diff --git a/examples/transformations/catch22.ipynb b/examples/transformations/catch22.ipynb
index a551f67ef6..1a921944c9 100644
--- a/examples/transformations/catch22.ipynb
+++ b/examples/transformations/catch22.ipynb
@@ -276,7 +276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You may find that some time series cannot extract certain features from it. This may happen when division by zero occurs, or the input value is zero. Simply, it means we cannot extract the feature from the time series. However, we may still want a number for calculations and therefore 'replace_nans' allows us to replace NaN with zero."
+    "You may find that some time series cannot extract certain features from it. This may happen when division by zero occurs, or the input value is zero. Simply, it means we cannot extract the feature from the time series. However, we may still want a number for calculations and therefore ``'replace_nans'`` allows us to replace ``NaN`` with zero."
    ]
   },
   {
@@ -323,7 +323,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pycatch22 is the original implementation of catch22 based on \\[1\\]. Aeon allows you to use pycatch22 by setting the parameter 'use_pycatch22' to true. The difference of the two is that pycatch22 uses C as their backend while python uses the Numba library, which assembles python code into C. Aeon also regularly maintains their catch22 library, and therefore there should be barely any discrepancy between outputs. Pycatch22 has a few issues with their implementation such as at times struggling to run on windows. If you are using the aeon library for a certain task, but want to use pycatch22 for transformation of the data, it is recommended to use aeon's catch22 with the parameter 'use_pycatch22' set to true. If you do that, you may encounter a warning that pycatch22 has not been installed and therefore will use aeon's catch22, if that happens just install the pycatch22 library.\n",
+    "Pycatch22 is the original implementation of catch22 based on \\[1\\]. Aeon allows you to use pycatch22 by setting the parameter ``'use_pycatch22'`` to true. The difference of the two is that pycatch22 uses C as their backend while python uses the Numba library, which assembles python code into C. Aeon also regularly maintains their catch22 library, and therefore there should be barely any discrepancy between outputs. Pycatch22 has a few issues with their implementation such as at times struggling to run on windows. If you are using the aeon library for a certain task, but want to use pycatch22 for transformation of the data, it is recommended to use aeon's catch22 with the parameter ``'use_pycatch22'`` set to true. If you do that, you may encounter a warning that pycatch22 has not been installed and therefore will use aeon's catch22, if that happens just install the pycatch22 library.\n",
     "\n",
     "Currently, pycatch22 has an issue where the output features extracted using Python yield different values compared to those extracted using the native C code. Aeon's catch22 implementation extracts the same results as pycatch22's C code. Therefore, the extracted results may differ."
    ]
diff --git a/examples/transformations/minirocket.ipynb b/examples/transformations/minirocket.ipynb
index c23d15623a..457350c211 100644
--- a/examples/transformations/minirocket.ipynb
+++ b/examples/transformations/minirocket.ipynb
@@ -19,10 +19,10 @@
     "\n",
     "### 1.1 Imports\n",
     "\n",
-    "Import example data, `MiniRocket`, `MiniRocketClassifier`, `MiniRocketRegressor`,\n",
-    "`RidgeClassifierCV` (scikit-learn), and ``numpy``.\n",
+    "Import example data, ``MiniRocket``, ``MiniRocketClassifier``, ``MiniRocketRegressor``,\n",
+    "``RidgeClassifierCV`` (scikit-learn), and ``numpy``.\n",
     "\n",
-    "You can use the `MiniRocket`transform directly, in a pipeline, or in our baked in `MiniRocketClassifier` or `MiniRocketRegressor`.\n",
+    "You can use the ``MiniRocket``transform directly, in a pipeline, or in our baked in ``MiniRocketClassifier`` or ``MiniRocketRegressor``.\n",
     "\n",
     "**Note**: ``MiniRocket`` is compiled by ``numba`` on import.  The compiled functions are\n",
     "cached, so this should only happen once (i.e., the first time you import ``MiniRocket``)."
@@ -30,32 +30,34 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:08:58.368462Z",
+     "start_time": "2024-11-25T11:08:58.349939Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:03.214929Z",
      "iopub.status.busy": "2020-10-12T17:43:03.214184Z",
      "iopub.status.idle": "2020-10-12T17:43:03.216304Z",
      "shell.execute_reply": "2020-10-12T17:43:03.216990Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:08:58.368462Z",
-     "start_time": "2024-11-25T11:08:58.349939Z"
     }
    },
+   "outputs": [],
    "source": [
     "# !pip install --upgrade numba"
-   ],
-   "outputs": [],
-   "execution_count": 1
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:10:18.327182Z",
      "start_time": "2024-11-25T11:08:59.095253Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from sklearn.linear_model import RidgeClassifierCV\n",
@@ -66,26 +68,17 @@
     "from aeon.datasets import load_basic_motions  # multivariate dataset\n",
     "from aeon.regression.convolution_based import MiniRocketRegressor\n",
     "from aeon.transformations.collection.convolution_based import MiniRocket"
-   ],
-   "outputs": [],
-   "execution_count": 2
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:10:23.328664Z",
      "start_time": "2024-11-25T11:10:23.234728Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "X_train, y_train = load_arrow_head(split=\"train\")\n",
-    "minirocket = MiniRocket()  # by default, MiniRocket uses ~10_000 kernels\n",
-    "minirocket.fit(X_train)\n",
-    "X_train_transform = minirocket.transform(X_train)\n",
-    "# test shape of transformed training data -> (n_cases, 9_996)\n",
-    "X_train_transform.shape"
-   ],
    "outputs": [
     {
      "data": {
@@ -98,48 +91,45 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 5
+   "source": [
+    "X_train, y_train = load_arrow_head(split=\"train\")\n",
+    "minirocket = MiniRocket()  # by default, MiniRocket uses ~10_000 kernels\n",
+    "minirocket.fit(X_train)\n",
+    "X_train_transform = minirocket.transform(X_train)\n",
+    "# test shape of transformed training data -> (n_cases, 9_996)\n",
+    "X_train_transform.shape"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### 1.4 Fit a Classifier"
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "We suggest using `RidgeClassifierCV` (scikit-learn) for smaller datasets (fewer than ~10,000 training examples), and using logistic regression trained using stochastic gradient descent for larger datasets.\n",
+    "We suggest using ``RidgeClassifierCV`` (scikit-learn) for smaller datasets (fewer than ~10,000 training examples), and using logistic regression trained using stochastic gradient descent for larger datasets.\n",
     "\n",
-    "**Note**: For larger datasets, this means integrating MiniRocket with stochastic gradient descent such that the transform is performed per minibatch, *not* simply substituting `RidgeClassifierCV` for, e.g., `LogisticRegression`.\n",
+    "**Note**: For larger datasets, this means integrating MiniRocket with stochastic gradient descent such that the transform is performed per minibatch, *not* simply substituting ``RidgeClassifierCV`` for, e.g., ``LogisticRegression``.\n",
     "\n",
-    "**Note**: While the input time-series of MiniRocket is unscaled, the output features of MiniRocket may need to be adjusted for following models. E.g. for `RidgeClassifierCV`, we scale the features using the sklearn StandardScaler."
+    "**Note**: While the input time-series of MiniRocket is unscaled, the output features of MiniRocket may need to be adjusted for following models. E.g. for ``RidgeClassifierCV``, we scale the features using the sklearn StandardScaler."
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:10:26.380394Z",
      "start_time": "2024-11-25T11:10:26.343196Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "scaler = StandardScaler(with_mean=False)\n",
-    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
-    "X_train_scaled_transform = scaler.fit_transform(X_train_transform)\n",
-    "classifier.fit(X_train_scaled_transform, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03]))"
-      ],
       "text/html": [
        "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -550,6 +540,11 @@
        "       2.15443469e+02, 1.00000000e+03]))</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;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03]))"
       ]
      },
      "execution_count": 6,
@@ -557,34 +552,33 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "source": [
+    "scaler = StandardScaler(with_mean=False)\n",
+    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
+    "X_train_scaled_transform = scaler.fit_transform(X_train_transform)\n",
+    "classifier.fit(X_train_scaled_transform, y_train)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Or just use the provide baked in ``MiniRocketClassifier`` which contains\n",
     "the scaler and classifier."
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:10:28.558282Z",
      "start_time": "2024-11-25T11:10:28.438303Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "mr = MiniRocketClassifier()\n",
-    "mr.fit(X_train, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "MiniRocketClassifier()"
-      ],
       "text/html": [
        "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -991,6 +985,9 @@
        "  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>MiniRocketClassifier()</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;MiniRocketClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MiniRocketClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "MiniRocketClassifier()"
       ]
      },
      "execution_count": 7,
@@ -998,40 +995,43 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 7
+   "source": [
+    "mr = MiniRocketClassifier()\n",
+    "mr.fit(X_train, y_train)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### 1.5 Load and Transform the Test Data"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:10:36.310828Z",
      "start_time": "2024-11-25T11:10:36.188643Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "X_test, y_test = load_arrow_head(split=\"test\")\n",
     "X_test_transform = minirocket.transform(X_test)"
-   ],
-   "outputs": [],
-   "execution_count": 8
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### 1.6 Classify the Test Data"
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 2 Multivariate Time Series\n",
     "\n",
@@ -1039,18 +1039,14 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:10:40.698690Z",
      "start_time": "2024-11-25T11:10:40.561965Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "X_test_scaled_transform = scaler.transform(X_test_transform)\n",
-    "print(\" Score =\", classifier.score(X_test_scaled_transform, y_test))\n",
-    "print(\" Score = \", mr.score(X_test, y_test))"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -1061,7 +1057,11 @@
      ]
     }
    ],
-   "execution_count": 9
+   "source": [
+    "X_test_scaled_transform = scaler.transform(X_test_transform)\n",
+    "print(\" Score =\", classifier.score(X_test_scaled_transform, y_test))\n",
+    "print(\" Score = \", mr.score(X_test, y_test))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1070,28 +1070,28 @@
     "### Load the Training Data\n",
     "\n",
     "**Note**: Input time series must be *at least* of length 9.  Pad shorter time series\n",
-    "using, e.g., `Padder` (`aeon.transformers.collection`)."
+    "using, e.g., ``Padder`` (``aeon.transformers.collection``)."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:10:43.874489Z",
+     "start_time": "2024-11-25T11:10:43.846456Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:10.054652Z",
      "iopub.status.busy": "2020-10-12T17:43:10.034190Z",
      "iopub.status.idle": "2020-10-12T17:43:10.394311Z",
      "shell.execute_reply": "2020-10-12T17:43:10.394905Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:10:43.874489Z",
-     "start_time": "2024-11-25T11:10:43.846456Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_train, y_train = load_basic_motions(split=\"train\")"
-   ],
-   "outputs": [],
-   "execution_count": 10
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1102,25 +1102,25 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:10:45.517801Z",
+     "start_time": "2024-11-25T11:10:45.415754Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:10.410718Z",
      "iopub.status.busy": "2020-10-12T17:43:10.410103Z",
      "iopub.status.idle": "2020-10-12T17:43:11.186318Z",
      "shell.execute_reply": "2020-10-12T17:43:11.186801Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:10:45.517801Z",
-     "start_time": "2024-11-25T11:10:45.415754Z"
     }
    },
+   "outputs": [],
    "source": [
     "mr = MiniRocket()\n",
     "mr.fit(X_train)\n",
     "X_train_transform = mr.transform(X_train)"
-   ],
-   "outputs": [],
-   "execution_count": 11
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1131,33 +1131,22 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:10:48.940610Z",
+     "start_time": "2024-11-25T11:10:48.898236Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:11.190556Z",
      "iopub.status.busy": "2020-10-12T17:43:11.190017Z",
      "iopub.status.idle": "2020-10-12T17:43:11.396461Z",
      "shell.execute_reply": "2020-10-12T17:43:11.397135Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:10:48.940610Z",
-     "start_time": "2024-11-25T11:10:48.898236Z"
     }
    },
-   "source": [
-    "scaler = StandardScaler(with_mean=False)\n",
-    "X_train_scaled_transform = scaler.fit_transform(X_train_transform)\n",
-    "\n",
-    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
-    "classifier.fit(X_train_scaled_transform, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03]))"
-      ],
       "text/html": [
        "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -1568,6 +1557,11 @@
        "       2.15443469e+02, 1.00000000e+03]))</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03]))"
       ]
      },
      "execution_count": 12,
@@ -1575,7 +1569,13 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 12
+   "source": [
+    "scaler = StandardScaler(with_mean=False)\n",
+    "X_train_scaled_transform = scaler.fit_transform(X_train_transform)\n",
+    "\n",
+    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
+    "classifier.fit(X_train_scaled_transform, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1586,24 +1586,24 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:10:54.416071Z",
+     "start_time": "2024-11-25T11:10:54.338117Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:11.401025Z",
      "iopub.status.busy": "2020-10-12T17:43:11.400273Z",
      "iopub.status.idle": "2020-10-12T17:43:12.450777Z",
      "shell.execute_reply": "2020-10-12T17:43:12.451162Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:10:54.416071Z",
-     "start_time": "2024-11-25T11:10:54.338117Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_test, y_test = load_basic_motions(split=\"test\")\n",
     "X_test_transform = mr.transform(X_test)"
-   ],
-   "outputs": [],
-   "execution_count": 13
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1614,23 +1614,20 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:10:57.265809Z",
+     "start_time": "2024-11-25T11:10:57.240878Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:12.494679Z",
      "iopub.status.busy": "2020-10-12T17:43:12.453795Z",
      "iopub.status.idle": "2020-10-12T17:43:12.548017Z",
      "shell.execute_reply": "2020-10-12T17:43:12.548575Z"
     },
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:10:57.265809Z",
-     "start_time": "2024-11-25T11:10:57.240878Z"
-    }
+    "scrolled": true
    },
-   "source": [
-    "X_test_scaled_transform = scaler.transform(X_test_transform)\n",
-    "classifier.score(X_test_scaled_transform, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -1643,7 +1640,10 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 14
+   "source": [
+    "X_test_scaled_transform = scaler.transform(X_test_transform)\n",
+    "classifier.score(X_test_scaled_transform, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1653,13 +1653,15 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:10:59.040716Z",
      "start_time": "2024-11-25T11:10:59.035170Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from sklearn.linear_model import RidgeClassifierCV\n",
     "from sklearn.pipeline import make_pipeline\n",
@@ -1670,13 +1672,11 @@
     "    StandardScaler(with_mean=False),\n",
     "    RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)),\n",
     ")"
-   ],
-   "outputs": [],
-   "execution_count": 15
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Or just use the provide baked in ``MiniRocket`` classifier"
    ]
@@ -1688,41 +1688,27 @@
     "### 3.3 Load and Fit the Training Data\n",
     "\n",
     "**Note**: Input time series must be *at least* of length 9.  Pad shorter time series\n",
-    "using, e.g., `Padder` (`aeon.transformers.collection`)."
+    "using, e.g., ``Padder`` (``aeon.transformers.collection``)."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:11:02.046780Z",
+     "start_time": "2024-11-25T11:11:01.913155Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:12.557100Z",
      "iopub.status.busy": "2020-10-12T17:43:12.556478Z",
      "iopub.status.idle": "2020-10-12T17:43:12.885951Z",
      "shell.execute_reply": "2020-10-12T17:43:12.886625Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:11:02.046780Z",
-     "start_time": "2024-11-25T11:11:01.913155Z"
     }
    },
-   "source": [
-    "X_train, y_train = load_arrow_head(split=\"train\")\n",
-    "\n",
-    "# it is necessary to pass y_train to the pipeline\n",
-    "# y_train is not used for the transform, but it is used by the classifier\n",
-    "minirocket_pipeline.fit(X_train, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Pipeline(steps=[('minirocket', MiniRocket()),\n",
-       "                ('standardscaler', StandardScaler(with_mean=False)),\n",
-       "                ('ridgeclassifiercv',\n",
-       "                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03])))])"
-      ],
       "text/html": [
        "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -2141,6 +2127,14 @@
        "       2.15443469e+02, 1.00000000e+03])))])</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\">MiniRocket</label><div class=\"sk-toggleable__content fitted\"><pre>MiniRocket()</pre></div> </div></div><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 fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler(with_mean=False)</pre></div> </div></div><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 fitted\">&nbsp;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('minirocket', MiniRocket()),\n",
+       "                ('standardscaler', StandardScaler(with_mean=False)),\n",
+       "                ('ridgeclassifiercv',\n",
+       "                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03])))])"
       ]
      },
      "execution_count": 16,
@@ -2148,7 +2142,13 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 16
+   "source": [
+    "X_train, y_train = load_arrow_head(split=\"train\")\n",
+    "\n",
+    "# it is necessary to pass y_train to the pipeline\n",
+    "# y_train is not used for the transform, but it is used by the classifier\n",
+    "minirocket_pipeline.fit(X_train, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2159,31 +2159,31 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T11:11:31.335457Z",
+     "start_time": "2024-11-25T11:11:31.003725Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-10-12T17:43:12.890535Z",
      "iopub.status.busy": "2020-10-12T17:43:12.889866Z",
      "iopub.status.idle": "2020-10-12T17:43:13.897048Z",
      "shell.execute_reply": "2020-10-12T17:43:13.897624Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T11:11:31.335457Z",
-     "start_time": "2024-11-25T11:11:31.003725Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_test, y_test = load_arrow_head(split=\"test\")\n",
     "\n",
     "minirocket_pipeline.score(X_test, y_test)\n",
     "minirocket_pipeline.fit(X_train, y_train)\n",
     "pred = minirocket_pipeline.predict(X_test)\n"
-   ],
-   "outputs": [],
-   "execution_count": 18
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Time series regression\n",
     "\n",
@@ -2191,22 +2191,14 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-25T11:11:35.125060Z",
      "start_time": "2024-11-25T11:11:34.966868Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "from aeon.datasets import load_covid_3month\n",
-    "\n",
-    "X_train, y_train = load_covid_3month(split=\"train\")\n",
-    "X_test, y_test = load_covid_3month(split=\"test\")\n",
-    "mr = MiniRocketRegressor()\n",
-    "mr.fit(X_train, y_train)\n",
-    "mr.score(X_test, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -2219,11 +2211,19 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 19
+   "source": [
+    "from aeon.datasets import load_covid_3month\n",
+    "\n",
+    "X_train, y_train = load_covid_3month(split=\"train\")\n",
+    "X_test, y_test = load_covid_3month(split=\"test\")\n",
+    "mr = MiniRocketRegressor()\n",
+    "mr.fit(X_train, y_train)\n",
+    "mr.score(X_test, y_test)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### References\n",
     "[1] Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb. A Very Fast (Almost)\n",
@@ -2236,11 +2236,11 @@
    "source": []
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": ""
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/transformations/preprocessing.ipynb b/examples/transformations/preprocessing.ipynb
index 99dcd8567f..311a35e3af 100644
--- a/examples/transformations/preprocessing.ipynb
+++ b/examples/transformations/preprocessing.ipynb
@@ -2,53 +2,45 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "# Preprocessing time series with aeon\n",
     "\n",
     "It is common to need to preprocess time series data before applying machine learning\n",
-    "algorithms. So algorithms can handle these characteristics, or `aeon` transformers can be used to preprocess collections of time\n",
+    "algorithms. So algorithms can handle these characteristics, or ``aeon`` transformers can be used to preprocess collections of time\n",
     "series into standard format. This notebook demonstrates three common use cases\n",
     "\n",
     "1. [Rescaling time series](#Rescaling-time-series)\n",
     "2. [Resizing time series](#Resizing-time-series)\n",
     "3. [Dealing with missing values](#missing-values)\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "## Rescaling time series\n",
     "\n",
     "Different levels of scale and variance can mask discriminative patterns in time\n",
     "series. This is particularly true for methods that are based on distances. It common\n",
     "to rescale time series to have zero mean and unit variance. For example, the data in\n",
-    "the `UnitTest` dataset is a subset of the [Chinatown dataset]\n",
-    "(https://timeseriesclassification.com/description.php?Dataset=Chinatown. These are\n",
+    "the ``UnitTest`` dataset is a subset of the [Chinatown dataset](https://timeseriesclassification.com/description.php?Dataset=Chinatown). These are\n",
     "counts of pedestrians in Chinatown, Melbourne. The time series are of different means"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from aeon.datasets import load_unit_test\n",
-    "\n",
-    "X, y = load_unit_test(split=\"Train\")\n",
-    "np.mean(X, axis=-1)[0:5]"
-   ],
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-17T13:48:57.655001Z",
      "start_time": "2024-11-17T13:48:57.631756Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -66,19 +58,24 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 3
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from aeon.datasets import load_unit_test\n",
+    "\n",
+    "X, y = load_unit_test(split=\"Train\")\n",
+    "np.mean(X, axis=-1)[0:5]"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "np.std(X, axis=-1)[0:5]"
-   ],
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-17T13:48:59.467239Z",
      "start_time": "2024-11-17T13:48:59.458263Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -96,34 +93,30 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 4
+   "source": [
+    "np.std(X, axis=-1)[0:5]"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "We can rescale the time series in three ways:\n",
     "1. Normalise: subtract the mean and divide by the standard deviation to make all\n",
     "series have zero mean and unit variance."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from aeon.transformations.collection import Normalizer\n",
-    "\n",
-    "normalizer = Normalizer()\n",
-    "X2 = normalizer.fit_transform(X)\n",
-    "np.round(np.mean(X2, axis=-1)[0:5], 6)"
-   ],
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:01.643630Z",
      "start_time": "2024-11-17T13:49:01.627083Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -141,19 +134,23 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 5
+   "source": [
+    "from aeon.transformations.collection import Normalizer\n",
+    "\n",
+    "normalizer = Normalizer()\n",
+    "X2 = normalizer.fit_transform(X)\n",
+    "np.round(np.mean(X2, axis=-1)[0:5], 6)"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "np.round(np.std(X2, axis=-1)[0:5], 6)"
-   ],
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:02.670358Z",
      "start_time": "2024-11-17T13:49:02.648594Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -171,32 +168,28 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "source": [
+    "np.round(np.std(X2, axis=-1)[0:5], 6)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "2. Re-center: Recentering involves subtracting the mean of each series"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "2. Re-center: Recentering involves subtracting the mean of each series"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from aeon.transformations.collection import Centerer\n",
-    "\n",
-    "c = Centerer()\n",
-    "X3 = c.fit_transform(X)\n",
-    "np.round(np.mean(X3, axis=-1)[0:5], 6)"
-   ],
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:04.345033Z",
      "start_time": "2024-11-17T13:49:04.332065Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -214,32 +207,32 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 7
+   "source": [
+    "from aeon.transformations.collection import Centerer\n",
+    "\n",
+    "c = Centerer()\n",
+    "X3 = c.fit_transform(X)\n",
+    "np.round(np.mean(X3, axis=-1)[0:5], 6)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "3. Min-Max: Scale the data to be between 0 and 1"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "3. Min-Max: Scale the data to be between 0 and 1"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from aeon.transformations.collection import MinMaxScaler\n",
-    "\n",
-    "minmax = MinMaxScaler()\n",
-    "X4 = minmax.fit_transform(X)\n",
-    "np.round(np.min(X4, axis=-1)[0:5], 6)"
-   ],
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:06.135780Z",
      "start_time": "2024-11-17T13:49:06.116831Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -257,19 +250,23 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 8
+   "source": [
+    "from aeon.transformations.collection import MinMaxScaler\n",
+    "\n",
+    "minmax = MinMaxScaler()\n",
+    "X4 = minmax.fit_transform(X)\n",
+    "np.round(np.min(X4, axis=-1)[0:5], 6)"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "np.round(np.max(X4, axis=-1)[0:5], 6)"
-   ],
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:07.094710Z",
      "start_time": "2024-11-17T13:49:07.072733Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -287,17 +284,19 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 9
+   "source": [
+    "np.round(np.max(X4, axis=-1)[0:5], 6)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "There is no best way to do this, although for counts such as this it is more common\n",
     "to MinMax scale, so that the data still has some interpretation as proportions."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
@@ -309,12 +308,17 @@
     "number of time points. Currently, most of aeon's collection estimators\n",
     "(classification, clustering or regression) require equal-length time\n",
     "series. Those that can handle unequal length series are tagged with\n",
-    "\"capability:unequal\"."
+    "\"``capability:unequal``\"."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-17T13:49:10.589292Z",
+     "start_time": "2024-11-17T13:49:10.576923Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:31:58.456171Z",
      "iopub.status.busy": "2020-12-19T14:31:58.455565Z",
@@ -323,31 +327,26 @@
     },
     "pycharm": {
      "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-17T13:49:10.589292Z",
-     "start_time": "2024-11-17T13:49:10.576923Z"
     }
    },
+   "outputs": [],
    "source": [
     "from aeon.classification.convolution_based import RocketClassifier\n",
     "from aeon.datasets import load_basic_motions, load_japanese_vowels, load_plaid\n",
     "from aeon.utils.validation import has_missing, is_equal_length, is_univariate"
-   ],
-   "outputs": [],
-   "execution_count": 10
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "If you want to use an estimator that cannot internally handle missing values, one\n",
     "option is to convert unequal length series into equal length. This can be\n",
     " done through padding, truncation or resizing through fitting a function and\n",
     " resampling."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
@@ -356,13 +355,18 @@
     "### Unequal or equal length collections time series\n",
     "\n",
     "If a collection contains all equal length series, it will store the data in a 3D\n",
-    "numpy of shape `(n_cases, n_channels, n_timepoints)`. If it is unequal length, it is\n",
+    "numpy of shape ``(n_cases, n_channels, n_timepoints)``. If it is unequal length, it is\n",
     "stored in a list of 2D numpy arrays:"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-17T13:49:17.636972Z",
+     "start_time": "2024-11-17T13:49:17.599882Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:31:59.194445Z",
      "iopub.status.busy": "2020-12-19T14:31:59.193903Z",
@@ -371,22 +375,8 @@
     },
     "pycharm": {
      "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-17T13:49:17.636972Z",
-     "start_time": "2024-11-17T13:49:17.599882Z"
     }
    },
-   "source": [
-    "# Equal length multivariate data\n",
-    "bm_X, bm_y = load_basic_motions()\n",
-    "X = bm_X\n",
-    "print(f\"{type(X)}, {X.shape}\")\n",
-    "print(\n",
-    "    f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n",
-    "    f\"length = {is_equal_length(X)}\"\n",
-    ")"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -397,28 +387,28 @@
      ]
     }
    ],
-   "execution_count": 11
-  },
-  {
-   "cell_type": "code",
    "source": [
-    "# Unequal length univariate data\n",
-    "plaid_X, plaid_y = load_plaid()\n",
-    "X = plaid_X\n",
-    "print(type(plaid_X), \"\\n\", plaid_X[0].shape, \"\\n\", plaid_X[10].shape)\n",
+    "# Equal length multivariate data\n",
+    "bm_X, bm_y = load_basic_motions()\n",
+    "X = bm_X\n",
+    "print(f\"{type(X)}, {X.shape}\")\n",
     "print(\n",
     "    f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n",
     "    f\"length = {is_equal_length(X)}\"\n",
     ")"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:18.797171Z",
      "start_time": "2024-11-17T13:49:18.626506Z"
+    },
+    "collapsed": false,
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [
@@ -433,24 +423,26 @@
      ]
     }
    ],
-   "execution_count": 12
+   "source": [
+    "# Unequal length univariate data\n",
+    "plaid_X, plaid_y = load_plaid()\n",
+    "X = plaid_X\n",
+    "print(type(plaid_X), \"\\n\", plaid_X[0].shape, \"\\n\", plaid_X[10].shape)\n",
+    "print(\n",
+    "    f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n",
+    "    f\"length = {is_equal_length(X)}\"\n",
+    ")"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:19.575389Z",
      "start_time": "2024-11-17T13:49:19.526275Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "vowels_X, vowels_y = load_japanese_vowels(split=\"train\")\n",
-    "X = vowels_X\n",
-    "print(\n",
-    "    f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n",
-    "    f\"length = {is_equal_length(X)}\"\n",
-    ")"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -460,31 +452,33 @@
      ]
     }
    ],
-   "execution_count": 13
+   "source": [
+    "vowels_X, vowels_y = load_japanese_vowels(split=\"train\")\n",
+    "X = vowels_X\n",
+    "print(\n",
+    "    f\"univariate = {is_univariate(X)}, has missing ={has_missing(X)}, equal \"\n",
+    "    f\"length = {is_equal_length(X)}\"\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "\n"
+   "source": [
+    "\n"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "series_lengths = [array.shape[1] for array in plaid_X]\n",
-    "\n",
-    "# Find the minimum and maximum of the second dimensions\n",
-    "min_length = min(series_lengths)\n",
-    "max_length = max(series_lengths)\n",
-    "print(\" Min length = \", min_length, \" max length = \", max_length)"
-   ],
+   "execution_count": 14,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:21.081698Z",
      "start_time": "2024-11-17T13:49:21.061750Z"
+    },
+    "collapsed": false,
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [
@@ -496,38 +490,38 @@
      ]
     }
    ],
-   "execution_count": 14
+   "source": [
+    "series_lengths = [array.shape[1] for array in plaid_X]\n",
+    "\n",
+    "# Find the minimum and maximum of the second dimensions\n",
+    "min_length = min(series_lengths)\n",
+    "max_length = max(series_lengths)\n",
+    "print(\" Min length = \", min_length, \" max length = \", max_length)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "There are two basic strategies for unequal length problems\n",
     "1. Use an estimator that can internally handle missing values\n",
     "2. Transform the data to be equal length by, for example, truncating or padding series\n",
     "\n",
-    "Estimators with the tag `\"capability:unequal_length\": True` have the capability to\n",
+    "Estimators with the tag ``\"capability:unequal_length\": True`` have the capability to\n",
     "handle unequal length series. For classification, regression and\n",
     "clusterign, the\n",
     "current list is"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:23.280238Z",
      "start_time": "2024-11-17T13:49:23.143830Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "from aeon.utils.discovery import all_estimators\n",
-    "\n",
-    "all_estimators(\n",
-    "    type_filter=[\"classifier\", \"regressor\", \"clusterer\"],\n",
-    "    tag_filter={\"capability:unequal_length\": True},\n",
-    ")"
-   ],
    "outputs": [
     {
      "data": {
@@ -553,33 +547,42 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 15
+   "source": [
+    "from aeon.utils.discovery import all_estimators\n",
+    "\n",
+    "all_estimators(\n",
+    "    type_filter=[\"classifier\", \"regressor\", \"clusterer\"],\n",
+    "    tag_filter={\"capability:unequal_length\": True},\n",
+    ")"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "You can pass these estimators unequal length series and they will work as expected.\n"
+   "metadata": {},
+   "source": [
+    "You can pass these estimators unequal length series and they will work as expected.\n"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:25.499271Z",
      "start_time": "2024-11-17T13:49:25.466359Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n",
     "\n",
     "knn = KNeighborsTimeSeriesClassifier()\n",
     "model = knn.fit(plaid_X, plaid_y)"
-   ],
-   "outputs": [],
-   "execution_count": 16
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "If time series are unequal length, collection estimators will raise an error if they\n",
     "do not have the capability to handle this characteristic. If you want to use them, \n",
@@ -587,20 +590,14 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:27.034532Z",
      "start_time": "2024-11-17T13:49:27.001467Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "rc = RocketClassifier()\n",
-    "try:\n",
-    "    rc.fit(plaid_X, plaid_y)\n",
-    "except ValueError as e:\n",
-    "    print(f\"ValueError: {e}\")"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -610,7 +607,13 @@
      ]
     }
    ],
-   "execution_count": 17
+   "source": [
+    "rc = RocketClassifier()\n",
+    "try:\n",
+    "    rc.fit(plaid_X, plaid_y)\n",
+    "except ValueError as e:\n",
+    "    print(f\"ValueError: {e}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -625,7 +628,12 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-17T13:49:29.582165Z",
+     "start_time": "2024-11-17T13:49:29.437476Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:01.245270Z",
      "iopub.status.busy": "2020-12-19T14:32:01.244733Z",
@@ -634,23 +642,8 @@
     },
     "pycharm": {
      "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-17T13:49:29.582165Z",
-     "start_time": "2024-11-17T13:49:29.437476Z"
     }
    },
-   "source": [
-    "from aeon.transformations.collection import Padder, Resizer, Truncator\n",
-    "\n",
-    "pad = Padder()\n",
-    "truncate = Truncator()\n",
-    "resize = Resizer(length=600)\n",
-    "X2 = pad.fit_transform(plaid_X)\n",
-    "X3 = truncate.fit_transform(plaid_X)\n",
-    "X4 = resize.fit_transform(plaid_X)\n",
-    "print(X2.shape, \"\\n\", X3.shape, \"\\n\", X4.shape)"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -662,23 +655,27 @@
      ]
     }
    ],
-   "execution_count": 18
+   "source": [
+    "from aeon.transformations.collection import Padder, Resizer, Truncator\n",
+    "\n",
+    "pad = Padder()\n",
+    "truncate = Truncator()\n",
+    "resize = Resizer(length=600)\n",
+    "X2 = pad.fit_transform(plaid_X)\n",
+    "X3 = truncate.fit_transform(plaid_X)\n",
+    "X4 = resize.fit_transform(plaid_X)\n",
+    "print(X2.shape, \"\\n\", X3.shape, \"\\n\", X4.shape)"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:49:36.601769Z",
      "start_time": "2024-11-17T13:49:35.625172Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.title(\"Before and after padding: PLAID first case (shifted up for unpadded)\")\n",
-    "plt.plot(plaid_X[0][0] + 10)\n",
-    "plt.plot(X2[0][0])"
-   ],
    "outputs": [
     {
      "data": {
@@ -692,50 +689,43 @@
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 19
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.title(\"Before and after padding: PLAID first case (shifted up for unpadded)\")\n",
+    "plt.plot(plaid_X[0][0] + 10)\n",
+    "plt.plot(X2[0][0])"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "You can put these transformers in a pipeline to apply to both train/test split\n"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "You can put these transformers in a pipeline to apply to both train/test split\n"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
-    "# Unequal length univariate data\n",
-    "from aeon.pipeline import make_pipeline\n",
-    "\n",
-    "train_X, train_y = load_plaid(split=\"Train\")\n",
-    "test_X, test_y = load_plaid(split=\"Test\")\n",
-    "steps = [truncate, rc]\n",
-    "pipe = make_pipeline(steps)\n",
-    "pipe.fit(train_X, train_y)\n",
-    "preds = pipe.predict(test_X)\n",
-    "accuracy_score(train_y, preds)"
-   ],
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
     "ExecuteTime": {
      "end_time": "2024-11-17T13:50:05.966304Z",
      "start_time": "2024-11-17T13:49:37.145088Z"
+    },
+    "collapsed": false,
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [
@@ -750,30 +740,40 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 20
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Unequal length univariate data\n",
+    "from aeon.pipeline import make_pipeline\n",
+    "\n",
+    "train_X, train_y = load_plaid(split=\"Train\")\n",
+    "test_X, test_y = load_plaid(split=\"Test\")\n",
+    "steps = [truncate, rc]\n",
+    "pipe = make_pipeline(steps)\n",
+    "pipe.fit(train_X, train_y)\n",
+    "preds = pipe.predict(test_X)\n",
+    "accuracy_score(train_y, preds)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Missing Values\n",
     "\n",
-    "Missing values are indicated by `NaN` in numpy array. You can test whether any `aeon`\n",
+    "Missing values are indicated by ``NaN`` in numpy array. You can test whether any ``aeon``\n",
     " data structure contains missing values using the utility function"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:50:06.076875Z",
      "start_time": "2024-11-17T13:50:06.065907Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "X = np.random.random(size=(10, 2, 200))\n",
-    "has_missing(X)"
-   ],
    "outputs": [
     {
      "data": {
@@ -786,20 +786,20 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 21
+   "source": [
+    "X = np.random.random(size=(10, 2, 200))\n",
+    "has_missing(X)"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:50:06.186109Z",
      "start_time": "2024-11-17T13:50:06.180126Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "X[5][0][55] = np.NAN\n",
-    "has_missing(X)"
-   ],
    "outputs": [
     {
      "data": {
@@ -812,11 +812,14 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 22
+   "source": [
+    "X[5][0][55] = np.NAN\n",
+    "has_missing(X)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "There are a range of strategies for handling missing values. These include:\n",
     "\n",
@@ -824,24 +827,18 @@
     "some algorithms (such as decision trees) to internally deal with missing values,\n",
     "usually be using it as a distinct series value after discretisation. We do not yet \n",
     "have many estimators with this capability. Estimators that are able to internally \n",
-    "handle missing values are tagged with `\"capability:missing_values\": True`."
+    "handle missing values are tagged with ``\"capability:missing_values\": True``."
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:50:06.436013Z",
      "start_time": "2024-11-17T13:50:06.296331Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "from aeon.utils.discovery import all_estimators\n",
-    "\n",
-    "all_estimators(\n",
-    "    tag_filter={\"capability:missing_values\": True},\n",
-    ")"
-   ],
    "outputs": [
     {
      "data": {
@@ -861,11 +858,17 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 23
+   "source": [
+    "from aeon.utils.discovery import all_estimators\n",
+    "\n",
+    "all_estimators(\n",
+    "    tag_filter={\"capability:missing_values\": True},\n",
+    ")"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "2. Removing series with missing: this is often desirable if the train set size is\n",
     "large, the number of series with missing is small and the proportion of missing\n",
@@ -878,25 +881,19 @@
     " often desirable if the train set size is small and the proportion of missing values\n",
     " is low. You can do this with the transformer ``SimpleImputer``. This interpolates \n",
     " each series and each channel independently. So for example a mean interpolation \n",
-    " of series with two channels `[[NaN,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]` would be \n",
-    " `[[2.0,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]`. "
+    " of series with two channels ``[[NaN,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]`` would be \n",
+    " ``[[2.0,1.0,2.0,3.0],[-1.0,-2.0,-3.0,-4.0]]``. "
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:52:23.133162Z",
      "start_time": "2024-11-17T13:52:23.118202Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "from aeon.transformations.collection import SimpleImputer\n",
-    "\n",
-    "imput = SimpleImputer(strategy=\"mean\")\n",
-    "X2 = imput.fit_transform(X)\n",
-    "has_missing(X2)"
-   ],
    "outputs": [
     {
      "data": {
@@ -909,21 +906,23 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 26
+   "source": [
+    "from aeon.transformations.collection import SimpleImputer\n",
+    "\n",
+    "imput = SimpleImputer(strategy=\"mean\")\n",
+    "X2 = imput.fit_transform(X)\n",
+    "has_missing(X2)"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:52:23.825897Z",
      "start_time": "2024-11-17T13:52:23.811936Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "imp2 = SimpleImputer(strategy=\"median\")\n",
-    "X3 = imp2.fit_transform(X)\n",
-    "has_missing(X3)"
-   ],
    "outputs": [
     {
      "data": {
@@ -936,21 +935,21 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 27
+   "source": [
+    "imp2 = SimpleImputer(strategy=\"median\")\n",
+    "X3 = imp2.fit_transform(X)\n",
+    "has_missing(X3)"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-11-17T13:52:24.602058Z",
      "start_time": "2024-11-17T13:52:24.582111Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "imp3 = SimpleImputer(strategy=\"constant\", fill_value=0)\n",
-    "X4 = imp3.fit_transform(X)\n",
-    "has_missing(X4)"
-   ],
    "outputs": [
     {
      "data": {
@@ -963,14 +962,18 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 28
+   "source": [
+    "imp3 = SimpleImputer(strategy=\"constant\", fill_value=0)\n",
+    "X4 = imp3.fit_transform(X)\n",
+    "has_missing(X4)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": ""
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/transformations/rocket.ipynb b/examples/transformations/rocket.ipynb
index c992fc1952..0f0de1b6a1 100644
--- a/examples/transformations/rocket.ipynb
+++ b/examples/transformations/rocket.ipynb
@@ -25,45 +25,47 @@
     "\n",
     "## 1 Imports\n",
     "\n",
-    "Import example data, ROCKET, and a classifier (`RidgeClassifierCV` from scikit-learn), as well as NumPy and `make_pipeline` from scikit-learn.\n",
+    "Import example data, ROCKET, and a classifier (``RidgeClassifierCV`` from scikit-learn), as well as NumPy and ``make_pipeline`` from scikit-learn.\n",
     "\n",
     "**Note**: ROCKET compiles (via Numba) on import, which may take a few seconds."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 33,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:32.515016Z",
+     "start_time": "2024-11-25T17:01:32.504509Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:46.441933Z",
      "iopub.status.busy": "2020-12-19T14:32:46.441213Z",
      "iopub.status.idle": "2020-12-19T14:32:46.443225Z",
      "shell.execute_reply": "2020-12-19T14:32:46.444014Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:32.515016Z",
-     "start_time": "2024-11-25T17:01:32.504509Z"
     }
    },
+   "outputs": [],
    "source": [
     "# !pip install --upgrade numba"
-   ],
-   "outputs": [],
-   "execution_count": 33
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 34,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:33.167188Z",
+     "start_time": "2024-11-25T17:01:33.161609Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:46.448396Z",
      "iopub.status.busy": "2020-12-19T14:32:46.447602Z",
      "iopub.status.idle": "2020-12-19T14:32:51.904418Z",
      "shell.execute_reply": "2020-12-19T14:32:51.905034Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:33.167188Z",
-     "start_time": "2024-11-25T17:01:33.161609Z"
     }
    },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from sklearn.linear_model import RidgeClassifierCV\n",
@@ -72,9 +74,7 @@
     "from aeon.datasets import load_basic_motions  # multivariate dataset\n",
     "from aeon.datasets import load_gunpoint  # univariate dataset\n",
     "from aeon.transformations.collection.convolution_based import Rocket"
-   ],
-   "outputs": [],
-   "execution_count": 34
+   ]
   },
   {
    "cell_type": "markdown",
@@ -91,24 +91,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 35,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:34.603321Z",
+     "start_time": "2024-11-25T17:01:34.573759Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.908710Z",
      "iopub.status.busy": "2020-12-19T14:32:51.908101Z",
      "iopub.status.idle": "2020-12-19T14:32:51.918987Z",
      "shell.execute_reply": "2020-12-19T14:32:51.919508Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:34.603321Z",
-     "start_time": "2024-11-25T17:01:34.573759Z"
     }
    },
-   "source": [
-    "X_train, y_train = load_gunpoint(split=\"train\")\n",
-    "X_train = X_train[:5, :, :]\n",
-    "y_train = y_train[:5]\n",
-    "print(X_train.shape)"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -118,7 +113,12 @@
      ]
     }
    ],
-   "execution_count": 35
+   "source": [
+    "X_train, y_train = load_gunpoint(split=\"train\")\n",
+    "X_train = X_train[:5, :, :]\n",
+    "y_train = y_train[:5]\n",
+    "print(X_train.shape)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -129,24 +129,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 36,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:35.852821Z",
+     "start_time": "2024-11-25T17:01:35.837832Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.923023Z",
      "iopub.status.busy": "2020-12-19T14:32:51.922451Z",
      "iopub.status.idle": "2020-12-19T14:32:52.164365Z",
      "shell.execute_reply": "2020-12-19T14:32:52.164864Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:35.852821Z",
-     "start_time": "2024-11-25T17:01:35.837832Z"
     }
    },
-   "source": [
-    "rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels\n",
-    "rocket.fit(X_train)\n",
-    "X_train_transform = rocket.transform(X_train)\n",
-    "print(X_train_transform.shape)"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -156,7 +151,12 @@
      ]
     }
    ],
-   "execution_count": 36
+   "source": [
+    "rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels\n",
+    "rocket.fit(X_train)\n",
+    "X_train_transform = rocket.transform(X_train)\n",
+    "print(X_train_transform.shape)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -169,35 +169,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We recommend using `RidgeClassifierCV` from scikit-learn for smaller datasets (fewer than approx. 20K training examples), and using logistic regression trained using stochastic gradient descent for larger datasets."
+    "We recommend using ``RidgeClassifierCV`` from scikit-learn for smaller datasets (fewer than approx. 20K training examples), and using logistic regression trained using stochastic gradient descent for larger datasets."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 37,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:38.060428Z",
+     "start_time": "2024-11-25T17:01:38.038775Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.168847Z",
      "iopub.status.busy": "2020-12-19T14:32:52.168155Z",
      "iopub.status.idle": "2020-12-19T14:32:52.284816Z",
      "shell.execute_reply": "2020-12-19T14:32:52.285506Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:38.060428Z",
-     "start_time": "2024-11-25T17:01:38.038775Z"
     }
    },
-   "source": [
-    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
-    "classifier.fit(X_train_transform, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03]))"
-      ],
       "text/html": [
        "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -608,6 +600,11 @@
        "       2.15443469e+02, 1.00000000e+03]))</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;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03]))"
       ]
      },
      "execution_count": 37,
@@ -615,7 +612,10 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 37
+   "source": [
+    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
+    "classifier.fit(X_train_transform, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -626,24 +626,24 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 38,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:39.178929Z",
+     "start_time": "2024-11-25T17:01:39.136007Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.289448Z",
      "iopub.status.busy": "2020-12-19T14:32:52.288717Z",
      "iopub.status.idle": "2020-12-19T14:32:53.307829Z",
      "shell.execute_reply": "2020-12-19T14:32:53.308341Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:39.178929Z",
-     "start_time": "2024-11-25T17:01:39.136007Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_test, y_test = load_gunpoint(split=\"test\")\n",
     "X_test_transform = rocket.transform(X_test)"
-   ],
-   "outputs": [],
-   "execution_count": 38
+   ]
   },
   {
    "cell_type": "markdown",
@@ -654,22 +654,20 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 39,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:40.547350Z",
+     "start_time": "2024-11-25T17:01:40.533334Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:53.312125Z",
      "iopub.status.busy": "2020-12-19T14:32:53.311628Z",
      "iopub.status.idle": "2020-12-19T14:32:53.409775Z",
      "shell.execute_reply": "2020-12-19T14:32:53.410342Z"
     },
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:40.547350Z",
-     "start_time": "2024-11-25T17:01:40.533334Z"
-    }
+    "scrolled": true
    },
-   "source": [
-    "classifier.score(X_test_transform, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -682,7 +680,9 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 39
+   "source": [
+    "classifier.score(X_test_transform, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -699,23 +699,23 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 40,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:41.782580Z",
+     "start_time": "2024-11-25T17:01:41.767897Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:53.413597Z",
      "iopub.status.busy": "2020-12-19T14:32:53.412786Z",
      "iopub.status.idle": "2020-12-19T14:32:53.775638Z",
      "shell.execute_reply": "2020-12-19T14:32:53.776690Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:41.782580Z",
-     "start_time": "2024-11-25T17:01:41.767897Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_train, y_train = load_basic_motions(split=\"train\")"
-   ],
-   "outputs": [],
-   "execution_count": 40
+   ]
   },
   {
    "cell_type": "markdown",
@@ -726,24 +726,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 41,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:42.949980Z",
+     "start_time": "2024-11-25T17:01:42.918211Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:53.794896Z",
      "iopub.status.busy": "2020-12-19T14:32:53.794345Z",
      "iopub.status.idle": "2020-12-19T14:32:54.613570Z",
      "shell.execute_reply": "2020-12-19T14:32:54.614198Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:42.949980Z",
-     "start_time": "2024-11-25T17:01:42.918211Z"
     }
    },
-   "source": [
-    "rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels\n",
-    "rocket.fit(X_train)\n",
-    "X_train_transform = rocket.transform(X_train)\n",
-    "X_train_transform.shape"
-   ],
    "outputs": [
     {
      "data": {
@@ -756,7 +751,12 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 41
+   "source": [
+    "rocket = Rocket(n_kernels=100)  # by default, ROCKET uses 10,000 kernels\n",
+    "rocket.fit(X_train)\n",
+    "X_train_transform = rocket.transform(X_train)\n",
+    "X_train_transform.shape"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -767,30 +767,22 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 42,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:44.038154Z",
+     "start_time": "2024-11-25T17:01:44.002549Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:54.618359Z",
      "iopub.status.busy": "2020-12-19T14:32:54.617890Z",
      "iopub.status.idle": "2020-12-19T14:32:54.836560Z",
      "shell.execute_reply": "2020-12-19T14:32:54.837249Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:44.038154Z",
-     "start_time": "2024-11-25T17:01:44.002549Z"
     }
    },
-   "source": [
-    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
-    "classifier.fit(X_train_transform, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03]))"
-      ],
       "text/html": [
        "<style>#sk-container-id-6 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -1201,6 +1193,11 @@
        "       2.15443469e+02, 1.00000000e+03]))</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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03]))"
       ]
      },
      "execution_count": 42,
@@ -1208,7 +1205,10 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 42
+   "source": [
+    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
+    "classifier.fit(X_train_transform, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1219,24 +1219,24 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 43,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:45.150937Z",
+     "start_time": "2024-11-25T17:01:45.106121Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:54.841004Z",
      "iopub.status.busy": "2020-12-19T14:32:54.840351Z",
      "iopub.status.idle": "2020-12-19T14:32:55.906455Z",
      "shell.execute_reply": "2020-12-19T14:32:55.907064Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:45.150937Z",
-     "start_time": "2024-11-25T17:01:45.106121Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_test, y_test = load_basic_motions(split=\"test\")\n",
     "X_test_transform = rocket.transform(X_test)"
-   ],
-   "outputs": [],
-   "execution_count": 43
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1247,22 +1247,20 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 44,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:46.229312Z",
+     "start_time": "2024-11-25T17:01:46.215072Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:55.910253Z",
      "iopub.status.busy": "2020-12-19T14:32:55.909743Z",
      "iopub.status.idle": "2020-12-19T14:32:56.008364Z",
      "shell.execute_reply": "2020-12-19T14:32:56.008931Z"
     },
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:46.229312Z",
-     "start_time": "2024-11-25T17:01:46.215072Z"
-    }
+    "scrolled": true
    },
-   "source": [
-    "classifier.score(X_test_transform, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -1275,7 +1273,9 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 44
+   "source": [
+    "classifier.score(X_test_transform, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1285,32 +1285,32 @@
     "\n",
     "## 4 Pipeline Example\n",
     "\n",
-    "We can use ROCKET together with `RidgeClassifierCV` (or another classifier) in a pipeline.  We can then use the pipeline like a self-contained classifier, with a single call to `fit`, and without having to separately transform the data, etc.\n",
+    "We can use ROCKET together with ``RidgeClassifierCV`` (or another classifier) in a pipeline.  We can then use the pipeline like a self-contained classifier, with a single call to ``fit``, and without having to separately transform the data, etc.\n",
     "\n",
     "### 4.1 Initialise the Pipeline"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 45,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:47.349648Z",
+     "start_time": "2024-11-25T17:01:47.345129Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.012465Z",
      "iopub.status.busy": "2020-12-19T14:32:56.011939Z",
      "iopub.status.idle": "2020-12-19T14:32:56.013801Z",
      "shell.execute_reply": "2020-12-19T14:32:56.014399Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:47.349648Z",
-     "start_time": "2024-11-25T17:01:47.345129Z"
     }
    },
+   "outputs": [],
    "source": [
     "rocket_pipeline = make_pipeline(\n",
     "    Rocket(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
     ")"
-   ],
-   "outputs": [],
-   "execution_count": 45
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1321,33 +1321,22 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 46,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:49.740497Z",
+     "start_time": "2024-11-25T17:01:48.459632Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.017692Z",
      "iopub.status.busy": "2020-12-19T14:32:56.017166Z",
      "iopub.status.idle": "2020-12-19T14:32:56.420648Z",
      "shell.execute_reply": "2020-12-19T14:32:56.421247Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:49.740497Z",
-     "start_time": "2024-11-25T17:01:48.459632Z"
     }
    },
-   "source": [
-    "# it is necessary to pass y_train to the pipeline\n",
-    "# y_train is not used for the transform, but it is used by the classifier\n",
-    "rocket_pipeline.fit(X_train, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Pipeline(steps=[('rocket', Rocket()),\n",
-       "                ('ridgeclassifiercv',\n",
-       "                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03])))])"
-      ],
       "text/html": [
        "<style>#sk-container-id-7 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -1764,6 +1753,13 @@
        "       2.15443469e+02, 1.00000000e+03])))])</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\">Rocket</label><div class=\"sk-toggleable__content fitted\"><pre>Rocket()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator 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;RidgeClassifierCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('rocket', Rocket()),\n",
+       "                ('ridgeclassifiercv',\n",
+       "                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03])))])"
       ]
      },
      "execution_count": 46,
@@ -1771,7 +1767,11 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 46
+   "source": [
+    "# it is necessary to pass y_train to the pipeline\n",
+    "# y_train is not used for the transform, but it is used by the classifier\n",
+    "rocket_pipeline.fit(X_train, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1782,21 +1782,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 47,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T17:01:50.960464Z",
+     "start_time": "2024-11-25T17:01:49.763086Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.425026Z",
      "iopub.status.busy": "2020-12-19T14:32:56.424348Z",
      "iopub.status.idle": "2020-12-19T14:32:57.602704Z",
      "shell.execute_reply": "2020-12-19T14:32:57.603291Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T17:01:50.960464Z",
-     "start_time": "2024-11-25T17:01:49.763086Z"
     }
    },
-   "source": [
-    "rocket_pipeline.score(X_test, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -1809,14 +1807,16 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 47
+   "source": [
+    "rocket_pipeline.score(X_test, y_test)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": ""
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/transformations/sast.ipynb b/examples/transformations/sast.ipynb
index 3e7072df36..a8970537fa 100644
--- a/examples/transformations/sast.ipynb
+++ b/examples/transformations/sast.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "## Overview\n",
     "\n",
-    "SAST [1]_ first randomly selects k time series from each class (they are called reference time series). Then SAST generates all the subsequences of the specified lengths from these reference time series. These subsequences are then used to transform a time series dataset, replacing each time series by the vector of its distance to each subsequence.\n",
+    "SAST [1] first randomly selects k time series from each class (they are called reference time series). Then SAST generates all the subsequences of the specified lengths from these reference time series. These subsequences are then used to transform a time series dataset, replacing each time series by the vector of its distance to each subsequence.\n",
     "\n",
     "[1] Mbouopda, Michael Franklin, and Engelbert Mephu Nguifo. \"Scalable and accurate subsequence transform for time series classification.\" Pattern Recognition 147 (2023): 110121, https://www.sciencedirect.com/science/article/abs/pii/S003132032300818X, https://uca.hal.science/hal-03087686/document\n",
     "\n",
@@ -28,25 +28,27 @@
     "\n",
     "## 1 Imports\n",
     "\n",
-    "Import example data, SAST, and a classifier (`RidgeClassifierCV` from scikit-learn), as well as NumPy and `make_pipeline` from scikit-learn.\n",
+    "Import example data, SAST, and a classifier (``RidgeClassifierCV`` from scikit-learn), as well as NumPy and ``make_pipeline`` from scikit-learn.\n",
     "\n",
     "**Note**: SAST compiles (via Numba) on import, which may take a few seconds."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:19.000929Z",
+     "start_time": "2025-02-19T16:46:18.281830Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:46.448396Z",
      "iopub.status.busy": "2020-12-19T14:32:46.447602Z",
      "iopub.status.idle": "2020-12-19T14:32:51.904418Z",
      "shell.execute_reply": "2020-12-19T14:32:51.905034Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:19.000929Z",
-     "start_time": "2025-02-19T16:46:18.281830Z"
     }
    },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from sklearn.linear_model import RidgeClassifierCV\n",
@@ -55,9 +57,7 @@
     "from aeon.classification.shapelet_based import SASTClassifier\n",
     "from aeon.datasets import load_classification\n",
     "from aeon.transformations.collection.shapelet_based import SAST"
-   ],
-   "outputs": [],
-   "execution_count": 1
+   ]
   },
   {
    "cell_type": "markdown",
@@ -74,23 +74,23 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:19.007891Z",
+     "start_time": "2025-02-19T16:46:19.003992Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.908710Z",
      "iopub.status.busy": "2020-12-19T14:32:51.908101Z",
      "iopub.status.idle": "2020-12-19T14:32:51.918987Z",
      "shell.execute_reply": "2020-12-19T14:32:51.919508Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:19.007891Z",
-     "start_time": "2025-02-19T16:46:19.003992Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_train, y_train = load_classification(\"UnitTest\", split=\"train\")"
-   ],
-   "outputs": [],
-   "execution_count": 2
+   ]
   },
   {
    "cell_type": "markdown",
@@ -101,25 +101,25 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:23.655540Z",
+     "start_time": "2025-02-19T16:46:19.139803Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.923023Z",
      "iopub.status.busy": "2020-12-19T14:32:51.922451Z",
      "iopub.status.idle": "2020-12-19T14:32:52.164365Z",
      "shell.execute_reply": "2020-12-19T14:32:52.164864Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:23.655540Z",
-     "start_time": "2025-02-19T16:46:19.139803Z"
     }
    },
+   "outputs": [],
    "source": [
     "sast = SAST()\n",
     "sast.fit(X_train, y_train)\n",
     "X_train_transform = sast.transform(X_train)"
-   ],
-   "outputs": [],
-   "execution_count": 3
+   ]
   },
   {
    "cell_type": "markdown",
@@ -132,35 +132,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We recommend using `RidgeClassifierCV` from scikit-learn for smaller datasets (fewer than approx. 20K training examples), and using logistic regression trained using stochastic gradient descent for larger datasets."
+    "We recommend using ``RidgeClassifierCV`` from scikit-learn for smaller datasets (fewer than approx. 20K training examples), and using logistic regression trained using stochastic gradient descent for larger datasets."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:23.667169Z",
+     "start_time": "2025-02-19T16:46:23.657545Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.168847Z",
      "iopub.status.busy": "2020-12-19T14:32:52.168155Z",
      "iopub.status.idle": "2020-12-19T14:32:52.284816Z",
      "shell.execute_reply": "2020-12-19T14:32:52.285506Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:23.667169Z",
-     "start_time": "2025-02-19T16:46:23.657545Z"
     }
    },
-   "source": [
-    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
-    "classifier.fit(X_train_transform, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03]))"
-      ],
       "text/html": [
        "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -582,6 +574,11 @@
        "       2.15443469e+02, 1.00000000e+03]))</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>RidgeClassifierCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03]))"
       ]
      },
      "execution_count": 4,
@@ -589,7 +586,10 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 4
+   "source": [
+    "classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))\n",
+    "classifier.fit(X_train_transform, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -600,24 +600,24 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:23.693290Z",
+     "start_time": "2025-02-19T16:46:23.674752Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.289448Z",
      "iopub.status.busy": "2020-12-19T14:32:52.288717Z",
      "iopub.status.idle": "2020-12-19T14:32:53.307829Z",
      "shell.execute_reply": "2020-12-19T14:32:53.308341Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:23.693290Z",
-     "start_time": "2025-02-19T16:46:23.674752Z"
     }
    },
+   "outputs": [],
    "source": [
     "X_test, y_test = load_classification(\"UnitTest\", split=\"test\")\n",
     "X_test_transform = sast.transform(X_test)"
-   ],
-   "outputs": [],
-   "execution_count": 5
+   ]
   },
   {
    "cell_type": "markdown",
@@ -628,22 +628,20 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:23.705394Z",
+     "start_time": "2025-02-19T16:46:23.700306Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:53.312125Z",
      "iopub.status.busy": "2020-12-19T14:32:53.311628Z",
      "iopub.status.idle": "2020-12-19T14:32:53.409775Z",
      "shell.execute_reply": "2020-12-19T14:32:53.410342Z"
     },
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:23.705394Z",
-     "start_time": "2025-02-19T16:46:23.700306Z"
-    }
+    "scrolled": true
    },
-   "source": [
-    "classifier.score(X_test_transform, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -656,7 +654,9 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "source": [
+    "classifier.score(X_test_transform, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -666,30 +666,30 @@
     "\n",
     "## 3 Pipeline Example\n",
     "\n",
-    "We can use SAST together with `RidgeClassifierCV` (or another classifier) in a pipeline.  We can then use the pipeline like a self-contained classifier, with a single call to `fit`, and without having to separately transform the data, etc.\n",
+    "We can use SAST together with ``RidgeClassifierCV`` (or another classifier) in a pipeline.  We can then use the pipeline like a self-contained classifier, with a single call to ``fit``, and without having to separately transform the data, etc.\n",
     "\n",
     "### 3.1 Initialise the Pipeline"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:23.716427Z",
+     "start_time": "2025-02-19T16:46:23.714150Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.012465Z",
      "iopub.status.busy": "2020-12-19T14:32:56.011939Z",
      "iopub.status.idle": "2020-12-19T14:32:56.013801Z",
      "shell.execute_reply": "2020-12-19T14:32:56.014399Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:23.716427Z",
-     "start_time": "2025-02-19T16:46:23.714150Z"
     }
    },
+   "outputs": [],
    "source": [
     "sast_pipeline = make_pipeline(SAST(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)))"
-   ],
-   "outputs": [],
-   "execution_count": 7
+   ]
   },
   {
    "cell_type": "markdown",
@@ -700,35 +700,22 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:23.752245Z",
+     "start_time": "2025-02-19T16:46:23.725945Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.017692Z",
      "iopub.status.busy": "2020-12-19T14:32:56.017166Z",
      "iopub.status.idle": "2020-12-19T14:32:56.420648Z",
      "shell.execute_reply": "2020-12-19T14:32:56.421247Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:23.752245Z",
-     "start_time": "2025-02-19T16:46:23.725945Z"
     }
    },
-   "source": [
-    "X_train, y_train = load_classification(\"UnitTest\", split=\"train\")\n",
-    "\n",
-    "# it is necessary to pass y_train to the pipeline\n",
-    "# y_train is not used for the transform, but it is used by the classifier\n",
-    "sast_pipeline.fit(X_train, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Pipeline(steps=[('sast', SAST()),\n",
-       "                ('ridgeclassifiercv',\n",
-       "                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
-       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
-       "       2.15443469e+02, 1.00000000e+03])))])"
-      ],
       "text/html": [
        "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -1156,6 +1143,13 @@
        "       2.15443469e+02, 1.00000000e+03])))])</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\"><div><div>SAST</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>SAST()</pre></div> </div></div><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>RidgeClassifierCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.RidgeClassifierCV.html\">?<span>Documentation for RidgeClassifierCV</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
        "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
        "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('sast', SAST()),\n",
+       "                ('ridgeclassifiercv',\n",
+       "                 RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       4.64158883e-01, 2.15443469e+00, 1.00000000e+01, 4.64158883e+01,\n",
+       "       2.15443469e+02, 1.00000000e+03])))])"
       ]
      },
      "execution_count": 8,
@@ -1163,7 +1157,13 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 8
+   "source": [
+    "X_train, y_train = load_classification(\"UnitTest\", split=\"train\")\n",
+    "\n",
+    "# it is necessary to pass y_train to the pipeline\n",
+    "# y_train is not used for the transform, but it is used by the classifier\n",
+    "sast_pipeline.fit(X_train, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1174,23 +1174,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-19T16:46:23.781486Z",
+     "start_time": "2025-02-19T16:46:23.762476Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.425026Z",
      "iopub.status.busy": "2020-12-19T14:32:56.424348Z",
      "iopub.status.idle": "2020-12-19T14:32:57.602704Z",
      "shell.execute_reply": "2020-12-19T14:32:57.603291Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2025-02-19T16:46:23.781486Z",
-     "start_time": "2025-02-19T16:46:23.762476Z"
     }
    },
-   "source": [
-    "X_test, y_test = load_classification(\"UnitTest\", split=\"test\")\n",
-    "\n",
-    "sast_pipeline.score(X_test, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -1203,7 +1199,11 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 9
+   "source": [
+    "X_test, y_test = load_classification(\"UnitTest\", split=\"test\")\n",
+    "\n",
+    "sast_pipeline.score(X_test, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1217,22 +1217,16 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-02-19T16:46:23.795361Z",
      "start_time": "2025-02-19T16:46:23.791501Z"
     }
    },
-   "source": [
-    "clf = SASTClassifier(seed=42)\n",
-    "clf"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "SASTClassifier(seed=42)"
-      ],
       "text/html": [
        "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -1650,6 +1644,9 @@
        "  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>SASTClassifier(seed=42)</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  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  sk-toggleable__label-arrow\"><div><div>SASTClassifier</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>SASTClassifier(seed=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SASTClassifier(seed=42)"
       ]
      },
      "execution_count": 10,
@@ -1657,7 +1654,10 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 10
+   "source": [
+    "clf = SASTClassifier(seed=42)\n",
+    "clf"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1668,21 +1668,16 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-02-19T16:46:23.825075Z",
      "start_time": "2025-02-19T16:46:23.805052Z"
     }
    },
-   "source": [
-    "clf.fit(X_train, y_train)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "SASTClassifier(seed=42)"
-      ],
       "text/html": [
        "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -2100,6 +2095,9 @@
        "  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>SASTClassifier(seed=42)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SASTClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SASTClassifier(seed=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SASTClassifier(seed=42)"
       ]
      },
      "execution_count": 11,
@@ -2107,7 +2105,9 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 11
+   "source": [
+    "clf.fit(X_train, y_train)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2118,15 +2118,13 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-02-19T16:46:23.851369Z",
      "start_time": "2025-02-19T16:46:23.833347Z"
     }
    },
-   "source": [
-    "clf.score(X_test, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -2139,7 +2137,9 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 12
+   "source": [
+    "clf.score(X_test, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2147,73 +2147,73 @@
    "source": [
     "### 4.4 Interpretability\n",
     "\n",
-    "SASTClassifier's interpretability can be achieved by visualizing the most important subsequences used for the transformation.\n",
+    "``SASTClassifier``'s interpretability can be achieved by visualizing the most important subsequences used for the transformation.\n",
     "\n",
     "The importance of features can be computed by different means. In this demo, we considere the absolute values of the weights of the ridge classifier as the feature importances.\n",
     "\n",
-    "#### 4.4.1 Interpretability of class `1`"
+    "#### 4.4.1 Interpretability of class ``1``"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-02-19T16:46:24.377310Z",
      "start_time": "2025-02-19T16:46:23.859834Z"
     }
    },
-   "source": [
-    "fig = clf.plot_most_important_feature_on_ts(\n",
-    "    X_test[y_test == \"1\"][0, 0], clf._classifier.coef_\n",
-    ")"
-   ],
    "outputs": [
     {
      "data": {
+      "image/png": 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uREQC0do3jAw6vNrsSkQkEK19A96YpAwSEXOse7Mig1aZXYmIBKJ1bymDAlyDF9FXrFjBhRdeSEJCAhaLhYULF9Z67O23347FYuHZZ591ej4rK4uZM2cSFRVFq1atuPnmm8nPz3c6ZsuWLYwePZpmzZrRqVMn/vOf/zS0VBHXykuFrx6AwythwW1gtZpdkYgEkrw0+FoZJCImqcyg5FWwYDaUl5tdkYgEkrw0+Or+igy6TRkkIp6Vnw5f3Wdk0OeaBwWqBi+iFxQUMHjwYF566aU6j1uwYAGrVq0iISGh2mszZ85k+/btLF26lMWLF7NixQpmz55tez03N5eJEyfSpUsX1q9fz5NPPsk//vEP5s6d29ByRVxnxyKgYtEqa7+xkCUi4ik7F4G1YrJ24gAc+s3cekQksDhl0EE4rAwSEQ9SBomImRwzKPsQHPrV3HrEFA1eRJ8yZQqPPfYYl1xySa3HHD16lLvuuot58+YRGhrq9NrOnTtZsmQJr7/+OiNGjGDUqFG88MILfPjhh6SkpAAwb948iouLefPNN+nfvz9XXXUVd999N88880xDyxVxne0LnB9vnGdOHSISmKpm0CZlkIh4kOZBImKm7QudHyuDRMSTqmaQzsUCkst7opeXl3Pdddfxpz/9if79+1d7feXKlbRq1Yrhw4fbnpswYQJBQUGsXr3adsyYMWMICwuzHTNp0iR2797NiRMnavy5RUVF5ObmOn2JuEzusepXnm9fAEV55tQjXkcZJG6Vl1r9ynNlkDhQBolb1ZRBOxZCof47E4MySNwqL7X6VZ/KIHGgDBK3ykuDg784P7d9oTIoALl8Ef3f//43ISEh3H333TW+npqaSmxsrNNzISEhxMTEkJqaajsmLi7O6ZjKx5XHVPXEE08QHR1t++rUqVNTfxURu50OrVxCWxp/lhRU/zRSApYySNxqR00ZdLL6laESsJRB4lbKIDkNZZC41c4vUQZJXZRB4lY1rQeVnoLtn5tWkpjDpYvo69ev57nnnuPtt9/GYrG48q1P68EHHyQnJ8f2lZyc7NGfL37OcYJ2gcMmtxvf93wt4pWUQeJWyiA5DWWQuFVtGaRbmaWCMkjcSvMgOQ1lkLiV48WTyqCA5tJF9J9//pn09HQ6d+5MSEgIISEhHDp0iPvvv5+uXbsCEB8fT3p6utP3lZaWkpWVRXx8vO2YtLQ0p2MqH1ceU1V4eDhRUVFOXyIukZtib+XStg8MmQnt+hqPk1dB5l7zahOvoQwSt6kpg2L7GY+TV0PmHvNqE6+hDBK3ccqg3tUzKCPJvNrEayiDxG1yj9nbSbXpVZFBFW1jj6xRBgmgDBI3cmwn1aankUFxA4zHR9ZCxm7zahOPc+ki+nXXXceWLVvYtGmT7SshIYE//elPfPvttwCMHDmS7Oxs1q9fb/u+H374gfLyckaMGGE7ZsWKFZSUlNiOWbp0KX369KF169auLFnk9HZ8YR/3vwQsFhh6rf25Tfr0UUTcyLGNQv/p1TNIV0CIiDs5ZZDmQSLiYTuVQSJiotPNg3QuFlAavIien59vWyAHOHDgAJs2beLw4cO0adOGAQMGOH2FhoYSHx9Pnz59AOjbty+TJ0/m1ltvZc2aNfz666/ceeedXHXVVSQkJABwzTXXEBYWxs0338z27dv56KOPeO6557jvvvtc95uL1JfjrTv9pxt/DroSgkKM8aYPoKzU01WJSKDYsdA+7n+J8adjBm1WBomIG502gz5UBomI+zidi1Vm0BVVzsVKqn2biIhL1DQPGngFBIUa480fKoMCSIMX0detW8fQoUMZOnQoAPfddx9Dhw5lzpw59X6PefPmkZiYyPjx47ngggsYNWoUc+fOtb0eHR3Nd999x4EDBxg2bBj3338/c+bMYfbs2Q0tV6Rpco4aLVvAaOESW9HGJaId9J5sjPNTYd8yc+oTEf/m2EahXaI9g1q2dcigNNj7vTn1iYh/UwaJiJmqtrRzzKA+U4xxQboySETcw7GdVNve9nZ2Lds4Z9CepebUJx4X0tBvGDduHFartd7HHzx4sNpzMTExzJ8/v87vGzRoED///HNDyxNxLadWLtOdXxt6LexabIw3vg+9J3msLBEJEFXbSTkaep09gza9D30me64uEQkMjhnUb7rza44ZtPE9ZZCIuF5NLe0qDb0Odn5pjDe+b1/QEhFxFcd2Uv2m15BBi4zxpnmQeIGnqxMTuLQnuojfcbx1p+rJY8/zoWWsMd79DRRkeqoqEQkU2xfYx9UyaAJExBljZZCIuENNLe0qOWZQ0hLIz/BUVSISKGpqo1Cpx3iIiDfGyiARcQfHc7FqGXRelQxK91xdYhotoovUJucIJK82xrH9IDbR+fXgEBh8lTEuL4EtH3u2PhHxb44Z1K7vaTKoFLZ85Nn6RMS/1dbSrlJwCAy+2hiXl8JWzYNExIVqaydVSfMgEXGn2tpJVQoOgSEO8yBlUEDQIrpIbepqo1Cp6q7MDWh1JCJSpx2L7OPaMmiIYwbNUwaJiOs0dB604T1lkIi4js7FRMRMVc/FHFu5VBqiDAo0WkQXqU1dbRQqtesDHc8yxunb4dgmd1clIoHC6fbB6TUf0643dBphjNO3Q8pGt5clIgGiPhnUtpc9gzJ2QsoGt5clIgGiPudibXtBp7ONsTJIRFypXvOgntB5pDHO2AVHlUH+TovoIjXJToYja41xbH9joao2Q2faxxvfd29dIhIYspPhyBpjHNvf+MCuNkOUQSLiYjlHHDKoX90ZNLTKHTEiIk2Vc7TulnaOdC4mIq7m1NKuhnZSjpzOxd5zb11iOi2ii9SkPrcP2l6/FEKaG+Otn0BJofvqEpHA4JRB0+s+tv8lENrCGG/9FEpOua0sEQkQDZoHKYNExMUanUGfKYNEpOkalEHTIbSlMd72GRSfdFtZYj4toovUpD637lRqFmU/pjAHdi12V1UiEih2LLSPa7uFuVKzKPsxRTmw6ys3FSUiAaM+bRQqhUc6Z9BOzYNEpIkaci4WHmlf5FIGiYgrNORczCmDcrUe5Oe0iC5SVfZhOLrOGMcNMHrtnY7aKYiIq2QftreTihtQdzupSkN1G6GIuEhDWtpVcmzpsknzIBFpgoa0k6qkdgoi4io5R+rfTqqSzsUChhbRRapqSBuFSl3OhdZdjfH+5cYimIhIYzQ6g7oZ4/0/wYlDLi9LRAJEQ25hrtTlHIjpboyVQSLSFE3NoAPKIBFpgsZkUOeRDhm0Ak4cdHlZ4h20iC5SldMtzPUMzaAgGFJ5FZYVNn3g8rJEJEA0JoMsFocrIKywWRkkIo3UkDYKlSwWhytBlUEi0gQNaSdVySmDgE3zXVqSiASQxs6DnO7KUwb5Ky2iizg6cQiOrjfG8QOhbc/6f++QqwGLMd40D8rLXV6eiPg5xwyKa2AGDb4GZZCINIlTS7uB9WtpV2nw1WCpOLVQBolIYzi2k6pvS7tKThk0XxkkIg1XraVdPdpJVVIGBQQtoos4crx1p75XPlSK7gg9fmeMsw/BoV9cVpaIBIjGtHKpFN0BepxnjLMPw8GfXVaWiAQIpwy6uGHfqwwSkaZq0rmYQwblHIaDK1xWlogEiKaci0UlQI/xxjgn2WgtJX5Hi+gijpxu3alnGwVHjrfw+NIGo1ar2RWICCiDRMRcjWkn5UgZJCJN0Zg2Co6UQSLSFI1pJ+VIGeT3tIguUunEQUjZYIzjB0GbHg1/jz5ToVkrY7xjERTmuKo69ygvg+8egsfi4KsHzK5GJLA5ZdDAxmVQ4lRo3toY7/ShDPpnPHx1vyZwImZqSku7Sn0ucM6gU9kuK88tysvgu4eVQSLe4MShxreTquSUQV/6QAaVw9I5RgYtvk8ZJGImp5Z2DWwnVanPFGgeY4x3fgmnTriuPndwyqB7lUH1oEV0kUrbF9rHjbnyASC0GQy6whiXnoJtnze1KvcpOQUfz4LfXoCyIlj7mvMnryLiWY3ZCb6qkHAYWJlBhbDts6bX5S4lp+CT640MKi2Eta8rg0TMtGOhfdyYq6+gegZt9/J50Cc3wG/PK4NEvEFT2klVCgmHQVcaY1+ZB/36nFHruje8OzNF/F1T2klVcsygsiIfy6A3vbteLxFidgEiXsMVJ49g7Ay/Zq4x3viecUVEUR4U5Vb86fhV8VxZMfS9CLqMbMpvUH8Fx+GDq+DIGufnv7ofuo6Glm09U4eI2DX19sFKQ2fCmleN8Yb3jDtkvC2DTmYZGZS82vn5rx8wMiiinWfqEBE7p4sJGvlBHhi3Mjckg4rzobTIhAy6GpJXOT+vDBIxj9O5WBMyaMhMWP2KMd74HiROq2cGXQhdzmnSr1BvtWXQVw9A1zHKIBEzNLWdVKWhM2H1y8Z4w3uQeGHtGVRc8ae3ZNDXf4JuYyAi1jN1+CAtoosAZB2AlI3GuP3gxrVRqNR+sHELYtpW47bop+t5G9C6t+CudcYGpe6UdQDmzYDje43HYREQ289YUD953DiBvPxt99YgIs5cnUHxAyF1q9Eextsy6MRBeP+yujPoinfcW4OIOHNFS7tK7QcZ75G6peEZdOdaaNWp8T+7Pk4chPdnwPE9xmNlkIj5XNFOqpJTBm1sQAa9WZFBnRv/s+ujrgw6lQVf3w9XvOveGkTEmWMGNbadVKX4gcb52LHNcGyT92dQaEuIH2Bc3HQqC766D654DywW99bho9TORQScr3xoytVXYITNGdc1/PtKT8GyR5r2s0/n6Hp443z74lVEPNz4DVw1z94/cPsC56vRRMT9XJlBAEO9NYM2wOsTHDIoDm78uiKDKvoH7liolgoinuaqq9AreXUGnW8/cVQGiXgHV8+DzpjV8O8pLXR/BqVsrJ5BN3wFV813yKAvlEEinuaUQdOb/n6NmgeZkEEtY4150JXzoEUb47mdXyqD6tDgK9FXrFjBk08+yfr16zl27BgLFixg+vTpAJSUlPDQQw/x9ddfs3//fqKjo5kwYQL/+te/SEhIsL1HVlYWd911F19++SVBQUFcdtllPPfcc0RERNiO2bJlC3fccQdr166lXbt23HXXXfz5z39u+m8sUhPHk8emtFGoNOxGY5EofSeER0F4ZA1fURAeASHN4LObjU0ntnwEI26DDsOaXkNVSd8avT9LThqP2/aBaz+1f9J5wVNGHeDQ1qWN6+sQkepMz6BbjCsP3JpB3xl992rNoCcdMugBtZYS8SRXnzwOu8E4Qaszgyq+QpvDpzcbGbT1YxhxO3R0VwbdACUFxmNlkIj3cPk86AbITGpEBn1SkUHDm15DVXuWwsfXO2RQb5j5KbTuYjye+hR8epMxVotNEc9y9cUEw26AzD2QvqN+GfTZLcbdcJ7MoDa94NrP7Bl0wVPw6Y3GWO3tatXgRfSCggIGDx7MTTfdxKWXXur02smTJ9mwYQMPP/wwgwcP5sSJE/zxj3/koosuYt26dbbjZs6cybFjx1i6dCklJSXceOONzJ49m/nz5wOQm5vLxIkTmTBhAq+88gpbt27lpptuolWrVsyePbuJv7JIFVn7jdtsANoPgZhuTX/PkDDjZKy+xj0I31R8SPTt/xlXh7vy9pl1bxm35VjLjcddznW++hxgwGXGJ467FsPJTPjmTzDjTdfVICI185oM+pMxdkcGrX8bFt8H1jLjcedzjAxqEWM/pmoGff0nuPwt19UgIjWr2k4qpnvT37OhGfS7vxknbADf/g1uWuLiDHoHFt+rDBLxRq5sJ1UpOLSJGfStazNow7vw5T0OGTTSuPrcMYP6X2pk0M4v1WJTxJOcMmigCzPoP/U/ftyDns2gTmfD1R9UyaBLKjJokdrb1aHB7VymTJnCY489xiWXVP90Jjo6mqVLl3LFFVfQp08fzj77bF588UXWr1/P4cOHAdi5cydLlizh9ddfZ8SIEYwaNYoXXniBDz/8kJSUFADmzZtHcXExb775Jv379+eqq67i7rvv5plnnmnirytSA1d/6tgYw2+CNhW9/w6vNILLFaxW+OExWHyPfQG9/yVw7efOC+hghPTUZ6BZK+Pxts+MSZyIuJdXZNCNxtUI4IYM+id8+Uf7pK3fdLhugfOkDewZZGst9TnscFEdIlI7V7dRaIxhNxhXZYKxydWOL1zzvlYr/Pg4fHl3IzPIRXWISO28YR7klEGrnXOxKSozaNFdDhl0MVy3sB4ZtEAZJOIJXpFBNxp3yIH7M6jvRTDri1oy6Okq7e1cVIcfcXtP9JycHCwWC61atQJg5cqVtGrViuHD7bcnTJgwgaCgIFavXm07ZsyYMYSFhdmOmTRpErt37+bEiRM1/pyioiJyc3OdvkTq5dBv9nHfC82pITgUJj5mf7x0jrFDc1OUFsPC38MKh6swRt4Jl70Joc1q/p7IOOerNhbfZ+zcLKelDJJGO7zSPvanDCorgYV/gBUOV2GMvBNmvFV3Bk1xyKCvlEH1pQySRnOaB11kTg3uyqAv7oCf/m1/rsEZdD8UHG9aHQFCGSSN5pUZ9HcoKWzae9aUQWffATPerj2DImKNlgqVlEH1pgySRnM6FzMrg0LclEF3VsmgP8Dl75wmgzQPqotbF9ELCwv5y1/+wtVXX01UVBQAqampxMbGOh0XEhJCTEwMqamptmPi4uKcjql8XHlMVU888QTR0dG2r06dOrn61xF/lbHL+DM8yjW3MDdW78nQbYwxPnEQ1sxt/HsV5cP8K2DzBxVPWGDyv2DSPyHoNP/bD7wc+lxgjAvS4Zu/NL6OAKIMkkZL95YMmgTdxhpjl2XQ/IonLDDpiXpm0AzoM9UYF2TYW11JnZRB0mjekkG9JkL3ccY4+xCsfrXx71VcYGTQpnkVTyiD3E0ZJI2WsdP4MyzSNW0UGqtqBq1xdQY9DpMfP30GDbgMEqcZY2VQvSmDpNHSKzMowt4dwAy9zofuvzPGLsmgK2HT+xVPVGbQEw3LoMo2v2LjtkX0kpISrrjiCqxWKy+//LK7fozNgw8+SE5Oju0rOTnZ7T9T/EBRHuRU/LfSLtG1facaymKBif8EKmr46cnGfepXVmJs3rf/R+NxcLjRy+rs39e/jmn/hWbRxuOtH8OurxpeR4BRBkmjFOVDjtHuzCsyaJKrMugG2PeD8Tg43OjpOfIP9a9jmkNrqa2fwM7FDa8jwCiDpFGcMqiP+RnkOA9a8SQUZDb8fcpKXZtB2z5Ve7t6UAZJoxQXQLa3ZtBTLsygt2DkHfWvY6oyqKGUQdIo3pZBk/4Jlopl2iZn0DLjcWMzqLK11LbP1GLTgVsW0SsX0A8dOsTSpUttV6EDxMfHk56e7nR8aWkpWVlZxMfH245JS0tzOqbyceUxVYWHhxMVFeX0JXJaGUn2cbs+5tVRqf0gGDrTGBflwE//atj3W63GhhF7vzceh0fD9YuM3nsNERkPkx1u+1l8r1oqnIYySBolc7d97A0ZFD8Qhl5rjBubQYvvgb1Ljcfh0UbPvf7TG/Y+kfEwRRnUEMogaRSvy6ABcMZ1xrgoF5Y3MoP2fGc8blIGObSiUnu701IGSaNkOGZQonl1VKqWQU807PutVvjqXocMioJZCxveZzkyThnUQMogaZTMJMBqjL0hg+L6w1AvzCC12LRx+SJ65QL6nj17+P7772nTpo3T6yNHjiQ7O5v169fbnvvhhx8oLy9nxIgRtmNWrFhBSUmJ7ZilS5fSp08fWreushmiSFNU3j4IENvXvDoc/e4hCG1hjNe+4bzQfzrLn7DfshMcBlfPh85nN66OwVdBr0nGOD8NljzYuPcRkdpVtlEA78mg8x6C0JbGuMEZ9C/Y6JBBV82DLiMbV8egK402V2C0llry18a9j4jUzjGD2nlJBv3OIYPWvem8yHY6P/0bNr5njJucQVdA7ynGWO3tRNwjw3Ee5AULWFAlg95yzsnT+enfsOFdY2zLoHMaV4cySMT9nOZB3pJB/2e0loFGZNB/7BkUFNq0DHJq86vWUpUavIien5/Ppk2b2LRpEwAHDhxg06ZNHD58mJKSEmbMmMG6deuYN28eZWVlpKamkpqaSnFxMQB9+/Zl8uTJ3HrrraxZs4Zff/2VO++8k6uuuoqEhAQArrnmGsLCwrj55pvZvn07H330Ec899xz33Xef635zEXCeuHlLaEa1h3PvMcbWMlj6cP2+b91bzptGXPIqdB3V+DosFrjwWeMqLoAtH8Lubxr/fiJSnTdmUGQ8jLrHGDckg9a/7Xzl+iWvQLfRja/DYoFpz9pbS235CHZ93fj3E5HqvHEBKzIORt1rjK1l8F19M+gd5yu2XJJBam8n4lYZXvhBXtUMqu88aMO7zhk0/WX7fleNoQwScb8ML7ygKTKucediG96F5Y/bH1/yioszSC02oRGL6OvWrWPo0KEMHToUgPvuu4+hQ4cyZ84cjh49yqJFizhy5AhDhgyhffv2tq/ffrPvuj1v3jwSExMZP348F1xwAaNGjWLuXPsGZtHR0Xz33XccOHCAYcOGcf/99zNnzhxmz57tgl9ZxIE3fvIIcM6dEGl8qETSEti/vO7jdy8xbrGpNOlxGHBp0+uISjA2n6j05T1w6kTT31dEDN64iA4wskoG7fux7uOTvjVuNa408Z/GpjRNFdXe2BS50uJ7dCuhiCt5bQbdAVEdjPGeb+29hWuT9J3R9qmSSzNIraVE3MbpXMwLWkpVcsqg7+qXQV/eY3888TFjk+KmUgaJuFeGt2bQnRDV0Rjv+Q72Lqv7+KoZdP6jrsmgau3tlEENXkQfN24cVqu12tfbb79N165da3zNarUybtw423vExMQwf/588vLyyMnJ4c033yQiIsLp5wwaNIiff/6ZwsJCjhw5wl/+otuXxA0qQzM8ylgw9hZhLWH8HPvjbx+C8rKajz2yHj69EazlxuORd9Z/04j6GHIN9DzfGOenwpK/ue69RQJdurdmUAuY8Hf74+/qyKCj643Na6wVr599h/FBoKsMvhp6TTTG+WnwrTJIxGWcMqiDubU4CmsB4x0yqK550NH1xobqbssgtbcTcZvK1pphkRDd0dxaHDUpg/5gnI+5ijJIxH3SKzMoAqI7mVuLo9DmDTgX2+CcQSN+D+fc5bpa1GLTiVs2FhXxCUV5kFOxa3e7RHN3Yq7JoCuh/WBjnLYVNs2vfszxfTD/cig5aTzuf6nxqaMrWSxw4XPGCTbA5vn2jUtFpPGK8iGncjd4L8yggVdA+yHGOG0bbJpX/Zjj+2DeFQ4ZdIlx9ZUr2TKo4lbCzR/AHmWQSJM5ZVAfL8ygyyHBuPOV9O32/RYcZe33UAY969zebs9S1/4MkUBUXADZfpZB/aYbd8K48ndRBom4h7dn0IAZkHCGMU7fYd/zxVHWfpjvmEEXG10JXJ1BVVtsJn3nuvf3MVpEl8DluFmeN926UykoyAjASj88apzwVsrPgPcvg5PHjcddRhl9r4Lc8L91dAeY9E/74zWvu/5niASaTIfN8nwigx4zPnysZMugTONxl3NhupsyKCrBOYPWKoNEmszXM6gg07MZNNmhFmWQSNM5bhrsTe2kKjUmgy551TMZtOY11/8MkUCTmQRYjbEvZ1BBhvG48zlwyVw3ZVB7mOTQ5ndt4GaQFtElcFXePgjes4lEVV1HQeI0Y5yfBr8+Z4yLC4xPHE8cMB6362vsvBwS7r5ahlwLEXHGeP+PRg0i0njpXriRTVVdz4W+Fxrj+mRQaDP31TJkJkTEG2NlkEjTOfUi9tIM6nIO9L3IGBekwy/PGuPKDMrabzz2RAYNvsYhg5Yrg0Sayhs3Nq6q3hmU6PkMcry4SkQazlv3x3PUZaRxdTkYi+W//NcYF5+E+Vc6Z9DV892cQVdDZHtjvP+ngM0gLaJL4PLWzbSqOv8RCAo1xr+9ACcOGf2HUzYYz0UmwLWfQvNW7q0jKMjeC6u08PQbDYpI3Rw/yPPmDJrw/6pk0EH45MYaMqi1e+sICoI+yiARl/GFBSyA8x0yaOWLRgZ9epPRhxg8nEFTjHFp4ek3GhSRumX4wAd5UI8Mag/XfubZDCorUgaJNFWGD1zQBDDhHxAcZox/exGyDlRk0Drjucj2MNPD86CyIth3ms1O/ZQW0SVw+cInjwBtesBZs41x6SmYO87YoRmMPuXXfuq5jXgSp9rHu7/2zM8U8VfefhtzpTY9YMRtxri0EOb+DvZ8azz2dAb1UQaJuIyvXEwQ0716BiUtMR6bOQ/apQwSaRKnczEvbClV6XQZNFPnYiI+KcMHM6isCF47D5K+MR6HRcLMT6CVhzZF7aN5kBbRJXBVhmZ4lNFnzpuN/ZP9k8VTWcafwWHGbYNx/T1XR7exENrSGCctqX2HaBE5vXQfyqAxD1TPoKBQEzJojDJIxFWcMqiDubWczpg/QfMYY2x2BoVFGOOkJVBW6rmfLeJvKu/IC4v03CJ0Y9WWQVe+D/EDPFeHMkjEddIrMygCoj20CN1Yox+oZR70PsQP9Fwd3UYbmQ3GRVUBmEFaRJfAVJQHOcnGuF2i9+3EXFXz1jD2r87PTX/ZmEh5Umgz6HmeMT55HJJXe/bni/iLonzIqdwN3kcyaNyDzs9d8opJGTTeGCuDRBrPKYP6+EAGtfKODAoJt2fQqSxlkEhjFRdAto9n0PSXoftYz9bhlEEnIHmVZ3++iL/wxQz63d+cn5v+P+g+zrN1VM2gwys9+/O9gBbRJTBlJNnH3nzrjqMzb4Yuo4xPHKc8CQNnmFOH0y08X5lTg4ivy3Rs5eIjGTT8Jug6uiKD/mNeBiUqg0SazCcz6EbvyCC1lRJpOl9paeeoagYNutycOtROQaTpMpMAqzH2lQwadoNx8UBQKEz+Nwy6wpw6ArytVIjZBYiYwnFDP2/eRMJRcCjcsBhKi9y76/Lp9J4ElmCwlhmhOfEx7//kVsTbpPvIRjaOgkPh+i/Nz6BeE5VBIk3l1ItYGdQgvc63Z9Cur5RBIo3hKxsbO/LGDNr9FUz6pzJIpKF8ZX88R8GhMGuRd2XQrq9g0uMBlUG6El0Ck69splWVxWJuYAK0iIHOI41x1v6KT3FFpEEcP8hTBjVMixjoco4xztrvfDWbiNSPLy5ggfdl0IkDzv8sRaR+Mnzwgzzwwgw6aO/rLCL1l+GDFzSBd2RQ89bQ9VxjnH0o4DJIi+gSmHzxk0dvkniBfax2CiIN54u3MXuTPg4ZtFsZJNJgvnoxgbdQWymRpnE6F/ORllLexKmdgjJIpMEylEFN0idwM0iL6BKYKkMzPAqiEsytxRc5LWAFXh8skSZLVwY1ieMHebu/Ma8OEV/llEEdzK3FF/VRBok0SeUdeWGREN3R3Fp8kTJIpGkqr54Oi4DoTubW4oucLqoMrPUgLaJL4CnKg5xkY9wuMaD6N7lMTDeI7WeMj6yDvDRz6xHxJUX5kFO5G7wyqFFad4XY/sZYGSTSME4Z1EcZ1Bitu0DcAGN8dB3kpZpbj4gvKS6AbGVQkzhl0HrIPWZuPSK+RBnUdK06Q9xAY5yyIaAySIvoEngyHHp469adxrNdAWGFJF0BIVJvmY6tXJRBjZaoDBJpFGWQa+hKUJHGUUs713DMIM2DROovMwmwGmNlUOMlBmZ3Ai2iS+Bx3NDPlzaR8DZ9AvcWHpEmSffRjWy8TZ8p9rEySKT+0n10Qz9v45hBAXTyKNJkvrqxsbfRPEikcbQ/nmsE6DxIi+gSeLSZlmskDIWIeGO8f7lxe7iInJ7jB3m6CrTx2g+FyPbGWBkkUn9awHKNhKEQWbGnxf6flEEi9ZWhD/JcwjGDDvxktCwVkdPL0AVNLtF+iH1fnQMrAiaDtIgugUdXgbpGUJD908eyItj3g7n1iPgKp9uYlUGNpgwSaRwtYLmGxVIlg5aZW4+Ir0jXB3ku4ZRBxbBXGSRSL7qo0jWqZdD35tbjIVpEl8BTGZrhUfarGKVxEqfaxwF0C49Ik6Q7ZFBUgrm1+Lo+yiCRBlMGuU6iWtuJNFjlHXlhkfarGKVxArQnsUiTpFdmUAREdzS3Fl8XgC1+G7yIvmLFCi688EISEhKwWCwsXLjQ6XWr1cqcOXNo3749zZs3Z8KECezZs8fpmKysLGbOnElUVBStWrXi5ptvJj/f+RbILVu2MHr0aJo1a0anTp34z3/+0/DfTqSqojzISTbG7RK1E3NTdRtj/OUDkPQtlJWaW4+ItyvKhxztBu8y3UYbJ+EASUuUQSKnowxyra4OGbRH8yCR0yougGxlkMs4ZlDSt1BWYm49It5OGeRaXUcbF2VAxTzI/zOowYvoBQUFDB48mJdeeqnG1//zn//w/PPP88orr7B69WpatmzJpEmTKCwstB0zc+ZMtm/fztKlS1m8eDErVqxg9uzZttdzc3OZOHEiXbp0Yf369Tz55JP84x//YO7cuY34FUUcZCTZx7p9sOlCwqHneGN8KguSV5tbj4i3c2rlogxqMqcMOgHJq8ytR8TbZSqDXCokHHpNMManTsDhlebWI+LtHOdBOhdrOscMKsxWBomcTmYSYDXGamnXdCFh0LMyg3ICIoMavIg+ZcoUHnvsMS655JJqr1mtVp599lkeeughLr74YgYNGsS7775LSkqK7Yr1nTt3smTJEl5//XVGjBjBqFGjeOGFF/jwww9JSUkBYN68eRQXF/Pmm2/Sv39/rrrqKu6++26eeeaZpv22Ik4b+mni5hJqpyBSf9rIxvUc20oFyG2EIo2Wrj6gLqd5kEj9qRex6/XRPEik3pzmQX3Mq8OfBNi5mEt7oh84cIDU1FQmTJhgey46OpoRI0awcqXxicTKlStp1aoVw4cPtx0zYcIEgoKCWL16te2YMWPGEBYWZjtm0qRJ7N69mxMnTtT4s4uKisjNzXX6EqlGEzfX63U+WIKN8a6vwGo1tx6TKIOkXpw+yNPEzSUcM2i3MkgZJHXK0IZ+LtfrfAgKMcaaBymDpG7a2Nj1HDNI8yBlkNRNFzS5Xs8JAZVBLl1ET01NBSAuLs7p+bi4ONtrqampxMbGOr0eEhJCTEyM0zE1vYfjz6jqiSeeIDo62vbVqVOnpv9C4n/SFZou1yIGupxjjE8ccP6LKYAog6Re0nXy6HLNW0PXc43xiYP2zYICjDJI6kULWK7XvBV0qcig7EOQvsPUcsyiDJJ6SdcHeS7nlEGHIW27qeWYRRkk9aKLKl2veSvoOsoYB0AGuXQR3UwPPvggOTk5tq/k5GSzSxJvVBma4VEQ2d7cWvyJ0y08X5lXh4mUQVIvlb1Aw6MgKsHcWvyJUzsFZZAySGqV7jAPUga5ToDdylwTZZDUS+UdeWGRENXB3Fr8SaLaSimDpF4qL7YJi4DojubW4k8CqLWdSxfR4+PjAUhLS3N6Pi0tzfZafHw86enpTq+XlpaSlZXldExN7+H4M6oKDw8nKirK6UvESVEe5FT8ZdouUTsxu1KfC+xjPw/N2iiD5LSK8iFHu8G7RaJjBn1jXh0mUgbJaSmD3KfPFPtY8yBlkNSsuMC4ShGUQa6mDFIGyekpg9zHMYP8/KJKly6id+vWjfj4eJYtW2Z7Ljc3l9WrVzNy5EgARo4cSXZ2NuvXr7cd88MPP1BeXs6IESNsx6xYsYKSkhLbMUuXLqVPnz60bt3alSVLIMlIso91+6Brte4CcQOM8dH1kHvM3HpEvFHlVeig2wddrVVniBtojJVBIjXLVAa5TavOEF+RQSkblEEiNXGcB+lczLWcMmgj5KaYW4+IN8pMAir6daulnWu16gTxg4zxsU2Qc9TUctypwYvo+fn5bNq0iU2bNgHGZqKbNm3i8OHDWCwW7rnnHh577DEWLVrE1q1bmTVrFgkJCUyfPh2Avn37MnnyZG699VbWrFnDr7/+yp133slVV11FQoJxW+k111xDWFgYN998M9u3b+ejjz7iueee47777nPZLy4ByGlDP03cXM7xavSkwLwSVKRO2sjGvRKVQSJ1SlcfULcKoFuZRRpFvYjdSxkkUjeneVAf8+rwVwHSVqrBi+jr1q1j6NChDB06FID77ruPoUOHMmfOHAD+/Oc/c9dddzF79mzOPPNM8vPzWbJkCc2aNbO9x7x580hMTGT8+PFccMEFjBo1irlz59pej46O5rvvvuPAgQMMGzaM+++/nzlz5jB79uym/r4SyDRxcy+1UxCpm9MHeZq4uZzjB3kB2pNYpE4Z2tDPrRLV2k6kTtrY2L0SNQ8SqZMuaHKvPoGxHhTS0G8YN24cVqu11tctFguPPPIIjzzySK3HxMTEMH/+/Dp/zqBBg/j5558bWp5I7dIVmm7VfghEJkBeCuz/yei9Gh5hdlUi3iNdJ49u1X6wsUlZ7lE48JOxD0Z4pNlViXgPLWC5V/wgiOoIuUfgwAplkEhV6fogz62qZlBhLjRTX3ARG11U6V7xAyG6k7EPoR9nkEt7oot4tcrQDI+CyPbm1uKPLBb7hhJlRbBvWd3HiwSayl6g4VEQlWBuLf7IKYOKYa8ySMRJusM8SBnketUy6Htz6xHxNpV35IVFGh96i2s5ZlB5iTJIpKr0ygyKgOiO5tbijwIkg7SILoGhKM/4RAyMTx21E7N76DZCkZoV5UOOdoN3uz5qpyBSI2WQZ2geJFKz4gLIVga5ndpKidRMGeQZAXAupkV0CQwZSfaxbh90n66jjatLAPZ8C2Wl5tYj4i0qr0IH3T7oTl1HG1fZAiR9C2Ul5tYj4i0ylUEe0WWUPYP2KINEbBznQToXcx+nDPpOGSRSKTMJqGhLrZZ27tN1FIRHG2M/zSAtoktgcNrQTxM3twkJh14TjPGpE3B4pbn1iHgLbWTjGSFh0LMigwqzlUEildLVB9QjQsKg1/nGuDAHDv1mbj0i3kK9iD2jWgb9am49It7CaR7Ux7w6/F1wqN9nkBbRJTBo4uY5fabax356C49Igzl9kKeJm1slOmSQ2imIGDK0oZ/HBMCtzCINpo2NPaeP2kqJVKMLmjzHz1vbaRFdAkO6QtNjep0PQSHGeNdXYLWaW4+IN0jXyaPH9Jxgz6DdyiARQAtYntTrfAgKNca7vlYGiUCVczF9kOdWjhm0WxkkAuiiSk/q6d8ZpEV0CQyVoRkeBZHtza3F3zVvBV3ONcbZhyBtu6nliHiFyl6g4VEQlWBuLf6ueSujHx8YGwilbTO1HBGvkO4wD1IGuVezaHsG5RyG1K3m1iPiDSrvyAuLhKgO5tbi75wyKBlSt5hbj4g3SK/MoAiI7mhuLf6uWRR0G22M/TCDtIgu/q8oz/ifF4xPHbUTs/v1vdA+3r7AvDpEvEFRvrGQAtoN3lMSp9nHyiAJdMogz+urDBKxKS4wPtQGZZCnKINE7JRBnufH52JaRBf/l5FkH+v2Qc/odzFYKuJl22d+dwuPSINUXoUOun3QU/pNVwaJVMpUBnlcX82DRGwc50E6F/OMvheDJdgYK4Mk0GUmARX/D6ilnWf0898M0iK6+D+nDf00cfOIiFjoWnELz4kDkLLR3HpEzOSYQdqTwTMi2kG3Mcb4xEFI2WBqOSKmSlcfUI+LaAfdxhrj7ENwVBkkAUy9iD3PcR6UfRiOrje3HhEzOc2D+phXRyBp2Ra6V86D/CuDtIgu/k8TN3MMuMw+3vaZeXWImC1DEzdTOGXQ5+bVIWK2DG3oZwrNg0QM2tjYHMogEYPTPEgZ5DF+mkFaRBf/l67QNEXfCyEoxBhvXwjl5aaWI2KadJ08miJxmn1n+O0LlEESuLSAZY6+yiARoMq5mD7I8xhlkIhBF1Waw0/PxbSILv6vMjTDoyCyvbm1BJIWMdDjPGOcewSOrDG3HhGzOGZQVIK5tQQSpww6Csmrza1HxCzpyiBTNG8NPccb47wUSF5lbj0iZqlsaxcWCVEdzK0lkDhl0DE4vNLcekTMkl6ZQREQ3dHcWgJJ81bQc4Ix9qMM0iK6+LeiPMhJNsbtErUTs6f56S08IvXmlEHaDd7jlEES6IryIeewMVYGeZ4ySAJdcYHRDxeUQWZQBkmgUwaZyw8zSIvo4t8ykuxj3T7oeX0ugOBwY7x9IZSXmVqOiMc5ZpBuH/S8PlMgpJkx3rEQykpNLUfE4zJ328fKIM9zyqAvlEESeDIcMkjnYp6nDJJAl5kEWI2xWtp5Xp8pENLcGPtJBmkRXfxb5e2DoJNHMzSLgt4TjXFBOhz8xdx6RDzNMYO0J4PnNYuCXpUZlAGHlEESYNLVB9RU4ZHOGXTwZ3PrEfE09SI2V3gk9J5kjE9mwsEV5tYj4mlO86A+5tURqMIj/C6DtIgu/k0TN/P1v9Q+9pNbeETqLUMTN9MNUAZJAMvQhn6m88NbmUXqTRsbm0/nYhLInOZByiBT+Nm5mBbRxb+lKzRN13sShLY0xjsXQWmxufWIeFK6Th5N18shg3YogyTAaAHLfL0mGpuZgeZBEnjS9UGe6Zwy6EtlkAQWXVRpvmoZVGRuPU3k8kX0srIyHn74Ybp160bz5s3p0aMHjz76KFar1XaM1Wplzpw5tG/fnubNmzNhwgT27Nnj9D5ZWVnMnDmTqKgoWrVqxc0330x+fr6ryxV/Vxma4VEQ2d7cWgJVWEujFxbAqROwf7mp5Yh4lGMGRSWYW0ugCmsBiRcY48Js2P+jqeWIeFS6Msh0YS3s86DCHGWQBJbKtnZhkRDVwdxaAlVYC2OfKjAyaN8P5tYj4knplRkUAdEdza0lUIU2h8SpxtgPMsjli+j//ve/efnll3nxxRfZuXMn//73v/nPf/7DCy+8YDvmP//5D88//zyvvPIKq1evpmXLlkyaNInCwkLbMTNnzmT79u0sXbqUxYsXs2LFCmbPnu3qcsWfFeVBTrIxbpeonZjN5Hgr8/bPzatDxJOcMki7wZvKqZ2CMkgCRFE+5Bw2xsogc6mliwSi4gLIVgZ5BWWQBCJlkPfwo3Mxly+i//bbb1x88cVMnTqVrl27MmPGDCZOnMiaNWsA4yr0Z599loceeoiLL76YQYMG8e6775KSksLChQsB2LlzJ0uWLOH1119nxIgRjBo1ihdeeIEPP/yQlJQUV5cs/iojyT7W7YPm6jkewqON8c7FUFJY9/Ei/sAxg3T7oLl6nGfPoF1fQckpc+sR8YTM3faxMshcPc6DZsogCTAZDhmkczFzOWbQ7q+VQRIYMpOAio4Yamlnru6/g2atjPHur6H4pKnlNIXLF9HPOeccli1bRlKSsXiwefNmfvnlF6ZMMW5jPHDgAKmpqUyYMMH2PdHR0YwYMYKVK1cCsHLlSlq1asXw4cNtx0yYMIGgoCBWr15d488tKioiNzfX6UsCXOXtg6CTR7OFhEPfaca4OA/2LjW3HjdQBkk1jhmkPRnMFRIOfS80xsV5sEcZJAEgXX1AvYZTBuUrgyQwqBex9wgJq5JB35lbjxsog6Qap3lQH/PqEL/KIJcvov/1r3/lqquuIjExkdDQUIYOHco999zDzJkzAUhNTQUgLi7O6fvi4uJsr6WmphIbG+v0ekhICDExMbZjqnriiSeIjo62fXXq1MnVv5r4Gk3cvIuf7cpclTJIqsnQxM2rKIMk0GRoQz+v0l8ZJAFGGxt7Fz9v6aIMkmqc5kHKINP5SQa5fBH9448/Zt68ecyfP58NGzbwzjvv8NRTT/HOO++4+kc5efDBB8nJybF9JScnu/XniQ9IV2h6lW5joUUbY5z0rdGjzI8og6SadJ08epWqGVTkX5uVK4OkGi1geRdlkASadH2Q51W6joEWbY1x0rfG3j1+RBkk1eiiSu/SdTS0bGeM93znsxnk8kX0P/3pT7ar0QcOHMh1113HvffeyxNPPAFAfHw8AGlpaU7fl5aWZnstPj6e9PR0p9dLS0vJysqyHVNVeHg4UVFRTl8S4CpDMzwKItubW4tAcCj0vcgYl5yE3d+YW4+LKYOkGscMikowtxaB4BDod7ExLj0FSUvMrcfFlEFSTboyyKsEh0C/6cZYGSSBoLKtXVgkRHUwtxapMg8qhN3KIPFz6ZUZFAHRHc2tRWrIIN9cD3L5IvrJkycJCnJ+2+DgYMrLywHo1q0b8fHxLFu2zPZ6bm4uq1evZuTIkQCMHDmS7Oxs1q9fbzvmhx9+oLy8nBEjRri6ZPFHRXmQU/Hpc7tE7cTsLfxoV2aROjllkHaD9xp+chuhyGkV5UPOYWOsDPIeft5WSsSmuACylUFeR/MgCRTKIO/kBxnk8kX0Cy+8kH/+85989dVXHDx4kAULFvDMM89wySWXAGCxWLjnnnt47LHHWLRoEVu3bmXWrFkkJCQwffp0APr27cvkyZO59dZbWbNmDb/++it33nknV111FQkJupJG6iEjyT7W7YPeo8s5EFFxN8nepXAq29RyRNzGMYN0+6D36DzSfmfSHmWQ+LHM3faxMsh7VMugE+bWI+IuGQ4ZpHMx7+GYQXu/VwaJ/8pMAqzGWC3tvEensyGyYk137zKfzCCXL6K/8MILzJgxgz/84Q/07duXBx54gNtuu41HH33Udsyf//xn7rrrLmbPns2ZZ55Jfn4+S5YsoVmzZrZj5s2bR2JiIuPHj+eCCy5g1KhRzJ0719Xlir9K3Wwfx/Yzrw5xFhQM/acb47Ji2P21qeWIuI0yyDsFBdvbKZSXwK6vTC1HxG2ObbGPlUHeIygY+hsXFimDxK+lKoO8UlCQcwbtXGxuPSLu4jQP0iK61/CDDHL5InpkZCTPPvsshw4d4tSpU+zbt4/HHnuMsLAw2zEWi4VHHnmE1NRUCgsL+f777+ndu7fT+8TExDB//nzy8vLIycnhzTffJCIiwtXlir86usE+TjjDvDqkOj+4hUfktBwzqIMyyKsogyQQpCiDvFZ/x5Yuam0nfkrnYt7LcR60XRkkfkrzIO/l4+diLl9EF/EKKZuMPy3BED/Q1FKkio5nQnRnY7zvRyg4bm49Iu5gy6AgZZC36TjcnkH7l0NBpqnliLjaiYJiyo5sNB4og7yPMkgCQYpDBrUfZG4t4qzDMGhVmUE/QX6GufWIuINjBsUrg7xKhzOgVRdjfMD3MkiL6OJ/ik9C+g5jHNsXwlqYW484s1hgQMUtPNYy2PmFufWIuJpjBrXrC2Etza1HnFks9s39rGWwQxkk/uW9FTuxpm0HILNFd7ZnlmK1Wk2uSmyUQeLvSgod5kGJmgd5G4vFfiWozsXEH5UUQsU8iLZ9IFwdLbyKUwaVw46FppbTUFpEF/+Tts2YEAAkDDG1FKmFbmUWf+aUQUPNrUVqNsAhg7YvMK8OETdI3rWWEEs5AD/kdGDq879w1uPLuP/jzezPyDe5OgGqtFNQBomfSdsG5aXGWPMg7+R0LqYMEj+Ttl0Z5O18+FxMi+jif9SDz/u1HwwxPYzxwV8gL9XcekRcyakfuiZuXil+ELTpaYwP/gK5x8ytR8RFMvOLaJFp30xri7U7ABl5RXy24QgWi8Ws0sRR/EBlkPgvp3MxzYO8UvxAaNPLGB/6FXJTzK1HxJXUD937xQ2AthX7Yh76DXKOmltPA2gRXfxPZf8r0MTNWznewoMVti80sxoR11IGeb+qGeRjtxGK1OaXPZkMCtpve7ylvLtt3KVNC7q1VVsFr6AMEn/mNA/SApZX0rmY+DOdi3k/H54HaRFd/E/lJ4/BYRDX39xapHbaGV78VWUGBYUan7KLd1JbKfFDPyVlMMhiLKIXW4PZZe1se21s73ZmlSU1ccqgz8yrQ8TVHOdB8ZoHea0ByiDxU5V3wwSF6FzMm/nouZgW0cW/FOZC5h5jHNcfQsLNrUdqF5sIsf2McfJqyD5sbj0irqAM8h2xiRBb8UHrkTVw4pC59Yg0UXm5lXW7D9PDYtyWv8vamWJCba9rEd3LOGXQWmWQ+IeiPMjYbYzj+mke5M3a9bEvMB5dBycOmlqOiEsU5UNmRQbF9oPQZubWI7Vr1xviBhpjH8ogLaKLfzm2GbAaY90+6P18eEMJkRo5ZpB68Hk/ZZD4kW0pOSScSiLIYmSQYyuXsOAgRvZoY1ZpUhtlkPibY1vQuZgPUQaJv0ndAlZjc3Wdi/mAAb53NboW0cW/pGgjG5/ieAuPevGJP1AG+RbHiZsP9eITqclPuzMYFLTP9rhyU1GAs7rF0CIsxIyypC5awBJ/o3mQb9G5mPgbbWzsW3zwXEyL6OJfHDeR0CeP3q9ND/utzMc2QWGOqeWINJk20/ItMd3ttzKnbIJT2WZWI9Iky5Myqmwq2sM2VisXL+WYQcc2w6kT5tYj0lQ6F/MtMd3s7RSUQeIPdC7mW1p3hfjKDNriExmkRXTxL5WfPIa2gLZ9zK1F6qfrKONPazkcXm1uLSJNVZlBIc2hXaK5tUj9VGYQVmN/BhEflHOyhI2HT9g2FT1lDWOPtYPt9bF9tIjutRwzSPMg8XW2eVAzaNfX3FqkfpRB4k9SHDIoVhnkE7qOrhhY4fAqU0upDy2ii/84mQXZFZsyxQ+CYN227BO6nGMfH/rVvDpEmsoxg9org3yGMkj8wC97M4m05tMlKB2A7daulBEMQEJ0M3rFRphZntRFGST+4tQJOHHAGOtczHcog8RfnDoBWRV35MUPhODQuo8X7+BjGaRFdPEfjj34dPug73AKzd/Mq0OkqZz6gCqDfEZnZZD4vp+S0p1auWwt72Ybj+3TDovFYkZZUh/KIPEXauXim3QuJv4iZZN9rHMx39F5pH3sAxmkRXTxH079r7SJhM+IiIU2vYxxygYoPmluPSKNpQzyTRHtoG1vY5yyEYoLzK1HpIGsVis/JWUw0GJfRN+sfui+wzGDjm2ConxTyxFpNM2DfFPLtvY2qMog8WXKIN/Usq29DWrKJq/PIC2ii/84qk0kfFblFRDlpXBkrbm1iDTWUV2B5bOUQeLDdqXmkZZbxGDHK9GtxpXoIUEWzunZ1qzSpL6UQeIPjuqOPJ+lDBJ/oM4Evqsyg6xlcGSNubWchhbRxX9UfvIYHgUx3c2tRRqmy7n2sQ/cwiNSI6cM6lH3seJdlEHiw35KygBgYMUieq61Ofut7QE4o0tropqpJ6jXUwaJP6hspRAWCW16mlqKNJAySPyBLYMilEG+xocySIvo4h/yUiEvxRgnDIEg/aftU3xsMwmRahwzqP1gZZCvUT9Q8WE/7c6gHdkkWLIA2FbeDWvFFF+tXHyEMkh8XX465B4xxjoX8z1dfKsnsUg1+RmQk2yM2w+BoGBTy5EG8qG+6G752+3o0aNce+21tGnThubNmzNw4EDWrVtne91qtTJnzhzat29P8+bNmTBhAnv27HF6j6ysLGbOnElUVBStWrXi5ptvJj/fu3vjiInU/8q3teoE0Z2N8ZG1UFpsbj0iDaUM8m3RHaGVYwYVmVuPSD3lF5Wy7lCW7Sp0gC1W+914WkT3Ecog8XVO86AhppUhjRTdEVp1McbKIPFFyiDfFt0BWnc1xkfWQUmhqeXUxeWL6CdOnODcc88lNDSUb775hh07dvD000/TunVr2zH/+c9/eP7553nllVdYvXo1LVu2ZNKkSRQW2v9BzZw5k+3bt7N06VIWL17MihUrmD17tqvLFX+hHny+r/IqrNJC578ERXzBUfXg83mVtxEqg8SH/LY3k5Iyq1M/9C3lxiJ624hw+rWPMqs0aajKDCorcv47RcQX6FzM9ymDxJepH7rvc8ygFO/NIJcvov/73/+mU6dOvPXWW5x11ll069aNiRMn0qOH0R/WarXy7LPP8tBDD3HxxRczaNAg3n33XVJSUli4cCEAO3fuZMmSJbz++uuMGDGCUaNG8cILL/Dhhx+SkpLi6pLFHzj+T6arQH2TWrqIL1MG+T5lkPigyn7ogyz7bM9VXok+tnc7goIsptQljaAMEl+meZDvUwaJLzuqDPJ5PpJBLl9EX7RoEcOHD+fyyy8nNjaWoUOH8tprr9leP3DgAKmpqUyYMMH2XHR0NCNGjGDlypUArFy5klatWjF8+HDbMRMmTCAoKIjVq1e7umTxdVar/arBFm3st8OKb/GhzSREnDhmUPMY++2w4luUQeJjrFZrxSK6lYFBBwDIskZwxGq0cBnbR61cfIoySHxV1XlQ5S354lu0N4P4KqcMag2tu5lbjzSOj2RQiKvfcP/+/bz88svcd999/O1vf2Pt2rXcfffdhIWFcf3115OamgpAXFyc0/fFxcXZXktNTSU2Nta50JAQYmJibMdUVVRURFGRvXdXbm6uK38t8WbZh+HkcWOccAZYdNWVT2rTA1rGQkE6HF4F5WU+tSGIMiiAOWZQB2WQz4rpDhFxkJ8Gh1dDWSkEu3ya5DbKoMCzL6OAIydO0YFM2lqMf99by7sDFoIsMLpnW3MLlIaJ6Q4R8ZCfCsnKIPEhOUegwLgrhoShmgf5qpjuENke8o4pg8S35B411hBAGeTLWnezZ5AXn4u5/Er08vJyzjjjDB5//HGGDh3K7NmzufXWW3nllVdc/aOcPPHEE0RHR9u+OnXq5NafJ15EG/r5B4vF/uljcR6kbjW3ngZSBgUwZZB/qJpBacog8W6VrVwqr0IH2FzRymVwp1a0bhlmSl3SSE4ZlA+pW8ytp4GUQQFM8yD/oAwSX6UM8g+OGVRSAKmbza2nFi5fRG/fvj39+vVzeq5v374cPnwYgPj4eADS0tKcjklLS7O9Fh8fT3p6utPrpaWlZGVl2Y6p6sEHHyQnJ8f2lZyc7JLfR3yANpHwHz58K7MyKIClaDMtv6EMEh9SuYg+OMjeD31rub0fuvggH7mVuSbKoACmczH/oQwSX6SNjf2HD2SQyxfRzz33XHbv3u30XFJSEl26GD1iu3XrRnx8PMuWLbO9npuby+rVqxk5ciQAI0eOJDs7m/Xr19uO+eGHHygvL2fEiBE1/tzw8HCioqKcviRA6JNH/+Ejm0nURBkUwJRB/sMHJm61UQYFllPFZazab7SRGmjZb3t+c3kPQIvoPsuHP8hTBgUwzYP8hzJIfJEyyH/4QAa5fBH93nvvZdWqVTz++OPs3buX+fPnM3fuXO644w4ALBYL99xzD4899hiLFi1i69atzJo1i4SEBKZPnw4YV65PnjyZW2+9lTVr1vDrr79y5513ctVVV5GQkODqksWXlZdDyiZjHJkAkTXfqSA+IrYfNIs2xod+MzYJEfFmThnUHqLam1qONFG7vtCslTE+9Jvx71fEC606cJzi0nIslDOoop1LmrUV6bSmdYtQBnVsZW6B0jjtEo1N0QAOK4PEBzhu6BcRD1E6V/dpbfsYm8OCMkh8g1MGxSmDfJ1jBnnpuZjLF9HPPPNMFixYwAcffMCAAQN49NFHefbZZ5k5c6btmD//+c/cddddzJ49mzPPPJP8/HyWLFlCs2bNbMfMmzePxMRExo8fzwUXXMCoUaOYO3euq8sVX5e1H4oqNg3Rp46+LygIOldcCXoqCzJ21328iNmUQf4lKMh+NfqpLMhUBol3+mm30cqliyWNKMtJALZUXIU+ulc7goO0qZZPcpoHnYCMXebWI3I6WfuhMMcYax7k+5zmQcog8QEnDkBhtjHWpqK+zzGDCrMhY6ep5dTE5YvoANOmTWPr1q0UFhayc+dObr31VqfXLRYLjzzyCKmpqRQWFvL999/Tu3dvp2NiYmKYP38+eXl55OTk8OabbxIREeGOcm1KyrzvUw45DacefJq4+QUfbukiAUj90P1PAGeQ5kG+Y0VFP/RBDq1ctpR3A9TKxecFcAaJD3Jso6B+6P4hgDNI8yAfpH7o/sfL22u6ZRHdF607mMV5Ty9n29Ecs0uRhlD/K//jA32wRGyUQf7Hyydu7lI5D9p6RPMgb3f4+En2ZxYAMCjIYRHdalyJPkaL6L4tQDNIfJTmQf4nQDNo/SFjHrQ5OdvsUqQhlEH+x8s/yAv4RXSr1crbvx7gqrmrSM46xW3vredEQbHZZUl96ZNH/9N+MIS2NMbqiy7ezimDNHHzC/GDIazizrcAyKCq86Db39c8yNv9lJRuGzstopd3Y0CHKNpFhptRlrhK/KCAyiDxcZoH+Z+4gRAWaYwDIIOsVivvrjxomwf9Yd4GsjQP8h1aRPc/8YO8OoMCehH9ZHEp9360iX98uYPScuNfzNHsU9z94UbKyr3rX5TUoKwUUrcY41ZdoEWMufWIawSHQOcRxjgvBU4cNLUckVo5ZVBnaNnG3HrENYJDoFNlBh0zei36qVPFZdz38WbNg3zMTxWtXIIoZ4DlIADJ5e04QZRaufgDxwzKTzV6Tot4o/IyOLbZGEd3hpZtza1HXMPxXMzPM+hUcRn3f7yZOV9sp6TMYR70geZBPsEpgzpBhOZAfiEoGDqfbYzz07wugwJ2Ef1gZgGX/u83Fm5Kqfbaz3syefb7JBOqkgbJ3A0lxmZa6sHnZwL0NkLxMY4ZpDth/EsAZNCh4wVc8r9fWbDxaLXXft6TyX+Xah7kjYpKy/ht33EAelqO0sJSBMAWa2U/9FjTahMXCoAMEj+QmQQlRmsp7U3lZwIggw4fP8mlL//G5zXMg37Zm8nT32lzea+XuQeK842xrkL3L17c0iVgF9E/33iUXal5tb7+wg97WbojzYMVSYOplYv/Ul908QWOGaQP8vxLAGTQ5xvqnge9+ONevtue6sGKpD7WHTzByeIyAAYH7bM9v6W8B5HhIQzt3MqkysSlAiCDxA/oXMx/BUAGLdh4lJ3Hcmt9/X/L9/Gt5kHeLUXnYn7LizMoYBfR7z6vJ+f2rPvW+/s+2sSBio2bxAup/5X/SjgDgit6uh76xdxaRGqjDPJfHRwy6KB/ZtDd43sxqmfdt97f//Fm9mfke6giqY/KVi4AAy32VkNbrN0Z1astocEBO7X3Lx00DxIfoHmQ/0oYCiHNjLGfZtCd5/VkdC/Ng3yaMsh/OWbQQV2J7hVCgoN4/qqhJEQb/2JiyOX64G+xUG47Jq+olNvfW8/J4lKzypS62D55tBibUYr/CG0GHYcb4xMHIaf6bXZ+pyATVr8K5eWnP1a8g+PVD+2HmFaGuEFIOHQ80xhnH4KcI+bW4wbBQRaev3ooHVo1B6B1bfOg9zUP8iY/7bYvog9yuBJ9W3k39UP3J04ZdBiyk82txxMKjsOqV4wet+IbHOdBCUNMK0PcIAAyKDjIwvNXOc+DbgheQpDDPCi/qJTb3ltPQZHmQV7pqM7F/FZImD2Dcg4bOeQlAnYRHaBNRDgvXzuM20O/ZmX4Xfy/0HcYG7TZ6ZjdaXn89bOtWL1sR9iAV1oEqduMcdte0CzK3HrE9Rz7YB1eaV4dnvDbi/BMP/jmz7D3e7OrkfpwzKA2yiC/5NSLzz8zKKZlGP+beQazQ79hVcU8aFyVeVBSWj5/0TzIKxzLOcXuNKMFTyil9LUYJxT7ytuTRwvGaBHdvwTSPGjlS/BMX1jyF9iz1OxqpD5Kix3mQT2hWbS59YjrBUAGtW4ZxivXDuPW0CWsCr+Lf4S+y7igTU7H7EnP58+fbdE8yNuUlUDqVmMc0wOatzK1HHEDp5Yu3pNBAb2IDjC4UyvOOXM44ZYSAG4I/q7aMYs2p/DWrwc9XJnUKW07lBv/ztSDz0958WYSLhfTDcqMzeFY86q5tUj9OGaQevD5pwDJoMGdWjHqrDMd5kHfVjvmy80pvKl5kOkcr0LvYzlMuMW4Mm6LtTu94yJIqLiaTvxEgGQQAK01D/I56Tvs/850LuafAiSDBnaMZsyIs+qcB3215Rhv/HKg2vNiIscM0rmYf/LSDAr4RXSAMdNmkRUaD8C44M10sxyrdszjX+9kzYEsT5cmtVH/K//X8SywBBtjL9tMwuV6T4ZWnY3x3u8hc6+59cjpKYP8X6ezICjEGPt5Bo2Zei1Zoe0BGBu8he6WlGrHaB5kPsd+6IOCHPqhl3dnXJ9YM0oSdwqgDKL3JPs8aN8PkLnH3Hrk9DQP8n8dzwyYDBp9wTVkhRnzoDHBW2ucBz3xzS5W7T/u6dKkNsog/+elGaRFdICgYCJH3257OKuGq9FLy63cMX8D6bmFnqxMHDz3/R5WJGVQUlaunZgDQXiEvb9ixi6jZ7i/CgqGM2+xP177mnm1SP049QFVBvmlsJb2/oqZuyE/o87DfVpQMJFj7POg64Krt1MoK7fyh3kbSNM8yBQlZeX8ssf+9+Agi70f+pby7uqH7o+cMijJ7zOIM2+1P14z17xapH50Lub/wlraFycDIIOixvze9rCm9aCycit3zt9Aao7mQV7hqM7F/F5YC/u/2+N7ID/d3HoqaBG9Qujw67EGG5uMzgheQUtOVTsmI6+IP8zbQHGpNv7ztMPHT/Lf75OY9eYazvrn9xzbafREslqCIW6AydWJ2wRALz6boddBSMWt+BvnQVGeufVI3VI2GX9agiF+oKmliBsFUAaFDptFeUjd86DMfM2DzLLxcDZ5DhubVV6JXma1cCC0B8O7tjarNHGnrg79QA97z1VYbjH0Wvs8aNN8KMw1tx6pW+VVoJYgzYP8WZfAyaCQYbMor8igGcEriOBktWMy84v5w7z1mgd5A8cMaj/I3FrEfZxaunhHBmkRvVKLGCyDLgcg0nKKS4N/rvGwdYdO8PjXOz1ZmQBfbbW32Dl1Mp92p4yTxyRrJ55dccSsssTdnDaT8I7QdJsWMVCRQRTnweYPza1Hald8EtIr/h6I7Wt8Si7+KcAyKGjQlYAxD7oseEWNh63XPMgUPyXZr75pRhG9LckAJFk7ckaPBMJDgs0qTdwpwDKIQVcY4+J82PyBufVI7UpOQdoOY9yur3HFsvinQMqg5q0JGmzMgyIshVxWy3rQhsPZPPbVDk9WJlWVFBo90QHaJSqD/JkXZpAW0R2NuM02vD74O6DmHZjf/u0gX2w66qGiBOCrrfa+ZP0shwixGJ/+bijtRpE+CfZfnc8GLMbYizaTcJuz7BnEmrmgXeC9U+pWsJYZY/Xg82+dRxBQGVRlHmSh5r9f3/7tIAs26gNsT1rusKmo4zxoS3kPtXLxZ50CN4NYMxfKNcf3Sqnb7POgDpoH+bVAmwedNds2nFXHPOjdlYf4fIPmQaZJ2wblFXfnqZWLf3PKIC2ie5/4gdDZuF2gZ1AKo4K21XroXz7bws5jus3QEw4dL2DbUfs/68FB9j6gW63dmTqwvRlliSc0bw1x/Y1x6lYozDG3HneLH2D/tDUzCfb/aG49UjOnfug6efRrzVvbW4YFQgbF9YcuowDoEXSsznnQg59vZUeK5kGekJx1ku0O/6wHBe23jbdauzG2tzYV9VvNWxlzAzAWLk9lm1mN+zlkEMf3wv4fzK1HaqZ5UOBoFm1v1xMoGdR1NGDMg0YHba310L8t0DzINE790IeYVoZ4gGMGpW2DUyfMrQctolc3wv7p4++bf1/rYYUl5dz+/npyTpV4oqqA5tjKBWBgRR9QgPTIfvRPiPJ0SeJJlX2wrOWQvMbcWjzB4QoIVmtjLa/kuBu8NtPyf7ZefFY4vNrUUjzCYR70h+bLaj3MNg86qXmQu1WfB9kX0TOi+tO5jVpK+TXbrcxWSA6sDNI8yEs5zoN0Faj/C7QMOstxPUjzIK+kc7HA4phBXnAupkX0qhKnQWQCAOeUraNXaGathx46fpL/t2i7pyoLWF9tcT55HGwxrkQvsoaQOGgEFovFjLLEU5w2kwiA2wgTp0FUB2OctASyDtR9vHhe5dUPwWEQ29/cWsT9Ai2D+kyFqI4AnF22jt5htc+DDmed5B9fah7kbtXnQcYierE1mF4DR5hRknhSAGcQe76DrP11Hy+eVzkPCgq13zEq/ivgMugCh3nQevqEZdR66OGsk/x9Ue137YmbpDhm0ABzaxH387IM0iJ6VcGhcOZNAFiw8kqfjXUe/vnGoxzMLPBEZQHpYGaB0y3MkZykR5BxMrnT2oUpQzqbVZp4Smfv25HZrYJDYPhNFQ+ssPZ1U8uRKgpz4PgeYxw3AELCzK1H3M8Ld4V3q+AQp3nQy7031Hn4go1HOaB5kNscPn6SrUftbYQiOEl3i30eNHlwF7NKE08JxHnQmQ7zoDWaB3mVojyj5SAYrYZCws2tR9wvIOdBNwP1mwct3JSieZAnFeVBxm5jHNdfGRQIvCyD3L6I/q9//QuLxcI999xje66wsJA77riDNm3aEBERwWWXXUZaWprT9x0+fJipU6fSokULYmNj+dOf/kRpaam7yzUMuxGCjf8Zexz5nNtHxtd5+AdrD3uiqoBU9RbmAQ6tXA6G96Zfe7Vy8XuRcdCmpzE+ugGKT5pbjycMu8GWQWx8D4o1MfMaxzbbx+oDGhgiYqFNL2OcEiAZdMYNDvOgBfz+NPOgD9doHuQu1edBBwmyGJtOHwzrrZZ2gSCiHbTtbYxTNgbGnMAhg9j4PhTlm1qOODi2GajY+F7zoMDQsi207WOMAyaDroeQZgB0T17IH86Jq/PwDzQP8pxjW1AGBRjHDDq2yfQ5gVsX0deuXcurr77KoEGDnJ6/9957+fLLL/nkk0/46aefSElJ4dJLL7W9XlZWxtSpUykuLua3337jnXfe4e2332bOnDnuLNeuZVsYcJkxLszhgYTNnNUtptbDP113hOJS7R7vDlVvYR5ksd/SGdZ5uFq5BIrKTx/LS+DoOnNr8YSWbWHgDGNcmANbPja3HrFz3MhGPfgChy2DSuHIWnNr8YSWbZwy6IH2m+qcB32y/ghFpWUeKi6wfLU1xenxIIt9c/WwzsM0DwoUAZlBlxvjohzY8pG59Yid04Z+mgcFjIDMoIp5UFEO98dvYkRd60GaB3lOis7FApIXZZDbFtHz8/OZOXMmr732Gq1bt7Y9n5OTwxtvvMEzzzzDeeedx7Bhw3jrrbf47bffWLVqFQDfffcdO3bs4P3332fIkCFMmTKFRx99lJdeeoni4mJ3lezMYVObkLWv8cQltfdaOl5QzLfbUz1RVUA5kFnAjmPOO147bqbVZ+gYT5ckZrFtJoFX3MLjEY4bjK6ZC1arebWIndNmWrr6IWAEeAYFr32Nf9UxD8oqKObb7Wm1vi6NczCzgG1HnedBgxzuyOuleVDgCMQMctxgdM1rmgd5C82DAlMgZtBZt9mGwWtfr3M9KKugmCXbtB7kEcqgwORFGeS2RfQ77riDqVOnMmHCBKfn169fT0lJidPziYmJdO7cmZUrVwKwcuVKBg4cSFyc/baZSZMmkZuby/btHtrAKmEodDzLGKfvoEfBJs7uXvunj/NX6xYeV/u6yi3MYOWMIKMX8SnC6d5XnzwGDC/bTMIjEoZAp4oN49J3wMFfTC1HME7gKz/5Dm1hv61M/F+gZ1DGTroXbGRk9za1Hj5/9SHP1BVAqrZyAStDK+ZBhYTRo98wzxcl5vCyfqAe0X4wdDrbGGfshAMrzK1HnOdBIc2hXaK59YjndBlpHwdMBg2CzhW/d8ZOuudv4Jwedc2DtB7kdlYrJFdmUDNo19fcesRzvCiD3LKI/uGHH7JhwwaeeOKJaq+lpqYSFhZGq1atnJ6Pi4sjNTXVdozjAnrl65Wv1aSoqIjc3FynryYbYf/0kTWvcs2I2jdvWrn/OPsz1K/PlRZXaeVydtBOEixZAKRGD8ESHGJGWWKGVp0hupMxTl4LpR66I6UB3JJBTlejv9r095OmOfgL5B41xp3PNjYeksDQqhNEV2xkfSRAM2j1q1wzovbNvFftz2Kf5kEuVfVighGWXXSwHAcq50GhZpQlZojuaMyFoCKDisytpwbuORercleemOvQb5CTbIw7j9A8KJBEd4RWFWshgZRBVe4MrmsetPpAFnvTNQ9yq8MrIafiw4pOyqCA4kUZ5PJF9OTkZP74xz8yb948mjVr5uq3r9UTTzxBdHS07atTp05Nf9O+F0FExWL+rq+Y1LGEmJZhtR6uDSVcZ39GPjurtHK5Ini5bRw2/FrPFiTmq7wKq/SUsaGEl3FLBvW7GCIqNvTb9RVkJzf9PaXxNr5vHw+ZaV4dYg5bBhU630rqJdyeQbu/ZlKHYtrUNQ/SVVgucyCzgO0pVeZBIctt45BhmgcFnMpbmQMpg/peBJHtjfHuryFbGWOqje/Zx5oHBZ6AzKALITLBGO/+mokJxbSN0HqQaTYogwJaZQaVFTnvz+FhLl9EX79+Penp6ZxxxhmEhIQQEhLCTz/9xPPPP09ISAhxcXEUFxeTnZ3t9H1paWnExxsnavHx8aSlpVV7vfK1mjz44IPk5OTYvpKTXbDYFBIGw28yxtZywje+xeXDOtZ6+Kfrj1BYog0lXKHq1VeRnOSCoNUA5BJBwtmXm1GWmMnxVuZ9P5pXRy3ckkHBoU4ZxNrXm/6e0jiFObDjC2PcrBUkTjO1HDGBYwbtD8wMCtv4FjOG1zEP2qB5kKvUNQ/KoyUdNA8KPJoHaR5kpsJc2L7QGIdHG4uLEliUQYRteIMZw2pfnP9M8yD3KcyFHQuNcXg09LvI1HLEBF5yLubyRfTx48ezdetWNm3aZPsaPnw4M2fOtI1DQ0NZtmyZ7Xt2797N4cOHGTnS6HMzcuRItm7dSnp6uu2YpUuXEhUVRb9+/Wr8ueHh4URFRTl9ucSwGyGo4nbZ9e9wzRntaj30xMkSbTDqIlVbuVwU/BvNLCUA7IufgiW0uRlliZl6jAcsxnjLh163wZT7MugGewZteAdKTrnmfaVhtn5q3AUBMOgKCPXcnVbiJXqchy2DNgduBl0ztPZ5UPbJEm2s5SJV50EXBq+kucVoI7Q37gIsYS3MKEvM5JhBWz6E8nJTy6nKrRkUXHHl54Z3NQ8yy7bPHOZBl4POxQKPMgg2vMs1Q2vvi559soRvtlXdz0RcYvvnUHLSGA+coQwKRFXPxUzKIJcvokdGRjJgwACnr5YtW9KmTRsGDBhAdHQ0N998M/fddx8//vgj69ev58Ybb2TkyJGcfbaxeczEiRPp168f1113HZs3b+bbb7/loYce4o477iA8PNzVJZ/mF4qD/tON8aksuhxbwrk9aw/OebqVucn2puezKzXP6TnHVi6tRt3s2YLEO7TqBN3HGuOs/UZPtEAQGQf9LzHGp04Yi7nieY63MA+9zrw6xDyOGXTigOmb2nhMlQzqkvINo3q2rfVwbazVdDW3tLNfcRM96iZPlyTeILojdB9njE8chMMBkkERsVXmQZ+YW0+g0jxIojtAj98Z44DKoHbQ/1JjXJhN56NfM7pX7fOgeas0D3ILx1YuZyiDApJjBmUfgkO/mlKGWzYWPZ3//ve/TJs2jcsuu4wxY8YQHx/P559/bns9ODiYxYsXExwczMiRI7n22muZNWsWjzzyiBnlwlkOG4yufpVrzqx9Q4k1B7LYm55X6+tyelVvYU60HGZw0H4AkoJ60LX/2WaUJd5giEMPWMf+1P6uyibH3nYFrN9L3Wbv/Rg/CNoPMrceMY8yyNho/azab2VeczCLPWmaBzVF1XlQH8thhlTMg/YEdaPbgJFmlCXeYGiAZpDTudhczYM8LW0HHF1vjOMGQvvB5tYj5nHsQx1IGVRlk+Nrzqx9HrTu0AmSNA9yrfSdcHSdMY4bCO2HmFqOmMgLMsgji+jLly/n2WeftT1u1qwZL730EllZWRQUFPD5559X63XepUsXvv76a06ePElGRgZPPfUUISEm7b7bcTgkDDXGqVuYGHWwzg0l5q/W5n9NUfXk0fEq9OSul2KxWDxbkHiPvtOMHmhg9GUsCpAd0DsOh4QzjHHqVji8ytx6Ao3jX9BnzDKvDjGfYwbtWAhFAXKSVCWDJkYeoG1E7XcGztfGWk1StZXLFcE/2cbJXS7TPCiQJU51yKAvAiiDhkGHYcY4bWvg3I3oLZzmQdeBMihwJU6DZgGYQR2GQYfhxjhtG+dH7KNdZB3zIN2V51qOGTT0WmVQIKuaQYW5dR/vBqZcie5zLBanKyBC173G5cO1oYQ77E3Pc2rlEkYJlwT/AkCRNZTOY683qzTxBqHNYUDF7XQlBfbNRQJB1avRxTNKi4y+jwDB4UYPPglcThl00r7JWiBwyKCQda9xRR0bjH6mjdYbbV9Gfg3zoJ8BYx7UcZzmQQEttDkMvMwYl5yE7QvMrceTqtwZLB5SWlxlHqRNjQNaaDMYUDEXDrQMasg8aMMRThVrHuQSpcWw+QNjHBxm7E0lgcsxg0pPmZJBWkSvrwGXQouK3lc7F3Ft39qvRM85VVLtamqpn6+2OG9Idn7QelpbjKuNfwk9h56da//LSgKE063M88yrw9P6XwItKzb027EIclPMrSdQ7P7a6MEKxi7wzVubW4+Yz7EX7KbAzaBr+4XWeiFQbmEpX23RPKgxvq7yz2180AZibPOgkfTqXPtFHBIgAnYeNN2eQTu/hJyjppYTMHZ/DSePG+O+06BFjLn1iPmGOrZTCKAM6jcdWsYa452LmZkYXOs8KK+wlMVbdK7mEknf2DMoURkkOM+DTDgX0yJ6fYWEGzszA5SX0mHfB3VuKKFbeBqn6ocPVzpspHWiz5W6hVmM2+naJRrjw7/B8X3m1uMpjhlkLYN1b5paTsBw3MTG8S9sCVwdznDIoJWQudfcejylSgYl7P2A0b3a1Xq4Wro0zlfV5kHLbeMTfa7QPEiM1krt+hrj5FUBlkE3GmPNgzxno+ZBUkXCGRDbzxgnr4LMPebW4ykhYTDcnkEJez9gbG/Ng9xOG4pKVQlDHTJoNWQkefTHaxG9IYbfBJZgY7z+La4bVntorjt0gt2pAdIjzEX2pOWx22ETjgQyGRW0DYDD5e0YNHqaWaWJN7FYnDeUCKQrQYffBEEVe0OsewuKT5pbj7/LToZ9PxjjVl2g6xhz6xHvoAwyxuve4tozap8HrT90gl2pnu9T6MuqtrRrz3HGBG0BILm8HYNGX2RWaeJNLBbnK0E3BdDmfo4ZtP5tKC4wtRy/l3ME9i4zxtGdods4M6sRbxHI86BhNzpl0HVn1H5R5cbD2ew8pnlQk+QchX2VGdRJGSQGkzNIi+gNEd3BuJ0foCCD8TmfnWZDiUMeKsw/VL36akbwCoIsVgB+aH4+veOjzShLvNGgK+0faG36AMoDpOdcVAL0rcigk5mw6iVz6/F3m+YDRgYx9FoI0l+ZUsExgzZ/GLAZND77E2K1sZbLVG1pNyP4J9s8aJnmQeIoYDOoPfS72BifzISV/zO3Hn+36QPs86CZmgeJ3aAr7YvJAZdB043xyeOMy/qE+KhmtR6ueVATbZ4P1nJjPEQZJA6qZlBZqcd+tP4rbKixfwWL8Y8t+NfnuHFwy1oP/XzjUW0o0QCO/VMtlHN58E8AlFstlA662qyyxBtFxkGvicY4LwX2/1j38f5k3F/tJ86/PAv56aaW47fKyx2u7rPAkGtMLUe8TGQc9J5kjPNSYF9gZlDwb89x4+AWtR66YMNRThZ7blLr6xxb2lWbBw3UPEgcRMRC78nGOO+Y/a6pQDD2L/Z50K/Pah7kLuXlDq1cNA+SKiLaQa/KeVDgZlDwb89xw+DmtR66cKPmQY1WXg4bHc7FHO/AEoloZ58H5ad6NIO0iN5QsYlwxvXGuDiPWcUf1rmhxJfaUKJektLy2JOeb3t8TtB2OgVlALCifBBjhw81qzTxVk6b2gTQrczt+sCwygzKh+X/Mrcef3VwBWRXXD3S4zyI1qbGUoXjbYSOPWP9XZUMuq6ojnlQUSmLN2uD0fqo2tLu7KCddK6YB/1SPoAxZ2oeJFUE9DzoBmNcnA/LnzC1HL918GfIrriruvs4aNXZ1HLECw0N1HlQb3tv9JICriv8gKA65kFfbtZ6UKMc+gVOHDTGyiCpiUnnYlpEb4xxD0JYBAARW9/jqm6naj1Ut/DUj+NV6ABXVFx9BfBz5GR6xUV6uiTxdr0mQYuKPnS7voKTWebW40kOGcT6tyFjt6nl+CVtYiOn09shg3Z/HbAZFLHtfa6pYx40Txtr1Uv1DUXtdzf8FDGF3poHSVW9JkLLin0JAjKDKv6fWP8OpO8ytx5/5PjBjOZBUhOnDPomsDJo7F9tGdTyNPMgrQc1kjJITqfX+c4ZVHDcIz9Wi+iNERkH595jjK1l3Mv8Wg/dlJzNjhRtKFEXq9XqdPIYRT6Tg9YCkGWNoPWQi80qTbxZSJjRCwugrBi2fWZuPZ4UEQuj7jHG1jJY+ndTy/E7p07Azi+NcfMY6HOBufWIdwoOdc6grZ+aW48nVcmge6h9Q5/NydlsT8nxTF0+zPFigijymeIwD2o1VPMgqUG1DPrE3Ho8KaKd8zzoe82DXOpUNuxcZIybt4bEaaaWI14q0DNo9L3G2FrOPdbar4LdfCSHbUc1D2qQU9mw4wtj3KwV9JlqZjXirRwzqLzEYxmkRfTGGnkHRLYHIDZlGRdE7K310PlrtMFoXZLS8tnr0MplevCvhFtKAFhQNprJQ3TrjtQiUG9lBjj7DohMMMZJ38CBFebW40+2fgplRcZ48FUQUvvGiRLgHDNoU+BmULuUH5gauafWQ3UVVt2qtrS7KHilbR60sGwUkwd3Mas08XZDAnke9AeHedAS2P9T3cdL/W39BEoLjfGgKzUPktoNvdY+DsQMiuoAQNtjy7kwMqnWQ+dpHtQw2z51zqDQ2jdvlQDnmEEeOhfTInpjhbWA8x6yPfx7+AdYKK/x0IUbUygo0oYStal+C/Ny23hN66n0jNUtzFKLuP7QfogxPrYJUreZWY1nVckgvnvI2IBFmm7Du/ax41/MIlXF9YeEil7VxzZD6lZz6/GkKhk0J6z2edAXmzQPqkvVlnaOrVzWtLpALe2kdnH9IOEMY5y6JfAyaPzD9seaB7mOY29ZzYOkLrF9nTPo2BZz6/Gk0OZwnj2DHq5jHrRo01HyNQ+qP7XVlPqK7Qsdhhnj1K3G+ZibaRG9KQZfDXEDAIgr2MnFwStrPCxfG0rUymq18pXD5qv9LQfpH2Rcub+pvAf9h5xtVmniK5w+fay9pYBfGnwVxA00xsc2G5/aA4UlZVitVhML82HHNhsnAWCcFMT1N7ce8X5OV4IGbgbFFexievBvNR6WX1TKIs2DalS1pV0/y0EGBh0EYHN5d/oOOcekysRnDA3gDBp0JcRXzINSt9hu5dY8qAmObbEvQrQfYv/nK1Ibp7vyAjeDYgt2c2lIzfOgguIyvth01JOV+a7UrcbFcaAMkvrx8LmYFtGbIigYJj5qe/hws08Ip7jGQ1/7eT/vrzrEsp1pbDuaw/H8Ik3ugN1peezLKLA9vsLh6quPysZxwcD2ZpQlvmTAZRBccZvplo+gtOb/B/1SlQxi2SNQcoor566i/9+/5bynlzPz9VXc//Fmnvp2N++tOsT3O5RBddImNtJQA2fYM2jrxwGdQQ81+1TzoAaq2tLucoeN1T8uG8fUQfFmlCW+xHEeFJAZ9Jj9ccU86OrXqs+Dnvx2l+ZB9VC6XleASgMNmAEhFe02tgRaBgXBxH/aHv5feO3rQW/8fID3lUGnVbrO4Y5gZZDUx4DL7Bm09WMoLXLrjwtx67sHgh7nQc8JsPd72pSlc0Pwt7xadmG1w/ZlFPDQQudWE2EhQcRHNTO+opvRMzaC6UM60LlNC09Vb7qvHW5hDqeY6cG/AnDKGsbutufTMzbCrNLEV7SIgcSpsP1zOHkc9nwLfav/P+i3evwOep4Pe5dCTjKsfoXUnIGcLC5jf0YB+x0+pKqqpgy6eEgCXdq09OAv4EVKCo3JP0BIc+MvZJHTad7aOYOSlkC/i8yuynMcMqhNWTo3Bi/hlbLqv//+es6DAi2DHK9CD6eYS4J/AaDQGsrOthPV0k5Or3lr6DvN2GA9EDOo+zjoNRH2fAe5R2DVy6TmDK7fPCg4iLjocNpHNQ/YDKq07mAWLy/bwXNH5hMBxoLEgBlmlyW+oHkrY/PZbZ/CqSxjr6Z+AbQhdvex0GsS7PmWmLIMbg7+hv+VVf/992fWMA9SBtmsO5jF/5bt4LkjHxAJyiCpP6cMOgG7v4H+093243Qluiuc/whYjH+Ud4V+QWty6/VtxaXlHM46yZqDWSzanMIzS5MY8+SPfLr+iDur9RpWq5XFDiePk4LWEm05CcDX5SP43aCeZpUmviaQb2UGpwyy/vw0ZXkZ9fq2mjJo7JPL+WRdsjur9V67FkNhtjHudzE0iza1HPEhgdxWCpwy6M7QRcQ0YR4USBlUtaXdxKB1tLIYC35fl49gnOZBUl+BvLkfVJkHPUNpfedBZeUkZ52qlkEfB1AG/bYvk6vnrmLGKytpse8bIqwVd8b0vchYmBCpD52L2TLojtBFtCGnXt+mDLKyct9xhwxaQqQ1z3hRGSQN4cFzMS2iu0Jcf1sfnghOcnfIgia93V8/28LOY/U7AfVFVquV5bvTmfHKSqerQ64IXm4bf1Q6jgsGqZWL1FP339l2R2fPd5CXZm49nhbXz/YXh6UojzuDP2/S2z34+Va/zqBabdDtg9JI3cdVyaBUU8vxuLh+MNT4f8aYBzU9g3ak+G8GWa1WfkrK4PJXVlZpabfcNv5YLe2kIbqNhaiOxnjv0sDLoNi+tgyyFOdxZ9CnTXq7v32+le0p9VsE80WOGXTNa6tZuf844NxOSvMgaZCqGZR7rO7j/U1sIpwxC4CWnOKPTZwHBUIGrUjK4IpXV3L1a6tsGeQ4D1IGSYN0GwvRnYzx3u8h1317MWkR3VV+938QarRhuTb4e7paGv8XR2m5leeX7XFVZV7DarWydEca01/6lRveWsv6Qydsr3W0pDMqeDsA+8vjyYs7ix7t1MpF6iko2NjgDsBaBls+NLceMzhk0MzgZXRTBjXMiUNwoOLkMaY7dDnX3HrEtwQFG5uNA1jLYXMgZtDfINS4/VgZVDOr1cr3O9KY/r/fuP7NNaxzmgdlcG6QMQ86WB5Hdruz1NJO6i8oGIYogyoz6JrgH+huafwJdCBmUAcyGBVktJo4VB7LhiBtrC4NUDWDAvFcbJxjBi1TBtXAarWybKeRQbPeXMPag8ogcZGgII+di2kR3VWi2sM5dwMQainjLyFN+5f2zbZUdqX6x1VY5eVWvt56jAue/4Vb313H5iPVP1V1vPLhk7JxTBuc4MkSxR9U3ZU50DZqiYyHc/8IKIMqnSwupay8nv8dON72NfRasFjcU5T4ryHX2MebAjWDXDcPWrI91W/uiCkvt/LN1mNMff4Xbnl3HZuTs6sdMyP4J4Isxn8zH5eN1TxIGk4Z5NJ50Lfb0wIqgy4Pccygcbzwwz4PVyk+zzGDAvJcLA5G3QNAiKWcv4Z80KS387cMWrLtGNNe+IWb36ltHrRCGSRN46F5kMsX0Z944gnOPPNMIiMjiY2NZfr06ezevdvpmMLCQu644w7atGlDREQEl112GWlpzu0XDh8+zNSpU2nRogWxsbH86U9/orS01NXlutY5d0FEHABTgtdyVtCuen2bhXL6Ww7QxeJ86+ULP+x1eYmeVFZu5YtNR5n83Ar+MG9DrX8JBFFuW0Qvs1pYUD6aaWrlIg3Vpgd0PscYZ+6Go+vNrccM59zFqfB2AEwOXsuZlsDNoOLScm59dx1/mLeewpKyug8uL7P3b7Q4fIot0hBtetjvYMhMgiPrzK3HDOfcBRHxgJFBI4J3n+YbDLVl0Is+nEFgzIMWbU5h8nMr+P28DeyoYx40I3iF8T1WCwvKxzBtkBbRpYFiukOXUcY4YDPoTts8aFLwOs6y7KzXt9U+D/LtK0Ebm0GflY3mx90ZbK3hwieRWjlm0PE9cGStufWYYeQdEGmsY0wMXs/IYGXQos0pTHnuZ25/fwPba2nVZ6Gcy0Ps60HKIGmUmG7QdbQxPr4Xkte45ceEuPoNf/rpJ+644w7OPPNMSktL+dvf/sbEiRPZsWMHLVsat7fce++9fPXVV3zyySdER0dz5513cumll/Lrr78CUFZWxtSpU4mPj+e3337j2LFjzJo1i9DQUB5//HFXl+w64RHGrYRfGldBzO+ymLXjryc1r5BjOYWk5tj/zMrJpdfJDUwMWsf5wRtoZ8mhyBrC1cUPscHaG4Cvtx5jT1oeveIizfytGuXb7an8+5td7M8sOO2xY4M2k2DJAuDH8iFMHjk0IHekFhcYOhMO/2aMN74PHYebW4+nhbWk+cSH4UvjatD3Oy1iw8RZpOYV1TuDrin+P9Zb+wC+m0Hl5Vb+/Olmft1r9Neb+fpqXp81nNYtw2r+hr3LILdiQ+ee50OUFq+kkYbMhEPGXIZN70OnM82tx9PCWsJ5/weL7gJgXqdFrJ3wiVMGpeYUkppbyPHsXHqf2sD5FucMmln8N9ZZEwH4etsxktLy6O1jGQTw3fZU/rVkl9PeL7UZE7SFjpZMAH4qH8zEs4fSta3mQdIIQ2fCoV+M8cb3AjKDmk+aY8ug9zp9yYaJ11ebB6XVkkHF1mCuKf4/ewZtTWV3ah594gMng1aUDyInNJZbRnQmPrqZu8sUf+OUQe9Dp7PMrcfTwloaLTYX3QkYGbR2gj2D0irPxXILyczOo/epDUy0rOP84PV+l0FLd6TxxDc765lBW5VB4hpDZsLBn43xpveh8wiX/wiL1ere+2wyMjKIjY3lp59+YsyYMeTk5NCuXTvmz5/PjBkzANi1axd9+/Zl5cqVnH322XzzzTdMmzaNlJQU4uKMK7tfeeUV/vKXv5CRkUFYWC0LIQ5yc3OJjo4mJyeHqKgod/6KzspK4ZVRkFHxqeOMN2HAZcb4VDbsWQq7FhvN7ovzq337yrJ+XF3ykO3xRYMTeP7qoR4o3HX+uzSJ5+rZw6ujJYPPw/5OrCUbgG/6P82ky24mKEitFKQRivLhqd5QUgDhUXD/bghrUa9vdXVmmJZB5WVGBqXvMB5f9gYMNLK2Phm0qrwvVxU/bHvsixn0r2928cpPzrcAdm/XknduPItOMVX+ezhxCN44H/Ir7oa68n3oe6GHKhW/45hBYZHwQJIyqIEZtLo8kSuL59geXzg4gRd8LIOe/T6JZ7+v3zyoAxksCLfPg5b0f5KJl92qeZA0TnGBkUHF+cogF2XQtEHtefGaMzxQuOs89/0e/vt9Ur2OrZpBC3o9wZiLb6ZNRLgbKxS/VS2DdhsLy/XgXxk0GtKNfU649HUYdLkxrsyg3V8ZfyqD6EAGn4f/nThlkLhCcQE81QeK8yAsomIe5NoMcntP9Jwc4xaMmJgYANavX09JSQkTJkywHZOYmEjnzp1ZuXIlACtXrmTgwIG2BXSASZMmkZuby/bt291dctMEh8DER+2Pv/9/sOY1ePdieLIHfH4L7FjoFJjFlnByrMYEd2TwDoY7tGD4cksKe9Orh6u3enflwXovoLcij3dC/2WbtJV2OIspl96gE0dpvPAI6H+JMS7KNU6SAk1QMJzvkEHLKjNoer0y6OygnU5tYHwxg6ouoAPszyjgkv/9xrajDrcFnsyC9y+zL6B3Oht6T/FQpeKXHDOoOA92fmluPWYICnaeBzUwg0YE7XJqwbB4Swp70/M8VHzTvbfyYL0X0KPJ552wfzvMg85k8qU3aR4kjRfWEvpPN8bKIMP3TcugryruyvMV7606VO/Fq2oZlHAml1w1W4tX0nhhLTUPqjYPeqR6Bm1f4JRBJZYwpwwaEUAZ9HbYf2wL6MogaTKneVA+7Fjk8h/h1kX08vJy7rnnHs4991wGDBgAQGpqKmFhYbRq1crp2Li4OFJTU23HOC6gV75e+VpNioqKyM3NdfoyTc8J0H2cMc4+BF8/APuXQ7lDT/fmMcatBlfNJ+22HTxWNsv20t0hC2xjqxVe+tE3eoIu2ZbK3xfV70OOcIp5p9nT9Ag6ZjzRphchMz8yPoQQaYqhjhuMvu+xH+tdGTQeuv/OGGcfrsigH2vNoPTbd/Bo2fW2l+6qkkEv+kg/vtNlUGZ+Efd+tMnYbLTkFHxwldGzEaBNL7j6A2WQNN3Qa+3jTYGaQROgx3nGuN4ZdIPtpTtDFtrGVqvv7M/w7fZU5jRgHvR2s6fpGZRiPNGmFyEzP1YGSdMNvc4+VgZBTsMzqOo8yFcy6Lvtqfz9i231OrZ6BvXUuZi4huM8KJDPxXqMN8a1ZlBrGHwNXDmPtNt38ohTBn1uG/tzBr3V7Bl6BR01nlAGias4zYPmufzt3bqIfscdd7Bt2zY+/LBpu6PXxxNPPEF0dLTtq1OnTm7/mbWyWCquBK1yJVGrznD2HXDDV/DAHpj+P0icSqf4tlgGXcHhcmMjnDHBWxlssQflF5uOsj/Du68EXX8oiz9+uLFeG+DGtgxhScd3GEzFJ5QRcXDtZ9Aixr1FSmDoPNLY2AbgwAqjXYcHeF0GTawtg/5QLYM6xrUleNDlHCqPBYwMGuKQQYs2p3h9Bq07ePoMimoWwv9mnkEw5fDZLZC82nhBGSSu1PlsiOlhjAM1g6COeVDNGRQyeAYHy40LJqpm0JebU9jn5Rm0/lAWd3/QgHlQp3cZSsXGqy1j4dpPlUHiGp1GQJuexlgZ5PxcPTNodPA2hlrsFxD4wl156w+d4K4PNlJerwwK5ptqGfQZtGzj3iIlMDhm0MGf4cRBj/xYr8ugiY+CpcpyW2UGXb8YHtgLl7wMfafRMa4tYYMvt2XQqODtnGGxX83tnxn0HmdQcfezMkhcqdNZxgVyYGRQ1gGXvr3bFtHvvPNOFi9ezI8//kjHjh1tz8fHx1NcXEx2drbT8WlpacTHx9uOSUtLq/Z65Ws1efDBB8nJybF9JScnu/C3aYT2g+DiF6HXJBj3N7j9V/jjFpj8OHQdVe0Ttj+cl8gr5RfbHjtehVVuhZd+rN6ewFvsTc/n5nfWUVRaXudx8VHN+Pu0vvw25Du6Zf5oPBkWATM/gdZdPFCpBASLBYZcY4yDwyBlo0d+rNdlUPxAuPilGjLoiZozaHzVDLJfhVVuhRe9+I6Y+mRQWHAQr80aTq/YCPjmL/ZWP8ogcTWnDAqHlA0e+bHel0EDHDLoQbj9lzoz6PdV5kF3Vcmgl7z4Kqx9GfWbB8VFhRvzoKFL6Zbxg/GkLYO6ur9QCQzKIEMDM+gP5/Wt9VzM2+8M3peRzy3vrK1XBs2Z2pffhi6je2UGhbaEmR8rg8R1qmbQ0QDNoLj+tWdQt9E1zoNe9tEM2t+oDFpmPKkMElerth7k2gxy+b0SVquVu+66iwULFrB8+XK6devm9PqwYcMIDQ1l2bJlXHaZseHm7t27OXz4MCNHjgRg5MiR/POf/yQ9PZ3YWOPKyKVLlxIVFUW/fv1q/Lnh4eGEh3tZ76Sh1zrfzlSHrm1bUjLgSlJ2fk6CJYvzgzfQr/QgO6xdAVi46Sh3j+9Jlzb1a4rvKem5hVz/5hqyT5bUeozFAv93QV+uG9mF8FXPw/rXjReCQuCKd6H9YA9VKwFjyEzjFrkBlxl/eoB3ZtBM5/Y2dejSpiVlA6/i6I7P6WA5zoTgjfQvPcj2igz6YlMKd5/Xi65tvTODck7VnUH/vXIII7q3gV/+C2tfM15QBom7DLkGmkUbm9kpg+p1aJc2LSkfeCVHdnxOR0sm44M30r/0ANutxjzSmAd5YQbl1X8edO3ZXWi2+gVY55hB70DCEM8UK4Fj8DXGBuvKoHpnUOc2LbDWkUFfVGRQNy/NoBN1ZBDYz8WarXkR1s01nqycByX41ubN4gOUQYYh19gX806jc5sWWBwy6LzgTQwo3c82q3GHtVdn0FuNzCBLsDJI3GPw1RAeaawHufhOT5dfiX7HHXfw/vvvM3/+fCIjI0lNTSU1NZVTp04BEB0dzc0338x9993Hjz/+yPr167nxxhsZOXIkZ599NgATJ06kX79+XHfddWzevJlvv/2Whx56iDvuuMP7gtGFfj++H6+WXmh77PjpY1m51es+fcwvKuXGt9dyNPtUncfNmdaPW0Z3J3z7p/D9P+wvXPSi0S9MxNWiEuDMWzw2afMXvx/fl7lOGWS/EtSXM+jhqf2YOqg9bPlYGSSeEZUAZ92qDGqgqhl0V5W78rztjpj8olJufGstR07UnUEPTTXmQc12fg7f/93+wkUvGL2bRVwtqr0yqBFuH9/X6VysWgZ52R0x+UWl3PR2fTKoL7eOqcigpXPsL1z4PPRSBokbKIMa5fbxfXm17CLbY1/JoOSsRmbQRS8og8Q9KjPIDa0SXb6I/vLLL5OTk8O4ceNo37697eujjz6yHfPf//6XadOmcdlllzFmzBji4+P5/HP75gnBwcEsXryY4OBgRo4cybXXXsusWbN45JFHXF2uV+neLoKT/a8h3doKgAuC19DLcsT2+ucbjpKcddKk6pwVl5bz+/fXsz2l7g07Zo/pzo3ndoN9P8IXf7C/MH4ODLnazVWKSEN0a9uSkwOuIa0ig6YEr6W3xX4r5OcbfS+Dbh3djZtGVWTQQocMOu9hZZCIl+nWtiWnBlxty6DJwWvpYzlse33BxqMcPu4dGVRSVr8MumVUN24e1c3YYH7h7+0vnPdQva9OExHP6Na2JUV1ZNDCTUc5dLzApOqclZSV84d5G9h29PQZdMvo7jVnUD2v0hcRz+jatiVFA64i1Wp8+DApeB2JPp5BN9sy6CfnDPqdMkh8k8sX0a1Wa41fN9xwg+2YZs2a8dJLL5GVlUVBQQGff/55tV7nXbp04euvv+bkyZNkZGTw1FNPERLi/zv13jahP3PLptoeO16NXlpu5X/Lzf/00Wq18tfPt/Dznsw6j7tocAJ/nZwIx7bAR9fZd6MefjOMus8DlYpIQ902vj9zy6bZHnvjHTH1zaALByfw4JS+DhlUcZvh8Jtg9P0eqFREGur2Cf15tcy778qzWq385bPTZ9C0Qe352wV9IXUrfHitPYOG3QijH/BApSLSUMa5mPdn0F8/28qKpIw6j7Nn0DbneZAySMRr3e4j52IPfn76DJo6qD3/Z8sgx3nQDTBGGSS+yW0bi0rj9IyN5ETfazlujQRgWtBKulmO2V7/ZN0Rjpww9yqsp77bzecbjtZ5zMjubXjy8kEE5SbDvMuhOM94oc9UuOBJo0GoiHidnrERZPe9lkxrFADTglbR3ZJie/3T9UdMvxq9Phl0dvcYnqo1g55SBol4qR7tIsjpO5OMigyaGrSaHhb7/++fbTA/g57+LqleGfT0FYMJyj1SJYMuUAaJeLEe7SLI6Vd7BnnDncHPLE3isw1H6jxmRDfHDJoBRRVXi/aeogwS8WLd20WQ55BBF3hhBv13aRKfrj99Bj1TawY9rQwSn6VFdC9024SBvF5xNXqwxcodIV/YXistt/Ly8n1mlcb7qw7x0o91//zE+EhenTWM8OIceH8G5KcaL3Q8Cy57HYKCPVCpiDTW7RMG8HrZBQAE1ZRBP3l3BvWJi+TV64bXkEFnKoNEfMDvzx/AGxXzoCCLlT9UyaD/mTgPmrf60Gl7sztn0GWQV3ExRMcz4bI3INj/76wU8WW3TzhdBpl3Jei81Yd44TR9kXvHRTB31nDCS3KNxavKDOowHGa8qQwS8XKO60E1nYuZmUHzVx/meWWQBDAtonuh3nGRpPW5jmyrsfPy9KBf6GRJs73+8bpkUk6zkZ47fLc9lTlfbKvzmPbRzXjrxjOJKs+DD66CzN3GC216wjUfQVgLD1QqIk3RKy6S9MTrOGGNAODioF+dMuiTdcmn3czTHeqbQW/fdCbR1ioZFNMDrlYGifiCnrGRZCRe65BBv9HZIYM+XW9OBi3dkcbDC+vOoPgoYx4UTT58cLUySMQHnS6DzLoz+Pt6ZtDbN55lz6CMXcYLMT3gmo+VQSI+oGdsBJl9ryPL4VysiyXV9rqZGfTQwq11HlN7BnXXepD4BS2ie6nZ5w/izdIpAIRYyvl98CLbayVlVl7x8JWgm5KzueuDjZRbaz8mslkI79x0Fu2LDsJr50HyauOFlrFw7Wdu2RlXRNzjtgmDebN0MmBk0B+qZpCHrwStbwa9fWNNGdTOyKCWbTxSq4g03ewJg3mjjnnQyx6+CmtzcjZ3fbCh7gwKD+Htm84kofhQRQatMl5QBon4nNvOd86gPwSbe2fw5uRs7mxoBh1eabygDBLxObdNGMSbFXcGB1usTudiZmTQliOuyKC2nilWxI20iO6lEuOjSO41i1xrcwBmBK8gAfsGVh+uSSY1p9AjtaRkn+LWd9dRVFpe6zFhwUG8Nms4vU+sgNcnwIkDxgst2sLMT6B1V4/UKiKu0Sc+kqO97Rl0WZUM+mhtMsdyPHMlaH0zaO51w+mTXVMGfQox3TxSq4i4Rp/4SFL6XEeu1bhiqWoGfbz2iMcy6FjOKW55dx2FJXVn0KuzhpGY/Qu8Ph6y9hsvVM6DlEEiPqV3XNUM+pkO2DfR8+SdwcdyjHlQvTIo51djHqQMEvFpveMiOdbbnkGXBv9MR4s5GZSaU8gt79SdQaHBlloyqI1xF0xMd4/UKuJuWkT3YrdOHMq7ZRMBCLOUcVvIl7bXisvKPXI1ekFRKbe8s46MvKI6j3vmikGcnfwmfHgNFOcbT8YPgtnLIWGI2+sUEde7deJQ3imbBBgZdHuVDHr1p/1ur6G+GfT05YMYeUQZJOJPbj1/KG87zIOqZpAn7og5WVy/DHrq8kGcc/StWjJoqNvrFBHXmz1xKG9VzINCq2SQp+4Mrsyg9Hpl0NtG+4TKjYzjB8LsH5VBIj7q1olDnDPIhO4EJ4tLueXdtafNoKcvH1w9g+IGGvOgDme4vU4RT9EiuhfrlxDF/h7XU2ANB+Cq4OW044Tt9Q/WHGbV/uNYrXXcU9ME5eVW7v1oEzuO5dZ53D8md2Xa7gfhx8fsT/a/FG76Flp1ckttIuJ+fdtHcaDnLFsGXRm8nFiHDJq/5jAr93lHBl2YpAwS8Td920dxsOcs8q3NgOoZ9MHaZI9k0PaUujPo75O6clHS3+CHx4CKWpRBIj4vMT6KQz2vt2XQFcHLiSPL9vqHa5L5bV+mWzPovo82NyCDHsWeQZdUZFBnt9QmIu6XGB/F4V72DLo8+CePZ9D9H29m29G6M2jOpC7VM6jfdLhZGST+R4voXu6micN5v2wCAOGWEm4LWWx7rai0nKvmrmLsk8v579IkDh0vcOnPfuq73Xy3I63OY+47sxk37JwNOyr7BFpg/Bxj12VtGiHi8246fzjvlZ0PVM+g4tJyrn7NyKBnliZxMNPzGXTvcGWQiD+7eeJw3j9NBo158ke3ZNDTS3fz7fa6M+ie4eHcsGs27FhY8YwySMSf3DzxDId5UKlzBpWVc81rq92WQc8sTWLJ9tQ6j7lneDg37L7NOYPOexhmvAVhLV1aj4h43s3nD3PKoKp35bkzg/77fRLfbKs7g/44LJwbd99ePYMuf1sZJH5Ji+hebkCHaHZ3u55CaygAM4OX0YYcp2MOZ53kuWV7GPvkci57+TfmrT5EzsmSJv3czzcc4X+nuU36jm6p3LXvVkir2CU+LBKu/hBG3w8WS5N+voh4hwEdoknqfj2nrGEAXFNLBj2/bA/jnjIy6P1Vh8g+Wdykn1ufDPpD11Tu3q8MEvFn/ROiSepRdwYlZ52yZdCl//vVJRm0YOMRXvrx9Bn0x/2zsSiDRPxW/4Ro9lTJoLYeyKCFG4/y4o91b6D8+65pRgalbjWeCIuEqz+AMQ8og0T8RL+EKPY6ZNDVwT/QjmynY9yRQV9sOsoLP5wug1K554AySAKLFtF9wA2TRvBB2XkANLcUc3PIN7Ueu/7QCf5vwTbO/Of3/P799SzdkUZxHZvx1WTdwSz++tnWOo6w8kDrn3kg7c9YTh43norpAbcugz6TG/SzRMT73TjROYNuCfm61mPXHzrBQwu3cdY/l7k9g/6UrgwSCQQ3nn8W88vGA0YG3VpHBm04nO2UQd9tT21wBq0/lMVfPq0rg+D+ahnUXRkk4qduPP8s5lVkUDNLSZ3zINdk0An+/NmWOo+5r/Uv/Dn9T84ZdMv30GdKg36WiHi/Gyc6Z9DN9cyg299rfAb96dP6ZNCflUEScLSI7gMGdWzFli43UGQNAWBW8HdEk1/n9xSXlfPNtlRufXcd5/xrGS/9uJecU6e/Oj056yS3vbee4rKagzaUUp5p/jZ3nnoZS3mp8WSP8caJY7s+DfvFRMQnDOwYzbau9gy6Lngprcir83uUQSLiKgM7RrOj6/UUVdyVd20DMmj2e+sblEFHTpxk9rt1Z9DTzd/iLqcMOg9u/UEZJOKnjAy6wZZB1wV/59YMuu29dbUuelVm0N2n/lc9g2ITG/aLiYhPGNAhmp3dHDNoKa2pu095cVk5S7YbGTTyiWW8+MMeZZCIC2gR3Ufce+k4Flp+B0CEpZCb6rgavarM/GKe/HY3o/71A/9ZsovM/Jp3Vs4vMnZ/P15Q860/Iyw7WRz+f1xqXWp/8py7YeYn0Lx1/X8ZEfE59146loUW42p078qgu5RBIgHgnkvHssAF86B/L9lFRl7jMugsy06+DH+Iy6pm0DXKIBF/d++lY/i8Yh7U0lJU553BVTU0gzLzG5BBI+9UBokEgHsuGcOCRmbQ8YJinvouiXP/9QP/+qb2DCpQBomclsXqrq18TZabm0t0dDQ5OTlERUWZXY5LJO/fTcK7IwmmjDKrhbll0/hv6QyKCW3Q+4SHBHHVmZ24dUx3OrY2Nr0qK7cy+911LNuVXu34WE7wt9B5TA/+zf5kcDhc9AIMvrJJv5OIt3B1ZgRCBr1WNo3/ll5GEWENeh9lkEh1yqDTSz6wm/bvnEMIpV6SQc/D4Kua9DuJeAtl0Ok5ZlC51cLcsqn8t3RGozLoyjM7MbtKBt323nq+31l9M+N2nOBvofO5JPhX+5PB4XDhczDk6ib9TiLeQhl0ekcOJhH/9kiXZdCto7vTKcbIoPJyK7OVQRLA6psZWkT3Nd/8FVa/bHu4u7wj95fczjZr9wa/VUiQhYuHdOD343rw0drDvPbzAefXKeWG4G+5J+QzIiyF9hfaDzECM2FII38JEe+jiVs9LXkQVv3P9jCpvAP3lfzewxk0GC58XhkkfkUZVE9L/garXrI9dE0GdefjdUeYu2K/8+t1ZtBzkDC00b+GiLdRBtWTmzLok3VHeLWGDLo++FvuCfmcSMsp+wvKIPFDyqB6+vb/YOWLtodJ5R24v+T3bG1EBv3/9u49OMp6v+P4ZxOScE+ASC6SQFAuU4FwDCZSq9Mp4dZqQS2COjVSBwsSPYCeGek5AnZ6jIoyFIpw+oekzqGIOAcZtYcZDSQeIYSKOToCpmAzEk4uSCgkAiFk99c/EhaCWZLsbvJc9v2a2Rl49jLfXx7n7fLLk2x0lEdzJqfqmb+8TTsPn9JvSmgQIheb6G6Nps8rHdgg7XtF8rb+mI3XE62tUQ/r1Qv3q0V9uv2SHo90438FU6OO6J/7FGpM1J/8x0y/IfJMWyXdmSdFRYe0DMBueOPWRR00yKdovR1Ng4BQ0KAu8nmlAxulfb/u8Qa93KdQY69vUN8EeXJX0yC4Eg3qIp+3dQNr77+0a9DWtgZd6ekGTVslZT1Jg+A6NKiLAjSoMPohFVx4IKgGdeTuqKN6uU+hxkWd8h+jQXAzNtHdGs2r6o5IuxZLtdc+NfnHoXdoc8IvtPVEP11s9gb1ssmq169itun+6IP+Yz555PtZnvpMXy31Hxry6IAd8catm+qOSrv+sX2DhtyhzUN6qEGTn1CfGWtoEFyLBnVTwAa9oK0n+ofUoF/GbNMDNzTIO/kJxUxfLQ0YFvLogB3RoG6qOyp9sFiq+cp/6GqDCk/014UgG5Sks/pVzG9pECIODeqm08da3we1a9CfacvQX2jr8dAa9MuYbfrb6FL/sdZ/i/29+kxfQ4PgWmyiuz2aktTSLP3hDemzNyTTFsnoWDXft1IfDXxY/1Fapa9One/SS8WoRU9F/5ee7bNLAzzXPmjiG88YJS/4NyWOu7snVgDYBm/cguC90tqfz9be0KAX9fGgv1PhARoEdBUNCoL3ivSHN1sb5GtpPRZCg/4h+vd6rs/vOmjQBiWO+/OeWAFgGzQoCJ01qPSUvqo616WXokGIdDQoCB01KCpGzfe9qN8Pmqe3D4beoCOe25W0YCMNguuxiR4J0bzqT19KHyyRfvj22rER2TJzN2v//yXoreITOvBdfdsdRolqUIanRhlRNRrtab1NiKpUques/+n1ZpDWmUe1YNE/aWIan7QM9+ONWwiqy1t/MuYnDXpLB84N0aZ9wTdo/qKVmpTG1edwPxoUgupyadcS6Ydj146NuEtm7uagG3TWDNSbvsf0yKKVykynQXA/GhSC6j+2vQ/quEFvFZ/Q/hOBGlSr0Z5qGoSIR4NCcJMGlZ4bok2dNCij7X3QrZ56/9NpECKNKzbRN23apLVr16q2tlaZmZnauHGjsrOzu/TciIqmJF1pkopfkfZvkNR2Svv0k/5iuSTpbNVRna86pmGXT2rw9R8KcQOv8ei33lyta5mngsfv019PTOmF4QHr8cYtRF1p0KljGtZUpcGeiwFfxms82ubN1Zst8/TKY/fpbybRIEQGGhSigA1aJskTVIN+/di9un9Sam9MD1iOBoWo5XLr58Uc2CAZX+uxIBrkMx5t807TGy2P0CBEFBoUopbLUnGBtP9fr2tQ37Z/i9EgoDOO30TfsWOHnnjiCW3ZskU5OTlav369du7cqYqKCg0fPrzT50dcNK86ebD1qvSz/9v5Y69z2cSozDder7U8qiNmlJ6fPlbPThvTQ0MC9sMbtzAJoUGHfOP0astjOmJGacX0sXqOBiGC0KAwOVnW+nuKQ2zQ8tyx+nkuDULkoEFhEnKDHtURk0GDEHFoUJhUHWq9Kv3sd9162mUTo//2jVVBy2M6YjK0LHeMluWO7aEhAfvpajPC89G9PWDdunVatGiRFi5cKEnasmWLPv74Y7399tt68cUXLZ7OxtLvlhZ/Ln26Rjr07zfc6ZES0qRhY9Q4YJSK6wfrd9/31f+0JKtaw2QUJUmaPyVN+X91e6+PDsAFwtCgR6aM0LM0CEAw0nPaGvSydOg3N9x5tUG3q3FARmuDTvbV8ZZkVZth8rU1aF7WCD03jQYBCEJ6jrR4v1T0slS25YY7r2vQwAwVn6FBAMIsLbv1fVBnDRowSsX18QEb9HMuZgI6ZMsr0Zubm9W/f3+9//77mjt3rv94Xl6ezp07p927d3f6GhH7ncfrVZe3fidycKo07HZpSIYU07fdQ+oamvSfZSdVVlmvfjHRmj0xRfOyRsjj8Vg0NGANrn7oAVcbNChFShzTYYNONzRp2/UNmpCieVNoECIPDeoBNAjoMhrUA2gQ0GU0qAdU/1GqKmtt0LDbpaGjO23QrAnJemRKGg1CxHH0lehnzpyR1+tVUlJSu+NJSUn69ttvO3zO5cuXdfnytU8Rbmho6NEZHSH1Z623m0ga3FfLp/NjOkCoaFAHutCg4TQICAsa1AEaBPQaGtQBGgT0GhrUgdTJrbeboEFA90RZPUC4FBQUKD4+3n9LS0uzeiQAEYQGAbASDQJgJRoEwEo0CEBvsOUmemJioqKjo1VXV9fueF1dnZKTkzt8zsqVK3X+/Hn/raqqqjdGBQBJNAiAtWgQACvRIABWokEAeoMtf51LbGyssrKyVFRU5P+d6D6fT0VFRcrPz+/wOXFxcYqLi+vFKQHgGhoEwEo0CICVaBAAK9EgAL3BlpvokrRixQrl5eVpypQpys7O1vr163XhwgUtXLjQ6tEAAAAAAAAAABHCtpvo8+fP1w8//KBVq1aptrZWkydP1p49e37yYaMAAAAAAAAAAPQU226iS1J+fn7AX98CAAAAAAAAAEBPs/UmeiiMMZKkhoYGiycB4ARXW3G1HaGiQQC6gwYBsBINAmAlGgTASl1tkGs30RsbGyVJaWlpFk8CwEkaGxsVHx8flteRaBCA7qFBAKxEgwBYiQYBsFJnDfKYcH2rz2Z8Pp+qq6s1aNAgeTyeTh/f0NCgtLQ0VVVVafDgwb0wYc9jTc7hxnU5bU3GGDU2Nio1NVVRUVEhvx4NYk1O4sZ1OW1NNCj8WJNzuHFdTlsTDQo/1uQcblyX09ZEg8KPNTmHG9fltDV1tUGuvRI9KipKI0aM6PbzBg8e7Ig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       "text/plain": [
        "<Figure size 1500x300 with 5 Axes>"
-      ],
-      "image/png": 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+      ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "source": [
+    "fig = clf.plot_most_important_feature_on_ts(\n",
+    "    X_test[y_test == \"1\"][0, 0], clf._classifier.coef_\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 4.4.2 Interpretability of class `2`"
+    "#### 4.4.2 Interpretability of class ``2``"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-02-19T16:46:24.666511Z",
      "start_time": "2025-02-19T16:46:24.387533Z"
     }
    },
-   "source": [
-    "fig = clf.plot_most_important_feature_on_ts(\n",
-    "    X_test[y_test == \"2\"][0, 0], clf._classifier.coef_\n",
-    ")"
-   ],
    "outputs": [
     {
      "data": {
+      "image/png": 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2Muh0bQZ9IXdPiKgxWs6gPZ/L3RMiaowmM2gdM6gZjHJ3gEg1TNXA11cBhWlA6i7g6k/l7lHrmaqBX/4JFGcCaXuBqD5A7GC5e0VE9phNwNezgcJUIGUnMOczuXvUeqZq4Jd7geIMZhCR0plNNXVQKpCyA5jzP7l71HpmE/DLfKDogphBkX2B9swgIkUym4BvZgMFKdrNoKh+zCAipbLOoOTtwFwNTDrXZVA6M6iJeCc6UVPlnBAn0AHxap0gyNsfKZzdIE6gAwAE4NcF4qQWESlP9glx8goAzmglgzaKE+gAmEFECmeTQes1kkEbxMkrAIAArHqAGUSkVDknxckrQLsZ9CsziEixck5pMIM2ihPoAJhBTcNJdKKmSttnaZflAflJ8vVFKolLbF9nHAR2fSRPX4jIuXTrDLoIXDwvW1ckc4AZRKQaDTLonHx9kUrid7avMw4BOz+Upy9E5FyaB2RQ5iFg5wfy9IWInLOug8rzgbyzsnVFMgeYQc3FSXSiprIOTUB83EXNyvKB47+Jbe9AADqxvf4loCBVrl4RkSNpGsug8gJmEJGaNMigffbPU4vyAuD4KrFtnUEbXgbyU2TrFhE50GAsxgwiIjfSXB1UCByrySCvANhmULJs3VI6TqITNZXWQvPIT4CpQmwPmAcMvlVsV5UAfzwuW7eIyIH6g8f0/fL0QypHfgKqy8X2gBuAIbeJ7aoS4PfH5OsXEdmntQmsIz9bMqj/9cCQ28U26yAiZdLcWOxnBxlUCvz+qDaWiiDSkgZjMZVn0NGfgeoysT3gemDoHWK7qlQcizGD7OIkOlFTVFcAmUds31P7BJb1ozsDrgemPAsERIivj6+y3CFKRPKrrgAyDtu+p/bBo/UjzP2vByZbZdCJ3yx3RhCR/OxlkNoHj/XroMnPAIGR4mtmEJGyVFcCmVrLoKWWdv0MOvmH5S51IpJfdaW45Js1TY3FbmAGNREn0YmaIuMwYK6yfS89UdzNWI1yzwApO8V2u15A9ADArw0wfZHlnN8fBSqKZOkeEdWTaSeDLiSqd+OXvLNAyg6x3a4nEDMQ8Au1zaA/HmMGESmFvQxKT1R3BiVvF9ttewAxg5hBREqWeRgwVdq+p+oMOgckbxPbjjLod2YQkWJkHWmYQRcOaCSDugOxgwDfEGD6K5ZzmEF2cRKdqCms73TQGcR/VpUA2Sfk6U9r1b/zQVez/lWfq4Euk8R2YRqwYVHDnyUi90uzl0GlQI4GMqh//QyaLLaZQUTKYS+DqsuA7OPy9Ke1DiyztK3roITZ9TLoZff3jYgasjcWU3UGORiLJcwGuk4R20Xp4j4xRCQ/h3XQMXn601oHreog67FYwlVA10vENjPILk6iEzWFdWj2nGlpq/ExQrPZUrjp9EC/ay3HdDpg5huA0Vd8vfMD8QorEcnLevko6wxS42OEZrNlGQVmEJE6pCda2pqog5qaQR/a/t6JSB6O6iAtZtClr1syaNdHzCAiJUh3MB+kxrGYIFgtaaezUwe9Dhj9xNe7PlL/MsYS4yQ6UVPUhqbeCAy6xfJ+2l55+tMaSVuBgprdlrtMAoKibI+HdQbGPSK2BTPw6wL1LltDpBVpVhk0WOUZlLzNsuN754lAcLTt8bBOwLhHxbZgBn59gBlEJLd0LWXQdiA/SWx3ngAEx9geD+sEjK/Z3FgwA6sWMIOI5JZWM4njiRnEOohIfrUZpDNoI4MunhfbnScAIbG2x9t0rJdBC5hBVjiJTtSYiiLLsi2RCUCH4QBqHndR45VH6420+l9v/5xRD4jr8wHiwHn3Z67vFxHZV1FsWbYlojcQZ5VBarwDy3oTmwE32D9n1P3iWumAePcDM4hIPpUlliUTInoBcSPEuycBldZBSyzt/g4yaOR99TLoU9f3i4jsqyyxLJmgiQxqwlhs5H3ivlWAuAfOrk9c3i0icsAmg3oDHUZaMkiVYzHrOsjRfNB94u8VYAbVw0l0osZcOAhAENsxgwCfIHHzBQDIPAJUlcvWtWarLAGO/iK2fYJtH0WyZvQGLltseb1uIVB4weXdIyI7Mg6KdwEA4qYvPkFAu5qLXKrMoJ/FtncQ0ONS++cZvYFZiy2v1y0ECtNd3TsisufCAUsGxQwCfAItF9qzjqosg0qBIzV1kHeQ8zrIJoNeYAYRyeXCwUYyqEy+vjVXZSlw5Gex7R0E9Jxl/7z6Y7H1LzKDiOSScchqLDYQ8A6wXGjPVHMGBQK9HGSQwcu2Dlr/AlCQ5ureqQIn0YkaY311MXaQ7T/NVeJu8WpxbBVQWSy2E64EvPwcnxs/Chh4k9iuLAJWP+Hy7hGRHdZ3WcUMsv2nuVos7NTi+G+2GeTt7/jc+JHAoJvFNjOISD5pzuogNWZQkdhOuIIZRKQGTsdiGs6gDiMsy4hWFgF/PO76/hFRQ87GYoKp5qZLlTjxuyWDel8pXhBwpMNwYPCtYruyGFjNDAI4iU7UOJvQHCj+M3aw/eNK15RHmK1dshDwDxfbR38GTv7lkm4RkRPp9jJokP3jSmf9+KCjpVysTXke8G8rto/+Apz80zX9IiLH0u0MHq0zSE3rgTa3DmIGEcnP7lhskP3jStfsDHrOkkHHVgInVrukW0TkhGbHYg6WcrE25TkgoJ3YPvYrcOIPl3RLTTiJTtSY2lA0+lnWpotRYWgWpAJnN4ntNh3Fuxsa4x8GTHvZ8vq3h8XlGIjIfWoHh0ZfcS1QQJ2Dx4I04OxGsd2mo7ieYGMaZNAjzCAid7OXQWqsgwrTLRkUGs8MIlKLdOsMqlmj15MyaPoiy+vfmUFEbldbBxl8xD3yAHWOxQovAGc3iO3QDkCHUY3/jF8bYJp1Bj3q8RnESXQiZ0rzLDsXR/cDDEaxHdUH0HuJbbXcgXVwGerWdu9/PaDTNe3n+l0LdBontguSgU2vuqR7RGRHaR5w8ZzYjuonrk8HAJGelEHXAJ3Gi21mEJF7Ocsgg7fYVsvg8eAyy5qm/a8H9E0cBtXPoI2vuKZ/RNRQ2UUg76zYjurrIIPUUgd9b5VB1zU9g/rOBTpPENsFKcDGRU5PJyIJleUDeWfEtnUGRSSoMIOs6qB+zcmgOcwgK5xEJ3LG3iPMAGD0ESfSASDnFFBe6N5+NZcgAInWO8Ff1/Sf1emAmW9a/pLY/r64mSERuV76fks7tn4G9RXbuaeA8gL39qu5BAE4IFEGbXsPyFDRXhREamadQbWPMAPipnfWGVSW79ZuNZvUdRAziMg9bDLIug6yzqDT6sigVtdBPuLr7f9V1zrwRGrmcCzmLd5cAIiT7Mwgj8FJdCJn0hyEJmBVyAnAhUR39ahl0vaKg1wAiB8tLqXQHG27AWMfFtvmamDTa5J2j4gccHQhD6i3Fl+iW7rTYmn7gJyTYrtFGdQVGPuI2BZMwOb/SNo9InLA3oZ+tawzSel1UPo+IOeE2O4wCgjr1Lyfb5BBrIOI3MLexsa1VJVB+4Hs42K7w0ggrHPzfj68CzDOKoM4FiNyjyaPxfZD0awzKG6EmCnNEd4FGPeo2BZMHv1kMCfRiZxpamgq/REem6uOTdhAwp7RCyybShxfBRRntbpbRNSIJl3Ig8oyqBl3Plgbs4AZRORuaQ7uAgXUtR7ogaWWtiQZ9BtQlNnqbhFRIxzdiQ544FjsASAgQmyf+J0ZROQOTb2Qp/gMsqqDmrKhqD2j7wcCI8X28d+BoozW90uFOIlO5ExtaPqENLxjIHZww/OUqLoCOLRCbBv9gN5XtOxzvHyBAfPEtrka2P+NNP0jIsdqL+T5BANh9e4YsM4gJW+qVV0BHLbOoCtb9jlGH2DgjWKbGUTkHtYZFN7V9phNHaTgwWN1pVUd5AskXNmyz6mfQYnMICKXS2tqBim5DnJRBu3/WpLuEZETtRfyvIOA8G62x2zGYgq+E726Eji0XGwbfICEq1r2OdYZJJg8NoM4iU7kSGE6UFxzdS1mQMONF9p2B7wCxLaSQ/PkaqA8X2z3mgX4Brf8swbfamnv/QIwm1vRMSJyqvACUHRBbNvNoG6Ad6DYTlNyBv0pbgwGtD6DBt0CoGZDUmYQkWtZZ1B0/4YZFN5NHFQCyq6DTv0JlOWJ7Z6zAN+Qln8WM4jIfYoygKJ0sa3qDPrLKoNmti6DBltl0L4vAbOp1d0jIgeKMoHCNLFtbywW3lW8wAco+0KelBlkUwd95ZEZ1OxJ9M2bN+Oyyy5DTEwMdDodfv75Z5vjgiDgmWeeQXR0NPz8/DBlyhScOnXK5py8vDzMmzcPwcHBCA0NxR133IHi4mKbcw4ePIixY8fC19cXcXFxeO01rvtFbubs0R0A0BvEMAXEXYqVurRAogSPD9YK6wR0mSS285OAs+tb93lE5Jiz5aQAMYOiB4jtwlTlPtYrxSPMtZhBRO7jbD10QBxM1tZBhWnKzSDrOqiljzDXssmgZOAMM4jIZRodi9XPIIUuLWBTB93Qus9q0xHoOllsM4OIXMtmLDaw4XG9XrzAB4gX/AovuKdfzWWdQQNam0HxQNcpYrsgGTi9rnWfp0LNnkQvKSlB//798f7779s9/tprr+Gdd97Bhx9+iJ07dyIgIADTpk1DeXl53Tnz5s3DkSNHsGbNGqxatQqbN2/G3XffXXe8sLAQU6dORXx8PPbu3Yv//Oc/eO655/Dxxx+34LdI1EKNTWAByl8PtDgbOL1GbAfFAJ0ntP4zB99mae/5vPWfR0T2NTZ4rP++Epd0KckR734ApMugIcwgIrdIa2YdpNgM+lNsB0UDnSe2/jOtM2gvM4jIZbQwFivJFZ/IA4DAKKCLBBnEsRiRezRpLKbw5TVtMiiSdZAEmj2JPmPGDLz44ou46qqG6+gIgoDFixfjqaeewhVXXIF+/frhq6++Qnp6et0d68eOHcPq1avx6aefYvjw4RgzZgzeffddLF26FOnp4uNa3377LSorK/G///0PCQkJuO6663D//ffjzTffbN3vlqg5mhKaMQofPB5aLq6ZBwD9rhHvXG2tHjPEIhAATvyh3CuuRGqnhcGjKzKo+3RmEJE7pDvZ2LiW0jfVOrTCNRkUFC22T/whLv9HRNJrbgYpcSx2eAVgrhLbrsigk6uBgrTWfyYRNaSFsVj9DDIYW/+Z3aaJN0cBHplBkq6Jfu7cOWRkZGDKlCl174WEhGD48OHYvn07AGD79u0IDQ3FkCFD6s6ZMmUK9Ho9du7cWXfOuHHj4O3tXXfOtGnTcOLECVy8eNHud1dUVKCwsNDmF1GLCYKlcAuIAIJj7Z+n9F3hpVxGoZbBCxh0k9j24A0l6mMGkaRsMqgdENLe/nlKn8ByWQbdLLaZQXWYQSQpQbAMHv3bAiFx9s9T+uDRVRk00KoO2scMAphBJDFBsGRKkzNIgXVQ4hJLW7IMMrIOsoMZRJKyyaBwILSD/fNUNRZr5VIutWwyyAzs+0qaz1UJSSfRMzLEdcgiIyNt3o+MjKw7lpGRgYiICJvjRqMRYWFhNufY+wzr76hv0aJFCAkJqfsVF+fgL1qipsg7a9mMM3YQoNPZPy80XgxVQAxZQXBL95ok8wiQcVBsxwwEInpK99mDboZlQwluagMwg0hiF89ZNuOMcZZBHcTBJSBOeCkqg44CFw6IbVdkkK6mhGEGAWAGkcQunrdkkLM6KCROvNAHKC+Dso4BFxLFdvQAIKKXdJ9tnUH7PHNjrfqYQSSp/CTLRnhNzqD9Cs6g/kBkb+k+u34Gmaql+2yVYgaRpKwzyNlYLKS9eNMloMAMOm65KSuqHzNIIpJOosvpySefREFBQd2vlJQUubtEamb9+KCjR3cAMUxrj5fliWGrFDZ3Pkh01bFWaAeg21SxXZgKnF4r7eerEDOIJNWU5aQAMYNqj5ddFCe+lOKAKzMoDuh6idguTAVOrZH281WIGUSSasojzEC9OuiieAFQKazroNZupFVfaJxtHcQMYgaRtJqyJwOg7AxyxR2gtULai0sqAOKmqqeZQcwgklRLxmLl+eLNmEpxwIV1UEisJYOK0i17YHkASSfRo6LENUozMzNt3s/MzKw7FhUVhaysLJvj1dXVyMvLsznH3mdYf0d9Pj4+CA4OtvlF1GJNDc36x5XyKLOpGjj4vdjWewF950j/HTab+/1P+s9XGWYQSaqpF/LqH1fKeqBuyaDbLW0P3NSmPmYQSUprdVAfF2TQYNZB1phBJKl0lWeQ2WSVQUaOxdyAGUSSaurNBPWPW4/h5NQgg+ZK/x3WYzEPyiBJJ9E7deqEqKgorFu3ru69wsJC7Ny5EyNHjgQAjBw5Evn5+di717Je0Pr162E2mzF8+PC6czZv3oyqqqq6c9asWYMePXqgTZs2UnaZyD6b0Bzo/FzrHZmVsg7WmXVASc3Fqu7TAP8w6b+j6yWWteJP/QUUpEr/HUSeKq2lGaSQweOZ9UBxzcVwV2VQt0uA4Jq14k/9BeTzjiMiyTTnQp51Bill8Hh2A1BcswRk92lAQLj032GdQafXMIOIpJRmnUFqrIM2AEU1G593mwYEtJX+O7pOsawVf2oNkJ8s/XcQeaoWZ5BS5oOsM2iqizJosiWDTq8FLipoVQYXavYkenFxMRITE5GYmAhA3Ew0MTERycnJ0Ol0WLBgAV588UWsXLkShw4dws0334yYmBhceeWVAIBevXph+vTpuOuuu7Br1y5s3boV8+fPx3XXXYeYGHGH1xtuuAHe3t644447cOTIESxbtgxvv/02HnroIcl+40QOmaot6/iGdGg8cJR45XHbu5a21I/u1DIYgUG3iG0P3FCCyGXMJqsMigMC2zk/X4l3YG13QwbpDR69qQ2Ry5hNQHqi2G5KBilxUy3rOkiqzfzq0xuAwayDiCRnNlnWEg9uDwRGOD1dkU/k2dRBLsyg2rEYBGYQkVRsMigWCIp0errNJLsSx2KurIM8MIOaPYm+Z88eDBw4EAMHin9QHnroIQwcOBDPPPMMAOCxxx7Dfffdh7vvvhtDhw5FcXExVq9eDV9f37rP+Pbbb9GzZ09MnjwZl156KcaMGYOPP/647nhISAj++usvnDt3DoMHD8bDDz+MZ555BnfffXdrf79Ejcs5AVSViu3YRq46AuLgsvYKXHqi/JtLpewCzm8R22FdgO7TXfddg24CdAax7WEbShC5TPYJoKpEbDd25wMgXugLqdkx/kKi/P8fpuwGzm0W22Gd3ZdB+7+W//dOpAU5J5uZQeHiXimAeAFQ7v8PU/cA5zaJ7bDOQI8ZrvuugfXroCrn5xNR43JOAZXFYrspYzHFZdBe4OxGsd2mE9DdlRl0IzOISGrWGdTkOihebCsxg3pc6rrvss6g/V97RAY1exJ9woQJEAShwa8vvvgCAKDT6bBw4UJkZGSgvLwca9euRffu3W0+IywsDEuWLEFRUREKCgrwv//9D4GBgTbn9OvXD1u2bEF5eTlSU1Px+OOPt/x3SdQcTd3IxlptuFaViBNgctrypqU95kHxCqGrBMdYJsiKLgAnV7vuu4g8RXPWAa07rzaDSsULgXL6280ZVDtBxgwikkZz1kOvVVsvKSGDrOug0QtcnEHRlgwqzmAGEUmhOWsR1z+vqhTIPi59n5rDpg5aID696yo2GZQJnPjDdd9F5ClaNBarOa+6DMg+Jn2fmsPdGdSzZpK+OBM48bvrvkshJF0TnUgTrJdkaXJoWq8HKuMjPJlHgJM1xVNwLNDvWtd/Jzf3I5JWc9YirqWU9UAzj1qKp+BYoN91rv9Obu5HJK2WTGApJYOyjgEnfhPbQTFAfzdkkM3mfqyDiFqtJRfylDIWyzoOHF8ltoOiXbeMgjWOxYikpeaxmBwZNNiz6iBOohPVV1d46YDoAU37GaWsSfz3W5b2qPsBo7frv7PLJMsjlKfXARfPu/47ibTM5mmYAU37GaWsB2qTQfe5P4POrGcGEbVWSzLIpg6ScV30Bhnk4/rv7DzJ8hj3mXVA3jnXfyeRllnXMaoei7krgyYCbTqK7TPrgbyzrv9OIi2zqYOasJwLoOCxmJsz6OwGIPeM679TRpxEJ7JWXQFkHBbbbbsBvsFN+7noAQB0YluuwWPeWeDwD2LbP9yy4Z6r6fW2G0rs/dI930ukRdWVQGZNBoV3A3xDmvZzMQMgfwadAw6vENt+Ye7NoMG31rxgBhG1SkszKLo/6jJIrsFj3jngkFUG1W766Wp6ve137WMGEbVYdSWQcUhsh3cF/EKb9nPWGSRXHXTxPHBoudj2C7MaH7mYzVgMrIOIWsM6g8K6NC+DdDXTq4rIoDbuzaC6sRg0XwdxEp3IWuZhwFyzGUJTH90BxMn2tjVr/2ceESfj3W3r24BgFtsj/gF4+7vvuwfeBOhr1tra/41HbChB5BKZhwFTpdhu6iPMAOATBLTrUfMZR4Cqcun71hibDPon4B3gvu8ecKNVBn0tFsBE1HytyqCeNZ8hUwZtewcQajZ3H/EP92ZQ/TqIGUTUMllHLBnUnLGYdQZlHZWpDrLKoOH/B/gEOj9fSgNvBPReYjvxW2YQUUtlHQVMNXM5zaqDAq3qoKNAVZn0fWuMTQb9w70ZNMAqg/ZrO4M4iU5krSVr8NU/31xluZvdXQovAIlLxLZ3EDD0Lvd+f1Ak0HOm2C7JAo7/5t7vJ9KKlqxFXP98c7XlTlJ3KbwgDtoAMYOG3ene77fJoGzLmshE1DytyaBYqwyqvYvLXYoyxMlrAPAOBIa5uQ4KjAB6zhLbzCCilpNkLCZHBmXKn0G9rDKodk1kImoeKcZigskDM6idJYNKc4Djv7r3+92Ik+hE1lqyiYS98939CM/29yx3bQy7s+mPHUmJm/sRtV5aCzY2tne+uzNox/uWDBp6h/gIobtZb6zFDCJqmdZkkPW6oe5e0mW7EjKIdRBRq7VqAssqg2Spg2ruXh1yO+Af5t7vBzgWI5JCqy7keXgG2YzFtLvBKCfRiazVhqbeCET1bd7PyrUrfGmeJaSMvuIyCnLoNB4I6yy2z23S/IYSRC6R3poMkmlTrdI8YHfNYM3gI18GdRxnlUGbmUFELVF7M4HO0Lo6yN0ZtMc6g+5133db6zhOXD8VYAYRtVR6ovjP1maQO8diZReB3Z+JbYMPMFKmDOo0TlxHHgDObwFyTsvTDyI1s6mD+jXvZ+Wqg5SSQR3H1sugU/L0w8U4iU5Uq6IYyDkhtiN6A16+zfv5qD6WdaDcGZo7PwKqSsT2wJvEx/nkUH9Dib1fyNMPIrWqLAGyj4vtiF6Al1/zfj7SKoPcOXjc9bElgwbdJC6tIocGGaTdOyCIXKKyBMg+JrYjercsgwzeYtudd2Dt+gSoLBbbzCAi9aosBbKsMqi5+zvZZJA76yCrDBp4IxAU5b7vtqbTMYOIWsMmg3o1P4MiEiwZ5NaxmHUGzVNQBn0hTz9cjJPoRLUuHLBsitfcR3cAwOgDRCaI7ZyTQHmhdH1zpKII2Pmh2NYbgdH3u/47nRkwz/IXR+K38mywSqRW1hnU3EeYATGDovqI7ZyTQHmBdH1zpKII2PGB2NYZgFEKyqD938qzsRiRWl04aFUHDXR+rj1Gb3ESCwByT7kpg4qBncwgIk3IOGjZFE+KDCrLl6xrDlUUAzv+K7Z1BvnHYv1vsB2LMYOIms46g2JamEG1T9DknpYng+SugzwggziJTlSrNWvw1ap7hEcALiS2tkeN2/sFUJ4vtvteA4R2cP13OhPQFuh1udguzQWOaXdDCSLJtWYNvrqfs36UObFV3WkS6wzqdw3QJt713+mMdQaV5TGDiJpD0joI7sugsotiu+9cBWRQOND7CrHNDCJqnjSJM8gdY7F9X1pl0BygTUfXf6czAeFA7yvFdtlF4NhKWbtDpCqSj8X2Oz5PKvUzKKyT67/TmfoZdPQXWbvjCpxEJ6olSWi6cU3iqnJg23s1L3TAmAWu/b6mstlYi48REjWZFBNY1j/n6scIqyvqZdCDrv2+prLOID7KTNR0UtdB7sig7e9ZXislgwYzg4haJF1lY7HqCmDbu5bXSskgjsWIWkaVYzGFZ5AG6yBOohPVqg05oy/QrlfLPsM6NF29HuiBJUBxhtjuNQto18O139dU8aOBtt3FdtLfwJn18vaHSC3SrDIoooUZFOvGDEq0yqCeMxWaQVuZQURNZV0HRfRu2We4tQ76Dii6ILZ7zgIierr2+5oqfhTQtiYPmUFETVdbBxl8VJJBS+tlUAtrN6l1GGnJoORtwOl18vaHSC2kyCB3XsizzqAeMxWaQduB02vl7Y/EOIlOBAClecDF82I7qh9gMLbsc9r1ALwCxLYrH98xVQNb37a8HvOQ676ruXQ6YPg9ltcr7gAuJsnXHyI1KM0DLp4T21F9AYNXyz6nbXfAO1Bsp7k6gxZbXo9VcgbdzgwiakzZRSDvrNhuVQZ1A7yDxLarM+jvxZbXzCAidSvLB/LOiG2pMsjVY7G/37K8VtpYbMT/WV7/cIdlnEtE9tlkUB9xffOWCLeug1w4iV4/g5RWB1ln0AptZRAn0YkAaR4fBAC9AYgZILYLUoDirFZ1y6EjP1mCqMuk1vXZFQbfDnSbKrbL8oDvbwKqyuTtE5GSWQ/0Wvr4ICBmUPQAsV2YChRltqpbDllnUOeJtuv/KcHg24Hu08V22UVg2Y3MICJnpMyg2jrIlRl09GfLhUdFZtBtzCCi5rDOIKnGYoVpQFFGq7rlkE0GTQDaKyyDBt0KdJ8htmszqLJU1i4RKZpkdZDekkFF6UDhhVZ1yyHrDOo0Hmg/xDXf01LWGVSer6kM4iQ6EWB7t1RrQhOw3cnZFVcfzWbg7zctr8c+LP13tJZeD8z+GGhTs7HFhQPAqocAQZC3X0RKJdWFPACItcogV6zFp5YMuuojIKyz+DrjILDqQWYQkSNSrIdeK8YNGbTlDctrZhCR+kmxFnHdz7t4LCYIwBYV1EGzPwLCuoivMw4xg4ickXQs5uJ10ZlBsuIkOhEgcWha78jsgtA8uRrIOiq244aL6/8qkV8b4LpvAS9/8fWBJcDuT+XtE5FSSXkhzzqDXDF4tM6g9sOAjmOk/w4p+IUC11pn0HfMICJHpLoDC3B9Bp360yqDhqogg2qW+WMGETkm5YU8l4/F/gSyjtR81xCg41jpv0MKviE1Y7GaDDq4FNj1ibx9IlKqNAkv5Ll8LFYvgzqNk/47pGA3gz6Wt08S4CQ6EWAJN59gy9WylnLlZhKCYHv31ZiHxDWnlCoyAbjcasfo1U8AyTvk6w+RUqVbZVB419Z9lit3ha+fQWOVnkG9gSves7xmBhHZlyZhBrnyDqwGGfSw+jIoabt8/SFSqtoLed5B4prCreHOsZjSMyiiF3Dl+5bXfz4JJG2Trz9ESlWXQYHi3gqtwbGYRYMM+pfqM4iT6ESF6UBxzXp5MQPER09aIzQe8AsT2+n7pH1k5fwWIG2P2I7sA3SfJt1nu0rfOcDI+WLbXA18f7Pr1ickUqPCC5ad1aP7S5BBHQD/cLGd5sIMikgAuqkgg/pc3TCDXLU+IZEaFWWI63YC0mRQSBzg31Zsp+2VOIP+BlJ3i23VZNBsYNR9YttcDSy/hRlEZK0oU1y/HJBmLGadQVKPxZK2Aqm7xHZEb8veB0qWcBUw6n6xba4GvmcGEdmwzqDoAeLeCq0R0h4IaCe2pR6LWWdQu16WdceVzG4Gpcvbp1bgJDqRlI/uAOKVwNo7IEpzgfyk1n8mIIbv5v9YXo95UNlXHa1Ned7yqGNxpjiJVV0pb5+IlELK5aQAMRdqs6wsT7rd0Otn0NiHWj/QdZf6GbT8FmYQUS0pl1EAbOugsovMIACY/JzlcWtmEJEtm/XQBzo+r6kaZNC51n8mYGcspqYMelbcfBAASrI4FiOyZjMWkyiDasdi5flA3tnWfyag8jrIXgZVyNunFlLJv3EiF5J6AguwXQdr1yetv/ooCMBfTwHnNouvwzqLV/TUwmAE5nwOBMeKr1N2io/yEJH0F/IA2wza/an0GdSmE9D7ytZ9pjsZjMDcL4Dg9uJrZhCRhZQb+tVyWQZtEl+rMYPmfF4vg56Ut09ESiH1hTyg3lhMogxa8zRwdqP4uk1HFY7F/ifepQ+Id7KufkLePhEphVrGYtYZFBoPJMxu3We6U20dVJdBu4HV6qyDOIlOlHnE0o4eIM1nWj/at/09YMNLrQvODS+LnwMA0AFTX2z9Y0buFtgOuPZrwOAtvt79CZC4RN4+ESmBdQbFDJDmM3vUy6D1L0qbQdNeEoshNQloW5NBPuJrZhCRyBUZVL8Oam0GbVzUsA5SfQZ9Cuz/Vt4+ESmBq8diO94H1r/Qygx6BdhWu8+TDpiqgTpoz2fA/m/k7RORErh6LLbjv8C6hdJmkCrHYuGayCBOohPlnhH/afSzXBlrrdhBwKWvW15v/o8YfC2x5U1g82uW15e/A/Sc2br+ySV2MDDTaiOMVQ8C6YmydYdIEfJqM8gXCOkgzWfGDLTNoC2vi5NQLaGpDBpkm0G/LmAGEeW6IoMGSJdBf78FbHrV8vqyt4Fes1rVPdnEDgJmvWl5vepBy2ZmRJ6qtg4y+Ih3V0ohZoDt3/db3mj5TU1/LwY2WY3jLlus3gyKGQjMesvyetVD0m++SqQ2rsig6P7ATKu/7/9+s+U3FNjNoMta20N5aCCDOIlOns1UbVknL7yLtGtKDbsLmGE18bTpleZPpO/4EFj3vOX1jNeAQTdL0z+5DLoZGHyb2K4uB5bdBJTkytsnIrmYqoG8mgwKc0UGWa2bt+lVZhAADLoJGHK72DZVMIPIs5lNlrU6lZhBOz8C1j5neT39VWDwLZJ0TzYDb2QGEdWyziCpx2JD72z9TU07PwbWPmt5Pf0VYPCtknRPNgPnAUPuENt1GZQjb5+I5GJTB3WW9mn/oXe0/oYCrWbQ0DvFtgoziJPo5Nnyk8QdggExNKU2/B4x6GptXARs+o/j863t/QJY/bjl9ZTnxc/TghmvAu2Hiu2CZOCH28XJRCJPU5AMmKvEdrgrMuhucdKp1sZFwKbXHJ9vbe+X9TLoOe1k0PRXmEFEAJAvQwZtfNXx+db2fQX88Zjl9eRngRH/J23/5GKTQSnAituYQeSZClIAU80Gl64Yi7XmpqZ9XwN/PGp5PflZYMQ/pO2fXKa/ArQfJrYLU5lB5LmsMyi8i/Sf35obChpk0DPayaBpi4C44WJbZRnESXTybLWPMANAeFfXfMeIfwDTXra83vAisPl1x+cDwIFl4jIDtcY/DoxZ4Ohs9TH6ANd8BQS0E1+f3Wi70zSRp3BLBv2fWKjU2vBS4/+/Hfwe+PUBy+vxjwNjHnRN/+RQl0ER4uuzG22XrCHyFO7KIJsbCl5u/IaCg8uBlfdbXo97DBj7kGv6J4f6GXRuEzOIPFPuaUvbVRlk96amRv5/O7QCWHmf5fW4RzWWQd71Mmiz7bJZRJ7CJoNcMIkOtOymJrsZ9LBr+icHozcw90sgMFJ8fW6z7ZI1Cib5JLrJZMLTTz+NTp06wc/PD126dMELL7wAwWrtH0EQ8MwzzyA6Ohp+fn6YMmUKTp06ZfM5eXl5mDdvHoKDgxEaGoo77rgDxcXFUneXPF2eGwaPADDyXnEDmlrrXxDXGbbn6C/Az/8HoOb/mVH3ARPUuXOxU8ExYnDqazbE2P1J63etJlIbd0xgAcDIf9pezFv/org+qD1HfwF+8pAMusYqg3Yxg8gDuasOas4NBUdXAj/dg7oMGjkfmPgv1/VNLswgIiD3rKXt1gxyckPBsV+BH++GbQb923V9k0twtDiRzrEYeTK3ZVAzbmrypAyaq746SPJJ9FdffRUffPAB3nvvPRw7dgyvvvoqXnvtNbz77rt157z22mt455138OGHH2Lnzp0ICAjAtGnTUF5eXnfOvHnzcOTIEaxZswarVq3C5s2bcffdd0vdXfJ07rj7odao+cAlL1her3te3CTC2sk/gRV3AIJZfD30TvFndDrX9k0uHUcDXSaJ7dJcy3pkRJ7CnRk08l5g6ouW1+sWihv2WTv5V00GmcTXWs+g+FFAl8liuyyPGUSex+0Z1MgNBSf/AlbcbsmgIXeIucUMItImWTPIzg0Fp9YAy2+zyqDbNZ5BI4GuU8R22UXbmzuIPIFbM6gJNzV5ZAZdIrbL823/eyiU5JPo27ZtwxVXXIGZM2eiY8eOmDNnDqZOnYpdu3YBEO9CX7x4MZ566ilcccUV6NevH7766iukp6fj559/BgAcO3YMq1evxqefforhw4djzJgxePfdd7F06VKkp6dL3WXyZO54fMfa6PvFtc1rrX0W2PqO2D67UdxUoXZt0gHzxPWztBqYtWrX4wOA1D3y9YNIDtYZFOaGDBp1H3DJQsvrtc8BW98W22c3Astu9LwMihtqaafulq8fRHJwewY5uaHg7Cbge6s6qP8N4oZczCAi7XL3WKxBBlndUHBus20d1P8G4NI3tJ9B7ZlB5MHcXQc5u6mpQQZd7xkZpLI6SPJJ9FGjRmHdunU4efIkAODAgQP4+++/MWPGDADAuXPnkJGRgSlTptT9TEhICIYPH47t27cDALZv347Q0FAMGTKk7pwpU6ZAr9dj586ddr+3oqIChYWFNr+IGlV7td03BPAPd893jlkgbkxTa83TwG8PA99dL+5ODAAJs4HL35V2h3qlam/5/1wNoekIM4hapDaDfEKAgLbu+c7RD4ibhNZa8wyw6iEPziB1FW6OMIOoRWoHj27NIDs3FNRmUHXNU6kJVwFXvMcMUhFmELVIXQYFW/ZKcrUGGfScmEFLrrPNINZBqsIMohapzSDvICAwwj3fae+mpvoZ1PtK4HLWQUok+X+RJ554Atdddx169uwJLy8vDBw4EAsWLMC8efMAABkZGQCAyMhIm5+LjIysO5aRkYGICNs/wEajEWFhYXXn1Ldo0SKEhITU/YqLi5P6t0ZaU1UGFKSK7fCu7r3CN/YhYNLTlte7PwWqSsV2j5nA7I8BvcF9/ZFT7CAANf/uVRCajjCDqNmqysUd4QHx7it3ZtCYB8Ud3mvt+cxzMyiGGUQeqroCyJcrgxbY3lCw5zOgqkRs97gUmP0JM0hlmEHUbNUVMtZBCxrPIIPRff2RE8di5KnkzKD6NzVZZ1D3GZ6VQTEDAV3N1LQKMkjySfTvv/8e3377LZYsWYJ9+/bhyy+/xOuvv44vv/xS6q+y8eSTT6KgoKDuV0pKiku/jzQg7xzqNmtw9fpX9ox7BJj4lO17XSYDcz8HDF7u749cfEOAdj3FduZhoLJU3v60EDOImu2izBk09mFgUv0MmuSBGRQMRPQS2xnMIPIgNnWQGx5hrq/+DQUA0HkiMIcZpEbMIGq2i+ct+0DJUgcxgwAAPkFARG+xnXkEqCyRtz8txAyiZpM7g+rf1ASIGTT3C8Do7f7+yEVlGST5pY1HH3207m50AOjbty+SkpKwaNEi3HLLLYiKigIAZGZmIjo6uu7nMjMzMWDAAABAVFQUsrKybD63uroaeXl5dT9fn4+PD3x8fKT+7ZCWuXv9K3vGPyoWaVveALpMBK78EDB64J/j9kOA7GOAuRq4cEDcYEJlmEHUbO5eB9SecY8CBm9g8+tA5wnAVR95bgZlHRU38bmQKG72pzLMIGo2d26m5ci4R2oy6D9A5/HAVR8DXr7y9EVOzCDyREoYizGDRO2HAFlHxAxKTwQ6jpa7R83GDKJmU8JYbOzDYgZteq0mgz7y3AzKPCxe1EjfD3QcI3ePHJL8TvTS0lLo663bYzAYYDaLV3g6deqEqKgorFu3ru54YWEhdu7ciZEjxYmzkSNHIj8/H3v37q07Z/369TCbzRg+fLjUXSZPpYTQBMTHCR8/D1zzFeDtL18/5KSydbCIJKGECSxAfJzw8fPAtV8zgwBmEHkOxWTQ/TUZ9A0zCGAGkedgBikHM4g8kVIyaNR9VhkUIF8/5KSiDJL8TvTLLrsML730Ejp06ICEhATs378fb775Jm6//XYAgE6nw4IFC/Diiy+iW7du6NSpE55++mnExMTgyiuvBAD06tUL06dPx1133YUPP/wQVVVVmD9/Pq677jrExMRI3WXyVHlnLG05QxPwnHU/HVFRaBJJJtc6g2S8kAcwg5hB5InymEGKYZ1BKbvk6weRO7EOUg7WQeSJmEHKYZNBe+TrRxNIPon+7rvv4umnn8Y///lPZGVlISYmBvfccw+eecay1s9jjz2GkpIS3H333cjPz8eYMWOwevVq+PpaHlv49ttvMX/+fEyePBl6vR5XX3013nnnHam7S55MSaHp6dr1EHfErixSfGgSScY6g+R6jJlEbXsAPsFARSGQshsQBPduLkQkB2aQclhnUCoziDwEx2LK0bY7M4g8D+sg5QjvBviEABUFis8gySfRg4KCsHjxYixevNjhOTqdDgsXLsTChQsdnhMWFoYlS5ZI3T0ii9rHdwIjxc0MSD56g7gz/LlNQFE6UJAKhLSXu1dErlWbQQER4sZyJB+9XsygsxuB4gwxg0Lj5O4VkWsxg5TDJoMymUHkGeoyqB3gGyJvXzydXg/EDgbObqjJoBQgtIPcvSJyrdoM8m8L+IXK2hWPp9cD7QcDZ9aLGZSfDLSJl7tXdkm+JjqRKpQXACXZYlvupVxIFDfM0uZjhKR15YVASc0G2swgZWjPDCIPUlEkDlIAZpBSMIPIk1QUiRetAWaQUnAsRp6kopgZpDQqqYM4iU6eiY8PKo+K1sEiajUlrUVMImYQeRKbOqizfP0gC2YQeZK8s5Y26yBlYAaRJ1HS/ngkUkkGcRKdPBPXv1Ke2CGWtoKvPBJJghfylKc9M4g8SO0jzAAHj0rBDCJPYp1BHIspQ+xgS5sZRFpnUwfxZgJFiB1kaSs4gziJTp6Jg0flCQgHwmr+AktPBKorZe0OkUsxg5THP8wykL9wAKiukLc/RK6UyzuwFIcZRJ6EGaQ8/mGW/xbMINI6ZpDy+IeJG4wCQMZBxWYQJ9HJM/HxHWWqfYTHVAFkHpK3L0SuxMJNmawzKOOwvH0haqGf9qfii63ncCi1AFUms/2TWAcpEzOIPAVvJlCmugyqBDI4FiMN41hMmawz6MJBefviACfRyTPVFW46IKyTrF0hKypZB4uo1awzqA0zSDG4nAJpwBfbkvDcr0dx2Xt/o+9zf+Kaj7bj1dXHseZoJkorq8WTmEHKZJNBu+TrB5Gr1U1gcSymKKyDyFPYLCnF5VwUQwUZxEl08jyCYCncQuMAo4+8/SELFYQmUatZZ1BIHODlK29/yMLmQh4ziNSnvMqEo+kFVq/N2HUuDx9sPIO7vtqDgrKqmgyqGTwyg5SFGUSeQBCA3FNiO6Q94OUnb3/IghlEnsA6g4KZQYqiggziJDp5npJsoKJQbPPRHWWJ7AMYawbzCg1NolYryQEqaia5uKmoskQmAMaaQpoZRCp0KK0AVSbB7rHoEF9Eh/gBpblAOTNIkZhB5AlK85hBShXBDCIPwAxSrojegJe/2FboygScRCfPw/WvlMvgBcQMFNsXzwPF2bJ2h8gluBaxcllnUH4SUJwlb3+Immlf0kWHxwZ1aCM2WAcpl00GJQNFmfL2h8gVuB66chmMQOwgsc0MIq3iWEy5DEYgpiaDCpKBogx5+2MHJ9HJ87BwUzYVPMJD1CrMIGWLYwaReu11NokeXzuJbp1BvANLcawzKE2Zd2ERtQonsJSNYzHSOo7FlE3hYzFOopPnsdlEgoNHxWHhRlrHCSxlYwaRSgmCgH3J+Q6PD+oQKjY4eFQ2ZhBpHcdiysYMIq3jWEzZFJ5BnEQnz8PQVDaFhyZRqzGDlC3WeoNj3gVK6pF6sQw5xRV2j3kb9UiICRFfMIOUjRlEWscMUrb2zCDSON5MoGwKr4M4iU6eJ++s+E+9FxDaQd6+UEPB0eIu2QCQtg8wm+TtD5HUcq0yKIQZpDjB0UBInNhO2weYquXtD1ETOVvKpV9sCLyNNWV/XR1kZAYpETOItK52Xwa9EQiNl7cv1FBQlOXvhnRmEGlQrlUdxPkg5QmKtPx3UWAdxEl08ixms6VwC+sM6A3y9ofsq70DoqoEyDomb1+IpGQ2W9YCDeskbp5CymOdQdnMIFKHfclNWA/dug5qwwxSLJs66Ki8fSGSktlsuZDHDFKuugwqZQaRtliPxdp0FDf0JuWpXZ2gugzIOiJvX+rhJDp5lsJUwFTzqDMfH1QuLulCWlWYBlSXi22uA6pczCBSIaebinaomUQvShcHJAAfYVYyZhBpVdEFcWIW4FhMyZhBpFXWGcSxmHIpOIM4iU6ehWvwqYNNaCpvHSyiFmMGqQMziFSmtLIaxzOKHB4fFB8qNphB6sAMIq3iWsTqwAwirWIGqYOCM4iT6ORZah9hBhiaShbdT1wvGlDclUeiVsljBqlCFDOI1OVASgFMZsHusbgwP0QE+YovbOogTqIrFjOItIoX8tQhuh9g8BbbzCDSkjzWQaoQ1VexGcRJdPIsnERXBy8/MTgBIOcEUOb4EXUiVWEGqYOXrziABICck0Bpnrz9IWqE0/XQa5dyAZhBamGdQbmnmEGkHbXroQPMICUz+ogX8wBmEGkL6yB1MPoA0f3Fdu5pRWUQJ9HJs1jf/cA1sJQtbpilnbZXvn4QSYl3YKlHe+sM2idfP4iaYF9T1kMH+BizmjCDSIs4FlMPjsVIizgWUw/rOkhBS7pwEp08S+3jO14BQFCUvH0h5xS8DhZRi9Xe/eDlDwRFy9sXcq79EEtbYY8RElkTBMHpneiD460m0fOYQarBDCItqp3AYgYpHzOItKh2LGb0A4Ji5O0LOafQDOIkOnmO6krgYpLYDu8C6HTy9oecU2hoErWYqQq4eF5sM4OUT8G7whNZO5dTgoulVXaP+XkZ0DMqSHxhnUFhzCDFs8mgXfL1g0gqpmrbDNJzKkLRWAeR1piqgYvnxHY4M0jxFJpB/FNDniM/CRBMYpuPMCtfaDwQ0E5sp+4BzGZ5+0PUWheZQaoS2gEIiBDbacwgUq59yfkOj/WPC4HRUFPu5ycD5mqxzUeYlc86g1L3MoNI/fKTmEFqEhIHBEaKbWYQaQEzSF1C2gOBNatHpCkngziJTp6D61+pi05nufpYnm+7kzaRGnEdUHWxyaAC2/9+RAqyl+uha5N1BlUUiJv7EamZzYZ+rIMUjxlEWmOdQRyLKZ9OZ1mdoKIQyDkpb39qcBKdPAd3YlYfLulCWpLHDFIdZhCpwP6mrofOCSz1YQaRlvBCnvowg0hLOBZTHwUu6cJJdPIcLNzUR4GhSdRizCD1YQaRwhWWV+FEZpHD4wN5J7q6MYNIS5hB6sMMIi1hBqmPAjPIJZPoaWlpuPHGGxEeHg4/Pz/07dsXe/bsqTsuCAKeeeYZREdHw8/PD1OmTMGpU7aPB+Xl5WHevHkIDg5GaGgo7rjjDhQXF7uiu+QpbJZS6CxfP6jpYgYCupqYUkhoErUYl5RSH5sM2uP8XCIZHEjJhyDYP9apbQDCArwtb3DwqD7MINIS3gWqPswg0hLWQeoTMwDQGcS2QjJI8kn0ixcvYvTo0fDy8sIff/yBo0eP4o033kCbNpY7YV577TW88847+PDDD7Fz504EBARg2rRpKC8vrztn3rx5OHLkCNasWYNVq1Zh8+bNuPvuu6XuLnmS2seY/cIA/zB5+0JN4xMERPQW25lHgApeSCMVq8ugNswgtfAJBCISxHbWEaDC8R2/RHLYl5Tv8JjNeugAM0iNbDLoKDOI1K02g3xDmUFq4R0ARDKDSCPqMiiEGaQW9TOovFDe/sAFk+ivvvoq4uLi8Pnnn2PYsGHo1KkTpk6dii5dxLvuBEHA4sWL8dRTT+GKK65Av3798NVXXyE9PR0///wzAODYsWNYvXo1Pv30UwwfPhxjxozBu+++i6VLlyI9PV3qLpMnqCwBimr+7PCqo7rUPsIjmIH0/fL2hailKkuBwjSxzQxSlzhmECnXXifroQ+KD7W8qCoDClPFNjfTUhdmEGlBVRlQkCK2WQepS/th4j8FM5C2T96+ELVUVRlQUFMHhXcVN60kdYirySAIQLr8GST5JPrKlSsxZMgQzJ07FxERERg4cCA++eSTuuPnzp1DRkYGpkyZUvdeSEgIhg8fju3btwMAtm/fjtDQUAwZYtnIYsqUKdDr9di5c6fUXSZPkHfW0mbhpi4KXAeLqNmYQerFDCKFMpuFpm8qygxSL+sMStklXz+IWoMZpF6sg0gL8s4BqFn/jhmkLgrLIKPUH3j27Fl88MEHeOihh/Cvf/0Lu3fvxv333w9vb2/ccsstyMjIAABERkba/FxkZGTdsYyMDERERNh21GhEWFhY3Tn1VVRUoKKiou51YaH8t/mTgtisf8X10FXFJjSVsQ6WPcwgcspmTwbeBaoqzCBSqNPZxSgqr7Z7LNDHiG4RQZY3uA6oejGDSAtyrddDZx2kKswg0gKOxdRLYRkk+Z3oZrMZgwYNwssvv4yBAwfi7rvvxl133YUPP/xQ6q+ysWjRIoSEhNT9iouLc+n3kcpw8Khe4V3FdcsA8cqjox3UZMYMIqe4qah6hXUR128FmEGkKPuSHN+FPiAuFAa91aPKzCD1YgaRFjCD1CucGUQawAxSr7DO4n4+gCIySPJJ9OjoaPTu3dvmvV69eiE5ORkAEBUVBQDIzMy0OSczM7PuWFRUFLKysmyOV1dXIy8vr+6c+p588kkUFBTU/UpJSZHk90MakctHCFVLrwdia5Z2KskC8pPl7Y8DzCByio8xq5deD7SvzaBsID9J3v44wAzyPPucrodef1NR6wzi4FFVrDOoNAe4eF7W7jjCDCKnbO5EZx2kKjqd5U5QZhCpVR4zSLVsMigXuHhO1u5IPok+evRonDhxwua9kydPIj4+HgDQqVMnREVFYd26dXXHCwsLsXPnTowcORIAMHLkSOTn52Pv3r1156xfvx5msxnDhw+3+70+Pj4IDg62+UVUx+bxHS7nojoKWwfLHmYQOcUMUjeFPUZoDzPI8+x1cif6oA6htm/wMWZ1YwaR2jGD1I0ZRGrHJaXUTUEZJPkk+oMPPogdO3bg5ZdfxunTp7FkyRJ8/PHHuPfeewEAOp0OCxYswIsvvoiVK1fi0KFDuPnmmxETE4Mrr7wSgHjn+vTp03HXXXdh165d2Lp1K+bPn4/rrrsOMTExUneZPEFt4RYUA3gHyNsXaj4FhSZRi9RlUDTgEyhvX6j52ls2OlfqhTzyLPmllTiTXeLw+MAO9e9EZwapGjOI1K72LtDAKGaQGjGDSO1q66DASMAnyPm5pDwKyiDJNxYdOnQofvrpJzz55JNYuHAhOnXqhMWLF2PevHl15zz22GMoKSnB3Xffjfz8fIwZMwarV6+Gr69v3Tnffvst5s+fj8mTJ0Ov1+Pqq6/GO++8I3V3yROU5gFleWKbVx3Vqf1gS5uFG6lN2UXx0TOAjw+qVSwziJRlf3K+w2PdIgIR4udleaMsX3wEH+AdoGrFDCI1K8sXl0MDWAepFTOI1Ky8gBmkdrGDAegACLJnkOST6AAwa9YszJo1y+FxnU6HhQsXYuHChQ7PCQsLw5IlS1zRPfI0XItY/fzaAG27AzkngQsHgKpywMu38Z8jUgKuRax+fm2Atj2AnBPAhYPMIJKds/XQB9dfDz2PjzCrnnUGZRwEqsoALz+5e0XUNMwg9fMLBdr1BLKPM4NIfbiUi/r5htRk0DEg45CsGST5ci5EimOzEzMn0VWrdkkXc5VYvBGpBTNIG6wz6MIBeftCHs/5euj1l3LhZlqaUJdB1cwgUhdmkDbULqfADCK1YQZpg3UGpSfK1g1OopP22Uxg8cqjalmvg5WyU75+EDUXN9PSBmYQKYTJLOBASr7D44PiQ23f4IU8bWAGkVrxLlBtsN6jihlEasKxmDYoJIM4iW6lsLwK6fllcneDpMYrj9rQYZSlffJP+fpB1Fx5zCBNiLfKoFN/ydcPF2IdpA4nMopQUmmyeyzEzwud29bbtI8TWNpgnUEntZlBpFG8kKcNHbSfQayDNIpjMW1QyFiMk+gABEHAqoPpmPzGJjz0fSIEQZC7SySl2sJNZwBC4+XtC7Vcux5AWGexnbRN3DCWSA3qMkgPtOkoa1eoFdp2t9y9krRVUxkkCAJ+O3gBU97YhAeXsQ5Sur1O1kMf2CEUer3O9k1mkDZYZ1DyNqAkV97+EDUVM0gb2nazTEBqLIOs66AFrIO0p+5Cng4I6yRrV6gV2nYDwruJ7eTtsmWQx0+iJ+eW4tbPd2P+kv3ILqrAjrN5+GFfmtzdIqkIguUOrDbxgNFb3v5Qy+l0QI9LxbZg0uydoKQx1hkUygxSNZ0O6FmbQWbNPBGTkleK277YjXuX7ENWUQV2nsvDir2pcneLnNjvZD30wfXXQ7fJoA6A0ceFPSOXqp9Bp7SRQaRxzCDtsBmLaSeDUvJKcbtVHbTrXB6Wsw7SDmaQttiMxVbL0gWPnUSvrDbj/Q2ncclbm7DpZLbNsZd/P4aLJZUy9YwkVZQBVJWIba5/pX49Z1rax3+Trx9ETVWcCVQWi20uo6B+Pawy6IS6M6jKZMZ/N4p10MYTDeugPNZBiuXsTvRB8fUm0YuzgMoisc1HmNWvB+sgUpmSbEsGcSymfhoai1WZzPhg4xlc8tYmbKhXBy1iHaQdJdlARaHY5lhM/WzGYr/L0gWPnUT/ePMZ/OfPE6ioNjc4lldSiUV/HJOhVyQ5rn+lLXHDAf9wsX16HVBVLm9/iBrDPRm0JW4Y4N9WbJ9eD1Spd93MjzefxWurT6C8qmEddLG0Cot+Zx2kRDnFFUjKLbV7TK8D+seF2r5pXQdxAkv9rDPojLoziDwE10PXlvZDNZNBn2w5i1dXH3dYB73MOkgbOBbTlvZDgIB2Yvv0OqDSfk3sSh47iX7LqI6IDHb8KMf3e1Kx86x21vnyWDaFGwePqqc3AN1niO2qEuDcJnn7Q9QYDh61RW8AekwX21UlwFn1ZtAtozoiKtjX4fHle1Oxg3WQ4uxPznd4rEdUMAJ9jLZvMoO0xSaDSoGzG2XtDlGjmEHaojcAPWrHYurOoJtHdkR0iOM6aMXeVGw/wzpI9ZhB2mKdQdVlsmSQx06iB/l64bnLEpye86+fDqGi2uSmHpFLMDS1p3YdLED1jxGSB+CFPO3RyJIugT5GPHe58zro36yDFGevk/XQB3UIbfgmM0h7uKQLqQkzSHs0sqRLk+qgn1kHqR4zSHtkHot57CQ6AEzvE4VJPSMcHj+TXYKPN511Y49IcjaP7zA0NaHzRMDoJ7ZPrgbMDR/BI1KMXC6loDmdJ1gy6IS6M2haQiSm9HJeB33EOkhR9jlZD31w/fXQAdZBWmSdQSdXA2ZO8JCCMYO0p/MEwMtfbKs8g6YlRGFKr0iHx89ml+DDjayDVM16Ep1jMW3oPN6SQSfcn0EePYmu0+nw/OUJ8PMyODzn3Q2ncT6nxI29IknVFm4GHyC4vbx9IWl4+wNdJort4kwgba+8/SFyJs8qg0KYQZrg7Q90mSS2S7KAtD3y9qcVdDodnmukDnpvw2mcYx2kCFUmMw6m5js8PqiDk0l0gzcQEueajpF72WRQNpCq3gwiD8AM0h4vP01l0PNXJMDf23Ed9P5G1kGqlldzEUTvBYR2kLcvJA3rDCrNAVJ3u/XrPXoSHQDiwvyxYEo3h8crq814+pfDEATBjb0iSZhNltAM7wLoPf6Pu3bYPEa4Sr5+EDljnUFhncU13EgbNJRB7dv446FLujs8XlltxtM/sw5SgmMXCu1ugAYAYQHeiA/3t32TGaRdGsog0jBmkHZpKINiQ/0arYOe+vkQ6yA1MpstF/KYQdrSc5al7eYM4qwigNvHdELPqCCHx7ecysHKA+lu7BFJIj8ZMFeJbT4+qC3dpwO6mvg68bu8fSFypCAFMFWKbWaQtlhn0HH1Z9BtozuiV3Sww+N/n87BL4msg+S2z+l66G2g0+ls3yxIBUwVYpv7wmgL6yBSA2aQdnWbpqkMunVUR/R2UgdtPZ2LnxPT3NgjkkQhM0izuk8DdDUXRY7/DrjxIhcn0QF4GfR46aq+qD/2sPbCqqMoKK1yX6eo9bgWsXYFtAXihovtnJNAzil5+0NkDzey0a6AcCBuhNjOPQVkn5S3P61kNOjx8lV9nNZBL/52FPmlle7rFDWwNznf4bFB8aEN37RZB7Sz5P0hGdlk0GnVZxBpVJ71WIwZpCkB4UCHkWJbAxlkNOjx8mzn80EvrjrGOkhtbMZizCBN8Q+zZFDeGXFOyE04iV5jcHwb3DDM8RpJOcWVeGX1cTf2iFrNunDjlUft6XGppa3ineFJw3KtNiJiBmlPT6sMkmFneKkN7NAGNw6Pd3g8p7gSr7IOkpWzO9EH21sPPY8ZpGnWGaTy5RRIo3I5FtO0HtrKoAFxobhphOM6KLekEq/8wTpIVZhB2iZTHcRJdCuPTe+JtoE+Do9/tysZe5Py3NgjqlVtMuPYhUKYzc14TMPmyiNDU3Os1+LTwGOEpEHMIG2zGTxqI4Mend4D7YKc1UEp2HOedZAcMgvLkZZfZveYUa9Dv/ahDQ8wg7TNOoNYB5ESMYO0raf2MuiRaT0Q4aQOWro7BbtZB6kHJ9G1TaaxGCfRrYT4eeGZy3o7PedfPx5Glcn+pk7kOgfTCjDj7S0Y9vJa3P/dfny/O8XhYLIOl1LQtvAuQLueYjtlF1CcJW9/iOqzWUqBGaQ54V2Adr3EdupuTWRQsK8XnpnlvA7690+sg+Tg7C703jHB8PO2s1kWJ7C0zSaD9gBFmfL2h6g+jsW0LawzEFFTM2gkg4J9vfDsZQlOz/n3T4dQWc06SBU4FtO2sE5ARM3/r2l7gKIMt3wtJ9HruaxfNMZ2a+vw+InMIny65Zwbe0QAsPVUDgDxcfKVB9Lx2A8HMfqV9Zj4+kZ8syPJ/g/VXnn0CQYC2rmpp+RWdVcfBeDkalm7QtRA7ZJS3kFAYIS8fSHX6GmVQSf+kLUrUpnVLxrjuzv+O/NEZhE+2XLW4XFyjX3JzjcVtau2DvIOZAZplXUGndRGBpGG2GRQpLx9Idfoob0MurRvFCb0cFwHncwsZh2kFrVjMa8AIChK3r6Qa9g8EeOeDOIkej06nQ4vXtkHPkbH/2reXncSKXmlbuwV/X06x+7753JKUGHvSnB1BZCfLLbDu8DpLiGkXj1nWdpcF52UhBnkGayXldJIBul0OrxwhfM66J11p5CcyzrInfY6uRN9YIfQhm9WVwL5NTcZMIO0S4MZRBrBDPIMGsyg2jrI14t1kKpVVwIXmUGaJ0MGcRLdjvjwANw/uZvD4+VVZjz9y2EIQjPW56YWK62sdnoHlt0nB/LOAaj578NHmLUrZiAQWHNV+exGoLJE1u4Q1bl4HhBqLvAxg7QreiAQFC22z24EKopl7Y5UOoT744EprIOUorSyGofTCh0eHxxv5050ZpBnsMmgTZrJINIAZpBniBkIBMWIbQ1lUFyYPx6Y3N3h8YpqM55iHaRs+UmAYBLbzCDtih4ABMeK7XObgIoil38lJ9EduGtsZ3SLCHR4fOOJbPx+yD1r7ni6XefyUGWy/xdUuyAf+/+dzm2ytMMdTwSQyun1QI8ZYru6HDizXt7+ENU6a5VBbZlBmmWdQaYKTWXQXWM7o3uk4zpo08ls/Hboght75Ll2n7+ISgfr0EcE+SA21K/hAdZBnqFBBq2Ttz9EtZhBnkGn02wG3Tm2E3pEBjk8vvlkNlYdZB2kWGc3Wtoci2mXTQZVAqddn0GcRHfA26jHy7P7Oj3n+V+PoLC8yk098lxbHSzlAgBjuraFrv6jOWYzsOtjy+tes0AaZvMIjzZ2hieVq59BPZlBmtbDKoNOaCeDvAx6vHxVY3XQUdZBbuCsDhrVJbxhHSQIrIM8SQ/WQaQwzCDPYr0msYYyyMugx8uz+zg9Z+GqoygoYx2kOIIA7PrE8ppjMW3rYb0uuusziJPoTgztGIZrh8Q5PJ5VVIE3/zrpxh55pi2nHA8eR3e1s5TL2fWWnZg7jgUine+wTSrXaZy4YREgbmhjqpa3P0RnNwC5p8R2/BggynkBTirXaay4eSwgbnCsoQwa0jEM1w9zXAdlF1XgjT9PuLFHnunvZtdBG4Ccmvo0fjQQ5fxiCKlcgwzihA7JjBnkWTqOA3yCxbbGMmhwfBiuH9bB4fHsogq88RfrIMU5uxHIqfnv0mEUEN1P1u6Qi3Uc69YM4iR6I568tCfCA7wdHl+yMxn5pZVu7JFnyS6qwPEMx+saje4a3vDNnVZ3Pgy/xwW9IkUx+gBdp4jtsotAyg55+0O0ixnkUYw+QDerDEreLm9/JPb4dOd10He7UnCxhHWQq+QUV+DoBcfrodudRGcd5FmsM6g8X3MZRCrEDPIsRm/LWEyDGfTE9J5oG+isDkpGHusgZeFYzLMYvYFul4jt8gIgaZtLv46T6I0I9ffGv2f2cni80mTG2mNZbuyRZ9l2xvHdV13aBSA6pN46oLlngFN/ie2QOKD7DBf2jhSDS7qQUuSdBU7+KbaD29s+XkbapdElXQCxDnpqVmN1UKYbe+RZtp3JdXisc7sAxNRfDz3vnHgXDlCTQTMb/iBpD5d0IaVgBnkmDY/FQvy98PSs3g6PV5kErD3KOkgx8s4BJ/4Q28GxXMrFU7hxSRdOojfBVQNjMaqLnTuea6w+zA1GXaWx9dAb2P0pgJpNSIfeARiMrukYKUu3SwCdQWwfXyWug0Ykh13MII/U7RJAX/PfWoMZdOWAWPtPftX48wjrIFfZ6mQpl8broNuZQZ7CJoN+01wGkYowgzxT1ymazqDL+8dgbDc7f+fWWM06SDmsM2gIM8hjdLsE0HuJbRdnkMsn0V955RXodDosWLCg7r3y8nLce++9CA8PR2BgIK6++mpkZtpevUtOTsbMmTPh7++PiIgIPProo6iulmedUZ1Oh7vHdXZ4fPOpbJRUaGcNVKUQBKF564BWFAP7vxHbRl9g0C0u7B0pil8boOMYsZ2fBGQdlbc/5JmYQZ7LL9Qqg5KBzCOydkdqYh3UxeHxzadyUMw6SHKCIOBvJzcT2K2D9n0ttg0+wKBbXdc5UhbrDCpIBjIPy9od8lDMIM/lFyquSwxoMoMamw/6+1QOirjRuvwqS4D9Vhk0+FZZu0Nu5Bsi7hEDAAUpQMYhl32VSyfRd+/ejY8++gj9+tku5P/ggw/i119/xfLly7Fp0yakp6dj9uzZdcdNJhNmzpyJyspKbNu2DV9++SW++OILPPPMM67srlOjurRFkI/9q1iV1WZsOpnt5h5p3/ncUqQXlNs9ptcBI+o/HXDgO6CiZt3QvnMB/zAX95AURcOPEZJKHFwKVBSI7b5zgADHd+6SBml4SRcAGNk5HEG+TuqgE6yDpJaUW4q0/DK7x/Q6YETnehlzcJlVBs1lBnkaLulCcmMGeTaNj8VGdA5HsKM6yGTGRtZB8ju4TFwTG6gZizl+eoA0yE1LurhsEr24uBjz5s3DJ598gjZt2tS9X1BQgM8++wxvvvkmJk2ahMGDB+Pzzz/Htm3bsGOHuCHgX3/9haNHj+Kbb77BgAEDMGPGDLzwwgt4//33UVkpz6YN3kY9JvWKcHicS7pIz9ndV/3jQhHs62V5QxCAXZ9YXnMDCc/Tw2r9+xO/ydcP8kz1M2gYM8jjWGfQce1lkLdRj8k9ndRBfJRZcs7qoH7tQxHi56wOutuFPSNFYh1EcmIGkcYzyMugx5RekQ6Psw6SmSDYbmo8jBnkcawn0V04FnPZJPq9996LmTNnYsqUKTbv7927F1VVVTbv9+zZEx06dMD27eJOztu3b0ffvn0RGWkJqWnTpqGwsBBHjth/RLqiogKFhYU2v6Q2PSHK4bH1x7NQUW2S/Ds9mbN1QMfWf4T57EYg54TYjh8NRPV1XcdImUI7AFE1T72k7wcK0tz69e7IIFKwc5uA7ONiu8MoILqf8/NJe0LjgOj+YvtCIlCQ6tavd0sd1MdJHXQsE+VVrIOk5GxfmAZrs57bDGQfE9sdRlr+LJLnsMmgA0B+ilu/nnWQh2MGUUh7IHqA2NZoBk1zUgdtOJ7FOkhO57dYMihuBBAzQNbukAxCYoGYgWI746C4xKYLuGQSfenSpdi3bx8WLVrU4FhGRga8vb0RGhpq835kZCQyMjLqzrGeQK89XnvMnkWLFiEkJKTuV1xcnAS/E1vje7SDj9H+v7LiimpsO50r+Xd6KpNZwLYzzVgHdOdHljavOnqunvItp+CODCIFs84g3n3luWyWdPnDrV/tjgwa170dfL3s10EllSanf29T84h1kOO6knUQ2aXxDCIFYwYRUG8spr0MGtetHfy8DHaPlVaanO7nRi7GsRgBbqmDJJ9ET0lJwQMPPIBvv/0Wvr6+Un+8Q08++SQKCgrqfqWkSH/l09/biPHd2zk8ziVdpHM4rQCF5fY3KfPzMmBgB8sSQcg7B5xcLbaDY4Ges9zQQ1IkN62DZY87MogU6uJ5y1/SQTHMIE/W0z2PEdrDOkhbjqQXoKDM/iZlYh0Uannj4nngpFUG9brM5f0jhbLOIDcvp8A6yIMxg6hWD21nkJ+3ARN6OKmDuKSLPC4mWcb+QdFAr8vl7Q/Jxw1jMckn0ffu3YusrCwMGjQIRqMRRqMRmzZtwjvvvAOj0YjIyEhUVlYiPz/f5ucyMzMRFSU+HhMVFYXMzMwGx2uP2ePj44Pg4GCbX67g7FHmNccyUW0yu+R7PY2zdUCHdw6Dt/UTAbs/BSCI7aF3AAb7G36QB4jqC4R0ENvntlg2FnEDd2UQKdCuT2CbQV5OTycNi+xjyaDzW4CyfLd9tSLqoKOsg6SyxcndbMM6hcHHaHUn3O5PAaHm3/vQ25lBnswmg/7WZAaRAjGDqFZkgrjEJqDZDHJWB63lfJA8rDNoCMdiHi2iNxAaL7aTtgJlFyX/Cskn0SdPnoxDhw4hMTGx7teQIUMwb968uraXlxfWrVtX9zMnTpxAcnIyRo4cCQAYOXIkDh06hKysrLpz1qxZg+DgYPTu3VvqLjfL5J6RMOp1do/llVRiT5L0/5E8kbNHocZYP8JcWQLs/1psG3yAQbe6tmOkbDqd5eqjuQo4tUbe/pD21c+gwbfK2h2SmU5neZTZXA2cXitvf1xgkpM66GJpFXadz3Nzj7TJ2XrotnVQKbDPOoNuc3HPSNHqZxDrIHI1mwzy5ljM0+l0licyNZpBE3tGwMtgvw7KL63CrnOsg9yqshTY95XYNnhzLObp3JBBkk+iBwUFoU+fPja/AgICEB4ejj59+iAkJAR33HEHHnroIWzYsAF79+7FbbfdhpEjR2LEiBEAgKlTp6J379646aabcODAAfz555946qmncO+998LHx0fqLjdLiL8XRnYJd3icjzK3XlmlCXudXIywWQf04DLL3cZ95wABjv/bkIeQcUkX8kAHv7dkUJ+rgYC2zs8n7ZNxSRd3CPHzwqj663Fb+ZN1UKuVVZqw53wT66BD3wPl+WKbGUSArEu6kAeqn0GBjpe6IA8h45Iu7hDs69VwXxIrXNLFzQ4tt2RQwmxmELl8LOaSjUUb89Zbb2HWrFm4+uqrMW7cOERFReHHH3+sO24wGLBq1SoYDAaMHDkSN954I26++WYsXLhQju42MC3B8SM8fx7JgCAIbuyN9uw+n4dKB49BtQ30Ro/IIPGFIAA7P7Yc5CY2BADxowDfELF9ag1QXSlvf0i7BIGb2FBDHUYBvqFi+9QaoLpC1u64wrSESIfH/jySCbOZdVBr7ElyXAeFB3ijZ5R1HcQMonpsMmitJjOIFKJBBt0jX19IOTqMBPxq9i/TaAY1Nh/EOshNWAeRPXEjLBl0WvoMcssk+saNG7F48eK6176+vnj//feRl5eHkpIS/Pjjjw3WOo+Pj8fvv/+O0tJSZGdn4/XXX4fRqIy1rqf2joTO/hM8uFBQjoOp7luHWYucPcI8qktb6GsfIz+3Gcg+JrbjRgAxA1zfOVI+gxfQbZrYrigU1yUmcoXzW6wyaDgQM1De/pAyGIxA95oMqizSZAZd4qQOyigsx8E01kGt4WxfmFFdreqg838DWUfFNjOIatXPoHPayyBSCOsMaj+MGUQig9EyFtNoBjmrgzILK5CYmu/W/nispK1A1hGx3X4oEDtY3v6QMhiMQPfpYruyWJw3lJAsd6KrXUSwLwZ3aOPwOB/haR1ng0ebdUB3Wd2FzquOZK3nTKBdL2Dsw0BYJ7l7Q1plfecDn4QhazYZ1Fnu3kguIsgXQ+Kd1EFc0qVVnK+HbrVs3S5mEDnQcybQrqeYQeHayyBSiF28C50c0HgGtQ30wdCOYQ6Pc2k7N7EZizGDyEptBo15SPKxmDJu7Vah6X2iHG4iuvpwBh6b1gM6R5cnyaG8kkocSS90eHx0t5pJ9ItJlvWug6KBXpe7oXekGr2vABKulLsXpGX5ybYZ1PsKeftDytLrcs3/mZiWEIXdDtbtXn34Ah6fzjqoJRqtg2pvJshPtqzzGBil+T9v1EwekEEks/oZxLEYWet1GdBb238mpidEOdxEdPWRDDwxoyfrIFfKTwGOrxLbgZH8O49s9Zwl5pAL8E70FnK2Dta5nBKcyip2Y2+0Y9sZx3dfdW4bgNhQP/HF7k8BoWa90CF3iEt4ENViwUKuZpNBtzODyJYHZJCzOuh8bilOZrIOaontZ3LhaGudTm0D0L6Nv/hi92eWDBrKOojq8YAMIplZZ9CQ2wGjt7z9IWXxgAya1sdxHZSUW4rjGUVu7I0H2sMMIidcmEGcRG+huDB/JMQEOzzOR3haxtkjzHV3X1WWAvu+EtsGb2Dwra7vGBFRLWYQEeLC/NEn1nEdxCVdWsbZknaja5dyqSoD9n0ptplBRORu1hmk92IGkUeKDfVDv/YhDo//ySV+XaeqDNhrnUG3ydsf8iicRG+F6U7uwuK66M0nCAK2nGrCJPqh74HyfLHd52ogsJ3rO0dEVOvQcqCsZhmLhNlAYIS8/SGSCesg6f19Otvhsbp9YZhBRCQn6wzqMxsIipS3P0QycfZUHm8mcKFDK4CymqV0Eq5iBpFbcRK9FaY7eYTnSHohUvJK3dgb9UvOK0XqxTK7x/Q6YGTncEAQgJ1WG4pyIy0icidB4KbGRDWc1UHHLhQiKbfEjb1Rv+TcUqTk2a+DdDpgZOe2DesgZhARuVODsRg38yPP5awOOp5RhPM5rIMkJwjc1JhkxUn0VugaEYjObQMcHucjPM3j7BHmvu1DEeLvBSRtBbKOiG+2HwrEDnJT74iIACRtAzIPi+3YIUDsYHn7QySjrhFB6NyOdZBUnNVB/WJDxDooeTuQeUh8kxlERO5mk0GDgfbMIPJcXdoFomtEoMPjrINcIHkHkFGTQTGDgPZD5O0PeRxOoreCTqdzuqEEH+FpHmfroY+pXQd0xweWN4f/n4t7RERUz47/WtrMICKnS7r8eSTTjT1RvybtC8MMIiI5MYOIbHBpOzdjBpHMOIneSs5Cc2/yRWQVlbuxN+plMgvYdibX4fHRXdsCp9cCx1eJbwRGAr0ud1PviIgAnF5nm0G9r5C3P0QK4OxR5r1JF5FVyDqoKcxmAVvPOLuZoC1wZj1w7FfxDWYQEbmbdQYFRAC9r5S1O0RK4KwO2p+cj4wC1kGSObMBOLZSbAe0AxKulLU75Jk4id5K/dqHIDrE1+4xQQDWHOVdWE1xNL0Q+aVVdo/5eukxKNII/LrA8ubEfwNGb/d0joioorheBv2LGUQEoG9sCGIc1EEA8CfroCY5esFxHeRj1GNQtBfw6wOWNyc8yQwiIvepKLbNINZBRACAhJhgxIb6OTy+5ijvRpdEZQnw6/2W1xP/BRh95OsPeSxOoreSTqfjrswScLYO6NCOYfDd/BJQkCK+0WkcMOhmN/WMiAjAuoVAQbLY7jgWGHSLvP0hUojGlrb7k3VQkzirg4Z1CoPv5peBfKsMGnyrezpGRAQA619gBhHZodPpnN6NziVdJLKuXgYNulXW7pDn4iS6BJyF5vYzuShwcGcRWThbB3RO2xRg1yfiC6MfcNk7gE7npp4RkcdL3gHs+lhsG/2Ay5lBRNacLW23/Wwu8ksr3dgbdXJWB13dNhXY+ZH4ghlERO6WvNM2gy57mxlEZMXZfNCOs3m4WMI6qFVSdgE7PxTbRl8xg/ScyiR58E+eBIZ2DEN4gP3H2arNAtYd56PMzpRXmbDrfJ7dYz6oxPSzLwMQxDcmPw2EdXJf54jIs1WVA7/Mh20GdZa1S0RKM8RJHWQyC1h7LMvNPVKX8ioTdp1zUgeds6qDJj3FDCIi96kqB1Za1UGT/g2Ed5G1S0RKM6hDG7QNtL+0iFgHcT6oxaorbMdiE5lBJC9OokvAoNfhkt6RDo9zSRfn9py/iMpqs91jj/v9Ap+CM+KL2CHcgZmI3GvTq0DuKbHNDCKyy6DXYWoC66CW2pd0ERUO6qBHfX+Bb/5p8UXsYGDEP9zYMyLyeJv/A+ScFNuxg4ER/5S3P0QK1Fgd9CeXdGm5zf8Bck6I7ZhBzCCSHSfRJeJsXfRNJ7NRWlntxt6oi6N1QBN053CL8Iv4Qu8FXPEeoDe4sWdE5NEuHAC2vi22mUFETk11UgdtPpWNkgrWQY5scVAH9dadx22wqoMuZwYRkRtdOAj8/ZbYZgYROeVsPmjzqRwUsw5qvoxDthl0xXuAwShvn8jjcRJdIqO6hiPQx/7/0BXVZmw6ke3mHqmHvXVAjajGa14fw4CaO7PGPwZE9HJzz4jIY5mqgF/uBQST+Hrco8wgIidGdQlHkIM6qLLajE0nWQc50qQ6aNwjQGRvN/eMiDyWqbpeHcQMInJmZOdwBPk6roM2nuDSds1Sm0HmmosPYx8GIhPk7RMROIkuGR+jAZN6Rjg8zkd47LtYUonD6QUN3r/bsAoJ+iTxRUQCMHqBeztGpDBllSZuUuxO294R734AxAwa86C8/SFSOB+jAZN6Oa6DuKSLffmllTiU1rAOusvwO/roz4svInoDYx5yb8eI3CSrsBxmsyB3N6i+be8AGQfFNjOIqFHeRj2m9HK2pAvXRW+W7e+KTwUDQLte4iQ6kQLwWQgJTe8ThZUH0u0eW3csC78dvID4cH90CPdHsK+Xm3unTNvP5kKoVzd30aXhAeOP4gudXnxsx2h/wzIiT7HmWCbu/24/Qvy8xBwJ80d8uD/iwwLQIVxsRwb5Qq/Xyd1V9cs+CWx8VWzr9MAV7zKDiJpgekIUfkm0XwetP56FVQfT6zIrxI91EABsP9OwDuqsS8cC4w/iC9ZBpGFms4Axr20ABKB9mB/iw/wRHx5gqXHC/dG+jT98vbiEiFvlnAI2viK2dXpxGRdmEFGjpiVE4af9aXaPrT+WyTqoqXJOAxsWiW2dHrjifWYQKQYn0SU0vns7+Bj1djeHKqqoxr1L9tW9buPvhQ5h/ugQHoD4MHFivVtEIPq3D/WoSbD666HrYcarXp/AR1fz2M7I+UDsIBl6RqQsybklAICCsiocTC3AwdSGdy76GPWIC/Ovy5T4MH/Etw3AkPg2COKFu6Yxm4GV8wFThfh65HxxIy0iatT4Ho7roOKKasxfsr/udai/V01WsQ6ypoMZr3p9DB9dzZNHI+9lBpFmZRaVo7ImL85ml+BsdgkA26WfdDogKtjXamJdnGTvGxuCjm0DZOi1/M5mF+O9Dafx0pV94ect8QUGsxlYeZ+lDhrxT6A9M4ioKcZ3bwdfLz3KqxrWQSWVpibVQf3ah8LgQXVQraLyKry55iRmD4hB3zXzmUGkWJxEl1CAjxHjurfDmqONP6pzsbQKF0sLcKDeRFhcmB/eu34Q+seFuqiXylJ/HdCbDGswRC/uAF8SEI+Aif+So1tEipOUW9roORXVZpzOKsbprGKb970Neiy4pBv+Mb4LdDrPK8qaZfcnQMpOsR3WGZjwpLz9IVIRf28jxndvh7+aUAfll1Yh304d1L6NH967YRAGeHAdNNS6DprAOoi0qym1jSAAFwrKcaGgHDvP5dkcG9utLRZfOwDhgT6u6qKilFeZ8N+NZ/DhxjOoNJkRFeyLx6b3lPZL9nwGJG8X2206ARP/Le3nE2mYn7cBE7pHYHUTlvJlHSQSBAG/HbqAhb8eRVZRBSKOf42+JbUZ1JEZRIrDNdElNt3JrsxNkZJXhju/2oPCcu2vfXw4rcCmeG6vy8ZjxqV1r82XvQ14+cnRNSLFScprfKDpSKXJjNdWn8AfXJPYuYtJwNrnLa8vfxfw9pevP0QqNL1P6+qg1ItluPPL3Sgo034ddCS9AOet6qBYZONxqzrINOttZhBpWnITJtGd2XIqB4//cFCi3ijbppPZmLZ4M95ZdwqVJvEu1483n8WpzCLpviQ/GVj7nOX15e8wg4iaSbI6yAP2wkrKLcEtn+/G/CX7kVVUgRjk4Kbizy0ncCxGCsRJdIlN7hUBL0Pr7vTMLqrAsl0pEvVIecqrTFi89iRmf7DN6l0BLxs/RYBOfGznd59LEdRzojwdJFKg1g40AeCddacg1F98l0SCAPz6AFAlLpuDIbcDHcfI2yciFZrcM7LVdVBOcSWW7U6WqEfKU1FtwttrT+Gq/9rWQYu8LHXQHz7TEdyLdRBpW1JeSas/Y+2xLBxJb7jEnVZkFpbj3iX7cMv/djW4c7/aLOCpnw9LU9sJAvDrAqCy5mnGwbcCnca1/nOJPMzEnhHwNrRumi2nuBLfeUAddMlbm7H5ZO0SXgJe9voMgbpyAEBZ3xuZQaRInESXWKi/N24Y1qHVn/Pd7mRNTnb9fSoHM97egsVrT9WtgQgAcwybMc5wCACQLoThRF/uvkxUq7zKhIzC8lZ/zvGMIrtrqROAxCXA2Q1iOzgWmPK88/OJyK4Qfy/MGx7f6s9ZujtFs3XQ9MVb8NbakzZ10NX6LTZ10LE+j8rVRSK3acpyLk2xbLf2bj6qNpnx+dZzmPzGJvx28ILD83aey8OP++xvZNgsB5YCZ9aJ7aAY4JKFrf9MIg8U4ueFeSNaPx+0TKN10NbTOZhhpw66Sv83JhgOAAAyhDZ4rvw6ubpI5BQn0V3gyUt74Zoh7Vu1IcTZ7BLsSbooYa/klVVUjvu/248bP9uJcznWd50IGKo7jqeNX9e986+qOzCsZ0e395FIqfJLq9A7OhiBPq3fxmKpBgearSIIQNI24E+rtc9nLQZ8g2XrEpHaPTGjJ64dEtfqOmj3ec+og4bojuNpL0sd9G/WQeQhgny9EBnc+vXMf9qfhvIqkwQ9UobElHxc8f5WPP/rURRXVDd6/ku/H0N+aWXLvkwQgKTtwOonLO/NegvwDWnZ5xERnpjRE9cNjYOxFXXQuZySBvtAqFlWUTkeWLof8z7dibP16qDBuhN4xqYOuh3LDhXi71M5DT+ISGbcWNQFfL0MeG1OfzwxoxeOpIvrfifnlSIpt6SuXVrZeKH33a5kDO0Y5oYeu47JLODbnUn4z+oTKLIqAsNQiKsMW3CdYSO66S13T/xoGoPk8DEY0Tlcht4SKVNUiC9+f2AsBEFAXkklkvJKkZxbiqTcUiTllYjtvFJkF1U0+lkrE9Pw1MxeCJBgQl4OKXmliAz2hbexldeAS3KBA98B+74Cck5Y3u93LdB9aus+m8jD+XoZ8OqcfnhiRk8cbkUdtHRXMoZ10kgd9OcJFJVb6qA2KMRsO3XQT6bRSAofg5FdWAeR9i2a3RdAX5RVmpBysaauyS2pyQsxK1IvlqLK5PxuzKLyavx+6AJmD2rvno67SEFZFf7z53F8uzMZTb0BVacDZvWLbv5Fy5Jc4OBSsQ7KPm55v+9coMf05n0WEdnwMRrwytX98Pj01tdBap8XMZkFLNmZhNfs1kF/41rDBnS3qoN+MY3COvNgjOgchuhQXzm6TOSU5LMoixYtwo8//ojjx4/Dz88Po0aNwquvvooePXrUnVNeXo6HH34YS5cuRUVFBaZNm4b//ve/iIyMrDsnOTkZ//jHP7BhwwYEBgbilltuwaJFi2A0qmfiJyzAG2O7tcPYbrbvC4KAnOJKJOeV4Of96fh6R5Ldn//90AU8e1kCQvy83NBb6R1MzcdTPx+uWz5CBzNG6Y/gesMGTNXvhrfO9i+OM+ZofOR7Jz6YN7hVd68RaZVOp0N4oA/CA30wqEObBsdLK6uRnFeKM1kluH/pfpjMDUdgJZUm/HbwAq4ZGueOLkuqpKIaN322E0G+Xlh83QB0aRfYvA8wm4Fzm4B9XwLHVgHmehv2hHcDpi2SrsNEHq5NE+qgXxLT8dV2+3XQb4cu4NnL1VsHHUotwL9/PtSgDrrOsAFT9Xvgo7O9w/SMORof+t7FOog8jp+3Ad0jg9A9MqjBMZNZQHp+GZLzSvHWmpMOn9RdujtFtZPogiDgl8R0vPjbMeQUN35DRK2+sSF46ao+6Nc+tGk/UFcHfQUcXwWY6t29Ht4VmP5K0ztORE45q4NySyqRlFuKlYlp+NJBHfT74Qw8X1qFEH/11kFP/XwIB6zqoJH6o7jesN5hHfSO1x1486r+uGpgLHQ61kKkPJLPSG/atAn33nsvhg4diurqavzrX//C1KlTcfToUQQEBAAAHnzwQfz2229Yvnw5QkJCMH/+fMyePRtbt24FAJhMJsycORNRUVHYtm0bLly4gJtvvhleXl54+eWXpe6y2+l0OrQL8kG7IB90ahuIZbtT6nZZt1ZeZcbKxDTcNLKj+zvZCiazgEW/H8NnW89BEIAIXMRcwyZca9iADvrsBufvNPfEUtMkhA6eg+9n9FPtYJlIbv7eRvSMCkbPqGD8kpiGv45m2j3vu93JqpxEf2HVUZyvWT915jtb8NTM3pg3vEPjBVbhBSDxG2Df10C+nSI1fjQw6Bag9+WAl58Lek5E1qzroM5tA7F0l/06qKLajF8S03CzSuug/209B3NNHTTHsAnXGjYiXp/V4Pyd5p5YZpqIoEFz8P2M/qodLBO5gkGvQ1yYP+LC/FFUXo09SXvtnrfrXB7OZBc3/wK7zHKKK/DA0v3Yejq3yT8T5GPEI9N64MYR8U274FZ4AUj8Ftj/NXDxfMPjHUYBg28Bel0OePs3vfNE1CI6nQ5tA33QNtAHXdoF4LvdKTbrg9eqrDbjp/2puHV0Jxl62XIms4BX/jiGz/4W66B2dfNBjuug700TEDhoDn6cMYB1ECma5JPoq1evtnn9xRdfICIiAnv37sW4ceNQUFCAzz77DEuWLMGkSZMAAJ9//jl69eqFHTt2YMSIEfjrr79w9OhRrF27FpGRkRgwYABeeOEFPP7443juuefg7e0tdbdlExbgjakJkVjlYMOY73al4MYR8aq5CicIAh5ZfgA/7U/DQN0p/NPrF0zS74dBZ3tHbI4QjBWmcfjeNAF+0T3x0lV9MSAuVJ5OE2nQdcPiHE6i70/Ox4mMIvSIanjHl1L9eSTDZj338ioznvr5MDYcz8Krc/qhbaCdNVVT9wBb3gBO/gkI9R6Z9G8LDLgBGHQz0LZbw58lIrdoE+CNaX2i8OuBdLvHv9uVgptUVgc9uvwAftyfhgG607jX6xdM1O+HUWc7OM4RgvGDaSyWmSbCN6onXrqqDwbaecKIiCwm94pA20Afh3drf787BU9e2svNvWq5gtIqXPPRdpzNLmn85BqX9Y/B0zN7ISK4CcscNFoHXS/eRMA6iEg2of7emNEnCr8k2q+Dlu5OwS2jOqqqDnpsxUH8sC8V/XWnMd9BHZQrBNXNB/lE9WIdRKrh8rVRCgrERzfCwsQ1Lffu3YuqqipMmTKl7pyePXuiQ4cO2L59O0aMGIHt27ejb9++Nsu7TJs2Df/4xz9w5MgRDBw4sMH3VFRUoKLCUlAVFha66rckueuHdXA4iX70QiEOpxWib3t1bO6yfE8qftqfhm66VCz1fhE+OstyCWZBhy3mvlhqmoi15sHw8fHFwzO646YR8TAauMctqZvSMmh89whEBfsio7Dc7vGlu5Px7GUJbu5Vy2QVluOJHw7aPbbueBamL96M7+8Zic7Wd59lHQM+vxQwWQ+0dUCXSeLEeY9LAaN2LsgSKS2DmuP6oXEOJ9GPXSjEobSCpi9XILPle1PxY00dtMz7hQZ10N/mPvjONKmuDnpoenfcPJJ1EKmfOzLIy6DHnMHt8eGmM3aPr9ibioen9mj9viluIAgCnvrlcJMn0DuG++OFK/tgbLd2TfuCrOMO6qCJ4sQ56yDSGDXXQdcN7eBwEv14RhEOpBao5obDH/al4Yd9qeiqS8X3DuqgpaaJWGMeAm9vHzw0vQduYR1EKuLSSXSz2YwFCxZg9OjR6NOnDwAgIyMD3t7eCA0NtTk3MjISGRkZdedYT6DXHq89Zs+iRYvw/PPPS/w7cI+RncMRF+aHlLwyu8e/252Mvu37urlXzZeUW4Lnfj0CAHjYuLwuMC8IYfjeNB7LTROQKoiF38x+0XhmVm9ENuUuCiIVUFoGGfQ6XDOkPd5Zf9ru8Z/2p+Hx6T3h62Vwc8+ax2wW8MiKg7hYWuXwnI7hAYgPD7B9c/2LloFjUAww8EbxV5t4F/aWSD5Ky6DmGNE5HB3C/JGcV2r3+He7UlQxiZ6UW4LnV4p10EON1UF9o/H0rN6ICmEdRNrgrgy6dmicw0n03JJKrDuWiRl9o13ej9b6JTHd4cVDa94GPf45sQv+b3yX5tVsG15iHUQeRd11UBg6hvvXLVtZ39JdyaqYRE/OLcWzvxwGADxkXFFXB2UIbfC9aTy+N01AqhABALi0bxSemZXAOohUx6WXe+69914cPnwYS5cudeXXAACefPJJFBQU1P1KSUlp/IcUQq/X4bqhHRweX5mYjtLKaofHlaDaZMaCZYkorTShj+4spht2AwAyhVBMrHgDb1XPRarQDvHh/vjy9mF4/4ZBnEAnTVFiBs0dEgdHT/7ll1bhzyP2L0oqyVfbz2PzyYZ7KdQK9DHirWsH2K4Jmr5f3DALAAKjgPv2ApP+zYEjaZoSM6ip9HodrnWyT8PKxDSUVCi/DnpwWSJKauqgGTV1UFa9OqhDmD++uG0o3p83iANH0hR3ZVCntgEY0TnM4fHvdis/+1LySvH0z4cbPW9st7b488FxWDCle/Mm0NMTgWMrxXZABHDfHtZBpHlqroN0Oh2udTYfdCAdxWqog74X66AE3TlcatgFAMgWQjCx4g28WX0NUoUIdAjzx+e3DcV/5w1mHUSq5LJJ9Pnz52PVqlXYsGED2re37JQeFRWFyspK5Ofn25yfmZmJqKiounMyMzMbHK89Zo+Pjw+Cg4NtfqnJnMHtHW4MU1xR7XC5F6V4b8Np7E/OBwA8Ylxueb/6SpTDB94GPe6f1BV/LhiH8d2b+BgikYooMYPiwvwxpmtbh8eX7lJ2cXkyswgv/3Hc6TkLr0hAXFi9TbDWv2hpj3uEm2SRR1BiBjXHXCd1UEmlCb8pvA56f8MZ7Kupgx62qYOuQDl84GXQ4f5JXfHXg+MwoUeETL0kch13ZpCzm4+2nMpG6kX7d3Mqgcks4OHvD6DIyYRYuyAfvHv9QHx1+zB0ahvg8DyHNrxkaY97BPBuwWcQqYza66CrB8fC6KAOKq00YVUTnlyR0383nsHepIsAbOug96uvQBl84WXQ4b6aOmgi6yBSMckn0QVBwPz58/HTTz9h/fr16NTJdifhwYMHw8vLC+vWrat778SJE0hOTsbIkSMBACNHjsShQ4eQlWXZuXfNmjUIDg5G7969pe6yIkQG+zoNk2UKvqtiX/JFvFuzZMQQ3XFMMBwAAKQKbbHUJG4e+/68QXhoag/FLx1BpDXXD3M80Nx+Nhfnc5q+mZU7VVSb8MDSRLs71dea2S8aVw2MtX0zaTtweq3YDukgrvtJRIoXEeyLST0d10FLdye7sTfNsy/5It5ZfwoAMFh3AhPt1UE3sA4iksr0PlEI8fOye0wQgO/3pLq5R0334aYz2HU+z+HxUH8v/Dp/DC7rH9OyjQSTdwKn/hLbIXHA4Ftb1lEicquIIF9M7uWsDlLufFBiSj7eXifWQYN0JzHJkAgASBPCscQ0GQDw3g2D8DDrINIAySfR7733XnzzzTdYsmQJgoKCkJGRgYyMDJSViet9h4SE4I477sBDDz2EDRs2YO/evbjtttswcuRIjBgxAgAwdepU9O7dGzfddBMOHDiAP//8E0899RTuvfde+Pj4SN1lxbjOyaPMe5Mu4mRmkRt70zTFFdV4cFkiTGYBgIBHvb6vO/Z29WxUwYgbhnfAJb0jHX8IEbnMlF6RCA9wvHHUsj3KLMje+Oskjl1wvCFQdIgvXr6yr+0AUxCA9S9YXk94nJtmEamIszpoX3K+IuugkkbqoEp44fphHTA1wf6TlETUfL5ehoYX0a0s35NS8/+kshxKLcBba046PWfRVX1bt8SBdR00/jHAqN2xM5HWOHvKJjElH8czlLdZaklFNRYs3V+XuY8YLXXQO3V1UBymsQ4ijZB8Ev2DDz5AQUEBJkyYgOjo6Lpfy5YtqzvnrbfewqxZs3D11Vdj3LhxiIqKwo8//lh33GAwYNWqVTAYDBg5ciRuvPFG3HzzzVi4cKHU3VWUCT3aITLYcaGjxLvRF/56BEk1G2CM0R/GcL249MIZczR+NI1F57YBeGpmLzm7SOTRvI16XD24vcPjK/amosrk+G5vOWw7nYNPtpx1eFynA96Y2x8h/vXuQju7AUjaKrbDuwL9rnNhL4lIauO7t0OUk/1SlLgE1cJfj9bVQaP1hzFCfwwAcNYchR9NY9GpbQCensU6iEhqzvZRuFBQ7nQ/FTmUVZrwwLL9qHYyuT93cPvWbYp6diNwfovYDusM9L++5Z9FRG43rns7RDu5iKbEOujF347WbYg6Sn8YowxHAQDnzZH4oa4O0uZqEuSZXLKci71ft956a905vr6+eP/995GXl4eSkhL8+OOPDdY6j4+Px++//47S0lJkZ2fj9ddfh9FolLq7imI06DF3sOOC8Md9qaioNrmxR86tPnzB6nFJweaq4+Lqq6HTG7H4ugHw99b2fzcipbtmiONcyS6qwPrjWQ6Pu1tBaRUeXn4AgpMbyO4a2xmj6q/1Lgi2a6FPeBIwMHuI1MRo0GPuEMcX/X7cn4ryKiXVQRlWT/MINnvCLK6eI9ZB17IOInKFXtHB6B8X6vC40paAeun3ozib7XgJvQ5h/nj28oSWf0GDOuhfgMH+kjdEpEwGvQ5znYzbftqfpqg66M8jGfhul3UdZJkPeqv6agh6L7zFOog0xmUbi1LLOLur4mJpFf48kunwuDtlFpbjiR8P1b2eot+HAfozAIBj5jisMo/Ag5d0R7/2oTL1kIhqdY0IxLCOYQ6PL92ljIGmIAj418+HcKGg3OE5vaKD8fDU7g0PnPgDSNsrtiMSgITZLuolEbnSNUPi4GgZ4PzSKvx5JMO9HXIgs7AcT/54sO71ZP0+DNSL+8McN8fhV/MILJjSzekkHxG1zvVOxk3rjmUhq8hxPeFO645l4psdjmstvQ5469oBCPRpxUTTyT+B1N1iu10voA/rICI1umZIe4d1UEFZFVYfVkYdlFVYjid+sNRBk/T7MaimDjppjsWv5lFYMLkbBrAOIo3hJLrCxIX5Y2y3tg6PL1PAXRVms4BHlh9AfmkVAEAHs80OzG9Wz8WQjuH4v/Fd5OoiEdVz3TDHA81NJ7ORnl/mxt7Y99P+NPx28ILD495GPd6+bgB8jPU2pDGbgQ0vWV5P+jeg519vRGoUF+aPMfWfNLGihKXtauugizZ10Iq6429Wz8Hg+HD8Y0JXubpI5BEu6x+DAG/7m9RVmwX8sDfNzT1qKLuoAo+tOOj0nPmTumFwfJuWf4nZDGywugt90r8BPTfvI1Kj9m38Ma5bO4fHlfCUjSAIeHTFwXp1kGU+6I3quRgYH45/TOB8EGkPZxkUyNnd6FtP5yIp1/GjgO7wxbbz2HIqp+71TP1O9NKLYZ5o7owdXsPx5jUDYNC3YEd5InKJGX2iEeRr/w4nswAsr1uaSR4peaV45pcjTs95ckZPdI8Manjg6E9A5mGxHTMI6HGpC3pIRO7ibGOtbWfkr4O+3G5bB12q34Xe+iQAwAFzZ2zzGoG3rmUdRORqAT5GXNY/xuHxZbuTIThbH87FBEHA4z8cRG5JpcNzBsSF4r5JrbzgduwXIKPmCeHoAUDPWa37PCKSlbON1neczcO5HJnroG3nsclq34kZ+l1IqKmDDpk7YqvXSCy+dgCMBk43kvbwT7UCXdI7EmEB3g6Pf79HvruwTmQU4ZXVx+teG2DCg1Z3X71RfQ0WXtkHcWH+cnSPiBzw8zbgqoGxDo9/vyelbld1dzOZBTy4LBHFFdUOzxnbrS1uGdnRzg9XAxtetrye9BQcPgNJRKrQWB0k593oJzOLsOgP2zrooXp3Xz1/OesgIne5bpjji27nc0ux/WyuG3tj69udyU73nfH3NmDxtQPg1ZqJJrOpXh30NOsgIpWb3CsSbQPVUQfpYcZD9eaDnmMdRBrGSfRa57cCxcrYxd3HaMBsJ5Ndy/ekotpkdmOPRBXVJjywdD8qqy3ffZXhb3TRi8sv7DT3REjCVFw5wHHficiB81uBYtdu8OnsKZe0/DL8fTrH4XFX+mDjaexJuujweBt/L7w+tz/09u7qPLgMyBXX30P8aKDLJBf1kkjjkra5PIOaytuox9WDnNRBe1NRJVMddP93tnXQlfqtNnVQUMI0zHbSdyJyoIUZ1L99CHpG2XlKrYZck02ns4rx4m9HnZ7z7GW90bFtQOu+6OD3QM5Jsd1hJNB1cus+j8hTKa4OcrzR+goZ66AHliaiwqYO+htd9ekAgN3m7gjoPd1pDUekdpxEB8TJ82XzgPeHAgeWibuby8zZ+sVZRRXYcML9E/6v/3kCxzOK6l57oRoPGH6se/25z4146ap+0PHuB6LmKc4Glt0IvD/MpRmUEBOCfu1DHB6XY8+FAyn5WLz2lNNzFs3uh8hg34YHqiuBTa9YXvMudKKWKc4Gls6ryaCliqiDrnWypEt2UQU2OLm701Xe+OtkgzpogfGHutefe8/DS1f1ZR1E1FwlOS3OIJ1O53Tpgz8OZyC/1PFyKq5QWW3GgmX7UV7leJJrau9IXDPEcb+bpLoS2LjI8pp1EFHLlOSIY7H3hgKJ3ymkDnKcDznFFVh3zP110Jt/ncSxC4V1rxvWQTfipdmsg0jbOIkOAOsXAmUXxV8/3Q0suQYokHd94K4RQRjiZIOZpbvcO9m17XQOPtlyzua9aw0bEKcXJ/M3mfvh5uuuR4i/l1v7RaQJ618AyvIsGfTtXCDfNXdOOSvI1hzNRE5xhUu+156yShMeXJaIaifLyFw7JA7T+0TZP7j/KyC/Jgu7TAbiR7mgl0QewCaD7nFpBjVV14hADO3opA5y892l287k4JMtZ23eu8awsa4O2mzqi5uvuwGh/o4fvyYiB1qZQVcOjIW30f6wtrLajJ/2u3eD0bfXncThtEKHx9sF+eCVqyW48SjxGyBfXIcYnScCHce07vOIPNX6F4HSXKA8H/j5/4Bv58heB3VuF4hhncIcHnf3BqPbz+Ti43p10FzDJnSoqYO2mPpg3nXzWAeR5nESHQAmPQMkzLa8PvUX8P4IYPen4m7nMnG2xt+GE1nIKCh3Sz+KK6rxaL1d5X1QifnGn+ten+uzAKO6tHVLf4g0Z9LTQJ+rLa9PrwH+OwLY9YnkGXR5/xj4eRnsHqsyCfhxn/suIP7nzxM462RjnPhwfzxzWW/7B6vKgM2vW15P+rfEvSPyIG7MoOZwtsHoxhNZuFBQ5pZ+FFdU49HlB21uTPNBJe4z/lT3+kyfBzCqK+sgohZpZQaF+nvjUkcX3AEs3ZXitg1GD6Tk44ONZ5ye8/rc/k73fWiSqnJg038sryc93brPI/Jkk54C+syxvD69VhF10PVOVifYdDIb6fnuqYNKKqrx6IoDTuugUwkLMJp1EHkATqIDQGA7YO7nwHVLgKBo8b3KIuC3h4EvZgI5p2Xp1qV9oxDkY7R7zCwAy920wegrfxxDWr2AvsmwBlE6cQ3jbV4jcP3sK93SFyJNCmwHzPkfcN13VhlUDPz+iOQZFOTrhVn9oh0eX7rbPQPN3efz8Pm2cw6PG/Q6vHXtAAQ4yEDs/hQoEtchRs9ZQOxgF/SSyEM0mkHOl1xylUv7RiPI11kd5J6Lfq/+cbxBHXSjdR1kHI4brp5t70eJqCkC2jrJoEublEHOloA6kVmExJR8iTrrWEW1CY8sPwBn+7TfOqojxndv1/ov2/MZUCSuQ4welwLtWQcRtVhAW2DOZ8D1S4GgGPG9ZmaQK8zoE41gB3WQIADfu2k+6NXVx5F6sX4dtBbRujwAwA7jMMybwzqIPAMn0a31nAn8cwcw6BbLe8nbgA9HA38vBkzVbu2Ov7cRVwyMcXh82Z4UmJ1VaRLYdjoH3+ywfVQoAGX4h3ElAMAs6BB71QvwMdq/s5WImqHnpcC9Oxtm0AejgL/fkiyDnD3lcja7BLvO5UnyPY6UVZrw2IqDTpcbvG9SVwzq4GAph4oi8d8HAEAHTPyX5H0k8kgOM2g0sOVNt9dBft4Gp5uVL9vthjroTA6+3pFk854/yvFPqzooZvaLrIOIpFCbQYNvtbyXvN0qg6oc/uiIzmHo5GSTzqW7XD/Z9M66UziVVezwePfIQDwxo2frv6iiWPz3UWsin8YjkkSPGcC9O4DBt1nea2IGuYKvlwFXDXRcB32/OwUmF9dB28/k4qvtDeug2vkgAIi6knUQeQ5OotfnFwpc/g5w80qgTUfxvepyYO2zwKeTgIxDbu2Os0eZUy+WYdWhCy777pKKajz2w8EG799mWI1wnbix1rnoaYjvPcxlfSDyOL4hDTPIVAGsfU7MoAsN/59srkEdQtE9MtDh8c/+PoeKalOrv8eR1/86gXNOlnEZEBeK+RO7Ov6AHR+K6xYC4uPfkQkS95DIgznKoHXPS5ZBzeFso/W0/DL8ejDdZd9dUlGNx1Y0UgdFTUNH1kFE0vENAS57G7jlV6BNJ/G92gz6ZBJw4YDdH9PpdE73ffn1YDpS8kpd0WMAwMHUfHy46azD48aaJ+x8HSyp1yw7PwRKc8R2wmwgqk/rP5OIRL4hwGWLm51BruLs5qf0gnL8esB1dVBpZTUe+6Hh7/dWw2q01Yn7PpyNmIqOfYa7rA9ESsNJdEc6jwf+sQ0YOR/Q1fxrunAA+HgCsO4FcR06N+gTG4I+scEOj9//3X5c8+F2rDuWKfndWK/80fCxnWAU427jbwAAE/TodPWLkn4nEdXoPB74x3YHGbSwVRkkDjQdF2R/Hc3EmFc34IONZ1BYLu0dF3vO5+F/Wx0v4+Jt1OP1uf1hNDj466nsIrDtXbGtMwATnpS0f0RUw4UZ1BwJMSHoGxvi8PgDSxMx98NtLqmD7D2+HIxi3GNcBQAwQ49Oc1gHEblEp3ENx2IZB4GPJzrMoKsHtYdRb3+zztJKEya+vhEPLUvE8QzHm362RO0yLs7uCL1vUjckxDjOsiYrywe2vSO2dXo+jUfkKrUZNOq+hhm09nm31UG9ooPRv73j7FiwLBFzPtiGtUelr4NeW30CKXn166ASmzqoI+sg8jCcRHfGOwCY9hJwxxqgXS/xPXM1sOV1cRCZddwt3XB2NzoA7Dqfhzu+3INpizdj+Z4UVFa3fvMLe48vAwIeNP6AYJ14F0dZ72ugb9et1d9FRA54+9dk0FogomaDTcEEbHmj1Rk0e2AsvB1NVAPILqrAq6uPY9Si9Vj0+zFJNjIurzLh0UaWcXn4ku7oGuHgLnlBADa+AlQUiK8HXA+0dXLHOhG1jtMMGu++OsjJ3egAsPv8RcnrIHuPLwMCFhh/rKuDSnrNZR1E5EqNZtAxm9PbBflgSq9Ihx9XbRbw4/40TF+8Bbd+vgvbz+RKsg/Mu+tO42Sm42VcekcH458Tu7T6eyAIwKZXgfKaOqj/9UBbZhCRy3j7A1NfBO5cC0TUPPkqmIC/37SbQa7i7G50ANiTdBF3frUHUxdvxvd7UiR5onjH2Vx8se18vXcFPGD8ESG1dVDPOdBH9Gj1dxGpCSfRm6L9EOCezcD4JwC9l/he9jHgk4lA4ncu//rLB8TArwmP/p3KKsajKw5i3Gsb8Mnmsyhq4R2kJRXVeLzeMi56mPG88QvcZvwTAGDSGRE4levvEblF+8HA3ZvEu64lyqA2Ad6Y1ieq0fOKK6rx0eazGPvaejy6/ABOZRa16PsA4I0mLONy59jO9g+aTcAfj4mPMAPiv4fxj7e4L0TUDHUZ9C+rDDruvjqof/ProI83n2lxHWTv8WU9zHjW+BVuN64GINZBQayDiNzDYQZNAhKX2Jx6bSMX3WptPJGN6z/ZgSvf34rfD11o8brCh1IL8MGmMw6PG/U6vD63P7yc3LjQJGYT8MfjwI7/iq9ZBxG5T+xg4O6NDTPo44nA/m9d/vWX9Y+Bv3fjddDprGI8VlMHfbSp5U8Ul1Y2XM6utg66w/gHgJo6aBrrIPI8nERvKqM3MPFJ4J5NljshqkqBn/8P+OVeoNJ1a+wF+3rhSicbStSXUViOl34/hlGvrMerq48jq7B5d5C+uvq4zWM7PqjEf73exi3GNXXv6aa+AIQ6vyJKRBIyegMTnhAv6NXeCdHKDLp5ZHyTz60yCVi+NxWXvLUZd365G7vP5zXr7q29SXn49O/GlnHpB4O9x7CryoDvbwZ2fWx575KFzCAidzJ6AxMet59BP7u2Dgry9cJVg5pXB738u/gkzSt/tKAO+qNhHfS+19t1NxIAgO6ShUCbpmcoEbWSwwz6B/DzP+syaFy3dogP92/yxx5ILcA/v92HyW9sxLc7k1Be1fQ7OJuyjMv8SV3RO8bx0pxNUlUGLL8V2PWR5b1LnmcGEblTbQb93xYgsmYfguoy4Jd/1mSQ4xuFWivQx4jZzaiDMgsrsOiP4xi9aD0W/XEMmc2sg15bfQLJVntI+KAS73m9Y1sHTXnOsncOkQfhJHpzRSYAd64DBt5keW//N8Cnk4Hsky772kemdkdsqF+zfqaovBofbDyDMa9twGurj6O0srrRn6n/+HIoivCt98uYbtgNAKgSDMievBj6kf9s3m+AiKQR2Vt8pHDQzZb39n8j3o2VfaJZHzW0YxhuGN78iei1x7Iw98PtuPGznU26M728yoRHlztfxuXBKd3RNSKo4YHSPOCrK4Dj4tp70BuBK/4LMIOI5BHZG7hrHTDoFst7iS3LoOZ4ZGqP5tdBFdX4cNMZjHl1A15tYh2042wuvrSqg0JQjG+8X8YMmzroLehH3du83wARScNuBn0rZlDWcRj0Orx8VV94G5s3zD2fW4p//3QYY1/bgB/3pTbpRoH31p/GCSd1UK/oYPxzQiuXnSvNA76+Cji2UnytM9TUQcwgIllE9BLHYoNvtbxnlUGu8vAlLauDPtp0FmNf3YBX/jiOkorG66Cd9ZZxCUExvvZehEsNuwAA1YIeWZPehH70fc3qC5FWcBK9Jbz9gSveA676CPCqudMh66i4RvGBZS75yvBAH/x+/1jcNCIePs0sCiurzfjvxjOY8sYm/H7ogsOisP7jy+112fjB+zkM0YsXB4oFX6wb9C7ajb2t5b8RImo9b3/g8neBqz62ZFD2MfGRwmZm0EtX9sFrV/dDXFjzijIA2Ho6FzPe3oKXfjuKYidF2ZtrTuKsk2Vc+seF4q6xnRoeuJgEfDYVSNkpvvYOBG5YBgyc1+y+EpGEvPyAy98BZn8CeAWI72Ufq6mDlrrkK8MCvFteB5nM+GDjGUx+YxN+O9hIHWT1+HIsxDpoaE0dVCL41NRBt7f8N0JErecogz6ZCBxYitFd22Ll/NGY2KNdsz86u6gCD31/ANd8tB1H0x1vQHo4rQD/3djYMi79mj2Zb+P/27vzqCiufA/g3+5mcQcjAqKiuC8RFxSGuMQEXDCLms3MMxFnHJMYiCbGZPTNJJq8eUejc4zRmBizuMzkGcXEDY0TRcENNSpqjEoQEVwABYMgKEv3fX9Ud0MDDTQ0dlX193MO50h1d3l/Vs43Py5V9+ZlAN+MBTISpe9dmwOTN7EPInI016bAU59UyqDGXeautbEPmhLaCU1cbe+DViWkInxpAmLP3rDaB90r0ePd7y37oM1uHyBYK90kUSjcsXfgcniPmFb/QogUTiPssZuKDOXn58PDwwN37txBq1YNfISuJreSgU2RUuNmMmgKELFYCtdGkHO3GOuPXMG6xHTcuWf7OlfDunlhwdN9q2zet2D7r+bfOvbRXMFat8Xw1uQBAG4KT/zT6x9YFPUStFZ2vSdSMntnxgPNoJip0i/yTAa+DIxbYlMGlekN+PFcFr44kIpz163/0GiNd0t3/O2J3ni6vx80mvKMOJn+O55fdQTWnnR202mxc+YwdPepdBd65lng2+eAu9nS9829gckxgN8Am8dGpATKzaDfgJjIqhkUsVj6hV8jyL1bjHWJ6VifeAV5RY3XB61xWwwfYx90S3hgSZv/wcLoKdUvO0WkcOrKoJeAiCWAWzNczMrH6gOXsf30DZTZuO65VgNMCe2Mt0b1gEdTV/PxkjIDnv70EC5mWb8LfVZYd7w1qofN5Zhl/QL8+zngbpb0fXNvaQLdb2D9z0kkY4rNoJwUaT7o5q/lxypkUGPIvVuM9YnpWFfPPmhotzb44Om+VZ4C/nDHeXxzWFp+s7cmHWvdPqrQB7XC4jb/wCL2QaRSdc0MTqLbQ0kR8OM70pIKJt59gefXAm0b0DzVorC4DJtOXMVXB9NwPe9e7R+owFWnwZ+HBWDm493R3N0FRy/n4sXVRwEAw7S/YJXrx2ihkdbOSjW0wytiHr6a9RwCvJrbvQ4iOVBs4wbYNYOEEDiSmotVCak4mJJj81BCAh7Ch+MfRk/flrhfqse45Qdx+Zb1u9DfHduz6qPOqfuAjVOAEuMPp226AS99z3X3SNWUn0HvAkn/Kj/m3Qd4fl2j9kFFJWXY9PNVfGmHPujY5VxMMvZBQ7W/YJXrMrTUSOdMNbTDK4Z5+HLWs+jStkVNpyVSLLVn0PW8e/jmUBo2HM9AUUnd1z0HAK8Wbpgb0RvPDGwPrVaDpT8lY/m+S1bf38u3JbZHD6v/XeiX44HvXmIfRE5F8Rm0+6/AqfXlxx5QHxRz4hq+PHgZ1363rQ9y0Rr7oLDuaOHuguNptzFpdSKEAB7RnsMXrh+b+6DLBl9MN8zD6lnPoSv7IFIpTqI/yNA0Ob0B2Dlb2uQGkB7teeoTIPD5Rv1rS/UG7PolE6sSLuNCpm13kPq2aoJ3x/bEsr0pyLhdhInag1jsuhquGqm5PGnojmklc/DGkyGYNqya5RaIVELRjZuJnTPo1xt3sPrAZcSezaxx06zKdFoNpj7SGWV6g8XawpX17+CB72c8AhddhR8yz3wnbZRqMC4P0yEY+ON3QPM29aqBSCnUm0HLgMAXGvWvLdMbsLOBfdAncSlIzy3CBO0hLHH9wtwHnTJ0w7SSOYh6IgR/Gd6lMYZPJAuqyKAz3wGxb1lm0JMfA/0nmd9yp6gU/z6WjjWH05Bzt8Sm0wd1ao3JIf54Z/NZq32Ri1aDrVFD8XB7j/rVcHaTtEmhwXh3aYchwB83sg8i1VNHBm0EYt+sMYMag6kP+iLhMs7b2Af5tHLHu2N6YcW+FFzJLcJ4Yx/kZuyDkgzd8OeSOXh9XAimj2AfROrFSXRHhCYgbSYREymtiWXS60kgfAHg1b1h575/BziyQmqu2gUCwa8CnYcBxqUThBA4mJKDLw6k4vClXBtPLvC6bjvedS1fT/k/+sGYWRqNfp18sPHVUD62Q6qmisYNaJQMunq7CF8fSsPOn5PxstiGCdrD+FV0xjr9GBw19AZgeza46bSInTkMPUzLuAgBHPoYiPug/E09nwCe/arRHoUkkhPVZFB1y9z1fAIY9YGd+qBPgbMbjX3QK0Dn4Xbrg17T7cBc1/I13ffog/BGaTT6dvLFJvZBpHKqz6DwBRZ3hN4v1eOHU9fx5cHLSKth35aKWqIIf3HZiYnaQzgvOmOdfjQSDX1QsQ+aGdYds+uzjIsQwOFPgL3zK4x7HPDs1+yDyCmoKoMqL7VZTQbVS8U+yLef1AcFjLDogw5dysEXCZdx6JKtTxQLvKqLxTzX8jXdTX1Qb38fbH7tEfZBpGqcRHdUaAJASSGwcw5w5v/Kj2l00g7OI+cCLbxtO1/pPeD4l8ChpcC93y1f8+4jhWfgJIsG68zVPHwYex4n0yu9vxodNLcwQ7cdk13izMfWl43CgrJIuLq44MdZw/n4Mqmeaho3QMqgXe9IO8Wb2CGDDAeXQnvfMlMuGjpinX40tuiH4T7c63zKd8b0RNRjxmVc8jKAg0uBk2vK3zB4mrSuu1Zn21iJFEp9GfQucLrCElMaHRAUCTw6F2jpY9v56tQHvQC4lS85Z0sf1B63MMNlO16q0Af9uywM88umwsXFFbtmDefjy6R66sqgImMfVHsG6Q0CsWdvYOGui8jKv1/t6dxRgpd1e/C6yzY8pLlr8dpFQ0es14/GFv1QdPJtW79lXPIygEPLgBNflx8b/GdpTWWdi23nIlIo1WVQ5aU2G9oH/fyV9PPSvduWr7XtDYSY5oPK+6Cz1/Lw4Y7zOFHHPug1lx142WWv+di3ZWF4v2wqdC6u2DVzeJV9ZIjUhpPojgxNkzMbgT3vlW+MBwBuLYBHZgKhUYB7LUGkL5Oav/iPgIIbNb+3iScw6GVgyHSgdScAgMEg8P2pa1j040XkFpY/ruiCMgzW/obHtEl4THsaPbTXLU61uHQSPtM/DUCDvz/Rm48vk1NQVeNmcnYT8NN75ZtSAdJjhUNn2T2D8kRzbNSPxL/0o3BN1DxJP7B9c2wep4UudQ+Q8pPlXfMA8Ph7wPC3zXdVEDkD58qgmUBodB0z6FsgflEd+iAPaUPT4OnmdYMNBoEfkq5j0Y8XLJZtMPVBI7Wn8bg2qZo+6AV8ph8PQIO/jevNx5fJKTh7BhUWl2H5vhR8fTDNvAGpDno8pzuAWS7fw09zu/LZLdwRzVEaOBlej0fVvna5vhTIOCr1QCl7LO+aB9gHkVNSZwbFAD/9veF9UMJHQP71mt9bTR8khMAPp65jYTV9UJAmBY/ppPmgntprFqdaUvoCVhr7oP8e1wuvjOhqQ9FEysRJdDmEJiDdjZW4Uno8r6TCnQstfICR86Sgq3yHgcEAnN8K7P9fILfipjUa6TeMI94BMk8Dx1cDV49ZflajBXpESL+NDHgU0Ghw514pvtx1BDdPxWKk9jSGaX9BK03VjSdKhQ5/LZ2OHwwjAEjr/vHxZXIWqmzcAGMGfQYcXmZbBl3YBuz7h9UMEplncHv/CrS5nWT5UaFBnGEQ1ujH4IihL0yPOLdFHkbqTuNx3RmMbnIeOtNmWRVpXYCnVwAD/ssupRMpidNlUHNv4LF5wMApNmbQC1IflHUWOLYauHq00l+oAXpGSHendxlZoQ9KxM1TO/Co9jSG19AHzS2dju+NfdAgf0/E8PFlchLMIMmlm3exYNs5tEzbhTkum9BVm2l+zSA02GoYik/LJqCPJh2RLv/BEO1vlf7CqhkEACjIBi4Zbx5I3Q8UV7NusdYFeGo5MHCy3conUgpVZ9DRz4BDn5RvFgzU3AcJAZw39UEpFV6oYx/UYywQ8mqVPij7VCxGapOs9kFlQou5ZdOxWf8oAGCgvyeXcSGnwUl0uYSmyd2b0m8QT6wBRIUd4b16AOEfSM0WAKTGAXEfAplnLD/fc5x0V4JPH8vjN5Kk8Dy3GdBX2hynbS+gaxiQfqjq+Yz0QoMk0R379QOw0xCCK6IdAMDdRcvHl8mpqLZxM7l7S8qgk2vKN+0EqsmgfdK65NVm0N8Bn74Whwuv/IxLO5aiV85PcNeUWbz2m6E9DhgCEay9iEBtWvXj0milTbO6jwL6TAS8ujW0UiJFcu4MWiBlDGDMoA+lmwUq6hEBhL1XJYNw47R0U8EvmwF9seVrXj2BbuFA+uGq5zMyCA2SRDfs0w/ELkMI0ox9kJuLFj+yDyIn4rQZ1Ka7tGeDKYMu74fY+wE0lTJjj34Q/ln2ApKFv8Xxvpo0zGq5D6P0h6CpRwYBGmMfNBroyz6InJdTZNCBxcCJb2rNIOz94IH3QdJ80B8s+qBdM4ehm3fLepdMpCSqmERfuXIllixZgqysLPTv3x8rVqxAcHBwnT4ru9A0yUmRJqgu7LA87v+ItPbvlYOWxzsNlX647FhL3XdvAafWAj9/U+sjz8WunogrC8Tu4n44YAhEHqoGIx9fJmej+sbNJOeSMYO2Wx73D5XugKoug8LmA/4hNZ429coV/Pz9x3g0fzva1fLIM5q2BrqNkn5g7BYGNHuoHoUQqQszyEoG+T8i9UG1ZBAKc4CTa4Gfv65DH+SBfWWB+LE4EAcN/fA7qv778PFlcjZOn0Ed/wC4uAFpBywOX205AHN+n4BjZdVvCKjTarD19aHo17pUyqAT39S+7ELT1tLEVvfR0g1Pzds0oCAidXCaDMpNlTLo/DbL41YySOqD5gP+f6j5vKY+qA4ZVGLug6T5oOr6oLkRvfDao+yDyHkofhJ948aNmDJlClatWoWQkBAsW7YMMTExSE5Ohrd37ZviyTY0TTKOSeulV16OxcQ3UJq46hZm23p4+lJpgv74aiAj0fJ8PcZIzVr7IOSXGPDxnt+wPjEdeoPlfwKP9miLb6YO4WM75FScpnEzqTWD+gFhC2zKICEEYk9n4MjOdZhYEotgbbL5tQsIgHfQ02gz4EmgfRA3DCWqxOky6Opxaa3iKo8hG/n2M/ZB4bb3QRdjpaf0Mo5UOF+g1AP1GGPug5btScG6xCtV+qARPdpiDfsgcjLMoEoqZFBqTiEWbP8VB1NyqrxtXkQvvFpxoklfZsygL6rPoO6jgQ6D2QcRVcIMqsSnnzR5bnMfVLcMKigxYNneFKw9UrUPGt7dC2umDoGLzsZNkokUTPGT6CEhIRgyZAg+/fRTAIDBYEDHjh3xxhtvYO7cubV+XvahCUhrXV2MBfYuKF/z86GuwON/k5Y10DYwtLJ/BW6nSRNWrdpV+5YLmflYlZCKhN9uoVUTV4T39sHciF627ypPpHBO17gBxgzaCeydXyGDukjLtjQgg+4Wl+HLA5dx4Uwimt7NgPALwqyJI7gsAlENmEEVMuixvwF9n3kgfdDFrHysipf6oBZNXBDe2wfzInqzDyKnwwwyZlDrAKkPqpRBQgj859dsfHssHSeu/I4ubZtjSmgnTBrib+XkqFMGEZHEaTMoeRewZ3752udWMqhess8Dty/XmEHJWQX4PP4S+yByeoqeRC8pKUGzZs2wefNmTJgwwXw8MjISeXl52LZtm/UPGykiNE30peWP8/QZD+hcH/gQhBDQcAd4cmJO2biZNGIGMVuI6oYZxD6IyJGYQXXPIOYFkf05dwaVAee3Sn9mH0TkEHXNDBerrzhQTk4O9Ho9fHx8LI77+Pjg4sWL1X6muLgYxcXlGynk51ez47lc6VyBfs85dAgMTKKGYQZVj9lC9GAwgxqGWUXUMM6UQcwLIvlRdga5sA8iUgjVPKOxcOFCeHh4mL86duzo6CERkRNhBhGRIzGDiMiRmEFE5EjMICJ6EGQ5ie7l5QWdTofs7GyL49nZ2fD19a32M/PmzcOdO3fMX1evXn0QQyUiAsAMIiLHYgYRkSMxg4jIkZhBRPQgyHI5Fzc3NwQFBSEuLs68JrrBYEBcXByio6Or/Yy7uzvc3d0f4CiJiMoxg4jIkZhBRORIzCAiciRmEBE9CLKcRAeA2bNnIzIyEoMHD0ZwcDCWLVuGwsJC/OlPf3L00IiIiIiIiIiIiIjISch2En3SpEm4desW3n//fWRlZWHAgAHYvXt3lc1GiYiIiIiIiIiIiIgai2wn0QEgOjra6vItRERERERERERERESNTdaT6A0hhAAA5OfnO3gkRKQEpqwwZUdDMYOIyBbMICJyJGYQETkSM4iIHKmuGaTaSfSCggIAQMeOHR08EiJSkoKCAnh4eNjlPAAziIhswwwiIkdiBhGRIzGDiMiRassgjbDXr/pkxmAw4MaNG2jZsiU0Gk2t78/Pz0fHjh1x9epVtGrV6gGMsPGxJuVQY11Kq0kIgYKCAvj5+UGr1Tb4fMwg1qQkaqxLaTUxg+yPNSmHGutSWk3MIPtjTcqhxrqUVhMzyP5Yk3KosS6l1VTXDFLtneharRYdOnSw+XOtWrVSxAW2BWtSDjXWpaSa7HHXgwkzqBxrUg411qWkmphBjYM1KYca61JSTcygxsGalEONdSmpJmZQ42BNyqHGupRUU10yqOG/4iMiIiIiIiIiIiIiUilOohMRERERERERERERWcFJdCN3d3fMnz8f7u7ujh6K3bAm5VBjXWqsqTGp8d+LNSmHGutSY02NSY3/XqxJOdRYlxprakxq/PdiTcqhxrrUWFNjUuO/F2tSDjXWpcaaABVvLEpERERERERERERE1FC8E52IiIiIiIiIiIiIyApOohMRERERERERERERWcFJdCIiIiIiIiIiIiIiKziJTkRERERERERERERkBSfRjVauXInOnTujSZMmCAkJwfHjxx09pHpbsGABNBqNxVevXr0cPSybHDhwAE899RT8/Pyg0WiwdetWi9eFEHj//ffRrl07NG3aFOHh4UhJSXHMYOuotpqmTp1a5bqNHTvWMYOto4ULF2LIkCFo2bIlvL29MWHCBCQnJ1u85/79+4iKikKbNm3QokULPPvss8jOznbQiOWLGSQvzCBmkLNhBskLM4gZ5GyYQfLCDGIGORtmkLwwg5hBcsVJdAAbN27E7NmzMX/+fJw6dQr9+/fHmDFjcPPmTUcPrd769u2LzMxM89ehQ4ccPSSbFBYWon///li5cmW1ry9evBjLly/HqlWrcOzYMTRv3hxjxozB/fv3H/BI6662mgBg7NixFtdtw4YND3CEtktISEBUVBSOHj2KPXv2oLS0FKNHj0ZhYaH5PW+99RZ27NiBmJgYJCQk4MaNG3jmmWccOGr5YQbJDzOIGeRMmEHywwxiBjkTZpD8MIOYQc6EGSQ/zCBmkGwJEsHBwSIqKsr8vV6vF35+fmLhwoUOHFX9zZ8/X/Tv39/Rw7AbAGLLli3m7w0Gg/D19RVLliwxH8vLyxPu7u5iw4YNDhih7SrXJIQQkZGRYvz48Q4Zj73cvHlTABAJCQlCCOm6uLq6ipiYGPN7Lly4IACIxMRERw1TdphB8sYMUg5mUP0wg+SNGaQczKD6YQbJGzNIOZhB9cMMkjdmkHI4QwY5/Z3oJSUlOHnyJMLDw83HtFotwsPDkZiY6MCRNUxKSgr8/PzQpUsXTJ48GRkZGY4ekt2kpaUhKyvL4pp5eHggJCRE0dcMAOLj4+Ht7Y2ePXtixowZyM3NdfSQbHLnzh0AwEMPPQQAOHnyJEpLSy2uVa9eveDv76/4a2UvzCDlYQbJFzPIdswg5WEGyRczyHbMIOVhBskXM8h2zCDlYQbJlzNkkNNPoufk5ECv18PHx8fiuI+PD7Kyshw0qoYJCQnB2rVrsXv3bnz++edIS0vD8OHDUVBQ4Oih2YXpuqjpmgHSozvr169HXFwcPvroIyQkJCAiIgJ6vd7RQ6sTg8GAN998E0OHDsXDDz8MQLpWbm5u8PT0tHiv0q+VPTGDlIcZJE/MoPphBikPM0iemEH1wwxSHmaQPDGD6ocZpDzMIHlylgxycfQAyP4iIiLMfw4MDERISAg6deqETZs2Ydq0aQ4cGdXkxRdfNP+5X79+CAwMRNeuXREfH4+wsDAHjqxuoqKicO7cOcWtt0b2xwxSJmYQqQUzSJmYQaQWzCBlYgaRWjCDlIkZpAxOfye6l5cXdDpdld1hs7Oz4evr66BR2Zenpyd69OiBS5cuOXoodmG6Lmq+ZgDQpUsXeHl5KeK6RUdHIzY2Fvv370eHDh3Mx319fVFSUoK8vDyL96vtWjUEM0h5mEHywwyqP2aQ8jCD5IcZVH/MIOVhBskPM6j+mEHKwwySH2fKIKefRHdzc0NQUBDi4uLMxwwGA+Li4hAaGurAkdnP3bt3kZqainbt2jl6KHYREBAAX19fi2uWn5+PY8eOqeaaAcC1a9eQm5sr6+smhEB0dDS2bNmCffv2ISAgwOL1oKAguLq6Wlyr5ORkZGRkqOpaNQQzSHmYQfLBDGo4ZpDyMIPkgxnUcMwg5WEGyQczqOGYQcrDDJIPp8wgh25rKhPfffedcHd3F2vXrhXnz58Xr7zyivD09BRZWVmOHlq9vP322yI+Pl6kpaWJw4cPi/DwcOHl5SVu3rzp6KHVWUFBgUhKShJJSUkCgFi6dKlISkoS6enpQgghFi1aJDw9PcW2bdvE2bNnxfjx40VAQIC4d++eg0duXU01FRQUiDlz5ojExESRlpYm9u7dKwYNGiS6d+8u7t+/7+ihWzVjxgzh4eEh4uPjRWZmpvmrqKjI/J7XXntN+Pv7i3379okTJ06I0NBQERoa6sBRyw8zSH6YQcwgZ8IMkh9mEDPImTCD5IcZxAxyJswg+WEGMYPkipPoRitWrBD+/v7Czc1NBAcHi6NHjzp6SPU2adIk0a5dO+Hm5ibat28vJk2aJC5duuToYdlk//79AkCVr8jISCGEEAaDQbz33nvCx8dHuLu7i7CwMJGcnOzYQdeippqKiorE6NGjRdu2bYWrq6vo1KmTmD59uuz/x11dPQDEmjVrzO+5d++eeP3110Xr1q1Fs2bNxMSJE0VmZqbjBi1TzCB5YQYxg5wNM0hemEHMIGfDDJIXZhAzyNkwg+SFGcQMkiuNEELU9a51IiIiIiIiIiIiIiJn4vRrohMRERERERERERERWcNJdCIiIiIiIiIiIiIiKziJTkRERERERERERERkBSfRiYiIiIiIiIiIiIis4CQ6EREREREREREREZEVnEQnIiIiIiIiIiIiIrKCk+hERERERERERERERFZwEp2IiIiIiIiIiIiIyApOohMRERERERERERERWcFJdCIiIiIiIiIiIiIiKziJTkRERERERERERERkBSfRiYiIiIiIiIiIiIis+H/T7X/CKLcyqAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1500x300 with 5 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "source": [
+    "fig = clf.plot_most_important_feature_on_ts(\n",
+    "    X_test[y_test == \"2\"][0, 0], clf._classifier.coef_\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2221,8 +2221,8 @@
    "source": [
     "#### 4.4.3 Interpretability summary\n",
     "\n",
-    "We can see that the most important subsequences matche the instance from class `1` quite exactly, meaning that these subsequences are characteristic of this class.\n",
-    "On the contrary, they failed to match the instance from class `2`. These subsequences can be called Shapelets for this dataset.\n",
+    "We can see that the most important subsequences matche the instance from class ``1`` quite exactly, meaning that these subsequences are characteristic of this class.\n",
+    "On the contrary, they failed to match the instance from class ``2``. These subsequences can be called Shapelets for this dataset.\n",
     "\n",
     "Feel free to play with different time series from the dataset, or with a different dataset"
    ]
diff --git a/examples/transformations/signature_method.ipynb b/examples/transformations/signature_method.ipynb
index 81d6d4b866..ae0c480763 100644
--- a/examples/transformations/signature_method.ipynb
+++ b/examples/transformations/signature_method.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "The ‘signature method’ refers to a collection of feature extraction techniques for multimodal sequential data, derived from the theory of controlled differential equations. In recent years, a large number of modifications have been suggested to the signature method so as to improve some aspect of it.\n",
     "\n",
-    "In the paper [\"A Generalised Signature Method for Time-Series\"](https://arxiv.org/abs/2006.00873) [1] the authors collated the vast majority of these modifications into a single document and ran a large hyper-parameter study over the multivariate UEA datasets to build a generic signature algorithm that is expected to work well on a wide range of datasets. We implement the best practice results from this study as the default starting values for our hyperparameters in the `SignatureClassifier` module.\n"
+    "In the paper [\"A Generalised Signature Method for Time-Series\"](https://arxiv.org/abs/2006.00873) [1] the authors collated the vast majority of these modifications into a single document and ran a large hyper-parameter study over the multivariate UEA datasets to build a generic signature algorithm that is expected to work well on a wide range of datasets. We implement the best practice results from this study as the default starting values for our hyperparameters in the ``SignatureClassifier`` module.\n"
    ]
   },
   {
@@ -147,7 +147,7 @@
     "### Overview\n",
     "We provide the following:\n",
     "- **aeon.transformers.panel.signature_based.SignatureTransformer** - An sklearn transformer that provides the functionality to apply the signature method with some choice of variations as noted above.\n",
-    "- **aeon.classification.feature_based.SignatureClassifier** - This provides a simple interface to append a classifier to the SignatureTransformer class."
+    "- **aeon.classification.feature_based.SignatureClassifier** - This provides a simple interface to append a classifier to the ``SignatureTransformer`` class."
    ]
   },
   {
@@ -176,7 +176,7 @@
    "metadata": {},
    "source": [
     "### Example 1: Sequential Data -> Signature Features.\n",
-    "Here we will give a very simple example of converting the sequential 3D GunPoint data of shape [num_batch, n_timepoints, num_features] -> [num_batch, signature_features]."
+    "Here we will give a very simple example of converting the sequential 3D GunPoint data of shape ``[num_batch, n_timepoints, num_features] -> [num_batch, signature_features]``."
    ]
   },
   {
@@ -263,9 +263,9 @@
    "source": [
     "### Example 2: Fine Tuning the Generalised Model\n",
     "As previously mentioned, in [1] the authors performed a large hyperparameter search over the signature variations on the full UEA archive to develop a 'Best Practices' approach to building a model. This required some fine tuning over the following parameters, as they were found to be very dataset specific:\n",
-    "- `depth` over [1, 2, 3, 4, 5, 6]\n",
-    "- `window_depth` over [2, 3, 4]\n",
-    "- `RandomForestClassifier` hyperparamters.\n",
+    "- ``depth`` over [1, 2, 3, 4, 5, 6]\n",
+    "- ``window_depth`` over [2, 3, 4]\n",
+    "- ``RandomForestClassifier`` hyperparamters.\n",
     "\n",
     "Here we show a simplified version of the tuning."
    ]
@@ -354,7 +354,7 @@
    "metadata": {},
    "source": [
     "## A Full Description of the Parameters\n",
-    "We conclude by giving further explanation of each of the parameters in the `SignatureClassifier` module and what values they can take."
+    "We conclude by giving further explanation of each of the parameters in the ``SignatureClassifier`` module and what values they can take."
    ]
   },
   {
@@ -366,37 +366,37 @@
     "Below we list each parameter and the values that they can take. For further details about what the options mean refer to [1].\n",
     "\n",
     "\n",
-    "**classifier** Needs to be any sklearn estimator. Defaults to `RandomForestClassifier()`.\n",
+    "**classifier**: Needs to be any sklearn estimator. Defaults to ``RandomForestClassifier()``.\n",
     "\n",
     "**augmentation_list**: list of tuple of strings, List of augmentations to be applied before the signature transform is applied. These can be any from:\n",
-    "- 'addtime' - Add an equally spaced time channel.\n",
-    "- 'leadlag' - The leadlag transform.\n",
-    "- 'ir' - Perform the invisibility reset transform.\n",
-    "- 'cumsum' - Perform a cumulative sum transform.\n",
-    "- 'basepoint' - Append zero to the start of the path to remove translational invariance.\n",
+    "- ``'addtime'`` - Add an equally spaced time channel.\n",
+    "- ``'leadlag'`` - The leadlag transform.\n",
+    "- ``'ir'`` - Perform the invisibility reset transform.\n",
+    "- ``'cumsum'`` - Perform a cumulative sum transform.\n",
+    "- ``'basepoint'`` - Append zero to the start of the path to remove translational invariance.\n",
     "\n",
-    "**window_name** str, The name of the window transform to apply. Can be any of:\n",
-    "- 'global' - A single window over all the data.\n",
-    "- 'expanding' - Multiple windows starting at the first datapoint that extend over the data (increasing width).\n",
-    "- 'sliding' - Multiple windows that slide along the data (fixed width).\n",
-    "- 'dyadic' - Partition the data into dyadic windows.\n",
+    "**window_name**: ``str``, The name of the window transform to apply. Can be any of:\n",
+    "- ``'global'`` - A single window over all the data.\n",
+    "- ``'expanding'`` - Multiple windows starting at the first datapoint that extend over the data (increasing width).\n",
+    "- ``'sliding'`` - Multiple windows that slide along the data (fixed width).\n",
+    "- ``'dyadic'`` - Partition the data into dyadic windows.\n",
     "\n",
-    "**window_depth**: int, The depth of the dyadic window. (Active only if `window_name == 'dyadic']`).\n",
+    "**window_depth**: ``int``, The depth of the dyadic window. (Active only if ``window_name == 'dyadic']``).\n",
     "\n",
-    "**window_length**: int, The length of the sliding/expanding window. (Active only if `window_name in ['sliding, 'expanding'].`)\n",
+    "**window_length**: ``int``, The length of the sliding/expanding window. (Active only if ``window_name in ['sliding, 'expanding'].``)\n",
     "\n",
-    "**window_step**: int, The step of the sliding/expanding window. (Active only if `window_name in ['sliding, 'expanding'].`)\n",
+    "**window_step**: ``int``, The step of the sliding/expanding window. (Active only if ``window_name in ['sliding, 'expanding'].``)\n",
     "\n",
-    "**rescaling**: str, The method of signature rescaling. Any of:\n",
-    "- 'pre' - Rescale the path before the signature transform.\n",
-    "- 'post' - Rescale the path after the signature transform.\n",
+    "**rescaling**: ``str``, The method of signature rescaling. Any of:\n",
+    "- ``'pre'`` - Rescale the path before the signature transform.\n",
+    "- ``'post'`` - Rescale the path after the signature transform.\n",
     "- None - No rescaling.\n",
     "\n",
-    "**sig_tfm**: str, String to specify the type of signature transform. Either of: ['signature', 'logsignature'].\n",
+    "**sig_tfm**: ``str``, String to specify the type of signature transform. Either of: [``'signature'``, ``'logsignature'``].\n",
     "\n",
-    "**depth**: int, Signature truncation depth.\n",
+    "**depth**: ``int``, Signature truncation depth.\n",
     "\n",
-    "**random_state**: int, Random state initialisation."
+    "**random_state**: ``int``, Random state initialisation."
    ]
   },
   {
diff --git a/examples/transformations/smoothing_filters.ipynb b/examples/transformations/smoothing_filters.ipynb
index 6a7776f04e..089b389617 100644
--- a/examples/transformations/smoothing_filters.ipynb
+++ b/examples/transformations/smoothing_filters.ipynb
@@ -9,8 +9,7 @@
     "\n",
     "> In statistics and image processing, to smooth a data set is to create an\n",
     "approximating function that attempts to capture important patterns in the data,\n",
-    "while leaving out noise or other fine-scale structures/rapid phenomena.\n",
-    "(https://en.wikipedia.org/wiki/Smoothing)\n",
+    "while leaving out noise or other fine-scale structures/rapid phenomena. Read more [here](https://en.wikipedia.org/wiki/Smoothing).\n",
     "\n",
     "In this notebook, we demonstrate the usage and results of different smoothing\n",
     "transformations."
diff --git a/examples/transformations/transformations.ipynb b/examples/transformations/transformations.ipynb
index 7d88735104..c46ca1dc26 100644
--- a/examples/transformations/transformations.ipynb
+++ b/examples/transformations/transformations.ipynb
@@ -2,6 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "# Transforming time series\n",
     "\n",
@@ -9,24 +12,24 @@
     "series machine learning. Transformation can involve extracting features that\n",
     "characterize the time series, such as mean and variance or changing the series into,\n",
     "for example, first order differences. We use the term transformer in the\n",
-    "`scikit-learn` sense, not to be confused with deep learning Transformers that employ\n",
+    "``scikit-learn`` sense, not to be confused with deep learning Transformers that employ\n",
     "an attention mechanism.  We call transformers that extract features\n",
-    "`series-to-vector` transformers and those that change the series into a different\n",
-    "representation that is still ordered `series-to-series` transformers.\n",
+    "``series-to-vector`` transformers and those that change the series into a different\n",
+    "representation that is still ordered ``series-to-series`` transformers.\n",
     "\n",
     "We further differentiate between transformers that act on a single series and those\n",
     "that transform a collection of series. Single series transformers are located in\n",
-    "transformations/series directory and inherit from `BaseSeriesTransformer`. For\n",
-    "example, `AutoCorrelationSeriesTransformer` is a `series-to-series` transformer that\n",
+    "transformations/series directory and inherit from ``BaseSeriesTransformer``. For\n",
+    "example, ``AutoCorrelationSeriesTransformer`` is a ``series-to-series`` transformer that\n",
     "finds the auto correlation function for a single series."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -45,25 +48,25 @@
     "transformer = AutoCorrelationSeriesTransformer(n_lags=10)\n",
     "acf = transformer.fit_transform(series)\n",
     "print(acf)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "Collection transformers are located in the transformations/collection directory and\n",
-    "inherit from `BaseCollectionTransformer`. For example, `Truncator` truncates all time\n",
+    "inherit from ``BaseCollectionTransformer``. For example, ``Truncator`` truncates all time\n",
     " series in a collection to the same length."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -83,25 +86,25 @@
     "trunc = Truncator(truncated_length=100)\n",
     "X2 = trunc.fit_transform(X)\n",
     "print(\"Truncated collection shape  =\", X2.shape)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
-    "`Truncator` is a `series-to-series` transformer\n",
+    "``Truncator`` is a ``series-to-series`` transformer\n",
     " that returns a new collection of time series of the same length. This can then be\n",
     " used, for example, by a classifier that only works with equal length series:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -112,8 +115,416 @@
     },
     {
      "data": {
-      "text/plain": "SummaryClassifier()",
-      "text/html": "<style>#sk-container-id-7 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\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-icon: #696969;\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}\n\n#sk-container-id-7 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-7 pre {\n  padding: 0;\n}\n\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  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\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  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\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     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-7 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\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-7 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-7 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  align-self: flex-end;\n  width: 50%;\n}\n\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-7 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  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\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-7 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\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  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\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-7 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\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  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\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  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\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-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}\n\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-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-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-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-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-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-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-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-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-7 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\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  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\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-7 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  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na: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}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-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\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\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  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-7 a.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}\n\n/* On 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-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-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SummaryClassifier()</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 fitted\">&nbsp;SummaryClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SummaryClassifier()</pre></div> </div></div></div></div>"
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\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-icon: #696969;\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",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\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",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\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",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\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",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\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-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 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",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\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-7 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",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\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-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\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",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\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-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\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",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\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",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\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-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",
+       "}\n",
+       "\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-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-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-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-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-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-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-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-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-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\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",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\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-7 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",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\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",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-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",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\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",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.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",
+       "}\n",
+       "\n",
+       "/* On 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-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-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SummaryClassifier()</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 fitted\">&nbsp;SummaryClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SummaryClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SummaryClassifier()"
+      ]
      },
      "execution_count": 25,
      "metadata": {},
@@ -130,26 +541,26 @@
     "    print(e)\n",
     "\n",
     "summary.fit(X2, y)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "Some collection transformers are supervised, meaning they fit a transform based on\n",
     "the class labels. For example, the shapelet transform finds shapelets that are good\n",
-    "at separating classes. This is a `series-to-vector` transformer that produces tabular\n",
-    " output shape `(n_cases, n_shapelets)`.\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+    "at separating classes. This is a ``series-to-vector`` transformer that produces tabular\n",
+    " output shape ``(n_cases, n_shapelets)``.\n"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -165,24 +576,24 @@
     "st = RandomShapeletTransform(max_shapelets=10, n_shapelet_samples=100)\n",
     "X2 = st.fit_transform(X, y)\n",
     "print(X2.shape)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "`series-to-vector` transformers produce output that is compatible with `scikit-learn`\n",
-    " estimators"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "``series-to-vector`` transformers produce output that is compatible with ``scikit-learn``\n",
+    " estimators"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -193,8 +604,416 @@
     },
     {
      "data": {
-      "text/plain": "RandomForestClassifier()",
-      "text/html": "<style>#sk-container-id-8 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\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-icon: #696969;\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}\n\n#sk-container-id-8 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-8 pre {\n  padding: 0;\n}\n\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  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\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  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\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     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-8 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\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-8 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-8 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  align-self: flex-end;\n  width: 50%;\n}\n\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-8 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  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\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-8 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\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  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\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-8 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\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  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\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  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\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-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}\n\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-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-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-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-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-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-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-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-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-8 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\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  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\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-8 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  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na: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}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-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\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\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  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-8 a.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}\n\n/* On 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-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-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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" 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/1.5/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>"
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\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-icon: #696969;\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",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\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",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\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",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\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",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\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-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 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",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\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-8 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",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\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-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\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",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\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-8 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\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",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\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",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\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-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",
+       "}\n",
+       "\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-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-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-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-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-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-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-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-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-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\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",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\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-8 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",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\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",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-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",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\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",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.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",
+       "}\n",
+       "\n",
+       "/* On 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-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-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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" 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/1.5/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>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
      },
      "execution_count": 27,
      "metadata": {},
@@ -210,13 +1029,13 @@
     "except ValueError as e:\n",
     "    print(e)\n",
     "rf.fit(X2, y)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "A list of all the available transformers can be found in the [API](https://www.aeon-toolkit.org/en/latest/api_reference/transformations.html). We currently have\n",
     "specific notebooks for the following transformers:\n",
@@ -231,19 +1050,16 @@
     "- [signature method](signature_method.ipynb)\n",
     "- [tsfresh](tsfresh.ipynb)\n",
     "\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/transformations/tsfresh.ipynb b/examples/transformations/tsfresh.ipynb
index d1d37d1761..4809cb8b48 100644
--- a/examples/transformations/tsfresh.ipynb
+++ b/examples/transformations/tsfresh.ipynb
@@ -17,47 +17,47 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:05.457198Z",
+     "start_time": "2024-11-25T14:07:05.449815Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:39.713903Z",
      "iopub.status.busy": "2020-12-19T14:30:39.713342Z",
      "iopub.status.idle": "2020-12-19T14:30:39.715128Z",
      "shell.execute_reply": "2020-12-19T14:30:39.715641Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T14:07:05.457198Z",
-     "start_time": "2024-11-25T14:07:05.449815Z"
     }
    },
+   "outputs": [],
    "source": [
     "# !pip install --upgrade tsfresh"
-   ],
-   "outputs": [],
-   "execution_count": 1
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:07.829632Z",
+     "start_time": "2024-11-25T14:07:06.056664Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:39.719083Z",
      "iopub.status.busy": "2020-12-19T14:30:39.718586Z",
      "iopub.status.idle": "2020-12-19T14:30:40.743724Z",
      "shell.execute_reply": "2020-12-19T14:30:40.744213Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T14:07:07.829632Z",
-     "start_time": "2024-11-25T14:07:06.056664Z"
     }
    },
+   "outputs": [],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.pipeline import make_pipeline\n",
     "\n",
     "from aeon.datasets import load_arrow_head, load_basic_motions\n",
     "from aeon.transformations.collection.feature_based import TSFresh, TSFreshRelevant"
-   ],
-   "outputs": [],
-   "execution_count": 2
+   ]
   },
   {
    "cell_type": "markdown",
@@ -73,27 +73,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:09.120656Z",
+     "start_time": "2024-11-25T14:07:09.090118Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:40.748159Z",
      "iopub.status.busy": "2020-12-19T14:30:40.747656Z",
      "iopub.status.idle": "2020-12-19T14:30:40.795200Z",
      "shell.execute_reply": "2020-12-19T14:30:40.795889Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T14:07:09.120656Z",
-     "start_time": "2024-11-25T14:07:09.090118Z"
     }
    },
-   "source": [
-    "X, y = load_arrow_head()\n",
-    "n_cases = 24\n",
-    "X_train = X[:n_cases, :, :]\n",
-    "y_train = y[:n_cases]\n",
-    "X_test = X[n_cases : 2 * n_cases, :, :]\n",
-    "y_test = y[n_cases : 2 * n_cases]\n",
-    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -103,7 +95,15 @@
      ]
     }
    ],
-   "execution_count": 3
+   "source": [
+    "X, y = load_arrow_head()\n",
+    "n_cases = 24\n",
+    "X_train = X[:n_cases, :, :]\n",
+    "y_train = y[:n_cases]\n",
+    "X_test = X[n_cases : 2 * n_cases, :, :]\n",
+    "y_test = y[n_cases : 2 * n_cases]\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -113,30 +113,25 @@
     "\n",
     "There are two versions of TSFresh feature extractors wrapped in aeon. The\n",
     "first is the unsupervised\n",
-    "`TSFresh` which by default extracts all 4662 features. See the\n",
+    "``TSFresh`` which by default extracts all 4662 features. See the\n",
     "documentation for parameter configuration."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:16.339473Z",
+     "start_time": "2024-11-25T14:07:11.573523Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:40.829452Z",
      "iopub.status.busy": "2020-12-19T14:30:40.828907Z",
      "iopub.status.idle": "2020-12-19T14:30:53.049755Z",
      "shell.execute_reply": "2020-12-19T14:30:53.050249Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T14:07:16.339473Z",
-     "start_time": "2024-11-25T14:07:11.573523Z"
     }
    },
-   "source": [
-    "t = TSFresh()\n",
-    "Xt = t.fit_transform(X_train)\n",
-    "Xt2 = t.transform(X_test)\n",
-    "print(f\"Train shape = {Xt.shape} test shape = {Xt2.shape}\")"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -146,32 +141,32 @@
      ]
     }
    ],
-   "execution_count": 4
+   "source": [
+    "t = TSFresh()\n",
+    "Xt = t.fit_transform(X_train)\n",
+    "Xt2 = t.transform(X_test)\n",
+    "print(f\"Train shape = {Xt.shape} test shape = {Xt2.shape}\")"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "The second is `TSFreshRelevant` which uses `y` to select the most\n",
-    "relevant features."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "The second is ``TSFreshRelevant`` which uses ``y`` to select the most\n",
+    "relevant features."
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "t = TSFreshRelevant()\n",
-    "t.fit(X_train, y_train)\n",
-    "Xt = t.transform(X_test)\n",
-    "Xt.shape"
-   ],
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-25T14:07:32.455607Z",
      "start_time": "2024-11-25T14:07:26.124172Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -185,7 +180,12 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 5
+   "source": [
+    "t = TSFreshRelevant()\n",
+    "t.fit(X_train, y_train)\n",
+    "Xt = t.transform(X_test)\n",
+    "Xt.shape"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -198,26 +198,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:07:41.090159Z",
+     "start_time": "2024-11-25T14:07:36.403997Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:30:53.062147Z",
      "iopub.status.busy": "2020-12-19T14:30:53.061631Z",
      "iopub.status.idle": "2020-12-19T14:31:09.307275Z",
      "shell.execute_reply": "2020-12-19T14:31:09.307781Z"
-    },
-    "ExecuteTime": {
-     "end_time": "2024-11-25T14:07:41.090159Z",
-     "start_time": "2024-11-25T14:07:36.403997Z"
     }
    },
-   "source": [
-    "classifier = make_pipeline(\n",
-    "    TSFresh(default_fc_parameters=\"efficient\", show_warnings=False),\n",
-    "    RandomForestClassifier(),\n",
-    ")\n",
-    "classifier.fit(X_train, y_train)\n",
-    "classifier.score(X_test, y_test)"
-   ],
    "outputs": [
     {
      "data": {
@@ -230,39 +223,34 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "source": [
+    "classifier = make_pipeline(\n",
+    "    TSFresh(default_fc_parameters=\"efficient\", show_warnings=False),\n",
+    "    RandomForestClassifier(),\n",
+    ")\n",
+    "classifier.fit(X_train, y_train)\n",
+    "classifier.score(X_test, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "For convenience and consistency of use we also have hard coded TSFresh classifier,\n",
     "regressor and clusterer."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from aeon.classification.feature_based import TSFreshClassifier\n",
-    "from aeon.clustering.feature_based import TSFreshClusterer\n",
-    "\n",
-    "cls = TSFreshClassifier(relevant_feature_extractor=False)\n",
-    "clst = TSFreshClusterer(n_clusters=2)\n",
-    "\n",
-    "cls.fit(X_train, y_train)\n",
-    "cls.score(X_test, y_test)\n",
-    "clst.fit(X_train)\n",
-    "print(cls.predict(X_test))\n",
-    "print(clst.predict(X_test))"
-   ],
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-25T14:08:02.405107Z",
      "start_time": "2024-11-25T14:07:50.878523Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -275,35 +263,41 @@
      ]
     }
    ],
-   "execution_count": 7
+   "source": [
+    "from aeon.classification.feature_based import TSFreshClassifier\n",
+    "from aeon.clustering.feature_based import TSFreshClusterer\n",
+    "\n",
+    "cls = TSFreshClassifier(relevant_feature_extractor=False)\n",
+    "clst = TSFreshClusterer(n_clusters=2)\n",
+    "\n",
+    "cls.fit(X_train, y_train)\n",
+    "cls.score(X_test, y_test)\n",
+    "clst.fit(X_train)\n",
+    "print(cls.predict(X_test))\n",
+    "print(clst.predict(X_test))"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
-    "By default, the `TSFreshClassifier` uses the supervised\n",
-    "`TSFreshRelevant` and the scitkit `RandomForestClassifier`.\n",
+    "By default, the ``TSFreshClassifier`` uses the supervised\n",
+    "``TSFreshRelevant`` and the scitkit ``RandomForestClassifier``.\n",
     " You can\n",
     "change this through the constructor"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from aeon.classification.sklearn import RotationForestClassifier\n",
-    "\n",
-    "cls = TSFreshClassifier(estimator=RotationForestClassifier(n_estimators=5))\n",
-    "cls.fit(X_train, y_train)\n",
-    "cls.score(X_test, y_test)"
-   ],
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-25T14:08:13.304452Z",
      "start_time": "2024-11-25T14:08:06.677532Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -317,34 +311,34 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 8
+   "source": [
+    "from aeon.classification.sklearn import RotationForestClassifier\n",
+    "\n",
+    "cls = TSFreshClassifier(estimator=RotationForestClassifier(n_estimators=5))\n",
+    "cls.fit(X_train, y_train)\n",
+    "cls.score(X_test, y_test)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "By default, the `TSFreshClusterer` uses the unsupervised `TSFresh`\n",
-    "and the `sklearn` clusterer `KMeans` with default parameters (which fits 8 clusters).\n",
-    " You can also configure this through the constructor."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "By default, the ``TSFreshClusterer`` uses the unsupervised ``TSFresh``\n",
+    "and the ``sklearn`` clusterer ``KMeans`` with default parameters (which fits 8 clusters).\n",
+    " You can also configure this through the constructor."
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "clst = TSFreshClusterer(estimator=KMeans(n_clusters=3))\n",
-    "clst.fit(X_train)\n",
-    "print(clst.predict(X_test))"
-   ],
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-25T14:08:38.025066Z",
      "start_time": "2024-11-25T14:08:33.300907Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -355,42 +349,37 @@
      ]
     }
    ],
-   "execution_count": 9
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "clst = TSFreshClusterer(estimator=KMeans(n_clusters=3))\n",
+    "clst.fit(X_train)\n",
+    "print(clst.predict(X_test))"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "The `TSFreshRegressor` uses the supervised\n",
-    "`TSFreshRelevant` and the scitkit `RandomForestRegressor`."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "The ``TSFreshRegressor`` uses the supervised\n",
+    "``TSFreshRelevant`` and the scitkit ``RandomForestRegressor``."
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "from aeon.regression.feature_based import TSFreshRegressor\n",
-    "\n",
-    "reg = TSFreshRegressor(relevant_feature_extractor=False)\n",
-    "from aeon.datasets import load_covid_3month\n",
-    "\n",
-    "X, y = load_covid_3month(split=\"train\")\n",
-    "reg.fit(X, y)"
-   ],
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-11-25T14:09:11.745540Z",
      "start_time": "2024-11-25T14:08:56.573376Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "TSFreshRegressor(relevant_feature_extractor=False)"
-      ],
       "text/html": [
        "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -797,6 +786,9 @@
        "  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>TSFreshRegressor(relevant_feature_extractor=False)</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;TSFreshRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TSFreshRegressor(relevant_feature_extractor=False)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "TSFreshRegressor(relevant_feature_extractor=False)"
       ]
      },
      "execution_count": 10,
@@ -804,14 +796,22 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 10
+   "source": [
+    "from aeon.regression.feature_based import TSFreshRegressor\n",
+    "\n",
+    "reg = TSFreshRegressor(relevant_feature_extractor=False)\n",
+    "from aeon.datasets import load_covid_3month\n",
+    "\n",
+    "X, y = load_covid_3month(split=\"train\")\n",
+    "reg.fit(X, y)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -821,29 +821,25 @@
     "\n",
     "``TSFresh`` transformers and all three estimators can be used with multivariate time \n",
     "series. The transform calculates the features on each channel independently then \n",
-    "concatenate the results. The full transform creates `777*n_channels` features."
+    "concatenate the results. The full transform creates ``777*n_channels`` features."
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 14,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T14:11:57.583864Z",
+     "start_time": "2024-11-25T14:11:57.545946Z"
+    },
     "execution": {
      "iopub.execute_input": "2020-12-19T14:31:09.311742Z",
      "iopub.status.busy": "2020-12-19T14:31:09.311092Z",
      "iopub.status.idle": "2020-12-19T14:31:09.380791Z",
      "shell.execute_reply": "2020-12-19T14:31:09.381304Z"
     },
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2024-11-25T14:11:57.583864Z",
-     "start_time": "2024-11-25T14:11:57.545946Z"
-    }
+    "scrolled": true
    },
-   "source": [
-    "X_train, y_train = load_basic_motions(split=\"train\")\n",
-    "X_test, y_test = load_basic_motions(split=\"test\")\n",
-    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -853,23 +849,23 @@
      ]
     }
    ],
-   "execution_count": 14
+   "source": [
+    "X_train, y_train = load_basic_motions(split=\"train\")\n",
+    "X_test, y_test = load_basic_motions(split=\"test\")\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "tsfresh = TSFresh()\n",
-    "X = tsfresh.fit_transform(X_train, y_train)\n",
-    "X.shape"
-   ],
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
     "ExecuteTime": {
      "end_time": "2024-11-25T14:12:19.453228Z",
      "start_time": "2024-11-25T14:11:58.795027Z"
+    },
+    "collapsed": false,
+    "pycharm": {
+     "is_executing": true
     }
    },
    "outputs": [
@@ -884,14 +880,18 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 15
+   "source": [
+    "tsfresh = TSFresh()\n",
+    "X = tsfresh.fit_transform(X_train, y_train)\n",
+    "X.shape"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": ""
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {