEwAAAAAAEhSBTi/4uKJR//XMhzGP2Swm3XPVVF0/3ivT72+QjE7mzJn7X2o46xv62Ts/1oufvtjtNoxMG6mSvBJNzp6scVnjND5rvApTCxleBAAAAADAIJQwgc5AXbbcMAzd9dxHcnmjV5dKtln0xyWzdFZRuvQ/l0itdbEv8rkybR89Xbe9+GUdbjrcpfvmJefpwtEX6pwR52h63nTlpeT14FMAAAAAAIBEwrLlPbR2e7n+/ZHNMY/9+vozdFXJCOmVe6Q3Hoh9gYt+oBdGTdYP3/yhvH7vce81zDFMl596ua4Ye4Wm5E4JrJYEAAAAAAAGBZYt7yetHp9+9vedMY/deM6YQJjz2ZvSG7+MfYGpn9f/5Y7Qva+vOO59JmVP0s3TbtbFoy9m3hsAAAAAAECg0xMPv7lPB2qjlxHPT3fo9gUTJU+L9PwyxZoF2cidoNWnTtdv37230+un29J1+6zbdfX4q5kLBwAAAAAAhBDonKTKhlaVvbon5rH/vGySUh1Wad3PpNpPo477zTbdN2WuHvnw951ef0bBDN17/r0qTC3stTYDAAAAAIDBIW6TsDQ3N+uUU07RbbfdFq8m9Mh/v7xbTnf0BM0lo7N0zRkjpYqPpDd/FfPc+0+/RI8cWNvptW+ccqP+cOkfCHMAAAAAAEBMceuh89Of/lRnnXVWvG7fI9sO1umvmw/FPHbnlcUymyT9/XYpxiTHT4wu1p/rt3d67dtm3qbFUxb3VlMBAAAAAMAgFJceOp988ol27dqlhQsXxuP2PWIYhn70wo6Yx66ePkIzThkm7XhO2v9m1PGdjiTda2uNea7ZZNaPzvkRYQ4AAAAAADihbgc6GzZs0KJFizRixAiZTCY9++yzUXXKyso0ZswYJSUlac6cOdq0aVPE8dtuu00rV6486UbH0/Pbjmjz/mNR5ck2i/5z4STJ0yr984dRx5tMJt06eqw8RnSvHavZqvsvvF/XnHZNn7QZAAAAAAAMLt0OdJxOp0pKSlRWVhbz+OOPP67ly5frrrvu0pYtW1RSUqIFCxaosrJSkvTcc89pwoQJmjBhQs9aHgctbp/ufWlXzGPfnDtOwzOTpU0PSXUHoo7/omCEDvqcMc9ded5KXXLKJb3aVgAAAAAAMHh1ew6dhQsXHneo1AMPPKClS5dqyZIlkqTVq1frxRdf1MMPP6w77rhDb7/9tv7yl7/oySefVFNTkzwejzIyMnTnnXfGvJ7L5ZLL5QrtNzQ0dLfJvWb1a3t1tD56yNTIrGR9/YJTJa9L2vhg1PHddpueSrbEvOaSqUt02djLer2tAAAAAABg8OrVOXTcbrc2b96s+fPnt9/AbNb8+fO1ceNGSdLKlSt18OBBffbZZ7r//vu1dOnSTsOcYP3MzMzQa/To0b3Z5C47Utei323YG/PYissnKclmkT54QmqqiDhmSLpveJGMGOdNz5uub5/x7d5vLAAAAAAAGNR6NdCprq6Wz+dTQUFBRHlBQYHKy8tP6porVqxQfX297r//fk2cOFHjx4/vjaZ22y//+bFaPf6o8tljsnXFtOGS3y+99euo4+tTkvWO2RNV7rA4dO8F98pmtvVJewEAAAAAwOAVt2XLJenGG288YR2HwyGHw6Fbb71Vt956qxoaGpSZmdn3jQuzu7xRT22JXqbcZJLuXFQsk8kkfbxWqv444rhH0i/yCiRFB0FfLf6qRqaN7KMWAwAAAACAwaxXe+jk5ubKYrGooiJy2FFFRYUKCwt7dO2ysjIVFxdr1qxZPbrOybjv5V3yxxgz9fkzR2nqyLZw6e3fRh1/IiNN+83RYU5ucq5unnZzbzcTAAAAAAAMEb0a6Njtds2YMUPr1q0Llfn9fq1bt05nn312j65dWlqqHTt26N133+1pM7vl/QPH9MrOyqhyu9Ws5Ze0rdRVuVPatyHiuFfSn7KGxbzmt8/4tlJtqb3dVAAAAAAAMER0e8hVU1OT9uzZE9rft2+ftm7dquzsbBUVFWn58uVavHixZs6cqdmzZ2vVqlVyOp2hVa9OVllZmcrKyuTz+Xp0ne56aMOnMctvPGeMRmQlB3Y2PRR1/LWUZB21mKLKJ2VP0ufGfa5X2wgAAAAAAIYWk2EYsRZg6tT69es1b968qPLFixdrzZo1kqQHH3xQ9913n8rLyzV9+nT9+te/1pw5c3qlwcE5dOrr65WRkdEr1+zM/hqn5t6/Xh2/ofQkq17/j3nKSrFLLXXSA5MlT3NEnZuHF2pTkj3qmr+e92vNK4r+/gAAAAAAwNDWncyj2z105s6dqxNlQMuWLdOyZcu6e+kB53/e2BcV5kjS/zvrlECYI0lb/y8qzNlrs8YMc0aljdIFoy7oi6YCAAAAAIAhpFfn0OlL/T0p8jGnW0+8dzCq3GYx6cZzxgR2/H5p0++j6jyenh7zml+e9GVZzJbebCYAAAAAABiCEibQ6e9JkZ9+/7BaPdErVF09faTyM5ICO3tekY7tizjuNJn0fIxuUUmWJF09/uq+aCoAAAAAABhiEibQ6U+GYeiJd6N750jS184/tX1ny5+ijr+YliqnKXqc1sKxC5XpyOy1NgIAAAAAgKErYQKd/hxy9eHheu2uaIwqP+vUbE0sbBtO5ayWPl4bVee5zNhLlX950pd7tY0AAAAAAGDoSphApz+HXP39w/KY5V+aObp958O/Sn5vxPGDVqs+sEUvVX567ukqzinu1TYCAAAAAIChK2ECnf5iGIbWbj8aVZ5it2jh1OHtBVv/L6rOi2kpMa/5ufGf67X2AQAAAAAAJEyg019Drj6uaNJnNc1R5fMm5ivZ3rZCVfl2qfyDiOOGpBezcqPOs5qsuvSUS/uiqQAAAAAAYIhKmECnv4Zcrd0ee7jVgqmF7TsfPRN1fJfdps9M3qjyc0eeq6ykrN5qHgAAAAAAQOIEOv1l7UfRgY7dYta8iXntBXtfjarz94zYK1hdPvbyXmsbAAAAAACARKATYX+NUzuPNkSVn3dartKTbIGdlmPSkfcjjhuSXk6PDnSSrcmaO3puH7QUAAAAAAAMZQQ6YV6O0TtHki6bEjbcat/rCkQ47bY57DoqT9R5c0fPVYot9kTJAAAAAAAAJythAp3+mBQ51vw5FrNJ84sL2gv2vxVV5+XU2KHNZWMu67W2AQAAAAAABCVMoNPXkyJXNLRqy4G6qPI5Y7OVnWpvLziyJeK4X9I/0tKjzkuzpem8kef1cisBAAAAAAASKNDpa6/tropZfln46lY+r3Q0crnyLUkOVVpMUeddVHSR7BZ7VDkAAAAAAEBPEei0ee3j2IHOJeHDrap2St6WiON/72S41YIxC3qtbQAAAAAAAOEIdCR5fX69/kl0oDOxIF3DM5PbCw5HDrdqMZn0Ulpq1HkZ9gydPfzsXm8nAAAAAACARKAjSdp2qF4Nrd6o8gsm5EYWVGyP2H0lJVlN5uivcMGYBbJZbL3aRgAAAAAAgCACHXU+3OqCCXmRBZU7I3ZfiNE7R5KuPe3aXmkXAAAAAABALAkT6PTlsuUbYgQ6STazZo3Jbi8wDKnio9ButcWst5OTos4bnzVeU3Km9HobAQAAAAAAghIm0OmrZcuPOd3adqguqvysU3OUZLO0FzirpJba0O7LqSnym6JXt7ri1CtkilEOAAAAAADQWxIm0Okr/9xZIcOILr/gtI7DrXZE7L6YGnu41eVjL++tpgEAAAAAAMQ05AOdFz84GrN87sTO58/Zb7XqwyRH1Dln5p+pEWkjerV9AAAAAAAAHQ3pQKe+2aM391RHlU8enqFT89IiC6s/CW2uTUuJeb0rTr2iV9sHAAAAAAAQS78HOnV1dZo5c6amT5+uqVOn6ve//31/NyFk46c18vqjx1tdMa0wunLNntDm+pTkqMNWs1ULxizo1fYBAAAAAADEYu3vG6anp2vDhg1KSUmR0+nU1KlTde211yonJ6e/m6J39tXELL90SqxAZ68kqdJi0XZH9HCrOcPnKNOR2avtAwAAAAAAiKXfe+hYLBalpASGLLlcLhmGISPWrMT94J1Pa6PKclLtOi2/w3Ard7PUcEiStCEleqlySZo3al6vtw8AAAAAACCWbgc6GzZs0KJFizRixAiZTCY9++yzUXXKyso0ZswYJSUlac6cOdq0aVPE8bq6OpWUlGjUqFG6/fbblZube9If4GTVN3u0s7whqnzOqdnRy47X7g1trk+JPX/OhaMv7NX2AQAAAAAAdKbbgY7T6VRJSYnKyspiHn/88ce1fPly3XXXXdqyZYtKSkq0YMECVVZWhupkZWVp27Zt2rdvnx599FFVVFSc/Cc4SZs+q425XPmcsTGGfrXNn9NiMuntGKtbTc6erMLUGMO0AAAAAAAA+kC3A52FCxfqJz/5ia655pqYxx944AEtXbpUS5YsUXFxsVavXq2UlBQ9/PDDUXULCgpUUlKi119/vdP7uVwuNTQ0RLx6wzufxp4/Z86p2dGFtZ9Kkt5OTpLLHP2VzR09t1faBAAAAAAA0BW9OoeO2+3W5s2bNX/+/PYbmM2aP3++Nm7cKEmqqKhQY2OjJKm+vl4bNmzQxIkTO73mypUrlZmZGXqNHj26V9r6dowJkYel2DQhPz26cv1hSdLGpNjz5xDoAAAAAACA/tSrgU51dbV8Pp8KCgoiygsKClReXi5J2r9/v84//3yVlJTo/PPP17e//W1Nmzat02uuWLFC9fX1uv/++zVx4kSNHz++x+2sb/Fox5Honj6zx2bLbDZFn9AQCHQ2xxhulZucq8nZk3vcJgAAAAAAgK7q92XLZ8+era1bt3a5vsPhkMPh0K233qpbb71VDQ0Nyszs2fLgr31cJX9X58+RpIbDqjeb9YndFnVoZsHM6EmUAQAAAAAA+lCv9tDJzc2VxWKJmuS4oqJChYU9mzS4rKxMxcXFmjVrVo+uI0n/3BF7EubzT+tkta36w3rf4ZARI7iZUTCjx+0BAAAAAADojl4NdOx2u2bMmKF169aFyvx+v9atW6ezzz67R9cuLS3Vjh079O677/boOh6fX+t3VUaVj8lJ0fj8tOgT3M1SS23M4VYSgQ4AAAAAAOh/3R5y1dTUpD179oT29+3bp61btyo7O1tFRUVavny5Fi9erJkzZ2r27NlatWqVnE6nlixZ0qOGlpWVqaysTD6fr0fX2X64Xo0ub1T5JcUFsYdONR6VFHv+nExHpsZljetRewAAAAAAALqr24HOe++9p3nz5oX2ly9fLklavHix1qxZo+uuu05VVVW68847VV5erunTp2vt2rVREyV3V2lpqUpLS3s8h86WA3Uxy+dOzI99Qv0hNZtM2uGwRx06M/9MmU292skJAAAAAADghLod6MydO1eGEWNG4TDLli3TsmXLTrpRfWnLgWNRZWaTNH10VuwTGo5oq8MhH/PnAAAAAACAASJhupf01qTIW/ZHBzqTCjOU6ugk22o8oi2dzJ8zs2Bmj9oCAAAAAABwMhIm0OmNSZGP1rfoaH1rVPmZp2R1flJTlbYlRQ+3SrYma2L2xJNuCwAAAAAAwMlKmECnN2zZXxezfMYpwzo9x9dUoQ8c0T10puVOk9Xc7RFrAAAAAAAAPZYwgU5vDLnaHGO4lSSdWdR5oLPHeVjN5uivqSSv5KTbAQAAAAAA0BMJE+j0xpCrWBMi56TaVZSd0uk521zVMcun508/6XYAAAAAAAD0RMIEOj3V6vHpoyP1UeVnnjJMphgrWAVtM5pjlp+ee3qvtQ0AAAAAAKA7EibQ6emQq51HG+TxRS+3frzhVvJ5tDXGNDljLKnKSso6qXYAAAAAAAD0VMIEOj0dcvXh4ejeOZJUMjqz03Nqaz/RAZstqnx68vCTagMAAAAAAEBvSJhAp6e2HYwOdEwmadrIzgOdbYffjlleknlqr7ULAAAAAACgu4ZMoPPBobqoslNzU5WeFN0DJ2hb9baY5dNzpvVWswAAAAAAALptSAQ6TpdXe6qaospPH5V13PO21n0SVZbu8+vUvCm91TQAAAAAAIBuS5hApyeTIm8/XC8jej5knT6q8+FWHr9HHzUfjT7H5ZI5JbfbbQAAAAAAAOgtCRPo9GRS5A8OxZ4Q+XiBzse1H6vV8EaVl7hcksXe7TYAAAAAAAD0loQJdHrigxgrXFnMJhUP7zzQ+bD6w5jlJa1uydL5vDsAAAAAAAB9bWgEOjEmRJ5QkK5ku6XTc3bV7opZPtXtkszW3moaAAAAAABAtw36QKe+xaP9Nc1R5acfZ7lySdpRsyOqbJTHowy/QQ8dAAAAAAAQV4M+0PmkojFm+dSRGZ2e4/F7tKduT1T5ZLcnsEEPHQAAAAAAEEcJE+ic7CpXuzsJdCYWdh7ofFr3qTx+T1T5JJc7sGGmhw4AAAAAAIifhAl0TnaVq4/LYwc6EwrSOj1nZ+3OmOWT3cFAp/O5dwAAAAAAAPpawgQ6JytWD52CDIeyUjpferyzCZEnu92B3jkmU6+1DwAAAAAAoLsGdaBjGIZ2x+ihc7zhVpK0sya6h06u16dcn5/5cwAAAAAAQNwN6kCnusmtY83Rc+FMPM5wK7/h1+5ju6PKJwWHW7HCFQAAAAAAiLNBHeh83MmEyBMK0js951DjITk9zqjy9vlz6KEDAAAAAADiq98DnYMHD2ru3LkqLi7W6aefrieffLLP7hVruJUkTSzsPND5sPrDmOWhFa7ooQMAAAAAAOKs37ubWK1WrVq1StOnT1d5eblmzJihyy+/XKmpqb1+r1g9dEwmaXx+50Ou3jn6TszyYnroAAAAAACAAaLf04nhw4dr+PDhkqTCwkLl5uaqtra2TwKdWCtcFWWnKMUe+2MbhqG3j74dVT7S49Uory+wQ6ADAAAAAADirNtDrjZs2KBFixZpxIgRMplMevbZZ6PqlJWVacyYMUpKStKcOXO0adOmmNfavHmzfD6fRo8e3e2Gn4hhGPo4xpCr482fs79hv446j0aVn9Xa2r7DkCsAAAAAABBn3Q50nE6nSkpKVFZWFvP4448/ruXLl+uuu+7Sli1bVFJSogULFqiysjKiXm1trb761a/qoYceOrmWn8DhuhY53b6o8onHCXSe/uTpmOVntYQFOmYCHQAAAAAAEF/dHj+0cOFCLVy4sNPjDzzwgJYuXaolS5ZIklavXq0XX3xRDz/8sO644w5Jksvl0tVXX6077rhD55xzznHv53K55HK5QvsNDQ1damenK1x1MiFyi7dFT33yVFS51TA0JyLQYcgVAAAAAACIr15d5crtdmvz5s2aP39++w3MZs2fP18bN26UFBgKdeONN+qiiy7S//t//++E11y5cqUyMzNDr64Oz9pd3hSzvLMeOq8fel0N7uiw6BJns4b5/e0FFgIdAAAAAAAQX70a6FRXV8vn86mgoCCivKCgQOXl5ZKkN998U48//rieffZZTZ8+XdOnT9eHH8ZeKlySVqxYofr6et1///2aOHGixo8f36W2xOqhYzWbNDY39uTLrx58NWb5Vxo6XIchVwAAAAAAIM76vbvJeeedJ394j5cTcDgccjgcuvXWW3XrrbeqoaFBmZmZJzxvd4wJkU/NS5XdGp1heXwebTi4Iap8pN+sEpc7spBJkQEAAAAAQJz1ag+d3NxcWSwWVVRURJRXVFSosLCwR9cuKytTcXGxZs2adcK6Xp9fe6qih1x1tsLV1qqtavREB0AX+SwydSw0W7rSXAAAAAAAgD7Tq4GO3W7XjBkztG7dulCZ3+/XunXrdPbZZ/fo2qWlpdqxY4fefffdE9bdX9sstze6F1Bn8+e8X/l+zPJ5nhiFDLkCAAAAAABx1u0hV01NTdqzZ09of9++fdq6dauys7NVVFSk5cuXa/HixZo5c6Zmz56tVatWyel0hla9OlllZWUqKyuTzxe9FHlHH8cYbiV1vsLVlsotUWV2s10lbm90ZYZcAQAAAACAOOt2oPPee+9p3rx5of3ly5dLkhYvXqw1a9bouuuuU1VVle68806Vl5dr+vTpWrt2bdREyd1VWlqq0tLSLs2hs7uTJctj9dDx+X3aVrktqnxa3jTZq9+LvgjLlgMAAAAAgDjrdjoxd+5cGYZx3DrLli3TsmXLTrpRPRVrhaskm1lF2SlR5btqd6nJEz3fzpn5Z0ofvR19cQIdAAAAAAAQZ706h05f6s6kyLFWuJpQkC6zOWqKY607sC6qTJLOyD9D8sWYRIchVwAAAAAAIM4SJtDp6qTIrR6fPqtpjirvbIWrWIFOkiVJMwtnSv4YgQ6TIgMAAAAAgDhLmECnq/ZWNcnnjx4SFmv+nP0N+/Vp/adR5eeNPE/J1mTJF2NSZJYtBwAAAAAAcZYwgU5Xh1zFmj9Hir3C1abyTTHrXlR0UWDDzypXAAAAAABg4EmYQKerQ652l0dPcCzF7qHzXnmMVawknTvy3MAGQ64AAAAAAMAAlDCBTlfF6qGTkWRVQYYjoswwDL1XER3ojMscp+ykbMnvlwx/9A3ooQMAAAAAAOIsYQKdrg65irXC1cTCdJlMkStcHWo6pMrmyqi6MwtnBjZi9c6RmEMHAAAAAADEXcIEOl0ZcuXx+XW0viWqfHx+9HCrD6s+jHmNmQXBQCfG/DkSQ64AAAAAAEDcJUyg0xXl9a2KscCVRmcnR5V9WB070CnJKwls+DrpocOQKwAAAAAAEGeDKtA5dCy6d44kjcyKDnQ+qvkoqiw7KVuFqYWBnU576FhPun0AAAAAAAC9YVAFOofrYgc6o4ZFBjoev0c7a3ZG1ZuWO619rp3OeugQ6AAAAAAAgDhLmECnK5MiH+60h05KxP7eur1q9bVG1ZuaO7V9p7MeOgy5AgAAAAAAcZYwgU5XJkU+XNccVWazmJSfHrlkeWfz50zLnda+0+kqVwQ6AAAAAAAgvhIm0OmKWEOuhmcmy2yOXLL8o+ro+XMkaUrOlPYdX2c9dBhyBQAAAAAA4mtwBToxhlzFmhA5Vg+d0emjlZWU1V7QaQ8dAh0AAAAAABBfgybQ8fsNHamLnhdnZIcJkZs9zdpTtyeqXsT8OdJxVrliyBUAAAAAAIivQRPoVDW55Pb5o8o79tDZWbtTfiO6XsT8OdJxhlwR6AAAAAAAgPhKmEDnRKtcHepshasOPXS2V2+PWS+6hw5DrgAAAAAAwMCUMIHOiVa5ijUhsiSNyjpxoGMxWTQpe1JkYadDrgh0AAAAAABAfCVMoHMisSZElqJ76MSaEPm0Yacp2dph8mRfJz10GHIFAAAAAADibPAEOnXNUWUmU2DZ8qDa1lodbjocVS9quJVEDx0AAAAAADBgDZ5AJ0YPnfx0h+zW9o/Y6fw5OTECHXroAAAAAACAAWrwBDox5tDpuMLVR9UfxTyXHjoAAAAAACCRxCXQueaaazRs2DB94Qtf6LVrVja6ospGZJ14/pxka7LGZY2LvmCnq1zRQwcAAAAAAMRXXLqbfPe739VNN92kP/3pT71yPY/Pr7rmjgGModPs1dJHz0hHtsqo3KXt3l1R5072GrL+3xejL9pYEftmFnroAAAAAACA+IpLOjF37lytX7++1653zOkObVvk023WJ/Qly3rlbG+U2qbNOWq16NjokVHnTq2vlGo/7vrN6KEDAAAAAADirNtDrjZs2KBFixZpxIgRMplMevbZZ6PqlJWVacyYMUpKStKcOXO0adOm3mhrp6qb2gOdH1gf0Tetf1OOqTGizk67Pea5U1zumOWdYg4dAAAAAAAQZ90OdJxOp0pKSlRWVhbz+OOPP67ly5frrrvu0pYtW1RSUqIFCxaosrLypBrocrnU0NAQ8eqoxhmYP2eK6TMttvwj5nV2dBLoFHcn0DFZpIzhXa8PAAAAAADQB7od6CxcuFA/+clPdM0118Q8/sADD2jp0qVasmSJiouLtXr1aqWkpOjhhx8+qQauXLlSmZmZodfo0aOj6tS09dD5T+tjMpuMmNfZ6YgOdFL8fhV5O1nNKpbiz0lJmV2vDwAAAAAA0Ad6dfyQ2+3W5s2btWLFilCZ2WzW/PnztXHjxpO65ooVK7R8+fLQfkNDQ1SoU93k0ummvbrAEr2KVdBOhyOqbJJhlTlv0okbYU+Txs2TLri96w0HAAAAAADoI70a6FRXV8vn86mgoCCivKCgQLt2ta8wNX/+fG3btk1Op1OjRo3Sk08+qbPPPjvmNR0OhxwOh8rKylRWViafzxdVp8bp1resz8duVO5EVV37W1X/c0nUoeKpX5Fm/2c3PiEAAAAAAED8xWWG31deeaXb55SWlqq0tFQNDQ3KzIwc9mSp3q3LLO9GnWM4MmT62j+1s3pbzGtOzpnc7XYAAAAAAADEW7fn0Dme3NxcWSwWVVRURJRXVFSosLCwR9cuKytTcXGxZs2aFXXs7KN/jnmOadbXpKRM7ajZEfP45GwCHQAAAAAAkHh6NdCx2+2aMWOG1q1bFyrz+/1at25dp0Oquqq0tFQ7duzQu+926IlzbL/Ocq6Lqu+SXTrrW5KknTU7o447LA6NzRzbozYBAAAAAADEQ7eHXDU1NWnPnj2h/X379mnr1q3Kzs5WUVGRli9frsWLF2vmzJmaPXu2Vq1aJafTqSVLouew6Y5O59DZWCaL/FH1X09fqPlpeZKknbXRgc7EYRNlNcdlxBkAAAAAAECPdDvReO+99zRv3rzQfnAFqsWLF2vNmjW67rrrVFVVpTvvvFPl5eWaPn261q5dGzVRcnfFnEPH7ZS2PRZV12NY9E7hDZovqd5Vr6POo1F1mD8HAAAAAAAkqm4HOnPnzpVhGMets2zZMi1btuykG9VlHz0ruRqiip/3nyNzdpEk6dP6T2OeOmHYhL5sGQAAAAAAQJ/p1Tl0+lLMSZG3Phqz7p+8lyo31SFJ2lu3N2adcVnjer2NAAAAAAAA/SFhAp2oSZEbK6T9b0bV2+E/RR8Ypyo71S7pOIFOJoEOAAAAAABITAkT6ETZ/XdJ0UO/nvKdL8mknLRAoBNryFV2UraykrL6tn0AAAAAAAB9JGECnaghVx+/FLPei745kqTctMCQq1iBDsOtAAAAAABAIkuYQCdqyNX+t6LqvOefoHLlSJJy0uyqaalRubM8qt6pmaf2aVsBAAAAAAD6UsIEOl3xqu+M0HZ2ql2bKzbHrDcxe2J/NQkAAAAAAKDXJUygE3OVqw42+KdJktKTrHJYLXq3/N2Y9WYWzOyTNgIAAAAAAPSHhAl0ooZcdVBrpOkjY4yk9vlz3qt4L6pebnKuxmSM6atmAgAAAAAA9Lm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qfnQ8g3WYVSxD6bMCAAAAAAYHn9+Qx+eXx+eX12fIZxgyjMD0IoYkw5D8oe3gMcmQIX+HelHntE0sEyht244x2czx6hkR9YwYZaGtE5wbeY3w4+FtCrbB2dgY3dBOJEygAwAAAADAUGcYhnx+Q97gy+eXx2eEApJgWeDdkMcfCEy8Pr88YfW9beXBczzB8rD9jue7g9dpu5bH217X7Y28TnhY4/b5Q/cNbvsTajbf/uN3NXe5LoEOAAAAAGDACwYZPsOQ3x/oiRHYDpT723pnBLYDdXxt+0ZbXZ+/i+cahnx+hfaDwYTXHxaUtAUfvg7hh89nRAQigeAl7HyfEQpWwsOXQCjTIUQJHg9eu60eICVQoNPVZcsBAAAAAO18fkMtHp+aXV41u31yur1qcfvk9vo7BAuBwMHToTdHV3p5hHqKRJwTfX54DxKPz4gIUULBSjC06RDKAIiUMIFOd+bQSUQ33nij6urq9Oyzz8a7KQAAAAD6gGEYoaEoHUOOYNARHLYSGN4SCD1a3N5AIOP2qaXt1ezpuO2V0xW53eLxyenyyuX1x/ujA+gDCRPonIw7nvpAuyu6PqFQb5tYkK57P3963O4PAAAADDZ+v6FWbyDcaHb51OzxhoKOZncgxHB7/aGQJLDdHpq4fX55vB32w+YN8bfNS+IPm6ekY1nEqwtlfiNwfqxJWQHgZA3qQGd3RaPeP1AX72YAAAAA6MDj8+uY063aZrdqnW7VNXt0rDnwXtcc3PeovqV92+kK9FQB0P8sZpOsZpPsFrNsVrOsZpNsFrNsluB7+7a17d0eth2rrjW4bTbJbDbJZJLMJpNMUmhbCqzubDaprTywrbZ6ZlPgvOC2wupJge2g4CLR4YtFm2SKKmuvbwqr17VzY9XTcetF3sPZ1KirV0W3JZaECXSYQwcAAAAYmHx+Q/UtgUDmmDMQ0BxrdqvWGSiraQrut783tnrj3Wygz5hNktUSCD2CwYfVYpLVHHwPlFnMbaGG2SRLx3pmUygMCW4Hzw+ea2s7P3Bu4LjNYpIlVC+wbTNHBi3W4H3Dzgm2N1Qv2Na2bbM5RuKBXtfQkNzlugkT6Az2OXQAAACA/mQYhlxef8QcLM1uX8TwpWa3V61tc7c43T41tHToRdPi0TGnWw2EM0OSySTZzMFQoj0oaA8lIgMKW4dAIxggmM0mWUyBQMNkUti2SRZzYN9sNsncVm5uKzeb2stinmsK9CoJndt2nY7nmtt6fETXbb9eeNgRCk/CAptgOBIIWQg/0D8SJtABAAAABrvgpLnhKw152+Zv8Xj9ERPjNodNlBsoaw9kWsPLO8wxE6zb4vGJhYMSm8kkJdssSrFblGy3KMVmVYojsJ9it0a8p9otSg6VBcod1ugeGx17hUQFMh2CDQDxM6gDnYkF6UP6/gAAAOhcq8enioZWNQWXcu7w3uLxRS7NHFzWuW1pZl/4Us5hSzcHVzAKP6fjUs2ByXajl4cmYOk/kXN5tPeuCL1MHfbN7T08rJ2UhY7FKDOHenoEenC0D39pC0us5tDwGVt4oGI1h0KbFLtFSbb2kMZhNUfMvwFgaBnUgQ4rTAEAAAxdrR6fKhtcOlrfooPHWnSgtlmHapt1oLZZB481q6LBFe8m4jhS7BZlJduUlWJXVopNw1LsSk+yBnqitPUwCQUdDqtSwnqqOKwW2a2RgU1gItf2niYEIQAS3aAOdAAAADD4GIahumaPyhtaVd7Qqor6wHt52HtFQ6uONXvi3VS0SbFblJ1qV3aqXcNS7KHt9v1AcDMsxa5hKTZlptjksFri3WwAGNAIdAAAADAgeHx+1TS5Vd3kanu5VdO2XdHgCoQ3bYGNy+uPd3OHJJNJykiytYUugfAlvBdNVrJNw1KjA5skG+EMAPS2hAl0Bvuy5WvWrIl3EwAAAKIYhiG/IXn9fvn9ks8ITM7r9vnl9vrl8vrk8ga2A/tt277AsfDyhlavGls9aox4D2zXtXhUR4+aXhMxUa7domRbcLttyFLoePskuZF1raHtlLDz0hxWJsIFgAEiYQIdli0HAADxElze2eNrDyvcbfvhYYbHZ8jt84UCDI/PaDvmk9cfmAjXZxjy+w352sIRf1hZsI7fiHz3+dW+HTo/7LihUFn4tdrPD79mWDgTfj0jMMmvz4iuj0iW4DLFHVYF6rgK0PGWao5ZFrxesCzGqkLtwUxkSBMeyCRZLSyZDABDQMIEOgAAAF1hGIZaPD41ubxqavUG3tu2m92+thWNvHK6Ass+O9tWNQrfb3a1121x++T2MbxnsLBbzRo1LFlF2SkaPSxFo7OTNSzFrlRH29LOwfe2kMRmMcvStiqR1dK+ehEAAPFGoAMAAAYEv99QkzsQvDS2etXkah+SEwxkGls9anR55WwLaRpb27fDwxt6lQxNVrNJ+ekOFWYmqTAzSQUZSSrMCGyPyAqEOHlpDgIZAMCgQKADAMAAEhza0+rxqdXT9t42D4nHZ8jr88vrN+Tx+eX1GfL628qD7xHbkXU9/rZzfH55/O3Da0JDfjoM2zE6DOeJGgZkqL1OW3noGsG6bUN2Og4t8huRw32C5wKdSXdYVZDZHtAUZiS172ckqSDTodxUwhoAwNBBoAMAQA+1enyqcbp1zOkO9SZxurxqDPUa8bS9+wLbbcN7AqFNW3Djbd8GBrs0h1VpDqvSk4IvmzKSbcpJtSsv3aHcNLtyUh3KbdvOTXOwShIAAB30e6Cze/duXXfddRH7jz32mK6++ur+bgoAAJ0yDEM1TreO1LW0vVpV43S1LansVo3TpVqnWzVNbjW5vPFuLgYZizk4V4tCc7a0lwXeLeYOx4Nlbe+WiHParmk2y2JSRD1z2+S+4dcJlYXqKaKe1WKWw2qWPfiymOWwWQLv1g7HrGY5rBbZreZQkMMqSQAA9Fy/BzoTJ07U1q1bJUlNTU0aM2aMLrnkkv5uBgBgiGtx+3SkviUU2Byua9XRupa2slYdrmuR20tvmaHAYTVHToTrCCzNHNxPtluUZAtMjmsPhhVtqxTZ24KKQKhhanu3tB1rr2+zBF4RgUtY0BJZRtgBAABOLK5Drp5//nldfPHFSk1NjWczAACDkNfn15G6Vu2vdWp/TbMO1DZrf41Th9t629Q63fFuInog1W5RWpJVqQ6r0h3WwLY98J7uCJRHbIfVSXVYleoILPWcarfIajHH++MAAAB0W7cDnQ0bNui+++7T5s2bdfToUT3zzDNRw6XKysp03333qby8XCUlJfrNb36j2bNnR13riSee0Fe/+tWTbjwAYGhzurxtQU2zDtY2R4Q3h4+1yMssuzGZTZLVYpbNHDmUx9Q2tCbYSyQ0JCdsiE74MB2zKXq4j8mkiJ4m5o7De0ztQ3gC9SOHCqU5AkFNepItFMJkJFmV5rC1lQdCGYbsAACAoa7bgY7T6VRJSYluuukmXXvttVHHH3/8cS1fvlyrV6/WnDlztGrVKi1YsEC7d+9Wfn5+qF5DQ4Peeust/eUvfznu/Vwul1wuV8R5AIDBzzAMNbq8qmxoVWWDS0fqW3WgtlkHapzaXxsIcKqbEqeXjckUmAg22GMkxWFVis2iJJtZSTZL2Ktt32pRsr192241y2oxyWoODPUJBjJWS6DcZm57b6sT2DbLGqxjNoXqMaQHAAAg8XU70Fm4cKEWLlzY6fEHHnhAS5cu1ZIlSyRJq1ev1osvvqiHH35Yd9xxR6jec889p0svvVRJSUnHvd/KlSt1zz33dLeZCefGG2/Un/70J0mS1WrVqFGj9MUvflE/+tGPTvgdAUAiCQ9qKhpcqmxse29wqaKxNRDgNLpU0dA6IFd8slvMykmzB16pjrZ3u3LSHMpOtSsjyab0pPYhPsFJYFPsFplMBCkAAADoHb06h47b7dbmzZu1YsWKUJnZbNb8+fO1cePGiLpPPPGEvv71r5/wmitWrNDy5ctD+w0NDRo9enTXGvT8t6XKnV2r2xfyJ0tX/abL1S+77DL98Y9/lMfj0ebNm7V48WKZTCb9/Oc/78NGAkDv8PkN1Trdqmp0qarJFXhvDAQ2lY2uAR/USIGwZnhWkkZkJmtEVrJGZiVpRFZy6JWf4VC6w0owAwAAgLjr1UCnurpaPp9PBQUFEeUFBQXatWtXaL++vl6bNm3SU089dcJrOhwOORwOlZWVqaysTD6fr+sNqtwpHXq36/XjzOFwqLCwUJI0evRozZ8/X//85z8JdADElcvrU2VET5pWVTUFetSEBzc1Trd8A3zOmsxkm4qyUzQyFNIkhW0nKyfVznAkAAAAJIS4rHKVmZmpioqKbp1TWlqq0tJSNTQ0KDMzs49aNnBs375db731lk455ZR4NwXAIOXzG6ppcqm8behTRUNraBhUoCzQoyaRVoMymaTCjCQVZafolJwUnZKT2r6dnarMFFu8mwgAAAD0il4NdHJzc2WxWKLCmoqKilDPk5N1Uj10EswLL7ygtLQ0eb1euVwumc1mPfjgg/FuFoAEYxiG6po9qmjrUVNRHwhnQvttYU1Vo0sDvENNTA6rORTSFGWnqig7WafkpGp0dopGDUtWks0S7yYCAAAAfa5XAx273a4ZM2Zo3bp1oaXM/X6/1q1bp2XLlvXo2kOhh868efP0//1//5+cTqd++ctfymq16vOf/3y8mwVggGifTLhtXprwnjTBoKYttHF7B+YcNSeSlWJTQXqS8jMcyk9PUkGGQwUZScpPdyg/w6FRw1KUn+5gDhsAAAAMed0OdJqamrRnz57Q/r59+7R161ZlZ2erqKhIy5cv1+LFizVz5kzNnj1bq1atktPpDK16dbJOqodO/uQe3bPHunn/1NRUjR8/XpL08MMPq6SkRP/zP/+jm2++uS9aB2CAaPX4oiYS7mw/UYOazGSbCjMig5r89Lawpi2wyUt30LsGAAAA6KJuBzrvvfee5s2bF9oPrkC1ePFirVmzRtddd52qqqp05513qry8XNOnT9fatWujJkrurpPqodONFaYGGrPZrP/6r//S8uXL9ZWvfEXJycnxbhKAbvD5DdU4TxzQVDW61NjqjXdzT4rDalZeWxATDGTy0pJCZcFXbppdDitBDQAAANCbuh3ozJ07V4Zx/EkXli1b1uMhVh0NhTl0OvriF7+o22+/XWVlZbrtttvi3RxgSPP7DTW0elTrdOtYs0fHnG7VNrsD7053RFhT3RRY8ekEj8oBy24xK79tqFOwV01BRvjwpySW7wYAAADiLC6rXJ2MoTCHTkdWq1XLli3Tf//3f+ub3/ymUlNT490kYFAwDENNLq+OOT2qcbp0rNmtWmdkSHOs2a1jTk/EfiJOIBzObJLy0ttDmcJMhwrSk9qGPbUHOFkpNoIaAAAAYIAzGSfqbjPABAOd+vp6ZWRkRBxrbW3Vvn37NHbsWCUlJcWphf1rKH5moCuOOd368HC99lQ26UBtsw7WNutIfWuoR43bl5hz0XQmO9UempOmINSjJiliPzfNIYuZoAYAAAAYqI6XeXSUMD10huKQKwBd4/H5tf1wvTbtq9X7B+r04eF6Ha5riXezesxqNik3zaHcdLvy0sLmpUlzKK+th01w+BNz1AAAAABDS8IEOkNxyBWAzm0/XK9/7arUps9qtXn/MTW7EyfsHZZi6xDOhIc17ZMKZyXbZKZHDQAAAIAYEibQAYAml1fPbz2ixzYd0IeH6+PdnAjJNovyMxwxetJEvnJSHbJbzfFuLgAAAIAER6ADYMCrb/Zo1bqP9cS7B+Xsh544dqtZ2Sl2DUu1KzvVpmEpdmWn2pWVYld2iq2t3B4qH5ZiV7KdIU8AAAAA+k/CBDrMoQMMPYZh6IUPjuqev+1QdZOrR9fKTXNo1LDktl4ygSAmO9WunLRAIJOT6tCwVJuyU+1KtllY5QkAAADAgJYwgQ5z6ABDy6Fjzfrhs9v1r91V3TrPajZp8vAMTR2ZqdNHZWrqiEyNy09Vij1hHncAAAAAcEL8wgEwoHh9fq156zP94h8fq8Vz4h55DqtZZxRlafbYHM0Zm60zirIIbwAAAAAMevzqATBgbD9crzue/kDbDzecsG7JqEx9ZU6Rrjx9hFIdPMoAAAAADC0J8yuIOXSAwavZ7dUv//mxHn7zM/n8xnHrXjQpX8svmaCpIxl6CQAAAGDoSphAhzl0gMHpX7sr9YNntutwXctx6+WmOXTPVVN0+bRCJiwGAAAAMOSZ490ABNx4440ymUwymUyy2WwqKCjQJZdcoocfflh+vz/ezQN6XVWjS9957H0t+eO7Jwxzrp9dpHXLL9QVpw8nzAEAAAAAJVAPnZNx91t365O6T+J2/9OyTtPd59zd5fqXXXaZ/vjHP8rn86miokJr167Vd7/7Xf31r3/V888/L6t1UP/jwhBhGIaeeO+gfvb3Xapv8Ry37ri8VK289nTNHpvdT60DAAAAgMQwqBOCT+o+0QdVH8S7GV3mcDhUWFgoSRo5cqTOPPNMnXXWWbr44ou1Zs0afe1rX4tzC4Ge2VPZpO8/86He2Vd73Hp2i1nfmjdO35w7Tg6rpZ9aBwAAAACJY1AHOoPBRRddpJKSEj399NMEOkhYFQ2tWvXKJ3rivYMnnPR49phs/ezaaRqfn9ZPrQMAAACAxEOgkwAmTZqkDz5InJ5GQFBDq0e/e22v/ueNfWr1HH8uqIwkq/7r8sn60szRMpuZJwcAAAAAjidhAp2hvGy5YRhMBIuE4vL69OeN+1X2rz061nz8eXIk6crTh+vORcXKT0/qh9YBAAAAQOJLmEDnZJYtPy3rtD5uVf/cf+fOnRo7dmyvXAvoSz6/oee3Hdb9L398wpWrJGlkVrJ+fPUUXTSpoB9aBwAAAACDR8IEOiejOytMDVSvvvqqPvzwQ91yyy3xbgrQKbfXr2ffP6zVr+3Vp9XOE9a3W8xafM4p+t78CUp1DOrHEAAAAAD0CX5JDSAul0vl5eURy5avXLlSV155pb761a/Gu3lAlGa3V49tOqg/vP6pjta3nrC+ySRdPX2kll8yQaOzU/qhhQAAAAAwOBHoDCBr167V8OHDZbVaNWzYMJWUlOjXv/61Fi9eLLPZHO/mASF1zW796a39WvPWvi7NkSNJF07I039cNlFTRnRtyCQAAAAAoHMEOgPEmjVrtGbNmng3AziuT6ua9NimA3r0nQNyurs2QfnpozJ1x2WTdM743D5uHQAAAAAMHXEJdPbt26ebbrpJFRUVslgsevvtt5WamhqPpgA4gWa3V3//sFxPvHtQmz6r7fJ5Y3JSdNuCibpi2nBWaQMAAACAXhaXQOfGG2/UT37yE51//vmqra2Vw+GIRzMAdMIwDG07VK/H3z2ov207oiaXt8vnTipM17fmjdflUwtltTBUEAAAAAD6Qr8HOh999JFsNpvOP/98SVJ2dnZ/NwFAJ+qbPXp262E9tumAdpU3duvcmacM07fmjdO8ifn0yAEAAACAPtbtP59v2LBBixYt0ogRI2QymfTss89G1SkrK9OYMWOUlJSkOXPmaNOmTaFjn3zyidLS0rRo0SKdeeaZ+tnPftajDwCg5/ZUNun2J7dp9s9e0V3Pf9StMOfCCXl64htn66/fPEcXTSogzAEAAACAftDtHjpOp1MlJSW66aabdO2110Ydf/zxx7V8+XKtXr1ac+bM0apVq7RgwQLt3r1b+fn58nq9ev3117V161bl5+frsssu06xZs3TJJZfEvJ/L5ZLL5QrtNzQ0nLCNfr+/ux8rYQ2lz4ret/1wvX67fo9e2l4uw+j6eXaLWZdNLdTXLzhVU0eyahUAAAAA9LduBzoLFy7UwoULOz3+wAMPaOnSpVqyZIkkafXq1XrxxRf18MMP64477tDIkSM1c+ZMjR49WpJ0+eWXa+vWrZ0GOitXrtQ999zTpbbZ7XaZzWYdOXJEeXl5stvtg7a3gGEYcrvdqqqqktlslt1uj3eTkEDe+bRGZev3asPHVd06b1Jhuq6bNVpXTx+pYan8OwcAAAAA8dKrc+i43W5t3rxZK1asCJWZzWbNnz9fGzdulCTNmjVLlZWVOnbsmDIzM7VhwwZ94xvf6PSaK1as0PLly0P7DQ0NoTCoI7PZrLFjx+ro0aM6cuRIL32qgS0lJUVFRUUym5l8FsdnGIbW765S2b/26L39x7p8XrrDqqumj9B1s0Zr2sjMQRuSAgAAAEAi6dVAp7q6Wj6fTwUFBRHlBQUF2rVrV+CGVqt+9rOf6YILLpBhGLr00kt15ZVXdnpNh8Mhh8OhsrIylZWVyefzHbcNdrtdRUVF8nq9J6yb6CwWi6xWKz+wcVw+v6GXth9V2b/2aufREw9ZDJo1Zpi+PKtIl08brmS7pQ9bCAAAAADorrgsW36iYVuxlJaWqrS0VA0NDcrMPP6cHSaTSTabTTabrSfNBBKa2+vXs+8f1v/32l7tq3Z26Ry71awvzRylxWeP0WkF6X3cQgAAAADAyerVQCc3N1cWi0UVFRUR5RUVFSosLOzRtbvaQwcY6lrcPv3l3QN6aMOnOlrf2qVz0hxW3XBWkW4+b6zy05P6uIUAAAAAgJ7q1UDHbrdrxowZWrduna6++mpJgVWY1q1bp2XLlvXo2t3poQMMRfUtHj3y9n79zxv7VOt0d+mcYSk23XTuWH317DHKTKFHGwAAAAAkim4HOk1NTdqzZ09of9++fdq6dauys7NVVFSk5cuXa/HixZo5c6Zmz56tVatWyel0hla9Oln00AFiq25y6eE39unPG/er0eXt0jmFGUlaesGpun72aKXY4zLyEgAAAADQAybDMIzunLB+/XrNmzcvqnzx4sVas2aNJOnBBx/Ufffdp/Lyck2fPl2//vWvNWfOnF5pcLCHTn19vTIyMnrlmkAiOlzXot9v+FSPbTogl9ffpXNOyUnRNy8cp2vOHCmHlYmOAQAAAGAg6U7m0e1AJ17Ce+h8/PHHBDoYsvZUNmn1a3v17PuH5fV37X++kwrT9a1543X51EJZLSxxDwAAAAAD0aAMdILooYOhavvhev12/R69tL1cXf1f7ZlFWSqdN14XTcpneXsAAAAAGOC6k3kweQYwgBmGodc/qdbvX/9Ur39S3eXzzj8tV6XzxmvO2GyCHAAAAAAYhBIm0GFSZAwlbq9fL3xwRA9t+FS7yhu7fN5lUwr1rXnjdPqorL5rHAAAAAAg7hhyBQwgDa0e/WXTAT38xmcqb2jt0jkWs0mfmz5C37xwnE4rSO/jFgIAAAAA+gpDroAE4vMbemtvtZ55/7DWbi9Xs7trvdDsVrOumzlaX7/gVI3OTunjVgIAAAAABhICHSBOdh5t0DPvH9ZzWw+rosHV5fPSHFbdcFaRbj5vrPLTk/qwhQAGHcOQDL/k94a9fB32O5b5Ytcx/Cd4GT2sY/TSdcKOAwAADHTN7i5XTZhAhzl0kOj8fkPbDtXplZ0V+ueOCn1c0dSt8/PTHVpy7lh9ZXaRMlNsfdRKAP3OMCS3M/Dytkietpe3NXrb2yp5miVPa1vdtv2I47HOb6vrbZWUUCOtAQAAhhZX1/9bjTl0gD7U6vHpzT3VemVnhV7ZWamqxq73xAmaUJCmpeefqs9NHym71dwHrQTQI4YhtdZLLbVSyzGp+Vhgu7ltv7VOcjVKrgbJ1dS23Si5w7YJWQAAACCpwWUo895G5tAB+pvb69eOow3asv+Y3v60Rq9/Uq0Wz8n1KjtnXI6+fsGpunBCHkuPA/3J3Sw5K6Wmqrb3Sqm5JhDOtBxrC2pqw7aPSQa9RwEAANC/CHSAk+Ty+rS30qndFQ3aebRR7x84pg8O1cvlPfl5GnLTHPrc9BH6/JmjVDyCHmhAj/j9gd4x4SFM+HtzTWRPmuAxb9dWmAMAAADiKWECHebQQbzUt3h0sLZZh441a09lk3aVN2p3eaM+rXbK5+/5MIkkm1mXFhfqmjNH6vzxubJaGFYFRPG6YocyEYFMTWRZax0T4Q41JnPnL5kkOjsCAIABzy+psUs1mUMHQ5phGKpr9uhofasOHWvWoWMtOtj2Hng1q7HV2+v3TbZZdMGEXF1SXKgFUwqUnsQkxxgiDCMwl0woeDnWeUgTHObUXCt5nPFu+cBlTZZsSe3vthTJmiTZktvfg9vWJMlik8zWDi9LN/etkskUKO80RDF1EqqcoM5xr3O8+qQ1AAAg8XUn80iYHjpAZ/x+Q80en5pavWpyedTkCm63vVo9crp9amz1qqHVo6pGlyobXapqaFVVk0seX/9kmvnpDl08uUCXFhfo7HE5SrJZ+uW+QJ+JGNJU02EYU02HUCas94y/90PSgc0kOdIDL3ta23bwPaO9zJ4aFr6EhzTJscOZ4DtBBgAAwJBEoIM+5fX5Vd3k1rFmt1xev1weX+Dd65fL65PLE7bt9bft+0Jl7mDdsHKn26emVo+aXF45XT41uQbmj0OzSZpUmKGLJ+dr/uQCTRuZKbOZH14YwIK9Z8InA3ZWhb2qA8GMs1pqrm6bDHiIDWmyp0nJ2VJylpSSHdhOyZaSh4Vth5cNk5KyJDNDKQEAANC7CHRwUtxev6qbAj1dKhtaVdHW46Wy0aWK0LtLNU6XEmtQ38nLTLbpjKIsnVk0TGcWDVPJ6EyGUiH+gr1omiojQ5rQflXYe5Xkc8W7xf3HbI0dwqRkSym5UlqBlJYnpeZLqbmBY1ZHvFsNAAAASCLQGTQMw1BD2zAjp8urxrDtplavGtvem1weubx++fyG/IYhn9+Qz6/2bcOQEaPcbxjy+Pw65vSoqsmlWqc73h85buwWs8blp2lSYbomtr0mFaarMCOJ5cXRP/y+QE+Z8B40UQFN2LGhMMQp2HMmJVZPmQ7vwW1HOsOVAAAAkLAIdBJAi9unqkaXqppaA+9tvV+O1LfoaF1r4L2+Ve4eLJeNSLlpdo0clqJRw5LbXoHt0cNSdEpOimysRIXe5PMG5pYJDmlqDg5tqoncD4Y1zTWDd6iTyRwYopSSE7vnTGchDT1nAAAAMMQkTKAzlJYtr2t268UPj2rt9nJtPVCnxgE6R0wisppNykmzKz89SfnpDuVnOJSX5lBeRpJGZQXCm5HDkpViT5j/aWCgcje395RpqpSaKtp60lS0lzW3BTatdfFubd+wONp6xOSEDWXKOU4PGuabAQAAiDfDMGTIkN/wyzAM+eWXz+8LlQXLfUYnZW3nhJf5DX/ENTuWhb86nu83/PKrk+0O1z3e8eNdW4ZktP1f+HcQ3A59NzJC++H1g+dHnRtWP7gf6/qh60hqdbZ2+Z8Vy5YPME+8d1A//tsOQhwFhjY5rGY5bObAts0ih9WsJJtF6UlWpTkCr1SHNbTfcTtYJ62tzGE1MywKPeNqkhoOS/UHpYYjYQFNRaAHTXDf3RjvlvYukyUQxqTmSak5gTlmgr1owkOa8H17KkOaAADoBR1/eIb/IO34YzX04zbsR25EnbYfmBHXC/sR3PHHcXeuF+va4ecHzw3+qA7fD3+PqN+hTFJEOzq7Vqh+2/cVbJcUo01h54Su30m7OjveWRvC//kFA4CYbex4zQ7fd8fvvdMw5AQBSVfLED++Fp92fnMny5YnEo/Przuf+0iPbToQ76b0iM1iUn56kvLSHSrIcCg/PUkFGQ7lpjmUbLfIYbXIYWsLaqzBgKZ9O3jcbjGzIhT6n9fVFtYcbns/1P5ef1hqOCS11se7lb3H4pDS2ib8Tc1vnwA4LT8Q3KTlt+/TcwYAoMCPTp/hk88f+Ou61/DK7297NwJ/xfcZYa8O9cLLT3iO39t+v7by4Hb48fB6nZ0TLOsYUhzvL/YRx7vwV/9YvRVCP447CWE6BgGdhSQAEAuBzgDx32t39VuYYzWbZDabZDGZZDGbZDJJlrb98HKzWTKbIsszk23KS3eEXoFhS4HhSwUZScpKthHEYGDz+wPBTPXHUvWetvePpZo9UuPReLeu52wpHcKYzkKaPMmRQQ8aABiADMOQ2++Wy+eS2+eW2xe5HTzm8Xnk8rkC235PdH2/O+b5Ln/7ueHHY4UzHUMXwgUAGDgIdAaAg7XN+sMb+7p9nsNq1sisZA3PStLwzGQVZiQpM9kWGl4UHGqUao8chmS38ld2DBF+fyCoObxZOvxe4L1yl+RtiXfLusHUNv9MTltPmrBQJjU3OrhxpMW7wQCQ0Lx+b8xQpLOwpMvlYUFMrPLwMo/fE++vAQCQAOIS6IwZM0YZGRkym80aNmyY/vWvf8WjGQPGI+/sV2czGU0ZkaFzx+cqP9grJi3wnpvmUFaKjflggHBNlYHQ5lBbeHN4i+QaYEOkTJa2UCYv8J4SNh9Nak6H/dxAmGO2xLvVAPpYZ0M6JJ1wqEewTmi+hvD5IoJDOWLM7xA+d0bH+SQ6zuUQc/94x8LmpYg1L0fUnBMxzo113eDwGo/fE3oP34713t1jPmPwL8ABAIgPk0yh3/Cmtv9r2wltW7rx3/5x66Hz1ltvKS2NvyS7vX498e7BmMfu+8Lp+uLM0f3cIiBBuJulo9sCPW8OvRcIb+rjNQeVKRDApBUEgpq0grCeM23baW3bydnMRYMuC/549fq98hre9m2/Vz6/Tx7DE1kWNn9EX60SETXxpTqECsf5Ad/VH/qd/ZAP3SNG/eCxrgYPXQkOjne/WGUxv/MO33+n82sAAAaM4I9us8ySSTIrsLCK2WQOHTebzKF6JpMpVCap/VjY8eA1QmWx6ne4VvAHfvhxs8ksi8kS2g5e96TK2q7bWZnZ1P65TTLJYrZEfBehV1fL2q7TsSxW3Y71wveD32fH9h3veMd7hNcPfsfh/+yD26F/J0zt4UvEdiCJaT83Vp1udsJoaGhQ5jcyu1SXIVdx9uquSh1rju5We8W04YQ5QDh3s3Rok/Tpa9K+16QjW6X++CuqySylD5cyRkqZI9veR7Xvp48I9LSx8Dgdanx+n1q8LWr2NgfePc1R282e5lCd8O0WT3RZcJhFMKwJzlsBAOie4A9ei8kS+AHatn+iH5Gd/SDs6o/E4PHgj+PgD+Bgm0LhQNh5He8Z/kO+Y1nHH/Xh2x2v1/FHbmfXC7UtxrU7nhdRR7Hb3LFex/tGhRVhYcmJjge/v6gwJey+0vHDloiApJN7MgICiaTbv0A2bNig++67T5s3b9bRo0f1zDPP6Oqrr46oU1ZWpvvuu0/l5eUqKSnRb37zG82ePTt03GQy6cILL5TZbNb3vvc93XDDDT3+IInq6S2HYpb/v7NP6eeWAAOIzyPV7pMqtksHN0kH35bKP5T83r65X8YoKfc0KWe8lDU6MrRJH05Yk+D8hl+t3tbIIOU42x1DmPCQJvy91dca748GACdkkkl2iz3wMtvlsDhC+w6LQzazLVRmNVtlNVtlNpllNbW9t+0Hw5GOQUmwXsdjHeuHXyvqnE7qhV/ParLKbDZH3yPGOfwgBzBUdPtXitPpVElJiW666SZde+21Uccff/xxLV++XKtXr9acOXO0atUqLViwQLt371Z+fr4k6Y033tDIkSN19OhRzZ8/X9OmTdPpp5/e80+TYJpcXq3fXRVVPjo7WbPHZMehRUA/M4zAkuBVu6TKHVL59kCIU/2J1BcTQqaPkEaeKRVMkXIntIc49tTevxdOitvnVpOn6bi9XSLClRihS1RvmISaBBvAYGIz20LBSTBQCe47LA7ZLG1hSlh5x/AlWKdj+NJZ/Yhj5kBIQ8ABAINTtwOdhQsXauHChZ0ef+CBB7R06VItWbJEkrR69Wq9+OKLevjhh3XHHXdIkkaOHClJGj58uC6//HJt2bKl00DH5XLJ5XKF9hsaGrrb5AHrjU+q5fZFd6e/ZvpIlv7G4OF1BUKb+kNS/cHAe91BqXp3YMUpd2Pf3NeeJo04Qxo1Uxo5I/DKGNE394J8fp+cXqecbqcaPY1yepxqdDeqyd2kJk/bq207eCz8PXjc7XfH+6MASDBWs1U2s61L7ycqs5qsoVCkYzgSClfMjuigJWw7PLgJDgEBAKAv9Oo4Arfbrc2bN2vFihWhMrPZrPnz52vjxo2SAj18/H6/0tPT1dTUpFdffVVf+tKXOr3mypUrdc899/RmMweMV3dVxCy/bOrwfm4J0EV+v9RaJzXXSs01gVdL2HZzbfux8PK+ZrIEet2MnNEe4OROYHWoHjAMQw3uBtW01qi2pVa1re2vmpaayP3WGjX2VTCHIaHjvAzhcyF0nHciaq6GTuakiDXfQ2cTUwbngug4l0P4nBjBY+H1TmbixY6fMdbxjnNXxLpP+CSWHeePCD839B3GmNMiqm745zrO/Bdd/c67NK9Gd+q2lXcMYOh9AgAYqno10KmurpbP51NBQUFEeUFBgXbt2iVJqqio0DXXXCNJ8vl8Wrp0qWbNmtXpNVesWKHly5eH9hsaGjR6dOJPFuz3G3p1V/Rwq+GZSZo8PD0OLcKQYBiS2ym5GsNeDZHbLXVSa33bqy4yoGk5Jg2ESVrt6dKY86Qx50qjZkmFp0v2lHi3asBr9bbqWOuxQEgTI5gJD2yOtR6T1+ijOYuGsCRLklJsKUq2Jre/W1OUYk2Rw+qI+JFqNVsD80a07QePBeeS6NizwGKyhMpirWrR41Uiwn5odwwDOp4bPHa8YIDJJwEAAHqm32f6PPXUU7Vt27Yu13c4HHI4HCorK1NZWZl8vn5Y1aYffHi4XtVNrqjyiybl8x+3Q41hSD53YGiS1yX52t69rdFlbqfkaZE8zW2vlrCyFsnjPE5Zs+RukmTE+xN3X2qeNHpO4FV0dmAoFRMVy+f3qd5dH9GDpqa1ptOgxulxxrvJCcNutivFFghawoOXZFtyewjTdjwYznSs23E7yZIUWu0EAAAA6Kle/UWUm5sri8WiiorIoUQVFRUqLCzszVslvFd3VcYsv3hyfj+3JAH5vO0BRzAICb27JL8v8DJ8gVWR/L5ArxJ/277h61AnVl1vh3J/2La3/VoRr7Aynyfw8ra2ta1V8ro72Y8O9oYsk0XKmxgYPpU3KfAqKJaGjZWGSNDZ7GkOBTNRQ51aayJ60dS56ob8stY2sy0iUIkIV9oCmPDyzsKWjnWsZgJDAAAADGy9+l+sdrtdM2bM0Lp160JLmfv9fq1bt07Lli3r0bVLS0tVWlqqhoYGZWZm9kJr4ytWoJNkM+uccblxaE0v87QGhuqEhu3Utw3jqQsM6/G0SN6WsB4kze3bwV4psYIarzvwPsR/wA4KZltgefC8yVL+JCm/OBDe5J4mWR3xbl2vMgxD9a561bTWqLqlWjUtNcftRTPYV2QyyaQ0W5rS7GlKtaUq3Z4eeLelK9Xe9m5LVaotNWZIExHQWFNks9ji/ZEAAACAuOh2oNPU1KQ9e/aE9vft26etW7cqOztbRUVFWr58uRYvXqyZM2dq9uzZWrVqlZxOZ2jVK0iNrR5tP1IfVX7uuFwl2QZod3y/LzCPSlOF1FgReA9/hcoq+27VIgx81iQpJUdKyZaSswPbqblS5qi2V1HgPa1AMif2yh/BSYPLneWhV0VzRSC06RDeeP2Day4ai8miYUnDlJ2UHXoNSxqmdHt6IKyxpUWEM6Fye5qSrcms+gIAAAD0gm4HOu+9957mzZsX2g9OWLx48WKtWbNG1113naqqqnTnnXeqvLxc06dP19q1a6MmSu6uwTSHzgeH6mXEmMbkvNPi3Dun5ZhUsUOq2iXV7Q8sLV13ILDMtLMqMOQIQ4c9XUrKlFKGBYKZYEATDGw6BjcpOYNqYuJmT7OONB3RUedRVTRXtAc3zeWqcFaoorliUPWmSbelKzs5OyKkCb2Ss5WTlKOcpBxlJ2Urw5FBKAMAAADEmckwYkULA1dwyFV9fb0yMjLi3ZyT8uCrn+j+f3wcVf5c6bkqGZ3V9w3wtErVuwPhTWXbq2KH1Hik7++NvmGySPZUyZbc9grbDpWnBgIXR3rYK6N9254WCHCSMgPlg3zSYcMwVNtaq711e3Wg8YAONx3W4cbDOtx0WIeaDqm2tTbeTewRq9mq7KRAEBMMZDoLarKTsmW32OPdZAAAAGDI607mkTC/2AZTD52tB+uiyuxWsyYP74OAyueRyj+QDm4KvCq2SzV76W3TG0xmyWwNe1nat00WyWqXLI7AnDDBV2g/KUZZF+rYUgKhjC2lLaRp27bYhsykwSej2dOsXbW7tPvYbu2t26s9dXu0t26v6lx18W5at2Q6MqNCmZzknOiwJjlb6bZ0VswDAAAABrGECXQGy6TIhmHo/QN1UeVTR2TIbu2FIQxel3TwHenT9dKBt6XDWwITEA9EZmtYMJEsWZMlW1JbeBEWhljsnbyH1wurHwxXTJb2kMVkDtsOlofVMVnCzjN3uIY1MN+LyRIITkLXYMjJQOTz+7Snbo+2V2/Xh9Ufanv1du2p2yPfAAwxHRZHexgTY7hTeFiTlZQlm5kJgAEAAAAEJEygM1gcrG1RjdMdVX5G0bCTv2j1J9KeV6S9r0qfvRFYNao/JWcHJrlNLwi8p+YF5lZJypSSstpebUN5kjLaQxxWp0EPGYaho86joeDmw+oPtaNmR1zntrGb7cpNzg2EMck5ge2kwHuovG0/2ZpMLxoAAAAAJyVhAp3BMuTq/YPHYpafUZTVvQvV7JW2Py199IxU+VHPGxaLPV3KKgosL50xQkorlNLypfS297QCKTU/0DsG6AcN7oZAcFPVHuDUtNb02/3tZrsKUgtUmFqogpTAe2FKYeC9rSzTkUlIAwAAAKDPJUygM1iGXMUabiVJ07syGbLPK+3+u/TuH6R9r/Veo5KypIIpUn6xlD85sJ07QUoexrwsiBu3z62Pj32sD6o+CIU3nzV81qf3TLYma2TaSI1KG6WR6SM1Mm2kRqSNCAU32UnZhDUAAAAABoSECXQGi/cPRPfQyUt3aGRWcucned3S+3+W3vilVH/w5G9uMgdCm8JpbeFNsVRQLKUPJ7hB3Hj9Xh1tOqoDjQe0v2G/9tXv00c1H2lX7S55/J4+uWdhaqHGZY7TuKxxGp81XmMzx2p0+mgCGwAAAAAJg0CnH/n8hnaWN0aVnzE6K/aPSL9Pev8RacN9Jxfk2NOk0XPaXrOlkTMCc9gAfchv+OX0ONXkblKjp1FN7iY1eZpU21qr6pZqVbdUq6q5KvDeUqWjzqPy+r191p6RaSM1LXeapuZO1bTcaZowbILS7Gl9dj8AAAAA6A8JE+gMhjl0Dh1rltvrjyovHhEjZKn4SHr+29LhzV2/gcksjThTGneRNG6eNGoWEw8PUYZhyOv3yuP3tL98nqiyVm+r3D63Wn2tcvlcavUG3jtuh++3+trOiVG3xdsip8cpQ0ZcPnemIzMU3ARDnOyk7Li0BQAAAAD6UsIEOoNhDp09lU0xy8fnh/UW8LQGeuS8uUrqSq8Fsy0Q4Ey5WppwWWB1qTjz+r1y+9yh0CAYInj93tCr033DK8Mw5Df88hk+GTLk8wfe/YY/9PIZvlA9v+GXX/6I41Ev+eX3B94Nw5DP8EUclxTaNmTIMIzQPSO2e3gsZr0Ox8Lbc7xj4cd9hi/qux7s7Ga7JudMjuh9Mzp9NEOmAAAAAAwJCRPoDAYnDHQOvis9+02p5pMTX2zYGGnmzdIZ/9YrIY7X71Vta62qmqtU2VypqpYqVbVUyelxyuV1hXpwhG+3elsD2x2Oe43BHyag/52aeaqm5k7V6bmna2reVE3ImiAbPdAAAAAADFEEOv3kw0P1WvnSrqhys0kaO8wuvfpT6fX7JSN6SFaEwmnS3BXShIWS2dylexuGoTpXnQ43HQ69jjQdUYWzQpUtlapqrlJNa02o9wcQb/nJ+ZqSO0Wn552uqblTNSVnitLt6fFuFgAAAAAMGAkT6CTiHDpOl1fPvH9YT285pC2dLFc+J6tBjjULpKNbj3+x7HHSJfdIk67sdEUql8+lz+o/0566Pdpbt1d76vboYONBHW46rBZvS88+DNCLshxZyk3OVV5ynvJS8nRKxikqyihSUXrgxaTFAAAAAHB8JsMw4jN76UkKzqFTX1+vjIyBu2LTPz4q1/ef3a6qRlendc42f6TfJ/1aaf7ola9CzFbp3O9KF9wu2dqXNjcMQ/sb9mtr1VZtrdyqbVXb9Gn9p/SyQZ+wmW1KsiTJYXXIYWl/JVmTAu9hx9Lt6UqzpYXe0+xpSrelK8ORodzkXOUk5TBUCgAAAABi6E7mkTA9dBKF32/ol698rN+8uue49f7N8k/dZf1f2fzH6XE04gzpqt8EhllJavW26u2jb2v9wfXacGiDqlqqerHlQ4dJJplN5ohXsMwkk0ymwMssc2C7K8fatiOOdagX3DebzJJJ0cdMZpnV4VjYNToeC2+TWWbZLDbZzG2v8G2zTVazNarcarZGBTMdt4Mvi9kS739sAAAAAIAwBDq9yO3163uPv6+/f1h+3HrftTylW2xPdV7BbA3Mk3Pu9+Q3m7W5/F09v/d5/eOzf6jZ29zLrT6xjr0zkixJ7T0zYvTQSLYmy26xRwQJ4e8R2yZrVFkoZDGZZDFZQqGJxWRpLzOFlckki9kSFdSE1wuGJcHrAgAAAACQyAh0etGPX9hxwjBnqeMV3WI6TpiTO1G69iG5C4r1/N5n9cftf9SBxgO93NJAL5W85Dzlp+QrL6XtPWw/uJ1hz6B3BgAAAAAAAwyBTi95avMh/fnt/Z0eLxmVqWVjDmv+5j9Jnc1aNPkq6ZrVWl/xrn76zBUqdx4/HDqRVFuqTs08VaPSR2lU2iiNSBuhkWkjNTJtpIanDmceEwAAAAAAEhSBTi/4uKJR//XMhzGP2Swm3XPVVF0/3ivT72+QjE7mzJn7X2o46xv62Ts/1oufvtjtNoxMG6mSvBJNzp6scVnjND5rvApTCxleBAAAAADAIJQwgc5AXbbcMAzd9dxHcnmjV5dKtln0xyWzdFZRuvQ/l0itdbEv8rkybR89Xbe9+GUdbjrcpfvmJefpwtEX6pwR52h63nTlpeT14FMAAAAAAIBEwrLlPbR2e7n+/ZHNMY/9+vozdFXJCOmVe6Q3Hoh9gYt+oBdGTdYP3/yhvH7vce81zDFMl596ua4Ye4Wm5E4JrJYEAAAAAAAGBZYt7yetHp9+9vedMY/deM6YQJjz2ZvSG7+MfYGpn9f/5Y7Qva+vOO59JmVP0s3TbtbFoy9m3hsAAAAAAECg0xMPv7lPB2qjlxHPT3fo9gUTJU+L9PwyxZoF2cidoNWnTtdv37230+un29J1+6zbdfX4q5kLBwAAAAAAhBDonKTKhlaVvbon5rH/vGySUh1Wad3PpNpPo477zTbdN2WuHvnw951ef0bBDN17/r0qTC3stTYDAAAAAIDBIW6TsDQ3N+uUU07RbbfdFq8m9Mh/v7xbTnf0BM0lo7N0zRkjpYqPpDd/FfPc+0+/RI8cWNvptW+ccqP+cOkfCHMAAAAAAEBMceuh89Of/lRnnXVWvG7fI9sO1umvmw/FPHbnlcUymyT9/XYpxiTHT4wu1p/rt3d67dtm3qbFUxb3VlMBAAAAAMAgFJceOp988ol27dqlhQsXxuP2PWIYhn70wo6Yx66ePkIzThkm7XhO2v9m1PGdjiTda2uNea7ZZNaPzvkRYQ4AAAAAADihbgc6GzZs0KJFizRixAiZTCY9++yzUXXKyso0ZswYJSUlac6cOdq0aVPE8dtuu00rV6486UbH0/Pbjmjz/mNR5ck2i/5z4STJ0yr984dRx5tMJt06eqw8RnSvHavZqvsvvF/XnHZNn7QZAAAAAAAMLt0OdJxOp0pKSlRWVhbz+OOPP67ly5frrrvu0pYtW1RSUqIFCxaosrJSkvTcc89pwoQJmjBhQs9aHgctbp/ufWlXzGPfnDtOwzOTpU0PSXUHoo7/omCEDvqcMc9ded5KXXLKJb3aVgAAAAAAMHh1ew6dhQsXHneo1AMPPKClS5dqyZIlkqTVq1frxRdf1MMPP6w77rhDb7/9tv7yl7/oySefVFNTkzwejzIyMnTnnXfGvJ7L5ZLL5QrtNzQ0dLfJvWb1a3t1tD56yNTIrGR9/YJTJa9L2vhg1PHddpueSrbEvOaSqUt02djLer2tAAAAAABg8OrVOXTcbrc2b96s+fPnt9/AbNb8+fO1ceNGSdLKlSt18OBBffbZZ7r//vu1dOnSTsOcYP3MzMzQa/To0b3Z5C47Utei323YG/PYissnKclmkT54QmqqiDhmSLpveJGMGOdNz5uub5/x7d5vLAAAAAAAGNR6NdCprq6Wz+dTQUFBRHlBQYHKy8tP6porVqxQfX297r//fk2cOFHjx4/vjaZ22y//+bFaPf6o8tljsnXFtOGS3y+99euo4+tTkvWO2RNV7rA4dO8F98pmtvVJewEAAAAAwOAVt2XLJenGG288YR2HwyGHw6Fbb71Vt956qxoaGpSZmdn3jQuzu7xRT22JXqbcZJLuXFQsk8kkfbxWqv444rhH0i/yCiRFB0FfLf6qRqaN7KMWAwAAAACAwaxXe+jk5ubKYrGooiJy2FFFRYUKCwt7dO2ysjIVFxdr1qxZPbrOybjv5V3yxxgz9fkzR2nqyLZw6e3fRh1/IiNN+83RYU5ucq5unnZzbzcTAAAAAAAMEb0a6Njtds2YMUPr1q0Llfn9fq1bt05nn312j65dWlqqHTt26N133+1pM7vl/QPH9MrOyqhyu9Ws5Ze0rdRVuVPatyHiuFfSn7KGxbzmt8/4tlJtqb3dVAAAAAAAMER0e8hVU1OT9uzZE9rft2+ftm7dquzsbBUVFWn58uVavHixZs6cqdmzZ2vVqlVyOp2hVa9OVllZmcrKyuTz+Xp0ne56aMOnMctvPGeMRmQlB3Y2PRR1/LWUZB21mKLKJ2VP0ufGfa5X2wgAAAAAAIYWk2EYsRZg6tT69es1b968qPLFixdrzZo1kqQHH3xQ9913n8rLyzV9+nT9+te/1pw5c3qlwcE5dOrr65WRkdEr1+zM/hqn5t6/Xh2/ofQkq17/j3nKSrFLLXXSA5MlT3NEnZuHF2pTkj3qmr+e92vNK4r+/gAAAAAAwNDWncyj2z105s6dqxNlQMuWLdOyZcu6e+kB53/e2BcV5kjS/zvrlECYI0lb/y8qzNlrs8YMc0aljdIFoy7oi6YCAAAAAIAhpFfn0OlL/T0p8jGnW0+8dzCq3GYx6cZzxgR2/H5p0++j6jyenh7zml+e9GVZzJbebCYAAAAAABiCEibQ6e9JkZ9+/7BaPdErVF09faTyM5ICO3tekY7tizjuNJn0fIxuUUmWJF09/uq+aCoAAAAAABhiEibQ6U+GYeiJd6N750jS184/tX1ny5+ijr+YliqnKXqc1sKxC5XpyOy1NgIAAAAAgKErYQKd/hxy9eHheu2uaIwqP+vUbE0sbBtO5ayWPl4bVee5zNhLlX950pd7tY0AAAAAAGDoSphApz+HXP39w/KY5V+aObp958O/Sn5vxPGDVqs+sEUvVX567ukqzinu1TYCAAAAAIChK2ECnf5iGIbWbj8aVZ5it2jh1OHtBVv/L6rOi2kpMa/5ufGf67X2AQAAAAAAJEyg019Drj6uaNJnNc1R5fMm5ivZ3rZCVfl2qfyDiOOGpBezcqPOs5qsuvSUS/uiqQAAAAAAYIhKmECnv4Zcrd0ee7jVgqmF7TsfPRN1fJfdps9M3qjyc0eeq6ykrN5qHgAAAAAAQOIEOv1l7UfRgY7dYta8iXntBXtfjarz94zYK1hdPvbyXmsbAAAAAACARKATYX+NUzuPNkSVn3dartKTbIGdlmPSkfcjjhuSXk6PDnSSrcmaO3puH7QUAAAAAAAMZQQ6YV6O0TtHki6bEjbcat/rCkQ47bY57DoqT9R5c0fPVYot9kTJAAAAAAAAJythAp3+mBQ51vw5FrNJ84sL2gv2vxVV5+XU2KHNZWMu67W2AQAAAAAABCVMoNPXkyJXNLRqy4G6qPI5Y7OVnWpvLziyJeK4X9I/0tKjzkuzpem8kef1cisBAAAAAAASKNDpa6/tropZfln46lY+r3Q0crnyLUkOVVpMUeddVHSR7BZ7VDkAAAAAAEBPEei0ee3j2IHOJeHDrap2St6WiON/72S41YIxC3qtbQAAAAAAAOEIdCR5fX69/kl0oDOxIF3DM5PbCw5HDrdqMZn0Ulpq1HkZ9gydPfzsXm8nAAAAAACARKAjSdp2qF4Nrd6o8gsm5EYWVGyP2H0lJVlN5uivcMGYBbJZbL3aRgAAAAAAgCACHXU+3OqCCXmRBZU7I3ZfiNE7R5KuPe3aXmkXAAAAAABALAkT6PTlsuUbYgQ6STazZo3Jbi8wDKnio9ButcWst5OTos4bnzVeU3Km9HobAQAAAAAAghIm0OmrZcuPOd3adqguqvysU3OUZLO0FzirpJba0O7LqSnym6JXt7ri1CtkilEOAAAAAADQWxIm0Okr/9xZIcOILr/gtI7DrXZE7L6YGnu41eVjL++tpgEAAAAAAMQ05AOdFz84GrN87sTO58/Zb7XqwyRH1Dln5p+pEWkjerV9AAAAAAAAHQ3pQKe+2aM391RHlU8enqFT89IiC6s/CW2uTUuJeb0rTr2iV9sHAAAAAAAQS78HOnV1dZo5c6amT5+uqVOn6ve//31/NyFk46c18vqjx1tdMa0wunLNntDm+pTkqMNWs1ULxizo1fYBAAAAAADEYu3vG6anp2vDhg1KSUmR0+nU1KlTde211yonJ6e/m6J39tXELL90SqxAZ68kqdJi0XZH9HCrOcPnKNOR2avtAwAAAAAAiKXfe+hYLBalpASGLLlcLhmGISPWrMT94J1Pa6PKclLtOi2/w3Ard7PUcEiStCEleqlySZo3al6vtw8AAAAAACCWbgc6GzZs0KJFizRixAiZTCY9++yzUXXKyso0ZswYJSUlac6cOdq0aVPE8bq6OpWUlGjUqFG6/fbblZube9If4GTVN3u0s7whqnzOqdnRy47X7g1trk+JPX/OhaMv7NX2AQAAAAAAdKbbgY7T6VRJSYnKyspiHn/88ce1fPly3XXXXdqyZYtKSkq0YMECVVZWhupkZWVp27Zt2rdvnx599FFVVFSc/Cc4SZs+q425XPmcsTGGfrXNn9NiMuntGKtbTc6erMLUGMO0AAAAAAAA+kC3A52FCxfqJz/5ia655pqYxx944AEtXbpUS5YsUXFxsVavXq2UlBQ9/PDDUXULCgpUUlKi119/vdP7uVwuNTQ0RLx6wzufxp4/Z86p2dGFtZ9Kkt5OTpLLHP2VzR09t1faBAAAAAAA0BW9OoeO2+3W5s2bNX/+/PYbmM2aP3++Nm7cKEmqqKhQY2OjJKm+vl4bNmzQxIkTO73mypUrlZmZGXqNHj26V9r6dowJkYel2DQhPz26cv1hSdLGpNjz5xDoAAAAAACA/tSrgU51dbV8Pp8KCgoiygsKClReXi5J2r9/v84//3yVlJTo/PPP17e//W1Nmzat02uuWLFC9fX1uv/++zVx4kSNHz++x+2sb/Fox5Honj6zx2bLbDZFn9AQCHQ2xxhulZucq8nZk3vcJgAAAAAAgK7q92XLZ8+era1bt3a5vsPhkMPh0K233qpbb71VDQ0Nyszs2fLgr31cJX9X58+RpIbDqjeb9YndFnVoZsHM6EmUAQAAAAAA+lCv9tDJzc2VxWKJmuS4oqJChYU9mzS4rKxMxcXFmjVrVo+uI0n/3BF7EubzT+tkta36w3rf4ZARI7iZUTCjx+0BAAAAAADojl4NdOx2u2bMmKF169aFyvx+v9atW6ezzz67R9cuLS3Vjh079O677/boOh6fX+t3VUaVj8lJ0fj8tOgT3M1SS23M4VYSgQ4AAAAAAOh/3R5y1dTUpD179oT29+3bp61btyo7O1tFRUVavny5Fi9erJkzZ2r27NlatWqVnE6nlixZ0qOGlpWVqaysTD6fr0fX2X64Xo0ub1T5JcUFsYdONR6VFHv+nExHpsZljetRewAAAAAAALqr24HOe++9p3nz5oX2ly9fLklavHix1qxZo+uuu05VVVW68847VV5erunTp2vt2rVREyV3V2lpqUpLS3s8h86WA3Uxy+dOzI99Qv0hNZtM2uGwRx06M/9MmU292skJAAAAAADghLod6MydO1eGEWNG4TDLli3TsmXLTrpRfWnLgWNRZWaTNH10VuwTGo5oq8MhH/PnAAAAAACAASJhupf01qTIW/ZHBzqTCjOU6ugk22o8oi2dzJ8zs2Bmj9oCAAAAAABwMhIm0OmNSZGP1rfoaH1rVPmZp2R1flJTlbYlRQ+3SrYma2L2xJNuCwAAAAAAwMlKmECnN2zZXxezfMYpwzo9x9dUoQ8c0T10puVOk9Xc7RFrAAAAAAAAPZYwgU5vDLnaHGO4lSSdWdR5oLPHeVjN5uivqSSv5KTbAQAAAAAA0BMJE+j0xpCrWBMi56TaVZSd0uk521zVMcun508/6XYAAAAAAAD0RMIEOj3V6vHpoyP1UeVnnjJMphgrWAVtM5pjlp+ee3qvtQ0AAAAAAKA7EibQ6emQq51HG+TxRS+3frzhVvJ5tDXGNDljLKnKSso6qXYAAAAAAAD0VMIEOj0dcvXh4ejeOZJUMjqz03Nqaz/RAZstqnx68vCTagMAAAAAAEBvSJhAp6e2HYwOdEwmadrIzgOdbYffjlleknlqr7ULAAAAAACgu4ZMoPPBobqoslNzU5WeFN0DJ2hb9baY5dNzpvVWswAAAAAAALptSAQ6TpdXe6qaospPH5V13PO21n0SVZbu8+vUvCm91TQAAAAAAIBuS5hApyeTIm8/XC8jej5knT6q8+FWHr9HHzUfjT7H5ZI5JbfbbQAAAAAAAOgtCRPo9GRS5A8OxZ4Q+XiBzse1H6vV8EaVl7hcksXe7TYAAAAAAAD0loQJdHrigxgrXFnMJhUP7zzQ+bD6w5jlJa1uydL5vDsAAAAAAAB9bWgEOjEmRJ5QkK5ku6XTc3bV7opZPtXtkszW3moaAAAAAABAtw36QKe+xaP9Nc1R5acfZ7lySdpRsyOqbJTHowy/QQ8dAAAAAAAQV4M+0PmkojFm+dSRGZ2e4/F7tKduT1T5ZLcnsEEPHQAAAAAAEEcJE+ic7CpXuzsJdCYWdh7ofFr3qTx+T1T5JJc7sGGmhw4AAAAAAIifhAl0TnaVq4/LYwc6EwrSOj1nZ+3OmOWT3cFAp/O5dwAAAAAAAPpawgQ6JytWD52CDIeyUjpferyzCZEnu92B3jkmU6+1DwAAAAAAoLsGdaBjGIZ2x+ihc7zhVpK0sya6h06u16dcn5/5cwAAAAAAQNwN6kCnusmtY83Rc+FMPM5wK7/h1+5ju6PKJwWHW7HCFQAAAAAAiLNBHeh83MmEyBMK0js951DjITk9zqjy9vlz6KEDAAAAAADiq98DnYMHD2ru3LkqLi7W6aefrieffLLP7hVruJUkTSzsPND5sPrDmOWhFa7ooQMAAAAAAOKs37ubWK1WrVq1StOnT1d5eblmzJihyy+/XKmpqb1+r1g9dEwmaXx+50Ou3jn6TszyYnroAAAAAACAAaLf04nhw4dr+PDhkqTCwkLl5uaqtra2TwKdWCtcFWWnKMUe+2MbhqG3j74dVT7S49Uory+wQ6ADAAAAAADirNtDrjZs2KBFixZpxIgRMplMevbZZ6PqlJWVacyYMUpKStKcOXO0adOmmNfavHmzfD6fRo8e3e2Gn4hhGPo4xpCr482fs79hv446j0aVn9Xa2r7DkCsAAAAAABBn3Q50nE6nSkpKVFZWFvP4448/ruXLl+uuu+7Sli1bVFJSogULFqiysjKiXm1trb761a/qoYceOrmWn8DhuhY53b6o8onHCXSe/uTpmOVntYQFOmYCHQAAAAAAEF/dHj+0cOFCLVy4sNPjDzzwgJYuXaolS5ZIklavXq0XX3xRDz/8sO644w5Jksvl0tVXX6077rhD55xzznHv53K55HK5QvsNDQ1damenK1x1MiFyi7dFT33yVFS51TA0JyLQYcgVAAAAAACIr15d5crtdmvz5s2aP39++w3MZs2fP18bN26UFBgKdeONN+qiiy7S//t//++E11y5cqUyMzNDr64Oz9pd3hSzvLMeOq8fel0N7uiw6BJns4b5/e0FFgIdAAAAAAAQX70a6FRXV8vn86mgoCCivKCgQOXl5ZKkN998U48//rieffZZTZ8+XdOnT9eHH8ZeKlySVqxYofr6et1///2aOHGixo8f36W2xOqhYzWbNDY39uTLrx58NWb5Vxo6XIchVwAAAAAAIM76vbvJeeedJ394j5cTcDgccjgcuvXWW3XrrbeqoaFBmZmZJzxvd4wJkU/NS5XdGp1heXwebTi4Iap8pN+sEpc7spBJkQEAAAAAQJz1ag+d3NxcWSwWVVRURJRXVFSosLCwR9cuKytTcXGxZs2adcK6Xp9fe6qih1x1tsLV1qqtavREB0AX+SwydSw0W7rSXAAAAAAAgD7Tq4GO3W7XjBkztG7dulCZ3+/XunXrdPbZZ/fo2qWlpdqxY4fefffdE9bdX9sstze6F1Bn8+e8X/l+zPJ5nhiFDLkCAAAAAABx1u0hV01NTdqzZ09of9++fdq6dauys7NVVFSk5cuXa/HixZo5c6Zmz56tVatWyel0hla9OlllZWUqKyuTzxe9FHlHH8cYbiV1vsLVlsotUWV2s10lbm90ZYZcAQAAAACAOOt2oPPee+9p3rx5of3ly5dLkhYvXqw1a9bouuuuU1VVle68806Vl5dr+vTpWrt2bdREyd1VWlqq0tLSLs2hs7uTJctj9dDx+X3aVrktqnxa3jTZq9+LvgjLlgMAAAAAgDjrdjoxd+5cGYZx3DrLli3TsmXLTrpRPRVrhaskm1lF2SlR5btqd6nJEz3fzpn5Z0ofvR19cQIdAAAAAAAQZ706h05f6s6kyLFWuJpQkC6zOWqKY607sC6qTJLOyD9D8sWYRIchVwAAAAAAIM4SJtDp6qTIrR6fPqtpjirvbIWrWIFOkiVJMwtnSv4YgQ6TIgMAAAAAgDhLmECnq/ZWNcnnjx4SFmv+nP0N+/Vp/adR5eeNPE/J1mTJF2NSZJYtBwAAAAAAcZYwgU5Xh1zFmj9Hir3C1abyTTHrXlR0UWDDzypXAAAAAABg4EmYQKerQ652l0dPcCzF7qHzXnmMVawknTvy3MAGQ64AAAAAAMAAlDCBTlfF6qGTkWRVQYYjoswwDL1XER3ojMscp+ykbMnvlwx/9A3ooQMAAAAAAOIsYQKdrg65irXC1cTCdJlMkStcHWo6pMrmyqi6MwtnBjZi9c6RmEMHAAAAAADEXcIEOl0ZcuXx+XW0viWqfHx+9HCrD6s+jHmNmQXBQCfG/DkSQ64AAAAAAEDcJUyg0xXl9a2KscCVRmcnR5V9WB070CnJKwls+DrpocOQKwAAAAAAEGeDKtA5dCy6d44kjcyKDnQ+qvkoqiw7KVuFqYWBnU576FhPun0AAAAAAAC9YVAFOofrYgc6o4ZFBjoev0c7a3ZG1ZuWO619rp3OeugQ6AAAAAAAgDhLmECnK5MiH+60h05KxP7eur1q9bVG1ZuaO7V9p7MeOgy5AgAAAAAAcZYwgU5XJkU+XNccVWazmJSfHrlkeWfz50zLnda+0+kqVwQ6AAAAAAAgvhIm0OmKWEOuhmcmy2yOXLL8o+ro+XMkaUrOlPYdX2c9dBhyBQAAAAAA4mtwBToxhlzFmhA5Vg+d0emjlZWU1V7QaQ8dAh0AAAAAABBfgybQ8fsNHamLnhdnZIcJkZs9zdpTtyeqXsT8OdJxVrliyBUAAAAAAIivQRPoVDW55Pb5o8o79tDZWbtTfiO6XsT8OdJxhlwR6AAAAAAAgPhKmEDnRKtcHepshasOPXS2V2+PWS+6hw5DrgAAAAAAwMCUMIHOiVa5ijUhsiSNyjpxoGMxWTQpe1JkYadDrgh0AAAAAABAfCVMoHMisSZElqJ76MSaEPm0Yacp2dph8mRfJz10GHIFAAAAAADibPAEOnXNUWUmU2DZ8qDa1lodbjocVS9quJVEDx0AAAAAADBgDZ5AJ0YPnfx0h+zW9o/Y6fw5OTECHXroAAAAAACAAWrwBDox5tDpuMLVR9UfxTyXHjoAAAAAACCRxCXQueaaazRs2DB94Qtf6LVrVja6ospGZJ14/pxka7LGZY2LvmCnq1zRQwcAAAAAAMRXXLqbfPe739VNN92kP/3pT71yPY/Pr7rmjgGModPs1dJHz0hHtsqo3KXt3l1R5072GrL+3xejL9pYEftmFnroAAAAAACA+IpLOjF37lytX7++1653zOkObVvk023WJ/Qly3rlbG+U2qbNOWq16NjokVHnTq2vlGo/7vrN6KEDAAAAAADirNtDrjZs2KBFixZpxIgRMplMevbZZ6PqlJWVacyYMUpKStKcOXO0adOm3mhrp6qb2gOdH1gf0Tetf1OOqTGizk67Pea5U1zumOWdYg4dAAAAAAAQZ90OdJxOp0pKSlRWVhbz+OOPP67ly5frrrvu0pYtW1RSUqIFCxaosrLypBrocrnU0NAQ8eqoxhmYP2eK6TMttvwj5nV2dBLoFHcn0DFZpIzhXa8PAAAAAADQB7od6CxcuFA/+clPdM0118Q8/sADD2jp0qVasmSJiouLtXr1aqWkpOjhhx8+qQauXLlSmZmZodfo0aOj6tS09dD5T+tjMpuMmNfZ6YgOdFL8fhV5O1nNKpbiz0lJmV2vDwAAAAAA0Ad6dfyQ2+3W5s2btWLFilCZ2WzW/PnztXHjxpO65ooVK7R8+fLQfkNDQ1SoU93k0ummvbrAEr2KVdBOhyOqbJJhlTlv0okbYU+Txs2TLri96w0HAAAAAADoI70a6FRXV8vn86mgoCCivKCgQLt2ta8wNX/+fG3btk1Op1OjRo3Sk08+qbPPPjvmNR0OhxwOh8rKylRWViafzxdVp8bp1resz8duVO5EVV37W1X/c0nUoeKpX5Fm/2c3PiEAAAAAAED8xWWG31deeaXb55SWlqq0tFQNDQ3KzIwc9mSp3q3LLO9GnWM4MmT62j+1s3pbzGtOzpnc7XYAAAAAAADEW7fn0Dme3NxcWSwWVVRURJRXVFSosLCwR9cuKytTcXGxZs2aFXXs7KN/jnmOadbXpKRM7ajZEfP45GwCHQAAAAAAkHh6NdCx2+2aMWOG1q1bFyrz+/1at25dp0Oquqq0tFQ7duzQu+926IlzbL/Ocq6Lqu+SXTrrW5KknTU7o447LA6NzRzbozYBAAAAAADEQ7eHXDU1NWnPnj2h/X379mnr1q3Kzs5WUVGRli9frsWLF2vmzJmaPXu2Vq1aJafTqSVLouew6Y5O59DZWCaL/FH1X09fqPlpeZKknbXRgc7EYRNlNcdlxBkAAAAAAECPdDvReO+99zRv3rzQfnAFqsWLF2vNmjW67rrrVFVVpTvvvFPl5eWaPn261q5dGzVRcnfFnEPH7ZS2PRZV12NY9E7hDZovqd5Vr6POo1F1mD8HAAAAAAAkqm4HOnPnzpVhGMets2zZMi1btuykG9VlHz0ruRqiip/3nyNzdpEk6dP6T2OeOmHYhL5sGQAAAAAAQJ/p1Tl0+lLMSZG3Phqz7p+8lyo31SFJ2lu3N2adcVnjer2NAAAAAAAA/SFhAp2oSZEbK6T9b0bV2+E/RR8Ypyo71S7pOIFOJoEOAAAAAABITAkT6ETZ/XdJ0UO/nvKdL8mknLRAoBNryFV2UraykrL6tn0AAAAAAAB9JGECnaghVx+/FLPei745kqTctMCQq1iBDsOtAAAAAABAIkuYQCdqyNX+t6LqvOefoHLlSJJy0uyqaalRubM8qt6pmaf2aVsBAAAAAAD6UsIEOl3xqu+M0HZ2ql2bKzbHrDcxe2J/NQkAAAAAAKDXJUygE3OVqw42+KdJktKTrHJYLXq3/N2Y9WYWzOyTNgIAAAAAAPSHhAl0ooZcdVBrpOkjY4yk9vlz3qt4L6pebnKuxmSM6atmAgAAAAAA9Lm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       "text/plain": [
        "<Figure size 1400x400 with 1 Axes>"
       ]
@@ -862,12 +862,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -897,7 +897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -915,7 +915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -935,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,12 +1051,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -1109,12 +1109,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -1164,12 +1164,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -1203,12 +1203,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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pKYmmpiYSE4/8A7yIiEgktbu9VDR2cOAIoVF5Ywc1La6I15QW5+wzHa37cVqcU7uliYiIyNBlGNBef4TQp1fXT3vdwOcZapzxEJcB8Zm97jMhPqPr3ny92R9NUmb+gJmHgiEFQyIiMox0enyUN5qh0f66NnZXt7K7qpXd1S3UtkZ2d7WUWEefzqJJWQlMzk5Qh5GIiIhEhs8LLRW9Qp8jdP142ge7yuBEJfUKdvoGPIcFP87YoE4ZbOahYEjBkIiIjBD1bW52V7Wwu7qVPdWt7Op6HOkuo8LUWOYUJjOnIJk5hSlMzUnEabdGtAYREREZQTqboGYX1OyA2p3m49qdZgA0lKd5xaQO2NVDfCbEpoNj4E1QjpWCoSAoGBIRkdGgsd0d6CzaVdUSCI2qIxQYOe1WZuYlBYKiOYXJ5CRFaxqaiIiI9DAMc+eu2p1Q03XrDoFaKwe7ur4sVkjIgaQCSMqH5IKuxwWQkGUGP3HpYBvcnWAVDAVBwZCIiIxmTe0e9tS0sKuqZzra7qpWKps7w37trMQo5hSYIdGcwhRm5iUR47SF/boiIiIyyPx+c8pXd9dPzU6o7eoG6mwa7OpM9mgz5EnuCn6SCns9LoDE3EEPfYKhYCgICoZEREQO19zpYXdVK3u6gqJd1a3sqWqhoil8gZHNamFqTkKfsGhsWqy6ikRERIYrnwfqi83Ap08ItBu8HYNbW0xq3y6f3qFPciHEpsEI+BlEwVAQFAyJiIgEr6XTw57q1q5paS2B6WnljeH54S451tFn+tmsgmQSo4f+t3MiIiKjirvNDHu6u366O4Dqi8HvjXw9Fisk5vUKeg7p+knMg6j4yNc1CBQMBUHBkIiIyIlrdXnZW93K9oPNbDjQyPrSRnZVtxDqnzAsFpiQER/oKJpTmMzEzARs1uH/jZ6IiMiQ19HYN/jpXgeoqTTytUQnQ8YUSJ8IKWP6TvVKyAWbPfI1DUEKhoKgYEhERCQ8Wjo9bCprYn1pA+tLG1l/oJH6NnfIrxPntDGrILlrF7QUZhcmkx4fFfLriIiIjCo+D1RtgbI15q18DdTtiXwdCTmQMRnSJ0PGpK77yeZuXiNgqle4KRgKgoIhERGRyDAMg9L6djMkKm1g/YFGtlU04/WH/seQwtTYrqAombljUpiem6SuIhERkaMxDGgqM8Of7iDo4Abwhn8zCsCc+pU8xgx8AiHQZLMbKDopMjWMUAqGgqBgSEREZPB0enxsKW/q6igyO4sOhmGB65RYB2dOzOCcyRmcOTGDjAR1FImIyCjmaoWK9VC2GsrXmvetVeG/rs0JaRMgfVLfEChtAjiiw3/9UUjBUBAUDImIiAwtB5s62NA19Wx9aQObyppwef0hvcbMvCTOmZzB2ZMymF2QjN1mDen5RUREhgy/z1wHqHc3UM12MEL7b2sfzvie8CdwPxlSxmrtnwhTMBQEBUMiIiJDm8fnZ8fBlkBH0frSBvbVtYfs/EkxDs6YmM45k8ygKDNR31iKiMgw1lrdsyZQ2WooXw/ulvBcKzatZ9pX7xAoMU/r/wwRCoaCoGBIRERk+Klvc7MhEBQ1suFAI62u0GyHOy0nMdBNNHdMCg51E4mIyFDl6YTKTWYA1B0GNYZph7D0SZB/MuTOgazpZiAUlxaea0nIKBgKgoIhERGR4c/nN9hb0xrYAW3DgUZ2VrVwoj/hJETZOWNiOmdPyuDsyRnkJMWEpmAREZFjZRhQX9yrG2gNVG4Gvyf014pJNUOg/PnmLXcuxCSH/joSdgqGgqBgSEREZGRq6fSwdn8DK3bVsGJXDcU1bSd8zinZCYGQaP6YVJx2dROJiEiYuNuhdFXfIKijPvTXsTog5yTIm98VBs2DlCJNBRshFAwFQcGQiIjI6FBa186KXdWs2FXDyj11dHh8J3S+OKeN0yekc/bkDM6ZnElesrqJRETkBNXthd1LYfd/YN8H4HOF/hrJY7o6gU42w6DsmdoRbARTMBQEBUMiIiKjj8vrY3VJA8t3mkHR7urWEz7nxMx4zp5khkQnF6UQZbeFoFIRERnRPJ2w/4OeMKi+OLTndyZA3tyeaWF58yE+I7TXkCFNwVAQFAyJiIhIWUO7OeVsZw0r99TS5j6xbqJYp43TxqcFgqKC1NgQVSoiIsNew34zBNq9FEreA29HaM5rsULmtJ4AKP9kc8Foq6Y9j2YKhoKgYEhERER6c3v9rNlfHwiKdlSe+Ba/49LjAlPOFhSlEu1QN5GIyKjhdUPph11dQUuhdmdozhuf3bM4dP7JkDMbouJDc24ZMRQMBUHBkIiIiPTnYFMHK3aaC1h/sLuWFpf3hM4X7bBy3pRMrpiTz9mTMrSAtYjISNRUZoZAe96G4uXgPsEpy/YYyJ0NefN6poUl5mmBaBmQgqEgKBgSERGRYHl8ftbtb2B5VzfRtoPNJ3S+lFgHl52Uy+Vz8phbmIxFP+CLiAxPPg8c+LhritjbUL31BE9oMcOfiRfC+PPNXcNsjpCUKqOLgqEgKBgSERGR41Xd3GmGRLtqeH9XDc2dx99NNCYtlstn53HFnDzGpseFsEoREQmLlkqzI2j3f2Dvu+A6sS8LiEmFCYu6wqDzIC4tNHXKqKZgKAgKhkRERCQUvD4/Gw40smJXDct31rC5vOm4zzWnMJkr5+Rx6Um5pMY5Q1iliIgcN58Xytf07CBWuenEz5k7xwyCJlxg7h5m1Rp0EloKhoKgYEhERETCoabFxXtd3UTv7a6hsd1zzOewWy2cMzmTK+bkcf7UTC1aLSISaa01sHeZGQTtWQadjSd2vugkc2rYxAthwvkQnxmSMkWORsFQEBQMiYiISLj5/AYbyxp5e1sV/9pQQXnjsW9NnBBl55KZOVw+J48FRalYrVqPSEQk5Px+qFjftVbQf8zHnOCvy9kze7qC8k8Gmz0kpYoEQ8FQEBQMiYiISCT5/Qar99Xz0vpyXt98kJbjWJcoNymaz8zJ48o5eUzMSghDlSIio4hhwIFPYMOfYcdr0F53YudzJsD4c2HiBeaaQYm5oalT5DgoGAqCgiEREREZLJ0eH+/sqOal9eUs31mNx3fsP5LNyEvk8tl5fHp2LpkJ0WGoUkRkhGoqg40vwIbnoX7viZ0rc1rPwtEFC8Cu9eFkaFAwFAQFQyIiIjIUNLS5eW3zQV5aV8a60sZjPt5qgTMmZnDFnFwWT88m1qmpCiIih3G3w47Xze6g4uUc9zQxRxyMOwcmLjKniCUXhLBIkdBRMBQEBUMiIiIy1Oyva+Ol9eW8vL6cfXXtx3x8rNPG4unZXDEnj9MnpGPTekQiMpr1niq29aXj31Y+fZIZAk28AMacBvao0NYpEgYKhoKgYEhERESGKsMwWH+gkZfXl/PqxgoajmNns4yEKD4zK5fL5+QxPTcRi0UhkYiMEic6VcweA0Vn9awVlFoU+hpFwkzBUBAUDImIiMhw4Pb6eW9XDS9tKGfptircXv8xn2NSVjyXz8nj8tl55CbHhKFKEZFBdqJTxaKTYcaVMPlSGHs6OPTfShneFAwFQcGQiIiIDDfNnR7e3FzJi+vL+Ki4/piPt1hgQVEqV87J56KZ2SRGO8JQpYhIhJzoVDGL1ewImv1FmHQxOLSQv4wcCoaCoGBIREREhrPyxg7+taGcl9aVs7u69ZiPd9qtXDIjmxvPGMfM/KQwVCgiEiYnOlUsY4oZBp30BUjIDn19IkNAsJmHNdQX9vl8fPe736WoqIiYmBjGjx/Pj370I3rnT4Zh8L3vfY+cnBxiYmJYtGgRu3fv7nOe+vp6rr32WhITE0lOTubGG2+ktbXvDzybNm3izDPPJDo6moKCAh555JFQfxwRERGRISsvOYbbzpnAf+4+i9fuPIObzigiIyH4BVHdXj8vb6jgU7/6gC/8ehVLt1Xh94/a7wxFZKhzt8Omv8P/XQ6PzYB3fnRsoVB0Mpx8E9z8Dtz2EZz+dYVCIkDI9zJ9+OGHeeqpp3juueeYPn06a9as4Stf+QpJSUl87WtfA+CRRx7h8ccf57nnnqOoqIjvfve7LF68mG3bthEdbbbuXXvttRw8eJClS5fi8Xj4yle+wi233MLzzz8PmMnXhRdeyKJFi3j66afZvHkzN9xwA8nJydxyyy2h/lgiIiIiQ5bFYmFGXhIz8pK4/+IpfLi3jpfWl/Pmlko6PL6gzvFxST0fl9RTlB7HDWcUcdXcPG17LyKDT1PFRMIu5FPJLrvsMrKysvjd734XeO2qq64iJiaGP/3pTxiGQW5uLvfeey/f+MY3AGhqaiIrK4tnn32Wq6++mu3btzNt2jRWr17N/PnzAXjzzTe55JJLKCsrIzc3l6eeeorvfOc7VFZW4nQ6Abj//vt5+eWX2bFjR1C1aiqZiIiIjGRtLi//2VbJS+sr+GB3DcfSDJQc6+DaBYVct3AsmYn6RUpEIkxTxURO2KBNJTvttNNYtmwZu3btAmDjxo188MEHXHzxxQCUlJRQWVnJokWLAsckJSWxYMECVq1aBcCqVatITk4OhEIAixYtwmq18vHHHwfGnHXWWYFQCGDx4sXs3LmThoaGI9bmcrlobm7ucxMREREZqeKi7FwxJ5//u+EUPnrgfB68dCrTc4P7Mqyx3cMT7+7l9Iff4d6/bWRbhX5uEpEwO+GpYkmaKiZyHELeH3z//ffT3NzMlClTsNls+Hw+fvKTn3DttdcCUFlZCUBWVlaf47KysgLvVVZWkpmZ2bdQu53U1NQ+Y4qKig47R/d7KSkph9W2ZMkSHnrooRB8ShEREZHhJTMxmpvOHMdNZ45jV1ULf1t9gBdWH6DV5e33OI/P4J/ryvjnujLOmJDOjWcWcfbEDKxWS4QqF5ERTVPFRAZdyIOhv/3tb/z5z3/m+eefZ/r06WzYsIG77rqL3NxcrrvuulBf7pg88MAD3HPPPYHnzc3NFBQUDGJFIiIiIpE3KSuBBy+bxtcXTeSvqw/wh5X7KG/sGPC4D/bU8sGeWiZkxnPTGUVcPiePaIctAhWLyIjTVA4b/3L8U8XSJ8OcazVVTCQEQh4MffOb3+T+++/n6quvBmDmzJns37+fJUuWcN1115Gdbf6lraqqIicnJ3BcVVUVs2fPBiA7O5vq6uo+5/V6vdTX1weOz87Opqqqqs+Y7ufdYw4VFRVFVFTwO3WIiIiIjGQJ0Q5uOnMc1582lje3VvKb90vYeKBxwOP2VLdy/4ub+Z+3dvJfp47hSwvHkB6vn7FEJAjV2+GDX8Dmv4MR3OL4AdFJMPNzZndQ7lywqHNRJBRCvsZQe3s7Vmvf09psNvx+PwBFRUVkZ2ezbNmywPvNzc18/PHHLFy4EICFCxfS2NjI2rVrA2Peeecd/H4/CxYsCIx577338Hg8gTFLly5l8uTJR5xGJiIiIiJHZrdZueykXF6+7TT+8dWFXDQ9O6jft+ra3Pxy2W5O+9k73P/PTeyuagl/sSIyPB1YDX/5Ijx5Kmx6IfhQyGKFiRfC556Fe3fBpY9C3jyFQiIhFPJdya6//nrefvttfv3rXzN9+nTWr1/PLbfcwg033MDDDz8MmFva/+xnP+uzXf2mTZv6bFd/8cUXU1VVxdNPPx3Yrn7+/PmB7eqbmpqYPHkyF154Iffddx9btmzhhhtu4LHHHgt6u3rtSiYiIiJyZPvr2vjDyn38bc0B2t3Bf6t/9qQMbj5zHKdPSMOiX9xERjfDgL3vwAePwb73j+3Y7qliMz8PiTkDjxeRwwSbeYQ8GGppaeG73/0uL730EtXV1eTm5nLNNdfwve99L7CDmGEYfP/73+eZZ56hsbGRM844gyeffJJJkyYFzlNfX88dd9zBq6++itVq5aqrruLxxx8nPj4+MGbTpk3cfvvtrF69mvT0dO68807uu+++oGtVMCQiIiLSv6Z2D89/UsqzH5ZQ1ewK+rgp2QncdOY4PjUrhyi71iESGVX8Ptj+ihkIHdwY/HGaKiYSUoMWDA0nCoZEREREguP2+nlj80F+834xW49h6/qMhCiuWziGaxeMISXOGcYKRWTQeV2w6a+w8pdQtye4YyxWGH++GQZNvkS7iomEkIKhICgYEhERETk2hmHwUXE9v/ugmLe3Vw98QJdoh5XPzsvnhtOLGJcRP/ABIjJ8uFph7bOw6gloqQjuGHs0zP0yLLwDUsaEtTyR0UrBUBAUDImIiIgcv701rfz+gxL+ua6MTo8/qGMsFjh/ShY3nVnEgqJUrUMkMpy118PHT8PHv4bOxuCOiUqCU26GBV+F+Iywlicy2ikYCoKCIREREZETV9/m5vmP9/Psh/upbQ1+HaKZeUncdGYRl8zMwWEL+Wa5IhIuTWVmd9DaZ8HTHtwx8Vlw6m0w/waI1u9eIpGgYCgICoZEREREQsfl9fHKhgp+90EJOyqD37o+OzGa608fyzWnFJIU4whjhSJyQmp3wwe/MNcR8nuCOyZlLJz+dZj1Ra0fJBJhCoaCoGBIREREJPQMw+CDPbX89v0SVuyqCfq4WKeNL5xcwK3njCczQb9AigwZ5evMHca2vwoE+etj1gw4426YdjnY7OGsTkSOQsFQEBQMiYiIiITXrqoWfvd+CS+tL8ftC24dohiHjZvOLOLms8aRGK0OIpFBYRhQ8h588HMoXh78cYUL4Yx7YOIF2m5eZJApGAqCgiERERGRyKhpcfGnj/bzx4/2U9/mDuqY5FgHt58zgS8tHEO0wxbmCkUEAL8fdr5udgiVrw3+uImLzQ6hMQvDV5uIHBMFQ0FQMCQiIiISWZ0eHy+tL+e37xezt6YtqGNykqK5e9Ekrpybh12LVIuEh88Dm/4GK38BtbuCO8ZihRlXwel3QfaMcFYnIsdBwVAQFAyJiIiIDA6/32DFrhp++0ExK/fUBXXMhMx4vnHhZBZPz9I29yKh4m6DdX+ED/8fNJcFd4wtCub8F5x2J6QWhbc+ETluCoaCoGBIREREZPBtq2jmyeV7eG3TwaDGzy5I5r6LprBwfFqYKxMZwToa4JPfwMdPQ3tw4SzOBDj5RnPb+YSs8NYnIidMwVAQFAyJiIiIDB1bypt45K2dvBfkTmZnTcrgW4snMyMvKcyViYwgzQfhoydgzR/A3RrcMXEZcOqtMP9GiEkOa3kiEjoKhoKgYEhERERk6Plwby0Pv7mTjQcagxr/qVm53HvBJMamx4W3MJHhrG4vrPwlbPwL+IJbAJ7kQjjta+a0MUdMeOsTkZBTMBQEBUMiIiIiQ5NhGLy1tYr/eWtHUItU260Wrj6lgK+dN5HMxOgIVCgyTLRWw7s/gXX/B4Y/uGMyppo7jM24EmyO8NYnImGjYCgICoZEREREhjavz8+L68p57O1dHGzqHHB8jMPGDWeM5b/PHk9itH6hlVHM02lOGXv/58FPGcs/Bc68x9x63qodAEWGOwVDQVAwJCIiIjI8dHp8/HHVfp5YvofGds+A45NjHdx2zni+vHAs0Q5bBCoUGSIMA7b8E95+CJpKgztmwiKzQ2jM6aAd/0RGDAVDQVAwJCIiIjK8NHd6eGZFMb/7oIQOj2/A8TlJ0dy1aCJXzc3HblMHhIxwBz6Bt74NZasHHmuxwrTPmIFQzqzw1yYiEadgKAgKhkRERESGp+qWTv7fsj385ZNSvP6Bf5wdnxHHNxdPZvH0bCzqiJCRpmE/vP0D2PriwGNtTph1DZz+dUgbH/bSRGTwKBgKgoIhERERkeFtf10bj/5nF69srAhq/KyCZO5bPJnTJqSHuTKRCOhshg9+DqueBJ9r4PHTr4RFP4CUMWEvTUQGn4KhICgYEhERERkZtlY08T9v7WT5zpqgxp85MZ37LprCjLykMFcmEgY+L6z/o7nbWFsQ/5/Pmw8XLYGCU8Jfm4gMGQqGgqBgSERERGRk+ai4joff3MH60sagxl92Ug73XjiZovS48BYmEip7lsF/HoTqbQOPTSowO4RmXKVFpUVGIQVDQVAwJCIiIjLyGIbB0m1V/M9bO9ldPfA23TarhS+cXMDXz59IVmJ0BCoUOQ7VO8xAaM/Sgcc6481t50+9DRwx4a9NRIYkBUNBUDAkIiIiMnL5/AYvrivjsaW7qGjqHHB8tMPKV04v4qtnjycpxhGBCkWC0FYLy5fAmj+AMcBOfBYrzPkSnPcgxGdGpj4RGbIUDAVBwZCIiIjIyNfp8fGnj/bzxLt7aGj3DDg+KcbBreeM5/rTxhLtsEWgQpEj8Lrg41/De/8LrqaBx487By78CWTPCHtpIjI8KBgKgoIhERERkdGjpdPDb94r5rcflNDuHqDzAshKjOKuRZP43Lx87DZrBCoUAQwDtv0Lln4PGvcPPD59khkITbxA6wiJSB8KhoKgYEhERERk9KlpcfGrd3bz/CeleHwD/yg8LSeRH18xg7mFKRGoTka18rXw1negdNXAY2NS4dxvw7zrwaapjyJyOAVDQVAwJCIiIjJ6lda18/OlO/nXxgqC+Yn46pMLuO+iKaTEOcNfnIwuTWWw7Iew6a8Dj7U6YMF/w1nfhJjksJcmIsOXgqEgKBgSERERkW0Vzfzvf3byzo7qAcemxDq476IpfH5+AVarpu3ICXK1wspfwof/D7wdA4+f+mm44CFIHRf+2kRk2FMwFAQFQyIiIiLS7ZOSeh5+cwdr9zcMOHZOYTI/vnwG03OTIlCZjDh+H2x4Ht75EbRWDTw+dw4s/imMOS38tYnIiKFgKAgKhkRERESkN8MwWLa9miX/3s7emrZ+x1ot8OWFY7nnwkkkRmuNFwlS8QpzHaGqzQOPTciFRd+HmZ8HqxZAF5Fjo2AoCAqGRERERORI3F4/v/ughMeX7abD0/8OZhkJUTx46VQ+PSsXi3aFkqOp3QNLvws73xh4rCMWzrgbFt4Bztjw1yYiI5KCoSAoGBIRERGR/pQ3dvDDV7fy1taBp/ssHJfGjy6fzoTMhAhUJsNGez2seARW/wb83gEGW2D2tXDeg5CYE5HyRGTkUjAUBAVDIiIiIhKMd3dU8/1XtlJa397vOIfNwo1njONr508g1mmPUHUyJHndsPq3sOJh6GwcePzYM2HxTyBnVthLE5HRQcFQEIL5Q/L7/bjd7ghXFnlOpxOr5i2LiIiIHFWnx8eTy/fy9PK9uH3+fsfmJcfwvU9N48JpWZpeNhrtXgr/vg/q9w48NnU8XPgjmHwJ6P8rIhJCCoaCMNAfktvtpqSkBL+//3/4RwKr1UpRURFOp3OwSxEREREZ0kpq2/j+K1t5b1fNgGPPnZzBQ5+eQWGa1okZFVqr4c37Ycs/Bx4bnQzn3A/zbwS7fgYXkdBTMBSE/v6QDMOgtLQUj8dDbm7uiO6m8fv9VFRU4HA4KCws1LdaIiIiIgMwDIN/b6nkh69uo7K5s9+xUXYrt587gVvOGke0wxahCiWiDAPW/xH+8yB0NvU/1mqHU26Bs74JsamRqU9ERiUFQ0Ho7w/J4/GwZ88ecnNzSUpKGqQKI6epqYmKigomTJiAw6HtVkVERESC0eby8viy3fzugxK8/v5/rB6bFstDn5nB2ZMyIlSdRETtHnjtLtj3/sBjJ18KF/wQ0ieEvSwRkWCDoZHbBnOCfD5zW9LRMrWq+3N2f24RERERGVhclJ0HLpnK6187k1PG9t/9sa+unet+/wm3/XktB5s6IlShhI3XDSv+B546beBQKHsmfPkVuOZ5hUIiMuQoGBrAaJlWNVo+p4iIiEg4TM5O4K//fSo///ws0uP7/2Lxjc2VnP/oCp55by+eARaxliHqwCfw67Pg3R+Dz3X0cdFJcNkv4JYVMO7siJUnInIsFAyJiIiIiISAxWLhyrn5LLv3HL68cEy/G0y1u3389I0dXPr4+3xSUh+5IuXEdDbD69+A310INdv7Hzv9Crh9Ncz/Cli1tpSIDF0KhkREREREQigpxsEPPzODV24/g1n5/a9Vuauqlc//ehX3/G0DNS39dJ7I4Nv+GjyxAFb/BuhnPanEfLjmBfjcs5CQFanqRESOm4IhEREREZEwmJmfxIu3nc5PrphBUkz/m3u8uK6c8x5dzh9X7cM3wCLWEmHNFfDCtfDXa6Glop+BFlhwK9z+EUy+OGLliYicKAVDI1BNTQ233norhYWFREVFkZ2dzeLFi1m5cuVglyYiIiIyqtisFq5dMIZ37j2bz83L73dsS6eX7/5rK5c/sZKNBxojU6Acnd8Pq39rdgnteK3/sVkz4KZlcPHPICohMvWJiISIfbALGA7u/+cmdla1DGoNk7MS+NlVJwU19qqrrsLtdvPcc88xbtw4qqqqWLZsGXV1dWGuUkRERESOJC0+iv/53Cw+f3IB3315Czsqj/6z5ebyJi5/ciVfPKWQby2eQlJs/91GEgbV2+HVr8OBj/sfZ4+Gc+6HhXeATf87icjwpGAoCDurWlhf2jjYZQSlsbGR999/n+XLl3P22ebOB2PGjOGUU04Z5MpERERE5OSxqbx65xk89+E+Hlu6iza374jjDAP+/HEpb26p5P6Lp3DV3HysVu0iG3aeTnj/UfjgMfB7+h9bdDZc9hikjY9MbSIiYaKpZCNMfHw88fHxvPzyy7hcWsBQREREZKhx2KzcdOY4lt17DpedlNPv2Lo2N9/8xya+8MwqdlQ2R6jCUWrfB/D0GfDeI/2HQjGpcPnT8OV/KRQSkREhLMFQeXk5//Vf/0VaWhoxMTHMnDmTNWvWBN43DIPvfe975OTkEBMTw6JFi9i9e3efc9TX13PttdeSmJhIcnIyN954I62trX3GbNq0iTPPPJPo6GgKCgp45JFHwvFxhhW73c6zzz7Lc889R3JyMqeffjrf/va32bRp02CXJiIiIiK9ZCdF86svzuVPNy5gXHpcv2NX72vg0sc/4MevbaPV5Y1QhaNERwO8cic8eynU7e5/7ElfgDtWw+xrwKIOLhEZGUIeDDU0NHD66afjcDj497//zbZt23j00UdJSUkJjHnkkUd4/PHHefrpp/n444+Ji4tj8eLFdHZ2BsZce+21bN26laVLl/Laa6/x3nvvccsttwTeb25u5sILL2TMmDGsXbuW//mf/+EHP/gBzzzzTKg/0rBz1VVXUVFRwSuvvMJFF13E8uXLmTt3Ls8+++xglyYiIiIihzhjYjr/vutMvnHhJKLsR//x3Oc3+O0HJSx+7D1W7KqJYIUjlGHAlhfhV6fAuv/rf2zyGPivF+HKZyAuPTL1iYhEiMUwjJDuh3n//fezcuVK3n///SO+bxgGubm53HvvvXzjG98AoKmpiaysLJ599lmuvvpqtm/fzrRp01i9ejXz588H4M033+SSSy6hrKyM3NxcnnrqKb7zne9QWVmJ0+kMXPvll19mx44dQdXa3NxMUlISTU1NJCYm9nmvs7OTkpISioqK+MHru4bV4tNHctNNN7F06VL2799/xPd7f97o6Ojjvo6IiIiIHL8D9e089OpW3t5ePeDYz83L58FLp2lx6uPReABevxd2v9X/OIsNFt5uLjDt7L+rS0RkqOkv8+gt5ItPv/LKKyxevJjPfe5zrFixgry8PG677TZuvvlmAEpKSqisrGTRokWBY5KSkliwYAGrVq3i6quvZtWqVSQnJwdCIYBFixZhtVr5+OOPueKKK1i1ahVnnXVWIBQCWLx4MQ8//DANDQ19OpS6uVyuPuvuNDcHN0/7RAKZoWLatGm8/PLLg12GiIiIiPSjIDWW3153Mku3VfGDV7ZS3thx1LF/X1vGil01/OSKmVwwLSuCVQ5jfh988gws+xF42vofmzMbPv045MyKSGkiIoMl5FPJiouLeeqpp5g4cSJvvfUWt956K1/72td47rnnAKisrAQgK6vvP15ZWVmB9yorK8nMzOzzvt1uJzU1tc+YI52j9zUOtWTJEpKSkgK3goKCE/y0Q09dXR3nnXcef/rTn9i0aRMlJSX8/e9/55FHHuEzn/nMYJcnIiIiIkG4YFoWb99zNrefOx6H7ehr2VS3uLj5/9bw9RfWU9/mjmCFw1DlZvjtInjz/v5DIUcsLP4p3LRMoZCIjAoh7xjy+/3Mnz+fn/70pwDMmTOHLVu28PTTT3PdddeF+nLH5IEHHuCee+4JPG9ubh5x4VB8fDwLFizgscceY+/evXg8HgoKCrj55pv59re/PdjliYiIiEiQYpw2vrl4ClfMyefBlzfzUXH9Ucf+a0MFH+yu5YefmcElM7OxaGHkHu52WPEwfPj/wPD1P3bCBXDpo5AyJjK1iYgMASEPhnJycpg2bVqf16ZOnco///lPALKzswGoqqoiJ6dne86qqipmz54dGFNd3Xdetdfrpb6+PnB8dnY2VVVVfcZ0P+8ec6ioqCiioqKO85MND1FRUSxZsoQlS5YMdikiIiIiEgITMuN5/qZT+cvqUpa8seOou5LVtbm5/fl1XDQ9mx9ePp3MBK0byd534LW7oWFf/+PiMuCin8GMq7TbmIiMOiGfSnb66aezc+fOPq/t2rWLMWPM1L2oqIjs7GyWLVsWeL+5uZmPP/6YhQsXArBw4UIaGxtZu3ZtYMw777yD3+9nwYIFgTHvvfceHo8nMGbp0qVMnjz5iOsLiYiIiIgMV1arhWsXjOGtu8/irEkZ/Y59c2slF/z8PV5cV0aI95kZPtrq4MX/hj9eMXAoNOe/4PZPYOZnFQqJyKgU8mDo7rvv5qOPPuKnP/0pe/bs4fnnn+eZZ57h9ttvB8BisXDXXXfx4x//mFdeeYXNmzfz5S9/mdzcXC6//HLA7DC66KKLuPnmm/nkk09YuXIld9xxB1dffTW5ubkAfPGLX8TpdHLjjTeydetW/vrXv/LLX/6yz1QxEREREZGRJC85hue+cjL/+7lZJEYfvfm/qcPDPX/byA3PruZg09EXsB5xDAM2/hWeOBk2vdD/2NTxcN2r8JknIDY1MvWJiAxBId+uHuC1117jgQceYPfu3RQVFXHPPfcEdiUDc8v673//+zzzzDM0NjZyxhln8OSTTzJp0qTAmPr6eu644w5effVVrFYrV111FY8//jjx8fGBMZs2beL2229n9erVpKenc+edd3LfffcFXWew29WPhu3bR9vnFRERERnuqps7+c7LW1i6rarfcQlRdr5z6VS+cHLByF57qL7EnDZW/G7/46x2OONuOPMb4NDPvSIycgW7XX1YgqHhQsFQj9H2eUVERERGAsMweG3TQb7/ytYBdyU7fUIaP7vyJApSYyNUXYT4fbDqCXj3p+AdoDsq/2T41OOQNa3/cSIiI0CwwVDIp5KJiIiIiEhkWCwWPjUrl6V3n8VlJ+X0O3blnjoW/+I9nvtwH37/CPluuGYX/H4xLP1u/6GQMwEu+V+44S2FQiIih1AwJCIiIiIyzKXFR/GrL87l6f+aR3r80XfhbXf7+P4rW/nCM6soqW2LYIUh5vfByl/C02dA2er+x06+FG7/GE65Gay2yNQnIjKMKBgSERERERkhLpqRzdv3nMVVc/P7Hbd6XwMX/eI9fvNeMb7h1j0U6BL6HvhcRx8Xnw2f/yNc8zwk5UWuPhGRYUbBkIiIiIjICJIc6+TRz8/iD185mZyko68d6fL6+ckb27nyqQ/ZVdUSwQqPk98HKx8Prkto/o1wxycw7dORqU1EZBhTMCQiIiIiMgKdOzmTt+4+i2tOKex33MYDjVz2+Af86p3deHz+CFV3jGp3w+8vMtcS6q9LKHUcfOXfcNnPITopcvWJiAxjCoZEREREREaoxGgHS66cyZ9vWkB+SsxRx7l9fv73P7v4zK9WsrWiKYIVDsDvgw//X1eX0Cf9DLTAglvhqythzGkRK09EZCRQMDTCXH/99Vx++eWDXYaIiIiIDCGnT0jnrbvO4vrTxmKxHH3ctoPNfOZXK3n0PztxeX2RK/BIuruE/vMgeDuPPi6lCL7yBlz8M3DGRq4+EZERwj7YBQwLr9wJ1dsHt4bMqfDp/ze4NYiIiIjIsBUXZecHn57OpSflcN8/NlF8lF3JvH6D//fOHt7aWskjn53F7ILkyBbq98FHT8I7P+4/EMICC74K539PgZCIyAlQMBSM6u0DL3AnIiIiIjIMnDw2lTe+fiaPLd3Fb94v5mibku2qauXKJ1dy85njuPuCSUQ7IrDVe+1u+NftcODj/selFMHlT2ramIhICGgqmYiIiIjIKBPtsPHAJVN58bbTmZQVf9RxfgN+/V4xF//yfVbvqw9fQb3XEhooFFrwVbhVawmJiISKgiERERERkVFqdkEyr955Bl87bwJ269EXHyqpbePzv17FD17ZSpvLG9oiavfAHy4Obi2h69+Aix8GZ1xoaxARGcUUDImIiIiIjGJRdhv3XDiZf91xOtNzE486zjDg2Q/3sfgX77FyT+2JX9jvgw9/BU+fHnyX0NjTT/y6IiLSh9YYCkbm1MGuYGjUICIiIiIj1vTcJF6+/XR+vWIvjy/bg9vnP+K4soYOrv3tx1xzSiEPXDKFxGjHsV+sdk/XWkIf9T8uZSx85kkFQiIiYaRgKBjaDUxERERERgGHzcod503kwunZfPMfm9h4oPGoY//ySSnv7qhmyZUzOXdKZnAX8Pvg46dh2Q8H2HGMXjuOadqYiEg4aSqZiIiIiIj0MSkrgRdvPY3vXDKVKPvRf2WobO7kK8+u5ht/30hTh6f/k9bthT9cAm99e4C1hMZqLSERkQhSMCQiIiIiIoexWS3cfNY43rzrLE4Zm9rv2H+sLWPxY+/x7s7qw9/0+2DVE/DUaQNPHTvlv+HWDzV1TEQkgiyGYRiDXcRgaW5uJikpiaamJhIT+y6019nZSUlJCUVFRURHRw9ShZEz2j6viIiIiATP7zf440f7efjNHbS7ff2O/dy8fB68bBpJMQ6zS+jl24JcS+gJGHtG6IoWERnl+ss8etMaQyIiIiIi0i+r1cJ1p43lvCmZ3P/iJlbuqTvq2L+vLeODXdX834x1TNz8GHg7+j/5KbfAoh9o2piIyCBRMCQiIiIiIkEpSI3lTzcu4IXVB/jJ69tpdXkPGzPGUsn/uH7NxPU7+z+ZuoRERIYErTEkIiIiIiJBs1gsXHNKIW/dfRZnTkzveR0/X7H9mzed93OKdYBQ6JRbutYSUigkIjLY1DEkIiIiIiLHLC85hv+74RReWH2A/3v9XR4ynhgwEPInjcF6+RNQdGaEqhQRkYGoY0hERERERI6LxTC4xv86rzvuGzAUetZ7IYs6fspy9+QIVSciIsFQx5CIiIiIiBy7ur3wrzug9MN+v20u9WfwLe9/85F/GjTD9X9YzefnmzuXJUY7IlauiIgcmTqGREREREQkeH4/fPQUPHU6lH7Y79DnvBdwkfthMxTq5W9rylj82Hus2FUTzkpFRCQICoZERERERCQ4tbvhDxfDm/f3uw29kTyGt0/5HY9Yb6Kd6COOOdjUyXW//4T7/rGJ5k5PuCoWEZEBKBgSEREREZH++byw8pfw9Blw4KP+x558M5ZbP2TRJZ/lrbvP4owJ6f0O/+uaA+oeEhEZRAqGRpjrr78ei8WCxWLB4XBQVFTEt771LTo7Owe7NBEREREZjqq2we8ugKXfA28/P1MmF8J1r8Kl/wtR8QDkp8TyxxtP4SdXzCDOaTvqoeoeEhEZPAqGRqCLLrqIgwcPUlxczGOPPcavf/1rvv/97w92WSIiIiIynPg8sOIR+PVZULGu/7En3wS3roKisw57y2KxcO2CMeoeEhEZorQrWRB+8OEP2N24e1BrmJg8kR+c9oOgxkZFRZGdnQ1AQUEBixYtYunSpTz88MNhrFBERERERoyKDeaOY1Wb+x+XMhY+/f+OGAgdqrt76PlPSvnp69tpc/uOOK67e+jqkwv49qVTtXOZiEiYKRgKwu7G3Wyq2TTYZRyXLVu28OGHHzJmzJjBLkVEREREhjqvy+wS+uAxMI4c3JgssOCrcP53wRkX9Om7u4fOmpjB/S9uYuWeuqOOfWH1AVbsquHhq07irEkZx/AhRETkWCgYGoFee+014uPj8Xq9uFwurFYrv/rVrwa7LBEREREZysrWwMu3Qe3O/selTYDPPAGFpx73pQpSY/nTjQuC6h76clf30HcunUqCuodEREJOwdAIdO655/LUU0/R1tbGY489ht1u56qrrhrsskRERERkKHK3w7s/gY+eBMN/9HEWK5z2NTjnfnDEnPBlj7V76L1dNfxM3UMiIiGnxadHoLi4OCZMmMCsWbP4/e9/z8cff8zvfve7wS5LRERERIaafSvh6dNh1a/6D4Uyp8FNb8MFD4UkFOqtu3vox5f3v3NZRVf30P3/3ESLdi4TEQkZdQwFYWLyxMEu4bhrsFqtfPvb3+aee+7hi1/8IjExof2HXERERESGIVcrvP0DWP2b/sdZ7XDmN+DMe8HuDFs5FouF/zp1DGdPyuC+f27iw73qHhIRiRQFQ0EIdjewoepzn/sc3/zmN3niiSf4xje+MdjliIiIiMhg2vsuvPI1aCrtf1z2SXD5k5A9MzJ10dM99OdPSlnyxnbaj7L2UHf30DWnFPDtS7T2kIjIidBUslHAbrdzxx138Mgjj9DW1jbY5YiIiIjIYOhsglfuhD9e3n8oZHPC+d+Dm9+JaCjUzWq18KVTx/DWXWdx2vi0fsf+5ZMDXPSL93l/d02EqhMRGXkshmEYg13EYGlubiYpKYmmpiYSExP7vNfZ2UlJSQlFRUVER0cPUoWRM9o+r4iIiMiosustePUuaKnof1z+yeaOYxmTI1LWQPx+Y8DuoW7XnFLIty+Zou4hEZEu/WUevaljSERERERkpGqvhxdvgec/338oZI+BxT+FG94aMqEQ9O0eWjhuoO6hUnUPiYgcBwVDIiIiIiIj0bZ/wROnwKa/9j9uzBlw60pYeDtYj74r2GAqSI3lzzct4EeXzyC2n53Lyhs7+NLvPuHev22kvs0dwQpFRIYvBUMiIiIiIiNJazX87cvmra2f7hlHHFzyv3Ddq5A2PnL1Hadj6R7657oyFv18BS+tL2MUr5whIhIUBUMiIiIiIiOBYcCmv8MTC8xuof6MOxduWwWn3AzW4fUrQaB76DPT++0eqm9zc/dfN/Ll339CaV17BCsUERlehte/AiIiIiIicrjmCvjLNfDiTdBRf/RxUUnw6V/Bl16ClDGRqy/ErFYLX1o4lrfuOotTx6X2O/b93bVc+IsV/HrFXrw+f4QqFBEZPhQMiYiIiIgMV4YB6/4IT5wKu/7d/9hJF8HtH8HcL4HFEpn6wqwgNZbnbzqVH31mOvFR9qOO6/T4WfLvHXz6VyvZVNYYuQJFRIYBBUMiIiIiIsNRYyn88Qp45Q5wNR19XEwKXPkbuOYFSMyNXH0R0t09tPSes7hgWla/Y7cdbObyJ1byo9e20ebyRqhCEZGhTcGQiIiIiMhw4vfD6t/Ckwuh+N3+x077DNz+CZz0+RHTJXQ0OUkx/ObL83n6v+aSmRB11HF+A373QQkXPvYe7+6sjmCFIiJDU9iDoZ/97GdYLBbuuuuuwGudnZ3cfvvtpKWlER8fz1VXXUVVVVWf40pLS7n00kuJjY0lMzOTb37zm3i9fVP95cuXM3fuXKKiopgwYQLPPvtsuD+OiIiIiMjgqdsLz30KXr8X3K1HHxeXAZ97Dj7/fxCfGbn6hoCLZuTw9r1n81+nFvY7rryxg6/8YTV3/mU9NS2uCFUnIjL0hDUYWr16Nb/+9a856aST+rx+99138+qrr/L3v/+dFStWUFFRwZVXXhl43+fzcemll+J2u/nwww957rnnePbZZ/ne974XGFNSUsKll17Kueeey4YNG7jrrru46aabeOutt8L5kUREREREIs/vg1VPwFOnw/4P+h970hfMLqHpl0ektKEoMdrBjy+fyT++upAJmfH9jn11YwWLfr6Cv60+oK3tRWRUClsw1NrayrXXXstvfvMbUlJSAq83NTXxu9/9jp///Oecd955zJs3jz/84Q98+OGHfPTRRwD85z//Ydu2bfzpT39i9uzZXHzxxfzoRz/iiSeewO12A/D0009TVFTEo48+ytSpU7njjjv47Gc/y2OPPRaujzQsXH/99VgsFiwWCw6Hg6ysLC644AJ+//vf4/drFwYRERGRYadyM/x+Mbz1bfB2HH1cQg5c81e48hmI7X+nrtFi/thUXv/aGdxzwSSctqP/6tPU4eFb/9zENb/5iOKafjqxRERGoLAFQ7fffjuXXnopixYt6vP62rVr8Xg8fV6fMmUKhYWFrFq1CoBVq1Yxc+ZMsrJ6Fo9bvHgxzc3NbN26NTDm0HMvXrw4cI4jcblcNDc397mNRBdddBEHDx5k3759/Pvf/+bcc8/l61//Opdddtlh0/FEREREZIjqbIY3H4Bfnw1lq/sfO+dLcNtHMPmiyNQ2jETZbXzt/Im88fUzOWVs/4HZR8X1XPTL9/nVO7txe/WlqoiMDkff0/EEvPDCC6xbt47Vqw//B6yyshKn00lycnKf17OysqisrAyM6R0Kdb/f/V5/Y5qbm+no6CAmJuaway9ZsoSHHnromD/Pwe9+F9eu3cd8XChFTZpIzo9+FNzYqCiys7MByMvLY+7cuZx66qmcf/75PPvss9x0003hLFVEREREToRhwJZ/wlvfgdbK/scmFcKnfwnjz4tMbcPYhMx4XrjlVP625gA/fWM7zZ1H/sLU7fXzv//ZxasbD7LkqpnMLUw54jgRkZEi5MHQgQMH+PrXv87SpUuJjo4O9elPyAMPPMA999wTeN7c3ExBQcGAx7l27aZj48ZwlhZ25513HrNmzeLFF19UMCQiIiIyVNXuNheWLlkx8NiTb4ZF34eohPDXNUJYrRauPqWQ86Zm8tCr23h908Gjjt1Z1cJVT33Il04dwzcXTyYh2hHBSkVEIifkU8nWrl1LdXU1c+fOxW63Y7fbWbFiBY8//jh2u52srCzcbjeNjY19jquqqgp0uWRnZx+2S1n384HGJCYmHrFbCMxOmsTExD630WTKlCns27dvsMsQERERkUO522HZD80t6AcKhVLHwfVvwKX/q1DoOGUmRPPEF+fyu+vmk5t09C+zDQP+b9V+Lvj5e/xn6wDdWyIiw1TIg6Hzzz+fzZs3s2HDhsBt/vz5XHvttYHHDoeDZcuWBY7ZuXMnpaWlLFy4EICFCxeyefNmqqurA2OWLl1KYmIi06ZNC4zpfY7uMd3nkMMZhoHFYhnsMkRERESktx1vwBML4P1Hwe85+jibE876Jnx1JYw9PXL1jWDnT81i6T1n85XTx9Lfj8mVzZ3c8se1fPWPa6lq7oxcgSIix8nj87CrfldQY0M+lSwhIYEZM2b0eS0uLo60tLTA6zfeeCP33HMPqampJCYmcuedd7Jw4UJOPfVUAC688EKmTZvGl770JR555BEqKyt58MEHuf3224mKigLgq1/9Kr/61a/41re+xQ033MA777zD3/72N15//fVQf6QRY/v27RQVFQ12GSIiIiIC0LAP/n0/7Pr3wGPHnQOXPArpE8Jd1agTF2Xn+5+azmdm53H/Pzexo7LlqGPf3FrJyj213HfxFL54SiFWq750FZHB1+5pZ1fDLrbXb2d73XZ21O9gd+NuXG2uoI4Py+LTA3nsscewWq1cddVVuFwuFi9ezJNPPhl432az8dprr3HrrbeycOFC4uLiuO666/jhD38YGFNUVMTrr7/O3XffzS9/+Uvy8/P57W9/y+LFi0Neb9SkiSE/Z6RreOedd9i8eTN33313iCoSERERkePidcGHj8N7/wveAbpPEnJg8U9h+hX029IiJ2x2QTKv3nkGv32/hF+8vQvXUXYla3F5efDlLby8vpwlV85kYpam84lI5DR2NrK93gx/ttdtZ3v9dvY378fAOO5zWgzDOP6jh7nm5maSkpJoamo6bL2hzs5OSkpKKCoqGnKLaPfn+uuvp6qqij/84Q/4fD6qqqp48803WbJkCeeccw4vv/wyNpvtsOOG6+cVERERGVb2vgtvfAPq9vQ/zmKDU2+Fc+7XOkKDYH9dG99+aTMr99T1O85hs3DrORO47ZzxRDsO/xlbROR4GYZBVXtVoANoe70ZAlW2Bb/ema/Dx/Zbtx8x8+htUDqGJLzefPNNcnJysNvtpKSkMGvWLB5//HGuu+46rNaQLyslIiIiIgNprjC3n9/64sBjC06Fy34OWdPDX5cc0Zi0OP504wJeXFfOj1/fRkP7kdd+8vgMHl+2m9c2VbDkipksGJcW4UpFZCTwG372N+8PBEA76nawo34HDa6GiFxfHUMjrGPoeI22zysiIiISET4vfPJrePen4G7tf2xsGlzwI5h1DejLvCGjrtXFT17fzovrywcce80pBdx/0VSSYrW1vYgcmcfnYU/jnp4QqN4MgTq8HSG/ljqGREREREQGU+lH8Pq9ULVlgIEWmP8VOO+7EJsakdIkeGnxUfz8C7O5fE4e33l5Mwfqj/7L218+OcDSbdX84NPTuHRmjnYEFhnl2j3t7GzYGZgO1r0otNfvDet1CxMKmZI6hbFRY/kaXxtwvDqG1DEEjL7PKyIiIhI2bbWw9Huw4c8Dj82ZbU4by5sX9rLkxHW4ffzi7V389oMSfP7+f406f0omP7x8BnnJMRGqTkQGU5OriW112wJTwUKxKPRA7BY745LHMSV1ClNTpzI1bSqTUyYT74wH+s88+pwnbBWKiIiIiIwmfh+sew7efgg6G/sfG5UEi74H874CVi1aPFzEOG08cMlUPjUrlwde3Mzm8qajjl22o5qPfr6Cey+czJcWjsFh0/RAkZGiw9vB9rrtbKndYt7qtnCg5UBYrxlti2ZS6iSmpk4NBEETUiYQZYs64XMrGBIREREROVEV6+G1e6Bi3cBjZ10DF/wQ4jPDX5eExYy8JF667TSeW7WfR/+zk3a374jj2tw+fvjaNv788X6+c+lUzp2cqellIsOMx+9hT8MettRtCQRBexv34jOO/Pc+FBKcCWYHUOpUpqSZIdDYxLHYwvRFgoKhAYyWmXaj5XOKiIiIhFRHI7zzY1j9WxhoukDGVLj0URh7eiQqkzCz26zceEYRi6dn8eDLW1i+s+aoY/fWtHHDs2s4c2I637l0KlOyjz6lQ0QGj9/wU9pc2icE2lG/A5fPFbZrZsZkMiVtClNSpzAtdRpT0qaQG5cb0RBZwdBR2GxmEud2u4mJGfnzgt1uN9DzuUVERESkH4YBm/4K/3kQ2o4eCADgiINz7odTbwWbdqsaafJTYvnD9Sfz2qaDPPTqVmpb3Ucd+/7uWi755ft84eRC7rlgEhkJJz4FRESOX1VbFVvqtrC1diubazeztW4rLe6WsF2ve1HoqWnmdLApqVNIj0kP2/WCpcWnj7IQk2EYlJaW4vF4yM3NxTqCtwz1+/1UVFTgcDgoLCxUe6uIiIhIf6q2wRvfgP0rBx477TOweAkk5YW/Lhl0je1ulryxg7+uGXitkTinjdvOncCNZxQR7dCXsyLh1uRqYmvd1p4QqHYr1R3VYbmWzWJjXPK4nulgqVOYnDqZBGdCWK53NMEuPq1gqJ8/JLfbTUlJCX6/fxCqiyyr1UpRURFOp3OwSxEREREZmlytsOJn8NFTMNBWw6nj4JL/gQmLIlObDCmr9tbx0Ktb2VE5cOdBXnIM37poMp+eFdmpIyIjWae3kx31O9hSuyXQCbS/eX9YrmW1WBmfPJ4ZaTOYkT6D6WnTQ7Yo9IlSMBSEYP6Q/H5/YJrVSOZ0Okd0V5SIiIjIcTMM2P4KvPkANJf3P9YWBWfeC6d/HRzRkalPhiSf3+Bvaw7w6H929ju9rNucwmQevHQa88akRKA6kZHD6/eyt3FvnxBod8PusC0OnR+fz4z0GYHb1NSpxDpiw3KtE6VgKAjB/iGJiIiIyChVtxfe+CbsXTbw2IkXwsUPm91CIl1aOj08tXwvv/2gBLd34JkIl52Uw30XTaEgdWj+oiky2KraqthQs4GNNRvZUruF7XXb6fR1huVaadFpfUKg6WnTSYkePuGtgqEgKBgSERERkSPydMAHj8EHv4CBdqNJzDcDoSmXgqYCyVGUNbTzyJs7eWVjxYBjnXZzx7PbzhlPQrQWLJfRy+PzsKN+BxtrNgbCoMq2yrBcK84Rx/S06YEQaGb6TLJis4b1FE8FQ0FQMCQiIiIiffj9sO0lWPZDaNjX/1irHU67E876JjjjIlKeDH/rShv48WvbWFfaOODYtDgn91w4iS/ML8Bu07IPMvLVdtSysXpjIAjaVrctLFvFO6wOpqRO6dMNNDZxLFbLyPp7pmAoCAqGRERERAQw1xHauwzefggqNw08fuyZcMn/QuaU8NcmI45hGLy26SA/+/cOyhs7Bhw/OSuB71w6lbMmZUSgOpHI8Pg97GrYFQiCNtZspLx1gHXcjoMFi7k4dPoMc4HojBlMSp6Ewzbyu/EUDAVBwZCIiIiIULYG3v4B7Ht/4LFxmbD4pzDzs5o2Jies0+Pj9ytLePLdvbS6BtjpDjhncgbfuWQqE7Miu+W1SCjUd9azqWYTG6rNKWFb67bS4R04GD1WefF5PSFQ+gympU0bsotDh5uCoSAoGBIREREZxWp2mlPGdrw28FiLFU65Bc79NkQnhb82GVVqWlw89vYuXvikFP8Av53ZrBauOaWAuxdNIi1+8LfDFjkSn9/HnsY95pSwriCotKU05NdJiUphZsbMnq3i06eTGp0a8usMVwqGgqBgSERERGQUaiqD5Utgw/NgDLxLFPknw6WPQs6s8Ncmo9qOymZ+8vp23t9dO+DYhCg7d5w3getPH0uU3RaB6kSOrsnVFJgOtrFmI5trNtPubQ/pNawWKxOTJzIrYxazM2czK2MWBQkFw3px6HBTMBQEBUMiIiIio0h7Pbz/KHzym4F3GgNIHgPnfgdmfg6sI2tBUhm6DMNg+a4afvL6dvZUtw44viA1hgcunsrFM7L1C7JEhN/wU9xYHNglbGPNRkqaSkJ+nURnIrMyZpm3zFnMTJ9JnEML/R8LBUNBUDAkIiIiMgq42+CjJ2Hl4+BqHnh8XAac9S2Ydz3YnWEvT+RIvD4/f/mklMfe3k19m3vA8fPHpPDgZdOYXZAc/uJkVGn3tLOpdhPrq9azoWYDm2s20+JpCek1uheI7h0EjcRdwiJNwVAQFAyJiIiIjGBeN6x7DlY8Am3VA493JsDpX4NTb4Oo+PDXJxKEpg4PT767hz+s3IfbN/DUx8tn5/Kti6aQmxwTgepkJKrvrGd91XrWVa9jXdU6ttdvx2f4QnqNeEc8J2WcxOwMc0rYzIyZJDi1qHqoKRgKgoIhERERkRHI74etL8I7P4KGfQOPtznNhaXPuAfi0sJensjx2F/XxsNv7uCNzZUDjo2yW7nlrHF89ezxxEXZI1CdDFeGYVDWWsa6qnWsr17P2qq17GveF/LrFCUVBbqBZmfMZlzyOHUDRYCCoSAoGBIREREZQQwD9iyDZT+Ays0Dj7dYYdYX4Zz7Ibkg7OWJhMLqffX86LVtbCprGnBsRkIU37xwMlfNy8dm1fpDYu4WtrtxN2ur1rK+ej3rq9ZT3RFER+UxiLXHMjNjZs+0sIxZJEVpN8fBoGAoCAqGREREREaIA6vh7R/A/g+CGz/lMjjvu5A5JaxliYSD32/wr43lPPLmTg42dQ44fmpOIt+9dCqnTUiPQHUylLh8LjbXbDa7garXsrF6I62egRc1PxaFCYV9dgqbkDwBm1U75Q0FCoaCoGBIREREZJir3mFOGdvxWnDjx5wBi34ABSeHtSyRSOhw+/jt+8U8tWIv7e6B14BZNDWTBy6ZyvgMraE1UjW5mthQvSGwPtDWuq14/J6QnT/aFs2M9Bl9FolOjU4N2fkltBQMBUHBkIiIiMgw1XgAli+BjX8BY+AFecmeCef/ACacD9rSW0aYquZOHv3PTv6+toyBfruzWS186qQcbj1nApOztdjvcFfZVhmYFra2ai17GveE9PwpUSnMyZzD3Ky5zM2cy5S0KTisjpBeQ8JHwVAQFAyJiIiIDDNtdfD+o7D6N+AbeAtvUsaaU8amXwlWLXQqI9vWiiZ+/Np2VhXXBTX+/CmZ3HbueOaNUcfHcOA3/BQ3FpvdQNXrWF+1noq2ipBeIy8+j3lZ8wJhUFFiERaF6cOWgqEgKBgSERERGSZcrfDRk7DycXC3DDw+LhPO/hbMvQ7szvDXJzJEGIbB29ur+ekb2ympbQvqmFOKUrn1nPGcMylDIcAQ4vF52Fq3lfXV681dw2rW0+QaeNHxYFmwMCllUqAbaE7mHLLiskJ2fhl8CoaCoGBIREREZIjzumHts/DeI9BWM/D4qEQ4/etw6q3gjAt7eSJDldvr508f7eeXy3bT1BHcGjPTchK59ZzxXDIzR7uYDYJObyebajaxtmota6rWsKlmE52+gRcXD5bT6mRG+oxAEDQrcxaJTv0ePJIpGAqCgiERERGRIcrvhy3/gHd+DI37Bx5vi4IFt8AZ90CspsWIdGtsd/P4sj3836p9eP3B/eo3Ji2W/z5rPFfNyyPKrt2lwqXd086G6g2sqVrD2qq1bK7dHNKFohOcCczJnMOczDnMy5rHtLRpRNmiQnZ+GfoUDAVBwZCIiIjIEGMYsPs/sOyHULVl4PEWK8y+Fs65H5Lyw1+fyDBV0djBb94v5oVPDtDhGXgHM4DMhChuOrOILy4YQ3yUPcwVjnwt7hbWV683g6DKtWyr24bX8Ibs/FmxWYFuoLlZc5mQPAGrRWurjWYKhoKgYEhERERkCCn9GN7+AZR+GNz4qZ8yF5bOmBzWskRGkvo2N89+uI/nPtwX9BSzxGg71502lutPG0tavDpOgtXY2cja6rXm1LDKNexs2Ik/mF0UgzQ+aTxzs+YGOoJy4nK0RpT00VRfT3JamoKh/igYEhERERkCDqw2dxrb9e/gxo89ExY9BPnzwluXyAjW6vLyl49L+e0HxVQ1u4I6Jtph5eqTC7n5rHHkJceEucLhp7ajNhACra1ey+6G3SE7t91iZ1r6NLMbqGuh6OTo5JCdX4Ynw+/HW1ODp6wMT3k57rIyPGXl5vOyMhoqKjhl5w4FQ/1RMCQiIiIySPw+2P4qrHoCyj4J7pjsk2DRD2D8eaBvxUVCwuX18dK6cp5esZd9de1BHWO3WvjM7Dy+evY4JmYlhLnCoauqrSqwPtCaqjWUNJWE7NzRtmhmZc5iXtY85mfNZ0b6DGLsCuNGG8Mw8DU2BoIed1cAFAh/Kiow3O6jHt/q83HKnt0KhvqjYEhEREQkwlwtsP5P8NFTwS0qDZA6Ds57EKZdAVatlyESDj6/wb+3HOSp5XvZWtEc9HEXTsvi1nPGM6cwJYzVDQ3lreWsqVwTCIMOtBwI2bnjHHHMzpzN/Kz5zM+az/S06ThsjpCdX4YuX2sbnvKyXuFPeVf4Yz73twcX2B6JgqEgKBgSERERiZCmMvj4aVj7HLiC/KUzPgvOvg/mfhn0C5JIRBiGwXu7a3lq+R4+Kq4P+riF49K47dzxnDEhfUSsc2MYBqUtpX2CoINtB0N2/kRnInOz5gaCoMmpk7FbtcD3SOR3ufCUVwTCnz7TvcrL8TU2hu3aCoaCoGBIREREJMzK15nTxba+BEZwOyERlQRn3AUL/huccWEtT0SObl1pA0++u5e3t1cFfczMvCRuPWc8i6dnY7MOn4DIMAyKm4r7BEE1HTUhO39KVIo5LSzbDIImpkzUjmEjhOH3462uxr2/FE/ZgcPW+fHWhO7/R8dKwVAQFAyJiIiIhIHfby4kveoJ2L8y+OOik+HkG2HhHRCbGrbyROTY7Kpq4enle/nXxgp8/uB+fRyXHsdXzx7P5XPycNqHXgDiN/zsadzTJwiq7wy+Q2ogGTEZzM+aHwiDxiWNGxGdVKOV4fXiqajAvb8U94FSPPtLcR84gLt0P54DZRiu4BZwjwi7HUduLs78PDrTM5j8P48oGOqPgiERERGREHK3wYbn4aMnob44+ONSx8Gpt8HsL6pDSGQIO1Dfzm/fL+aF1QdweYPbdj07MZqbzizimlMKiYsavKlSfsPP7obdrK5cHQiCGl2NITt/TlxOnyCoMKFQQdAw43e58Bw4gLu0FHdpKZ7SUtyl5nNPRQV4vYNdosliwZ6djSMvF2dePo588+bMz8ORl4c9KwuLzQYEn3koGFIwJCIiInJimg/C6t/Amt9DR0PwxxWeBqfdAZMuAqstfPWJSEjVtrr4w8oS/m/Vflo6g/tlOTnWwXULx3L9aWNJiXOGuULw+X3sbNjZpyOo2R38otoDKUgo6BME5cXnhezcEj6+1tauwKc79NmPpyv88VZVwRCJR2xpaTjy88zgJy+vK/zJw5mfjyMnB4szuL9DCoaCoGBIRERE5ARUbjani23+B/g9wR1jscH0K2DhbZA3L7z1iUhYtXR6+PPHpfz2/RJqW4ObShPrtHHNKYXcdGYROUmh237d6/eys34na6rWsLpyNeuq1tHiaQnZ+YuSinqCoKz5ZMVlhezcEjqB7d337zeneu0v7RP++OpDN13wRFgTEnp1+eT3BD95ZtePNTY2JNdRMBQEBUMiIiIix8jvhz1vw6pfQcmK4I+LSoR518Ep/w3JBeGrT0QirtPj45/ryvj1imJK64PbWtths3DFnDz+++zxjM+IP+Zrev1ettdtZ3XVatZUrmFd9TraPG3HfJ6jmZgyMbBj2NysuaTHpIfs3HJiDMPAV1eHu6TE7Pzpve5PaSn+1tbBLhFLdPTRO37y87FFKH9QMBQEBUMiIiIiQfJ0wKa/wqonoXZn8MclFcKpt8LcL0FUQvjqE5FB5/X5eWNLJU++u4cdlcF36ywoSuWz8/K5ZGbOUdch8vg9bK3dypqqNaypXMP66vW0e4MLoQZiwcKU1CmBaWHzMueRHJ0cknPL8fO1tuHevw93yT7c+/rehkL4Y0tNxVlQgGNMIc7CMTgLC3AUFOAsKMCWljYk1phSMBQEBUMiIiIiA2itgdW/NW/ttcEflzffXD9oyqfANngLzopI5BmGwfKdNTy5fA+r9wW/7lis08YlM3P47Lx85hQksK1hK2sqzalhG2o20OHtCEl9VouVKalTmJ81n5OzT2ZO5hySopJCcm45NobbjbusvCf0KSkJPB7Mbd672bOycBYWmuFPQSHOMYVm+FNYiC1h6H/ZoWAoCAqGRERERI6ieoc5XWzT38AX5Da8FitMuQxOuxMKTglvfSIyLKzeV89Ty/fyzo7q/gdavNiiD2CLK8YWW4I9dj9Ygly7bAA2i42pqVM5Oftk5mfPZ07mHBKcQ/+X+pHCMAy8VVW9wp99uPaZAZCnrBx8vsErzmYzt3YvLMRRWGB2/owpNDuBCgqwRkcPXm0hEGzmEfKvb5YsWcKLL77Ijh07iImJ4bTTTuPhhx9m8uTJgTGdnZ3ce++9vPDCC7hcLhYvXsyTTz5JVlbPAl6lpaXceuutvPvuu8THx3PdddexZMkS7PaekpcvX84999zD1q1bKSgo4MEHH+T6668P9UcSERERGR0MA4rfNReU3vN28Mc54mDul2HBf0NqUfjqE5Fh5+SxqZx8fSrbDzbz9Iq9vLqxAr8BWDzYYkqxxZZgiy3GFlOKxRqa7cBtFhvT06YzP9tcI2hO5hzince+jpEcG19zc6DjxxWY9rUf9759GB2h6fY6HhaHA0ehGfY4xxSaj7tujtxcLA7HoNU2VIQ8GFqxYgW33347J598Ml6vl29/+9tceOGFbNu2jbi4OADuvvtuXn/9df7+97+TlJTEHXfcwZVXXsnKlSsB8Pl8XHrppWRnZ/Phhx9y8OBBvvzlL+NwOPjpT38KQElJCZdeeilf/epX+fOf/8yyZcu46aabyMnJYfHixaH+WCIiIiIjl9dl7iy26gmo3hr8cYl5Zhg09zqISQ5beSIy/BWm2/jCmR1kFOxkacmH1Hn2YLGGplPEbrEzI30G87Pnc3LWyczOnE2sIzS7OklffpcLT2mpGfwcsvbPYO74ZY2NxTFmTE/4U9DT/WPPysJitQ5abcNB2KeS1dTUkJmZyYoVKzjrrLNoamoiIyOD559/ns9+9rMA7Nixg6lTp7Jq1SpOPfVU/v3vf3PZZZdRUVER6CJ6+umnue+++6ipqcHpdHLffffx+uuvs2XLlsC1rr76ahobG3nzzTeDqk1TyURERGRUa6+HNb+DT34DrVXBH5czCxbeCdMvB5u+aRWRwzW5mlhfvZ61VWtZU7mG7fXb8RmhCYIMw4bdM4YZaXP4/PRzWDTuFAVBIeatr8ddXIyruBj33mJcJcW4i0vwlJeb3aWDwOJ04hwzBufYsb1uY3COGTNkFnseagZtKtmhmpqaAEhNTQVg7dq1eDweFi1aFBgzZcoUCgsLA8HQqlWrmDlzZp+pZYsXL+bWW29l69atzJkzh1WrVvU5R/eYu+6666i1uFwuXK6eOfLNzc2h+IgiIiIiw4dhQMV6WP9H2PAXCHoxVwtMvhgW3g5jTgf9AC4ivdR21LKuap0ZBFWtYXfDbgxCEyAYfhu+jkJ87ePwtRfh6ygEw8kHe+GDT9o4ddxGPjuvgItnZB91VzM5nOHz4amowLV3L+7iElzF5r27uBhfY+PgFGWxmGv+FBUdEgCNxZGTjcVmG5y6Rriw/q3x+/3cddddnH766cyYMQOAyspKnE4nycnJfcZmZWVRWVkZGNM7FOp+v/u9/sY0NzfT0dFBTEzMYfUsWbKEhx56KCSfTURERGRYadgPm/9mLiZduyv44+wxMPuLcOptkD4hfPWJyLBS2VYZ2Dp+bdVa9jXvC9m5LdjxthfibesdBB29O/Gj4no+Kq7ne//aEtjV7JSxqVitCrAB/B0d5ro/e4vNLqASswvIvW8fhts9KDXZUlN7Qp8i8z5q7FgchYVYo6IGpabRLKzB0O23386WLVv44IMPwnmZoD3wwAPcc889gefNzc0UFBQMYkUiIiIiYdTRAFtfNsOg0g+P7dj4LDjlZph/I8SmhqU8ERkeDMPgQMuBQDfQ2qq1lLeWh+z80bZoZmXOCmwfPzN9Ji0dBv/aUMHf15axvT24mR7tbh//WFvGP9aWUZAaw1Vz87lqbj4FqSN/mplhGPgC079KcBfvNe/37sVTUTEoNVliYnqme3UFP86iInPqV1LSoNQkRxa2YOiOO+7gtdde47333iM/Pz/wenZ2Nm63m8bGxj5dQ1VVVWRnZwfGfPLJJ33OV1VVFXiv+777td5jEhMTj9gtBBAVFUWU0kcREREZybwu2L0UNr0Au94C3zF+G5w5HU67A2ZcBXb93CQyGvkNP8WNxX2CoJqOmpCdP9Yey5zMOczLmse8rHnMTJ+J45D1ytLi4YYzirjhjCK2VjTxz7XlvLyhnPq24P6bdqC+g1+8vZtfvL2bU8eljpipZobPh6e8/MjTv7qWcYkomw1nfv5h3T/OoiLsmZla92eYCPnfCsMwuPPOO3nppZdYvnw5RUV9tyydN28eDoeDZcuWcdVVVwGwc+dOSktLWbhwIQALFy7kJz/5CdXV1WRmZgKwdOlSEhMTmTZtWmDMG2+80efcS5cuDZxDREREZNQwDDjwMWz6K2x5ETobj/0cEy4w1w8ad47WDxIZZXx+Hzsbdgamha2rXkejqzFk5090JjI3ay7zs8zt4yenTsZuDf5X0em5SUzPTeL+i6fw7s5q/rG2jHd3VOP1B7eG0XCcaubv6MBdUnJY9497//5Bmf5lS03FOa6IqKJxfdf/yc/D4nRGvB4JrZDvSnbbbbfx/PPP869//YvJkycHXk9KSgp08tx666288cYbPPvssyQmJnLnnXcC8OGHZouzz+dj9uzZ5Obm8sgjj1BZWcmXvvQlbrrppj7b1c+YMYPbb7+dG264gXfeeYevfe1rvP7660FvV69dyURERGRYq91jhkGb/gqN+4/9eGc8zLjSXD8oc2ro6xORIcnj87C1bmugG2hD9QZaPa0hO39adBrzs+cHOoImJE/AagntduG1rS5zqtmaA+yobDnm44fKVDNfa5sZ/OzZi2vvHnMHsL178ZSVRX73L6sVR34+UUVFOMeNI2r8OJzjzCDInpIS2VokJILNPEIeDB2tVewPf/gD119/PQCdnZ3ce++9/OUvf8HlcrF48WKefPLJwDQxgP3793PrrbeyfPly4uLiuO666/jZz36G3d6TLC9fvpy7776bbdu2kZ+fz3e/+93ANYKhYEhERESGnbZa2PJPMwwqX3vsx1tsMOF8OOkLMPkScI78tTdERrt2TzubazcHdg3bWLORTl9nyM6fE5fD/KyeIGhM4piITiHaWtHEP9aW8a8NFUFPNestElPNfI2NuIqLce3Zg3tvVxBUXIz34MGwXK8/lujonu6fcUVEjR+Ps2gczrFjtPDzCDNowdBwomBIREREhgVPB+x8w1xEes/b4Pce+zly55ph0IyrID4j9DWKyJBR1VbF+pr1bKjewPrq9eys34nP8IXs/GMTxwZCoHlZ88iNzw3ZuU+E2+s/rqlm3WKdNi6akc2F07I4fUI6CdFH3wntSLoXgA50/+zZi2uvefPV1h7TuULBlpZmdv+MH0/UuK4uoHHjsOfkYLGGtoNLhiYFQ0FQMCQiIiJDlt8P+943w6Bt/wL3sU+VILnQDINmfh4yJoW+RhEZdD6/jz2Ne1hfvZ711WYYVNEW2l2oJqZMZF7mPOZlz2N+1nzSY9JDev5wONGpZnarhZPHpnLO5AzOnZLJxMz4QBeUYRh4q6rMBaC7u3/27sW9Z0/kF4C2WnEU5JvdP+PN4MdZNI6ocUXYem32JKOTgqEgKBgSERGRIadqm7mj2OZ/QPNxbAcdnQTTrzQDoYIFoG+FRUaUNk8bm2o2BbqBNtVuos3TFrLzWy1WpqZODXQDzc2cS3J0csjOPxiOd6qZxfCT2d5AYUs1M7z1zDUaKGiuIrqiFKMtdH/mQdUSExNY+8c5roioceNxjjMXgbZq8Wc5CgVDQVAwJCIiIkNC80HY8g/Y+Feo2nzsx1sdMGkxzLoaJl6obeZFRpDKtso+3UA7G3biN/whO7/damdm+kzmZZndQLMyZhHvjA/Z+YeSo001sxp+stvqKGypMm/N5n1BazXRPk9Ea7QmJRE1frx5mzAe57jxRI0fhz07W9O/5JgFm3mEZ2UtEREREemfqxW2v2ouIl2yAo7nF73ChXDS52Ha5RCbGvISRSSyvH4vuxt2B0Kg9TXrqWyrDOk1om3RzMqYFZgWNjN9JtH26JBeY6hy4OecmHYWZtfTUF/M/nVbcO/dS0ZDJc7jWbvtBNjS07sCoHHmGkDjJxA1YTy2tLSILtwtAgqGRERERCLH54Xi5eZUsR2vg6f92M+RNgFOuhpO+hykjA11hSISQa3uVjbVbAosFL2pZhPt3uP470I/0qLTmJM5h9mZs5mdOZtpqdNw2I5tUeXhxnC7cZeWmmv/7NnTsxD0vn3g6ekAisQy/PbsbHPXr/HjAuGPc9w4bf8uQ4qCIREREZFw8rqhdBXs/Le5zXxb9bGfIzYdZn7W7A7KnQv6Nllk2DEMg4NtB/tMC9vduDuk08IAJiRPYHbmbOZkzmFOxhzyE/JHbAeK3+3GvW8f7j17AgtAu/bswb1/P3gj2wF0MDaV0oQsShOyOJCQ2XWfRXZOGudMzuScyRmcUpRKtMMW0bpEgqFgSERERCTUGkth91LYs8ycJuZuPfZz2KNhyqVmd9D4c2GEf8MvMtJ4/V52NuwMLBK9vno91e3HEQz3I9oWzYz0GYGOoFkZs0iKSgrpNYYCv8uFu6Sk1zbwZhDkLi0Fny9idfiwcDA+PRAAlXYFQGXxGbiOsrZbcW0bxbUl/H5lCTEOG6dPSOPsyZmcOzmD/JTYiNUu0h8FQyIiIiInyuuC/Sth99uw522o3XmcJ7JA0VnmjmJTPwXR2hxDZDgwDIOy1jK21G4J3LbXb6fD2xHS62TEZJhTwjLMjqApaVNwWEdOaOzv6DADoL17ce3eE9gC3n3gAPhD21nVL4eDqLFjcI6fEFgHyDF+PHudqeza18S7O6pZV9qA/xi3cerw+Hh7ezVvbzcDwomZ8Zw7JZNzJmUwf2wqTrsWl5bBoV3JtCuZiIiIHI/6EjME2vM2lLx3fOsFdcucDrO+ADM+C0l5oatRRMKirqOOrXVb2Vy7mc21m9lau5VGV2NIr2HBwoSUCczJmBOYGpYXnzcipoX529pwFZeY3T979wamgXkOHIAI/npqcThwjhtH1ISutX+6dgNzFhZicfQfuDW2u3l/dy3v7qxmxc4a6trcJ1RLnNPGGRPTA9POcpJiTuh8IqDt6oOiYEhERESC5umAfSu7wqClULfnxM6XkAMzP2d2B2XPCE2NIhJy7Z52ttZtZWutGQRtqd1CRVtFyK8TY49hZvrMQAh0UsZJJDqH9+8ovsZGXMXF5ro/e4vNTqDivXgrDka0DktUVE8A1LUNfNSECTjy87HYT3wSjd9vsLm8ieU7a3h3ZzUbyxpPON+akp3AuVMyOXtSBrMLkrU2kRwXBUNBUDAkIiIi/arb27VW0Nuw733wdp7Y+aKSzHWDZn0Bxp4JVv2gLzKUePwedjfsZkvtlkAIVNxUHPIFogEyYzJ7FonOnMOk1EnDclqYYRh4a2pwFxeb6/4Ud3UAFRfjq62NaC2WmBiixo3r6v6ZEOgEcuTlYbFF7r+3da0u3ttdw7s7anhvdw2N7Z6BD+qH3WphSk4CswuSmVOQwuzCZIrS4rBah3/3mISXgqEgKBgSERGRPtztZgC0520zEGooOfFzZp8EEy+ACRdA/slg0xKPIkOB3/BT2lzKlrotgSBoR90O3P4TmxJ0JBYsTEqZFAiCZmfOJjcud1hNCzP8fjwVB3Hv3YNrb3HXNLBiXMXF+JubI1qLJTa2a+0fs/vHOcEMgRy5uVisQ2udHp/fYMOBRpbvrGb5zho2lzeF5LxJMQ5mFSSbYVFhMrPzk0mJc4bk3DJyKBgKgoIhERGRUc4woHa3OTVsz9vmVDGf68TOGZ0M488zw6Dx50NCVkhKFZETU9NeE+gC2lK7hS11W2hxt4TlWqnRqUxPm86M9BnMzpjNSRknEe+MD8u1Qs3wenGXHugJfvbuNdcBKinB6AjtYtoDscbF4Zwwnqhe3T9R48djz8kZcgFQsKpbOlmxs4blO81uopZOb8jOXZQex+xeYdGU7EQtaD3KKRgKgoIhERGRUcjVai4W3R0GNZae+Dlz55gdQRMvgNy56goSGWQt7ha21W3rEwRVtVeF5Vox9himpU1jZvpMpqdPZ2b6zGHRDRTYAr47+NlbbE4D27cfPCc29elYWRMSzEWfu9b+MYOg8dizs4f8n+OJ8Pr8rCtt5N2ubqLtB0PbeeW0W5mRm8icwpRAYJSfEjOi/0ylLwVDQVAwJCIiMgoYBtTs6FkrqHQV+E5wqkhMKkw43wyDxp8H8RmhqVVEjlmLu4VdDbvYUb8jEAaVNIVgGugR2C12JqZMZEb6jEAQNC5pHHbr0A2DfY2NuEpKcJfsM4Ofri4gT1lZZLeAB2ypqUSNG2cGQOPGB3YCs2dmKqwADjZ1sKJrAesPdtfS5vaF/Brp8VGBjqI5BcmcVJBMfNTQ/f+vnBgFQ0FQMCQiIjJCtdWaAdCet2H329BcdoIntEDevJ61gnJna+FokQjzG37KW8vZVb+LnQ072VG/g10NuyhvLQ/bNcckjmF6mtkFNCN9BlNSpxBtjw7b9Y6X4fHgLiszw5+S4l5BUDG+hoaI12PPzjY7gMaP6xsApaREvJbhyu31s7GskfWlDWw40Mj60kYONp3gBghHYLHApMyEnrWKCpOZmJmATQtbjwgKhoKgYEhERGQE8LqhajOUrem6rQ7NotGx6TBhUddaQedBbOqJn1NEgtLh7WBPw54+AdCuhl20edrCds206DRmZsw0Q6C0GUxPn05SVFLYrnc8vA0NgfDHXVKCq2Qf7pIS3KWl4A3dWjVBsVhwFBR0LQI9ztwFbPw4nOPGYYsfHuspDTeVTZ1sONDA+gONbChtZFNZEx2e0HcVxTltnJRvhkTdgVFmwtALRGVgCoaCoGBIRERkmDEMaC43w5/uEKhiw4kvGA1gsZq7hk24ACYuguxZMEwXNxUZLgzDoLq9mp0NO9lZvzNwX9pSGpYt4rvFOeICi0N3dwNlxWYNielMhseD+0BZr/CnV/dPY2PkC3I4iBo7Bue48T1dQOPH4xw7Fmu0woLB5PX52VXVyvoDDWwobWTDgUb21LQSjt/w85JjzKAoP5lJ2QlMyIwnNyl6SPydkaNTMBQEBUMiIiJDnLsdDm7oCoK6wqCWg6E7f3yW2RU0YRGMO0ddQSJh5PF52Nu0NxAAdU8Ja3Q1hvW6dqudKSlTAgtDz0yfydiksVgtgxv8mt0/JbiL+079cpeVRb77B7BER5vr/4zvHQBNwFmQj8XhiHg9cnyaOz1sOtBkdhZ1hUV1bSe4rt5RxDptjM+IZ2JmPOMz45nQdRuTGovdpi9WhgIFQ0FQMCQiIjKEGAbUF/cKgVZD5RYwQtgmb7FBwQKzI2jCBZA1Q11BImFQ31nPzvqd7GrYFQiCihuL8RrhDzyKkooCXUAz0mYwOXUyTpsz7Nc9EsPtNtf+OTT8KSnB19Q0KDVZExMPXwB63HgcucN3C3g5OsMwOFDfYXYVda1VtK2iGbcvfB15DpuFovQ4MyjK6AmNxmfEE+3Q+nyRpGAoCAqGREREBlFnE5Sv7ZkSVrYGOupDf52EXHMHsYkXmF1B0UNrzRCR4czn97G/ef9hU8FqOmrCfu1oWzQTUyYyKWUSU1KnMDl1MhOTJxLvjOz6Nn63G09ZGe7SUjylpbj3l+IuLcVduh9PWTn4Qr8GzICsVhx5eTjHFRE1tghnUZH5uKgIW3q6pv+Mci6vj20VzWw40BgIi0rr28N+XYsFClJiA51FvW+J0epKCwcFQ0FQMCQiIhIhfp+5ZXzvKWE1O4FQ/xhigcypkD/fXC8o/2TImGL+NCoix83tc1PaXMq+5n2UNJVQ0lRCcVMxexr34ArFGl8DyIrNYnLqZCanTGZS6iQmp0ymMKEQW4R2B/R3dOAuPWCGPaWlPY/3l+I5eJCwLOoSBGtCwhHDH8eYMVidg9MlJcNTXauraxc0MyzaUNpIiytyUxozE6KYkGlOS5vQa2paRnyUgswToGAoCAqGREREwqS1BsrX9ARB5evA3Rr668SmdQVAXUFQ7lyI1r/pIserobOBkqaSPgFQSVMJZa1lYV0MupvdamdC8gQmpZjhT3cYlBydHPZr+1pajtz1U3oAb3V12K9/VFYrjoL8w8IfZ1ERtrQ0/dIsYeH3GxTXtrK+tDGwC9rOqhZ8/sjGB4nRdiZmJTAho2+HUV5yDFar/r8/EAVDQVAwJCIiEgJttWY3UOWWnjCoYV/or2O1Q/bMnk6g/PmQUqRuIJFj5PV7KW8tNwOgpn2UNPcEQOFeCLq31OjUvgFQ6mSKkopwWMMzpcQwDHyNjV0dP93hj9n14y4txdfQEJbrBsualETU2LFd4c84nEVjze6fwkJ1/8iQ0OH2sbemlb01reypNm+7q1vZV9uGN8KBUbTDyviusCg/JYa85FjyUmLIS44mNzmGWKc9ovUMVQqGgqBgSEREJEiGAW01ZgBUsxOqt5v3NduhvS4810zM6zslLGcWOGLCcy2REajF3cK+pn2Hdf/sb9mP1x+5KSJWi5WxiWMD3T/d9+kxoV/rxjAMvDU1PV0/B/p2APlbWkJ6vWNms+HMzz8s/HGOG4ctJUXdPzIseXx+9te1d4VFLeZ9TSt7q9vo8AzCGltAapyT3ORo8pLN0Cg3OToQIOUmR5Ma5xwVf9+CzTwUo4mIiEgPw4DWajPw6RMA7QjPwtDd7NGQO6cnCMqbD0l54bueyAjhN/xUtlUecfpXJBaAPlSCIyGwBlB3ADQ+eTzR9uiQnN8wDHz19XgqKvCUl+Mp77qvqMBTUYH7wAGMjo6QXOtE2DMzcRYW4igs7Al/iopwFhRgUfePjDAOmzUwxQuyA6/7/QYVTR3srm5lb3XfLqOmDk9Ya6pvc1Pf5mZLefMR349x2Mjt6i4yA6MYcpPN+7yUGLITo7HbRs8ufeoYUseQiIiMRoYBLZVdHUBdt+qu+87G8F8/dVzfKWFZM8CmHUlEjqbV3UpZa5k59as7/Gk2p4J1+jojXo/damdMwhiKkooCQdCU1CnkxOWc0Lfwht9vdvyUV/SEPxV9wx+jM/Kf9zAWC46cHBxjCnEWjukKgQrMxwX5WGNjB7tCkSHLMAxqW929Oota2d3VaVTVHP7F7INhtUB2YjR5KX0Do9zkGPK7QqS4qKHfZ6OOIRERETEDoOaKnilgNb06gDqbIlODMwHy5/UEQXnzIS4tMtcWGSY6vB1UtFZQ3lpu3lrKqWiroKyljPLWcprdR/7WO9ySopIYlzSOsYljKUoqCtzy4vOwW4/9VwnD68VbVYW7V9DTE/5U4D14EMMT3k6CoNntOPJyA8GPc0xXB1DhGBz5eVr3R+Q4WSwWMhKiyEiIYuH4vj8PNHd6+nQXdYdHpfXtEd38z29ARVMnFU2dwJHXH0uOdfTpNMrvCo4yE6JIj48iPSGKOKdtWExZU8eQOoZERGQkMAxoLu/p+gncdoIrkr9QHmG7+PRJEKEtpUWGKo/Pw8G2g5S1mkFPRWsF5S3lgSCorjNMa3UFwWqxkh+fz9iksRQlFvUJgFKiU47pXH63G+/Bg4cFPoHHVVXgG5w1R47E4nSanT4FhWbXT3cH0JhCHDk5WOz6Hl1kKOj0+CipbWN3V1jUHR7tr2+j0xP+HROPV7TDaoZEXbeMBCdpcVGkxztJT+j1enwUiTH2kIdI6hgSEREZaQzDnObVWAqNB6C+uKf7p2YnuCO8qGpiPmROgYwpkDEZMqaa99ouXkYhn99HVXtVT8dPV9dP9+Pq9moMBvf72Fh7bJ/QpyipiKLEIgoTC3HaBu5+Mfx+fHV1eKqq8VZX462u6pny1RUEeWtqiOjX+kGwxMaaHT/dXT8FBYHwx56VhcU6etYRERmuoh02puYkMjWn788YhmFQ3+amorGT8sZ2yho6Ao/LG83H9W3uQaoaOj1+yho6KGsYeO0zp81KWryzKyxyBrqO+jzvepwS68RqDV2IpGBIRERkqDAM6GiAxv1d4U9XANT9uOlAhLt/uiQVmoFPIASaYnYBKQCSUcRv+KntqKWitcLs+uma6lXeUk5ZaxlVbVV4jcjt9NWf7Ljswzp/xiaOJTM284jfRhuGga+1FW9VFd7qajxVVXira7qeV+GprsZbVY23tha8Q+MzHsqekYEjNxdHXl6vdX8KcBYWYksP/e5nIjI0WCwW0uKjSIuPYmZ+0hHHtLu9VDR2UN7YSXlDB+WN7WZ41NBBeWMHlc2d+PyDH2i7fX4ONnVysGngddRsVgupcT0hUkYgRHL26VCKMoJbk03BkIiISKQYBrTVQlPpkYOfxlLwtA1efcmFPV0/mV336ZMhKn7wahKJAMMwaHI1Ud1RTU17DdXt1dR01FDV1tMBVNFagds/eN86HyrKFsWYxDF9On/GJo1lbOJYYh09Cx8bbre5mPOuClqqNuCt7g5/urp+qszgx2hvH8RPMwCrFXtWFo683J7wJ9d87MzLw56TgzUqarCrFJEhKtZpZ0JmAhMyE474vtfnp6rFRXlDR1eA1NHVeWQ+Lm/ooMMzdKbAAvj8BjUtLmpa+l+s2+8K7r/tCoZERERCpXur98bSo4c/3sHeRtkCKWN6On8yppidQOmTwBk3yLWJhJZhGLR4Wqhpr6Gmo6ZP6FPdXh14vbq9Go9/iCx43EuCM4H8+Hzy4vPIjc8lLz6PgoQCipKKyI7Nhqbmni6fXVV4q9+lqaqK2uqejh9fff1gf4yBORw4srP7hj6977MysTi0a6GIhIfdZjV3HUuOOeL7hmHQ2O45YmBU0WTe1w3idLVQUDAkIiISLL8fWqt6pnUdOuWr6QB4h8A2ygBYILXo8PV/0ieBU9soy/DX7mk/LOQ5tOOnpr1mULZyD1aMPYa8+LyeW2wu+aSQ44klvdNBVHMnvoZ6vPvr8dXX4a37BG/1a3RWVbGnpmbo7N41AEtUVKDDJxD25PWEP/aMDCw2LVAvIkOTxWIhJc5JSpyTGXlHnq7W4fZR0dRBZVMnta1mJ09tq5vaVlfPrcVNXZsLj2/wp60dSsGQiIiIq9UMfNpqzPvWavPWVt3zuLUaWivBN8S+EbJYIaWoZ+pXYA2gieA48jdfIkNZp7ezp7unK+g5UujTNpjTLoPktDgocuQw3kin0JdErieODJeTlA4b8a0+7E1teOvr8NWX4a3fiK++IbBjV+0g134srLGxXdO8+gY+3SGQLS1Na/yIyIgW47QxPiOe8Rn9T783DIOmDk9XWNQVHB0SItW0urtec+HyRmbHNQVDIiIyMrnbu4KdrrAnEPIcIfjxDOG1NQDiMs31fwK3AkgeA0kFkDIWHNGDXaHIERmGQbu3nfrOeho7G2lwNdDoaqShs4GGzl6PXebz+s56mt2DsMD6MXB4DJLaIbEdUtotFPiSyPPEk+mOJrXdSkKbn+hWN/amNoz6Rgx3MVB82HmG+H91AmxpadizMnFkZmHPzMSelYk9MxNHVvfzLGzJyQp+RESCYLFYSI51khzrZEJm/2MNw6DV5e0bILX1hEaHhktt7uNfB0nBkIiIDB9e1yGhTtURgp+uW6S3bj8RCTlmyHPE8CdfnT8yZLh97p5Ax9VAY2ejGfp0BTzdrzd0NgSCoKG4dg+AxTCI7YT4TojrhLhOg/iOvs8TOiDDFUVqp43EdohpcWPvPHRXrrquW1+R+Y73+FljY7FnZZm3zAwz6Mkwgx5HV/hjT0/H4hx4G3sREQk9i8VCQrSDhGgHRekDrwPZ4fZ1dRz1dCGVVdVx3y8GvpaCIRERiTzDAE8HdDZCR6O5RfuRHrfX9g1+OpsGs+rjZIHEXDPsOSz8KYTEPHX8yKDw+X20uFuod/Xq5unsFez06uzp7vQZctO3DINod0+YE98V7sT1ft79uMMMe+K6wqBYF1iDWuZh6K5RdER2u7l1e2ZmoKPH7PjpepyZiT0zC1u8FpsXERlJYpw2ClJjKUjtWUuyuTmZ+4I4VsGQiIgcP6974HDnaI99/W+vOWxYrGa40zvs6R0AJeaBXd+4S2gZhkGHt4NWTyst7pbArffz/t5rcbfQ7h0ak5ksfoMYN8S4zbDGDHF6Apzux3GB7h6j63VzvH2ot+aEitWKLSUFe1par7Dn8OldttRULFbrYFcrIiLDiIIhEZHRzO8HT5u5+LK7zezI6Ww4QqDTeORwZ6h1D4Sa1W6u7xPf65ZUcEjwkws2baMsx8bj99DqbqXV3Uqzp7nnsbt5wECn1WOO9RqHTmmKHIvf7NSJdWPeuyDabXTd03PvNoh29YyLcUGM2+i6N2/RQ3OmWURYk5Kwp6ZiS00179NSsaemmc/TUrGlppn3aWnYEhO1c5eIiISFgiERkeHE7wd3q3lztfb/+LDX2sx1dwKvt5n3o43FCnEZXUFP1iHBT1bXe1nm8+hk0Dfvo57H76Hd006Ht4N2bzsdnq57bwftnvZjetzmbqPF00KHtyOin8HmM4jy0Ofm9EC0p6tbpzuoOUJwY77ed9xoDnP6Y42NNUOc1BQz4AkEPWanjxn4pGFLScWekqz1e0REZEhQMCQiEmp+P3g7zZuno+u+HTyd4O0w7z3t5uvdAU0gzGnpCWxcrWaQ0/v9ob571qCxQFz64d09cV1hT3xGTwgUmwpWfes+khiGgcvn6rl5ex4HwpkjBDrdrw/0ONyLJ1v9BlFuiPL2DmzA6ekV5nh7hzoGzt4hj7d32HNIAOQ1zzdqpluFmMXp7NXNk9bT3dO7myc1DXtqCrbUVKwxWiheRESGHwVDIjLy+f3mejZe1yFhTUfX466wpr8A51jGeofZQqVDlcVqduz0F/J0vxebDjb9kzYUeP3ePgFNp68Tt89Np6+zT2BzpOdun5tOb+dRQ55Dx3Q/d53oelWGgc0Pdh84veDouiX5IM0LTh84vAYOr/l+n3Fdj+1eo2scRxnXc/yhnTsKbcLMbseWmIgtKQlbYiLWpERsScldr5mvWxOTAo9tiYmB59ZoLQwvIiIjn36KFpHQ8PvA5wG/p+vea9773ObN6+p7f7THXpcZ4vg8/R9z1PO4e0Kg7tf8g7cOhwBRiRCTbIY8MckQkxLcY2eCpnEdwjAMvIYXr7/n5vF78Pg9uH1u87Gv7/PA64c89vg8uP3uIx57pOPdPnfgeoHX/O7Aebx+L26fG5/hO6xui2Fg95lBic1nBiHdz+0+AqGMeTMOe9/pg5jer/m7z3XI2EOO6745Dgl2HL0CnO5gJ7jdqWTQWCxdoU5ST8iTlIg1sW/IY77fFfJ0jbPExmKxWAb7E4iIiAxZCoZEwsXvB8NnhhJ+rxmc+H0ReM3bK6DxHh7UHOn50UKd/s7h9/Z9D/1WNaI5YvsJcZK7Hqd0PU7pGROVGJZOHsMw8Bk+/IYfr9+L3/DjM3zmze8LPPb7e73e9Z7f8OM1vH2P7XVM93n7vObved3j9wRCGZ/h6xPSHBraBF7zefD5vPi8Hv5/e/cfW1V9/3H8dc69bamUFlr6A6T82PgKZc3Kr1KqiKtUyuZQJroNswiIcz9aFlogg6EDQkzJ2CJOMESDso1VCWxIBIPDYmuYFbEGMmBUcTZNpi0/hHaUH4We8/3j3t7e2x+X3729Pc9HcnPP+fw673PNJc3Lc8+xmq94Xlc8bS3bVvMV2VeuyLaaPe1XrshuviI1W56+5mbJsmTakml5fn5k2p5QxWV52tzed1fbV7Pk8s5zN3vmumy/PkuKtKQ7Opsf0G4HPWb78MZzbDiY2y1X794yY2JkBrz3ltm7t6evd2tfS6hjxsbJ1dd7BU9MDE/aAgDgNiEYulm27XnpZt8VuG9bHWxbfmMsv3lWkDlt5ytw/jXN8Wvr7OWrI9jrKuu0ravD+c3ewMUbuthWazhiW4F9VrPfOLvNuObWdQPG+a9nB/b5z/Ef11lAQ1ByTewOXr52Q7JltG8P6JfkNyZgHaNlu4M1DMlS63yrzZot85ptv7Vsvz7baF3H7ytsyWhdo2WM7VmrZQ3fcb3tvmP7r+E3r6UWyw6sTS1r2d66bc9a8u1LMtyyXBGyzZaXWzIjZJluyXDLNl2eMYbLt22bblmGWzJcsgyXZJiyLVu2bcu+YklnLdlnLNn215J1Spbl+X7aVrNk2bK930XbtgPePX3N3n9mLKnZe+aW9zvvPYZa1rNtGd55rW2WTMszx7A9V3mYtmR4X/77ptW+rcN9SzLkbfeu6z/OtL2hSsvLG4QE7Pu9u6z27QQjCDttw5w2gU7bMCegvU0AZERGcsUOAADdWNgHQ+vXr9eaNWtUW1urjIwMvfjii5owYcJ1rfHOD8fpjohr+L9Q9vX+ZX+dfwR1sHxL0039OdV2XSNI342sdzOud63rHG+0G9968naH/Vdfr3WKIUNuSe5rW8uv3+igrW2Vvv/2HY1pt5bRYX+w43RWb9v2DveNjj+/YPtGkPM3OhlrtBnT0Zr+Z250cN5t5wXMsQOP7euzpc7+RTDU7tMOE83eF4Aew+WSGR0tI7qXzOg7ZPbqdfXt6F4yegVuu2JiCHMAAHCwsA6GtmzZoqKiIm3YsEFZWVlau3at8vLyVFVVpaSkpGteZ+jnhmJcXJ4MAABunhEV5X1FyoyM8oQs/vt3RMvsFe0JbFq2o3vJiG6zHe0dE7DtCXTMXr141DkAALglDNu+7stguo2srCxlZmZq3bp1kiTLspSamqr58+dryZIl7cZfunRJly61PrmkoaFBqamp+mj4/ynGxaOLAQAIWy6XjIiIwJfb7Xnv1avzkMZ/P8q7Hxlk36/NjIps3Y+KkhkZKUVEcKUNAADoFhoaGhQXF6f6+nrFxsZ2Oi5srxhqampSZWWlli5d6mszTVO5ubmqqKjocE5xcbFWrlzZVSUCABAeTFOG2y253TJcLu+2S4bL7QlXXK42fR2P6zCc8Q9oIiJkRLa2qd24NvuRbeZ6X/K1RbaO4cbEAAAANyRsg6FTp06publZycnJAe3Jyck6duxYh3OWLl2qoqIi337LFUMAgPBkG4Zs0/OSd1vebZmmbO+7TO+7YUguUzJa2wxfu8u3bZimb98wTMllyjC9+y5PEGL63j0hiel2yzRbAhLP+LbvhtslBWk3XGb7fpfb1x7Q73J5A50IGW5XazDjdrfb9gU7HYU8LecNAAAARwrbYOhGREVFKSoqql17fYyhK65ru+zbvsarw1uGXffv9IKsb9/Epelt6w68/7Rx1WN3vl6QhdvpuLPTz7Sz823THuT+0h03tL1P83WPNzq+f7f/Z9LJmrbfR+3Z7vyz9z+ObXhudR14PL9JAfPbHN/wP4bnCVdGmz7baLnhteFtatPe9r+Fd98wWtez/Y5j+B/H1274avDcwdl3W+iAfsP7VC/Df54Cz8HobC3D8HxO/p+t6Znvq8X7Cljft98SKLSs03Ks1vMNGG8YntCgbZvajvGfZ7Zbp6XNMFpCCu++N9Aw1RpeGIYp03S1zvUGF75331yXDNM73jB9AYhpBI43TVfAsQ3D9IQPhuELT4yWcMUwPR+RafpClsA+Q4Z/ANNyDt61DL/Apv2+31rekMI313sucrlax7RsAwAAALgpYRsM9e/fXy6XS3V1dQHtdXV1SklJua61JpQfCPp7OwAAAAAAgJ4obK8dj4yM1Lhx41RaWuprsyxLpaWlys7ODmFlAAAAAAAA4SFsrxiSpKKiIs2ePVvjx4/XhAkTtHbtWjU2Nmru3LmhLg0AAAAAAKDbC+tg6Ec/+pFOnjyp3/72t6qtrdXo0aO1e/fudjekBgAAAAAAQHuGbdvXfX/knqKhoUFxcXGqr6/nHkMAAAAAAKDHuNbMI2zvMQQAAAAAAICbQzAEAAAAAADgUARDAAAAAAAADkUwBAAAAAAA4FAEQwAAAAAAAA4V1o+rv1ktD2RraGgIcSUAAAAAAAC3TkvWcbWH0Ts6GDp9+rQkKTU1NcSVAAAAAAAA3HqnT59WXFxcp/2ODobi4+MlSTU1NUE/pK6UmZmpAwcOhLoMH+oJjnqurrvVRD3BUU9w3a0eqfvVRD3BUU9w3a0eqfvVRD3BUU9w3a0eqfvVRD3BUU9w3a2e+vp6DR482Jd9dMbRwZBpem6xFBcXp9jY2BBX4+FyubpNLRL1XA31XF13q4l6gqOe4LpbPVL3q4l6gqOe4LpbPVL3q4l6gqOe4LpbPVL3q4l6gqOe4LpbPS1aso9O+7uoDlyj/Pz8UJcQgHqCo56r6241UU9w1BNcd6tH6n41UU9w1BNcd6tH6n41UU9w1BNcd6tH6n41UU9w1BNcd6vnWhn21e5C1IM1NDQoLi5O9fX13TLVAwAAAAAAuBHXmnk4+oqhqKgoLV++XFFRUaEuBQAAAAAA4Ja51szD0VcMAQAAAAAAOJmjrxgCAAAAAABwMoIhAAAAAAAAhyIYAsLQ+vXrNXToUPXq1UtZWVn66KOPAvorKip0//33q3fv3oqNjdXkyZN14cKFEFUL4Ga8//77mj59ugYOHCjDMPTmm28G9K9YsUIjR45U79691a9fP+Xm5mr//v2hKRbATSsuLlZmZqb69OmjpKQkzZgxQ1VVVQFjLl68qPz8fCUkJCgmJkYzZ85UXV1diCoGcDOu9p2vrq6WYRgdvrZu3RrCytGTEAwBYWbLli0qKirS8uXL9cknnygjI0N5eXk6ceKEJE8oNG3aNE2dOlUfffSRDhw4oIKCApkmX3cgHDU2NiojI0Pr16/vsP+uu+7SunXr9K9//Uv79u3T0KFDNXXqVJ08ebKLKwVwK5SXlys/P18ffvih9uzZo8uXL2vq1KlqbGz0jSksLNRbb72lrVu3qry8XF9++aUeeeSREFYN4EZd7Tufmpqqr776KuC1cuVKxcTE6Lvf/W6Iq0dPwc2ngTCTlZWlzMxMrVu3TpJkWZZSU1M1f/58LVmyRBMnTtQDDzygVatWhbhSALeaYRjavn27ZsyY0emYlseSvvvuu5oyZUrXFQfgtjh58qSSkpJUXl6uyZMnq76+XomJiSopKdGjjz4qSTp27JjS0tJUUVGhiRMnhrhiADej7Xe+I2PGjNHYsWO1cePGLq4OPRWXEABhpKmpSZWVlcrNzfW1maap3NxcVVRU6MSJE9q/f7+SkpJ09913Kzk5Wffdd5/27dsXwqoBdJWmpia9/PLLiouLU0ZGRqjLAXAL1NfXS5Li4+MlSZWVlbp8+XLA3wIjR47U4MGDVVFREZIaAdw6bb/zbVVWVurgwYOaN29eV5aFHo5gCAgjp06dUnNzs5KTkwPak5OTVVtbq//85z+SPPcc+elPf6rdu3dr7NixmjJlij777LNQlAygC+zcuVMxMTHq1auXnn/+ee3Zs0f9+/cPdVkAbpJlWVqwYIHuuecepaenS5Jqa2sVGRmpvn37Boxt+VsAQPjq6Dvf1saNG5WWlqa77767i6tDT0YwBPQglmVJkn72s59p7ty5GjNmjJ5//nmNGDFCr776aoirA3C75OTk6ODBg/rggw80bdo0/fCHP/TddwxA+MrPz9fhw4f1xhtvhLoUAF3gat/5CxcuqKSkhKuFcMsRDAFhpH///nK5XO2ePFJXV6eUlBQNGDBAkjRq1KiA/rS0NNXU1HRZnQC6Vu/evTV8+HBNnDhRGzdulNvt5r4DQJgrKCjQzp079d5772nQoEG+9pSUFDU1Nens2bMB41v+FgAQnjr7zvvbtm2bzp8/ryeeeKKLq0NPRzAEhJHIyEiNGzdOpaWlvjbLslRaWqrs7GwNHTpUAwcObPdY208//VRDhgzp6nI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       "text/plain": [
        "<Figure size 1400x400 with 1 Axes>"
       ]
@@ -1252,19 +1252,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2000/2000 [00:00<00:00, 14320.85it/s]\n"
+      "100%|██████████| 2000/2000 [00:00<00:00, 16666.12it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -1359,19 +1359,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1000/1000 [00:01<00:00, 602.45it/s]\n"
+      "100%|██████████| 1000/1000 [00:01<00:00, 646.61it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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Uf2mPafFzJpFINOXLly/HrFmzNNt9+vTB2rVrtY51dHTEpEmTEBoaColEgqNHj+Lbb7/Ff//9hzNnzuh8AC+WkZGBPXv2YNiwYeXubfM4lUqF33//HR07dixxnq5Ro0bh33//BVD0P+6tt97S/K0YorRE0bZt2zT/94Ci5MhPP/2k1duvsmMCavbrT038uyYiIiKqL2pMAic8PFxr+5133sHKlSvx999/o1WrVti2bRvUajVGjRqlteS4u7s7goKCcPDgwVK/7TQxMdFMyqlWq5GVlQW1Wo327dvj3LlzWnU3btyI/Pz8UuNu1KiR5uf8/Hyt5NCjLCwsSm0vPz9f64PXo8cW7weAp59+Gn5+fnj//fdhZWWFkJAQnDp1CnPmzIGpqanWebp06aI1NOCZZ57BiBEj0KpVK8yaNQt79uwp9RptbGy0hmsFBwfDwcEBXl5emuQNAM3PhqxQ9KilS5fqDGPR59GJa4uvtaTHrbRhLWV9zAEgIiJCq87o0aPRuHFjzJkzB1u2bNF6jLy8vNCxY0fNc3X06FF8/fXXcHZ2xhdffFHqNQJFSZtHde/eXe9kr2URFhZm8MovpT22j9Ypz/GPHmvI81ARmzdvhr29Pfr166f1fyQkJAQ2NjY4ePCgVgLH399fK3nzqLFjx+r0YnF3dy/z49y6dWuD4y/rc/Lo/hdeeAHt27dHamoqdu3ahQcPHug8npMnT9baHj58ODp27IiXXnoJK1euxAcffKA3ni1btkChUJRpqFNZ7d+/Hw8ePHji//PPP/8c7733Hu7du4f169dDoVAYPDxUrVbjt99+Q9u2bXV64BXr3bs3IiMjkZWVhf379+PixYuQyWRVFlOxmvz6UxP/romIiIjqixqTwHl0CVcACAgIgFgs1iw7e+vWLQiCoFOvWFmHIqxfvx5Lly7F9evXtYbrPP6tedeuXQ2IvoilpaXeeSWAom/rS5v7xNLSUjM86vFji/cDRW9+d+/ejVGjRmH48OEAit5ML168GJ9++qnWcB59AgMDMXToUGzbtg0qlUpnpZnHeXt76/RQsbe31+n6XrwaU1mSMPqEhIQYfEzxY1LS41ZZj3lJpk6dinnz5mHfvn2aBM7x48cxePBgnDx5UjPMYNiwYbCzs8OCBQvw2muvoVmzZk9s18LCAi4uLlpljo6O5X5sPTw89H4T/iSlPbaP1inP8Y8eW9Hnoaxu3bqF7OxsnV4KxVJSUrS2n9SbRt8+CwsL9O3bt2JBPkF5nhM/Pz/4+fkBKErmjB8/Hn379sWNGzee+Li++OKLeO+997Bv374SEzgbN26Ek5MTBg4cWK7rKalNExMTPP/88yXWeXS468svv4x27dppVjsqq8OHDyMxMRFTp04tsY6bm5tmCNmIESPw2WefoV+/frh165bOnEWVEVOxmvz6UxP/romIiIjqixqTwHnc4wkDtVoNkUiEf/75R2/CobSkBVA0AfK4ceMwbNgwTJ8+Ha6urjAxMUFERARiY2O16qamppZpDgIbGxvNuT08PKBSqZCSkqL1AVGhUCA9Pb3UJY89PDy0hj8VK+6y/ujxzZs3R3R0NK5evYrMzEw0a9YMlpaWmDp1Knr27Flq3D4+PlAoFJDJZCUOjyhWUoKnpPJHJ0AtaaJifY9tRkZGiR9AHmVpaalJFhUnJZKSknQSSklJSZq5Fkri4eGBgwcPQhAErVj1PeYlxdKgQQOteU++++47uLm56cwR8cwzz+Cjjz7CiRMnSk3glJZUAwx7bPPz85GdnV1qmwA0H0ydnJwgkUg0j8WjyvL4PPrc6Dv+0WMN+d2vCLVaDVdXV83Qmcc9njQrS4LqUSqVCqmpqWWKxcnJqcQeEyUp7TEtfs6eZMSIEfjhhx9w5MiREnsXFfPx8SlxTp/4+HgcPXoU48ePr9Ck0o/Kz8/H9u3b0bdv31Ln3ilmbm6OZ555Bp9//jny8/PLnBTYuHEjxGKx1hCp0owYMQJz5szBzp078dZbb1V6TMVq8utPTfy7JiIiIqovakwC59atW1rfaMfExECtVmtW5wgICIAgCPD390fjxo2f2FZJH2y3bNmCRo0aYdu2bVp15s+fr1O3Q4cOBs9BUPwN7JkzZ/D0009r6pw5cwZqtVrrG1p92rRpg4MHD0IqlWolVU6dOqXVfjGRSITmzZtrtv/++2+o1eoy9QC4ffu21mTAVcXR0VHvSiz6HtvnnnvO4DlwHn3MH03W3L9/HwkJCVqrn+jTpk0brFmzBteuXdNKqpT0mD8uJycHaWlpWh/8Hzx4oPfDV3GPr/IOq3ico6MjAN0VcfQ9tr///rvBc+CIxWK0bNkSZ86c0alz6tQpNGrU6InznrRo0QKmpqY4c+YMRo0apSlXKBS4cOGCVpmhv/vlFRAQgH379qFr165V8u3/vXv3qnQOHC8vL7i4uOh9Th6fiL0kxcNWSkvoCYKAO3fulDhP1q+//gpBECp1+NSff/6pd0Wr0uTn50MQBOTk5JTpeZXL5di6dSt69eplUBKhrI9deWJ6VE1+/amJf9dERERE9UWNSeB8++236N+/v2b7m2++AQBN1/znnnsOs2bNwoIFC7BhwwatBIwgCMjIyECDBg0AANbW1nrfYBf3ani0t8WpU6cQFRWltSQvUL45CJ566ik4OTlh1apVWm+gV61aBSsrKwwaNEhTlpaWhrS0NPj6+mpWERkxYgS++OILzQS4QNEHjbVr16JTp05PXK0jPz8f8+bNg4eHh9Y3yqmpqTq9Ci5evIg///wTAwcOhFhctSvJBwQEIDs7G5cuXUKrVq0AFH37un37dp265ZkDp3nz5mjSpAm+//57vPXWW5rneNWqVRCJRBgxYoSmbnZ2tmZSzeIePEOHDsXUqVOxcuVKrFixAkDR78fq1avh5eWlmT+ooKAASqVSJ2Hx8ccfQxAEDBgwQFPWuHFj7N27F4cOHdL6gP7rr78CQJkmji4LPz8/mJiY4MiRIxg2bJimfOXKlTp1yzMHDlD0O/nBBx/gzJkzmh5FN27cwIEDBzS/o8WuX78OKysrzd+Svb09+vbtiw0bNmDevHmax+6XX35Bbm4uRo4cqXWe8v7uG2LUqFFYuXIlPv74Y3z22Wda+woLC5GbmwsHB4dyt1/Vc+AARfPTrF+/Hvfu3dM8Lvv378fNmze1hgPp+9sHgB9//BEikQjt2rV7Yt1Vq1YhNTVV63f7UZs2bYKvr6/WKm0VtWnTJlhZWeHZZ5/Vu//x3iVAUQJz69at8PHx0doXHx+PvLw8vau+/f3338jKyioxUZSWloYGDRrofBlQPAn5o73rDImprGry609N/LsmIiIiqi9qTAInLi4OzzzzDAYMGICoqChs2LABL774ouZDTkBAAD755BPMmjULd+7c0ax6EhcXh+3bt2P8+PGaN4ghISH4/fffMW3aNHTo0AE2NjYYMmQIBg8ejG3btuHZZ5/FoEGDEBcXh9WrV6NZs2bIzc3Viqe8cxB8/PHHCA8Px8iRIxEWFoajR49iw4YN+PTTT+Hk5KSpu2LFCixYsEDrW/hOnTph5MiRmDVrFlJSUhAYGIj169fjzp07WitkAUUfRD09PdGsWTNIpVL89NNPuH37Nnbv3q2VZHj++edhaWmJLl26wNXVFVevXsX3338PKysrfP755wZfo6FGjx6NmTNn4tlnn8W7776LvLw8rFq1Co0bN9aZOLo8c+AAwJIlS/DMM8+gf//+GD16NKKjo7FixQq88cYbWpOTbt++Ha+++irWrl2rWSXL29sbU6ZMwZIlS6BUKtGhQwfs2LEDR48e1czFAQDJyclo27YtXnjhBc0Hwn///Rd///03BgwYgKFDh2rOM2nSJKxduxZDhgzBO++8Az8/Pxw+fBi//vor+vXrpzXxc0XY29tj5MiR+OabbyASiRAQEIBdu3bpzOMClG8OHAB4++238cMPP2DQoEF4//33YWZmhi+//BJubm547733tOo2bdoUPXv21KzSBACffvopunTpgp49e2L8+PFISEjA0qVL0b9/f63EgCG/+5cuXcKff/4JoKinXnZ2Nj755BMARUmRIUOGlHg9PXv2xFtvvYWIiAhcuHAB/fv3h5mZGW7duoXNmzdj+fLlWkk/Q1VkDpwVK1YgKytLszLPX3/9hYSEBABFk7oXJx1nz56NzZs3o3fv3pg8eTJyc3OxZMkStGzZUquX1aefforjx49jwIAB8PX1RUZGBrZu3YrTp0/jnXfe0Vrhyc/PD88//zxatmwJCwsLHDt2DL/99hvatGmjd6hQdHQ0Ll26hA8++KDEHo/Z2dmaRPzx48c11+jg4AAHBwdMmjRJq35GRgb++ecfDB8+vMSegQMHDoS3tzc6deoEV1dXxMfHY+3atbh//z5+//13rbpjxozB4cOHtYZ0Ftu4cSMkEolmDrHHbdiwAatXr8awYcPQqFEj5OTk4N9//0VkZCSGDBmCp556qlwxlVVNf/2pir9rIiIiIioDoZrNnz9fACBcvXpVGDFihGBrays4OjoKkyZNEvLz83Xqb926VejWrZtgbW0tWFtbC02aNBHCw8OFGzduaOrk5uYKL774ouDg4CAAEPz8/ARBEAS1Wi189tlngp+fnyCRSIS2bdsKu3btEsaOHaupUxm+//57ITg4WDA3NxcCAgKEZcuWCWq1Wu91Hzx4UKs8Pz9feP/99wV3d3dBIpEIHTp0EPbs2aNzjkWLFglNmjQRLCwsBEdHR+GZZ54Rzp8/r1Nv+fLlQseOHQUnJyfB1NRU8PDwEF5++WXh1q1bZbqWnj17Cs2bN9cp9/PzEwYNGqRTDkAIDw/XKtu7d6/QokULwdzcXAgODhY2bNiguf7Ksn37dqFNmzaCRCIRvL29hblz5woKhUKrztq1awUAwtq1a7XKVSqV5vfC3NxcaN68ubBhwwatOpmZmcLLL78sBAYGClZWVoJEIhGaN28ufPbZZzrnEQRBuH79ujBixAjBx8dHMDMzE/z8/IT3339fkMlkpV7L2LFjBWtra51yfY9ZamqqMHz4cMHKykpwdHQU3nrrLSE6OlrvdZbXvXv3hBEjRgh2dnaCjY2NMHjwYL2/PwCEnj176pQfPXpU6NKli2BhYSG4uLgI4eHhglQq1alX1t/94udR323s2LFluqbvv/9eCAkJESwtLQVbW1uhZcuWwowZM4T79+9r6pT0O37w4EEBgLB58+Yynaus/Pz8SryuuLg4rbrR0dFC//79BSsrK8HBwUF46aWXhOTkZK06e/fuFQYPHix4enoKZmZmgq2trdC1a1dh7dq1Ov+P3njjDaFZs2aCra2tYGZmJgQGBgozZ87U+zwJgiB88MEHAgDh0qVLJV5PXFxcidej7//t6tWrBQDCn3/+WWKbK1asELp16yY4OzsLpqamgouLizBkyBDhyJEjOnV79uyp939Mdna2YGFhITz33HMlnuf06dPCyJEjBV9fX0EikQjW1tZCu3bthC+//FJQKpXljkmf2vj6IwiV/3dNRERERKUTCYKeryeJiIiIiIjI6JRKJZYuXYrmzZs/sWdxSaRSKZYvX47evXtX6lBnIqp+VTsBChEREREREZXZBx98gDVr1qBz587lOt7Ozg52dnYYNmwY7t27V8nREVF1YgKHiIiIiIiokqxbtw4ikUhzs7CwgKenJ8LCwvD1118jJyenxGN37tyJDRs2YM+ePXoXIyiryZMnY+zYsRg1apRmJVQiqv2YwCEiIiIiIqpkCxcuxC+//IJVq1bhnXfeAQBMmTIFLVu2xKVLl/Qec+fOHfzzzz9aiw2U1xdffIHnn38eV69erXBbRFQzcA4cIiIiIiKiSrJu3Tq8+uqrOH36NNq3b6+178CBAxg8eDBcXV1x7do1WFpaVlOURFQbsQcOERERERGRETz11FOYN28e7t69iw0bNmjKr1+/jhEjRsDJyQkWFhZo3749/vzzT53js7KyMHXqVDRs2BASiQTe3t4YM2YM0tLSNHXkcjnmz5+PwMBASCQS+Pj4YPr06SgoKNBqKzIyEt26dYODgwNsbGwQHByM2bNnV93FE1GFmVZ3AERERERERPXFK6+8gtmzZ2Pv3r148803ceXKFXTt2hVeXl744IMPYG1tjT/++APDhg3D1q1b8eyzzwIAcnNz0b17d1y7dg2vvfYa2rVrh7S0NPz5559ISEiAs7Mz1Go1nnnmGRw9ehTjx49Hs2bNcPnyZXz11Ve4fv06/vrrLwDAlStXMHjwYLRq1QoLFy6ERCJBTEwMjh8/Xp0PDRGVggkcIiIiIiIiI/H29oa9vT1iY2MBFE047Ovri9OnT0MikQAA3n77bXTr1g0zZ87UJHCWLFmC6OhobNu2TVMGAHPnzkXxrBibNm1CZGQkDhw4gF69emnqNGvWDG+//TaOHTuGbt26ITIyEgqFAv/88w+cnZ2NdOVEVFEcQkVERERERGRENjY2yMnJQUZGBg4cOIBRo0YhJycHaWlpSEtLQ3p6OsLCwnDr1i0kJiYCALZu3YrWrVtrJW+KiUQiAMDmzZvRvHlzdO7cGQUFBZrb0KFDAQCHDh0CADg4OAAoWvVKrVZX/QUTUaWoVQmcI0eOYMiQIfD09IRIJMKOHTsMOv6jjz7SWtKv+GZtbV01ARMRERERET0mNzcXtra2iImJgSAImDdvHlxcXLRu8+fPBwCkpKQAAGJjY9GiRYsntnvr1i1ER0fD0tJS6+bl5QUASE1NBQA8//zz6Nq1K9544w24ublh9OjR+OOPP5jMIarhatUQKplMhtatW+O1117Dc889Z/Dx77//PiZMmKBV1qdPH3To0KGyQiQiIiIiIipRQkICsrOzERgYqEmYvP/++wgLC9Nb35AlxdVqNdq0aYNVq1bp3e/u7g4AsLS0xJEjR3Dw4EHs3r0be/bswe+//46nnnoKe/fuhYmJiYFXRUTGUKsSOAMHDsTAgQNL3C+XyzFnzhz8+uuvyMrKQosWLbBo0SLN+E8bGxvY2Nho6l+8eBFXr17F6tWrqzp0IiIiIiIi/PLLLwCAsLAwNGrUCABgZmaGvn37PvG4gIAAREdHl1rn/Pnz6NSpk2ZYVUnEYjH69OmDPn364Msvv8Rnn32GOXPm4ODBg6XGQkTVo1YNoSrNpEmTEBUVhd9++w2XLl3CyJEjMWDAANy6dUtv/TVr1qBx48bo3r27kSMlIiIiIqL65sCBA/j444/h7++Pl156Ca6urujVqxe+++47JCUl6dQvHvIEAMOHD8fFixexfft2nXrFkxiPGjUKSUlJenvgyGQy5ObmAgAyMjJ09rdp0wZA0ZfiRFQziYTiv/ZaRiQSYfv27Rg2bBgAID4+Ho0aNUJ8fDw8PT019fr27YuOHTvis88+0zq+oKAAnp6e+OCDDzBjxgxjhk5ERERERHXUunXr8Oqrr2LhwoXw9/dHYWEhHjx4gAMHDiAyMhJ+fn7466+/NPPZXL16Fd26dYNYLMabb76JRo0a4cGDB4iKikJCQgIuXrwIoGjenE6dOuHGjRt47bXXEBISgoyMDPz5559YvXo1WrduDbVajSFDhuDvv//GqFGj0L17dyiVSly9ehWbN2/Gvn370L59e0yZMgVHjhzBoEGD4Ofnh5SUFKxcuRIikQjR0dGwt7evzoeQiEpQq4ZQPcnly5ehUqnQuHFjrXK5XI4GDRro1N++fTtycnIwduxYY4VIRERERET1xIcffggAMDc3h5OTE1q2bImvvvoKr776KmxtbTX1mjVrhjNnzmDBggVYt24d0tPT4erqirZt22raAIqmgzh69Cjmz5+P7du3Y/369XB1dUWfPn3g7e0NoGhY1I4dO7Bs2TL8/PPP2LlzJ6ysrNCoUSNMmzZN81npmWeewZ07d/DTTz8hLS0Nzs7O6NmzJxYsWMDkDVENVmd64Pz+++946aWXcOXKFZ1Jt2xsbDQTdhXr06cP7Ozs9HZBJCIiIiIiIiKqSepMD5y2bdtCpVIhJSWl1Dlt4uLicPDgQfz5559Gio6IiIiIiIiIqPxqVQInNzcXMTExmu24uDhcuHABTk5OaNy4MV566SWMGTMGS5cuRdu2bZGamor9+/ejVatWGDRokOa4n376CR4eHk9c0YqIiIiIiIiIqKaoVUOoDh06hN69e+uUjx07FuvWrYNSqcQnn3yCn3/+GYmJiXB2dkbnzp2xYMECtGzZEgCgVqvh5+eHMWPG4NNPPzX2JRARERERERERGaxWJXCIiIiIiIiIiOojcXUHQERERERERERET8YEDhERERERERFRDVehSYw///xzzJo1C5MnT8ZXX31VYr3Nmzdj3rx5uHPnDoKCgrBo0SI8/fTTZT6PWq3G/fv3YWtrC5FIVJGQiYiIqAoJgoCcnBx4enpCLOb3RNWJ75+IiIhqh7K+fyp3Auf06dP47rvv0KpVqyfWO3HiBF544QVERERg8ODB2LRpE4YNG4Zz586hRYsWZTrX/fv34ePjU95QiYiIyMju3bsHb2/v6g6jXuP7JyIiotqltPdP5ZrEODc3F+3atcPKlSvxySefoE2bNiX2wHn++echk8mwa9cuTVnnzp3Rpk0brF69Wu8xcrkccrlcs52dnQ1fX1/cu3cPdnZ2hoZLRERERiKVSuHj44OsrCzY29tXdzj1WnZ2NhwcHPj+iYiIqIYr6/uncvXACQ8Px6BBg9C3b1988sknT6wbFRWFadOmaZWFhYVhx44dJR4TERGBBQsW6JTb2dnxDQgREVEtwCE71a/4OeD7JyIiotqhtPdPBg9O/+2333Du3DlERESUqX5ycjLc3Ny0ytzc3JCcnFziMbNmzUJ2drbmdu/ePUPDJCIiIiIiIiKqMwzqgXPv3j1MnjwZkZGRsLCwqKqYIJFIIJFIqqx9IiIiIiIiIqLaxKAEztmzZ5GSkoJ27dppylQqFY4cOYIVK1ZALpfDxMRE6xh3d3c8ePBAq+zBgwdwd3evQNi61Go1FApFpbZZn5iZmek8d0RERERERERUMxiUwOnTpw8uX76sVfbqq6+iSZMmmDlzpt4EQGhoKPbv348pU6ZoyiIjIxEaGlq+iPVQKBSIi4uDWq2utDbrIwcHB7i7u3PeAiIiIiIiIqIaxqAEjq2trc7S39bW1mjQoIGmfMyYMfDy8tLMkTN58mT07NkTS5cuxaBBg/Dbb7/hzJkz+P777yvlAgRBQFJSEkxMTODj4/PENdNJP0EQkJeXh5SUFACAh4dHNUdERES1lVLFL1OIiIiIqkK5VqF6kvj4eK0kSpcuXbBp0ybMnTsXs2fPRlBQEHbs2KGTCCqvwsJC5OXlwdPTE1ZWVpXSZn1kaWkJAEhJSYGrqyuHUxER1RPyQhWk+YXIKVBCJldBpiiETF4ImUKFPHkhcuWFyFOoHpYVIk+u0pQV3RdqjsuTq1CQl1vdl0RERERUJ1U4gXPo0KEnbgPAyJEjMXLkyIqeSi+VSgUAMDc3r5L265PiBJhSqWQCh4ioFhIEAfezC3AvIw9puXKk5ciRlqtAukwOaX4hpAVKSAsKkZNfdC8tUEJRyB4zRERERLVBpffAqS6ct6Xi+BgSEdUuhSo1ztzNxIHrKTgfn4nryTnIKSis7rCIiIxPEABBrf+mVj38+fE6qse2hf/Xh1C0XdT4/89RoW0YWL+s21S78HkjPXJkZapWZxI4RERE9YEgCIi6nY5t5xKx/9oDZOYpqzskIqIihQqgIBuQS4GCLKBACshzAGU+UJhfdF9802wXAMo8oLCg6KZSAurCh/dKQFUIqBT//1mt1K1TnHAhIqqt5GX7H1brEziCIECmUCO3oBBK1Lw3sTYSU/ZsISKiClOrBfx16T5WHozFjQc51R0OEdUHhXJAmgjkJAO5KYAs9eF9CiBLK/o5P/Nhwia7KAFDRERVptYncGRyFUb8egfAnWqORL/LH/WHrYVZhdo4dOgQevfujczMTDg4OFROYEREVGtcvS/FrO2XcfFeVnWHQkR1TX4WkHYTSL0OZMQBWfFA9r2i+5xksGcLEVHNUesTOLXVuHHjkJWVhR07dlR3KEREVEPJC1VYcSAGqw7FolDND1FEVAFqNZARCySeBe6fB1KuAak3gNzk6o6MiIjKiAkcIiKiGuh2ai7e3ngO15M5XIqIykFZANw7BcQdARJOA/cvAPLs6o6KiIgqgAmcGkAul2P69On47bffIJVK0b59eyxbtgwdOnTQqnf8+HHMmjULN2/eRJs2bbBmzRq0aNECAHD37l1MmjQJx44dg0KhQMOGDbFkyRI8/fTT1XFJRERUAQdvpODdX89zRSkiKjtBKOpRc/Mf4PYhIP5kzZ+TRmQCmFkBZpaAmQVgagGIzQATU8DE/P8/i80AEzNA/LDcxEx7n9gUEIkBkajoXmzycLuMN019kXb5/wN9eCeqpG2Uo34Z2yQ9+BiVCX+XqlduHvD5iFKrMYFTA8yYMQNbt27F+vXr4efnh8WLFyMsLAwxMTFwcnLS1Js+fTqWL18Od3d3zJ49G0OGDMHNmzdhZmaG8PBwKBQKHDlyBNbW1rh69SpsbGyq8aqIiMhQgiBg1eFYLPn3BleHJaLSCQKQdAG4+idw7S8g/ZZxzisyAaxdABsXwKoBYGFfdJPYARYOgIXdI9t2jyRpLAFTy///bFKxeSKJiOoMqbRM1ZjAqWYymQyrVq3CunXrMHDgQADADz/8gMjISPz444+YPn26pu78+fPRr18/AMD69evh7e2N7du3Y9SoUYiPj8fw4cPRsmVLAECjRo2MfzFERFRueYpCzNhyCbsuJVV3KERU08nSgIu/Aec3AKnXKrdtM2vAwQdw8AXsi++9ARtXwNq16N7CARCLS22KiIgqFxM41Sw2NhZKpRJdu3bVlJmZmaFjx464dk37BTk0NFTzs5OTE4KDgzV13n33XUycOBF79+5F3759MXz4cLRq1co4F0FERBVyLyMP4385i2tJZfv2pSqYiEWwkZjCRmIKa4kJrMyLfrYyN4H1wzJrc1NYPyyzkZjCSmIK6+L95kV11PI8BH1VbZdRooiICGzbtg3Xr1+HpaUlunTpgkWLFiE4OFhTp1evXjh8+LDWcW+99RZWr16t2Y6Pj8fEiRNx8OBB2NjYYOzYsYiIiICp6f/fUh06dAjTpk3DlStX4OPjg7lz52LcuHFa7X777bdYsmQJkpOT0bp1a3zzzTfo2LGjZn9BQQHee+89/Pbbb5DL5QgLC8PKlSvh5uZWyY8M1SoJZ4AT3wDXdwNqZcXasnYFXJsCLk0Al8ZF986Ni3rWcCgFEVGNxAROHfHGG28gLCwMu3fvxt69exEREYGlS5finXfeqe7QiIjoCY7HpGHSpnPIzKvgh7FHmJuI0dDZCq62FmhgYw5nGwmcrM1hZ2kGOwvT/99bmMHWwgx2lqawNDOBqBI+tEml6kq4gsp3+PBhhIeHo0OHDigsLMTs2bPRv39/XL16FdbW1pp6b775JhYuXKjZtrKy0vysUqkwaNAguLu748SJE0hKSsKYMWNgZmaGzz77DAAQFxeHQYMGYcKECdi4cSP279+PN954Ax4eHggLCwMA/P7775g2bRpWr16NTp064auvvkJYWBhu3LgBV1dXAMDUqVOxe/dubN68Gfb29pg0aRKee+45HD9+3BgPF9UkggDc3AMcXw7ER5WvDXMbwCsE8Gr38D4EsPOs3DiJiKjKMYFTzQICAmBubo7jx4/Dz88PAKBUKnH69GlMmTJFq+7Jkyfh6+sLAMjMzMTNmzfRtGlTzX4fHx9MmDABEyZMwKxZs/DDDz8wgUNEVEMJgoAfj8Uh4p/rUFVwiXCRCGjn64h+zdzQs7ELAl1tYGbC4Q2P2rNnj9b2unXr4OrqirNnz6JHjx6acisrK7i7u+ttY+/evbh69Sr27dsHNzc3tGnTBh9//DFmzpyJjz76CObm5li9ejX8/f2xdOlSAEDTpk1x7NgxLFu2TJPA+fLLL/Hmm2/i1VdfBQCsXr0au3fvxk8//YQPPvgA2dnZ+PHHH7Fp0yY89dRTAIC1a9eiadOmOHnyJDp37qw3PrlcDrlcrtmWlnE8PdVgtw8B+z4qWvbbEGIzwKcj4N8TaNSzKGHD+WaIiGo9JnCqmbW1NSZOnIjp06fDyckJvr6+WLx4MfLy8vD6669r1V24cCEaNGgANzc3zJkzB87Ozhg2bBgAYMqUKRg4cCAaN26MzMxMHDx4UCu5Q0RENUdWngLTt1xC5NUHFWpHLAJe6OiLCT0D4ONkVfoBpJGdXbSc8qOLBQDAxo0bsWHDBri7u2PIkCGYN2+ephdOVFQUWrZsqTWMKSwsDBMnTsSVK1fQtm1bREVFoW/fvlpthoWFab6UUSgUOHv2LGbNmqXZLxaL0bdvX0RFFfWuOHv2LJRKpVY7TZo0ga+vL6KiokpM4ERERGDBggXlfESoRkm+DOydW5TAKSszKyCwL9D0GaBx/6JJhImIqE5hAqcG+Pzzz6FWq/HKK68gJycH7du3x7///gtHR0edepMnT8atW7fQpk0b/PXXXzA3NwdQ1K07PDwcCQkJsLOzw4ABA7Bs2bLquBwiInqCA9cfYM72aCRlV2x532A3Wywa0QptfBwqJ7B6RK1WY8qUKejatStatGihKX/xxRfh5+cHT09PXLp0CTNnzsSNGzewbds2AEBycrLOHDTF28nJyU+sI5VKkZ+fj8zMTKhUKr11rl+/rmnD3NwcDg4OOnWKz6PPrFmzMG3aNM22VCqFj49PWR4SqinkucChCODkKkBQle2Yht2Btq8ATYcA5kzkEhHVZbU+gWMtMcGWFxqioZ8fJBaS6g5Hh41E/0O8bt06zc8WFhb4+uuv8fXXX+ut26tXLwgP15MdPHiw3jrffPNNxQIlIqIqdfNBDj77+xoO3UitcFuvd/PHzAFNYG7KYVLlER4ejujoaBw7dkyrfPz48ZqfW7ZsCQ8PD/Tp0wexsbEICAgwdpgGk0gkkEhq3nshKqMbe4Dd7wHShNLrWtgDIa8CIWMBJ648SkRUX9T6BI5IJIK1uRg2FqawsODYXiIiqjkEQcDZu5lYffg29l2r2HApAJCYivH58JZ4tq13JURXP02aNAm7du3CkSNH4O395MexU6dOAICYmBgEBATA3d0d//33n1adBw+KntfieXPc3d01ZY/WsbOzg6WlJUxMTGBiYqK3zqNtKBQKZGVlafXCebQO1SGKPGDvHODMT6XXtfMGukwC2r4MSGyrPjYiIqpR+NUdERFRJYtNzcXKQzHot+wIRqyOqpTkjae9BbZM6MLkTTkJgoBJkyZh+/btOHDgAPz9/Us95sKFCwAADw8PAEBoaCguX76MlJQUTZ3IyEjY2dmhWbNmmjr79+/XaicyMhKhoaEAAHNzc4SEhGjVUavV2L9/v6ZOSEgIzMzMtOrcuHED8fHxmjpURyRHA9/3LD15Y9UAGPA58O45oPNEJm+IiOqpWt8Dh4iIqLqp1QIuJGRh75UHiLyajNhUWaW237GhE1a+3A7ONhweU17h4eHYtGkTdu7cCVtbW81cMvb29rC0tERsbCw2bdqEp59+Gg0aNMClS5cwdepU9OjRA61atQIA9O/fH82aNcMrr7yCxYsXIzk5GXPnzkV4eLhm6NKECROwYsUKzJgxA6+99hoOHDiAP/74A7t379bEMm3aNIwdOxbt27dHx44d8dVXX0Emk2lWpbK3t8frr7+OadOmwcnJCXZ2dnjnnXcQGhpa4gTGVAtFbwV2hAOF+SXXEZsBXd8Fuk1l0oaIiJjAISIiKg9FoRonYtOw9+oDRF59gNQceekHlcOYUD/MHdSM891U0KpVqwAUzSv3qLVr12LcuHEwNzfHvn37NMkUHx8fDB8+HHPnztXUNTExwa5duzBx4kSEhobC2toaY8eOxcKFCzV1/P39sXv3bkydOhXLly+Ht7c31qxZo1lCHACef/55pKam4sMPP0RycjLatGmDPXv2aE1svGzZMojFYgwfPhxyuRxhYWFYuXJlFT06ZFRqFXDgE+DYl0+u59cNGPwl4BJsnLiIiKjGEwnFs+PWYFKpFPb29sjOzoadnZ3WvoKCAsTFxcHf3x8WFhbVFGHdwMeSiKh0mTIF1h6Pw8ZT8UiXKarsPGYmInw8tAVGd/StsnNUhSe9ZpNx8bmogQoVwPa3gCvbSq5jZg0MiADajQFEIuPFRkRE1aasr9nsgUNERFQG0gIlfjhyGz8di4NMUcblfcvJ3c4C377UDiF+jlV6HiIyInku8PvLwO2DJdfxbAcMXwM0qPmrnhERkfExgUNERFSKvVeSMXt7NNJyq2aY1KP6NnXDkhGt4GhtXuXnIiIjKcgGfnkOSDxTcp0ObwJhnwGm/NsnIiL9mMAhIiIqQU6BEvN3XsG284lVfi4LMzFmDWyKMaF+EHHYBFHdIc8BNowoOXkjNgMGLQVCxho3LiIiqnVqfwJHECBWyopeHFF1cxGUm8SW45eJiGqhq/eleHvjWdxJz6vycw1u5YFZTzeFl4NllZ+LiIxIkQdsGg0k/Kd/v8QOeOFXoGE348ZFRES1Uu1P4ChyEbytT3VHUbIP7gEWlT9x4KFDh9C7d29kZmbCwcGh0tsnIqqvBEHAH2fu4cOdVyAvVFfpuUL8HDEjLBidGjWo0vMQUTVQFQJ/vALcPaZ/v7Ur8PJWwKOVceMiIqJay6A1SVetWoVWrVrBzs4OdnZ2CA0NxT///FNi/XXr1kEkEmnduLpRkXHjxmkeEzMzM/j7+2PGjBkoKCio7tCIiOqtPEUh3tt8ETO3Xq7S5E3vYBf88VYotkwIZfKGqC4SBOCf6UDMPv377byA1/YweUNERAYxqAeOt7c3Pv/8cwQFBUEQBKxfvx5Dhw7F+fPn0bx5c73H2NnZ4caNG5ptjuv/vwEDBmDt2rVQKpU4e/Ysxo4dC5FIhEWLFlV3aERE9U5MSg7e3ngONx/kVkn7zjYSDA/xwqj2PghwsamScxBRDRH1LXDmJ/37bNyAsX9xpSkiIjKYQT1whgwZgqeffhpBQUFo3LgxPv30U9jY2ODkyZMlHiMSieDu7q65ubm5VTjoukIikcDd3R0+Pj4YNmwY+vbti8jISACAXC7Hu+++C1dXV1hYWKBbt244ffq0ThvHjx9Hq1atYGFhgc6dOyM6Olqz7+7duxgyZAgcHR1hbW2N5s2b4++//zba9RER1RZbziZgyDfHKz154+NkiTe6+eOPt0JxanYfzBrYlMkborruxh5g71z9+6waAGP+ZPKGiIjKpdxz4KhUKmzevBkymQyhoaEl1svNzYWfnx/UajXatWuHzz77rMTeOsXkcjnk8v8v1SqVSssbZq0RHR2NEydOwM/PDwAwY8YMbN26FevXr4efnx8WL16MsLAwxMTEwMnJSXPc9OnTsXz5cri7u2P27NkYMmQIbt68CTMzM4SHh0OhUODIkSOwtrbG1atXYWPDDw5ERMVk8kJ8uPMKtp5LqLQ2W3jZoX8zd/Rv7oZgN1v2PCWqTzLvANvHAxB095laAi9tBlybGDsqIiKqIwxO4Fy+fBmhoaEoKCiAjY0Ntm/fjmbNmumtGxwcjJ9++gmtWrVCdnY2vvjiC3Tp0gVXrlyBt7d3ieeIiIjAggULDA2t1tm1axdsbGxQWFgIuVwOsViMFStWQCaTYdWqVVi3bh0GDhwIAPjhhx8QGRmJH3/8EdOnT9e0MX/+fPTr1w8AsH79enh7e2P79u0YNWoU4uPjMXz4cLRs2RIA0KhRI+NfJBFRDXXkZirm7LiMexn5FW7L094Cr3b1x9OtPLiSFFF9pSwA/hgLFGTr2SkChv8AeIUYPSwiIqo7DE7gBAcH48KFC8jOzsaWLVswduxYHD58WG8SJzQ0VKt3TpcuXdC0aVN89913+Pjjj0s8x6xZszBt2jTNtlQqhY+Pj6Gh1ni9e/fGqlWrIJPJsGzZMpiammL48OG4dOkSlEolunbtqqlrZmaGjh074tq1a1ptPPr4Ojk5ITg4WFPn3XffxcSJE7F371707dsXw4cPR6tWnCyPiOq3exl5+HzPdey+lFThtmwlpng/LBijO/pAYmpSCdERUa3172wg6YL+ff0/BpoOMWo4RERU9xg0Bw4AmJubIzAwECEhIYiIiEDr1q2xfPnyMh1rZmaGtm3bIiYm5on1JBKJZqWr4ltdZG1tjcDAQLRu3Ro//fQTTp06hR9//LHS2n/jjTdw+/ZtvPLKK7h8+TLat2+Pb775ptLaJyKqTe5l5GHhX1fRZ+nhSknedA1sgH+n9sDYLg2ZvCGq7679BZwp4T1cy1FA6CTjxkNERHWSwQmcx6nVaq35ap5EpVLh8uXL8PDwqOhp6xyxWIzZs2dj7ty5CAgIgLm5OY4fP67Zr1Qqcfr0aZ2eTo9OIJ2ZmYmbN2+iadOmmjIfHx9MmDAB27Ztw3vvvYcffvih6i+GiKiGUKkFHI9JQ/jGc+i55CB+Oh4Hhapiy4OLRMC0fo3x82ud4MnhUkQkSwP+mqJ/n3MwMHhZ0T8OIiKiCjJoCNWsWbMwcOBA+Pr6IicnB5s2bcKhQ4fw77//AgDGjBkDLy8vREREAAAWLlyIzp07IzAwEFlZWViyZAnu3r2LN954o/KvpA4YOXIkpk+fjlWrVmHixImYPn06nJyc4Ovri8WLFyMvLw+vv/661jELFy5EgwYN4Obmhjlz5sDZ2RnDhg0DAEyZMgUDBw5E48aNkZmZiYMHD2old4iI6qIMmQJn7mTgWEwa/r6cjLTcsn3JUBbONuZYProtugY6V1qbRFSLCQKwexqQl6a7z8wKGPUzIOECEkREVDkMSuCkpKRgzJgxSEpKgr29PVq1aoV///1XM4lufHw8xOL/d+rJzMzEm2++ieTkZDg6OiIkJAQnTpwocdLj+s7U1BSTJk3C4sWLERcXB7VajVdeeQU5OTlo3749/v33Xzg6Omod8/nnn2Py5Mm4desW2rRpg7/++gvm5uYAino8hYeHIyEhAXZ2dhgwYACWLVtWHZdGRFQlBEHA3fQ8nL6TgTN3MnH6bgZup8qq5Fwd/Z3wzQtt4WZnUSXtE1EtFL0VuLpT/76nv+CKU0REVKlEgiDoWeewZpFKpbC3t0d2drbOfDgF+fm4e+sK/Pz8YCGRVFOETyCxrTXdZgsKChAXFwd/f39YWPADChHVPEqVGlfvSzUJmzN3M5CWq6jy807sFYD3+jWGqUmFRx7XeU96zSbj4nNRxWTpwIr2QH6G7r7gQcDojbXmPSAREVWvsr5mG7wKVY0jEkFtZl2UKGHSgYioTskpUOJ8fBbO3MnA6TuZuHAvC/lKldHO72Rtji9HtUavYFejnZOIaon9H+lP3lg6AUO+YvKGiIgqXe1P4BARUZ2hUgs4dTsdh2+lIio2HdGJ2VBXUz/RTv5O+JpDpohIn3ungXM/6983+EvAhklfIiKqfEzgEBFRtbt6X4qt5xLw18X7SMmpvEmHy8PSzATT+jXGq10bcsgUEelSq4omLtanyWCg+bPGjYeIiOoNJnCIiKhaCIKAYzFpWH04Fsdj0qs7HABA72AXLBzaAj5OVtUdChHVVGd+ApIv6ZabWQEDPjd+PEREVG8wgUNEREZ3OSEb83ZG48K9rOoOBQDQ2M0GcwY1Q8/GLtUdChHVZAVS4FCE/n09pgMOPsaNh4iI6hUmcIiIyGgKlCp8te8Wvj8SW21z2zwqyNUGb/ZohOfaenG4FBGV7vhyIE9Pj8EGQUDoJOPHQ0RE9QoTOEREZBQ3knPw9saziE2VVXco6NHYBa9380ePIGeIuFIMEZWFNAmI+lb/voGLAFNz48ZDRET1DhM4RERU5fZdfYDJv52HTGG8JcAf18LLDkNaeWJQKw94O3KOGyIy0KEIoDBftzzgKSCwj/HjISKieocJHCIiqjJqtYBvDsTgq/03IRh5yJSPkyU6+DkhpKEjugY4o6GztXEDIKK6I+0WcP4X/fv6fmTUUIiIqP5iAoeIiKrExXtZ+OzvazgVl1Hl5xKLgGaedmjv54QODZ3QvqEj3Owsqvy8RFRPHFkCCGrd8pajAI/Wxo+HiIjqJSZwqsm4ceOQlZWFHTt2VHcoRESVKkVagEV7bmDruYQqO4eVuQna+jpoEjZtfB1gI+FLGhFVgfRY4PJm3XITc+CpucaPh4iI6q069W73oxMf4VbWrWqNIcghCB91+ahaYyAiqi7/XE7CzK2XIC0orNR2nW0k6OjvqEnYNPWw5apRRGQcx77U3/smZBzg6Gf0cIiIqP6qUwmcW1m3cCn1UnWHQURUL/14LA4f77paae01cbfF0DZe6NvUFYGuNlwtioiML/MucPE33XKxGdB1svHjISKieq1OJXCIiKh6/HnxfqUkb8QiYGALD4zv0QitfRwqHhgRUUUcXw6o9fQobPsSYO9t/HiIiKheYwKHiIgq5EZyDmZuqXjvx2Yedlg8ohVaeNlXQlRERBWUm6J/5SmRCdBtqvHjISKieo8JHCIiKjdpgRITNpxFvlJVoXbCewdgSt/GMOO8NkRUU5z+EVApdMtbPQ84NjR6OERERHUqgRPkEFTdIdSIGIiIjEGtFjDt9wuIS5OVuw0LMzEWj2iNZ1p7VmJkREQVpCwATq/Rs0MEdJ9m9HCIiIiAOpbA4epPRETGs3z/Ley7llLu432cLPHdy+3RzNOuEqMiIqoE0VuAvDTd8sYDAGd+WUdERNWjTiVwiIjIOPZeScby/bfKffyzbb0wf0gzOFiZV2JURESVQBCAqJX694W+bdxYiIiIHsEEDhERGeR2ai6m/XGxXMd2buSEuYOacaJiIqq54g4DKVd0y91aAg27Gz8eIiKih5jAqSbr1q2r7hCIiAyWr1Dh7Y3nkCvXs6xuKZaMaIURId4QiURVEBkRUSU59Z3+8s4TAf7/IiKiasQEDhERlYkgCJiz4zKuJ+cYfOy7TwViZHufKoiKiKgSSe8DN/follu7Ai1HGD8eIiKiR3C9ViIiKpMfjt7GtnOJBh/Xo7ELJvdtXAURERFVsvMbAEGtW97+NcBUYvx4iIiIHsEEDhERlWrDybuI+Oe6wcf5OFni69FtYCLmsAMiquHUKuDcz7rlIjHQbozx4yEiInoMh1AREVGJVGoBS/69gdWHYw0+1tLMBN+/0p4rTRFR7RCzH8i+p1seFAbYexk/HiIioscwgUNERHolZxdgyu/ncfJ2RrmOXzKyFZp62FVyVEREVeTsOv3lIeOMGQUREVGJDBpCtWrVKrRq1Qp2dnaws7NDaGgo/vnnnyces3nzZjRp0gQWFhZo2bIl/v777woFTEREVUsQBOy8kIgBy4+UO3nzRjd/DG7lWcmRERFVEWmS/smL7byAoH7Gj4eIiEgPg3rgeHt74/PPP0dQUBAEQcD69esxdOhQnD9/Hs2bN9epf+LECbzwwguIiIjA4MGDsWnTJgwbNgznzp1DixYtKu0iiIioYpQqNc7ezcShG6n49b94ZOcry91Wh4aOmDmwSSVGR0RUxS7+Cggq3fJ2YwCxifHjISIi0kMkCIJQkQacnJywZMkSvP766zr7nn/+echkMuzatUtT1rlzZ7Rp0warV68usU25XA65XK7Zlkql8PHxQXZ2NuzstLvjFxQUIC4uDv7+/rCwsKjIpdR7fCyJ6pfErHwcvZmKQzdScSwmDbnywgq36Wwjwe53u8HNjv9D6iupVAp7e3u9r9lkXHwuykgQgJWdgdTHJmoXiYEplwF77+qJi4iI6o2yvmaXew4clUqFzZs3QyaTITQ0VG+dqKgoTJs2TassLCwMO3bseGLbERERWLBgQXlDIyIiPe5l5OFEbBpOxWXg1O0MJGblV2r7ElMxfhgTwuQNEdUuSRd1kzcA0Kg3kzdERFSjGJzAuXz5MkJDQ1FQUAAbGxts374dzZo101s3OTkZbm5uWmVubm5ITk5+4jlmzZqllfgp7oFDRESGKVCq8PflJGw8FY+zdzOr7DwiEbB0VGu09XWssnMQEVWJi7/pL2/9gnHjICIiKoXBCZzg4GBcuHAB2dnZ2LJlC8aOHYvDhw+XmMQpD4lEAolEUmnt1UTjxo3D+vXrAQCmpqbw9vbGyJEjsXDhQg5fIqIKi0uTYdOpu9h8NgFZeeWfz6YszExEWDKiNSctJqLaR6UEorfolpvbAE0GGT8eIiKiJzA4gWNubo7AwEAAQEhICE6fPo3ly5fju+++06nr7u6OBw8eaJU9ePAA7u7u5Qz3yZLmzYP85q0qabusJI2D4PHxx2WqO2DAAKxduxZKpRJnz57F2LFjIRKJsGjRoiqOkojqIkEQcDwmHd8dicXRW2lGOaetxBSrXwlB10Bno5yPiKhSxR4AZKm65U2fAcytjB8PERHRE5R7DpxiarVaa8LhR4WGhmL//v2YMmWKpiwyMrLEOXMqSn7zFvIvXqyStquCRCLRJLN8fHzQt29fREZGMoFDRAYRBAH7rqVgxYFbuJiQbbTzNnKxxqqXQhDsbmu0cxIRVaoSh0+NNm4cREREZSA2pPKsWbNw5MgR3LlzB5cvX8asWbNw6NAhvPTSSwCAMWPGYNasWZr6kydPxp49e7B06VJcv34dH330Ec6cOYNJkyZV7lXUAdHR0Thx4gTMzc2rOxQiqkXO3s3AiNVRePPnM0ZN3jzXzgt/TerG5A3VGhEREejQoQNsbW3h6uqKYcOG4caNG1p1CgoKEB4ejgYNGsDGxgbDhw/X6UkcHx+PQYMGwcrKCq6urpg+fToKC7VXcDt06BDatWsHiUSCwMBArFu3Tieeb7/9Fg0bNoSFhQU6deqE//77z+BYqILkOcCNv3XL7byAht2NHw8REVEpDErgpKSkYMyYMQgODkafPn1w+vRp/Pvvv+jXrx+Aojc1SUlJmvpdunTBpk2b8P3336N169bYsmULduzYgRYtWlTuVdRSu3btgo2NDSwsLNCyZUukpKRg+vTp1R0WEdUCablyhG86h+Groqp0cuLH2VmY4usX2uLLUW1gLalwJ04iozl8+DDCw8Nx8uRJREZGQqlUon///pDJZJo6U6dOxV9//YXNmzfj8OHDuH//Pp577jnNfpVKhUGDBkGhUODEiRNYv3491q1bhw8//FBTJy4uDoMGDULv3r1x4cIFTJkyBW+88Qb+/fdfTZ3ff/8d06ZNw/z583Hu3Dm0bt0aYWFhSElJKXMsVAlu/gsUFuiWtxwJiA16i0xERGQUIkEQhOoOojRPWhO9oKAAcXFx8Pf3R/LYcdU+hMqydWs0/L2E7riPGDduHBITE7Fq1SrIZDIsW7YMpqamWLNmjRGi1O/Rx5ITKRPVXH9fTsLcHdHIkCmMdk4TsQijO/hgSt/GcLGt25PMU8U86TW7JklNTYWrqysOHz6MHj16IDs7Gy4uLti0aRNGjBgBALh+/TqaNm2KqKgodO7cGf/88w8GDx6M+/fva1bZXL16NWbOnInU1FSYm5tj5syZ2L17N6KjozXnGj16NLKysrBnzx4AQKdOndChQwesWLECQNFwdB8fH7zzzjv44IMPyhRLWdSW56La/PYScH2XbvmE44A7v2wkIiLjKetrdp36+lTSOKi6QzAoBmtra82E0D/99BNat26NH3/8Ea+//npVhUdEtZhSpcbCv67il5N3jXZONzsJBrfyxAsdfRHoamO08xJVtezsoiGHTk5OAICzZ89CqVSib9++mjpNmjSBr6+vJmkSFRWFli1bapI3ABAWFoaJEyfiypUraNu2LaKiorTaKK5TPB+gQqHA2bNntYaci8Vi9O3bF1FRUWWORR+5XK41L6FUKi3PQ1M/FEiBW5G65c6NAbfmxo+HiIioDOpUAqesqz/VRGKxGLNnz8a0adPw4osvwtLSsrpDIqIaRFqgxIRfzuJEbHqVnsfMRIQODZ3QK9gFvYJdEeRqA5FIVKXnJDI2tVqNKVOmoGvXrpph3cnJyTA3N4eDg4NWXTc3NyQnJ2vqPJq8Kd5fvO9JdaRSKfLz85GZmQmVSqW3zvXr18sciz4RERFYsGBBGR4Bws1/AZWeRTiaDQP4P4+IiGooDvCtQUaOHAkTExN8++231R0KEdUgMnkhxv70X5Ulb4JcbfBSJ198/0oIzn/YH5ve7IzxPQLQ2M2WyRuqk8LDwxEdHY3ffit9yHNtMmvWLGRnZ2tu9+7dq+6Qaq4r2/WXN3/WuHEQEREZoE71wKntTE1NMWnSJCxevBgTJ06EtbV1dYdERNWsQKnCmz+fwfn4rEpr08rcBM+09kSvYBd0aOiEBjac04bqj0mTJmHXrl04cuQIvL29NeXu7u5QKBTIysrS6vny4MEDuLu7a+o8vlpU8cpQj9Z5fLWoBw8ewM7ODpaWljAxMYGJiYneOo+2UVos+kgkEkgk/HsuVYEUiNmnW+4cDLg2NX48REREZcQeONVk3bp12LFjh075Bx98gJSUFCZviAhKlRqTNp2rtJ43wW62+Hhoc5ya3QefD2+FAS08mLyhekMQBEyaNAnbt2/HgQMH4O/vr7U/JCQEZmZm2L9/v6bsxo0biI+PR2hoKAAgNDQUly9f1lotKjIyEnZ2dmjWrJmmzqNtFNcpbsPc3BwhISFaddRqNfbv36+pU5ZYqAJu7tE/fKr5MA6fIiKiGo09cIiIaqiPd13FvmsppVd8ApEIeLqFB17r1hDtfB05JIrqrfDwcGzatAk7d+6Era2tZi4Ze3t7WFpawt7eHq+//jqmTZsGJycn2NnZ4Z133kFoaKhm0uD+/fujWbNmeOWVV7B48WIkJydj7ty5CA8P1/R8mTBhAlasWIEZM2bgtddew4EDB/DHH39g9+7dmlimTZuGsWPHon379ujYsSO++uoryGQyvPrqq5qYSouFKuDqTv3lHD5FREQ1HBM4REQ10B9n7uHnqPKvNiUSAc+19cbbvQMQ4MLVo4hWrVoFAOjVq5dW+dq1azFu3DgAwLJlyyAWizF8+HDI5XKEhYVh5cqVmromJibYtWsXJk6ciNDQUFhbW2Ps2LFYuHChpo6/vz92796NqVOnYvny5fD29saaNWsQFhamqfP8888jNTUVH374IZKTk9GmTRvs2bNHa2Lj0mKhclLmA7EHdMs5fIqIiGoBkSAIQnUHUZonrYleUFCAuLg4+Pv7w8LCopoirBv4WBLVDFfvSzHs2+NQqNTlOr5nYxfMeroJmrjblV6ZqJI96TWbjIvPhR43/gF+Ha1b3mM68NRc48dDRESEsr9mswcOEVENIi9UYdofF8qVvLGzMMXCoS0wtI0nh0oREelz42/95cEDjRsHERFROdSZBE4t6EhU46nV5fu2n4gqz6pDsbienGPwcY3dbPDj2A7wcbKqgqiIiOoAtRq4sUe33NYD8Ghr/HiIiIgMVOsTOGZmZhCJREhNTYWLiwu/dS4HQRCgUCiQmpoKsVgMc3Pz6g6JqF5KyMzDqkOxBh8X5GqD38eHwtGaf7tERCVKPAvI9EwM33gAIObCrEREVPPV+gSOiYkJvL29kZCQgDt37lR3OLWalZUVfH19IeabGKJq8dnf1yAvNKwnnK+TFTa80YnJGyKi0tzYrb+8ySDjxkFERFROtT6BAwA2NjYICgqCUqms7lBqLRMTE5iamrIHE1E1OR6Thr8vJxt0jIOVGX55vSPc7DjpOBFRqW78o1tmZg007G78WIiIiMqhTiRwgKIEhImJSXWHQURkMLVawCe7rxl0jFgErHihHfwaWFdRVEREdUh6LJB6Xbc8sA9gxiQ4ERHVDhwrQ0RUzf68eB/XkqQGHfN2r0B0C3KuooiIiOoYfb1vACD4aePGQUREVAFM4BARVSN5oQpf7L1h0DGNXKzxbp+gKoqIiKgO0pfAEYmBxmHGj4WIiKicmMAhIqpGm07FIyEz36BjPhzcDOam/PdNRFQmBdlAfJRuuW8oYOVk/HiIiIjKiZ8AiIiqSa68ECsOxBh0TN+mbugV7FpFERER1UG3DwOCSrecvW+IiKiWYQKHiKia/HDkNtJlijLXNzMRYe6gplUYERFRHRQTqb88sJ9x4yAiIqogJnCIiKpBao4ca47eNuiYlzr5oaEzV50iIiozQQBi9uuW23oCrkyIExFR7cIEDhFRNVhx4BZkCj1d+ktgbW6CSU8FVmFERER1UOp1QJqoWx7UFxCJjB8PERFRBTCBQ0RkZHfTZdj0X7xBx7zRvRGcbSRVFBERUR0Vs09/eWBf48ZBRERUCZjAISIyIkEQMGd7NJQqoczHNLA2x5s9GlVhVEREddQtPfPfiEwA/57Gj4WIiKiCmMAhIjKi307fw7GYNIOOmfRUIGwkplUUERFRHSXP1b98uE9HwNLB6OEQERFVFBM4RERGkpiVj093XzPoGG9HS7zYybeKIiIiqsPuHANUelb6C+xj/FiIiIgqgUEJnIiICHTo0AG2trZwdXXFsGHDcOPGjSces27dOohEIq2bhYVFhYImIqptBEHArG2XkSsvNOi49/o3hsTUpIqiIiKqw0qc/4bLhxMRUe1kUALn8OHDCA8Px8mTJxEZGQmlUon+/ftDJpM98Tg7OzskJSVpbnfv3q1Q0EREtc3Wc4k4cjPVoGOaethhaGuvKoqIiKgOEwQgRs/8N9YugHsr48dDRERUCQyaVGHPnj1a2+vWrYOrqyvOnj2LHj16lHicSCSCu7t7+SIkIqrlsvOUiPjbsKFTADD76SYQi7nMLRGRwTLjgMw7uuUBfQAxZxAgIqLaqUKvYNnZ2QAAJyenJ9bLzc2Fn58ffHx8MHToUFy5cuWJ9eVyOaRSqdaNiKi2WrbvJtJleuZheIIRId7oHuRSRREREdVxtw/rL+f8N0REVIuVO4GjVqsxZcoUdO3aFS1atCixXnBwMH766Sfs3LkTGzZsgFqtRpcuXZCQkFDiMREREbC3t9fcfHx8yhsmEVG1upYkxc9Rdww6xs1OgnmDmlVNQERE9UFcCQkcLh9ORES1WLkTOOHh4YiOjsZvv/32xHqhoaEYM2YM2rRpg549e2Lbtm1wcXHBd999V+Ixs2bNQnZ2tuZ279698oZJRFRtVGoBc7Zfhlow7LjPnm0JeyuzqgmKiKiuU6uBuCO65S5NAVs348dDRERUSQyaA6fYpEmTsGvXLhw5cgTe3t4GHWtmZoa2bdsiJiamxDoSiQQSiaQ8oRER1Ri/RN3Bufgsg455rq0X+jTlBwwionJLuQLkpeuWN+pl9FCIiIgqk0E9cARBwKRJk7B9+3YcOHAA/v7+Bp9QpVLh8uXL8PDwMPhYIqLaIjY1F4v/vWHQMU7W5vhwCIdOERFVSEnz3zTi8CkiIqrdDOqBEx4ejk2bNmHnzp2wtbVFcnIyAMDe3h6WlpYAgDFjxsDLywsREREAgIULF6Jz584IDAxEVlYWlixZgrt37+KNN96o5EshIqoZcgqUmLjhLPIUKoOO+2BAEzhYmVdRVERE9YS++W9EJoBfV+PHQkREVIkMSuCsWrUKANCrVy+t8rVr12LcuHEAgPj4eIgfWZ4xMzMTb775JpKTk+Ho6IiQkBCcOHECzZrxW2YiqnuUKjXe3ngONx/kGnRcax8HjAgxbEgqERE9plAB3DmuW+7VDrCwM348RERElcigBI4glD4T56FDh7S2ly1bhmXLlhkUFBFRbaRWC5i97TKO3koz6DiRCFj4THOIxaIqioyIqJ5IPAsoZbrlXH2KiIjqgHJNYkxERNoKlCp8sPUSdly4b/CxY0MborWPQ+UHRURU35S0fDgnMCYiojqACRwiogq6kybD2xvP4WqS1OBjPe0t8H5YcBVERURUD+mbwNjUEvDpaPxYiIiIKhkTOEREFfDP5STM2HIJOfLCch3/6XMtYSPhv2IiogpTyICE07rlvp0BU4nx4yEiIqpk/NRARFQOikI1Iv65hrXH75S7jfE9GqF3sGvlBUVEVJ/FRwFqpW45lw8nIqI6ggkcIiIDJWTmIXzTeVy8l1XuNkIbNcB0Dp0iIqo8+lafAjiBMRER1RlM4BARGeDg9RRM+f0CsvP1fMtbRgEu1lj9cgjMTMSVGBkRUT13V08CR2IHeLQ2fixERERVgAkcIqIyUKsFrDgYg2X7bkIQyt+Os4051r3aEfZWZpUXHBFRfaeQAYnndMt9OwNiE+PHQ0REVEaCICBPUbb5NJnAISIqRU6BEu/9cRF7rz6oUDtO1uZY/1pH+DhZVVJkREQEALj3n/75b/y6Gj8WIiKqN9RqAdICJdJlCmTKFMjKU0KmKEROQSFk8kLkyrV/1tweluXIi+4LC/LKdD4mcIiInuDWgxy89ctZ3E6TVagdV1sJNr7RCUFutpUUGRERaegbPgUADbsZNw4iIqpTMmQKxKXlIjGrAElZ+biflY/72QW4n5WPB9ICZOYpoVJXoHu+gZjAISIqwa5L9zFjyyXkKVQVaqetrwO+fbEdPB0sKykyIiLSom8CYzNrzn9DRERlUqhS43pyDq4mSXEzOQc3HuTgenIOUnPk1R2aFiZwiIgeU6hS4/N/rmPNsbgKt/VGN3/MGNAE5qacsJiIqEoo84HEM7rlvp0BE843RkREuhSFapy9m4lTcek4cycT5+IzK/ylrTEwgUNE9IjUHDkmbTqHU3EZFWrH1sIUS0a0xoAW7pUUGRER6ZVwBlApdMsbcv4bIiIqIggCYlJycTwmDcdj0xEVm45cedkmDq5JmMAhIkLRP/Wo2HRM++MikqUFFWqrhZcdVr4YAt8GnKyYiKjK3Tmmv9yP898QEdVXgiAgITMfUbHpOB6bhhOx6TVuOFR5MIFDRPVWnqIQ/8Vl4NzdTBy6mYpLCdkVbvPlzr6YO6gZLMy4bC0RkVHom8DY1BLwbGv8WIiIqFrkK1SITc3F+fhM/HcnE6fjMir8pWxNxAQOEdUbKrWA6MRsHItJw9FbqTh3NwsKlbpS2rYwE2PR8FYY2sarUtojIqIyKJQDCad1y306Aqbmxo+HiIiqjCAISJYWIDZFhttpubidKkNsatF9YlZ+dYdnFEzgEFGdFp+eh6MxqUXjXWPSkZ2vrPRzeDta4rtXQtDc077S2yYioidIPAsU6vmGtWF348dCRESVIl+h0iRoNEmah9u1YaLhqsQEDhHVKdl5SpyITcPRmDQcu5WG+Iy8Kj1f9yBnfD26LRyt+U0vEZHR6Vs+HOAExkRENZwgCEjJkSMmJRe3U3MRWw9705QHEzhEVOspVWpsP5+IP07fw7n4TKgF45w3vHcApvULholYZJwTEhGRtjtHdctMLQCvEOPHQkREOlRqAQmZeYhJydXcbqXkIjYlFzm1cBWo6sYEDhHVWiq1gJ0XEvHVvltV3tPmUbYSUywd1Rr9m3OJcCKiaqNS6p//xrsDYCoxfjxERPWQIAiQKVRIzZHjXkYe4jPyNPd30vNwOzUX8sLKmXOyJhOJAEcrczhamcHe0gzWElPYWpjCRmJa9PPDe5uHZcW34npqeR4afVX6eZjAIaJaKT49D+/8dh4X72UZ9byN3Wyw+uUQNHKxMep5iYjoMcmXAKWe5L1fF+PHQkRURxQoVcjMUyBDpkCmTImMPAUyZQ+38/5/n55bdJ8pU1baoiA1lUgE+DpZwcfRCp4OFvB0sISnvSU8HSzhbi+Bk7UE9pZmFeqVL5WWbW4fJnCIqNZJkRbghR9OGn187OBWHlg0vBWsJfzXSURU7eJP6i/3DTVuHEREtYCiUI2k7HwkZOYjMTMfCZl5SMjKR1qudoKmvk8SbGEmRhsfBzT3tEewuy2auNsiyNUWluYm1R0aACZwiKiWyVeoMP6Xs0ZN3piIRZg1sAle7+YPkYjz3RAR1Qj6EjgiE8C7vfFjISKqAaQFSs08M3fSiiYDLk7YPMgpgGCkeSJrEzMTETr5N0CPxs7o0NAJLbzsYWYiru6wSsQEDhHVGoIgYMbWS7hgxGFTvYJdMGtgUwS72xrtnEREVApB0J/AcW8BSPj/mojqtnyFCleTpLieLMWtB7mITc3FrQe5SJYWVHdoNZ5IBLTwtEeXwAboEuCMDg0dYWVee9IiNTe1RET0mN2Xk/DXxftVfh5biSle6OiLfdN6Yt2rHZm8Iaojjhw5giFDhsDT0xMikQg7duzQ2j9u3DiIRCKt24ABA7TqZGRk4KWXXoKdnR0cHBzw+uuvIzc3V6vOpUuX0L17d1hYWMDHxweLFy/WiWXz5s1o0qQJLCws0LJlS/z9999a+wVBwIcffggPDw9YWlqib9++uHXrVuU8EHVBxm1AlqJbzuFTRFTHqNQCohOz8XPUHby/+SIGfHUELT76F8NXncCc7dFYd+IOjt5KY/KmBCIR0MTdFmNC/bD65RBcmNcff73TDbMGNkXPxi61KnkDsAcOEdUShSo1Fu25XiVt20hM0blRA3QJaICW3vZo5W0PiWnNGOdKRJVHJpOhdevWeO211/Dcc8/prTNgwACsXbtWsy2RaK9m9NJLLyEpKQmRkZFQKpV49dVXMX78eGzatAkAIJVK0b9/f/Tt2xerV6/G5cuX8dprr8HBwQHjx48HAJw4cQIvvPACIiIiMHjwYGzatAnDhg3DuXPn0KJFCwDA4sWL8fXXX2P9+vXw9/fHvHnzEBYWhqtXr8LCwqIqHp7a5d4p/eW+nY0bBxFRJZPJCxGbmosr96U4eisVx2PSkZ2vrO6wag0XWwkCXKzRxscRHf0dEeLrBHsrs+oOq9KIBKHmj4STSqWwt7dHdnY27OzsqjscIqoGOy8kYvJvFyqlLROxCG19HNAtyBndg5zR2tsBpjV4rCtRbVJbXrNFIhG2b9+OYcOGacrGjRuHrKwsnZ45xa5du4ZmzZrh9OnTaN++aJ6VPXv24Omnn0ZCQgI8PT2xatUqzJkzB8nJyTA3NwcAfPDBB9ixYweuXy9KQj///POQyWTYtWuXpu3OnTujTZs2WL16NQRBgKenJ9577z28//77AIDs7Gy4ublh3bp1GD16dJmusbY8F+Xy5zvAuZ91y6ddB+w8jB8PEVEZCIKAHHkhUqRypEgLkJIjxwNpAZKyCxCbmovYlFzcz2ZPmtKYm4jR0NkKAS42aORi/fC+6Gc7i9qZrCnra7ZBPXAiIiKwbds2XL9+HZaWlujSpQsWLVqE4ODgJx63efNmzJs3D3fu3EFQUBAWLVqEp59+2pBTE1E9JggCVh++XaE2Alys0T3IBV0DndG5kRNsa+k/dyKqWocOHYKrqyscHR3x1FNP4ZNPPkGDBg0AAFFRUXBwcNAkbwCgb9++EIvFOHXqFJ599llERUWhR48emuQNAISFhWHRokXIzMyEo6MjoqKiMG3aNK3zhoWFaRJHcXFxSE5ORt++fTX77e3t0alTJ0RFRZWYwJHL5ZDL5ZptqVRa4cejxtI3/42DH5M3RFQt5IUqpObIkZarQFqOHGm58ofbRWWpOXKk5BTggVSOfGX9XuXJEM42Rb1pGrnYIOBhoibAxQZejpYVWrK7NjMogXP48GGEh4ejQ4cOKCwsxOzZs9G/f39cvXoV1tbWeo8pSzdhIqInOXs3E9eSDP8g8kxrT3QLcka3QGd4OlhWQWREVJcMGDAAzz33HPz9/REbG4vZs2dj4MCBiIqKgomJCZKTk+Hq6qp1jKmpKZycnJCcnAwASE5Ohr+/v1YdNzc3zT5HR0ckJydryh6t82gbjx6nr44+ERERWLBgQTmuvJaRpQNpN3XLOf8NEVWBAqUKMSm5uJuehwcPe80U954pTspwiFP5mYpFaOhsjUCt3jRFSRt7S37h+jiDEjh79uzR2l63bh1cXV1x9uxZ9OjRQ+8xy5cvx4ABAzB9+nQAwMcff4zIyEisWLECq1ev1ntMvfoGiYhKtem/eIPqm4pFOPBeL/g2sKqiiIioLnq0Z0vLli3RqlUrBAQE4NChQ+jTp081RlY2s2bN0urZI5VK4ePjU40RVZES57/pZNw4iKhOUarUuPkg5+EtF7ce5OBWSi7iM/K4/HYlsDQzQYBrUaIm0NUGga62CHS1gV8Dqxq9bHdNU6FJjLOzswEATk5OJdYprZuwPvXmGyQiKlWGTIHdl5LKXF9iKsaRGb3hZsdJPomoYho1agRnZ2fExMSgT58+cHd3R0qK9spHhYWFyMjIgLu7OwDA3d0dDx480KpTvF1anUf3F5d5eHho1WnTpk2J8UokEp1Jl+uk+Cj95eyBQ0QGyClQ4vSdDJy9m4mzdzNx8V42hzdVkJ2FKXwbWMHXyQo+TlbwcSy6b+RsDS8HS4jr6bCnylTuBI5arcaUKVPQtWvXJw6FKq2bsD715hskIirVz1F3IC9Ul7n+qPY+TN4QUaVISEhAenq6JokSGhqKrKwsnD17FiEhIQCAAwcOQK1Wo1OnTpo6c+bMgVKphJlZUdfvyMhIBAcHw9HRUVNn//79mDJliuZckZGRCA0tSkD4+/vD3d0d+/fv1yRspFIpTp06hYkTJxrj0ms2fT1wLBwA5yfPyUhE9ZtaLeBCQhYOXk/B8Zg0XEzIhkrNrjUlsTQzgZO1ORytzeBoZY4G1uZwtDaHk9XDe2tzOFqZa+o4WJrD3JQ9aapauRM44eHhiI6OxrFjxyozHgD16BskInqifIUK60/cMeiY17r5l16JiOql3NxcxMTEaLbj4uJw4cIFODk5wcnJCQsWLMDw4cPh7u6O2NhYzJgxA4GBgQgLCwMANG3aFAMGDMCbb76J1atXQ6lUYtKkSRg9ejQ8PT0BAC+++CIWLFiA119/HTNnzkR0dDSWL1+OZcuWac47efJk9OzZE0uXLsWgQYPw22+/4cyZM/j+++8BFK2QNWXKFHzyyScICgrSLCPu6emptWpWvaQsAO6f1y336QSI+cGBiLQVKFX4Ly4De68mY++VB0jJkZd+UB1na2EKb0creDlYwtux6OZiK9FOyFiZw9LcpLpDJT3KlcCZNGkSdu3ahSNHjsDb2/uJdUvrJkxUkwiCgEJ1IZRqpeYmCAJUgkrnXg011Gp10b2g//b4cWqoITwcRCtA0Dpv8faj+zV1hP/Xf7SeVpmmqv79wiODdx8/l7E9eu1PcuxWGnLMEmFqX7Z2g1xtcDFLiYtZJZy3ng1gLuvjTFSZ8nLzqjuEEp05cwa9e/fWbBf39h07dixWrVqFS5cuYf369cjKyoKnpyf69++Pjz/+WOtLpY0bN2LSpEno06cPxGIxhg8fjq+//lqz397eHnv37kV4eDhCQkLg7OyMDz/8EOPHj9fU6dKlCzZt2oS5c+di9uzZCAoKwo4dO7R6NM+YMQMymQzjx49HVlYWunXrhj179sDCop73MLx/HlApdMt9Oxs/FiKqcQRBwPXkHBy7lYYjt1LxX1yGQT256wovB0sEuhat2OTrZAkvRyt4O1rCy9Gy1i6zTUVEggGfaARBwDvvvIPt27fj0KFDCAoKKvWY559/Hnl5efjrr780ZV26dEGrVq1KnMT4cWVdE53qLkEQkF+YD6lCihxFjuY+R5EDmVKGgsIC5KvyUVBYUPRzYb5OmVwlh1Kt1ErQFKoLoVQpUSj8/74KLwIiARALgFj98P7hz5py4f8/QwCKR4mKhMfuAU3CRvRIud7tR9opbrO0/XisTlUy2nmMcZK6dC1E5aQsUOGvT67zNbsGqJPvn45+CezXM0/iq3sAP86BQ1QfpUgLcCwmDUdvpeFYTBpS61EvG2cbc7TwskcTdzsEudogyK0oaWMtqdBUt1QNyvqabdAzGx4ejk2bNmHnzp2wtbXVzGNjb28PS8uiJXrHjBkDLy8vREREACi9mzDVb1KFFPdz7yNZloz0/HSkF6QjLT9N83N6fjqy5FnIUeRAJZRjUjFBgEQJWBcA1nLAQgHYKIvKim8WCsC8ELBQPCwvBEwLAVMVYKp+eK+5CSWUAybqx5Iw6sd+rvyHj4ioxslVqfBX6dWIyif+pG6ZiTng2db4sRBRtchTFOK/uAwce5iwuZ6cU90hGYWpWIS2vg7o6O+EVt4OaOllDw97C4hE/GqvPjEogbNq1SoAQK9evbTK165di3HjxgEA4uPjIX5kDHJZuglT3ZZVkIXY7FjEZsUiLjsO93Pv477sPhJzEpGjNPAfriDAugBwkAGOuQIccgFHGeDw8GebAsC6QIBNAWBVULRtWv96TRIREdU9arX+CYw92wJm9XxoGVEdplSpcSkhGydiihI25+IzoVTV/WHibnYSBLraoLGbLUIbNUBoQAPYcvhTvWdQAqcso60OHTqkUzZy5EiMHDnSkFNRLSQIAhJzE3Ep9RIup13GrcxbiMmKQXpBukHtmKgEeGYA7pkCXLMA1ywBbg/vXbOLessQERFRPZN2AyjI0i3n/DdEdYpKLeBakhQnYtNwIjYdp+MyIFPUreW9RSKggbU5XG0t4GongdvDe78G1gh0tUEjF2vOVUN6cXAclZtaUON6xnVE3Y/C+ZTzuJx2GRkFGQa1YZ8rIDBJgG8K4JsqwCdNgGc6e80QERHRY+Kj9Jf7MIFDVJsJgoDY1Fwcu1WUsDkVl4HsfGV1h1Uu1uYmcLaVwMVGAmcbCZxtzeFiY/HwXgI3u6JEjbONBGYmnGCBDMcEDhkksyATh+4dwon7J3Aq6RQy5ZllPlakFuD/AAhOENA4UUDQ/aIeNURERESlitczfAooWkKciGoVQRBwLj4Le6KTEHn1Ae6k19wVDIs525gjwMUGng6WcLWVwMX2YULG9v+JGStzfrymqsXfMCpVen469sfvR+TdSJxOPm3QZMJOUgGt44puLeME2BZUYaCVRGRuDpGFBURmZto3U1PdMjMziMyL7mFiCpFYDJiYAGIRRGITwEQMkaioTCQWA2IxRCZiQPxYHXFRmchEDIiK6kGEoknJNBOTPfKzSPRwOSnRw83ifY/tf/iz3nY0beH/k589qZ0qVHz+DSfv4uRtw3pxjerggx5BzmU9kaGhGc5oE8lxwjqqmaQyGTBieHWHQXWRvh44zsGAdQPjx0JE5ZKYlY9tZxOw7Xwi4tJk1R2OXlbmJmjpZY+mHnaaOWgCXW3gZG1e3aERMYFD+qnUKkQlRWHrza04dO+QQctru2UK6HxdQOh1NRolV12MjxPb2sLE0REmdnYwsbOD2N4OJnb2Rdv2dhDb2UFsbQ2xpRXEVpYQW1hAVPyzpSVEFpYQW1pAZGJivKBJIyo2HYuPCIC3b5mPCXCxRtikHuyCSlSTSKXVHQHVRdIkIOuubrkve98Q1QbXkqRYcSAGf0cnoQzTqhqVg5UZugQ0QCf/Bgjxc0QTd1uY8r0l1VBM4JCWHEUOfr/xOzbf2Iz7svtlPs6qQED3aAG9LqsRUAVJG1MXF5j5+MDM2wtmnp4wdXEpujm7wNTVBaYNGkD8cCl7qn3yFIWYufWSwcd9+mxLJm+IiOqDe3qWDwcA31DjxkFEBolOzMbX+29h79UH1R2Kluaedghr7o6nmriimYcdxGL2bKbagQkcAgCk5qViw7UN+OPGH8hV5pb5uKBEAf3OqxF6TYCkgqtDiczMYB4QAEnjIEiCgiAJCIS5ny/MvLwgtuDyoHXZ4j03EJ9h2NjnkSHe6NyI3eaJiOqF+JISOJzAmKgmSsjMw+f/XMeuS0nVHQoAwNLMBJ0bOaF7kAv6NXODj5NVdYdEVC5M4NRzOYoc/BT9E365+gvkKnmZjhEJAtrGCBh9WoKGd/PLd2KxGJKgIFi2bg3LNm1g2aolzBs2hMiUv5L1zcnb6Vh34o5BxzhZm2P2002rJiAiIqp59M1/Y+0KOPobPxYiKpG8UIVVh2Kx6lAs5IXVt6ysSAS09LJH9yBndAt0QTs/B0hMOU0C1X78tFxPKdVK/HHjD6y+uBpZ8qyyHSQIGHHfB4MP5cIqPg2AYckbSVAgrLt2g3XXrrBs2xYmNtYGx011S56iEDO2GD50au6gpnDkRHJERPWDPAdIvqxb7tvZiBPHE1FpLt7LwvubL+JWStl781cmLwfLooRNkDO6BjjzvSLVSUzg1EOXUy/jo6iPcDPzZpnqu1q6YlxhR3Tcdh3q6OtlP5GJCaw7dYJtWBhsevaAmbt7OSOmuurT3dcMHjrVNbABnm3rVUURERFRjZNwBhD0fJPP+W+IagSVWsDqw7FYFnkThWrjzVBsa2GK0EYN0DXQGd2DnOHvbP3/lVWJ6igmcOqRPGUevjn/DTZd3wS1vjdCj+nk0QmvOA2E/7qDkO3bgbJ2grQMCYH90Gdg268fTB0dKxY01Vm7Lt3HxlPxBh1jbirGJ8Na8sWZiKg+uXdKfzlXoCKqdsnZBZj6+wVE3U6v8nNJTMUI8XNEl4CipE1LL3uuFkX1DhM49cSV9CuYeWQm7kr1LMH5CBFE6OfXD681GwuPPReQ+sFnkOWV3kNCbGcH+2FD4ThyJCRBQZUVNtVRd9Jk+GCrnu7wpXivX2P4O3PoHRFRvaJv/hszK8C9lfFjISIAgCAI+Cc6GbO3X0ZWnrJKzmEqFqGNjwO6BDRAaIAz2vo6wMKM89hQ/cYETh0nCAJ+vvozvjr3FQrVT14mqptXN0xpNwUNs81xP3wmHlwu/QO2qYcHGrw6Dg4jRkBsxdncqXRZeQq8tv40cuWGLVvW1tcBb3RvVEVRERFRjaQqBO6d1i33CgFMzIwfDxHhXkYePtwZjYM3Uiu9bX9na/Rt6opuQS5o7+cIawk/rhI9in8RdVieMg9zj89F5N3IJ9YLdAjEzI4z0cm9E7K3bUPcJ59CyH/yBMWmHh5wmRQO+2eegciMb6CobLLzlXh9/RncTpUZdJy5qRhLRrSGiZhDp4iI6pUH0YBSz2sG578hMjpFoRo/HL2Nr/ffqtQVprwcLPFsWy8Ma+uJQFfbSmuXqC5iAqeOSpYl490D7+JaxrUS65iLzTGh9QSMaz4O4nw5EqdOQ86ePU9sV2xvD+fx4+H48ksQSySVHTbVYQ+kBRj703+4npxj8LHv92+MQFebKoiKiIhqNM5/Q1QjRMWmY97OaMRU4gpTnfydEN47EN0CnSHml3REZcIETh10I+MGJuybgLT8tBLrtHZpjU+6foKG9g2huHsXd94OhyI29ont2j/7LFxnTOfExGQQQRCw/1oKPth2CWm5CoOPb+vrgNe7cegUEVG9FH9ST6EI8O5g9FCI6qO0XDk++/satp1LrLQ2uwQ0wLt9gtC5UYNKa5OovmACp4459+AcJu2fhByl/l4OIojwRss38Habt2EqNoXs5EkkTJ4CdXZ2iW2aN2wI9wULYN2pY1WFTXWQIAg4F5+J5ftjcORm+cZI21mY4uvRbTl0ioioPhIE/Qkct+aAhb3x4yGqRzJlCqw5dhvrjt+BTKGqlDYbuVhj3qBm6N3EtVLaI6qPmMCpQ44lHsPUg1NRoCrQu99eYo8lPZYg1LNo3Hjm5s1I/mgBoCr5n7LDC6PhNmMGxJaWVRIz1S0FShWibqfj4PUUHLiegoTMJ8+lVJolI1vDx4mTYxMR1UvZ94Cc+7rlvp2NHwtRPZGVp8Cao3FYd+KOwQtOlMRULMLbvQMR3jsAElOuIkVUEUzg1BEn7p/AuwfehVKtfxm/RvaNsOKpFfCx8wEApP3wA1KXfllieyb29vD47FPY9ulTJfFS7SYIAtJyFbibLsPtNBmu3pfiUkIWrtyXVtqkdq919UdYc/dKaYuIiGqh+BLmv/FhAoeosmXnKbHm2G2sPV55iRsA8GtgheWj26KNj0OltUlUnzGBUwecfXAWkw9MLjF5E+oRii97fQkbcxsIgoDUpUuRvubHEtuTNGsKn2+/hZmHR1WFTLXAo0mauDQZ7qbnIS5dhjsPf67MF/fH9WzsgllPN6my9omIqBa4p2/+G3ACY6JKlJ2nxI8PEzc5lfzebng7bywY2hw2XAqcqNLwr6mWu5J2BeH7w0scNtXPrx8+7/45zE3MIQgCUj7/HBnrfy6xPdsBA+D52acQW3HYSn0gCALSZQrcSZPhTnoe7qTJEJcuw910Ge6kVW2SpiTNPOzw7UvtYGYiNvq5iYioBtHXA8fWE7D3MX4sRHVMSk4B1p+4g5+j7iKnoHLf79lKTPHJsy0wtI1XpbZLREzg1Gr3c+8jfH84ZEqZ3v3PBT2HDzt/CBOxSVHPmy+XPTF54xweDudJ4RCJOGFsXSMIAu5l5ON6shSxqTLEpuYW3VJyIa3kF+2KaOZhh59f78hvaoiI6ruCbOBBtG65b2eA71OIyu3mgxz8dCwO284lQqGqnGHvjxrS2hPzBjWFq51FpbdNREzg1Fo5ihyE7w9HekG63v1DA4Zifuh8iEVFvRjSVq5E+g8/6G9MJIL7h/Pg+MILVRUuGVm+QoWTcek4eycTFxOycDkxG1l5+ofY1RSd/J3ww9j2sLMwq+5QiIiouiWcBiDolnMCYyKDFShV2BOdjI2n7uL0ncwqOUfDBlb4eFgLdA9yqZL2iagIEzi1UKG6EO8ffh8xWTF694c1DMOCLgs0yZvM3/9A2jcr9DdmagrPRZ/DftCgqgqXjCQ2NRcHr6fg8M1UnIrLgKKSJhM2hmFtPPH58FawMOPKBEREhCdMYMz5b4jKQq0WcPpOBnZcuI+/LychO79qvsgzNxHj7d4BmNAzgO/jiIyACZxa6OtzX+PE/RN693X16oqI7hEwERf9A809fBjJCxfqb8jEBF5Ll8IurH9VhUpVLF+hwp8XE7HhZDwuJ2ZXdzgGszI3wUfPNMfIEG8O3SMiov/TN4GxmTXg1sL4sRDVEmq1gEuJ2fjnchL+vHgfSdn658isLN2DnLFwaAv4O1tX6XmI6P+YwKll9sfvx9ora/Xua+zYGEt7LoWZuGgISn70FSRMnQaoVLqVRSJ4LlrE5E0tdS8jD2uP38GWs/dq1Bw2hnimtSdmDmwCLwfL6g6FiIhqEpUSSDijW+7dHjDhW1eiR2XKFIi6nY4D11Nw6EYK0nIVVX5OT3sLfPB0Uwxp5cEv4IiMzOBXwSNHjmDJkiU4e/YskpKSsH37dgwbNqzE+ocOHULv3r11ypOSkuDu7m7o6eu1e9J7mHtsrt59LpYu+LbPt7A2K8qAF6amIiE8HEJent76Hp98DPvBHDZV28SlybDiQAx2XEiESq1nboBaoEdjF0zuE4QQP8fqDoWIiGqi5MuAUs/7F99Q48dCVEMUqtRIyMzH7bRc3E6V4XpyDs7FZ+J2qv7FTKqCp70Fwp8KxMgQH5ibcrVQoupgcAJHJpOhdevWeO211/Dcc8+V+bgbN27Azs5Os+3q6mroqes1hUqBaYenIVeZq7PPXGyOb576Bu7WRQkxQalEwtSpKHzwQG9bzu++A4fhw6s0Xqpc2XlKfLX/Jn6JuovCWpi4aexmg6eauGFEiDcCXW2qOxwiIqrJ7pUw/40v57+hukFRqEZWvgLZeUpk5SuRladEVp4C2cU/5yuQlafU2k7OLoBSVT3vAT3sLfB270CMau8NiSnnuSGqTgYncAYOHIiBAwcafCJXV1c4ODgYfBwV+fbCt7iecV3vvlmdZqG5c3PN9oNFi5F/5qzeuvYjhsN54sQqiZEqX6FKjV9P38OXe28gs4avIvUoa3MTtG/ohD5NXdE72BU+TlbVHRIREdUW8XrmvxGJAe8Oxo+F6AmUKjWy8pTIzFMgU6ZAZp7iYcJF+TAB83D7YVl2ngJZ+UrkKfRMb1ADudtZILx3AEZ18GHihqiGMNpA4jZt2kAul6NFixb46KOP0LVr1xLryuVyyOVyzbZUKjVGiDXW2QdnsTZa/7w3zwQ8g+FB/+9Nk/3XLmRu2KC3rnWXUHjMn8+xqrVEdGI2pm+5hGtJNff331Qsgo+TFfwaWMHf2RotvezRytsBjZytIRbz94yIiAwkCPoTOG7NAYmt8eOhekmtFpAmk+NBthzJ0gIkSwvwIPvhvbQAydlF97V1HsLSuNlJ8HavQDzfwYcrSxHVMFWewPHw8MDq1avRvn17yOVyrFmzBr169cKpU6fQrl07vcdERERgwYIFVR1arZCryMWcY3MgQLfLZKBDIOZ2nqtJyCju3UPyRx/pbcfM2xteX34JkZlZVYZLlUBRqMaKA7fw7aHYGjHPjYlYBB9HSzR0tkbDBtZo2MBK87OXoyXMTDgGmoiIKknWXSA3Wbfcp7PxY6E6T1Goxp10GWJScnHrQS5iUnMRk5KL26m5kBeqqzs8o/N1ssIb3f0xqj0TN0Q1VZUncIKDgxEcHKzZ7tKlC2JjY7Fs2TL88ssveo+ZNWsWpk2bptmWSqXw8fGp6lBrpC/OfIHE3ESdcjOxGT7v/jksTYtW8BGUSiS+/z7UMt2JzEQWFvBe8Q1MOIStxotOzMb7my/ienKOUc9bnKTxa2ANf2dr+D1M0vgzSUNERMYUX9L8N0zgUMWkSAtwOTEblxKycTVJitiUXNzNyKsRX5ZVt9be9hjfIwADWrjDhD2oiWq0almLsWPHjjh27FiJ+yUSCSQSiREjqpnOJJ/B1ltb9e57p+07CHb6f2Is9dtvUXDxkt66Hh9/DIsmTaokRqocmTIFlu27iY2n4qv0jYSpWIRgd1sEu9siwMUGAS42CHS1hq+TNVcTICKi6ndPz/ApgAkcKrN8hQqxD3vS3ErJwY3kXEQnZiNZWlDdodUolmYmeKa1J17q7ItW3g7VHQ4RlVG1JHAuXLgADw+P6jh1raFQKbAgSv8wshC3EIxpNkazLfvvP6R/973euvbDn4P9kMFVEiNVXKFKjV9O3sWyyJtVMo7aytwEXQIaoGugM9r6OqKJuy27xBIRUc2lrweOnTdg7238WKhaqdUC8pQqyOSFyJUXIreg8P8/ywshzVciJUeOB1I5UnIKkJojxwNpQa1a9KE6tPV1wHNtvTC0rRfsLDi1AlFtY3ACJzc3FzExMZrtuLg4XLhwAU5OTvD19cWsWbOQmJiIn3/+GQDw1Vdfwd/fH82bN0dBQQHWrFmDAwcOYO/evZV3FXXQj5d/xB3pHZ1yS1NLfNL1E5iIiz6Eq2UyJM2aXTTp32PMGzaE++zZVR0qldO1JClmbLmEy4nZldqurcQUz7bzwoDm7ghp6MhVA4iIqHbIzwJSruqWc/nwWkkQBEjzC5GSU5RUKVqhSaH5WZpflJCRyQshUxRCJv9/skYmL0SeUqXv7S2VQ6CrDYa18cQzrb3g24ArgxLVZgYncM6cOYPevXtrtovnqhk7dizWrVuHpKQkxMfHa/YrFAq89957SExMhJWVFVq1aoV9+/ZptUHa4rLj8MPlH/TuC28TDm/b/38LlfLVcigTdefIgZkZPJd+AbG1dVWFSeUkL1Th2wMxWHkoFoWVOFwq2M0WY7s0xNA2nrCWVEvnOiIiovJLOA3oWbQBvqFGD4XKpkCpwu1UGW6l5OBeRh4SswpwPytfc5PVkuWy66LWPg7o08QVfZu6oamHLVehJaojDP6U16tXLwhPSIevW7dOa3vGjBmYMWOGwYHVZ4tPL4ZSrdv9s6lTU7zU9CXNdt65cyUuGe46ZQosmzevshipfC7cy8KMLRdx80FupbXZ3NMOU/s2Rp+mrnxxJiKi2kvf8uEA4MMeODVFcnYBom6n4URMOs7czcTddBk4B3DN4OVgiXZ+jugR5Ixewa5wseV8okR1Eb+mr2GOJBzBsUTdCZ7FIjE+6vIRTMVFT5laLkfSnLl6h05Ztg+B06vjqjpUMkB2nhJf7L2BDafuVlp3YGcbCWaEBWNEiDfEXDGAiIhqu/go3TJzW8CNX0hVl+w8JY7cSsWJ2HScvJ2OuDTd1U7JuKzNTdDIxQaNXKzRyNkGjd1s0M7PEW52FtUdGhEZARM4NYhSpcSS00v07nuxyYto1qCZZjtt5Soo4uJ06okkEnh8/DFEYq4oVBM8kBbgr4v3sepQLNJlikpp00Qswhvd/DHpqUDYcvI5IiKqCwrlQMIZ3XKfDoCYc7kZU2qOHHuvJmNPdDKiYtMrdbg3aTMzEcHByhwOlmZwsDKDvaU5HKzM/r/9yD4HS3O42UngYithj2uieowJnBpk0/VNeicudrJwwsQ2EzXb8thYpP/4o942XN6ZBIm/f1WFWK8JgoA8hQpZ+Upk5SmQna9Edp7y4bYSWfmKou08JTLyFIhLkyE1R16pMQS72eKLka3R0tu+UtslIiKqVonnAJWe10y/LsaPpR5SqQUcuZmKX/+Lx/7rKVAxaWMQMxMRHK3M4WRtDntLMzhamT9MwBQlXoqTMlrbVmawNDNhMoaIDMIETg2RVZCF1RdX6933Ttt3YGduB6AoifDg00+BQt0lpy2aN4fTuHFVGWadlp4rx80HubiVkoM7aXlIy5UjXSZHWo4C6TIFsvMVUKqq5w2NiViEt3sFYNJTgVxVioiI6p67x/WX+3U1bhz1jFKlxrZzCVhxMAb3MvKrO5wax9xEDFc7CdztLOBmbwF3Owutn93sJHCyNoeNxJSJGCIyCiZwaog1l9cgV6k7sW0TpyZ4NvBZzXbOv3shO6FnjLipKTw++xQiUz6lZaFSC7h6X4qTt4vGdF+4l1VpQ5wqW2tve3wyrCV73RARUd1194RumYkE8Gxn/FjqAaVKje3nEvHNwVtM3ABwsDJDoIsNAl21b572lpxnkIhqFH7arwGSZcn49fqvevfN7DATJg/Hfqvz8vBg0SK99ZxeeQUWwcFVFmNtJwgCYlNzceRmGk7EpuFUXAZyCnR7MdUkrrYSzBzQBM+29eKbByIiqrtUhcC9U7rl3u0BM07MWpkEQcDuy0n/a+/Ow6Ks2j+Af2dnHfZdEBQRcEHFDbPMQjGX160y990s9U2t/GmZS/ZmZZuZ5pZiqbllWlqUaWopmiLugBuKC4uA7LLNPL8/zEmcQRkYZoHv57q4Ys723IOe5vHmPOfgo5gkpGQXmTocoxOJgMZudmjp44AWDRwQ7KlEEw87uNjKuYKGiCwCEzhmYPmp5ShVa6/+6OrbFW0922peZy5fgfLUVK12Ujc3uE6aVKsxWqpzt3LxfdxN/HI2Fam5xaYOp0rEImBs5wBMjQyCrYJTlIiI6ri000Cp9ipk7n9jWJcy8jFn5zkcvpxl6lCMws1egUA3OzTxuLeaJsjDHs28lTwAgogsGv91aGJXcq/gh0s/aJWLRWL8t/V/Na9Lr19H9tq1OsdwnzEDEjvbWovR0tzOL8HOkzexLe4GEtPyTR2OXoI87PDR82Fo5eto6lCIiIiMQ9fjUwDgF2HcOOoolVrAqj+v4NPfLqBUpTZ1ONUik4hgp5DCViGF3T9ftgopHG1k8FBawd3+3ulM9793V1rBjr8EI6I6iP9nM7Ev47+EWtD+MO3dqDcCnQI1r29/9jmEsjKtdjZt20LZu1etxmgprtwuwIoDV7A9/obJNhuuLqlYhFe7BmJS18bcpJiIiOqXFB17+4kkgG9748dSx1zNLMQbW0/h+LU7JotBLAKcbRVwsvn3dCYnGzkcbWWw/ycRY6uQwlYuha1CoknOaF5bSXlvRET0DyZwTOh81nnsubZHq1wmluHVVq9qXt89cwZ5P/+sPYBEAo933qn3z+yeu5WLZfsv4+czqRAsK28DAGjj54j/9W+BEC+lqUMhIiIyLrVa9wocrzBAYW/8eOoIQRCw/mgK3t+dgLtlqlq/noutHIHudmjkZocGTtbwcbSGt6M1vB2t4KG0gkwirvUYiIjqAyZwTGjl6ZU6ywc1HQQfOx8A9z6AMz5apLOd44svwKppUK3FZ+7ScovxYUwifoi/aepQqsXZVo6ZPYLxfHgDblJMRET1U2YScDdbu5z731Rbau5dzNh2Gn9ezKy1awS42qJjIxdENHZBxwBnuCu52TQRkTEwgWMiF+5cwN6UvVrlNlIbjGsxTvO6YP9+FB07ptVOZGMDt3q6cXFpuRorDlzGsv2XjfJbJUNzsZVjRIQ/Rj3hDwdrbqRHRET12LVDussbPmHcOOoAQRCw4+RNzNl5zuAnbdrIJega7I5ng90R0dgFXg7WBh2fiIiqhusZTWTV6VU6ywcHD4aLtQsAQCgvR8Ynn+hs5zJ2DKSurrUWn7m6mJ6P/ssO4ZM9FywqeePvYoPB7f2wfFg4Ds18Bq9FNmHyhojIyA4ePIg+ffrA29sbIpEIO3bsqFAvCALmzJkDLy8vWFtbIzIyEhcvXqzQJjs7G0OHDoVSqYSjoyPGjh2LgoKKJyidPn0aTz75JKysrODr64uPPvpIK5atW7ciODgYVlZWaNGiBX5+6FHpqsRSJ1S6gXFH48Zh4bIKSvDK+hOYtvmUwZI3ErEI3UM9sGJ4OE680w1Lh7TBgDYNmLwhIjIhrsAxgeTcZPx69VetciuJFYaHDte8zvnhB5ReuqzVTuLmCpfRo2s1RnP03d8pmPfjOZSUm/4EBRu5BI7WMjjYyOFoLYOjzb0vB+t7m/PdL/NxtEFjd1vYyDnViIhMrbCwEGFhYRgzZgwGDBigVf/RRx/hiy++wLp16xAQEIB33nkHUVFROH/+PKys7j0iMnToUKSmpmLPnj0oKyvD6NGjMWHCBGzcuBEAkJeXh+7duyMyMhLLly/HmTNnMGbMGDg6OmLChAkAgMOHD2Pw4MFYuHAhevfujY0bN6Jfv344ceIEmjdvXuVYLJ4g6E7guIcCNs7Gj8dC/XYuDW/9cAaZBaUGGc9aJsGITg0x9okAPhpFRGRmRIJg/tu+5uXlwcHBAbm5uVAqLX+j17f/ehs/Xv5Rq3x46HDMaDcDAKAuKcHlbt1RnpGh1c7z3flwevHFWo/TXJSr1HhvdwKiD1+t9WuJRUBDF1s0cbdDoLsdPB2s4GKrgIudHM628n+SNDKehkBEVAlL+cwWiUT44Ycf0K9fPwD3Vrx4e3vj9ddfxxtvvAEAyM3NhYeHB6Kjo/HSSy8hISEBoaGhOHbsGNq2bQsAiImJQc+ePXHjxg14e3vjq6++wttvv420tDTI5XIAwMyZM7Fjxw4kJiYCAAYNGoTCwkLs2rVLE0/Hjh3RqlUrLF++vEqx6FJSUoKSkhLN67y8PPj6+prvn0X2FeCL1trl7cYBvXSvQKZ/5RWXYf6P5/H9iRsGGc9KJsaICH9MeKoRXO0UBhmTiIiqpqr3T1wWYGTX869j95XdWuVysRyjm/27qiZn82adyRt548Zw1PFbw7qqsKQcr244gQMXbtfK+HKJGK38HNGxkQs6NnJGa18nWMuZnCEiqm+Sk5ORlpaGyMhITZmDgwM6dOiA2NhYvPTSS4iNjYWjo6MmeQMAkZGREIvFOHr0KPr374/Y2Fg89dRTmuQNAERFReHDDz/EnTt34OTkhNjYWEyfPr3C9aOiojSPdFUlFl0WLlyI+fPnG+LHYRxXK9v/hhsYP84fiRmYtf0M0vKKDTLewDYN8H/PNYW7PVfcEBGZMyZwjOzrM19DJWjv3TKgyQC42bgBANR37yJzpe49ctxffx0iaf34Y8spKsXo6GOIT8kx6LhBHnbo2tQdTzZxQ1t/J1jJmLAhIqrv0tLSAAAeHh4Vyj08PDR1aWlpcHd3r1AvlUrh7OxcoU1AQIDWGPfrnJyckJaW9tjrPC4WXWbNmlUhMXR/BY7Zuvqn7nI/JnAqY+hVNw1dbLDo+TC0D+Aja0RElqB+ZALMxO2i29h5eadWuVQsxZjmYzSv72z8DqpM7aMfrcJawq7r07UYofnIyCvG8K//RlJ6vkHGc7WTo28rHwxs0wCh3ma4jJyIiKiGFAoFFAoLefRFEIDkg9rlrkGA0sv48ViA0zdyMGnjCVzPvmuQ8YZ19MOs50Jgq+A/B4iILAX/j21Em5I2oVytfTJA38Z94WV372ZFXViIrNWrdfZ3m/JfiESiWo3RHGQVlOCllUdwJbOwxmO1bOCAV7o0RmSoB2QSHrpGRES6eXp6AgDS09Ph5fVvAiE9PR2tWrXStMl46PHm8vJyZGdna/p7enoiPT29Qpv7rx/X5sH6x8Vi8bIuAfmp2uX+Txo/FjMnCALWHrqKhb8koExV860rPZVW+Oj5lngqyM0A0RERkTHxX7RGUlxejK1JW7XKRRBhbPOxmtfZ6zdAdeeOVjvr8HDYPlH3lxQXlZZjzLrjNU7etA9wxvqxHbBz0hN4roUXkzdERPRIAQEB8PT0xN69ezVleXl5OHr0KCIiIgAAERERyMnJQVxcnKbNvn37oFar0aFDB02bgwcPoqysTNNmz549aNq0KZycnDRtHrzO/Tb3r1OVWCxe8gHd5QFPGTcOM5dTVIqXv43Du7vOGyR507+1D36d+hSTN0REFoorcIxk95XduFOinZjp6tsVvsp7z6erCgqQtWaNzv5uU6bU+dU35So1pmyMx6nrOdUew9/FBm/3CkVkiHud/3kREZF+CgoKcOnSJc3r5ORknDx5Es7OzvDz88PUqVPx3nvvoUmTJpqju729vTUnVYWEhKBHjx4YP348li9fjrKyMkyePBkvvfQSvL29AQBDhgzB/PnzMXbsWPzf//0fzp49i8WLF+Ozzz7TXPe1115Dly5d8Mknn6BXr17YtGkTjh8/jpUrVwK4d0LW42KxeMmV7H/DFTgA7q262X7iJt7/OQFZhTU/HtzZVo73+zdHj+Z8PI2IyJIxgWMEgiBgfcJ6nXXDQ4drvr/z7bdQ5+ZqtbFp3x62HTvUWnzmYsGu89ibqH3yVlWIRMCEJxthevcgHvFNREQ6HT9+HF27dtW8vr/h78iRIxEdHY0ZM2agsLAQEyZMQE5ODjp37oyYmBhYWf17Ms+GDRswefJkPPvssxCLxRg4cCC++OILTb2DgwN+++03TJo0CeHh4XB1dcWcOXMwYcIETZtOnTph48aNmD17Nt566y00adIEO3bsQPPmzTVtqhKLxVKrdW9g7NEcsHUxfjxm5lJGPmbvOIsjV7INMl63UA+8378F3OwtZH8kIiKqlEgQhJqvx6xlVT0T3VwdvnUYL+95Was8xDkEm3tvhkgkgrqwEJeeeRYqHQmchuu/hc0DR5bWRV/uu4iPf7tQrb6ONjIsHdIGTwS6GjgqIiLSl6V/ZtclZvtnkXYWWP6EdnnHV4EeC40fj5m4W6rCl39cxMqDVwzyuJS9Qop5/2mGAW18uCqZiMjMVfUzmytwjGD9+cpX39z/QL2zeYvO5I1tp4g6nbwRBAFfHbhc7eRNIzdbrBnZDv6utgaOjIiIiGqFrtOngHq9/80fiRmY8+NZg50w9USgCz56Pgw+jtYGGY+IiMwDEzi17EruFfx5U3uZsKu1K3r49wAAqEtKkL12rc7+rq++WqvxmZJaLWDB7vNYe+hqtfq3bOCA6NHt4WwrN2xgREREVHt0PT4lEgMN6/5hDQ9Lzb2Ld386j1/OphlkPFu5BDN7hmBoez+IxVx1Q0RU1zCBU8s2JmzUWf5S05cgk8gAALk//IDy27e12li3Da+zq28EQcCM709jW9yNavXv2MgZq0a0hb2VzMCRERERUa1RlQNX/9Iu92oFWDkYPRxT2hF/E+/sOIv8knKDjNc50BUfDGyBBk42BhmPiIjMj95nKx88eBB9+vSBt7c3RCIRduzY8dg++/fvR5s2baBQKBAYGIjo6OhqhGp5ckty8ePlH7XKFRIFXmj6AgBAKCtD1qrVOvu7vjyxVuMzpUW/JlU7eRPm64g1o9oxeUNERGRp0k4BJXna5fXo8ancu2X473fxmLr5pEGSNxKxCP/XIxjfjGnP5A0RUR2ndwKnsLAQYWFhWLp0aZXaJycno1evXujatStOnjyJqVOnYty4cfj111/1DtbSbLuwDXfLtZ9l7t2oN5ytnAEAubt3o+zmTa02Vs2awbazjg3+6oANR69h2f7L1err72KDNSPbwkbOxWNEREQWp9L9b+rH8eFHrmThuc8P4sdTtwwynpeDFTZP6IhXnm7MR6aIiOoBvf8V/Nxzz+G5556rcvvly5cjICAAn3zyCQAgJCQEf/31Fz777DNERUXp7FNSUoKSkhLN67w8Hb+pMXNl6jJsTNT9+NSwkGEAAEGtRtbKVTrbuEx8uU6eGLA3IR3v7Dhbrb6ONjJEj24PFzseg0lERGSRLu/TLhNLAb8I48diRKXlany65wJWHLwMQ53/+kywOz55IQxO3AuQiKje0HsFjr5iY2MRGRlZoSwqKgqxsbGV9lm4cCEcHBw0X76+vrUdpsH9fu13ZBRlaJVHeEUg0CkQAJC/53eUXrmi1UYe2Bj2zz5b6zEa28nrOZi8MR7qaty4KKRirBzelqdNERERWarSQiDliHa5b0dAXnc/3y9l5KP/skNYfsAwyRtnWzkWPd8SX49sy+QNEVE9U+sJnLS0NHh4eFQo8/DwQF5eHu7e1X1U4qxZs5Cbm6v5un79em2HaVCCIODb89/qrBseOlzTJnPFcp1tXCdMgEhc6380RpWcWYgx0cdwt0yld1+5RIyVI9qifYBzLURGRERERnH1EKAq1S5v3NX4sRiBIAjYfCwFvZf8hXO3DLOafHB7X+yd3gUvtPWtkyu1iYjo0cxyIxGFQgGFwnIfkzl1+xTOZJ7RKvdX+uMJn3v72hT++SdKzidotZH5+kLZs2etx2hMGfnFGLHmKLILddy0PYZCKsayoW3QJcitFiIjIiIio7m8V3d542eMG4cR5BeX4e0fzhpsr5tgT3u816852vrzl1lERPVZrSdwPD09kZ6eXqEsPT0dSqUS1tbWtX15k3jU6hux6N7KmswVK3W2cRk3DiKpWebVqiW3qAyj1x7D9Wzdq60excFahq9HtuXNChERUV2ga/8ba+d7R4jXIVczCzFm3TFcuV1Y47Fs5BJMiwzCqCf8IZPUrdXZRESkv1rPFERERODnn3+uULZnzx5ERNTNzepuFdzC7ym/a5Ur5Ur0btQbAFAUF4e7cXFabaTu7nDo36+2QzSazIISDP/6bySk6r9s2F4hxXfjOyLUW1kLkREREZFR5VwHMi9olzfuCtShx8aPXsnCy+vjkFNUVuOxuod6YO5/msHHsW7+wpOIiPSndwKnoKAAly5d0rxOTk7GyZMn4ezsDD8/P8yaNQs3b97EN998AwCYOHEivvzyS8yYMQNjxozBvn37sGXLFuzevdtw78KMfJf4HdSCWqv8haAXYCOzAQBkrVqts6/zmNEQy+vGZnSpuXcxbPVRXK7Gb5+kYhGWDw9n8oaIiKiu0LX6BgAa151DG7bF3cCs7adRpqrZTsXOtnK8378FejT3NFBkRERUV+idwDl+/Di6dv13s7np06cDAEaOHIno6GikpqYiJSVFUx8QEIDdu3dj2rRpWLx4MRo0aIDVq1dXeoS4JSsqK8L3F77XKpeKpHgp+CUAQHHSBRTs36/VRuLgAKcXX6ztEI3i9I0cjFt3HBn5JY9vrMOHA1viiUBXA0dFREREJlNpAqdubGC86uAV/O9n7b0N9dUlyA2LXmgJd3srA0RFRER1jd4JnKeffhrCI85AjI6O1tknPj5e30tZnB2XdiC/LF+rvJt/N3ja3vstStbXulffOA0bBrGNTa3GV9tUagHfxF7FhzGJKC7TXoVUFdO7BWFgeAMDR0ZEREQmo1YBV/Zrl7uHAkpvo4djaEv/uIRFvybVaAy5VIy3ngvGyE7+PF2KiIgqVXd2yzUxtaDGhoQNOuuGh9w7Orzs5k3k7f5Zq15kbQ2nYUNrNb7adiE9H//3/WnEp+RUe4yhHfww5ZlAwwVFREREpnfjOFCco11eB06fWvz7RXz2u469ffQQ4qXE4pdaIcjD3kBRERFRXcUEjoEcvHEQKfkpWuWt3FqhhVsLAEDW2mhApdJq4/jC85A6OdV2iLWipFyFZX9cxrL9l2r0zHf3UA+827c5f+tERERU11yI0V1uwQkcQRDw6Z4LWLLv0uMbP8KEpxrh9e5BUEglBoqMiIjqMiZwDGT9+fU6y4eFDgMAlGdnI2fbNu0GUilcRo2qxchqT9y1O5j5/WlczCio0Tjt/J3wxeDWkIiZvCEiIqpzLvyqXSazBfw7Gz8WAxAEAR/GJGH5gcvVHsPVTo7PBrXCk03cDBgZERHVdUzgGEBSdhKOph3VKvey9cKzfvdOV7izfj2E4mKtNg69e0PmbVnPfxeUlGNRTCK+OXINj9gOqUpaNnDA6hHtYCXjb56IiIjqnJwUIOOcdnnjroBUYfx4akgQBPxvdwJW/5Vc7TE6B7ri00Fh3KiYiIj0xgSOAaxP0L36ZkjwEEjFUqgLC5G9YaPONi7jxtZmaAb3R2IG3v7hDG7laiej9NXO3wlfj2oHpZXMAJERERGR2dG1+gYAgnoYNw4DEAQB8386j+jDV6s9xqhO/pjdKwRSidhwgRERUb3BBE4NZd7NxO4ru7XKraXWGBA0AABwZ+tWqHNztdrYPfMMFIGWsWnvldsF+DAmEb+eSzfIeJ0DXbFyRDhs5PwrSEREVGdVlsBp0t24cdSQWi3gnZ1nseGo9n6HVSEWAfP+0wwjIvwNGxgREdUr/NdzDW1N2ooydZlWef/A/lDKlRBKS5G9NlpnX5fx42o5uprLLizFF3svYv2RayhX1/B5qX8MaO2D9we04GNTREREdVlpIZB8ULvcuw1g72H8eKpJrRYwa/sZbD5+vVr95RIxlg1tg8hQy3nPRERknpjAqYESVQk2JW3SKhdBhKEh944Fz/1pF8rTtVet2LRtC5vWrWs9xuoqKVdh3eGrWLLvEvKLyw0ypkgEzIgKxsQujXjaFBERUV135QCgKtEut6DHp0rL1Xh96yn8dOpWtfrLpWKsHB6Op5u6GzgyIiKqj5jAqYFfkn9BdnG2VnkX3y7wU/pBUKuRtXq1zr7muvpGEATsOp2KD2MScePOXYON6+NojQ8GtuBpC0RERPVFZceHB0UZN45quluqwisb4rA/6Xa1+lvJxFg9oh06N3E1cGRERFRfMYFTTYIg4Nvz3+qsGx4yHACQv3cvSpO1TylQBAXB9qmnajW+6jh+NRvv7U7Ayes5BhtTJAJGRvjjzaimsFXwrxsREVG9oFYBST9rl9t7AV5hxo9HT7l3yzBu3TEcu3qnWv1t5BJ8PbIdIhq7GDgyIiKqz/gv6mo6knoEF+5c0Cpv6tQU7TzbQRAEZK2qbPXNeLN6hOjGnSIs/CURu0+nGnTcIA87fDCwJdr4ORl0XCIiIjJzKUeAQh0rV5p0v/fbHTOWkV+MUWuO4XxqXrX628oliB7THu38nQ0cGRER1XdM4FTTuvPrdJYPCx0GkUiEwqN/o/j0aa16mY8PlM+Zx7PfRaXlWL7/MlYcvIKScrXBxpVJRJjUNRCvPh0IuZTHZBIREdU7CT/qLg/9j3Hj0FNiWh7GRh/HzZzqPUZur5Aiekx7hDfkL6+IiMjwmMCphkt3LuHQzUNa5a7WrugZ0BMAkLVypc6+zmPHQCQ17Y9drRaw89RNfPhLEtLyig06dhs/R3w4sCWaeNgbdFwiIiKyEGo1cF5HAsfKAfA3v0fI7/sjMQOTN55AYamqWv2VVlJ8O7YDwnwdDRsYERHRP5jAqYZvE3TvfTMkeAjkEjnunj6NwkPaCR6JszMcBwyo7fAe6eT1HMz/6RziU3IMOq6tXIIZPYIxvGNDiMXmvTSaiIiIatHNOCBfx6lNTXsCUrnx46mC6EPJeHfXeaiF6vV3tJFh/dgOaO7jYNjAiIiIHsAEjp4y72Zi1+VdWuVWEiu8EPTCvTbLvtLZ13n4MIitrGo1vsqk5Rbjo5hEbI+/afCxI0M8ML9vM/g4Wht8bCIiIrIwCTt1l4f2NW4cVVCmUmPBrvP4JvZatcdwtpVj/dgOCPVWGjAyIiIibUzg6Glz0maUqku1yvsG9oWjlSOKz59Hwf79WvViOzs4DR1qhAgryr1bhhUHLmPNoWQUlxlunxsAaO6jxFs9Q9CpMY/HJCIiIgCCAJzXkcCR2wGNuho/nkfIKijB5I3xiL2SVe0xPJVWWD+uPQLd+eg4ERHVPiZw9FBcXozNiZu1ykUQYXjovaPDM79arrOv0/BhkCiN95uZ4jIV1h+5hi//uIScojKDju2ptMKbUU3Rv7UPH5ciIiKif6WeAnJStMuDogCZaVYh63L2Zi5e/jau2psVA0CAqy2+HdseDZxsDBgZERFR5ZjA0cNPV37CnZI7WuVP+z6NhsqGKL5wAfl79mjVi21s4DxihDFChEotYEf8TXy650KNbkp0sZVL8MrTjTG2cyNYyyUGHZuIiIjqgLPf6y43o8endp68if/7/nSNViaHeinxzdj2cLVTGDAyIiKiR2MCp4rUghrfnPtGZ92I0HvJmazlK3TWOw0ZDKlT7R4nKQgC/kjKwEcxSUhMyzfo2GIR8FJ7P0yNbAJ3e/P57RkRERGZEbUKOLNNu1xqDQRGGj+eh6jUAj6KScSKg1dqNE57f2esHtUWSiuZgSIjIiKqGiZwqmhvyl5czbuqVR7qEopwj3CUXLmCvF9+0aoXWVnBefToWourpFyFmLNp+PqvZJy+kWvw8bsEueHtXiEI4rHgRERE9ChX/9J9+lRwL0Bua/x4HpBTVIop38Xjz4uZNRqnV0svfPJCGKxkXIlMRETGxwROFQiCgFWnV+msGxk6EiKRCFkrVtzbuO8hToNehNTFxeAxXc0sxHd/p2Br3A1kF2pvqlxTjdxs8U6vUHQNdjf42ERERFQHnd6iu7zlIOPG8ZDTN3IwaeMJXM+u2aPlU54JxLTIIO7/R0REJsMEThUcvnUYCdkJWuU+dj7o7t8dJVeSkbtrt1a9SC6H85ixBoujqLQcP59Jw7a46zhyJdtg4z7I3kqKqZFBGBHREDKJuFauQURERHVM2V3dp0/ZuAKNTXP6lCAI+Cb2Gv63OwGlqurvdyOTiPDBgJYYGN7AgNERERHpjwmcKlh1RvfqmzHNx0AqliL9yyWASqVV7/j885B51GwFi1ot4NjVbGyLu4Gfz6SisFT7OoYgFgGD2/thercguHBDPiIiItJH0i9AqY49+Fo8D0iMv1dMSbkKs74/g+3xN2s0jqONDCuGhaNDI8OvpiYiItIXEziPcSL9BOLS47TK3azd0DewL4oTE5H3s/beN5DJ4DKu+qtvrmUVYvuJm9gef6PGS34fJ6KRC+b0CUWIl/GOOSciIqI6pNLHp140bhy4t9/Ny9/G4WhyzVYrB3nYYeXwtvB3Ne3+PURERPcxgfMYq8+s1lk+stlIKCQKXP98sc56p0GDIPP2rvJ1VGoBJ6/nYG9COvYmZCAp3bAnSeni72KDmc8FI6qZJ0QiPs9NRERE1VCQAVzao13uEgh4tzFqKNeyCjE6+hiu3C6s0Tg9mnni4xfDYKfgrTIREZmPan0qLV26FIsWLUJaWhrCwsKwZMkStG/fXmfb6OhojH7oFCaFQoHi4uLqXNqozmWew583/9QqV8qVeCHoBRTFx6Ng/36tepG1NVxfnvDY8QtKyvHnhdv4PSED+5MykFULmxHr4mqnwGuRTfBSO1/uc0NEREQ1c3IDoC7XLm85CDDiL4jirt3B+G+O1+hwB5EImB4ZhEldA7lZMRERAJVKhbKyMlOHYfEkEgmkUmmNF07oncDZvHkzpk+fjuXLl6NDhw74/PPPERUVhaSkJLi7697vRalUIikpSfPaUlZ7LIlforN8WMgw2MhscK2S1TfOw4ZB6uams+52fglizqbit/PpOHolu0ab6unLTiHFy081wpjOAbDlb5SIiIioptRqIG6djgqRUU+fijmbhv9uikdpefXvq+wVUnz+Uis8G+JhwMiIiCxXQUEBbty4AUHHacukPxsbG3h5eUEul1d7DL3/Ff/pp59i/PjxmlU1y5cvx+7du7FmzRrMnDlTZx+RSARPT89qB2kKcelxOHTrkFa5rcwWQ0KGoODPv1B09KhWvdjeHi5jx1QoEwQBfyRlYO2hqzh0KRNqI//9l0lEGNaxISZ3DeQGxURERGQ4Vw8Cd5K1yxs/Azg1NEoIW45dx8ztp2t0f9XIzRarRrRFYzc7wwVGRGTBVCoVbty4ARsbG7i5uVnMIgxzJAgCSktLcfv2bSQnJ6NJkyYQi6v3JIxeCZzS0lLExcVh1qxZmjKxWIzIyEjExsZW2q+goAANGzaEWq1GmzZt8P7776NZs2aVti8pKUFJSYnmdV5enj5h1pggCJWuvhkROgJKiS2SP/pQZ73LmNGQODpqXiek5mHej+dqvJFedYhFQN9WPpjeLQi+zjZGvz4RERHVcXHRusvDRxnl8qsOXsH/fk6o0RiRIe74dFArKK2Mf1oWEZG5KisrgyAIcHNzg7W1tanDsXjW1taQyWS4du0aSktLYWVlVa1x9ErgZGZmQqVSwcOj4tJSDw8PJCYm6uzTtGlTrFmzBi1btkRubi4+/vhjdOrUCefOnUODBg109lm4cCHmz5+vT2gGdST1iM6Tp5RyJYaHDkfOtu9RcvGSVr3E2RlOw0cAuJcEWnvoKt7/OQHlRl5yYyUTo2+YD17u0giN+JskIiIiqg0FGUDCLu1yW3eg6XO1emlBELDo1yQs23+52mNIxCK80b0pXn6qEfe7ISKqBFfeGE51V908qNY3QomIiEBERITmdadOnRASEoIVK1ZgwYIFOvvMmjUL06dP17zOy8uDr69vbYcK4N4NwZfxX+qsG918NGxKRbj8xRc6693+OwUSO1sUlpRj5vYz+OnUrdoMVUuQhx2GtPdD/zYN4GDN3yIRERFRLTq+BlDr2Niy9TBAUnv3ISq1gHd2nsXGoynVHsPdXoGlQ9ugnb+zASMjIiKqXXolcFxdXSGRSJCenl6hPD09vcp73MhkMrRu3RqXLmmvYLlPoVBAoTDNXi37UvbhdOZprXJnK2cMCR6CrC+WQ5Wt/TiUPLAxHJ9/HpdvF2Dit3G4mFFgjHBhI5egZwsvvNjWF+38nZghJSIiotpXVgwcW62jQgS0GVFrly0tV2PalpPYfTq12mMEe9pj7eh28HLgIwFERJURBAGFpWoUFJejDOZ3CpWdouYnOlkivRI4crkc4eHh2Lt3L/r16wcAUKvV2Lt3LyZPnlylMVQqFc6cOYOePXvqHWxtK1WV4pO4T3TWjWsxDtL0bGSv03XSAuAxYwZOpRZgxNdHkVes4yhNA+sQ4IznwxugZwsvnihFRERExnX2e6DwtnZ50+cA54BauWRxmQovfxuHAxd0XLeKugS5YenQNrDjvRMR0SMVlqjw/HdXAVw1cSS6nZnXHfa1uHfZvHnzsGPHDpw8ebLWrlEden96TZ8+HSNHjkTbtm3Rvn17fP755ygsLNScSjVixAj4+Phg4cKFAIB3330XHTt2RGBgIHJycrBo0SJcu3YN48aNM+w7MYDvEr/D9fzrWuXuNu54vsnzSJ8yHUJpqVa97RNPICOkDUZ9dbhWkze+ztYY0LoBBrZpAD8XbkpMREREJiAIwJGvdNd1fKVWLllarsarG07UKHkztIMf5v+nGaSSmu9BQERE5mnUqFFY98+iC6lUCmdnZ7Rs2RKDBw/GqFGjDLIPjSnpncAZNGgQbt++jTlz5iAtLQ2tWrVCTEyMZmPjlJSUCj+UO3fuYPz48UhLS4OTkxPCw8Nx+PBhhIaGGu5dGMCd4jtYcWqFzrr/tv4vyg8cQsH+/dqVYjGspkzDi9HHkFNk+KVl3g5WeDbEA71beqGdvzM32SMiIiLTurIfSD+jXe7RAvB/0uCXK1ep8dqmeOxLzKj2GLOeC8aEpxrVy+X2RET1TY8ePbB27VqoVCqkp6cjJiYGr732GrZt24Yff/wRUqnlrsKsVuSTJ0+u9JGp/Q8lOT777DN89tln1bmMUS07uQz5Zfla5SHOIejl+QySx/fV2c/++ecx6UgurmYVGSQOkQgIa+CIyBB3PBvigWBPe95sEBERkfk4uEh3ecdX7t3IGNh7uxPwy9m0avWViEX4aGBLDAzXffIpERHVPQqFQrNHr4+PD9q0aYOOHTvi2WefRXR0NMaNG4eUlBRMmTIFe/fuhVgsRo8ePbBkyRKtE7dXrFiB9957D1lZWejduzdWrVoFBwcHAPdyHzNmzMC5c+cgk8nQrFkzbNy4EQ0bNqy192a5qScDOpd1DlsubNFZ92a7N5H11XKUp2pvlidxdsaG5r1w7ET1l/MCgLVMgiebuCIyxANdg93hZm+aDZyJiIiIHunqIeDaIe1yW3eg+UCDX27D0WuIPny1Wn3lUjGWDmmDbqEej29MRER12jPPPIOwsDBs374dY8aMQd++fWFnZ4cDBw6gvLwckyZNwqBBgyosSLl06RK2bNmCn376CXl5eRg7dixeffVVbNiwAeXl5ejXrx/Gjx+P7777DqWlpfj7779rffGFZT8AZgAqtQrvxr4LtaDWqnvW71k0v22F7LXROvuWT5iCFfHVT970bOGJVSPaIn5ON6wc0RYvtvNl8oaIiMhE5s2bB5FIVOErODhYU19cXIxJkybBxcUFdnZ2GDhwoNbJnCkpKejVqxdsbGzg7u6ON998E+XlFffH279/P9q0aQOFQoHAwEBER0drxbJ06VL4+/vDysoKHTp0wN9//10r71lvla2+6TQFkFkZ9FKHL2di7s5z1eprp5Bi3ej2TN4QEZFGcHAwrl69ir179+LMmTPYuHEjwsPD0aFDB3zzzTc4cOAAjh07pmlfXFyMb775Bq1atcJTTz2FJUuWYNOmTUhLS0NeXh5yc3PRu3dvNG7cGCEhIRg5ciT8/Pxq9T3U+wTOpqRNOJ91XqtcJpZhWvPJuDVzFqDWTu5Yt2uHeXf9oBb0v6aHUoGtEyOwbGg4uoV6wEomqU7oREREZGDNmjVDamqq5uuvv/7S1E2bNg0//fQTtm7digMHDuDWrVsYMGCApl6lUqFXr14oLS3F4cOHsW7dOkRHR2POnDmaNsnJyejVqxe6du2KkydPYurUqRg3bhx+/fVXTZvNmzdj+vTpmDt3Lk6cOIGwsDBERUUhI6P6e8AYxPW/gSt/aJdbOwNtxxj0UjfuFOHVDSdQXo0bLWdbOb4b3xERjV0MGhMREVk2QRAgEomQkJAAX19f+Pr6aupCQ0Ph6OiIhIQETZmfnx98fHw0ryMiIqBWq5GUlARnZ2eMGjUKUVFR6NOnDxYvXoxUHU/tGFq9TuCkF6ZjSfwSnXXjW4yH9bodKL18WbtSKsW5F1/G39fu6H3NDgHO2DXlSbTzd9a7LxEREdUuqVQKT09PzZerqysAIDc3F19//TU+/fRTPPPMMwgPD8fatWtx+PBhHDlyBADw22+/4fz581i/fj1atWqF5557DgsWLMDSpUtR+s8plsuXL0dAQAA++eQThISEYPLkyXj++ecr7Bf46aefYvz48Rg9ejRCQ0OxfPly2NjYYM2aNY+MvaSkBHl5eRW+DEYQgD1zdddFvAoo7Ax2qTKVGv/9Lr5ah0N4O1hhy8sRaNHAwWDxEBFR3ZCQkICAgACDjbd27VrExsaiU6dO2Lx5M4KCgjT3BLWl3iZwBEHA3MNzUVhWqFXnr/TH0PJwZK1Zq7Ov7bgJmB2v3e9xBrf3xYZxHfiYFBERkZm6ePEivL290ahRIwwdOhQpKSkAgLi4OJSVlSEyMlLTNjg4GH5+foiNjQUAxMbGokWLFhU2QIyKikJeXh7OnTunafPgGPfb3B+jtLQUcXFxFdqIxWJERkZq2lRm4cKFcHBw0Hw9+JvFGrsQA6Qc1i5XOADtJxjuOgA+//0CTqTk6N3Pz9kGW1/phEB3wyWTiIiobti3bx/OnDmDgQMHIiQkBNevX8f169c19efPn0dOTk6F07JTUlJw69YtzesjR45ALBajadOmmrLWrVtj1qxZOHz4MJo3b46NGzfW6vuotwmcLUlbcOiWjk34ALwTOg0ZM3Q/OmXVrBk+d+uIO3r+VujZYHe8168FpJJ6+yMnIiIyax06dEB0dDRiYmLw1VdfITk5GU8++STy8/ORlpYGuVwOR0fHCn08PDyQlnbvhKS0tDSt0yvuv35cm7y8PNy9exeZmZlQqVQ629wfozKzZs1Cbm6u5uvBG9MaUZUDv8/TXffEFMDKcKtd/rqYiWX7dax+fgxvBytsHN8BPo7WBouFiIgsU0lJCdLS0nDz5k2cOHEC77//Pvr27YvevXtjxIgRiIyMRIsWLTB06FCcOHECf//9N0aMGIEuXbqgbdu2mnGsrKwwcuRInDp1Cn/++Sf++9//4sUXX4SnpyeSk5Mxa9YsxMbG4tq1a/jtt99w8eJFhISE1Op7q5enUF3NvYqPj3+ss+4/jfrAa/H3KHgg03afSCbDjZffwPZ9+j2DHuxpjy8Gt4ZEzOPAiYiIzNVzzz2n+b5ly5bo0KEDGjZsiC1btsDa2vwTAwqFAgpFLazyjf8GuJ2oXW7nCXR81WCXySwowbQtJyHoue2No40MG8Z3RAMnG4PFQkRElismJgZeXl6QSqVwcnJCWFgYvvjiC4wcORJi8b0FFTt37sSUKVPw1FNPVThG/EGBgYEYMGAAevbsiezsbPTu3RvLli0DANjY2CAxMRHr1q1DVlYWvLy8MGnSJLz88su1+t7qXQKnTFWGt/56C8WqYq06d2t3vHqhIfL2fa6zr2Lcy5h+rECv64lEwIcDW8JWUe9+1ERERBbN0dERQUFBuHTpErp164bS0lLk5ORUWIWTnp4OT09PAICnp6fWaVH3T6l6sM3DJ1elp6dDqVTC2toaEokEEolEZ5v7YxhVYSbw+3zddU/PBOS2BrmMWi3g9S2ncDu/RK9+UrEIy4a2QYCrYeIgIqJ7bBUSbBvsD/+GDaGwMr8tQOwq+fd1dHS0ztMdH+bn54edO3dWWj9v3jzMmzcPAPDKK69o1Xt4eOCHH36oUqyGVO+e51l0fBHOZJ7RWfe+3VDkfb5UZ51V+/aYKmqh943Fi+G+CPN11DdMIiIiMrGCggJcvnwZXl5eCA8Ph0wmw969ezX1SUlJSElJQUREBIB7p1OcOXOmwmlRe/bsgVKp1DxTHxERUWGM+23ujyGXyxEeHl6hjVqtxt69ezVtjOr3uUBxjna5SxOg9XCDXWbNoWQcuHBb737z+zZDp8auBouDiIjuEYlEsJWLYWclhb2VzOy+RKL6+XRLvUrg/HT5J3yX+J3OurFOveD07iqgTHtvG4mLC1Z0HolTt/L1up6TjQwzejR9fEMiIiIyuTfeeAMHDhzA1atXcfjwYfTv3x8SiQSDBw+Gg4MDxo4di+nTp+OPP/5AXFwcRo8ejYiICHTs2BEA0L17d4SGhmL48OE4deoUfv31V8yePRuTJk3SPNo0ceJEXLlyBTNmzEBiYiKWLVuGLVu2YNq0aZo4pk+fjlWrVmHdunVISEjAK6+8gsLCQowePdq4P5CUI0D8et11PRYCEsOsLj5zIxcfxuh4ROsxXmrni6EdGhokBiIiIktQb57rScpOwrux7+qsC5b7ovfy0yjLydGuFInwy38mYtOVu3pfc26fZnCxM7/lZkRERKTtxo0bGDx4MLKysuDm5obOnTvjyJEjcHNzAwB89tlnEIvFGDhwIEpKShAVFaV5Fh4AJBIJdu3ahVdeeQURERGwtbXFyJEj8e67/95/BAQEYPfu3Zg2bRoWL16MBg0aYPXq1YiKitK0GTRoEG7fvo05c+YgLS0NrVq1QkxMjNbGxrWqtBDYob1kHAAQ0gdo0s0glykoKceU706gTKXfxjdBHnaY26eZQWIgIiKyFCJB0HerOOPLy8uDg4MDcnNzoVQq9e6fVpiGoT8PRUaR9ubDSlhh9W+NgLjTOvvGdRmA2U6d9L5mlyA3RI9uV2+XdhERUf1U089sMpwa/Vn8/Cbw90rtcpkNMPkY4NCgxvEJgoDpW07hh/ibevWzkonx4+TOCPKwr3EMRESkW3FxMZKTkxEQEAArKytTh1MnPOpnWtXP7Dq/AievNA+v/P6KzuSNRCVg8X7fSpM3Zxq1wTuOHfW+po1cgv/1b87kDREREVmey/t0J28AoMv/GSR5AwAbjqbonbwB7q1wZvKGiIjqozq9B05xeTGm/jEVl3IuadWJ1QIWHfCF7d8JOvtecfTBO6EDIYj0/xHNei6YR1kSERGR5cm7BXw/Xnddg3ZAxGSDXObv5GzM/+mc3v16tfTCS+18DRIDERGRpamzK3Dult/FlH1TcCztmFadrFzA3F8d0OD0VZ19s6yUmNthNEqk+u9fM6CND4Z15IZ6REREZGFUZcDWUUBRpnad1Brot9wgGxcnpOZh7Lpjeu974+Nojff7t+AKZyIiqrfqZAKnqKwIk/ZOwvH041p11iUC5uxUoPHlbJ1982Q2eLvTeGRaO+p93dZ+jryxICIiIssjCMAvM4DrR3XXd5sPuAbW+DIpWUUYseZv5BeX69VPIhbhi8Gt4WAtq3EMRERElqrOJXAy72Ziyt4pOJt1VqvOLUfAW9tF8Ekv0tm3SKrA7E7jcU3ppfd1PZVWWDEsHFYyid59iYiIiEzq0GLg+BrddcG9gfYTanyJjPxiDPv6KG7nl+jdd3q3IIQ3dKpxDERERJasTiVwkrKTMHnfZKQVpmnVtUhWY9pOAXZ3dS/XLZbIMKfjWFx00v+5akcbGdaMagd3JXfnJiIiIgtzegvw+1zddU4BQN+lQA1XFyel5eOV9XFIydb9S7RH6dTYBRO7NK7R9YmIiOqCOrOJ8a4ruzDilxFayRuJSsCgAyq8vVldafImX2aNWU9MxDnXRnpf18lGho3jOiLUm0elEhERkYU5+z3ww8u666TWwIvfANV4rPy+vOIyfBSTiN5L/sSVzEK9+7vYyvHZoFaQiPl4OhERPd6oUaMgEokgEokgk8ng4eGBbt26Yc2aNVCr1aYOr8YsfgVOUVkR/nf0f/jx8o9adT6ZAqb8pEIj7QU5GllWSrxdzcem3OwV+HZsewR7MnlDREREFub0FuCHiYCg64ZWBDz/NeDVUu9hi0rLcTQ5Gz+duoWYs2koKlVVKzypWISlQ9vAgyuciYhM68cpQIbu05uNxj0E+M+SKjXt0aMH1q5dC5VKhfT0dMTExOC1117Dtm3b8OOPP0Iqtdw0iEVFXlymQk5WEdLyinEzpwh/pBzE4ZxVKEHF0xKsSwQMPKRGz2MCpI9IsiUrPTG/w2ik27roHUsrX0d8NawNvBys9e5LVCWCjhVjusruVVSxbSX9q9q20usTEf2jrNjUEdDjCMK9PW8qe2wKAJ77CAju9chhVGoBKdlFuJCej4vp+biQXoCLGQW4lJGv9wlTuszuFYKOjfS/RyMiIgPLSABuaJ/ubK4UCgU8PT0BAD4+PmjTpg06duyIZ599FtHR0Rg3bpyJI6w+i0rgtH3vd4gVNhDJsqBwj4FMeaZCvbRcQNfT95I3zgWPHutP75b4tM0gFFfjqPBhHf3wTu9QKKR1cMNitRoQVIC6HFD/819B/dBr1T/fP/i6/F7fCq//afO48TTflz30+p/vVWUPta1Je9U/v2kU7v1XUN/LU9z/vkK58FC5UEn5/e8fok+yhYiorijh/+PMWtnde6dNnfim8jZPvg50uLdpsSAIuJ1fguTMQlzLKkJyViGuZRUiObMIl28XoLS8dpajj38yACM7+dfK2EREVP8888wzCAsLw/bt25nAMRYr6W3IPI5D5BQHkejfGwZFqYCnzwjoe0QN17xHj1EuEuOHkKcQ06QDnEX5ECHvn335BIggQAT889+K3wOAp70CUyMD0a6hBLh97qGEQfm9BIGqvJLvyx5KOqi0kwzCw2WPa1PTBIuOhAuTC0RERHVKas5dZJaIocq8DJ89E2GTfb7Stoe9hmNLRm/cXn0Et/NLcOPO3Wo/AlVdz4c3wFs9QyCq4cbJREREDwoODsbp06dNHUaNWFYCp9ESCDb/huyTKaBbvBpPnxFgU4UTKeXKMvh3zMEc542Yg436B1AK4Gf9uxERERGZSvfP9mOYzRHMlH4HG9HdStstK/8PPkruASDVeME9JDLEAx8MaMHkDRERGZwgCBb/+WJRCZxykQhedwREJArolKBGQHoVO4oEODcthFuLPIjr4FNPRERERJVZK/8Iz8guVVqvFkSYXz4C61RRRoxKW/sAZ3w5pDWkkjpzSCoRUd3gHmLqCAwSQ0JCAgICAgwQjOlUK4GzdOlSLFq0CGlpaQgLC8OSJUvQvn37Sttv3boV77zzDq5evYomTZrgww8/RM+ePfW+7gdfl6NJvn4ZGFuPEni0yYXCoVzv6xERERFZurbiiwB0/8axQLDC62UT8au68vs4Y3iyiSu+GhYOKxl/00ZEZHaqePqTOdu3bx/OnDmDadOmmTqUGtH7VxybN2/G9OnTMXfuXJw4cQJhYWGIiopCRkaGzvaHDx/G4MGDMXbsWMTHx6Nfv37o168fzp49q3ewXjlVb6twKkWDJ7Pg+3QWkzdERERED0lU++I/pe+ZNHkjEgFTnglE9Oj2sFNY1MJwIiIyUyUlJUhLS8PNmzdx4sQJvP/+++jbty969+6NESNGmDq8GtE7gfPpp59i/PjxGD16NEJDQ7F8+XLY2NhgzZo1OtsvXrwYPXr0wJtvvomQkBAsWLAAbdq0wZdfflnj4HWxcilFg87ZCOieCXufElj4I25EREREBre5/Gn0K30XVwRvk8UQ7GmPLS9H4PXuTSER84aNiIgMIyYmBl5eXvD390ePHj3wxx9/4IsvvsDOnTshkVj2Sk+9ftVRWlqKuLg4zJo1S1MmFosRGRmJ2NhYnX1iY2Mxffr0CmVRUVHYsWNHpdcpKSlBScm/uxLn5T36aCmRWICy4V04NSmEtXNZFd4JERERUf1zXe2Gt8rH4k91S5PF4GIrx6SugRgR0ZD73RARkUFFR0cjOjra1GHUGr0SOJmZmVCpVPDw8KhQ7uHhgcTERJ190tLSdLZPS0ur9DoLFy7E/PnzHx2MSICtRwmUfndh36AYEjmPvyYiIiLSpUSQYa0qCovLB+AurIx+fZEIiGjkgufDG6BnCy/udUNERFQNZvmw8axZsyqs2snLy4Ovry8kChWUPiWw8yyBrVcJpFZqE0ZJREREZP5+KO+EFcJg3IKrUa+rtJKiYyMXPBHoishQD/g4Whv1+kRERHWNXgkcV1dXSCQSpKdXPL87PT0dnp6eOvt4enrq1R4AFAoFFAqFVnnj3rfhYMVnpImIiIiq6p3ysRBLbGptfBu5BE3c7dDEwx5N3O0Q5GGPJh528Hawhph72xARERmMXgkcuVyO8PBw7N27F/369QMAqNVq7N27F5MnT9bZJyIiAnv37sXUqVM1ZXv27EFERITewXJDYjMhkgBiKSD+578i8UOvJf98/6jXUkB8v58UEMv+rdd8PfBa8qh6mXb7B9tIZP++Fokf+hJVfA3RA+WV1T3YT/RAna6/oJX8pa1qW6ONaYDrExEBQF4+8EGAqaMgA5NJRPB1tkGAiy0autgiwNXmn//awseRiRoiIiJj0PsRqunTp2PkyJFo27Yt2rdvj88//xyFhYUYPXo0AGDEiBHw8fHBwoULAQCvvfYaunTpgk8++QS9evXCpk2bcPz4caxcudKw76Ta7v/DW8//Ppg8kEgrJhIkD34v05GEeDDh8EDS4eHEyCPbiCsvqzCOpJoJl0rGu5+0ICIi0kUlM3UEpINcIoZCJoaVTAJrmQRW/3x//8v6gToXOznc7BRws7eCm70CXg5W8Ha05klRRET1kCBwr1lDMcTPUu8EzqBBg3D79m3MmTMHaWlpaNWqFWJiYjQbFaekpEAs/vdEgU6dOmHjxo2YPXs23nrrLTRp0gQ7duxA8+bN9Y/2tdOAUlm9hMvD/2USgoiIiOqBU3O7w8nRwdRhEBGRBbl/3HZpaSmsrbmHmSEUFRUBAGSy6v+ySyRYQEotLy8PDg4OyM3NhVKpNHU4REREVAl+ZpsP/lkQEVF1CYKAlJQUlJWVwdvbu8IiDdKPIAgoKipCRkYGHB0d4eXlpdWmqp/ZZnkKFRERERERERGZhkgkgpeXF5KTk3Ht2jVTh1MnODo6PvIwp6pgAoeIiIiIiIiIKpDL5WjSpAlKS0tNHYrFk8lkmsfSaoIJHCIiIiIiIiLSIhaLYWVlZeow6B98kI2IiIiIiIiIyMwxgUNEREREREREZOaYwCEiIiIiIiIiMnMWsQfO/ZPO8/LyTBwJERERPcr9z+r7n91kOrx/IiIisgxVvX+yiAROVlYWAMDX19fEkRAREVFVZGVlwcHBwdRh1Gu8fyIiIrIsj7t/sogEjrOzMwAgJSVF55tp164djh07Vq2xa9LXUGPUpH9eXh58fX1x/fp1KJVKo8dg6T+/utDfHGKoz3Ogpn3Zn3Ogpv3NbQ7k5ubCz89P89lNpvOo+ydT/503dQyGmDeWfP9p6uubQ/+ajmHq69e0v6k/Oyz951cX+ptDDJwD//ar6v2TRSRwxOJ7W/U4ODjo/OFKJJJq/9Br0tdQYxgiBqVSaZIY6sLPz9L7m0MMpu4PmG4O1LQv+3MOGKI/YH5z4P5nN5nOo+6fzOHvrDnEUJN5Y8n3n6a+vjn0r+kYpr6+oWLgvyHqb39ziMHU/QHzmwOPu3+qE3dXkyZNMklfQ41hiBhqqrox1IWfn6X3N4cYTN3fEEz5/xH25xyo73OAjM8c/s6aQwymur6pf36mvr459K/pGKa+vqFiqCn+G8Jy+5tDDKbubwjGngMiwQJ2GczLy4ODgwNyc3NrnGGra/izofqOc4DqO3ObA+YWT33GP4vK8WdD9R3nANV35jYHqhqPRazAUSgUmDt3LhQKhalDMTv82VB9xzlA9Z25zQFzi6c+459F5fizofqOc4DqO3ObA1WNxyJW4BARERERERER1WcWsQKHiIiIiIiIiKg+YwKHqBaIRCLs2LHD1GEQmQznABER6YufHVTfcQ7Q4zCBQ1QFo0aNQr9+/UwdBpHJcA4QEZG++NlB9R3nABkaEzhERERERERERGaOCRwzx6yt+fH398fnn39eoaxVq1aYN2+eSeKp6zgHzA/ngHFxDhDpj/PG/PCzw7g4B8wP54Bx1dU5wAQOEREREREREZGZYwLHgsTExKBz585wdHSEi4sLevfujcuXL2vqr169CpFIhO3bt6Nr166wsbFBWFgYYmNjTRg1keFwDlB9xzlApD/OG6rvOAeovqtLc4AJHAtSWFiI6dOn4/jx49i7dy/EYjH69+8PtVpdod3bb7+NN954AydPnkRQUBAGDx6M8vJyE0VNZDicA1TfcQ4Q6Y/zhuo7zgGq7+rSHJCaOgCquoEDB1Z4vWbNGri5ueH8+fNo3ry5pvyNN95Ar169AADz589Hs2bNcOnSJQQHBxs13rpKLBZDEIQKZWVlZSaKpn7hHDAPnAOmwzlApD/OG/PAzw7T4RwwD5wDplOX5gBX4FiQixcvYvDgwWjUqBGUSiX8/f0BACkpKRXatWzZUvO9l5cXACAjI8NocdZ1bm5uSE1N1bzOy8tDcnKyCSOqPzgHzAPngOlwDhDpj/PGPPCzw3Q4B8wD54Dp1KU5wBU4FqRPnz5o2LAhVq1aBW9vb6jVajRv3hylpaUV2slkMs33IpEIALSWh1H1PfPMM4iOjkafPn3g6OiIOXPmQCKRmDqseoFzwDxwDpgO5wCR/jhvzAM/O0yHc8A8cA6YTl2aA0zgWIisrCwkJSVh1apVePLJJwEAf/31l4mjqj/UajWk0nvTZdasWUhOTkbv3r3h4OCABQsWMHtuBJwDpsU5YHqcA0T647wxLX52mB7ngGlxDpheXZsDTOBYCCcnJ7i4uGDlypXw8vJCSkoKZs6caeqw6o2MjAwEBgYCAJRKJTZt2lShfuTIkRVeP/x8K9Uc54BpcQ6YHucAkf44b0yLnx2mxzlgWpwDplfX5gD3wDFz97O2YrEYmzZtQlxcHJo3b45p06Zh0aJFpg6vzrtz5w527dqF/fv3IzIy0tTh1EucA6bFOWB6nANE+uO8MS1+dpge54BpcQ6YXl2dA1yBY+YezNpGRkbi/PnzFeofzNL6+/trZW0dHR2Zya2BMWPG4NixY3j99dfRt29fU4dTL3EOmBbngOlxDhDpj/PGtPjZYXqcA6bFOWB6dXUOMIFjpu7cuYNDhw5h//79mDhxoqnDqbd++OEHU4dQb3EOmAfOAdPhHCDSH+eNeeBnh+lwDpgHzgHTqetzgAkcM8WsLdV3nANU33EOEOmP84bqO84Bqu/q+hwQCea4LoiIiIiIiIiIiDS4iTERERERERERkZljAoeIiIiIiIiIyMwxgWMGFi5ciHbt2sHe3h7u7u7o168fkpKSKrQpLi7GpEmT4OLiAjs7OwwcOBDp6ema+lOnTmHw4MHw9fWFtbU1QkJCsHjx4gpjpKamYsiQIQgKCoJYLMbUqVON8faIHstYc2D79u3o1q0b3NzcoFQqERERgV9//dUo75HoUYw1B/766y888cQTcHFxgbW1NYKDg/HZZ58Z5T0SGRLvnYh4/0RUH++fmMAxAwcOHMCkSZNw5MgR7NmzB2VlZejevTsKCws1baZNm4affvoJW7duxYEDB3Dr1i0MGDBAUx8XFwd3d3esX78e586dw9tvv41Zs2bhyy+/1LQpKSmBm5sbZs+ejbCwMKO+R6JHMdYcOHjwILp164aff/4ZcXFx6Nq1K/r06YP4+Hijvl+ihxlrDtja2mLy5Mk4ePAgEhISMHv2bMyePRsrV6406vslqineOxHx/omoXt4/CWR2MjIyBADCgQMHBEEQhJycHEEmkwlbt27VtElISBAACLGxsZWO8+qrrwpdu3bVWdelSxfhtddeM2jcRIZijDlwX2hoqDB//nzDBE5kIMacA/379xeGDRtmmMCJTIT3TkS8fyKqD/dPXIFjhnJzcwEAzs7OAO5lBcvKyhAZGalpExwcDD8/P8TGxj5ynPtjEFkSY80BtVqN/Px8zhMyO8aaA/Hx8Th8+DC6dOlioMiJTIP3TkS8fyKqD/dPUqNfkR5JrVZj6tSpeOKJJ9C8eXMAQFpaGuRyORwdHSu09fDwQFpams5xDh8+jM2bN2P37t21HTKRQRlzDnz88ccoKCjAiy++aLD4iWrKGHOgQYMGuH37NsrLyzFv3jyMGzfO4O+DyFh470TE+yei+nL/xASOmZk0aRLOnj2Lv/76q9pjnD17Fn379sXcuXPRvXt3A0ZHVPuMNQc2btyI+fPnY+fOnXB3d6/2tYgMzRhz4M8//0RBQQGOHDmCmTNnIjAwEIMHD65J2EQmw3snIt4/EdWX+ycmcMzI5MmTsWvXLhw8eBANGjTQlHt6eqK0tBQ5OTkVsofp6enw9PSsMMb58+fx7LPPYsKECZg9e7axQicyCGPNgU2bNmHcuHHYunVrhSWVRKZmrDkQEBAAAGjRogXS09Mxb948JnDIIvHeiYj3T0T16v7J6LvukBa1Wi1MmjRJ8Pb2Fi5cuKBVf3/zpW3btmnKEhMTtTZfOnv2rODu7i68+eabj70mN+Ijc2LMObBx40bByspK2LFjh2HfBFENmOJz4L758+cLDRs2rFH8RMbGeyci3j8R1cf7JyZwzMArr7wiODg4CPv37xdSU1M1X0VFRZo2EydOFPz8/IR9+/YJx48fFyIiIoSIiAhN/ZkzZwQ3Nzdh2LBhFcbIyMiocK34+HghPj5eCA8PF4YMGSLEx8cL586dM9p7JdLFWHNgw4YNglQqFZYuXVqhTU5OjlHfL9HDjDUHvvzyS+HHH38ULly4IFy4cEFYvXq1YG9vL7z99ttGfb9ENcV7JyLePxHVx/snJnDMAACdX2vXrtW0uXv3rvDqq68KTk5Ogo2NjdC/f38hNTVVUz937lydYzycFaxKGyJjM9Yc6NKli842I0eONN6bJdLBWHPgiy++EJo1aybY2NgISqVSaN26tbBs2TJBpVIZ8d0S1RzvnYh4/0RUH++fRIIgCFV72IqIiIiIiIiIiExBbOoAiIiIiIiIiIjo0ZjAISIiIiIiIiIyc0zgEBERERERERGZOSZwiIiIiIiIiIjMHBM4RERERERERERmjgkcIiIiIiIiIiIzxwQOEREREREREZGZYwKHiIiIiIiIiMjMMYFDRLXi6aefxtSpU00dBhEREZHF4P0TET0KEzhEZHL79++HSCRCTk6OqUMhIiIisgi8fyKqf5jAISIiIiIiIiIyc0zgEFGNFRYWYsSIEbCzs4OXlxc++eSTCvXffvst2rZtC3t7e3h6emLIkCHIyMgAAFy9ehVdu3YFADg5OUEkEmHUqFEAALVajYULFyIgIADW1tYICwvDtm3bjPreiIiIiGoD75+ISF9M4BBRjb355ps4cOAAdu7cid9++w379+/HiRMnNPVlZWVYsGABTp06hR07duDq1auamwxfX198//33AICkpCSkpqZi8eLFAICFCxfim2++wfLly3Hu3DlMmzYNw4YNw4EDB4z+HomIiIgMifdPRKQvkSAIgqmDICLLVVBQABcXF6xfvx4vvPACACA7OxsNGjTAhAkT8Pnnn2v1OX78ONq1a4f8/HzY2dlh//796Nq1K+7cuQNHR0cAQElJCZydnfH7778jIiJC03fcuHEoKirCxo0bjfH2iIiIiAyO909EVB1SUwdARJbt8uXLKC0tRYcOHTRlzs7OaNq0qeZ1XFwc5s2bh1OnTuHOnTtQq9UAgJSUFISGhuoc99KlSygqKkK3bt0qlJeWlqJ169a18E6IiIiIjIP3T0RUHUzgEFGtKiwsRFRUFKKiorBhwwa4ubkhJSUFUVFRKC0trbRfQUEBAGD37t3w8fGpUKdQKGo1ZiIiIiJT4v0TEenCBA4R1Ujjxo0hk8lw9OhR+Pn5AQDu3LmDCxcuoEuXLkhMTERWVhY++OAD+Pr6Ari3BPhBcrkcAKBSqTRloaGhUCgUSElJQZcuXYz0boiIiIhqH++fiKg6mMAhohqxs7PD2LFj8eabb8LFxQXu7u54++23IRbf2yPdz88PcrkcS5YswcSJE3H27FksWLCgwhgNGzaESCTCrl270LNnT1hbW8Pe3h5vvPEGpk2bBrVajc6dOyM3NxeHDh2CUqnEyJEjTfF2iYiIiGqM909EVB08hYqIamzRokV48skn0adPH0RGRqJz584IDw8HALi5uSE6Ohpbt25FaGgoPvjgA3z88ccV+vv4+GD+/PmYOXMmPDw8MHnyZADAggUL8M4772DhwoUICQlBjx49sHv3bgQEBBj9PRIREREZEu+fiEhfPIWKiIiIiIiIiMjMcQUOEREREREREZGZYwKHiIiIiIiIiMjMMYFDRERERERERGTmmMAhIiIiIiIiIjJzTOAQEREREREREZk5JnCIiIiIiIiIiMwcEzhERERERERERGaOCRwiIiIiIiIiIjPHBA4RERERERERkZljAoeIiIiIiIiIyMwxgUNEREREREREZOb+H4mwgjKhTIV7AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -1404,19 +1404,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1000/1000 [00:01<00:00, 548.73it/s]\n"
+      "100%|██████████| 1000/1000 [00:01<00:00, 627.14it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x400 with 2 Axes>"
       ]
@@ -1489,7 +1489,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -1503,7 +1503,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/exams/td_note_2017.ipynb b/_doc/practice/exams/td_note_2017.ipynb
index a1ae0fb..f644b7e 100644
--- a/_doc/practice/exams/td_note_2017.ipynb
+++ b/_doc/practice/exams/td_note_2017.ipynb
@@ -52,25 +52,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(2.996890181837922, 2.8750295096923186),\n",
-       " (4.264526460045277, 2.324063943726332),\n",
-       " (4.718648422500299, 3.052469543647318),\n",
-       " (2.442689562115705, 3.861870829036456),\n",
-       " (0.13558433730903707, 0.5754835901808546),\n",
-       " (5.59230695209076, 1.6209924216651825),\n",
-       " (7.610357428256408, 3.3202733390571373),\n",
-       " (8.678403330137792, 4.96766236219644),\n",
-       " (9.427259745518597, 6.385862058140737),\n",
-       " (9.273956381823456, 4.938275166261537)]"
+       "[(6.049169429448925, 4.718935755862216),\n",
+       " (4.627795365365467, 3.8048865083718906),\n",
+       " (6.474192602737176, 5.038157546858375),\n",
+       " (5.257002270747765, 3.824981234020261),\n",
+       " (3.6776757481314792, 2.3846063467327263),\n",
+       " (4.806391225454085, 3.6643444372898646),\n",
+       " (8.524342428472508, 3.7743268407183237),\n",
+       " (8.293296420000031, 6.157037850737982),\n",
+       " (1.1448844698514993, 1.8337680880279141),\n",
+       " (0.47468792840570706, 1.3511445685085155)]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,16 +103,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(5.523441805914873, 3.850511796328412, 25.88928454527569, 38.98854258182378)"
+       "(5.486540369984782, 3.9453189049350192, 25.815898347439713, 39.24240425818958)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -147,16 +147,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-0.5446995618974346, 6.859128128176218)"
+       "(-0.46893829734349396, 6.518167804342026)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,25 +183,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('vert', 5.132157444058703),\n",
-       " ('vert', 6.088324149707968),\n",
-       " ('rouge', 0.16315983779393228),\n",
-       " ('rouge', 0.9717657424738734),\n",
-       " ('rouge', 2.843197432779423),\n",
-       " ('rouge', 0.7204386278807904),\n",
-       " ('bleu', 21.89226869979884),\n",
-       " ('rouge', -0.16605748011543708),\n",
-       " ('rouge', -0.02903894820027486),\n",
-       " ('rouge', 0.5787816483863786)]"
+       "[('bleu', 19.91792858279941),\n",
+       " ('vert', 6.552945331178698),\n",
+       " ('bleu', 21.607291021269408),\n",
+       " ('vert', 6.299785531549073),\n",
+       " ('vert', 7.258812927878141),\n",
+       " ('rouge', -0.6578105813916351),\n",
+       " ('rouge', 0.522136653538479),\n",
+       " ('bleu', 21.469646109623795),\n",
+       " ('rouge', 1.3477906240590773),\n",
+       " ('vert', 7.922241071711617)]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -233,16 +233,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'bleu': 0, 'rouge': 2, 'vert': 1}"
+       "{'vert': 0, 'bleu': 1, 'rouge': 2}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,20 +271,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  0.,  0.],\n",
-       "       [ 0.,  1.,  0.],\n",
-       "       [ 1.,  0.,  0.],\n",
-       "       [ 1.,  0.,  0.],\n",
-       "       [ 0.,  0.,  1.]])"
+       "array([[1., 0., 0.],\n",
+       "       [0., 1., 0.],\n",
+       "       [1., 0., 0.],\n",
+       "       [0., 0., 1.],\n",
+       "       [0., 0., 1.]])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -317,24 +317,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([[ 1.,  0.,  0.,  1.],\n",
-       "        [ 0.,  1.,  0.,  1.],\n",
-       "        [ 1.,  0.,  0.,  1.],\n",
-       "        [ 1.,  0.,  0.,  1.],\n",
-       "        [ 0.,  0.,  1.,  1.]]), array([[ 21.15485572],\n",
-       "        [  6.37882494],\n",
-       "        [ 21.37124634],\n",
-       "        [ 21.77476221],\n",
-       "        [  2.03305199]]))"
+       "(array([[1., 0., 0., 1.],\n",
+       "        [0., 1., 0., 1.],\n",
+       "        [1., 0., 0., 1.],\n",
+       "        [0., 0., 1., 1.],\n",
+       "        [0., 0., 1., 1.]]),\n",
+       " array([[ 4.7323734 ],\n",
+       "        [21.71040171],\n",
+       "        [ 4.84924589],\n",
+       "        [ 1.78526719],\n",
+       "        [ 0.34311133]]))"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,18 +363,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 20.92499253],\n",
-       "       [  6.14818418],\n",
-       "       [  1.09988478]])"
+       "array([[ 5.94180865],\n",
+       "       [20.97848259],\n",
+       "       [ 1.05427197]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,20 +397,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 20.92499253],\n",
-       "       [  6.14818418],\n",
-       "       [ 20.92499253],\n",
-       "       [ 20.92499253],\n",
-       "       [  1.09988478]])"
+       "array([[ 5.94180865],\n",
+       "       [20.97848259],\n",
+       "       [ 5.94180865],\n",
+       "       [ 1.05427197],\n",
+       "       [ 1.05427197]])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,16 +433,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 1.]), array([  1.77635684e-15]))"
+       "(array([1.]), array([-5.32907052e-15]))"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -487,25 +488,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('vert', 6.0084452843428675),\n",
-       " ('rouge', 2.155449750270483),\n",
-       " ('rouge', 2.1132607428792447),\n",
-       " ('vert', 6.897729973062269),\n",
-       " ('rouge', 0.7637316114791164),\n",
-       " ('vert', 5.566787193134299),\n",
-       " ('vert', 5.848567708215508),\n",
-       " ('bleu', 19.722503065860707),\n",
-       " ('rouge', 0.8043492141543047),\n",
-       " ('bleu', 21.675781652825997)]"
+       "[('rouge', 0.09329495469271787),\n",
+       " ('rouge', 0.789609852660202),\n",
+       " ('vert', 4.714873982539039),\n",
+       " ('rouge', 2.0829663032267485),\n",
+       " ('vert', 6.745691356870579),\n",
+       " ('rouge', 0.05932073340343336),\n",
+       " ('vert', 6.772334898512818),\n",
+       " ('vert', 5.8598815352891425),\n",
+       " ('rouge', 2.30150566164451),\n",
+       " ('vert', 6.549148011451264)]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -542,16 +543,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'bleu': 2, 'rouge': 4, 'vert': 4}"
+       "{'rouge': 5, 'vert': 5}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -578,18 +579,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'bleu': 20.69914235934335,\n",
-       " 'rouge': 1.4591978296957873,\n",
-       " 'vert': 6.080382539688736}"
+       "{'rouge': 1.0653395011255224, 'vert': 6.1283859569325685}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -627,20 +626,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[6.080382539688736,\n",
-       " 1.4591978296957873,\n",
-       " 1.4591978296957873,\n",
-       " 6.080382539688736,\n",
-       " 1.4591978296957873]"
+       "[1.0653395011255224,\n",
+       " 1.0653395011255224,\n",
+       " 6.1283859569325685,\n",
+       " 1.0653395011255224,\n",
+       " 6.1283859569325685]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -662,16 +661,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1.0, 1.7763568394002505e-15)"
+       "(1.0, -8.881784197001252e-16)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -699,25 +698,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 6.00844528,  6.08038254, -0.07193726,  0.        ,  1.        ,\n",
+       "array([[ 0.09329495,  1.0653395 , -0.97204455,  1.        ,  0.        ,\n",
        "         0.        ],\n",
-       "       [ 2.15544975,  1.45919783,  0.69625192,  1.        ,  0.        ,\n",
+       "       [ 0.78960985,  1.0653395 , -0.27572965,  1.        ,  0.        ,\n",
        "         0.        ],\n",
-       "       [ 2.11326074,  1.45919783,  0.65406291,  1.        ,  0.        ,\n",
+       "       [ 4.71487398,  6.12838596, -1.41351197,  0.        ,  1.        ,\n",
        "         0.        ],\n",
-       "       [ 6.89772997,  6.08038254,  0.81734743,  0.        ,  1.        ,\n",
+       "       [ 2.0829663 ,  1.0653395 ,  1.0176268 ,  1.        ,  0.        ,\n",
        "         0.        ],\n",
-       "       [ 0.76373161,  1.45919783, -0.69546622,  1.        ,  0.        ,\n",
+       "       [ 6.74569136,  6.12838596,  0.6173054 ,  0.        ,  1.        ,\n",
        "         0.        ]])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -742,27 +741,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[  5.62221004e+01,   5.56972711e+01,   5.24829301e-01,\n",
-       "         -2.53176124e+00,  -4.77901369e-01,   3.00966261e+00],\n",
-       "       [  5.56972711e+01,   5.56972711e+01,  -1.92890933e-16,\n",
-       "         -2.53176124e+00,  -4.77901369e-01,   3.00966261e+00],\n",
-       "       [  5.24829301e-01,  -1.92890933e-16,   5.24829301e-01,\n",
-       "         -5.54535166e-17,   7.40725030e-17,  -1.24510807e-17],\n",
-       "       [ -2.53176124e+00,  -2.53176124e+00,  -5.54535166e-17,\n",
-       "          2.66666667e-01,  -1.77777778e-01,  -8.88888889e-02],\n",
-       "       [ -4.77901369e-01,  -4.77901369e-01,   7.40725030e-17,\n",
-       "         -1.77777778e-01,   2.66666667e-01,  -8.88888889e-02],\n",
-       "       [  3.00966261e+00,   3.00966261e+00,  -1.24510807e-17,\n",
-       "         -8.88888889e-02,  -8.88888889e-02,   1.77777778e-01]])"
+       "array([[ 7.96951427e+00,  7.12067761e+00,  8.48836657e-01,\n",
+       "        -1.40640179e+00,  1.40640179e+00,  0.00000000e+00],\n",
+       "       [ 7.12067761e+00,  7.12067761e+00,  2.25880946e-16,\n",
+       "        -1.40640179e+00,  1.40640179e+00,  0.00000000e+00],\n",
+       "       [ 8.48836657e-01,  2.25880946e-16,  8.48836657e-01,\n",
+       "        -4.00913870e-17,  4.00913870e-17,  0.00000000e+00],\n",
+       "       [-1.40640179e+00, -1.40640179e+00, -4.00913870e-17,\n",
+       "         2.77777778e-01, -2.77777778e-01,  0.00000000e+00],\n",
+       "       [ 1.40640179e+00,  1.40640179e+00,  4.00913870e-17,\n",
+       "        -2.77777778e-01,  2.77777778e-01,  0.00000000e+00],\n",
+       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00]])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -781,25 +780,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -816,73 +815,73 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>56.222</td>\n",
-       "      <td>55.697</td>\n",
-       "      <td>0.525</td>\n",
-       "      <td>-2.532</td>\n",
-       "      <td>-0.478</td>\n",
-       "      <td>3.010</td>\n",
+       "      <td>7.970</td>\n",
+       "      <td>7.121</td>\n",
+       "      <td>0.849</td>\n",
+       "      <td>-1.406</td>\n",
+       "      <td>1.406</td>\n",
+       "      <td>0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>55.697</td>\n",
-       "      <td>55.697</td>\n",
-       "      <td>-0.000</td>\n",
-       "      <td>-2.532</td>\n",
-       "      <td>-0.478</td>\n",
-       "      <td>3.010</td>\n",
+       "      <td>7.121</td>\n",
+       "      <td>7.121</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>-1.406</td>\n",
+       "      <td>1.406</td>\n",
+       "      <td>0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.525</td>\n",
-       "      <td>-0.000</td>\n",
-       "      <td>0.525</td>\n",
-       "      <td>-0.000</td>\n",
+       "      <td>0.849</td>\n",
        "      <td>0.000</td>\n",
+       "      <td>0.849</td>\n",
        "      <td>-0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-2.532</td>\n",
-       "      <td>-2.532</td>\n",
+       "      <td>-1.406</td>\n",
+       "      <td>-1.406</td>\n",
        "      <td>-0.000</td>\n",
-       "      <td>0.267</td>\n",
-       "      <td>-0.178</td>\n",
-       "      <td>-0.089</td>\n",
+       "      <td>0.278</td>\n",
+       "      <td>-0.278</td>\n",
+       "      <td>0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.478</td>\n",
-       "      <td>-0.478</td>\n",
+       "      <td>1.406</td>\n",
+       "      <td>1.406</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>-0.278</td>\n",
+       "      <td>0.278</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>-0.178</td>\n",
-       "      <td>0.267</td>\n",
-       "      <td>-0.089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>3.010</td>\n",
-       "      <td>3.010</td>\n",
-       "      <td>-0.000</td>\n",
-       "      <td>-0.089</td>\n",
-       "      <td>-0.089</td>\n",
-       "      <td>0.178</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        0       1       2       3       4       5\n",
-       "0  56.222  55.697   0.525  -2.532  -0.478   3.010\n",
-       "1  55.697  55.697  -0.000  -2.532  -0.478   3.010\n",
-       "2   0.525  -0.000   0.525  -0.000   0.000  -0.000\n",
-       "3  -2.532  -2.532  -0.000   0.267  -0.178  -0.089\n",
-       "4  -0.478  -0.478   0.000  -0.178   0.267  -0.089\n",
-       "5   3.010   3.010  -0.000  -0.089  -0.089   0.178"
+       "        0       1       2       3       4      5\n",
+       "0   7.970   7.121   0.849  -1.406   1.406  0.000\n",
+       "1   7.121   7.121   0.000  -1.406   1.406  0.000\n",
+       "2   0.849   0.000   0.849  -0.000   0.000  0.000\n",
+       "3  -1.406  -1.406  -0.000   0.278  -0.278  0.000\n",
+       "4   1.406   1.406   0.000  -0.278   0.278  0.000\n",
+       "5   0.000   0.000   0.000   0.000   0.000  0.000"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -890,7 +889,7 @@
    "source": [
     "import pandas\n",
     "\n",
-    "pandas.DataFrame(c).applymap(lambda x: \"%1.3f\" % x)"
+    "pandas.DataFrame(c).map(lambda x: \"%1.3f\" % x)"
    ]
   },
   {
@@ -904,7 +903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -916,16 +915,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.829591905722086, 1.2193824894893863)"
+       "(-0.999999999999999, 7.193725458058086)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -938,16 +937,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.4723463712054069, 16.100975199731273)"
+       "(0.7639529913942368, 0.7639529913942368)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -980,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -990,7 +989,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -1004,7 +1003,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/ml/winesc_color_roc.ipynb b/_doc/practice/ml/winesc_color_roc.ipynb
index 63822cd..142db53 100644
--- a/_doc/practice/ml/winesc_color_roc.ipynb
+++ b/_doc/practice/ml/winesc_color_roc.ipynb
@@ -51,10 +51,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/xadupre/install/scikit-learn/sklearn/linear_model/_logistic.py:474: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
       "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
       "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
       "Please also refer to the documentation for alternative solver options:\n",
       "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
@@ -66,7 +67,8 @@
       "text/html": [
        "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -78,20 +80,21 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-1.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
        "#sk-container-id-1 {\n",
@@ -211,12 +214,21 @@
        "/* Toggleable label */\n",
        "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
@@ -234,9 +246,7 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -262,9 +272,9 @@
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -318,7 +328,6 @@
        "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
@@ -364,23 +373,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -390,7 +401,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -400,7 +412,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -440,7 +453,7 @@
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -452,6 +465,7 @@
        "\n",
        "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
@@ -468,7 +482,489 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
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+       "    const originalStyle = element.style;\n",
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+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
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+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
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+       " * @returns \"light\" or \"dark\"\n",
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+       "\n",
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+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
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+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
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+       "        // Otherwise fall back to the next heuristic.\n",
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       ],
       "text/plain": [
        "LogisticRegression()"
@@ -519,40 +1015,40 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>8.591782</td>\n",
+       "      <td>4.959154</td>\n",
        "      <td>white</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-4.745667</td>\n",
+       "      <td>-5.770727</td>\n",
        "      <td>red</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>5.874864</td>\n",
+       "      <td>10.241925</td>\n",
        "      <td>white</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>6.381441</td>\n",
+       "      <td>2.645375</td>\n",
        "      <td>white</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-3.017183</td>\n",
-       "      <td>red</td>\n",
+       "      <td>11.503256</td>\n",
+       "      <td>white</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      score  color\n",
-       "0  8.591782  white\n",
-       "1 -4.745667    red\n",
-       "2  5.874864  white\n",
-       "3  6.381441  white\n",
-       "4 -3.017183    red"
+       "       score  color\n",
+       "0   4.959154  white\n",
+       "1  -5.770727    red\n",
+       "2  10.241925  white\n",
+       "3   2.645375  white\n",
+       "4  11.503256  white"
       ]
      },
      "execution_count": 5,
@@ -576,7 +1072,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x300 with 1 Axes>"
       ]
@@ -602,7 +1098,7 @@
     {
      "data": {
       "text/plain": [
-       "(1545,)"
+       "(1538,)"
       ]
      },
      "execution_count": 7,
@@ -626,7 +1122,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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hn2ZqaoqkpCSF+SUSCV6+fKl0xocPHyqsWTx69AgymUz+oYednR2ICDVr1pSvxRRXTsG5uLhg/fr18v2927ZtCxMTE+zbtw+zZs3K80WR8yFxjx495POYmppi//79mDlzZpE+kN27d6/CgWsfbwr4lJ2dHWQyGe7evSv/ALUwjhw5glq1auHo0aMKpeDn55drrI6ODtzd3eHu7g6ZTIZx48Zh06ZNmDNnjvw/PjMzMwwfPhzDhw/Hu3fv0K5dO8ybN6/YRW9hYQFDQ0NIpVK4uroWODZnbezT12Jx/vsE/nv+C1pJsrCwgL6+Pu7fv5/rtn/++QeampryEv4458c7DhQmp52dHW7fvo2OHTsWuBJWWs8F8KEb8tpM9+my29nZAfiwsvC5313O+B9++AE//PADHj58iGbNmmHVqlXYs2dPgfN5eHhg3LhxuH//Pg4ePAh9fX24u7srsUSfV9RsHyuzI2OnTp0KAwMDeHt7Iz4+Ptft0dHRWLt2LQCgW7duAIA1a9YojMlZk+jevbt8mp2dHS5cuKAwbvPmzYVaQ/lUQECAwvV169YBALp27QoA+OabbyASiTB//vxcewYQEd68eaP0YwIfDjW3t7fHmjVr8P79ewCAvr4+Jk+ejPv372PWrFm55gkMDMTOnTvh5uaGr7/+Wj7PtGnTcO/ePUybNi3PvRf27NmDa9eu5ZulTZs2cHV1lV8KKvrevXtDU1MTCxYsyHVASF6PnSPnDejjMVevXkVERITCuE+fT01NTfkadGZmZp5jKlWqhNq1a8tvLw6RSIS+ffvi119/zbNoExIS5D/nFMvHr0WpVFrsAwAtLCzQrl07bN++HU+fPlW4Lef5E4lE6Ny5M06cOKFwCoD4+Hjs27cPbdu2lW9KyCtnWlqa/D+kggwYMADPnz/Hli1bct2WkZEh34vNyMgI5ubmuf4uN2zYUIglLli3bt1w5coVhddwQkIC9u7dqzDOzc0NRkZGWLJkCbKysnLdT87vLj09Xf43l8POzg6GhoaFeg317dsXIpEI+/fvx+HDh9GjRw8YGBgUZdFyKW62j5XZGr2dnR327dsHDw8PNGjQQOHI2MuXL+Pw4cMYNmwYAKBp06bw8vLC5s2bkZSUBGdnZ1y7dg27du1C79694eLiIr9fb29vjBkzBn379kWnTp1w+/ZtnDlzBubm5kpnjI2NRc+ePdGlSxdERERgz549GDx4MJo2bSpfhkWLFmHGjBl4/PgxevfuDUNDQ8TGxuLYsWMYNWoUJk+eXKTnZ8qUKejfvz927twp/2Bq+vTpuHXrFpYtW4aIiAj07dsXenp6uHjxIvbs2YMGDRrk+gPNOcJ41apVCAsLQ79+/WBtbY24uDgcP34c165dw+XLl4uU8VO1a9fGrFmzsHDhQjg5OeGbb76BWCzGn3/+iSpVqsh3XfxUjx49cPToUfTp0wfdu3dHbGwsNm7ciIYNGyp8JuDt7Y3ExER06NAB1apVw5MnT7Bu3To0a9ZM/p9ew4YN0b59e7Rs2RJmZma4fv06jhw5gvHjx5fIMi5duhRhYWFwcHDAyJEj0bBhQyQmJuLmzZs4d+4cEhMTAQCNGjXC119/jRkzZiAxMRFmZmY4cOAAsrOzi53hp59+Qtu2bdGiRQuMGjUKNWvWxOPHjxEYGIjIyEgAwKJFixASEoK2bdti3Lhx0NLSwqZNm5CZmYnly5fL76tz586oXr06RowYgSlTpkAkEmH79u2wsLDI9UbyKU9PTxw6dAhjxoxBWFgY2rRpA6lUin/++QeHDh3CmTNn5Jskvb29sXTpUnh7e6NVq1a4cOECHjx4UOznYurUqdi9eze6dOmCCRMmyHevrFGjhsJnM0ZGRvj555/h6emJFi1aYODAgfJlDAwMRJs2bbB+/Xo8ePAAHTt2xIABA9CwYUNoaWnh2LFjiI+Px8CBAz+bx9LSEi4uLli9ejVSU1Ph4eFR7GXMUdxsCoq9346SHjx4QCNHjiRbW1vS0dEhQ0NDatOmDa1bt07hoIusrCyaP38+1axZk7S1tcnGxibPA6akUilNmzaNzM3NSV9fn9zc3OjRo0f57l756cFKRP/t4nX37l3q168fGRoakqmpKY0fPz7PA6Z+/fVXatu2LRkYGJCBgQHVr1+ffHx86P79+wUue0EZpFIp2dnZkZ2dncLuhFKplHbs2EFt2rQhIyMj0tXVpUaNGtH8+fMLPILwyJEj1LlzZzIzMyMtLS2qXLkyeXh4UHh4eIEZi2L79u3UvHlzEovFZGpqSs7OzhQSEiK//dPd9GQyGS1ZsoRq1KhBYrGYmjdvTqdOncq1G1rOMuQcqFS9enUaPXo0vXz5Uj5m0aJFZG9vTyYmJqSnp0f169enxYsXk0QikY8pzu6VRETx8fHk4+NDNjY2pK2tTdbW1tSxY0favHmzwrjo6GhydXUlsVhMVlZWNHPmTAoJCcn3gCll/P3339SnTx8yMTEhXV1dqlevHs2ZM0dhzM2bN8nNzY0qVapE+vr65OLiQpcvX851Xzdu3CAHBwf5c7p69epCHzAlkUho2bJl1KhRI/nvu2XLljR//nxKTk6Wj0tPT6cRI0aQsbExGRoa0oABA+jVq1f57l6pzFHpf/31Fzk7OxfqgKmwsDByc3MjY2Nj0tXVJTs7Oxo2bBhdv36diIhev35NPj4+VL9+fTIwMCBjY2NycHCgQ4cOFTrPli1bCAAZGhrm2RcFHTD1qY+fn5LIlkPj/++cMcaYmuLTFDPGmJrjomeMMTXHRc8YY2qOi54xxtQcFz1jjKk5LnrGGFNz5eI702QyGV68eAFDQ8Ninf+GMcZUBREhNTUVVapUyXX215JWLor+xYsX8nN1MMaYOnn27JnCKcpLQ7ko+pzT8D579qxQp/9kjDFVl5KSAhsbmzL5/ohyUfQ5m2uMjIy46BljaqUsNkfzh7GMMabmuOgZY0zNcdEzxpia46JnjDE1x0XPGGNqjoueMcbUHBc9Y4ypOaWL/sKFC3B3d0eVKlWgoaGB48ePf3ae8PBwtGjRAmKxGLVr18bOnTuLEJUxxlhRKF30aWlpaNq0KQICAgo1PjY2Ft27d4eLiwsiIyMxceJEeHt748yZM0qHZYwxpjylj4zt2rUrunbtWujxGzduRM2aNbFq1SoAQIMGDXDx4kX8+OOPcHNzU/bhGWOMKanUT4EQEREBV1dXhWlubm6YOHFivvNkZmYiMzNTfj0lJaW04hWIiJCYJkHU82RIsmX5jrv9bxKypITSPpL5n5epeJGUAWM97dJ9IMaYUr6u9QUmu9UTOka+Sr3o4+LiYGVlpTDNysoKKSkpyMjIgJ6eXq55/P39MX/+/NKOlktyRhbWn3+ILX/EwvYLfTx+k17mGRhj5Y+FoVjoCAVSyZOazZgxA76+vvLrOWd5Kw3J6VnY8kcMNoQ/goz+m55XybeobpLv/UQnpKFfy2rQLOW1+pSMbDSrbgJTfV6rZ0woKSmpMDL676yTlka6Aqb5vFIvemtra8THxytMi4+Ph5GRUZ5r8wAgFoshFpf+O+S7zGw0XXA213QTfW2Md6mNZjYmqGqqB2sjXf7CE8YYACAqKgpDe3eEr68vpk+fLnScQin1ond0dERQUJDCtJCQEDg6Opb2Q+frj4cJWHn2AW4/S5JPMzPQwaLejdHMxgRVTPJ+A2KMVWx//fUXOnbsiNevX+PIkSOYNGlSmayUFpfSRf/u3Ts8evRIfj02NhaRkZEwMzND9erVMWPGDDx//hy//PILAGDMmDFYv349pk6diu+++w7nz5/HoUOHEBgYWHJLoYR/4lLgue2awjT7mmY4NFq4Nx7GmOq7ffs2OnbsiDdv3qBVq1Y4e/ZsuSh5oAhFf/36dbi4uMiv52xL9/Lyws6dO/Hy5Us8ffpUfnvNmjURGBiISZMmYe3atahWrRq2bt0qyK6Vb95losuaP+TXB9nbYIhDDTSualzmWRhj5UdkZCRcXV3x5s0bfPXVVzh79ixMTEyEjlVoGkREnx8mrJSUFBgbGyM5OblY3zAVcjceI3+5DgCY6FoHE13rllRExpiaunXrFlxdXZGYmAh7e3ucOXOmREq+pHqtMCrUuW5G7/5Q8rZf6HPJM8YK5erVq0hMTISDg0O5W5PPoZK7V5aGs3fi5LtPdvuysrBhGGPlxpgxY2BkZITu3bvD2Lh8buatMGv0xyOfy38e36G2gEkYY6ru9u3bSEpKkl8fPHhwuS15oAIVfc4nET90qgt9nQrzjwxjTEl//vkn2rdvj06dOimUfXlWIYr+WWI6Tv8dB+DDwVCMMZaXa9euyQteV1cXIpFI6EglQu2LXiojOC0Pk1+vbWlYwGjGWEV19epVdOrUCcnJyXByckJQUBAMDdWjL9S+6N+k/XcWzO/a1ISj3RcCpmGMqaIrV66gc+fOSElJQbt27dSq5IEKUPQfm+veUOgIjDEV83HJOzs7IzAwEJUqVRI6VomqMJ9KlvZZJRlj5ZOpqSkMDAzQsmVLnDp1CgYGBkJHKnEVpugZYywv9erVw8WLF2Ftba2WJQ9w0TPGKqA//vgDGRkZ6Ny5MwDAzs5O4ESli4ueMVahXLhwAd26dUN2djbCw8Px9ddfCx2p1FWoD2MZYxXb77//jm7duiEtLQ3t2rVD06ZNhY5UJtS+6JPSs4SOwBhTAeHh4fKSd3Nzw4kTJ/L9ljt1o/ZFP+NoFAAofB8sY6xiCQsLQ7du3ZCeno4uXbrg+PHjFabkgQpQ9O+zpAAAl3oWAidhjAnhr7/+Qvfu3ZGRkYGuXbvi2LFj0NVV7S/zLmkV5sPYYW1qCh2BMSaAhg0bolevXkhJScHRo0fLzdf/laQKU/SMsYpJS0sLu3fvhlQqrZAlD1SATTeMsYrn7NmzGDt2LGQyGYAPZV9RSx7gNXrGmJo5c+YMevXqhczMTDRu3Bg+Pj5CRxIcr9EzxtRGcHCwvOR79eqFkSNHCh1JJXDRM8bUQlBQkLzke/fujUOHDkFHR0foWCqBi54xVu4FBgaiT58+kEgk+Oabb7jkP8FFzxgr1968eYOBAwdCIpGgb9++OHDgALS1+StDP8ZFzxgr17744gvs3bsXQ4YMwf79+7nk88B73TDGyiWJRCLfPNOzZ0/07NlT4ESqS63X6CXZMtx5kSJ0DMZYCTt+/DgaNWqE2NhYoaOUC2pd9PuvPZX/rM3fJciYWjh27Bj69++PR48e4aeffhI6Trmg1kWfkJop/7mlramASRhjJeHo0aMYMGAAsrOzMXjwYKxYsULoSOWCWhd9jmGtbSHWEgkdgzFWDEeOHJGX/JAhQ7Br1y5oafHHjIVRIYqeMVa+HT58GAMHDoRUKoWnpyeXvJK46BljKk0qlWLp0qWQSqUYOnQoduzYAZGI/0NXBhc9Y0yliUQinDlzBn5+fti+fTuXfBFw0TPGVFJMTIz8Z3Nzc8ybN49Lvoi46BljKmfv3r2oW7cuNm/eLHQUtcBFzxhTKXv27MHQoUMhlUpx48YNoeOoBS56xpjK2L17N4YOHQqZTIZRo0bh559/FjqSWuCiZ4yphF27dsHLywtEhNGjR+Pnn3+GpiZXVEngZ5ExJridO3di+PDhICKMHTsWGzZs4JIvQfxMMsYEFxsbCyLCuHHjEBAQwCVfwvjQMsaY4ObNm4evvvoK3bt3h4YGn4CwpPHbJmNMECdPnkR6ejoAQENDAz169OCSLyVc9IyxMrd582b07NkTPXr0QGZm5udnYMXCRc8YK1MbN27E6NGjAQDNmjXjL/EuA1z0jLEys2HDBowdOxYA4Ovri1WrVvHmmjJQpKIPCAiAra0tdHV14eDggGvXrhU4fs2aNahXrx709PRgY2ODSZMm4f3790UKzBgrnwICAuDj4wMAmDx5MlauXMklX0aULvqDBw/C19cXfn5+uHnzJpo2bQo3Nze8evUqz/H79u3D9OnT4efnh3v37mHbtm04ePAgZs6cWezwjLHyYdOmTRg/fjwAYOrUqVi+fDmXfBlSuuhXr16NkSNHYvjw4WjYsCE2btwIfX19bN++Pc/xly9fRps2bTB48GDY2tqic+fOGDRo0Gf/C2CMqY+vvvoKJiYmmDZtGpYuXcolX8aUKnqJRIIbN27A1dX1vzvQ1ISrqysiIiLynKd169a4ceOGvNhjYmIQFBSEbt265fs4mZmZSElJUbgwxsqvFi1aICoqCv7+/lzyAlCq6F+/fg2pVAorKyuF6VZWVoiLi8tznsGDB2PBggVo27YttLW1YWdnh/bt2xe46cbf3x/Gxsbyi42NjTIxGWMqYP369QorgNWqVeOSF0ip73UTHh6OJUuWYMOGDbh58yaOHj2KwMBALFy4MN95ZsyYgeTkZPnl2bNnpR2TMVaCVq1ahe+//x5ubm7896sClDoFgrm5OUQiEeLj4xWmx8fHw9raOs955syZA09PT3h7ewMAvvzyS6SlpWHUqFGYNWtWnue0EIvFEIvFykRjjKmIlStXYsqUKQCAiRMnolq1agInYkqt0evo6KBly5YIDQ2VT5PJZAgNDYWjo2Oe86Snp+cq85yvAyMiZfMyxlTY8uXL5SXv5+eHBQsW8OYaFaD0Sc18fX3h5eWFVq1awd7eHmvWrEFaWhqGDx8OABg6dCiqVq0Kf39/AIC7uztWr16N5s2bw8HBAY8ePcKcOXPg7u7O3//ImBpZunQpZsyYAeDDScr8/PwETsRyKF30Hh4eSEhIwNy5cxEXF4dmzZohODhY/gHt06dPFdbgZ8+eDQ0NDcyePRvPnz+HhYUF3N3dsXjx4pJbCsaYoA4dOiQv+fnz52Pu3LkCJ2If06BysP0kJSUFxsbGSE5OhpGRUaHnW3nmPtaHPcKw1raY17NRKSZkrGJ7//49+vTpgzZt2mD27NlCxykXitprRcHno2eMFRkRQUNDA7q6ujh58iS0tLhSVBGf1IwxViQLFizAlClT5DtVcMmrLv7NMMaUNn/+fMybNw8A0L17d7i4uAgbiBWIi54xVmhEhHnz5mHBggUAgGXLlnHJlwNc9IyxQiEi+Pn5yY9qX7FiBSZPnixwKlYYXPSMsc8iIsyZM0e+W/SqVavg6+srcCpWWFz0jLHPioyMxJIlSwB8OFX5pEmTBE7ElMFFzxj7rObNm2Pnzp14+/YtJkyYIHQcpiQuesZYnogIqamp8oN5hg4dKnAiVlS8Hz1jLBciwrRp0+Dg4JDvd02w8oOLnjGmgIgwZcoUrFixAv/88w/OnTsndCRWTLzphjEmR0SYPHkyVq9eDQDYsGEDvv32W4FTseLiomeMAfhQ8r6+vlizZg0A4Oeff8aYMWOEDcVKBBc9YwxEhEmTJmHt2rUAgE2bNmHUqFECp2IlhYueMYa3b9/i1KlTAIDNmzdj5MiRAidiJYmLnjEGMzMzhIWF4eLFixg0aJDQcVgJ471uGKugZDIZrl+/Lr9uY2PDJa+muOgZq4BkMhl8fHzw9ddf4/Dhw0LHYaWMN90wVsHIZDKMHTsWmzdvhoaGBtLT04WOxEoZFz1jFYhMJsOYMWOwZcsWaGhoYNeuXfD09BQ6FitlXPSMVRAymQyjR4/G1q1boampiV27dvHBUBUEFz1jFYBMJsOoUaOwbds2aGpqYvfu3Rg8eLDQsVgZ4aJnrALQ0NCAjo4ONDU1sXfvXgwcOFDoSKwM8V43jFUAGhoaWL9+Pa5cucIlXwFx0TOmpqRSKdavXw+JRAIA0NTUxFdffSVwKiYELnrG1JBUKsWwYcPw/fff49tvvwURCR2JCYi30TOmZrKzs+Hl5YV9+/ZBJBJhwIAB0NDQEDoWExAXPWNqJDs7G0OHDsX+/fuhpaWFAwcOoG/fvkLHYgLjomdMTWRnZ8PT0xMHDhyAlpYWDh06hD59+ggdi6kALnrG1MSoUaPkJX/48GH07t1b6EhMRfCHsYypCS8vL5iYmODIkSNc8kwBr9EzpiacnZ0RGxsLExMToaMwFcNr9IyVU1lZWRg1ahT+/vtv+TQueZYXLnrGyiGJRAIPDw9s2bIFXbt2xfv374WOxFQYb7phrJzJKfnjx49DLBZj8+bN0NXVFToWU2Fc9IyVIxKJBAMGDMCJEycgFotx/PhxdOnSRehYTMVx0TNWTmRmZqJ///44efIkxGIxTpw4ATc3N6FjsXKAt9EzVk4sXLgQJ0+ehK6uLn777TcueVZoXPSMlRPTpk2Dq6srTp48ic6dOwsdh5UjvOmGMRUmlUohEokAAIaGhjh79iyfoIwpjdfoGVNR79+/R48ePeDv7y+fxiXPioKLnjEV9P79e/Tu3RvBwcFYtGgRnj59KnQkVo5x0TOmYjIyMtCrVy+cOXMG+vr6CAoKQvXq1YWOxcox3kbPmArJKfmQkBAYGBggKCgI7dq1EzoWK+e46BlTEenp6ejVqxfOnTsHAwMDnD59Gk5OTkLHYmqgSJtuAgICYGtrC11dXTg4OODatWsFjk9KSoKPjw8qV64MsViMunXrIigoqEiBGVNXp0+fxrlz51CpUiUEBwdzybMSo/Qa/cGDB+Hr64uNGzfCwcEBa9asgZubG+7fvw9LS8tc4yUSCTp16gRLS0scOXIEVatWxZMnT/gse4x9om/fvli/fj2aNWuGNm3aCB2HqRGli3716tUYOXIkhg8fDgDYuHEjAgMDsX37dkyfPj3X+O3btyMxMRGXL1+GtrY2AMDW1rZ4qRlTE2lpaZBIJDA1NQUA+Pj4CJyIqSOlNt1IJBLcuHEDrq6u/92BpiZcXV0RERGR5zy//fYbHB0d4ePjAysrKzRu3BhLliyBVCrN93EyMzORkpKicGFM3bx79w7dunVDp06dkJSUJHQcpsaUKvrXr19DKpXCyspKYbqVlRXi4uLynCcmJgZHjhyBVCpFUFAQ5syZg1WrVmHRokX5Po6/vz+MjY3lFxsbG2ViMqbyckr+woULePjwIWJiYoSOxNRYqe9HL5PJYGlpic2bN6Nly5bw8PDArFmzsHHjxnznmTFjBpKTk+WXZ8+elXZMxspMamoqunbtij/++APGxsYICQlBixYthI7F1JhS2+jNzc0hEokQHx+vMD0+Ph7W1tZ5zlO5cmVoa2vLz9cBAA0aNEBcXBwkEgl0dHRyzSMWiyEWi5WJxli5kFPyly5dkpf8V199JXQspuaUWqPX0dFBy5YtERoaKp8mk8kQGhoKR0fHPOdp06YNHj16BJlMJp/24MEDVK5cOc+SZ0xdpaSkoEuXLrh06RJMTExw7tw5LnlWJpTedOPr64stW7Zg165duHfvHsaOHYu0tDT5XjhDhw7FjBkz5OPHjh2LxMRETJgwAQ8ePEBgYCCWLFnCexewCichIQGxsbEwNTXFuXPn0KpVK6EjsQpC6d0rPTw8kJCQgLlz5yIuLg7NmjVDcHCw/APap0+fQlPzv/cPGxsbnDlzBpMmTUKTJk1QtWpVTJgwAdOmTSu5pWCsHLCzs0NYWBjS0tJ4mzwrUxpEREKH+JyUlBQYGxsjOTkZRkZGhZ5v5Zn7WB/2CMNa22Jez0almJCxvCUnJyMqKgpt27YVOgpTMUXttaLgs1cyVkqSkpLQuXNnuLq6IiQkROg4rALjomesFLx9+xadOnXCtWvXUKlSpTxPD8JYWeGzVzJWwnJK/saNGzA3N0doaCiaNGkidCxWgXHRM1aCEhMT0alTJ9y8eRPm5uY4f/48vvzyS6FjsQqOi56xEpKcnAxXV1fcunULFhYWOH/+PBo3bix0LMZ4Gz1jJcXAwAD16tWDpaUlwsLCuOSZyuA1esZKiJaWFnbv3o1///2XT8XNVAqv0TNWDK9fv8aCBQvkp/jQ0tLikmcqh9foGSuihIQEdOzYEVFRUUhNTcWKFSuEjsRYnniNnrEiePXqFTp06ICoqChUrlwZ3t7eQkdiLF+8Rs+YknJK/s6dO6hcuTLCwsJQr149oWMxli9eo2dMCfHx8XBxccGdO3dQpUoVhIeHc8kzlcdFz1ghSaVSdOnSBXfv3kXVqlURHh6OunXrCh2Lsc/iomeskEQiEebPn49atWohPDwcderUEToSY4XCRc+YEnr27Il79+6hdu3aQkdhrNC46BkrwIsXL9CpUyfExMTIp/FXYLLyhouesXw8f/4c7du3x7lz5zBs2DCUg+/oYSxPXPSM5eH58+dwcXHBw4cPUaNGDezatQsaGhpCx2KsSLjoGfvEv//+i/bt2+Phw4ewtbVFeHg4atasKXQsxoqMi56xjzx79gzt27fHo0ePULNmTYSHh/O5a1i5x0fGMvaRCRMmIDo6Wl7y1atXFzoSY8XGRc/YRzZv3gwiwk8//QQbGxuh4zBWIrjoWYWXnp4OfX19AIC5uTmOHTsmcCLGSpZab6MPvhMndASm4h4/fozGjRtj06ZNQkdhrNSobdE/jE/Fo1fvAAC62iKB0zBVFBsbi/bt2yM2NharV6/G+/fvhY7EWKlQ26JPzsiS/+zpWEPAJEwVxcTEoH379njy5Anq1KmD8+fPQ1dXV+hYjJUKtS36HLZf6KOqiZ7QMZgKiY6ORvv27fH06VPUrVsX4eHhqFq1qtCxGCs1/GEsq1BySv7ff/9FvXr1EBYWhsqVKwsdi7FSpfZr9Ix97Pjx4/j3339Rv359hIeHc8mzCoHX6FmF4uvrCx0dHfTv3x/W1tZCx2GsTPAaPVN7MTExSE9PBwBoaGjg+++/55JnFQoXPVNr9+/fR9u2bdGjRw952TNW0XDRM7X1zz//oH379nj58iVev37NRc8qLC56ppbu3bsHFxcXxMXF4csvv0RoaCjMzc2FjsWYILjomdq5e/euvOSbNGmC8+fPw8LCQuhYjAmG97phaiWn5F+9eoWmTZsiNDQUX3zxhdCxGBMUr9EztZKZmYmsrCw0a9aMS56x/8dr9EytNG/eHOHh4ahWrRrMzMyEjsOYSuCiZ+XeX3/9hbS0NDg6OgIAmjRpInAixlQLb7ph5drt27fRoUMHuLm54caNG0LHYUwlcdGzcisyMhIdO3bEmzdvUL9+fdjZ2QkdiTGVxEXPyqVbt27JS97e3h5nz56FiYmJ0LEYU0lc9KzcuXnzJjp27IjExEQ4ODhwyTP2GVz0rFy5d+8eXF1d8fbtW3z99dc4e/YsjI2NhY7FmErjvW5YuVKzZk04ODggOTkZwcHBMDIyEjoSYyqvSGv0AQEBsLW1ha6uLhwcHHDt2rVCzXfgwAFoaGigd+/eRXlYxqCrq4tjx47hzJkzXPKMFZLSRX/w4EH4+vrCz88PN2/eRNOmTeHm5oZXr14VON/jx48xefJkODk5FTksq5iuXbuGOXPmgIgAfCh7Q0NDgVMxVn4oXfSrV6/GyJEjMXz4cDRs2BAbN26Evr4+tm/fnu88UqkUQ4YMwfz581GrVq1iBWYVy9WrV9GpUycsWrQIGzduFDoOY+WSUkUvkUhw48YNuLq6/ncHmppwdXVFREREvvMtWLAAlpaWGDFiRKEeJzMzEykpKQoXVvFcuXIFnTp1QkpKCpycnODp6Sl0JMbKJaWK/vXr15BKpbCyslKYbmVlhbi4uDznuXjxIrZt24YtW7YU+nH8/f1hbGwsv9jY2CgTk6mBiIgIdO7cGampqXB2dkZQUBAqVaokdCzGyqVS3b0yNTUVnp6e2LJli1Jf+jBjxgwkJyfLL8+ePSvFlEzVXLp0SV7y7du3R2BgIJc8Y8Wg1O6V5ubmEIlEiI+PV5geHx+f55ctR0dH4/Hjx3B3d5dPk8lkHx5YSwv379/P87B1sVgMsVisTDSmJhITE9G9e3e8e/cOHTp0wMmTJ6Gvry90LMbKNaXW6HV0dNCyZUuEhobKp8lkMoSGhsrPHPix+vXrIyoqCpGRkfJLz5494eLigsjISN4kw3IxMzPDunXr0LlzZy55xkqI0gdM+fr6wsvLC61atYK9vT3WrFmDtLQ0DB8+HAAwdOhQVK1aFf7+/tDV1UXjxo0V5s85VP3T6axiIyJoaGgAADw9PfHtt9/KrzPGikfpovfw8EBCQgLmzp2LuLg4NGvWDMHBwfIPaJ8+fQpNTT6zAiu8Cxcu4IcffsDJkyflmwC55BkrORqUcxSKCktJSYGxsTGSk5MLfTTk9ceJ6LcxArZf6CN8ikspJ2RF9fvvv6Nbt25IT0/H2LFjsWHDBqEjMVYmitJrRcWr3kwwYWFh8pJ3c3PDqlWrhI7EmFriomeCOH/+PLp374709HR06dIFx48fh56entCxGFNLXPSszJ07dw7du3dHRkYGunXrhmPHjkFXV1foWIypLS56VqakUil8fX3x/v17dO/eHUePHuWSZ6yUcdGzMiUSiRAYGIhx48bh119/5QPjGCsDXPSsTCQkJMh/trGxQUBAAJc8Y2WEi56VuuDgYNSsWROHDh0SOgpjFRIXPStVp0+fRq9evZCWlobDhw+jHBy2wZja4aJnpSYoKAi9e/eGRCJBnz59sG/fPj7ilTEBcNGzUnHq1Cn06dMHEokEffv2xcGDB6GtrS10LMYqJC56VuJOnjyJb775BhKJBP369cP+/fu55BkTEBc9K3Hnz59HVlYW+vfvj3379nHJMyYwpc9eydjnrF69Gk2aNIGnpye0tPglxpjQeI2elYhLly5BIpEA+HCK4eHDh3PJM6YiuOhZsf36669o3749PDw85GXPGFMdXPSsWI4cOQIPDw9kZ2ejUqVKEIlEQkdijH2Ci54V2eHDhzFw4EBIpVJ4enpi586dXPSMqSAuelYkBw8exKBBgyCVSuHl5YUdO3ZwyTOmorjomdIOHTqEIUOGQCqVYvjw4di2bRuXPGMqjHeLYEqzsrKCWCzGwIEDsWXLFv4yeMZUHBc9U5qzszP+/PNP1K9fn0uesXKA/0pZoRw4cABRUVHy6w0bNuSSZ6yc4L9U9lm//PILBg8ejA4dOuDZs2dCx2GMKYmLnhVo165dGDZsGIgIffv2RdWqVYWOxBhTEhc9y9eOHTswfPhwEBHGjh2LDRs28OYaxsoh/qtledq+fTtGjBgBIsK4ceMQEBDAJc9YOcV/uSyXEydOyEt+/PjxWL9+PX8zFGPlGO9eyXLp0KEDWrdujZYtW2Lt2rVc8oyVc1z0LBdDQ0OEhIRAT0+PS54xNcCbbhgAYNOmTfD395df19fX55JnTE3wGj3Dzz//jHHjxgEA7O3t0bFjR4ETMcZKEq/RV3ABAQHykv/hhx/QoUMHgRMxxkoaF30Ftm7dOowfPx4AMGXKFKxYsYI31zCmhrjoK6i1a9fif//7HwBg2rRpWLZsGZc8Y2qKi74Cun37NiZOnAgAmD59Ovz9/bnkGVNj/GFsBdS0aVOsWbMGr169wqJFi7jkGVNzXPQViEQigY6ODgBgwoQJAqdhjJUV3nRTQaxcuRKtW7fG27dvhY7CGCtjXPQVwIoVKzBlyhTcuHEDhw8fFjoOY6yMcdGruWXLlmHq1KkAgHnz5mHUqFECJ2KMlTUuejXm7++P6dOnAwDmz58PPz8/gRMxxoTAH8aqqSVLlmDWrFkAgIULF2L27NkCJ2KMCYWLXg0lJiYiICAAALB48WLMnDlT4ESMMSFx0ashMzMzhIWFITg4WH70K2Os4uKiVyOPHj1C7dq1AQB169ZF3bp1BU7EGFMFRfowNiAgALa2ttDV1YWDgwOuXbuW79gtW7bAyckJpqamMDU1haura4HjmfKICH5+fmjcuDHOnj0rdBzGmIpRuugPHjwIX19f+Pn54ebNm2jatCnc3Nzw6tWrPMeHh4dj0KBBCAsLQ0REBGxsbNC5c2c8f/682OHZfyW/YMECZGZm4s6dO0JHYoypGlKSvb09+fj4yK9LpVKqUqUK+fv7F2r+7OxsMjQ0pF27dhX6MZOTkwkAJScnF3qeP2PfUI1pp8h5+flCz1PeyGQymj17NgEgALRq1SqhIzHGCqkovVZUSq3RSyQS3LhxA66urvJpmpqacHV1RURERKHuIz09HVlZWTAzM8t3TGZmJlJSUhQuTBERYfbs2Vi0aBEAYPXq1fD19RU4FWNMFSlV9K9fv4ZUKoWVlZXCdCsrK8TFxRXqPqZNm4YqVaoovFl8yt/fH8bGxvKLjY2NMjHVHhFh5syZWLJkCQBgzZo1mDRpksCpGGOqqkyPjF26dCkOHDiAY8eOQVdXN99xM2bMQHJysvzy7NmzMkyp+ogIT58+BQD89NNPfCZKxliBlNq90tzcHCKRCPHx8QrT4+PjYW1tXeC8K1euxNKlS3Hu3Dk0adKkwLFisRhisViZaBWKpqYmdu3aBU9PT3Tp0kXoOIwxFafUGr2Ojg5atmyJ0NBQ+TSZTIbQ0FA4OjrmO9/y5cuxcOFCBAcHo1WrVkVPW4EREQ4ePAipVAoA0NLS4pJnjBWK0ptufH19sWXLFuzatQv37t3D2LFjkZaWhuHDhwMAhg4dihkzZsjHL1u2DHPmzMH27dtha2uLuLg4xMXF4d27dyW3FGqOiPDDDz9g4MCBGDVqFIhI6EiMsXJE6SNjPTw8kJCQgLlz5yIuLg7NmjVDcHCw/APap0+fQlPzv/ePn3/+GRKJBP369VO4Hz8/P8ybN6946SsAIsKkSZOwdu1aAIC9vT1/9R9jTClFOgXC+PHjMX78+DxvCw8PV7j++PHjojwEw4eSnzhxIn766ScAwKZNm/h88owxpfG5blQUEeF///sf1q9fD+DDqSS8vb0FTsUYK4/4i0dUlK+vL9avXw8NDQ1s3bqVS54xVmRc9CqqQ4cOEIvF2Lp1K0aMGCF0HMZYOcabblSUu7s7oqOjUbVqVaGjMMbKOV6jVxEymQyzZ89GTEyMfBqXPGOsJHDRqwCZTIYxY8Zg8eLF6NixIzIyMoSOxBhTI7zpRmAymQyjR4/G1q1boampiYULF0JPT0/oWIwxNcJFLyCZTIaRI0di+/bt0NTUxO7duzF48GChYzHG1AwXvUBkMhm8vb2xY8cOaGpqYs+ePRg0aJDQsRhjaoiLXiCLFi3Cjh07IBKJsHfvXnh4eAgdiTGmpvjDWIGMGzcOLVq0wL59+7jkGWOlitfoyxARyU9IZm5ujmvXrkEkEgmcijGm7niNvoxIpVIMHToUmzZtkk/jkmeMlQUu+jKQnZ2NoUOHYs+ePfj+++/x5MkToSMxxioQ3nRTyrKzs+Hp6YkDBw5AS0sLBw8eRI0aNYSOxRirQLjoS1F2dja+/fZbHDx4ENra2jh8+DB69eoldCzGWAXDRV9KsrKyMGTIEBw+fBja2to4cuQIevbsKXQsxlgFxEVfSo4dOyYv+V9//RXu7u5CR2KMVVBc9KWkf//++Oeff9CiRQv06NFD6DiMsQqMi74ESSQSZGVlwcDAABoaGpg7d67QkRhjjHevLCkSiQQeHh7o0aMH0tPThY7DGGNyXPQlQCKRYMCAATh+/DgiIiIQGRkpdCTGGJPjTTfFlJmZif79++PkyZMQi8U4ceIEWrduLXQsxhiT46IvhszMTPTr1w+nTp2Crq4uTpw4gc6dOwsdizHGFHDRF1FmZib69u2LwMBA6Orq4uTJk3B1dRU6FmOM5cLb6Ivo8ePHuHz5MvT09HDq1CkuecaYyuI1+iKqV68ezp07h6SkJHTo0EHoOIwxli8ueiW8f/8eDx48QJMmTQAALVq0EDgRY4x9Hm+6KaSMjAz06tULbdu2RUREhNBxGGOs0LjoCyGn5M+ePQuZTIasrCyhIzHGWKHxppvPSE9PR69evXDu3DkYGBjg9OnTcHJyEjoW+39SqZTfeJlK0tbWVplvkeOiL0B6ejrc3d1x/vx5VKpUCadPn0bbtm2FjsXw4ft34+LikJSUJHQUxvJlYmICa2tr+XdFC4WLPh/p6eno0aMHwsLCUKlSJQQHB6NNmzZCx2L/L6fkLS0toa+vL/gfEmMfIyKkp6fj1atXAIDKlSsLmoeLPh8ikQh6enowNDREcHAwn9ZAhUilUnnJf/HFF0LHYSxPenp6AIBXr17B0tJS0M04XPT5EIvF+PXXXxV2p2SqIWebvL6+vsBJGCtYzms0KytL0KLnvW4+8u7dO2zYsAFEBADQ1dXlkldhvLmGqTpVeY3yGv3/S01NRbdu3XDx4kW8fPkSCxcuFDoSY4yVCF6jx4eS79q1Ky5evAhjY2P+Em+m8tq3b4+JEycKHYOVExW+6FNSUtClSxdcunQJJiYmOHfuHL766iuhYzGmdogIc+fOReXKlaGnpwdXV1c8fPiwwHlSU1MxceJE1KhRA3p6emjdujX+/PNPhTHx8fEYNmwYqlSpAn19fXTp0kXhfh8/fgwNDY08L4cPHwYAvHnzBl26dEGVKlUgFothY2OD8ePHIyUlRX4/R48eRadOnWBhYQEjIyM4OjrizJkzJfgMlZ4KXfQ5JX/58mWYmpri3LlzaNWqldCxWAUhkUiEjlCmli9fjp9++gkbN27E1atXYWBgADc3N7x//z7feby9vRESEoLdu3cjKioKnTt3hqurK54/fw7gw5tH7969ERMTgxMnTuDWrVuoUaMGXF1dkZaWBgCwsbHBy5cvFS7z589HpUqV0LVrVwCApqYmevXqhd9++w0PHjzAzp07ce7cOYwZM0ae5cKFC+jUqROCgoJw48YNuLi4wN3dHbdu3SrFZ62EUDmQnJxMACg5ObnQ8/wZ+4ZqTDtFzsvP53l7dnY2tWnThgCQqakp3bhxo6TislKWkZFBd+/epYyMDKGjKMXZ2Zl8fHxowoQJ9MUXX1D79u2JiCgqKoq6dOlCBgYGZGlpSd9++y0lJCTI53v37h15enqSgYEBWVtb08qVK8nZ2ZkmTJiQ72M9evSIevbsSZaWlmRgYECtWrWikJAQhTEA6NixYwrTjI2NaceOHfLrz549o4EDB5KpqSnp6+tTy5Yt6cqVK0ovu0wmI2tra1qxYoV8WlJSEonFYtq/f3+e86Snp5NIJKJTp04pTG/RogXNmjWLiIju379PAOjvv/+W3y6VSsnCwoK2bNmSb55mzZrRd999V2DmtWvXUrVq1Qoc07BhQ5o/f36+txf0Wi1KrxVVhV2jF4lEGD16NMzNzREaGspnoizHiAjpkmxBLvT/e2gV1q5du6Cjo4NLly5h48aN8tNcN2/eHNevX0dwcDDi4+MxYMAA+TxTpkzB77//jhMnTuDs2bMIDw/HzZs3C3ycd+/eoVu3bggNDcWtW7fQpUsXuLu74+nTp4XO+u7dOzg7O+P58+f47bffcPv2bUydOhUymQwA8Mcff6BSpUoFXvbu3QsAiI2NRVxcnML3NhgbG8PBwSHfkwRmZ2dDKpVCV1dXYbqenh4uXrwI4MMXAAFQGKOpqQmxWCwf86kbN24gMjISI0aMyHfZX7x4gaNHj8LZ2TnfMTKZDKmpqTAzM8t3jKqo0HvdeHp6omfPnjA2NhY6CiuGjCwpGs4VZlvp3QVu0Ncp/J9RnTp1sHz5cvn1RYsWoXnz5liyZIl82vbt22FjY4MHDx6gSpUq2LZtG/bs2YOOHTsC+PBmUa1atQIfp2nTpmjatKn8+sKFC3Hs2DH89ttvGD9+fKGy7tu3DwkJCfjzzz/lZVa7dm357a1atUJkZGSB92FlZQXgw5HMH1//+Pac2z5laGgIR0dHLFy4EA0aNICVlRX279+PiIgIeY769eujevXqmDFjBjZt2gQDAwP8+OOP+Pfff/Hy5cs873fbtm1o0KBBngdBDho0CCdOnEBGRgbc3d2xdevWfJdt5cqVePfuncKbsqqqUGv0b9++xZAhQxReAFzyrCy1bNlS4frt27flp9nIudSvXx8AEB0djejoaEgkEjg4OMjnMTMzQ7169Qp8nHfv3mHy5Mlo0KABTExMUKlSJdy7d0+pNfrIyEg0b9483zVWPT091K5du8CLoaFhoR8vL7t37wYRoWrVqhCLxfjpp58waNAgaGp+qC5tbW0cPXoUDx48gJmZGfT19REWFoauXbvKx3wsIyMD+/bty3dt/scff8TNmzdx4sQJREdHw9fXN89x+/btw/z583Ho0CFYWloWaxnLQoVZo3/79i06deqEGzdu4NmzZ/j9999V5mAGVjx62iLcXeAm2GMrw8DAQOH6u3fv4O7ujmXLluUaW7lyZTx69KhIuSZPnoyQkBCsXLkStWvXhp6eHvr166fwAbCGhkauTU8fnwk05xD+/Pzxxx/yDzPzs2nTJgwZMgTW1tYAPuwh8/F5X+Lj49GsWbN857ezs8Pvv/+OtLQ0pKSkoHLlyvDw8ECtWrXkY1q2bInIyEgkJydDIpHAwsICDg4Oee5YceTIEaSnp2Po0KF5Pp61tTWsra1Rv359mJmZwcnJCXPmzFHIfODAAXh7e+Pw4cPl5itEi1T0AQEBWLFiBeLi4tC0aVOsW7cO9vb2+Y4/fPgw5syZg8ePH6NOnTpYtmwZunXrVuTQykpMTESnTp1w8+ZNmJubIyAggEtejWhoaCi1+USVtGjRAr/++itsbW2hpZV7Gezs7KCtrY2rV6+ievXqAD6stDx48KDA7ceXLl3CsGHD0KdPHwAf3lAeP36sMMbCwkLhv9uHDx8iPT1dfr1JkybYunUrEhMT81yrV2bTTc2aNWFtbY3Q0FB5saekpODq1asYO3ZsgfcBfHiDNDAwwNu3b3HmzBmFzV85cv47f/jwIa5fv57nQY/btm1Dz549YWFh8dnHzPksIudzAADYv38/vvvuOxw4cADdu3f/7H2oDGU/vT1w4ADp6OjQ9u3b6c6dOzRy5EgyMTGh+Pj4PMdfunSJRCIRLV++nO7evUuzZ88mbW1tioqKKvRjFmevm7b+56h58+YEgCwsLJR6XKaayvNeN5/uKfP8+XOysLCgfv360bVr1+jRo0cUHBxMw4YNo+zsbCIiGjNmDNWoUYNCQ0MpKiqKevbsSZUqVSpwr5s+ffpQs2bN6NatWxQZGUnu7u5kaGioMM/AgQOpQYMGdPPmTfrzzz+pQ4cOpK2tLd/rJjMzk+rWrUtOTk508eJFio6OpiNHjtDly5eLtPxLly4lExMTOnHiBP3111/Uq1cvqlmzpsLvsUOHDrRu3Tr59eDgYDp9+jTFxMTQ2bNnqWnTpuTg4EASiUQ+5tChQxQWFkbR0dF0/PhxqlGjBn3zzTe5Hv/hw4ekoaFBp0+fznVbYGAgbd++naKioig2NpZOnTpFDRo0oDZt2sjH7N27l7S0tCggIIBevnwpvyQlJeW7zKqy143SRW9vb08+Pj7y61KplKpUqUL+/v55jh8wYAB1795dYZqDgwONHj260I9ZnKKvOX4nASBLS0uFXbBY+aVORU9E9ODBA+rTpw+ZmJiQnp4e1a9fnyZOnEgymYyIiFJTU+nbb78lfX19srKyouXLl39298rY2FhycXEhPT09srGxofXr1+ea5/nz59S5c2cyMDCgOnXqUFBQUK7dKx8/fkx9+/YlIyMj0tfXp1atWtHVq1eLtPwymYzmzJlDVlZWJBaLqWPHjnT//n2FMTVq1CA/Pz/59YMHD1KtWrVIR0eHrK2tycfHJ1ex5uwGqa2tTdWrV6fZs2dTZmZmrsefMWMG2djYkFQqzXXb+fPnydHRkYyNjUlXV5fq1KlD06ZNo7dv38rHODs7E4BcFy8vr3yXWVWKXoOo8PuHSSQS6Ovr48iRI+jdu7d8upeXF5KSknDixIlc81SvXh2+vr4Kh2v7+fnh+PHjuH37dp6Pk5mZqfDvUkpKCmxsbJCcnAwjI6NCZb3+OBH9NkYgK/EFpL/Nwfnz59GwYcPCLShTae/fv0dsbCxq1qyZa9c7xlRJQa/VlJQUGBsbK9VrRaXUXjevX7+GVCpVahepuLg4pcYDgL+/P4yNjeUXGxsbZWIq0NPTRVhYGJc8Y6zCUslPsGbMmKGwW1POGr0y7CwqYeO3LaCrLUKDeqq/+xNjjJUWpYre3NwcIpEI8fHxCtPj4+Plu099ytraWqnxwIcv/RCLxcpEy8XUQAddGgv79V2MMaYKlNp0o6Ojg5YtWyI0NFQ+TSaTITQ0FI6OjnnO4+joqDAeAEJCQvIdzxhjrGQpvenG19cXXl5eaNWqFezt7bFmzRqkpaVh+PDhAIChQ4eiatWq8Pf3BwBMmDABzs7OWLVqFbp3744DBw7g+vXr2Lx5c8kuCWOMsTwpXfQeHh5ISEjA3LlzERcXh2bNmiE4OFj+gevTp08VDj1u3bo19u3bh9mzZ2PmzJmoU6cOjh8/jsaNG5fcUrAKKeeAFsZUlaq8RpXavVIoZbkbElN9MpkMDx8+hEgkgoWFBXR0dPhIZ6ZSiAgSiQQJCQmQSqWoU6dOrnPvlGWvqeReN4wVRFNTEzVr1sTLly/x4sULoeMwli99fX1Ur149zxOslSUuelYu6ejooHr16vJzljOmakQiEbS0tFTiv00uelZuaWhoQFtbG9ra2kJHYUylVajz0TPGWEXERc8YY2qOi54xxtRcudhGn7MHaEpKisBJGGOsZOT0WVns4V4uij41NRUAinUWS8YYU0Wpqaml/t3V5eKAKZlMhhcvXsDQ0FCpXZVyznr57NkztTzQSt2XD1D/ZeTlK/+KuoxEhNTUVFSpUqXU97MvF2v0mpqaqFatWpHnNzIyUtsXGaD+yweo/zLy8pV/RVnG0l6Tz8EfxjLGmJrjomeMMTWn1kUvFovh5+dX7C8xUVXqvnyA+i8jL1/5Vx6WsVx8GMsYY6zo1HqNnjHGGBc9Y4ypPS56xhhTc1z0jDGm5sp90QcEBMDW1ha6urpwcHDAtWvXChx/+PBh1K9fH7q6uvjyyy8RFBRURkmLRpnl27JlC5ycnGBqagpTU1O4urp+9vkQmrK/vxwHDhyAhoYGevfuXboBS4Cyy5iUlAQfHx9UrlwZYrEYdevWVenXqbLLt2bNGtSrVw96enqwsbHBpEmT8P79+zJKq5wLFy7A3d0dVapUgYaGBo4fP/7ZecLDw9GiRQuIxWLUrl0bO3fuLPWcn0Xl2IEDB0hHR4e2b99Od+7coZEjR5KJiQnFx8fnOf7SpUskEolo+fLldPfuXZo9ezZpa2tTVFRUGScvHGWXb/DgwRQQEEC3bt2ie/fu0bBhw8jY2Jj+/fffMk5eOMouX47Y2FiqWrUqOTk5Ua9evcombBEpu4yZmZnUqlUr6tatG128eJFiY2MpPDycIiMjyzh54Si7fHv37iWxWEx79+6l2NhYOnPmDFWuXJkmTZpUxskLJygoiGbNmkVHjx4lAHTs2LECx8fExJC+vj75+vrS3bt3ad26dSQSiSg4OLhsAuejXBe9vb09+fj4yK9LpVKqUqUK+fv75zl+wIAB1L17d4VpDg4ONHr06FLNWVTKLt+nsrOzydDQkHbt2lVaEYulKMuXnZ1NrVu3pq1bt5KXl5fKF72yy/jzzz9TrVq1SCKRlFXEYlF2+Xx8fKhDhw4K03x9falNmzalmrMkFKbop06dSo0aNVKY5uHhQW5ubqWY7PPK7aYbiUSCGzduwNXVVT5NU1MTrq6uiIiIyHOeiIgIhfEA4Obmlu94IRVl+T6Vnp6OrKwsmJmZlVbMIivq8i1YsACWlpYYMWJEWcQslqIs42+//QZHR0f4+PjAysoKjRs3xpIlS1Tye3GLsnytW7fGjRs35Jt3YmJiEBQUhG7dupVJ5tKmqh1TLk5qlpfXr19DKpXCyspKYbqVlRX++eefPOeJi4vLc3xcXFyp5Syqoizfp6ZNm4YqVarkeuGpgqIs38WLF7Ft2zZERkaWQcLiK8oyxsTE4Pz58xgyZAiCgoLw6NEjjBs3DllZWfDz8yuL2IVWlOUbPHgwXr9+jbZt24KIkJ2djTFjxmDmzJllEbnU5dcxKSkpyMjIgJ6eniC5yu0aPSvY0qVLceDAARw7dgy6urpCxym21NRUeHp6YsuWLTA3Nxc6TqmRyWSwtLTE5s2b0bJlS3h4eGDWrFnYuHGj0NFKRHh4OJYsWYINGzbg5s2bOHr0KAIDA7Fw4UKho6m1crtGb25uDpFIhPj4eIXp8fHxsLa2znMea2trpcYLqSjLl2PlypVYunQpzp07hyZNmpRmzCJTdvmio6Px+PFjuLu7y6fJZDIAgJaWFu7fvw87O7vSDa2kovwOK1euDG1tbYhEIvm0Bg0aIC4uDhKJBDo6OqWaWRlFWb45c+bA09MT3t7eAIAvv/wSaWlpGDVqFGbNmlXq52Uvbfl1jJGRkWBr80A5XqPX0dFBy5YtERoaKp8mk8kQGhoKR0fHPOdxdHRUGA8AISEh+Y4XUlGWDwCWL1+OhQsXIjg4GK1atSqLqEWi7PLVr18fUVFRiIyMlF969uwJFxcXREZGquS3jxXld9imTRs8evRI/iYGAA8ePEDlypVVquSBoi1fenp6rjLPeVMjNTjtlsp2jKAfBRfTgQMHSCwW086dO+nu3bs0atQoMjExobi4OCIi8vT0pOnTp8vHX7p0ibS0tGjlypV079498vPzU/ndK5VZvqVLl5KOjg4dOXKEXr58Kb+kpqYKtQgFUnb5PlUe9rpRdhmfPn1KhoaGNH78eLp//z6dOnWKLC0tadGiRUItQoGUXT4/Pz8yNDSk/fv3U0xMDJ09e5bs7OxowIABQi1CgVJTU+nWrVt069YtAkCrV6+mW7du0ZMnT4iIaPr06eTp6Skfn7N75ZQpU+jevXsUEBDAu1eWhHXr1lH16tVJR0eH7O3t6cqVK/LbnJ2dycvLS2H8oUOHqG7duqSjo0ONGjWiwMDAMk6sHGWWr0aNGgQg18XPz6/sgxeSsr+/j5WHoidSfhkvX75MDg4OJBaLqVatWrR48WLKzs4u49SFp8zyZWVl0bx588jOzo50dXXJxsaGxo0bR2/fvi374IUQFhaW599UzjJ5eXmRs7NzrnmaNWtGOjo6VKtWLdqxY0eZ5/4Un6aYMcbUXLndRs8YY6xwuOgZY0zNcdEzxpia46JnjDE1x0XPGGNqjoueMcbUHBc9Y4ypOS56xhhTc1z0jDGm5rjoGWNMzXHRM8aYmuOiZ4wxNfd/f9LOkgbiQQcAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -677,7 +1173,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x300 with 2 Axes>"
       ]
@@ -718,7 +1214,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 2 Axes>"
       ]
@@ -763,7 +1259,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -796,7 +1292,7 @@
     {
      "data": {
       "text/plain": [
-       "0.994822102226496"
+       "0.9973206433743456"
       ]
      },
      "execution_count": 14,
@@ -825,7 +1321,7 @@
     {
      "data": {
       "text/plain": [
-       "0.9165399409607299"
+       "0.951975"
       ]
      },
      "execution_count": 15,
@@ -915,32 +1411,28 @@
        "      <td>-1.000000</td>\n",
        "      <td>-0.250000</td>\n",
        "      <td>0.50000</td>\n",
-       "      <td>1.250000</td>\n",
+       "      <td>1.25000</td>\n",
        "      <td>2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>101.0</td>\n",
-       "      <td>0.925869</td>\n",
-       "      <td>0.007364</td>\n",
-       "      <td>0.913118</td>\n",
-       "      <td>0.919359</td>\n",
-       "      <td>0.92586</td>\n",
-       "      <td>0.932542</td>\n",
-       "      <td>0.938355</td>\n",
+       "      <td>0.946323</td>\n",
+       "      <td>0.008481</td>\n",
+       "      <td>0.926092</td>\n",
+       "      <td>0.941026</td>\n",
+       "      <td>0.94812</td>\n",
+       "      <td>0.95325</td>\n",
+       "      <td>0.958874</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     count      mean       std       min       25%      50%       75%  \\\n",
-       "th   101.0  0.500000  0.879005 -1.000000 -0.250000  0.50000  1.250000   \n",
-       "AUC  101.0  0.925869  0.007364  0.913118  0.919359  0.92586  0.932542   \n",
-       "\n",
-       "          max  \n",
-       "th   2.000000  \n",
-       "AUC  0.938355  "
+       "     count      mean       std       min       25%      50%      75%       max\n",
+       "th   101.0  0.500000  0.879005 -1.000000 -0.250000  0.50000  1.25000  2.000000\n",
+       "AUC  101.0  0.946323  0.008481  0.926092  0.941026  0.94812  0.95325  0.958874"
       ]
      },
      "execution_count": 17,
@@ -960,7 +1452,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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vYvLkyQgKCkJ5eTmuXbuGPXv2IC8vDy4uLg16H1T59NNPcezYMfTu3RvvvPMO/vWvf2Ht2rWoqqrSOG+2LpFIhGXLluGtt95CcHAwJk2aBCcnJ/z222+oqKiQpWgFBQVh165dSEhIQHBwMOzs7DBy5EjWa3711VcYNmwYevTogdjYWFl6kIODg97XSJDRaoy8CVGVHoR66QoM8zLVwcvLiwHAfPrpp6zXLC4uZmJiYhgXFxdGIBAwnTt3VrgOwyimBzEMwxw9epTp1KkTIxAImLZt2zJbt25lTQ+6desW07dvX8ba2poBIEuXqZ8eJLVy5UqmXbt2jKWlJePu7s5MmzZNIb2lX79+TMeOHRXKqSxtqb7nz58zM2bMYJo1a8bY2toyI0eOZO7du8f6OouLi5m4uDjGy8uLsbS0ZDw8PJhBgwYx69atU3sfVelB1dXVTLNmzZg+ffqovIavry/TrVs3uWOHDx9mBgwYwIhEIsbS0pLx9fVlEhISlKYBVVRUMEuWLGGCg4MZOzs7RiAQMK1bt2amT5/O5OTkqH0dT58+ZRITE5mAgABGIBAwLi4uTM+ePZklS5Yw1dXVDMMoT+9hGPbPT32qnn/58mUmPDycsbOzY2xsbJgBAwYwZ8+elTtHWerUyZMnGQDMyZMn5Y7/+OOPTM+ePRlra2tGJBIxISEhzI4dO2SPP3v2jJk0aRLj6OjIAJB9rtjSgxiGYY4fP8706tVLdr2RI0cyN27ckDtH+vdRUlLCWvb6fwuq8BiGQ72uhBDCQdRHSQghalCgJIQQNShQEkKIGhQoCSFEDQqUhBCiBgVKQkxMLBZj0aJF+OWXX0xdFKIEBUozV39x38aKbfHihmxOJt1P59SpUw0uW0PvExsbi8OHD+u0kr6ur4M2YtMOzcwhxIRWrFiBq1ev4uzZs7C2tjZ1cYgSFChJo7B+/Xq5rRDMQXV1NcrLy5GamqrztMS+ffvi+fPnWq/Mnp2drbDkH1GOAiVpFEy9C58hCAQCfPTRRw26Bp/P12nzLdqNUzv0lWJGMjIyEBwcDKFQCH9/f6xdu1bhHFUbymva55ecnIyOHTvCxsYGTk5O6N69u2yjKClNNglTtuEZW7+bsg3WNPHnn38iIiICtra2cHNzw8yZMxVWA5I6f/48hg4dCgcHB9jY2KBfv344c+aMye5z//59xMbGwtPTE1ZWVvD19cW0adNkW96yvVe3b99GZGQkPDw8IBQK0bJlS0yYMEFudR62Psrc3Fy89tprcHZ2ho2NDV555RWFle6l9/vhhx/w2WefoWXLlhAKhRg0aBDrQifmgmqUZuLatWsYMmQIXF1dsWDBArx48QLz58+Hu7u7Xu+zfv16zJgxA+PGjcO7776LyspKXL16FefPn8ekSZMAGG+TME08f/4cgwYNQkFBAWbMmAFPT09s2bIFJ06cUDj3xIkTGDZsGIKCgjB//nzw+Xxs3LgRAwcOxC+//IKQkBCj3qewsBAhISEoLS3F22+/jXbt2uH+/fvYs2cPKioqWJvb1dXVCA8PR1VVFaZPnw4PDw/cv38fhw4dQmlpKRwcHFjLX1xcjJ49e6KiogIzZsxAs2bNsGnTJowaNQp79uzBmDFj5M5fvHgx+Hw+Zs2aBbFYjC+//BL//ve/cf78eZX/Ho2WxstnEE6LiIhghEIhk5+fLzt248YNxsLCQm6FIWWrsTCMZqvOjB49mnUlobo03SRM2SoubCvQsK1UpEl5pZu6/fDDD7Jj5eXlTEBAgNw9JBIJ07p1ayY8PFxu06uKigrG19eXGTx4sNHvExUVxfD5fNbNzaTPrf9eXblyhQHA7N69W2V566+09N577zEAmF9++UV27OnTp4yvry/j4+Mj24BOer/27dszVVVVsnNXrFjBAGCuXbum8r6NFTW9zUBtbS2OHDmCiIgIuf1B2rdvj/DwcL3ey9HREX/++afSvaMZI24Spomff/4ZzZs3x7hx42THbGxsFNaRzMrKwu3btzFp0iQ8fvxYVuby8nIMGjQIp0+fVjmYpO/7SCQSpKSkYOTIkaxpQ8pSvqQ1xiNHjmi1d/XPP/+MkJAQ9O7dW3bMzs4Ob7/9NvLy8nDjxg2582NiYuRqtA3Z1KwxoKa3GSgpKcHz58/RunVrhcfatm3LujOdrj788EMcP34cISEhCAgIwJAhQzBp0iT06tVLVhZjbBKmqfz8fAQEBCgElvqbU92+fRsAlO7XA7xMDK+746Eh71NdXY2ysjKtN+Xy9fVFQkICli5dim3btqFPnz4YNWoU3njjDaXNbmn52RZ4rrv5Xd2y6HNTs8aAAmUT09CNytq3b4/s7GwcOnQIqamp2Lt3L1avXo2PP/4YCxcu1GqTMC5smiYlLfdXX32Frl27sp5jZ2dntPs0ZDX3r7/+GpMnT8aBAwdw9OhRzJgxA0lJSfj111/RsmVLna9bV2PYVEyfKFCaAVdXV1hbW8tqK3XV34RJ+s1ff18dbbbLtbW1xfjx4zF+/HhUV1dj7Nix+Oyzz5CYmKjVJmH6KIs63t7euH79OhiGkQvM9d8X6SZYIpFIp4299H0fV1dXiEQi1v2DNNG5c2d07twZ8+bNw9mzZ9GrVy+sWbMGn376qdLyK9uwS/p4U0Z9lGbAwsIC4eHhSElJQUFBgez4zZs3FTapEolEcHFxwenTp+WOr169WqN7PX78WO53gUCADh06gGEY1NTUaLVJmDRo1C1LbW2t0ia7LoYPH47CwkLs2bNHdqyiokLhHkFBQfD398eSJUvw7NkzleU2xn34fD4iIiJw8OBBXLp0SeE8ZTW3srIyhQ3IOnfuDD6frzRVSVr+Cxcu4Ny5c7Jj5eXlWLduHXx8fNChQwelz20KqEZpJhYuXIjU1FT06dMH77zzjmyHx44dO8rt3ggAb731FhYvXoy33noL3bt3x+nTp/HHH39odJ8hQ4bAw8MDvXr1gru7O27evImVK1dixIgRsg3SNN0krGPHjnjllVeQmJiIv/76C87Ozti5c6dedxqcMmUKVq5ciaioKGRmZqJ58+bYsmULbGxs5M7j8/n49ttvMWzYMHTs2BExMTFo0aIF7t+/j5MnT0IkEuHgwYNGvc/nn3+Oo0ePol+/fnj77bfRvn17PHjwALt370ZGRgYcHR0VynHixAnEx8fjtddeQ5s2bfDixQts2bJF9gWmzJw5c7Bjxw4MGzYMM2bMgLOzMzZt2oS7d+9i7969NIvHhCPuRM/S09OZoKAgRiAQMH5+fsyaNWtYNyCrqKhgYmNjGQcHB8be3p55/fXXmYcPH2qUbrN27Vqmb9++TLNmzRgrKyvG39+f+eCDDxixWCx3nqabhN25c4cJCwtjrKysGHd3d+ajjz5ijh07prf0IIZhmPz8fGbUqFGMjY0N4+Liwrz77rtMamoq6yZYV65cYcaOHSt7fd7e3szrr7/OpKWlmeQ++fn5TFRUFOPq6spYWVkxfn5+TFxcnCw1p356UG5uLvPmm28y/v7+jFAoZJydnZkBAwYwx48fl7su20Zsd+7cYcaNG8c4OjoyQqGQCQkJYQ4dOiR3jvR+9dOPVKWdmQPaXIwQQtRo4vVpQghRjwIlIYSoQYGSEELUoEBJCCFqUKAkhBA1KFASQogalHCuI4lEgsLCQtjb25vF5l2EmBOGYfD06VN4enrqJVmeAqWOCgsL4eXlZepiEEJUuHfvnl4WAqFAqSPpdL179+5BJBKZuDSEkLrKysrg5eUl+zttKAqUOpI2t0UiEQVKQjhKX91iNJhDCCFqUKAkhBA1KFASQoga1EdJiJnILXmG/L8q4NPMFr4utqYujlmhQElII1daUY0ZO7Jw+vY/q7D3be2K5Ind4GBjacKSmQ9qehPSyM3YkYUzOY/kjp3JeYTpO66YqETmhwIlIRyQW/IMJ7Mf4u6jcq2fd/p2CWrrrb9dyzA4fbtE6+sRdtT0JsSEGtpszv+rQuXjeY/Lqb9SD6hGSYgJNaTZXFpRjVUnclSe49OMgqQ+UKAkxEQa2myesvkSLhc8YX3MgsdD39auVJvUEwqUhJiIJs1mNqUV1Rj3zVlczHsCiZKtAf/P2xHJE7s1tIjkbxQoCTERb2cblY8razbP2JGFy/nsNUmpdwYEUGqQHlGgJMRE/Fzt0Le1KyzqLdygqtksba5L1Fyb+ib1iwIlISaUPLEbegW4yB3rFeCitNmsrrnO54H6Jg2A0oMIMSEHG0tsjg3B3UflyHtcrnb6obrmepC3E/VNGgDVKAnhAF8XWwxo6waGYVQmnitrrvMBBHs7YffUntQ3aQBUoySEA7RJPE+e2A3Td1yRO7f33+cSw+AxDKMkwYCoUlZWBgcHB4jFYlrhnDRY1HcXcCbnkVxOpQWPh14BLtgcG8L6HE2b602Rvv8+qUZJiIlJR7Lrq5t4zhYIfV0oQBoL9VESYmK6Jp4T46FASYiJ6Zp4ToyHAiUhJqZL4jkxLgqUpEnTdR1IfdM28ZwYFw3mkCaJa9snaJt4ToyLapSkSeLq9gnSxHMKktxCgZI0OVzYPoErTX6iGWp6kyZHXTrO9B2XsS32FYM0wbnW5CeaoRolaXLUpePcKCxT2QRvSG1Q0yY/1Ti5hWqUpMmRpuNkKFnXUcKAdUZMQ2uDmszAcbKxpBonB1GNkjRJyRO7oYOn6jnA9WfENHQASJMZOFwdZGrqKFCSJsnBxhL/U5OjuPpkDsQVNQD0MwCkrslvweOZfJCJsONEoFy1ahV8fHwgFAoRGhqKCxcuKD23pqYGixYtgr+/P4RCIQIDA5Gamip3zjfffIMuXbpAJBJBJBKhR48eOHz4sNw5/fv3B4/Hk/uZOnWqQV4f4SZlM2KkLueXympy+piPrW4GTv0Aqcs9iGGYPFDu2rULCQkJmD9/Pi5fvozAwECEh4fj4cOHrOfPmzcPa9euRXJyMm7cuIGpU6dizJgxuHLln6ZJy5YtsXjxYmRmZuLSpUsYOHAgRo8ejd9//13uWlOmTMGDBw9kP19++aVBXysxPnWDIskTu6FbK0fWx+rW5PQ1H1vVDBya881dJl+PMjQ0FMHBwVi5ciUAQCKRwMvLC9OnT8ecOXMUzvf09MTcuXMRFxcnOxYZGQlra2ts3bpV6X2cnZ3x1VdfITY2FsDLGmXXrl2xfPlyncpN61FymzYDLyezHyJm40Wl1+rUQoRtsa9g+o4rCmtG8vFy+4Xd03pqVT5lM3B0WZeSKNL336dJa5TV1dXIzMxEWFiY7Bifz0dYWBjOnTvH+pyqqioIhUK5Y9bW1sjIyGA9v7a2Fjt37kR5eTl69Ogh99i2bdvg4uKCTp06ITExERUVyptXVVVVKCsrk/sh3KXNoIim6UJstUEJgIv5TxD13QVZf6YmlG39QHO+ucmk6UGPHj1CbW0t3N3d5Y67u7vj1q1brM8JDw/H0qVL0bdvX/j7+yMtLQ379u1DbW2t3HnXrl1Djx49UFlZCTs7O+zfvx8dOnSQPT5p0iR4e3vD09MTV69exYcffojs7Gzs27eP9b5JSUlYuHBhA18xMQZtF8LVNF3or4pqbI4NwWtrziIz/wkkddpi0iCsrtaXW/IM+X9VwNlGgK+P/sFa46U539zT6PIoV6xYgSlTpqBdu3bg8Xjw9/dHTEwMNmzYIHde27ZtkZWVBbFYjD179iA6Ohrp6emyYPn222/Lzu3cuTOaN2+OQYMG4c6dO/D391e4b2JiIhISEmS/l5WVwcvLy0CvkjSEJgMv9YNP8sRu+Pe3v+J6ofKWwvQdl/FZRGdczHui8Ji61cjZugLqqxtsafVybjFp09vFxQUWFhYoLi6WO15cXAwPDw/W57i6uiIlJQXl5eXIz8/HrVu3YGdnBz8/P7nzBAIBAgICEBQUhKSkJAQGBmLFihVKyxIaGgoAyMnJYX3cyspKNoou/SHcpMugiCbpQjcKyzB3/zWV5ygbmWbrCqiP0oC4y6SBUiAQICgoCGlpabJjEokEaWlpCv2J9QmFQrRo0QIvXrzA3r17MXr0aJXnSyQSVFVVKX08KysLANC8eXPNXwDhJF0XwpU+T9kfhYSByhonwB6EleVgKkNpQNxj8vSghIQErF+/Hps2bcLNmzcxbdo0lJeXIyYmBgAQFRWFxMRE2fnnz5/Hvn37kJubi19++QVDhw6FRCLB7NmzZeckJibi9OnTyMvLw7Vr15CYmIhTp07h3//+NwDgzp07+OSTT5CZmYm8vDz8+OOPiIqKQt++fdGlSxfjvgHEIHQdFNFkxk5rNzutgrC6roD66gZbmvPNDSbvoxw/fjxKSkrw8ccfo6ioCF27dkVqaqpsgKegoAB8/j/xvLKyEvPmzUNubi7s7OwwfPhwbNmyBY6OjrJzHj58iKioKDx48AAODg7o0qULjhw5gsGDBwN4WZM9fvw4li9fjvLycnh5eSEyMhLz5s0z6msnhqPrQrjSJvjAr9OVnnP74TM42VjiSZ1R7vbN7TFrSBvW89V1BUhJ04B8XWxplSGOMXkeZWNFeZTmjS2fsS4LHg9dWohQwzC4fv+f5riyYKbuevWfq898SulIe1MaQdf33ycFSh1RoDRv4ooaTN9xReUoNQDweZBLE1IWzNiu17e1K2YNaYPHFdVyQSy35JnKGu3JWf01CnhstdJOniJ8PqYzung5qn1+Y6bvv0+TN70J4SJp033nhQLM2ad8pFtSr5qhLE1Im64AXdKb2LCNtF8vLMOoVWeoGa8lkw/mEMJlIb7OOj1P2ci1Jnvi6GPOt7qR9oycElq6TQsUKAlRQVmqkbo/nIYsYKGPfb7V1UrrLk5M1KNASYgabKlGvVu7oqd/swYFM23vqc2cb01H2ilnUzPUR0mIGsr6F9kGaPS1gEVD9/lWN39dipZu0wyNeuuIRr2JlDSYWfB4qGUYzqThqBq5N/el22jUmxCOcbKxxPwDeUqTw42dx1j3fptjQ3D1z1J8tP+aXL4nLd2mHapR6ohqlERKWXJ4iK8zLC34Rptdo242T1Naus2sFu4lpLFTtenYudzHyMiRb/YackdFdYsVa5KaRNhRoCSkATRJw6lL16XU1C2OoY9dIoly1EdJzI4x+wQ1TcOpT9PZNZoujqGv2TyEHQVKYjZMseKONA2HbdMxfaTlqGpO1x2xph0cDYua3sRsaLOhmD4ZKiFdm+a0PmbzEOWoRknMgrYbiulT/eRwaT5lM1sBlhz5Q+eEdG2b08kTuxksAb6po0BJzAIX+uiU5VP+GNdLYSk1TWjbnNZ2Nk9TXKdSVxQoiVngQh+dsqY/AJ1mwCjr/6y7EjobdTs40urp2qM+SmIWTN1HZ6j0nIYujsGGLaBn3C7Bv7/9ldKIlKAaJTEbpuyjM1TTv6GLY9SnrC9XgpeL+g5YcopqlywoUBKzoe+gog1DN/3VNac1pcmOkBm3S/DWpovYPa1ng+9nLqjpTcyOKabqmbrpz4ZtNo8mCfISABfzn+C1NWchrrPTZFNGgZIQPWHrT1S1ja2hlFZUI+q7Cxj4dTpiNl7EgCWnEPXdBYgrapQGdDaZ+U9ou4i/UaAkRE+kTf8Dcb3QqcXLFWukm3lJA5UxqEu8ZwvobGi7iH9QoCREz74++gduFj6VO6avGUL6WBxDGtBPzuqPTi1EUFe3pO0iaDCHEL0y1AwhQyyO4etii22xryB200Vcyn+i9Dk0T5xqlITolSaBSheazmPXZTbPnmk9EezjBH69qiXNE/8HBUpC9MgQaULGWBzj26hg9A5wlTtG88T/QU1v0uhweY6yrtMOVTHG4himzEFtDChQkkYjq+AJ5h24LrdJVidPET4f0xldvBxNV7B69D1DyNCLY9Slr8R2c0Obi+mINhczHraBjPq4OO1On7UzZRuYmfOWsw1Bm4uRJmfGjixkqAiSAPDL39PuuESfM4QMsTgG0RwnAuWqVavg4+MDoVCI0NBQXLhwQem5NTU1WLRoEfz9/SEUChEYGIjU1FS5c7755ht06dIFIpEIIpEIPXr0wOHDh+XOqaysRFxcHJo1awY7OztERkaiuLjYIK+P6E46kKFqWwUAYPBy2t3gpem4eq/UCCUzrrq5jxtjgnFyVn9sjg3RqQatLheTKDJ5oNy1axcSEhIwf/58XL58GYGBgQgPD8fDhw9Zz583bx7Wrl2L5ORk3LhxA1OnTsWYMWNw5co/aRItW7bE4sWLkZmZiUuXLmHgwIEYPXo0fv/9d9k5M2fOxMGDB7F7926kp6ejsLAQY8eONfjrJdrRZBGHum4/fGb0mTDG1JBaqqqpjUQ1k/dRhoaGIjg4GCtXrgQASCQSeHl5Yfr06ZgzZ47C+Z6enpg7dy7i4uJkxyIjI2FtbY2tW7cqvY+zszO++uorxMbGQiwWw9XVFdu3b8e4ceMAALdu3UL79u1x7tw5vPLKK2rLTX2UxpFb8gwDv07X+nl8HtA7wJX67+poSv2cZtVHWV1djczMTISFhcmO8fl8hIWF4dy5c6zPqaqqglAolDtmbW2NjIwM1vNra2uxc+dOlJeXo0ePHgCAzMxM1NTUyN23Xbt2aNWqlcr7lpWVyf0Qw9NmEYe6aJ6yPNr3u2FMGigfPXqE2tpauLu7yx13d3dHUVER63PCw8OxdOlS3L59GxKJBMeOHcO+ffvw4MEDufOuXbsGOzs7WFlZYerUqdi/fz86dOgAACgqKoJAIICjo6PG901KSoKDg4Psx8vLS8dXTbTFNpBhY6nZR5fmKb9kqBlDTYXJ+yi1tWLFCrRu3Rrt2rWDQCBAfHw8YmJiwOfLv5S2bdsiKysL58+fx7Rp0xAdHY0bN27ofN/ExESIxWLZz7179xr6UoiG2AYybnwyDME+TmoXdJDmGDb1AQwu7CnUmJk04dzFxQUWFhYKo83FxcXw8PBgfY6rqytSUlJQWVmJx48fw9PTE3PmzIGfn5/ceQKBAAEBAQCAoKAgXLx4EStWrMDatWvh4eGB6upqlJaWytUqVd3XysoKVlZWDXi1pKHqJ0N/GxWskNgtJe17c7KxRNR3F5r8Rlrazhji8uwnUzBpjVIgECAoKAhpaWmyYxKJBGlpabL+RGWEQiFatGiBFy9eYO/evRg9erTK8yUSCaqqqgC8DJyWlpZy983OzkZBQYHa+xLukNY0f4z/Z/1HKWmOoaaLSTQFmuRi0sg4O5NPYUxISEB0dDS6d++OkJAQLF++HOXl5YiJiQEAREVFoUWLFkhKSgIAnD9/Hvfv30fXrl1x//59LFiwABKJBLNnz5ZdMzExEcOGDUOrVq3w9OlTbN++HadOncKRI0cAAA4ODoiNjUVCQgKcnZ0hEokwffp09OjRQ6MRb8ItXVo64tD0PgozYQy15FljpcnURlVfLOY2Mq4NkwfK8ePHo6SkBB9//DGKiorQtWtXpKamygZ4CgoK5PofKysrMW/ePOTm5sLOzg7Dhw/Hli1b5JrQDx8+RFRUFB48eAAHBwd06dIFR44cweDBg2XnLFu2DHw+H5GRkaiqqkJ4eDhWr15ttNdN9K9+09xQOyM2dsrmc9MXi3Imz6NsrCiPkvvU5WAG+zjh26jgJtVXqcrJ7IeI2ah8GminFiJsi32lUbxfZpVHSYghqcvBzMx7wrn54aakbmT8RmFZk+zbBShQEjOXPLEburVyZH2MtmWVJ/1iURYUmnISPwVKYtYcbCwRNzBA5Tm0Les/kid2QwdP1U3VppicToGSmD11TcqmXFOqz8HGEv9Ts3RbU0xOp0BJzJ66JqVUU6wpsdF13x1zRoGSNAnJE7vh/7ydVJ7TFGtKytBCwfIoPUhHlB7UOL225iwy859AUudTb65LjemDPrezMOa0SH3/fZo84ZwQY2KbH96Ua0rq6GOzMbY9jxrbfHuqUeqIapSNG23LajymWDCYapSE6AFty2oc5jItkgZzCCEGYy4LBlOgJIQYjLksGEyBkhBiMNrkZHJ5FXrqoySEGFTyxG4qMw2yCp5g3oHruH7/nw37uDYqTqPeOqJRb0K0Uz/TgC1tSKqho+I06k0IaZTqZxrM2JGFDJYgCXBvVFzjPsrCwkLMmjWLdT9rsViMDz74QGGTMEIIYSNNG5KoOY8ro+IaB8qlS5eirKyMtRrr4OCAp0+fYunSpXotHCHEPKlLG5Liyqi4xoEyNTUVUVFRSh+PiorCoUOH9FIoQoh5U5c2xOeBUysVaRwo7969i1atWil9vGXLlsjLy9NHmQghHKPv1B1123T0DnDl1Px7jQdzrK2tkZeXpzRY5uXlwdraWm8FI4SYXkMWtFC3WhBb2lAnTxE+H9MZXbwc9fYa9EHj9KARI0bA09MT69evZ338rbfeQmFhIX7++We9FpCrKD2INAW6LGihbXA1xAIlJtuFcdasWdi4cSNmzZolN7pdXFyM999/H99//z1mzZrV4AIRQrhBOjJdW68uVTd1h82MHVk4k/NI7tiZnEdK9yXydbHFgLZunOmPZKNx03vAgAFYtWoV3n33XSxbtgwikQg8Hg9isRiWlpZITk7GwIEDDVlWQogRabKgRf3gpo/Vgoy5wK+mtEo4/+9//4tXX30VP/zwA3JycsAwDNq0aYNx48ahZcuWhiojIcQEdFnQQpfgKsXlBX61npnTokULzJw50xBlIYRwiHRkWlkfJVvAa8hqQaqa7KbepkPjQPm///2P9biDgwPatGmDHj166K1QhBBuULegRX26BFeA+wv8ahwoly1bxnq8tLQUYrEYPXv2xI8//ghnZ2e9FY4QYloONpbYHBuiMDKdW/IMl+89Ye1HZAuu7ZvbY9aQNkrv05AmuzHoZfWg3NxcvPHGG+jatStWr16tj3JxHqUHkaZIm37E3+6VYm7KNY2WT8steYaBX6crve/JWf21CpQmSw9Sxc/PD4sXL8bRo0f1cTlCCEdpk/rz9dE/cLPwqUbnarPArynobYXzVq1aoaioSKfnrlq1Cj4+PhAKhQgNDcWFCxeUnltTU4NFixbB398fQqEQgYGBSE1NlTsnKSkJwcHBsLe3h5ubGyIiIpCdnS13Tv/+/cHj8eR+pk6dqlP5CWkKtMmr1CUHM3liN/QKcJE7xpWthPW2HuW1a9fg7e2t9fN27dqFhIQErFmzBqGhoVi+fDnCw8ORnZ0NNzc3hfPnzZuHrVu3Yv369WjXrh2OHDmCMWPG4OzZs+jW7eUbmp6ejri4OAQHB+PFixf46KOPMGTIENy4cQO2tv98M02ZMgWLFi2S/W5jo3rEjpCmTJt+RF36HJX1h3KBxoGSbR1K4OValJmZmXj//fcRHR2tdQGWLl2KKVOmICYmBgCwZs0a/PTTT9iwYQPmzJmjcP6WLVswd+5cDB8+HAAwbdo0HD9+HF9//TW2bt0KAAo1zO+//x5ubm7IzMxE3759ZcdtbGzg4eGhdZkJaYq0Sf1pSJoQF7cS1rjp7ejoCCcnJ4UfHx8fjBs3DoMHD2YNbKpUV1cjMzMTYWFh/xSIz0dYWBjOnTvH+pyqqioIhUK5Y9bW1sjIyFB6H7FYDAAKI/Lbtm2Di4sLOnXqhMTERFRUKP8WrKqqQllZmdwPIU2JuhV/5h/4HeKKGpXnWvB46O7thLzH5ZzcREwZjUe909PZR6REIhFat24NOzs7XL9+HZ06ddL45oWFhWjRogXOnj0rl4c5e/ZspKen4/z58wrPmTRpEn777TekpKTA398faWlpGD16NGpra1FVVaVwvkQiwahRo1BaWioXTNetWwdvb294enri6tWr+PDDDxESEoJ9+/axlnXBggVYuHChwnEa9SZNibiiRiH1R6r+Yhls5zrZWOLJ38EUMNzMG32Pejc4Pejp06fYsWMHvvvuO1y6dAm1tbUaP1eXQFlSUoIpU6bg4MGD4PF48Pf3R1hYGDZs2IDnz58rnD9t2jQcPnwYGRkZKqdZnjhxAoMGDUJOTg78/f0VHq+qqpILxGVlZfDy8qJAaQRcnPvblGmbyiPtc1x9MgeX80u1WolIV5zZXOz06dP47rvvsHfvXnh6emLs2LFYuXKlVtdwcXGBhYWFwl47xcXFSvsOXV1dkZKSgsrKSjx+/Bienp6YM2cO/Pz8FM6Nj4/HoUOHcPr0abVz0UNDQwFAaaC0srKClZWVpi+N6AGX5/42ZdoO1Pi62IJhGFzMe6JwLldm3qijVXpQUVERFi9ejNatW+O1116DSCRCVVUVUlJSsHjxYgQHB2t1c4FAgKCgIKSlpcmOSSQSpKWlqZ0SKRQK0aJFC7x48QJ79+7F6NGjZY8xDIP4+Hjs378fJ06cgK+vr9qyZGVlAQCaN2+u1WsghqPtcl3EOAy1WAaXaRwoR44cibZt2+Lq1atYvnw5CgsLkZyc3OACJCQkYP369di0aRNu3ryJadOmoby8XDYKHhUVhcTERNn558+fx759+5Cbm4tffvkFQ4cOhUQiwezZs2XnxMXFYevWrdi+fTvs7e1RVFSEoqIiWdP8zp07+OSTT5CZmYm8vDz8+OOPiIqKQt++fdGlS5cGvybScLquhUgMT9lADR9AsLeT3hfL4AKNA+Xhw4cRGxuLhQsXYsSIEbCwsNBLAcaPH48lS5bg448/RteuXZGVlYXU1FS4u7sDAAoKCvDgwQPZ+ZWVlZg3bx46dOiAMWPGoEWLFsjIyICjo6PsnG+++QZisRj9+/dH8+bNZT+7du0C8LIme/z4cQwZMgTt2rXD+++/j8jISBw8eFAvr8lc6HufFG009hqIuWNLDpcAuJj/BFHfXZCNfktxfeaNOhoP5vz666/47rvvsGvXLrRv3x7/+c9/MGHCBDRv3hy//fYbOnToYOiycoo5z/XmQt+gvuf+Ev2oP7D22pqzyMx/AkmdKKJsgIZtFNxsR73Ly8uxa9cubNiwARcuXEBtbS2WLl2KN998E/b29g0uUGNhzoFSl31SzLkchP3Ls7u3Ey7lKw7QSCn7MjPGzBuTL4pha2uLN998ExkZGbh27Rref/99LF68GG5ubhg1alSDC0RMi0t9g1ye+9vUsA2sXVYRJAHl3SONYY+c+ho017tt27b48ssvkZSUhIMHD2LDhg36KhcxES6tC8jlub9NibJFdSVqnsf1ARpt6GVRDAsLC0RERCAiIkIflyMmxMXRSS7O/W1K1H158nlg7aM0p38zvS2zRsxDYx+dJPqn7sszyNtJ7ndz7B7R2zJrxHxou08KMW/q9sFpCt0jetkKoiky51FvKXP/8BPNGTO1Rx9Mnh5EXmoKgZKQ+hrLlydnFsUghDQ9xh5Y48rKURQoCSGcw4XZYXXRqDchhHO4tnIUBUpCCKdwaXaYFAVKQgincHHlKAqUhBBO4eLsMAqURGOmXJ+SNB1cnB1Go95ELa6NQBLzx7XZYZRwrqOmlHBO60ISQ9AkR1LXBHdKOCdGpWyJrcayex7hHm1aKFxZOYr6KIlKXByBJI0b13IkNUGBkihVWlGNVSdyVJ5jTouzEv1QNejHxRxJTVDTmyg1Y0cWrhSUsj5mjouzkobRpEnNpRX0tUE1SsJK2Te/1P95O9L6lESOJk1qLuZIaoICJWGl7pv/nQEBBk8NorzNxkPTJjUXcyQ1QU1vwsqU3/yUt9n4aNOk5lqOpCYoUBJW6pb/N+Q3v6omHOVtcpM2X6yNcXdNanoTpUyxr3ZjHRVt6nRpUjem/b2pRkmUMsU3f2MdFSWNs0mtKQqURC1jzo5orKOipHE2qTVFgZJwiin7Rol+cGXaoT5RHyXhHFP0jRKiCtUoiQJT73xnzk040jhxoka5atUq+Pj4QCgUIjQ0FBcuXFB6bk1NDRYtWgR/f38IhUIEBgYiNTVV7pykpCQEBwfD3t4ebm5uiIiIQHZ2ttw5lZWViIuLQ7NmzWBnZ4fIyEgUFxcb5PUpo4+Ean0mZZdWVCPquwsY+HU6YjZexIAlpxD13QWIK2oafG1dNKZRUWLeTL4e5a5duxAVFYU1a9YgNDQUy5cvx+7du5GdnQ03NzeF8z/88ENs3boV69evR7t27XDkyBEkJCTg7Nmz6NbtZdNs6NChmDBhAoKDg/HixQt89NFHuH79Om7cuAFb25d/dNOmTcNPP/2E77//Hg4ODoiPjwefz8eZM2c0KndD1rvTR0K1IZKyad1JYi70vR6lyQNlaGgogoODsXLlSgCARCKBl5cXpk+fjjlz5iic7+npiblz5yIuLk52LDIyEtbW1ti6dSvrPUpKSuDm5ob09HT07dsXYrEYrq6u2L59O8aNGwcAuHXrFtq3b49z587hlVdeUVvuhvxDaBuQ2JrC+g5quSXPMPDrdKWPn5zVn2p2xOQ07RYyq4V7q6urkZmZicTERNkxPp+PsLAwnDt3jvU5VVVVEAqFcsesra2RkZGh9D5isRgA4OzsDADIzMxETU0NwsLCZOe0a9cOrVq1Uhooq6qqUFVVJfu9rKxMg1eoSJuFcJXVGt8f0kbvi+kaK3/R1P2fpHHKKniCeQeu4/r9f/7ujDmt1aSB8tGjR6itrYW7u7vccXd3d9y6dYv1OeHh4Vi6dCn69u0Lf39/pKWlYd++faitrWU9XyKR4L333kOvXr3QqVMnAEBRUREEAgEcHR0V7ltUVMR6naSkJCxcuFDLV6hIm4CkbCrfX+VVbE9lvYamDJ2/SPO3iS7YPjdSxpzWyonBHG2sWLECrVu3Rrt27SAQCBAfH4+YmBjw+ewvJS4uDtevX8fOnTsbdN/ExESIxWLZz71793S6jrqAVCyuxN1H5Sqn8l0vVF2blV5DG36udgj2cQJffgYa6xQ0XQaQGuOq1sT0ZuzIQgZLkASMO63VpDVKFxcXWFhYKIw2FxcXw8PDg/U5rq6uSElJQWVlJR4/fgxPT0/MmTMHfn5+CufGx8fj0KFDOH36NFq2bCk77uHhgerqapSWlsrVKlXd18rKClZWVjq8SnnKEqql5uy7BgDo5Km6X6VTCxFuFj5VeQ1Na2zSb+2LeU8UHqubv6hrrZD23SG6UPa5qc8Y01pNWqMUCAQICgpCWlqa7JhEIkFaWhp69Oih8rlCoRAtWrTAixcvsHfvXowePVr2GMMwiI+Px/79+3HixAn4+vrKPTcoKAiWlpZy983OzkZBQYHa++oDW0J1fTfU1Bo/H9NZ7TU0rbGx1fb4AIK9nbA5NkQWBHWtFdK+O0QX6j43UsaY1mryhPOEhARER0eje/fuCAkJwfLly1FeXo6YmBgAQFRUFFq0aIGkpCQAwPnz53H//n107doV9+/fx4IFCyCRSDB79mzZNePi4rB9+3YcOHAA9vb2sn5HBwcHWFtbw8HBAbGxsUhISICzszNEIhGmT5+OHj16aDTi3VB1E6p/zX2MxL9rgHVJ/v4vnwdI6lQapSPbXVo6qr2GJjU2Zd/aEgAX85/IntuQWiHN3ya6UPe54fOA3gHGWezX5IFy/PjxKCkpwccff4yioiJ07doVqampsgGegoICuf7HyspKzJs3D7m5ubCzs8Pw4cOxZcsWuSb0N998AwDo37+/3L02btyIyZMnAwCWLVsGPp+PyMhIVFVVITw8HKtXrzboa5WqO/Lr4SBUeW4HT5HcSJ+0KazNNVQ1TTQdXGrIqDjN3ya6UNdN1TvA1WjTWk2eR9lY6ZKnxdbH193bCZfyFfsGpU7O6g8Asql8TjaWOl1DVY1Sk/zJhuZZiitqFJbgolFvog7b56aTpwifj+mMLl6OSp9nVnmUTQ1bH9+VglI42Vii7PkLlbWt+onmulyDjaa1vYbWCmn+NtEFVz43jS49qLFSle7zpKIGfq7y//i9Alzw/pA2cmk46q7xf96OCtfQpGmi6Wo9+ljVh+ZvE12Y+nNDNUojUdfHd/vhMwAvmxWJw9ph7em7GL3qn3nnfVu7Ynz3lsqeDuDlzog+zWy1/ubV9Ftbm293moFDzAkFSiNRN4IndfPBU8TvuIKy5y/kjp/JeYSK6hdKnvVSsbgSPs1efvPqQtMFV1WdRzNwiDmipreRKNt8qT5pM5qteX0p/wmCfZyUXmPOvmsKS6MZe2/sKZsvISNHPo2IZuCQxo5qlEbEtvmStqJ7+sDa8k+V1ziT8whTt2bC0oJvtJpdaUU13tp0iXX0nWbgkMaOapRGJO3jOzmrP5LGdtbpGh09HdReo5ZhcC73sVFrdjN2ZOGyihQlgGbgkMaLAqUJ+LrYYmJIK6X7IDvZWLIe7+7thLzH5bKambpEc0m9DNn6iwjoq1kuHY2XqDmPZuCQxoqa3iakbB/kzyI6YW7KdbnjIut/4VL+E8RsvAjgn3UpdXG9UIz5B37XW7Nc3Yi+MaeaEWIINDNHR/rM/K+fbiNNrfkXn4cXEgarT+bgcn4pa6I3AIUkcD6gsnYX7OOk9HqGWB092McJ30YF06g3MRqamWOGpOk20s29NJmeKG1G/xjfCwDkntO7tSueVtbgtz9LFRbU6NbKkXU5tYYMuCibtcMHEOTthN1Te2p1PcrBJFxDgZJD2KY4qhsgeVxeLZcE7mwjwNdH/8CVe6UK5/YKcMH47i1VzgvXdW0/tm6E3q21W7SAcjAJV1Gg5AhVy52pIh0gkdZK2eaCS2t2m2NDkFvyTKPraUsfc3JVrXdJu0ASU6JRb47QZECkLmVbNLDNBa+7tqSyxHe26+lC1zm56dkPlc5jN9Zy/4QoQ4GSI9RNcQzydpL7nW0xCk1XEtfH4hb6Iu2Xjf57NF8ZysEkpkRNb45Qt4yZJs1aTVcS58rSVQB7c5sN5WASU6IaJYeoq+mpa9Zq26w29dJVyroK6tJXlwAhDUE1Sg7RR01PWRK7KZrV6miyeRRXy06aFgqUHKTpcmdsuNSsVqW0ohqrTuSoPGdLbAj6tHY1UokIUY4CpZlqSLA1himbL+FyAXs+p7RfloIk4QoKlMSoVC3HJvV/3o7U3CacQoM5xKg0WY7tnQEBNBOHcArVKJsILsyfVjb7qD5KBSJcQ4HSzHFp/jQtx0YaK2p6mzlV86eNKbfkGYrElSrPCfJ2or5JwklUozRjypq6xtzDhq1GW5+uy7ERYixUozRjms79NiRNpij2bu2Kb6ODDV4WQnRFNUozpuncb0NRN3izeGxnhPo1oz5JwnlUozRjms79NsTe3y+b3Kr7Qd0dhBQkSaNANUozp2rutyFHxGfsyMKNwjKV51AaEGksTF6jXLVqFXx8fCAUChEaGooLFy4oPbempgaLFi2Cv78/hEIhAgMDkZqaKnfO6dOnMXLkSHh6eoLH4yElJUXhOpMnTwaPx5P7GTp0qL5fGifU3Ut8Y0wwTs7qj82xIXCwsTTYiLi67Wv5PNCKQKRRMWmg3LVrFxISEjB//nxcvnwZgYGBCA8Px8OHD1nPnzdvHtauXYvk5GTcuHEDU6dOxZgxY3Dlyj9/2OXl5QgMDMSqVatU3nvo0KF48OCB7GfHjh16fW1cU39JNWVLnOljRXF1g0gdPEWUBkQaFZMGyqVLl2LKlCmIiYlBhw4dsGbNGtjY2GDDhg2s52/ZsgUfffQRhg8fDj8/P0ybNg3Dhw/H119/LTtn2LBh+PTTTzFmzBiV97aysoKHh4fsx8nJSeX55saQI+LqBpGSJ/4fTVEkjYrJAmV1dTUyMzMRFhb2T2H4fISFheHcuXOsz6mqqoJQKJQ7Zm1tjYyMDK3vf+rUKbi5uaFt27aYNm0aHj9+rPU1GjNDjogbel8eQozNZIHy0aNHqK2thbu7u9xxd3d3FBUVsT4nPDwcS5cuxe3btyGRSHDs2DHs27cPDx480OreQ4cOxebNm5GWloYvvvgC6enpGDZsGGpra5U+p6qqCmVlZXI/jZmhgxmX9uUhpKEa1aj3ihUrMGXKFLRr1w48Hg/+/v6IiYlR2lRXZsKECbL/79y5M7p06QJ/f3+cOnUKgwYNYn1OUlISFi5c2KDyc40hV0NvLAsIE6IJkwVKFxcXWFhYoLi4WO54cXExPDw8WJ/j6uqKlJQUVFZW4vHjx/D09MScOXPg5+fXoLL4+fnBxcUFOTk5SgNlYmIiEhISZL+XlZXBy8tLq/twYQWfuowRzLi+gDAhmjBZoBQIBAgKCkJaWhoiIiIAABKJBGlpaYiPj1f5XKFQiBYtWqCmpgZ79+7F66+/3qCy/Pnnn3j8+DGaN2+u9BwrKytYWVnpdH0ureDDhoIZIaqZdNQ7ISEB69evx6ZNm3Dz5k1MmzYN5eXliImJAQBERUUhMTFRdv758+exb98+5Obm4pdffsHQoUMhkUgwe/Zs2TnPnj1DVlYWsrKyAAB3795FVlYWCgoKZI9/8MEH+PXXX5GXl4e0tDSMHj0aAQEBCA8PN8jr5MoKPoZkiNk9hHCFSfsox48fj5KSEnz88ccoKipC165dkZqaKhvgKSgoAJ//TyyvrKzEvHnzkJubCzs7OwwfPhxbtmyBo6Oj7JxLly5hwIABst+lzeXo6Gh8//33sLCwwNWrV7Fp0yaUlpbC09MTQ4YMwSeffKJzjVEVLqzgY0hcry0Tog88hlGxqTJRqqysDA4ODhCLxRCJRErPO5n9EDEbLyp9fGNMMAa0dTNEEY0i6rsLOJPzSC5xXbo52ObYEBOWjDRlmv59asrkUxjNnalX8DEkQ87uIYRLKFAaGNeSr/XZl3j+ruokfWOsd0mIMTSqPMrGypD5iprSZ1+iJquWA427tkxIXRQojYALydeqRt617UtUt2q5tI+yMQ9SEVIXBUojMlW+oj5H3jXZcpamKhJzQ4HSwLgwG0eTlYI0LZu6ayWN7YyJIa00LhshjQEFSgPhUn6hPkfe1V3rFb9mGl+LkMaCRr0NhEuzcfQ58s61UXxCjIECpQFwMb9Qn8ue0RJqpKmhprcB6LNPUF/0OfLOhVF8QoyJAqUBcHk2jj5H3mnVIdJUUNPbAKgfjxDzQoHSQKgfjxDzQU1vA6F+PELMBwVKA6N+PEIaP2p6E0KIGhQoCSFEDQqUhBCiBgVKQghRgwZzdCTdaqisrMzEJSGE1Cf9u9TXlmAUKHX09OlTAICXl5eJS0IIUebp06dwcHBo8HVoF0YdSSQSFBYWwt7eHrx6M3DqKisrg5eXF+7du6eX3eDMCb037Oh9UU7T94ZhGDx9+hSenp5yW17rimqUOuLz+WjZsqXG54tEIvrQK0HvDTt6X5TT5L3RR01SigZzCCFEDQqUhBCiBgVKA7OyssL8+fNhZWVl6qJwDr037Oh9Uc5U7w0N5hBCiBpUoySEEDUoUBJCiBoUKAkhRA0KlIQQogYFSiP77LPP0LNnT9jY2MDR0dHUxTGZVatWwcfHB0KhEKGhobhw4YKpi8QJp0+fxsiRI+Hp6Qkej4eUlBRTF8nkkpKSEBwcDHt7e7i5uSEiIgLZ2dlGLQMFSiOrrq7Ga6+9hmnTppm6KCaza9cuJCQkYP78+bh8+TICAwMRHh6Ohw8fmrpoJldeXo7AwECsWrXK1EXhjPT0dMTFxeHXX3/FsWPHUFNTgyFDhqC8vNx4hWCISWzcuJFxcHAwdTFMIiQkhImLi5P9Xltby3h6ejJJSUkmLBX3AGD2799v6mJwzsOHDxkATHp6utHuSTVKYlTV1dXIzMxEWFiY7Bifz0dYWBjOnTtnwpKRxkIsFgMAnJ2djXZPCpTEqB49eoTa2lq4u7vLHXd3d0dRUZGJSkUaC4lEgvfeew+9evVCp06djHZfCpR6MGfOHPB4PJU/t27dMnUxCWn04uLicP36dezcudOo96Vl1vTg/fffx+TJk1We4+fnZ5zCcJyLiwssLCxQXFwsd7y4uBgeHh4mKhVpDOLj43Ho0CGcPn1aqyUO9YECpR64urrC1dXV1MVoFAQCAYKCgpCWloaIiAgAL5tTaWlpiI+PN23hCCcxDIPp06dj//79OHXqFHx9fY1eBgqURlZQUIC//voLBQUFqK2tRVZWFgAgICAAdnZ2pi2ckSQkJCA6Ohrdu3dHSEgIli9fjvLycsTExJi6aCb37Nkz5OTkyH6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+      "image/png": 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",
       "text/plain": [
        "<Figure size 300x300 with 1 Axes>"
       ]
@@ -989,7 +1481,7 @@
    "source": [
     "score = clr.decision_function(X_test)\n",
     "dfsc = pandas.DataFrame(score, columns=[\"score\"])\n",
-    "dfsc[\"color\"] = y_test.ravel()\n",
+    "dfsc[\"color\"] = y_test.values.ravel()\n",
     "dfsc[\"score_abs\"] = dfsc[\"score\"].abs()"
    ]
   },
@@ -1000,7 +1492,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x400 with 2 Axes>"
       ]
@@ -1063,17 +1555,17 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>394</th>\n",
-       "      <td>-11.912383</td>\n",
+       "      <td>-10.560451</td>\n",
        "      <td>red</td>\n",
-       "      <td>11.912383</td>\n",
+       "      <td>10.560451</td>\n",
        "      <td>1</td>\n",
        "      <td>0.997475</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>395</th>\n",
-       "      <td>-12.204326</td>\n",
+       "      <td>-12.879144</td>\n",
        "      <td>red</td>\n",
-       "      <td>12.204326</td>\n",
+       "      <td>12.879144</td>\n",
        "      <td>1</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -1083,8 +1575,8 @@
       ],
       "text/plain": [
        "         score color  score_abs  count  count_sum\n",
-       "394 -11.912383   red  11.912383      1   0.997475\n",
-       "395 -12.204326   red  12.204326      1   1.000000"
+       "394 -10.560451   red  10.560451      1   0.997475\n",
+       "395 -12.879144   red  12.879144      1   1.000000"
       ]
      },
      "execution_count": 21,
@@ -1135,17 +1627,17 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1227</th>\n",
-       "      <td>13.561302</td>\n",
+       "      <td>12.948889</td>\n",
        "      <td>white</td>\n",
-       "      <td>13.561302</td>\n",
+       "      <td>12.948889</td>\n",
        "      <td>1</td>\n",
        "      <td>0.999186</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1228</th>\n",
-       "      <td>13.671458</td>\n",
+       "      <td>13.120824</td>\n",
        "      <td>white</td>\n",
-       "      <td>13.671458</td>\n",
+       "      <td>13.120824</td>\n",
        "      <td>1</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -1155,8 +1647,8 @@
       ],
       "text/plain": [
        "          score  color  score_abs  count  count_sum\n",
-       "1227  13.561302  white  13.561302      1   0.999186\n",
-       "1228  13.671458  white  13.671458      1   1.000000"
+       "1227  12.948889  white  12.948889      1   0.999186\n",
+       "1228  13.120824  white  13.120824      1   1.000000"
       ]
      },
      "execution_count": 22,
@@ -1213,7 +1705,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x400 with 2 Axes>"
       ]
@@ -1264,7 +1756,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -1278,7 +1770,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/ml/winesc_multi.ipynb b/_doc/practice/ml/winesc_multi.ipynb
index 7975365..1db972d 100644
--- a/_doc/practice/ml/winesc_multi.ipynb
+++ b/_doc/practice/ml/winesc_multi.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,15 +46,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -66,31 +82,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-1.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -102,7 +119,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -110,7 +127,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -120,7 +137,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -136,14 +153,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -151,28 +168,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -190,24 +207,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -215,27 +241,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -243,79 +267,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -325,18 +348,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -352,23 +375,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -378,7 +403,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -388,7 +414,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -423,12 +450,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -438,31 +465,514 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
       ],
       "text/plain": [
-       "LogisticRegression(solver='liblinear')"
+       "LogisticRegression()"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,22 +980,22 @@
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "\n",
-    "clr = LogisticRegression(solver=\"liblinear\")\n",
+    "clr = LogisticRegression()\n",
     "clr.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "55.07692307692308"
+       "np.float64(45.84615384615385)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -493,7 +1003,7 @@
    "source": [
     "import numpy\n",
     "\n",
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -505,7 +1015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -543,8 +1053,8 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -553,9 +1063,9 @@
        "      <th>1</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>47</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -563,8 +1073,8 @@
        "      <th>2</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>332</td>\n",
-       "      <td>184</td>\n",
+       "      <td>224</td>\n",
+       "      <td>296</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -573,9 +1083,9 @@
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>169</td>\n",
-       "      <td>541</td>\n",
-       "      <td>10</td>\n",
+       "      <td>196</td>\n",
+       "      <td>521</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -583,9 +1093,9 @@
        "      <th>4</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>217</td>\n",
-       "      <td>22</td>\n",
+       "      <td>36</td>\n",
+       "      <td>233</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -593,9 +1103,9 @@
        "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>42</td>\n",
-       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -604,7 +1114,7 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -614,17 +1124,17 @@
        "</div>"
       ],
       "text/plain": [
-       "   0  1    2    3   4  5  6\n",
-       "0  0  0    4    7   0  0  0\n",
-       "1  0  0   47   22   0  0  0\n",
-       "2  0  0  332  184   0  0  0\n",
-       "3  0  0  169  541  10  0  0\n",
-       "4  0  0   19  217  22  0  0\n",
-       "5  0  0    3   42   5  0  0\n",
-       "6  0  0    0    1   0  0  0"
+       "   0  1    2    3  4  5  6\n",
+       "0  0  0    0    5  0  0  0\n",
+       "1  0  0   23   37  1  0  0\n",
+       "2  0  0  224  296  0  0  0\n",
+       "3  0  0  196  521  0  0  0\n",
+       "4  0  0   36  233  0  0  0\n",
+       "5  0  0    6   45  0  0  0\n",
+       "6  0  0    0    2  0  0  0"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -645,7 +1155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -683,8 +1193,8 @@
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -693,9 +1203,9 @@
        "      <th>4</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>47</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -703,8 +1213,8 @@
        "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>332</td>\n",
-       "      <td>184</td>\n",
+       "      <td>224</td>\n",
+       "      <td>296</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -713,9 +1223,9 @@
        "      <th>6</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>169</td>\n",
-       "      <td>541</td>\n",
-       "      <td>10</td>\n",
+       "      <td>196</td>\n",
+       "      <td>521</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -723,9 +1233,9 @@
        "      <th>7</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>217</td>\n",
-       "      <td>22</td>\n",
+       "      <td>36</td>\n",
+       "      <td>233</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -733,9 +1243,9 @@
        "      <th>8</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>42</td>\n",
-       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -744,7 +1254,7 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -754,17 +1264,17 @@
        "</div>"
       ],
       "text/plain": [
-       "   3  4    5    6   7  8  9\n",
-       "3  0  0    4    7   0  0  0\n",
-       "4  0  0   47   22   0  0  0\n",
-       "5  0  0  332  184   0  0  0\n",
-       "6  0  0  169  541  10  0  0\n",
-       "7  0  0   19  217  22  0  0\n",
-       "8  0  0    3   42   5  0  0\n",
-       "9  0  0    0    1   0  0  0"
+       "   3  4    5    6  7  8  9\n",
+       "3  0  0    0    5  0  0  0\n",
+       "4  0  0   23   37  1  0  0\n",
+       "5  0  0  224  296  0  0  0\n",
+       "6  0  0  196  521  0  0  0\n",
+       "7  0  0   36  233  0  0  0\n",
+       "8  0  0    6   45  0  0  0\n",
+       "9  0  0    0    2  0  0  0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -793,15 +1303,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {\n",
+       "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -813,31 +1324,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-2.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 {\n",
+       "#sk-container-id-2 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 pre {\n",
+       "#sk-container-id-2 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -849,7 +1361,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -857,7 +1369,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-container {\n",
+       "#sk-container-id-2 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -867,7 +1379,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -883,14 +1395,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -898,28 +1410,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
+       "#sk-container-id-2 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -937,24 +1449,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -962,27 +1483,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -990,79 +1509,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1072,18 +1590,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1099,23 +1617,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1125,7 +1645,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1135,7 +1656,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1170,12 +1692,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1185,31 +1707,572 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegre...r=&#x27;liblinear&#x27;)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;liblinear&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
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+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=LogisticRegression(solver='liblinear'))"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1223,22 +2286,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "54.95384615384615"
+       "np.float64(51.323076923076925)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -1250,15 +2313,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {\n",
+       "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1270,31 +2334,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-3.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 {\n",
+       "#sk-container-id-3 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 pre {\n",
+       "#sk-container-id-3 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1306,7 +2371,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1314,7 +2379,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-container {\n",
+       "#sk-container-id-3 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1324,7 +2389,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1340,14 +2405,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1355,28 +2420,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
+       "#sk-container-id-3 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1394,24 +2459,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1419,27 +2493,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1447,79 +2519,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1529,18 +2600,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1556,23 +2627,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1582,7 +2655,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1592,7 +2666,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1627,12 +2702,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1642,31 +2717,556 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsOneClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsOneClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsOneClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegre...r=&#x27;liblinear&#x27;)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes * (<br>n_classes - 1) / 2` OVO problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;liblinear&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
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+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "}\n",
+       "\n",
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+       "\n",
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+       " * @returns \"light\" or \"dark\"\n",
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+       "\n",
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+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
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+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
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+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
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+       "            // If the text is very bright we have a dark theme\n",
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+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
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+       "\n",
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       ],
       "text/plain": [
        "OneVsOneClassifier(estimator=LogisticRegression(solver='liblinear'))"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1680,27 +3280,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "55.138461538461534"
+       "np.float64(52.246153846153845)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1738,8 +3338,8 @@
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1748,9 +3348,9 @@
        "      <th>4</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>46</td>\n",
-       "      <td>23</td>\n",
-       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -1758,18 +3358,18 @@
        "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>332</td>\n",
-       "      <td>183</td>\n",
+       "      <td>304</td>\n",
+       "      <td>212</td>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>169</td>\n",
-       "      <td>524</td>\n",
+       "      <td>182</td>\n",
+       "      <td>508</td>\n",
        "      <td>27</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1778,9 +3378,9 @@
        "      <th>7</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>18</td>\n",
-       "      <td>200</td>\n",
-       "      <td>40</td>\n",
+       "      <td>22</td>\n",
+       "      <td>210</td>\n",
+       "      <td>37</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -1788,9 +3388,9 @@
        "      <th>8</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>32</td>\n",
-       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>35</td>\n",
+       "      <td>9</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -1799,7 +3399,7 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1810,16 +3410,16 @@
       ],
       "text/plain": [
        "   3  4    5    6   7  8  9\n",
-       "3  0  0    5    6   0  0  0\n",
-       "4  0  0   46   23   0  0  0\n",
-       "5  0  0  332  183   1  0  0\n",
-       "6  0  0  169  524  27  0  0\n",
-       "7  0  0   18  200  40  0  0\n",
-       "8  0  0    6   32  12  0  0\n",
-       "9  0  0    0    1   0  0  0"
+       "3  0  0    3    2   0  0  0\n",
+       "4  0  0   40   20   1  0  0\n",
+       "5  0  0  304  212   3  1  0\n",
+       "6  0  0  182  508  27  0  0\n",
+       "7  0  0   22  210  37  0  0\n",
+       "8  0  0    7   35   9  0  0\n",
+       "9  0  0    0    2   0  0  0"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1848,15 +3448,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-5 {\n",
+       "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1868,31 +3469,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-4.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-4.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1904,7 +3506,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1912,7 +3514,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-container {\n",
+       "#sk-container-id-4 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1922,7 +3524,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1938,14 +3540,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1953,28 +3555,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
+       "#sk-container-id-4 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1992,24 +3594,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2017,27 +3628,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2045,79 +3654,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2127,18 +3735,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2154,23 +3762,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -2180,7 +3790,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2190,7 +3801,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2225,12 +3837,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -2240,31 +3852,498 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-4');</script></body>"
       ],
       "text/plain": [
        "DecisionTreeClassifier()"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2278,22 +4357,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "59.323076923076925"
+       "np.float64(59.07692307692308)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -2305,15 +4384,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-6 {\n",
+       "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -2325,31 +4405,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-5.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-5.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 {\n",
+       "#sk-container-id-5 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 pre {\n",
+       "#sk-container-id-5 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2361,7 +4442,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2369,7 +4450,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-container {\n",
+       "#sk-container-id-5 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2379,7 +4460,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2395,14 +4476,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel {\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2410,28 +4491,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
+       "#sk-container-id-5 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2449,24 +4530,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2474,27 +4564,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2502,79 +4590,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-6 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label-container {\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2584,18 +4671,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2611,23 +4698,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -2637,7 +4726,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2647,7 +4737,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2682,12 +4773,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -2697,31 +4788,556 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=DecisionTreeClassifier())</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
-      ],
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">DecisionTreeClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-5');</script></body>"
+      ],
       "text/plain": [
        "OneVsRestClassifier(estimator=DecisionTreeClassifier())"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2733,22 +5349,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "53.35384615384615"
+       "np.float64(53.41538461538462)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -2760,15 +5376,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-7 {\n",
+       "<style>#sk-container-id-6 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -2780,31 +5397,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-6.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-6.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 {\n",
+       "#sk-container-id-6 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 pre {\n",
+       "#sk-container-id-6 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2816,7 +5434,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2824,7 +5442,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-container {\n",
+       "#sk-container-id-6 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2834,7 +5452,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2850,14 +5468,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel {\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2865,28 +5483,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-serial {\n",
+       "#sk-container-id-6 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2904,24 +5522,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable {\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2929,27 +5556,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2957,79 +5582,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label-container {\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-7 div.sk-estimator {\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3039,18 +5663,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3066,23 +5690,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -3092,7 +5718,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3102,7 +5729,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3137,12 +5765,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -3152,31 +5780,540 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsOneClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsOneClassifier(estimator=DecisionTreeClassifier())</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsOneClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">DecisionTreeClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes * (<br>n_classes - 1) / 2` OVO problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-6');</script></body>"
       ],
       "text/plain": [
        "OneVsOneClassifier(estimator=DecisionTreeClassifier())"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3188,22 +6325,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "62.58461538461538"
+       "np.float64(58.83076923076923)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -3215,15 +6352,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-8 {\n",
+       "<style>#sk-container-id-7 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -3235,31 +6373,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-7.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-7.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 {\n",
+       "#sk-container-id-7 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 pre {\n",
+       "#sk-container-id-7 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3271,7 +6410,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3279,7 +6418,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-container {\n",
+       "#sk-container-id-7 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3289,7 +6428,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3305,14 +6444,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel {\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3320,28 +6459,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-8 div.sk-serial {\n",
+       "#sk-container-id-7 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3359,24 +6498,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable {\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3384,27 +6532,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3412,79 +6558,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-8 div.sk-label label {\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-8 div.sk-label label {\n",
+       "#sk-container-id-7 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-label-container {\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-8 div.sk-estimator {\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3494,18 +6639,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3521,23 +6666,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -3547,7 +6694,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3557,7 +6705,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3592,12 +6741,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -3607,31 +6756,594 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "\n",
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+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-7');</script></body>"
       ],
       "text/plain": [
        "RandomForestClassifier()"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3645,35 +7357,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "69.2923076923077"
+       "np.float64(66.4)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-9 {\n",
+       "<style>#sk-container-id-8 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -3685,31 +7398,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-8.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-8.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 {\n",
+       "#sk-container-id-8 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 pre {\n",
+       "#sk-container-id-8 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3721,7 +7435,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3729,7 +7443,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-container {\n",
+       "#sk-container-id-8 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3739,7 +7453,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3755,14 +7469,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel {\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3770,28 +7484,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-9 div.sk-serial {\n",
+       "#sk-container-id-8 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3809,24 +7523,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable {\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3834,27 +7557,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3862,79 +7583,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-9 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-9 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label-container {\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-9 div.sk-estimator {\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3944,18 +7664,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3971,23 +7691,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -3997,7 +7719,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4007,7 +7730,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4042,12 +7766,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -4057,31 +7781,652 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=RandomForestClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=RandomForestClassifier())</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=RandomForestClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">RandomForestClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: RandomForestClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
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+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "    element.setAttribute('title', fullParamName);\n",
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+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
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+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
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+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
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+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
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+       "        return 'light';\n",
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+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
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+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
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+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-8');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=RandomForestClassifier())"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4093,22 +8438,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "69.41538461538461"
+       "np.float64(66.27692307692308)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -4120,15 +8465,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-10 {\n",
+       "<style>#sk-container-id-9 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -4140,31 +8486,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-9.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-9.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 {\n",
+       "#sk-container-id-9 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 pre {\n",
+       "#sk-container-id-9 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-hidden--visually {\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4176,7 +8523,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4184,7 +8531,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-container {\n",
+       "#sk-container-id-9 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4194,7 +8541,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4210,14 +8557,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel {\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4225,28 +8572,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item {\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-10 div.sk-serial {\n",
+       "#sk-container-id-9 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4264,24 +8611,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable {\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-10 label.sk-toggleable__label {\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4289,27 +8645,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4317,79 +8671,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-10 div.sk-label label {\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-10 div.sk-label label {\n",
+       "#sk-container-id-9 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label-container {\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-10 div.sk-estimator {\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4399,18 +8752,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-10 div.sk-estimator:hover {\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4426,23 +8779,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -4452,7 +8807,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4462,7 +8818,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4497,12 +8854,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link {\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -4512,31 +8869,658 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=hidden_layer_sizes,-array-like%20of%20shape%28n_layers%20-%202%2C%29%2C%20default%3D%28100%2C%29\">\n",
+       "            hidden_layer_sizes\n",
+       "            <span class=\"param-doc-description\">hidden_layer_sizes: array-like of shape(n_layers - 2,), default=(100,)<br><br>The ith element represents the number of neurons in the ith<br>hidden layer.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('activation',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=activation,-%7B%27identity%27%2C%20%27logistic%27%2C%20%27tanh%27%2C%20%27relu%27%7D%2C%20default%3D%27relu%27\">\n",
+       "            activation\n",
+       "            <span class=\"param-doc-description\">activation: {'identity', 'logistic', 'tanh', 'relu'}, default='relu'<br><br>Activation function for the hidden layer.<br><br>- 'identity', no-op activation, useful to implement linear bottleneck,<br>  returns f(x) = x<br><br>- 'logistic', the logistic sigmoid function,<br>  returns f(x) = 1 / (1 + exp(-x)).<br><br>- 'tanh', the hyperbolic tan function,<br>  returns f(x) = tanh(x).<br><br>- 'relu', the rectified linear unit function,<br>  returns f(x) = max(0, x)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27sgd%27%2C%20%27adam%27%7D%2C%20default%3D%27adam%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'sgd', 'adam'}, default='adam'<br><br>The solver for weight optimization.<br><br>- 'lbfgs' is an optimizer in the family of quasi-Newton methods.<br><br>- 'sgd' refers to stochastic gradient descent.<br><br>- 'adam' refers to a stochastic gradient-based optimizer proposed<br>  by Kingma, Diederik, and Jimmy Ba<br><br>For a comparison between Adam optimizer and SGD, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`.<br><br>Note: The default solver 'adam' works pretty well on relatively<br>large datasets (with thousands of training samples or more) in terms of<br>both training time and validation score.<br>For small datasets, however, 'lbfgs' can converge faster and perform<br>better.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=alpha,-float%2C%20default%3D0.0001\">\n",
+       "            alpha\n",
+       "            <span class=\"param-doc-description\">alpha: float, default=0.0001<br><br>Strength of the L2 regularization term. The L2 regularization term<br>is divided by the sample size when added to the loss.<br><br>For an example usage and visualization of varying regularization, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('batch_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=batch_size,-int%2C%20default%3D%27auto%27\">\n",
+       "            batch_size\n",
+       "            <span class=\"param-doc-description\">batch_size: int, default='auto'<br><br>Size of minibatches for stochastic optimizers.<br>If the solver is 'lbfgs', the classifier will not use minibatch.<br>When set to \"auto\", `batch_size=min(200, n_samples)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate,-%7B%27constant%27%2C%20%27invscaling%27%2C%20%27adaptive%27%7D%2C%20default%3D%27constant%27\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: {'constant', 'invscaling', 'adaptive'}, default='constant'<br><br>Learning rate schedule for weight updates.<br><br>- 'constant' is a constant learning rate given by<br>  'learning_rate_init'.<br><br>- 'invscaling' gradually decreases the learning rate at each<br>  time step 't' using an inverse scaling exponent of 'power_t'.<br>  effective_learning_rate = learning_rate_init / pow(t, power_t)<br><br>- 'adaptive' keeps the learning rate constant to<br>  'learning_rate_init' as long as training loss keeps decreasing.<br>  Each time two consecutive epochs fail to decrease training loss by at<br>  least tol, or fail to increase validation score by at least tol if<br>  'early_stopping' is on, the current learning rate is divided by 5.<br><br>Only used when ``solver='sgd'``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate_init,-float%2C%20default%3D0.001\">\n",
+       "            learning_rate_init\n",
+       "            <span class=\"param-doc-description\">learning_rate_init: float, default=0.001<br><br>The initial learning rate used. It controls the step-size<br>in updating the weights. Only used when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('power_t',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=power_t,-float%2C%20default%3D0.5\">\n",
+       "            power_t\n",
+       "            <span class=\"param-doc-description\">power_t: float, default=0.5<br><br>The exponent for inverse scaling learning rate.<br>It is used in updating effective learning rate when the learning_rate<br>is set to 'invscaling'. Only used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_iter,-int%2C%20default%3D200\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=200<br><br>Maximum number of iterations. The solver iterates until convergence<br>(determined by 'tol') or this number of iterations. For stochastic<br>solvers ('sgd', 'adam'), note that this determines the number of epochs<br>(how many times each data point will be used), not the number of<br>gradient steps.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">600</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shuffle',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=shuffle,-bool%2C%20default%3DTrue\">\n",
+       "            shuffle\n",
+       "            <span class=\"param-doc-description\">shuffle: bool, default=True<br><br>Whether to shuffle samples in each iteration. Only used when<br>solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Determines random number generation for weights and bias<br>initialization, train-test split if early stopping is used, and batch<br>sampling when solver='sgd' or 'adam'.<br>Pass an int for reproducible results across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the optimization. When the loss or score is not improving<br>by at least ``tol`` for ``n_iter_no_change`` consecutive iterations,<br>unless ``learning_rate`` is set to 'adaptive', convergence is<br>considered to be reached and training stops.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>Whether to print progress messages to stdout.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous<br>call to fit as initialization, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=momentum,-float%2C%20default%3D0.9\">\n",
+       "            momentum\n",
+       "            <span class=\"param-doc-description\">momentum: float, default=0.9<br><br>Momentum for gradient descent update. Should be between 0 and 1. Only<br>used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=nesterovs_momentum,-bool%2C%20default%3DTrue\">\n",
+       "            nesterovs_momentum\n",
+       "            <span class=\"param-doc-description\">nesterovs_momentum: bool, default=True<br><br>Whether to use Nesterov's momentum. Only used when solver='sgd' and<br>momentum > 0.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=early_stopping,-bool%2C%20default%3DFalse\">\n",
+       "            early_stopping\n",
+       "            <span class=\"param-doc-description\">early_stopping: bool, default=False<br><br>Whether to use early stopping to terminate training when validation<br>score is not improving. If set to True, it will automatically set<br>aside ``validation_fraction`` of training data as validation and<br>terminate training when validation score is not improving by at least<br>``tol`` for ``n_iter_no_change`` consecutive epochs. The split is<br>stratified, except in a multilabel setting.<br>If early stopping is False, then the training stops when the training<br>loss does not improve by more than ``tol`` for ``n_iter_no_change``<br>consecutive passes over the training set.<br>Only effective when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Must be between 0 and 1.<br>Only used if early_stopping is True.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_1',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_1,-float%2C%20default%3D0.9\">\n",
+       "            beta_1\n",
+       "            <span class=\"param-doc-description\">beta_1: float, default=0.9<br><br>Exponential decay rate for estimates of first moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_2',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_2,-float%2C%20default%3D0.999\">\n",
+       "            beta_2\n",
+       "            <span class=\"param-doc-description\">beta_2: float, default=0.999<br><br>Exponential decay rate for estimates of second moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.999</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('epsilon',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=epsilon,-float%2C%20default%3D1e-8\">\n",
+       "            epsilon\n",
+       "            <span class=\"param-doc-description\">epsilon: float, default=1e-8<br><br>Value for numerical stability in adam. Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1e-08</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3D10\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=10<br><br>Maximum number of epochs to not meet ``tol`` improvement.<br>Only effective when solver='sgd' or 'adam'.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_fun',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_fun,-int%2C%20default%3D15000\">\n",
+       "            max_fun\n",
+       "            <span class=\"param-doc-description\">max_fun: int, default=15000<br><br>Only used when solver='lbfgs'. Maximum number of loss function calls.<br>The solver iterates until convergence (determined by 'tol'), number<br>of iterations reaches max_iter, or this number of loss function calls.<br>Note that number of loss function calls will be greater than or equal<br>to the number of iterations for the `MLPClassifier`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">15000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-9');</script></body>"
       ],
       "text/plain": [
        "MLPClassifier(hidden_layer_sizes=30, max_iter=600)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4550,35 +9534,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "52.800000000000004"
+       "np.float64(49.66153846153846)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-11 {\n",
+       "<style>#sk-container-id-10 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -4590,31 +9575,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-10.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-10.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 {\n",
+       "#sk-container-id-10 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 pre {\n",
+       "#sk-container-id-10 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-hidden--visually {\n",
+       "#sk-container-id-10 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4626,7 +9612,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4634,7 +9620,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-container {\n",
+       "#sk-container-id-10 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4644,7 +9630,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4660,14 +9646,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel {\n",
+       "#sk-container-id-10 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4675,28 +9661,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item {\n",
+       "#sk-container-id-10 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-serial {\n",
+       "#sk-container-id-10 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4714,24 +9700,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable {\n",
+       "#sk-container-id-10 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-11 label.sk-toggleable__label {\n",
+       "#sk-container-id-10 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4739,27 +9734,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-10 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4767,79 +9760,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-10 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-10 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label-container {\n",
+       "#sk-container-id-10 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-11 div.sk-estimator {\n",
+       "#sk-container-id-10 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4849,18 +9841,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-11 div.sk-estimator:hover {\n",
+       "#sk-container-id-10 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4876,23 +9868,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -4902,7 +9896,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4912,7 +9907,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4947,12 +9943,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link {\n",
+       "#sk-container-id-10 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -4962,34 +9958,718 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
-       "                                            max_iter=600))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
-       "                                            max_iter=600))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: MLPClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
+       "                                            max_iter=600))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">MLPClassifier... max_iter=600)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: MLPClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=hidden_layer_sizes,-array-like%20of%20shape%28n_layers%20-%202%2C%29%2C%20default%3D%28100%2C%29\">\n",
+       "            hidden_layer_sizes\n",
+       "            <span class=\"param-doc-description\">hidden_layer_sizes: array-like of shape(n_layers - 2,), default=(100,)<br><br>The ith element represents the number of neurons in the ith<br>hidden layer.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('activation',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=activation,-%7B%27identity%27%2C%20%27logistic%27%2C%20%27tanh%27%2C%20%27relu%27%7D%2C%20default%3D%27relu%27\">\n",
+       "            activation\n",
+       "            <span class=\"param-doc-description\">activation: {'identity', 'logistic', 'tanh', 'relu'}, default='relu'<br><br>Activation function for the hidden layer.<br><br>- 'identity', no-op activation, useful to implement linear bottleneck,<br>  returns f(x) = x<br><br>- 'logistic', the logistic sigmoid function,<br>  returns f(x) = 1 / (1 + exp(-x)).<br><br>- 'tanh', the hyperbolic tan function,<br>  returns f(x) = tanh(x).<br><br>- 'relu', the rectified linear unit function,<br>  returns f(x) = max(0, x)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27sgd%27%2C%20%27adam%27%7D%2C%20default%3D%27adam%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'sgd', 'adam'}, default='adam'<br><br>The solver for weight optimization.<br><br>- 'lbfgs' is an optimizer in the family of quasi-Newton methods.<br><br>- 'sgd' refers to stochastic gradient descent.<br><br>- 'adam' refers to a stochastic gradient-based optimizer proposed<br>  by Kingma, Diederik, and Jimmy Ba<br><br>For a comparison between Adam optimizer and SGD, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`.<br><br>Note: The default solver 'adam' works pretty well on relatively<br>large datasets (with thousands of training samples or more) in terms of<br>both training time and validation score.<br>For small datasets, however, 'lbfgs' can converge faster and perform<br>better.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=alpha,-float%2C%20default%3D0.0001\">\n",
+       "            alpha\n",
+       "            <span class=\"param-doc-description\">alpha: float, default=0.0001<br><br>Strength of the L2 regularization term. The L2 regularization term<br>is divided by the sample size when added to the loss.<br><br>For an example usage and visualization of varying regularization, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('batch_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=batch_size,-int%2C%20default%3D%27auto%27\">\n",
+       "            batch_size\n",
+       "            <span class=\"param-doc-description\">batch_size: int, default='auto'<br><br>Size of minibatches for stochastic optimizers.<br>If the solver is 'lbfgs', the classifier will not use minibatch.<br>When set to \"auto\", `batch_size=min(200, n_samples)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate,-%7B%27constant%27%2C%20%27invscaling%27%2C%20%27adaptive%27%7D%2C%20default%3D%27constant%27\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: {'constant', 'invscaling', 'adaptive'}, default='constant'<br><br>Learning rate schedule for weight updates.<br><br>- 'constant' is a constant learning rate given by<br>  'learning_rate_init'.<br><br>- 'invscaling' gradually decreases the learning rate at each<br>  time step 't' using an inverse scaling exponent of 'power_t'.<br>  effective_learning_rate = learning_rate_init / pow(t, power_t)<br><br>- 'adaptive' keeps the learning rate constant to<br>  'learning_rate_init' as long as training loss keeps decreasing.<br>  Each time two consecutive epochs fail to decrease training loss by at<br>  least tol, or fail to increase validation score by at least tol if<br>  'early_stopping' is on, the current learning rate is divided by 5.<br><br>Only used when ``solver='sgd'``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate_init,-float%2C%20default%3D0.001\">\n",
+       "            learning_rate_init\n",
+       "            <span class=\"param-doc-description\">learning_rate_init: float, default=0.001<br><br>The initial learning rate used. It controls the step-size<br>in updating the weights. Only used when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('power_t',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=power_t,-float%2C%20default%3D0.5\">\n",
+       "            power_t\n",
+       "            <span class=\"param-doc-description\">power_t: float, default=0.5<br><br>The exponent for inverse scaling learning rate.<br>It is used in updating effective learning rate when the learning_rate<br>is set to 'invscaling'. Only used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_iter,-int%2C%20default%3D200\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=200<br><br>Maximum number of iterations. The solver iterates until convergence<br>(determined by 'tol') or this number of iterations. For stochastic<br>solvers ('sgd', 'adam'), note that this determines the number of epochs<br>(how many times each data point will be used), not the number of<br>gradient steps.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">600</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shuffle',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=shuffle,-bool%2C%20default%3DTrue\">\n",
+       "            shuffle\n",
+       "            <span class=\"param-doc-description\">shuffle: bool, default=True<br><br>Whether to shuffle samples in each iteration. Only used when<br>solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Determines random number generation for weights and bias<br>initialization, train-test split if early stopping is used, and batch<br>sampling when solver='sgd' or 'adam'.<br>Pass an int for reproducible results across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the optimization. When the loss or score is not improving<br>by at least ``tol`` for ``n_iter_no_change`` consecutive iterations,<br>unless ``learning_rate`` is set to 'adaptive', convergence is<br>considered to be reached and training stops.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>Whether to print progress messages to stdout.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous<br>call to fit as initialization, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=momentum,-float%2C%20default%3D0.9\">\n",
+       "            momentum\n",
+       "            <span class=\"param-doc-description\">momentum: float, default=0.9<br><br>Momentum for gradient descent update. Should be between 0 and 1. Only<br>used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=nesterovs_momentum,-bool%2C%20default%3DTrue\">\n",
+       "            nesterovs_momentum\n",
+       "            <span class=\"param-doc-description\">nesterovs_momentum: bool, default=True<br><br>Whether to use Nesterov's momentum. Only used when solver='sgd' and<br>momentum > 0.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=early_stopping,-bool%2C%20default%3DFalse\">\n",
+       "            early_stopping\n",
+       "            <span class=\"param-doc-description\">early_stopping: bool, default=False<br><br>Whether to use early stopping to terminate training when validation<br>score is not improving. If set to True, it will automatically set<br>aside ``validation_fraction`` of training data as validation and<br>terminate training when validation score is not improving by at least<br>``tol`` for ``n_iter_no_change`` consecutive epochs. The split is<br>stratified, except in a multilabel setting.<br>If early stopping is False, then the training stops when the training<br>loss does not improve by more than ``tol`` for ``n_iter_no_change``<br>consecutive passes over the training set.<br>Only effective when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Must be between 0 and 1.<br>Only used if early_stopping is True.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_1',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_1,-float%2C%20default%3D0.9\">\n",
+       "            beta_1\n",
+       "            <span class=\"param-doc-description\">beta_1: float, default=0.9<br><br>Exponential decay rate for estimates of first moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_2',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_2,-float%2C%20default%3D0.999\">\n",
+       "            beta_2\n",
+       "            <span class=\"param-doc-description\">beta_2: float, default=0.999<br><br>Exponential decay rate for estimates of second moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.999</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('epsilon',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=epsilon,-float%2C%20default%3D1e-8\">\n",
+       "            epsilon\n",
+       "            <span class=\"param-doc-description\">epsilon: float, default=1e-8<br><br>Value for numerical stability in adam. Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1e-08</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3D10\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=10<br><br>Maximum number of epochs to not meet ``tol`` improvement.<br>Only effective when solver='sgd' or 'adam'.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_fun',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_fun,-int%2C%20default%3D15000\">\n",
+       "            max_fun\n",
+       "            <span class=\"param-doc-description\">max_fun: int, default=15000<br><br>Only used when solver='lbfgs'. Maximum number of loss function calls.<br>The solver iterates until convergence (determined by 'tol'), number<br>of iterations reaches max_iter, or this number of loss function calls.<br>Note that number of loss function calls will be greater than or equal<br>to the number of iterations for the `MLPClassifier`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">15000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-10');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
        "                                            max_iter=600))"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5001,22 +10681,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "52.800000000000004"
+       "np.float64(50.153846153846146)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -5029,7 +10709,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -5043,7 +10723,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/ml/winesc_multi_stacking.ipynb b/_doc/practice/ml/winesc_multi_stacking.ipynb
index 7b94f5a..d004f24 100644
--- a/_doc/practice/ml/winesc_multi_stacking.ipynb
+++ b/_doc/practice/ml/winesc_multi_stacking.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -30,7 +30,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 7,
      "metadata": {
       "image/png": {
        "width": 400
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,15 +82,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {\n",
+       "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -102,31 +103,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-2.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 {\n",
+       "#sk-container-id-2 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 pre {\n",
+       "#sk-container-id-2 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -138,7 +140,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -146,7 +148,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-container {\n",
+       "#sk-container-id-2 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -156,7 +158,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -172,14 +174,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -187,28 +189,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
+       "#sk-container-id-2 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -226,24 +228,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -251,27 +262,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -279,79 +288,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -361,18 +369,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -388,23 +396,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -414,7 +424,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -424,7 +435,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -459,12 +471,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -474,31 +486,572 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1500)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1500)</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegre...max_iter=1500)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression(max_iter=1500)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1500</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -513,16 +1066,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "54.70769230769231"
+       "np.float64(54.09230769230769)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -530,7 +1083,7 @@
    "source": [
     "import numpy\n",
     "\n",
-    "numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(clr.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -542,7 +1095,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -580,19 +1133,19 @@
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>18</td>\n",
-       "      <td>0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -600,9 +1153,9 @@
        "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>339</td>\n",
-       "      <td>198</td>\n",
-       "      <td>0</td>\n",
+       "      <td>325</td>\n",
+       "      <td>210</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -610,9 +1163,9 @@
        "      <th>6</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>184</td>\n",
-       "      <td>527</td>\n",
-       "      <td>8</td>\n",
+       "      <td>166</td>\n",
+       "      <td>531</td>\n",
+       "      <td>16</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -620,8 +1173,8 @@
        "      <th>7</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>23</td>\n",
-       "      <td>210</td>\n",
+       "      <td>15</td>\n",
+       "      <td>230</td>\n",
        "      <td>23</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -630,9 +1183,9 @@
        "      <th>8</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>33</td>\n",
-       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>34</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -641,7 +1194,7 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -652,16 +1205,16 @@
       ],
       "text/plain": [
        "   3  4    5    6   7  8  9\n",
-       "3  0  0    3    5   0  1  0\n",
-       "4  0  0   40   18   0  0  0\n",
-       "5  0  0  339  198   0  0  0\n",
-       "6  0  0  184  527   8  0  0\n",
-       "7  0  0   23  210  23  0  0\n",
-       "8  0  0    3   33   9  0  0\n",
-       "9  0  0    0    1   0  0  0"
+       "3  0  0    4    2   0  0  0\n",
+       "4  0  0   28   26   1  0  0\n",
+       "5  0  0  325  210   2  0  0\n",
+       "6  0  0  166  531  16  0  0\n",
+       "7  0  0   15  230  23  0  0\n",
+       "8  0  0    6   34   4  0  0\n",
+       "9  0  0    0    2   0  0  0"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -690,16 +1243,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "68.9846153846154"
+       "np.float64(67.6923076923077)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,7 +1262,7 @@
     "\n",
     "rfc = RandomForestClassifier()\n",
     "rfc.fit(X_train, y_train)\n",
-    "numpy.mean(rfc.predict(X_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(rfc.predict(X_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -721,7 +1274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {
     "scrolled": false
    },
@@ -729,9 +1282,10 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -743,31 +1297,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-3.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-3 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-3 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -779,7 +1334,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -787,7 +1342,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-3 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -797,7 +1352,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -813,14 +1368,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -828,28 +1383,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
+       "#sk-container-id-3 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -867,24 +1422,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -892,27 +1456,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -920,79 +1482,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1002,18 +1563,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1029,23 +1590,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1055,7 +1618,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1065,7 +1629,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1100,12 +1665,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1115,31 +1680,594 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-3');</script></body>"
       ],
       "text/plain": [
        "RandomForestClassifier()"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1160,23 +2288,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "66.83076923076922"
+       "np.float64(65.72307692307693)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "rf_test = clr.decision_function(X_test)\n",
-    "numpy.mean(rfc_y.predict(rf_test).ravel() == y_test.ravel()) * 100"
+    "numpy.mean(rfc_y.predict(rf_test).ravel() == y_test.values.ravel()) * 100"
    ]
   },
   {
@@ -1188,16 +2316,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7269546464802059, 0.6626763197852255, 0.6701197104064432)"
+       "(0.610804863925883, 0.6194730746960047, 0.585865662999421)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1218,12 +2346,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -1253,7 +2381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1277,16 +2405,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.5556820682740744, 0.7318154142418192, 0.7349919877544187)"
+       "(0.5566906703022872, 0.7535939393939393, 0.7387052091528319)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1321,12 +2449,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Sy/ToJzXf/Z49e6JSqfjjjz9YunQpRkZGdO/eXWe/NLK8d0cCW7du1fgWDxo0SCxbtkwsWLBAdOjQQZiYmIhevXpp6ia44LVu3VosWLBAs/+hC97ixYs1Hg6LFi0Sffr0EQULFhQODg5Jend86P4mRGLXrQULFmhct9q3b69Vd/LkyRqXs2nTpolFixaJn376SRQpUkRMnz79o+//YzZs2LAhkRtefHy8aNGihQBEzZo1xZw5c8TSpUtFp06dhIGBgShZsqQICgrSakepVIqOHTsKQFSoUEFMmjRJrFixQkyaNElUrlxZAOLEiRMftTM1/PLLL5rr8fvvv4t58+aJTp06iREjRmjqJFxfb29vMX/+fPH9998n64KXlNdDgq/yhwCif//+ifr51H28efOmMDExEaVLlxbz588XU6ZMEW5ubqJs2bICEPfv3/9k36lp40PvjgoVKoiGDRuKiRMniuXLl4sff/xRmJqaikaNGmnqJLjg5c2bV0ycOFFMnTpVFCpUKFkXvA8/UwnumQnugsmxe/duLde40aNHJ+sa17t3bwGIBg0aiFmzZon58+eLQYMGiTx58mh82dOzn5R+91esWCEAsWrVKk3ZmjVrBCAWLFjwUbtTyhcj0kIIcevWLdGzZ0/h6uoqTExMhLW1tahevbqYN2+e1kKVuLg4MX78eFGwYEFhbGwsXFxcklzMolQqxfDhwzVO/D4+PuLOnTvJuuB9TKSvX78uWrZsKaytrUWOHDnEgAEDklzMsmnTJlGjRg1haWkpLC0tRfHixUX//v1FQEDAR9/7x2xQKpXCzc1NuLm5abkIKpVKsXLlSlG9enVhY2MjzMzMRMmSJcX48eM/6oS/ceNG4e3tLezt7YWRkZFwdnYWbdq0EX5+fh+18XNYsWKFKF++vDA1NRU5cuQQtWrVSrTQZP78+aJ48eLC2NhY5MqVS/Tt2zfZxSwfklqRTsl93LZtmyhTpowwMzMTrq6uYurUqZovc0pEOjVtfCjSS5YsETVr1hQ5c+YUpqamws3NTQwbNkyEhYVptX/hwgXh4+MjrKyshIWFhfD09Ez0DzatIi2E+vNcokQJYWpqKtzd3ZNdZCKE2sfbw8NDmJubC2tra1G6dGnx008/JbmKWN/9pOS7//jxY2Fra6v1DzCBZs2aCUtLS3Hv3r1P2v4pFELoakwukXy5jBs3jvHjx/PixQscHBwy2hxJNuKLmJOWSCSSLxUp0hKJRJKJkSItkUgkmRg5Jy2RSCSZGDmSlkgkkkyMFOlMzLFjx5gwYUKa4mVIJJKsjRTpTMCDBw9QKBSsWrVKq9zDw4M9e/bQo0ePZM/18/NDoVDg5+enXyMlmZrM+jnIrHZlJaRIZ2LMzc3Zvn07/v7+muXHktTx7Nkzxo0bh7+/v977ioqKYty4cVKQJDpFinQmJ2fOnOzevZuIiAji4uIy2pwsx7Nnzxg/fny6ifT48eOlSEt0ihTpLEChQoUYOXJkimNKZzciIyMz2gSJRG9IkUa95FehUHDr1i2+++47bG1tcXR05JdffkEIwePHj2nSpAk2Njbkzp2bGTNmJGojODiY7t27kytXLszMzChbtiyrV69OVO/169d06dIFW1tb7Ozs6Ny5c7JRvm7evEnLli2xt7fHzMwMDw8PtmzZkqL3dPr0aerXr4+trS0WFhbUqlWL48ePa9V58+YNP/zwA66urpiamuLk5ES9evW0AqV/7HrdvHmT1q1bY2NjQ86cORk0aJAm0l4C8fHx/Prrr7i5uWFqaoqrqys///yzJvZuAgqFIsnY0q6urnTp0kWzv2rVKhQKBYcPH6Zfv344OTlpZZ15Hz8/PypVqgRA165dUSgUieb+N2zYgIeHB+bm5jg4OPDdd9/x9OlTrXZq165N7dq1E7XfpUsXTYjSBw8eaOIRjx8/XtPX++/pw/tZsWJFtm3bptVmXFwc48ePp0iRIpiZmZEzZ05q1KjB/v37k3yPn0KfnwOAp0+f0r17d/LkyYOpqSkFCxakb9++xMbGJnvO0aNHadWqFfnz58fU1BQXFxcGDx6sFcYWICgoiK5du5IvXz5MTU1xdnamSZMmWtH7zp07h4+PDw4ODpibm1OwYEG6deum1Y5KpWL27NmULFkSMzMzcuXKRe/evRMlLUhJWxnBFxFPWle0adOGEiVKMGXKFHbu3Mlvv/2Gvb09S5YsoU6dOkydOpW1a9cydOhQKlWqRM2aNQF4+/YttWvX5s6dOwwYMICCBQuyYcMGunTpwuvXrxk0aBCgDj3ZpEkTjh07Rp8+fShRogSbN2+mc+fOiWy5du0a1atXx9nZmeHDh2NlZcX69etp3rw569evp2XLlsm+j0OHDtGgQQM8PDwYO3YsBgYGrFy5kjp16nD06FEqV64MQJ8+fdi4cSMDBgzA3d2dV69ecezYMW7cuEGFChU+eb1at26Nq6srkydP5tSpU8ydO5fQ0FBNLGqAHj16sHr1alq2bMmPP/7I6dOnmTx5Mjdu3GDz5s2puj/v069fPxwdHRkzZkyyI+kSJUowYcIExowZQ69evfj6668BqFatGqAW/K5du1KpUiUmT57M8+fPmTNnDsePH+fixYvY2dml2B5HR0cWLVpE3759adasGc2bNwegTJkywLv7mTdvXkaMGIGlpSXr16+nadOmbNq0iWbNmgHqf4CTJ0+mR48eVK5cmfDwcM6dO8eFCxeoV69eqq6Rvj8Hz549o3Llyrx+/ZpevXpRvHhxnj59ysaNG4mKisLExCTJ8zZs2EBUVBR9+/YlZ86cnDlzhnnz5vHkyRM2bNigqdeiRQuuXbvG999/j6urK8HBwezfv59Hjx5p9r29vXF0dGTEiBHY2dnx4MED/v33X63+evfurbnXAwcO5P79+8yfP5+LFy9y/PhxjI2NU9xWhpDmEE1fAAkRzt4PZxofHy/y5csnFAqFJvO3EEKEhoYKc3NzrSh4CYld16xZoymLjY0VVatWFVZWVpqs01u2bBGAmDZtmlY/X3/9daLEp3Xr1hXu7u5aCUlVKpX46quvhJubm6bsw4hkKpVKFClSRPj4+GglD42KihIFCxYU9erV05TZ2tpqRXpL7fX6VFLW9E76mxRnz55NdG2FUN8fJycnUapUKa0odjt27BCAGDNmjKbsw0hzCXwYZe3FixfJvo+6deuK0qVLa0VaVKlUolq1aqJIkSKasrJlyyYbGe9jZMTnICGsbVKRFxP6TCpiXlJJdidPniwUCoV4+PChEEL9PQM+GqJ38+bNyUZ+TCClyWdT0lZGIac73uN9VzdDQ0MqVqyIEILu3btryu3s7ChWrJhWktJdu3aRO3du2rVrpykzNjZm4MCBREREcPjwYU09IyMj+vbtq9VPQjLTBEJCQjh06BCdO3dGoVAQHR1NdHQ0MTExNG3alLt37/LkyZMk34O/vz+3b9+mffv2vHr1SpNsMzIykrp163LkyBFNEH07OztOnz7Ns2fPPut6fSopa0Yl/U0J586dIzg4mH79+mFmZqYp/+abbyhevHiabPuQhPvZunVr3rx5o7knr169wsfHh9u3b2umWOzs7Lh27VqSmUZSg74/ByqVii1bttCoUSNNurb3+Vh6uvezokRGRvLy5UuqVauGEIKLFy9q6piYmODn55dsLsWEXzo7duxI9qF6SpPPpqStjEKK9Ht8mHDT1tYWMzOzRKEpbW1ttT44Dx8+pEiRIomyWiek50rI6/fw4UOcnZ0T5Sv8MCnonTt3EEIwfPhwzM3NtV4JKbBevHiR5HtI+HJ37twZR0dHrdfy5cuJiYnRZLCYNm0aV69excXFhcqVKzNu3LgkM2Qnx6eSsmZU0t+UkFwSW1Dnc0yLbR+ScD9/+eWXRPdk7NixwLskqBMmTOD169cULVqU0qVLM2zYME2+ydSg78/BixcvCA8Pp1SpUqm27dGjR3Tp0gV7e3usrKxwdHSkVq1aABqbTE1NmTp1Krt37yZXrlzUrFmTadOmERQUpGmnVq1atGjRgvHjx+Pg4ECTJk1YuXKl1vOO95PPfngdIiIiNNc9JW1lFHJO+j2SGpnpOwlnUiSMcEaNGqXJMfghyeVBTDh3+vTpyeYGTPgn0bp1a77++ms2b97Mvn37mD59OlOnTuXff//VpINKDcmNnjI66W9aUSgUSd7vlKZRS7gnCbnvkiLhH1nNmjW5e/cuW7duZd++fSxfvpxZs2axePHijy5qSq7PjPgcfAylUkm9evUICQlh+PDhFC9eHEtLS54+fUqXLl20UqX98MMPNGrUiC1btrB3715++eUXJk+ezKFDhyhfvjwKhYKNGzdy6tQptm/fzt69e+nWrRszZszg1KlTWFlZpTj5bErayiikSOuAAgUKcPnyZVQqldZo+ubNm5rjCX8PHjxIRESE1k3/MGFmQu6/+Ph4TY7BlOLm5gaos6inJBGrs7Mz/fr1o1+/fgQHB1OhQgUmTpyYoi/np5KyZlTS3/dJ7h/E+0ls69Spo3UsICAgkW1JjSw/HG0n11fC/TQ2Nk7RPbG3t6dr16507dqViIgIatasybhx41Il0vr+HDg6OmJjY8PVq1dTbBOoM2nfunWL1atX06lTJ015ct4rbm5u/Pjjj/z444/cvn2bcuXKMWPGDNasWaOp89VXX/HVV18xceJE/vrrLzp06MA///xDjx49Up3k9mNtZRRyukMHNGzYkKCgINatW6cpi4+PZ968eVhZWWl+yjVs2JD4+HgWLVqkqadUKhOtJnRycqJ27dosXbo0kTsYoPWT70M8PDxwc3Pj999/JyIiItHxhGkSpVKplbgzod88efKk+Cfep5KyZlTS3/extLQESPQPoGLFijg5ObF48WKt97t7925u3LiRyLabN29qTTFdunQpkSubhYVFkn0l3M8lS5Yk+U/n/XY/TCRrZWVF4cKFU/2zW9+fAwMDA5o2bcr27ds5d+5couPJ/dJMKsmuEEKTKDqBqKioRO6cbm5uWFtba+wKDQ1N1E/Cr4aEOilNPpuStjIKOZLWAb169WLJkiV06dKF8+fP4+rqysaNGzl+/DizZ8/G2toagEaNGlG9enVGjBjBgwcPcHd3599//030JQG1ANaoUYMyZcrQs2dP3NzcCAwM5Pjx4wQGBiY7T2lgYMDy5ctp0KABJUuWpGvXruTNm5enT5/i6+uLjY0N27dv582bN+TLl4+WLVtStmxZrKysOHDgAGfPnk3SDzwp7t+/T+PGjalfvz4nT55kzZo1tG/fnrJlywJQtmxZOnfuzNKlS3n9+jW1atXizJkzrF69mqZNm+Lp6alpq0ePHvTp04cWLVpQr149Ll26xN69e9OcqsrNzQ07OzsWL16MtbU1lpaWVKlShYIFCzJ16lS6du1KrVq1aNeuncYFz9XVlcGDB2va6NatGzNnzsTHx4fu3bsTHBzM4sWLKVmyJOHh4Zp65ubmuLu7s27dOooWLYq9vT2lSpWiVKlSmvtZunRpevbsSaFChXj+/DknT57kyZMnXLp0CQB3d3dq166Nh4cH9vb2nDt3TuMelxrS43MwadIk9u3bR61atejVqxclSpQgMDCQDRs2cOzYsSRdGIsXL46bmxtDhw7l6dOn2NjYsGnTpkQPB2/dukXdunVp3bo17u7uGBkZsXnzZp4/f07btm0BWL16NQsXLqRZs2a4ubnx5s0bli1bho2NjWaAUKtWLXr37s3kyZPx9/fH29sbY2Njbt++zYYNG5gzZw4tW7ZMUVsZRgZ5lWQqElzKXrx4oVXeuXNnYWlpmah+UklNnz9/Lrp27SocHBw0mZ4/dPsSQohXr16Jjh07ChsbG2Frays6duwoLl68mKSb2N27d0WnTp1E7ty5hbGxscibN6/49ttvxcaNGzV1kksKevHiRdG8eXNNQtICBQqI1q1bi4MHDwohhIiJiRHDhg0TZcuWFdbW1sLS0lKULVtWLFy4MMXXKyVJWdMz6W9ybN26Vbi7uwsjI6NE13ndunWaRLf29vaiQ4cO4smTJ4naWLNmjShUqJAwMTER5cqVE3v37k0y0emJEyeEh4eHMDExSeSOl5L7+dtvv4nKlSsLOzs7YW5uLooXLy4mTpyolfk8KTLicyCEEA8fPhSdOnUSjo6OwtTUVBQqVEj0799fxMTEJGvX9evXhZeXl7CyshIODg6iZ8+e4tKlS1r35uXLl6J///6iePHiwtLSUtja2ooqVaqI9evXa9q5cOGCaNeuncifP78wNTUVTk5O4ttvvxXnzp1LZOenks+mpq30Rgb9l6QamZRVIkk/5Jy0RCKRZGKkSEskEkkmRoq0RCKRZGLknLREIpFkYuRIWiKRSDIxUqQlEokkE5MlFrOoVCqePXuGtbV1muJASCQSSWZBCMGbN2/IkydPouBs75MlRPrZs2e4uLhktBkSiUSicx4/fpxsdiHIIiKdsKz68ePH2NjYZLA1EolEknbCw8NxcXHR6FtyZAmRTpjisLGxkSItkUi+KD41hSsfHEokEkkmRoq0RCKRZGKkSEskEkkmRoq0RCKRZGKkSEskEkkmRoq0RCKRZGKkSEskEkkmJtUifeTIERo1akSePHlQKBRs2bLlk+f4+flRoUIFTE1NKVy4MKtWrfoMUyUSiST7kWqRjoyMpGzZsokyRSfH/fv3+eabb/D09MTf358ffviBHj16sHfv3lQbK5FIJNmNVK84bNCgAQ0aNEhx/cWLF1OwYEFN5uESJUpw7NgxZs2ahY+PT2q7l0gkkkyFSqhQCRVGBvpZwK33ZeEnT57Ey8tLq8zHx4cffvgh2XNiYmKIiYnR7IeHh+vLPIlEIvk4rx/BmaXw8ja8fa116EBMBLONI6huUISR3TfqpXu9i3RQUBC5cuXSKsuVKxfh4eG8ffsWc3PzROdMnjyZ8ePH69s0iUQi+TgqFWzoCk/PaYoEEGBizB5LC/6wswUgLvYuI4TQSyjlTBlgaeTIkQwZMkSznxAtSiKRSNIFlRIenYJzK+DpOaIUCk6Xb8FxIzjy+haBca81VW0iHelfqK/eYt3rXaRz587N8+fPtcqeP3+OjY1NkqNoAFNTU0xNTfVtmkQikWjz/BrBx2dx7OlR7quieWRsxKO8uXlgakZ86BlNNaEyIj6iGMrIogyr1YnGVQvrzSS9i3TVqlXZtWuXVtn+/fupWrWqvruWSCSSFBEaHcrOoxPYf38XF02METYmgMm7CkJFPqt8OKiKciLABWWkGygN8f/FCztrC73almqRjoiI4M6dO5r9+/fv4+/vj729Pfnz52fkyJE8ffqU//3vfwD06dOH+fPn89NPP9GtWzcOHTrE+vXr2blzp+7ehUQikXwG115d4+8bf7P77nZiUYGpWpjLWLtSJm818tu6kt86P662rry484Km/zzVnHvmp6/0LtDwGSJ97tw5PD09NfsJc8edO3dm1apVBAYG8ujRI83xggULsnPnTgYPHsycOXPIly8fy5cvl+53Eokkw3gW8Yyfj/3M+efnNWUlYmJpkrsqdT0nk9vaWau+v78/9Zq1w7LN7wCM9nHDySFnutiqEEKIdOkpDYSHh2Nra0tYWJjMzCKRSD6byLhIDj06xO/nfickOgQjhRE+UdG0C31JmYr9UdQbl+icixcvUu+bplh1WqgpuzLOG2sz4zTZklJdy5TeHRKJRKIr3sS+we+xH/sf7uf40+PEqmIBKGHqwKynT8n75jk4lgDPkUmef/r0aQyqdtLsd6nmmmaBTg1SpCUSyReHUqVk/8P97Li3gxPPThCnitMcy29sQ4PXr+j24CIWQoBdfmj5Bxgl7VF2w64KFkWeAWBnYcy4xiXT5T0kIEVaIpF8McSr4tl1fxfLLi/jQfgDTXlBU3u8FdbUe3qTouGPUADYFYCaQ6FsOzDUHhlfunQJJ+d8NF1+kefh71Y/r+leJX3eyHtIkZZIJFmeOFUcO+7uYNmVZTx+8xgAW2Mr2kRG0zD4EW5x75wZyOEKNYdBmTaJxBng7Nmz+DRqhk2XJVrlZ0bVxcnaTJ9vI0mkSEskkixLnDKOrXe3svzKcp5GqN3jcpjmoLOlG239d2CpjAUjc8hfGVwqQ/6voLBXkuIMcObMGby9vYnLU05TltPShJMj62JilDHh96VISySSLEesMpbNtzez/OpygiKDALA3taObVTFaPbyCxc1/1RVLNIZGc8DC/pNtnj59Gm9vb8LDwynQZxgAuWxMOf2z1yfO1C9SpCUSSZYhRhnDplub+OPqHwRHBQPgYGRFN5UFLW9dxFx1WV3R2BIaTIHyHSEFMTVOnTqFj48P4eHhVGrUkeD/yn1K5tbTO0k5UqQlEkmmJyouik23N7Hy6kpevH0BgJOBKd1DX9M89DFmCcs98lWGcu2hZDMwt0tR26dOncLb25s3b95Qq1YtHri30Rwb2yh9PTmSQoq0RCLJtIREh/DXjb/4++bfhMeq48rnFgb0ePWSZm8i1NE1bPJB2bZqLw2H1Ac6ypEjB5aWlnh4eLBjxw4qTz1KZKySdpVdMDTQT2S71CBFWiKRZDoev3nM/679jy13thCtjAYgf7yKrq9DafImEmMDIyjZHDw6g2tNMPj8h3rFihVjz6HDzDzxipK/+mnKv/uqQBrfhW6QIi2RSDINN0NusuLKCvY+3ItKqAAoGRNLt9dh1I16i6F1HvD8ASp0ButcH2/sIxw9epSoqCjWPLEjODyGgOdvEtUp5GD12e3rEinSEokkw7nw/AJLLi/hxLMTmrLqUW/pFhZOpegYFAVrQqWeUKwhGKZNtjrM28uRG88wtHECXiY6/mf3ylR3c8AgE0x1gBRpiUSSgQRFBjHjzFT2PDoAgIEQ+ERG0S0snOIKcyjbBSp1B8diae5LpRKMW3OQ40/j/xPod6zuVhkbMyPKudjpLcPK5yJFWiKRpDux8TH878RElt7fyltUGAhB8zcRdA8LJ59LdajRAUo0AhPLz2r/wPXnrDxxHzsLE+6/iOR6YOJk1n9188DC3IySeWwwNsyYhSopQYq0RCJJP14/5sjJ35kWeIiHhuqi8tHRjIw1o0TpvmovjRyf/8Dutx3XWX7s/ifrzWtTmmpFM94HOiVIkZZIJPrn2UUeH57MtNAL+FmagyE4KFUMsSrBtzV/QFGgWpo8NHZeDqT/XxcSlTevkBcHRSQzZs7kzb2L1K5clm0b/sbMLP1jcHwuUqQlEonOEULwLOIptwK2cuv6egIinnDY3JxYS3OMBHznVIXeNSdiZfX5HhoJ/XRZeZbDt15olS/sUIF67rkwNjQgPj6eK/+aEG5fgn83/pPlklxLkZZIJGkiKi6KO6/vEBAawK2QW9wKDeDWqxtE/OffDIClOhdg1ZxlGFHjVwrZFdJJ37/uuKEl0IPqFmFAncJac8xGRkb8+eefKJXKLCfQIEVaIpF8JgEhASzwX4DfYz8EibPwGQlBobh4ilm5ULRwA0rnr0UFpwpp9p4IDHvLYr+7rD75UKv88LDaFMipftC4b98+Nm/ezIIFCzAwMMDIyAgjo6wpd1nTaolEkmE8DH/IAv8F7Lm/RyPOOY2tKRb1hqKR4RSNjaWoMKZQ2Y4YV/serHXzgM4vIJh+ay8QFatMdGxr/+oagd67dy9NmjQhJiaGUqVK0b9/f530n1FIkZZIJCkiMCKQxZcXs/XOVpRCLZQ++b3o+yYatwt/qSuZ28NXg6FyTzDPoZN+n71+y8SdN9h5JVCrPKelCaO/LUH9ks6Ym6hdRfbs2UPTpk2JiYmhSZMm9OzZUyc2ZCRSpCUSyUd5+fYlyy4vY8OtDZpcgTXz1eR7l/oU3z8RXtxQV6zcC+qOBVPdLKd+HBLFriuBLD58l9CodzkK+9V240fvYomCH+3atYtmzZoRGxtL06ZNWbduHSYmJjqxJSORIi2RSJIkLCaMFVdX8PfNv3kb/xaASrkrMbDsAMrdOwHruoEyFiydoOlCKFJPp/2P23aNgzeDNfs2ZkbMalOOr4s4JhLonTt30rx5c2JjY2nevDn//PMPxsbpl9Fbn0iRlkgkGoQQXHpxiX9v/8ueB3s04lzaoTTfl/+eryzzo9jaD+75qU8o2gAazwMrR53bcuO/VYLV3HJSxMmKbjUKauad3+fVq1e0bduW2NhYWrRowd9///3FCDRIkZZIJEBodCjb727n39v/cjfsrqa8aI6iDCg3gNo2hVGcWQrnV0FMuDpvYP1J4NE1RZlPUkvY2ziehald+DpXc/1ohpScOXOydu1a1q9fz8qVK78ogQYp0hJJtkUlVJwKPMW/t//l4KODxKviATAzNMPb1ZsWRVpQPh4UJxfAtc3w38NCnMtB82XgWFSn9gghCI9W2/DnyQea8q8K5UyyfmxsrGbOuXHjxjRu3Fin9mQWpEhLJNmMoMggttzZwubbm3kW+UxT7p7TnRZFWtCggA/WD47DrtHw8Pi7E12/hmrfQ+F6aVrCnRSHb72g84ozSR6zNU88Mt6yZQvDhg1j3759FCxYUKe2ZDakSEsk2YB4VTyHHx9m0+1NHH92XBNQ39rYmm8KfUPzIs0pYZ0f/P+CpZ4Q8t+Uh4ERlGoJVfuBc1md2yWE4MGrqCQFWqGAZR0rJirfvHkzrVu3Jj4+nrlz5zJr1iyd25WZkCItkXzhnAs6x8TTE7nz+o6mrGKuijQv0px6BephFhUCZ5fDuRXwNlRdwcwWKnZTu9XZ5NGbbV9NPsjz8BjN/jCfYvSqqV4ybqBQJPLi+Pfff2nTpg3x8fG0b9+e6dOn6822zIIUaYnkC+Xl25fMODeDHfd2AGBrakuLIi1oVrgZrtb54e4h2NgdAna/m2/OURC+6qfOuK0jf+fk6Pm/c1oCbW9pQo+vCyYb23njxo20bdsWpVJJhw4dWLVqVZZd6p0avvx3KJFkM+JV8awLWMf8i/OJiItAgYIWRVswqPwg7OJj4eKfcH41vH4v9kX+auopjWINwcBQ7zZuv/SM/defa/avT/DBwiR5OdqwYQPt2rVDqVTSsWNHVq5ciaGh/u3MDEiRlki+IC69uMRvp37jZshNQP0wcHTlnykdEQrbBsHNHfCfFwdmtlC2ndqNzql4utr5y9armu1tA6p/VKCVSiVTpkxBqVTSqVMnVqxYkW0EGqRISyRfBKHRocy+MJt/b/8LgLWJNYNK9qBleDiG/3R99yAQIF8ltTCXbAYmFhlib2y8+sHlL9+6Uzqv7UfrGhoasnfvXubPn88vv/ySrQQapEhLJFkalVCx6fYm5lyYQ1hMGABNHCszODKenFuHq5dtA5hYQ5nWULEr5C6dgRZDvFKliWTn7Z4r2dCl9+7do1Ah9UNEBwcHxo0bl14mZiqkSEskWZRrr64x8dRErry8AkBRA0tGvXhBhfsb31VyLqv20ijVUu8PAj+FEIJVJx6w9vQjTZmZcdKj4rVr19K5c2cWLlxIr1690svETIkUaYkkixEWE8bc87PZcHsTAoGlSjAg9DVtwx+pv9CWjlCiMZT/DvJWyGhzNVx5Gsb47de1yhytE2dKWbNmDZ07d0alUnH+/Pn0Mi/TIkVaIskiqOJj+ff0dObc3cRroQ7d2SAikmEhoTia2kPFVup55gLV08VDIzWERsbSeL569WIOC2M6flWApuXzJqr3559/0rlzZ4QQ9OrVi0WLFqW3qZkOKdISSWbn9WOuHJ3MpOd+XP1veqBwbCw/RyipVLgxNPhPmA0z19f57osIjt95iUKh4Jct77w5ahRxZIh3sUT1V69eTdeuXRFC0Lt3bxYuXIiBjpefZ0Uy112VSCTveHqe0OOzmBN8gn+tLBDGhlipBP0si9C2Wn+MC9XOdMIMEKdUUX/2Ee6+iEx0rLCTFXPblktUvmrVKrp164YQgr59+zJ//nwp0P+R+e6wRJKdUSkhYBfKk/PZGHqVuTlsCbdWx1Bu5FCBIbWm4GDlnMFGJk9MvJL5h+5oCXSNwg5YmxnhbGvO8AbFkvTmuH//PkII+vXrx/z589OcrPZLQoq0RJIZiIlQBzc6tRD/qKdMymnPDQd7AIpZ5+fn6r9SIVfmeQj4IUIIrgeG883cY1rll8Z6JxnF7kPGjRtHpUqV+Oabb6RAf4AUaYkkIwl/BqeXwPmV+Iu3LLaz5bidOsC9tbElA8oPpHWx1hgZZN6valBYNF9NPpiofEH7Ch8V6O3bt1O3bl0sLCxQKBR8++23+jQzy5J577xE8qUiBDy7oBbnq5u4YGzIohw2nDJXr7wzVBjSpHATBpYfSE7zpAPeZzRvouP4aeNldl8NSnSsUdk8zG5TLlEEu/dZunQpvXv3xtPTk927d2NqmtgVT6JGirREkl68CIArG+HqJgi5y1kzUxY72XPG3AwAI4URjQs3pkepHrjYuGSwsckjhKD0uH2Jyr8qZM/fPb/65HTF4sWL6du3LwDlypX7IjJ66xMp0hKJPnn9SC3KVzbB8ysI4LSZKYudc3PeTC1ORgZGNC3clB6le5DXKrHvcGYj5r+4Gwms712VCvntMEomxOj7LFy4kP79+wMwZMgQfv/9dzkH/Qk+y8dlwYIFuLq6YmZmRpUqVThzJum0NwnMnj2bYsWKYW5ujouLC4MHDyY6OvqzDJZIMj0RL+DMMvjDB2aXhgPjEM+vcMLCks4Fi9HTORfnzUwwNjCmTbE27Gq2i7FVx2YJgQYIj47TbF8b70PlgvYpEugFCxZoBHro0KFSoFNIqkfS69atY8iQISxevJgqVaowe/ZsfHx8CAgIwMnJKVH9v/76ixEjRrBixQqqVavGrVu36NKlCwqFgpkzZ+rkTUgkmYKQ+7B3FNza8y6IPgr8XSsy3ULB5bdBwFtMDExoWbQl3Up1I5dlroy0ONX4BQTTZeVZzb6RYcpEdsmSJQwYMACAn376iSlTpkiBTiGpFumZM2fSs2dPunbtCqjnl3bu3MmKFSsYMWJEovonTpygevXqtG/fHgBXV1fatWvH6dOn02i6RJJJUMbD6UVwaCLEv1WX5alATMmmLFCEser2RsRbgZmhGa2KtaJrya44WjhmrM2fQftlpzhx95Vmv3ReW0yNUrb8vFKlStjZ2dG7d28mT54sBToVpEqkY2NjOX/+PCNHjtSUGRgY4OXlxcmTJ5M8p1q1aqxZs4YzZ85QuXJl7t27x65du+jYsWOy/cTExBAT8y6tTnh4eGrMlEjSj6ArsO17eHZRve/6NTT8nWsGSkYdG8XdMHUc58ZujRnsMRgHc4cMNPbzeBwSxdfTfLXKetUsxMgGKU8UUKFCBa5cuULevHmlQKeSVIn0y5cvUSqV5Mql/RMtV65c3Lx5M8lz2rdvz8uXL6lRowZCCOLj4+nTpw8///xzsv1MnjyZ8ePHp8Y0iSR9iYuGI9Pg+Bx1phNTW/D+lbiy7Vh6dRnLLi9DKZTkNMvJ2Kpj8czvmdEWp5joOCWjt1wlIOgNV56GJTruP6Yedhaf9siYP38+Hh4eVK1aFYB8+fLp3NbsgN4Xx/v5+TFp0iQWLlzIhQsX+Pfff9m5cye//vprsueMHDmSsLAwzevx48f6NlMiSTkPjsPi6nB0hlqgSzSGAWe4VagqHXZ/x+JLi1EKJT6uPmxusjlLCfTz8Gi8Zh5m4/kniQS6nIsd9yc3TJFAz5gxg++//x4fHx/5/U0jqRpJOzg4YGhoyPPnz7XKnz9/Tu7cuZM855dffqFjx4706NEDgNKlSxMZGUmvXr0YNWpUkkFUTE1NpXO7JPMRHQb7x8L5lep9q9zwze/EF2vAqmurWOC/gHhVPLamtoyuMpr6BetnrL2pICo2njZLTiUS5mktyuDmZEl5lxwYfGRxyvv8/vvvDBs2DIAffvhBjqDTSKpE2sTEBA8PDw4ePEjTpk0BUKlUHDx4UPPk9kOioqISCXFCjjIhxGeYLJGkMxHBcHa5+hX134OzCp0RXuM5/OoSC3e250bIDQBq56vN2Gpjs9Tc853gN3jNPKJVZmNmxM6BX+Nin7ociNOmTWP48OEAjB07NtumvNIlqfbuGDJkCJ07d6ZixYpUrlyZ2bNnExkZqfH26NSpE3nz5mXy5MkANGrUiJkzZ1K+fHmqVKnCnTt3+OWXX2jUqFG2SygpyWIE34CTC+DyelD+9yDb3g3Vt7PwNYxn8cFemqzcVsZWjKg8gsZujbPUg7F1Zx8xfNMVzb6pkQEnRtQhp1Xqf8lOmTJF41Qwbtw4xo4dqzM7szOpFuk2bdrw4sULxowZQ1BQEOXKlWPPnj2ah4mPHj3SGjmPHj0ahULB6NGjefr0KY6OjjRq1IiJEyfq7l1IJLpCCLjnqxbnOwfeleetiKpqP/ZbWLDk6hxuh94GwMLIgnbF29GpZCfszewzyOjUER2nJCImnhn7Avj7zLv54j613BiRCo+N91m/fr1GoMePH8+YMWN0YqsEFCILzDmEh4dja2tLWFgYNjY2GW2O5EskPkYdV+PkAgi+pi5TGEDxb1F+1Y99ylCWXF6qcamzMraifYn2dCzRETszu4yzO5WsOfWQ0e9lSdGUd69CjSKfP0UTHR1Ns2bNqF69OqNHj06LidmGlOqajN0hyd5EhcC5P9TLuCP+eyBubAkVOhJfqSe7w26w9MIkHoQ/AMDa2Jrv3L+jQ4kO2JraZpzdn8EvW67y56mHicqPj6hDXjvzz2pTCIFCocDMzIzt27djZCQlRdfIKyrJnry8A6cWqgPtJ6wStM4DVXqDRxfOhd1h4vFh3Hl9BwAbExs6uXeifYn2WJtYZ6Dhn8fmi0+0BHpjn6pUdE3b9MyECRMIDw9n+vTpKBQKKdB6Ql5VSfYiLhoOTlALNP/N9DmXharfQ8mmhMZFMPP8DLbc2QKAraktXUp2oW2xtliZWGWY2Wll8LpLmm2/obVxdbBMU3vjx4/XeG588803eHpmHV/wrIYUaUn2Iegq/NsTgq+r94s2gGoDoEB1VAi23NnCzPMzCYtR+wq3LNqSHyr8kOWmNT7k/MNQzfYwn2JpEmghBOPGjWPChAkATJ06VQq0npEiLfnyUang1AL1CFoZC5aO0GQBFPUB4FboLX479RsXg9XxN4rmKMovX/1COadyGWh02omIiefq0zDaLj2lKWtfOf9ntyeEYOzYsZrVwtOnT2fo0KFptlPycaRIS75swp7A5j7w4Kh6v1hDaDQXrByJioti8eXF/HntT+JFPOZG5vQv158OJTpk6pyCKaHRvGOJVg9ObVGaHJaflwVFCMEvv/yicZ2dMWMGQ4YMSbOdkk+TtT+JEsnHuLIRdg5RL+c2toD6kxHlO3Et5Dr7Av5k572dBEcFA+CV34vhlYeT2zLp8AZZicHr/BMJ9NhG7rSp9PmjaH9/fyZNmgSowxUPHjw4TTZKUo4UacmXx5vnsG8UXNkAgCpvBS57DmV/6HX2/1ufwMhATdU8lnn4ucrP1HKplVHWppm3sUqWHrnHosN3iI7TTm11dbwPVqZp/5qXL1+eVatWERoayqBBg9LcniTlyMUski+H2Eg4MR+Oz0EVF4m/mTn7ilRnv+q1ZsQMYG5kTq18tahXoB4189XEzMgsA41OO64jdiZZnlYvDiEEb968kd85PSEXs0iyDyolXFwDvpN4GRXMX7bWbLF15YVCBRG3ALA0tqS2S23qFahH9TzVs7wwA6hUgnsvI7TKBtUtQoPSuSnqZJ3iqHVJIYRg+PDhbN++HV9f32SjXEr0jxRpSdZFCHV8jf1jeBwSwGpbGzY75CNWAaDC2tgaz/ye1CtQj6p5qmJq+OWEv1WpBE0XHufyk3dzz/cmNUyTMCcghGDYsGHMmDEDgAMHDvDdd9+luV3J5yFFWpI1CbwE+37hxtMTrLC1YV++PKj+iz5X2qE0XUt1pXa+2hgbGmewofph/bnHWgLduGwenQn00KFDNUmiFy5cKAU6g5EiLclahD1BHPyVM7e2sMLWmhN5nTWHquetTvdS3amYq2KWCheaUp69fsuaUw9Z6HdXqzzgt/opTgj7MYQQDBkyhNmzZwOwaNEi+vTpk+Z2JWlDirQkaxAdRvzRGRy6vIoV1mZcc3YCwAADfAr60K1UN4rbf16YzcxOVGw8/154mmT0upVdKulMoAcPHsycOXMAWLJkCb169Upzu5K0I0VakrlRxhF+ejGbL8zjLzNDnjmol2ibGhjTrEgLOpfsTD7rLzM9U0RMPMduv6DPmgta5RYmhnSrXpABdQpjZqybxBmhoaHs2LEDgKVLl9KzZ0+dtCtJO1KkJZkTIXh0cRVrz81ms3E8b63VD/1yGFnS2v072pdon2WC7H8urRef5HpguFbZzNZlaV5B9/+U7O3t8fX15dixY7Rr107n7Us+HynSkkyFEIKzV/7kz/NzOayIRpgqAAMKm+bku3L9+KZw4y/CfS454pQqtl96xvE7r7QEulHZPMxtW06nc+0qlYoLFy5QsWJFAFxcXKRAZ0KkSEsyBTHKGHZdWc2ay8u5Jd6CAYCCmmbOfPfVCL7K7/lFPgz8kM0XnvLTpstaZZfHeWNjplsvFZVKRf/+/Vm2bBl///03rVq10mn7Et0hRVqSoQRFBrH+yko23tpAqIgDwFyloolZPtrXnEDBvFUy2ML0IV6pYvaB28z3vaMp83bPRdfqBfUi0H379mXp0qUoFAqioqJ02r5Et0iRlqQ7QgguBF/gr2urOfjYD+V/wfed4+NpZ5yb5l6TsXX5KmONTCeEECw6fJdpewK0yhe0r8A3ZZyTOevzUalU9OnTh2XLlqFQKFi9ejUdO3bUeT8S3SFFWpJuRMdHs/v+btZe/5OA17c15ZXeRtPByIlanr9i5FYnAy1Mfy4+fp1IoDf0qUqlNKa2SgqVSkXv3r1Zvnw5BgYGrF69Wi5UyQJIkZboFSEE119dZ+vdrey6t4uwWPUqOTOVim8iomhvaE/R2hOhRCPIBnPOH9J84QnN9rQWZWhaPi8mRgY670elUtGrVy/++OMPDAwM+PPPP2nfvr3O+5HoHinSEr3wPPI5O+7tYNvdbdwLu6cpzxMXT7vwNzRT2GBbawKUbQeG2fNjGKd8F1a0YenctK7kore+FAoFJiYmGBgYsHbtWtq2bau3viS6RYYqleiMqLgoDj0+xLY72zgVeArx31yzqYA6kZE0iYjkK8wx/HooVOoBxl+uK92nEEJQcOQuzb7/mHrYWXxe1pSUolKpOH/+PJUqVdJrP5KUIUOVStKNWGUsSy4vYc31NUTFv/MUqKCwoEnwY7wjo7AysoCqA9WJX82ydmLXtBKvVLHqxAPNvoOVqV4EWqlUsmjRInr16qUZRUuBznpIkZakicsvLjPm+BjuhqmD/uSzykfjfJ58e3EzLkHXwcAYKvWEmsPAyimDrc1YhBB4/u7Hg1faLm/nRnvpvC+lUkmXLl1Ys2YNR44cYd26ddnCz/xLRIq05LN4G/+WBRcX8OeNP1EJFTnNcvJzlZ+pZ5EfxdqWEPYYLJ2gwwbIUy6jzc0UHLoZnEigxzZy13k/8fHxdO7cmb/++gtDQ0Nat24tBToLI0VakmrOPz/PmONjePTmEQCNCjXip0o/YRccACvrw9tQyFkYvtsEOVwz1thMQnh0HN1Xn9PsX/ilHvafmbn7Y8THx9OpUyf+/vtvjIyM+Oeff2jRooXO+5GkH1KkJSkmKi6K2Rdm8/fNvwFwsnBibNWx1MxXE27uhI3dID4a8laE9uvBMmcGW5x5WHX8gWZ7UrPSehPojh078s8//2BkZMT69etp1qyZzvuRpC9SpCWfRAjBoUeHmH5uOk8jngLQokgLfqz4I9Ym1nBuBez8EYQKitaHlivBxCKDrc48XHkSxsz96lyL5saGtK+SXy/99OrVSyPQGzZsoGnTpnrpR5K+SJGWJIsQgpOBJ5l7YS7XXl0DII9lHsZWG0u1PNXUOQYP/QZHpqtPqNAJvpmVbf2ek+LCo1CtBSvz25fXW1+dO3dm8+bNrFq1iiZNmuitH0n6Ir9NkiS59OIScy/M5UzQGQDMjczp6N6RbqW6YWlsqRbo3cPhzBL1CbVHQq3h2XLV4IdExymZsS+AoPAYtl96pikfWLcIdUvk0lu/tWrV4v79+9jZ2emtD0n6I0VaosXt0NvMvTgXv8d+ABgbGNOmWBt6lO5BTvP/5phVKtg1FM79ASjg25lQsVtGmZxpuPo0jIM3gpl14FaiY5Oaldb5NEdcXBz9+/dn4MCBlCpVCkAK9BeIFGkJAGExYUw5M4Wd93YiEBgoDGji1oQ+ZfuQxyrPu4oqFez4AS6sBhTQZAGU75BRZmcarj0L49t5xxKV//KtO+Vc7PAokEOn/cXGxtK2bVs2b97M7t27uX37NmZm2XcF55eMFGkJL9++pOe+ntx5rY5lXK9APQaUH0Ah20LaFVVK2DYQ/NeAwgCaLoaybTLA4syDSiXYcP4xwzdd0ZRVdrWnemEHBtQpjKGB7qd/YmNjadOmDVu2bMHU1JSlS5dKgf6CkSKdzXkW8Yye+3ry6M0jHM0dmeM5h9KOpRNXVClhSz+4/A8oDKH5UijdMv0NzmScfRCiJdC9axZiZMMSeusvNjaW1q1bs3XrVkxNTdmyZQv169fXW3+SjEeKdDbmQdgDeu7vSVBkEHmt8rLMexku1klEYhMCtvZ/J9At/4CS0v/2SWgUbZae0uyPa+ROl+oF9dZfTEwMrVq1Yvv27ZiamrJ161Z8fHz01p8kcyBFOpsSEBJAr/29CIkOoaBtQZbVW0Yuy2Q8D47NhEt/g4GR2gfavXH6GpvJ+Ov0I3wDgtl//bmmrG0lF70KNMCvv/7K9u3bMTMzY+vWrXh7e+u1P0nmQIp0NuTSi0v0PdCXN7FvKGFfgsX1FmNvlkwmkIA9cPBX9XbD6dleoMPexvHz5itaZR4FcjC5eRJTRDpm+PDhnD59muHDh+PlpfugTJLMiRTpbIQQgr0P9zLm+Bjexr+lvFN55tedj41JMrFsXwTAph6AgIrdpZsdEBj2VrM9zKcYJZytqVNcf77PSqUSQ0NDAKytrdm3b58MlpTNkCKdTTj5TL1y8OqrqwBUda7KbM/ZWBgns3z7bSj83Q5i30CB6lB/Sjpam3np+McZzXZ/z8J67Ss6OppmzZpRs2ZNRo4cCSAFOhsiRfoL58qLK8y5MIfTQacB9crBTu6d6FWmFyaGyQT5USlhY3cIuQu2LtD6f2Ck36whWYGHryJ58SYGgLx25nrtKzo6mqZNm7J3716OHDlChw4dyJ9fPzE/JJkbKdJfKHdf32XexXkcfHQQSGblYHIcHA93D4KRObT9Cywd0sHizM2T0ChqTffT7P/Vs4re+nr79i1NmzZl3759WFhYsGvXLinQ2Rgp0l8YUXFRTD83nX9v/4tKqDBQGNCoUCP6leunvXIwOa5uguNz1NtNF4BzGf0anAWIjlNSY6qvZt+zmCMFclrqpa+3b9/SpEkT9u/fj6WlJbt27aJmzZp66UuSNZAi/QUREBLA0MNDeRD+AACv/F4MKD8ANzu3lDUQdAW2DlBvVx8EpWSweIDlR99lO3exN2dxRw+99BMVFUWTJk04cOAAlpaW7N69m6+//lovfUmyDgafc9KCBQtwdXXFzMyMKlWqcObMmY/Wf/36Nf3798fZ2RlTU1OKFi3Krl27PnqOJOUIIVh3cx3td7bnQfgDnCycWOGzglmes1Iu0A+Owd/tIS4K3OpA3bH6NToLMefgbc32wSG1MTUy1Es/u3fv5sCBA1hZWbFnzx4p0BLgM0bS69atY8iQISxevJgqVaowe/ZsfHx8CAgIwMkpcaLR2NhY6tWrh5OTExs3biRv3rw8fPhQRuvSEeGx4Yw7MY79D/cDUDNfTX6r/hs5zFIY0Cf0Aez7BW5sU+/ncIUWf4CBfoQoK3H09gtGbLpCnFIAMNS7KCZGnzWuSREtWrRg/vz5lCtXjurVq+utH0nWQiGEEKk5oUqVKlSqVIn58+cDoFKpcHFx4fvvv2fEiBGJ6i9evJjp06dz8+ZNjI2NP8vI8PBwbG1tCQsLw8YmGZ/ebMjlF5f56chPPI14ipGBET9U+IFO7p1S5qYV8waOzoSTC0AZow6Y5NEVPEfJtFfA7AO3mH3gtlbZ1fE+WJnqdoYwMjKS2NhYcuTQbZQ8SeYnpbqWqk9cbGws58+f1/hsAhgYGODl5cXJkyeTPGfbtm1UrVqV/v37s3XrVhwdHWnfvj3Dhw/XOOl/SExMDDExMVpvRvIOIQSrr61mzoU5xIt48lrlZXrN6UkHRvoQlUq9xPvgeIj4b1lzwVpQfzLkKqlfw7MIYVFxWgI9pF5R2lZ20blAR0RE8M033xAZGcmBAwfkr0tJkqTqU/fy5UuUSiW5cmmvsMqVKxc3b95M8px79+5x6NAhOnTowK5du7hz5w79+vUjLi6OsWOTnvecPHky48ePT41p2Yo9D/Yw4/wMALwLeDOu2jh1rsFP8fS8Ohfhs4vq/RwFwWciFGsoM6r8hxCCerMOa/b3/lCTYrlTcG1TSUREBA0bNuTo0aPY2Nhw7949KlSooPN+JFkfvXt3qFQqnJycWLp0KYaGhnh4ePD06VOmT5+erEiPHDmSIUOGaPbDw8NxcUkiOls2RCVULL28FIAuJbswxGNIyqY3Qu7Bym8g/i2YWEOtYVClDxiZ6tnirINKJfh6mi/B/y1YKZ7bWi8C/ebNGxo2bMixY8ewtbVl3759UqAlyZIqkXZwcMDQ0JDnz59rlT9//pzcuXMneY6zszPGxsZaUxslSpQgKCiI2NhYTEwSr2QzNTXF1FSKR1IcfnyYO6/vYGlsSc8yPVMm0ELA9h/UAu3yFbT5E6wSP+TN7nz/90Wevn4Xm2Nd76o67+PNmzc0aNCA48ePY2try/79+6lUqZLO+5F8OaTqUbWJiQkeHh4cPHhQU6ZSqTh48CBVqyb9ga5evTp37txBpVJpym7duoWzs3OSAi1JHiEEy64sA6BtsbbJB0b6EP+/4P5hMDKDZoukQCfBy4gYdl4J1Oyf+bkutuaf96A7OcLDw6lfvz7Hjx/Hzs6OAwcOSIGWfJJU+xMNGTKEZcuWsXr1am7cuEHfvn2JjIyka9euAHTq1EnrwWLfvn0JCQlh0KBB3Lp1i507dzJp0iT69++vu3eRTTgddJorL69gamjKd+7fpeykiGDY+7N6u/ZIsC/08frZkKjYeCr+dkCzv6B9BZxsdJ+O6sWLF9y/f58cOXJw4MABKlasqPM+JF8eqZ6TbtOmDS9evGDMmDEEBQVRrlw59uzZo3mY+OjRIwwM3mm/i4sLe/fuZfDgwZQpU4a8efMyaNAghg8frrt3kU1Yfnk5AC2KtMDBPIXxNHYPh+jXkLsMVB2gP+OyIE9fv2X+odv4BbzQlJXNZ0vdEvr5peHm5oavry+RkZFyDlqSYlLtJ50RSD9pdaD+73Z9h5HCiF3Nd+Fs5fzpk04tgj0j1D7QPQ9BnvL6NzQLMXP/Lea+t5qwsJMVB4bU0mkfYWFhXLlyhRo1aui0XUnWRy9+0pKMY/kV9Sj6W7dvPy3QKhUcGAsn5qr3aw6TAv0Bz16/1Qj0V4XsaVDKGc9iuh1Bv379Gh8fHy5dusT27dupV6+eTtuXZA+kSGcBboXewu+xHwoUdCv1iewo8bHqpLFX1qv36/wCX/+odxuzEm+i46g25ZBmv2IBezpXc9VpH6GhoXh7e3Pu3Dly5syZZMgEiSQlSJHO5ETHRzP62GgA6hWoR0HbjyQ7jXkD676De37qrN6N50H5DuljaBbi3wtPNdul89oyyKuITtsPDQ2lXr16nD9/HgcHBw4ePEiZMjLkq+TzkCKdiRFC8OupX7kRcoMcpjkYWnFo8pWV8bC2NTw6AcaW6mwqRWSy0qSIU6rdQa3NjNj+vW7nikNCQqhXrx4XLlzAwcGBQ4cOUbq0/pPUSr5cpEhnYtYHrGfb3W0YKAyYXmv6x+eij0xXC7SpDXTaCnml90ByvIyIBcCrhG4TyIaFheHl5cXFixdxdHTk0KFDlCpVSqd9SLIf+ou7KEkT/sH+TDmrTv46uMJgqjh/JF3To1NwZJp6+5uZUqA/wtIjd1l8+C4AsfGqT9ROHZaWlhQrVgwnJyd8fX2lQEt0ghxJZ0JCokP40e9H4lXxeBfwpnPJzslXfvsaNvUEoYIybaBMq3SzM6sxbc9NFvrd1ex7uev2YZ6RkRF//vknT548wdXVVadtS7IvciSdyRBCMO7EOILfBlPIthATqk9IPj6HELBzCIQ9ArsC0PD39DU2C7H29EMtgV7fuyrNyudLc7svX75kwoQJmrAHRkZGUqAlOkWOpDMZm+9sxvexL8YGxkyrOQ1L448kPL30jzpxrMJQnU3FLHsu9EmOsLdx9PnzPCfvvdIq3zmwBiXz2Ka5/RcvXlC3bl2uXLnCmzdvmD59eprblEg+RIp0JuJR+COmnFHPQw8sP5Bi9sWSr3zPD3b95+1RewS4yEA97/PPmUeM+PdKovIlHT10ItDBwcHUrVuXq1ev4uzsTI8ePdLcpkSSFFKkMwnxqnhGHh3J2/i3VMpdiU4lOyVdMS4aDv0KJ9XpyyhQXS5WeY/zD0NYfvQ+u68GacpszIyY0bocHgVyYG+Z9siLwcHB1KlTh2vXruHs7Iyvry/Fin3kH6pEkgakSGcSll1ZxuWXl7E2tmZi9YkYKJJ4XPD8mvohYfA19b5HV3VmFZk0FiEExUbvIVap7bGxpKMHPiWTjnX+OTx//pw6depw/fp18uTJg6+vL0WLFtVZ+xLJh0iRzgRcfnGZJZeWADD6q9GJ/aFVKji1UJ2XUBkLFg7QZD4Ua5AB1mZO7r2M1BLoVh758C6ZmzrFdefBoVQqqV+/PtevXydv3rz4+vpSpIhuVytKJB8iRTqDCYwIZOjhoSiFkoYFG9KwUEPtCmFPYUsfuH9EvV+0vnq5twzcryE2XkXrxe8SId+Z2AAjQ907LhkaGjJ+/HgGDx7M3r17KVy4sM77kEg+RIp0BhIcFUz3fd0JjAzE1caVUV+N0q5wbQtsHwjRYWBsoZ7a8Ogqk8a+x4hNl/nn7GOtMn0IdAKNGzemfv36MquQJN2QftIZREh0CD339eTxm8fktcrLMu9l2umwnl6ADV3UAp2nAvQ+ChW7SYH+jwcvI3H7eVcigT4+oo5O+3n27Bn16tXj3r17mjIp0JL0RI6kM4CwmDB67evFvbB75LLIxXLv5eS2/ODh1sHxgIASjaDlSjDUbb69rM7Afy6iVL3LV3FsuCf5cljotI+nT5/i6enJ7du36dKlC4cPH05Z4l+JRIdIkU5nImIj6HugLwGhAeQ0y8ly7+Xks/5g5dtdX7UftIExeE+UAp0ECfmEijhZsW9wTZ2L5/sCXaBAAVavXi0FWpIhyOmOdGb8yfFceXkFO1M7lnkvw9XWVbuCEP+NooFK3SFHgXS3MbNz5n4IV56GAfCjd1Gdi+eTJ0+oXbs2t2/fxtXVFT8/PwoW/Egcb4lEj8iRdDri+8iXPQ/2YKAwYH7d+RTJkYT71vWt8OwimFjB1x+JH51Nabf0lNYy7wr5c+i0/cePH+Pp6cndu3cpWLAgvr6+FCgg/1FKMg4p0unEm9g3/HbqNwA6l+xMWcey2hWiw+DMUjjx30rCqgPAyjGdrcycRMcpOX0/hC0Xn2oJ9IxWZXGyMdNpX4MGDdIItJ+fH/nz59dp+xJJapEinU7MOj+L4LfB5LfOT7+y/d4diHwFpxfB6aUQo/4JT67SUG1AxhiayYhTqvhq8kFeR8VplZ8b7YWDlanO+1u6dClCCObOnYuLi4vO25dIUosU6XTgbNBZNtzaAMC4auMwMzKDN0FwYh6cWwlxkeqKjiXUcThKNgNDeWuCw6OpPOmgVlkl1xwM8ymuU4GOiorCwkLtGeLg4MDmzZt11rZEklakEuiZGGUM40+qHwS2KNKCSrkqwrHZ4DsJlDHqSrnLQM1hUPxbMJDPcgEq/rZfk+YqgcvjvLEx062ny4MHD6hTpw7Dhw+nd+/eOm1bItEFUhH0zNEnR3kY/hAHcweGlO0Hm3rAgbFqgc5XGdpvgN5HwL2xFGjg2O2XuI7YqSXQNQo78GDKNzoX6Pv371O7dm3u37/PzJkziY6O1mn7EokukCNpPXPs6TEA6jvXwGZNKwj0BwMjqD8FKvWQKwj/IzQylpn7b/HnqYda5fqKw3Hv3j08PT159OgRRYoU4dChQ5iZ6fYhpESiC6RI6xEhBCeenQCg+oUNEBoI5vbQejUUrJnB1mUezj8MocWik1plLSrk4/dWZfSygOTu3bt4enry+PFjihYtiq+vL3ny5NF5PxKJLpAirUfuh90nMDIQEyHweB2kfjDY7m+wlwsjEoiJV2oJdNFcVkxqVpqKrvZ66e/u3bvUrl2bJ0+eUKxYMXx9fXF2dv70iRJJBiFFWo8cO78IgIpvozHPXx3argVzu4w1KpPhezNYs/17q7K09Eh7ctiPsWXLFp48eULx4sXx9fUld27dJQSQSPSBFGl9cecgx29vAXMzqtkUhtabwFjOeSbgezOY0Vuu8vT1W02ZvgUaYMiQIZiYmNCqVSsp0JIsgRRpffDsItHrO3He2Q6AGg3mSYF+DyEEXVed1SrrW9tNb/3du3eP3LlzY2FhgUKh4Pvvv9dbXxKJrpEirWtC7sHaVhw3UhJjYEAui1wUyiEzeCQQHaek/9oLmv0WFfLRqmI+KulpDjogIABPT0+KFy/Ojh07NItWJJKsghRpXRLzBta0RES+YFmBgoA6JZYMcakePccpBTWm+vIyIkZTri8PDoCbN2/i6elJUFAQDg4OWisLJZKsghRpXXJ4GoTc5XDOfFwzUGJuZE6XUl0y2qoMZ97B28zYfytR+YEhuo8DncCNGzeoU6cOQUFBlC5dmoMHD+Lg4KCXviQSfSJFWle8CIBTCxHAQmcXiHxKu+LtsDfTz8/4rMTGC0+09i1NDDn1c12sdbyCMIHr169Tp04dnj9/TpkyZaRAS7I0UqR1gRCwaxio4jlUuDo3Ih9jYWRBl5JdMtqyDCVOqWLd2cc8fBUFwJTmpWlUNg+Wpvr72F2/fh1PT0+Cg4MpW7YsBw8eJGfOnHrrTyLRN1KkdcHNnXD/MCpDUxZaGkI4dCjRgRxmug1In5UQQtBh2WnOPAjRlFUuaK9XgQaIiYkhLi6OcuXKceDAASnQkiyPFOm0olKB3xQADpRryq2Qk1gZW9G5ZOcMNixjeBMdR+slp7gRGK5VPrFZKQo5Wum9//Lly+Pn50e+fPmwt5dTTZKsjxTptBKwE55fIc7EmoXxgQB0dO+IraltBhuW/kTHKSk9bl+i8kM/1tKrQF++fJnIyEiqVq0KQJkyZfTWl0SS3kiRTgtCwOGpACwtXp274dexM7XjO/fvMtiw9Ofk3Vd0WH5Ks29lasSW/tUo7GSt134vXbpE3bp1iY2NxdfXFw8PD732J5GkN1Kk08LNHRB0hWuWdiyLCABg1FejsDGxyWDD0pfImHjaLXsn0E7WppwZ5aX3fv39/fHy8uLVq1dUqlQJNzf9rVqUSDIKKdKfy+tHsP0HYhQwKk8+lPHh+Lj6UN+1fkZblu4sOXxXs92mogsDvZLIgq5jLl68iJeXFyEhIVSuXJm9e/diZ2en934lkvRGivTnEBMBf7eDqJcsyFeEu/Hh2JvZM6rKqIy2LEP49+JTzfbUlvqfD75w4QJeXl6EhoZSpUoV9u7di61t9nsGIMkeyHxNqUWlgs294flVAq2dWG2szmI9ruq4bOlyFxuv4kmoOpLdii4V9d7fjRs3NAL91VdfsW/fPinQki8aKdKpxW+yei7a0ATfat1QoaKCUwU883tmtGXpTrxSRdHRuzX7OS11l8E7OQoWLEiVKlWoWrUqe/fuxcYme83/S7IfnyXSCxYswNXVFTMzM6pUqcKZM2dSdN4///yDQqGgadOmn9NtxnN9KxyZpt5uNAe/N/cB8HTJfgJ9IzCcwqN2a5WVzKN/wTQzM2Pz5s1SoCXZhlSL9Lp16xgyZAhjx47lwoULlC1bFh8fH4KDgz963oMHDxg6dChff/31ZxuboYTcg60D1NtVB/DGvRFnn6tjItd2qZ1xdmUQ47df09q/9Zt+EsYCnDlzhl9++QUhBKAWamtr/br2SSSZhVR/q2bOnEnPnj3p2rUr7u7uLF68GAsLC1asWJHsOUqlkg4dOjB+/HgKFSqUJoMzhPgY2NAVYsIhf1XwGs/xZ8eJV8XjauOKq61rRluYrqw59ZBT99TLvesWd+LBlG8wMdKPQJ8+fZp69erx22+/sXjxYr30IZFkZlL1zYqNjeX8+fN4eb3zgTUwMMDLy4uTJ08me96ECRNwcnKie/fuKeonJiaG8PBwrVeGsn8MBPqrM323+AMMjTj8+DCQ/UbROy8HMnrLVc1+/zr6S2hw6tQp6tWrR3h4OF9//TUdO3bUW18SSWYlVSL98uVLlEoluXLl0irPlSsXQUFBSZ5z7Ngx/vjjD5YtW5bifiZPnoytra3m5eLikhozdcuN7XD6vxFcsyVgmxeVUHHi2QkAauarmXG2pTPbLj2j/1/vsqpMa1GGCvn149Fy8uRJvL29efPmDbVq1WLXrl1YWek/9odEktnQq3fHmzdv6NixI8uWLUtVPN+RI0cSFhameT1+/FiPVn6E149ga3/1drWBUNQbgJshNwmJDsHCyIJyjuUyxrYM4Mf1/prtnl8XpHUl/fzzPH78uEaga9euzc6dO6VAS7ItqVrM4uDggKGhIc+fP9cqf/78eZKZl+/evcuDBw9o1KiRpkylUqk7NjIiICAgyaW8pqammJrq353rk5yYD9FhkK8S1B3zrvi/UXTl3JUxNtRP4PrMxN0XEaw4dp84pfrB3TCfYvT31M80R0hICN988w0RERHUqVOH7du3y5RXkmxNqkbSJiYmeHh4cPDgQU2ZSqXi4MGDmghk71O8eHGuXLmCv7+/5tW4cWM8PT3x9/fP2GmMTxEfA1fWq7drj4D3xDhBpKvlrZYRlqUrz8OjqTvjMGtPP9KU9amlvxgZ9vb2zJs3D29vbynQEgmfsSx8yJAhdO7cmYoVK1K5cmVmz55NZGQkXbt2BaBTp07kzZuXyZMnY2ZmRqlSpbTOT4iv8GF5puPWXngbCtbOUOidH3RUXBQXgy8CUD1P9YyyLl3wvRlM11VnNfvlXOwY37gkhga6z0sohNDkO+zYsSPfffedTOArkfAZIt2mTRtevHjBmDFjCAoKoly5cuzZs0fzMPHRo0cYGHwBCxkv/a3+W6Y1GBhqiv8J+Id4VTz5rPKR3yZ/Bhmnf649C9MS6FJ5bdjSXz//lI4cOcKPP/7I9u3bNdNmUqAlEjUKkbBCIBMTHh6Ora0tYWFh6bPKLOIFzCwOqnjodxqcigNwJ/QOrXe0Jk4Vx4RqE2hWpJn+bckAhBAUHLlLsz/UuygD6ugnst3hw4dp2LAhUVFR9O3bl4ULF+qlH4kks5FSXZNR8JLiyga1QOepoBHoOFUco46PIk4VR618tWhauGnG2qgnYuKVTNp5Q7PfokI+vT0k9PX15dtvvyUqKgofHx9mzJihl34kkqyMFOmk8P9L/bdce03R8ivLuf7qOjYmNoytOvaL+zn+MiKG0/dCmL73Jg/+y+4NMKN1Wb30d+jQIb799lvevn1L/fr12bx5M2ZmZnrpSyLJykiR/pDAy/D8ChiaQKkWANwKvcXSS0sB+LnKzzhaOGakhTqn1nRfHr4nzAms753YY0cXHDhwgEaNGhEdHU3Dhg3ZtGmTFGiJJBmkSH9IwgPDYg3AQp1teunlpcSLeDxdPGlYsGEGGqd7Ttx5mUig21XOT59ahSiQ01Ln/SmVSoYMGUJ0dDTffPMNmzZtyhw+8RJJJkWK9PvERr2b6iirnup4HP6Y/Q/3AzCg/IAvapojOk5J++WnNft3JzXUi3vd+xgaGrJz506mTJnCzJkzpUBLJJ/gC/CV0yGX10H0a7ArAEXqAbD6+mpUQkWNvDUomqNoxtqnY47ceqHZntO2nF4F+sWLd325uLiwYMECKdASSQqQIp2AEHB6iXq7ci8wMCQkOoQtd7YA0K1Ut4yzTQ+oVIJef57X7Dcpl1dvfe3Zs4eCBQuyfv16vfUhkXypSJFO4J4fvLgBxpZQ/jsA/rrxFzHKGErlLEXFXPrP35cehEfHUXeGH4V+fucH3cojn9762717N02aNCEyMpINGzaQBdzyJZJMhZyTTiBhFF2uPZjbEa+KZ13AOgC6luqa5eeiH7yM5KeNlznzICTRsXGNS+qlz127dtGsWTNiY2Np1qwZf/31V5a/jhJJeiNFGtSpsW7tUW9X6Q3Ag7AHvI55jYWRBXXz181A43TDjsvPEgn00Z88cbHXTwCjHTt20KJFC2JjY2nRogV///03xsZffsRAiUTXSJEGOL0UEFC4Hjiolz/fCFGvuituXxzD92J3ZEUevIzk9323AKjmlpPh9YtTJp+t3ka127dvp0WLFsTFxdGyZUv++usvKdASyWciRTrmDVxco96u0kdT/L5IZ1Vi41XsuPyMIesvacraVHKhrIudXvs9dOgQcXFxtGrVirVr10qBlkjSgBTpy+sh9g3kLAxudTTFN0NuAllbpP8+84ix295l9R7mU0yvXhwJzJw5kzJlytCxY0eMjORHTCJJC9nbu0MIOLdSvV2xO/wXYlWpUnLzlVqk3XO6Z5R1aeZ9P+hRDUvoLVASqFNexcbGAuowo127dpUCLZHogOwt0k/OquN0GJlB2baa4jU31vAm7g3WxtYUsi2UgQZ+HucfhlJ9yiEO3gwGoH2V/PSsqb/3sWnTJmrXrk2bNm00Qi2RSHRD9h7qnFuh/luqhSZOx72we8y9MBeAwRUHZ7kchi8jYmix6IRWWfvK+ktOsHHjRtq2bYtSqcTKygpDw6z9kFUiyWxkX5GOCoGr/6q3K6pXE8ar4hl9bDSxqliq56lOyyItM9DAz+OfM9q5CLtUcyW3rX4izG3YsIF27dqhVCrp2LEjK1eulCItkeiY7CvSAbtBGQO5SkNeDwBWXVvFlZdXsDa2Zly1cVlu4UVETLzG1Q5gRAP9PfRct24dHTp0QKlU0rlzZ/744w8p0BKJHsi+c9LhT9V/81YAhYLAiEAW+qtTNw2vPJzclrkz0LjUc+9FBKXG7tXsz2tXXm99rV+/XiPQXbt2lQItkeiR7DuSjniu/mulTqB78NFB4lRxlHMsR2O3xhloWOpQqQQ/brjE5otPNWXmxoY0KptHb33mypULU1NT2rZty7Jly76MxMMSSSZFirSVEwB+T/wA8CrglaWmOdzH7iE6TqXZ/6aMM9NbltFrn7Vq1eLs2bMUL15cCrREomeysUir3dOwciIiNoLzQeqwnbVdamecTang2eu3fDP3qJZAbxtQnTL57PTS3z///EPJkiUpXbo0AO7uWdd/XCLJSmRjkX433XH82XHiRTyuNq4UsCmQsXalgOg4JfVmHiYyVqkpuzepIQZ6Ctr/v//9jy5dupAzZ04uXLiAi4uLXvqRSCSJyZ6/VeOiITxQvW3lxOHHhwGola9WBhqVcnxvBmsE2trUiIu/1NObQK9evZouXboghKBFixbkzav/ZeUSieQd2XMk/eCo2v3O2hmlbX6OPj0KQC2XrCHSfdde0GxfGe+jt35WrlxJ9+7dEULQt29f5s+fL+egJZJ0JnuKdMB/WUmKNeDKq6u8jnmNjYkN5Z3057aWFiJi4nn4KpLg8Bi6rjqrKe+lx6XeK1asoEePHggh6NevH/Pnz89SD1Qlki+F7CfSQqgXsgAUa8iVl1cA8MjlgZFB5rsc8UoVXjMOExQenejYD15F9NLn1q1b6d69OwADBgxg7ty5UqAlkgwi86mSvgn0hzeB6lyGrl9z8/SvAJSwL5GxdiVBnFKF781gjUA7WZsSE6+iXeX8DK9fTG/CWadOHapVq4aHhwdz5syRAi2RZCDZT6QTRtGF64CxmSZudDH7YhloVGKUKkGRUbs1+woFnBhRByND/c8JW1tbs3//fszNzaVASyQZTPZ7CnR9q/pvsW+IVcZy7/U9IHONpM8/DMHtvWzeAIPqFtGrQC9ZsoTJkydr9i0sLKRASySZgOw1kg6+AS9ugqEJFGvA7de3iRfx2JraZppYHQv97jBtT4BW2c1f62NmrL/YGIsWLaJfv34AVK5cmbp1s37iXYnkSyF7ifS1zeq/bnXB3I6AJ4cAdYqsjB41voyIYdqem6w/90RTNqphCb0G6wdYsGABAwYMAODHH3+kTp06nzhDIpGkJ9lHpIV4J9IlmwFw45U62WxGT3UsPnyXKbtvapXtG1yTorms9drvvHnzGDhwIADDhg1j6tSpGf7PSiKRaJN9RDr4Bry8BYamUKwBAAGh6mmFjHpoqFQJio7ejVIlNGXWpkas7VlF7wI9Z84cfvjhBwCGDx/O5MmTpUBLJJmQ7CPSQZfVf10qg5kNQghuh94GoFiOjBHpv8880hLo/3WrTM2ijnrv99KlSxqBHjFiBJMmTZICLZFkUrKPSL/5L1aHbT4AgqOCiYiLwFBhiKuNa4aYdOz2S832nYkN0sW9DqBs2bLMnj2b4OBgfvvtNynQEkkmJhuJdJD6r7Xai+NemNr1zsXaJUOSzapUgj3X1Db1qFEwXQQ6NjYWExMTAAYNGqT3/iQSSdrJPn7SCSNpa2fgnUgXstWv90RydF/9LgaHi72F3vv7/fffqVatGqGhoXrvSyKR6I7sI9IJoUkTRtL/LWIpZJf+Iv06KhbfgBea/abl9Rv+c/r06QwbNozz58+zYcMGvfYlkUh0S/YR6Yj/pjus1CJ9N+wukP4jaSEEVScf0uxfHueNrbn+plumTp3KTz/9BMC4cePo1auX3vqSSCS6J/uIdNR/P/MtcgLwIOwBkP4i3WXlWd7GqQP2uzvbYGOmP4GePHkyI0aMAGD8+PGMHTtWb31JJBL9kD0eHMbHQuwb9baFPfGqeEKiQwDIZZkr3czYey2Iw7feTXPs+L6G3vqaNGkSo0aNAuDXX39l9OjReutLIpHoj+wh0tGv/9tQgJktr2NCEQgMFAbkMM2RbmaM2nxFs31+tJfeUl6FhISwYMECACZOnMjPP/+sl34kEon+yR4iHaUeNWNmCwaGvHyr9k+2M7XD0EB/gYvex//xa15GxALQtFweclqZ6q0ve3t7fH192bNnj2bZt0QiyZpkD5F++998tLl61Pzq7SsAHMwd0s2E5UfvabYnNiutlz7u3LlD4cKFAShatChFixbVSz9JoVQqiYuLS7f+JJLMjrGxMYaGaR8EfpZIL1iwgOnTpxMUFETZsmWZN28elStXTrLusmXL+N///sfVq1cB8PDwYNKkScnW1wtv/xtJW9gDaEbS6SnSL97EAPBNGWcsTXX7v1EIwbhx45g6dSrbtm3D29tbp+1/qu+goCBev36dbn1KJFkFOzs7cufOnaZVvalWi3Xr1jFkyBAWL15MlSpVmD17Nj4+PgQEBODk5JSovp+fH+3ataNatWqYmZkxdepUvL29uXbtGnnz6tc/WMMHI+kTz04A6tWG6cXp++p/FCXz2Oi0XSEEY8eO5ddf1WnArl27lq4inSDQTk5OMlGARPIfQgiioqIIDg4GwNnZOU2NpYrKlSuL/v37a/aVSqXIkyePmDx5corOj4+PF9bW1mL16tUp7jMsLEwAIiwsLLXmqjm5UIixNkKs7yyeRz4X5VaXE6VWlRJXX179vPZSyb5rQaLA8B2iwPAd4sqT1zprV6VSidGjRwtAAGLGjBk6azslxMfHi+vXr4uXL1+ma78SSVbh5cuX4vr16yI+Pj7RsZTqWqr8pGNjYzl//jxeXl6aMgMDA7y8vDh58mSK2oiKiiIuLg57e/tk68TExBAeHq710gkKA9YFrCNexFPBqQIlc5bUTbsfQaUS9PzfOc1+YScrnbQrhGD06NH89ttvAMycOZMhQ4bopO2UkjAHbWGh/2XtEklWJOG7kZbnNakS6ZcvX6JUKsmVS9u3OFeuXAQFBaWojeHDh5MnTx4tof+QyZMnY2trq3m5uOhmWkIlBBtvbQSgQ4kOOmnzU8w9dFuzvfi7CjpJgyWE4Oeff2bSpEkAzJ49m8GDB6e53c9FTnFIJEmji+9Guq44nDJlCv/88w+bN2/GzMws2XojR44kLCxM83r8+LFO+n8kYgiJDsHU0BTP/J46afNjXH7ymtkH3ol0/VJpmJd6DyEEjx49AmDu3Lkyop1E8gWTqgeHDg4OGBoa8vz5c63y58+fkzv3xxO5/v7770yZMoUDBw5QpkyZj9Y1NTXF1FSHfsRCHVj/pioSUAf5NzbQ33Lsy09e03j+ca2yjX2q6qx9AwMDVq9eTceOHalfv77O2pVIJJmPVI2kTUxM8PDw4ODBg5oylUrFwYMHqVo1eRGaNm0av/76K3v27KFixYqfb+3nEqV2ubtuoALUiWf1yYcC3d/TjYquyc/BpwQhBOvWrUOpVMf9MDIykgKdRh4/fky3bt3IkycPJiYmFChQgEGDBvHq1Sud9vP9999TokTSeTQfPXqEoaEh27Zt+2Q7rq6uKBQKFAoFFhYWlC5dmuXLl+vMzlWrVmFnZ6ez9j63ny5dumjep7GxMQULFuSnn34iOjpa77ZlRlI93TFkyBCWLVvG6tWruXHjBn379iUyMpKuXbsC0KlTJ0aOHKmpP3XqVH755RdWrFiBq6srQUFBBAUFERERobt38SnCnwFwA/WKv+I59SfSNwLfPeTsUs2V6xN8GOaTtv6EEPz444+0bduWXr16IYT49EmSj3Lv3j0qVqzI7du3+fvvv7lz5w6LFy/WDDhCQkJ01lf37t25efMmJ06cSHRs1apVODk50bBhwxS1NWHCBAIDA7l69SrfffcdPXv2ZPfu3Tqz9XMYN24cXbp00Wmb9evXJzAwkHv37jFr1iyWLFmSbQOEpVqk27Rpw++//86YMWMoV64c/v7+7NmzR/Mw8dGjRwQGBmrqL1q0iNjYWFq2bImzs7Pm9fvvv+vuXXyKsCcI4Gas2l/a3d5db13N2HdLsz3mW3csTNK2cEUIweDBg5k1axYAlStXlg/qdED//v0xMTFh37591KpVi/z589OgQQMOHDjA06dPNcGpQD2CnTRpEt26dcPa2pr8+fOzdOlSrfYeP35M69atsbOzw97eniZNmvDgwQMAypUrR4UKFVixYoXWOUIIVq1aRefOnTEyMiI2NpYBAwbg7OyMmZkZBQoUYPLkyVrnWFtbkzt3bgoVKsTw4cOxt7dn//79muOvX7+mR48eODo6YmNjQ506dbh06ZLm+KVLl/D09MTa2hobGxs8PDw4d+4cfn5+dO3albCwMM0odty4cTq62qnH1NSU3Llz4+LiQtOmTfHy8tJ6n9mJz3pwOGDAAB4+fEhMTAynT5+mSpUqmmN+fn6sWrVKs//gwQOEEIle6foBCH/KfWMjXivfYmJgQuEchfXSzaXHrzlwQz1fXyqvTZoDKAkh+OGHH5gzZw4AS5YsoXfv3mm2U58IIYiKjU/3V2p+XYSEhLB371769euHubm51rHcuXPToUMH1q1bp9XmjBkzqFixIhcvXqRfv3707duXgAB1tvm4uDh8fHywtrbm6NGjHD9+HCsrK+rXr09srPrXW/fu3Vm/fj2RkZGaNv38/Lh//z7dunUD1A+Bt23bxvr16wkICGDt2rW4urom+R5UKhWbNm0iNDRUkxINoFWrVgQHB7N7927Onz9PhQoVqFu3ruaXQYcOHciXLx9nz57l/PnzjBgxAmNjY6pVq8bs2bOxsbEhMDCQwMBAhg4dmuJrqk+uXr3KiRMntN5nduLLj92hUkH4M45YqL+MlXJXwtRQP8GNDt5490B1UhrjcwghGDhwIPPnzwfUy+t79OiRpjbTg7dxStzH7E33fq9P8Enxr5bbt28jhEh2nrhEiRKEhoby4sULzSrahg0b0q9fP0DtRjpr1ix8fX0pVqwY69atQ6VSsXz5cs2vnJUrV2JnZ4efnx/e3t60b9+eH3/8kQ0bNmimBlauXEmNGjU0MVYePXpEkSJFqFGjBgqFggIFCiSybfjw4YwePZqYmBji4+Oxt7fXfC6OHTvGmTNnCA4O1jx4//3339myZQsbN26kV69ePHr0iGHDhlG8uHoKrkiRIpq2bW1tUSgUn3QCSA927NiBlZUV8fHxxMTEYGBgoPkuZDe+/KD/IfcgPpqjlpYAfJ3va7119ST0LQANSuWmTD67NLU1ZMgQ5s+fj0KhYPny5VlCoLMaqRl9v++RlCBkCUt+L126xJ07d7C2tsbKygorKyvs7e2Jjo7m7l11BiA7OzuaN2+umfIIDw9n06ZNdO/eXdNuly5d8Pf3p1ixYgwcOJB9+/YlsmPYsGH4+/tz6NAhqlSpwqxZszRBtS5dukRERAQ5c+bU2GFlZcX9+/c1dgwZMoQePXrg5eXFlClTNOWp4ejRo1rtT5o0ibVr12qVrV27NtXtvo+npyf+/v6cPn2azp0707VrV1q0aJGmNrMqX/5IOtCfCIWCC6bqn0pf59WfSP978SkAdhZp/1lWp04dFi1axMKFCzU/h7MC5saGXJ/gkyH9ppTChQujUCi4ceMGzZo1S3T8xo0b5MiRA0dHR02ZsbG2y6ZCoUClUnsLRURE4OHhkaQwvd9G9+7dqVu3Lnfu3MHX1xdDQ0NatWqlOV6hQgXu37/P7t27OXDgAK1bt8bLy4uNGzdq6jg4OFC4cGEKFy7Mhg0bKF26NBUrVsTd3Z2IiAicnZ3x8/NLZEeCN8W4ceNo3749O3fuZPfu3YwdO5Z//vknyeuQHBUrVsTf31+zP3fuXJ4+fcrUqVM1ZR8ueEstlpaWmn8+K1asoGzZsvzxxx9a/9SyC9lCpE+ZmxGvgAI2Bchvk18v3byNVWq2vyqUNnc7gEaNGnH37t30C0KlIxQKRZofluqbnDlzUq9ePRYuXMjgwYO15qWDgoJYu3YtnTp1SvED2goVKrBu3TqcnJywsUk+gJanpycFCxZk5cqV+Pr60rZtWyz/+4WXgI2NDW3atKFNmza0bNmS+vXrExISkmQYBRcXF9q0acPIkSPZunUrFSpUICgoCCMjo2TnsuFdGNvBgwfTrl07Vq5cSbNmzTAxMdG4eH4Mc3NzjYCCOn55eHi4VpkuMTAw4Oeff2bIkCG0b98+0XOEL50vf7rjmVqkQX+j6Og4JSXG7NHsl8prm+o2VCoVo0eP5t69d3Gns5pAZyXmz59PTEwMPj4+HDlyhMePH7Nnzx7q1atH3rx5mThxYorb6tChAw4ODjRp0oSjR49y//59/Pz8GDhwIE+ePNHUUygUdOvWjUWLFnHy5MlEo8KZM2fy999/c/PmTW7dusWGDRvInTv3R32KBw0axPbt2zl37hxeXl5UrVqVpk2bsm/fPh48eMCJEycYNWoU586d4+3btwwYMAA/Pz8ePnzI8ePHOXv2rGZu3tXVlYiICA4ePMjLly+JiopK3UVNBUqlEn9/f63XjRs3kq3fqlUrDA0NNRmHshNftkgLAYGXeWisHtnpaxFLv7UXNNuFnawo5GD5kdqJUalU9OnTh4kTJ1K3bl3evn2raxMlH1CkSBHOnTtHoUKFaN26NW5ubvTq1QtPT09Onjz50QBgH2JhYcGRI0fInz8/zZs3p0SJEnTv3p3o6OhEI+suXboQFhZGyZIltbyiQO1eN23aNCpWrEilSpV48OABu3btwsAg+a+pu7s73t7ejBkzBoVCwa5du6hZsyZdu3alaNGitG3blocPH5IrVy4MDQ159eoVnTp1omjRorRu3ZoGDRowfvx4AKpVq0afPn1o06YNjo6OTJs2LRVXNHVERERQvnx5rVejRo2SrW9kZMSAAQOYNm2alodMdkAhssDKiPDwcGxtbQkLC/voz8lEhNyDueX5Jl8eHhkbsdJnJRVz63bFY0y8kmKj1aPogg6W+A6tnarzVSoVvXv3Zvny5Zrl3t99951ObdQX0dHR3L9/n4IFC340FotEkl352HckpbqWuScP00rgJZTAMyP128xrpfvpg+VH72u2JzYtlapzVSoVPXv2ZMWKFRgYGPDnn3/Svn17XZsokUiyMF+2SD/z56WhIfEKMFQY4mjh+OlzUklw+Lt4Ah6uKc88rlKp6NGjBytXrsTAwIA1a9bQrl07ndsnkUiyNl+2SIc+IPa/J/RmRmYYGej27T4OiWL1yYcADKxTGFOjlLuB/fbbb6xcuRJDQ0PWrl1LmzZtdGqbRCL5MviyRRrdT7fHxqtoufgEryJiefr63QM+81S6nfXr14+tW7cyfPhwWrdurWszJRLJF8IXLtK6QwjBQr+7TN8bkOhY9cI5aV0xX4raSPC9dXBw4MyZMzpJ+S6RSL5cvniRvvef+52ZYdq8D648DdMSaIUCNverjoOVCflyfDrHn1KppEuXLtSoUUMTJEkKtEQi+RRfvEivsFO7tjRyS94HMyW8nwZrVpuyNCmbN8VR7uLj4+ncuTN//fUX69ato379+kkGz5FIJJIP+aJF+qIyggtmZhgrDOjo3jHV5z8OiWLK7psEPH/DnWB1kgJ3Zxualf/01EYC8fHxdOzYkX/++QcjIyPWrVsnBVoikaSYL1qkNyjVabMa2RTHycIp1eeP336NAzeCtcpGNEj5qsX4+Hi+++471q1bh7GxMRs2bKBJkyaptkMikWRfvmiRfi3UwWLKWeRJ9bmD/rmoJdDTWpShSC4ryrnYpej8uLg4OnTowIYNGzA2Nmbjxo00btw41XZIJJLszZcdu+MzCQh6w1b/Z5r9PT98TetKLpTPnyPFkdE2b96sEehNmzZJgc5kpCTZacLx9181atTIQKsl2ZEveiT9uTwJfRf96+TIOjjbpj40YqtWrbh58yYVKlTg22+/1aV5Eh1Rv359Vq5cSVxcHOfPn6dz584oFAqtuMgrV67UysqeXVM4STIOKdJJcO6hOmFt2Xy2qRLo2NhY4uLisLS0RKFQMGbMGH2ZKNEBCclOQR2bOSHZ6fsibWdnlynSSUmyL1Kkk+BmYDgAz8NjUnxObGwsbdq04fXr1+zcuRMLi0/7Tn+RCAFx+otDnCzGFmrn9c8kIdmp9LyRZDakSH/A/ZeR+Aa8AKBj1ZR9YWNjY2ndujVbt27F1NQUf39/qlWrpk8zMy9xUTAp9Q9q08zPz8AkdXG8U5LstF27dlqLjtasWUPTpk11YbFEkiK+bJFWxan/Ghp/vN577Lj07oFhmXyfzrASExNDq1at2L59O6ampmzdujX7CnQWw9PTk0WLFhEZGcmsWbMwMjJKlOx01qxZeHl5afadnZ3T20xJNufLFWkhICYCTA3BJuUju4V+6uzJ7s42fF3k46FNY2JiaNmyJTt27MDMzIytW7fi7e2dJrOzPMYW6lFtRvSbSlKS7DR37tx6y90nkaSEL1ekI56DMhYwB+uUiXTP/53jbZzat9rL/ePZjmNiYmjRogU7d+7EzMyM7du3a424si0KRaqnHTID2T3ZqSTz8uX6SYc+fLedgumO11Gx7L/+XLPf8auPz0cnJPk0Nzdnx44dUqC/ALJzslNJ5uXLFelUxpL+Zes1zfbZUV44Wpt+tH6xYsU4cOAAO3bsoG7dup9loSRzkZ2TnUoyL1/udEcqEEKw/b8HhmbGBskKdHR0NLdu3aJMmTIAVKhQId1slOiWVatWJVk+YsQIRowYAag/FxJJRvMFj6RTztHbLzXba3tUSbLO27dvadKkCTVq1ODkyZPpZZpEIsnmZHuRvhEYTqcVZzT7HgXsE9VJEOh9+/ahUqmIi4tLTxMlEkk2JltPd8QpVbRYdEKz39/TLVGdqKgomjRpwoEDB7C0tGT37t18/fXX6WmmRCLJxmRrkd59NYioWLXL3TelnRnmox0rOioqikaNGnHo0CGsrKzYvXu3jIImkUjSlWwr0vFKFQP/vqjZ/7VpKa3jUVFRfPvtt/j6+mJlZcWePXuoXr16epspkUiyOdl2TvrW8wjN9jCfYthbaoegNDQ0xNzcHGtra/bu3SsFWiKRZAjZdiTd689zmu3+nomX/ZqamrJp0yYtlzuJRCJJb7LlSHr2gVs8CX0LoOUTHRERwcKFCzX+sWZmZlKgJRJJhpLtRtJ915xn99Ugzf7W/uppjDdv3tCwYUOOHTtGYGAgv/76a0aZKJFIJBqynUjvufZOoDf1rUoeO3PevHlDgwYNOH78OLa2tjIfoUQiyTRkq+kO1xE7SVjpu7lfNTwK2BMeHk79+vU5fvw4dnZ2HDhwgEqVKmWsoZJ04fHjx3Tr1o08efJgYmJCgQIFGDRoEK9evdJpP99//z0lSpRI8tijR48wNDRk27Ztn2zH1dVVkxDXwsKC0qVLs3z5cp3ZuWrVKuzs7HTW3uf2k5Ikwfogpf2md4LibCPSQ9b5a+2XcLbRCPSJEyfIkSMHBw4coGLFihljoCRduXfvHhUrVuT27dv8/fff3Llzh8WLF3Pw4EGqVq1KSEiIzvrq3r07N2/e5MSJE4mOrVq1CicnJxo2bJiitiZMmEBgYCBXr17lu+++o2fPnuzevVtntn4O48aNo0uXLjpts379+gQGBnLv3j1mzZrFkiVLGDt2bKraqF27drIxWtLa78qVKwkMDNS8UvJP9nP5okU6ITyOUiX49+JTTfmDKd9gbAANGzbk5MmTGoH28PDIGEMl6U7//v0xMTFh37591KpVi/z589OgQQMOHDjA06dPGTVqlKauq6srkyZNolu3blhbW5M/f36WLl2q1d7jx49p3bo1dnZ22Nvb06RJEx48eABAuXLlqFChAitWrNA6RwjBqlWr6Ny5M0ZGRsTGxjJgwACcnZ0xMzOjQIECTJ48Wesca2trcufOTaFChRg+fDj29vbs379fc/z169f06NEDR0dHbGxsqFOnDpcuXdIcv3TpEp6enlhbW2NjY4OHhwfnzp3Dz8+Prl27EhYWphkdjhs3TkdXO/UkJAl2cXGhadOmmiTBmaXfhATFCS97+8ThJHTFFy3SEQbqtxcZ/S5Hnd/Q2oDaD7p37944ODhw8OBBGdFORwghiIqLSvdXaiLWhYSEsHfvXvr165couH/u3Lnp0KED69at02pzxowZVKxYkYsXL9KvXz/69u1LQEAAAHFxcfj4+GBtbc3Ro0c5fvw4VlZW1K9fn9jYWEA9ml6/fr1WCFQ/Pz/u379Pt27dAJg7dy7btm1j/fr1BAQEsHbtWlxdXZN8DyqVik2bNhEaGoqJyTsf/1atWhEcHMzu3bs5f/48FSpUoG7duppfBh06dCBfvnycPXuW8+fPM2LECIyNjalWrRqzZ8/GxsZGMzocOnRoiq+pPklIEvz++/yS+/2QL/rBYZCRWpzXnXoD5MDE0ABXh3dZQzp27Ejjxo2xtf10LkNJyngb/5YqfyUdSVCfnG5/GosUptC6ffs2Qohk54lLlChBaGgoL168wMnJCVD/6urXrx8Aw4cPZ9asWfj6+lKsWDHWrVuHSqVi+fLlKP7LWL5y5Urs7Ozw8/PD29ub9u3b8+OPP7JhwwbN1MDKlSupUaMGRYsWBdTz00WKFKFGjRooFIokM5cPHz6c0aNHExMTQ3x8PPb29vTo0QOAY8eOcebMGYKDgzE1VbuW/v7772zZsoWNGzfSq1cvHj16xLBhwyheXB0CoUiRIpq2bW1tUSgU5M6dO0XXUZ+kJElwRvabngmKv9iRtFKoePHfRbzxWP02C9ib0aFDBwIDAzX1pEBnX1Iz+n7fXz5ByIKDgwH1FMKdO3ewtrbGysoKKysr7O3tiY6O5u5ddc5MOzs7mjdvrpnyCA8PZ9OmTVr5FLt06YK/vz/FihVj4MCB7Nu3L5Edw4YNw9/fn0OHDlGlShVmzZqlycF46dIlIiIiyJkzp8YOKysr7t+/r7FjyJAh9OjRAy8vL6ZMmaIpTw1Hjx7Van/SpEmsXbtWq2zt2rWpbvd9PD098ff35/Tp03Tu3JmuXbsmShL8IZMmTdKy4ejRo/Tp00er7NGjRzrpd9asWfj7+2te9erVS9P7/Rhf7Ej6RcxrlAoFhkIg4q1pWS4Xh+YO5cDJwzx+/JjDhw9rRj0S3WFuZM7p9qczpN+UUrhwYRQKBTdu3KBZs2aJjt+4cYMcOXLg6PguEbGxsXYKNoVCgUqlAtSLoDw8PJIUpvfb6N69O3Xr1uXOnTv4+vpiaGhIq1atNMcrVKjA/fv32b17NwcOHKB169Z4eXmxceNGTR0HBwcKFy5M4cKF2bBhA6VLl6ZixYq4u7sTERGBs7Mzfn5+iexI8KYYN24c7du3Z+fOnezevZuxY8fyzz//JHkdkqNixYr4+/tr9ufOncvTp0+ZOnWqpixXro/nCP0UKUkS/CF9+vShdevWmv0OHTrQokULmjdvrinLk+fj+U5T2m96Jij+rJH0ggULcHV1xczMjCpVqnDmzJmP1t+wYQPFixfHzMyM0qVLs2vXrs8yNjW8iA0DIIcSwIB9yydz8eRhHBwcWLBggRRoPaFQKLAwtkj3V2ruZ86cOalXrx4LFy7k7du3WseCgoJYu3Ytbdq0SXGbFSpU4Pbt2zg5OWkENOH1/i81T09PChYsyMqVK1m5ciVt27bF0lI7aa+NjQ1t2rRh2bJlrFu3jk2bNiXraeLi4kKbNm0YOXKkxo6goCCMjIwS2eHg4KA5r2jRogwePJh9+/bRvHlzVq5cCYCJiQlKpfKT79fc3FyrbXt7e6ytrbXKrK2tU3TtUkJCkuDRo0cnul/vY29vr2WDubl5ontiZJTycWlK+9U3qRbpdevWMWTIEMaOHcuFCxcoW7YsPj4+mp9+H3LixAnatWtH9+7duXjxIk2bNqVp06ZcvXo1zcZ/jHiV+sNmrFL/pL1y8F8cHR3x9fWldOnSeu1bkvmZP38+MTEx+Pj4cOTIER4/fsyePXuoV68eefPmZeLEiSluq0OHDjg4ONCkSROOHj3K/fv38fPzY+DAgTx58kRTT6FQ0K1bNxYtWsTJkycTjc5mzpzJ33//zc2bN7l16xYbNmwgd+7cH/UpHjRoENu3b+fcuXN4eXlRtWpVmjZtyr59+zTJkkeNGsW5c+d4+/YtAwYMwM/Pj4cPH3L8+HHOnj2rmZt3dXUlIiKCgwcP8vLlS6KiolJ3UVOBUqnUmi7w9/fnxo0bydbPqCTBmSE5capFeubMmfTs2ZOuXbvi7u7O4sWLsbCwSORelMCcOXOoX78+w4YNo0SJEvz6669UqFAhXR4CJBC0djhOTk74+vpSqlSpT58g+eIpUqQI586do1ChQrRu3Ro3Nzd69eqFp6cnJ0+eTJVLlYWFBUeOHCF//vw0b96cEiVK0L17d6Kjo7GxsdGq26VLF8LCwihZsiRVqmg/YLW2tmbatGlUrFiRSpUq8eDBA3bt2oWBQfJfU3d3d7y9vRkzZgwKhYJdu3ZRs2ZNunbtStGiRWnbti0PHz4kV65cGBoa8urVKzp16kTRokVp3bo1DRo0YPz48QBUq1aNPn360KZNGxwdHZk2bVoqrmjqiIiI+H979x7S1N/HAfyts7MlmBWmbqWVll2snkpRtEIKIVCs/koo1orKerKHSqgsq0VXiYpArJ6uxo9qVJT1lNjFkrALUSlI2lW7URsJlaOLc+7z/BHb77e8tHNs29n6vOD84el77PNu8+Np53u+B+PGjXPasrKyOh3vrYcEy+HhxAEk4uqJxWJBcHAwzpw543QlU6fT4dOnTzh//ny7Y6Kjo5GXl4fly5c79un1epSWljrN3/ynlpYWtLS0OL5ubm5GVFQUPn/+3O5N35mq+yX496NdUFtsqC3qiwrDfzFy5EjXgjKXfP/+HY2NjRg8eDBUKpW3y2FMdrr6GWlubkZoaOgv+5qoM+mmpia0tbW1uygQEREBo9HY4TFGo1HUeADYvn07QkNDHVtUVJSYMn/Uav67yf/vr/3coBljPkmWU/DWrFmDz58/O7Y3b96I/h7jhk3Gf0KnQTtgDhL/Fe+GKhljzP1ETcELCwuDQqGAyWRy2m8ymTqdAB8ZGSlqPPDj1kz7ZHypBmrikDPD9Ys/jDEmR6LOpAVBQEJCAioqKhz7bDabY1GajqSkpDiNB4CrV692Op4xxtjfRN/MkpeXB51Oh8TERCQlJWHPnj348uUL5s2bBwCYM2cO+vfv71gYZtmyZUhLS8OuXbuQmZkJg8GA+/fvt1ughjHGWHuim3R2djY+fPiADRs2wGg0YuzYsSgvL3dcHHz9+rXTlKHU1FScOHEC69atw9q1azF06FCUlpbyVDg/Yr/zjjHm7Hf8bIiaguctrk5VYZ5ls9nw7NkzKBQK9OvXD4Ig8J2cjOHHujAWiwUfPnxAW1sbhg4d2m6+u6t9zW/X7mDuFxgYiMGDB+P9+/d49+6dt8thTHaCg4MRHR3d5Q1Jv8JNmnWLIAiIjo6G1Wp1ad0Hxv4UCoUCQUFB3f7fJTdp1m32Z8L9vFIcY6z7ZHkzC2OMsR+4STPGmIxxk2aMMRnzic+k7bMEm5ubvVwJY4z9HvZ+9qtZ0D7RpM1mMwBIWg2PMcbkzGw2d/msVZ+4mcVms+Hdu3cICQkRNZ3Fvg71mzdv/PImGH/PB/h/Rs7n+6RmJCKYzWZoNJou51H7xJl0YGAgBgwYIPn4Xr16+e0bBPD/fID/Z+R8vk9Kxq7OoO34wiFjjMkYN2nGGJMxv27SSqUSer2+2w8QkCt/zwf4f0bO5/vcndEnLhwyxtifyq/PpBljzNdxk2aMMRnjJs0YYzLGTZoxxmTM55t0cXExBg0aBJVKheTkZNy7d6/L8adPn8bw4cOhUqkwevRolJWVeahSacTkO3jwICZNmoQ+ffqgT58+SE9P/+W/h7eJff3sDAYDAgICMGPGDPcW+BuIzfjp0yfk5uZCrVZDqVQiLi5O1u9Tsfn27NmDYcOGoWfPnoiKisKKFSvw/ft3D1Urzs2bN5GVlQWNRoOAgACUlpb+8pjKykqMHz8eSqUSQ4YMQUlJSfeKIB9mMBhIEAQ6cuQIPXr0iBYuXEi9e/cmk8nU4fhbt26RQqGgHTt2UF1dHa1bt4569OhBtbW1Hq7cNWLzzZo1i4qLi6m6uprq6+tp7ty5FBoaSm/fvvVw5a4Rm8+usbGR+vfvT5MmTaLp06d7pliJxGZsaWmhxMREysjIoKqqKmpsbKTKykqqqanxcOWuEZvv+PHjpFQq6fjx49TY2EiXL18mtVpNK1as8HDlrikrK6OCggI6e/YsAaBz5851Ob6hoYGCg4MpLy+P6urqqKioiBQKBZWXl0uuwaebdFJSEuXm5jq+bmtrI41GQ9u3b+9w/MyZMykzM9NpX3JyMi1atMitdUolNt/PrFYrhYSE0LFjx9xVYrdIyWe1Wik1NZUOHTpEOp1O9k1abMZ9+/ZRTEwMWSwWT5XYLWLz5ebm0pQpU5z25eXl0YQJE9xa5+/gSpNetWoVxcfHO+3Lzs6mqVOnSv57ffbjDovFggcPHiA9Pd2xLzAwEOnp6bhz506Hx9y5c8dpPABMnTq10/HeJCXfz75+/YrW1lb07dvXXWVKJjXfpk2bEB4ejvnz53uizG6RkvHChQtISUlBbm4uIiIiMGrUKGzbtk2Wz4+Uki81NRUPHjxwfCTS0NCAsrIyZGRkeKRmd3NHj/GJBZY60tTUhLa2NkRERDjtj4iIwOPHjzs8xmg0djjeaDS6rU6ppOT72erVq6HRaNq9aeRASr6qqiocPnwYNTU1Hqiw+6RkbGhowPXr1zF79myUlZXh+fPnWLJkCVpbW6HX6z1Rtsuk5Js1axaampowceJEEBGsVisWL16MtWvXeqJkt+usxzQ3N+Pbt2/o2bOn6O/ps2fSrGuFhYUwGAw4d+4cVCqVt8vpNrPZDK1Wi4MHDyIsLMzb5biNzWZDeHg4Dhw4gISEBGRnZ6OgoAD79+/3dmm/RWVlJbZt24a9e/fi4cOHOHv2LC5duoTNmzd7uzTZ8tkz6bCwMCgUCphMJqf9JpMJkZGRHR4TGRkparw3Sclnt3PnThQWFuLatWsYM2aMO8uUTGy+Fy9e4OXLl8jKynLss9lsAICgoCA8efIEsbGx7i1aJCmvoVqtRo8ePaBQKBz7RowYAaPRCIvFAkEQ3FqzGFLyrV+/HlqtFgsWLAAAjB49Gl++fEFOTg4KCgq6XFfZF3TWY3r16iXpLBrw4TNpQRCQkJCAiooKxz6bzYaKigqkpKR0eExKSorTeAC4evVqp+O9SUo+ANixYwc2b96M8vJyJCYmeqJUScTmGz58OGpra1FTU+PYpk2bhsmTJ6OmpkaWT+2R8hpOmDABz58/d/wCAoCnT59CrVbLqkED0vJ9/fq1XSO2/0IiP1hGyC09RvIlRxkwGAykVCqppKSE6urqKCcnh3r37k1Go5GIiLRaLeXn5zvG37p1i4KCgmjnzp1UX19Per1e9lPwxOQrLCwkQRDozJkz9P79e8dmNpu9FaFLYvP9zBdmd4jN+Pr1awoJCaGlS5fSkydP6OLFixQeHk5btmzxVoQuic2n1+spJCSETp48SQ0NDXTlyhWKjY2lmTNneitCl8xmM1VXV1N1dTUBoN27d1N1dTW9evWKiIjy8/NJq9U6xtun4K1cuZLq6+upuLj4z56CR0RUVFRE0dHRJAgCJSUl0d27dx1/lpaWRjqdzmn8qVOnKC4ujgRBoPj4eLp06ZKHKxZHTL6BAwcSgHabXq/3fOEuEvv6/ZMvNGki8Rlv375NycnJpFQqKSYmhrZu3UpWq9XDVbtOTL7W1lbauHEjxcbGkkqloqioKFqyZAl9/PjR84W74MaNGx3+TNkz6XQ6SktLa3fM2LFjSRAEiomJoaNHj3arBl6qlDHGZMxnP5NmjLE/ATdpxhiTMW7SjDEmY9ykGWNMxrhJM8aYjHGTZowxGeMmzRhjMsZNmjHGZIybNGOMyRg3acYYkzFu0owxJmPcpBljTMb+D9HYaG8x3maWAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -1356,15 +2484,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {\n",
+       "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1376,31 +2505,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-4.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-4.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1412,7 +2542,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1420,7 +2550,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-container {\n",
+       "#sk-container-id-4 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1430,7 +2560,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1446,14 +2576,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1461,28 +2591,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
+       "#sk-container-id-4 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1500,24 +2630,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1525,27 +2664,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1553,79 +2690,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1635,18 +2771,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1662,23 +2798,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1688,7 +2826,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1698,7 +2837,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1733,12 +2873,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1748,31 +2888,594 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
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+       "\n",
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+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
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+       "            // If the text is very bright we have a dark theme\n",
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       ],
       "text/plain": [
        "RandomForestClassifier()"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1793,18 +3496,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.42, 0.58],\n",
-       "       [0.89, 0.11],\n",
-       "       [0.64, 0.36]])"
+       "array([[0.26, 0.74],\n",
+       "       [0.76, 0.24],\n",
+       "       [0.66, 0.34]])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1816,7 +3519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1825,16 +3528,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.5556820682740744, 0.7731918190932655)"
+       "(0.5566906703022872, 0.7760471471663816)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1849,12 +3552,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -1893,25 +3596,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/xadupre/install/scikit-learn/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
       "  warnings.warn(\n",
-      "/home/xadupre/install/scikit-learn/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
       "  warnings.warn(\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-7 {\n",
+       "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1923,31 +3681,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-5.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-5.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 {\n",
+       "#sk-container-id-5 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 pre {\n",
+       "#sk-container-id-5 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1959,7 +3718,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1967,7 +3726,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-container {\n",
+       "#sk-container-id-5 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1977,7 +3736,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1993,14 +3752,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel {\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2008,28 +3767,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-serial {\n",
+       "#sk-container-id-5 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2047,24 +3806,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable {\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2072,27 +3840,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2100,79 +3866,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label-container {\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-7 div.sk-estimator {\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2182,18 +3947,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2209,23 +3974,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -2235,7 +4002,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2245,7 +4013,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2280,12 +4049,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -2295,37 +4064,1186 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;ovrlr&#x27;,\n",
-       "                                LogisticRegression(solver=&#x27;liblinear&#x27;)),\n",
-       "                               (&#x27;rf&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StackingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StackingClassifier(estimators=[(&#x27;ovrlr&#x27;,\n",
-       "                                LogisticRegression(solver=&#x27;liblinear&#x27;)),\n",
-       "                               (&#x27;rf&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>ovrlr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;ovrlr&#x27;, LogisticRegression()),\n",
+       "                               (&#x27;rf&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StackingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=estimators,-list%20of%20%28str%2C%20estimator%29\">\n",
+       "            estimators\n",
+       "            <span class=\"param-doc-description\">estimators: list of (str, estimator)<br><br>Base estimators which will be stacked together. Each element of the<br>list is defined as a tuple of string (i.e. name) and an estimator<br>instance. An estimator can be set to 'drop' using `set_params`.<br><br>The type of estimator is generally expected to be a classifier.<br>However, one can pass a regressor for some use case (e.g. ordinal<br>regression).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;ovrlr&#x27;, ...), (&#x27;rf&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('final_estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=final_estimator,-estimator%2C%20default%3DNone\">\n",
+       "            final_estimator\n",
+       "            <span class=\"param-doc-description\">final_estimator: estimator, default=None<br><br>A classifier which will be used to combine the base estimators.<br>The default classifier is a<br>:class:`~sklearn.linear_model.LogisticRegression`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=cv,-int%2C%20cross-validation%20generator%2C%20iterable%2C%20or%20%22prefit%22%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator, iterable, or \"prefit\", default=None<br><br>Determines the cross-validation splitting strategy used in<br>`cross_val_predict` to train `final_estimator`. Possible inputs for<br>cv are:<br><br>* None, to use the default 5-fold cross validation,<br>* integer, to specify the number of folds in a (Stratified) KFold,<br>* An object to be used as a cross-validation generator,<br>* An iterable yielding train, test splits,<br>* `\"prefit\"`, to assume the `estimators` are prefit. In this case, the<br>  estimators will not be refitted.<br><br>For integer/None inputs, if the estimator is a classifier and y is<br>either binary or multiclass,<br>:class:`~sklearn.model_selection.StratifiedKFold` is used.<br>In all other cases, :class:`~sklearn.model_selection.KFold` is used.<br>These splitters are instantiated with `shuffle=False` so the splits<br>will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>If \"prefit\" is passed, it is assumed that all `estimators` have<br>been fitted already. The `final_estimator_` is trained on the `estimators`<br>predictions on the full training set and are **not** cross validated<br>predictions. Please note that if the models have been trained on the same<br>data to train the stacking model, there is a very high risk of overfitting.<br><br>.. versionadded:: 1.1<br>    The 'prefit' option was added in 1.1<br><br>.. note::<br>   A larger number of split will provide no benefits if the number<br>   of training samples is large enough. Indeed, the training time<br>   will increase. ``cv`` is not used for model evaluation but for<br>   prediction.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('stack_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=stack_method,-%7B%27auto%27%2C%20%27predict_proba%27%2C%20%27decision_function%27%2C%20%27predict%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27auto%27\">\n",
+       "            stack_method\n",
+       "            <span class=\"param-doc-description\">stack_method: {'auto', 'predict_proba', 'decision_function', 'predict'},             default='auto'<br><br>Methods called for each base estimator. It can be:<br><br>* if 'auto', it will try to invoke, for each estimator,<br>  `'predict_proba'`, `'decision_function'` or `'predict'` in that<br>  order.<br>* otherwise, one of `'predict_proba'`, `'decision_function'` or<br>  `'predict'`. If the method is not implemented by the estimator, it<br>  will raise an error.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for `fit` of all `estimators`.<br>`None` means 1 unless in a `joblib.parallel_backend` context. -1 means<br>using all processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('passthrough',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=passthrough,-bool%2C%20default%3DFalse\">\n",
+       "            passthrough\n",
+       "            <span class=\"param-doc-description\">passthrough: bool, default=False<br><br>When False, only the predictions of estimators will be used as<br>training data for `final_estimator`. When True, the<br>`final_estimator` is trained on the predictions as well as the<br>original training data.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity level.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>ovrlr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"ovrlr__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"rf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"final_estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-5');</script></body>"
       ],
       "text/plain": [
-       "StackingClassifier(estimators=[('ovrlr',\n",
-       "                                LogisticRegression(solver='liblinear')),\n",
+       "StackingClassifier(estimators=[('ovrlr', LogisticRegression()),\n",
        "                               ('rf', RandomForestClassifier())])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2335,7 +5253,7 @@
     "\n",
     "model = StackingClassifier(\n",
     "    [\n",
-    "        (\"ovrlr\", LogisticRegression(solver=\"liblinear\")),\n",
+    "        (\"ovrlr\", LogisticRegression()),\n",
     "        (\"rf\", RandomForestClassifier()),\n",
     "    ]\n",
     ")\n",
@@ -2344,16 +5262,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7196235911146174"
+       "0.7486097777777779"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2376,11 +5294,16 @@
    "source": [
     "La validation croisée a été escamotée par gain de temps mais faire l'impasse est risquée dans le cas général."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -2394,7 +5317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/py-base/exercice_json_xml.ipynb b/_doc/practice/py-base/exercice_json_xml.ipynb
index 434e0a0..22fa798 100644
--- a/_doc/practice/py-base/exercice_json_xml.ipynb
+++ b/_doc/practice/py-base/exercice_json_xml.ipynb
@@ -217,13 +217,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -362,26 +355,36 @@
     {
      "data": {
       "text/plain": [
-       "['coloriage_carre.ipynb',\n",
-       " 'histogramme_rapide.ipynb',\n",
-       " 'dummy.json',\n",
-       " 'exercice_json_xml.ipynb',\n",
-       " 'exercice_regex.ipynb',\n",
+       "['code_liste_tuple.ipynb',\n",
        " 'tableau_contingence.ipynb',\n",
-       " 'nbheap.ipynb',\n",
-       " 'recherche_dichotomique.ipynb',\n",
-       " 'classes_2048.ipynb',\n",
-       " 'profile_gini.ipynb',\n",
-       " 'pivot_gauss.ipynb',\n",
-       " 'profiling_example.ipynb',\n",
        " 'classes_metro.ipynb',\n",
-       " 'exercice_pi.ipynb',\n",
-       " 'code_multinomial.ipynb',\n",
+       " 'profiling_example.ipynb',\n",
+       " 'tests_unitaires.ipynb',\n",
+       " 'scrapping.ipynb',\n",
+       " 'histogramme_rapide.ipynb',\n",
+       " 'profile_gini.ipynb',\n",
+       " 'classe_de.ipynb',\n",
+       " 'de_rotation.ipynb',\n",
        " 'images',\n",
-       " 'code_liste_tuple.ipynb',\n",
+       " 'nbheap.ipynb',\n",
+       " 'exercice_pi.ipynb',\n",
+       " 'profiling_conv.ipynb',\n",
+       " 'exercice_hash.ipynb',\n",
+       " 'classe_iterateur.ipynb',\n",
        " 'tri_nlnd.ipynb',\n",
+       " 'dame_prise.ipynb',\n",
+       " 'classe_user_p.ipynb',\n",
+       " 'coloriage_carre.ipynb',\n",
+       " 'classe_permutation.ipynb',\n",
        " 'fleurs.csv',\n",
+       " 'recherche_dichotomique.ipynb',\n",
+       " 'classes_2048.ipynb',\n",
+       " 'exercice_regex.ipynb',\n",
+       " 'exercice_json_xml.ipynb',\n",
+       " 'pivot_gauss.ipynb',\n",
        " 'structures_donnees_conversion.ipynb',\n",
+       " 'classe_tree.ipynb',\n",
+       " 'code_multinomial.ipynb',\n",
        " 'exercice_serialisation_json.ipynb']"
       ]
      },
@@ -907,7 +910,7 @@
     }
    ],
    "source": [
-    "df_html = pandas.read_html(html_text)\n",
+    "df_html = pandas.read_html(StringIO(html_text))\n",
     "df_html[0].tail()"
    ]
   },
@@ -928,7 +931,7 @@
     }
    ],
    "source": [
-    "df_html = pandas.read_html(html_text + html_text)\n",
+    "df_html = pandas.read_html(StringIO(html_text + html_text))\n",
     "len(df_html)"
    ]
   },
@@ -1797,18 +1800,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from zipfile import ZipFile\n",
     "\n",
@@ -1819,9 +1813,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['data.zip']"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import glob\n",
     "\n",
@@ -1847,7 +1852,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -1861,7 +1866,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/py-base/scrapping.ipynb b/_doc/practice/py-base/scrapping.ipynb
index 18cba6f..006ee7c 100644
--- a/_doc/practice/py-base/scrapping.ipynb
+++ b/_doc/practice/py-base/scrapping.ipynb
@@ -380,7 +380,6 @@
     "import shutil\n",
     "import requests\n",
     "\n",
-    "\n",
     "for e, pokemon in enumerate(liste_pokemon):\n",
     "    print(e, pokemon)\n",
     "    url = \"https://img.pokemondb.net/artwork/{}.jpg\".format(pokemon)\n",
diff --git a/_doc/practice/tds-base/module_file_regex_correction.ipynb b/_doc/practice/tds-base/module_file_regex_correction.ipynb
index 3dfb03c..f858e6d 100644
--- a/_doc/practice/tds-base/module_file_regex_correction.ipynb
+++ b/_doc/practice/tds-base/module_file_regex_correction.ipynb
@@ -275,9 +275,7 @@
     "            return [ math.cos(x) for x in seq ]        \n",
     "        if True :\n",
     "            print (\"Ce message n'apparaît que si ce programme est le point d'entrée.\")\n",
-    "        \"\"\".replace(\n",
-    "        \"        \", \"\"\n",
-    "    )\n",
+    "        \"\"\".replace(\"        \", \"\")\n",
     "    with open(\"monmodule3.py\", \"w\", encoding=\"utf8\") as f:\n",
     "        f.write(code)"
    ]
diff --git a/_doc/practice/tds-base/texte_langue_correction.ipynb b/_doc/practice/tds-base/texte_langue_correction.ipynb
index 095216c..87a56d6 100644
--- a/_doc/practice/tds-base/texte_langue_correction.ipynb
+++ b/_doc/practice/tds-base/texte_langue_correction.ipynb
@@ -82,10 +82,8 @@
    "outputs": [],
    "source": [
     "with open(\"texte.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "    f.write(\n",
-    "        \"\"\"Un corbeau sur un arbre perché tenait en son bec un fromage.\n",
-    "Maître Renard, par l'odeur alléché, Lui tint à peu près ce langage :\"\"\"\n",
-    "    )"
+    "    f.write(\"\"\"Un corbeau sur un arbre perché tenait en son bec un fromage.\n",
+    "Maître Renard, par l'odeur alléché, Lui tint à peu près ce langage :\"\"\")"
    ]
   },
   {
diff --git a/_doc/practice/years/2025/seance4_algo.ipynb b/_doc/practice/years/2025/seance4_algo.ipynb
index 2a5226a..c351d64 100644
--- a/_doc/practice/years/2025/seance4_algo.ipynb
+++ b/_doc/practice/years/2025/seance4_algo.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Algorithmes\n",
+    "# Algorithmes, voyageur de commerce, distance d'édition\n",
     "\n",
     "## Voyageur de commerce"
    ]
@@ -17,11 +17,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.90080623, 0.2350022 ],\n",
-       "       [0.56755441, 0.54858335],\n",
-       "       [0.60316886, 0.99559521],\n",
-       "       [0.87287745, 0.22318813],\n",
-       "       [0.21085165, 0.0701609 ]])"
+       "array([[0.41511992, 0.25980581],\n",
+       "       [0.28882207, 0.18110005],\n",
+       "       [0.43436194, 0.89716744],\n",
+       "       [0.51101688, 0.41073324],\n",
+       "       [0.73591566, 0.73214563]])"
       ]
      },
      "execution_count": 1,
@@ -45,7 +45,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x73893a0a5370>]"
+       "[<matplotlib.lines.Line2D at 0x79588b67de80>]"
       ]
      },
      "execution_count": 2,
@@ -54,7 +54,7 @@
     },
     {
      "data": {
-      "image/png": "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",
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",
       "text/plain": [
        "<Figure size 300x300 with 1 Axes>"
       ]
@@ -77,7 +77,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x738939c39b50>]"
+       "[<matplotlib.lines.Line2D at 0x79588b2b2a50>]"
       ]
      },
      "execution_count": 3,
@@ -86,7 +86,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 1 Axes>"
       ]
@@ -136,18 +136,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mieux -1 3 -0.15191965099971733 [0, 3, 14, 19] []\n",
-      "mieux -1 11 -0.5172511323315206 [19, 14, 3, 0, 16, 6, 10, 17, 15, 11, 18, 2] []\n",
-      "mieux 11 13 -0.0513745444570991 [1, 12] [12, 1]\n",
-      "mieux 11 17 -0.0044994455150375035 [12, 1, 7, 9, 4, 5] [5, 4, 9, 7, 1, 12]\n",
-      "mieux 15 17 -0.03444152981270798 [1, 12] [12, 1]\n",
-      "mieux 11 13 -0.12384307859548493 [5, 4] [4, 5]\n"
+      "mieux -1 2 -0.015414159550566658 [0, 11, 1] []\n",
+      "mieux 3 5 -0.034186569174776416 [13, 3] [3, 13]\n",
+      "mieux 6 14 -0.053147377441806753 [4, 16, 2, 9, 12, 14, 17, 7] [7, 17, 14, 12, 9, 2, 16, 4]\n",
+      "mieux 7 9 -0.01748283747037216 [17, 14] [14, 17]\n",
+      "mieux 13 15 -0.19987294649097015 [4, 19] [19, 4]\n",
+      "mieux 2 4 -0.0075636338480231935 [10, 3] [3, 10]\n",
+      "mieux 6 9 -0.2253313144570693 [7, 14, 17] [17, 14, 7]\n",
+      "mieux 8 10 -0.14891254917547306 [7, 12] [12, 7]\n",
+      "mieux 3 6 -0.0478332555189685 [10, 13, 15] [15, 13, 10]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x738937b2a870>]"
+       "[<matplotlib.lines.Line2D at 0x79588b355be0>]"
       ]
      },
      "execution_count": 4,
@@ -156,7 +159,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 1 Axes>"
       ]
@@ -167,8 +170,10 @@
    ],
    "source": [
     "def elimine_croisements(villes, chemin):\n",
+    "    n_iteration = 0\n",
     "    C = chemin\n",
-    "    while True:\n",
+    "    while n_iteration < 10 + villes.shape[0]:\n",
+    "        n_iteration += 1  # pour éviter une boucle infini\n",
     "        mieux = 0\n",
     "        for i in range(-1, villes.shape[0]):\n",
     "            for j in range(i + 2, villes.shape[0] - 1):\n",
@@ -284,9 +289,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/years/2025/seance5_algo2.ipynb b/_doc/practice/years/2025/seance5_algo2.ipynb
index a0b3ea5..734a5da 100644
--- a/_doc/practice/years/2025/seance5_algo2.ipynb
+++ b/_doc/practice/years/2025/seance5_algo2.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Algorithmes\n",
+    "# Porfilage d'algorithmes\n",
     "\n",
     "## Mesurer le temps"
    ]
diff --git a/_doc/py/c_regex/regex.rst b/_doc/py/c_regex/regex.rst
index 3c5d69a..2b4925c 100644
--- a/_doc/py/c_regex/regex.rst
+++ b/_doc/py/c_regex/regex.rst
@@ -275,7 +275,7 @@ et :func:`re.match` retournent toutes des objets :func:`re.match` :
     date 7 : 08/03/1941     date 8 : 8/1/1980     date 9 : 30/6/1976"""
 
     import re
-    expression = re.compile("([0-3]?[0-9]/[0-1]?[0-9]/([0-2][0-9])?[0-9][0-9])[^\d]")
+    expression = re.compile("([0-3]?[0-9]/[0-1]?[0-9]/([0-2][0-9])?[0-9][0-9])[^\\d]")
     print(expression.search(s).group(1,2)) # affiche ('14/9/2000', '20')
     c = expression.search(s).span(1)       # affiche (9, 18)
     print(s[c[0]:c[1]])                    # affiche 14/9/2000
diff --git a/_latex/ensae/td_note_2010_rattrape.py b/_latex/ensae/td_note_2010_rattrape.py
index acb0016..1aad043 100644
--- a/_latex/ensae/td_note_2010_rattrape.py
+++ b/_latex/ensae/td_note_2010_rattrape.py
@@ -33,11 +33,7 @@ def get_tour():
 Metz	6,11729002	49,0734787
 Sedan	4,896070004	49,68407059
 Grenoble	5,684440136	45,13940048
-Annecy	6,082499981	45,8782196""".replace(
-        ",", "."
-    ).split(
-        "\n"
-    )
+Annecy	6,082499981	45,8782196""".replace(",", ".").split("\n")
     # ligne d'avant : on d�coupe l'unique cha�ne de caract�res
 
     # ligne suivant : on d�coupe chaque ligne en colonne
diff --git a/_unittests/ut_datasets/test_data_ts.py b/_unittests/ut_datasets/test_data_ts.py
index e0acdf0..d8705d3 100644
--- a/_unittests/ut_datasets/test_data_ts.py
+++ b/_unittests/ut_datasets/test_data_ts.py
@@ -4,7 +4,6 @@
 
 
 class TestDataTs(ExtTestCase):
-
     def test_generate_sells(self):
         df = generate_sells()
         self.assertEqual(len(df), 731)
diff --git a/_unittests/ut_tools/test_helpers.py b/_unittests/ut_tools/test_helpers.py
new file mode 100644
index 0000000..3be2c09
--- /dev/null
+++ b/_unittests/ut_tools/test_helpers.py
@@ -0,0 +1,13 @@
+import unittest
+from teachpyx.ext_test_case import ExtTestCase
+from teachpyx.tools.helpers import total_size
+
+
+class TestHelpers(ExtTestCase):
+    def test_total_size(self):
+        res = total_size([4, (5,), {"r": 5}, {4.5}])
+        self.assertGreater(res, 10)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/_unittests/ut_tools/test_pandas.py b/_unittests/ut_tools/test_pandas.py
new file mode 100644
index 0000000..4833207
--- /dev/null
+++ b/_unittests/ut_tools/test_pandas.py
@@ -0,0 +1,19 @@
+import unittest
+from teachpyx.ext_test_case import ExtTestCase
+from teachpyx.tools.pandas import read_csv_cached
+
+
+class TestPandas(ExtTestCase):
+    def test_read_csv_cached(self):
+        df = read_csv_cached(
+            "https://github.com/sdpython/teachpyx/raw/main/_data/paris_54000.zip"
+        )
+        df2 = read_csv_cached(
+            "https://github.com/sdpython/teachpyx/raw/main/_data/paris_54000.zip"
+        )
+        self.assertEqual(df.shape, df2.shape)
+        self.assertEqual(list(df.columns), list(df2.columns))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/_unittests/ut_tools/test_profiling.py b/_unittests/ut_tools/test_profiling.py
index 91e8bba..72bfdef 100644
--- a/_unittests/ut_tools/test_profiling.py
+++ b/_unittests/ut_tools/test_profiling.py
@@ -1,7 +1,3 @@
-"""
-@brief      test tree node (time=5s)
-"""
-
 import os
 import sys
 import time
diff --git a/_unittests/ut_xrun_doc/test_measure_time.py b/_unittests/ut_xrun_doc/test_measure_time.py
index d50876a..e4f1f50 100644
--- a/_unittests/ut_xrun_doc/test_measure_time.py
+++ b/_unittests/ut_xrun_doc/test_measure_time.py
@@ -9,6 +9,11 @@ def test_measure_time(self):
         self.assertIsInstance(res, dict)
         self.assertIn("average", res)
 
+    def test_measure_time_max_time(self):
+        res = measure_time(lambda: cos(5), max_time=0.2)
+        self.assertIsInstance(res, dict)
+        self.assertIn("average", res)
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)
diff --git a/clean.sh b/clean.sh
index 73e51b6..16f5457 100644
--- a/clean.sh
+++ b/clean.sh
@@ -4,3 +4,15 @@ rm essai.txt
 rm *.bin
 rm *.prof
 rm *.log
+rm lisezmoi*
+rm etatcivil*
+rm exemple.txt
+rm *.pdf
+rm *.dbf
+rm *.xlsx
+rm _unittests/*/temp_* -rf
+rm _doc/c_data/lisezmoi*
+rm _doc/c_data/etatcivil*
+rm _doc/c_data/exemple.txt
+rm _doc/c_data/*.pdf
+rm _doc/c_data/*.dbf
diff --git a/pyproject.toml b/pyproject.toml
index e114b30..702f8a4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
 ]
-dependencies = ["numpy", "matplotlib", "pandas", "scikit-learn>=1.2"]
+dependencies = ["numpy", "matplotlib", "pandas", "scikit-learn>=1.2", "tqdm"]
 description = "Teaching material, algorithm, machine learning"
 keywords = ["python", "teaching", "algorithmic", "machine learning"]
 license = {file = "LICENSE.txt"}
@@ -68,7 +68,6 @@ dev = [
     "sphinx-gallery",
     "sphinx-issues",
     "sphinx-runpython",
-    "tqdm",
     "ujson",
     "wheel",
 ]
diff --git a/requirements-dev.txt b/requirements-dev.txt
index ac15ba5..e98d633 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -42,7 +42,6 @@ sphinx-issues
 git+https://github.com/sdpython/sphinx-runpython.git
 statsmodels
 torch
-tqdm
 transformers
 ujson
 xarray
diff --git a/requirements.txt b/requirements.txt
index 89986db..b8238d7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@ matplotlib
 numpy
 pandas
 scikit-learn>=1.2
+tqdm
diff --git a/teachpyx/datasets/wines.py b/teachpyx/datasets/wines.py
index e7b534d..eb4caf4 100644
--- a/teachpyx/datasets/wines.py
+++ b/teachpyx/datasets/wines.py
@@ -3,7 +3,6 @@
 import pandas
 from .data_helper import get_data_folder
 
-
 __all__ = ["load_wines_dataset"]
 
 
diff --git a/teachpyx/tools/data_helper.py b/teachpyx/tools/data_helper.py
index dcc3a57..fb287df 100644
--- a/teachpyx/tools/data_helper.py
+++ b/teachpyx/tools/data_helper.py
@@ -26,14 +26,14 @@ def decompress_zip(filename, dest: str, verbose: bool = False) -> List[str]:
                 finalfolder = os.path.split(tos)[0]
                 if not os.path.exists(finalfolder):
                     if verbose:
-                        print(f"creating folder {finalfolder!r}")
+                        print(f"[decompress_zip] creating folder {finalfolder!r}")
                     os.makedirs(finalfolder)
                 if not info.filename.endswith("/"):
                     with open(tos, "wb") as u:
                         u.write(data)
                     files.append(tos)
                     if verbose:
-                        print(f"unzipped {info.filename!r} to {tos!r}")
+                        print(f"[decompress_zip] unzipped {info.filename!r} to {tos!r}")
             elif not tos.endswith("/"):
                 files.append(tos)
         elif not info.filename.endswith("/"):
@@ -45,7 +45,7 @@ def download(
     url: str, dest: str = ".", timeout: int = 10, verbose: bool = False
 ) -> str:
     """
-    Download one file.
+    Downloads one file.
 
     :param url: url
     :param dest: destination folder
@@ -57,13 +57,13 @@ def download(
     dest_zip = os.path.join(dest, filename)
     if not os.path.exists(dest_zip):
         if verbose:
-            print(f"downloads into {dest_zip!r} from {url!r}")
+            print(f"[download] downloads into {dest_zip!r} from {url!r}")
         with urlopen(url, timeout=timeout) as u:
             content = u.read()
         with open(dest_zip, "wb") as f:
             f.write(content)
     elif verbose:
-        print(f"already downloaded {dest_zip!r}")
+        print(f"[download] already downloaded {dest_zip!r}")
     return dest_zip
 
 
@@ -83,12 +83,12 @@ def download_and_unzip(
     dest_zip = os.path.join(dest, filename)
     if not os.path.exists(dest_zip):
         if verbose:
-            print(f"downloads into {dest_zip!r} from {url!r}")
+            print(f"[download_and_unzip] downloads into {dest_zip!r} from {url!r}")
         with urlopen(url, timeout=timeout) as u:
             content = u.read()
         with open(dest_zip, "wb") as f:
             f.write(content)
     elif verbose:
-        print(f"already downloaded {dest_zip!r}")
+        print(f"[download_and_unzip] already downloaded {dest_zip!r}")
 
     return decompress_zip(dest_zip, dest, verbose=verbose)
diff --git a/teachpyx/tools/display/pygame_helper.py b/teachpyx/tools/display/pygame_helper.py
index 56142d3..2b0f028 100644
--- a/teachpyx/tools/display/pygame_helper.py
+++ b/teachpyx/tools/display/pygame_helper.py
@@ -1,7 +1,6 @@
 import math
 from typing import List, Optional, Tuple
 
-
 MOUSE = "mouse"
 KEY = "key"
 
@@ -305,9 +304,7 @@ def build_diff_image(
 
 
 def display_line(ligne: List[Tuple[float, float]], screen, pygame):
-    """
-    Affiche une ligne à l'écran.
-    """
+    """Affiche une ligne à l'écran."""
     color = 0, 0, 0
     for p in ligne:
         pygame.draw.line(screen, color, p, p)
diff --git a/teachpyx/tools/display/video_helper.py b/teachpyx/tools/display/video_helper.py
index 403baaf..cf8102e 100644
--- a/teachpyx/tools/display/video_helper.py
+++ b/teachpyx/tools/display/video_helper.py
@@ -41,14 +41,8 @@ def make_video(
     By default, the video will have the size of the first image.
     It will resize every image to this size before adding them to the video.
     """
-    if len(images) == 0:
-        raise ValueError("No image to convert into a video.")
-    from cv2 import (
-        VideoWriter,
-        VideoWriter_fourcc,
-        imread,
-        resize,
-    )  # pylint: disable=E0401
+    assert images, "No image to convert into a video."
+    from cv2 import VideoWriter, VideoWriter_fourcc, imread, resize
 
     fourcc = VideoWriter_fourcc(*format)
     vid = None
diff --git a/teachpyx/tools/graphviz_helper.py b/teachpyx/tools/graphviz_helper.py
index c919b6b..311bb0f 100644
--- a/teachpyx/tools/graphviz_helper.py
+++ b/teachpyx/tools/graphviz_helper.py
@@ -56,10 +56,7 @@ def find_graphviz_dot(exc: bool = True) -> str:
     return "dot"
 
 
-def run_subprocess(
-    args: List[str],
-    cwd: Optional[str] = None,
-):
+def run_subprocess(args: List[str], cwd: Optional[str] = None):
     assert not isinstance(
         args, str
     ), "args should be a sequence of strings, not a string."
@@ -91,14 +88,14 @@ def run_subprocess(
     p.stdout.close()
     if raise_exception:
         raise RuntimeError(
-            "An error was found in the output. The build is stopped.\n{output}"
+            f"An error was found in the output. The build is stopped.\n{output}"
         )
     return output
 
 
 def run_graphviz(filename: str, image: str, engine: str = "dot") -> str:
     """
-    Run :epkg:`Graphviz`.
+    Runs :epkg:`Graphviz`.
 
     :param filename: filename which contains the graph definition
     :param image: output image
diff --git a/teachpyx/tools/pandas.py b/teachpyx/tools/pandas.py
new file mode 100644
index 0000000..165e544
--- /dev/null
+++ b/teachpyx/tools/pandas.py
@@ -0,0 +1,46 @@
+import hashlib
+import os
+import re
+from pathlib import Path
+from urllib.parse import urlparse, unquote
+import pandas
+
+
+def _filename_from_url(url):
+    parsed = urlparse(url)
+    path = parsed.path
+    name = unquote(path.split("/")[-1])
+    name = re.sub(r"[^\w.\-]", "_", name)
+    assert name, f"unable to create a filename from {url!r}"
+    h = hashlib.sha1(url.encode()).hexdigest()[:8]
+    return f"{os.path.splitext(name)[0]}_{h}.csv"
+
+
+def read_csv_cached(
+    filepath_or_buffer: str, ignore_cache: bool = False, **kwargs
+) -> pandas.DataFrame:
+    """
+    After the data is loaded with :func:`pandas.read_csv`,
+    the data is cached. This is interesting when the data is downloaded.
+    The second call reuses the cached data.
+    The cached dataframe is stored in
+    ``.cache/teachpyx/<filename>_<hash>.csv``.
+
+    :param filepath_or_buffer: Any valid string path is acceptable.
+        The string could be a URL. Valid URL schemes include http,
+        ftp, s3, gs, and file. For file URLs, a host is expected.
+        See :func:`pandas.read_csv`.
+    :param ignore_cache: ignore the cache, overwrites it if it exists
+    :param kwargs: other argument for :func:`pandas.read_csv`
+    :return: dataframe
+    """
+    cache_dir = Path.home() / ".cache" / "teachpyx" / "pandas"
+    cache_dir.mkdir(parents=True, exist_ok=True)
+
+    cache_name = cache_dir / _filename_from_url(filepath_or_buffer)
+    if cache_name.exists() and not ignore_cache:
+        return pandas.read_csv(cache_name)
+
+    df = pandas.read_csv(filepath_or_buffer, **kwargs)
+    df.to_csv(cache_name, index=False)
+    return df
diff --git a/teachpyx/video/tsp_kohonen_pygame.py b/teachpyx/video/tsp_kohonen_pygame.py
index 4c197b9..7c74209 100644
--- a/teachpyx/video/tsp_kohonen_pygame.py
+++ b/teachpyx/video/tsp_kohonen_pygame.py
@@ -1,4 +1,5 @@
 import os
+from tqdm import tqdm
 from ..practice.tsp_kohonen import (
     ENSEMBLE,
     iteration,
@@ -11,9 +12,7 @@
 
 
 def display_neurone(neurones: ENSEMBLE, screen, bn: int, pygame):
-    """
-    Dessine les neurones à l'écran.
-    """
+    """Dessine les neurones à l'écran."""
     color = 0, 0, 255
     color2 = 0, 255, 0
     for n in neurones:
@@ -25,9 +24,7 @@ def display_neurone(neurones: ENSEMBLE, screen, bn: int, pygame):
 
 
 def display_ville(villes: ENSEMBLE, screen, bv: int, pygame):
-    """
-    Dessine les villes à l'écran.
-    """
+    """Dessine les villes à l'écran."""
     color = 255, 0, 0
     color2 = 0, 255, 0
     for v in villes:
@@ -98,14 +95,9 @@ def pygame_simulation(
         wait_event(pygame)
     images = [] if folder is not None else None
 
-    iter = 0
-    while iter < max_iter:
-        iter += 1
-
-        if iter % 1000 == 0:
-            print("iter", iter)
+    for n_iter in tqdm(list(range(max_iter)), desc="optimizing"):
 
-        if iter % maj == 0:
+        if n_iter % maj == 0:
             modifie_structure(neurones, compte_n, tour)
             dist *= alpha
             f2 = tuple(w * beta for w in fs)
@@ -119,16 +111,13 @@ def pygame_simulation(
         empty_main_loop(pygame)
         pygame.display.flip()
 
-        if images is not None and iter % 10 == 0:
+        if images is not None and n_iter % 10 == 0:
             images.append(screen.copy())
 
     if first_click:
         wait_event(pygame)
 
     if folder is not None:
-        print("saving images")
-        for it, screen in enumerate(images):
-            if it % 10 == 0:
-                print("saving image:", it, "/", len(images))
+        for it, screen in enumerate(tqdm(images, desc=f"saving images in {folder!r}")):
             image = os.path.join(folder, "image_%04d.png" % it)
             pygame.image.save(screen, image)
diff --git a/teachpyx/video/tsp_kruskal_pygame.py b/teachpyx/video/tsp_kruskal_pygame.py
index 5e330aa..b430ac9 100644
--- a/teachpyx/video/tsp_kruskal_pygame.py
+++ b/teachpyx/video/tsp_kruskal_pygame.py
@@ -2,6 +2,7 @@
 import math
 import random
 from typing import List, Optional, Tuple
+from tqdm import tqdm
 from ..practice.tsp_kruskal import (
     ENSEMBLE,
     DISTANCE,
@@ -144,9 +145,7 @@ def distance(p1, p2):
 
 
 def display_ville(villes, screen, bv, pygame):
-    """
-    Dessine les villes à l'écran.
-    """
+    """Dessine les villes à l'écran."""
     color = 255, 0, 0
     color2 = 0, 255, 0
     for v in villes:
@@ -156,9 +155,7 @@ def display_ville(villes, screen, bv, pygame):
 
 
 def display_chemin(neurones, bn, screen, pygame):
-    """
-    Dessine le chemin à l'écran.
-    """
+    """Dessine le chemin à l'écran."""
     color = 0, 0, 255
     color2 = 0, 255, 0
     for n in neurones:
@@ -170,9 +167,7 @@ def display_chemin(neurones, bn, screen, pygame):
 
 
 def display_arbre(villes, arbre, mult=1, screen=None, pygame=None):
-    """
-    Dessine le graphe de poids minimal dꧩni par arbre.
-    """
+    """Dessine le graphe de poids minimal dꧩni par arbre."""
     if mult == 2:
         color = 0, 255, 0
         li = 4
@@ -443,10 +438,6 @@ def pygame_simulation(
         wait_event(pygame)
 
     if folder is not None:
-        if verbose > 0:
-            print("saving images")
-        for it, screen in enumerate(images):
-            if verbose > 0 and it % 10 == 0:
-                print(f"saving image: {it}/{len(images)}")
+        for it, screen in enumerate(tqdm(images, desc=f"saving images in {folder!r}")):
             image = os.path.join(folder, "image_%04d.png" % it)
             pygame.image.save(screen, image)