From d2d6748fa737b29a568443248ab73c8726580f7c Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Wed, 26 Feb 2025 14:39:59 +0530
Subject: [PATCH 01/11] check_fit_det resolve trial for  sast,rsat

---
 aeon/base/_base.py             | 2 ++
 aeon/testing/testing_config.py | 8 ++++----
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/aeon/base/_base.py b/aeon/base/_base.py
index 41ac7010f3..83bcfe4fd7 100644
--- a/aeon/base/_base.py
+++ b/aeon/base/_base.py
@@ -444,5 +444,7 @@ def _clone_estimator(base_estimator, random_state=None):
 
     if random_state is not None:
         _set_random_states(estimator, random_state)
+        if hasattr(estimator, "seed"):
+            estimator.seed = random_state
 
     return estimator
diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py
index 3d05d6679d..7ee0b0ee03 100644
--- a/aeon/testing/testing_config.py
+++ b/aeon/testing/testing_config.py
@@ -48,10 +48,10 @@
         "check_save_estimators_to_file",
     ],
     # needs investigation
-    "SASTClassifier": ["check_fit_deterministic"],
-    "RSASTClassifier": ["check_fit_deterministic"],
-    "SAST": ["check_fit_deterministic"],
-    "RSAST": ["check_fit_deterministic"],
+    # "SASTClassifier": ["check_fit_deterministic"],
+    # "RSASTClassifier": ["check_fit_deterministic"],
+    # "SAST": ["check_fit_deterministic"],
+    # "RSAST": ["check_fit_deterministic"],
     "MatrixProfile": ["check_persistence_via_pickle"],
     # missed in legacy testing, changes state in predict/transform
     "FLUSSSegmenter": ["check_non_state_changing_method"],

From fe94723965f5f895cc357ddf81cf851c464dfd2d Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Wed, 26 Feb 2025 15:29:37 +0530
Subject: [PATCH 02/11] Commented parts removed

---
 aeon/testing/testing_config.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py
index 7ee0b0ee03..8d3e8c9b3e 100644
--- a/aeon/testing/testing_config.py
+++ b/aeon/testing/testing_config.py
@@ -47,11 +47,6 @@
         "check_persistence_via_pickle",
         "check_save_estimators_to_file",
     ],
-    # needs investigation
-    # "SASTClassifier": ["check_fit_deterministic"],
-    # "RSASTClassifier": ["check_fit_deterministic"],
-    # "SAST": ["check_fit_deterministic"],
-    # "RSAST": ["check_fit_deterministic"],
     "MatrixProfile": ["check_persistence_via_pickle"],
     # missed in legacy testing, changes state in predict/transform
     "FLUSSSegmenter": ["check_non_state_changing_method"],

From ed3dd8000aa293cf109766a01ab4362e4a8b8cdb Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sat, 1 Mar 2025 01:17:43 +0530
Subject: [PATCH 03/11] seed replaced with random_state

---
 aeon/base/_base.py                                   |  2 --
 aeon/classification/shapelet_based/_rsast.py         | 10 +++++-----
 aeon/classification/shapelet_based/_sast.py          | 10 +++++-----
 .../collection/shapelet_based/_rsast.py              | 12 ++++++------
 .../collection/shapelet_based/_sast.py               | 12 ++++++------
 5 files changed, 22 insertions(+), 24 deletions(-)

diff --git a/aeon/base/_base.py b/aeon/base/_base.py
index 83bcfe4fd7..41ac7010f3 100644
--- a/aeon/base/_base.py
+++ b/aeon/base/_base.py
@@ -444,7 +444,5 @@ def _clone_estimator(base_estimator, random_state=None):
 
     if random_state is not None:
         _set_random_states(estimator, random_state)
-        if hasattr(estimator, "seed"):
-            estimator.seed = random_state
 
     return estimator
diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py
index 48025229b2..07d01a8fb1 100644
--- a/aeon/classification/shapelet_based/_rsast.py
+++ b/aeon/classification/shapelet_based/_rsast.py
@@ -30,7 +30,7 @@ class RSASTClassifier(BaseClassifier):
     "None"=Extract randomly any length from the TS
     nb_inst_per_class : int default = 10
         the number of reference time series to select per class
-    seed : int, default = None
+    random_state : int, default = None
         the seed of the random generator
     estimator : sklearn compatible classifier, default = None
         if None, a RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)) is used.
@@ -68,7 +68,7 @@ def __init__(
         n_random_points=10,
         len_method="both",
         nb_inst_per_class=10,
-        seed=None,
+        random_state=None,
         classifier=None,
         n_jobs=1,
     ):
@@ -77,7 +77,7 @@ def __init__(
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        self.seed = seed
+        self.random_state = random_state
         self.classifier = classifier
 
     def _fit(self, X, y):
@@ -100,7 +100,7 @@ def _fit(self, X, y):
             self.n_random_points,
             self.len_method,
             self.nb_inst_per_class,
-            self.seed,
+            self.random_state,
             self.n_jobs,
         )
 
@@ -110,7 +110,7 @@ def _fit(self, X, y):
                 if self.classifier is None
                 else self.classifier
             ),
-            self.seed,
+            self.random_state,
         )
 
         self._pipeline = make_pipeline(self._transformer, self._classifier)
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index c5ef836c63..89209c5da6 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -32,7 +32,7 @@ class SASTClassifier(BaseClassifier):
         the stride used when generating subsquences
     nb_inst_per_class : int default = 1
         the number of reference time series to select per class
-    seed : int, default = None
+    random_state : int, default = None
         the seed of the random generator
     estimator : sklearn compatible classifier, default = None
         if None, a RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)) is used.
@@ -71,7 +71,7 @@ def __init__(
         length_list=None,
         stride: int = 1,
         nb_inst_per_class: int = 1,
-        seed: Optional[int] = None,
+        random_state: Optional[int] = None,
         classifier=None,
         n_jobs: int = 1,
     ) -> None:
@@ -80,7 +80,7 @@ def __init__(
         self.stride = stride
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        self.seed = seed
+        self.random_state = random_state
 
         self.classifier = classifier
 
@@ -104,7 +104,7 @@ def _fit(self, X, y):
             self.length_list,
             self.stride,
             self.nb_inst_per_class,
-            self.seed,
+            self.random_state,
             self.n_jobs,
         )
 
@@ -114,7 +114,7 @@ def _fit(self, X, y):
                 if self.classifier is None
                 else self.classifier
             ),
-            self.seed,
+            self.random_state,
         )
 
         self._pipeline = make_pipeline(self._transformer, self._classifier)
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index 199e52ed1c..0049ad79eb 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -64,7 +64,7 @@ class RSAST(BaseCollectionTransformer):
 
     nb_inst_per_class : int default = 10
         the number of reference time series to select per class
-    seed : int, default = None
+    random_state : int, default = None
         the seed of the random generator
     n_jobs : int, default -1
         Number of threads to use for the transform.
@@ -103,14 +103,14 @@ def __init__(
         n_random_points: int = 10,
         len_method: str = "both",
         nb_inst_per_class: int = 10,
-        seed: Optional[int] = None,
+        random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parllel Processing
     ):
         self.n_random_points = n_random_points
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        self.seed = seed
+        self.random_state = random_state
         self._kernels = None  # z-normalized subsequences
         self._cand_length_list = {}
         self._kernel_orig = []
@@ -144,9 +144,9 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "RSAST":
         X_ = np.reshape(X, (X.shape[0], X.shape[-1]))
 
         self._random_state = (
-            np.random.RandomState(self.seed)
-            if not isinstance(self.seed, np.random.RandomState)
-            else self.seed
+            np.random.RandomState(self.random_state)
+            if not isinstance(self.random_state, np.random.RandomState)
+            else self.random_state
         )
 
         classes = np.unique(y)
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index ffc513815c..95a2a6ac8a 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -56,7 +56,7 @@ class SAST(BaseCollectionTransformer):
         the stride used when generating subsequences
     nb_inst_per_class : int, default = 1
         the number of reference time series to select per class
-    seed : int, default = None
+    random_state : int, default = None
         the seed of the random generator
     n_jobs : int, default -1
         Number of threads to use for the transform.
@@ -97,7 +97,7 @@ def __init__(
         lengths: Optional[np.ndarray] = None,
         stride: int = 1,
         nb_inst_per_class: int = 1,
-        seed: Optional[int] = None,
+        random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parallel processing
     ):
         super().__init__()
@@ -111,7 +111,7 @@ def __init__(
         self._source_series = []  # To store the index of the original time series
         self.kernels_generators_ = {}  # Reference time series
         self.n_jobs = n_jobs
-        self.seed = seed
+        self.random_state = random_state
 
     def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
         """Select reference time series and generate subsequences from them.
@@ -135,9 +135,9 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
         )
 
         self._random_state = (
-            np.random.RandomState(self.seed)
-            if not isinstance(self.seed, np.random.RandomState)
-            else self.seed
+            np.random.RandomState(self.random_state)
+            if not isinstance(self.random_state, np.random.RandomState)
+            else self.random_state
         )
 
         classes = np.unique(y)

From c690af4aea43aab647f9311bf7baf2f9c7807b04 Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sat, 1 Mar 2025 01:29:46 +0530
Subject: [PATCH 04/11] sast example corrected

---
 examples/transformations/sast.ipynb | 1706 ++++++++++++++++++++++++++-
 1 file changed, 1657 insertions(+), 49 deletions(-)

diff --git a/examples/transformations/sast.ipynb b/examples/transformations/sast.ipynb
index f7dbd9c251..a77ae51191 100644
--- a/examples/transformations/sast.ipynb
+++ b/examples/transformations/sast.ipynb
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:46.448396Z",
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.908710Z",
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.923023Z",
@@ -102,15 +102,7 @@
      "shell.execute_reply": "2020-12-19T14:32:52.164864Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sast = SAST()\n",
     "sast.fit(X_train, y_train)\n",
@@ -133,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.168847Z",
@@ -146,11 +138,415 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-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-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     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-2 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-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 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-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  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-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  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-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  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-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* 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-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  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-2 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-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><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><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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifierCV</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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>"
+       "       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",
@@ -158,7 +554,7 @@
        "       2.15443469e+02, 1.00000000e+03]))"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -177,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.289448Z",
@@ -201,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:53.312125Z",
@@ -215,10 +611,10 @@
     {
      "data": {
       "text/plain": [
-       "0.9795918367346939"
+       "0.9090909090909091"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.012465Z",
@@ -265,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.017692Z",
@@ -278,17 +674,421 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-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-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     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-3 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-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 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-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  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-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  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-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  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-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* 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-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  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-3 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-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
        "                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
+       "       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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
        "                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">SAST</label><div class=\"sk-toggleable__content\"><pre>SAST()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">RidgeClassifierCV</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "       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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">SAST</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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>"
+       "       2.15443469e+02, 1.00000000e+03]))</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('sast', SAST()),\n",
@@ -298,7 +1098,7 @@
        "       2.15443469e+02, 1.00000000e+03])))])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -320,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.425026Z",
@@ -333,10 +1133,10 @@
     {
      "data": {
       "text/plain": [
-       "0.956268221574344"
+       "0.8636363636363636"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -359,25 +1159,429 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</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\">SASTClassifier</label><div class=\"sk-toggleable__content\"><pre>SASTClassifier(seed=42)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-4 {\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-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     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-4 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-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 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-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  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-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  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-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  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-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* 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-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  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-4 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-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SASTClassifier(random_state=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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SASTClassifier<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>SASTClassifier(random_state=42)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
-       "SASTClassifier(seed=42)"
+       "SASTClassifier(random_state=42)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "clf = SASTClassifier(seed=42)\n",
+    "clf = SASTClassifier(random_state=42)\n",
     "clf"
    ]
   },
@@ -390,19 +1594,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">SASTClassifier</label><div class=\"sk-toggleable__content\"><pre>SASTClassifier(seed=42)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-5 {\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-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     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-5 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-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 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-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  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-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  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-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  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-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* 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-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  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-5 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-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SASTClassifier(random_state=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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SASTClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SASTClassifier(random_state=42)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
-       "SASTClassifier(seed=42)"
+       "SASTClassifier(random_state=42)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -420,16 +2028,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9650145772594753"
+       "0.8636363636363636"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -453,12 +2061,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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       "text/plain": [
        "<Figure size 1500x300 with 5 Axes>"
       ]
@@ -482,12 +2090,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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zr4VVr1s9ExGJRtu/hFevgQ0fWD0TEYlGGz80MWjbF1bPRESi0eo34NVp5ppMolJca98wZcoUpkyZ0uRrGRkZzJs3z+O5GTNmcOKJJ7Jr1y569OhBcXExL7zwAi+//DKTJk0CYObMmXTv3p358+dzzjnnsH79ej766CMWLVrESSedBMDzzz/PKaecwsaNGxkwYEBrpy3iuyPb4D8XQcUh2PA+DJgCaTlWz0pEokXBGnj5EqgphS3zTQxKbGf1rEQkWuz6DmZeDvXVsHMh3LUNYmKtnpWIRIvN80wigaMe9q+F6ausnpGIRJPVb8CbN5lxyV74yQJr5yOWCHhN9OLiYmw2G+3btwdg6dKl1NbWMnny5MZjcnNzycvLY+HChQB8++23ZGRkNC6gA5x88slkZGQ0HnO06upqSkpKPL5E/Kb8sLlwrDhkHjvssGeJtXOSkKIYJAFVvBdeucIsoAPUVUGBLh7FRTFIAurQFnj1arOADlBVBIc2WTolCS2KQRJQ+1bAf39oFtABinZC6X5LpyShRTFIAmrHN/D2z1yPC1ZDTYV18xHLBHQRvaqqit/+9rdMmzaN9PR0AAoKCkhISKBDhw4ex2ZnZ1NQUNB4TFZW1jGfl5WV1XjM0R588MHG+ukZGRl0797dzz+NRK3aSph9DRzZ6vn8vuXWzEdCkmKQBExVsVlAL/XsL6IYJO4UgyRgyg7CK5dB5RHP5xWDxI1ikARM0S6YdSXUlns+rxgkbhSDJGAObjTrQfU1rucc9WYhXaJOwBbRa2trufrqq7Hb7Tz99NPHPd7hcGCz2Rofu4+9HePu7rvvpri4uPFr9+7dbZ+8iJPdDnN+Aru/M4+T3W7+7FtmzZwkJCkGSUDU18J/fwAH1prH7jFor2KQuCgGSUDUVJgM9MId5rFikHihGCQBUVlkEgnKGrLOdS0mXigGSUCU7odXLjdJTaAYJIFZRK+treXKK69k+/btzJs3rzELHSAnJ4eamhoKCws93nPgwAGys7Mbj9m//9jtWQcPHmw85miJiYmkp6d7fIn4bN7vYd07ZhyfCte/DSmZ5vG+5eBwWDY1CS2KQeJ3DgfM/YWreVZyB7jxQ4hLMo914iZuFIPE7+z18NbNsLehfF1aLtzg1lBUWaDiRjFI/K6uBl67Dg5uMI879oHr3nS9rht54kYxSPyuptzsginaZR7nDIUrX3a9rvOgqOT3RXTnAvrmzZuZP38+nTp18nh99OjRxMfHezQgzc/PZ82aNYwbNw6AU045heLiYr7//vvGY7777juKi4sbjxEJuO+ehW+fMmNbLFz5EuSOgK6jzHOVha7MLBERf1vwMKx4xYxjE+HqVyFrkDmBA9PsuLLQ+/tFRNrK4YCP7oYN75nHCWlw7X8hezB07GueK1htFrlERPzN4YB3b4MdX5nHKZ3g2jcgdxQkdzTPKaFJRAKlvg7e+BHkrzCP07vBtNeh+0kQm2Ce0428qNTqRfSysjJWrFjBihUrANi+fTsrVqxg165d1NXVcfnll7NkyRJeeeUV6uvrKSgooKCggJoac5KdkZHBTTfdxB133MGnn37K8uXLue666xg6dCiTJk0CYNCgQZx77rncfPPNLFq0iEWLFnHzzTczdepUBgwY4L+fXsSbDe/Dh79xPT7/Ueh/thnnjnI9r0xQEQmEFbPgiwdcjy99FnqeYsa5I13P71sR1GmJSJRY9DR8/6wZx8SZRALnDTxnMkF9NRxYZ838RCSyff4XWPWaGcclwTWvQae+YLO5zoMqDkGxSnaIiJ85HPDhXbDpI/M4MQOuewPSu0BcAmTnmecPb3aVeZGo0epF9CVLljBy5EhGjjR/vH71q18xcuRI7r33Xvbs2cO7777Lnj17GDFiBF26dGn8WrhwYeNnPP7441x88cVceeWVnHrqqaSkpDB37lxiY2Mbj3nllVcYOnQokydPZvLkyQwbNoyXX375mPmI+N2epfDGTUBDZsPpd8CYG12veyxgaQuPiPjZ1s/h3dtdjyf/GYZc4nrscSNPMUhE/GzdO/DxPa7HF/wN+k10PdZ5kIgE0tKX4MtHGh7Y4LJ/Qvexrte76jxIRAJo4ZOw5AUzjomHq142u4Gd3M+D8lcGd25iubjWvmHChAk4mtk21dxrTklJScyYMYMZM2Z4PaZjx47MnDmztdMT8c2R7abuVV2leTz0Cjjr957HuAfNvTpxExE/2r/WNBK115nHJ/4PnHKb5zFdtRtGRAJk13fw1v/QmEgw/jcw8jrPY47ZkXcjIiJ+sXk+vPdL1+Nz/wqDLvA8xj0G7V0Ggy8KztxEJPKteRPm3et6fNFT0Ge85zFdR7kW2fcug95nBG9+YrmANBYVCUsVR0zn5YpD5nHP0+Civ5ttg+7SsiG9qxnnrzCNt0REfFWyD165AqpLzOMB55mLx6NjUKd+kNDOjHUjT0T85fBWePVqqKsyj4dfAxPuPva4LsPA1nAJoRgkIv6Svwpe/yE4Gq6tTr4VTv7pscd57IZRMoGI+MnOhTDHLeac+TsYfvWxxykGRTUtoosA1FbBq9fA4S3mceYAuHomxCU2fbwzcNaUud4jItJWVSXwypVQstc87joaLnsBYmKPPTYmFrqMMOOSPVB2IGjTFJEIVX4IZl4GlUfM497j4YInj72JB5CQCp0HmvGBdVBbGbx5ikhkKtptEglqyszjQReacnZNSe8CaV3MeN9KsNuDM0cRiVwHN5n1oPqGhukjr4cz7mz62MwBEJ9ixiopFXW0iC7icMDbP4Xdi8zjdtmmcURyB+/v6XrUNkIRkbaqrzOZV/tXm8fte5oGWgkp3t/TVTWJRcRPaqtMBnrhdvM4a7Cp/xmX4P09znIKjnooWB34OYpI5KoqMQvoZQXmcbcT4dLnIKaZpQpnDKouhiPbAj9HEYlcZQfhlcugqsg87jsRpj7edCIBQGwcdBluxkW7TCKCRA0toovsWgRr55hxfCpMew3a92j+PWqqJSL+svkT2PqZGSd3gOvehHadm3+PR28G3cgTER+seQP2LDbjtC5w7euQlNH8e3JHuMY6DxIRXyx7CQ6uN+OOfeCa2RCf3Px7VE5BRPxl0dNmMRwgZyhc+RLExjf/Ho8YtCJgU5PQo0V0EedJG8BZ93gGRG904iYi/uIeg855EDL7H/89Ho39tIAlIj444BaDpj4BGd2O/x7tyBMRfzmwwTW+5FlI7XT892hHnoj4y0G3GHT5i5CYdvz3HNNkXaKFFtFFSgtc404tWLwCky3asY8ZF6yG+lr/z0tEooN7DGrJAjpAh16uklP7lpmyVCIibdGWGJSdBzENWVq6eBQRX5Tmu8ad+rXsPV20I09E/MQZg2wx0KF3y96jZIKopUV0kZJ9rnFaTsvf58xGr6vyzOISEWmNtsQgm80Vg8oPuhqSioi0lvsCVktjUFwiZA8x40ObTU1jEZG2cMag2MTme1K5S+1kesgAFKwy/WVERNqipCEGpWaZeuct0aE3JDaUvtNumKiiRXQR9wys9NyWv09beETEHxpjkM00Nm6pXGVAiIgfOBewEjMgIbXl72vMwnJA/kq/T0tEooQzBqV38d7IrynOGFRbAYc2+n9eIhL56uug/IAZp3dp+ftiYlz9YcoKPJOiJKJpEV3EeeIWEw/JHVv+vq6qSSwifuCMQamdj9/Exp16M4iIrxwOVwZWa3bjgXoziIjvaiuhstCM01qxgAVqsi4ivis/AA67Gbc2Bmk9KCppEV3EuYCV1sXcUWypnGGmbhboxE1E2sZud2Wityb7AXTiJiK+qyqGukozbm0M0o08EfGV+47gVi+i6zxIRHzkUdJON/Lk+LSILtGtrhoqDptxazOwEttB5gAzPrAOaqv8OzcRiXzlB8FRb8atPXFL6+Iq/7JvuZqLikjr+XLx2HkgxCWbsS4eRaQtfIlBXYYDDeVfdCNPRNqixJdFdJX3jUZaRJfo5pH90MpFdHDdfbTXwf41/pmTiESPtjT0c7LZXCdvVcVwZJv/5iUi0cGXGBQbB12GmXHRTqg44r95iUh08CUGJaVDZn8zLlhjkqNERFrDlxiU0Q1SMs1YCU1RQ4voEt3a2lTUqasa+4mIDzxu5PkYg7SVWURay9cYpCwsEfGFr9dizhhkr4X9a/0zJxGJHh4xqJWZ6Dab61qsshAKd/htWhK6tIgu0a3UrYtymzLRtYAlIj7wOQapFp+I+KDExxikG3ki4gtfY5B6M4iIL3wpKQVaD4pCWkSX6OZrBlb2EIiJM2OduIlIa/krAwsUg0Sk9XyOQe438nTxKCKt5EtjUThqV7BikIi0ks+L6LqRF220iC7Rzdfsh/gks5AOcHAjVJf6Z14iEh18jUGpnaB9DzPOXwn2ev/MS0Sigy+1QAE69oXEdDPWxaOItJavC1g5Q8EWa8bKAhWR1nI2Fo1NhOQOrX+/buRFHS2iS3TzNQML3DJBHZC/yucpiUgU8XU3DLgyIGorzM08EZGWalzAskG77Na/PyYGcke4Pqskv9nDRUQ8OGNQUgYkpLT+/fHJkDXYjA+uh5py/81NRCKfMwaldzE1zlurXRakdzPj/BVgt/ttahKatIgu0c3XDCzQFh4RaTtnDIpNgJSObfsM1eITkbZyLnqndobY+LZ9hsd5kGKQiLSQw+GKQW3JQnfq2hCDHHYoWO37vEQkOtRWQlWRGfsSg5zJBDVlcHizr7OSEKdFdIluzgWshDRITGvbZ3hs4dEiuoi0gjMGpeW0LfsBdCNPRNrGXg9l+8043ZeLR/VmEJE2qCqGukoz9mkBS03WRaQNfC0n5aT1oKiiRXSJXh7ZD23MQgfoPAjiksxYGVgi0lJ11VBx2Iz9kf0AOnETkZYrPwiOhj4K/rp41HmQiLSUvxawdCNPRNqiJBAxSOdBkU6L6BK9qkuhtqFuni+L6LFxkDPMjAu3Q8UR3+cWbIe3woH1quElEkwe9dB9iEFJGdCpvxnvXwN1Nb7NywoHN8LBTebmpogEhz9K2gFkdIeUTma8d1n4/R47HLB/rTkXCre5i4Qzf8WgrMGmKSCE5wKW3W76ahXtsnomItHFXzHIPaEpHG/k1dfBvhVQss/qmYSFOKsnIGIZfzQVdeo6CvZ8b8b5K6DvWb59XjBtng+vXGbGKZ2g1+nQ+wzoPR469W17iQkRaZ4/moo65Y40Nfjqa+DAWs+tzaFu9Rvw5k1m3C67If40fHXoZenURCKav2KQzWaysLbMg8ojZiGoQ0/f5xcsi/8JH9xpxund3GLQ6ZDRzdq5iUQyf12LxSVATh7sXQqHt0BlESS393V2wfPlI/DFA2bcoZeJP70aYpAvC3si0jx/xaDkDtCxDxzZZvoy1Ne2vc+MFT75HXz3DzPu1M91HtTrdEjNtHZuIUiL6BK9St3utPl6gnJ0Lb5wWkT/7hnXuOIwrHvbfIG5qHZf0Grf3YoZikQmf8agrqNg9X/NeO+y8FpEX/S0a1y2H1a/br4A2vdw3dTrdbpvdZtFxFOJn8+Dtswz433LwmcR3eHwjEEle2DlLPMF0LGva0G91xnQrrM18xSJRH6NQaPMIjqYhKY+E3z7vGCpr4Pvn3U9Ltxhvpb9xzzOHOC2oHVa25vQi8ix/JWJDuY86Mg2qKsyO/y7DPPt84KlphyW/tv1+PAW87XkX+Zxdp4rBvUcZ3ZAR7lWl3P58ssvueCCC8jNzcVms/H22297vO5wOLj//vvJzc0lOTmZCRMmsHbtWo9jqquruf3228nMzCQ1NZULL7yQPXv2eBxTWFjI9ddfT0ZGBhkZGVx//fUUFRW1+gcU8cqvWaBhWgerqhi2fWHG8amQmO75euk+WDUb3rkFnsiDv42Ad38Oh9R1WsRn/twN49FcNIxiUPEe10VvYjoktPN8vWgXLJ8Jb90Mjw2EGWPg/Tu15VnEH/y9I88pnHozHFhnLnoBktpDXLLn60e2wtIX4Y0fwf/1g6dPgY/+F0r3B32qIhHH3zvynMLpPGjXt67+OCmdIDbB8/VDG2Hx8/Df6+HhPvDM6TD//vAsHyoSavzVlwHCtzfDlk9dDZ5TO0PMUXnW+9eYZINXr4aHesHzZ8HnD5rSyFGq1Yvo5eXlDB8+nKeeeqrJ1x9++GEee+wxnnrqKRYvXkxOTg5nn302paWu/8jTp09nzpw5zJ49m6+//pqysjKmTp1KfX194zHTpk1jxYoVfPTRR3z00UesWLGC66+/vg0/oogX/sx+6NQPEtLMOJxO3DZ9DPZaMx71A7hrO9z8GUy632TTH30xWbgdlr0E//1B0KcqEnH8GYNyhoEt1ozDKQatf881PuU2+M0OuGkenPU7k/HgrHHqdHizuZic89OgTlMkIgVqR144xaB177rGZ94Dv90JN3wA438LPcZBzFHbsQ+sg0V/h/d/Fdx5ikQif2aBhuuNvPVuMWjKw/DbXfCDd+D0O6DbWNe5HQAOKFgFXz8O8+4N+lRFIo6/GotC+DZZd49BlzwDv9kJ174J434OXUYAbqV9HXaT/LTgr6YMVZRqdTmXKVOmMGXKlCZfczgcPPHEE9xzzz1ceumlALz00ktkZ2cza9YsfvKTn1BcXMwLL7zAyy+/zKRJkwCYOXMm3bt3Z/78+ZxzzjmsX7+ejz76iEWLFnHSSScB8Pzzz3PKKaewceNGBgwY0NafV8TFnxlYMTGmocSOr6Bkr8lQSsv27TODYd07rvGgC0yT1K6jzddpvzQNCvcuge1fwfYvYfd3ZtH9wDoo3gsZXa2bu0i482cGVkIKZA0y2QIH1kNNhXku1LmfuA26wNQP7H6i+Trj11BbZfpNbP/SfO1ZAo56k7lVVQJJ6d4/W0Sa588YlJZjPqN0H+SvNI3yYlqdqxN8HjFoKsQlQq9TzdeZd5ttzrsWuWJQ/gpzEbntC1OGIVaVMUXarHER3WZ6ovgi8wSzq7a23DTICwd2O6yfa8axCdB/MsQnm1I0znI0VSXmnGf7l7B9gam3DLBlvilHpd5VIm3njEFJGb5fN+UMA1tMw0JzmNzIq6s2SZUAiRmmbF1cAvSfZL4AKgth50LXedCBdeb5zfPh7D9aM2+L+fXsdvv27RQUFDB58uTG5xITExk/fjwLFy4EYOnSpdTW1nock5ubS15eXuMx3377LRkZGY0L6AAnn3wyGRkZjceI+MyfGVgQfllYNeVm+w6YrTs9Tj72mLgEU/tqwm/gxvfNwrrTzm+CM0+RSOXPDCxwdYZ31LsuskJZ2QFzUgZmN0/WoGOPiU8yGeln/Q5u+gTG/Mg877Cbm3oi0nbODKzYBP/U2XVmYVWXmDIooe7QFtfFYLexTSdUJKRCv4lw9h/gfz6HIZeY52vKzM0CEWk7Zwxql+X7DamYWOgy3IyLd0H5Id8+Lxj2LnGdC/Y5s+nEgKR0OOEcOOcv8NOvXX23SvNdpahEpPUcDtfvn69Z6ACJ7UwPAzDnFrVVvn9moG37wpyzAQyYYtZ+jpbcAQaeD1Meglu+bchOBw6sjdqyUn5dRC8oMBkt2dmed5Kzs7MbXysoKCAhIYEOHTo0e0xWVtYxn5+VldV4zNGqq6spKSnx+BJplnsGVjs/LGB5bOEJg7uPm+e56l8NnGpOPo+n16mu8Y6vAzOvMKUYJK3mPHFLTDcnXr4Kt1p8G94DHGY86MKWZVMpBnmlGCSt1njxmOOfbMajm6yHuvXuu/EubNl7errFoJ2KQe4Ug6RV7PWmmTj4ZwELwq+ki/uO4MFtiUFKaHKnGCStUlVkmoCC/2OQvc7sDg517iXtWhqDep3mGu+MzgTngOyztB11Iu5wOI557mhHH9PU8c19zoMPPtjYhDQjI4Pu3bu3YeYSVZzZDymZTd91a62wu3ic6xq3NGh2O9HVbEInbh4Ug6RVHA5XDPJHFjqEXy2+tsQgXTx6pRgkrVJbBZUNGUT+unj02JEXoedB7hePOxSD3CkGSauUHzQ75yBAMSjEz4McDlcMssXCgPNa9j7FIK8Ug6RV/FkP3Smc1oPq62Dj+2Ycn+ra5XI8uhbz7yJ6To5ZCDg6W/zAgQON2ek5OTnU1NRQWFjY7DH79x/b9f7gwYPHZLk73X333RQXFzd+7d692+efRyKY3Q5lDf9O/RU02/eE5Ibt0PuWm5OjUOVe/yqpPfQ6vWXvS0hxZbse3mJqvwugGCStVF1q6naC/xbRs4aYsgwQ+idulYWmrh5ARg/X1sDjaZcFnfqb8b7lpiyVAIpB0kpl7vXQ/RSDwmkBq2iXa445w6BDr5a9L/MEk3wBpla6vT4g0wtHikHSKv4uaQfhdSOvYBUU7TTj3qe3vKRW7kiISzLjKM0C9UYxSFolIDEojBKadn5trscATmjox9ASPU6msdmoFtF917t3b3Jycpg3b17jczU1NSxYsIBx48YBMHr0aOLj4z2Oyc/PZ82aNY3HnHLKKRQXF/P99983HvPdd99RXFzceMzREhMTSU9P9/gS8arisNlmA5Dup0V0m82VCVpxCIpD+A/31s+hptSMB5xnmvm1VC/dfWyKYpC0ij8b+jnFJUB2nhkf3gxVxf753EDY+KErBg+6oHWlJJwxyF4Hu79v/tgoohgkrRKIGJTSETr0NuP8VSbLKVS5Z6G3tJQLmFjVs+FapLo4PLZrB4likLSKewxqqh9BW3TsYxoEgkkmCOWEJvcyCoMuaPn74hJNDwcwtd+Ldvl3XmFMMUhaJRAxKCcPYhrWVUL9Rl5bY1BKR8geYsYFq0P7ejNAWr2IXlZWxooVK1ixYgVgmomuWLGCXbt2YbPZmD59Og888ABz5sxhzZo13HDDDaSkpDBt2jQAMjIyuOmmm7jjjjv49NNPWb58Oddddx1Dhw5l0iTTAXbQoEGce+653HzzzSxatIhFixZx8803M3XqVAYMGOC/n16il7+bijqFSxbW+jbUv3Lq6V4HS4voIm0SjBgUyk3v2lKDz0kxSMR3JQGOQXWVcHCD/z7X33yJQSqnIOK7QMQgm80Vg8oPeH6PUNN4LWaDga1YwALFIBF/CMS1WFwiZA8244MbobrMP5/rb3Z7Q28qIDYR+k9u3fudJV0cdrMrL8q0ehF9yZIljBw5kpEjzR+oX/3qV4wcOZJ7770XgLvuuovp06dzyy23MGbMGPbu3csnn3xCWlpa42c8/vjjXHzxxVx55ZWceuqppKSkMHfuXGJjXY0NX3nlFYYOHcrkyZOZPHkyw4YN4+WXX/b15xUxApGBBZ5beEK1nEJ9LWz8wIwT2plu8K3R4yRTuw904ibSVoHIfoDwaKpVXQpbPzPjdjmm10JreDQXVQwSaZNgxKBQzcIqLYDd35lx5gDo3MoEHdUDFfFdMK7FQjUGHdwIhzaZcY+TIa3pcrVeqcGxiO8CHoMcoZvQtOd7V2PnfhMhMa3544/mcS0WfTEorrVvmDBhAo5mtkbZbDbuv/9+7r//fq/HJCUlMWPGDGbMmOH1mI4dOzJz5szWTk+kZQKdgQWhe+K2w63+Vf/JEJ/UuvcnpkGX4ebnO7geyg9Daif/z1MkkgUsBoVBLb7Nn0B9tRkPmgoxrbyfn55rSkYUboe9S6C2suV1/ETECMZumL3LYNQP/PfZ/rLhPaDhWqa1WegAWYMhuYM5l9r5jcnoam0cE4l2gYpBRzdZb02ZgmDxKKPQhhjUbYzpgVNfo2QCkbYqCUBNdDDnQUtfNON9yzwXnEOFrzEoypMJdMYn0SlQGVjpXVyNSvetNBdWocaXUi5Oqosu4ptAZT9kngDxKWYcqjfy2lqDz53z5K2+BvYs8X1OItEmUDGoy3AaG06F6o08X2NQTAz0aKiLXlloEgpEpHUCdS129I28ULT+Hdd40NTWvz8+GbqOMePC7aFdtkYkVDU2FrVBu1buBmnO0TfyQo3D4eoLExMHA85t/WekZkLngWa8b4XZZRxFtIgu0SlQ2Q/gygStLoYj2/z72b6y18P6hvpXcUnQ7+y2fY7H3Ud1hhdptUDFoNi4hkUsTLOp8kP++2x/qK2EzQ2NxZM7etY3b41eikEiPglUBlZimqs8yv61UFftv8/2h4ojrq3H7XtCzrC2fY6zuSgoBom0hTMGxSaanR3+kt4VUrPMeN/y0GsuemS7acYHZsG/fY+2fY5ikIhvnIvo7bLM9ZO/dB5k1lkgNG/k7VtumhID9D6j7fHXGYMc9bD7e//MLUxoEV2iU6AysCC0m4vu/t402gHoNwkS27Xtc3qcQmOmmWrxibSeRwwK0I08MNkBoWTrZ1BbbsYDz2v7SavqgYr4xnnxmJje9nMBb5znQfZa2L/Gv5/tq40fmAs+MLvxbLa2fU6U1wMV8ZkzBqXltP33sCk2mysTtKrIZGqHEmcGKLStjIKTYpBI29nrXTXBnVUE/CU2znWDvnC7q4xuqPBXDIriki5aRJfo5Mx+iImDFD/X8+4awnXR1/uhjAJAcnvIyTPjgjWh98dBJNQ5Y1BqZ4iN9+9nh3JvBo8yChe1/XM69ISM7ma8ezHU1fg2L5Fo4nB4LmD5Wyg3WfdXDMoZZm5AgLl4DLVsV5FQVlsFlUfM2N8LWBDaJV08ymr6EIO6n2SuYyHqFrBEfFZ2ABwNZXcDEYNCtaSLw+GKQbYYGNiGclJOvdx2E0dZbwYtokt0aty+k+P/ZlChevHoUf8qHk5oQ/0rd41lGBywa5FvnyUSTex2KGvIRA/EAlbXEI1BdTWw8UMzTkyHPuN9+zxnBkRdZejdLBAJZdUlUFthxoGOQaF08VhVAts+N+O0XOg6uu2fFRMLPU424/KDcGiz7/MTiRZlAdyNB6HbZL14L+xZbMZZQ6BT37Z/VkKq62bBoU1mUVBEWqY0QCXtnEJ1PejAeji8xYx7jIN2ndv+WWk50LEhhu1dCjUVvs8vTGgRXaJPXQ1UNNQJTg/AnceUjqbOJkDBKqiv8//3aIt9y6B4txn3GW+yyX2hbYQibVNxGOwNccHf5aQAOvSGxAwzDqWLx+1fml4RACecA3GJvn2eez1QxSCRlgtkSTuA7DxXhmQoxaBNH5tmxGCa+fmaRKGyUiJtE6imok6hWlpzw3uusS87gp2iuJyCiE+iNQb5qyqBk3M9yF7rukEYBbSILtEn0NkP4MrCqq2AQxsD8z1ay1/1r5x6qKGNSJsEsrExmIWh3BFmXFYAJfuaPTxoPE7c/BCD3LcRKgaJtFxJgGNQfBJkDTbjgxugptz/36Mt/B2D1GRdpG0CHYPadXaVfNu3wtQ/DgXu12KDFYNELBPoa7FO/Vwl30JqEd19PcjfN/KiJwZpEV2iT6AzsCD0tvA4HK46oLYYGHi+75+Z2sl0nwbIXwnVpb5/pkg0CHT2A4ReSRd7PWx434zjU0xjY1917GNKcgHs/i50dv2IhLpgxiCH3ZwjWK2mArbMN+OUTM+dLG2VOwLiU814h+qii7RYUK7FGjJBa8tNuROrlR9yZYt37Ou60eiLHieb6zqIuprEIj4JdAyKiYEuw824ZC+U7vf/92itw1tdzd67joGMrr5/ZpTuhtEiukSfQGc/wFENbZYG5nu0xoF1cGSrGfc8FVIz/fO5zi08jnrY9Z1/PlMk0kVjDNq50FVGq98kSEjx/TNtNlcMqikLjYU6kXAQ6AwsCL0YtGW+qw78wPNNTXNfxcZD9xPNuHQfFG73/TNFokE0xqAN77kaGQ6+0JzD+Cop3TQ5BjiwFiqO+P6ZItGgJMA10SH0YpBHU2M/7IQBaN8d2vcw4z2Loa7aP58b4rSILtEnGBlYuSNc9UBXv+75Pa2wzs9bmJ1UD1Sk9YKRgdV1jGu87D9QWRSY79NS/i6j4OSeTaoYJNIywYhB3ca6xt8/B7WVgfk+LRWoGOTRHyZ6srBEfBKMazH3GLToH9bvVlvn51rETiptJ9J67o1FgxGDvn3K+t1qgYpBPRtiUF1VaNwsCAItokv0CUb2Q2IajPqBGdeUwfz7A/N9Wsqj/tVU/31uT108irRaMGJQRlcYcokZVxyCBQ8F5vu0hN0O6xuaacUmmKai/tLT7eJRMUikZYKxGyZrMPQeb8ZFu2DhU4H5Pi1RV22aioJputz7DP99tnsMiqKtzCI+CUYWaM9TocsIM96/Bpa+GJjv0xKVRbB9gRlndPcs++mrKC2nIOIT5yJ6bCIkdwjM9zjhHFN6Eszv5tq3AvN9WqJoN+xrKO+ZPdQ1L3+IwmQCLaJL9AlGBhbAmb+DpPZmvPJV2P194L5Xcw5vNVv8wNwR9efd1rRs6NTfjPctC53mYSKhLBgZWABn/wniks34u2fhwIbAfa/m7F3qunHQ50yz/dhfOg8w9Y0Bdn0bOs3DREKZx3lQgBawbDaY8hDYGsqmfPUoFO8JzPc6nm0LoLrEjAdMgbgE/31211EQl2TGUXLxKOIz5wJWYgYkpAbme8TEwJSHXY8/+7N15U42fQT2hkz4QRf4p5SLU89TgIbP26EdeSIt4oxBaTn+/X10F5cI5/7V9fiT31u3VrLhPdfYX6VcnKKwMoEW0SX6BCMDC0zjzbN+53r8wa9NRmawrXvHNfbnFmYnZzkFe52phSUizXNmYMXEQ3LHwH2f9t3h9F+ZsaMePrzLmq2E691ikL9P3Gw2VwyqLnE1zBER75wXj6mdTV3vQMkaBCf+jxnXVZoLSCsEMgbFJbq2bBfvMln3IuKdw+G5gBVIPU6CYVebcVWRWUi3QqDKaoLJos0eYsYFq6Gq2L+fLxJpaiuhstCM07oE9nudcA70n2zGJXvh68cD+/28CWQM6tDLlZi6+3uor/Xv54cgLaJL9HFmYMWnmrIrgTT6RshqOLHJXwErZgb2+zUlEE0k3PVSOQWRVnG/eIwJ8J/hcbe7Gr5sX+CZiRAMDofrxM0WCwPO8//3UAwSaTm73XUeFOgFLIAJv4WUTma89q3gZ0rW18GGD8w4PhX6nuX/7+GRhaWaxCLNqi5xNfkNRgyadD8ktDPjpS9C/qrAf0931WWw9VMzbpcN3U/y//dojEEO2LXI/58vEkmCsRvP3TkPmsQpgG+ehCNBbkJeut/s1gXIPAGyBvr38202V0mX2grYt8K/nx+CtIgu0ccZONO7BG77jlNsnNnO7DT/D743+CveA3U1LTu2aDfsW27GOcPMnUJ/Uy0+kZarqzE1yiHw2Q8A8clwzgOuxx//r28N/hwOk2nZ0gZdBauhaKcZ9zoNUgKQee/RXFQxSKRZFYfMzhQIbEk7p+T2MPE+1+MPf+Nbgz+HAwp3trx0085voLKhhEP/s01M9DePeqDRsZVZpM2CVdKu8Xt0gTN+bcYOu4lBvuzKs9c3xKAW7i7eMs803AMYeH5gkicUg0RaLtgxKLMfnHKLGddXwye/a/7446mvNWs8LY1jG98HGo71Z0NRd1FW0kWL6BJdqkuhptSMg7GABdD7dP80+LPb4Z1b4fEh5mvBI8ev7efRUDQAWehgGhg6F+f3LIHaqsB8H5FIUBbk7AeAgVOhzwQzLtoFC2e07XPqa+G/P4AnhsLfhpnPOd624UDvhAGz28fZf2LnN9aUzRIJF8Eqaedu5HX+afBXUwEvX2Liz4zR8P3zx68vGowY1G2saZoMupEncjxWxKCTfwYd+5rxroWw5s22fU5VMfzrHBOD/nEKLPvP8a97AllGwUkJTSItV2pBDDrj12YnCphdwVs+bdvnlB2AZ8fDE3nw7Bmw6vXjl08JRgyKsl3BWkSX6OKxfSdIi+jge4M/hwM+uBOWN5SDKT8An/8ZHhsM799hmoc2JRgXjwA9GwJnfTXsXRK47yMS7oKd/QBmx8257g3+HjMZDK1hr4c5P3XFlJK9JpPisSHw8T3eGwY2nrjZYGCAsh9iYlzZ6JWFcHB9YL6PSCSwIgbFxPre4K+uxtzE2/a5eVy43ZwXPT4EPv2T2a58NLsd1jeUsIpNdNUl9bf4ZOg62oyPbHP1vRCRY3lciwUpBvmjwV9NBcy6ytX/6eAGePd2k1jgLbGptgo2f2LGyR08F5r8KTUTOjeUaNi3wiSNiUjTrIhBiWlw9h9djz/6betrh1cWwsuXwoG15nHBKnjrx/C3Ed4TmyqOwI6vzLh9D+gyvE3TP65O/SA1y4x3LfJtx2EY0CK6RBcrsh/A9wZ/n/4BlrxgxrYY8wWmUdfif5qMrNnXwq7vXO8p3e+qi5c5ADoP8P3n8MajnILqgYp4ZVUMyhro2eBvXisa/Dkc8P6vYM0b5nFMnOu1mlL49il4Yhi8+WPPOngHN8KhjWbc42RIy/bpR2iWahKLtIwVGVjgW4M/ez28dbMpiwCu2qJgLiq/+j+TlfXOrXDA7Sbanu9du3/6TQxsHxyVlRJpGati0AmTXTfSSveZhIKWqquG165z1RV27jyBoxKb7jQ30py2fgY1ZWY84PzANnJ2xiBHvWnuJyJNs+pabOiV0O1EMz60Cb5/ruXvrS6DV66E/avNY/cYVLLHe2LTxg/B3rCgPejCwJUyttlcMaim1DXPCKVFdIkuVmRgObW1wd9Xj3l2cr74Gbh9GZz0U9MkCwCH+bx/TYZ/ToJ17zRkjDYs1AcyCx1Ui0+kpazIfnDyaPA3B7Z/dfz3OBww715Y+m/zOCYOrpoJty6GUT802Z1gLtpWvw7PjYd/T4VNH5s45BSo7YNOikEiLWNlDGpLgz+HA+b+Ata9bR7HJcEP3oGffGUW5Z039eprzG69p0+GmZfDti+Cs4XZSeUURFrGymsx9wZ/C5/0XPD2pr7OJAk4m4MmpsNN8+DHn8Lgi49KbHoenhzVsOD+nWdZzUBfiykGibSMVTEoJqahV17DQvYXfzXlWY6nrhpeu9YkBgCkZMLPFsIN78MJ57qOayqxySMGXeSvn6RpUVTSRYvoEl2syn6AtjX4W/xPk4XudN7/wfCroGNvE4R/tdY07Grn9rPsWWy2PH/wa9dzgWoi4dS+J6R3M+Pd37e88alItLEyBrWlwd9Xj5oLTQBscMmzMGAKdD4BLnwSfrkWxrstzoPZNjjrSvjcLd4Nmuqvn6Jp2UMhoSHLdOc3vjUNE4lkVmVgQesb/DkcJrtq+cvmcUwcXPmyuWnWZRhc+iz8YhWc+gtIzHC9b8s8+M9FsOhp1/sGnHvs5/tT95NcJbMi/OJRxCdWxiCPBn818PFxGvzZ7eYmnrOUXVwyTPsv5I6AbmPgypfg58uPTWxaP9ckNq181TyVkObqTRMoUbSAJeKTUreSa+0CuEu2KV1Hwajrzbi6xHOdpyn1dfDGj0xiAJhznevnQGZ/8zs/7bXmE5s2fWSeS+sCXccE5EdqFEU38rSILtHFqproTsc0+HvK+7ErXzPbAp0m3gcn3ux5THIHUyZm+mq4+B+QNdjtxYYL0/Y9IWeYP2bvnc3mygStq4R9ywP7/UTCldUxyL3B34G1zTf4++45+OxPrsdTH4ehl3se064znHm3WUyf+rircRfQGINyR7p24QRKbJwpGQNQfhAObQ7s9xMJV1bHoKMb/K19y/uxCx42WVVgsj0vfd6UZHCX0dXUGf3VWpNlmuEeaxpiUO8zzPlSICW2M7EOTBmrsoOB/X4i4aoxBtmCv4AFng3+Nr7vvcGfw2ESnlY09KOKiYerZ0LPUzyP69DLe2KTMwadcI6pyx5IaTmu2Lp3qanhLiLHci6iJ6abv93Bdta9rhv/y18xv69NsdtNmTpn9YL4FLj2dZNE4K65xCZnDBo41WTCB1LngZDc0Yx3LjTzj1BaRJfo4pH9YMHF4zEN/h5tuiHfhvfh7Z/RGPhO+6WrpnpT4hJgxDSztee6t6DPma7XRl4fuPpX7jzuPqqcgkiT3GNQugUxqKUN/la8Ch+67WY5+48w5kbvnxufDGN+BLctgatfhR5u9YFHXuf7vFuil2KQyHE5Lx5j4o+60AqSljb4W/QP+MJtN8sFf4O8S71/bmKayTD9+XK4/F+QO8r1miUxKLKzsETazBmDUjsHtka4Ny1t8PfFg/DdP8zYFgOXvwD9Jnn/3GMSm4a4Xgt2DLLXuhqgioiLw+Fq/m3FWhC4EpDMhMyuvKMXnB0O00Nv1WzzODYBrp5l+ssc73OPSWyywYhr/P1THCsmxlUXvarI1QA1AmkRXaKLRwZWkLcQOmUNhJN+YsZ1leYC0t22L+D1G8xWHICxP/YswdAcm800z/rB23DLd3Dtm2YBPhjctxGqsZ9I05wxKKFdYJvcNed4Df7Wz4V3bnE9Pv1OUy6hJWJiYOB58KMP4adfw/Vvw+gf+WPWx9dTMUjkuJwLWGk5gc9K8uaEydD/HDMu2evZ9wVg2ctmYcvpnAdg1A9a9tmxcZB3Gdz8mfn64XswpJnFd39SDBJpnt3uOg+y6joMjt/gb+FTsOAh1+MLn2p5PeHGxKZv4EefmK++Zx7/ff6gGCTSvKpis/4C1sagsT82mdtgbnites3z9c/+ZHosgEm+vPzFlscR98SmH75nzoW6jvbf3JsTJetBWkSX6OI8cUvpFPhtdc0Z/xvTFALMVmZnI7zd38Or00ydPoBhV8GUR9qWSZ41EPpPMheUwdCxj2t75K5Fx6+1LBKNGi8eLcp+cPLW4G/rZ6b2nqMhI+LEn8BZx6kZ6k3OUHPCF6yFutwRZqsjmHqgqosu4qmuGioOm7HVMehctwZ/3zwJR7ab8do5MPfnruPG/xZOubX1n2+zmYvG3qcHZzcemBuUziaDykQXOVbFIVeSULCbirprrsHf0pfgk3tcx577EIy8tvXfw2YzMaG5zFF/6+m2C1AxSORYVjY2dhcb77krb/59UFVixl8/YaoVOF38dNt6S8XEmHOgrqOOf6y/uMegHZG7K9jvV7Z1dXX87ne/o3fv3iQnJ9OnTx/++Mc/YnfbouBwOLj//vvJzc0lOTmZCRMmsHatZ7p/dXU1t99+O5mZmaSmpnLhhReyZ08TZS9EWspud8vAsvjiMbk9TDqqwV/+Snjlcqht2NY84Hy46GnrMsVay2ZzlXSpKYOCldbORyTUVJeazulgbfYDNN3gb9cimH2t6ybe8GnmBC9YC1C+io2H7g2ZZaX7oHC7tfMRCTWhsBvPqVNf1+J4fbVpILp5Prx5s+sm3sm3wITfev+MUJOUYW4eAuxf23SpLJFoZmVT0aM11eBvzZumkajTmffAyT+1Zn5t0b67qwfNnsXmxqmIuJSGUAzqeyYMusCMy/bDl4/Akn+ZBXWn8/4Phl9tzfzaIjvPVe9958KITWjy++rcQw89xDPPPMNTTz3F+vXrefjhh3nkkUeYMWNG4zEPP/wwjz32GE899RSLFy8mJyeHs88+m9LS0sZjpk+fzpw5c5g9ezZff/01ZWVlTJ06lfr6en9PWaJF5RFTIw6sX0QHGHGdqwnV/jXwz0lmixFA7/Gmpmewssj9xb0eqDrDi3gKlewHp6Mb/L10AdQ2NKIadAFcOCN8buI5uW9lVgwS8RRqMeiMO11N+Da8B7OvcZ2njbzOlHEJl5t4To0xyAG7vrV0KiIhx+NGXgjEII8GfzPhrf+hsR/VKbe5kg3CiTMG1VV5b1goEq1CLQZN/gvEJZnxoqfhPbceeBPvhRNvtmZebRUT62q+XHEIDm60dj4B4ver42+//ZaLLrqI888/n169enH55ZczefJklixZApgs9CeeeIJ77rmHSy+9lLy8PF566SUqKiqYNWsWAMXFxbzwwgs8+uijTJo0iZEjRzJz5kxWr17N/Pnz/T1liRahlP0ADVsJ3Rr8ObM/u401jSPik6yZly88avFpAUvEQ6jFoKMb/DljUJ8z4bIXwu8mHqixn0hzQikDC45t8OeMQYMvhgueDL8FdFAygUhzQi0GeTT4A+wNpShH/QAm/1kxSCTShNq1WIeerr5T9joab+KdOh1Ov8OqWfmmp/u1WGSWdPH7Ivppp53Gp59+yqZNmwBYuXIlX3/9Needdx4A27dvp6CggMmTJze+JzExkfHjx7NwoSk+v3TpUmpraz2Oyc3NJS8vr/GYo1VXV1NSUuLxJeIh1DKwwJQeGO7WLTk7D659HRLbWTcnX3QeYOrNA+z8FuzRs3NEMUiOK9SyH8CzwR9A95Pg6les7Rnhi66jXRkdUXbxqBgkxxWKMWiYW4M/gH6T4NLnTTZTOOpximscoReP3igGyXGF4rWYe4M/MI2Ipz4RngvoEBULWN4oBslxhWIMOnU6pHdzPR7zI9O7KlxFwY08vy+i/+Y3v+Gaa65h4MCBxMfHM3LkSKZPn84115iFwoIC8w83Ozvb433Z2dmNrxUUFJCQkECHDh28HnO0Bx98kIyMjMav7t27+/tHk3AXatkPTpP/Av3OhhPOhevnQHKH478nVNlsroYS1cWmJmiUUAyS4wrVGDT1ceh9Bgy6EKb9FxJSrZ5R28Ulmt08AMW7oGiXtfMJIsUgOa5Qy8ACc95wyTPQ/WQYdjVc+TLEJVg9q7ZL6QhZQ8y4YLWrTF8UUAyS4wrFGBQbb27cdR1tFq8ueTZ8b+IBdOjlukm6+3uor7V0OsGkGCTH5eyPB6ETgxJS4PIXIGcYjPs5nPdo+N7EA8gZDgkNCaERWhfd74vor732GjNnzmTWrFksW7aMl156if/7v//jpZde8jjOdtQ/DIfDccxzR2vumLvvvpvi4uLGr927d/v2g0jk8cjACoGa6E6pneC6N2Daa9Auy+rZ+C5KS7ooBslxhWoMyugKP5wLV71smh6HO/fO8Dub3r0WiRSD5LhCNQZ16gs3fQyXPmsuJsOdMwvLYYdd31k7lyBSDJLjCtUY1GUY3PyZSSoI55t4YBbfnDGotgL2rbB0OsGkGCTH1biIboN22c0eGlQ9ToaffgWT/xR+/aiOFhtndjYDlBXAkW3WzicA/P7/0K9//Wt++9vfcvXVVzN06FCuv/56fvnLX/Lggw8CkJNj7vgcnVF+4MCBxuz0nJwcampqKCws9HrM0RITE0lPT/f4EvHgkf0QQidukcZjC0/0bCNUDJLjco9B6YpBAdNTMUgxSJrknoGlGBQ4UVpOQTFIjssZg2LiXeUfxf8UgxSDpGklDTEotbPZhSKBEeHrQX5fRK+oqCDmqLsnsbGx2O12AHr37k1OTg7z5s1rfL2mpoYFCxYwbpzJHhs9ejTx8fEex+Tn57NmzZrGY0RaLVSzHyJN1hBIam/GOxdCw+++SNRzj0HtQmQLYSTqNtZcoENU7YYROS7nAlZCO9PUUwKjZ+TXAxVpE2cMSusS3uUKQl0vt13BikEihr0eyvabcaiUcolUEV6ZwO+L6BdccAF/+ctfeP/999mxYwdz5szhscce45JLLgFMGZfp06fzwAMPMGfOHNasWcMNN9xASkoK06ZNAyAjI4ObbrqJO+64g08//ZTly5dz3XXXMXToUCZNmuTvKUu0cJ642WLN3UcJjJgYVzmFyiNwcIO18xEJFc5F9JTM8N8uHMoSUkxtUzBbCEvymz9eJFo4Y5ASCQKrXWfIHGDG+5ZDdZm18xEJBXXVUHHYjLUTJrA69YPUhhKhuxZBfZ218xEJBeWHwFFvxqHSVDRS5Y6EuGQz3vFNxNVF9/si+owZM7j88su55ZZbGDRoEHfeeSc/+clP+NOf/tR4zF133cX06dO55ZZbGDNmDHv37uWTTz4hLc2VFfP4449z8cUXc+WVV3LqqaeSkpLC3LlziY0N40YfYq3G7Iec8K81FeqitJyCiFd2u2cGlgRWhG8jFGm1qhKoaVjMVQZW4DXWRa+H3YusnYtIKPDYEawYFFDuddFrSqFgpbXzEQkFpSHY2DhSxSVA9xPNuGQPFO6wdDr+5veVxLS0NJ544gl27txJZWUlW7du5c9//jMJCa6sO5vNxv33309+fj5VVVUsWLCAvLw8j89JSkpixowZHD58mIqKCubOnasOy9J29bVQftCMtYAVeL3PcI1XzbZuHiKhovII2GvNWBlYgacYJOLJfQFLGViB5x6DVr5m3TxEQoXHIrpiUMApBol4UgwKLo9rsciKQUrHleig7IfgyhkKOcPMeO9S2LvM2vmIWK1E2Q9B1et0aN/DjLfMj8jO8CKtogys4DphCiR3NON1b0PZQUunI2I5xaDgGnKJq5zCyldVVkpE12LBNfwaU0YZYOm/TVJrhNAiukQHZWAFl80GY3/serzkBevmIhIKlP0QXDGxMOZHrsdL/mXdXERCgWJQcMUnwajrzbi+Bpa/bO18RKyma7HgSu4AQy834+oSWP26tfMRsZpiUHBldIUBU8y4NB82fmDtfPxIi+gSHZT9EHxDL4fEDDNe/QZUFlo7HxErKQYF38jrIbahlNzymVBbae18RKykDKzgG30jYDPjJS+Cvd7S6YhYSjEo+NwTmhb/M+Ka+4m0iq7Fgu/oGBQhtIgu0cEjA0v1iIMiIRVGTDPjuipYMcva+YhYSTEo+FIzzXZmMDfx1s6xdj4iQfLm0j1c/o+F/PXDDXy2YT/FlbWKQVbo2Bv6n23Gxbtg8zxr5yNiJcWg4MsdAV3HmPH+NbD7e0unI2IpxaDg6z0eOvUz4+1fwsGN1s7HT7SILtHBI/tBQTNoxt7kGi9+Aex26+YiYiX3GKTGosEToRkQIs35Zushluws5JkFW/nRv5cw4o+f8PXyVa4DFIOCRzFIxCjNd42VBRo8ikEiRklDDIqJh5RO1s4lWsTEwBi39aAIKa+pRXSJDrrzaI3M/uYOJMCRrbD9C0unI2IZxSBrdBtrGh2DmhxL1Fi844jHY4cDUqrdGlu20wJW0PSbpCbHIuBaRE9Ig8Q0a+cSTYZcYuqjg5ocS3RzxqC0LqZ/mwTHiGtcTY5XzIKacmvn4wdaRJfo4J79oAys4PLIgFCDUYlSzhgUEwcpmdbOJZqoybFEmYLiKnYfObb+f5atCAB7SibEJQR5VlHMo8mxw9RGF4lGzmQCXYcFV3yS6REDanIs0auuGiobEgwUg4IrApscaxFdooNzASs+BRLTrZ1LtBlwnivzduMHULzH2vmIWMEZg9rlmK1tEjxDr3DFfTU5lgj3/VFZ6AA27GRj/t3HaCdM8Hk0OX5ZTY4l+lSVQE2ZGauUS/CNUZNjiXIqJ2WtCGtyrCt5iQ7O7Adt3wm+2DgYfYMZO+yw9N9WzkYk+Oprobxh+6yyH4JPTY4liizefuwiekdKibc1LJooBgVfaiYMvtiMKwth7dtWzkYk+DxK2uVaN49o1bGPKS0FanIs0UkxyFq5I6DraDMuWA17Fls6HV9pEV0iX3WZ2ToCqkVslVE/BFusGS99CepqrJ2PSDB5nLgp+8ESY9TkWKLD0fXQAXJsbrsvFIOsoeZ+Es1K3ZqrKwZZQzFIolmJYpDlIigGaRFdIp/7ApYysKyR3gUGTTXj8gOwYa618xEJJmU/WK/zCdD7DDNWk2OJUMUVtWzcX3rM81kei+iKQZbofiJkO5scL4F9y62dj0gweVyLKQZZov/ZkKEmxxKlFIOs597keO0cKD9k7Xx8oEV0iXzKfggNajAq0UoxKDQoBkmEW7rrSJNlJnNsbtnpikHWsNlg7FE7YkSihbJArRcT21AbHdTkWKKOrsWsF58MI68z4zBvcqxFdIl8HlmgykS3TK/TIfMEM975DexfZ+18RIJFMSg0qMmxRLjvtzfdNDfbIxNdMcgyanIs0UrnQaHBo8nxTKitsnY+IsGiGBQaxvzINV7yr7BtcqxFdIl8HtkPCpqWsdk8M0GX/Mu6uYgEk3sMUkkp68TGH9Xk+CVLpyPib03VQwfIxm2xVjHIOontYPg1ZlxXCStnWzsfkWAp1bVYSGjX2a3J8RFY97aVsxEJnpJ811iZ6NZxb3JctAu2fGrtfNpIi+gS+XTnMXQMvxriU8x45WyoPrZ2q0jEUQwKHe5NjpepybFEjqraelbtKWryNWWihxCPki7/pMn6OyKRxv08qF22dfOQiGruJ9JipQ2L6AlpkJhm7VyiXQTEIC2iS+QrVRZoyEjKgGFXmnFNKaz6r7XzEQkGZWCFDvcmx2X7YcN71s5HxE9W7C6itr7pBdlusUVmEBMHKZnBm5Qcq/MAU94O4PAW2L7A2vmIBIMzCzS1M8QlWDuXaOfe5HjPYti3wtLpiAScw+FaRNdakPX6T4aM7ma8+RMo3GHpdNpCi+gS+TyyH7R9x3JjjmqspSwsiXTOGBSfquyHUKAGoxKBFm9vupQLQBfnInq7HIjRqb/lIiALS6TF7HYoazgPUhkF6x3d5HiJzoMkwlWXQG2FGSsGWS8CmhzrTFoin/POY3JHiE+ydi4CXYZB95PM+MBa2LXI2vmIBJpzET29i7l4EWt5NDn+Gg6st3Y+In7wvZd66PHUkVZfZB4oAys0DDzfldSx4QMo3mvtfEQCqeIw2OvMOC3X2rmIMfQKU9YCYNXrUFlk6XREAsqjrKZiUEgY+QOIiTfj5S+HXZNjLaJLZHM4XIFTZRRCh7KwJFpUl5kMCFAMChU227E7YkTCWF29nWU7C5t8Lcu9qagysEKDR5PjetOfQSRSeZS0UwwKCYntYIR7k+NXrZ2PSCCVKAaFnHadYcjFZlxxGNa9Y+l0WkuL6BLZKo5AfUPjOGVghY7BF0FKJzNe9w6UHbB2PiKBoqaioUlNjiWCrM8vpbymvsnXeie5/dtWBlboGO3W5Hjpv6G+1tLpiASM+3lQumJQyBijJscSJRSDQlMYJ1VqEV0im7IfQlNcIoz6gRnba2HZf6ydj0igKAaFpuT2ZjszqMmxhL3FXkq5AJySWe16oBgUOtJzTVkXUJNjiWzKAg1NWQPV5Fiig67FQlP3kyA7z4z3fA/5K62dTytoEV0im7JAQ9foG4GG+tBLXgR701l0ImFNMSh0Hd1gVFlYEqaaW0Qf1r7S9UAxKLSMVVkpiQI6DwpdY4/KRheJRIpBoclmgzE/cj0Oo/MgLaJLZPPIflDQDCkdekL/yWZcsgc2fWztfEQCwT0GqaRUaOkyDLqdaMZqcixhyuFwNLuI3s+9nItiUGjpPR469TfjHV/BgQ3WzkckEEp1LRayBk6FdtlmrCbHEqm0HhS6hl3panK8OnyaHAdkEX3v3r1cd911dOrUiZSUFEaMGMHSpUsbX3c4HNx///3k5uaSnJzMhAkTWLt2rcdnVFdXc/vtt5OZmUlqaioXXnghe/bsCcR0JZLpzmNoC+NaWCItohgU2hSDJMxtP1TOobKaJl9LiIs5qrGoYlBIsdk8M0GXhE8WlkiL6TwodKnJsUQD9xjkvGkkoSExzfSpAqitMH2qwoDfF9ELCws59dRTiY+P58MPP2TdunU8+uijtG/fvvGYhx9+mMcee4ynnnqKxYsXk5OTw9lnn01pqStbZvr06cyZM4fZs2fz9ddfU1ZWxtSpU6mvV8kHaYVSZYGGtH4ToX1PM976KRTrRplEGGVghbajmxyHSQaEiFNzWegjurcntizf9YRiUOgZfg3EJZvxytlQ1/QNEZGwVdIQg2LiXX9vJXSMcmtyvOxllbaTyFPaEINSO0NcgrVzkWO5JxOESZ88vy+iP/TQQ3Tv3p0XX3yRE088kV69ejFx4kT69u0LmCz0J554gnvuuYdLL72UvLw8XnrpJSoqKpg1axYAxcXFvPDCCzz66KNMmjSJkSNHMnPmTFavXs38+fP9PWWJZMp+CG0xsa67jwA7vrZuLiKB4BGD1Mwm5MQnQd5lZmyvhd3fWzsfkVb6fnuh19fG9urgikHxqSbjR0JLcntXg9HqkrBqrCXSIs4FrLQciFEl2ZCT0RX6TDDj0n1wZJul0xHxK7vddR6k67DQlDUIckea8YG1UH7Y2vm0gN//kr377ruMGTOGK664gqysLEaOHMnzzz/f+Pr27dspKChg8uTJjc8lJiYyfvx4Fi5cCMDSpUupra31OCY3N5e8vLzGY45WXV1NSUmJx5dI44mbLdbcfZTQ0/NU13hn07/f4UAxSJrkjEEpnSAu0dq5SNPcY9AuxSAJL81loo/t1dF18ZjexZQPkdDTSzFIIlRdDVQcMmMlM4WuXroWkwhVcciUKgJIy7V2LuKdx7XYt9bNo4X8voi+bds2/vGPf9C/f38+/vhjfvrTn/Lzn/+c//zHpOYXFJiT+exsz3pE2dnZja8VFBSQkJBAhw4dvB5ztAcffJCMjIzGr+7du/v7R5Nw5NxC2C7bZD1L6Ok2BmLizDiMG/spBskxHA637AddPIasHqe4xopBEkb2l1Sx60hFk6/F2GB0ThzUNJRKVAwKXYpBEqnKtBsvLCgGSaTyaCqqGBSyPGJQFC6i2+12Ro0axQMPPMDIkSP5yU9+ws0338w//vEPj+NsR2XDOByOY547WnPH3H333RQXFzd+7d6927cfRMJffS2UHzRj1UMPXQmp0GW4GR/aGBZbeJqiGCTHqDgC9Q31bbWAFbrSsqFjHzPeuxRqq6ydTxspBkWf77d7z0If1CWdtFq3v6eKQaErcwAkNyQO7frWbD8PQ4pBcgz3knbpygINWbmjILahVnQY74ZRDJJjKAaFh2hfRO/SpQuDBw/2eG7QoEHs2rULgJwccwfo6IzyAwcONGan5+TkUFNTQ2FhoddjjpaYmEh6errHl0S5sv1AQ3MUXTyGtjALnE1RDJJjlCr7IWz0GGf+t74G9i2zdi5tpBgUfZYcr5SLMrDCQ0yM6zyostAkFIQhxSA5hmJQeIhPgq6jzfjINijdb+182kgxSI6ha7HwkNrJJBSA6Q1TU27tfI7D74vop556Khs3ep78bdq0iZ49ewLQu3dvcnJymDdvXuPrNTU1LFiwgHHjzEXs6NGjiY+P9zgmPz+fNWvWNB4jclxq6Bc+ImARXeQYamwcPnqc7BorBkmY+H6H96aiJ/buqBgUThSDJBIpBoUPxSCJRIpB4cMZg+x1sGeJtXM5Dr8vov/yl79k0aJFPPDAA2zZsoVZs2bx3HPPceuttwKmjMv06dN54IEHmDNnDmvWrOGGG24gJSWFadOmAZCRkcFNN93EHXfcwaeffsry5cu57rrrGDp0KJMmTfL3lCVSeWQ/KGiGNC2iSyRyj0EqKRXaerrdoN+pGCShr7iylg0F3pummaaiikFho4dikESgUl2LhQ33GKRrMYkUWg8KHz3DJwbF+fsDx44dy5w5c7j77rv54x//SO/evXniiSe49tprG4+56667qKys5JZbbqGwsJCTTjqJTz75hLS0tMZjHn/8ceLi4rjyyiuprKxk4sSJ/Pvf/yY2Vs0hpYV05zF8OLfwHNro2sKTkGr1rER8oxgUPjr2gdQsKD8Au78De72aUUtIW7azEIej6dd6Z6bSOS1RMSicdBkOcclQVxnWjf1EPCgGhY/uJwI2wAE7w7cuuogHxaDwEUZJlX7PRAeYOnUqq1evpqqqivXr13PzzTd7vG6z2bj//vvJz8+nqqqKBQsWkJeX53FMUlISM2bM4PDhw1RUVDB37lx1WJbWUQZWeAmjLTwiLaIMrPBhs7liUHUJHFhn7XxEjuP7ZuuhNzSpVAZW+IhLgG5jzLh4FxTvsXY+Iv6gHXnhI7k9ZA8x4/1roMr7TieRsFGab/43Jh5SOlk7F2le+x6Q1tD8dfdiqK+zdj7NCMgiukhI0J3H8BJGW3hEWkQxKLyopIuEkcXbj9NUFNQbJty4Z2EpBkkkcMaghHaQmNb8sWI9Zwxy2GHP99bORcQfnIvoaTmmibeELpsNejbEoNpyKFhp7XyaoX9JbnYfqeDV73dZPQ3xF2fQBC1ghYMw2sIj0iLOGGSLhdTO1s5Fjs8jBkXnVua1+4p5f1X+8Q8US1XV1rNqT7HX149ZRE/pBHGJQZiZ+KSnzoMkwjhjkK7DwkNP3ciTCFJXDRWHzbgFMai6rp5nF2ylsqY+wBMTrzyuxUK3tJ3fa6KHI7vdwSvf7eSvH26gorae/lntGOO8AJHwVdKwEBCXDEkZ1s5Fjs+5had0n2sLT6xClISxEmU/hJXsPJMtV1NmTtwcDpMVEQVq6uw89dlmnv5iKwlxMQzrlkH3jilWT0u8WLm7iJp6e5OvdU5LpGenFLDb3TKwtIAVFrqNBVuMyQLVIrqEu+pSqCk1Y+2ECQ9hsoAl0iKt2I23cncRv35jJZv2l3GgtJrfTx0c4MlJkzx25C2EU261bi7NiPqr+p2Hy5n2z0X8/p21lNfU43DAXW+soqpWd6DCWm0VFO8247ScqFkICWthtIVH5LgqC6H8oBnr4jE8xMY1NNbCLD4W7rB0OsGyak8RF8z4mic/20Kd3UFFTT2/fWsVDm9dK8Vyi5uph35ir47YbDYo2gn2WvOkYlB4SEyDnGFmfGAdVHj//1kk5B3a7BrrRl54SM+F9j3NeO8Sk8krEq4OHz8GVdXW89cPN3DJ09+waX8ZAP/6ZjtLd+rvryWyBruSX50JTSEoahfR7XYHL36znXOf+IpF2zx/SbYdKuexeZssmpn4xYb3oLbCjJ3N4iT0KQNCIsXqN4CGP/zdFYPCRo/o6c1QVVvPQx9t4JKnF7Jxf6nHa99sOcyr3++2aGZyPN/vKPT6WmNT0dWvu55UDAof7r0ZdqsmsYQx9xjU4yTr5iGt44xBdVWwb4WlUxHxyarmY9DSnYWc/+RXPLNgK3a3tVqHA36tpFprxMS4zlkrDsHhLdbOx4uoXUTfXVjBgx9uoNLLL8c/v9rG8l3eL1IkxK14xTUeMc26eUjrHL2FRyRcKQaFJ/ebrhG+iL58VxH/+GIr9famszwe+GA9e4sqgzwrOZ56u4NlO5tZRO/d0ZRyaYxBNhh+dXAmJ77ziEE6D5IwVVcDq14z49hEyLvM2vlIy0XReZBEsKoSWPeOGSdlwIDzPV52OBw88MF6th4sb/Lt2w6W8/h8JdVawj0Gheh6UNQuovfslMovJ53g9XW77kCFr+I9sPVzM27fA3qeZu18pOXCZAuPSLP2r4N9y824y3DIybN2PtJy3cZATLwZR3hTrVP6duKqMd29vl5WXcfdb61WWZcQsz6/hLLquiZfS0uMY2BOull8dZYj6jMe2nv//1lCTA819pMIsPljV0O/gedDcgdr5yMtF0U78iSCrZ0DdQ2JIHmXQ3ySx8s2m42/XjqUhFjvy6HPf7mNFbuLAjhJaVLP0I9BUbuIDnDz6b0Z3s17w8ktB8p48tPNXl+XELXyVRrLKIy4Vg39wkmYbOERaZZHFvp11s1DWi8+GXJHmvHhzVB20Nr5BNg9UweRk57k9fUvNx3k9aV7gjgjOZ7m6qGP7tWB2BgbLFcMClvtsqBTPzPetxxqtRtEwpB7DBp5rXXzkNbL7A8pncx41yKzs0kk3Kw4fgzqn53GLyb19/oRdgf8+vWVVNcpqTaockeaHUygRfRQFBcbw8OXDyc+1nvTyWe/3MbqPcVBnJX4xOGAFbNcj4dfY91cpG3CYAuPiFf1tW5bmBNg6OXWzkdazz0G7Y7s3gzpSfE8eOnQZo/503vrKCiuCtKM5HiaW0Qf26sjVJfCurfNE4kZMGhqcCYm/uOMQfZa2LvU2rmItFbpftj8iRmnd4U+Z1o7H2kdm821I6aqCA5usHQ6Iq12aDPs/s6MOw+C3FFeD/3JGX0Y2tV7Uu3mA2XM+FRJfUEVlwhdR5tx4Q4oybd0Ok2J6kV0gAE5afz8LO93oOrtDn79xkpq6nQXNizs+haObDPj3mdAh57WzkdaLwy28Ih4tfkTKG/IXh5wHqR0tHY+0nruMSgKyimcOTCLS0d19fp6aVUd98xRWZdQ4HA4+H6793roJ/buCGvfdjVWz7vU7K6Q8NIjumKQRJhVr4GjIXNz+NUQE2vtfKT13MtKqTeDhJujs9Bt3hNm42JjeOSKYc0m1f5jwVbW7FVSbVD1DO0YFPWL6AA/ndCXIbnpXl/fUFDKU5/rDlRY0Bbm8BcGW3hEvPLYwqwYFJa6n+Qah+CJWyDcO3UwndMSvb7+6YYDvL1ibxBnJE3ZcbiCQ2XVTb6WEBfDsG4ZR108KgaFJTX2k3DlcBxV0k6lXMKSejNIuLLXw8rZZmyLhWFXHfctA3PSue3M5pNq73xdSbVB5XEjL/R2BWsRHYiPjeGRy4cTF+P9DtTTn29h7T7dgQpp1WWmiQRAYjoMusDa+UjbhMEWHpEmlR00zbQA0rpA37OsnY+0TUpHs/0TIH+V+dsS4dqnJPDAJc2Xdbn/3XUcKFVZFyst3u69lMvwbhkkFu9wLbpmDnD9LZXw0rEPtMs2493fm0UBkXCwd5mr/EePU6BTX2vnI23TZRjEp5hxCC5giXi19TMobVg7OOEc02ekBW45sy+DujSfVPv0F0qqDZruJwINa7MheCNPi+gNBuemc8uZ/by+Xmd38OvXV1FbrztQIWvdO1BbbsZDLoGEFGvnI20X4lt4RJq06jWw15nxsKu0hTmcOWOQox72LLZ2LkFy9uBsLhqR6/X14spafv/2GpV1sdD3x6uH3ootzBLC3GsS15RCwWpr5yPSUitmusYjplk3D/FNbDx0G2vGJXugaJe18xFpqeXuMajlO2FMUu2wZpNqn/psC+vzS3yZnbRUUgbk5Jnx/jVQWWTpdI6mRXQ3t53Zj4E5aV5fX5dfwjNfbA3ijMTdxoJSSqtqvR/g3lBUW5jDW4hv4RE5xtFbmBWDwptHDAq9DIhAuf+CIWS2S/D6+sdr9/PeKu0OskqzTUV7Zhy1hfnqIM1KAkLnQRJuaqtg9ZtmHJ9iEpokfKmki4SbiiOw8QMzTsk0meitkNc1g59N8L57pq6hV6KSaoOkMQY5Qi6hSYvobhLiTFmX2GbuQD352WY2FpQGcVbidOusZYz84zyuevZbnv5iC+v2lbgy4o5sh51fm3Gn/q675xKeQnwLj8gx8lfAgXVm3O1EyPReW0/CQJQuondITeBPF+U1e8x9767lsJe63BI4B0qq2Hm4osnXbDY4kdVQ0lC3vv/ZkJYdxNmJ32lHnoSbDe9BdUPp08EXQaL3xDQJAz2j8zxIwtiaN6G+xoyHXWV2VLTSbWf144Tsdt6/xd4SnvtyW1tnKK3hcSMvtM6DtIh+lKHdMvjp+D5eX6+tN3eg6nQHKqj2FFaw5UAZdXYH320/wsMfbeS8J7/ixAc+5c7XV2L3aCg6TVuYw93RW3iq1I9AQtzyo8ooSHhr3x0yupvxniVQ38wuqAgzZWgXzh/axevrR8pruPfdtUGckQAs3lHo9bVBOemkrp3tekJlFMJfdh4kNCxC7lpkdjuJhDI1FI0s3caaXU2gRXQJD8t9LyeVGBfL/13RfFLt3+ZvZtN+JdUGXAjvyNMiehN+PrE//bO834FataeY57/aHsQZyYJNB5t8/mBpNWv3FBKzyrmFOQaGXxPEmUnAuG/h2f29pVMRaVZtFax+3YzjkmHIpdbOR/yjx8nmf2srIH+ltXMJsj9cNISOqd7Lury/Kp8PV6usSzA1V8rljO5xsP498yC5I5wwJUizkoCJiW3YlQeU7YcjynyTEFa8B7Z+bsbte0LPU62dj/guIRW6DDfjgxtMqQyRULV/rdkVDObfbU7zuyqbM6xbe/7nDO9JtTX1dn79xiol1QZaehfo0MuM9y6FutDZBatF9CYkxsXyyBXDaeYGFI/P28SWA7oDFSwLNja9iA7wgy47oXi3edB3ovmFk/AXwlt4RDxs/ACqisx48IWQ5L27u4SRKC3pApDZLpH7LxzS7DG/f2cNR8prgjQjWbj1kNfXzo/5FuobLi6GXQlx3m+ASBhROQUJFytfBRp2S4y4FmK0xBAReo5zjUMsE1TEg0dVAt/7Uv1iYn/6NZNUu3J3Ef/8Wkm1AdejIQbVV8PeZdbOxY3+wnkxont7bj79+Heg6u3aXhloNXV2Fm497PX1SVXzXQ9URiFyhPAWHhEP2sIcmdwvHqOwN8MFw7pwzhDvdbUPldXwh7kq6xIM+4oq2bS/zOvrA/PfcT1QDIocPdwXsKIvBkmYcDhgxayGBzYYoR3BEaOHejNIGKivhVWvmXFsAgy93OePTIqP5eHLhzWbVPvYvE1sOeD93Ez8IESTCbSI3oxfnn0CfTJTvb6+fFcR/9IdqIBbtquQsuq6Jl/LSagic88n5kFyBxhwXhBnJgEVwlt4RBqV7IOtn5lx+x7Q63Rr5yP+kzkAktqb8a5vwR5d2zZtNht/ujiP9ineGzO9s2If89btD+KsotOXXkraAUzOPEL8/hXmQc5Q6DIsOJOSwOs6CmIafv+i8EaehIld37rKDfU+w5wLSWRwlrUDxSAJXZs+hoqG3XoDzoOUjn752FE9OnDTab29vl5TZ+euN1YqqTaQQnRXsBbRm5EUH8sjVwxrtkfl/32ykYLiquBNKgp90Uwpl1uzVmGra/jvP/QKiEsM0qwkKEJ0C49Io5WvgqNhcXX4NG1hjiQxMa6Tt8ojcHiztfOxQFZaEvddMLjZY3739mqqauuDNKPo5K0vDMCP2rllB/phC7OEkPhks5AOcGQrlOqGlYQgj8bqikERJTUTMk8w4/wVUFNh6XREmrQicDHojskD6N1MUu2yXUW8tni3X7+nuOnUD1I7m/Gu78AeGtcbuto/jtE9O3LjOO93oKrr7Mz6bmcQZxR9mrt4PLf2U9cDbWGOPCG6hUcEMFuYPWrwaQtzxOmp3gwXj+jKxIFZXl/fX1LNB2oyGjC19Xa+3tx0PfQ46hhV9LF5EBNvkgkksrhnYe1WaTsJMdVlsHaOGSemw8Cp1s5H/M8Zg+x1sHeJtXMROVrZAZOJDpDWBfqe5dePd5Z1aS6p9oWvt+FwKBs9IGw2146Y6mI4sN7a+TTQInoL/PqcAfTslOL19fdW5+sXJ0D2l1SxPr+kydf62vbSuXi1eZCd5+ogLpEjRLfwiACw+3uTHQimjIuz/JBEDsUgbDYbf7lkKGlJcV6PeX+VFtEDZfmuIkq9lLSbnLCGhKqGnjEDpkBqpyDOTILCo8l6dMYgCWHr34XacjMecgkkeL9eljClGCShbNV/wdGQnTz8aoiJ9fu3GNurIz88pZfX17ceLGfj/lK/f19pEILXYgFfRH/wwQex2WxMnz698TmHw8H9999Pbm4uycnJTJgwgbVrPZtTVVdXc/vtt5OZmUlqaioXXnghe/bsCfR0m5ScEMtDl3mvMbntYDnr8/WLEwjN1QG9Oc3tl2jEtTR7i1DCU4hu4REBYMVM11g7YSJTlxEQl2zGIXLiZoWcjCR+d/4gr69/ufkgxRW1QZxR9Fiw6YDX137sXspFZRQiU4+TgIbzWzX2k1CjUi6RT7uCJVQ5HJ6lXAJY0u6ucwfQrUOy19ffW6lkkoDpEXq7ggO6iL548WKee+45hg3zXIB++OGHeeyxx3jqqadYvHgxOTk5nH322ZSWuhaip0+fzpw5c5g9ezZff/01ZWVlTJ06lfp6axbRTu7TiUFd0r2+/t6qfUGcTfT4wssieiz1nG9fYB7ExMGwK4M4KwmaEN3CI0JNOaxp2MKckAaDL7R2PhIYcQnQbYwZF+2C4r3WzsdCl4zsRkZy001Ga+sdfLyuIMgzig7eStp1pIQRlQ3lPdrlQN+JQZyVBE1yB8hq6EtQsBqqlbQjIeLIdtj5tRl36g/dxlo7HwmM9j0hLdeM9yyG+qZ3RokE3b7lcGCdGXc/CTL7BexbpSTEcfXY7l5ff2/VPlWmCJScYRDfUJd+17fm5onFAraIXlZWxrXXXsvzzz9Phw4dGp93OBw88cQT3HPPPVx66aXk5eXx0ksvUVFRwaxZswAoLi7mhRde4NFHH2XSpEmMHDmSmTNnsnr1aubPnx+oKR/X1GFdvL723iqVdPG3umbqgJ4Rs4q0uoYtzCecaxqfSGQKwS08IqyfCzUNixlDLoYE701nJMw5b+RBVMeghLgYzhmS7fX191TSxe8OlFaxZm/TJe0ujv2GGEfDYsbwqyDWe7kdCXPOGOSwmzJiIqFgxSzXeKR2BEcs94SmmjIoWGXtfEScPLLQA78jeOqwXK+v7Thcwdp9TZ+viY9i46B7w03a0nwosr4fZcAW0W+99VbOP/98Jk2a5PH89u3bKSgoYPLkyY3PJSYmMn78eBYuNOn5S5cupba21uOY3Nxc8vLyGo+xQnOL6LuOVLB6b3EQZxP5Vu4ppriy6e3hV8cvcD3Q9sHIFoJbeERY7lbKRTEosulGXqPzm7mA+GbLIY6U1wRxNpHvq01NJxKAg2sTv3I9DOAWZgkBPce5xlEegyRE2O2w8lUztsXAsKutnY8ElkcMUoNjCQG1VbD6dTOOSzY9GQKsV2YqeV29V6aYq8oUgdPDLQaFQG+GgCyiz549m2XLlvHggw8e81pBgdnum53tmc2UnZ3d+FpBQQEJCQkeGexHH3O06upqSkpKPL78rWenVIZ2zfD6urKw/MvbFuYOlDDRtsw8SM2CfmcHcVYSdCG4hacpwYhBEiIKd8COhgWsTv3MFkKJXN1PNIsEEBInbt4EIwaN69uJDilNl3Sptzv4aI1KuviTt/OgIbYd9LXvMA+6jYXOJwRvUhJ8HjfyQncBS+dBUWT7Aijebcb9JkG690QziQAeMSh0E5oUg6LIxvehqiGBdfBFkOR9cdufzh/qPZnkfVWmCJwQ683g90X03bt384tf/IKZM2eSlJTk9TjbUVu+HA7HMc8drbljHnzwQTIyMhq/unf3XrPIF81lo+sXx78WbGy6mdZFsQuJQ1uYo0YIbuFpSrBikISAFa+6xiOmaQtzpEtMg5yhZnxgHVQWWjsfL4IRg+JjYzg3r7nSdsrC8Zd6u4OvNje9iH5FrNtuPDU1jnwZXSGjhxnvWQx1obnjQ+dBUSTIZRTEYlmDILEhkXBn6CY0KQZFEY+mxsGLQc2tBe4prGTF7qKgzSWqdB1j+iBCZC6iL126lAMHDjB69Gji4uKIi4tjwYIFPPnkk8TFxTVmoB+dUX7gwIHG13JycqipqaGwsNDrMUe7++67KS4ubvzavXu3v380AM5v5hdnb1Ely/WL4xeHy6pZ5aU8ji4eo1CIbeFpSrBikFjMbnfVAbXFwPBrrJ2PBEdjDHKEbE3iYMWgC5o5D1q07TAHS6sD8n2jzao9RRRWHFvSLoFaLoptyASMS4K8S4M8M7GEMwurrgryV1g6FW90HhQlKotMXxgwjW8HTLF0OhIEMbHQo2HXZcUhOLzV2vl4oRgUJYr3wtbPzLh9D+h5WtC+dfeOKQzv3t7r6++rMkVgJKRAlxFmfGgTlHsrdxgcfl9EnzhxIqtXr2bFihWNX2PGjOHaa69lxYoV9OnTh5ycHObNm9f4npqaGhYsWMC4ceYidfTo0cTHx3sck5+fz5o1axqPOVpiYiLp6ekeX4HQrUMKI3u09/r6eyv1i+MPX2851ORN7sG2HQyJachEzh1l7oxL5AuxLTxNCVYMEovt+AqKd5lxnzMh3fu2PokgPUO/N0OwYtBJfTqR2S6xydfsDvhojc6D/MFbKZdJMUvpYCszDwZdAEneywxKBAmD3gw6D4oSa98yN3MAhl4BcU3/PZAIEwYlXRSDosTKV4GGhaLh0yAmYG0em9RcMsn7q/Ox20Nzp0bY6xk6pe38/i8uLS2NvLw8j6/U1FQ6depEXl4eNpuN6dOn88ADDzBnzhzWrFnDDTfcQEpKCtOmTQMgIyODm266iTvuuINPP/2U5cuXc9111zF06NBjGpVaobnOvB/oF8cvFmxs+uLx0li3RlpB3LojFguxLTwSxVbOdo0Vg6JHGCxgBUtsjI3zhuZ4fX2usnD8wtsiusd5kHbjRQ+PJuvRHYPEYu7nQYpB0UMxSEKBw3FUDAr+juDzhnpfRM8vrmLZrtAs+xj2QuhaLLi3bRrcddddTJ8+nVtuuYUxY8awd+9ePvnkE9LS0hqPefzxx7n44ou58sorOfXUU0lJSWHu3LnExsZaMWUP5w/t4rUEbkFJFUt26hfHF3a7w8vFo4NzYxebYUwc5F0W1HmJhUJsC49Eqboa08gGICENBpxv7XwkeNplQce+Zrx3GdRWWjsfi53fzAXE4h1H2F9SFcTZRJ7C8hpWNlEesB0VnB6z2jxI6wK9xwd3YmKdzgMguaMZ715kSouJBFvJPtj9nRl3Hghdhls7HwmerqMgtmHXQZQnE4iFDqyHw5vNuMc46NAr6FPIbZ/MmJ4dvL7+npJJAiPaFtG/+OILnnjiicbHNpuN+++/n/z8fKqqqliwYAF5eXke70lKSmLGjBkcPnyYiooK5s6dGzLNIXIykhjbs6PX19VYyzdr95VwuPzYpknDbNvoZmtYPO0zwdThk+gRQlt4JErt+NLVCX7AuRDvvXm2RCBnDLLXmoX0KDa2V0ey05vewu9wqCakr77acoimNjWeFbOcRFtDY/VBFwZ9C7NYyGZzXUBWFsKhjdbOR6LT+vdc48EXq7F6NIlLhK6jzbhwO5QWNH+8SCCsf9c1HnKxZdNorsHo+6vzqVdlCv9L6Whu3gLkr4SacsumorPvNmquwegHqwv0i+ODBZsONPn8lFi3Zm6DLgzSbCRkhNDdR4lS69xO3BSDok8Y1AMNlpgYW7PbWZVM4BtvJe08zoMGKwZFnR4nu8Yh2ptBIpz7ApZiUPRRDBKreVyLXWDZNKY0U5niYGk1328/EtwJRQtnDLLXwZ7Flk1Di+htNGVoDjFefnEOlVXz3bbDwZ1QBPFayiWm4eLRFgMDVUYh6rgvYK1+HUr3WzcXiT71dbChIQMrPgX6Wd+fQ4LMPQYtfwUqiyybSihorj/Msl1F7C2K7pI3beWtpF0yVUyIWWkepHb2/Pco0aHnONd4yYtRX1ZKgqzsIOz8xow79oWswdbOR4LPPQZ9/zzU11o3F4k+h7bAgbVm3G0spHs/Dw207PQkTuylyhRB18MtBi36h2Wl7bSI3kZZaUmc1LuT19fVWKttiitrWbar6JjnB9l20TumYdG056mQmhnciYn1UjpC/8lmXLYf3rhRJ28SPLsWQkXDzdF+k0ydfokuHfuYk3YwW5nn/DSq6xKP6tGeru2Tvb7+vi4g2mR9QQmHyqqPeX5CzEqSbQ2l7gaeDzHW9wiSIOsyAjJPMOP9q+G9X5n6SSLBsOE9cDT8zRt8oUq5RKNep0Faw8LlroUw7z5r5yPRZf07rnEI7AieOtz7Iv5Hawqoq4/ea4SA6X+2q6Tzpo/g60ctmYYW0X0wdbj3rcwfrcnXL04bfLPlUJOlcM712MJ8URBnJCHlor+7Tt52fqOTNwke9+2DikHRyWaDy/4JSe3N400fwlfWnLyFApvN1mxpO9VFb5svWlLKJQQuHsUCsXFwxUtmNxTAylmw5AVr5yTRY71K2kW9+GS48iWIiTePF/0dVr9h7ZwkeqwLrXJSU/K8V6Y4XF7Dom0q6eJ3KR3NtRgN/+E/+wtsmR/0aWgR3QdT8roQ6+U3p7CiloVbVdKltbzWAXWWcsEGA6cGb0ISWtplwZX/0cmbBJfdDuvnmnFsomtHhESfDr3g8hdoPHn7/C+wOfgnb6Hi/Gbqoq/cU8yuwxVBnE1kaKqUSyI1nBWz3DxIag+9zwjupCR0ZA+Gi55yPf7wt7D7e+/Hi/hDZSFs/9KMM3pA7khr5yPW6X4inPug6/G7t8P+ddbNR6JD4U7IX2HGXYab83GLZbZLZFxf79URVNIlQPpNgjPvaXjggDd/bP59BJEW0X3QMTWBcX29l3TRL07rOBxN1wHta9vLCTF7zYPuJ0G694t2iQLdx8KUv7oe6+RNAm3PYigrMOO+Z0FSurXzEWsdc/J2ExTusHJGlhnWLYMeHb2XNnpvtc6DWqOkqpZlOwuPef60mNW0s1WZBwPPh9j4IM9MQkreZXDyrWZsr4X//gDKDlg7J4lsGz80jdxApVwExv4Yhl9jxrUV8Np1UFVs7ZwksjmTmSCkdsJMbWZH5kdrC6hVZYrAOP0OOGGKGVcWmhgUxD4xWkT30QXNNNb6aE0BNXX6xWmpTfvLKCipOub5c2PcOu+GwNYdCQFjboLh08zYefIW5U3+JIDWudXgUwwSMCdvA84z46oiE4Nqoi/r+nglXd5bqZIurbFwy2HqmihpNyXW7TwohC4exUJn/8H0CAIozYfX1SdGAmhdaNUiFovZbDD1ccgZah4f2Rr1fWIkwNaHZlnNc4bkEOelMkVRRS1fbzkU5BlFiZgYuOQZ068KoGBVUPvEaBHdR5OHZHv9xSmpquPrLU2XJ5FjfbGx6Sya82K/cz0YdEGQZiMhzWaDqY9BzjDzWCdvEigOhyv7ISYOBkyxdj4SGhpP3vqaxwWr4f3obPLXXBbOuvwSth0sC+JswtuCTceeB8VTx9kxS8yDhDToe2aQZyUhKTYervg3pDX8/u38Wn1iJDCqSmDrZ2ac1sXVYFuiW3wyXDXT1Sdm4wdR3SdGAqhkH+xuWA/qPAgy+1s7HzcdUhM4tV8zJV2UTBI4ye3hqlcs6ROjRXQftU9J4PT++sXxh6ZKuXS37WdITEONo9yR0L5HkGclIct58tbYoTm6m/xJgOxbDsW7zLj3Ga5/byJJGSYGNZ68vQqL/2ntnCwwuEs6fTJTvb7+nhqMtojD4WiyL8wpMWvJsDXscjjhHIhLDPLMJGS1y4IrX1afGAmszZ9AfY0ZD5xqbiKLgPrESHCsf881DsEdwc0lk3yyroDquvogzibKWNQnRn8F/WBqMyVdPlm3n6pa/eIcT3l1HYt3HNvB2NVQlJDauiMhokNPuEwnbxJAIbp9UELE0SdvH90ddU3+jlvSRf1hWmTLgTL2FTdV0k7nQdIM9YmRQPMoaacYJEdRnxgJtBC/Fps8JIeE2KaXVUur6vhyk0q6BJQFfWK0iO4HZw/J9vqLU1Zd12SGtXj6duthautVB1TaoN9EOEsnbxIADofr4tEWYzKwRI6WdxmccpsZO0/eSvdbO6cgay6ZYNP+MjbtLw3ibMJTU+eKMdiZHLvUPIhPMYsVIkdTnxgJlJpy2DzPjFMyoec4a+cjoUl9YiRQyg/Bzm/MuGNfyBps7XyakJEczxknNFOZQskkgRfkPjFaRPeD9KR4xg/o7PX197WV+bi+aKIOaBcOMzJmi3mQnQed+gZ5VhI2TtPJmwTA/rVwZJsZ9zwVUr2fIEmUm/QH6HmaGZfmw+s3RFWTvwE5afTPauf1dZV0Ob4vmijlcmLMBjJtJeZBv0mQkBLkWUlYUJ8YCZQt86Gu0owHng8xsdbOR0KT+sRIoGx4DxwNf8sGX2j+3oWg5pJJ5qsyReAFuU+MFtH9pLlaSPPX76eyRr843jgcjiYvHs+NddvCrCx0aY5O3iQQ3LcPKgZJc2Lj4IoXIa3hJHrXQph3r7VzCrLmLiDeW7UPh+KxVxU1dXy/vamSdm6N1UNwC7OEkPhkuOplV5M/9YkRf1jnXkZB50HSDPWJkUBwLycVwtdikwZnkxDX9NJqeU09X2wMbHkRoaFPzH+C0idGi+h+MnFQNolefnEqaur5XL84Xm0/VM6ewspjnj/XvZSLTtzkeBpP3hoa3OnkTXzlfvE4SKVc5DiOOXl7Oqqa/E0d7j2ZYNvBctbnq6SLN4u2Haam3jNr2Ia98TzIEZsA/SdbMTUJJ2ryJ/5UWwWbPjbjpAzodYa185HQpz4x4k+VhbD9SzPO6AG5I62dTzPaJcZxZjOVKeZqR2ZwdD8xKH1itIjuJ+0S4zhrYJbX11ULybum6oB2poixto3mQeYJ0HlgkGclYUknb+IvBzfBwfVm3P0kSPeeZSvSqPtYmPKQ6/G7t5uyQFGgb+d2DOqS7vV1nQd5t6CJ3XgjbVvIthUBYOs7EZK8/7cVadRvkvrEiH9s+xxqGm5+Djgf4hKsnY+EB/WJEX/Z+CHY68w4hEu5ODW3I/Oz9QeoqKkL4myiWFN9YqqK/fottIjuR83+4mw4QHm1fnGa0lQpl8mxS4ixNWz9HhT6QVNCSN6lnidv8+5TWRdpvfWB3z5otztYtO0weworqLfr32jEGPMjGHGtGddWwGd/sXY+QdRcabv3VuWrpIsXTSUTTHEvaafdeNIaR/eJ+fIRS6cjYSoIpVzKqutYsuMI+0uqsOs8KHIc3Sfm2xnWzkfC07rwKqs5cVAWyfFN942orK3n0/WqTBEUTfWJWfyCX79FnF8/LcqdNTCLlIRYKpqof15Va+euN1Yxskd7undMoXuHFLp3TCYtKd6CmYaOqtp6Fm07fMzz58bo4lF8MOkPsG+FacJ26XO6CSOt53HidkFAvsX+0iqufm4RAHExNnLbJ9O9Y3LD34cUunVIbvx7kdkuAZv+HYcHmw3OfxT2rzF9Gi6MnovHqcO68MjHG5t8bdeRCn739hpOyE5r/HferUMKyQnR3ahux6Fydhw+uhG2o3ER3RETh23AlOBPTMKXs0/Mc2dC79NhysNWz0jCTX0tbPzAjBPaQZ8zA/JtVu0pYtrzpvdDYlwMXTskN14jO8+FnI8zkuN1HuSmrt5OXGyI5kM6+8Q8Ox6GXgYT77d6RhJuqkpg62dmnNYFuo21dj4tkJIQx1mDsnjfS+mWv3++hR2Hyk1ca4hxndMSFdfc+C2uOfvEPH8WnPQzOHW675/pRovofpScEMvEQdnMXdn0luX3V+fz/mrPX6r2KfGNJwc9OqZybl4Ow7tlRM0v03fbj1Bd51kHtD2lnBJjahc52vfE5ryLJNJSsXFwzSxISDMXkyKtcWQ7FKwy4y4joEPPgHyb3UdcvSDq7A52Halg15EK4Ngbi8nxsW6L6smc3KcTkwZnEx+qF1DRLj4ZfvCuqSMbJX/PAXp2SmVo1wxW72162+Qr3+065rnMdomNFxO9MlO5cHgu/bLaBXqqIaOpLPShtu10sx0CwNb7DEjuEOxpSbhLyoCbP4Pk9lbPRMLR9i/NLgaAE86B+KSAfJs9budB1XV2th0sZ9vB8iaPTUuMo1vDOVD3jimcOSCLcX07ERMTPX9jASpr6nlmwVbmrtrH+7efHro3ottlwS3fKgZJ22z+BOqrzXjg1LC5nr9gWBevi+gbCkrZUODZHygxLsYjaWps746cMySbxLgQ/b0OELvdwetLd/O3+Zt55eaT6Z2Z6vuHdugFty8LSAzSIrqfTR3WxesielOKKmopqihuvOB8ZsFWpuTl8Pdpo6LipKCpOqBnxy4lzmYW1m1hUP9KQlRShtUzkHC1PvBbmAF2Hzk6+9S7ytp6Nh8oY/OBMgBe+nYn3Tsm89bPTqVzWmKgpii+iNILx6nDunhdRG/KobJqDpVVs3xXEQBPfrqZn5zRh7vPGxSgGYaW45ZyCYMtzBKiojQGiR+sC3xJO4DdhS0/DyqtrmN9fgnr80sAeOHr7Yzs0Z5Xbz6ZJC8lFCKJw+HgvVX5PPjBevYVVwHw7JdbmT7pBItn1gzFIGkr9xgURlUJJgzIIjUhlvImKlM0pbrOztaD5WxtuHn48qKdZKcn8tYtp9K1fXIgpxoyluw4wh/mrmu8dvjL++v45w/9tPMgQDEoPG7phJHxJ3SmXaJv9yY+XFPAC19v99OMQtfCLYd4t4kbDlM8SrlcHLwJiYjAUaVcLgrYt2nNxWOT7z9Sya/+u8I/kxHxk/ObqYveUs9+uY1P1hb4YTahy+FwMHflPr7ZcujoVxpL2jlsMSYDS0QkWOz1sOF9M45Lhv5nB+xbtSaZoCnLdxXxl/fX+2k2oWvN3mKuenYRt7+6vHEBHUzy3d6iymbeKRKGaipgy3wzTsmEHuOsnU8rJMXHcvbgbJ8+Y39JNbfNWhbxfYTyiyv5xezlXP7Mtx7JN/PXH2gywSSUaBHdz5LiYzlnSI7Pn/Psl1upOarMSaTYfaSCn7y8hGn//I5DZdUer6VRwWkxqwGob5cLuaOsmKKIRKvivbB3iRlnDYHMfgH7VnsKfb/w+WrzIVbuLvJ9MiJ+0q1DCqN7+l5+5O9fbPXDbELTmr3FXPnst9z+6vJjStoNsO2mT4y5geDoMQ7adbZiiiISrXYuhIqGm3v9J0GCH7bVe+GP86DZi3dxsLT6+AeGocNl1dz91ioueOprvt9x5JjXq2rtPPhB5N9EkCizZT7UNtxgG3i+KdMaRi4ckevzZyzfVcTCrceW94wEVbX1zPh0M2f93wLeWdF0BY8/zl1LbX3oroVqET0Abj+rH4lxvv2nPVRWw4drmq6nFK7Kq+t45OMNTHxsAR+v3d/kMWfFLCPBZra/xA65MGzqX4lIhFg/1zUO8PZBXzOwnP7z7U6/fI6Iv9wx+QSfK7Gt3F3Eqj1FfplPqHBfEFm8o7DJY9xLucQMDtxOGBGRJq0Pzm488H1HHkBtvYPXFh/bbyOc1dbb+edX25jwf1/w6ve7aS4h9b1V+Xy3LTIX2yRKBamsZqCcOSDLL8kk//l2h++TCSEOh4MPV+cz6bEFPDpvE5W13kvebD1YHtLXt1qhDIBemam8/tNTfP7leTmE/+G0hsPhYM7yPZz16Bf8/fPmM+zPUx1QEbGSx8VjYGPQ8O7tObFXR7pkJPm04Dh31T6OlNf4b2IWiPQti9FmXN9M/vmDMQzMSfPpc0L5BLo1aupaviDiUdJukEq5iEgQ2e2uZILYBNNUNEAcDgen9OnEyB7tyWznW2+XV77bRV0IZy22xhcbD3DuE1/y5/fXU1pV16L3/PG9dTqPkshQVw0bPzLjpAzodYa182kDm83GizeO5bJR3UjxofHvvHX72Rch5ZrW55dwzfOL+Nkry1q8A+mJ+ZsoqgjN61u/74148MEHeeutt9iwYQPJycmMGzeOhx56iAEDBjQe43A4+MMf/sBzzz1HYWEhJ510En//+98ZMmRI4zHV1dXceeedvPrqq1RWVjJx4kSefvppunXr5u8pB8Swbu1582fjKKuuY/eRCvNVWMnuIxXsKaxg95FKdhdWUNFM04ElOwtZt6+EwbnpQZy5f63cXcQf5q5lWUPDsOakUMX4mJUA2FMyielxcoBnJyLipnS/2cYM0KkfZAW2seH/ujVOrK6rZ19RVcPfCtffiD0NfzuaWySvqbPz+pLd/GR834DON1A+WlPAv77ZzpNXjyQnI8nq6YifTByUzcRB2RRV1DT+ez7m33dhZbM31ueu3Mc95w2iQ2pCEGfuX19sPMAf31vHtoamUc3pY9vHgJg9ANi7jSUm3fctwSIiLbZnMZQ27ITucyYkBe4a1Gaz8cTVIxsfV9bUm2tk59+Io/5eNLegnF9cxacbDvilpKpVth8q58/vrePTDQda9b7h3dtz3wWDsfm6/UskFGz9HGpKzXjAeRAXnud/6UnxPHrlcP562VD2FVUedR7sWhM8VOb9+s7ugFe/38Udkwd4PSbUHSmv4bF5G5n13S7srbjP17V9Mr87fxAZyfGBm5wP/L6IvmDBAm699VbGjh1LXV0d99xzD5MnT2bdunWkppqaag8//DCPPfYY//73vznhhBP485//zNlnn83GjRtJSzNZS9OnT2fu3LnMnj2bTp06cccddzB16lSWLl1KbGz4dOBulxjHoC7pDOpy7EmIw+HgUFkN5z35lddabi8v2smDlw4N9DT97kh5DQ9+sJ7Xl+5p8XsmxKwgyVYLQMzgCyEmfP5/FpEIsOE9oOEv/OCL8LkeRSskxsXSOzOV3plN1x4tq67jrjdW8sHqppstzvxuJz8+vQ+xMeF1EbX7SAV3vbGSkqo6znvyKx6/agTjT1AN6EjSPiWB9ikJDO2WccxrdruDbYfKmfK3L6mtP/bsurrOzutLd/M/Z4TfDaJdhyu4f+5aPmvFgsi5Me6lXC4OwKxERJrhUUYhuOWkkhNi6Z+dRv/spncwFVfUct0L33k0oHP38rc7w3IRvaKmjr/N38y/vtne5N9BbzqnJfKbcwdy6ciuxITZuZ+IVxbGoECIj42hZ6dUenZq+vquoqaO37+9ljeXNb1m9ur3u7n9rP4k+FgqOtjsdgcvL9rJo59spKSFO2oAkuNjuWVCX24+ow9J8aG7Fuj3/zc++ugjbrjhBoYMGcLw4cN58cUX2bVrF0uXLgXMwvETTzzBPffcw6WXXkpeXh4vvfQSFRUVzJo1C4Di4mJeeOEFHn30USZNmsTIkSOZOXMmq1evZv78+f6esmVsNhud0xK5Zmx3r8e8vXwvxZW1QZyV73YfqeCSp7/h9aV7SKKaxkWpZrRLjOOObhtcT6iUi4j4yuEwHd5busU1iKVcWqtdYhw/OrW319d3H6lkwabWZS9ZrabOzm2zljWeXB0pr+GH//qehz/aEDHbsqV5MTE2+mW149y8Ll6PmbloF/bWpK+EgJW7i7jw71/z2Yb9DedBx9c5LZGbM9e4nhh0QYBmJyJRw26H2haWA3A4YF3DeVBMHAyYErh5tUFGSjw/OKWn19e/3nKIrQfLgjgj3xVV1HDN89/x7JfbWryAnhAbw0/H9+XzOydw+ehuWkCX0Gavh9qqlh1bXwsb3jfjhHZmN0yES0mI46bTvF/fHSqrDrs+ifV2B7/87wrue3dtqxbQLxqRy2d3juf2if1DegEdglATvbjY3C3u2LEjANu3b6egoIDJkyc3HpOYmMj48eNZuNBso1+6dCm1tbUex+Tm5pKXl9d4TCS55qQeXrMHK2vrecvLnalQVFVbz09nLmXn4XLujfsPG5Ju5KuE6dwX9xLjYtYQh+cvks0GV47pxue/OIm+RQ3/3yZ3gF6nWTB7EYkYdju8/TN4oAvMGA0f32NKtdR7+WNecQS2f2XG7XtAl+HBm2sLje7Zodka0+HWR+Ohjzawcs+xGWVPf7GVa55fRH5xZNQBlOO7/mTvCyO7jlSwYPPBIM7GN4fLqvmfl5dQVlHJc/GPsSHpRj5OuItfx81mhG0LNjxvECXExvCzCX354se96VC8zjzZZQR08P7fRETkuGoq4D8Xwl9y4JnT4fMHYd8K74kF+SuguKFBZ6/TIaVjsGbaYhcMz6V9ivft/TMXhc95kMPh4M7XV7Jyd1GL3zNpUDaf/PIMfjtlIO0S/V5QQMS/Kovgn5PgL9nw/Fnw5SOwf533GLTjK6gqMuMTzoH46CjxODg3nTHN9FIMp7gG8OSnm3lnxb4WH5/XNZ03fnoKf7t6JF0ykgM4M/8JaPR1OBz86le/4rTTTiMvLw+AggKzFT07O9vj2OzsbHbu3Nl4TEJCAh06dDjmGOf7j1ZdXU11tSvbp6SkxG8/R6B1yUjm7EHZfLS26Z/t5UU7uWFcr7CodfbAB+tZu6+EH8Z+wo/iTFOI7jEHuTHmY27kY0ocKXxuH8H8+lEUdR3Pry86kWHd2pu7jjUN2QMDzofY0Kx/JOJNOMegiLTgIVj5qhkf2QrfPmW+kjuaE7MB50HfsyCxnTlmw/vgaOhRMejCoJZyaSmbzcYPTunF/85Z3eTrX2w6yK7DFfTolBLkmbXeJ2sLeOHr7V5fX7yjkG+3HubSUeHRByUUhHMMGtvL3CDaUFDa5Osvf7uTMwdkBXlWrWe3O/jlf1eyv6Sa++JeYXKs2YU5IGYPA2L2cGvcuxx0ZDC/fhTz7aNIPGEid00dQa/MVPjmSdcHDQ6tnTAiLRHOMSjiOBww9+dmUQqgYJX5WvBXSO9qsswHTDGL5XENTT3XveN6f4jGoKT4WK4c053nvtzW5OtvLN3Dr88ZQEpC6C8wv/D1duavb9kOwn5Z7bh36mDOULm7ZikGhRB7Pbx1M+xbZh7vXWq+PvsztO9prsMGngc9TnGt+7jHoBDbERxo15/SkyU7C5t8bfGOQtbnlzRZHjrULNxyiCc/29yiYzPbJXDXOQPDckdNQDPRb7vtNlatWsWrr756zGtHLwg7HI7jLhI3d8yDDz5IRkZG41f37t5LpISi5ranbTtYzjdbDgdxNm3zwep8/vPtTk6OWcfv415ufL7W4dqOkW6r4KLYhcxIeIr/HL6GYZ/9EL57Fpa/4vqgED1xE2lOuMegiLL+PXOhCIANbG5/6iqPmMX1/14PD/eBV66AJf+CVa+5jgnhGnwXj8wlzUv2kcNhaqOHuj2FFdz5+spmj7lsVDctoLdSOMcgm83G9c2cB32+8QC7j1QEcUZt848FW/ly00GuiP2CG+M+BqDOEYPd4Tp37Wwr5pq4z3kh4VGe3nMZvebdDMtehjVvuj5oUOjGIBFvwjkGRZxvn4LVr5txzFHnDCV7YfE/YeZl8HBf+O8PYeVrbgtYNhg4NajTbY1rT+rhNc+htKqOt5e3PAPSKst2FfLXDzcc97j0pDjuu2AwH/7idC2gt4BiUAj5/C+w+RMzjjkqObJoJ3z3D3jpAnikL7z5Y1jzlquUS1wy9D87uPO12JS8LmS2895E9T9hsNv4QGkVP5+94rhVVONjbfzPGX347M4JXDm2e9gtoEMAF9Fvv/123n33XT7//HO6dXNdCOfkmIYfR2eUHzhwoDE7PScnh5qaGgoLC70ec7S7776b4uLixq/du3f788cJuFP6dqJv56YbDgC8vGhH8CbTBjsPl/ObN1bRlYP8Pf5vxNnMduV/1F3AqOpnub3mNubax1EV267xPTZ7HWz7Aj68CzY2BM3EdOgzIfg/gIiPwj0GRYwDG2DOT1yPJ90Pv94KlzxrFscTXDGI+mpzgvfeL13ZWmm50HVMUKfcGikJcVw22vvi8n+X7Kaqtj6IM2odUwd9ebM18vplteNPFw8J4qwiQ7jHoItHdA3rG0SLdxzhsXmbGGHbwp/j/tX4/D11N3Fi9dPcVXsznzOGuhi37cl1leb8593bTCkFgKwhkNkvuJMX8YNwj0ERY+tnMO9e1+PLX4RfroPzH4N+kyDWbaGmphTWvQ1z/geONGR39zwV2oXuzp+enVKbbUD+n2934GhpLxwLFFfUcvus5dQ10+sjxmZuFnx+5wRuPLU38bHh1VTQKopBIWLtHPjqUTO2xcJ1b8AvVsK5D0HvM8xzTlXF5obfGzdCeUPpvv6TIMH7ulgkSoiL4eqxPby+/vbyvZRUhW6fxHq7g1++toJDZc33ATprYBYfTz+D/z1vEOlJ4Vt5wu8R2eFwcNttt/HWW2/x2Wef0bu3Z6H83r17k5OTw7x58xqfq6mpYcGCBYwbNw6A0aNHEx8f73FMfn4+a9asaTzmaImJiaSnp3t8hRObzdZsTdB56/azryg068NW19Vz26zl1FaX81zCY3Syme3YX9QP55G6qyglhc1Z5zBi+psk/e8O+ME7cNJPIaOJQHHCua5thSJhJNxjUESoLITZ17hKQ+VdBqf+wtT1HH41XPkfs6B+7Zsw5keQ1kQzw0EXQExoX6w0l7FbVFHL3JWhm4X1yMcbWNFM/c+k+BievnZUWGzFDjXhHoNSE49zg2hx6N4gOlJew+2zltPRfoRnEh4n0WZuEv2n7mxeqz+TQ2SQ3+cKhv/6Q+J+ux2ufhVGXg+pTSwEaTeehKlwj0ER4cg2eP1GcDT0XjjjLhNTMrrC2Jvgujfhrm1wxUsw7GrTh+poYRCDmtvBvaGglKVeyiJYzeFwcOcbK9nbzDV9Vloic28/jb9cMpRO7XRN3BqKQSGgYA28fYvr8eQ/mwTJDr3g5J/CD+fCXVvhshdgyKUmgfJoUbobb9pJPfCWlF1ZW8+bS0O3T+LfP9/SbOWM+Fgbz/9gDP+6YSx9Orfzely48PtV6q233sqsWbN45513SEtLa8w4z8jIIDk5GZvNxvTp03nggQfo378//fv354EHHiAlJYVp06Y1HnvTTTdxxx130KlTJzp27Midd97J0KFDmTRpkr+nHDIuHd2Nhz/eSEXNsReJdgfM+m4Xd54zwIKZNe/BDzawem8RT8Y/x5AYkym23Z7Nz2tvxU4MKQmx/P3aUXTv2FCnt88E83XuX2H/Wtj4IWz6CHDAWfdY9WOISDiz15vtgM5MqpyhcOFTx9Y2j08yGQ79J5msrPwVsOEDk5EenwKn3xH0qbdW387tOK1fJl9vOdTk6y8v2skVY0JvC+v8dft5/ivvddAB/nhRHidke2+eKpHtupN78u+FO5p8rbCilvdW5XN5MwvtVrDbHfzqvys4UlLKqwlPkGMzizff2Qfyx7rrAcjNSOLJq0fSITUBSDB1QAeeZ+LW3qWw8QPY8imkdDJJBiIirVVdBrOvdWvMdy5MuPvY4xLTYMjF5qu+DnYvMtdi276AjO7mBl+IG39CFt07JrP7SNOL0f/5didjeoVeY9R/fbODeev2e309xgZ/u3okQ3IzgjgrET+pOAKzp0FtQ/m94dfAyT879rjkDjD0cvNVVwM7vzExaMdXkDXIxKYolNs+mbMHZ/Px2qZjRKj2Sfx262GemL+p2WPunjKIswc3XVEkHPl9Ef0f//gHABMmTPB4/sUXX+SGG24A4K677qKyspJbbrmFwsJCTjrpJD755BPS0lwXzo8//jhxcXFceeWVVFZWMnHiRP79738TGxtLpEpPiufikV2Z9d2uJl+fvXgXP5/Yn4S40MmS/GhNPv9euIP/iX2PC2O/BaDMkcT/1N5BCeYu0wOXDKVvU3ecbDbIyTNf438dzGmLSKT57E+wZb4Zp3SCq16BhOM02LTZIHek+QqzG3jXndzT6yL6qj3FrNxdxPDu7YM7qWbsLarkjuPUQb90ZFeuCLEFUgmuflntGNe3Ewu3Np3N8vKinSG3iP7cV9v4YuMBHoz7N6NjTDOlfY6O3FLzC+qIIzbGxoxpzgX0o8TEQvcTzdek+4M7cRGJHA4HvP0zOLDOPO7UHy597vg762LjoNdp5iuMxMbYuPaknl7rin+4Jp+DpYPpnBY6mdwrdhfx1w/XN3vM9EkncErfTkGakYgf1dfB6zeYeudgrq2mPn5sMtPR4hKg75nmS/jBKb28LqJvO1jOwq2HObVfZpBn5d3B0mp+Pns5zVSnYvLgbG48tVfQ5hQMASnn0tSXcwEdTOmS+++/n/z8fKqqqliwYAF5eXken5OUlMSMGTM4fPgwFRUVzJ07NyqaQzS3Pe1QWQ0frskP4myat+twBb9+YxVnxKzkN3GzG5//Ve3P2OwwF7lXj+3OxSO7WjVFEYkGa96Erx83Y1ssXPFv6OA9lkaCSYOyyM1I8vp6KDWgqa23c/usZRRXeq/l17dzKn+6OC/ksisk+Jo7D1q5u4hVe4qCN5njWLLjCI98vJFrYz/lmrjPAahyxPM/Nb/iMCaT8NfnDGB0z9DLiBSRCPLV/8H6d804MR2ueRWSIjub+cox3b0mltXWO3htcdNJaVYorqzltlnLqK33vtJ0ar9O/H979x0eRbk9cPy76SQkoaQRmoA06YQiSFFABBVFVMCCeO0N5YLXcv1d4dpAUURFFBUL6BVEERFBRAmhtxCKdAghISQEElJI2+zu/P6YTULbTdvszO6ez/PkcWd3kpzJroeZd973nKdvkH4YwkX9OQWOx6mPg8JhzLfgW0fbmFxQ31YNaWmnT+L8zUnOC6YCpSsxz+TZroPepH4dZtzVxe2u7/QzpVkA0C4qhF52lp8t0MnAiNFkYcL3O2lQfJKPfD/C26CeFMwyjeIPS08A2kUFM/U2aQ4nhKhF6Xth6dPl2ze9pTatcXM+3l7c29t2A5pf95wiK9/oxIhse3fVIXYmZ9t83d/Hi4/v606QjaaSwrMMaR9JVIj+bxCdyzcy4fsEuisHmOrzTdnzL5U8yt9KSwBuaBvOY/1bahWiEMITHPod1rxp3TDAnV9AWGtNQ3KGBkF+jOgcbfP177YmYzJbnBjRlSmKwgs/7ubkOdt10MOD/Zk1phvetgoiC6FnuxfC5tnqYy8fGL0AQvW1atBVuFKfxDlrj7L+yJVXRYNaB332vd0JDXTdBqK2yCC6Dt1vZxbWjhPn2H8q14nRXNm0lQc4ejKdz3xnEmpQ6179YY7hA9MoAAL9vJl9b3cCfN23/I4QQmP5mfD9vWCynkx0vQ96P65tTE40pmczfL2vfMFlNFlYvCPFyRFdbs3B08xdl2h3n9du70C7KGn+JFQV3iDafYpzGt8gslgUtTxRTipz/Gbha1B72XxuupmlFrUsQlRIAO+N7oqXDIoIIWrLmcOw5FHAOsN50P9Bm5s0DcmZ7DVaT8sp4q+DGU6M5sq+3pRkszwDqNUuPhjTVVelZ4SotNSdsOzZ8u3h70DzPtrF4wbujGlCHRtjaBYFvt+m/SqbLYmZzFxtvw76i8Pa0VVHpUUdSQbRdWhYhyjC7HTjXrBF21lYq/al8/XGRN7z/ZS2XmqX4COWxkwqeRLF+pF6846OXB3h+p13hRA6ZTbB4vGQYz2RaByjNgp1s+Vi9oQH+zO8YyObr3+79QRme0Xqatmp7EIm/2C/DvrIrtGM1mETVKGtsb2a2rxBVGyysDhe2xtEX2xIZOPBk8z1m0m4QZ3YsN7ckemmewDK6qA3uFIddCGEcISiHLWJX7F1ctU1t7tEc3RH6tq0Hp2b2C5bo/UK7j0ns3lrhf066M8Nbk1fHdU4FqLSzmfAovvBbC3nEfMg9HxY05DcQWmfRFu+35aC0aTdKpuz54t5roI66EPaR/JwvxbOC8rJZBBdh/x8vLi3l+1BhaUJqXZry9amlKwC/rV4N894L2WY93YAcpVAHiuZxHnUJn6jezThjm6yhEcIUYtW/0ft4g4QFGGtvWe7BIS7slc/OiWrkLjD2szCKjFbePb7BM4V2P63qmV4EG/e0cnt6uSJmosIDmCYvRtEW5KxaHSDKP7EOd7+/SDTfL+gs9dxAJIt4UwomYAZdebQ5KFt6GmnNJ8QQtSIxQJLHoNMtZkxER3g9jkeNZGglL3SBxuOnuXYmfNOjKZcTmEJT1dQB71vq4ZMGOT+pXeEGzIZ4YcHIDdV3W7aW52FLhzCfp/EYs36JFosCv9ctIvTubbroDeuV4d37+7s1td3MoiuU/f0bmazLlphiZklO086OSK1PMAz3yfQy7iVyb4/AmBRDDxb8jTHFfVit01kXf57W0d7P0YIIWpm1/ewZY762MsXxiyAENt1Md1ZTPP6tIsKtvm6VrOwZq4+zI4T52y+7u/jxcf3Sh10YZu9gZHkrALijpxxYjSq7AIjE/63kwcNKxjlvQGAAsWfx0omk436/+HANuE8MaCV02MTQniQtW/B4d/VxwH1YOx34O+ZK4BHdImmnp2au99qsIJbURRe+mkPKVm2axeH1fVn1tiuUgdduKbfX4Lkzerj4EYwej74SEkiR2nfKISeV9W3+boWeQ3gk7hjduug+1hXYtYLdO+VmDKIrlONQutwY/tIm68v2HICRXHuLKy3fz9I7skDvO87p+y5GaYxrLV0A6COrzcf39udOn5SB10IUUtO7YJfnyvfvnkGNLtWs3C0ZjAYeKDPVTZfX3v4DMmZBc4LCIg9lMEna4/Z3WfqbR1o30jqoAvbel6lrxtEiqLw/OLdNMvbyb99vit7fnLJExxU1BrukSH+zBzdReqgCyFqz4HlsG6G+tjgBXd/DQ3cd9l8RQJ8ve2Whfsx/iQFRpMTI1IbYK/8O93m6wYDfDC2KxHBnreCUriBhG9hxzz1sbcfjPkOgqO0jckNjbNzfbc96RwH0pzbJ3Hb8Sze++OQ3X1eHNaO7s1sD/67CxlE1zF7yzgSz+Sz8Wim02KJPZTBvA2JvOkzj2CDeld9ubk3n5hHlO3z+siOtI60fcErhBA1YjHDr8+W197r8RD0+Ie2MenAyG7RBNuY0a0oam10ZzmTV8zzFdRBv61LNGN7Sh10YZ/BYLDbNC72UAYpWc67QfTNpiTWHzjJdJ/P8Taokxhmm25npaU3AF4G+HBsNxra6WkjhBA1UpQDv00q377xdWh1g3bx6MR9vZvZrGSTV2RiacIpp8VyIC2XN3+zXwd9wqDWXCd10IUryjsNv79cvn3r+9AkRrt43FhFfRLnO3EySU5BSYV10Ae3i+CR/p5xQ1cG0XWsT6uGtAoPsvn6gi1JTokj83wx/1q8hxFem+nrvR+AFEs4/yp5HFDPWO6KacJdMVIHXQhRi3Z8CWnWAdrIjjDsbW3j0YlAPx/utJN/f9iRQlGJudbjUBSFF37cTWa+0eY+LcKCeGuU1EEXlTOya2Nd3CA6fDqPaSsP8pj3cq7yOg3ANktbZpruLttn8tC29G7Z0CnxCCE81NrpcF7NQbQZDn2e1jYenWjeMIiBbcJtvj5/c5JTVnAXlZiZuHAXRrPtpn/XtmzAc4OlDrpwUatfLW9m3OVe6Ha/tvG4MT8fL+6poE9iblHt90lUFIVXlu4lLafI5j7RoQG8e3cXj7m+k0F0HTMYDHZrgq7ef5pT2bZrrTmCoii8+NNeCs9n83++35Y9P9X0AIWoS9BaR9Tltds71GocQggPd/4MrHm9fPvmd8HHveutVYW9GbvZBSX8urv2Z2F9u+UEsYds16n2s9ZBryt10EUlBflXcINoe+3fICo2mXlu4S7Czek87fMLACbFi/+U/AOL9TS6f+swnhwoddCFELUo/W/YOld97FMHhr/tkY1EbbG3gvtgeh7xdvq0OMo7vx/i0Ok8m6+H1fXjw7HdpA66cE1JG2HPQvVxQCgMfd3+/qLG7q2gT+JP8bXfJ3HprlSW77HdyNTbWge9fpDnXJfLILrOjYppQqCNGuMWRV1eXJt31r/flsKfB07znM8SIg3ZAPxp7sZfFnXZToCvFx/f151APxkUEULUoj+nqsuYQZ350LyPpuHoTavwuvSzszR4/uYTlNiZGVVTRzPyeKOC5ctTRlzDNdFSB11Uzf12JhOcKyjh54TUWv397/1xmANpuUzxWUCAQZ3x87X5Jg5Z66BHBPvz/piuUgddCFF7FAVWPA+K9abhgMlQ33Zu9EQD20TQtEEdm69/ufE4Fnu1CGpo3eEzfLnxuM3XDQZ4f0xXIkKkDrpwQeYSNQeVGvwqBElJotrWKLQOQ9pH2Hx9wZYTFJtqbzJJSlYBry7dZ3efF25qS0zzBrUWgx7JIHqp7BRY9QqYirWO5CIhAb6M7NbY5utz1yUy8uONLE1IxWhy7ABJ4pnzvL58P20MKTzkvRKAIsWXqabxZfu8emsH2kgddCFq7sxh+PO/YHZu8yOXkLwFdllXwviHwo2vaRuPTtkbbNybmkP/t2P5OPYoWXbKrVSH0WThuYW7KLbzb9CwDlHc26uZQ3+vcLDUeIibAZbau9lSHVdH1KVvK9tlUl5espdx87ay5uBphw+QbDp6ls/XJzLIayc3escDcFqpxyzTnWX7vD+mq92alUKISkpcC1s+UQeMxcV2L4TkzerjBq2g77PaxqND3l4G7utt+zxoxd50hrwfx4LNSeQXO/Zc+1y+kecX2+8H8/iAVvRvbbvkjNCB/csg4buK9/NE2z6DDLWsL426Qoz0pHKWB+w0GE08k89102N5f/VhMvJsl1upDrNFYdIPu8izky/7tw7j0f4tHfp7XYEMogOUFMEP42DzbPhqOOTU/rKIqrC3PA1g98kcJi7aRb+31/DRX0fIPF/zGwElZgv/XLSLwhITr/t+hY9BvaieY7qdk4p6N2xI+0i7dZqEEJVUlAsL74UNM2HBSLV0iVCZTfDbhTMf/gN15SLkSoa0jyA61PYMp/TcImasOkSfaX/x0k97OJRue8lxVcxcfZh9p2x3iI8I9mea1EHXt/NnYNE4iH1DzUWlqz50oqLzoPVHzvLQ1zsYPDOObzYlcd4BAyQ5BSVMXrwbP8XIVJ9vyp5/s+R+zhMIwKP9W0hzOCEcITsZFv8Dfn8JljwKRuc1Dda9wmxY/Z/y7ZvfAR+5cXclo3s0xc/H9vBG4pl8/vPLPvpM+4u3Vhzg5Lmaf84UReHlJXvJyLN9/d0hOoRJN7ap8e8StSjjICx9En55CpZPApNjJ5y4tNw0iJ1m3TDALTPB68qVEoTj9a2gT+LZ88V88NcR+k2PZdIPu/g71THn8J/GHWN7ku0yWPUCfXn37i4euRJTBtEB0nZBhnUZemo8zB0Ix9drGtKF2kWF0OuqipdIZOQV897qw/SZvoZ/Ld7NfjuDGhX58K8j7D6Zw+1eG+ntdRCAJEskc823AhBW15+375RBESEcImUrnLMuAU1aD58NhJPx2sakFzvmwem96uOoztDjIW3j0TEfby/u7V3xbO9ik4WF21O4adY67vtiC3/ur/4M3i2Jmcxdd8zuPu+N7uJRdfJc0vE4yLXWzT+8Ej67ofy8SAeGtI8kqhJL4I+fzWfKsn30eesv3li+n5Ss6g2QKIrCv61NlJ70WUYzL/XG5ibzNSyzqKWk2kUF8/xNbav184UQlzjyBxRmqY/3LoZ5N0KW7dIYHiX2Lci3Tq5ofxtcPUTbeHSsQZAfIzpHV7hfbpGJz9YlMuCdWJ78Np5tx7OqXR51cfxJft+XbvN1fx8vPhjb1e7gvtCBg8vBeF59vGMefH2LOngs1Jt4RuvEm+4PQJMYbePxMBX1SSxlNFtYsjOVWz/awN2fbmLl3jRM1SzluedkNu+vPmx3n+mjOhHpoeWpJJsDNLsWHv4D6lkHHwrOwvzbYdNs3SwptNc07lJGk4XF8Se5+cP1jP1sM6v2pWOuwgDJ9qQsPo49SjAFvOL7v7Lnp5rGU4w6EDLj7s40lOXLQjhG6xvhwd+gbpS6nZsKXw2DnfO1jUtr5zNgzRvl2zLzoUJjejbDz7vy/7RvPJrJI/N3cMN7a/lyw3HyqtDlPaewhEmLdtn9Z/Kh61rI8mVX0OkuuP9HqFNf3c46Bp8Phn0/axuXlY+3F/dV4gZRqbxiE19sOM7AGbE8Nn8HWxIzqzRA8nNCKr/tSaOZ4TRPev8KQInizaumBwEDfj5efHhPN/x9JB8J4RA9H4HR88Gvrrp9+m91QsGR1drGpbW0PbD9c/WxbyAMm2Z/f1HhyqULWRRY+Xc6o+du5taPNvBj/Mkq1Rc+kZnPf5fZrxf8f7e05+oIKX2qewOeh9s/Bm/r+MbJbTB3AJzYpG1cWju+Xr2xCeo54pCpmobjqUbFNCHIRp/EK9medI4nv9vJwBlrmRt3jJyCyl/fFRhNTFy4C5Od8cPRPZowrGOjSv9MdyOD6KUadYHH4qDVYHVbMcMfr8CPD4ExX9vYgFs6NeLallUv2L8lMYvHF8Rzy4frWX+k4hIRuUUl/HPRLiwK/NPnRyKszURXmXuw1tIVgPF9mnNDW9sNDoQQ1dDsWng8Dppeq26bjbBsAvz6nO56NTjNH/+BYuuKmm7joGlPbeNxAeHB/jw3pHWVv+9EZgGvLd9P/3di+Xrj8Uo1If3P0r85lWO7/l7byGBeGCYzdV3G1UPgsbUQ1UndLsmHxQ/CH/+ni14ND/dvwVUNA6v0PRYF/th/mrGfbWHsZ1sqtcQ1JauAV3/ZByhM9fkGf2sz0Xnm4RxVmgDw8vB20g9GCEe75nZ4dA00tP4bVpQD392ty14NTmGxwG+TQbEe+8AXILSJtjG5gC5N63FXTNX/TvtO5fL84t0MejeOX3alVnjj1WS2MHHRLvKNtgfdb2gbbrdfjdCZbvfDw6sg1FquNj8DvhkBW+fqZmKlU13aTHTIVAj0rAaSehES4MsLw9pV+ftSswuZtvIg/d5ew9y4Y5W6SfjGbwdIPGt7/LN5w0CmjOhQ5VjciQyiXyiwAdy3GPpfkCz2LYEvhkCm/eXqtc3Ly8CXD/bkH9ddVa3lYAfT8xg3bxv/+GobRzNs18GdumwfJ88V0t5wgvHeqwAoVPx43TQOUBt8vXxz++odhBDCvuAoGP8r9Hqs/Ln4r629GlI1C0sTSRthz0L1cUA9mflQBU9d34rpozoRHlz11ULZBSVM/XU/N81ax18HTtu8iPxlVyrLdp+y+XP8vL2YNbYrAb4yU9el1L8KHvoDOo8tf27TR/DtHZB/VrOwAAL9fPjxyb7c3jWa6pRf3Ho8ixGzNzD5h92k27j5Y7Yo/HPRLs4Xm7jRK55B3rsASFMa8JHpDkBtojTeTpMnIUQNhLdVB9Lb3mJ9QlF7NSy6T3e9Gmrd7v+ps2FBvbFw7dPaxuNCpo3qxOQb2xDs71Pl703NLuS5hbsY9ckmdibbrgc8O/YoCcnZNl9vGOTHO3d1kdKnria6mzqxssVAddtigpUvwM+Pe16vhi2fwBm1rC+NY6DbA9rG4+Ee6NOcWWO60shO/ytb8opNTFt5kCEz41ixN83m9d2f+0/zv63JNn+Ot5eB98d0JagaudWdyCD6pby81cZ1Y74FP+sso4z9an3Qw6s0DS3Qz4cpIzqw+aVBPD+0DZEhVR8giT10hptmrefVX/4mK//ihhnL95xiyc5UQOE136/wNqj/c802jeSkEo6vt4FZY2RQRIha5eMHN8+AkZ+Cj/UfydR4dUmhjno11KpLZz4MfhWCpHlfZRkMBsb2asbGFwfx/pgudG4SWuWfkXgmn4e/2cH987ZyIO3i/honzxXwfz//bff7XxjWlvaNQqr8e4UO+AXCHZ/C8BngZT1JPr5O7ReTulPT0MLq+vPB2G6se+EGHhvQkpCAqp3EKwr8tPMkN7y7lll/HqbAePEM+0/WHmXHiXMEUMwU3/JyWm+U3E8+dajvwU2UhHCagBD1OmzQfwDr/2uHVuiuV0OtKjwHq18t377lXfX8UFSKr7cXEwa3ZvO/B/Pf2zrQIsx2Uz5bEpKzGTVnExO+T7isAenO5HN8tOao3e9/+87O1ZrMIHQgqCHcvwSum1j+3J5FMG+o5/RqyEmFtdOtGwa4+V3wkqFDLRkMBkZ2a8y6F25g9r3diGlev8o/IyWrkKe+28nouZvZnZJ90Wtn8op58ac9dr9/wqCr6d6s6r/X3RiU6nbR0Lnc3FxCQ0PJyckhJKSaF/JnDqszH85eUFT/+pdhwAu6SCJGk4WVf6fx5caky/4nqIzgAB+eHdSaB/o2J/O8kWGz1pFbZOJOr3W85/cpAImWKIYZ38aILy8Nb8cTA1s5+CiE0AeH5AxH/7y03bDofsi23hE2eMPQ1+Hap8CdZ7Zs/hhW/Vt9HN0NHvlLaqHXgKIoxJ84x1cbk/i9ij0yALwMMLpHUyYNbUPDIH/u+WwL25KybO7f7+ow5j/USwYaq0iXOejEZlg8Hs6fVre9/eGW96D7uBrH5wj5xSaWJKTy1cbjJJ6peum9qJAA/nVTW+7o1pi9qTnc+ckmTBaFyT4/MMFnKQDrzR0ZV/IyYODT+2MY1jHKsQchhE7oMgcd/RN+fBiKstVt3yAY+TF0uKPG8ena8klqc0OADqPg7q+0jcfFWSwKaw9n8NXGJNYfqfqqKj8fLx7p14Inr2+FwWDg5g/Wk2yncfW9vZvx1h2dahKyR9JlDtq3FJY+pZa4A3V17J3zoLWbN/hd/GB5X5weD8OtMzUNR1zZ7pRsvtp4nN/2plFirvqw7h3dGvPCsLZEhQTw0NfbiT1ku/xzt2b1WPx4H3yq0HvL1VQ2Z8ggekWK89TEeWBZ+XNthsEdc6FOvRrH6Sg7k9UBkhV706o8QNK8YSChdXzZczKHEPJZ4z+ZMIM68/AB44uss3Th2pYN+O6Ra/GWQRHhpnR54gZQkAU/PQzH1pQ/1/FOuO0j8Kv6zBrdy02D2T2tXeAN8Ohf6hJC4RCp2YUs2HyC77clk1NY+SYzAEF+3vRq0cDuCVZoHV9WTRxAVDWWGno63eag3DR1ID1la/lzMf+A4W+Djz5m2VksCuuOnOHLjUmsO1xx/5dLdWocSm5RCScyC2hhSON3vxfxN5gwKt4MN07nmNKYMT2a8vZdnWsheiH0Qbc56FySOqEgfW/5c32fhcFTwNsNl5SfSlBn3aOoNw0m7ICQaK2jchuHT+fx1cYkfk44SVFJ1Wrth9X14+qIumxJtD2RoGVYEMuf7Uegnxt+NmuZbnNQxkF1YmVm6eoDAwx6BfpN1sXESoc7FgsLRqqPAxvCMzukFrrOZeQW8e2WE3y7NfmyahMVCfD1ot/V4fx54LTNfQL9vFn5XH+aN3TDsYcLyCC6I5OwosDGWfDXa+XNXUKbQac7odUgaNpbNxeSaTnqAMn/tiWTXYUuvKWm+nzNgz5/ALDC3IunSiYSHODD7xMH0LheHUeHK4Ru6PbEDcBihtg3Yf175c81aKUOprcaBE16gLdvzX6HXvz0SHkX+JgHYcQHmobjrgqMJn5OSOWrjUkczTjvsJ87577u3NzJc7u114Suc5DJqK4O2f55+XMR16izQVsNUleM6GS1yNGMPL7elMRP8akUllTcQOliCvN9pzPAWx2sm2O6jXdMY2neMJAVz/b3+BqQwr3pOgcZC2D5RLWkQqlGXdVmpK0GQVRn9xjMslhg3hC1jB/Aja/Ddc9qG5ObOpdv5PvtySzYfII0O03Sq8LHy8CSp/rSuUk9h/w8T6PrHFSUAz8/oZaWKtW0N7QfoeagiGvcY5WwyQif9IXMI+r2bbN1s/pQVKyoxMyy3af4csNxDqbb7oNYVe/c2ZnRPZs67OfplQyiOzgJA+pM0B8fUuvUXcg3EK7qpybQVoMgrI3mSbTQaObbLSf4cM0R8opMFX8D0MGQxDK/V/A2KBQo/gwufpc0GvLhPd24rYvMgBDuTdcnbqUO/Ao/P2mdpX0Bv2BoMQBa3aDmoIYuWnbp+Hr45lb1cZ0GMCFeZj7UMkVR+OtABtNWHuBYNcphXOiumCa8e3cXB0XmeVwiB+36Hyz/J5guGXAIqActB5afB9Vr5pjfVwPZBUY+jUvkyw3HMZorN9vwJq9tzPWbBUCq0pAhxTMwetXhxyf60E1qQAo3p/scpCiw7TP1hp7lkmubwIbQ0noO1OoG1525Hf8N/GodNA9vB09scJ9JEjpVYrbwc0Iq7/1xiNO5xTX6Wf+6qS1P33C1gyLzPLrPQRaLOqEp9k3gkiG0ulHW67DB0PJ6qBte89+nhQ3vw59T1cdNesFDq9zjBqWHURSF9UfO8taKAzUeTB/WIYpP7u/uEU2SZRC9Ni4eAc6dgGXPqE22bAlpUj6Y1fJ6TQeBsvKNfPDnYb7dmmy3zIsBCz/5TaW7l7pMaXrJWD4138Yd3Rrz/piuTopWCO3o/sSt1JnD8MvTcHKb7X3qX1U+mNViAARUvbGk05lL4NN+5V3gR3wIMeO1jcmDlJgtfL8tmfdXH+ZcNVYxNWsQyIrn+lNXZupWm8vkoFO7YNkESLfTfKhh6/IcdFU/8K/ruN9fRSlZBbz9+0GW70mzu18divjT/180NmQC8LhxIqssvfjnkDY8N6S1M0IVQlMuk4NObIZfn4Ozh2zvE3FN+YB6s75qw2S9K8iCj2Kg0FoqZPxyaNFf25g8SIHRxNy4ROauO1blMi8APa+qz8LH+kjp0xpwmRx0ZDWseF4tNWVLVOfy86Bm1+qmaoFd2SnwcS8oKQCDFzy2FhrJ5BhXZrYo/BifwoxVhzl7vuo3CSOC/Vk1cQD1gzyjsbUMotdW0ix1/gwcj4Ojf6kz1M+n29jRAEHhav30gFB1tlZA6AXbFz5XH+pGQt0I9bED7/YczcjjrRUHWXMw47LXvLBwn/efvO77tbqvJZrhxulE1Atm5cT+hATIDAjh/lzmxK3sF6RBYqyaf47FQoGNRkkGLzUHXZZ7rrBdp541B0WqzznrjrO5BDbMgtg31O3GPeDh1TLzQQM5BSXMjj3C15uSKt2gxssAi5/oQ0xzWTVQEy6Xg7JTrPlnDSSuLW/8dykvH2sOuiDv2MpFZedBkeAf7NAcFH8ii9eWH7hiI3ZfTLzgs5BHfdRl2nHmzowveZHuzerzg5s3URKilMvloKzE8nOg4+ugOPfK+3n5luegiq7HAhtckIOcePOvpAhWTIaEb9XtTnfDnV847/eLMmk5hcxYdYglO1Mr/T3B/j6seK4/TRu4wM0aHXOpHKQocPZI+XlQ0np18PlKvP0hKMz29deF23UaQLA1Bzmz95WxAJY8CgeXq9u9HoObZzjv94tadb7YxKdrj/H5+kSKTZW/Sbjg4V70b+2iqyqqQQbRa/vE7UKKAhkHypPoiY2XL3WuKm//8gH14Cj1cXCUul03Sl0iFBimLl/0C6r0heaWPQf4ZdUfBGYfop0hhXZeybQ2pBJgKJ95eK/x32xWOrLosT70aiGDIsIzuNSJ26UsFji9tzwHJW8Bc9WailzGJ6D8QjI40pp3LnwcruafwLDKz/BSFMhLg9P7IWMfnN6nPj576IJ4DerMh+iuNYtf1MiJzHymrTjI7/ts3SAu9+zg1ky6sY0TonJvrp2DzOoM9dIcdHLb5eUWqso3sPycp/SCsuxcKFIdFAuyngf5Vq5ni8Vs4c+t8fyxZg0Rhcdo65VCW0MKrQyn8DWo9dOLFR9uMr7NGd8mrPCAJkpClHLpHGQuUeuIl+ag1PjyPlbV5Rt0wTnPFa7HgiLU/BMUVvlZphYL5CSXn/+UngtlHgPF2sPBL1htJhocVbP4RY3sOZnNG8sPsC3JdhPRUu+P6cId3Zo4ISr35tI5yFQMKdusOegvSNtd85/pF3zlHFR6PRZ0wXiQTyVnClvM6gz60/sgYz+c/lvNRVmJlJWoCQpXm4nWqVfzYxC6kppdyDu/H+SXXacq3Peh61rw6ohrnBCVfrjNIPqcOXOYMWMGaWlpdOjQgVmzZtG/f8VL25yaNC9VUgTJm6x3JTdC/hkozL68jrGj+ASoCTSoYfmgVlCYOpvCPxTOHS9PkLZmq1r9ar6WCSXP8tT1rXhhWLvaiVcIHXLpE7dLGfPhxCZ1pUzyJsjPVGeJGh3XQPIivoHW3NOwfFCrNCf51VW72ZdeLF7aU+JSPR+BW96zv49wmq2Jmbzx2wH2puZc8fUuTevx4xN98JWZujXmVjmoKFedlXVsDSRvVcsTFGZDSc3q7tvkG2Q9Bwq7JA81VPPT2cPWi8X9UHzlz3KpD00jmWkazTt3dWZ0D/dvoiREKbfKQYXnIDHOelNvh5qDinJszxStKb9g9borKOyCPGTNSd5+6mSB0/vUSVcVnYvdNA36PFU7cYoqURSFVfvSeWvFQZKzrvzZubVzIz66p5tH1AuubW6Vg/LPqqv0jq2B1J1qTirKAVNh7fw+/5BLrsUuGBvy8lFzz+l9atnMivLgyE+h6z21E6fQhYTkc7zx2wHiT1z5urxtZDC/PHMdAb7eTo5MW24xiL5o0SLGjRvHnDlzuO6665g7dy5ffPEF+/fvp1kz+02rNE2atphN6lLDomz1YrIox/plfVyQCeczIC9d/e/5dPW5WmTBiyQlkgOWpuy1tGSB+UZu6dGaN+/oJIMiwqO41YmbLWbTBTknW31clouy1Xqc50+rX3mn1RxU0aB3TRm8Iay1Wr+0cYw6iO4bULu/U1SJxaLwc0Iq76w6eFHTrWsahfDlgz2JCpX3yxE8IweVlJ/7FGZfnotKz4POp5fnoCL7g941ZTL4cNTciINKU+ItbfifeTDPDmnPs4OvlkER4VE8IgeZjBdfe12ah/IzLzgPSlf/a6tMjKN4+0F4W4joAFddB93GOa+cnqiUYpOZ+ZtO8OGaI+QVla+yGtgmnI/v6y79YBzEM3JQ8eXXX0U51kH2bPVarDT3lI4J1dZEzFI+AWoj48iOaj+/TndJDvIAiqLw2940pq88yMlz5Td3WoUH8fU/enlkeSq3GETv3bs33bt355NPPil7rn379owcOZJp06bZ/V5dJs3qMBkhP+PiQa280+rs9oLM8q/8s+p/S5cCXklQOER2UE/SIq9RH4e3I6PIwNpDZ0jPKaJH8/r0adVQLhyFx/GIE7fqMBVbLyhLb/BZc1DBWWveySp/XJhlf/l0cCN1sDzyGvVELeIaCGsjg+YuotBoZuPRs+xJzaFdVDDXtw0n0E8uHB1FcpANJYXWgfXTF19YFljPe/JLz4Ws+Qg7p7UhTcrPf0rPhRq2JjnHxNrDGRQYzfRp2ZAuTes56+iE0A3JQTYYCy4ZWLfe6Cu99iq7Djtb8cSDes3Kz39Kz4UatAJv+bfUFeQUlLDuyBmOZpynU+NQrm8bLj0zHEhykA3G/PLznwvHhC68DivNQ7b60wBggPpXqedApV8RHaBBC/DyrBnHolxRiZnNxzJJSMnm6oi6XN823GN7IlY2Z+j2X2yj0Uh8fDwvvfTSRc8PHTqUTZs2XbZ/cXExxcXlM+Ryc2t51oCz+PhBaBP1qyIWi7pM+cILysJsCG2sJsi6V24KEOGLLFkWoobcNwf5qxd99eyv/gHUHFSUffGNvaIc60VjB3Wps3BZdfy8GXJNJEOuidQ6FHEFbpuDfOtA/ebqV0UsZuuM9gsuKI3n1YvGiPZq49IraNbQjwf6XOXIqIXwOG6bg/wC1UGmBi0q3tdsUgfSy27sZaoDYA2vVmd6BrjwQJ4gNNCXEV2itQ5D2OC+OSgIGrZSvypiLlFzUNlNvrNqqeGwNhDRzrnNSoVLCPD15oZ2EdzQLkLrUFyGbgfRz549i9lsJjLy4ov1yMhI0tMvb3Q2bdo0/vvf/zorPH3y8lIvEOvUB67WOhohPIrkINQcFNhA/QprrXU0QngUyUGoM6mCrLWIhRBOJTkIdUZ53XCbE5eEELVHchDg7Wttyi4DokLUFt2vP7q0rIiiKFcsNfLyyy+Tk5NT9pWSkuKsEIUQQnKQEEJTkoOEEFqSHCSE0JLkICGEM+h2JnpYWBje3t6XzTrPyMi4bHY6gL+/P/7+/s4KTwghLiI5SAihJclBQggtSQ4SQmhJcpAQwhl0OxPdz8+PmJgYVq9efdHzq1evpm/fvhpFJYQQQgghhBBCCCGEEMKT6HYmOsCkSZMYN24cPXr0oE+fPnz22WckJyfzxBNPaB2aEEIIIYQQQgghhBBCCA+g60H0MWPGkJmZyWuvvUZaWhodO3ZkxYoVNG/eXOvQhBBCCCGEEEIIIYQQQngAXQ+iAzz11FM89dRTWochhBBCCCGEEEIIIYQQwgPpfhC9uhRFASA3N1fjSIQQrqA0V5TmjpqSHCSEqArJQUIILUkOEkJoSXKQEEJLlc1BbjuInpeXB0DTpk01jkQI4Ury8vIIDQ11yM8ByUFCiKqRHCSE0JLkICGEliQHCSG0VFEOMiiOutWnMxaLhVOnThEcHIzBYKhw/9zcXJo2bUpKSgohISFOiNA53PG45Jhcg6sdk6Io5OXlER0djZeXV41/nuQglTselxyTa3C1Y5Ic5HjueEzgnsclx6Q9yUGO547HBO55XHJM2pMc5HjueEzgnsclx6S9yuYgt52J7uXlRZMmTar8fSEhIS7xBleVOx6XHJNrcKVjcsSsh1KSgy7mjsclx+QaXOmYJAfVDnc8JnDP45Jj0pbkoNrhjscE7nlcckzakhxUO9zxmMA9j0uOSVuVyUE1v8UnhBBCCCGEEEIIIYQQQrgpGUQXQgghhBBCCCGEEEIIIWyQQXQrf39/pkyZgr+/v9ahOJQ7Hpcck2twx2OqTe7693LH45Jjcg3ueEy1yR3/Xu54TOCexyXHJNzx7+WOxwTueVxyTMId/17ueEzgnsclx+Q63LaxqBBCCCGEEEIIIYQQQghRUzITXQghhBBCCCGEEEIIIYSwQQbRhRBCCCGEEEIIIYQQQggbZBBdCCGEEEIIIYQQQgghhLBBBtGFEEIIIYQQQgghhBBCCBtkEN1qzpw5tGjRgoCAAGJiYli/fr3WIVXb1KlTMRgMF31FRUVpHVaVrFu3jhEjRhAdHY3BYGDp0qUXva4oClOnTiU6Opo6depw/fXXs2/fPm2CrYKKjuvBBx+87L279tprtQm2EqZNm0bPnj0JDg4mIiKCkSNHcujQoYv2cdX3ytkkB+mL5CDJQZ5GcpC+SA6SHORpJAfpizvmIHfLPyA5yJEkB+mL5CDJQXolg+jAokWLmDhxIq+88goJCQn079+f4cOHk5ycrHVo1dahQwfS0tLKvvbu3at1SFWSn59Ply5dmD179hVff+edd5g5cyazZ89m+/btREVFceONN5KXl+fkSKumouMCGDZs2EXv3YoVK5wYYdXExcXx9NNPs2XLFlavXo3JZGLo0KHk5+eX7eOq75UzSQ7SH8lBkoM8ieQg/ZEcJDnIk0gO0h93zEHuln9AcpCjSA7SH8lBkoN0SxFKr169lCeeeOKi59q1a6e89NJLGkVUM1OmTFG6dOmidRgOAyg///xz2bbFYlGioqKU6dOnlz1XVFSkhIaGKp9++qkGEVbPpcelKIoyfvx45fbbb9ckHkfIyMhQACUuLk5RFPd5r2qb5CB9kxzkOiQHVY/kIH2THOQ6JAdVj+QgfXPHHOSO+UdRJAdVl+QgfZMc5Do8IQd5/Ex0o9FIfHw8Q4cOvej5oUOHsmnTJo2iqrkjR44QHR1NixYtGDt2LImJiVqH5DDHjx8nPT39ovfM39+fgQMHuvR7Vmrt2rVERETQpk0bHn30UTIyMrQOqdJycnIAaNCgAeD+75UjSA5yPe7+uZYc5FkkB7ked/9cSw7yLJKDXI87f65dOf+A5KDqkBzketz5cy05SP88fhD97NmzmM1mIiMjL3o+MjKS9PR0jaKqmd69ezN//nxWrVrF559/Tnp6On379iUzM1Pr0Byi9H1xp/es1PDhw/nuu+9Ys2YN7733Htu3b2fQoEEUFxdrHVqFFEVh0qRJ9OvXj44dOwLu/V45iuQg1+POn2vJQZ5HcpDrcefPteQgzyM5yPW46+falfMPSA6qLslBrsddP9eSg1yDj9YB6IXBYLhoW1GUy55zFcOHDy973KlTJ/r06UOrVq345ptvmDRpkoaROZY7vWelxowZU/a4Y8eO9OjRg+bNm/Pbb78xatQoDSOr2DPPPMOePXvYsGHDZa+543vlaO70N5Ic5LokB3kud/obSQ5yXZKDPJc7/Y0kB7kmV84/IDmoptzpbyQ5yDVJDnINHj8TPSwsDG9v78vugmRkZFx2t8RVBQUF0alTJ44cOaJ1KA5R2lnand+zUo0aNaJ58+a6f+8mTJjAsmXLiI2NpUmTJmXPe9J7VV2Sg1yPJ32uJQe5P8lBrseTPteSg9yf5CDX4ymfa1fJPyA5qCYkB7keT/lcSw7SJ48fRPfz8yMmJobVq1df9Pzq1avp27evRlE5VnFxMQcOHKBRo0Zah+IQLVq0ICoq6qL3zGg0EhcX5zbvWanMzExSUlJ0+94pisIzzzzDkiVLWLNmDS1atLjodU96r6pLcpDr8aTPteQg9yc5yPV40udacpD7kxzkejzlc633/AOSgxxBcpDr8ZTPteQgnXJaC1MdW7hwoeLr66vMmzdP2b9/vzJx4kQlKChISUpK0jq0apk8ebKydu1aJTExUdmyZYty6623KsHBwS51PHl5eUpCQoKSkJCgAMrMmTOVhIQE5cSJE4qiKMr06dOV0NBQZcmSJcrevXuVe+65R2nUqJGSm5urceT22TuuvLw8ZfLkycqmTZuU48ePK7GxsUqfPn2Uxo0b6/a4nnzySSU0NFRZu3atkpaWVvZVUFBQto+rvlfOJDlIfyQHSQ7yJJKD9EdykOQgTyI5SH/cMQe5W/5RFMlBjiI5SH8kB0kO0isZRLf6+OOPlebNmyt+fn5K9+7dlbi4OK1DqrYxY8YojRo1Unx9fZXo6Ghl1KhRyr59+7QOq0piY2MV4LKv8ePHK4qiKBaLRZkyZYoSFRWl+Pv7KwMGDFD27t2rbdCVYO+4CgoKlKFDhyrh4eGKr6+v0qxZM2X8+PFKcnKy1mHbdKVjAZSvvvqqbB9Xfa+cTXKQvkgOkhzkaSQH6YvkIMlBnkZykL64Yw5yt/yjKJKDHElykL5IDpIcpFcGRVGUys5aF0IIIYQQQgghhBBCCCE8icfXRBdCCCGEEEIIIYQQQgghbJFBdCGEEEIIIYQQQgghhBDCBhlEF0IIIYQQQgghhBBCCCFskEF0IYQQQgghhBBCCCGEEMIGGUQXQgghhBBCCCGEEEIIIWyQQXQhhBBCCCGEEEIIIYQQwgYZRBdCCCGEEEIIIYQQQgghbJBBdCGEEEIIIYQQQgghhBDCBhlEF0IIIYQQQgghhBBCCCFskEF0IYQQQgghhBBCCCGEEMIGGUQXQgghhBBCCCGEEEIIIWyQQXQhhBBCCCGEEEIIIYQQwob/B7W/j9qWfFPdAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1500x300 with 5 Axes>"
       ]
@@ -525,7 +2133,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "aeon-venv",
    "language": "python",
    "name": "python3"
   },
@@ -539,7 +2147,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,

From f6cc5ba72f429a896ea229a707292b0bb7cef99b Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sat, 8 Mar 2025 10:41:39 +0530
Subject: [PATCH 05/11] Depreciation for seed parameter added

---
 aeon/classification/shapelet_based/_rsast.py         | 12 ++++++++++++
 aeon/classification/shapelet_based/_sast.py          | 12 ++++++++++++
 .../collection/shapelet_based/_rsast.py              | 12 ++++++++++++
 .../collection/shapelet_based/_sast.py               | 12 ++++++++++++
 4 files changed, 48 insertions(+)

diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py
index 07d01a8fb1..516f11d9d8 100644
--- a/aeon/classification/shapelet_based/_rsast.py
+++ b/aeon/classification/shapelet_based/_rsast.py
@@ -7,6 +7,7 @@
 __all__ = ["RSASTClassifier"]
 
 import numpy as np
+from deprecated.sphinx import deprecated
 from sklearn.linear_model import RidgeClassifierCV
 from sklearn.pipeline import make_pipeline
 
@@ -32,6 +33,8 @@ class RSASTClassifier(BaseClassifier):
         the number of reference time series to select per class
     random_state : int, default = None
         the seed of the random generator
+    seed : int, default= None
+        Deprecated and will be removed in v1.2. Use `random_state` instead.
     estimator : sklearn compatible classifier, default = None
         if None, a RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)) is used.
     n_jobs : int, default -1
@@ -63,6 +66,12 @@ class RSASTClassifier(BaseClassifier):
         "python_dependencies": "statsmodels",
     }
 
+    # TODO: remove 'seed' in v1.2
+    @deprecated(
+        version="1.1",
+        reason="The 'seed' parameter will be removed in v1.2.",
+        category=FutureWarning,
+    )
     def __init__(
         self,
         n_random_points=10,
@@ -70,6 +79,7 @@ def __init__(
         nb_inst_per_class=10,
         random_state=None,
         classifier=None,
+        seed=None,
         n_jobs=1,
     ):
         super().__init__()
@@ -77,6 +87,8 @@ def __init__(
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
+        if seed is not None:
+            random_state = seed
         self.random_state = random_state
         self.classifier = classifier
 
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index 89209c5da6..cea57293ea 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -11,6 +11,7 @@
 from operator import itemgetter
 
 import numpy as np
+from deprecated.sphinx import deprecated
 from sklearn.linear_model import RidgeClassifierCV
 from sklearn.pipeline import make_pipeline
 
@@ -34,6 +35,8 @@ class SASTClassifier(BaseClassifier):
         the number of reference time series to select per class
     random_state : int, default = None
         the seed of the random generator
+    seed : int, default=None
+        Deprecated and will be removed in v1.2. Use `random_state` instead.
     estimator : sklearn compatible classifier, default = None
         if None, a RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)) is used.
     n_jobs : int, default -1
@@ -66,6 +69,12 @@ class SASTClassifier(BaseClassifier):
         "algorithm_type": "shapelet",
     }
 
+    # TODO: remove 'seed' in v1.2
+    @deprecated(
+        version="1.1",
+        reason="The 'seed' parameter will be removed in v1.2.",
+        category=FutureWarning,
+    )
     def __init__(
         self,
         length_list=None,
@@ -73,6 +82,7 @@ def __init__(
         nb_inst_per_class: int = 1,
         random_state: Optional[int] = None,
         classifier=None,
+        seed: int = None,
         n_jobs: int = 1,
     ) -> None:
         super().__init__()
@@ -80,6 +90,8 @@ def __init__(
         self.stride = stride
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
+        if seed is not None:
+            random_state = seed
         self.random_state = random_state
 
         self.classifier = classifier
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index 0049ad79eb..d38ae5d284 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 import pandas as pd
+from deprecated.sphinx import deprecated
 from numba import get_num_threads, njit, prange, set_num_threads
 
 from aeon.transformations.collection import BaseCollectionTransformer
@@ -66,6 +67,8 @@ class RSAST(BaseCollectionTransformer):
         the number of reference time series to select per class
     random_state : int, default = None
         the seed of the random generator
+    seed : int, default= None
+        Deprecated and will be removed in v1.2. Use `random_state` instead.
     n_jobs : int, default -1
         Number of threads to use for the transform.
 
@@ -98,11 +101,18 @@ class RSAST(BaseCollectionTransformer):
         "python_dependencies": "statsmodels",
     }
 
+    # TODO: remove 'seed' in v1.2
+    @deprecated(
+        version="1.1",
+        reason="The 'seed' parameter will be removed in v1.2.",
+        category=FutureWarning,
+    )
     def __init__(
         self,
         n_random_points: int = 10,
         len_method: str = "both",
         nb_inst_per_class: int = 10,
+        seed=None,
         random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parllel Processing
     ):
@@ -110,6 +120,8 @@ def __init__(
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
+        if seed is not None:
+            random_state = seed
         self.random_state = random_state
         self._kernels = None  # z-normalized subsequences
         self._cand_length_list = {}
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index 95a2a6ac8a..35edc08c77 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -3,6 +3,7 @@
 from typing import Optional, Union
 
 import numpy as np
+from deprecated.sphinx import deprecated
 from numba import get_num_threads, njit, prange, set_num_threads
 
 from aeon.transformations.collection import BaseCollectionTransformer
@@ -58,6 +59,8 @@ class SAST(BaseCollectionTransformer):
         the number of reference time series to select per class
     random_state : int, default = None
         the seed of the random generator
+    seed : int, default=None
+        Deprecated and will be removed in v1.2. Use `random_state` instead.
     n_jobs : int, default -1
         Number of threads to use for the transform.
         The available CPU count is used if this value is less than 1
@@ -92,12 +95,19 @@ class SAST(BaseCollectionTransformer):
         "algorithm_type": "shapelet",
     }
 
+    # TODO: remove 'seed' in v1.2
+    @deprecated(
+        version="1.1",
+        reason="The 'seed' parameter will be removed in v1.2.",
+        category=FutureWarning,
+    )
     def __init__(
         self,
         lengths: Optional[np.ndarray] = None,
         stride: int = 1,
         nb_inst_per_class: int = 1,
         random_state: Optional[int] = None,
+        seed: int = None,
         n_jobs: int = 1,  # Parallel processing
     ):
         super().__init__()
@@ -111,6 +121,8 @@ def __init__(
         self._source_series = []  # To store the index of the original time series
         self.kernels_generators_ = {}  # Reference time series
         self.n_jobs = n_jobs
+        if seed is not None:
+            random_state = seed
         self.random_state = random_state
 
     def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":

From 17002b3b03fd98dbb1d40f244659e8a30da02354 Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sat, 8 Mar 2025 10:56:05 +0530
Subject: [PATCH 06/11] seed parameter put to end

---
 aeon/classification/shapelet_based/_rsast.py             | 2 +-
 aeon/classification/shapelet_based/_sast.py              | 2 +-
 aeon/transformations/collection/shapelet_based/_rsast.py | 2 +-
 aeon/transformations/collection/shapelet_based/_sast.py  | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py
index 516f11d9d8..42390e0acb 100644
--- a/aeon/classification/shapelet_based/_rsast.py
+++ b/aeon/classification/shapelet_based/_rsast.py
@@ -79,8 +79,8 @@ def __init__(
         nb_inst_per_class=10,
         random_state=None,
         classifier=None,
-        seed=None,
         n_jobs=1,
+        seed=None,
     ):
         super().__init__()
         self.n_random_points = n_random_points
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index cea57293ea..65144b98a5 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -82,8 +82,8 @@ def __init__(
         nb_inst_per_class: int = 1,
         random_state: Optional[int] = None,
         classifier=None,
-        seed: int = None,
         n_jobs: int = 1,
+        seed: int = None,
     ) -> None:
         super().__init__()
         self.length_list = length_list
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index d38ae5d284..a65831e2a6 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -112,9 +112,9 @@ def __init__(
         n_random_points: int = 10,
         len_method: str = "both",
         nb_inst_per_class: int = 10,
-        seed=None,
         random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parllel Processing
+        seed=None,
     ):
         self.n_random_points = n_random_points
         self.len_method = len_method
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index 35edc08c77..bb7f73a79b 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -107,8 +107,8 @@ def __init__(
         stride: int = 1,
         nb_inst_per_class: int = 1,
         random_state: Optional[int] = None,
-        seed: int = None,
         n_jobs: int = 1,  # Parallel processing
+        seed: int = None,
     ):
         super().__init__()
         self.lengths = lengths

From 2730594c6e4acb290fdeafe100842aad7983aeff Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sat, 10 May 2025 11:33:41 +0530
Subject: [PATCH 07/11] modified pr_pytest.yml for workflow

---
 .github/workflows/pr_pytest.yml | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml
index ae1c792243..417c540bfd 100644
--- a/.github/workflows/pr_pytest.yml
+++ b/.github/workflows/pr_pytest.yml
@@ -3,10 +3,12 @@ name: PR pytest
 on:
   push:
     branches:
-      - main
+      - tdmvdc
+      - seed
   pull_request:
     branches:
-      - main
+      - tdmvdc
+      - seed
     paths:
       - "aeon/**"
       - ".github/workflows/**"

From 15947efab727842be2440e43715771b957c9ca0b Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sat, 10 May 2025 15:14:10 +0530
Subject: [PATCH 08/11] temporary seed param removed

---
 .github/workflows/pr_pytest.yml                          | 2 --
 aeon/classification/shapelet_based/_rsast.py             | 6 +++---
 aeon/classification/shapelet_based/_sast.py              | 6 +++---
 aeon/transformations/collection/shapelet_based/_rsast.py | 6 +++---
 aeon/transformations/collection/shapelet_based/_sast.py  | 6 +++---
 5 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml
index 417c540bfd..87316e94fa 100644
--- a/.github/workflows/pr_pytest.yml
+++ b/.github/workflows/pr_pytest.yml
@@ -3,11 +3,9 @@ name: PR pytest
 on:
   push:
     branches:
-      - tdmvdc
       - seed
   pull_request:
     branches:
-      - tdmvdc
       - seed
     paths:
       - "aeon/**"
diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py
index 42390e0acb..73af16a2da 100644
--- a/aeon/classification/shapelet_based/_rsast.py
+++ b/aeon/classification/shapelet_based/_rsast.py
@@ -80,15 +80,15 @@ def __init__(
         random_state=None,
         classifier=None,
         n_jobs=1,
-        seed=None,
+        # seed=None,
     ):
         super().__init__()
         self.n_random_points = n_random_points
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        if seed is not None:
-            random_state = seed
+        # if seed is not None:
+        #     random_state = seed
         self.random_state = random_state
         self.classifier = classifier
 
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index 65144b98a5..08caffba19 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -83,15 +83,15 @@ def __init__(
         random_state: Optional[int] = None,
         classifier=None,
         n_jobs: int = 1,
-        seed: int = None,
+        # seed: Optional[int] = None,
     ) -> None:
         super().__init__()
         self.length_list = length_list
         self.stride = stride
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        if seed is not None:
-            random_state = seed
+        # if seed is not None:
+        #     random_state = seed
         self.random_state = random_state
 
         self.classifier = classifier
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index a65831e2a6..211cc6bcdc 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -114,14 +114,14 @@ def __init__(
         nb_inst_per_class: int = 10,
         random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parllel Processing
-        seed=None,
+        # seed=None,
     ):
         self.n_random_points = n_random_points
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        if seed is not None:
-            random_state = seed
+        # if seed is not None:
+        #     random_state = seed
         self.random_state = random_state
         self._kernels = None  # z-normalized subsequences
         self._cand_length_list = {}
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index bb7f73a79b..61bbe149ba 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -108,7 +108,7 @@ def __init__(
         nb_inst_per_class: int = 1,
         random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parallel processing
-        seed: int = None,
+        # seed: int = None,
     ):
         super().__init__()
         self.lengths = lengths
@@ -121,8 +121,8 @@ def __init__(
         self._source_series = []  # To store the index of the original time series
         self.kernels_generators_ = {}  # Reference time series
         self.n_jobs = n_jobs
-        if seed is not None:
-            random_state = seed
+        # if seed is not None:
+        #     random_state = seed
         self.random_state = random_state
 
     def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":

From 2d86987da6569a1e5ef06c7129c56973264e93dc Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sun, 25 May 2025 13:20:45 +0530
Subject: [PATCH 09/11] check with warns

---
 aeon/classification/shapelet_based/_rsast.py  | 29 +++++++++++++++----
 aeon/classification/shapelet_based/_sast.py   | 20 +++++++++++--
 .../collection/shapelet_based/_rsast.py       | 22 +++++++++++---
 .../collection/shapelet_based/_sast.py        | 20 +++++++++++--
 4 files changed, 75 insertions(+), 16 deletions(-)

diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py
index 73af16a2da..1cb789b457 100644
--- a/aeon/classification/shapelet_based/_rsast.py
+++ b/aeon/classification/shapelet_based/_rsast.py
@@ -68,9 +68,9 @@ class RSASTClassifier(BaseClassifier):
 
     # TODO: remove 'seed' in v1.2
     @deprecated(
-        version="1.1",
-        reason="The 'seed' parameter will be removed in v1.2.",
-        category=FutureWarning,
+    version="1.1",
+    reason="The 'seed' parameter will be removed in v1.2.",
+    category=FutureWarning,
     )
     def __init__(
         self,
@@ -80,18 +80,35 @@ def __init__(
         random_state=None,
         classifier=None,
         n_jobs=1,
-        # seed=None,
+        seed=None,
     ):
         super().__init__()
         self.n_random_points = n_random_points
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        # if seed is not None:
-        #     random_state = seed
+        
+        # Handle deprecated seed parameter
+        if seed is not None:
+            import warnings
+            warnings.warn(
+                "The 'seed' parameter is deprecated and will be removed in v1.2. "
+                "Use 'random_state' instead.",
+                FutureWarning,
+                stacklevel=2
+            )
+            if random_state is None:
+                random_state = seed
+            else:
+                raise ValueError(
+                    "Cannot specify both 'seed' and 'random_state'. "
+                    "Use 'random_state' only."
+                )
+        
         self.random_state = random_state
         self.classifier = classifier
 
+
     def _fit(self, X, y):
         """Fit RSASTClassifier to the training data.
 
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index 08caffba19..90f4f2d24c 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -83,15 +83,29 @@ def __init__(
         random_state: Optional[int] = None,
         classifier=None,
         n_jobs: int = 1,
-        # seed: Optional[int] = None,
+        seed = None,
     ) -> None:
         super().__init__()
         self.length_list = length_list
         self.stride = stride
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        # if seed is not None:
-        #     random_state = seed
+        # Handle deprecated seed parameter
+        if seed is not None:
+            import warnings
+            warnings.warn(
+                "The 'seed' parameter is deprecated and will be removed in v1.2. "
+                "Use 'random_state' instead.",
+                FutureWarning,
+                stacklevel=2
+            )
+            if random_state is None:
+                random_state = seed
+            else:
+                raise ValueError(
+                    "Cannot specify both 'seed' and 'random_state'. "
+                    "Use 'random_state' only."
+                )
         self.random_state = random_state
 
         self.classifier = classifier
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index 211cc6bcdc..a85cc2a80a 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -114,15 +114,12 @@ def __init__(
         nb_inst_per_class: int = 10,
         random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parllel Processing
-        # seed=None,
+        seed=None
     ):
         self.n_random_points = n_random_points
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        # if seed is not None:
-        #     random_state = seed
-        self.random_state = random_state
         self._kernels = None  # z-normalized subsequences
         self._cand_length_list = {}
         self._kernel_orig = []
@@ -130,6 +127,23 @@ def __init__(
         self._classes = []
         self._source_series = []  # To store the index of the original time series
         self._kernels_generators = {}  # Reference time series
+        # Handle deprecated seed parameter
+        if seed is not None:
+            import warnings
+            warnings.warn(
+                "The 'seed' parameter is deprecated and will be removed in v1.2. "
+                "Use 'random_state' instead.",
+                FutureWarning,
+                stacklevel=2
+            )
+            if random_state is None:
+                random_state = seed
+            else:
+                raise ValueError(
+                    "Cannot specify both 'seed' and 'random_state'. "
+                    "Use 'random_state' only."
+                )
+        self.random_state = random_state
         super().__init__()
 
     def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "RSAST":
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index 61bbe149ba..24e895c527 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -108,7 +108,7 @@ def __init__(
         nb_inst_per_class: int = 1,
         random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parallel processing
-        # seed: int = None,
+        seed = None,
     ):
         super().__init__()
         self.lengths = lengths
@@ -121,8 +121,22 @@ def __init__(
         self._source_series = []  # To store the index of the original time series
         self.kernels_generators_ = {}  # Reference time series
         self.n_jobs = n_jobs
-        # if seed is not None:
-        #     random_state = seed
+        # Handle deprecated seed parameter
+        if seed is not None:
+            import warnings
+            warnings.warn(
+                "The 'seed' parameter is deprecated and will be removed in v1.2. "
+                "Use 'random_state' instead.",
+                FutureWarning,
+                stacklevel=2
+            )
+            if random_state is None:
+                random_state = seed
+            else:
+                raise ValueError(
+                    "Cannot specify both 'seed' and 'random_state'. "
+                    "Use 'random_state' only."
+                )
         self.random_state = random_state
 
     def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":

From 7c3ebf2a83a29cea7bb4ec503ec13e8ce76fb838 Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sun, 25 May 2025 13:56:29 +0530
Subject: [PATCH 10/11] check

---
 aeon/classification/shapelet_based/_rsast.py  | 15 +++++++-----
 aeon/classification/shapelet_based/_sast.py   | 11 ++++++---
 aeon/testing/testing_config.py                | 16 +++++++------
 .../collection/shapelet_based/_rsast.py       |  7 ++++--
 .../collection/shapelet_based/_sast.py        | 24 ++++++++++++-------
 examples/transformations/sast.ipynb           |  2 +-
 6 files changed, 47 insertions(+), 28 deletions(-)

diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py
index 1cb789b457..6b17b6d207 100644
--- a/aeon/classification/shapelet_based/_rsast.py
+++ b/aeon/classification/shapelet_based/_rsast.py
@@ -68,9 +68,9 @@ class RSASTClassifier(BaseClassifier):
 
     # TODO: remove 'seed' in v1.2
     @deprecated(
-    version="1.1",
-    reason="The 'seed' parameter will be removed in v1.2.",
-    category=FutureWarning,
+        version="1.1",
+        reason="The 'seed' parameter will be removed in v1.2.",
+        category=FutureWarning,
     )
     def __init__(
         self,
@@ -87,15 +87,19 @@ def __init__(
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
+
+        # Store the seed parameter (required for sklearn compatibility)
+        self.seed = seed
         
         # Handle deprecated seed parameter
         if seed is not None:
             import warnings
+
             warnings.warn(
                 "The 'seed' parameter is deprecated and will be removed in v1.2. "
                 "Use 'random_state' instead.",
                 FutureWarning,
-                stacklevel=2
+                stacklevel=2,
             )
             if random_state is None:
                 random_state = seed
@@ -104,11 +108,10 @@ def __init__(
                     "Cannot specify both 'seed' and 'random_state'. "
                     "Use 'random_state' only."
                 )
-        
+
         self.random_state = random_state
         self.classifier = classifier
 
-
     def _fit(self, X, y):
         """Fit RSASTClassifier to the training data.
 
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index 90f4f2d24c..d2299518e3 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -83,13 +83,17 @@ def __init__(
         random_state: Optional[int] = None,
         classifier=None,
         n_jobs: int = 1,
-        seed = None,
+        seed=None,
     ) -> None:
         super().__init__()
         self.length_list = length_list
         self.stride = stride
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
+        
+        # Store the seed parameter (required for sklearn compatibility)
+        self.seed = seed
+        
         # Handle deprecated seed parameter
         if seed is not None:
             import warnings
@@ -97,7 +101,7 @@ def __init__(
                 "The 'seed' parameter is deprecated and will be removed in v1.2. "
                 "Use 'random_state' instead.",
                 FutureWarning,
-                stacklevel=2
+                stacklevel=2,
             )
             if random_state is None:
                 random_state = seed
@@ -106,10 +110,11 @@ def __init__(
                     "Cannot specify both 'seed' and 'random_state'. "
                     "Use 'random_state' only."
                 )
+        
         self.random_state = random_state
-
         self.classifier = classifier
 
+
     def _fit(self, X, y):
         """Fit SASTClassifier to the training data.
 
diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py
index 8d3e8c9b3e..b9f905a0d4 100644
--- a/aeon/testing/testing_config.py
+++ b/aeon/testing/testing_config.py
@@ -24,7 +24,6 @@
 
 # exclude estimators here for short term fixes
 EXCLUDE_ESTIMATORS = [
-    "REDCOMETS",
     "HydraTransformer",  # returns a pytorch Tensor
 ]
 
@@ -47,18 +46,17 @@
         "check_persistence_via_pickle",
         "check_save_estimators_to_file",
     ],
+    # needs investigation
+    "SASTClassifier": ["check_fit_deterministic"],
+    "RSASTClassifier": ["check_fit_deterministic"],
+    "SAST": ["check_fit_deterministic"],
+    "RSAST": ["check_fit_deterministic"],
     "MatrixProfile": ["check_persistence_via_pickle"],
     # missed in legacy testing, changes state in predict/transform
     "FLUSSSegmenter": ["check_non_state_changing_method"],
-    "InformationGainSegmenter": ["check_non_state_changing_method"],
-    "GreedyGaussianSegmenter": ["check_non_state_changing_method"],
     "ClaSPSegmenter": ["check_non_state_changing_method"],
     "HMMSegmenter": ["check_non_state_changing_method"],
     "RSTSF": ["check_non_state_changing_method"],
-    # Keeps length during predict to avoid recomputing means and std of data in fit
-    # if the next predict calls uses the same query length parameter.
-    "QuerySearch": ["check_non_state_changing_method"],
-    "SeriesSearch": ["check_non_state_changing_method"],
     # Unknown issue not producing the same results
     "RDSTRegressor": ["check_regressor_against_expected_results"],
     "RISTRegressor": ["check_regressor_against_expected_results"],
@@ -68,6 +66,10 @@
 EXCLUDED_TESTS_NO_NUMBA = {
     # See issue #622
     "HIVECOTEV2": ["check_classifier_against_expected_results"],
+    # Other failures
+    "TemporalDictionaryEnsemble": ["check_classifier_against_expected_results"],
+    "OrdinalTDE": ["check_classifier_against_expected_results"],
+    "CanonicalIntervalForestRegressor": ["check_regressor_against_expected_results"],
 }
 
 
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index a85cc2a80a..5a23c7e831 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -114,7 +114,7 @@ def __init__(
         nb_inst_per_class: int = 10,
         random_state: Optional[int] = None,
         n_jobs: int = 1,  # Parllel Processing
-        seed=None
+        seed=None,
     ):
         self.n_random_points = n_random_points
         self.len_method = len_method
@@ -128,13 +128,16 @@ def __init__(
         self._source_series = []  # To store the index of the original time series
         self._kernels_generators = {}  # Reference time series
         # Handle deprecated seed parameter
+        # Store the seed parameter (required for sklearn compatibility)
+        self.seed = seed
         if seed is not None:
             import warnings
+
             warnings.warn(
                 "The 'seed' parameter is deprecated and will be removed in v1.2. "
                 "Use 'random_state' instead.",
                 FutureWarning,
-                stacklevel=2
+                stacklevel=2,
             )
             if random_state is None:
                 random_state = seed
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index 24e895c527..e425879fde 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -107,20 +107,24 @@ def __init__(
         stride: int = 1,
         nb_inst_per_class: int = 1,
         random_state: Optional[int] = None,
-        n_jobs: int = 1,  # Parallel processing
-        seed = None,
+        n_jobs: int = 1,
+        seed=None,
     ):
         super().__init__()
         self.lengths = lengths
         self.stride = stride
         self.nb_inst_per_class = nb_inst_per_class
-        self._kernels = None  # z-normalized subsequences
-        self._kernel_orig = None  # non z-normalized subsequences
-        self._start_points = []  # To store the start positions
-        self._classes = []  # To store the class of each shapelet
-        self._source_series = []  # To store the index of the original time series
-        self.kernels_generators_ = {}  # Reference time series
+        self._kernels = None
+        self._kernel_orig = None
+        self._start_points = []
+        self._classes = []
+        self._source_series = []
+        self.kernels_generators_ = {}
         self.n_jobs = n_jobs
+        
+        # Store the seed parameter (required for sklearn compatibility)
+        self.seed = seed
+        
         # Handle deprecated seed parameter
         if seed is not None:
             import warnings
@@ -128,7 +132,7 @@ def __init__(
                 "The 'seed' parameter is deprecated and will be removed in v1.2. "
                 "Use 'random_state' instead.",
                 FutureWarning,
-                stacklevel=2
+                stacklevel=2,
             )
             if random_state is None:
                 random_state = seed
@@ -137,8 +141,10 @@ def __init__(
                     "Cannot specify both 'seed' and 'random_state'. "
                     "Use 'random_state' only."
                 )
+        
         self.random_state = random_state
 
+
     def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
         """Select reference time series and generate subsequences from them.
 
diff --git a/examples/transformations/sast.ipynb b/examples/transformations/sast.ipynb
index a77ae51191..ab81ee794b 100644
--- a/examples/transformations/sast.ipynb
+++ b/examples/transformations/sast.ipynb
@@ -1159,7 +1159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {

From 2c4e9160dc64492b2e0d0cf6b90f89647fecb106 Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sun, 25 May 2025 14:14:45 +0530
Subject: [PATCH 11/11] main push

---
 .github/workflows/pr_pytest.yml               |  19 +-
 aeon/classification/shapelet_based/_rsast.py  |   3 +-
 aeon/classification/shapelet_based/_sast.py   |   8 +-
 aeon/testing/testing_config.py                |   8 +-
 .../collection/shapelet_based/_rsast.py       |   1 +
 .../collection/shapelet_based/_sast.py        |   9 +-
 examples/transformations/sast.ipynb           | 456 ++++++++++--------
 7 files changed, 275 insertions(+), 229 deletions(-)

diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml
index 87316e94fa..cf1baee900 100644
--- a/.github/workflows/pr_pytest.yml
+++ b/.github/workflows/pr_pytest.yml
@@ -3,10 +3,8 @@ name: PR pytest
 on:
   push:
     branches:
-      - seed
+      - main
   pull_request:
-    branches:
-      - seed
     paths:
       - "aeon/**"
       - ".github/workflows/**"
@@ -24,10 +22,10 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
-      - name: Setup Python 3.10
+      - name: Setup Python 3.11
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
 
       - if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'no numba cache') }}
         name: Restore numba cache
@@ -35,7 +33,7 @@ jobs:
         with:
           cache_name: "test-no-soft-deps"
           runner_os: ${{ runner.os }}
-          python_version: "3.10"
+          python_version: "3.11"
 
       - name: Install aeon and dependencies
         uses: nick-fields/retry@v3
@@ -57,13 +55,15 @@ jobs:
       fail-fast: false
       matrix:
         os: [ ubuntu-24.04, macOS-14, windows-2022 ]
-        python-version: [ "3.9", "3.10", "3.11", "3.12" ]
+        python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ]
         # skip python versions unless the PR has the 'full pytest actions' label
         pr-testing:
           - ${{ (github.event_name == 'pull_request' && !contains(github.event.pull_request.labels.*.name, 'full pytest actions')) }}
         exclude:
           - pr-testing: true
             python-version: "3.10"
+          - pr-testing: true
+            python-version: "3.12"
 
     steps:
       - name: Checkout
@@ -90,6 +90,7 @@ jobs:
 
       - uses: ./.github/actions/cpu_all_extras
         with:
+          python_version: ${{ matrix.python-version }}
           additional_extras: "dev"
 
       - name: Show dependencies
@@ -109,10 +110,10 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
-      - name: Setup Python 3.10
+      - name: Setup Python 3.11
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
 
       - name: Disable Numba JIT
         run: echo "NUMBA_DISABLE_JIT=1" >> $GITHUB_ENV
diff --git a/aeon/classification/shapelet_based/_rsast.py b/aeon/classification/shapelet_based/_rsast.py
index 6b17b6d207..73ab8571b3 100644
--- a/aeon/classification/shapelet_based/_rsast.py
+++ b/aeon/classification/shapelet_based/_rsast.py
@@ -87,10 +87,9 @@ def __init__(
         self.len_method = len_method
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-
         # Store the seed parameter (required for sklearn compatibility)
         self.seed = seed
-        
+
         # Handle deprecated seed parameter
         if seed is not None:
             import warnings
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index d2299518e3..d36cfd1150 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -90,13 +90,14 @@ def __init__(
         self.stride = stride
         self.nb_inst_per_class = nb_inst_per_class
         self.n_jobs = n_jobs
-        
+
         # Store the seed parameter (required for sklearn compatibility)
         self.seed = seed
-        
+
         # Handle deprecated seed parameter
         if seed is not None:
             import warnings
+
             warnings.warn(
                 "The 'seed' parameter is deprecated and will be removed in v1.2. "
                 "Use 'random_state' instead.",
@@ -110,11 +111,10 @@ def __init__(
                     "Cannot specify both 'seed' and 'random_state'. "
                     "Use 'random_state' only."
                 )
-        
+
         self.random_state = random_state
         self.classifier = classifier
 
-
     def _fit(self, X, y):
         """Fit SASTClassifier to the training data.
 
diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py
index b9f905a0d4..678c289e6d 100644
--- a/aeon/testing/testing_config.py
+++ b/aeon/testing/testing_config.py
@@ -46,12 +46,8 @@
         "check_persistence_via_pickle",
         "check_save_estimators_to_file",
     ],
-    # needs investigation
-    "SASTClassifier": ["check_fit_deterministic"],
-    "RSASTClassifier": ["check_fit_deterministic"],
-    "SAST": ["check_fit_deterministic"],
-    "RSAST": ["check_fit_deterministic"],
-    "MatrixProfile": ["check_persistence_via_pickle"],
+    "MatrixProfile": ["check_fit_deterministic", "check_persistence_via_pickle"],
+    "LeftSTAMPi": ["check_anomaly_detector_output"],
     # missed in legacy testing, changes state in predict/transform
     "FLUSSSegmenter": ["check_non_state_changing_method"],
     "ClaSPSegmenter": ["check_non_state_changing_method"],
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index 5a23c7e831..244746400e 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -127,6 +127,7 @@ def __init__(
         self._classes = []
         self._source_series = []  # To store the index of the original time series
         self._kernels_generators = {}  # Reference time series
+
         # Handle deprecated seed parameter
         # Store the seed parameter (required for sklearn compatibility)
         self.seed = seed
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index e425879fde..ff0ae4f10c 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -110,6 +110,7 @@ def __init__(
         n_jobs: int = 1,
         seed=None,
     ):
+
         super().__init__()
         self.lengths = lengths
         self.stride = stride
@@ -121,13 +122,14 @@ def __init__(
         self._source_series = []
         self.kernels_generators_ = {}
         self.n_jobs = n_jobs
-        
+
         # Store the seed parameter (required for sklearn compatibility)
         self.seed = seed
-        
+
         # Handle deprecated seed parameter
         if seed is not None:
             import warnings
+
             warnings.warn(
                 "The 'seed' parameter is deprecated and will be removed in v1.2. "
                 "Use 'random_state' instead.",
@@ -141,9 +143,8 @@ def __init__(
                     "Cannot specify both 'seed' and 'random_state'. "
                     "Use 'random_state' only."
                 )
-        
-        self.random_state = random_state
 
+        self.random_state = random_state
 
     def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
         """Select reference time series and generate subsequences from them.
diff --git a/examples/transformations/sast.ipynb b/examples/transformations/sast.ipynb
index ab81ee794b..9ccc35daef 100644
--- a/examples/transformations/sast.ipynb
+++ b/examples/transformations/sast.ipynb
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:46.448396Z",
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.908710Z",
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:51.923023Z",
@@ -102,7 +102,18 @@
      "shell.execute_reply": "2020-12-19T14:32:52.164864Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_24972\\3935276173.py:1: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  sast = SAST()\n",
+      "C:\\Users\\HP\\aeon_gsoc\\aeon\\aeon\\base\\_base.py:114: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  self.__init__(**params)\n"
+     ]
+    }
+   ],
    "source": [
     "sast = SAST()\n",
     "sast.fit(X_train, y_train)\n",
@@ -125,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.168847Z",
@@ -138,7 +149,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -168,15 +179,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -188,7 +199,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -196,7 +207,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -206,7 +217,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -222,14 +233,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -237,28 +248,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -276,14 +287,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -293,7 +304,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -301,13 +312,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -316,12 +327,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -329,79 +340,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -411,18 +422,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -509,7 +520,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -524,27 +535,27 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeClassifierCV(alphas=array([1.00000000e-03, 4.64158883e-03, 2.15443469e-02, 1.00000000e-01,\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><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><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;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",
+       "       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>"
       ],
@@ -554,7 +565,7 @@
        "       2.15443469e+02, 1.00000000e+03]))"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:52.289448Z",
@@ -597,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:53.312125Z",
@@ -611,10 +622,10 @@
     {
      "data": {
       "text/plain": [
-       "0.9090909090909091"
+       "0.8636363636363636"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -638,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.012465Z",
@@ -647,7 +658,16 @@
      "shell.execute_reply": "2020-12-19T14:32:56.014399Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_24972\\3993780687.py:1: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  sast_pipeline = make_pipeline(SAST(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)))\n"
+     ]
+    }
+   ],
    "source": [
     "sast_pipeline = make_pipeline(SAST(), RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)))"
    ]
@@ -661,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.017692Z",
@@ -671,10 +691,18 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP\\aeon_gsoc\\aeon\\aeon\\base\\_base.py:114: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  self.__init__(**params)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {\n",
+       "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -704,15 +732,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 {\n",
+       "#sk-container-id-2 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 pre {\n",
+       "#sk-container-id-2 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -724,7 +752,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -732,7 +760,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-container {\n",
+       "#sk-container-id-2 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -742,7 +770,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -758,14 +786,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -773,28 +801,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
+       "#sk-container-id-2 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -812,14 +840,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -829,7 +857,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -837,13 +865,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -852,12 +880,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -865,79 +893,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -947,18 +975,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1045,7 +1073,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1060,33 +1088,33 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
        "                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
+       "       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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;sast&#x27;, SAST()),\n",
        "                (&#x27;ridgeclassifiercv&#x27;,\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])))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">SAST</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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",
+       "       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 fitted\">SAST</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 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>"
       ],
@@ -1098,7 +1126,7 @@
        "       2.15443469e+02, 1.00000000e+03])))])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1120,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-19T14:32:56.425026Z",
@@ -1133,10 +1161,10 @@
     {
      "data": {
       "text/plain": [
-       "0.8636363636363636"
+       "0.9090909090909091"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1159,13 +1187,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_24972\\2934984393.py:1: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  clf = SASTClassifier(random_state=42)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {\n",
+       "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -1195,15 +1231,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 {\n",
+       "#sk-container-id-3 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 pre {\n",
+       "#sk-container-id-3 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1215,7 +1251,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1223,7 +1259,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-container {\n",
+       "#sk-container-id-3 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1233,7 +1269,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1249,14 +1285,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1264,28 +1300,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
+       "#sk-container-id-3 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1303,14 +1339,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -1320,7 +1356,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1328,13 +1364,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1343,12 +1379,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1356,79 +1392,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1438,18 +1474,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1536,7 +1572,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1551,31 +1587,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SASTClassifier(random_state=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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SASTClassifier<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>SASTClassifier(random_state=42)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SASTClassifier(random_state=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 \">&nbsp;SASTClassifier<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>SASTClassifier(random_state=42)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "SASTClassifier(random_state=42)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1594,13 +1630,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP\\aeon_gsoc\\aeon\\aeon\\base\\_base.py:114: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  self.__init__(**params)\n",
+      "C:\\Users\\HP\\aeon_gsoc\\aeon\\aeon\\classification\\shapelet_based\\_sast.py:134: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  self._transformer = SAST(\n",
+      "C:\\Users\\HP\\aeon_gsoc\\aeon\\aeon\\base\\_base.py:114: FutureWarning: Call to deprecated method __init__. (The 'seed' parameter will be removed in v1.2.) -- Deprecated since version 1.1.\n",
+      "  self.__init__(**params)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-5 {\n",
+       "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -1630,15 +1678,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1650,7 +1698,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1658,7 +1706,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-container {\n",
+       "#sk-container-id-4 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1668,7 +1716,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1684,14 +1732,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1699,28 +1747,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
+       "#sk-container-id-4 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1738,14 +1786,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -1755,7 +1803,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1763,13 +1811,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1778,12 +1826,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1791,79 +1839,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1873,18 +1921,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1971,7 +2019,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1986,31 +2034,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SASTClassifier(random_state=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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SASTClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SASTClassifier(random_state=42)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SASTClassifier(random_state=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 fitted\">&nbsp;SASTClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SASTClassifier(random_state=42)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "SASTClassifier(random_state=42)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2028,7 +2076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -2037,7 +2085,7 @@
        "0.8636363636363636"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2061,12 +2109,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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MrkREAtHaN4wMOrza7EpEJBCtfQPemKQMEhFzrHuzIoNWmV2JiASidW8pgwJcgxfRV6xYwYUXXkhCQgIWi4WFCxfWeuztt9+OxWLh2WefdXo+KyuLmTNnEhUVRatWrbj55pvJz893OmbLli2MHj2aZs2a0alTJ/7zn/80tFQR18pLha8egMMrYcFtYLWaXZGIBJK8NPhaGSQiJqnMoORVsGA2lJebXZGIBJK8NPjq/ooMuk0ZJCKelZ8OX91nZNDnmgcFqgYvohcUFDB48GBeeumlOo9bsGABq1atIiEhodprM2fOZPv27SxdupTFixezYsUKZs+ebXs9NzeXiRMn0qVLF9avX8+TTz7JP/7xD+bOndvQckVcZ8cioGLRKmu/sZAlIuIpOxeBtWKyduIAHPrN3HpEJLA4ZdBBOKwMEhEPUgaJiJkcMyj7EBz61dx6xBQNXkSfMmUKjz32GJdcckmtxxw9epS77rqLefPmERoa6vTazp07WbJkCa+//jojRoxg1KhRvPDCC3z44YekpKQAMG/ePIqLi3nzzTfp378/V111FXfffTfPPPNMQ8sVcZ3tC5wfb5xnTh0iEpiqZtAmZZCIeJDmQSJipu0LnR8rg0TEk6pmkM7FApLLe6KXl5dz3XXX8ac//Yn+/ftXe33lypW0atWK4cOH256bMGECQUFBrF692nbMmDFjCAsLsx0zadIkdu/ezYkTJ2r8uUVFReTm5jp9ibhM7rHqV55vXwBFeebUI15HGSRulZda/cpzZZA4UAaJW9WUQTsWQqH+OxODMkjcKi+1+lWfyiBxoAwSt8pLg4O/OD+3faEyKAC5fBH93//+NyEhIdx99901vp6amkpsbKzTcyEhIcTExJCammo7Ji4uzumYyseVx1T1xBNPEB0dbfvq1KlTU38VEbudDq1cQlsaf5YUVP80UgKWMkjcakdNGXSy+pWhErCUQeJWyiA5DWWQuNXOL1EGSV2UQeJWNa0HlZ6C7Z+bVpKYw6WL6OvXr+e5557j7bffxmKxuPKtT+vBBx8kJyfH9pWcnOzRny9+znGCdoHDJrcb3/d8LeKVlEHiVsogOQ1lkLhVbRmkW5mlgjJI3ErzIDkNZZC4lePFk8qggObSRfSff/6Z9PR0OnfuTEhICCEhIRw6dIj777+frl27AhAfH096errT95WWlpKVlUV8fLztmLS0NKdjKh9XHlNVeHg4UVFRTl8iLpGbYm/l0rYPDJkJ7foaj5NXQeZe82oTr6EMErepKYNi+xmPk1dD5h7zahOvoQwSt3HKoN7VMygjybzaxGsog8Rtco/Z20m16VWRQRVtY4+sUQYJoAwSN3JsJ9Wmp5FBcQOMx0fWQsZu82oTj3PpIvp1113Hli1b2LRpk+0rISGBP/3pT3z77bcAjBw5kuzsbNavX2/7vh9++IHy8nJGjBhhO2bFihWUlJTYjlm6dCl9+vShdevWrixZ5PR2fGEf978ELBYYeq39uU369FFE3MixjUL/6dUzSFdAiIg7OWWQ5kEi4mE7lUEiYqLTzYN0LhZQGryInp+fb1sgBzhw4ACbNm3i8OHDtGnThgEDBjh9hYaGEh8fT58+fQDo27cvkydP5tZbb2XNmjX8+uuv3HnnnVx11VUkJCQAcM011xAWFsbNN9/M9u3b+eijj3juuee47777XPebi9SX4607/acbfw66EoJCjPGmD6Cs1NNViUig2LHQPu5/ifGnYwZtVgaJiBudNoM+VAaJiPs4nYtVZtAVVc7FSqp9m4iIS9Q0Dxp4BQSFGuPNHyqDAkiDF9HXrVvH0KFDGTp0KAD33XcfQ4cOZc6cOfV+j3nz5pGYmMj48eO54IILGDVqFHPnzrW9Hh0dzXfffceBAwcYNmwY999/P3PmzGH27NkNLVekaXKOGi1bwGjhElvRxiWiHfSebIzzU2HfMnPqExH/5thGoV2iPYNatnXIoDTY+7059YmIf1MGiYiZqra0c8ygPlOMcUG6MkhE3MOxnVTb3vZ2di3bOGfQnqXm1CceF9LQbxg3bhxWq7Xexx88eLDaczExMcyfP7/O7xs0aBA///xzQ8sTcS2nVi7TnV8bei3sWmyMN74PvSd5rCwRCRBV20k5GnqdPYM2vQ99JnuuLhEJDI4Z1G+682uOGbTxPWWQiLheTS3tKg29DnZ+aYw3vm9f0BIRcRXHdlL9pteQQYuM8aZ5kHiBp6sTE7i0J7qI33G8dafqyWPP86FlrDHe/Q0UZHqqKhEJFNsX2MfVMmgCRMQZY2WQiLhDTS3tKjlmUNISyM/wVFUiEihqaqNQqcd4iIg3xsogEXEHx3Oxahl0XpUMSvdcXWIaLaKL1CbnCCSvNsax/SA20fn14BAYfJUxLi+BLR97tj4R8W+OGdSu72kyqBS2fOTZ+kTEv9XW0q5ScAgMvtoYl5fCVs2DRMSFamsnVUnzIBFxp9raSVUKDoEhDvMgZVBA0CK6SG3qaqNQqequzA1odSQiUqcdi+zj2jJoiGMGzVMGiYjrNHQetOE9ZZCIuI7OxUTETFXPxRxbuVQaogwKNFpEF6lNXW0UKrXrAx3PMsbp2+HYJndXJSKBwun2wek1H9OuN3QaYYzTt0PKRreXJSIBoj4Z1LaXPYMydkLKBreXJSIBoj7nYm17QaezjbEySERcqV7zoJ7QeaQxztgFR5VB/k6L6CI1yU6GI2uNcWx/Y6GqNkNn2scb33dvXSISGLKT4cgaYxzb3/jArjZDlEEi4mI5RxwyqF/dGTS0yh0xIiJNlXO07pZ2jnQuJiKu5tTSroZ2Uo6czsXec29dYjotoovUpD63D9pevxRCmhvjrZ9ASaH76hKRwOCUQdPrPrb/JRDawhhv/RRKTrmtLBEJEA2aBymDRMTFGp1BnymDRKTpGpRB0yG0pTHe9hkUn3RbWWI+LaKL1KQ+t+5UahZlP6YwB3YtdldVIhIodiy0j2u7hblSsyj7MUU5sOsrNxUlIgGjPm0UKoVHOmfQTs2DRKSJGnIuFh5pX+RSBomIKzTkXMwpg3K1HuTntIguUlX2YTi6zhjHDTB67Z2O2imIiKtkH7a3k4obUHc7qUpDdRuhiLhIQ1raVXJs6bJJ8yARaYKGtJOqpHYKIuIqOUfq306qks7FAoYW0UWqakgbhUpdzoXWXY3x/uXGIpiISGM0OoO6GeP9P8GJQy4vS0QCRENuYa7U5RyI6W6MlUEi0hRNzaADyiARaYLGZFDnkQ4ZtAJOHHR5WeIdtIguUpXTLcz1DM2gIBhSeRWWFTZ94PKyRCRANCaDLBaHKyCssFkZJCKN1JA2CpUsFocrQZVBItIEDWknVckpg4BN811akogEkMbOg5zuylMG+Sstoos4OnEIjq43xvEDoW3P+n/vkKsBizHeNA/Ky11enoj4OccMimtgBg2+BmWQiDSJU0u7gfVraVdp8NVgqTi1UAaJSGM4tpOqb0u7Sk4ZNF8ZJCINV62lXT3aSVVSBgUELaKLOHK8dae+Vz5Uiu4IPX5njLMPwaFfXFaWiASIxrRyqRTdAXqcZ4yzD8PBn11WlogECKcMurhh36sMEpGmatK5mEMG5RyGgytcVpaIBIimnItFJUCP8cY4J9loLSV+R4voIo6cbt2pZxsFR4638PjSBqNWq9kViAgog0TEXI1pJ+VIGSQiTdGYNgqOlEEi0hSNaSflSBnk97SILlLpxEFI2WCM4wdBmx4Nf48+U6FZK2O8YxEU5riqOvcoL4PvHoLH4uCrB8yuRiSwOWXQwMZlUOJUaN7aGO/0oQz6Zzx8db8mcCJmakpLu0p9LnDOoFPZLivPLcrL4LuHlUEi3uDEoca3k6rklEFf+kAGlcPSOUYGLb5PGSRiJqeWdg1sJ1WpzxRoHmOMd34Jp064rj53cMqge5VB9aBFdJFK2xfax4258gEgtBkMusIYl56CbZ83tSr3KTkFH8+C316AsiJY+5rzJ68i4lmN2Qm+qpBwGFiZQYWw7bOm1+UuJafgk+uNDCothLWvK4NEzLRjoX3cmKuvoHoGbffyedAnN8BvzyuDRLxBU9pJVQoJh0FXGmNfmQf9+pxR67o3vDszRfxdU9pJVXLMoLIiH8ugN727Xi8RYnYBIl7DFSePYOwMv2auMd74nnFFRFEeFOVW/On4VfFcWTH0vQi6jGzKb1B/Bcfhg6vgyBrn57+6H7qOhpZtPVOHiNg19fbBSkNnwppXjfGG94w7ZLwtg05mGRmUvNr5+a8fMDIoop1n6hARO6eLCRr5QR4YtzI3JIOK86G0yIQMuhqSVzk/rwwSMY/TuVgTMmjITFj9ijHe+B4kTqtnBl0IXc5p0q9Qb7Vl0FcPQNcxyiARMzS1nVSloTNh9cvGeMN7kHhh7RlUXPGnt2TQ13+CbmMgItYzdfggLaKLAGQdgJSNxrj94Ma1UajUfrBxC2LaVuO26KfreRvQurfgrnXGBqXulHUA5s2A43uNx2ERENvPWFA/edw4gbz8bffWICLOXJ1B8QMhdavRHsbbMujEQXj/sroz6Ip33FuDiDhzRUu7Su0HGe+RuqXhGXTnWmjVqfE/uz5OHIT3Z8DxPcZjZZCI+VzRTqqSUwZtbEAGvVmRQZ0b/7Pro64MOpUFX98PV7zr3hpExJljBjW2nVSl+IHG+dixzXBsk/dnUGhLiB9gXNx0Kgu+ug+ueA8sFvfW4aPUzkUEnK98aMrVV2CEzRnXNfz7Sk/Bskea9rNP5+h6eON8++JVRDzc+A1cNc/eP3D7Auer0UTE/VyZQQBDvTWDNsDrExwyKA5u/Loigyr6B+5YqJYKIp7mqqvQK3l1Bp1vP3FUBol4B1fPg86Y1fDvKS10fwalbKyeQTd8BVfNd8igL5RBIp7mlEHTm/5+jZoHmZBBLWONedCV86BFG+O5nV8qg+rQ4CvRV6xYwZNPPsn69es5duwYCxYsYPr06QCUlJTw0EMP8fXXX7N//36io6OZMGEC//rXv0hISLC9R1ZWFnfddRdffvklQUFBXHbZZTz33HNERETYjtmyZQt33HEHa9eupV27dtx11138+c9/bvpvLFITx5PHprRRqDTsRmORKH0nhEdBeGQNX1EQHgEhzeCzm41NJ7Z8BCNugw7Dml5DVUnfGr0/S04aj9v2gWs/tX/SecFTRh3g0NaljevrEJHqTM+gW4wrD9yaQd8ZffdqzaAnHTLoAbWWEvEkV588DrvBOEGrM4MqvkKbw6c3Gxm09WMYcTt0dFcG3QAlBcZjZZCI93D5POgGyExqRAZ9UpFBw5teQ1V7lsLH1ztkUG+Y+Sm07mI8nvoUfHqTMVaLTRHPcvXFBMNugMw9kL6jfhn02S3G3XCezKA2veDaz+wZdMFT8OmNxljt7WrV4EX0goICBg8ezE033cSll17q9NrJkyfZsGEDDz/8MIMHD+bEiRP88Y9/5KKLLmLdunW242bOnMmxY8dYunQpJSUl3HjjjcyePZv58+cDkJuby8SJE5kwYQKvvPIKW7du5aabbqJVq1bMnj27ib+ySBVZ+43bbADaD4GYbk1/z5Aw42SsvsY9CN9UfEj07f8ZV4e78vaZdW8Zt+VYy43HXc51vvocYMBlxieOuxbDyUz45k8w403X1SAiNfOaDPqTMXZHBq1/GxbfB9Yy43Hnc4wMahFjP6ZqBn39J7j8LdfVICI1q9pOKqZ709+zoRn0u78ZJ2wA3/4Nblri4gx6BxbfqwwS8UaubCdVKTi0iRn0rWszaMO78OU9Dhk00rj63DGD+l9qZNDOL9ViU8STnDJooAsz6D/1P37cg57NoE5nw9UfVMmgSyoyaJHa29Whwe1cpkyZwmOPPcYll1T/dCY6OpqlS5dyxRVX0KdPH84++2xefPFF1q9fz+HDhwHYuXMnS5Ys4fXXX2fEiBGMGjWKF154gQ8//JCUlBQA5s2bR3FxMW+++Sb9+/fnqquu4u677+aZZ55p4q8rUgNXf+rYGMNvgjYVvf8OrzSCyxWsVvjhMVh8j30Bvf8lcO3nzgvoYIT01GegWSvj8bbPjEmciLiXV2TQjcbVCOCGDPonfPlH+6St33S4boHzpA3sGWRrLfU57HBRHSJSO1e3UWiMYTcYV2WCscnVji9c875WK/z4OHx5dyMzyEV1iEjtvGEe5JRBq51zsSkqM2jRXQ4ZdDFct7AeGbRAGSTiCV6RQTcad8iB+zOo70Uw64taMujpKu3tXFSHH3F7T/ScnBwsFgutWrUCYOXKlbRq1Yrhw+23J0yYMIGgoCBWr15tO2bMmDGEhYXZjpk0aRK7d+/mxIkTNf6coqIicnNznb5E6uXQb/Zx3wvNqSE4FCY+Zn+8dI6xQ3NTlBbDwt/DCoerMEbeCZe9CaHNav6eyDjnqzYW32fs3CynpQySRju80j72pwwqK4GFf4AVDldhjLwTZrxVdwZNccigr5RB9aUMkkZzmgddZE4N7sqgL+6An/5tf67BGXQ/FBxvWh0BQhkkjeaVGfR3KCls2nvWlEFn3wEz3q49gyJijZYKlZRB9aYMkkZzOhczK4NC3JRBd1bJoD/A5e+cJoM0D6qLWxfRCwsL+ctf/sLVV19NVFQUAKmpqcTGxjodFxISQkxMDKmpqbZj4uLinI6pfFx5TFVPPPEE0dHRtq9OnTq5+tcRf5Wxy/gzPMo1tzA3Vu/J0G2MMT5xENbMbfx7FeXD/Ctg8wcVT1hg8r9g0j8h6DT/2w+8HPpcYIwL0uGbvzS+jgCiDJJGS/eWDJoE3cYaY5dl0PyKJyww6Yl6ZtAM6DPVGBdk2FtdSZ2UQdJo3pJBvSZC93HGOPsQrH618e9VXGBk0KZ5FU8og9xNGSSNlrHT+DMs0jVtFBqragatcXUGPQ6THz99Bg24DBKnGWNlUL0pg6TR0iszKMLeHcAMvc6H7r8zxi7JoCth0/sVT1Rm0BMNy6DKNr9i47ZF9JKSEq644gqsVisvv/yyu36MzYMPPkhOTo7tKzk52e0/U/xAUR7kVPy30i7RtX2nGspigYn/BCpq+OnJxn3qV1ZibN63/0fjcXC40cvq7N/Xv45p/4Vm0cbjrR/Drq8aXkeAUQZJoxTlQ47R7swrMmiSqzLoBtj3g/E4ONzo6TnyD/WvY5pDa6mtn8DOxQ2vI8Aog6RRnDKoj/kZ5DgPWvEkFGQ2/H3KSl2bQds+VXu7elAGSaMUF0C2t2bQUy7MoLdg5B31r2OqMqihlEHSKN6WQZP+CZaKZdomZ9Ay43FjM6iytdS2z9Ri04FbFtErF9APHTrE0qVLbVehA8THx5Oenu50fGlpKVlZWcTHx9uOSUtLczqm8nHlMVWFh4cTFRXl9CVyWhlJ9nG7PubVUan9IBg60xgX5cBP/2rY91utxoYRe783HodHw/WLjN57DREZD5MdbvtZfK9aKpyGMkgaJXO3fewNGRQ/EIZea4wbm0GL74G9S43H4dFGz73+0xv2PpHxMEUZ1BDKIGkUr8ugAXDGdca4KBeWNzKD9nxnPG5SBjm0olJ7u9NSBkmjZDhmUKJ5dVSqlkFPNOz7rVb46l6HDIqCWQsb3mc5Mk4Z1EDKIGmUzCTAaoy9IYPi+sNQL8wgtdi0cfkieuUC+p49e/j+++9p06aN0+sjR44kOzub9evX25774YcfKC8vZ8SIEbZjVqxYQUlJie2YpUuX0qdPH1q3rrIZokhTVN4+CBDb17w6HP3uIQhtYYzXvuG80H86y5+w37ITHAZXz4fOZzeujsFXQa9Jxjg/DZY82Lj3EZHaVbZRAO/JoPMegtCWxrjBGfQv2OiQQVfNgy4jG1fHoCuNNldgtJZa8tfGvY+I1M4xg9p5SQb9ziGD1r3pvMh2Oj/9Gza+Z4ybnEFXQO8pxljt7UTcI8NxHuQFC1hQJYPecs7J0/np37DhXWNsy6BzGleHMkjE/ZzmQd6SQf9ntJaBRmTQf+wZFBTatAxyavOr1lKVGryInp+fz6ZNm9i0aRMABw4cYNOmTRw+fJiSkhJmzJjBunXrmDdvHmVlZaSmppKamkpxcTEAffv2ZfLkydx6662sWbOGX3/9lTvvvJOrrrqKhIQEAK655hrCwsK4+eab2b59Ox999BHPPfcc9913n+t+cxFwnrh5S2hGtYdz7zHG1jJY+nD9vm/dW86bRlzyKnQd1fg6LBa48FnjKi6ALR/C7m8a/34iUp03ZlBkPIy6xxg3JIPWv+185folr0C30Y2vw2KBac/aW0tt+Qh2fd349xOR6rxxASsyDkbda4ytZfBdfTPoHecrtlySQWpvJ+JWGV74QV7VDKrvPGjDu84ZNP1l+35XjaEMEnG/DC+8oCkyrnHnYhveheWP2x9f8oqLM0gtNqERi+jr1q1j6NChDB06FID77ruPoUOHMmfOHI4ePcqiRYs4cuQIQ4YMoX379rav336z77o9b948EhMTGT9+PBdccAGjRo1i7lz7BmbR0dF89913HDhwgGHDhnH//fczZ84cZs+e7YJfWcSBN37yCHDOnRBpfKhE0hLYv7zu43cvMW6xqTTpcRhwadPriEowNp+o9OU9cOpE099XRAzeuIgOMLJKBu37se7jk741bjWuNPGfxqY0TRXV3tgUudLie3QroYgreW0G3QFRHYzxnm/tvYVrk/Sd0fapkkszSK2lRNzG6VzMC1pKVXLKoO/ql0Ff3mN/PPExY5PiplIGibhXhrdm0J0Q1dEY7/kO9i6r+/iqGXT+o67JoGrt7ZRBDV5EHzduHFartdrX22+/TdeuXWt8zWq1Mm7cONt7xMTEMH/+fPLy8sjJyeHNN98kIiLC6ecMGjSIn3/+mcLCQo4cOcJf/qLbl8QNKkMzPMpYMPYWYS1h/Bz7428fgvKymo89sh4+vRGs5cbjkXfWf9OI+hhyDfQ83xjnp8KSv7nuvUUCXbq3ZlALmPB3++Pv6sigo+uNzWusFa+ffYfxQaCrDL4aek00xvlp8K0ySMRlnDKog7m1OAprAeMdMqiuedDR9caG6m7LILW3E3GbytaaYZEQ3dHcWhw1KYP+YJyPuYoySMR90iszKAKiO5lbi6PQ5g04F9vgnEEjfg/n3OW6WtRi04lbNhYV8QlFeZBTsWt3u0Rzd2KuyaArof1gY5y2FTbNr37M8X0w/3IoOWk87n+p8amjK1kscOFzxgk2wOb59o1LRaTxivIhp3I3eC/MoIFXQPshxjhtG2yaV/2Y4/tg3hUOGXSJcfWVK9kyqOJWws0fwB5lkEiTOWVQHy/MoMshwbjzlfTt9v0WHGXt91AGPevc3m7PUtf+DJFAVFwA2X6WQf2mG3fCuPJ3UQaJuIe3Z9CAGZBwhjFO32Hf88VR1n6Y75hBFxtdCVydQVVbbCZ957r39zFaRJfA5bhZnjfdulMpKMgIwEo/PGqc8FbKz4D3L4OTx43HXUYZfa+C3PC/dXQHmPRP++M1r7v+Z4gEmkyHzfJ8IoMeMz58rGTLoEzjcZdzYbqbMigqwTmD1iqDRJrM1zOoINOzGTTZoRZlkEjTOW4a7E3tpCo1JoMuedUzGbTmNdf/DJFAk5kEWI2xL2dQQYbxuPM5cMlcN2VQe5jk0OZ3beBmkBbRJXBV3j4I3rOJRFVdR0HiNGOcnwa/PmeMiwuMTxxPHDAet+tr7LwcEu6+WoZcCxFxxnj/j0YNItJ46V64kU1VXc+Fvhca4/pkUGgz99UyZCZExBtjZZBI0zn1IvbSDOpyDvS9yBgXpMMvzxrjygzK2m889kQGDb7GIYOWK4NEmsobNzauqt4ZlOj5DHK8uEpEGs5b98dz1GWkcXU5GIvlv/zXGBefhPlXOmfQ1fPdnEFXQ2R7Y7z/p4DNIC2iS+Dy1s20qjr/EQgKNca/vQAnDhn9h1M2GM9FJsC1n0LzVu6tIyjI3gurtPD0Gw2KSN0cP8jz5gya8P+qZNBB+OTGGjKotXvrCAqCPsogEZfxhQUsgPMdMmjli0YGfXqT0YcYPJxBU4xxaeHpNxoUkbpl+MAHeVCPDGoP137m2QwqK1IGiTRVhg9c0AQw4R8QHGaMf3sRsg5UZNA647nI9jDTw/OgsiLYd5rNTv2UFtElcPnCJ48AbXrAWbONcekpmDvO2KEZjD7l137quY14Eqfax7u/9szPFPFX3n4bc6U2PWDEbca4tBDm/g72fGs89nQG9VEGibiMr1xMENO9egYlLTEemzkP2qUMEmkSp3MxL2wpVel0GTRT52IiPinDBzOorAheOw+SvjEeh0XCzE+glYc2Re2jeZAW0SVwVYZmeJTRZ86bjf2T/ZPFU1nGn8Fhxm2Dcf09V0e3sRDa0hgnLal9h2gROb10H8qgMQ9Uz6CgUBMyaIwySMRVnDKog7m1nM6YP0HzGGNsdgaFRRjjpCVQVuq5ny3ibyrvyAuL9NwidGPVlkFXvg/xAzxXhzJIxHXSKzMoAqI9tAjdWKMfqGUe9D7ED/RcHd1GG5kNxkVVAZhBWkSXwFSUBznJxrhdovftxFxV89Yw9q/Oz01/2ZhIeVJoM+h5njE+eRySV3v254v4i6J8yKncDd5HMmjcg87PXfKKSRk03hgrg0QazymD+vhABrXyjgwKCbdn0KksZZBIYxUXQLaPZ9D0l6H7WM/W4ZRBJyB5lWd/voi/8MUM+t3fnJ+b/j/oPs6zdVTNoMMrPfvzvYAW0SUwZSTZx958646jM2+GLqOMTxynPAkDZ5hTh9MtPF+ZU4OIr8t0bOXiIxk0/CboOroig/5jXgYlKoNEmswnM+hG78ggtZUSaTpfaWnnqGoGDbrcnDrUTkGk6TKTAKsx9pUMGnaDcfFAUChM/jcMusKcOgK8rVSI2QWImMJxQz9v3kTCUXAo3LAYSovcu+vy6fSeBJZgsJYZoTnxMe//5FbE26T7yEY2joJD4fovzc+gXhOVQSJN5dSLWBnUIL3Ot2fQrq+UQSKN4SsbGzvyxgza/RVM+qcySKShfGV/PEfBoTBrkXdl0K6vYNLjAZVBuhJdApOvbKZVlcVibmACtIiBziONcdb+ik9xRaRBHD/IUwY1TIsY6HKOMc7a73w1m4jUjy8uYIH3ZdCJA87/LEWkfjJ88IM88MIMOmjv6ywi9Zfhgxc0gXdkUPPW0PVcY5x9KOAySIvoEph88ZNHb5J4gX2sdgoiDeeLtzF7kz4OGbRbGSTSYL56MYG3UFspkaZxOhfzkZZS3sSpnYIySKTBMpRBTdIncDNIi+gSmCpDMzwKohLMrcUXOS1gBV4fLJEmS1cGNYnjB3m7vzGvDhFf5ZRBHcytxRf1UQaJNEnlHXlhkRDd0dxafJEySKRpKq+eDouA6E7m1uKLnC6qDKz1IC2iS+ApyoOcZGPcLjGg+je5TEw3iO1njI+sg7w0c+sR8SVF+ZBTuRu8MqhRWneF2P7GWBkk0jBOGdRHGdQYrbtA3ABjfHQd5KWaW4+ILykugGxlUJM4ZdB6yD1mbj0ivkQZ1HStOkPcQGOcsiGgMkiL6BJ4Mhx6eOvWncazXQFhhSRdASFSb5mOrVyUQY2WqAwSaRRlkGvoSlCRxlFLO9dwzCDNg0TqLzMJsBpjZVDjJQZmdwItokvgcdzQz5c2kfA2fQL3Fh6RJkn30Y1svE2fKfaxMkik/tJ9dEM/b+OYQQF08ijSZL66sbG30TxIpHG0P55rBOg8SIvoEni0mZZrJAyFiHhjvH+5cXu4iJye4wd5ugq08doPhcj2xlgZJFJ/WsByjYShEFmxp8X+n5RBIvWVoQ/yXMIxgw78ZLQsFZHTy9AFTS7Rfoh9X50DKwImg7SILoFHV4G6RlCQ/dPHsiLY94O59Yj4CqfbmJVBjaYMEmkcLWC5hsVSJYOWmVuPiK9I1wd5LuGUQcWwVxkkUi+6qNI1qmXQ9+bW4yFaRJfAUxma4VH2qxilcRKn2scBdAuPSJOkO2RQVIK5tfi6PsogkQZTBrlOolrbiTRY5R15YZH2qxilcQK0J7FIk6RXZlAERHc0txZfF4Atfhu8iL5ixQouvPBCEhISsFgsLFy40Ol1q9XKnDlzaN++Pc2bN2fChAns2bPH6ZisrCxmzpxJVFQUrVq14uabbyY/3/kWyC1btjB69GiaNWtGp06d+M9//tPw306kqqI8yEk2xu0StRNzU3UbY/zlA5D0LZSVmluPiLcryocc7QbvMt1GGyfhAElLlEEip6MMcq2uDhm0R/MgkdMqLoBsZZDLOGZQ0rdQVmJuPSLeThnkWl1HGxdlQMU8yP8zqMGL6AUFBQwePJiXXnqpxtf/85//8Pzzz/PKK6+wevVqWrZsyaRJkygsLLQdM3PmTLZv387SpUtZvHgxK1asYPbs2bbXc3NzmThxIl26dGH9+vU8+eST/OMf/2Du3LmN+BVFHGQk2ce6fbDpQsKh53hjfCoLklebW4+It3Nq5aIMajKnDDoByavMrUfE22Uqg1wqJBx6TTDGp07A4ZXm1iPi7RznQToXazrHDCrMVgaJnE5mEmA1xmpp13QhYdCzMoNyAiKDGryIPmXKFB577DEuueSSaq9ZrVaeffZZHnroIS6++GIGDRrEu+++S0pKiu2K9Z07d7JkyRJef/11RowYwahRo3jhhRf48MMPSUlJAWDevHkUFxfz5ptv0r9/f6666iruvvtunnnmmab9tiJOG/pp4uYSaqcgUn/ayMb1HNtKBchthCKNlq4+oC6neZBI/akXsev10TxIpN6c5kF9zKvDnwTYuZhLe6IfOHCA1NRUJkyYYHsuOjqaESNGsHKl8YnEypUradWqFcOHD7cdM2HCBIKCgli9erXtmDFjxhAWFmY7ZtKkSezevZsTJ07U+LOLiorIzc11+hKpRhM31+t1PliCjfGur8BqNbcekyiDpF6cPsjTxM0lHDNotzJIGSR1ytCGfi7X63wICjHGmgcpg6Ru2tjY9RwzSPMgZZDUTRc0uV7PCQGVQS5dRE9NTQUgLi7O6fm4uDjba6mpqcTGxjq9HhISQkxMjNMxNb2H48+o6oknniA6Otr21alTp6b/QuJ/0hWaLtciBrqcY4xPHHD+iymAKIOkXtJ18uhyzVtD13ON8YmD9s2CAowySOpFC1iu17wVdKnIoOxDkL7D1HLMogySeknXB3ku55RBhyFtu6nlmEUZJPWiiypdr3kr6DrKGAdABrl0Ed1MDz74IDk5Obav5ORks0sSb1QZmuFRENne3Fr8idMtPF+ZV4eJlEFSL5W9QMOjICrB3Fr8iVM7BWWQMkhqle4wD1IGuU6A3cpcE2WQ1EvlHXlhkRDVwdxa/Emi2kopg6ReKi+2CYuA6I7m1uJPAqi1nUsX0ePj4wFIS0tzej4tLc32Wnx8POnp6U6vl5aWkpWV5XRMTe/h+DOqCg8PJyoqyulLxElRHuRU/GXaLlE7MbtSnwvsYz8Pzdoog+S0ivIhR7vBu0WiYwZ9Y14dJlIGyWkpg9ynzxT7WPMgZZDUrLjAuEoRlEGupgxSBsnpKYPcxzGD/PyiSpcuonfr1o34+HiWLVtmey43N5fVq1czcuRIAEaOHEl2djbr16+3HfPDDz9QXl7OiBEjbMesWLGCkpIS2zFLly6lT58+tG7d2pUlSyDJSLKPdfuga7XuAnEDjPHR9ZB7zNx6RLxR5VXooNsHXa1VZ4gbaIyVQSI1y1QGuU2rzhBfkUEpG5RBIjVxnAfpXMy1nDJoI+SmmFuPiDfKTAIq+nWrpZ1rteoE8YOM8bFNkHPU1HLcqcGL6Pn5+WzatIlNmzYBxmaimzZt4vDhw1gsFu655x4ee+wxFi1axNatW5k1axYJCQlMnz4dgL59+zJ58mRuvfVW1qxZw6+//sqdd97JVVddRUKCcVvpNddcQ1hYGDfffDPbt2/no48+4rnnnuO+++5z2S8uAchpQz9N3FzO8Wr0pMC8ElSkTtrIxr0SlUEidUpXH1C3CqBbmUUaRb2I3UsZJFI3p3lQH/Pq8FcB0laqwYvo69atY+jQoQwdOhSA++67j6FDhzJnzhwA/vznP3PXXXcxe/ZszjzzTPLz81myZAnNmjWzvce8efNITExk/PjxXHDBBYwaNYq5c+faXo+Ojua7777jwIEDDBs2jPvvv585c+Ywe/bspv6+Esg0cXMvtVMQqZvTB3mauLmc4wd5AdqTWKROGdrQz60S1dpOpE7a2Ni9EjUPEqmTLmhyrz6BsR4U0tBvGDduHFartdbXLRYLjzzyCI888kitx8TExDB//vw6f86gQYP4+eefG1qeSO3SFZpu1X4IRCZAXgrs/8novRoeYXZVIt4jXSePbtV+sLFJWe5ROPCTsQ9GeKTZVYl4Dy1guVf8IIjqCLlH4MAKZZBIVen6IM+tqmZQYS40U19wERtdVOle8QMhupOxD6EfZ5BLe6KLeLXK0AyPgsj25tbijywW+4YSZUWwb1ndx4sEmspeoOFREJVgbi3+yCmDimGvMkjESbrDPEgZ5HrVMuh7c+sR8TaVd+SFRRofeotrOWZQeYkySKSq9MoMioDojubW4o8CJIO0iC6BoSjP+EQMjE8dtROze+g2QpGaFeVDjnaDd7s+aqcgUiNlkGdoHiRSs+ICyFYGuZ3aSonUTBnkGQFwLqZFdAkMGUn2sW4fdJ+uo42rSwD2fAtlpebWI+ItKq9CB90+6E5dRxtX2QIkfQtlJebWI+ItMpVBHtFllD2D9iiDRGwc50E6F3Mfpwz6ThkkUikzCahoS62Wdu7TdRSERxtjP80gLaJLYHDa0E8TN7cJCYdeE4zxqRNweKW59Yh4C21k4xkhYdCzIoMKs5VBIpXS1QfUI0LCoNf5xrgwBw79Zm49It5CvYg9o1oG/WpuPSLewmke1Me8OvxdcKjfZ5AW0SUwaOLmOX2m2sd+eguPSIM5fZCniZtbJTpkkNopiBgytKGfxwTArcwiDaaNjT2nj9pKiVSjC5o8x89b22kRXQJDukLTY3qdD0EhxnjXV2C1mluPiDdI18mjx/ScYM+g3cogEUALWJ7U63wICjXGu75WBolAlXMxfZDnVo4ZtFsZJALookpP6unfGaRFdAkMlaEZHgWR7c2txd81bwVdzjXG2Ycgbbup5Yh4hcpeoOFREJVgbi3+rnkrox8fGBsIpW0ztRwRr5DuMA9SBrlXs2h7BuUchtSt5tYj4g0q78gLi4SoDubW4u+cMigZUreYW4+IN0ivzKAIiO5obi3+rlkUdBttjP0wg7SILv6vKM/4nxeMTx21E7P79b3QPt6+wLw6RLxBUb6xkALaDd5TEqfZx8ogCXTKIM/rqwwSsSkuMD7UBmWQpyiDROyUQZ7nx+diWkQX/5eRZB/r9kHP6HcxWCriZdtnfncLj0iDVF6FDrp90FP6TVcGiVTKVAZ5XF/Ng0RsHOdBOhfzjL4XgyXYGCuDJNBlJgEV/w+opZ1n9PPfDNIiuvg/pw39NHHziIhY6FpxC8+JA5Cy0dx6RMzkmEHak8EzItpBtzHG+MRBSNlgajkipkpXH1CPi2gH3cYa4+xDcFQZJAFMvYg9z3EelH0Yjq43tx4RMznNg/qYV0cgadkWulfOg/wrg7SILv5PEzdzDLjMPt72mXl1iJgtQxM3Uzhl0Ofm1SFitgxt6GcKzYNEDNrY2BzKIBGD0zxIGeQxfppBWkQX/5eu0DRF3wshKMQYb18I5eWmliNimnSdPJoicZp9Z/jtC5RBEri0gGWOvsogEaDKuZg+yPMYZZCIQRdVmsNPz8W0iC7+rzI0w6Mgsr25tQSSFjHQ4zxjnHsEjqwxtx4RszhmUFSCubUEEqcMOgrJq82tR8Qs6cogUzRvDT3HG+O8FEheZW49ImapbGsXFglRHcytJZA4ZdAxOLzS3HpEzJJemUEREN3R3FoCSfNW0HOCMfajDNIiuvi3ojzISTbG7RK1E7On+ektPCL15pRB2g3e45RBEuiK8iHnsDFWBnmeMkgCXXGB0Q8XlEFmUAZJoFMGmcsPM0iL6OLfMpLsY90+6Hl9LoDgcGO8fSGUl5lajojHOWaQbh/0vD5TIKSZMd6xEMpKTS1HxOMyd9vHyiDPc8qgL5RBEngyHDJI52KepwySQJeZBFiNsVraeV6fKRDS3Bj7SQZpEV38W+Xtg6CTRzM0i4LeE41xQToc/MXcekQ8zTGDtCeD5zWLgl6VGZQBh5RBEmDS1QfUVOGRzhl08Gdz6xHxNPUiNld4JPSeZIxPZsLBFebWI+JpTvOgPubVEajCI/wug7SILv5NEzfz9b/UPvaTW3hE6i1DEzfTDVAGSQDL0IZ+pvPDW5lF6k0bG5tP52ISyJzmQcogU/jZuZgW0cW/pSs0Tdd7EoS2NMY7F0Fpsbn1iHhSuk4eTdfLIYN2KIMkwGgBy3y9JhqbmYHmQRJ40vVBnumcMuhLZZAEFl1Uab5qGVRkbj1N5PJF9LKyMh5++GG6detG8+bN6dGjB48++ihWq9V2jNVqZc6cObRv357mzZszYcIE9uzZ4/Q+WVlZzJw5k6ioKFq1asXNN99Mfn6+q8sVf1cZmuFRENne3FoCVVhLoxcWwKkTsH+5qeWIeJRjBkUlmFtLoAprAYkXGOPCbNj/o6nliHhUujLIdGEt7POgwhxlkASWyrZ2YZEQ1cHcWgJVWAtjnyowMmjfD+bWI+JJ6ZUZFAHRHc2tJVCFNofEqcbYDzLI5Yvo//73v3n55Zd58cUX2blzJ//+97/5z3/+wwsvvGA75j//+Q/PP/88r7zyCqtXr6Zly5ZMmjSJwsJC2zEzZ85k+/btLF26lMWLF7NixQpmz57t6nLFnxXlQU6yMW6XqJ2YzeR4K/P2z82rQ8STnDJIu8GbyqmdgjJIAkRRPuQcNsbKIHOppYsEouICyFYGeQVlkAQiZZD38KNzMZcvov/2229cfPHFTJ06la5duzJjxgwmTpzImjVrAOMq9GeffZaHHnqIiy++mEGDBvHuu++SkpLCwoULAdi5cydLlizh9ddfZ8SIEYwaNYoXXniBDz/8kJSUFFeXLP4qI8k+1u2D5uo5HsKjjfHOxVBSWPfxIv7AMYN0+6C5epxnz6BdX0HJKXPrEfGEzN32sTLIXD3Og2bKIAkwGQ4ZpHMxczlm0O6vlUESGDKTgIqOGGppZ67uv4NmrYzx7q+h+KSp5TSFyxfRzznnHJYtW0ZSkrF4sHnzZn755RemTDFuYzxw4ACpqalMmDDB9j3R0dGMGDGClStXArBy5UpatWrF8OHDbcdMmDCBoKAgVq9eXePPLSoqIjc31+lLAlzl7YOgk0ezhYRD32nGuDgP9i41tx43UAZJNY4ZpD0ZzBUSDn0vNMbFebBHGSQBIF19QL2GUwblK4MkMKgXsfcICauSQd+ZW48bKIOkGqd5UB/z6hC/yiCXL6L/9a9/5aqrriIxMZHQ0FCGDh3KPffcw8yZMwFITU0FIC4uzun74uLibK+lpqYSGxvr9HpISAgxMTG2Y6p64okniI6Otn116tTJ1b+a+BpN3LyLn+3KXJUySKrJ0MTNqyiDJNBkaEM/r9JfGSQBRhsbexc/b+miDJJqnOZByiDT+UkGuXwR/eOPP2bevHnMnz+fDRs28M477/DUU0/xzjvvuPpHOXnwwQfJycmxfSUnJ7v154kPSFdoepVuY6FFG2Oc9K3Ro8yPKIOkmnSdPHqVqhlU5F+blSuDpBotYHkXZZAEmnR9kOdVuo6BFm2NcdK3xt49fkQZJNXookrv0nU0tGxnjPd857MZ5PJF9D/96U+2q9EHDhzIddddx7333ssTTzwBQHx8PABpaWlO35eWlmZ7LT4+nvT0dKfXS0tLycrKsh1TVXh4OFFRUU5fEuAqQzM8CiLbm1uLQHAo9L3IGJechN3fmFuPiymDpBrHDIpKMLcWgeAQ6HexMS49BUlLzK3HxZRBUk26MsirBIdAv+nGWBkkgaCyrV1YJER1MLcWqTIPKoTdyiDxc+mVGRQB0R3NrUVqyCDfXA9y+SL6yZMnCQpyftvg4GDKy8sB6NatG/Hx8Sxbtsz2em5uLqtXr2bkyJEAjBw5kuzsbNavX2875ocffqC8vJwRI0a4umTxR0V5kFPx6XO7RO3E7C38aFdmkTo5ZZB2g/cafnIbochpFeVDzmFjrAzyHn7eVkrEprgAspVBXkfzIAkUyiDv5AcZ5PJF9AsvvJB//vOffPXVVxw8eJAFCxbwzDPPcMkllwBgsVi45557eOyxx1i0aBFbt25l1qxZJCQkMH36dAD69u3L5MmTufXWW1mzZg2//vord955J1dddRUJCbqSRuohI8k+1u2D3qPLORBRcTfJ3qVwKtvUckTcxjGDdPug9+g80n5n0h5lkPixzN32sTLIe1TLoBPm1iPiLhkOGaRzMe/hmEF7v1cGif/KTAKsxlgt7bxHp7MhsmJNd+8yn8wgly+iv/DCC8yYMYM//OEP9O3blwceeIDbbruNRx991HbMn//8Z+666y5mz57NmWeeSX5+PkuWLKFZs2a2Y+bNm0diYiLjx4/nggsuYNSoUcydO9fV5Yq/St1sH8f2M68OcRYUDP2nG+OyYtj9tanliLiNMsg7BQXb2ymUl8Cur0wtR8Rtjm2xj5VB3iMoGPobFxYpg8SvpSqDvFJQkHMG7Vxsbj0i7uI0D9Iiutfwgwxy+SJ6ZGQkzz77LIcOHeLUqVPs27ePxx57jLCwMNsxFouFRx55hNTUVAoLC/n+++/p3bu30/vExMQwf/588vLyyMnJ4c033yQiIsLV5Yq/OrrBPk44w7w6pDo/uIVH5LQcM6iDMsirKIMkEKQog7xWf8eWLmptJ35K52Ley3EetF0ZJH5K8yDv5ePnYi5fRBfxCimbjD8twRA/0NRSpIqOZ0J0Z2O870coOG5uPSLuYMugIGWQt+k43J5B+5dDQaap5Yi42omCYsqObDQeKIO8jzJIAkGKQwa1H2RuLeKswzBoVZlBP0F+hrn1iLiDYwbFK4O8SoczoFUXY3zA9zJIi+jif4pPQvoOYxzbF8JamFuPOLNYYEDFLTzWMtj5hbn1iLiaYwa16wthLc2tR5xZLPbN/axlsEMZJP7lvRU7saZtByCzRXe2Z5ZitVpNrkpslEHi70oKHeZBiZoHeRuLxX4lqM7FxB+VFELFPIi2fSBcHS28ilMGlcOOhaaW01BaRBf/k7bNmBAAJAwxtRSphW5lFn/mlEFDza1FajbAIYO2LzCvDhE3SN61lhBLOQA/5HRg6vO/cNbjy7j/483sz8g3uToBqrRTUAaJn0nbBuWlxljzIO/kdC6mDBI/k7ZdGeTtfPhcTIvo4n/Ug8/7tR8MMT2M8cFfIC/V3HpEXMmpH7ombl4pfhC06WmMD/4CucfMrUfERTLzi2iRad9Ma4u1OwAZeUV8tuEIFovFrNLEUfxAZZD4L6dzMc2DvFL8QGjTyxgf+hVyU8ytR8SV1A/d+8UNgLYV+2Ie+g1yjppbTwNoEV38T2X/K9DEzVs53sKDFbYvNLMaEddSBnm/qhnkY7cRitTmlz2ZDArab3u8pby7bdylTQu6tVVbBa+gDBJ/5jQP0gKWV9K5mPgznYt5Px+eB2kRXfxP5SePwWEQ19/cWqR22hle/FVlBgWFGp+yi3dSWynxQz8lZTDIYiyiF1uD2WXtbHttbO92ZpUlNXHKoM/Mq0PE1RznQfGaB3mtAcog8VOVd8MEhehczJv56LmYFtHFvxTmQuYeYxzXH0LCza1HahebCLH9jHHyasg+bG49Iq6gDPIdsYkQW/FB65E1cOKQufWINFF5uZV1uw/Tw2Lclr/L2pliQm2vaxHdyzhl0FplkPiHojzI2G2M4/ppHuTN2vWxLzAeXQcnDppajohLFOVDZkUGxfaD0Gbm1iO1a9cb4gYaYx/KIC2ii385thmwGmPdPuj9fHhDCZEaOWaQevB5P2WQ+JFtKTkknEoiyGJkkGMrl7DgIEb2aGNWaVIbZZD4m2Nb0LmYD1EGib9J3QJWY3N1nYv5gAG+dzW6FtHFv6RoIxuf4ngLj3rxiT9QBvkWx4mbD/XiE6nJT7szGBS0z/a4clNRgLO6xdAiLMSMsqQuWsASf6N5kG/RuZj4G21s7Ft88FxMi+jiXxw3kdAnj96vTQ/7rczHNkFhjqnliDSZNtPyLTHd7bcyp2yCU9lmViPSJMuTMqpsKtrDNlYrFy/lmEHHNsOpE+bWI9JUOhfzLTHd7O0UlEHiD3Qu5ltad4X4ygza4hMZpEV08S+VnzyGtoC2fcytReqn6yjjT2s5HF5tbi0iTVWZQSHNoV2iubVI/VRmEFZjfwYRH5RzsoSNh0/YNhU9ZQ1jj7WD7fWxfbSI7rUcM0jzIPF1tnlQM2jX19xapH6UQeJPUhwyKFYZ5BO6jq4YWOHwKlNLqQ8toov/OJkF2RWbMsUPgmDdtuwTupxjHx/61bw6RJrKMYPaK4N8hjJI/MAvezOJtObTJSgdgO3WrpQRDEBCdDN6xUaYWZ7URRkk/uLUCThxwBjrXMx3KIPEX5w6AVkVd+TFD4Tg0LqPF+/gYxmkRXTxH449+HT7oO9wCs3fzKtDpKmc+oAqg3xGZ2WQ+L6fktKdWrlsLe9mG4/t0w6LxWJGWVIfyiDxF2rl4pt0Lib+ImWTfaxzMd/ReaR97AMZpEV08R9O/a+0iYTPiIiFNr2MccoGKD5pbj0ijaUM8k0R7aBtb2OcshGKC8ytR6SBrFYrPyVlMNBiX0TfrH7ovsMxg45tgqJ8U8sRaTTNg3xTy7b2NqjKIPFlyiDf1LKtvQ1qyiavzyAtoov/OKpNJHxW5RUQ5aVwZK25tYg01lFdgeWzlEHiw3al5pGWW8RgxyvRrcaV6CFBFs7p2das0qS+lEHiD47qjjyfpQwSf6DOBL6rMoOsZXBkjbm1nIYW0cV/VH7yGB4FMd3NrUUapsu59rEP3MIjUiOnDOpR97HiXZRB4sN+SsoAYGDFInqutTn7re0BOKNLa6KaqSeo11MGiT+obKUQFglteppaijSQMkj8gS2DIpRBvsaHMkiL6OIf8lIhL8UYJwyBIP2n7VN8bDMJkWocM6j9YGWQr1E/UPFhP+3OoB3ZJFiyANhW3g1rxRRfrVx8hDJIfF1+OuQeMcY6F/M9XXyrJ7FINfkZkJNsjNsPgaBgU8uRBvKhvuhu+dvt6NGjXHvttbRp04bmzZszcOBA1q1bZ3vdarUyZ84c2rdvT/PmzZkwYQJ79uxxeo+srCxmzpxJVFQUrVq14uabbyY/37t744iJ1P/Kt7XqBNGdjfGRtVBabG49Ig2lDPJt0R2hlWMGFZlbj0g95ReVsu5Qlu0qdIAtVvvdeFpE9xHKIPF1TvOgIaaVIY0U3RFadTHGyiDxRcog3xbdAVp3NcZH1kFJoanl1MXli+gnTpzg3HPPJTQ0lG+++YYdO3bw9NNP07p1a9sx//nPf3j++ed55ZVXWL16NS1btmTSpEkUFtr/Qc2cOZPt27ezdOlSFi9ezIoVK5g9e7aryxV/oR58vq/yKqzSQue/BEV8wVH14PN5lbcRKoPEh/y2N5OSMqtTP/Qt5cYietuIcPq1jzKrNGmoygwqK3L+O0XEF+hczPcpg8SXqR+673PMoBTvzSCXL6L/+9//plOnTrz11lucddZZdOvWjYkTJ9Kjh9Ef1mq18uyzz/LQQw9x8cUXM2jQIN59911SUlJYuHAhADt37mTJkiW8/vrrjBgxglGjRvHCCy/w4YcfkpKS4uqSxR84/k+mq0B9k1q6iC9TBvk+ZZD4oMp+6IMs+2zPVV6JPrZ3O4KCLKbUJY2gDBJfpnmQ71MGiS87qgzyeT6SQS5fRF+0aBHDhw/n8ssvJzY2lqFDh/Laa6/ZXj9w4ACpqalMmDDB9lx0dDQjRoxg5cqVAKxcuZJWrVoxfPhw2zETJkwgKCiI1atXu7pk8XVWq/2qwRZt7LfDim/xoc0kRJw4ZlDzGPvtsOJblEHiY6xWa8UiupWBQQcAyLJGcMRqtHAZ20etXHyKMkh8VdV5UOUt+eJbtDeD+CqnDGoNrbuZW480jo9kUIir33D//v28/PLL3Hffffztb39j7dq13H333YSFhXH99deTmpoKQFxcnNP3xcXF2V5LTU0lNjbWudCQEGJiYmzHVFVUVERRkb13V25urit/LfFm2Yfh5HFjnHAGWHTVlU9q0wNaxkJBOhxeBeVlPrUhiDIogDlmUAdlkM+K6Q4RcZCfBodXQ1kpBLt8muQ2yqDAsy+jgCMnTtGBTNpajH/fW8u7AxaCLDC6Z1tzC5SGiekOEfGQnwrJyiDxITlHoMC4K4aEoZoH+aqY7hDZHvKOKYPEt+QeNdYQQBnky1p3s2eQF5+LufxK9PLycs444wwef/xxhg4dyuzZs7n11lt55ZVXXP2jnDzxxBNER0fbvjp16uTWnydeRBv6+QeLxf7pY3EepG41t54GUgYFMGWQf6iaQWnKIPFula1cKq9CB9hc0cplcKdWtG4ZZkpd0khOGZQPqVvMraeBlEEBTPMg/6AMEl+lDPIPjhlUUgCpm82tpxYuX0Rv3749/fr1c3qub9++HD58GID4+HgA0tLSnI5JS0uzvRYfH096errT66WlpWRlZdmOqerBBx8kJyfH9pWcnOyS30d8gDaR8B8+fCuzMiiApWgzLb+hDBIfUrmIPjjI3g99a7m9H7r4IB+5lbkmyqAApnMx/6EMEl+kjY39hw9kkMsX0c8991x2797t9FxSUhJduhg9Yrt160Z8fDzLli2zvZ6bm8vq1asZOXIkACNHjiQ7O5v169fbjvnhhx8oLy9nxIgRNf7c8PBwoqKinL4kQOiTR//hI5tJ1EQZFMCUQf7DByZutVEGBZZTxWWs2m+0kRpo2W97fnN5D0CL6D7Lhz/IUwYFMM2D/IcySHyRMsh/+EAGuXwR/d5772XVqlU8/vjj7N27l/nz5zN37lzuuOMOACwWC/fccw+PPfYYixYtYuvWrcyaNYuEhASmT58OGFeuT548mVtvvZU1a9bw66+/cuedd3LVVVeRkJDg6pLFl5WXQ8omYxyZAJE136kgPiK2HzSLNsaHfjM2CRHxZk4Z1B6i2ptajjRRu77QrJUxPvSb8e9XxAutOnCc4tJyLJQzqKKdS5q1Fem0pnWLUAZ1bGVugdI47RKNTdEADiuDxAc4bugXEQ9ROlf3aW37GJvDgjJIfINTBsUpg3ydYwZ56bmYyxfRzzzzTBYsWMAHH3zAgAEDePTRR3n22WeZOXOm7Zg///nP3HXXXcyePZszzzyT/Px8lixZQrNmzWzHzJs3j8TERMaPH88FF1zAqFGjmDt3rqvLFV+XtR+KKjYN0aeOvi8oCDpXXAl6Kgsydtd9vIjZlEH+JSjIfjX6qSzIVAaJd/ppt9HKpYsljSjLSQC2VFyFPrpXO4KDtKmWT3KaB52AjF3m1iNyOln7oTDHGGse5Puc5kHKIPEBJw5AYbYx1qaivs8xgwqzIWOnqeXUxOWL6ADTpk1j69atFBYWsnPnTm699Van1y0WC4888gipqakUFhby/fff07t3b6djYmJimD9/Pnl5eeTk5PDmm28SERHhjnJtSsq871MOOQ2nHnyauPkFH27pIgFI/dD9TwBnkOZBvmNFRT/0QQ6tXLaUdwPUysXnBXAGiQ9ybKOgfuj+IYAzSPMgH6R+6P7Hy9trumUR3RetO5jFeU8vZ9vRHLNLkYZQ/yv/4wN9sERslEH+x8snbu5SOQ/aekTzIG93+PhJ9mcWADAoyGER3WpciT5Gi+i+LUAzSHyU5kH+J0AzaP0hYx60OTnb7FKkIZRB/sfLP8gL+EV0q9XK278e4Kq5q0jOOsVt763nREGx2WVJfemTR//TfjCEtjTG6osu3s4pgzRx8wvxgyGs4s63AMigqvOg29/XPMjb/ZSUbhs7LaKXd2NAhyjaRYabUZa4SvyggMog8XGaB/mfuIEQFmmMAyCDrFYr7648aJsH/WHeBrI0D/IdWkT3P/GDvDqDAnoR/WRxKfd+tIl/fLmD0nLjX8zR7FPc/eFGysq961+U1KCsFFK3GONWXaBFjLn1iGsEh0DnEcY4LwVOHDS1HJFaOWVQZ2jZxtx6xDWCQ6BTZQYdM3ot+qlTxWXc9/FmzYN8zE8VrVyCKGeA5SAAyeXtOEGUWrn4A8cMyk81ek6LeKPyMji22RhHd4aWbc2tR1zD8VzMzzPoVHEZ93+8mTlfbKekzGEe9IHmQT7BKYM6QYTmQH4hKBg6n22M89O8LoMCdhH9YGYBl/7vNxZuSqn22s97Mnn2+yQTqpIGydwNJcZmWurB52cC9DZC8TGOGaQ7YfxLAGTQoeMFXPK/X1mw8Wi1137ek8l/l2oe5I2KSsv4bd9xAHpajtLCUgTAFmtlP/RY02oTFwqADBI/kJkEJUZrKe1N5WcCIIMOHz/JpS//xuc1zIN+2ZvJ099pc3mvl7kHivONsa5C9y9e3NIlYBfRP994lF2pebW+/sIPe1m6I82DFUmDqZWL/1JfdPEFjhmkD/L8SwBk0Ocb6p4HvfjjXr7bnurBiqQ+1h08wcniMgAGB+2zPb+lvAeR4SEM7dzKpMrEpQIgg8QP6FzMfwVABi3YeJSdx3Jrff1/y/fxreZB3i1F52J+y4szKGAX0e8+ryfn9qz71vv7PtrEgYqNm8QLqf+V/0o4A4Ireroe+sXcWkRqowzyXx0cMuigf2bQ3eN7Mapn3bfe3//xZvZn5HuoIqmPylYuAAMt9lZDW6zdGdWrLaHBATu19y8dNA8SH6B5kP9KGAohzYyxn2bQnef1ZHQvzYN8mjLIfzlm0EFdie4VQoKDeP6qoSREG/9iYsjl+uBvsVBuOyavqJTb31vPyeJSs8qUutg+ebQYm1GK/whtBh2HG+MTByGn+m12fqcgE1a/CuXlpz9WvIPj1Q/th5hWhrhBSDh0PNMYZx+CnCPm1uMGwUEWnr96KB1aNQegdW3zoPc1D/ImP+22L6IPcrgSfVt5N/VD9ydOGXQYspPNrccTCo7DqleMHrfiGxznQQlDTCtD3CAAMig4yMLzVznPg24IXkKQwzwov6iU295bT0GR5kFe6ajOxfxWSJg9g3IOGznkJQJ2ER2gTUQ4L187jNtDv2Zl+F38v9B3GBu02emY3Wl5/PWzrVi9bEfYgFdaBKnbjHHbXtAsytx6xPUc+2AdXmleHZ7w24vwTD/45s+w93uzq5H6cMygNsogv+TUi88/MyimZRj/m3kGs0O/YVXFPGhclXlQUlo+f9E8yCscyznF7jSjBU8opfS1GCcU+8rbk0cLxmgR3b8E0jxo5UvwTF9Y8hfYs9TsaqQ+Sosd5kE9oVm0ufWI6wVABrVuGcYr1w7j1tAlrAq/i3+Evsu4oE1Ox+xJz+fPn23RPMjblJVA6lZjHNMDmrcytRxxA6eWLt6TQQG9iA4wuFMrzjlzOOGWEgBuCP6u2jGLNqfw1q8HPVyZ1CltO5Qb/87Ug89PefFmEi4X0w3KjM3hWPOqubVI/ThmkHrw+acAyaDBnVox6qwzHeZB31Y75svNKbypeZDpHK9C72M5TLjFuDJui7U7veMiSKi4mk78RIBkEACtNQ/yOek77P/OdC7mnwIkgwZ2jGbMiLPqnAd9teUYb/xyoNrzYiLHDNK5mH/y0gwK+EV0gDHTZpEVGg/AuODNdLMcq3bM41/vZM2BLE+XJrVR/yv/1/EssAQbYy/bTMLlek+GVp2N8d7vIXOvufXI6SmD/F+nsyAoxBj7eQaNmXotWaHtARgbvIXulpRqx2geZD7HfuiDghz6oZd3Z1yfWDNKEncKoAyi9yT7PGjfD5C5x9x65PQ0D/J/Hc8MmAwafcE1ZIUZ86AxwVtrnAc98c0uVu0/7unSpDbKIP/npRmkRXSAoGAiR99uezirhqvRS8ut3DF/A+m5hZ6sTBw89/0eViRlUFJWrp2YA0F4hL2/YsYuo2e4vwoKhjNvsT9e+5p5tUj9OPUBVQb5pbCW9v6KmbshP6POw31aUDCRY+zzoOuCq7dTKCu38od5G0jTPMgUJWXl/LLH/vfgIIu9H/qW8u7qh+6PnDIoye8ziDNvtT9eM9e8WqR+dC7m/8Ja2hcnAyCDosb83vawpvWgsnIrd87fQGqO5kFe4ajOxfxeWAv7v9vjeyA/3dx6KmgRvULo8OuxBhubjM4IXkFLTlU7JiOviD/M20BxqTb+87TDx0/y3++TmPXmGs765/cc22n0RLJagiFugMnVidsEQC8+m6HXQUjFrfgb50FRnrn1SN1SNhl/WoIhfqCppYgbBVAGhQ6bRXlI3fOgzHzNg8yy8XA2eQ4bm1VeiV5mtXAgtAfDu7Y2qzRxp64O/UAPe89VWG4x9Fr7PGjTfCjMNbceqVvlVaCWIM2D/FmXwMmgkGGzKK/IoBnBK4jgZLVjMvOL+cO89ZoHeQPHDGo/yNxaxH2cWrp4RwZpEb1Sixgsgy4HINJyikuDf67xsHWHTvD41zs9WZkAX221t9g5dTKfdqeMk8ckayeeXXHErLLE3Zw2k/CO0HSbFjFQkUEU58HmD82tR2pXfBLSK/4eiO1rfEou/inAMiho0JWAMQ+6LHhFjYet1zzIFD8l2a++aUYRvS3JACRZO3JGjwTCQ4LNKk3cKcAyiEFXGOPifNj8gbn1SO1KTkHaDmPcrq9xxbL4p0DKoOatCRpszIMiLIVcVst60IbD2Tz21Q5PViZVlRQaPdEB2iUqg/yZF2aQFtEdjbjNNrw++Dug5h2Y3/7tIF9sOuqhogTgq632vmT9LIcIsRif/m4o7UaRPgn2X53PBizG2Is2k3Cbs+wZxJq5oF3gvVPqVrCWGWP14PNvnUcQUBlUZR5koea/X9/+7SALNuoDbE9a7rCpqOM8aEt5D7Vy8WedAjeDWDMXyjXH90qp2+zzoA6aB/m1QJsHnTXbNpxVxzzo3ZWH+HyD5kGmSdsG5RV356mVi39zyiAtonuf+IHQ2bhdoGdQCqOCttV66F8+28LOY7rN0BMOHS9g21H7P+vBQfY+oFut3Zk6sL0ZZYknNG8Ncf2NcepWKMwxtx53ix9g/7Q1Mwn2/2huPVIzp37oOnn0a81b21uGBUIGxfWHLqMA6BF0rM550IOfb2VHiuZBnpCcdZLtDv+sBwXtt423Wrsxtrc2FfVbzVsZcwMwFi5PZZtZjfs5ZBDH98L+H8ytR2qmeVDgaBZtb9cTKBnUdTRgzINGB22t9dC/LdA8yDRO/dCHmFaGeIBjBqVtg1MnzK0HLaJXN8L+6ePvm39f62GFJeXc/v56ck6VeKKqgObYygVgYEUfUID0yH70T4jydEniSZV9sKzlkLzG3Fo8weEKCFZrYy2v5LgbvDbT8n+2XnxWOLza1FI8wmEe9Ifmy2o9zDYPOql5kLtVnwfZF9EzovrTuY1aSvk1263MVkgOrAzSPMhLOc6DdBWo/wu0DDrLcT1I8yCvpHOxwOKYQV5wLqZF9KoSp0FkAgDnlK2jV2hmrYceOn6S/7dou6cqC1hfbXE+eRxsMa5EL7KGkDhoBBaLxYyyxFOcNpMIgNsIE6dBVAdjnLQEsg7Ufbx4XuXVD8FhENvf3FrE/QItg/pMhaiOAJxdto7eYbXPgw5nneQfX2oe5G7V50HGInqxNZheA0eYUZJ4UgBnEHu+g6z9dR8vnlc5DwoKtd8xKv4r4DLoAod50Hr6hGXUeujhrJP8fVHtd+2Jm6Q4ZtAAc2sR9/OyDNIielXBoXDmTQBYsPJKn411Hv75xqMczCzwRGUB6WBmgdMtzJGcpEeQcTK509qFKUM6m1WaeEpn79uR2a2CQ2D4TRUPrLD2dVPLkSoKc+D4HmMcNwBCwsytR9zPC3eFd6vgEKd50Mu9N9R5+IKNRzmgeZDbHD5+kq1H7W2EIjhJd4t9HjR5cBezShNPCcR50JkO86A1mgd5laI8o+UgGK2GQsLNrUfcLyDnQTcD9ZsHLdyUonmQJxXlQcZuYxzXXxkUCLwsg9y+iP6vf/0Li8XCPffcY3uusLCQO+64gzZt2hAREcFll11GWlqa0/cdPnyYqVOn0qJFC2JjY/nTn/5EaWmpu8s1DLsRgo3/GXsc+ZzbR8bXefgHaw97oqqAVPUW5gEOrVwOhvemX3u1cvF7kXHQpqcxProBik+aW48nDLvBlkFsfA+KNTHzGsc228fqAxoYImKhTS9jnBIgGXTGDQ7zoAX8/jTzoA/XaB7kLtXnQQcJshibTh8M662WdoEgoh207W2MUzYGxpzAIYPY+D4U5Ztajjg4thmo2Phe86DA0LIttO1jjAMmg66HkGYAdE9eyB/Oiavz8A80D/KcY1tQBgUYxww6tsn0OYFbF9HXrl3Lq6++yqBBg5yev/fee/nyyy/55JNP+Omnn0hJSeHSSy+1vV5WVsbUqVMpLi7mt99+45133uHtt99mzpw57izXrmVbGHCZMS7M4YGEzZzVLabWwz9dd4TiUu0e7w5Vb2EeZLHf0hnWebhauQSKyk8fy0vg6Dpza/GElm1h4AxjXJgDWz42tx6xc9zIRj34Aoctg0rhyFpza/GElm2cMuiB9pvqnAd9sv4IRaVlHiousHy1NcXp8SCLfXP1sM7DNA8KFAGZQZcb46Ic2PKRufWIndOGfpoHBYyAzKCKeVBRDvfHb2JEXetBmgd5TorOxQKSF2WQ2xbR8/PzmTlzJq+99hqtW7e2PZ+Tk8Mbb7zBM888w3nnncewYcN46623+O2331i1ahUA3333HTt27OD9999nyJAhTJkyhUcffZSXXnqJ4uJid5XszGFTm5C1r/HEJbX3WjpeUMy321M9UVVAOZBZwI5jzjteO26m1WfoGE+XJGaxbSaBV9zC4xGOG4yumQtWq3m1iJ3TZlq6+iFgBHgGBa99jX/VMQ/KKijm2+1ptb4ujXMws4BtR53nQYMc7sjrpXlQ4AjEDHLcYHTNa5oHeQvNgwJTIGbQWbfZhsFrX69zPSiroJgl27Qe5BHKoMDkRRnktkX0O+64g6lTpzJhwgSn59evX09JSYnT84mJiXTu3JmVK1cCsHLlSgYOHEhcnP22mUmTJpGbm8v27R7awCphKHQ8yxin76BHwSbO7l77p4/zV+sWHlf7usotzGDljCCjF/EpwuneV588Bgwv20zCIxKGQKeKDePSd8DBX0wtRzBO4Cs/+Q5tYb+tTPxfoGdQxk66F2xkZPc2tR4+f/Uhz9QVQKq2cgErQyvmQYWE0aPfMM8XJebwsn6gHtF+MHQ62xhn7IQDK8ytR5znQSHNoV2iufWI53QZaR8HTAYNgs4Vv3fGTrrnb+CcHnXNg7Qe5HZWKyRXZlAzaNfX3HrEc7wog9yyiP7hhx+yYcMGnnjiiWqvpaamEhYWRqtWrZyej4uLIzU11XaM4wJ65euVr9WkqKiI3Nxcp68mG2H/9JE1r3LNiNo3b1q5/zj7M9Svz5UWV2nlcnbQThIsWQCkRg/BEhxiRllihladIbqTMU5eC6UeuiOlAdySQU5Xo7/a9PeTpjn4C+QeNcadzzY2HpLA0KoTRFdsZH0kQDNo9atcM6L2zbxX7c9in+ZBLlX1YoIRll10sBwHKudBoWaUJWaI7mjMhaAig4rMracG7jkXq3JXnpjr0G+Qk2yMO4/QPCiQRHeEVhVrIYGUQVXuDK5rHrT6QBZ70zUPcqvDKyGn4sOKTsqggOJFGeTyRfTk5GT++Mc/Mm/ePJo1a+bqt6/VE088QXR0tO2rU6dOTX/TvhdBRMVi/q6vmNSxhJiWYbUerg0lXGd/Rj47q7RyuSJ4uW0cNvxazxYk5qu8Cqv0lLGhhJdxSwb1uxgiKjb02/UVZCc3/T2l8Ta+bx8PmWleHWIOWwYVOt9K6iXcnkG7v2ZSh2La1DUP0lVYLnMgs4DtKVXmQSHLbeOQYZoHBZzKW5kDKYP6XgSR7Y3x7q8hWxljqo3v2ceaBwWegMygCyEywRjv/pqJCcW0jdB6kGk2KIMCWmUGlRU578/hYS5fRF+/fj3p6emcccYZhISEEBISwk8//cTzzz9PSEgIcXFxFBcXk52d7fR9aWlpxMcbJ2rx8fGkpaVVe73ytZo8+OCD5OTk2L6Sk12w2BQSBsNvMsbWcsI3vsXlwzrWevin649QWKINJVyh6tVXkZzkgqDVAOQSQcLZl5tRlpjJ8VbmfT+aV0ct3JJBwaFOGcTa15v+ntI4hTmw4wtj3KwVJE4ztRwxgWMG7Q/MDArb+BYzhtcxD9qgeZCr1DUPyqMlHTQPCjyaB2keZKbCXNi+0BiHRxuLixJYlEGEbXiDGcNqX5z/TPMg9ynMhR0LjXF4NPS7yNRyxAReci7m8kX08ePHs3XrVjZt2mT7Gj58ODNnzrSNQ0NDWbZsme17du/ezeHDhxk50uhzM3LkSLZu3Up6errtmKVLlxIVFUW/fv1q/Lnh4eFERUU5fbnEsBshqOJ22fXvcM0Z7Wo99MTJEm0w6iJVW7lcFPwbzSwlAOyLn4IltLkZZYmZeowHLMZ4y4det8GU+zLoBnsGbXgHSk655n2lYbZ+atwFATDoCgj13J1W4iV6nIctgzYHbgZdM7T2eVD2yRJtrOUiVedBFwavpLnFaCO0N+4CLGEtzChLzOSYQVs+hPJyU8upyq0ZFFxx5eeGdzUPMsu2zxzmQZeDzsUCjzIINrzLNUNr74uefbKEb7ZV3c9EXGL751By0hgPnKEMCkRVz8VMyiCXL6JHRkYyYMAAp6+WLVvSpk0bBgwYQHR0NDfffDP33XcfP/74I+vXr+fGG29k5MiRnH22sXnMxIkT6devH9dddx2bN2/m22+/5aGHHuKOO+4gPDzc1SWf5heKg/7TjfGpLLocW8K5PWsPznm6lbnJ9qbnsys1z+k5x1YurUbd7NmCxDu06gTdxxrjrP1GT7RAEBkH/S8xxqdOGIu54nmOtzAPvc68OsQ8jhl04oDpm9p4TJUM6pLyDaN6tq31cG2s1XQ1t7SzX3ETPeomT5ck3iC6I3QfZ4xPHITDAZJBEbFV5kGfmFtPoNI8SKI7QI/fGeOAyqB20P9SY1yYTeejXzO6V+3zoHmrNA9yC8dWLmcogwKSYwZlH4JDv5pShls2Fj2d//73v0ybNo3LLruMMWPGEB8fz+eff257PTg4mMWLFxMcHMzIkSO59tprmTVrFo888ogZ5cJZDhuMrn6Va86sfUOJNQey2JueV+vrcnpVb2FOtBxmcNB+AJKCetC1/9lmlCXeYIhDD1jH/tT+rsomx952BazfS91m7/0YPwjaDzK3HjGPMsjYaP2s2m9lXnMwiz1pmgc1RdV5UB/LYYZUzIP2BHWj24CRZpQl3mBogGaQ07nYXM2DPC1tBxxdb4zjBkL7webWI+Zx7EMdSBlUZZPja86sfR607tAJkjQPcq30nXB0nTGOGwjth5hajpjICzLII4voy5cv59lnn7U9btasGS+99BJZWVkUFBTw+eefV+t13qVLF77++mtOnjxJRkYGTz31FCEhJu2+23E4JAw1xqlbmBh1sM4NJeav1uZ/TVH15NHxKvTkrpdisVg8W5B4j77TjB5oYPRlLAqQHdA7DoeEM4xx6lY4vMrcegKN41/QZ8wyrw4xn2MG7VgIRQFyklQlgyZGHqBtRO13Bs7XxlpNUrWVyxXBP9nGyV0u0zwokCVOdcigLwIog4ZBh2HGOG1r4NyN6C2c5kHXgTIocCVOg2YBmEEdhkGH4cY4bRvnR+yjXWQd8yDdledajhk09FplUCCrmkGFuXUf7wamXInucywWpysgQte9xuXDtaGEO+xNz3Nq5RJGCZcE/wJAkTWUzmOvN6s08QahzWFAxe10JQX2zUUCQdWr0cUzSouMvo8AweFGDz4JXE4ZdNK+yVogcMigkHWvcUUdG4x+po3WG21fRn4N86CfAWMe1HGc5kEBLbQ5DLzMGJechO0LzK3Hk6rcGSweUlpcZR6kTY0DWmgzGFAxFw60DGrIPGjDEU4Vax7kEqXFsPkDYxwcZuxNJYHLMYNKT5mSQVpEr68Bl0KLit5XOxdxbd/ar0TPOVVS7WpqqZ+vtjhvSHZ+0HpaW4yrjX8JPYeenWv/y0oChNOtzPPMq8PT+l8CLSs29NuxCHJTzK0nUOz+2ujBCsYu8M1bm1uPmM+xF+ymwM2ga/uF1nohUG5hKV9t0TyoMb6u8s9tfNAGYmzzoJH06lz7RRwSIAJ2HjTdnkE7v4Sco6aWEzB2fw0njxvjvtOgRYy59Yj5hjq2UwigDOo3HVrGGuOdi5mZGFzrPCivsJTFW3Su5hJJ39gzKFEZJDjPg0w4F9Mien2FhBs7MwOUl9Jh3wd1biihW3gap+qHD1c6bKR1os+VuoVZjNvp2iUa48O/wfF95tbjKY4ZZC2DdW+aWk7AcNzExvEvbAlcHc5wyKCVkLnX3Ho8pUoGJez9gNG92tV6uFq6NM5X1eZBy23jE32u0DxIjNZK7foa4+RVAZZBNxpjzYM8Z6PmQVJFwhkQ288YJ6+CzD3m1uMpIWEw3J5BCXs/YGxvzYPcThuKSlUJQx0yaDVkJHn0x2sRvSGG3wSWYGO8/i2uG1Z7aK47dILdqQHSI8xF9qTlsdthE44EMhkVtA2Aw+XtGDR6mlmliTexWJw3lAikK0GH3wRBFXtDrHsLik+aW4+/y06GfT8Y41ZdoOsYc+sR76AMMsbr3uLaM2qfB60/dIJdqZ7vU+jLqra0a89xxgRtASC5vB2DRl9kVmniTSwW5ytBNwXQ5n6OGbT+bSguMLUcv5dzBPYuM8bRnaHbODOrEW8RyPOgYTc6ZdB1Z9R+UeXGw9nsPKZ5UJPkHIV9lRnUSRkkBpMzSIvoDRHdwbidH6Agg/E5n51mQ4lDHirMP1S9+mpG8AqCLFYAfmh+Pr3jo80oS7zRoCvtH2ht+gDKA6TnXFQC9K3IoJOZsOolc+vxd5vmA0YGMfRaCNJfmVLBMYM2fxiwGTQ++xNitbGWy1RtaTcj+CfbPGiZ5kHiKGAzqD30u9gYn8yElf8ztx5/t+kD7POgmZoHid2gK+2LyQGXQdON8cnjjMv6hPioZrUernlQE22eD9ZyYzxEGSQOqmZQWanHfrT+K2yosX8Fi/GPLfjX57hxcMtaD/1841FtKNEAjv1TLZRzefBPAJRbLZQOutqsssQbRcZBr4nGOC8F9v9Y9/H+ZNxf7SfOvzwL+emmluO3yssdru6zwJBrTC1HvExkHPSeZIzzUmBfYGZQ8G/PcePgFrUeumDDUU4We25S6+scW9pVmwcN1DxIHETEQu/JxjjvmP2uqUAw9i/2edCvz2oe5C7l5Q6tXDQPkioi2kGvynlQ4GZQ8G/PccPg5rUeunCj5kGNVl4OGx3OxRzvwBKJaGefB+WnejSDtIjeULGJcMb1xrg4j1nFH9a5ocSX2lCiXpLS8tiTnm97fE7QdjoFZQCwonwQY4cPNas08VZOm9oE0K3M7frAsMoMyofl/zK3Hn91cAVkV1w90uM8iNamxlKF422Ejj1j/V2VDLquqI55UFEpizdrg9H6qNrS7uygnXSumAf9Uj6AMWdqHiRVBPQ86AZjXJwPy58wtRy/dfBnyK64q7r7OGjV2dRyxAsNDdR5UG97b/SSAq4r/ICgOuZBX27WelCjHPoFThw0xsogqYlJ52JaRG+McQ9CWAQAEVvf46pup2o9VLfw1I/jVegAV1RcfQXwc+RkesVFerok8Xa9JkGLij50u76Ck1nm1uNJDhnE+rchY7ep5fglbWIjp9PbIYN2fx2wGRSx7X2uqWMeNE8ba9VL9Q1F7Xc3/BQxhd6aB0lVvSZCy4p9CQIygyr+n1j/DqTvMrcef+T4wYzmQVITpwz6JrAyaOxfbRnU8jTzIK0HNZIySE6n1/nOGVRw3CM/VovojREZB+feY4ytZdzL/FoP3ZSczY4UbShRF6vV6nTyGEU+k4PWApBljaD1kIvNKk28WUiY0QsLoKwYtn1mbj2eFBELo+4xxtYyWPp3U8vxO6dOwM4vjXHzGOhzgbn1iHcKDnXOoK2fmluPJ1XJoHuofUOfzcnZbE/J8UxdPszxYoIo8pniMA9qNVTzIKlBtQz6xNx6PCminfM86HvNg1zqVDbsXGSMm7eGxGmmliNeKtAzaPS9xthazj3W2q+C3Xwkh21HNQ9qkFPZsOMLY9ysFfSZamY14q0cM6i8xGMZpEX0xhp5B0S2ByA2ZRkXROyt9dD5a7TBaF2S0vLZ69DKZXrwr4RbSgBYUDaayUN0647UIlBvZQY4+w6ITDDGSd/AgRXm1uNPtn4KZUXGePBVEFL7xokS4BwzaFPgZlC7lB+YGrmn1kN1FVbdqra0uyh4pW0etLBsFJMHdzGrNPF2QwJ5HvQHh3nQEtj/U93HS/1t/QRKC43xoCs1D5LaDb3WPg7EDIrqAEDbY8u5MDKp1kPnaR7UMNs+dc6g0No3b5UA55hBHjoX0yJ6Y4W1gPMesj38e/gHWCiv8dCFG1MoKNKGErWpfgvzctt4Teup9IzVLcxSi7j+0H6IMT62CVK3mVmNZ1XJIL57yNiARZpuw7v2seNfzCJVxfWHhIpe1cc2Q+pWc+vxpCoZNCes9nnQF5s0D6pL1ZZ2jq1c1rS6QC3tpHZx/SDhDGOcuiXwMmj8w/bHmge5jmNvWc2DpC6xfZ0z6NgWc+vxpNDmcJ49gx6uYx60aNNR8jUPqj+11ZT6iu0LHYYZ49StxvmYm2kRvSkGXw1xAwCIK9jJxcErazwsXxtK1MpqtfKVw+ar/S0H6R9kXLm/qbwH/YecbVZp4iucPn2svaWAXxp8FcQNNMbHNhuf2gOFJWVYrVYTC/NhxzYbJwFgnBTE9Te3HvF+TleCBm4GxRXsYnrwbzUell9UyiLNg2pUtaVdP8tBBgYdBGBzeXf6DjnHpMrEZwwN4AwadCXEV8yDUrfYbuXWPKgJjm2xL0K0H2L/5ytSG6e78gI3g2ILdnNpSM3zoILiMr7YdNSTlfmu1K3GxXGgDJL68fC5mBbRmyIoGCY+anv4cLNPCKe4xkNf+3k/7686xLKdaWw7msPx/CJN7oDdaXnsyyiwPb7C4eqrj8rGccHA9maUJb5kwGUQXHGb6ZaPoLTm/wf9UpUMYtkjUHKKK+euov/fv+W8p5cz8/VV3P/xZp76djfvrTrE9zuUQXXSJjbSUANn2DNo68cBnUEPNftU86AGqtrS7nKHjdU/LhvH1EHxZpQlvsRxHhSQGfSY/XHFPOjq16rPg578dpfmQfVQul5XgEoDDZgBIRXtNrYEWgYFwcR/2h7+X3jt60Fv/HyA95VBp1W6zuGOYGWQ1MeAy+wZtPVjKC1y648Lceu7B4Ie50HPCbD3e9qUpXND8Le8WnZhtcP2ZRTw0ELnVhNhIUHERzUzvqKb0TM2gulDOtC5TQtPVW+6rx1uYQ6nmOnBvwJwyhrG7rbn0zM2wqzSxFe0iIHEqbD9czh5HPZ8C32r/z/ot3r8DnqeD3uXQk4yrH6F1JyBnCwuY39GAfsdPqSqqqYMunhIAl3atPTgL+BFSgqNyT9ASHPjL2SR02ne2jmDkpZAv4vMrspzHDKoTVk6NwYv4ZWy6r///nrOgwItgxyvQg+nmEuCfwGg0BrKzrYT1dJOTq95a+g7zdhgPRAzqPs46DUR9nwHuUdg1cuk5gyu3zwoOIi46HDaRzUP2AyqtO5gFi8v28FzR+YTAcaCxIAZZpclvqB5K2Pz2W2fwqksY6+mfgG0IXb3sdBrEuz5lpiyDG4O/ob/lVX//fdn1jAPUgbZrDuYxf+W7eC5Ix8QCcogqT+nDDoBu7+B/tPd9uN0JbornP8IWIx/lHeFfkFrcuv1bcWl5RzOOsmag1ks2pzCM0uTGPPkj3y6/og7q/UaVquVxQ4nj5OC1hJtOQnA1+Uj+N2gnmaVJr4mkG9lBqcMsv78NGV5GfX6tpoyaOyTy/lkXbI7q/VeuxZDYbYx7ncxNIs2tRzxIYHcVgqcMujO0EXENGEeFEgZVLWl3cSgdbSyGAt+X5ePYJzmQVJfgby5H1SZBz1DaX3nQWXlJGedqpZBHwdQBv22L5Or565ixisrabHvGyKsFXfG9L3IWJgQqQ+di9ky6I7QRbQhp17fpgyysnLfcYcMWkKkNc94URkkDeHBczEtortCXH9bH54ITnJ3yIImvd1fP9vCzmP1OwH1RVarleW705nxykqnq0OuCF5uG39UOo4LBqmVi9RT99/Zdkdnz3eQl2ZuPZ4W18/2F4elKI87gz9v0ts9+PlWv86gWm3Q7YPSSN3HVcmgVFPL8bi4fjDU+H/GmAc1PYN2pPhvBlmtVn5KyuDyV1ZWaWm33Db+WC3tpCG6jYWojsZ479LAy6DYvrYMshTncWfQp016u799vpXtKfVbBPNFjhl0zWurWbn/OODcTkrzIGmQqhmUe6zu4/1NbCKcMQuAlpzij02cBwVCBq1IyuCKV1dy9WurbBnkOA9SBkmDdBsL0Z2M8d7vIdd9ezFpEd1Vfvd/EGq0Ybk2+Hu6Whr/F0dpuZXnl+1xVWVew2q1snRHGtNf+pUb3lrL+kMnbK91tKQzKng7APvL48mLO4se7dTKReopKNjY4A7AWgZbPjS3HjM4ZNDM4GV0UwY1zIlDcKDi5DGmO3Q519x6xLcEBRubjQNYy2FzIGbQ3yDUuP1YGVQzq9XK9zvSmP6/37j+zTWsc5oHZXBukDEPOlgeR3a7s9TSTuovKBiGKIMqM+ia4B/obmn8CXQgZlAHMhgVZLSaOFQey4YgbawuDVA1gwLxXGycYwYtUwbVwGq1smynkUGz3lzD2oPKIHGRoCCPnYtpEd1VotrDOXcDEGop4y8hTfuX9s22VHal+sdVWOXlVr7eeowLnv+FW99dx+Yj1T9Vdbzy4ZOycUwbnODJEsUfVN2VOdA2aomMh3P/CCiDKp0sLqWsvJ7/HTje9jX0WrBY3FOU+K8h19jHmwI1g1w3D1qyPdVv7ogpL7fyzdZjTH3+F255dx2bk7OrHTMj+CeCLMZ/Mx+XjdU8SBpOGeTSedC329MCKoMuD3HMoHG88MM+D1cpPs8xgwLyXCwORt0DQIilnL+GfNCkt/O3DFqy7RjTXviFm9+pbR60QhkkTeOheZDLF9GfeOIJzjzzTCIjI4mNjWX69Ons3r3b6ZjCwkLuuOMO2rRpQ0REBJdddhlpac7tFw4fPszUqVNp0aIFsbGx/OlPf6K0tNTV5brWOXdBRBwAU4LXclbQrnp9m4Vy+lsO0MXifOvlCz/sdXmJnlRWbuWLTUeZ/NwK/jBvQ61/CQRRbltEL7NaWFA+mmlq5SIN1aYHdD7HGGfuhqPrza3HDOfcxanwdgBMDl7LmZbAzaDi0nJufXcdf5i3nsKSsroPLi+z92+0OHyKLdIQbXrY72DITIIj68ytxwzn3AUR8YCRQSOCd5/mGwy1ZdCLPpxBYMyDFm1OYfJzK/j9vA3sqGMeNCN4hfE9VgsLyscwbZAW0aWBYrpDl1HGOGAz6E7bPGhS8DrOsuys17fVPg/y7StBG5tBn5WN5sfdGWyt4cInkVo5ZtDxPXBkrbn1mGHkHRBprGNMDF7PyGBl0KLNKUx57mduf38D22tp1WehnMtD7OtByiBplJhu0HW0MT6+F5LXuOXHhLj6DX/66SfuuOMOzjzzTEpLS/nb3/7GxIkT2bFjBy1bGre33HvvvXz11Vd88sknREdHc+edd3LppZfy66+/AlBWVsbUqVOJj4/nt99+49ixY8yaNYvQ0FAef/xxV5fsOuERxq2EXxpXQczvspi1468nNa+QYzmFpObY/8zKyaXXyQ1MDFrH+cEbaGfJocgawtXFD7HB2huAr7ceY09aHr3iIs38rRrl2+2p/PubXezPLDjtsWODNpNgyQLgx/IhTB45NCB3pBYXGDoTDv9mjDe+Dx2Hm1uPp4W1pPnEh+FL42rQ9zstYsPEWaTmFdU7g64p/j/WW/sAvptB5eVW/vzpZn7da/TXm/n6al6fNZzWLcNq/oa9yyC3YkPnnudDlBavpJGGzIRDxlyGTe9DpzPNrcfTwlrCef8Hi+4CYF6nRayd8IlTBqXmFJKaW8jx7Fx6n9rA+RbnDJpZ/DfWWRMB+HrbMZLS8ujtYxkE8N32VP61ZJfT3i+1GRO0hY6WTAB+Kh/MxLOH0rWt5kHSCENnwqFfjPHG9wIyg5pPmmPLoPc6fcmGiddXmwel1ZJBxdZgrin+P3sGbU1ld2oefeIDJ4NWlA8iJzSWW0Z0Jj66mbvLFH/jlEHvQ6ezzK3H08JaGi02F90JGBm0doI9g9Iqz8VyC8nMzqP3qQ1MtKzj/OD1fpdBS3ek8cQ3O+uZQVuVQeIaQ2bCwZ+N8ab3ofMIl/8Ii9Xq3vtsMjIyiI2N5aeffmLMmDHk5OTQrl075s+fz4wZMwDYtWsXffv2ZeXKlZx99tl88803TJs2jZSUFOLijCu7X3nlFf7yl7+QkZFBWFgtCyEOcnNziY6OJicnh6ioKHf+is7KSuGVUZBR8anjjDdhwGXG+FQ27FkKuxYbze6L86t9+8qyflxd8pDt8UWDE3j+6qEeKNx1/rs0iefq2cOroyWDz8P+TqwlG4Bv+j/NpMtuJihIrRSkEYry4aneUFIA4VFw/24Ia1Gvb3V1ZpiWQeVlRgal7zAeX/YGDDSytj4ZtKq8L1cVP2x77IsZ9K9vdvHKT863AHZv15J3bjyLTjFV/ns4cQjeOB/yK+6GuvJ96HuhhyoVv+OYQWGR8ECSMqiBGbS6PJEri+fYHl84OIEXfCyDnv0+iWe/r988qAMZLAi3z4OW9H+SiZfdqnmQNE5xgZFBxfnKIBdl0LRB7XnxmjM8ULjrPPf9Hv77fVK9jq2aQQt6PcGYi2+mTUS4GysUv1Utg3YbC8v14F8ZNBrSjX1OuPR1GHS5Ma7MoN1fGX8qg+hABp+H/504ZZC4QnEBPNUHivMgLKJiHuTaDHJ7T/ScHOMWjJiYGADWr19PSUkJEyZMsB2TmJhI586dWblyJQArV65k4MCBtgV0gEmTJpGbm8v27dvdXXLTBIfAxEftj7//f7DmNXj3YniyB3x+C+xY6BSYxZZwcqzGBHdk8A6GO7Rg+HJLCnvTq4ert3p35cF6L6C3Io93Qv9lm7SVdjiLKZfeoBNHabzwCOh/iTEuyjVOkgJNUDCc75BByyozaHq9MujsoJ1ObWB8MYOqLqAD7M8o4JL//ca2ow63BZ7Mgvcvsy+gdzobek/xUKXilxwzqDgPdn5pbj1mCAp2ngc1MINGBO1yasGweEsKe9PzPFR807238mC9F9CjyeedsH87zIPOZPKlN2keJI0X1hL6TzfGyiDD903LoK8q7srzFe+tOlTvxatqGZRwJpdcNVuLV9J4YS01D6o2D3qkegZtX+CUQSWWMKcMGhFAGfR22H9sC+jKIGkyp3lQPuxY5PIf4dZF9PLycu655x7OPfdcBgwYAEBqaiphYWG0atXK6di4uDhSU1NtxzguoFe+XvlaTYqKisjNzXX6Mk3PCdB9nDHOPgRfPwD7l0O5Q0/35jHGrQZXzSftth08VjbL9tLdIQtsY6sVXvrRN3qCLtmWyt8X1e9DjnCKeafZ0/QIOmY80aYXITM/Mj6EEGmKoY4bjL7vsR/rXRk0Hrr/zhhnH67IoB9rzaD023fwaNn1tpfuqpJBL/pIP77TZVBmfhH3frTJ2Gy05BR8cJXRsxGgTS+4+gNlkDTd0Gvt402BmkEToMd5xrjeGXSD7aU7Qxbaxlar7+zP8O32VOY0YB70drOn6RmUYjzRphchMz9WBknTDb3OPlYGQU7DM6jqPMhXMui77an8/Ytt9Tq2egb11LmYuIbjPCiQz8V6jDfGtWZQaxh8DVw5j7Tbd/KIUwZ9bhv7cwa91ewZegUdNZ5QBomrOM2D5rn87d26iH7HHXewbds2Pvywabuj18cTTzxBdHS07atTp05u/5m1slgqrgStciVRq85w9h1ww1fwwB6Y/j9InEqn+LZYBl3B4XJjI5wxwVsZbLEH5RebjrI/w7uvBF1/KIs/frixXhvgxrYMYUnHdxhMxSeUEXFw7WfQIsa9RUpg6DzS2NgG4MAKo12HB3hdBk2sLYP+UC2DOsa1JXjQ5RwqjwWMDBrikEGLNqd4fQatO3j6DIpqFsL/Zp5BMOXw2S2QvNp4QRkkrtT5bIjpYYwDNYOgjnlQzRkUMngGB8uNCyaqZtCXm1PY5+UZtP5QFnd/0IB5UKd3GUrFxqstY+HaT5VB4hqdRkCbnsZYGeT8XD0zaHTwNoZa7BcQ+MJdeesPneCuDzZSXq8MCuabahn0GbRs494iJTA4ZtDBn+HEQY/8WK/LoImPgqXKcltlBl2/GB7YC5e8DH2n0TGuLWGDL7dl0Kjg7ZxhsV/N7Z8Z9B5nUHH3szJIXKnTWcYFcmBkUNYBl7692xbR77zzThYvXsyPP/5Ix44dbc/Hx8dTXFxMdna20/FpaWnEx8fbjklLS6v2euVrNXnwwQfJycmxfSUnJ7vwt2mE9oPg4heh1yQY9ze4/Vf44xaY/Dh0HVXtE7Y/nJfIK+UX2x47XoVVboWXfqzensBb7E3P5+Z31lFUWl7ncfFRzfj7tL78NuQ7umX+aDwZFgEzP4HWXTxQqQQEiwWGXGOMg8MgZaNHfqzXZVD8QLj4pRoy6ImaM2h81QyyX4VVboUXvfiOmPpkUFhwEK/NGk6v2Aj45i/2Vj/KIHE1pwwKh5QNHvmx3pdBAxwy6EG4/Zc6M+j3VeZBd1XJoJe8+CqsfRn1mwfFRYUb86ChS+mW8YPxpC2Durq/UAkMyiBDAzPoD+f1rfVczNvvDN6Xkc8t76ytVwbNmdqX34Yuo3tlBoW2hJkfK4PEdapm0NEAzaC4/rVnULfRNc6DXvbRDNrfqAxaZjypDBJXq7Ye5NoMcvm9ElarlbvuuosFCxawfPlyunXr5vT6sGHDCA0NZdmyZVx2mbHh5u7duzl8+DAjR44EYOTIkfzzn/8kPT2d2FjjysilS5cSFRVFv379avy54eHhhId7We+kodc6385Uh65tW1Iy4EpSdn5OgiWL84M30K/0IDusXQFYuOkod4/vSZc29WuK7ynpuYVc/+Yask+W1HqMxQL/d0FfrhvZhfBVz8P6140XgkLgineh/WAPVSsBY8hM4xa5AZcZf3qAd2bQTOf2NnXo0qYlZQOv4uiOz+lgOc6E4I30Lz3I9ooM+mJTCnef14uubb0zg3JO1Z1B/71yCCO6t4Ff/gtrXzNeUAaJuwy5BppFG5vZKYPqdWiXNi0pH3glR3Z8TkdLJuODN9K/9ADbrcY80pgHeWEG5dV/HnTt2V1otvoFWOeYQe9AwhDPFCuBY/A1xgbryqB6Z1DnNi2w1pFBX1RkUDcvzaATdWQQ2M/Fmq15EdbNNZ6snAcl+NbmzeIDlEGGIdfYF/NOo3ObFlgcMui84E0MKN3PNqtxh7VXZ9BbjcwgS7AySNxj8NUQHmmsB7n4Tk+XX4l+xx138P777zN//nwiIyNJTU0lNTWVU6dOARAdHc3NN9/Mfffdx48//sj69eu58cYbGTlyJGeffTYAEydOpF+/flx33XVs3ryZb7/9loceeog77rjD+4LRhX4/vh+vll5oe+z46WNZudXrPn3MLyrlxrfXcjT7VJ3HzZnWj1tGdyd8+6fw/T/sL1z0otEvTMTVohLgzFs8NmnzF78f35e5ThlkvxLUlzPo4an9mDqoPWz5WBkknhGVAGfdqgxqoKoZdFeVu/K87Y6Y/KJSbnxrLUdO1J1BD0015kHNdn4O3//d/sJFLxi9m0VcLaq9MqgRbh/f1+lcrFoGedkdMflFpdz0dn0yqC+3jqnIoKVz7C9c+Dz0UgaJGyiDGuX28X15tewi22NfyaDkrEZm0EUvKIPEPSozyA2tEl2+iP7yyy+Tk5PDuHHjaN++ve3ro48+sh3z3//+l2nTpnHZZZcxZswY4uPj+fxz++YJwcHBLF68mODgYEaOHMm1117LrFmzeOSRR1xdrlfp3i6Ck/2vId3aCoALgtfQy3LE9vrnG46SnHXSpOqcFZeW8/v317M9pe4NO2aP6c6N53aDfT/CF3+wvzB+Dgy52s1VikhDdGvbkpMDriGtIoOmBK+lt8V+K+TnG30vg24d3Y2bRlVk0EKHDDrvYWWQiJfp1rYlpwZcbcugycFr6WM5bHt9wcajHD7uHRlUUla/DLplVDduHtXN2GB+4e/tL5z3UL2vThMRz+jWtiVFdWTQwk1HOXS8wKTqnJWUlfOHeRvYdvT0GXTL6O41Z1A9r9IXEc/o2rYlRQOuItVqfPgwKXgdiT6eQTfbMugn5wz6nTJIfJPLF9GtVmuNXzfccIPtmGbNmvHSSy+RlZVFQUEBn3/+ebVe5126dOHrr7/m5MmTZGRk8NRTTxES4v879d42oT9zy6baHjtejV5abuV/y83/9NFqtfLXz7fw857MOo+7aHACf52cCMe2wEfX2XejHn4zjLrPA5WKSEPdNr4/c8um2R574x0x9c2gCwcn8OCUvg4ZVHGb4fCbYPT9HqhURBrq9gn9ebXMu+/Ks1qt/OWz02fQtEHt+dsFfSF1K3x4rT2Dht0Iox/wQKUi0lDGuZj3Z9BfP9vKiqSMOo+zZ9A253mQMkjEa93uI+diD35++gyaOqg9/2fLIMd50A0wRhkkvsltG4tK4/SMjeRE32s5bo0EYFrQSrpZjtle/2TdEY6cMPcqrKe+283nG47WeczI7m148vJBBOUmw7zLoTjPeKHPVLjgSaNBqIh4nZ6xEWT3vZZMaxQA04JW0d2SYnv90/VHTL8avT4ZdHb3GJ6qNYOeUgaJeKke7SLI6TuTjIoMmhq0mh4W+//vn20wP4Oe/i6pXhn09BWDCco9UiWDLlAGiXixHu0iyOlXewZ5w53BzyxN4rMNR+o8ZkQ3xwyaAUUVV4v2nqIMEvFi3dtFkOeQQRd4YQb9d2kSn64/fQY9U2sGPa0MEp+lRXQvdNuEgbxecTV6sMXKHSFf2F4rLbfy8vJ9ZpXG+6sO8dKPdf/8xPhIXp01jPDiHHh/BuSnGi90PAsuex2Cgj1QqYg01u0TBvB62QUABNWUQT95dwb1iYvk1euG15BBZyqDRHzA788fwBsV86Agi5U/VMmg/5k4D5q3+tBpe7M7Z9BlkFdxMUTHM+GyNyDY/++sFPFlt084XQaZdyXovNWHeOE0fZF7x0Uwd9ZwwktyjcWrygzqMBxmvKkMEvFyjutBNZ2LmZlB81cf5nllkAQwLaJ7od5xkaT1uY5sq7Hz8vSgX+hkSbO9/vG6ZFJOs5GeO3y3PZU5X2yr85j20c1468YziSrPgw+ugszdxgttesI1H0FYCw9UKiJN0SsukvTE6zhhjQDg4qBfnTLok3XJp93M0x3qm0Fv33Qm0dYqGRTTA65WBon4gp6xkWQkXuuQQb/R2SGDPl1vTgYt3ZHGwwvrzqD4KGMeFE0+fHC1MkjEB50ug8y6M/j7embQ2zeeZc+gjF3GCzE94JqPlUEiPqBnbASZfa8jy+FcrIsl1fa6mRn00MKtdR5TewZ113qQ+AUtonup2ecP4s3SKQCEWMr5ffAi22slZVZe8fCVoJuSs7nrg42UW2s/JrJZCO/cdBbtiw7Ca+dB8mrjhZaxcO1nbtkZV0Tc47YJg3mzdDJgZNAfqmaQh68ErW8GvX1jTRnUzsiglm08UquINN3sCYN5o4550Msevgprc3I2d32woe4MCg/h7ZvOJKH4UEUGrTJeUAaJ+JzbznfOoD8Em3tn8ObkbO5saAYdXmm8oAwS8Tm3TRjEmxV3BgdbrE7nYmZk0JYjrsigtp4pVsSNtIjupRLjo0juNYtca3MAZgSvIAH7BlYfrkkmNafQI7WkZJ/i1nfXUVRaXusxYcFBvDZrOL1PrIDXJ8CJA8YLLdrCzE+gdVeP1CoirtEnPpKjve0ZdFmVDPpobTLHcjxzJWh9M2judcPpk11TBn0KMd08UquIuEaf+EhS+lxHrtW4YqlqBn289ojHMuhYzilueXcdhSV1Z9Crs4aRmP0LvD4esvYbL1TOg5RBIj6ld1zVDPqZDtg30fPkncHHcox5UL0yKOdXYx6kDBLxab3jIjnW255Blwb/TEeLORmUmlPILe/UnUGhwZZaMqiNcRdMTHeP1CriblpE92K3ThzKu2UTAQizlHFbyJe214rLyj1yNXpBUSm3vLOOjLyiOo975opBnJ38Jnx4DRTnG0/GD4LZyyFhiNvrFBHXu3XiUN4pmwQYGXR7lQx69af9bq+hvhn09OWDGHlEGSTiT249fyhvO8yDqmaQJ+6IOVlcvwx66vJBnHP0rVoyaKjb6xQR15s9cShvVcyDQqtkkKfuDK7MoPR6ZdDbRvuEyo2M4wfC7B+VQSI+6taJQ5wzyITuBCeLS7nl3bWnzaCnLx9cPYPiBhrzoA5nuL1OEU/RIroX65cQxf4e11NgDQfgquDltOOE7fUP1hxm1f7jWK113FPTBOXlVu79aBM7juXWedw/Jndl2u4H4cfH7E/2vxRu+hZadXJLbSLifn3bR3Gg5yxbBl0ZvJxYhwyav+YwK/d5RwZdmKQMEvE3fdtHcbDnLPKtzYDqGfTB2mSPZND2lLoz6O+TunJR0t/gh8eAilqUQSI+LzE+ikM9r7dl0BXBy4kjy/b6h2uS+W1fplsz6L6PNjcggx7FnkGXVGRQZ7fUJiLulxgfxeFe9gy6PPgnj2fQ/R9vZtvRujNozqQu1TOo33S4WRkk/keL6F7uponDeb9sAgDhlhJuC1lse62otJyr5q5i7JPL+e/SJA4dL3Dpz37qu918tyOtzmPuO7MZN+ycDTsq+wRaYPwcY9dlbRoh4vNuOn8475WdD1TPoOLScq5+zcigZ5YmcTDT8xl073BlkIg/u3nicN4/TQaNefJHt2TQ00t38+32ujPonuHh3LBrNuxYWPGMMkjEn9w88QyHeVCpcwaVlXPNa6vdlkHPLE1iyfbUOo+5Z3g4N+y+zTmDznsYZrwFYS1dWo+IeN7N5w9zyqCqd+W5M4P++30S32yrO4P+OCycG3ffXj2DLn9bGSR+SYvoXm5Ah2h2d7ueQmsoADODl9GGHKdjDmed5Lllexj75HIue/k35q0+RM7Jkib93M83HOF/p7lN+o5uqdy171ZIq9glPiwSrv4QRt8PFkuTfr6IeIcBHaJJ6n49p6xhAFxTSwY9v2wP454yMuj9VYfIPlncpJ9bnwz6Q9dU7t6vDBLxZ/0ToknqUXcGJWedsmXQpf/71SUZtGDjEV768fQZ9Mf9s7Eog0T8Vv+EaPZUyaC2HsighRuP8uKPdW+g/PuuaUYGpW41ngiLhKs/gDEPKINE/ES/hCj2OmTQ1cE/0I5sp2PckUFfbDrKCz+cLoNSueeAMkgCixbRfcANk0bwQdl5ADS3FHNzyDe1Hrv+0An+b8E2zvzn9/z+/fUs3ZFGcR2b8dVk3cEs/vrZ1jqOsPJA6595IO3PWE4eN56K6QG3LoM+kxv0s0TE+9040TmDbgn5utZj1x86wUMLt3HWP5e5PYP+lK4MEgkEN55/FvPLxgNGBt1aRwZtOJztlEHfbU9tcAatP5TFXz6tK4Pg/moZ1F0ZJOKnbjz/LOZVZFAzS0md8yDXZNAJ/vzZljqPua/1L/w5/U/OGXTL99BnSoN+loh4vxsnOmfQzfXMoNvfa3wG/enT+mTQn5VBEnC0iO4DBnVsxZYuN1BkDQFgVvB3RJNf5/cUl5XzzbZUbn13Hef8axkv/biXnFOnvzo9Oeskt723nuKymoM2lFKeaf42d556GUt5qfFkj/HGiWO7Pg37xUTEJwzsGM22rvYMui54Ka3Iq/N7lEEi4ioDO0azo+v1FFXclXdtAzJo9nvrG5RBR06cZPa7dWfQ083f4i6nDDoPbv1BGSTip4wMusGWQdcFf+fWDLrtvXW1LnpVZtDdp/5XPYNiExv2i4mITxjQIZqd3RwzaCmtqbtPeXFZOUu2Gxk08ollvPjDHmWQiAtoEd1H3HvpOBZafgdAhKWQm+q4Gr2qzPxinvx2N6P+9QP/WbKLzPyad1bOLzJ2fz9eUPOtPyMsO1kc/n9cal1qf/Kcu2HmJ9C8df1/GRHxOfdeOpaFFuNqdO/KoLuUQSIB4J5Lx7LABfOgfy/ZRUZe4zLoLMtOvgx/iMuqZtA1yiARf3fvpWP4vGIe1NJSVOedwVU1NIMy8xuQQSPvVAaJBIB7LhnDgkZm0PGCYp76Lolz//UD//qm9gwqUAaJnJbF6q6tfE2Wm5tLdHQ0OTk5REVFmV2OSyTv303CuyMJpowyq4W5ZdP4b+kMiglt0PuEhwRx1ZmduHVMdzq2Nja9Kiu3MvvddSzblV7t+FhO8LfQeUwP/s3+ZHA4XPQCDL6ySb+TiLdwdWYEQga9VjaN/5ZeRhFhDXofZZBIdcqg00s+sJv275xDCKVekkHPw+CrmvQ7iXgLZdDpOWZQudXC3LKp/Ld0RqMy6MozOzG7Sgbd9t56vt9ZfTPjdpzgb6HzuST4V/uTweFw4XMw5Oom/U4i3kIZdHpHDiYR//ZIl2XQraO70ynGyKDyciuzlUESwOqbGVpE9zXf/BVWv2x7uLu8I/eX3M42a/cGv1VIkIWLh3Tg9+N68NHaw7z28wHn1ynlhuBvuSfkMyIshfYX2g8xAjNhSCN/CRHvo4lbPS15EFb9z/YwqbwD95X83sMZNBgufF4ZJH5FGVRPS/4Gq16yPXRNBnXn43VHmLtiv/PrdWbQc5AwtNG/hoi3UQbVk5sy6JN1R3i1hgy6Pvhb7gn5nEjLKfsLyiDxQ8qgevr2/2Dli7aHSeUduL/k92xtRAb9//buPTjKer/j+GcTknBPgEgukkBQLlOBcAwmUqvTKeHWakEtgjo1UgcLEj2AnhnpOQJ2eoyKMhSKcPqHpM6hiDgHGbWHGQ0kHiGEijk6AqZgMxJOLkgoJAIhZPfXPxIWglmS7G7yXPb9mtkZePYy318e5+3yy5NsdJRHcyan6pm/vE07D5/Sb0poECIXm+hujabPKx3YIO17RfK2/piN1xOtrVEP69UL96tFfbr9kh6PdON/BVOjjuif+xRqTNSf/MdMvyHyTFsl3ZknRUWHtAzAbnjj1kUdNMinaL0dTYOAUNCgLvJ5pQMbpX2/7vEGvdynUGOvb1DfBHlyV9MguBIN6iKft3UDa++/tGvQ1rYGXenpBk1bJWU9SYPgOjSoiwI0qDD6IRVceCCoBnXk7qijerlPocZFnfIfo0FwMzbR3RrNq+qOSLsWS7XXPjX5x6F3aHPCL7T1RD9dbPYG9bLJqtevYrbp/uiD/mM+eeT7WZ76TF8t9R8a8uiAHfHGrZvqjkq7/rF9g4bcoc1DeqhBk59QnxlraBBciwZ1U8AGvaCtJ/qH1KBfxmzTAzc0yDv5CcVMXy0NGBby6IAd0aBuqjsqfbBYqvnKf+hqgwpP9NeFIBuUpLP6VcxvaRAiDg3qptPHWt8HtWvQn2nL0F9o6/HQGvTLmG362+hS/7HWf4v9vfpMX0OD4Fpsors9mpLU0iz94Q3pszck0xbJ6Fg137dSHw18WP9RWqWvTp3v0kvFqEVPRf+Xnu2zSwM81z5o4hvPGCUv+Dcljru7J1YA2AZv3ILgvdLan8/W3tCgF/XxoL9T4QEaBHQVDQqC94r0hzdbG+RraT0WQoP+Ifr3eq7P7zpo0AYljvvznlgBYBs0KAidNaj0lL6qOtell6JBiHQ0KAgdNSgqRs33vajfD5qntw+G3qAjntuVtGAjDYLrsYkeCdG86k9fSh8skX749tqxEdkyczdr//8l6K3iEzrwXX3bHUaJalCGp0YZUTUa7Wm9TYiqVKrnrP/p9WaQ1plHtWDRP2liGp+0DPfjjVsIqstbfzLmJw16SwfODdGmfcE3aP6ilZqUxtXncD8aFILqcmnXEumHY9eOjbhLZu7moBt01gzUm77H9MiilcpMp0FwPxoUguo/tr0P6rhBbxWf0P4TgRpUq9GeahqEiEeDQnCTBpWeG6JNnTQoo+190K2eev/TaRAijSs20Tdt2qS1a9eqtrZWmZmZ2rhxo7Kzs7v03IiKpiRdaZKKX5H2b5DUdkr79JP+Yrkk6WzVUZ2vOqZhl09q8PUfCnEDr/Hot95crWuZp4LH79NfT0zpheEB6/HGLURdadCpYxrWVKXBnosBX8ZrPNrmzdWbLfP0ymP36W8m0SBEBhoUooANWibJE1SDfv3Yvbp/UmpvTA9YjgaFqOVy6+fFHNggGV/rsSAa5DMebfNO0xstj9AgRBQaFKKWy1JxgbT/X69rUN+2f4vRIKAzjt9E37Fjh5544glt2bJFOTk5Wr9+vXbu3KmKigoNHz680+dHXDSvOnmw9ar0s//b+WOvc9nEqMw3Xq+1PKojZpSenz5Wz04b00NDAvbDG7cwCaFBh3zj9GrLYzpiRmnF9LF6jgYhgtCgMDlZ1vp7ikNs0PLcsfp5Lg1C5KBBYRJygx7VEZNBgxBxaFCYVB1qvSr97HfdetplE6P/9o1VQctjOmIytCx3jJblju2hIQH76WozwvPRvT1g3bp1WrRokRYuXChJ2rJliz7++GO9/fbbevHFFy2ezsbS75YWfy59ukY69O833OmREtKkYWPUOGCUiusH63ff99X/tCSrWsNkFCVJmj8lTfl/dXuvjw7ABcLQoEemjNCzNAhAMNJz2hr0snToNzfcebVBt6txQEZrg0721fGWZFWbYfK1NWhe1gg9N40GAQhCeo60eL9U9LJUtuWGO69r0MAMFZ+hQQDCLC279X1QZw0aMErF9fEBG/RzLmYCOmTLK9Gbm5vVv39/vf/++5o7d67/eF5ens6dO6fdu3d3+hoR+53H61WXt34ncnCqNOx2aUiGFNO33UPqGpr0n2UnVVZZr34x0Zo9MUXzskbI4/FYNDRgDa5+6AFXGzQoRUoc02GDTjc0adv1DZqQonlTaBAiDw3qATQI6DIa1ANoENBlNKgHVP9RqiprbdCw26Whoztt0KwJyXpkShoNQsRx9JXoZ86ckdfrVVJSUrvjSUlJ+vbbbzt8zuXLl3X58rVPEW5oaOjRGR0h9Wett5tIGtxXy6fzYzpAqGhQB7rQoOE0CAgLGtQBGgT0GhrUARoE9Boa1IHUya23m6BBQPdEWT1AuBQUFCg+Pt5/S0tLs3okABGEBgGwEg0CYCUaBMBKNAhAb7DlJnpiYqKio6NVV1fX7nhdXZ2Sk5M7fM7KlSt1/vx5/62qqqo3RgUASTQIgLVoEAAr0SAAVqJBAHqDLX+dS2xsrLKyslRUVOT/neg+n09FRUXKz8/v8DlxcXGKi4vrxSkB4BoaBMBKNAiAlWgQACvRIAC9wZab6JK0YsUK5eXlacqUKcrOztb69et14cIFLVy40OrRAAAAAAAAAAARwrab6PPnz9cPP/ygVatWqba2VpMnT9aePXt+8mGjAAAAAAAAAAD0FNtuoktSfn5+wF/fAgAAAAAAAABAT7P1JnoojDGSpIaGBosnAeAEV1txtR2hokEAuoMGAbASDQJgJRoEwEpdbZBrN9EbGxslSWlpaRZPAsBJGhsbFR8fH5bXkWgQgO6hQQCsRIMAWIkGAbBSZw3ymHB9q89mfD6fqqurNWjQIHk8nk4f39DQoLS0NFVVVWnw4MG9MGHPY03O4cZ1OW1Nxhg1NjYqNTVVUVFRIb8eDWJNTuLGdTltTTQo/FiTc7hxXU5bEw0KP9bkHG5cl9PWRIPCjzU5hxvX5bQ1dbVBrr0SPSoqSiNGjOj28wYPHuy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",
       "text/plain": [
        "<Figure size 1500x300 with 5 Axes>"
       ]
@@ -2090,12 +2138,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1500x300 with 5 Axes>"
       ]
@@ -2133,7 +2181,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "aeon-venv",
+   "display_name": "aeon_cenv",
    "language": "python",
    "name": "python3"
   },
@@ -2147,7 +2195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,