diff --git a/experiments/mnist/mnist_classifier_from_scratch.py b/experiments/mnist/mnist_classifier_from_scratch.py
index 5c5c719..3f52905 100644
--- a/experiments/mnist/mnist_classifier_from_scratch.py
+++ b/experiments/mnist/mnist_classifier_from_scratch.py
@@ -17,7 +17,8 @@
 The primary aim here is simplicity and minimal dependencies.
 """
 
-
+import json
+import os
 import time
 
 import datasets
@@ -76,14 +77,29 @@ def accuracy(params, batch):
     return jnp.mean(predicted_class == target_class)
 
 
+def update_experiments_json(filename, config, results):
+    print("Saving results in:", filename)
+    experiments = []
+    if os.path.exists(filename):
+        with open(filename) as f:
+            experiments = json.load(f)
+    experiments.append((config, results))
+    with open(filename, "w") as f:
+        json.dump(experiments, f, indent=4)
+
+
 if __name__ == "__main__":
+    # Param scales: 0.5, 1, 2, 4, 8
+    # Step size: 0.0005, 0.001, 0.002, 0.004, 0.008, 0.016, 0.03
+
     layer_sizes = [784, 1024, 1024, 10]
     param_scale = 1.0
     step_size = 0.001
     num_epochs = 10
     batch_size = 128
 
-    training_dtype = np.float16
+    use_autoscale = False
+    training_dtype = np.float32
     scale_dtype = np.float32
 
     train_images, train_labels, test_images, test_labels = datasets.mnist()
@@ -102,21 +118,27 @@ def data_stream():
     batches = data_stream()
     params = init_random_params(param_scale, layer_sizes)
     # Transform parameters to `ScaledArray` and proper dtype.
-    params = jsa.as_scaled_array(params, scale=scale_dtype(param_scale))
+    if use_autoscale:
+        params = jsa.as_scaled_array(params, scale=scale_dtype(param_scale))
     params = jax.tree_map(lambda v: v.astype(training_dtype), params, is_leaf=jsa.core.is_scaled_leaf)
 
     @jit
-    @jsa.autoscale
     def update(params, batch):
         grads = grad(loss)(params, batch)
         return [(w - step_size * dw, b - step_size * db) for (w, b), (dw, db) in zip(params, grads)]
 
+    if use_autoscale:
+        update = jax.jit(jsa.autoscale(update))
+
+    # num_epochs = 1
+
     for epoch in range(num_epochs):
         start_time = time.time()
         for _ in range(num_batches):
             batch = next(batches)
             # Scaled micro-batch + training dtype cast.
-            batch = jsa.as_scaled_array(batch, scale=scale_dtype(1))
+            if use_autoscale:
+                batch = jsa.as_scaled_array(batch, scale=scale_dtype(1))
             batch = jax.tree_map(lambda v: v.astype(training_dtype), batch, is_leaf=jsa.core.is_scaled_leaf)
 
             with jsa.AutoScaleConfig(rounding_mode=jsa.Pow2RoundMode.DOWN, scale_dtype=scale_dtype):
@@ -131,3 +153,15 @@ def update(params, batch):
         print(f"Epoch {epoch} in {epoch_time:0.2f} sec")
         print(f"Training set accuracy {train_acc:0.5f}")
         print(f"Test set accuracy {test_acc:0.5f}")
+
+    filename = os.path.join(os.path.dirname(__file__), "mnist_experiments.json")
+    config = (
+        param_scale,
+        step_size,
+        num_epochs,
+        use_autoscale,
+        str(np.dtype(training_dtype)),
+        str(np.dtype(scale_dtype)),
+    )
+    results = (float(train_acc), float(test_acc))
+    update_experiments_json(filename, config, results)
diff --git a/experiments/mnist/mnist_experiments.json b/experiments/mnist/mnist_experiments.json
new file mode 100644
index 0000000..5d4be58
--- /dev/null
+++ b/experiments/mnist/mnist_experiments.json
@@ -0,0 +1,492 @@
+[
+    [
+        [
+            1.0,
+            0.001,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9698333144187927,
+            0.9395999908447266
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.002,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9852166771888733,
+            0.9461999535560608
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.004,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.996566653251648,
+            0.9515999555587769
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.008,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9987166523933411,
+            0.9506999850273132
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.016,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9988166689872742,
+            0.9508999586105347
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.0005,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9377166628837585,
+            0.9182999730110168
+        ]
+    ],
+    [
+        [
+            0.5,
+            0.001,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9377999901771545,
+            0.9174999594688416
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.001,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9698333144187927,
+            0.9395999908447266
+        ]
+    ],
+    [
+        [
+            2.0,
+            0.001,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9885833263397217,
+            0.9485999941825867
+        ]
+    ],
+    [
+        [
+            4.0,
+            0.001,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.994533360004425,
+            0.9509999752044678
+        ]
+    ],
+    [
+        [
+            8.0,
+            0.001,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9990000128746033,
+            0.9532999992370605
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.001,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9681500196456909,
+            0.9382999539375305
+        ]
+    ],
+    [
+        [
+            2.0,
+            0.001,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.09871666878461838,
+            0.09799999743700027
+        ]
+    ],
+    [
+        [
+            0.5,
+            0.001,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.937749981880188,
+            0.9177999496459961
+        ]
+    ],
+    [
+        [
+            4.0,
+            0.001,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.09871666878461838,
+            0.09799999743700027
+        ]
+    ],
+    [
+        [
+            8.0,
+            0.001,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.09871666878461838,
+            0.09799999743700027
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.0005,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9381999969482422,
+            0.9164999723434448
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.002,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9878666996955872,
+            0.9483000040054321
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.004,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9959666728973389,
+            0.9512999653816223
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.008,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9985166788101196,
+            0.9535999894142151
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.016,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.09871666878461838,
+            0.09799999743700027
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.03,
+            10,
+            false,
+            "float16",
+            "float32"
+        ],
+        [
+            0.09871666878461838,
+            0.09799999743700027
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.03,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9887833595275879,
+            0.9354999661445618
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.0005,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9521499872207642,
+            0.9299999475479126
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.03,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9432500004768372,
+            0.8903999924659729
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.001,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9711666703224182,
+            0.9406999945640564
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.002,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9886666536331177,
+            0.9470999836921692
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.004,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9954167008399963,
+            0.950499951839447
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.008,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9991999864578247,
+            0.9512999653816223
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.016,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9982333183288574,
+            0.9509999752044678
+        ]
+    ],
+    [
+        [
+            1.0,
+            0.03,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9432500004768372,
+            0.8903999924659729
+        ]
+    ],
+    [
+        [
+            0.5,
+            0.001,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9539000391960144,
+            0.9296000003814697
+        ]
+    ],
+    [
+        [
+            2.0,
+            0.001,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9904500246047974,
+            0.9488999843597412
+        ]
+    ],
+    [
+        [
+            4.0,
+            0.001,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.996483325958252,
+            0.9496999979019165
+        ]
+    ],
+    [
+        [
+            8.0,
+            0.001,
+            10,
+            false,
+            "float32",
+            "float32"
+        ],
+        [
+            0.9984333515167236,
+            0.9526999592781067
+        ]
+    ]
+]
diff --git a/experiments/mnist/mnist_experiments_plot.ipynb b/experiments/mnist/mnist_experiments_plot.ipynb
new file mode 100644
index 0000000..904406a
--- /dev/null
+++ b/experiments/mnist/mnist_experiments_plot.ipynb
@@ -0,0 +1,1135 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cbad259b-8b2c-415b-a5d1-7d9a2694b9e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "import pandas as pd\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "7ce8ae78-2f0e-46f3-9ed5-375a496a821f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"./mnist_experiments.json\"\n",
+    "with open(filename, \"r\") as f:\n",
+    "    raw_experiments = json.load(f)\n",
+    "\n",
+    "\n",
+    "def convert_to_df(raw_experiments):\n",
+    "    def convert_entry(v):\n",
+    "        return {\n",
+    "            \"Init. param scale\": v[0][0],\n",
+    "            \"Learning rate\": v[0][1],\n",
+    "            \"Num epochs\": v[0][2],\n",
+    "            \"Autoscale\": v[0][3],\n",
+    "            \"Training dtype\": v[0][4],\n",
+    "            \"Test dtype\": v[0][5],\n",
+    "            \"Training acc.\": v[1][0],\n",
+    "            \"Test acc.\": v[1][1],\n",
+    "        }\n",
+    "    data = [convert_entry(v) for v in raw_experiments]\n",
+    "    df = pd.DataFrame(data)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "0be79d98-5c22-4597-a91c-3e1a3334bde1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Init. param scale</th>\n",
+       "      <th>Learning rate</th>\n",
+       "      <th>Num epochs</th>\n",
+       "      <th>Autoscale</th>\n",
+       "      <th>Training dtype</th>\n",
+       "      <th>Test dtype</th>\n",
+       "      <th>Training acc.</th>\n",
+       "      <th>Test acc.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.969833</td>\n",
+       "      <td>0.9396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.985217</td>\n",
+       "      <td>0.9462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0040</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.996567</td>\n",
+       "      <td>0.9516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0080</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.998717</td>\n",
+       "      <td>0.9507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0160</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.998817</td>\n",
+       "      <td>0.9509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.937717</td>\n",
+       "      <td>0.9183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.937800</td>\n",
+       "      <td>0.9175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.969833</td>\n",
+       "      <td>0.9396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.988583</td>\n",
+       "      <td>0.9486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.994533</td>\n",
+       "      <td>0.9510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.999000</td>\n",
+       "      <td>0.9533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.968150</td>\n",
+       "      <td>0.9383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.937750</td>\n",
+       "      <td>0.9178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.938200</td>\n",
+       "      <td>0.9165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.987867</td>\n",
+       "      <td>0.9483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0040</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.995967</td>\n",
+       "      <td>0.9513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0080</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.998517</td>\n",
+       "      <td>0.9536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0160</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0300</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0300</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>float16</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.988783</td>\n",
+       "      <td>0.9355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.952150</td>\n",
+       "      <td>0.9300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0300</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.943250</td>\n",
+       "      <td>0.8904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.971167</td>\n",
+       "      <td>0.9407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.988667</td>\n",
+       "      <td>0.9471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0040</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.995417</td>\n",
+       "      <td>0.9505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0080</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.999200</td>\n",
+       "      <td>0.9513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0160</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.998233</td>\n",
+       "      <td>0.9510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0300</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.943250</td>\n",
+       "      <td>0.8904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.953900</td>\n",
+       "      <td>0.9296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.990450</td>\n",
+       "      <td>0.9489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.996483</td>\n",
+       "      <td>0.9497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>10</td>\n",
+       "      <td>False</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>float32</td>\n",
+       "      <td>0.998433</td>\n",
+       "      <td>0.9527</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Init. param scale  Learning rate  Num epochs  Autoscale Training dtype  \\\n",
+       "0                 1.0         0.0010          10       True        float16   \n",
+       "1                 1.0         0.0020          10       True        float16   \n",
+       "2                 1.0         0.0040          10       True        float16   \n",
+       "3                 1.0         0.0080          10       True        float16   \n",
+       "4                 1.0         0.0160          10       True        float16   \n",
+       "5                 1.0         0.0005          10       True        float16   \n",
+       "6                 0.5         0.0010          10       True        float16   \n",
+       "7                 1.0         0.0010          10       True        float16   \n",
+       "8                 2.0         0.0010          10       True        float16   \n",
+       "9                 4.0         0.0010          10       True        float16   \n",
+       "10                8.0         0.0010          10       True        float16   \n",
+       "11                1.0         0.0010          10      False        float16   \n",
+       "12                2.0         0.0010          10      False        float16   \n",
+       "13                0.5         0.0010          10      False        float16   \n",
+       "14                4.0         0.0010          10      False        float16   \n",
+       "15                8.0         0.0010          10      False        float16   \n",
+       "16                1.0         0.0005          10      False        float16   \n",
+       "17                1.0         0.0020          10      False        float16   \n",
+       "18                1.0         0.0040          10      False        float16   \n",
+       "19                1.0         0.0080          10      False        float16   \n",
+       "20                1.0         0.0160          10      False        float16   \n",
+       "21                1.0         0.0300          10      False        float16   \n",
+       "22                1.0         0.0300          10       True        float16   \n",
+       "23                1.0         0.0005          10      False        float32   \n",
+       "24                1.0         0.0300          10      False        float32   \n",
+       "25                1.0         0.0010          10      False        float32   \n",
+       "26                1.0         0.0020          10      False        float32   \n",
+       "27                1.0         0.0040          10      False        float32   \n",
+       "28                1.0         0.0080          10      False        float32   \n",
+       "29                1.0         0.0160          10      False        float32   \n",
+       "30                1.0         0.0300          10      False        float32   \n",
+       "31                0.5         0.0010          10      False        float32   \n",
+       "32                2.0         0.0010          10      False        float32   \n",
+       "33                4.0         0.0010          10      False        float32   \n",
+       "34                8.0         0.0010          10      False        float32   \n",
+       "\n",
+       "   Test dtype  Training acc.  Test acc.  \n",
+       "0     float32       0.969833     0.9396  \n",
+       "1     float32       0.985217     0.9462  \n",
+       "2     float32       0.996567     0.9516  \n",
+       "3     float32       0.998717     0.9507  \n",
+       "4     float32       0.998817     0.9509  \n",
+       "5     float32       0.937717     0.9183  \n",
+       "6     float32       0.937800     0.9175  \n",
+       "7     float32       0.969833     0.9396  \n",
+       "8     float32       0.988583     0.9486  \n",
+       "9     float32       0.994533     0.9510  \n",
+       "10    float32       0.999000     0.9533  \n",
+       "11    float32       0.968150     0.9383  \n",
+       "12    float32       0.098717     0.0980  \n",
+       "13    float32       0.937750     0.9178  \n",
+       "14    float32       0.098717     0.0980  \n",
+       "15    float32       0.098717     0.0980  \n",
+       "16    float32       0.938200     0.9165  \n",
+       "17    float32       0.987867     0.9483  \n",
+       "18    float32       0.995967     0.9513  \n",
+       "19    float32       0.998517     0.9536  \n",
+       "20    float32       0.098717     0.0980  \n",
+       "21    float32       0.098717     0.0980  \n",
+       "22    float32       0.988783     0.9355  \n",
+       "23    float32       0.952150     0.9300  \n",
+       "24    float32       0.943250     0.8904  \n",
+       "25    float32       0.971167     0.9407  \n",
+       "26    float32       0.988667     0.9471  \n",
+       "27    float32       0.995417     0.9505  \n",
+       "28    float32       0.999200     0.9513  \n",
+       "29    float32       0.998233     0.9510  \n",
+       "30    float32       0.943250     0.8904  \n",
+       "31    float32       0.953900     0.9296  \n",
+       "32    float32       0.990450     0.9489  \n",
+       "33    float32       0.996483     0.9497  \n",
+       "34    float32       0.998433     0.9527  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_experiments = convert_to_df(raw_experiments) \n",
+    "df_experiments\n",
+    "# df_experiments[\"Step size\"] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "a1392b70-a863-4629-ba8a-fb3300455e5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filter_init_scale_exp(df, autoscale: bool, training_dtype: str):\n",
+    "    mask = df[\"Learning rate\"] == 0.001\n",
+    "    mask &= df[\"Autoscale\"] == autoscale\n",
+    "    mask &= df[\"Training dtype\"] == training_dtype\n",
+    "    df = df[mask]\n",
+    "    # df.set_index(np.arange(len(df)), inplace=True)\n",
+    "    df.set_index(df[\"Init. param scale\"], inplace=True)\n",
+    "    df = df.sort_index().drop_duplicates()\n",
+    "    df = df[[\"Training acc.\", \"Test acc.\"]]\n",
+    "    \n",
+    "    suffix = training_dtype\n",
+    "    suffix += \" AutoScale\" if autoscale else \" Normal\"\n",
+    "    df = df.rename(columns={\"Training acc.\": \"Train acc. \" + suffix, \"Test acc.\": \"Test acc. \"+ suffix})\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "df_fp16_autoscale_exp = filter_init_scale_exp(df_experiments, True, \"float16\")\n",
+    "df_fp16_noscale_exp = filter_init_scale_exp(df_experiments, False, \"float16\")\n",
+    "df_fp32_noscale_exp = filter_init_scale_exp(df_experiments, False, \"float32\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "2845cf78-81dc-4102-a1b9-9e2b5d66e9c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Train acc. float16 AutoScale</th>\n",
+       "      <th>Test acc. float16 AutoScale</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Init. param scale</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>0.937800</td>\n",
+       "      <td>0.9175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>0.969833</td>\n",
+       "      <td>0.9396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>0.988583</td>\n",
+       "      <td>0.9486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>0.994533</td>\n",
+       "      <td>0.9510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>0.999000</td>\n",
+       "      <td>0.9533</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Train acc. float16 AutoScale  Test acc. float16 AutoScale\n",
+       "Init. param scale                                                           \n",
+       "0.5                                    0.937800                       0.9175\n",
+       "1.0                                    0.969833                       0.9396\n",
+       "2.0                                    0.988583                       0.9486\n",
+       "4.0                                    0.994533                       0.9510\n",
+       "8.0                                    0.999000                       0.9533"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_fp16_autoscale_exp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "68b639f4-b95c-4da0-86ea-542f522f005c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Train acc. float16 AutoScale</th>\n",
+       "      <th>Test acc. float16 AutoScale</th>\n",
+       "      <th>Train acc. float16 Normal</th>\n",
+       "      <th>Test acc. float16 Normal</th>\n",
+       "      <th>Train acc. float32 Normal</th>\n",
+       "      <th>Test acc. float32 Normal</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Init. param scale</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>0.937800</td>\n",
+       "      <td>0.9175</td>\n",
+       "      <td>0.937750</td>\n",
+       "      <td>0.9178</td>\n",
+       "      <td>0.953900</td>\n",
+       "      <td>0.9296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>0.969833</td>\n",
+       "      <td>0.9396</td>\n",
+       "      <td>0.968150</td>\n",
+       "      <td>0.9383</td>\n",
+       "      <td>0.971167</td>\n",
+       "      <td>0.9407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>0.988583</td>\n",
+       "      <td>0.9486</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "      <td>0.990450</td>\n",
+       "      <td>0.9489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>0.994533</td>\n",
+       "      <td>0.9510</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "      <td>0.996483</td>\n",
+       "      <td>0.9497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>0.999000</td>\n",
+       "      <td>0.9533</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "      <td>0.998433</td>\n",
+       "      <td>0.9527</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Train acc. float16 AutoScale  Test acc. float16 AutoScale  \\\n",
+       "Init. param scale                                                              \n",
+       "0.5                                    0.937800                       0.9175   \n",
+       "1.0                                    0.969833                       0.9396   \n",
+       "2.0                                    0.988583                       0.9486   \n",
+       "4.0                                    0.994533                       0.9510   \n",
+       "8.0                                    0.999000                       0.9533   \n",
+       "\n",
+       "                   Train acc. float16 Normal  Test acc. float16 Normal  \\\n",
+       "Init. param scale                                                        \n",
+       "0.5                                 0.937750                    0.9178   \n",
+       "1.0                                 0.968150                    0.9383   \n",
+       "2.0                                 0.098717                    0.0980   \n",
+       "4.0                                 0.098717                    0.0980   \n",
+       "8.0                                 0.098717                    0.0980   \n",
+       "\n",
+       "                   Train acc. float32 Normal  Test acc. float32 Normal  \n",
+       "Init. param scale                                                       \n",
+       "0.5                                 0.953900                    0.9296  \n",
+       "1.0                                 0.971167                    0.9407  \n",
+       "2.0                                 0.990450                    0.9489  \n",
+       "4.0                                 0.996483                    0.9497  \n",
+       "8.0                                 0.998433                    0.9527  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_scale_exp = pd.concat([df_fp16_autoscale_exp, df_fp16_noscale_exp, df_fp32_noscale_exp], axis=1)\n",
+    "df_scale_exp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "3285658f-3977-4d22-8cbe-45b2470b6198",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f77189;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bb9832;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#50b131;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#36ada4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3ba3ec;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e866f4;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.9677975592919913, 0.44127456009157356, 0.5358103155058701),\n",
+       " (0.7350228985632719, 0.5952719904750953, 0.1944419133847522),\n",
+       " (0.3126890019504329, 0.6928754610296064, 0.1923704830330379),\n",
+       " (0.21044753832183283, 0.6773105080456748, 0.6433941168468681),\n",
+       " (0.23299120924703914, 0.639586552066035, 0.9260706093977744),\n",
+       " (0.9082572436765556, 0.40195790729656516, 0.9576909250290225)]"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "palette = sns.color_palette(\"husl\")\n",
+    "colors = [\n",
+    "    palette[0], palette[0],\n",
+    "    palette[2], palette[2],\n",
+    "    palette[4], palette[4],\n",
+    "]\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f56416a-7cc3-4504-bbf8-e8090541335f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "6b531345-1ee3-45cc-ad38-fb2603667dea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0.0, 1.05)]"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style(\"darkgrid\")\n",
+    "sns.set(rc={'figure.figsize':(12,8)})\n",
+    "# sns.set_context(\"paper\")\n",
+    "\n",
+    "ax = sns.lineplot(data=df_scale_exp, palette=colors[:6], markers=True)\n",
+    "ax.set(ylim=(0, 1.05))\n",
+    "# ax = sns.lineplot(data=df_fp32_noscale_exp[[\"Training acc.\", \"Test acc.\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14a19118-91d2-48b6-9686-1299572a26a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "818eb73f-30d9-4e11-a78a-d50b37a2d182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Train acc. float32 Normal</th>\n",
+       "      <th>Test acc. float32 Normal</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Learning rate</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0005</th>\n",
+       "      <td>0.952150</td>\n",
+       "      <td>0.9300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0010</th>\n",
+       "      <td>0.971167</td>\n",
+       "      <td>0.9407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0020</th>\n",
+       "      <td>0.988667</td>\n",
+       "      <td>0.9471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0040</th>\n",
+       "      <td>0.995417</td>\n",
+       "      <td>0.9505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0080</th>\n",
+       "      <td>0.999200</td>\n",
+       "      <td>0.9513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0160</th>\n",
+       "      <td>0.998233</td>\n",
+       "      <td>0.9510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0300</th>\n",
+       "      <td>0.943250</td>\n",
+       "      <td>0.8904</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Train acc. float32 Normal  Test acc. float32 Normal\n",
+       "Learning rate                                                     \n",
+       "0.0005                          0.952150                    0.9300\n",
+       "0.0010                          0.971167                    0.9407\n",
+       "0.0020                          0.988667                    0.9471\n",
+       "0.0040                          0.995417                    0.9505\n",
+       "0.0080                          0.999200                    0.9513\n",
+       "0.0160                          0.998233                    0.9510\n",
+       "0.0300                          0.943250                    0.8904"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def filter_learning_rate_exp(df, autoscale: bool, training_dtype: str):\n",
+    "    mask = df[\"Init. param scale\"] == 1\n",
+    "    mask &= df[\"Autoscale\"] == autoscale\n",
+    "    mask &= df[\"Training dtype\"] == training_dtype\n",
+    "    df = df[mask]\n",
+    "    # df.set_index(np.arange(len(df)), inplace=True)\n",
+    "    df.set_index(df[\"Learning rate\"], inplace=True)\n",
+    "    df = df.sort_index().drop_duplicates()\n",
+    "    df = df[[\"Training acc.\", \"Test acc.\"]]\n",
+    "    \n",
+    "    suffix = training_dtype\n",
+    "    suffix += \" AutoScale\" if autoscale else \" Normal\"\n",
+    "    df = df.rename(columns={\"Training acc.\": \"Train acc. \" + suffix, \"Test acc.\": \"Test acc. \"+ suffix})\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "df_fp16_autoscale_lrexp = filter_learning_rate_exp(df_experiments, True, \"float16\")\n",
+    "df_fp16_noscale_lrexp = filter_learning_rate_exp(df_experiments, False, \"float16\")\n",
+    "df_fp32_noscale_lrexp = filter_learning_rate_exp(df_experiments, False, \"float32\")\n",
+    "\n",
+    "df_fp32_noscale_lrexp\n",
+    "# df_experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "9d6e5bf6-9a48-4991-9524-c3c0c0ba8ae4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_lr_exp = pd.concat([df_fp16_autoscale_lrexp, df_fp16_noscale_lrexp, df_fp32_noscale_lrexp], axis=1)\n",
+    "df_lr_exp.index = np.log10(df_lr_exp.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "840cfe4d-1650-4d51-b008-4f13fe65a130",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0.0, 1.05)]"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9sAAAKvCAYAAAB6Tdy/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddZhd1b3/8ffactzG4+7uIQQCBAJFCqUKNSgV2vKrUbn01ttbt1ugpXLb3t4KVGhpKW4lWAIhQIi7TmT0uO291++PPTPJkCChSSYJ39fzzHPm7GPrnLXnzP7sZUprrRFCCCGEEEIIIcQRY/R1AYQQQgghhBBCiJONhG0hhBBCCCGEEOIIk7AthBBCCCGEEEIcYRK2hRBCCCGEEEKII0zCthBCCCGEEEIIcYRJ2BZCCCGEEEIIIY4wCdtCCCGEEEIIIcQRJmFbCCGEEEIIIYQ4wqy+LsC/Q2uN5+m+LsZxwTCUfBYnKanbk5fU7clL6vbkJPV68pK6PXlJ3Z68+qpuDUOhlHpF9z2hw7bnadrb831djD5nWQY1NVEymQKO4/V1ccQRJHV78pK6PXlJ3Z6cpF5PXlK3Jy+p25NXX9ZtbW0U03xlYVu6kQshhBBCCCGEEEeYhG0hhBBCCCGEEOIIk7AthBBCCCGEEEIcYRK2hRBCCCGEEEKII0zCthBCCCGEEEIIcYRJ2BZCCCGEEEIIIY4wCdtCCCGEEEIIIcQRJmFbCCGEEEIIIYQ4wiRsCyGEEEIIIYQQR5iEbSGEEEIIIYQQ4giTsC2EEEIIIYQQQhxhEraFEEIIIYQQQogjTMK2EEIIIYQQQghxhEnYFkIIIYQQQgghjjAJ20IIIYQQQgghxBEmYVsIIYQQQgghhDjCJGwLIYQQQgghhBBHmIRtIYQQQgghhBDiCJOwLYQQQgghhBBCHGEStoUQQgghhBBCiCNMwrYQQgghhBBCCHGESdgWQgghhBBCCCGOsMMO29u2beOLX/wil1xyCRMmTOCiiy56RY/TWvPzn/+cM888kylTpvC2t72NZ5999nBfXgghhBBCCCGEOO4ddtjesGEDDz/8MEOHDmXkyJGv+HG/+MUvuP7667nyyiv52c9+RkNDA1dddRU7duw43CIIIYQQQojjjFIK0zRQSvV1UcQJRvYdcbI67LC9cOFCHn74Ya6//nomTpz4ih5TLpf52c9+xlVXXcWVV17JvHnz+MEPfkAqleKXv/zlYRdaCCGEEEIcH0zTIBG2qEmFqamJUJMKkwhbmKaMVhQvrXvfqUuFSdlQJ/uOOMkc9p5sGIe/8y9fvpxcLsf555/fsy0QCLBo0SIWL1582M8nhBBCiKNDWpjE4TBNg1jSwo6EKHiKzy3OUfAUdiRELCmhSbw40zRIxQIYjzxN5Y7FqFiEyh2LMR59mlQsIPuOOCkck7148+bNAIwYMaLX9pEjR9Lc3EypVDoWxRBCCCHEi5DWSfFqRAMGlgrQWS7zoXsyLN5R4UP3ZOgsl7FUgGhA9h9xaNGAgfvAUrx8kco58/nc4hyVc+bj5Yq4Dy6VfUecFKxj8SKZTIZAIEAwGOy1PZFIoLUmnU4TCoVe1XNblvwhdh8IyQHRyUfq9uQldXvyOhHr1jAU4ZiJpQLkHc03H8vx2XlRopEQsXCFYs7F83RfF/PY0Lr3j+dfmlqh80XMUhlct+c21XNfQHtoz38OhabsaKquxlaaoKHJVTyyDqAVjudRcqu4WmMRwNWQc/JUPI+ICjEt7NLpdrAkFyJImNPCDgWvg8V5G9eLEVBh8m6JtM5jESBGnKJXpU2nMbTFXCvILKuDe509PFUexhQjzhsDrTzobuDuyhQSOoXSJu3kKOMS1TFMbLKUKOAQ0CE+yS4stYGfqkHs8sZwlbOPSazjxkCQld58bB3kdeNjvH2i5mP3l9iadgHYmnb56P0lrj8nQDJoE/nT3Vxsz0cDv8k9Sq1X4WPR2WwzY/j9JzQK/N81qAOun1Nt5gOlDSy16vnv8ATGuWm+VHiOZiPMZ6Izez0O9j/2wN8/VVjJBDfNz0JjWGbXc3lpMwure7gjMIjbg4NRaAyUX48HlqXrOaLa4Zu5pwG4NjYHFHw59wwJXeX7kYnsMSIv+tqqa5+aW23hkvJ2VlkpbgmNYJib473F9bSoID+JTkB1vYH9j+/ej+j5uby4iWFujn8Eh7DWTnJ2uZmZ1TaesBt4LNDUdb+DywBgoAlrl/cV1oGGm6Lj0cAVhQ1EtcOfw8NpN/zjdKNr/+WA1+7+XMY6nZxa2cc2M8bDwf40OUXOK+8krWz+GR7a+3Fddbn/efzfzyjvpjGoefr8izCtWsZfMJoPP5Bna9ple8blpvPPIHD/YwQCFuEHlvqPCtjogA221XXZdb3X7xZYFhzQK+dE/D4Wr8yJUrfHJGwfLYahqKmJ9nUxjhuJRLiviyCOEqnbk5fU7cnreKxb7WmoVqFcQZerUKlAuQo1CVBhOstlPtoVmrZnXK4/J0QqGITMZuKr95D1crTrTkJegEZqKXpFdrIH2zMYpgfi6Cqr9RYcDyZ6wwmiWc5GOrVilDOEAEHWqB10UmSo25+Em2CLsZdms5N+Tj2nVkK0G7t4MFDEcptYWExgs4fboi3k9CAmFIdSwuXpyFY8DKbnRuICy6M7qCjN5FwTV3Zs5fb657grfDqDiw18ZO8mlqVW86fUNKKVodSW47QGiuwO5Yk6YZqKSXKWw85IDsuzedPuDOe3L+ezU1rZxjuY3+bwmW2P8Nlpz/OccR3xagS0Sc52cRTYXgC0ScVQeMoETL67/k7a44/y34POpeieyTU7nmBs5V9cN2EAhfIVL1FDNQBE3SJ/f+4P/GzaAyw2fguYzFnxB/45/BH+mPwMrjf4JZ6jPwBWy1qm7XmE/5vTSqZ6LvncHt62/kluPuUpdjpvPOg190v2/FbY8CRt8adYO6iWsldDbvc6OiurWDmhH4VyDNuEa2ZG+fziXE/Q7rY17fK9J/N8fUEMqzNNudEEwNyyC9MpUho/i6J1iMPQF4xeKGeKmDu246QM2kcEyeY9zE3b8UJJWia8sr8xZ3cbZm43bcNHsT0UI9eex2zZQWe/erYMiL/s4+NOCXPLTgBWzTgPAL19D6ZTZP34OWwNvvAzPFj/dCvmjl10pmyeHNFIMethbt1FOZTk8QlNr+h9XLjtGczcblYOH8PDsYGM27edOS272NyvkfsGDHpF7+PqbQ8BcNsMf2jnOzY8jukUuX/8KWwNv/z7uKhlDafv2M3O1DB+P2I0k7N7uGDHU2RDSf5vwphX9D7GNm9hQLxKa8Fj/mnj+PD9mV4naj70YIEbFi3g+d0lIptbGbZj8yvviquAQAACNipoQzBAOWAT6tpG0EYFu27vuk4g4N83EPBvP/C+Af85sC0ZWnOcOh7/1x7omITtRCJBpVKhXC73at3OZDIopUgmky/x6BfneZpMpnCkinnCMk2DRCJMJlPEdb2+Lo44gqRuT15StyevI1W3uVI7lXKOpBdDVWF7dgudpTR1XgNhJ8Lm0jZ2O/uocxqY7UTZ523iXtqwKwM5P5cAdyu/Se2k4IxlescwcrgsbtiOo0zm7RtFVZk83thMyTC4ZsY5LIzqnqAN+1snb1gU4A/7kizeZZIzqmRtB9sLEHKCVAwomRMBk4taNvOePY/zhTktZMpfYmJuDz9afwf/fcpSdlR/vv+N6Vn+pUHvwWwWfHvLXbTHV/DHQedSds4itGcJ4ysb+XtDPwrli7m9JxfNAODxnsOHyQCsj5W4YtNzPDN4M+vML7EuZvG+LWvZ3LCHjfZEXHMkHHhcFoDNgQMrD7aH1uKh2Rl2yBdHsDO4F4C9oQKVSj/a7N715B4iBTjBIF7AxFP+Z+mGgmBFUFRRlAh7Job2yFsFFC711TAm0Gp34qkq/aoRvIFNBOwoAb2aAdUa9KAmQtFaosYq6twKA5wUHWaanfZear0IY6oDKBh51gS2ENUWU2r6Qc1YhupdtFv/4JzoeJwZExlvbCRo/5l57iSSRHjWWkUnnUzVoxlEA+uNTexQuxjDYIZMG4ptnsICdyV1wSzzJo/HNU7jPOdprMAfODu8gJZchM+eEmV7xu0VuIclTT47L4rneFTmTuN/i9vRKMID51FR8BUnh0Pe7xDQ3TGgp+2z+zrE+seoTD+bSZ7BDU4zoSaPyvizqdWK/642d933hY/rfX3owElUjIlcVrV5nbuHgf0HU7EGcIZjMcbZi1KKQMCiXHFwte79nBoMpamMOAeAzxf2oYHAhadRUfDeYpW819rzel6vckB3e+7AfjVUZi1iWNXkY6U2Ug0GlUmLiLmK/1do73n8C19bH/BcjQunUbEmc2YhxKhqJ2P7D6cSHMTkUoArS50v+RloDUFDU3njuQBc1pnB02C8/gzKhuactKLDzfa6f+/34V9OaGikPOc86ss2F2Zz9K8NUZ76OgKOwXkd+Z779Vzqgz+L2MKZlKNw0bgXP1Hz3acK/NeCGGc3nUWo4TQme2mmVNuZUm5lRLEdo1KFahVVcaBSRTnO/hctV/wTitkDP9N/T3frOgEbbVtdLe1+CN/f6r6/9f2gbT2P9Vvge7a9oCX+tU4pf94QrXXPPvhi+vI4KpEIv+IWdaX1y72VF3fdddexcuVK/vnPf77k/Z544gmuvPJK/v73vzNu3Lie7d/61re49957efDBB1/V67uuR3t7/lU99mRiWQY1NVE6OvI4jhy0n0ykbk9eR7tuAyGLRCxEJleiUnKO+POf7FytKTsurjapeB6tpXZKbpWE3Q9Hw7bcZvLVMv3Dw5nTGOb51sd5dLei3hzMpfURdjjP8KcdZULuUGp1I3udNrZ5e4l4Cfp7/UnrEluMfZg6yBtzBhdkV3D1sGdoL3+YeWmXL21+iPfNeZxtlesxdBBPvfQ/9W9uuJtM7GF+MPgcys7ZfHDnUqaU/sWnJ9aTL7/3JR9rG/DA5bV8fnGOxTsqB92+YHDAP+i9uZ3qS+yqi8w2rrY38Gn1OFuK1zLGyPPD6Gb+S/+NJ3JfxMTDBDyqgEtQmQSUSVWXcSgTVjafSWVR1jP8TyZCxpnEu5KK8eGt/Cy9nZ3lWfQzG1HKY6+7CwPNMHswpqFodrajqTIiOJAPD/BYUniUu9qG0Wj354r+QXY6K7mn1SRsDKQh0EDOTdPu7CFhxxkYGkRFF9lT2UbIDDCndhTDYg5LOlawtxhnYmos41Im6/KrafOi1Bn9CagQbZVmHK9EQ6iReCBOrtpOycuSsJM0hVM4XoF0JUPQDJMMJPG0Q8HJYho2USuO1pqKV0JhYBuBngPME631LBH2J0c7sFfEsKTZ0yuiWiiRKR7f30Hyv7ZvJONBytrgQ/fnDjpRc+M5ce7bUuJnz5Uo9c7iTK1TfHmWTcHR7ClohsYVptZQdVDVKlT8S1WpYnouMdsg35HDK1W6bq+iqk5XWN9/XXUH+APuo6pHd9/t6SJvWweEdf+yJ9R3B3T74FCve27vfT/sEyvEm6ZBNGAQCAXQxRIqHKJSqpCveC8apPvy77a2NvqKw/YxadmeMWMGsViMu+66qydsV6tV7r33XhYsWHAsiiCEEK8ZgZCFEQjyucU5PntKlAAc94Hb9TRG1xntdEVTcqDqQcXTlF2XiuuBsik5VdornVRdGJ1sYEzK49E9K3i+Lc7Y1DAWDbJ5oPk+Hm4eSF1wCIoQuwq7yFULJOx+2CpKW7mdbDVPUCW4dpBB2VnMDXssstUFvCeaY6K1jM8VmslV3sv+dqruJtTuz3EIAAGvyp3P/pTFkx/mfvvnQIgL7v4jKwY/wrKaj+B43d0qU8BIANZ3v2lvIADbShsI7umgPNzA1YPZbbWgPA/bAwjjHeJ4ydYuCgetKoRQ6LHD6RcqMdo1adf7iE4aTiIR48zcHjaZO+kXbMIyIe21ELAMGiP9CJomRbeDgKl4ZneEz847dOvkdadE2ZF2+OpsGwONqcBQYBpgKuX/riBs9sMM9Ofb3ulUXLCMFBVzJp/Wfiv0/hD5wjlaIgf8XgcM47u9bh/Af3LqCx6TesH1ST2/ecAc3sycA24dw5mMGXrg/ePAgd1uo0B9r2c8LTq/1/WJ9dN6HdgNp3e33cZwXVf5fbYRJWztH+pmKJtEoLbnulKKoNm7++OJFrQB8hWPWLhCKhjkpvMCfPOJvD/e31I42j9YFuJQcoUqyXiQm86J9QTuYUmTm86JEbE0Z/eD0+psNmc0K9s1qzo8VndoRiT8v5PnWjXfec5hbErxrbk2ZctmZ9liWI3C7PpbUpaBWRPFfbWBrDvE94Ty3mG8exsVP7jvD/H7w/yh7t8d4hX0PBaKR+aDpSvEv3B8u+0HeP3CFvoX3N7dKr8/xB8Q+o9CiO+eld59YCnlR5+GYhnCQczTZ5JaOJfOXOWE7gF42GG7WCzy8MMPA7Br1y5yuRx33303AHPmzKG2tpYrrriC5uZm7rvvPgCCwSBXX301N9xwA7W1tYwZM4abb76Zzs5O3vvelz7jLoQQ4pVL1oQoeyYfuiezf7KZ8xIkwxbpjt4rPziepuD4/5STAUXV0+zMaaoevX4cD6qepux6FN0qrlac0hQiGchx/64023MRTu+XYnQyz+3bNrOirZGYXU/F1ewr7cPxFDGrAUcrOitpqh5EzCj/c2aYv2//Jb9d+2ZcHeam022e7fgDv1k7jZIz6gXvTOEHXUX3GNOzgu2MD27i186fSBe+yeadGV6/bDn31PyeDaXubsse3WNYm3ueqxaoJQtU77sfQksoDJtHVYeprl9FJLcKPXl/AFRao1UFRZVk1SDgeeQCObSq0K/s9ykem65nZc3z1JeTmOEgE90RTK1uJOCWGeAlKFlFOu1OEirIINUAlku70UbEspna2IAz6yK+Soa93i4aIylyZ7+Lb1hX0uYa2LaJbfgt0JYBlgKlDuz73P2eJvO1XtsG8b6D9pAXznPiB8xEGGxLcf05oYNaJ2O2Ilh1iKQOnDbqxdmGwj7ghP+JGCDFK+e6Hrm0QzTgEQkF+PqCGJ7rUS2UX7JVSgjX9Uhny8QiNjedl+g5URP0HNLZKq7rYRmKMSnFmBS8ERPX05S7dql0RRM2YWRX+F7VrvnacoeIBRNqFBNrDKY2wIzkv9GJXKn9XcL597uj9+jVEt8VtrtC/f5wfohtvUL8Aa3zB26j65u6p2X+yIV4wA/s3UG8u4W9V6v7/lb6A2/vHez3h/5oXRz3gaW49z2+/0WKZdx7/evR02aSKZ643yOHHbbb2tr42Mc+1mtb9/X/+7//Y+7cuXieh+v27vPx/ve/H601v/rVr2hvb2f8+PH88pe/ZPDgl5rkQwghRDdPa4oORG3/wOLZVo9MRTO70SBkwuq8Ymrc5CP3vWCymXsyXH9Ogt9t8bhtQ5nT+tl8ZDJ8f+WfWLL7zQyJaX40P8hP1/6AB3d85GVK4f/b6BfRGMYSbt6Uo1S9kEazQp2zmdt3PEi+fA37D0kaAdgHXdsSAJSqmuBf72Zv4gk8LgHC2H+4HZ1agpsaianLRFzQqkLByhP0PPqVQkCF5mgbtucxfHOJ6N7nGDyjTFAvZu6+MPaujcwaGcOI3MHEzhrqKhY7oi3k7TwjsnEGFEO0BzN0hjL0q8YYZygUY/lsaxuWeTuj4wnCtTP5camKYT1G0o5iBg44qOg5WAhAIIq2bXKLruCcgM05XWPvLGsK82qijDuMlhQPGNr10/1J2UC/V/Tof5+0Top/h+t6ZIoequSyo7CBv2/7FRErxntG/WdfF00c57oDdzDk8fUFMbK5MulS9UXvbxqKSNfJvNcNMVk0yOjpZt5Z0UQsKDiwrEWzrMWF9S7RJzsYn1JMqFFMqlWMiCtMo49PAh4Y4qNHK8Q7vYL7gYG+d6u707vr/AG3Uz2gRb676N0hvnAEQnw0TODzV/st2ofgPvI0wXPmoUou/8bI5z512GF70KBBrFu37iXv89vf/vagbUoprr76aq6++urDfUkhhDjpFB1NIetSLmviJuzIaZ5v98hVIVvRZKoenWWHkmuRq0JnxaHo+LP5/uXcAPc238wvV5+P40X50XzFMx3/y1XTP/qSswL/14IE/9jYTrniYLaXWJN+AngzbrmCtXYn+zrXYKg2koSIYpKmnaLK0OjFaHTCtBtt7Anso6kSZuDtHZTMp6gZAmHHYNr9UF/ZwOjxuyh7v+O85mHYnsfD/ddhey4Ldw8m5ho8V7sdxygxo60BO1PkwuRgpoZ+yvBsgsGFAPHMUGbtvp9U5VFqKxHKhkslqAiYYWw75J8pz3Z1aauxcRvH8R01GR20YaRNadypvCNwBm8/sAtcYMxBZ9QxzV6f0ZQDfnfY32ncY39H8pOZtE6KI0FrjeNWea7jMUJmlHdrB1Od0AvfiGOkXKpSfomQ/WJMQxHtCt8LB5qcMcBga7ar23m73+08X9Vd4du/X10QfnGG31K9MaMZHldYfR2+j5SjGeKd7u70LwjnB7bEHxjYe1rnD9Ei330CIBFF5wp+1/FDKZbRxRKGoXDd10jYFkII4euezMjVmq0ZTbYK2aomV8UPzVV/W64rPGermnePCTCupoMfr0qzbN8g3jzS5MIhndy09kHWtF3yglewDvl7qbmVPS2rMYwBDDSGEHi+TII0P+E2/mPuJS867vaBLQ9zae42LntmGHG3wpWDTEx9HQv3DCPk2rwvNZCK8RNGZetIVIO0BPNUTIdUJUzUCVDtmlnZ0gYKBTTy2z3Q3UFbm3G+u3ZyV9exfRC0OTs32P+nP8D/5z850OCP/xpmU7Zthgb8H23blAI2oYBNsGuSl+wBM7VWgIOn7hJH0oGtk4ah8Dx9wrYkiL4zJDqGNw/9EGMT01GvfMEmIY4IUylGJhQjE3DJMBNlKtoJ8diWPM+3uaxu9ydUU0rRnNd8ZolD3IZfn2ljGopNaY+hJ1P4PlKU6hrTfWRDvALqkmEIBw8duMNBVDiEVz6yXeGPJQnbQojXPK01JRc/GHcH5K7LbGX/741huGyUxdasy5eecgiaip+fEWBN57N84cmJL/Mq/njXts4ie4vPsTq9EYPL0XuLBNvWs7vyEAGrhrMqo0lWyzwQ+xcVo513N49nRMbgkbplZIMtXLJzEE3La1iUDDAjeDcjsjX0LyYYNqAf9rz5BAIvPu72nNR0FqQ347mt6IDNhR3T/TPg9TaObTEu0BWMB9pUAjaJAyZMKR0wYUr1oC7Vh24tFicmrfUJ24Ig+p6hTM4dcFlfF0MIwA/fY2osGpTFRUMMPO2fEAfYV9TELBgc87uVt5U0n1riEDJhXEoxsdZgUo1iZFJhS/g+KjRQKVUxT5/ZM0b7QObpM6mUKif0iV8J20KIk0Z3aM4d0MJ8YGvzhUMMwpbiF2sctmQ0V441GZMy+P6KKo/tefnnHxXXXN5Y4kerriVTvRHb8bCeX8cD+Z9iGp+mVkfp71p0GjvZZ29jZCnEvM469thbWdz0DKOzNuf9Yxglex+njH6G/sUnec/ymXh4fKh/mHj1QWa0rcPSBu+wKoSdOEZXq/HUjn5AP7Rl4cWCjLZrGG0H0QMCVINBSMYIpmpQhuLWjTdy/Tn/j+89WeVTc2xu3Xgj75/8KXQiSvZdl6IN84RaEkQIcWLZW9zB7Tt+TcUr8+FxX+/r4gjRw1CKRNf8ktPqDX6z0CbbFb73FDRx2z9ueLZN82yb35sr2B2+awwm1ipGS/g+ovIVj9TCuYA/RvvA2cjNhXPJ5k7sfm3/1jrbfU3W2fbJ+pAnr9dq3WqtKbu9u2LXhxQDooq1nR5L9noMjinOHmiytsPjJ6vcntZn5yW+0W48BXZn7+fG9SNJV4fzH00dWPpevt8RplS9GAuPKGVyxh5s8szI1xGvFnmk7imUkeXqDYO5oHUnn555L1sjST62ehLz26LcOnQlW+LtnLtrNFM7+rMx3sauSIZhuRRD8zVUDAdPaYKu3/qrQ0EIBTGjYRzbxgsE0KGAvz0Y7Pldd/8eDEL3pfXirceJsIUyPQLhKJ6nUcofZmUYikoxj3aN436925PBa/Xv9mQn9frKtZX38Nnlb8PA5Aez/0HEivV1kV6S1O3J63Dr1tOaHTnNqnbNyg6PVe2azAuGkgcMuGmBTW1QsS3rMSAq4fvfJetsCyHEq1R2/Rm0U0F/aall+zTZqmbRIAOlFP+zxqG11D3euavLduXg0PyWEQZvGlLkkR1bubN5LHPjFRYVW/lny+3syL9w4sUqcV2l1nHIWXvJ2nsZnQ1Q84vNrO+3DIbazM0NYfr9KXbGNxKdvIyJ1T/zreVnkrZL3DDhcVKVEB9Z46/tOyO/iYhjM73dRRsBvrD+PEJ2FDMYwR0W4JLQSLQVRI8NUA4GGRQKMLArKOeDQQj7wbkaCvhjkNn/T6JwBP9J5CseqViA6j2P4T66HCoOBCzM02ZgnTWHzuKJfXZYCHFiqAv2481DP8Sw2HiC5gvXVRfi+GUoxdC4YmgcLhhqorVmR757wjU/gNsG1AT85TOvW+rgafjv+Tb9I4odOU1TGAKmhO/DcdCcIeXiCd11/EAStoU4jtkB/4veDpjHxdn2dEXTUdakAopUULG2w5/ps2d88wsmB8tVoeJBQwh+fkYAz3H5znP++1hAK+2FDTy2awKd7qFbPUztoo1OArpAzaN7MNqX8PjctUTsMznj+Qai7ZvYeep9JCMbuXbVFEblbG6acC9rUzv42OpTmdcyhDsHreP5mj0saB1OU3UIUzr68yGVYbDjUlMXJRKezk/3ziQcjFOdEyYcCvLp0CJ0MEBhkt+ifEpXcK6GglQtC6UULzJvZp9yXY/OXIXo/BkEzz6l19nhbK4iM0oLIY4ZGbctTgZKKYbEFENicMEQv+ddR8Xf3lLUhEy/caAp7N/2+SerFF0Ym1RMrFVMqjUYk1QSvl+hk3HOEAnbQhyHTNMgFrEpGxafW5zjs/OiJOMGuUL13w5MrqcxDUXZ1WxMv2AysO5xzpXeoflTUy3G1Rj8dGWVJS3wgaEVLkwWWLHT4ub2l+8eWMiXiH33N/x4/APYoU8wIhcicPPTLBn+BE7jcE7NDGbh3gY2J7Zw59CnmNQZ5j9WTWNXdB9fmHkvtaUwb91zCVUVZGKunYR7Fwu8c3Cb6nlH+hQMK8CEIf2IBlJ8JPR+LCtKYG6cQijImaFLOCMYRIeCZIMB6gI2dV3jlQtd5bOAw19w5Ph0Mp8dFkKcODorbdy+41e0lHdz7YQf9HVxhDgilFLUBv3f+0cUvzrTpqPst4i3lTSmgqoHKzs0Kzs0f9zkYSkYk/LX+J5YYzA2pQhK+H7NkLAtxHHGNA2S8SAFBz50T4ataZftGZebFsVIxoOks2Vc16Pq6V7drnstNdUzOZhmbMrgkmEmy1s8vvOsw9Co5jvjy3Rmqnx+bfwVlal820M0V5aye9BMQsYsgo88T0vpce6akiZhnMmi3U1E3SJ/HfE4SmX55KopNJU9rp90F/sie/jic3NRThMh1yIV/QwX7ZtNPDqVQcZAZjmrmRNSzB88kOGhfvRnKk0N/eH8KTQFTX4YeCORcC25GRF0MMCngteAUlTxA/LsA8sJdM17wmt9ZPLJeHZYCHHiCBohHtt3Fx4uraXd1If693WRhDjilFLUdo2UqAspfnmmTXMBVrV7ftfzDo/2Mqzu0Kzu0IAfvr80y2JSrcHugqY2iITvk5iEbSH6mNaaoguZCmQqmoakjekZXHN/pmet5K1plw/dl+P6cxJo00S1p/nCCpN1hZf/E7a2bCd6x6O0Jh3KDe8g35Im9vCtLBm+kUjqo/QvedRXXHZHd9ESaWZGe5wZ7THW1mzkyabVzG6LM33fYNbWFCjUfoNpmVouzlzIvngSO3APtvUU7wt/EoIhthirCBhBRs9oIhGq51JVoWpB6vWzycX681bzTVweDBE+JU5eKeYAc7rKWQJqgdO6rneH5Wj353TEPnEhhBBHW9iK8pZhH6YxNIiEXdvXxRHimFBKMTAKA6Mm5w72j/F2F2BVR1f4bvdoK8OQmB+uf/Ccw9as5rrpFjMbDPYWNMkAhCwJ3ycLCdtCHGFlV/vBuaqpC/pjm1e0eaxs9xibMpjZYLB0r8fNG10yL5gMzDbggcsTfH5xridod9uadvnek3m+viDGrh//DhpOR4VGEXcrhL0iFaudsFtkQiaITZ5H+63AUBlev344Ri7H7ZNvp9a8my+sm4e2omyo3UE0/m6uKFzAHCbx69ADPBh6mrFNZ3PeoIupMYMUdJZRI0fB3IupNzK8rTSRutEDyZ23gJB2+XblLcTsJCXD71N1LWf3lNcBZjO+57oG4iSO9scvhBDiOHF2/zf3dRGE6FNKKQZEYUDUZNEgP3y3liARUDieP2Gso/eH7xtXOazp0IxOKibW+GO+x6YUYQnfJywJ20K8jKqn2Znz128eX2Pgepq/bPbIVveH6mzXZabiTwjW7UMTTM4dbPJ8u8dfNntc2FhmTqYNb6/HttyAXq8T8Kok3Sq/XRHk03OjbM+4vQL3sKTJdadEKWQKPNO4l7aGT7AoM5BPtr6edYlWvlb/B5Jeik/EPks5CMv4LQBDz/8F+cipjGvdRFW5cNUbyMWGcVbbaOY4GfpNn0UxNIALq/O5EIhacarKZDrTmM67APCAWho5m1E95TGUSU2w8ah97kIIIU5sRSfP7Tt/zZbcWj498UcY6sWXLhTitUApRUPY/90yFDedbtNSgoawwtP+JLSuhrWdmrWdmlu3eJgKRib2j/keXyPh+0QiYVu85uwravYVewfkQwXnQVHFF2ba7C3AtU84RC343dkBDAV/2ey+5HrOFpqkqhJcsY3wwxuYWA2RDtUzY3MLgfQmdg3fzvxQLZdtHUpdtcpXp/+VtkgHX958KVPGX4e2FNefE+Kj95fYmnYZljS5/pwQMVuhzBCJMy9k5PZ9NA6cQuF1byJV7eTNLUlSgXoq9aeggG+UbiFmJ7GNMJ5SXDPge73KOLPuzF7X43bqiH/WQgghXrsCZpDH9t1F0c2xLbee4fHxL/8gIV5DlFI0doVvQyluPC3AvqJmZbvXs9TYviKsT2vWpzV/3eJhKLh6vN+Y017ShCyISPg+bknYFickV2vyVQ7ZstwdnN89xiQVVHzzmSor2zWfmGIxq8Hgj5tcHtz18jN6B7vWqk8EIBmAZEDhlivYmSyvT4BdLpMs5UkWs0RybURybQzMVynarSxpWkfQNblo2yR2h7P8ZO4dhB2LT+96G159Pf8adBsVo0qk/xeJ1YykNvcE6XKOlvNnU6k4BAM2f9lwPdef81G+92SVT83xr39gymcoFypMSM5mwuT9U4PF7dRBy6zIZDRCCCH6kqksLh3yPmJ2in7hIX1dHCFOCI1hxcKBJgsH+tf3Ff2x3is7/Mu9RRgQ9cP1zRv9Y9orxppcPMyks6yxDYjaEr6PFxK2RZ/ztKbg9A7ODWEYFjdY3eHx0C6PQTHFJcNM1nV6fH25Q6768hNmXTTUIBVUVF26nt9/RFPYn7wibisSAUjYinjXZcLWJJwyyVKOmnyGwMPtNHZm+N9MK2ZnjshihxU1e0jU7mZsup45rYO5v/9Gbhq7jDktg/j4qtNYW+dw+5A11LspLhp6FeFkALJ34NomLR+7nIgd59wdRQJGEKthOm6glv/nfI+gGcZQBvmKRxB4YMdtAPzXgk/zP89/lwd23MYHpnyGfEXWShZCCHFiOLPfpX1dBCFOaI1hReNAk7O6wndLUZPqWn6spaTx2B++b93scud2j+EHdDufUKMkfPchCdviiNJaU3b9WRRdrXm21Z/84bR+Bpah+O16h+a8JlOlpzU6WwXvBcn59UMNrhpnsK+ouX+Xx9Q6P2wHTcgesCBy1PJbnuMHBuau68mA/8Xy3vEWWkN919IMbx3gcFkkg9GZwUhnYG+aQnov8Q4HM53joaYNPB9Nc+6u0QSLcX404TGWjNrB+9bN4uzcKNbVtXPnoHUU68JMHTWNaCwFzjI6h6TIXfgBkl6WhTsDNIQGUuk/ExP4fvXvxKwkqmt954sHv6fX+w1b0Z7fXdej7JT544WP92z7wJTP+K3aTvnfXmdbCCGEOFYcz+GOnb9hbeYZPjb+O4TMSF8XSYgTWkN4f3D+8iyb1pImbvvX93WF700ZzaaM5u9bPRQwPLF/wrUJNYqYhO9jRsK2eEnlrnV6g6ZiV16zKe2RqfqtxNmqH3wzld7duCM2/OYsf7Xjry930MDUOoOaIDzdotmWO3SbdNjcH5xrg/6XwMiEwdtHwaCuM3YDIoofzbdI2IqY7U8ucRDXRaWzGM1+oB7emaGYbiHeUaGUbeWBujWkAyXetWk6Gs0H5v+NXLLC9c0X0eDFeKj/ZjYm2hgTHEddYAKR5E5gBy1zR5IddhXDKqtY1PEEoxNTqdTOY6w3g//2Xk/EigEQN1NcNvxjvYp0uOOhMx0VoIJtG6RSUTo781SrErKFEEKcWExlsrT1PlrLu1mfeY4pNfP6ukhCnFTqQ/uPhT873aat5Hc3X9Xhj/1uLsDmjGZzRnP7Nj98v36owXvGWeSrGk9DPCDh+2iRsP0aUvVeZFKwF0wQdtkok3EpgxtWOv44kDEmbxhu8uQ+j/9b777s6+QqftdwUykm1ipMBV2ZnUuGGZTc/aH6wG7c9iGC8+CYYnBs/+ylAVMxJAoqX0S1+GGazk7cdAehzhK7S9tYFlpPohri7N0j2Rhv46vTH6AuEeWHay9EmVVuHvkcAG+onEUo3kDUSpCjleaLZhFunMXMfD2jqu0kp5xNOTqK11enc6nxeYKmP4PFuPAMxiVn9JTJNoLYXUtfHWla974UQgghjleJVICgHaRcLZPprAD+BFAXDHwXKBgWG9fHJRTi5FcXUiwYYLKga9Gb9nLXmO92zaoOj115v2s6wL+aPX651uXcwQYfnGBRdDRVz1+aTBwZEraPgUDIIhELkcmVqJScI/KcjqfJVf1wnHfBy5ZpbndI2jCn0WBb1g/GUUtx7VSL9rLmvf+qvvwTAwsHaMal/C7a4Adx8MeDTKpVB3TV9v8YE12XB3bjNrq6S39ttt3ruc8a+AqX/ahUMdIZVGcWr6MDI50h0JlnZXUVG83tTGqrZ1y6kX8MXsOfhq9gYXEkV22fRXP9Hv404nlGZeo5szqLaF2KqnEf7ZESuTedh6pJcXpnkXiwjvzsi8CKc215KjE7gW0E0cDZybf0KkrUlrWhhRAnP9s2iYcsHEsTtIKUnTKWo8iWHKrVlz/RKkTQDpIuZ0kG40ClZ/tpTRf2XaHEca37e6dQdQmGg5SLZSK2Kd87R1BtUHF6f5PTu+bN7eiaRA2guaDRQENX6/hjezx+vMplaKxrzHetwcQaJeH73yBh+ygLhCyMQJDPLc7x2VOiBOCgwO1pPzhnq9A/4gfVR3a7tBThzAEGtSHFHzY4PNeme1qg8wdldv+f2vR6xZxGA8eD5a2a2qAflLvHchgKErbfXcQPyQePdx5X4/8Fvm2kyeWjTEJd+Xhuo8HcRuPIfDCeh8rmMTozOB1tBNMljM4Mj3pL2Ovt5YItI4lWg3xn8mKerd3NtVtOY1bbQJaNWcMDAzahvYmM0SMIJRpwDc2+AQGK486mPprj1HKEgUPHUDjvrUS0yzfKZ5IKNKANCw28q+E/ehWlJthwZN6TEEKcoGzbJBmxcR5cSvB1p/UEpur9j5JcOJd0ATnwPQl42kPjobUGFJbhHwYWnTyudtBoPO2i0WjtEbZiPWOs9xZ34nXdpyE0ENsIUHIL7CluwzANIgGDmDOdlkKIgKGpjQd7ApPWmvt2/5GVnU/ynpHXURNs7MNPQRwver53HliCdfap7Mh6DIgG8B54nOTZp8j3zlFSE9wfnN8/3uItIzTdnUub835u2Jbzh33esd0fwjgk1nvMdyoo4fuVkrB9FHUH7Q/dm2Fr2mV7xuWGRQn+tcPlns0Vsl0zb+eq0D0a9zdn2SQC8KdNLjvzMDqpqA0pdhf8NfYOpIBYV6tybcQkYniMjPs7f7+I4iOTzJ5JwmxD8buFNhGLnkm6Xs6/PXNhqYzRmcHraCeQLpBP7+HJ6jKq5RwXbRxBSZW5ds4dpAMlfr3kzdiexd/mPk5LOM/0PQ3EjYFYdhitNHvGpijFFzAyVEuFjfRftIB8/elMdbJ8272aZKAOR5k0Aleyv4u3oUxZAku8Jh2qO6cQLyYesnAeXIr70JM458zvCUzqoSf928+cQ3upAq7n//NB9br0tL/dMP0zu1WvjOM5aDwsFSBg+kNt0pU2PO3idYU5Dw+tPRJ2LWEriqdddhU242mNxmNwdBSmsshWO9ld3NbzGE/7jzOUwYSUvwzi7uI2mgtb0NqjJtjIyPgkAFZ2LKWz0or2Xw2tXTytGRYby4j4RAD+tec2im4eT3uc0nAudcEmMtUOHtx9a09AVUpjB02KpQoXD7qKkBlhR34j/9rzNzSapF3HJUPeC8Bj++5kVedTvYKrh8fI+ETOH/hOAP667Wdsya1Bozmn/1uZVjsfgG88/0H//l2P7X79K0Zex4j4BFpLu/n+6o/jaQ+F4lsz/9Tzmn/e+pOez9TrCtVN4UF8aeqvAfjztp/wwO4/A7Cw35u4bPhHAfj2ymtoLm45aL+4bNjHWNj/jQB8+bkrcLV/pv9r035HU3gw23LrGTdwNPXhfgC0F13+371Zbr4kSTgUIBUKUHbKZDoqPN32MFtyq1mdXsb8xgv+zT1WnAziIQvngSXo9jSlisc192a5+cI40fY0zgNLiJ81l3YJ20fdgcH53WMtLh6mWd3hdzlf2a7Zntv/c9cOP7HMbFB8foZN1fOX4pXw/eIkbB8lLwzaAFvTLh+5L8OPzkmwLQu3bSj3ekzU8peoSgRgdoPB6KQfpgEuHGJwWj+jVwt01AZTKSzLoKYmSkdHHsfx/wiitr9GX6/nP9IzD3ZNREZHGiudozm9gTWVldRnbObsrOf58DZuHL+EgYUEX3r2bDrCWX4z935//en1QwkaAUqWi1awe3p/+idGMjuYoWC78PY3k6sZw5vKr+etyiRu11BVBrOYyKwDihCx4kSs+JF9X0KcwLq75Jl2oKd1sjYuXYFfSGuNqx087eHh9rTeld0SRTfvB7KuUOdpF0OZNIT8AXAd5X2kq2142iNu1/Rs35RdSdHJo0xNuGiSzeSpVqsMCQ6nMTIEggEe33c3bqWILhaYHTqFGBHaqy08mX8Mz/P81/VcnIYUnudx0eD3YK1Yx5bCOpaxDE+7pNwBnF6di+tqHjGfYVMigxMJYxk57EIrpNM0FuYwozCWuFPmvpq/02y3k9ZDOSU3l4ELLmFUMs/3Vn2cQkuMEoN4w7SPEB41i/7T5jC04vmB6eIkNd++lsd3Vli3Pk1wTTNvXHoPtw1ZxS2DHcruZLTSBAN3Yqg8Q3OTGDfyegCealnMjuJmACZ1NPH1tbtBKT4ydwXtXSdEQ4E7MY02XK+OCYX/YPBuk+mFzXx36o8ByJWu4nW7s9jaYldNheXhDYAiZN+BZe7C8+JUS+/m7H1B5uSb2Zm8lz8Neppc6X00WAEuHOZy7mCT+1bexHJvCo47jK4zBIDB0GIHb2l7mFP7mdxV93t2ZC7G0zXseXINH2l7iM5gJ7cOzVN1JqBUhXj4B/4+Up3DpnaNbTik97qsN6cDkNJpLn/or6Dg4YYSzwUWAIpE+Cso5VFxZrBi23ieWb6T9xfWsqv+cVaHSmSLH2XX9npud5txG+tZnXkPoElEvoKhClScyRTKV/Lj3VH+X/Ex6tR2WhpLpItfQqF4YJfLuc4e1JoV7Lb/A89LdL1P/6cjY/H8nc8w+YLpeJ5Ne+6nAKzbuI7Q6ofQwJbAB8lTi2VuJBH+NgYGudL7+O3aM/nLmgKq6tCmfoUGovbjBB57miDb+YM3kEj/CJdP6GRQPMDXH6+Sq2q+9EiO/5wXZW9xLVMbJrHsgedY63wdjeZXK0P8xvRPANboCv/trgDgb14T//D8Fu8fmGuoUQ7PhBq43h0KwIfGwJyVT5HWFp/0xh/y7/oLk6HfgASb0h4/XFYEx+E9agenqk6qnuL/4Z+AOYM2LmsoUp0xkW89U6U552EUO9Ba9+whb6GZhbQB8FEmUolEmdVk8q4xFvbTK/lRS4qNRGmkzJdYB8Bf6c+DNIBtocMh/7lch0n5fXxQbwXg52ooK0kQw+Ebeg0Ad4yfxz27/Vf+krOaumwHa4nxU2N4z3vrLpcZCvD1M/3jnifWdHD7Nv97/ZPuBgZSYhthrjdH+e/B28m0C6bhac3nn3T8eW48r9fzfdDdzEjytBDgO/Gp6HCI1w02OKO0G3vlOr5oTaSMwRQvzTvc7QD8jzmcjWYcry7V81xGawdnuXs5190LwHfssXQSYLTO8h5nK9XpE7i52sSaDo9IMc8X2p6iMn44mdPmoRT81xN5f995osCXLzmPcMDAKfv7iblzNzoUQoeD6FAQzFc4JFG8Kqmg4tR+ilP7+b1ZMxXNqq41vle1a7bmNDVdjXnrOjVfeMphUq3ia7NtXO33wK2R8N1DwvZRkoiF+NziXE/Q7rY17fL9J/N8fUGMWTWuP+75EDNrv3ts76rp7tp9TGkN+QKqI4OZybK2czk7SpuY0taPIS0Bbq1dwt2D1nHhjrG8cdskNg7YwO/GPM2sykDmlk4nHLDJBsq0qCLVCaOJpSJMM3dQF+1P5wcvw0qmuK50Hgm7lqiVoKIUb2B6ryLUSlczIV6x7i55lQeXUDnntP2tk/9aQnLhPHZnsjRnd+Bq12/l067fOqYMxiSmArCvuJPm4lY87ZEK1PW0/K3qfIqOyr6uFrfu1kWXYbFxPS2ID+7+KwUng6td5jdeQH2oP5lqB3fv+n1XaPWDbffrvnXYNYTNGFuy67m7+Y+4WpO0G3nDkA8SMuGxlttY3rqYgpPA1RpTFzHNNGOS05lTfzWlssd923/JHuchPDxODV7JQGMWnufxi/T/4KLxlIGh9mFba9Cey7nul4i6Q6jqNLcF/Va9ijONNxQ+SjUWpXb4U/xx2/dw3JGUqucABlqbRLwwc7IFwl6VePAm7qp7jnL1NOLmOxkYaeDadffzh/qfsjnUSL70PkARCd5KwFrB2zbO487G63BiiubCaDQGWhukKlG+u+FeisEN/GHKTtL5b6MxiYduIFS+Da0NfrP2XcBIpufb2Nn/EVBQzP6cn6qug3BnOrQD7RAN/oxI8G4IQ0fl+/w2GebM9s0UzF1siXbQmf84KyLTaVrl8P1TYWdhIznzvRQrl3HXFpMPTzcYGDP56mM5/6D30RyfO9UmaBVZvMOmudqPNwKegqI3hXzlfQAE7MdA5SnSyO82dP/PO6PrB56MgOKXoDVVdwwF990ARO2nCZlFqnogj3rToQlqdqRJlUMYKFoqb+VvdQccVFf8/w8DwttJZfNUvCae5w38rRZqilmGZmzGdTbwhLqA7ZU4y1s9zh1sMqrUn1XOAnLGlF5/L+ss2KBXMD/fxqwxZ7Kz8zzKTopN7MBsXUPCLlFfO4+t9iwsXeailjsxUKwY+Xqe3Gfj90kbDo5fF6rajNl8l/+ZhKZT6ZpI8+3DP4FpwOqtNdxTmExLAN61ZSlvaB/O+EiCH/ebzVYDthpAB9D1f/Aj284nmW5jRXgcv+43hc0GlHavoCHXxif3LuQ/h/t/d61FUIUs858P8/MJk0gfsJQkgAMUdy0G4PyB7+ZPG/zPdNi+AvZOv+dCYsKbyIRSjMuE+NHSyyidezrfi43jX80enQBYoP1eYqfsnsyQbY8Au6mMHcrXT43RVvS44p8Zcl1zvCzf63DlHRl+fO44qq6Hu3EnmWEj/QJVu366PsHAcn/S0mL/GeztP9h/tWdWE3CKOEMnsbfOD9uVqkvgqRVYVojmKb2PFbp5uXYgQdmDXY4N2FS27CbQvhGUwY7pfi+I7L4MVnoX1RkT2VOAHQUFhHs9V3FHC4GW1QBsnzaTSkExrOTfZm3axh4jwZZ4lGqpSmC1f7/0wBibm4b5H3q2uzeiSUOmSmCTf5+9IwawMRUjVS0SeN7f1jF8LhszXcd5e3cS2LWdcnwg60f33mcBrPL+lUnS2QprVI3/3jdswy52UI00sHqcv+9ld7b13HdNpwbi8ILDycrGZuzcHtxgkpUT50BJM6sBjPZO7JXrWTV1LgUzQE26HXvLegA2jxrBc4kktB/Q49JIMWHvVuzdGwBYOWkGLYEYRiaPvXEDzvBBbPUaeLZNEyOAvWYj5tsvIFuB/3dv733n7XdkufHcBEPiQUrZEsHf/h2T/a+lu05m6FAQHQ5SPuMUvIFN/qe9dSdGZ6brthAqGkbjQsUFZcIr7N0p9ksEFPOaFPOa9ofvStduuCPnn6DqDt9bM5pPLXEYGIWJNUbPWt+1odfu5660PnHnOXZdj/b2fF8X45AO1bINMCxpctO5CbxK+YhNlnaolu1XSlcqmOkcuqOTpzsepr24m9e1TCLQWeBbg/7Ghngrn3/2LEZl6/j+pEdYVr+Lq9bPZFHzaG4dupK/DF/JGe3jeF/hEtbWdnBP9ElGRsazqP9bKMWD7HP3Uhts6lkWSxyef6duxfHH6+r2ClDSOXSoQEtnO8VKkbJXouIWCZlRJtXMRWvNU62PsCm7npJbYXRiNqPjswhZ8IctX6Cj0kpnqZGya+LoDKaxhv857042doTwPM3ghMk7b0/zh0uSlKtw37YyJafCret/44c9DAyVJhL8C2EzxltrbmZF2mBbaRttxsdQqspw9RYs5114GqqRH7O9cg+uV0+2+BnARGOS0jWkSOFqhRn5CGlzGxVnKob7DSwV4PJhLdzc/HY8L0l77n8Bg3DgL0RDv+VHG9/Dx2vfwD7r4HV3P7XjUfambuGuAZtozfoBJmT/g3j4BqbWzGdj21fYmtMkvE0EUx8EoP+er7MiMueg5wpaD5CIfAuA+N7/YXN4KP3KnbgN/mSIudL7KVbeCsCHJz3IX7Z/m3J1Aeni5w56rmS1yLvSn+OBAZtoNd9JS9F/jt+0PMhf4r9jXWgE6/k8AGP1j2jUS1nQPJmvD/rsIfeJn+y6n6i5nV83tvIv4zMAzORPDB+1F6VM/rzhwwCc6m1mbPB3GMrgzur72a2bMNEoXFD+eLtT+q1jRngNVjrP7zvfhKfDzIqUmJ98jIIqcVfHVFqr/WhMhPnMNMW6zHKW74jzfKae688fxt6C1+ugFyBmK248N0FT1OD7j3ZyzVgouDmW7qvyVJvfcnfZ0CKpgGJvyeauvX6Lm9YuynFRCgwU14zSgGZVWvFIi0IBbxhh0Zjw14i9bUMFXJf5tS4T4h5o+PV2q+fwWhkKggEUsGgADCplyDqa2/b6y0xOizlMjvv/a/+yJ0ApGGJw0uT0/iaqPc3iPS57yibqgAN2BYwLV5lcb+HV13DvDpd8yaWxlGFB3E9VT2UD7KqaWGguqSsRi4V4xqphZWdXu3FnBlWtooCE6XF6tAjAqqLNjqoN4SALJyQxlGL3vjzrtmZAaWYFi6RMl5xr8GQpDBoUGm9QP5RSKAXzO7cRqJRpc03WVEIoNBOsErWmQ9lTPBXtj65JMiiqGFLqxNy6k+WVCFWtUHS30Prvd4RVITF3Aq6neb5dY63fQqNXYpBZBg1r3RBlzyCqXEYZRZyRQ9iRaCBd0Zj72jCb9wEahSaFQ3/l98zb7oUwGmrof8o4lFJ86dEcy/c6zGiy+PJpMYKVMnrps3S2F9ijgzxjrmFNfA8LBr2ZxvBAAjt3M3qf31raqm1atQ0oRqoittJko3G2jvFbsQfYDnVPLaeqFRv0odfqHjJlKIH6BPmqZueqnaiONANVmRrl4GlYrf1jkRqq9K8P40wey4a0h1OsEN6ynXLF6W74ZYAq02D4ZwWec2NUxwwnmQgyNG5grVzPptYKOUyCeEwwC6AUO70g+zwbL5XAHeR3rVe5AjXbtjHa9PeNDW6YNBYWmmmWfwy7beJkmqt+I8u05g2E8zk6PZP1XuSAWvTp2hQzJvkNEft2dbJjUwsAU6w8UeWR1RYrHf9xI8wSNfMnobVmyT6NtXk7OL2PPSeaBZKGS0EbLEsNxqtJMSyuGJxtxdy2i8cqcVwUDUaV8bb/d7GiGqFDBXCHD+p5HnPzDgarEiMsf994vBKnrBU1hsM0u4A7YjArjBStJY3dmebM/E4CQ/pRGTEMx9N89Yl8z77zuVNj5CoejZZDuT3LN+/eS7sR5NSOrbxrz7MH1Xvh7RfjDh0IQOifD2I/v+6Q+4c2DJzRwyi98byuuskTfGhJTzDvDu86FNzfkp6MS0v6y8hWNEXXn+H8wV0uN650eWG4HBCBibUGk2r8SdfqXiR8H86k0n15jFxbG8U0X1lDqITto+iFgftoBG146Z1Nuy5Gvki2bTur259EFYqc3jGajtwuvtn/LxSMMj99/A0AXHn6X6iYLj9ceiH9inG+PP1+1iVb+cjmhcxhGnc3rWJjeC/zoguYVD+f9miFQsClLtSvZ1kscWQFgxaRWIhCrkS5fOT2GbGf1hpHg+NB0XEoOCUKTplEoETQDGCoBnblNVuym4gFdmGoHIMj09mS6UfJddheuJVoYDdlt8ja9llUXIOq1oyNz8YmRrqSYWflq2CswdL1pNSvcQyThshTrM76Aawj/yM8rwHbXMG06C18wfoU/5XuzxLn4JnwL/GaWRf/KG1mmvbcL3C9YVjmKupjn+Ivr3+S5XtaGBCr5RtP7D/o/c9TQ+zIKH6yvMCGjv0n/wJmM2Prv0DIiDDkuau5tdFvKZurr8SmTCD/Oh6MXQHAeYm/UE2toOTU83jzRw/5WV7W+UWw9rEnNJ1/WVcD8NFxBfJLrqNClN+mvgnAjMIDzC/cyaJdo/nAuLezJ3jw+/zktkcYW17CrkiObw74TxSa2blnuNBaTmjR+fx23Uh2Zl0GdGzlvZ3/wFAmj0ROY1l4GCbgqCqG0qhknHENmtcPLRF4di13tA1glxGnhgpXGOswDIslqpHnqMMIh3j33BrClmLb3gKL12UwDYVh+EsYGoYiaMIltSUwDdbHG9mQMzAUnB4pEFEeHa7BmqJNLB5iUKBKTdDAVYrnO/wJKg2F/1xdl4OiipClqLia1pK/PRmAsOUfiGQqGlOBbfhLH4I/qaZxhFtnahMhypkC5VCYLy0p9uw7Xz0lTKBUJJiI0J4pHdHXPNHIyc9D6+5R4z66nOyps7n8753cckkKHl9M8rT5pAvVniEsN679LCs6HueNQ67mdQPf3rcFP4DUbd+ojQfxHlqKbk+Tv+Q8Lr8jyy0Xxgn8/V7yqRpqz57FvnSJdz1YpeTC9DrFFyd6qFKJe3ZqnKrDdDPLgNH90BH/ONR+agXW1p1QKqOKZYxyGVUs+fNNANVxIyldei4Axu59RP/31pcsY+4Dl6O7usuH7ngIY1/bweG863evoQ6vf9eku67rnyWxXntBPVfVrOla43tlh2ZrRvPCv6qBUfjvU20sQ9FR1tQEVU9u+uaSPJ89JfqyeUnC9jFwvIdt4LB3nFfDcqpkcht5fsdTNOYijEs3sKyyjFsSDzE6XctHVs9jbbKFr0x/gMZilB8tfT1lw+HKBX8B4OdPvo1IvIGfDHsI7ACXRN5Afc0IdoQ7MOMp6iIDsAz7ZUohjqTucbdFZbK3oGmKKMLaPaHH3XaH2qrnB9uqByFz/1wCmzMeJRfCJgxP+F9gqzs89hY0SsGZA8yebUv35ih7VcquS8BIoLVJyioxPPYwFbfA0pahbMs34WmPM+r/xFsTl7OY/vx2c4WcU2Rc7EcMN6ss0B/gkx2DD1ne+viFzGs4k1HuR/jhthAAycgnCVgreNPet/HTsN+FNhr8OZGgP+FQa+Y2NNGDnisR/jJB+zEMp5a9hT8CMKdpE23lTxMouGys/IIqjaT085yf+wnv3TCLrw4/i8U1I3qeQ6EJeC4XtaxlbvFveErzu/q30WbVMqTcykd33cuAr36ZbZkXb53sFzX4yY8fx9QeyjSIzZvEpcP9z3Xtr+5hbaAWQ8GbMhuxDdgVjLM83A9TwbQpTaTGDaboaJ55djd2814Mw583wjAUpqGYaOQIWAbZSIytYydgKOgf9KhZuQbXMNhNCMNQxCx/RQRtGGSx0A21GNGQH0BzecxqFWUYKMsE00Ab/iWmAYZx3HcDPBEP3HsC05rNZMeO7glMsXUbMMeP6BWYXqtOxHo9FroDk3v/ErxvfILdBWiMutz028v4xIBrMc6aS3vWb+18pv0R9hS3Ma3mNPpHhvVtwQ8gdds3DpyN3Dn7VJrzmgFRhfXA41hnn9LzvdNR1jzX5hG3FTMb/OODDz9SYXfBD203nub3cNmV1z0r7nSzLINUKkLnvjRuzu9doBN+DweVzWGt2oDqCuaqVDrgdz+k5z/0jp4gH/nfWzF373vR91OZNZnyotMAMDdsJfKXu/wu76HgQQGdUIjyGXN6Ws2NXXvANHvuR8A+7v/XvVL56v4J11a1azZnNCOTiu+cYpOpaK54qMq7JgR5+8TIYTVQStg+Bo73sG2aBtGAgRUKYJgGnuvhlCrkKx6uexg7heuyYfcS9nZuZGplJHVpkz85t7EkuJI3bZ/Cwh1D+POw5/nrsFWc3TyS962fzVP1O/nBpEcZmanla8++jvY6gx+PeJgmo4krQ+/ESyZYG9pJqmYotckhPV1bRd/bf9C7iezYMVz+905uviRFfN16zPEjX/agV2vtB9oDgm13d53OsmZf0Q+9I+J+i1qm4v8Tq3owNmUwMKrwtOavWzyqnu4Vjh1P84ZhJgNjBq1FzU+er+C4Hq+rr3JqvIhyNdduTJB1NOND7VzauIlSY4JfrB7M5kzyoLJOthYzwb6HodX+/LL6ftpUkKbINgYnv0s1n2Zz7hqy3ikosnyo+C3esHsatyTG8Kv6qQc914hiM9kmvxU2W7yWUvV8wKM+fh4/f/xS7rvgKm7a4YfmVPQaRhb38b5V7+BD49/wgmdyAIeBqauYXTebKZWr+OUGyNs5hvNzGt2tzNszjdtTb8H2XCL2/QyfahEIx1m6ZxpWczvBYpmmUpCYozC0w+jyaoY6BQwVZtmEs1ETRjI0aTC53E75tofYEEjhmQYxw6OfqoBlkjcDuKaBXjQfyzQwDYW97HlUpYq2TP8ftGmgTdM/c26aWCMHY9UmKFU8vvREYX/r5LwIwYCBk8lTKlb8wGoa0jXuKDlRD9y7T/KVTZPdOU3/mCLontgn+Y6kE7Vej7ZeyzedcwoVpfjco1ewJ7OV/z33fnIlfdzvP1K3fefVrLPtas0/tno81+YxLK64smueoy8vq7KiTTMqqZhWp5hWbzChzqShLnZE6tbY04LKFVDFUk8YV6VyT0B3xg6nOm0CANaKtYTveOhFn0srRe4/ru4J1NEb/w8jm+91uw4FIex3aa/MmYIz3p/4ztjd4k8a16vre6jrvkH/f/xxLF/VdFZgYFSxos3j2bTJe6ZE+Oj9hzf09kQJ2zJB2lFimgaxpNWz3AmAaZmYsTDKLZNLOz2Bu+qWWLtvCenMLs50ZqM6M3zT/RF7VStfWft6GtsM/jb9XjYm2vjEyvkMaB1MdXQr7QNztFidwBCGOk1MzmfpHx9J+dQZDEvO4rrIQmpHjyC3aCgBw+ATvB/oXpEbRjHy2H4or3HZiibvgKdhQNT/ct2W9diW1ZRcKLtQcqEhbjC/oUx4xHC+9Ei2a2bXLJ+fN4pYOk88EeGPKzq4bY+No+FbgzuoV1Wey1l8ZW89Dr3PhJpo/h5+Fset8Ei1ll/p0QB8pfoQc6qwLVDDD6xpAJw96Dne9PwSRmRr+f2QNx7yfTSnv8s7Ji6kxp7FMx0KMNla+l/+N3QLv3j0jeybcBU5K0i6tJ41rV+FVujMfxN6zSMPhnbZZGxnj/0kczsGM6DaQbC2Hle1sC3vj7dy7bXYjomh8pRLWYzODGPc3dQ17KZilrFsxay6+TToBKmlq1hn9yPoWuR4joraTUDD6Pw0jESKuSmXmvoszcX1DNp+Oo25AoMGJbi5+Bglq4Q2q0SMIGEzhOrXD3eS3/ND5fLMKW7pCrZX+sF2rMkM0wQriDYvxevXAJbJuQNAjS2ApisEdwVa4/Se9z2/69KyDMwhQym/7y0MOOCfRHdHXbPr50DVWZNfch9LxIN4DzxOtCPDV954Ppf/vZOvnh4ncutdqNoEgbPmUtInx5lyceRVqy7tVRfLMhgcD5PPFslL8BAvo1p1SRcgftZc0vkq5UqFPYWdaMtgQ/s26u2Bve7/TPsjLG97mNObXt8zMaN47er+3gEodQWq0sv0ADWV4tLhZk/PLPCH1+Srfs/tDWnNhrTmz5s9wpbDdfNsZhw8Wumwef0aXvF9ncljyY4Z3rvV/IDWcxy3V8u1jkXxNP7tjovS2u/+XiwBaVRp/xKe1tYdBP+19EVf262rofCBy7qeWBP6+/3oUKBX6zrdQT0UxEsl/Jb0YyRqK6JdLzelzmDh2OiLTir9zSX+pNKtpdwxK9+RJmH7KIkGDAJmkLfdcSqut/9LwzQs/njh43xj+RXQmeMrG95AKdvKDaf462Se9UgbIdcmPbeddLhAW7WFJt3IuEwTURUl2DSYyrDJnJUYyZy4QcOZoyi+dRALmmqY3HVmpwKEgBGHLpo4BK39mRXLLgQMCFkKrf2JZMou1IZgZFfX5vt3uuwpasoOB4RkzVkDTOZ1LZPwsceqpCuamQ0GH5nk/5l951mHlR2aphD8bGoJLJOHdwf525beB7O3vzlBW9Hjqjt7z8757jsy3HhunAGGx9MbHqQ56K9T2nnPnQzJVinWJXCGvuXg94bHe8zrwPYomWdhlN6DUlX+at3J/OfHkUzUUz+5lnRlD0+1/Z0RqsCY1klcFF7Do01bKFsFwEFRBVVlD8vprExheAw+GtjGr83fYxmbACgmg/zXvsfYFG3l7033E3NT2LEaBob/gq3+SaIty0fzl2KaivWB7TxnbMZ2zmFQ3QA+ZLRSHZ9kvRmj6H6LUEeBUM4lYHkErBpiw6+lcFqc8abJr0wTHQz4E5f4FQhjzmWRdf4hz+hqIAn489DOg6HzAL8NO9T10+2F0ULHolRnTnr5naj7/tFDT9xzLGRLDsmzT8F5YAlBU/PjcxMETI2qTfR0yRPi5TiOR7rj+O01Jo4/BwYmQ5l8ZuKNNIQGYRkHH2auaH+cpa33kbBrJWyLI8ZQiu/Os2kr+b31nm31LzNVGBgzAYf2kuY/n6wytc7g3EEGI5NHsQVYKejuPp56+bsXrnzT/itVp6vFvNTTpd1trOu52atNUR0/8gUhvozqWiqNA1tcK1XsNRtf+rXf/Drc0f6qCoFHnsJau2l/KH9B93evJok7vGv4nedBpQrBwL/V5T1fqPDZeVG2Z9yDWrY/Oy9KvlB5iUcf/yRsHwVKKQIhf/yI6zm4+oAzdF1H8luLmzEsjWppI4rBuM4GokTID67DjPfnqkAddqSGpovGkKtt4OJouGdHLgP1XT+A33r2GtA95tfuWiItXdG0FP1W4dFJRdBUtJU0S/f6Y39nNiiGxg3KruYnq1zKrn/fkktXUNZcN91iaNxgU0bz6SV+PV09weR1g/2zpV9Z5uABCwcafGSS/znft9Njffrg0RdjrDw0RMA06az46wzmd7YSWvsMRr5ILD6DYLgBlelkxa030D6qjr01s2gIDybuKYburhDyHP6xfjyLhie4+ZIUX3ok29MV+EunxYlYipLbxqa6P5D07kPhQGoSbrQfg8MZamOXo6gSUIr/nnsblgH/WvN/3JL2d7yQ/RBRazEBbFJqCKVFp1EfsHlr/XJWdi4hYISpr59BYe7pfMAyGZpbRkVVCZhhAlbcvwx+kKHJ8YQtxVlnjWZc8T0EjTABI0jglDhDlcFQYCHvPGQ9evQsmMPwA7Z3f5WOpmupk5qDH/uiHRGVOqZnZY9XB7YwuaUKg+N+l7zgWXNlzK0Q4ph5qfHYs+vPJhGoZXrtaceuQOI1oy6kWDjQZOFAv7V7e0ExusYkk4Zn2zz2FuHenR5T6wxGJqHqaf662WNqnWJ00p9/pM/Zlj/WO37wHDAAztgROGMP0aTmeftbzbspRemc+b26u/e0rnf9rsP7Jzk2OjOYrR0vWjRn+GCKXWFbdWSI/fzm/V3ee8am75/NvTJvRs/7MPa0+GXrmfHdXzM9bCq89Zu5aeEQPvRgYf+Y7YURQus3Y4waQvFVfIzHCwnbR4FhKHSxhHqRPxKAz0z4GpGdVQpvGQo1Ka5NfqBnxsISvUPIiTao3vF0T5fokguWgqaI/+W1usNjT8FfpqZ7wqunWzyWtfgB+X3jTKK2YnWHx/+scSm5vbtYBwy4+Rz/RMa/mj3+d53/hXL9fJvBMdhb1Pxirb8tFTQZGvdn9128+9BdIfNd50FC5v4v14oLOA5GvsjIqI02TRpDCpXNE1j6LAvKtYxzLIKVEkY1i3azaKNAbN9WnIHvxKpr4L3jdvDY6l9SdJr5QajEf2w4g69xF78avYz7B27khu4zJfl/gg0jY3P5uH4jOhYhNaWGd9y5gFMGnM3nTv00V/zT5XOnBvjD2q+yZPeD/P6CxSwcMA9LBQkYQeKzL6AQ6k/SyXN15zACRoigGeqZuXjemMuZ4V3qh2Ej1KulobudcxHjWDTgrT3bu7+mz+G9L1vf/cNDX/Y+4th5NV3yhBDiSHu67V883/EElw75AMnA/la5CalZTEjNeolHCnFkGEoxKmn0BOhTmwySAcUzrR6Ta/1t6zo1t2xyuWUTXDnW5JJh/rFpS1HTED4OgvfhMIyeydx6BGyqsw9er/3FlOfPojp57EHd3+kK6F7T/q70quRPfNi7y3tvldn7e68EH3oCa+uuXrfrgE114iiCb15E8KnnuWnhZL65rMxnZwUJPv081ilTTvheeRK2jwLP06gDzhKNSI7ji6f8lK8u+SDbMn5XjtlDz6EtWaSv56dLVzSFqt/Ke2BAHhxTDOwaV/yXzS6FqmZIXPUE5P9d57AhrUkG4DPT/BbF329wuG2Lh/OCtzSzQfH5Gf59btvi8lSLJhHYH7Y3ZzR37/DD8GUjTaK2PxnXluzBn03Z9Vu4lVLEbagL+TNaazSgqAkqTm0yCJnQ1PUlaRuKK8cYhDyHsFMhEA0RClkETRi5eyvBpTsYni/y10IVu5CG1VnCXd8X75xYoHlmfzLVdnbnzmT0Uys4J5jnczPvJWuXeeHw19mFRdTVNdAYSbMl8hgAJVIU33geXjRCqhSiX6ZMIlBLIlBL3E6RsGsZGBlJZfIp/nt0Svz+gsUA5MoVbjw3TtTSfGT6l/nI9C9TcctcPvwTB302YSvKnPqzD7k9fIgZsoUQQoij5e5df2Bbfh1jEtM5tfF1vW5bm17OsraHmJQ6hWm181/kGYQ4skKWYmbD/hnNwT+GnN/P4Lk2v3UbYF9Rc/XiKv0jMLXOYFq9weRaRcQ6wcL3q6Brk7i1B09oeyjewCayn35/rxncObBbe7GEju7PQzoawUslulrW/f6MqlKFZ9bg1KewZk9G3/UwX3/TOVRuvR/r7LkUqt4J3ytPwvZRoLWmUqoQjIUxDYt3j/8PWgph3jX+M3zzqQ8DUClVXjZoe9oPwGUXwhYETYXj+dPnF10oO36rbxUPtbPA6fWauAW785o/bHQpuprzB5vMbDBwteZDi/11Chf0N3jfeL/qv7rMYfMhQu27x+yfeOJvW1wKDsxr2h+2t2b9ctQfMNhVQa+gbSoImn5rdLeRCQPH83otyzCxVvG2kQYhUxHp2iOHxxVfmGERsvwvwqCpCJn+790WNsHZ8QqqUsWL+X2O+wc9Pt/2JCpfRK0qogpFnEKWRW4HWdPvhDLw3HfgDBrO4r3/4NG2e8jqvWRSJdINJfJ2lUW7RnHVhllow+D2xBK2bfeXeRgxYhxD507DjCgy+vau96yIWUnidg2JQC1ufcp/jcgIrhx5HXG7hlSgDifqd/c5l/dwLu95yXov5jwCkSrOQ08SO28+DWSJBeNU73kM66w5FAon9peOEEKIk9+k1Fy25dexsnPpQWF7decyFu/9BxW3JGFb9KlRSYNPTfWPk7sPV7dkNaaC3QXYXfC4e4eHqWBsSvHlWVbPcEYBWBY6ZqFjL9+oU7r4nP1XPA9KFVSpTMBSxAc1UP31bej1WykvXQGui9PaQeSqS6na5gkduCVsHyX5iodyy/zxwsdpL3q8/R+d/O71U7h60kP84MkMuaKH1poPT/Sr4MFdLn/f6lFyNV+bbdMYVqxs13xpmd/989opJqf3N6l69GzrzWHc/ADxmN9K/egev6V4ep2ffk2l6Cj7YTh3QG+McFeYDZt+MA6ZipAFqcD++5w3yMDVMDyx/8vljcNNFg3SRA84y/f6oSbnDjL957E45JfR20YdPMfyhBqDCTWGP8lVuYJqK5IMBZjR4E80Ze7Y7a+DWCj4IbpQQBWKGKUqBbPCiv6dtCyaQqbaQbbSQaHwDOlAkc+tOZOAZ/F401Z+Mn4JAKOy9fyn8zYAtubW8pS1ElK9y9g+tpHsRVdBMMCkHRYDKnuJ2zU0xoZQXjgGU2u+WPgVCbuGmJ3EUAcvnxS3U5zaeP4h6unl9Yy7PcOfzuvDD1zI7y9YjHHGbBl3K4QQ4oQwqWYud+z6P1Z3PoWrHUy1/5BzWu1pVL0yUyVoi+OEecAEX3MbDf5voc3Kds2zrR7PtnnsLkDB2X9se/s2l7Udmmn1ijMHGBLAD5dhQCSEjoSIxoM4Dy7FW78VY8wwrLe9DuePd+Ot34rzwBLiZ83tGR53IpKwfZQoBeWqSb7i8eVH/eWbvvpYlv+cFyMeUPxkeYFtaY8PT/TvX3Bge053/e53iT6wFbfSNeQ4aMKQmOpq7e0KyrYiGbGJ2f7j60OK944zCZn+usndvjXXwjYhYe//QvjabAv1MjMIvnvswbvJlLqDJ2WLBxTxF250XVShhCoU8RrreiZ5CzyxHKOtE5X3gzP5AoVKmoxZIB0o0TplEO0jUgyKjGJie5DAM6v49uSH2VmTJm2X+dxzZzK21EB7qMyPRj0IWx7c/5pd46H3nTeT2vhAgsYGrL1P++F4yBiccf7SV7PqzmJgZITfKm3XkLBrSdg1RKw4dK07fsmQg8csK6UYFD26y6Z1j7tNWJrfX7CYslMmkz2xZ2MUQgjx2jE8Np6IGafgZtmaW8vI+P5VHUbEJzAiPqEPSyfES4tYijmNijmN/vHg3oImXdnfffOJPR5rOjUr2vxJdAE2pD06yzCpVhF+DXQ5P1K6V1IBsM4+hULVI3LVpTgPLDkpVlKRsH2URONhdmQ9rrm3s9fyTVfekebGcxPcuCjBLc9lesYfz2k0GBJTBE3o1zXWeGRCcfPZNgHTn+QB/Msfze896/ILF3WPBxQXDT24tfVQSxy8XNA+iNZQqaLyRTAUOuUvXKjaOgkse/6A1uci5PNk3SxZu8zgQpLsx69iq7uFp9v+RSH9DP3TNpdu9882fHfSYpbXN/d+ra2wsN+bGN//HZTnz6Q1sJhWXQBg34WzGdB0FrZVYcTarX5YDtR2heYa4nYNwdQ0XDPCWD2YHw87+6D3OiE1mwmp2Yf3/o+xQtYhWBOkkJUJroQQQpw4DGUyITWbZW0PsrJjaa+wDV29y1ofYEh0DHMbFvVRKYV4ZZoiqmeyX/AnU3umVeOhe1rF79jm8fBuD9uA35xlE7YUZVdjG/uP48XBDlxJpbsHZ9U2e10/kUnYPkry2SKNoSB/uDjJlx/N9Szf9JXTYlhoKvkiFw/bH4gbw4rGF8x6aBqq11J5R43n+a3P+QJeTbJnCSVrxVqs7c2oQrEnQKt8EeW6bIl10DayhrZZw5laO594qczetY/xf6OXk46VSAdKZO0KWvknGn6z7EpUuUxzZSv3NN8MSRidHM7rJp+FjoQJFfdArpmIGSPe1cKcCNQwODoKr7GOSmMd78h+DoUiYdf6M5saASKEuG7yT17y7RnqtbE0mhBCCHE8mV2/kIgVY1xy5kG3bcis4L7df2Jico6EbXHCGZMyGJPqva0+DI1hvwdpd8v2rZtd7t7hdU20pphaZ1AfkuD9QgeupHKo6ycyCdtHieN4OLkiwaDFl0+L8fZ/pPnKaTHMaoVS+Si3UmoNVcfvou26ePW1/vZSmeDip7pCc6EnPFfLOaqGR9QJUHjnJSyLbqK5sJVC6wqy7m7S8RLp2jLnNo/ivMwYAL4x9SFydgU2wXXhm4jVDKc8fRwr7bt6FaV7ArGWD76BhJ1gcH4kZ/d/Cwk7RWNoME7dOADe5nyGdxifwzYCvJgXnhUXQgghxPFreu3pTK89/ZC3TaqZy+7iVibXzDvGpRLi6HjnaIt3jNIUDjjMX9OhyVbh0T0ej+4BcBkcVVw8zOCcQQf3QhUnHwnbR5Ftm0QDJkU0Pz43ga004YCJ5+nD7xLheahiCZUvoqNhdLRr8rBN27DXb6FSqRDszBLM+wHaccu0hQp0NATY97oZZKrtZEqt0PEU79g8DYD7Bmzg5nHPUbQcTtk3hI9sPRuqLv/acxtr0ssgjP/TZdeM/uQufgc6GmbA+rUUnRxxuwbLsNCRMMn553JVe+SAMdD+BGIHTooyODqat0VHH/T2wpYsTSWEEEKcjKpehYKT7bXedv/wUN498jN9WCohjjylFNEDRnt+aZbF+rQ/0dpzbZqNac2OvKbSFQO01vxwhcvwhGJWg8HgmLR6n2wkbB8ltm2SjNg4DywheO5chgZstK7i3buU5NmnkC5AteCHZ1Us4/Xfv0h84F9LMNLZA7puF6BQpGBV6QyUaJk/htTkedQF+8G+Vn5V+A3pQIl0vxKfXHk6NU6Y1TX7+NbUh/0nXP+3nudWgxWXDv8QKhrFU3GK6acB6BxVR/6SKwGY1DyH2kAjiYA/aVj3WtCNoUHooD9G+1MTf3TQew6ZEU5pOPcofaJCCCGEONE80XI3v9/8Q6bWnMr7x3yp1227i9t4qvUBkoF6zmi6uI9KKMTRYxmKCTWKCTUGbx8N2Yrm+XbNmJQfqpsL8Mgej0f2QGdZ855xfjR7usVjeEJRG5TwfaKTsH2UxEMWzgNLcB9YSuj803nHnQv4/QWLKT2wFIDErImUv/cLFAqtFBs++jq25zeQqXYwZOcGZu3wu37/aMJjrB/VStou4xpdU5JX7+QtbRUWDXgbeuggltBMkRIAzW88BTs1CVvtIrjuya5W5tT+cdB2DaVB47GNIFOrTXzNOYO4XUPY3N+yvGjA247thyWEEEKIk1J9cAAVr8Tq9DI87fZaLnNbbh3/3PkbhkTHSNgWrwnxgOLUfvsDdNyG940zebbNY2aDP8dQ0dF86xkHR8NFQwzeO96Pa57WMtHaCUjC9lHSPY293rUPAEf7Azi+e+oyOkJP0/FoM+YpLjc8dSk6GubpvQ/wjz3/B8Dc0bOYNOYidCRMZ3kF7ZUdPc8bNmMk7BpsIwiAN6CJS82rScSiWNUodbHJaCvOIB3nhrl3v2QZo3aCqJ04Gm9fCCGEEIIR8QlEzBhVr8Le0k76h4f23DYhOYtZdQuZmJrdszqLEK8liYDiwqEmFx6wilB7GYbGFZszmkFd3cq11lzzSJX+EcXUeoNpdYohMSV/MycACdtHSThqomybwPveREd+H57n0FrcwxfeeAsArcU9fOi+S8h86r0Yhkm/tocYW5hOwq5hZGIK1X5TAXhz/hN42utpoe4O2Qc6Z+Cbey39Ba9iSS8hhBBCiCPMVBafmng9TeFBBx3DJAK1fOAFXcuFeK0bGFV8b55NuqLpXq57R16zpwh7ippn2vwB3zVBmFZn8I7RJnUyw/lxS2mt9cvf7fjkuh7t7fm+LsYhNTTEecedC3C0g+c5ONrBUhaGYWEpi99fsJgNu7aStGv/7WD8wnW2xclD6vbkJXV78pK6PTlJvR4dbeW9LGt9EEOZLBrw1j4pg9TtyetkqVutNTvz8Eyrx7NtHqvaNRUPDAW/XWgTsRTrOz2W7POYVmcwvkZhGyd3AO/Luq2tjWK+wvWZpWX7KKm4ZX5/wWIAOgutvP+BC/jF2XeSitT33J46YFZOIYQQQoiT1bPtj/F8xxNcMuS9JOyanu17itu5dftPqQk0cE7/t0jPPCEOQSnF4BgMjplcPMyk4mrWdmp25jWRrubvJ/Z63LbV429bPK6fbzM4BpmKJl2BQVHp9dpXXlkkF4etkHXRlSrVex4jFanHMCxSkXqq9zyGrlQpZE+OhdqFEEIIIV7O7Tt/zSP7bmd151O9to+OT2FqzXzOHXAZrpZjIyFeiYCpmFJncMGQ/WO9J9UanDnAYFRCMahr3uPH9nh89LEq719cZVv2xG3ZP5FJy/ZRUq26pAsQP2M2AFbXWtPGGbNJF6qHv862EEIIIcQJalJqLjvyG1jZsaTXMqEBM8g1477RhyUT4uQws8HomdG8W7qisQ1Il6Ep7LdsL2/xuHmjy7R6xbQ6gzGpk7/LeV+SsH0UVasu7VWXhKn5/QWLKVfLZLKVvi6WEEIIIcQxNSk1h7t2/Y5V6acOWgIsW+3k6bZ/UXCyXDDoXX1YSiFOLpeNsrh0uGZbVhPq6m7+TKvHxoxmY0bzl80eIRMm1ypOaTJYONB8mWcUh0vC9jGQ6awAErKFEEII8do0Ij6RsBkj72TYmlvLiPjEnts6Kvv4w5YfEjTCnDvgMizD7sOSCnFyCZqKMan9LdeXjjAZkVA826Z5ts0jU4GnWjRhS7NwoH+fe3e4xG3F5DpFzJZW73+HhG0hhBBCCHFUmcpifHImy9sfZmXn0l5he1BkFFNr5jM8Nh5HV7GQsC3E0VIbVJw10OSsgeBpzdas5tlWzYiEH6pdrfntBpdcFcalFN+c6/89thY1NUEwpcv5YZGwLYQQQgghjrrZ9QsJmWFGJ6b22m4oQ8ZtC9EHDKUYkVCMSOzfVnHhrAEGz7T6y4h1++YzDnuKmtP6GXxookTIV0o+KSGEEEIIcdTNrDuTmXVnHvK2klvgmfbFtJSauXjwVce2YEKIHmFLcdU4PyK6WgNQcjStJU3BgeoBk5r/zxoHV8O0OoNJtYqodDk/iIRtIYQQQghxzDieQ9HNEbdTPduKTp5fb/wmCoOz+72ZqJ148ScQQhwTZtfa3CFL8auzbLZk/NnNAVxP81CzR8GBu3d4GArGJP0Zzs8cYNAUkeANss62EEIIIYQ4Rpa03Mu1yy7mj1tv6LW9JtjArLqzeN3Ay3GR5VGFON6YSjEqaTA0vj8+fnyyxYVDDAZEwNOwtlNzyyaXvUW/RTxf1dy7w2Vf1/XXImnZFkIIIYQQx0RdsImSm2dV58FLgH1gzJf7rmBCiMNiGorZjYrZjX743lfUPNvq8Xy7ZnyN36q9ol1z02oXcLl2isnp/U08rSm7fnf1F7Isg2g8TD5bxHG8g24/EUnLthBCCCGEOCa6lwAru0X2lnb2uq3qVVjetpi/bvs5Wr92W8KEOBE1hhXnDjb55FQLu2vG8oDhz2huKBhf48fOrVnNux6s8rknq6xs7x2orVCQHVkPKxQ85uU/WqRlWwghhBBCHBOmsrh2wg/pFx5C0Az1us3VDr/Y8BVc7XBa4wU0hgf1USmFEEfCzAaDmQ0GRUf3tGSv7dS4GlZ3aLpXEctUNE91mJwTVVxzb5o/XJwkGLQol50+LP2RIWFbCCGEEEIcM0NjYw65PWRGmFu/iJAZ7tW9XAhxYjuwy/gFQ0ym1xs81+YxJqmwLINQIMCZSYOvPpYjV9V8+dEcXz4tRsi2cUrlE7pLuYRtIYQQQghxTD3fsYSVnUu4aNCVvWYlv3LUdX1XKCHEMdE/ougf8U+oReNh2rMe19ydJlf1h48s3+vw9n+k+fG5CQbHw6Q78n1Z3H+LjNkWQgghhBDH1N+2/5yH9vyN1Z1P9druaZdVnU/x120/x9UnfhdSIcRLy2eLNIbgDxcnmdHktwPPaLK4+eIkjSH/9hOZhG0hhBBCCHFMTUrNBWBl59KDbvvF+q9wd/Pv2Zpbe6yLJYQ4xhzHo5QrYlUrfPm0GDFb8ZXTYpjVCqXciT8ruYRtIYQQQghxTE3sCturOp/E0/sPpg1lckrDIuY3XEDQCPdV8YQQx1i57GCj+fG5CSz0STE5GsiYbSGEEEIIcYyNjE8iZEbJOWm25dYxPD6+57bLhn+sD0smhOgrTqnM4K51tk8W0rIthBBCCCGOKcuwGJ+cCRzclVxrzabsSv6x49cUnFxfFE8I0QccxyPdkT/hu44fSFq2hRBCCCHEMTez7kxsI8CI+IRe25VS/HrjN9lX2smQ6Gim1Z7WRyUUQoh/j4RtIYQQQghxzM2pP5s59Wcf8rZZdWexr7STmJU8xqUSQogjR8K2EEIIIYToM552KboFola8Z9sbhryvD0skhBBHhozZFkIIIYQQfeLJ1vv55LI3cMuWHx102878Ju5tvoWO8r4+KJkQQvz7pGVbCCGEEEL0iaRdR97JsLrzKTztYaj97UC/3/JDNmWfJ2xGOb3p9X1YSiGEeHUkbAshhBBCiD4xMj6ZkBkh72bZW9pB//DQntum1cwnbEZJBRr6sIRCCPHqSdgWQgghhBB9wjIsPj7+e/QPDyNsRXvddt7Ayzlv4OV9VDIhhPj3yZhtIYQQQgjRZ0bEJx4UtLu1lJp5eM/f2VPcfoxLJYQQ/z4J20IIIYQQok+tST/Nn7f+mFw13Wv7n7f+mN9v+QFPtz3cRyUTQohXT7qRCyGEEEKIPvXHLTfQXNzC0NhY5tSf07N9cs088k6W+mC/PiydEEK8OhK2hRBCCCFEn5pUM5fm4hZWdiztFbZPb7qI05su6sOSCSHEqyfdyIUQQgghRJ+alJoLwKrOJ/G01+u2dKWNJ1ruYXt+fV8UTQghXjUJ20IIIYQQok+Nik8maITJOp0Hhep/7Pg1v974DZ7Yd08flU4IIV4dCdtCCCGEEKJPWYbN+ORMAFZ2Lu1128TUHIZEx1Af6t8XRRNCiFdNxmwLIYQQQog+N7PuTAxlMiQ6ptf2GXULmFG3oI9KJYQQr56EbSGEEEII0efmNixibsOiQ95WcHKsSy8nEahlZHzSMS6ZEEK8OtKNXAghhBBCHDc87VF08r223dt8Czet/wIP7f5rH5VKCCEOn4RtIYQQQghxXHiq9UE+8/SbuGXrj3ptn5CaTVNoCI3hQX1UMiGEOHzSjVwIIYQQQhwX4naKTLWdlV1LgBnKbxcaHZ/C16b/to9LJ4QQh0datoUQQgghxHGhewmwXDXN3tKOnu1KKSpumdWdT7Eu/UwfllAIIV45adkWQgghhBDHBcuw+ej4bzMgPJyoneh126P7/sktW69nfHImY5PT+6iEQgjxyknYFkIIIYQQx43RiamH3D4+OZNUoIHG0MBjXCIhhHh1JGwLIYQQQojjyobMczzfsZTzBlzW08LdLzyUb8/4M0qpPi6dEEK8MhK2hRBCCCHEceV3m3/A7uJWBkdHMbt+IeCP23a1w9bsWspukQmp2X1cSiGEeGkyQZoQQgghhDiuTErNBWBl59Je25e3LebbK6/hL9tu6otiCSHEYZGwLYQQQgghjiuTa04BYFXXEmDdxiVnELWSNIUH43hOXxVPCCFeEelGLoQQQgghjivdS4Blqu3syG9kaGwM4K/D/f1Zt/Wsvy2EEMcz+aYSQgghhBDHFcuwGZecCcCqF3QlVyh25jfxfMeSviiaEEK8YtKyLYQQQgghjjsz6k4HNAMiI3ptX5d5hh+s/gRJu47vzLxVZicXQhy3JGwLIYQQQojjzryG1zGv4XUHbR8Zn0jMSjI4OoqimyNixfugdEII8fIkbAshhBBCiOOW1pqyVyRkRgCwjSDfm/U3DGX2ccmEEOKlyZhtIYQQQghxXFrW9hDXLX8Lt2z5Ua/thjJpKTWzouOJPiqZEEK8PAnbQgghhBDiuBQx43RUWlj5giXAdhe28rlnLudn679E1av0YQmFEOLFSdgWQgghhBDHpdGJKT1LgO0tbu/Z3i88lNpAE8OiY8lUO/qwhEII8eJkzLYQQgghhDgu2UaAa8Z9gwHhYSQCtT3blVL81/Q/YBlyKCuEOH5Jy7YQQgghhDhujUvO6BW0u1mGRabSzoqOx/ugVEII8fIkbAshhBBCiOPa5uxq/rHj1xScbM+2bLWTTz/9Rn689j/JVjv7rnBCCPEipO+NEEIIIYQ4rv1m07fYXdzGwMhwZtadCUDcTjEwMhKlFOlKG3E71adlFEKIF5KwLYQQQgghjmsTU3PYXdzGyo6lPWEb4LOTb8I2An1XMCGEeAnSjVwIIYQQQhzXJqXmArCycyla657tthGg6ORZ0fFEr+1CCHE8kLAthBBCCCGOa6MTUwkYIdLVNnYWNvZsd7wqn3n6Tdy49jr2lLa/xDMIIcSxJ2FbCCGEEEIc12wjwLjkdMBv3e5mGTbD4xNoDA0iU2nvq+IJIcQhHfaY7U2bNvFf//VfPPPMM0SjUS655BI+/vGPEwi89HiZjo4OfvjDH7J48WI6OzsZNGgQ73jHO7j88stfdeGFEEIIIcRrw7Sa03G1S1NoSK/t14z9BkEz1EelEkKIF3dYYTudTnPFFVcwbNgwbrjhBvbu3cu3vvUtSqUSX/ziF1/ysR/72MfYvHkz1157Lf3792fx4sV8+ctfxjRN3vrWt/5bb0IIIYQQQpzcTmu6kNOaLjxoe9AMUfXKbMg8z5jENCxD5v8VQhwfDuvb6JZbbiGfz3PjjTeSSqUAcF2Xr3zlK1x99dU0NTUd8nEtLS0sXbqUb37zm7zxjW8EYN68eTz//PPccccdEraFEEIIIcQrorWm6lUImMGe61945l20V/by6YnXMzoxtY9LKIQQvsMas7148WLmzZvXE7QBzj//fDzP47HHHnvRxzmOA0A8Hu+1PRaLycyRQgghhBDiFVnetpjPPfN2bt7y3z3blFKMjE8kZdeTqXb2WdmEEOKFDqtle/PmzbzpTW/qtS2RSNDQ0MDmzZtf9HH9+/fntNNO46c//SnDhw+nX79+LF68mMcee4zvfe97r67kXSxL5ngzTaPXpTh5SN2evKRuT15StycnqdfjQyQQobXcjJOuYJoKpRQAV475D0JmuOf64ZC6PXlJ3Z68TpS6PaywnclkSCQSB21PJpOk0+mXfOwNN9zAJz7xCS680B9rY5omn//85znvvPMOpwi9GIaipib6qh9/skkkwn1dBHGUSN2evKRuT15Stycnqde+NTdxKsE1ITorrRTsFgbFhwNQQxTXc9jYuZohiZGErcM/PpS6PXlJ3Z68jve6PSYzSGit+exnP8vWrVv5/ve/T0NDA48//jjf+MY3SCaTPQH8cHmeJpMpHOHSnnhM0yCRCJPJFHFdr6+LI44gqduTl9TtyUvq9uQk9Xr8uGb8fzEwMoKo00BHR75n+9ef/SCbsqv48PivMav+zFf8fFK3Jy+p25NXX9ZtIhF+xS3qhxW2E4kE2Wz2oO3pdJpkMvmij/vXv/7F3XffzT/+8Q/Gjh0LwNy5c2lra+Nb3/rWqw7bAI4jfzjdXNeTz+MkJXV78pK6PXlJ3Z6cpF773rj4bODgY8Ch0XHsLmwjXe58VXUkdXvykro9eR3vdXtYndxHjBhx0NjsbDZLS0sLI0aMeNHHbdy4EdM0GTNmTK/t48ePZ9++fRSLxcMphhBCCCGEeA3bnl/PnTt/S8HJ9Wy7ZPB7+cHsf3BG08V9WDIhhNjvsML2ggULePzxx8lkMj3b7r77bgzDYP78+S/6uIEDB+K6LuvWreu1fdWqVdTV1REOH9997YUQQgghxPHjF+u/ym07/oc16ad7toWtKAqDXYXN5KovPZeQEEIcC4cVti+77DKi0SjXXHMNjz76KLfeeivf+c53uOyyy3qtsX3FFVewaNGinusLFixgwIABfPSjH+Xvf/87TzzxBN/97nf529/+xjvf+c4j926EEEIIIcRJb1JqLgCrOpf22v7T9V/gK8/9f/buMzyqql/D+L1nJr0nJCT0XkMnBAQ7qCiKCghiAWyAgAKKYjtWECsiKiBdURBfK4ooIoqIItgggNI7hCSkkT7lfIgMRFoCCTuZPL9zeb3Jml2e2Svh5D9r7bUH8VvK9yakEhEpqkTFdkhICHPnzsVqtTJs2DBeeeUVevfuzdixY4ts53Q6cTgc7u8DAwOZM2cOzZo14+WXX2bo0KH88MMPjB07lsGDB5fOOxERERGRSiE2rLDYTkj7FZfL5W6vFdAIL4sPmfY0k5KJiBxjuI7/F6qCcTicHD6cdeYNPZzNZiEsLIDU1KxyvUCAlJz61nOpbz2X+tYzqV/LlwJnHiPXXEuBM4//azmLGgH1AcixZ2Gz2PCy+BT7WOpbz6W+9Vxm9m14eECxVyMv308BFxERERH5Dy+LD42D2wCQcNxUcj9bAF4WH5JzD5CWn2JWPBERQMW2iIiIiFRArcM70zi4DVV8oou0v7/9NR79ox8rExeZlExEpFCJnrMtIiIiIlIeXFT1Oi46yWO+agTUw4KVjIJUE1KJiByjYltEREREKjS7swCbxQuADlW6EhdxOX62AJNTiUhlp2nkIiIiIlIh/XH4R/7vz9t4f8dEd5uv1R8/WwAZBamk5h0yMZ2IVHYqtkVERESkQvIyvDmYs/uER4B9ufddHlx7PV/te9/EdCJS2anYFhEREZEKqVFwa7wMb9LykziYs8vdXs2/DgDpBVqRXETMo3u2RURERKRC8rb6MLjx01Tzq0sV3xh3e/OQDrzc/lOCvcJMTCcilZ2KbRERERGpsFqGXXBCm7fVB2+rDzn2LLIdR4jwqWpCMhGp7DSNXEREREQqtP3ZO/hm/wJy7Fnuth8Tv2DUmmv5aNcUE5OJSGWmkW0RERERqdDe+ucxDuXuI9KnGm0iLgIK79t24iAlL9HkdCJSWWlkW0REREQqtNjQeAAS0la72+oENmF8mwU80kIj2yJiDhXbIiIiIlKhNT+u2D76CDCrYaOKbwwFznyNbouIKVRsi4iIiEiF1ji4DV6GN6n5SezP2eFu//PwT4xc04PZW8ebmE5EKisV2yIiIiJSoXlbfWgU0hqAhNRjU8lj/GpT4MwjJe8gDpfdpHQiUllpgTQRERERqfBahV1AriOHUO8q7rYo3+o81WouMX61MQzDxHQiUhmp2BYRERGRCu+S6Bu4JPqGIm2GYRSuSu5ykJqXQrhPlEnpRKQy0jRyEREREfEoTpfD/fW2zARGr+nJpE1jTEwkIpWRim0RERER8Qh/Hf6JZ/66g/d3vOZui/atRY7jCOn5yWTbM80LJyKVjqaRi4iIiIhHsBhW9mZvI8ueicvlwjAMAryCeaLlTGL8a2M19KeviJw/GtkWEREREY/QKLg1NsOb1PxDHMzZ5W6vEVAfC1bS8pNNTCcilY0+3hMRERERj+Bj9eWuhk9Qzb8OVX1rutsTc/bwyoaROHDwUruPsRgabxKRsqdiW0REREQ8RtuIi05oi/CJJseRhdPlIDnvAFG+1U1IJiKVjYptEREREfEoiTl7SUj7hc5RV+Nr9cdm8eKh2MlE+9XCy+JjdjwRqSRUbIuIiIiIR5m0aQzJefuJ8ImmdXgXAGoGNAQgsyCNIK9QE9OJSGWhG1ZERERExKPEhsYDkJC22t2WWZDGE3/cysO/9SHfkWdWNBGpRFRsi4iIiIhHiQ37t9hOXY3L5QIg0BZCnjMXp8vB3uxtZsYTkUpC08hFRERExKM0Dm6DzfDmcH4iB3N2EeNfB8MwGN5kPFV8quFvCzQ7oohUAhrZFhERERGP4mP1pXFwa6DoVPJaAY3wtwWSZc80KZmIVCYqtkVERETE47QI60idgCYEHrcYWoEzn+fW3c3oNdeSkX/YvHAiUiloGrmIiIiIeJxLo2/kspheRdq8LN64/v2/7Uc20Tq8s0npRKQyULEtIiIiIh7HMAz3106XE4tROKFzYP2HCfGKINg73KxoIlJJaBq5iIiIiHikdak/M27dPSzYMcndVjOgIcHe4eQ6st0rlYuIlAUV2yIiIiLioVzsyvqH9Wm/uAtrl8vFpI1jGLmmB/tzdpicT0Q8mYptEREREfFIhY8A8yIl7yAHc3cDx6aXO10OtmUmmBlPRDyc7tkWEREREY/kY/VjYINHqOZXh2jfWu72XrWH0N86ikjfaiamExFPp2JbRERERDxWhyqXn9BWI6A+UPgoMMOlP4dFpGxoGrmIiIiIeLSUvIP8kPg5eY4cd9vsrc8zck0PNqevMzGZiHgyfZQnIiIiIh7tlQ0jSc47QJh3FVqGXQAU3rNd4MxjS/pfXMCFJicUEU+kkW0RERER8WjNQzsAkJC62t3Wvfot/F/LWVxba6BJqUTE06nYFhERERGPFhsaD0BC2mr3I8Cq+delRkB9XDixOwvMjCciHkrFtoiIiIh4tCYhbbEZXiTnHSAxd4+7/aNdU7n/l2v55cB3JqYTEU+lYltEREREPJqP1Y+GwS2BwtHto5wuB1n2TBKSfzMrmoh4MC2QJiIiIiIeLza0I5kF6fhbA91tF1XtSVzUpbSt2Y6M9DwT04mIJ1KxLSIiIiIer2tMH7pVu6lIW1W/GthsFiyGFbvTjiZ9ikhp0r8oIiIiIuLxDMNwf310kTSAJXvnc9fSq/jh4GdmxBIRD6ZiW0REREQqhYTU1byQMIwPdk52tzlcDg7nJrExTfdti0jp0jRyEREREakU7C472zITSM9PoW+dERiGQXxkV2KjWxFjaQSuMx9DRKS4NLItIiIiIpVCk5A27keAHcrdC0AV32haR3XE2+qD0+UwOaGIeBKNbIuIiIhIpeBr9ee2+mOo5leHSN/q7vbvdi/iw39m0i78EnrWutPEhCLiSTSyLSIiIiKVRqfIK6kd2BiLcezPYCcODubsZlO67tsWkdKjkW0RERERqVTS8pPZkPYr7SMuw2bzp33VixjedBwNAlubHU1EPIiKbRERERGpNFwuFy8kDCMl7yDBXuG0ibyAUJ9w2la5CLvdidPlLDLqLSJytvQviYiIiIhUGoZh0Dy0AwDrU39xt/+Z8hPPrbub+TteMymZiHgaFdsiIiIiUqnEhsYDkJC2Gpfr2PO+dmdtZkPaGrNiiYiH0TRyEREREalUmoS0xWrYSM7bT2LOHsJpSpPQNtzR4DGahrQzO56IeAiNbIuIiIhIpeJr9adhUEsA1qeudrd1jLyCEO+IIqPdIiJnSyPbIiIiIlLpxIbFk16Qgo/V1922JWMdn+6eQYh3BPc0etLEdCLiCVRsi4iIiEil0zXmJq6o1g+b7dhET6thY0vmX/hZA3G47FgN/aksImdP/4KIiIiISKVz/OO9jk4brxPYmFvrPUCTkHYqtEXknOlfERERERGplDakreHr/e/TKKIZ11cfjMWwclHV64DCAtwwDJMTikhFpgXSRERERKRSynfm8nf67/x68Ht3277s7by+6WFe//sh84KJiEfQyLaIiIiIVEpN/50ufjB7L4k5e4jwqo6PxY+EtF+wGFZy7Fn42QLMjikiFZSKbRERERGplHyt/tze4EEaVW1MJNVwOqCKbwy31B1N/aBYfK3+ZkcUkQpMxbaIiIiIVFoXRl9DWFgAqalZOHECcHF0T5NTiYgn0D3bIiIiIlKppeel8vOhb8h35AGQkneQtzc/xQsJw0xOJiIVmYptEREREam0XC4XY1bcyvR/nuWfjD8A8LMG8HvKCrZlJpCSl2hyQhGpqFRsi4iIiEilZRgGraM6ArAhbTUA/rYgbqk3iodj3yTUO8LMeCJSganYFhEREZFKrW1UZwAS/i22AS6sei31g2KxGlriSETOjoptEREREanUWlbpgNWwcih3H4dy9gKQVZDBnK0TeOavO3G6HCYnFJGKSMW2iIiIiFRq/l6BNAxuCRwb3fax+vP74R/Ym72V3VlbzIwnIhWU5sWIiIiISKXXIiye5NxE97Rxm8VG3zojCPWOpJpfXZPTiUhFpGJbRERERCq9K2r0pVvMzUXaOkddbVIaEfEEmkYuIiIiIpXeyRZCK3DmM3/Hazz91yDyHDkmpBKRikzFtoiIiIgIsCn9NyZtHMPHu6YBYDO8WJf6M/uyt7M54y+T04lIRaNp5CIiIiIiQI49iw3pv5Kcd4Abaw/GMAyur3kXXhYfGgS1MDueiFQwKrZFRERERICmIe2wGFYSc/dwKHcfUb7ViY/sZnYsEamgVGyLiIiIiAB+tgBurnM/0X61CfeuCoDL5eKzPTPZkPYr9zYeR5hPpMkpRaSi0D3bIiIiIiL/uji6J41DWmOzFI5JGYbBhrRf2ZX1D5vSfzM5nYhUJBrZFhERERE5TrY9k43pa2kZegHeVh+uqt6ffGcesaHxZkcTkQpExbaIiIiIyL9cLhfPrruLlLyD3N/0JZqHdqBdxCVmxxKRCkjTyEVERERE/mUYBk1D2gGQkLra3f7tgQ95ecP97M3aZlY0EalgVGyLiIiIiByn+b/TxRPSjhXbG9PWsjnjTzamrzErlohUMJpGLiIiIiJynOMfAZaUu59I32pcEn09LcM60SKsk9nxRKSC0Mi2iIiIiMhx/G2B1A+MBWDDv6PbLcM6cUn09UT4VDUzmohUICq2RURERET+o0VYR8K8o4q0/Zy0hMmbxvJP+h8mpRKRikTTyEVERERE/qNrTB+urHYzhmG42/5J/4P1aT8T41+bxiFtTEwnIhWBim0RERERkf+wWbxOaOsYeSUxfnVoqfu2RaQYVGyLiIiIiJzE3+m/8+2BD6nhX5/ra91Fk5C2NAlpa3YsEakgdM+2iIiIiMhJZNkzWJe6it9SfnC3/Xn4J97e/BR/HP7RxGQiUhGo2BYREREROYmmIe2wYCUxdzfJuQcA2Jq5jrUpy/nz8EqT04lIeadp5CIiIiIiJ+FvC6JPnXuJ8atNiHcEAO0jLsHb4qP7tkXkjFRsi4iIiIicwuUxvYt8XyewKXUCm5qURkQqEk0jFxERERE5jVxHNn8eXkmBMx+Af9L/ZO7WF1h1aInJyUSkPFOxLSIiIiJyCi6Xi6f/GsRb/zzGlox1AOzK+pufkhazNuU7k9OJSHmmYltERERE5BQMw6BxcBsANqStBqBFWCe6xdzEFdX6mRlNRMo5FdsiIiIiIqcRGxYPQMK/xXaMX2361BmmZ26LyGmp2BYREREROY1mIe2xYOVAzi5S8g4CsOvIZhbseJ3vDnxscjoRKa9UbIuIiIiInIa/LYh6Qc0ASEgtHN3en7Od7w5+xM9JWiRNRE5Oj/4SERERETmD2NB4DuXuw+GyA9A0JI6Lql5H85A4k5OJSHmlYltERERE5Ay6VbuJq6rfgsUonBga6h3BrfUeMDmViJRnKrZFRERERM7Ay+JzQltizh5WJS3B1+pP9+q3mJBKRMoz3bMtIiIiIlIMWzL+Yuo//8eiPXMAOJS7j6/2zeOHg5/hcrnMDSci5Y6KbRERERGRYkjPT+H3wz+wNmU5AI2CWxFfpRvX1bwDF06T04lIeVPiYnvbtm0MGjSI1q1b07lzZ1588UXy8/OLtW9iYiIPP/wwHTt2pGXLlnTv3p3PP/+8xKFFRERERM63pqFxGFg4kLOTlLxEfKx+3NnwcS6IugqLYTU7noiUMyW6Zzs9PZ0BAwZQp04dJk+eTGJiIhMmTCA3N5f/+7//O+2+hw4dom/fvtStW5dnn32WwMBAtmzZUuxCXURERETETAG2IHrVHky0Xy2CvUIBSM1L4uekJRQ48+lZ605zA4pIuVKiYnvBggVkZWXxxhtvEBoaCoDD4eDpp59m8ODBVK1a9ZT7vvTSS0RHRzNjxgys1sJP/jp16nT2yUVEREREzrMrqvUr8n1GwWE+3TMDX6s/19QYgM2i9YdFpFCJppGvWLGCTp06uQttgO7du+N0Ovnpp59Oud+RI0f46quv6N+/v7vQFhERERGpiPIdeaxP/QW7s4CaAQ3pUKUrN9S6B6fLYXY0ESlHSvTR2/bt2+nVq1eRtuDgYCIjI9m+ffsp99uwYQMFBQXYbDZuvfVW/vjjD0JDQ7n++usZOXIkXl5eZ5cesNm0xpvVainyv+I51LeeS33rudS3nkn96rlK2rcul4un/hhAcu4BxrR4jaah7RjS9MmyjChnSb+3nqui9G2Jiu2MjAyCg4NPaA8JCSE9Pf2U+yUnJwPw+OOPc9NNNzF8+HDWrVvH66+/jsVi4YEHHihh7EIWi0FYWMBZ7euJgoP9zI4gZUR967nUt55LfeuZ1K+eqyR92yKyPcv3LGJz9m9cUPciMvPTWbH3K5JyDjCw+agyTClnQ7+3nqu89+15uanE6Sx8FMIFF1zA2LFjAejYsSNZWVnMmjWLYcOG4evrexbHdZGRkV2qWSsiq9VCcLAfGRk5OBx67IQnUd96LvWt51Lfeib1q+c6m75tHNCO5Sxi7YGV9Kx2D4fzUpiR8CIGFrpF3Yy/LaiMU0tx6PfWc5nZt8HBfsUeUS9RsR0cHExmZuYJ7enp6YSEhJx2PygssI/XqVMnpk6dyq5du2jcuHFJorjZ7frFOcrhcOp6eCj1redS33ou9a1nUr96rpL0bePAdhhY2Je9g8SsA0T4VOWCyKuI9qtNgd2JXc/cLlf0e+u5ynvflmiSe7169U64NzszM5OkpCTq1at3yv0aNGhw2uPm5eWVJIaIiIiIiGkCvIKpF9QMgIS01QAMbPAIV1XvT4BGtUXkXyUqti+66CJWrVpFRkaGu23JkiVYLBY6d+58yv2qV69Oo0aNWLVqVZH2VatW4evre8ZiXERERESkPIkNjSfAFky+IxeAPEcOKw99yYIdk0xOJiLlRYmK7X79+hEQEMCwYcNYuXIlH330ES+++CL9+vUr8oztAQMG0K1btyL7jho1iu+++45x48bx008/MXXqVGbNmsXAgQPx9/cvnXcjIiIiInIedI25iVfaf0q3ajcB4MLFvO2v8N3Bj0nK3W9yOhEpD0p0z3ZISAhz587l2WefZdiwYQQEBNC7d29GjSq66qLT6cThKPqcwcsuu4xXX32Vt956i/nz5xMVFcWIESO45557zv1diIiIiIicRz7Woov7+lr9uSjqWgJswXhZvE1KJSLlieFyuVxmhzhbDoeTw4ezzI5hOpvNQlhYAKmpWeV6gQApOfWt51Lfei71rWdSv3quc+nbrRnr+T7xU2L86nBNjdvKKKGcLf3eei4z+zY8PKDYq5GX76eAi4iIiIiUU4fzD/Fr8resSV4GgN1pZ23yd7y3/VUcLrvJ6UTEbCq2RURERETOQvOQOAws7M/ZweG8Q1gMg3nbX+GHxM/YdeQfs+OJiMlKdM+2iIiIiIgUCvAK5vpad1LVtxYBtmAshpUuUT1w4iDAFmJ2PBExmYptEREREZGz1L36rUW+711nqElJRKS80TRyEREREZFzUODMZ1P6b9idBbhcLtal/swHO98g15FtdjQRMZGKbRERERGRs+RyuXjyzwFM3DiarZnrMQyDD3ZMZtmBD/kn/Q+z44mIiVRsi4iIiIicJcMwaBAUC0BC2moAOkVewYVRPQj1jjQzmoiYTMW2iIiIiMg5iA2LByAhtbDY7lFzILfVH0PtwEZmxhIRk6nYFhERERE5B83+8wgwgC0Z6/h09wxS//1eRCofFdsiIiIiIucg0CuEuoFNANjw71Tyj3ZNZfG+d9mQtsbMaCJiIj36S0RERETkHMWGxrM/Zxc5jiwA2kVcQhXfGCJ9q5ucTETMomJbREREROQcda12E1dVvxWbpfDP627VbjI5kYiYTdPIRURERETOka/V311oH7Unaytf7XuPAzm7TEolImbSyLaIiIiISCnYnrmBHxI/J8avNldV78/ne2bxV+pPAMRUr21yOhE531Rsi4iIiIiUgqTc/fyctIQa/vW5qnp/Wod3ASDGT4W2SGWkYltEREREpBQ0C43DwGBv9jZS85LoHHU1naOuNjuWiJhExbaIiIiISCkI8gqlR42BVPWrib8tEIBDOXtJSFtNvaDm1Pn38WAiUjmo2BYRERERKSXX1hxY5Psl+99n5aEv6RrTR8W2SCWj1chFREREREqRw2VnS8Zf2J12WoR2pElwW2r41zc7loicZxrZFhEREREpJS6Xiyf/HMCh3L080GwSbSIuok3ERWbHEhETaGRbRERERKSUGIZB3cCmAGxIWw1AWn4yPx1azJaMv8yMJiLnmYptEREREZFSFBsaD0BC2q8ALD/4CXO3vcCKxEVmxhKR80zFtoiIiIhIKTr2CLCtpOUn0zy0A3UCm1IzoKHZ0UTkPNI92yIiIiIipSjIK5TagU3YeWQTCWmr6RJ1DY+2mGp2LBE5zzSyLSIiIiJSymJD4/G2+HKkIB2ArIIM1qYsZ0PaGpOTicj5opFtEREREZFS1jWmD92r34KXxRuAn5O/ZuHON2gWEkfz0DiT04nI+aBiW0RERESklPnbAot83ywkjmp+dakd2MikRCJyvqnYFhEREREpAzuPbOLHxC+I9qtFt2p9ear1HLMjich5pHu2RURERETKwMGcPfx46At+SfoGgDxHDutTf+Gvwz+ZnExEzgeNbIuIiIiIlIHmoR0wMNjz7yPA/kn/g5lbn6OmfwNahXc2O56IlDEV2yIiIiIiZSDIK5Srqt9CtF9NfK3+NA1tT4RPNHUCm+J0ObAYVrMjikgZUrEtIiIiIlJGbqh1t/trX6s/z7f9wMQ0InI+6Z5tEREREZEy5HQ52J65EYfLjt1pZ0vGX/yessLsWCJSxjSyLSIiIiJSRlwuF0/+OZDE3N2MaT6ZAmc+r216gFCvKrQJvxDDMMyOKCJlRCPbIiIiIiJlxDAMagU0ACAhbTUNg1sQ5h1Fo+BW5DlzTE4nImVJxbaIiIiISBmKDYsHCottL4sPE9ou5K5G/4ev1d/kZCJSllRsi4iIiIiUoeahhcX2nqwtpOen4MLFriObWZuy3ORkIlKWdM+2iIiIiEgZCvYKo3ZAY3Zl/cOGtF+pGdCQcevvxsfiR+uwLtgsXmZHFJEyoJFtEREREZEyFhsaj83wJi0/mer+9YjyrU7jkNYcsWeYHU1EyohGtkVEREREyljXajdxVfVb8LH6AvBs6/e0ErmIh9PItoiIiIhIGQuwBbkL7aMSc/bwa/IykxKJSFnTyLaIiIiIyHmw68hmfjq0mKp+NWgXcSlP/HkrBgbNQtoT6BVidjwRKWUa2RYREREROQ/252zn+8RP+Dnpa0K9I6gV0IhGwa3JtKeZHU1EyoBGtkVEREREzoPmIR0A2J21mfT8FB5tMQ2LobEvEU+lYltERERE5DwI9g6nW0xfov1q4m3xxWJYSM1LYkvmOuIiLtOCaSIeRsW2iIiIiMh50qfOve6v8x15PPbHzdhdBdQOaExVvxomJhOR0qZ5KyIiIiIi55HL5WJP1hasFisNgltSJ6AJWfZ0s2OJSCnTyLaIiIiIyHnicrl4+q9B7M/ZwcOxb3J/0xexGvqTXMQTaWRbREREROQ8MQyDav51AUhIXY3VsJFlz+S3lO+xO+0mpxOR0qRiW0RERETkPIoNjQcgIW01LpeL//vzNqZtfpKdRzaZnExESpOKbRERERGR8yg2tPARYLuy/iGzIJXGwa2J8atNjiPL5GQiUpp0g4iIiIiIyHkU7B1OrYBG7M7azIb0X7mjwWPYLF5mxxKRUqaRbRERERGR8yw2NB6LYSUlLxGbxYs8Ry4JqavJdWSbHU1ESolGtkVEREREzrOuMX24strN+NkCAHgh4V72Zm9jaOPnaBN+ocnpRKQ0qNgWERERETnPAr1CinzfMLglWfZM8hw5JiUSkdKmYltERERExAR7s7bxc9ISIn2r0bv2UPrVuR/DMMyOJSKlRPdsi4iIiIiYYHfWZpYeWMhPh77Cy+KDw+VgS8Y6MgpSzY4mIqVAxbaIiIiIiAmaH/cIsIyCVN74eywvbRjBH4d/NDmZiJQGTSMXERERETFBiHcEl0X3oqpfTWyGjQZBsezK2kyBM8/saCJSClRsi4iIiIiYpF/d+9xfd6vWj6tr3I7F0ORTEU+g32QRERERERO5XC72Z+/Ey+IFwJ6sLaTmHTI5lYicKxXbIiIiIiImcblcPLfuLp76awA7j/zN3G0v8Oy6u1iVtMTsaCJyjlRsi4iIiIiYxDAMovxqAJCQtpp6gc3wsfiR78w1OZmInCvdsy0iIiIiYqLY0Hh+S/me9am/MKb5ZDpHXY3t3ynlIlJxaWRbRERERMREzUPjgcJHgOU6s7EaNhJz9pKce8DkZCJyLlRsi4iIiIiYKNQ7gpr+DQDYkPYrn+yezhN/3sK3BxaanExEzoWKbRERERERk8WGxWNgISl3H3UCm2A1bOQ6csyOJSLnQPdsi4iIiIiY7PKYPlwR048Ar2AKnHlMjFuEr9Xf7Fgicg5UbIuIiIiImCzYK8z9tZfFBy8gLT+ZfEeue7VyEalYVGyLiIiIiJQD+7N38EvSUiJ9Y8h35vHBzsnERVzG3Y2eNDuaiJwF3bMtIiIiIlIObD+ykSX73+PHQ19SK6ARBgbZ9iNmxxKRs6SRbRERERGRciD26CPAjvxNpG81Xmn/GYFeISanEpGzpWJbRERERKQcCPWuwsVVryPKtyZeFh8CbEFk2TM5UpBGVb+aZscTkRJSsS0iIiIiUk7cUu8B99drkr9jxpZnaRzcmtHNJ5qYSkTOhu7ZFhEREREpZw7l7KW6fz1cODliz8DpcpodSURKSCPbIiIiIiLlhMvlYvz6wezK+oexzd/ihbYfEuYTZXYsETkLGtkWERERESknDMMgwicagA3pawjziSLPkUtizh6Tk4lISanYFhEREREpR46uSp6Qtpq/039n1Jprmbr5/0xOJSIlpWJbRERERKQcaR7aAYCdRzYR4lUFh6uAbPsRch3ZJicTkZLQPdsiIiIiIuVImE8kNfzrszd7G7uz/mFcm/lE+ERjGIbZ0USkBDSyLSIiIiJSzhwd3T6Ys5sqvjE4XA4Sc/aanEpESkIj2yIiIiIi5UzXmD50q9aXYK8w9mVv54WE4XhbfHip3cca4RapIDSyLSIiIiJSzoR4RxDsFQZAlG8NnC4HDped1Pwkk5OJSHFpZFtEREREpBw6kLOLNcnLiPCJ5omWM4n0jcFiWM2OJSLFpJFtEREREZFyaGvGer7YO5cfExdR1a8GYJCce8DsWCJSTCq2RURERETKodh/F0nbcWQT+7K3M+a3G3nyrwEUOPNMTiYixaFp5CIiIiIi5VCYTxSdI6+mql9NQrwisGLFwOBgzm5qBjQ0O56InIGKbRERERGRcmpAg4fdX49uPpEqPjHYLF4mJhKR4tI0chERERGRci4lL5Eo3xrYLF4czjtkdhwRKQYV2yIiIiIi5ZTL5WLC+qE88vtNbMtI4NHfb2bs733IKEg1O5qInIGKbRERERGRcsowDIK9wgH4J/MPfK1+WA0be7O2mpxMRM5E92yLiIiIiJRjsWHx/Jm6koTU1dzd6EnCvCPxtfqbHUtEzkAj2yIiIiIi5VhsaEeg8BFgwbYwfK3+pOen4HK5TE4mIqejYltEREREpBwL94miml9dXDjZkPYr49cNZsxvN3IwZ5fZ0UTkNFRsi4iIiIiUc7Fh8QDsy9mOny0QA4NdWZtNTiUip6N7tkVEREREyrmuMX24PLo3YT6RHMzZTYAtmCCvULNjichpqNgWERERESnnQr2ruL+O9qsFQLY9E2+LHzaL/qQXKY/0mykiIiIiUgEk5uxlbcpywn2i+D1lBetSVzG62UQah7Q2O5qInITu2RYRERERqQD+yfidz/bMYEXi5/hYfXHhZFfWP2bHEpFT0Mi2iIiIiEgF0Dy0cJG07Zkbuan2CG6odQ8RPlVNTiUip6JiW0RERESkAojwqUp8lW5U9atJpG8MgV4h5DvyMAwDL4u32fFE5D9UbIuIiIiIVBB3Nnzc/fW87a+w6tAS7mz4GO0iLjEvlIiclO7ZFhERERGpYNLzU7AZ3thd+WzP3GB2HBE5CY1si4iIiIhUEC6Xi5c33MeWzHUMa/w8F1e9lmi/2mbHEpGTKPHI9rZt2xg0aBCtW7emc+fOvPjii+Tn55foGHPmzKFx48YMHjy4pKcXEREREam0DMPA3xYMwN7sbcT418GJg3xHnsnJROS/SlRsp6enM2DAAAoKCpg8eTKjRo1i4cKFTJgwodjHSEpK4s033yQiIqLEYUVEREREKrvYsMJVyRPSVvP5nlmMWnMdq5IWm5xKRP6rRNPIFyxYQFZWFm+88QahoaEAOBwOnn76aQYPHkzVqmd+9MBLL73EZZddxv79+88qsIiIiIhIZRbrfgTYBhoHtybXkcXWjPVcEn2DyclE5HglGtlesWIFnTp1chfaAN27d8fpdPLTTz+dcf+1a9fy7bff8sADD5Q4qIiIiIiIFD4CLMavDi6chHpX4dEWU7mj4WNmxxKR/yhRsb19+3bq1atXpC04OJjIyEi2b99+2n0dDgfPPvssQ4YMISoqquRJRUREREQEODa6fTgvkTqBTTGwUODUfdsi5UmJppFnZGQQHBx8QntISAjp6emn3ff9998nJyeHgQMHlijgmdhsenqZ1Wop8r/iOdS3nkt967nUt55J/eq5KmrfXlmzD91q9KKKbwzL9n/Mot1zuTTmenrWHmR2tHKjovatnFlF6dvz8uivlJQUXn/9dV544QW8vb1L7bgWi0FYWECpHa+iCw72MzuClBH1redS33ou9a1nUr96rorWt2Ecm20akhFIRsFhtmWv09/GJ1HR+laKr7z3bYmK7eDgYDIzM09oT09PJyQk5JT7TZo0icaNG9O+fXsyMjIAsNvt2O12MjIy8Pf3x2Yred3vdLrIyMgu8X6exmq1EBzsR0ZGDg6H0+w4UorUt55Lfeu51LeeSf3quSpy3ybl7ue35B/wsfjxUIvXqR/cnNTULLNjlRsVuW/l9Mzs2+Bgv2KPqJeowq1Xr94J92ZnZmaSlJR0wr3cx9uxYwdr1qwhLi7uhNfi4uKYPn06F110UUmiuNnt+sU5yuFw6np4KPWt51Lfei71rWdSv3quiti365JXs3DHWzQIasFDsW+AE3LsuXhZSm8mqSeoiH0rxVPe+7ZExfZFF13E1KlTi9y7vWTJEiwWC507dz7lfo8++qh7RPuo8ePH4+vry+jRo2ncuPFZRBcRERERqbxiw+JhB2zP3MjKxMUs3vcusaEd6F9vlNnRRIQSFtv9+vXj3XffZdiwYQwePJjExERefPFF+vXrV+QZ2wMGDGD//v0sXboUgKZNm55wrODgYPz9/YmPjz/HtyAiIiIiUvlE+ETTPuJSqvrWxGpYSc7bz98Zv5sdS0T+VaJiOyQkhLlz5/Lss88ybNgwAgIC6N27N6NGFf30zOl04nA4SjWoiIiIiIgUdU+jpwDIdWTjbxtP45A25gYSETfD5XK5zA5xthwOJ4cPaxEIm81CWFgAqalZ5fqeBSk59a3nUt96LvWtZ1K/ei5P6dvMgjQCbSEYhoHDZcdqnJeHDpVrntK3ciIz+zY8PKBsFkgTEREREZHyw+VyMXHjaP7J+IOb645kReLnVPWryeBGT5sdTaTSU7EtIiIiIlJBGYaBt9UXFy72Zm9jb/Y2UvOTcLqcWIzijb6JSNlQsS0iIiIiUoHFhsazLnUV+7N3cFfD/6NpSDsV2iLlgH4LRUREREQqsNjQwqf7bM/cSGxoB4K8QnG6tFixiNk0si0iIiIiUoFV8Y0h2q8WB3N280Pi56xLXYWX4c3o5hPNjiZSqanYFhERERGp4GJD4zmYs5tDufvYlpmA1bCR58jBx+pndjSRSkvFtoiIiIhIBXdZdC8urtqTKN8aNA5uTcPgliq0RUymYltEREREpIKr4hvj/rpj5BVA4WPBDMMwK5JIpadiW0RERETEA6TkJfLn4R8Bg3Wpq8iyZ/B4y+lmxxKptFRsi4iIiIh4gPWpq/hg52TqB8WyPXMjLpyk5h0izCfK7GgilZKKbRERERERDxAb2hGAHZmbuLXuaOoHtyDUO9LkVCKVl4ptEREREREPUMU3htZhXajqV5PWERcS5BWKy+UyO5ZIpaViW0RERETEQ9zbZBwAGfmHmfLP4+zL3sEzrd/FYlhMTiZS+ei3TkRERETE41jYkLqGQ7l72ZO11ewwIpWSRrZFRERERDyEy+Xi9U0PsSn9N3rWuosmwW2oGVDf7FgilZKKbRERERERD2EYBhbDihMHBlA3qKnZkUQqLU0jFxERERHxILFh8QCsS13FrC3jeOT3vuQ5ck1OJVL5qNgWEREREfEgsaGFxfb2Ixv5O/13UvIOsjVzncmpRCofTSMXEREREfEgkb7ViPKtwaHcvcRV6UqLsHjqB8WaHUuk0tHItoiIiIiIhzk6ug1OmoS0xcvibWoekcpII9siIiIiIh7m8pjeXFi1B9G+tXl/+2tsTP+VB5tPJtQ7wuxoIpWGRrZFRERERDxMpG81qvvXw2qxsuPIRg7l7mNT+lqzY4lUKhrZFhERERHxQKl5SaxLXUXTkPZcU2MATULamB1JpFLRyLaIiIiIiAf64/CPvLfjVbYf2UDr8M74Wv3NjiRSqajYFhERERHxQEeft701cz0f7ZrGM3/dyb7s7SanEqk8NI1cRERERMQDRflWJza0I1X9arLzyCb2Zm9lY9oaqvvXMzuaSKWgYltERERExEPd1/QFADakreHCqj1oGtLe5EQilYeKbRERERERD1c/qDk+Fj8MwzA7ikiloWJbRERERMRDuVwu3vznETakraFbTF+2ZK7j+pp30lgrk4uUOS2QJiIiIiLioQzDwOFy4HDZ2Zi+hm2Z69mQ9qvZsUQqBRXbIiIiIiIeLDa0cFVyFy5uq/cgl0TfYHIikcpBxbaIiIiIiAeLDe0IwP7sHcRVuZxwnyiTE4lUDiq2RUREREQ8WFW/GkT6VMfhsvPZ7llM2jiG31NWmB1LxOOp2BYRERER8XCxYR0A2JL5FxvSf2Vd6iqTE4l4Pq1GLiIiIiLi4S6L7s0FkVfjcjnYkrme2LB4syOJeDwV2yIiIiIiHq6qXw3313WCmpqYRKTyULEtIiIiIlIJpOensD7tFw7nJrI/ZyexYfF0ibrG7FgiHkvFtoiIiIhIJbA2ZTkf7JxMpE81kvL2Y3flq9gWKUNaIE1EREREpBI4+rztw/mJXF39NnrUGGhuIBEPp5FtEREREZFKIMq3Bk2C2xLtV4vLom8k2Dvc7EgiHk3FtoiIiIhIJWAYBqObTwRgS8Y6Pto9lRr+DehW7SaTk4l4Jk0jFxERERGpZA5k7+TnpK9Zk/Kd2VFEPJZGtkVEREREKgmXy8XUzf/H+tRf6BJ5DR0iu5odScRjaWRbRERERKSSMAyDPEcOdlc+0f61aRLS1uxIIh5LI9siIiIiIpVIbFg8G9PX8FvKcpJz9xPkFca1NQeaHUvE42hkW0RERESkEmkR2hGAXVmb+T7xU1YlfYXL5TI5lYjn0ci2iIiIiEglEuVbgyo+1UjO209sSEcuir4WFy4MDLOjiXgUjWyLiIiIiFQihmEQGxoPQFW/GrQO74LFUFkgUto0si0iIiIiUslcFnMjHSO7EWAL4aNdU3HhonftoWbHEvEo+ghLRERERKSSifarRb2g5uQ4Mvl6/3xWJC7C4bKbHUvEo2hkW0RERESkEsosSONA9i5iQ+OJr9KtcJE03bYtUmo0si0iIiIiUgmtTl7K7G3P43DZiY/shs3iZXYkEY+iYltEREREpBI6ukja5oy/+HT3DN7fPtHkRCKeRdPIRUREREQqoaq+NWkQ1IJQ7yos3vcuFqxcX+tu/G2BZkcT8QgqtkVEREREKiHDMHgo9g0AAreHUiOgnh4BJlKKVGyLiIiIiFRyN9UZhtWwYRhaIU2ktKjYFhERERGppFwuF9O3PMP61J+5JPp6jhSkM6DBw2bHEvEImiciIiIiIlJJGYZBlj2DPGcO3+xfwE9Ji0nOPWB2LBGPoGJbRERERKQSO7oqeZh3FH1q34uP1c/kRCKeQcW2iIiIiEglFhtWWGxnFBzmoqrXEeQVam4gEQ+hYltEREREpBKL9q1FhE80dlcBH+2ayqwt43C6HGbHEqnwVGyLiIiIiFRihmG4p5L/eOhLfkn+ht1Zm01OJVLxaTVyEREREZFK7tLoG2gfcRkb09bgZfEiyCvc7EgiFZ6KbRERERGRSq6af10AGoe0NjeIiAdRsS0iIiIiImTZM9mQ9it7s7aSUZBKv7r34Wv1NzuWSIWlYltERERERPjp0GL+t+stvAxvClz5tI24mJZhncyOJVJhaYE0ERERERFxL5LmcDnoGt2HSN9qJicSqdg0si0iIiIiIsT41aZOYFOifWtyRfWbCfWOMDuSSIWmYltERERERDAMg0dbTAVgXeoqFu/9le7VbyHMJ9LkZCIVk6aRi4iIiIhIEYv2zOX7xE/YlL7W7CgiFZZGtkVEREREBACXy8WsrePYn7ODDhFdqeZfx+xIIhWWRrZFRERERAQonEqeXnCYAmcedYOaUiewqdmRRCosFdsiIiIiIuJ2dFXy1clL+XjXNA5k7zQ3kEgFpWJbRERERETcYkM7ALDryD8s2f8+f6WuMjmRSMWkYltERERERNxi/OoQ5h2FCxdNgttSO7Cx2ZFEKiQV2yIiIiIi4mYYhnsqeTX/ujQNaWdyIpGKSauRi4iIiIhIEZdG30C7iIvxswbw5d53aBYSR90gLZYmUhIqtkVEREREpIgaAfWB+szZOoFVSV+RbT+iYlukhCpFse10OnE47GbHKDNOp0FurpX8/DwcDpfZcaQUqW/LntVqw2LRHTUiIiL/levIJtgrnAZBLagX1NzsOCIVjkcX2y6Xi4yMw+TkHDE7SplLTrbgdDrNjiFlQH1b9vz8AgkODscwDLOjiIiIlBs/HPyMJfvfo3loB9pFXGx2HJEKx6OL7aOFdmBgGN7ePh79h7TVamjk00Opb8uOy+UiPz+PI0dSAQgJiTA5kYiISPnRIqwjH+2eyj/pf7Bk33zqBDamSUhbs2OJVBgeW2w7nQ53oR0YGGx2nDJns1mw2zX66YnUt2XL29sHgCNHUgkKCtOUchERkX/F+NWhpn8DCpz5fLx7Kp0ir1SxLVICHltsOxwO4Ngf0iIip3L03wmHw47F4m1yGhERkfLBMAwebzmDLZnrWLz3XRoEtTQ7kkiF4rHF9lGePHVcREqH/p0QERE5OcMwaBTcigZNW2AxNPtLpCQ8vtgWEREREZGz43K5mLvtBf5K/YmuMTdRM6ABLcM6mR1LpELQx1MiIiIiInJShmGQnHeALHsGn+2ZwfKDH5sdSaTC0Mh2OdelS/szbvPoo09y3XU9z+r4w4ffg7+/Py+++NpZ7V+eFRQU8NJL41m16kfS0tK4777RZGZmsmDBPJYu/bHUz/f772tJSFjH7bffUaQ9NTWVuXNnsmHDerZu3YzNZjvl+fPy8nj33dl8/fVikpOTCAsLp1u3Kxg69P5iZfjgg/eYPHki11xzHY888n9n9T4OHNjP4sWL6NnzRqpUiSzx/rm5ucyf/y7Lln3DgQP78fHxpVq16sTFxTN48LCzynQqv/++lvvuG8KMGe/QpEmzUj22iIiIFIoNjWdzxp/4WwNpHNzG7DgiFYaK7RIwDAOLxcDpdOFynZ9HMU2dOrvI90OGDKJ377507XqVu6169RpnffwHHhiL1eqZExyWLPmSr79ezGOPPUW1ajWIiYnh008/KrPz/fHHbyxYMO+EYjs5+RDLln1D06bNadKkKVu3bjnp/k6nk0ceeYD9+/cxaNDdxMRU4+DBA+zdu7vYGb75ZgkAP/ywnAceGIu3d8kX+zpwYD+zZ0+nc+cLz6rYfvzxh9i4cQO33TaIRo0ak5mZwaZNG/nxxx9KvdgWERGRshcbGs/Hu6dR4MznsuheZscRqTBUbBeD1WohwNuCt683rpxcDD8/8nPzycp34nCU7SOZYmNbnNAWFRV90vaj8vJy8fHxLdbx69atd9bZyrtdu3YSEVGFK67obmqO+vUbsmjRNwDMnDntlMX24sWfs3FjAvPm/Y8qVaq424v76K/du3fxzz+baN++A2vX/srPP6/k4osvK503UUx79+7hl19W8dhjT9G9ew93+yWXXK5CW0REpIKq7l+PUO9I0vKTWLR3NnUCm9Iu4mKzY4mUeyq2z8BqtRAa6I1j2WryVv4GOXng54P1wnaEXhZP2pH8Mi+4T2fmzGksWDCPN96YxquvvsSWLf9w111D6d//NqZMmczPP6/kwIH9BAQE0qpVG0aMGF2kkPvvNPKjx5s6dTYvv/w8mzf/TbVq1Rk+fBTx8adfDGP+/HksW/YNe/bswsvLm2bNmjN8+Chq1apdZLuEhHXMnDmNDRsScLlc1KlTl3vuGUpcXEcA8vPzmTNnBkuXfk1y8iFCQ8No374Djz32VLGvS+/e13Lw4AHg2FT8Dz/8/KTbHjx4gDfemMiaNatxOBy0bNmaYcNGUr9+A/c2X331BZ9//gk7d+7A5XLRoEFD7r33Ppo1i3Vft9mzpxc5X+vWbXnjjbeL/dzmzz//lEsv7Vqkf0pi6dIlGIbBQw89xpAhd/DNN1+dUGx36dKee++9n/79b3O3LVz4Pq+//iorV651T8sGuOuu293brFy5FjjztcrMzAA46Xv473U4Uz8nJKzj3Xdn8/ffm8jKOkKNGrXo1+8WrrrqmtNeB5fLxfz58/j8809ITDxAlSpR9O59E3373lKcyygiIiL/YRgGsaEdWHnoS77eP59aAY1UbIsUQ+Urtl0uKLAXe/OAYF8cy1bjWLrqWGNOHo5vCr8PuKAtGTkFxT+/lw1K+TFDBQUFPPnko/Tp05/Bg4cRHBwCQGrqYW67bRBVqkSSlpbKggXvMXz4PcybtxCb7dRdb7fbeeaZx+ndux8DB97Fe+/N5fHHH+J//1tESEjoKfdLSkqkV6+bqFo1muzsLD799COGDr2D+fM/dmdat+5P7r9/KM2bt+Dhhx8nKCiIv//eSGLiQfdxHn/8IX77bQ233TaI5s1bkJaWyg8/LC/RNRk//iXee28uf/75O+PGvQxARMSJBWB2dhYjRgzGMAwefPARvL19eOedWQwbdjdz586natVooLDIvOqqa6hevQYFBQV8++3XDB9+D3PmzKdWrdpce+31JCUdYunSJUyaNBWAgICAYue12+1s3vw3F1zQhWef/T9++OE7DMNCx44X8OCDDxESEn7GYyxd+jWtWrWhWrXqXHZZVz7//BOOHDlCYGBgsXM0btyE0aMf5tVXX+DRR5+kVq06JbpWtWrVxs/PnzfeeI177hlGmzbt8Pf3P+m5ztTPBw8eoEWLVlx/fS+8vX1Yv/4vJkx4FpfLVWTU/L8mTXqZRYs+5fbb76BZs1gSEtYxZcpkfHx8uP763sW+FiIiInLMJdHX0yi4FUv2LaBZSHtcLpcenSlyBpWr2Ha58H/3U6z7Dp55W4AAP7wfH1w4on0Sjh9/w+fSDgS99B5k5RTrkPYa0eTcen2pFtx2u53Bg4dx6aXdirQ/+uiTx7I6HMTGtuSGG67m99/X0qFDx1Mer6CggCFDhtOpUxcAatWqTZ8+1/HLL6u48sqrT7nfffc9UOR8cXHx9OhxBcuXL6NnzxsBmDLldapXr8mkSVOwWq0ARbKsWfMLq1at5Mknn6Nbt2P3pR//dXE0atSE8PAqeHl5n3bK/ZdfLuLgwQO8++5C6tSpC0CbNm3p1asHCxfOZ8SIUQAMGnS3ex+n00lcXDybNm3gq6++YPDgYURFVSUyMgqLxXLa851Kenoadrud9957h9at2zB+/MukpaXy1luvM3bsg0yZMuu0+2/atIG9e3fTr1/h6G3Xrlfxv/99wPffL6NHj+IvnhcQEOi+DvXq1S+y6FhxrlVAQCBjxz7BCy88x8MPj8JqtVK/fkMuvvhSbrqpP35+fkDx+rlr1yvdX7tcLlq1asOhQ4l89tnHpyy29+3by0cfLeTBBx9x/8zFxcWTm5vL7NnTue66G4s900BERESOqRXQiFoBjegYeeWZNxYRoLIV24CrBDWuERyA60h24dTxk8nJg6ycwu2KWWyXlc6dLzyh7eeff2Lu3Jns2LGNrKwsd/uePbtOW2xbLBbat493fx8TUw0fHx8OHTp02gwJCeuZMWMKmzf/Q0ZG+nHnK1zgKzc3lw0bEhg8eJi70P6vtWvX4OvrW6TQKkt//fUH9erVdxePAMHBIbRvH8+6dX+623bu3MG0aW+SkLCO1NTD7vY9e3aVSo6jC+75+/szbtxL7oXNwsLCGTVqGL/9toZ27eJOuf/SpUuw2WxcdllXoPBe/2rVqrN06ZISFdunU9xrdfnl3YiLi2fVqh/5/fe1/PbbGqZPn8I333zFjBnv4ufnV6x+zsjIYNasafz44w8kJyfhcDgACAkJOeU+a9asBuCSSy7Dbj82g6V9+w68995cDh1KJDo65mwvgYiISKWW58glIe0XDuXsJdynKvGR3c68k0glVrmKbcMoHFUu5jRyw2IQERIIfj4nL7j9fCA4gMxbb8DlLObq5GUwjdzX1xd/f/8ii2ht2rSBsWNHc+GFF3PrrQMIDQ3HMAwGDx5IXl7+aY/n4+ODl5dX0dheXuTnn+JDB+DgwYOMHj2cJk2aMmbMI1SpEomXlxdjxox075eZmYHT6TztCtcZGelERFQ5b9OSMjMzCQs7cYp2eHg4O3ZsAwqnT48ePZzQ0FBGjBhF1aox+Ph4M2HCc+Tnn/5aFldgYBCGYdCiRcsiK4i3adMOq9XKjh3bTllsO51Oli37hjZt2mEYFjIzMwG48MKL+fDDBSQnJ53VquL/VZxrdVRwcDBXXXUNV111DS6Xi5kzpzFnzgy++OIz+vTpV6x+Hj/+KRIS1jFw4F3UrVufgIAAPvnkf3z33dJT7pOenobL5eKaa7qe9PXERBXbIiIiZ2v5wY/5ePc0AMK8I+lQpaumkoucRuUqtqGw0PX2OvN2gAvIzy3AemE79z3ax7Ne2I783AJcp7n/+Xw42T9yK1Z8T2BgIM88M8E9bfbogmFlYfXqVeTkZDNu3EsEBQUBhdPbjx/hDgwMwmKxkJycdMrjBAeHkJKSfN7uAwoODmb37hNHpw8fPkxQUDBQOGJ/6FAiL7wwkYYNG7m3yco6AkSVSg5fX1+io6ud8vXTFfW//baGlJQUUlJS6N790hNe//bbr+nX71YAvL29sduLrjFwtDg/k+Jcq5MxDIP+/W9jzpwZ7Nq1499jnb6f8/LyWLVqJcOHj6J3737u9jM9ci84OATDMHjrrRknfGAEnLBYn4iIiBTf0UeAQeGCaQXOfLytPmbHEim3dPPiGWTlO7FeFo/1igsKR7KhcDXyKy7Aelk8WfnmrUR+Onl5udhstiKFzDfffFWG58vDMIwiC69999237qm/AH5+fjRv3oIlS74s0n689u07kJube9rRy9LUsmVrtm/fyu7dO91tGRkZrF37Ky1btgYKryVQpHhbv/4vDhzYX+RYhaP/Zz/S3blzF9av/4u8vGMzCH7/fS0Oh4PGjZuecr+lS5fg5+fHa6+9xeuvTy3yX4MGjdzP3gaIjIxyF7xHHZ16ffz7AE6YAVGca5WdneW+XsfbvbvwVoLw8AjgzP1cUFCA0+kscs2zs7NYuXLFKa8D4B79T09Pp0mTZif85+9f/EXrREREpKjq/vWI8atNm/Au9KgxSIW2yBlUvpHtEnI4nKQdySegSzt8unb69znbvuTn5pNp8mO/TicuLp6FC+czceKLXHTRpSQkrOPrrxeX2fmOFjnjxz9Nz543smPHdhYsmEdgYFCR7YYMGcH99w9h5Mh7ueGGPgQFBbF589+EhITSo0dP4uLi6dSpM88//wz79u2lWbNYMjIy+P77ZTzzzPNA4WO4Jkx4ltdee4s2bdqdU+5rrrmWhQvfZ8yYkdx991D3CttWq5WbbroZgObNW+Dn58+rr77ArbcOJCnpEDNnTiMysuiodu3adXE4HCxcOJ8WLVoSEBDgXs17+fJvgcJ7vx0Op/v7pk2bu6c133zzbSxZsphHHnmAPn36kZaWxtSpk2nVqjVt27Y/af68vDxWrFjOxRdfRvv2HU7y/q5j0qSX2b17J7Vq1eGSSy7nww/n06RJc2rVqs033ywmKanovfg1a9bGarXy5ZefYbVasdmsNGnSrFjXavfuXTz88Ci6d7+Wli1b4+fnx86dO5g3bw6BgYFcffW1AGfs58DAQJo2bca8eXMIDQ3FarUxb94cAgICSUs7fML7PKpWrdrceGMfnnvu/7j55tto1iwWu93Onj27+eOPtTz//Cun/XkQERGRUzMMg6dazcXuKmBbZgKb0n/jgqiSLWIrUpmo2C4Gh8NJRo4TI9eBxWLgzMs543RWs3Xq1IWhQ0fw0UcLWbx4ES1atOLFF1/j5ptvLJPz1a/fgEcffZJZs97moYdG0bBhI5577gWeeGJske1atWrN5MnTmD59CuPHP4XFYqVu3XrcffdQ9zbPPfcis2dP57PPPmbWrLcJD48gLu7Ygm0ulwuHw1EqfeDvH8DkydOYPPlVXnxxPE6ngxYtWvHmm9Pdj/0KD4/g2Wcn8OabrzF27APUrFmLMWMe5b335hY5VufOF3LDDX2YN28OqamHadWqDW+88TbACdfh6PePPvqkuwCtWjWayZOnMmnSKzz22MP4+vpy4YUXM3LkA6ecUv/zzys5cuTIKZ893a3bVbz55mt8880S7rprCAMH3kVq6mFmz56OxWJw3XU30qdPY9544zX3PqGhoYwa9RDvv/8OX3+9GIfDwcqVa4t1rapXr8l1193ImjWr+eKLT8nOzqZKlUjatYvj9tvvKHK/9Jn6+cknx/HSS+MZN+4pgoND6N27Hzk52SxYMO+0fTpy5Bhq1arNZ599zJw5M/Dz86dWrdpceunlp91PREREzswwDA5k7+TVjaPwsfjRoUpXbBaVFCInY7jKe9V4Gg6Hk8OHs076WkFBPikpB4iIiMHLy/uk23gSm81SZIE08Rzq27Jnxr8XNpuFsLAAUlOz1L8eRn3rmdSvnkt9WzIul4t521/hp6TFtAztxG31xxDkFWp2rJNS33ouM/s2PDwAq7V4d2Prnm0RERERESkWwzA4mLMbp8tB09D25bbQFikPVGyLiIiIiEixxYYV3va1JnkZKxO/NDmNSPmlYltERERERIotNrSw2N6auZ53tr9Elr14jxEVqWxUbIuIiIiISLHV8K9PqFcVAOoHNSdbxbbISanYFhERERGRYjMMwz2VvE5gUyJ9q5mcSKR80jr9IiIiIiJSIhdX7UlsaEdCvSP5MXERF1a91uxIIuVOiYvtbdu28dxzz/HHH38QEBBAz549GTlyJN7ep35czqFDh5gzZw4//fQTu3fvJigoiLi4OEaPHk316tXP6Q2IiIiIiMj5VTuwMVX9ajJqzbU4XHYah7Qlyld/14scr0TFdnp6OgMGDKBOnTpMnjyZxMREJkyYQG5uLv/3f/93yv02bNjA0qVL6dWrF61atSI1NZUpU6bQp08fvvjiC8LDw8/5jYiIiIiIyPljNWzU8K8PQJ4j2+Q0IuVPiYrtBQsWkJWVxRtvvEFoaCgADoeDp59+msGDB1O1atWT7teuXTu++uorbLZjp2vbti2XXHIJn376KXfcccfZvwMRERERETnvlh34H7uy/qFlWCdqBjQ0O45IuVOiBdJWrFhBp06d3IU2QPfu3XE6nfz000+n3C84OLhIoQ0QHR1NeHg4hw4dKlliERERERExXfPQDgBsSv+dHw5+hsNlNzmRSPlSopHt7du306tXryJtwcHBREZGsn379hKdeMeOHaSkpFC/fv0S7fdfNtvJPy9wOo1zOm550aVL+zNu8+ijT3LddT0xDHC5Sn6OLVv+YcWK77nllgH4+vqeRUpzrVnzC5MnT2TPnt34+PiwZMn3dOnSnnvvvZ/+/W8r9fPNnDmNDh060qJFqyLty5Z9w3ffLWXjxg0kJR067fkTEtYzffoUNm5MwDAM6tSpy5gxj9CwYeMi2xnGsf892rd2u52ePa8kIyODDz9cRHR09Fm9j8WLF2GzeXHFFVed1f4JCeuZPXs6W7b8w5EjRwgPD6dJk6bcfPPtNG8ee1bHPJXhw+/B39+fF198rVSP+19Wq3HKf1NK/1yWIv8rnkN965nUr55LfXv26gQ3JMq3Oil5iby341VqBzekQXDp/g1wLtS3nqui9G2Jiu2MjAyCg4NPaA8JCSE9Pb3Yx3G5XDz33HNERUVxzTXXlCRCERaLQVhYwElfy821kpxsOa9/PJeFGTPmFPn+rrsG0qdPP6688liBVL16TeDsf9i2bdvC7NnT6du3Hzab/1lnNcv48c/QoEFDHnroEXx8fN39XVZ9P3v2dAIDA2jTpk2R9h9++I4DB/bTpcuFfPLJR6c8/9q1vzJ69H306NGT228fiMNhZ8OGBAoK8k+Z9/i+/eWXX9y/b9999w233z7wrN7HV199gb+/H1dffXWJ9/3rrz8ZNuweOnbsxMMPP0pAQAB79uzmhx++559/NtKqVcuzynQqhmFgGKf+cO1cOZ0GFouFkBD/8/6BU3Cw33k9n5w/6lvPpH71XOrbszOl22dM/P0xkrIP4BdgPeXf5mZS33qu8t63pjz6a/Lkyfzyyy/MmDEDf/+zL+6cThcZGSdfjCE/Pw+n04nD4cJud571OQC8vKwE+drIzLVTUOA44fuy1KTJiZ8ORkZWLdJ+dPTT4XCe1ci201m4k91+7tfqfMvOziYp6RB33jmY2NjWAO73UBp9fyonO/ZTT43HYiksBj/55KOTbmO32xk37hl69+7Hvffe527v0OGCItmPMozCQvv4vl2y5CsCA4OoXr0GX3+9mP79bz+r9+ByuXC5TjxncXz00YfExMQwfvzLWK1WAFq3bs+1196I0+ks9et+LlmLw+Fw4XQ6SU/PJienbH+nj7JaLQQH+5GRkYPDUbF+7+T01LeeSf3qudS3525g3UcxMDAMg9TULLPjuKlvPZeZfRsc7FfsQc4SFdvBwcFkZmae0J6enk5ISEixjrFw4ULefPNNxo0bR6dOnUpy+pM61R/fDsdZVJ0n4eVlJcTfC/uyXwi5vCPZBVb8vSzu79OzKfOC+0y+/HIRH3zwHnv27CY4OITu3Xtw111D3EVQZmYmb701iZ9//omMjHRCQ8No0aIlTz/9PIsXL2L8+KcB6NGjKwDR0TH873+LTnquXbt2MmvWNNavX0d6ehoxMdW45pqe9O3b311oAuTn5zNnzgyWLv2a5ORDhIaG0b59Bx577Cn3NgkJ65g5cxobNiTgcrmoU6cu99wzlLi4jsV638dnnzDhWSZMeJbu3XsUOcfxPv30Iz744D0OHjxARESVf0eW73DnTk5O5u233+SPP34nJSWZqKgoLr20K4MG3e1+tN3Raf1vvTWJt96aBMDrr0+lbdv2Rd7/qaxd+ysHDuynT59+xXqPRwvso/+bk5PDTz+toGvXK2nQoCETJ77Etm1bqV+/gXufmTOnsWDBPJYu/bHIsa666hL69LmZO+8czPDh9/Dnn78XeU+DBt3NnXcOLta1yszMICwszP0zdrz/Xocz9fOUKZP5+eeVHDiwn4CAQFq1asOIEaOpUqXKaa/Nzp07mDp1Mn/88RsOh4M2bdoxcuQYqlevUZxLe1Jl+QHNqc9Z+h9OSPmgvvVM6lfPpb49Oy6XiwU7J/Hn4Z+4olo/Okd1x9davmZKqm89V3nv2xIV2/Xq1Tvh3uzMzEySkpKoV6/eGfdfunQpTz31FPfddx+9e/cuWdLSlF9w+tdtVvi3YAjytWJf9guOpT/j2nUAv75XUfDuEpybdxa+fkkHDh8ttl0uKDjDwhDeXucYvqgFC+YxZcpk+vXrz/DhI9m5cydvv/0WTqeToUNHADB58qusXr2KIUNGEB0dQ0pKMr/8sgqATp26MGDAncydO5NXXplMQEAg3qfJmJR0iFq16tCtW3f8/f3ZunUzM2dOIycnmzvuuMe93eOPP8Rvv63httsG0bx5C9LSUvnhh+Xu19et+5P77x9K8+YtePjhxwkKCuLvvzeSmHiw2O+9U6cuTJz4JqNGDWPAgDvp1KkLYWFhJ932f/9bwGuvvUzv3n254IILWb/+L2bPns6RI0cYPnwkAOnpaQQHhzBixCiCgoLYs2c3s2a9TUpKMo8++iQAU6fOZsiQQfTu3ZeuXQun8tetW7fYmTdsWE9ISAh//72R++4bwv79+4iJqc6AAXfQvXuPM+7/44/fk5OTQ7duV1G3bj1ef/1Vli5dQv36w4udAeCBB8by7LNP4OPjy7Bhhe8/KioKKN61aty4KXPmzGD69ClccUV3ateuc9LzFKefU1MPc9ttg6hSJZK0tFQWLHiP4cPvYd68hScsrHjUvn17GTLkDurVq8+jjz6FxWLwzjuzuP/+obz//kfuD0dERESkbBmGwd6sbaTmH+KDna8T4RNN6/DOZscSKRdKVGxfdNFFTJ06tci920uWLMFisdC58+l/qVavXs3o0aPp06cPw4YNO/vEpSDolRmnfT3n+iuwNy1cuC33l/X4XxyHa9cBnJt3kv/sVAAsjepguziOvEXfw6WFU4CNnFwCJ8057bEzHxl6rvHdsrOzmDnzbfr3v51hw0ZgtzuJi+uIl5eNyZMn0r//bYSEhLJp0wa6dr2qSDHXteuVAISFhblHAhs3blpkpfmTad++A+3bF6486XK5aNmyNbm5uXz00UJ3sb1mzS+sWrWSJ598jm7djt1bfvzXU6a8TvXqNZk0aYp7dLRDh+KNaB8VFhZGkybNAKhevQaxsS1Oup3D4WDOnBlcfvkVjBw5xn0uu93OggXzuO22gYSEhFK/fgN3MQnQokUrfH39GDfuSUaPfhhfX1/3OaKiok95vtM5fDiFnJxcxo9/hrvuGkydOvVYunQJ48Y9RXh4BPHxp5/tsXTpEiIjo2jdui0Wi4V27TqwdOkSBg8ehmEUf1HAunXr4e8fgL+/f5H3Udxr1b//bWzYsJ65c2cyd+5MgoNDiI/vxPXX96JVq2P3shenn49+kHH0/LGxLbnhhqv5/fe1p/yZmD17OsHBwUyc+CY+Pj4AxMa24qabevLFF59x4419in0tRERE5NzEhsWzJXMdvlZ/XC5zZ3yKlCclWm2oX79+BAQEMGzYMFauXMlHH33Eiy++SL9+/Yo8Y3vAgAF069bN/f22bdsYNmwYderUoWfPnvz555/u/3bv3l1676YMOBJTsP+wBtuNXYu0227siv2HNTh27jcpGaxfv46cnGwuvfRy7Ha7+7/27ePJy8tj+/ZtADRq1ISvvvqC999/l+3bt57TOfPy8pg5cxp9+17PpZd24pJLOvL222+RkpJMdnbh/fNr167B19fXXdD/V25uLhs2JNC9+zUnnYZc2nbt2klaWhqXXVa0Dy+7rBsFBQVs3LgBKPzwYOHC97n11j5cdllnLrmkI8888zgOh4P9+/eWShan00V+fh533HEPvXr1pV27OMaOfYIWLVrxzjuzTrtvWloav/76C5dd1s09VbtbtytJTDzIunV/lkq+4l4rf/8AJk58k7ffnsOgQXfTsGEjli//luHD72HRok+B4vfzzz//xJAhd3DllRdz8cXx3HBD4YJte/bsOuU+a9b8QpcuF2G1Wt0/90FBQTRq1Ji//954jldBRERESiI2NB4o/FsqNqxkgycinqxEI9shISHMnTuXZ599lmHDhhEQEEDv3r0ZNWpUke0KFyY79qnWX3/9RWZmJpmZmdx8881Ftr3hhhuYMGHCObyFkst84K7Tb2A7Vhg4u3XGFuhDwaxPimxi//hbvO64gawOreHf2wRcfr5nPnYpSk9PA+COO2496euHDiUCMGrUQwQHT+ODD+bx1luTiIqqym23DeKGG0o+lX/KlMksWvQJgwbdTePGTQkKCuLHH39g7tyZ5Ofn4+/vT0ZGOhERVU450pqZmYHT6aRKlcgSn/9sHF1nICwsvEh7eHi4Ow/AwoXv8+abk+jf/3batm1PUFAQmzZt5NVXXyA/P79UsgQFBQHQrl3RR7q1b9+Bjz5aeNp9v/tuKQ6Hgwsu6OJ+T23btsfb25ulS5cUGVE+W8W9Vkc1axZLs2aFi/Xt37+P4cPvYcqUyVx77fXF6udNmzYwduxoLrzwYm69dQChoeEYhsHgwQPJyzv1NU9LS2PhwvksXDj/hNdsttK9VUNEREROr4Z/A0K8IkgvSOGb/Qu4MKoHwd7hZ95RxMOVeDXy+vXrM2fOnNNu8+677xb5/sYbb+TGG28s6anKTgnumw7y98a+7Becm3cWTh3vexX2Dwrv2bYv+4WgS+M5nJlXuLFhlPo92afNFlQ4lX/cuJeoVi3mhJX4YmKqARAYGMj99z/A/fc/wLZtW/nww/m88soE6tWrX+ICbfnyb+nZ80ZuvXWgu23VqpVFtgkODiElJRmXy3XSgjswMAiLxUJyclKJzn22jt7ykJqaWqT98OHDwLHruHz5Mjp3voghQ47d/7xz545SzVK37qnXNsjPzzvtvkuXLgHg/vtPvBVh+fJvGTlyDDabDW9vH+z2omsH2O12cnJyzpivuNfqZKpVq86ll3blgw/e4/DhlGL184oV3xMYGMgzz0xwj9YfPHigGDlD6NSp80mni5/LEw5ERESk5AzDoHloB1YlfcVne2YS7BXGhVWvNTuWiOlMefRXRZKZayfk8sLpMLbLO5Jd4MT/jhuwL/sF2+UdSc8+w2JrZSg2tiW+vr4kJSVy+eWXF2slvvr1G3DffaP54ovP2LlzB61atXGPBJ6p2IPCaeTHjxw6HA6WLfumyDbt23fgvffm8t13S7n88itOOIafnx/Nm7dgyZIv6dfv1jKfSl6rVm1CQ8NYvvxbLr74Unf7d98txcvLi2bNmgOQl5eLl1fRD0u++earE45ns9mKda1OJj6+EzabjbVrf6VevWMriK9Zs5rGjZuecr+DBw+QkLCO66/vxWWXdSvy2tatm3n99Vf55ZdVdOlyEVFRURQUFLBv3173/fi//bamyGwTAC8vrxNGj4t7rQ4fTiE8POKEnHv27Mbb25vAwCC8vb3P2M95ebnYbLYiH8qc7Jr/V/v2HdixYxsNGzY+L7ciiIiIyOldXLUnuY5sdh75Byj+OjIinkzF9hkUFDhIz4agS+NJzy6goMBBgZe1yPdmCQoK4s47h/DWW5NJTk6iVau2WK1W9u/fy48/rmDcuBfx9fVl6NA7uPDCS6lXrz5Wq4UlS77Ey8vLPapdp04dAD7++EMuvPASfH19izxK6nhxcfEsWvQpdevWIyQklE8++ZD8/6zuHhcXT6dOnXn++WfYt28vzZrFkpGRwfffL+OZZ54HYMiQEdx//xBGjryXG27oQ1BQEJs3/01ISCg9evQEoG/f64mOjmHSpCnndJ2sVisDB97Ja6+9TFhYOJ06dWbDhvW8//479OlzMyEhoe7cH364gI8++oCaNWvz9deL2bv3xHu1a9euy8qVK2jVqg1+fn7UqlUbf/8AduzYzs6dx1br3759K8uXf4uvrx+dOhUuIBgeHkHv3v2YPn0KYFCnTl2+/fZrNmxYzyuvTD7lezg6qn3zzbed8Girli1b8+67c1i6dAldulxEx44X4OfnxwsvPMcttwwgKSmRDz9cgLe3zwnvY8mSL1i5cgVVqlShSpVIqlSJLNa1euGF53A4HFxyyWXUqFGLrKwsvv9+GatW/chNN93sXg38TP0cFxfPwoXzmTjxRS666FISEtbx9deLz9ind945mLvuup3Ro0dw3XU3EB4ezuHDKfzxx++0atW6yGJ8IiIiUvbqBjXlrob/h82i8kLkKP02FENBgePY471O8r2Zbr75ViIjI1m48H0+/HABNpuN6tVrcMEFF7ofm9SiRSu+/vpL9u/fj8ViUK9eA154YSJ16hQ+sqpRoybcccc9fPHFZ7z//jtERVU95XO2R40aw0svPc/EiS/h6+tL9+49uOiiS3nhheeKbPfccy8ye/Z0PvvsY2bNepvw8Aji4uLdr7dq1ZrJk6cxffoUxo9/CovFSt269bj77mNTpB0OxwmjsWerd+9+2Gw2Fix4n08++ZCIiCoMGnQ3t99+h3ubgQPvJi0tjRkzpgFwySWXM3Lkgzz8cNE1CUaPfphJk17mwQfvIy8vz/2c7e++W8rs2dPd2y1Z8iVLlnx5wnPLhwwZjp+fH/Pnv0taWiq1a9fl+edfPu1q7EuXLqFFi1YnfYa0zWajW7cr+fzzT8jOziYkJJTnnnuRN96YyCOPPEjDho14/PGnGTFicJH9brnldvbt28Nzzz3JkSOZ7udsF+da3XjjTSxZ8iXvvDOblJRkfH19qVatBmPHPlFk1fsz9XOnTl0YOnQEH320kMWLF9GiRStefPE1br759Led1KhRk+nT5zJ9+hRefXUCOTk5RERUoVWrNtSv3/C0+4qIiEjZ2Zi2lkM5e2gR3okIn2iz44iYynC5XC6zQ5wth8PJ4cNZJ32toCCflJQDRETE4OXl+c/ctdks5fqB7nL21Ldlz4x/L2w2C2FhAaSmZql/PYz61jOpXz2X+rb0fLXvPT7Z/TYA19e8i6tr3GZqHvWt5zKzb8PDA7Bai/dQrxI9+ktERERERORkmoXEub92UmHH80RKjaaRi4iIiIjIOasZ0IBw76pE+dWgc6TWTxFRsS0iIiIiIufMYlgY1/Z9DAx2Z20h35lPVb8T15sRqSw0jVxEREREREqF1bCxcOebjF8/mO8PfmJ2HBFTqdgWEREREZFS4XK5SM7bj4FBjiPb7DgiplKxLSIiIiIipcIwDLIKMnDhom5QU7PjiJhKxbaIiIiIiJSa2LBOAPyWvJwD2TvNDSNiIhXbIiIiIiJSamJDOwDwd8bvLNo71+Q0IuZRsS0iIiIiIqWmZkBD/K1BAGQWHDY5jYh59Oivcq5Ll/Zn3ObRR5/kuut6ntXxhw+/B39/f1588bWz2r88Kygo4KWXxrNq1Y+kpaVx332jyczMZMGCeSxd+mOpn+/339eSkLCO22+/o0h7amoqc+fOZMOG9WzduhmbzXbK8+fl5fHuu7P5+uvFJCcnERYWTrduVzB06P2nPO+BA/vp0+c6rFYr8+Z9SM2atdyvbdnyD4MG3cLrr0+lbdsz/yyZ7eh7efbZCVx6aVez44iIiMhZsBgWWoR1ZHXyUmoHNDE7johpVGyXc1Onzi7y/ZAhg+jduy9du17lbqte/eyfX/jAA2OxWj1zgsOSJV/y9deLeeyxp6hWrQYxMTF8+ulHZXa+P/74jQUL5p1QbCcnH2LZsm9o2rQ5TZo0ZevWLSfd3+l08sgjD7B//z4GDbqbmJhqHDx4gL17dxfr/A6Hg3femcVjjz11rm9FRERE5JxcEn09zULjqO5Xj71Z26gRUN/sSCLnnYrtci42tsUJbVFR0SdtPyovLxcfH99iHb9u3Xpnna2827VrJxERVbjiiu6m5qhfvyGLFn0DwMyZ005ZbC9e/DkbNyYwb97/qFKlirvdZrNgtzvPeJ62bdvzzTdfMWjQ3VSrVr10wh+nJD9XIiIiUrnVD4rlYM5unlt/F01D2jGq2atmRxI571RsF0NwqDc+Xj4ntOcV5JGRlm9ComNmzpzGggXzeOONabz66kts2fIPd901lP79b2PKlMn8/PNKDhzYT0BAIK1atWHEiNFFCrn/TiM/erypU2fz8svPs3nz31SrVp3hw0cRH9/ptFnmz5/HsmXfsGfPLry8vGnWrDnDh4+iVq3aRbZLSFjHzJnT2LAhAZfLRZ06dbnnnqHExXUEID8/nzlzZrB06dckJx8iNDSM9u07lGjEtnfvazl48ABwbCr+hx9+ftJtDx48wBtvTGTNmtU4HA5atmzNsGEjqV+/gXubr776gs8//4SdO3fgcrlo0KAh9957H82axbqv2+zZ04ucr3XrtrzxxttYLMWbOfD5559y6aVdi/RPSfTo0ZPdu3fx7ruzefjhx0+5ndPp5J13ZvHFF5+RkpJMTEw1brqpP9df38u9zdGfg0mTpjBp0ivun6vQ0FDGj3+aGTPeYdq0N1m37k+ioqoyevTDtGsXx4wZU1m06FMArrnmOu655173+9+1ayezZk1j/fp1pKenERNTjWuu6Unfvv2LfY1ERESk4qgT0AQwyLYfweVyYRiG2ZFEzqtKWWznOXIA8Lb4YhgGeY5cwIWXxRuLYaXAmYfT5cRqeGGz2PDx8qHvlxfgcNrdx7BabHxwzSogH6fLSYEzDwAfq99pz3H09dJUUFDAk08+Sp8+/Rk8eBjBwSEApKYe5rbbBlGlSiRpaaksWPAew4ffw7x5C7HZTt31drudZ555nN69+zFw4F28995cHn/8If73v0WEhISecr+kpER69bqJqlWjyc7O4tNPP2Lo0DuYP/9jd6Z16/7k/vuH0rx5Cx5++HGCgoL4+++NJCYedB/n8ccf4rff1nDbbYNo3rwFaWmp/PDD8hJdk/HjX+K99+by55+/M27cywBERJxYxGZnZzFixGAMw+DBBx/B29uHd96ZxbBhdzN37nyqVo0GCgvyq666hurVa1BQUMC3337N8OH3MGfOfGrVqs21115PUtIhli5dwqRJUwEICAgodl673c7mzX9zwQVdePbZ/+OHH77DMCx07HgBDz74ECEh4Wc8hpeXF/37385bb01iwIC7iI6OPul2b745if/9bwG3334HLVq0YtWqH3n55edxOOz06tXXvV1BQQFPP/04N9107Odq8+a/AXjuuSfp2bMX/frdyrx5c3jssYfo3v0asrKyePzxp9m4MYGZM6dRr14Drrii8JaHpKRD1KpVh27duuPv78/WrZuZOXMaOTnZ3HHHPcW+ViIiIlIxrEtdBbgI845UoS2VUqUstkf8WvjH/yvtPyPIK5Tx6wdzIGcnDzR7jcYhbZi5ZRy/H/6Bm+uO5NLoGwBwOO04XMeKbY6b1XswZxdP/TWQQFsIr8Z9ftpzvN3ph1J/P3a7ncGDh3Hppd2KtD/66JPurx0OB7GxLbnhhqv5/fe1dOjQ8ZTHKygoYMiQ4XTq1AWAWrVq06fPdfzyyyquvPLqU+53330PFDlfXFw8PXpcwfLly+jZ80YApkx5nerVazJp0hSsVitAkSxr1vzCqlUrefLJ5+jW7dh96cd/XRyNGjUhPLwKXl7ep51y/+WXizh48ADvvruQOnXqAtCmTVt69erBwoXzGTFiFACDBt3t3sfpdBIXF8+mTRv46qsvGDx4GFFRVYmMjMJisZz2fKeSnp6G3W7nvffeoXXrNowf/zJpaam89dbrjB37IFOmzCrWcXr2vIF58+Ywb94cHnxw7Amvp6Wl8dFHH3Dzzbdx552DgcLrn5aWxuzZM7j++t7ufrHb7dxzz71cfvkV7v2PFtu9evXlhht6AxAZGcntt/fj7783MW1a4RoD8fGdWLlyBcuXf+suttu370D79oWPAnG5XLRs2Zrc3Fw++mihim0REREP1Cw0jk/2TGdT+m/syvyH2kGNzY4kcl5VymLbE3XufOEJbT///BNz585kx45tZGVludv37Nl12mLbYrHQvn28+/uYmGr4+Phw6NCh02ZISFjPjBlT2Lz5HzIy0o87X+ECX7m5uWzYkMDgwcPcBd1/rV27Bl9fX7p2vfK05yotf/31B/Xq1XcX2gDBwSG0bx/PunV/utt27tzBtGlvkpCwjtTUY4+w2LNnV6nkcLlcAPj7+zNu3Et4e3sDEBYWzqhRw/jttzW0axd3xuP4+PjSr98tzJgxlQED7jjh9Y0bE7Db7Ses9H355d349tuv2bNnd5FrcfQDl/+Kizv281GzZuFtAv/NV7NmLXffQ+FK6/PmzeGbb74iMfEgdvuxD6+ys7Px9/c/4/sTERGRiqNmQEMCbSEcsaczc+t4nmmjZ25L5VIpi+3JHZYAhVO8AR5tMY2j08gB7mz4GINcj2A1vIp1vGi/2u5jnukcZcHX1xd/f/8ii2ht2rSBsWNHc+GFF3PrrQMIDQ3HMAwGDx5IXt7p7zP38fHBy6voe/fy8iI/P++U+xw8eJDRo4fTpElTxox5hCpVIvHy8mLMmJHu/TIzM3A6nVSpEnnK42RkpBMRUeW8TTXKzMwkLOzEKdrh4eHs2LENKJxqPnr0cEJDQxkxYhRVq8bg4+PNhAnPkZ9fOvfsBwYGYRgGLVq0dBfaAG3atMNqtbJjx7ZiFdsA11/fm/fem8t7773DNddcW+S1zMwM9/s7XlhYBECRD0mO/lydKu9RR39Wjm872n78z8yUKZNZtOgTBg26m8aNmxIUFMSPP/7A3Lkzyc/PV7EtIiLiYSyGhSdazmTs732wWazkO/Lwtp64DpKIp6qUxfZ/75v2sRZdYdnLcuI/AlaLrcjUcavl2KWzGJaTHPP05yhNJytMV6z4nsDAQJ55ZoJ78amjC4aVhdWrV5GTk824cS8RFFRYdNnt9iLFW2BgEBaLheTkpFMeJzg4hJSU5PO2iEZwcDC7d584On348GGCgoKBwhH7Q4cSeeGFiTRs2Mi9TVbWESCqVHL4+voSHV3tlK+XpKj39/enb99bmDt3Jh07XlDkteDgwveUmnqYyMhj2VNTU/59PcTdVtrXf/nyb+nZ80ZuvXWgu23VqpWleg4REREpX8J8Inm5/acEeYWaHUXkvNMSwMWQV5DHB9es4n/X/ur+74NrVpFXcOqRXrPl5eVis9mKFEzffPNVGZ4vD8Mwiiy89t133+JwONzf+/n50bx5C5Ys+bJI+/Hat+9Abm4u3323tMyyHq9ly9Zs376V3bt3utsyMjJYu/ZXWrZsDRReS6DIaP/69X9x4MD+IscqHMk9+5Huzp27sH79X+TlHfu5+v33tTgcDho3blqiY/XqdRNeXt7Mn/9ukfamTWOx2WwsX76sSPt3331LWFg4NWvWOuv8Z5KXl4fNduwaOhwOli37pszOJyIiIuZzuVws3f8BT/55Owmpv5odR+S8qpQj2yVV+Hgvcx/xVVJxcfEsXDifiRNf5KKLLiUhYR1ff724zM53dIrz+PFP07PnjezYsZ0FC+adMLV4yJAR3H//EEaOvJcbbuhDUFAQmzf/TUhIKD169CQuLp5OnTrz/PPPsG/fXpo1iyUjI4Pvv1/GM888DxQ+hmvChGd57bW3aNOm3Tnlvuaaa1m48H3GjBnJ3XcPda9GbrVauemmmwFo3rwFfn7+vPrqC9x660CSkg4xc+a0IiPDALVr18XhcLBw4XxatGhJQEAAtWrVAQpHdaHw3m+Hw+n+vmnT5kRHxwBw8823sWTJYh555AH69OlHWloaU6dOplWr1rRt275E7ysgIJA+ffq5H0d2VGhoKL169eX999/B29ub5s1b8PPPP7F06RJGjRpzynvpS0NcXDyLFn1K3br1CAkJ5ZNPPiQ/v6DMziciIiLmMwyD9am/cCBnF9O3PMVrcV9qZXKpNDSy7aE6derC0KEjWLlyBWPHjuavv/5wP0u7LNSv34BHH32Sf/7ZxEMPjeLbb7/muedeIDAwsMh2rVq1ZvLkaRiGwfjxT/HYYw+xYsX37oIT4LnnXqR373589tnHPPjgfbzxxkT8/I5Ny3e5XDgcDveiYufC3z+AyZOn0aBBQ158cTzPPFP4OLI335zufuxXeHgEzz47gdTUw4wd+wALF85nzJhHqV69RpFjde58ITfc0Id58+Zwzz0DefHF8e7XnnhiLE88MZbly78lPz/P/f3vv691b1O1ajSTJ08lPz+fxx57mMmTJ9KxY2defnnSWf0/pT59bj7p48eGDbufQYPu5osvPuOhh0by888/8eCDjxR57FdZGDVqDK1bt2XixJeYMOFZ6tVrwO23DyrTc4qIiIj5WocXLrjqcNnJKDh8hq1FPIfhKo2KxSQOh5PDh7NO+lpBQT4pKQeIiIjBy8v7pNt4EpvNUmSBNPEc6tuyZ8a/FzabhbCwAFJTs9S/HkZ965nUr55LfVv2dh7ZxPj1Q/C1+vNq+8+xWYq3CPG5Ut96LjP7Njw8AKu1eGPWGtkWEREREZEyUyugMUG2UHId2fySpPVapPJQsS0iIiIiImXGYlioH9wCgHnbX6HAWbHWQhI5Wyq2RURERESkTHWN7oOv1R8/WyBJufvPvIOIB9Bq5CIiIiIiUqYahbTiqVZzCfWugsXQeJ9UDvpJFxERERGRMhfmHcnurC38lvKD2VFEzguNbIuIiIiISJn7Yu9cFu2dDcAr7T8jyCvU3EAiZUwj2yIiIiIiUuZiQ+PdX+/L3m5iEpHzQyPbIiIiIiJS5moHNibQFkKkb3WifGuYHUekzKnYFhERERGRMmcxLExo+yFeFm8O5e7D5XJhGIbZsUTKjKaRi4iIiIjIeWGz2Hjiz1t54s9bSMzdY3YckTKlke1yrkuX9mfc5tFHn+S663qe9Tm2bPmHFSu+55ZbBuDr63vWxzHLmjW/MHnyRPbs2Y2Pjw9LlnxPly7tuffe++nf/7ZSP9/MmdPo0KEjLVq0KtK+bNk3fPfdUjZu3EBS0qHTnj8hYT3Tp09h48YEDMOgTp26jBnzCA0bNj7leceNe4qvvvqCK6+8mieeeKbIa4888gCZmZm88cbb5/4Gz4Nx457i77838u67C82OIiIiIueRgQWHywHA5vR1RPvVMjmRSNlRsV3OTZ06u8j3Q4YMonfvvnTtepW7rXr1c7vnZcuWzcyePZ1evfpWyGJ7/PhnqF+/AQ88MBYfH58yP9/s2dPx8/M/odhevnwZ+/fv44ILuvDZZx+fcv/fflvDmDH3c/XV13Hrrbdjt9vZuHEDubm5xTr/t99+zR133HPO/S4iIiJyvhmGgZ/VHwCLppCLh1OxXQLBod74ePmQV5BHRlr+eTlnbGyLE9qioqJP2l4ZZWdnk5R0iDvvHEyrVm1MzfLMM89jsRTemXGqYttutzNhwrP07t2Pe++9z93eqVOXYp2jRo1a5Ofn8c47s3jkkf8799D/kZeXi49PxfvARURERCqO1uEXsjd7G78f/oGOkVdis6gkEc+kn+wS8PHy4ZbFF/He1SuA81NsF8fixYv44IP32LNnN8HBIXTv3oO77hqC1WoFIDMzk7femsTPP/9ERkY6oaFhtGjRkqeffp7FixcxfvzTAPTo0RWA6OgY/ve/RSc9165dO5k1axrr168jPT2NmJhqXHNNT/r27e8uNAHy8/OZM2cGS5d+TXLyIUJDw2jfvgOPPfaUe5uEhHXMnDmNDRsScLlc1KlTl3vuGUpcXMdiv++j2SdMeJYJE56le/ceRc5xvE8//YgPPniPgwcPEBFRhR49enL77Xe4cycnJ/P222/yxx+/k5KSTFRUFJde2pVBg+7G29sbODat/623JvHWW5MAeP31qbRt277I+z+VtWt/5cCB/fTp069Y7/G/vLxs9OnTl8mTJzJo0N1ER8eccttt27by5puvsW7dn1itVuLi4hk+fDTR0dHubbp0ac/gwcPJzMxgyZIvycnJYenSFfTufS0XXNCFGjVqsmDBe2RmZnLJJZcxZsyj7Nq1g1dffYEtWzZTt249xo79P+rXb+A+5vz581i27Bv27NmFl5c3zZo1Z/jwUdSqVfus3rOIiIh4ltjQeL7YO4eEtNVszVxHk5C2ZkcSKROVstjOc+SctN3L4o3FsOJ0OSlw5rnbfax+7q/tLjsA+Y4cXO5WAx9r4WhggTMf57/3oVgMK14W73/PmQu4ihyrNCxYMI8pUybTr19/hg8fyc6dO3n77bdwOp0MHToCgMmTX2X16lUMGTKC6OgYUlKS+eWXVUDhiOqAAXcyd+5MXnllMgEBgXh7e53yfElJh6hVqw7dunXH39+frVs3M3PmNHJysrnjjnvc2z3++EP89tsabrttEM2btyAtLZUffljufn3duj+5//6hNG/egocffpygoCD+/nsjiYkHi/3eO3XqwsSJbzJq1DAGDLiTTp26EBYWdtJt//e/Bbz22sv07t2XCy64kPXr/2L27OkcOXKE4cNHApCenkZwcAgjRowiKCiIPXt2M2vW26SkJPPoo08ChdP6/zuVv27dusXOvGHDekJCQvj7743cd98Q9u/fR0xMdQYMuIPu3XsU6xg9elzPO+/M4p13ZvHQQ4+ddJvExIMMG3Y31avX4IknniU/P4+3357CiBH3MHfufPz9A467NvNp1qwFY8c+gcNhd7evXLmCunXrM2bMI+zfv4/Jkydis3mxYcM6+va9hfDwcKZMmcwTTzzMvHkfuj9sSEpKpFevm6haNZrs7Cw+/fQjhg69g/nzPyY4OKTY10pEREQ8U53AxtgML+yuAtYdXqViWzxWpSy2R/x61UnbH2j2Go1D2pCUu58n/rwFCxamdfuCKn5BABzOTcLptJOcc5Dq/44OJucc5MmVw3mqdeG91R/vmsayg/8D4PLo3vStW1jwjl8/mAM5O3m70w+l9j6ys7OYOfNt+ve/nWHDRmC3O4mL64iXl43JkyfSv/9thISEsmnTBrp2vapIMde165UAhIWFue/9bdy4KaGhoac9Z/v2HWjfvgMALpeLli1bk5uby0cfLXQX22vW/MKqVSt58snn6Nbt2LU+/uspU16nevWaTJo0xT0C36FD8Ua0jwoLC6NJk2ZA4X3rp5pa73A4mDNnBpdffgUjR45xn8tut7NgwTxuu20gISGh1K/fwF14A7Ro0QpfXz/GjXuS0aMfxtfX132Os53Kf/hwCjk5uYwf/wx33TWYOnXqsXTpEsaNe4rw8Aji4zud8Rg+Pj7cfPNtTJv2JgMH3kVUVNUTtlm48H0cDjsTJ77hLnAbNWrCrbf2YfHiRfTufWxkPSgohPHjXzrpozcmTHgFL6/CD1/++OM3Fi36hJdffp2OHS8AwOl08fDDo9i2bSsNGzYC4L77HnDv73A4iIuLp0ePK1i+fBk9e95YgqslIiIinshiWGke2oG/Un/C9u/AlIgnqpTFdnEZhoUqftHcsvgi7C47Tqcdu8vO0G+vw2KxYTNsvHf1CtOeD7h+/TpycrK59NLLsdvt2O1OANq3jycvL4/t27fRpk07GjVqwldffUFERBU6duxEvXoNznDkU8vLy2PevDl8881XJCYexG4/NhKanZ2Nv78/a9euwdfX113Q/1dubi4bNiQwePAwd6Fdlnbt2klaWhqXXda1SPtll3Xj3Xdns3HjBjp16ozL5eLDD+fz+eefsH//fvLzj81u2L9/7zldt6OcThf5+XkMHTqCXr36AtCuXRy7du3knXdmFavYBrj++t7MmzeXefPmMHr0wye8/tdff9K2bfsiI8m1a9ehQYOGrFv3V5Fiu2PHC076M9y6dVt3oQ1Qs2ZtLBYL7drFHddWuILooUOJ7mI7IWE9M2ZMYfPmf8jISHdvu2fP7mK9NxEREfF8XWP60Dw0jvqBLch35OFtLftFbkXOt0pZbE/usOSk7UenfEf6VnNvk1uQ++892pCWe5i7l17F9G5fEeob4X59bOxb7mPcWHsw19e6Cyj81O6oR1tMg+MmnpeG9PQ0AO6449aTvn7oUCIAo0Y9RHDwND74YB5vvTWJqKiq3HbbIG64oXeJzzllymQWLfqEQYPupnHjpgQFBfHjjz8wd+5M8vPz8ff3JyMjnYiIKqf8ECIzMwOn00mVKpElPv/ZyMzMBCAsLLxIe3h4uDsPFI4Gv/nmJPr3v522bdsTFBTEpk0befXVF8jPL5179IOCCmdJtGtX9JFu7dt34KOPiv8YLF9fX/r2vYXZs6dz++13nvB6ZmaGu/g9XlhYRJECGI5dh/8KDAws8r3NZsPHx6dIAX7066MfTBw8eJDRo4fTpElTxox5hCpVIvHy8mLMmJFFPrwQERGRyq1xSBtWJH7O/B2TGNzoGdpGXGR2JJFSVymL7TPdN20xLO5tMtMKyKQAgMjIcCwWG6G+ESQlZR53vGOrN3udYirM8duUlqCgYADGjXuJatVicDicRV6PiakGFBZN99//APff/wDbtm3lww/n88orE6hXr36JV/Bevvxbeva8kVtvHehuW7VqZZFtgoNDSElJxuVynbTgDgwMwmKxkJycVKJzn63g4MLrlJqaWqT98OHDwLHruHz5Mjp3voghQ4a7t9m5c0epZqlbt94pXytpMdqrVx/mz3+X99+fe8JrwcHBpKYePqE9NTWFmjX/u1BZ6c3MWL16FTk52Ywb95L7gwW73X5CgS8iIiISaAvFhYuN6WtUbItHOvPyyVKEzSg/n0/ExrbE19eXpKREmjZtRpMmRf8LCQk9YZ/69Rtw332jgWOFpM1WdHTydPLy8tzbQ+E9ucuWfVNkm/btO5Cbm8t33y096TH8/Pxo3rwFS5Z8icPhKNZ7PRe1atUmNDSM5cu/LdL+3XdL8fLyolmz5kDhY6+OH7UF+Oabr044ns1mO+tR2vj4TthsNtau/bVI+5o1q2ncuGmJjuXvH8BNN93MZ599fMIHCS1btua339aQkZHhbtu9eyfbtm2lZctW/z1UqcnLy8MwDGy2Y78n33337XnpZxEREalYfG2Fz9s+UqAP5cUzlZ/KsQLIK8jjvatXkFdQPqbDBgUFceedQ3jrrckkJyfRqlVbrFYr+/fv5ccfVzBu3Iv4+voydOgdXHjhpdSrVx+r1cKSJV/i5eXlHtWuU6cOAB9//CEXXngJvr6+RR7ldLy4uHgWLfqUunXrERISyieffEh+fsEJ23Tq1Jnnn3+Gffv20qxZLBkZGXz//TKeeeZ5AIYMGcH99w9h5Mh7ueGGPgQFBbF589+EhITSo0dPAPr2vZ7o6BgmTZpyTtfJarUycOCdvPbay4SFhdOpU2c2bFjP+++/Q58+N7s/lIiLi+fDDxfw0UcfULNmbb7+ejF79+494Xi1a9dl5coVtGrVBj8/P2rVqo2/fwA7dmxn587t7u22b9/K8uXf4uvrR6dOnQEID4+gd+9+TJ8+BTCoU6cu3377NRs2rOeVVyaX+L317t2PBQvmkZCwjtatj63kedNN/fnyy0WMHj2c22+/g/z8PKZPn0LVqtFcffW1JT5PcR29n3v8+Kfp2fNGduzYzoIF8wgMDCqzc4qIiEjF1CqsM1/tm8em9LVk2TMJsOnvBfEsKrZLICMtn/L0fG2Am2++lcjISBYufJ8PP1yAzWajevUaXHDBhe7RxRYtWvH111+yf/9+LBaDevUa8MILE6lTp/CRVY0aNeGOO+7hiy8+4/333yEqquopn7M9atQYXnrpeSZOfAlfX1+6d+/BRRddygsvPFdku+eee5HZs6fz2WcfM2vW24SHRxAXF+9+vVWr1kyePI3p06cwfvxTWCxW6tatx913D3Vv43A4Sm1EtHfvfthsNhYseJ9PPvmQiIgqDBp0N7fffod7m4ED7yYtLY0ZM6YBcMkllzNy5IM8/PCoIscaPfphJk16mQcfvI+8vDz3c7a/+24ps2dPd2+3ZMmXLFny5QnPLR8yZDh+fn7Mn/8uaWmp1K5dl+eff7nEq7FD4S0CvXr1Ze7cmUXaq1aN5o033ubNN1/jmWcex2KxEhfXgREjRhd57Fdpq1+/AY8++iSzZr3NQw+NomHDRjz33As88cTYMjuniIiIVEx1AhvjZfEh35HLjwcXcVWN/mZHEilVhsvlKt1Vu84jh8PJ4cNZJ32toCCflJQDRETE4OXl+Y8UsNks7tXIxbOob8ueGf9e2GwWwsICSE3NUv96GPWtZ1K/ei71rXmCQ71xGg5shg0vqzcFjny8rN7kFeT9O8h1btS3FUNwqDc+Xj7u/i/Oz4GZfRseHoDVWry7sXXPtoiIiIiInHc+Xj7c+c2VeFm9uWXxRXhZvem/+EJ8vPQYsMrEx8uH/osv9MifA00jFxERERERUxydZGt32f/93slviatYuvUzMuypdK9+C7UCGvL5nllsy9xA4+A29K4zlA1pv7J473uEe0fSIbIrqXmJ/HjoSwJswVxXcxDV/evzxt9PUkAuXWP6gdPgx8RF2F12+tUZweH8Q/yY+AWHcvfSNvxiagTUZ0PaavZl76RT5BXER3Zjyb73+fPwSqJ9azGgwcMk5e7j/R2TsBpWLo/pTYGzgK/2vQtAz5p3EhsWz6wt4zicd4iOkd0I8a7CHykrSCtI4YpqffGx+LE6+Ru2ZW6gYVBLmoa2Z3fWZjamraVpSFt61BzIqkNfsfzgJwTYgrm74f9hs3jz1j+Pk23PoEeNAVgMK9/sX0CuI5uLq15HfGQ3Fu+dx7bMDTQNaUeNgPr8k/4H+3N20j7iEuoENuWPwyvc7+Potfo5aSkx/rW4vf5DbE7/iw93vQnA3Q2fJMqvOrO2jPv/9u47vqb7f+D4696bPW4WgkoQJGapJkiFb9UqtWrTUqMpmlCUFuVHW5t+jVCCIGZqdVCzaG1VatfeI7Gy9x2/P/LNrSsJGTdDvJ+PRx8P93M+53w+n/O+J6fv+zmD+4k3+Y9rOxwsSnDk4U7DvvJzfY+TT/Zz/PEflLWuYBjH1dhzeNhVw7dUK85FHjHEI31f7bi3BlszNZ96TeJh0h3WXJ9Nii6ZYT7fGL4P6d+D4kKSbSGEEEIIIUSBUjum3ba19r0DPEoMR6fT8CgxnLXvHQSgvNqDAbva4FuyJRZKK/aEbwLgfuJNOlcYxJPkB1yOPQnA0cfGb8BJ0SUxuOo0TkceAeCfyLR6ViobkrQJJGr7s+Tyt8Skpr0m9Wb8RaP1K9pVI1WXws+3QwzLu1UcTII2josxJwA4H33MaJ0ll79ldr0tXIj5m6iUh1z6X9/U5k7EpEZSr0RTDj3YxoX/rX8z/iK/ha83rG9tlvZMnY23golNTXvLzN3E67jZVOaf/7U1/+IYozZXXJtBTSdfrsf9w4WY41yIOQ6ArZmaeE0M5WwqcSv+kmHf3Yy/aLSv0sf/691Qwz44E3WEptadDONYcW2GUZs34y9Swb4q9xNvcj76GOejjxmNQ6lQYh/lzOrr/zWUPbuv9Og48OBXLsecIrj5FkpYl2ZN6/1G34M1rfen7T9HC5PcUlBYJNkWQgghhBBCFChLc0s+2NoYjV6DTqdBo9cw6Ld2KJVmmCnMWN16Hx3c/PGwr0GqLpnSVu6k6lNQmzsB4KmujW/Jd7kaexZrlS1avYZEbTwKlJS2dsdcaUHfKqM4G3OIB/HhoFfgblsFD/sauFi6Us6mEo+SbdDqNVgqrTFXWpCsTaSmU33qujRGgYKy1hVJ1iWiUpihVCgpafUaLcp058STfdia2aNHT7wm7TWrJSxLA9Cl/KecjzrGzfhLqBQqSlqVpYZjPTzsa3A19gxRKY9I1adgrrTAUmlNqi4FN9sq+JZsCcBrNh48TLqLAqWhX53cB/J7xE/YmqlRoCBeE4MePdYqO1QKFc3LdsXNtgpnIg9jrrTARmVHvZLNeM3Gg4vRfxuNI31fOZi74Of6HgCuVm48SLoLYNi/Xcp/yqZbi7BW2aJSqEjUxhv2lZXSBh+XpjhalODAg1+xVFqj0aXQrGxXnCxKkaiNp5xNJUM80vcVQJPS76NSqHC0KElJq7KUsC79wu9BUXtAdU7IA9KKCXmIVvElsc1/8oA0YUoS2+JJ4lp8SWwLR/pDsQAeJYYz6Ld2LGj2CyWs05JWUzwkTWJb9OX2eyAPSBNCCCGEEEKITMREpZCcmgxACevSKJVmJk20xcuhuH8P5DJyIYQQQgghRIFLS6RSKFnSHjNFWlry8GFs4XZKFLji/D2QmW0hhBBCCCFEoUlOTWZ1632GGU7xaiqO3wOZ2RZCCCGEEEIUmvSZTfFqK47fA5nZFkIIIYQQQgghTExmtos4Pz/vF9YZM2Y87dq1z9X2AwM/wcbGhunTZ+dq/aIsNTWVGTMmc+jQfqKiohgyZDixsbGEha1i1679Jm/vxIm/OHv2NL179zMq/+efcwQHz+fatavExsbg5OSMj099/P0HUaJESUO9n37ayB9/7OHq1SskJSVRoUJFevXqQ5Mm7zy33fv379GlSztUKhWrVq3Hzc3dsOzy5Yv07fsBc+cupG7dF3+XClv6WL79dipNmjQr7O4IIYQQQgiRa5JsF3ELFy4z+jxwYF86d+5Gs2bvGspee61crrf/+eejsv3o+pfN9u2/smPHVr76agJly5ajTJky/PTTxnxr7++/jxMWtipDsh0bG0v58hVo27YDTk7O3Lt3l+XLl3DhwnkWL16BhUXaq6ZWrFhK/fq+dOjQGRsba/bs2c3o0SMYN+5rWrZ874Xta7VaVqxYyldfTciP4QkhhBBCCCFyQJLtHLCwMkNtZ0VMXBIpSZoCabNmzVoZykqVKp1pebrk5CQsLa2ytf2KFT1y3bei7ubNG7i4lKBFi1aF2o969RpQr14Dw+e6db1xdXVl2LBALl78h1q1agOwdOlqHB0dDfV8fBoQHn6PNWtWZivZrlvXm507t9G3rz9ly75m8nHk5HslhBBCCCHEq654TmnmAwsrM5QWlny1Lw6lhSUWVkXjd4qQkGCaN2/EuXNnGTCgL++88xYbN64HYMGCIHr37kbz5o3o0KEV48eP4dGjR0brBwZ+whdfDM2wvatXrzBoUH+aNm1Ir15dOXr08Av7snbtKj7+uDctW/6HNm2a88UXQ7l162aGemfPnmbYsABatPgPzZs3xt//I44dO2JYnpKSwqJF39OlS3uaNPHl/fdbM2nShBztl86d2xIWtooHDyLw8/PGz8+b+/fvZVo3PPw+Y8d+QcuW/6FZMz+GDw/k6tUrRnW2bdvCoEH9adXqHd59twmBgZ9w/vxZw/KQkGCWLVtMYmKiob3AwE+y7J9a7QikXeqe7ulEO12VKl48fPgwW2Nu06Y9Tk7OrFy57Ln1dDody5cvoXPntjRp4kvPnp0yzPinfw/Onzf+Xm3duhk/P28uXDjPsGEBNG3akB49OnLs2FF0Oh2LFn1P27YtaNu2BQsXzkOn0xm2efPmDcaPH03Hju/RtGlDPvywC2vXrjKqI4QQQgghRHFRNDLGApak0WernpkSzJQKzCzSEu1BO2O4Ea3lVoyWBS3UKLWQkJj63G1YqkChUJCs1aPXg5WZwhRDMJKamsr48WPo0qUnAwYEoFY7ABAZ+YRevfpSokRJoqIiCQtbTWDgJ6xatQ4zs6xDr9Fo+OabsXTu3J0+fT5m9epQxo79gg0bNuPg4Jjleg8fRtCpU1dcXUuTkBDPTz9tZNCgfqxdu8nQp9OnT/LZZ4OoUaMWX345Fnt7ey5cOE9ERLhhO2PHfsHx48fo1asvNWrUIioqkj/+2JujfTJ58gxWrw7l5MkTTJo0EwAXlxIZ6iUkxDN48AAUCgUjRozGwsKSFSuWEhDgT2joWlxdSwNpCfm7777Ha6+VIzU1ld9+20Fg4CcsX74Wd/fytG3bgYcPH7Br13bmzFkIgK2trVFbWq0WnU7HvXt3+f77uXh6VuX11+s8dxxnzpykQoWK2Rqzubk5PXv25vvv5/DRRx9TunTpTOvNnz+HDRvC6N27H7Vq1ebQof3MnDkFrVZDp07dDPVSU1P5+uuxdO367/fq0qULAEycOJ727TvRvfuHrFq1nK+++oJWrd4jPj6esWO/5vz5s4SEBOPhUZkWLdJueXj48AHu7hVo3rwVNjY2XLlyiZCQYBITE+jXL+sfJoQQQgghhHgZvZLJdo/dz0+Q0/lXU9HB0xK9+b+JNsCNaC2DdsYwp5maHy5q+ely1u+CW97EHAcLGHlYw+14PT+2tDDJGJ6m0WgYMCCAJk2aG5WPGTPe8G+tVkvNmq/z/vutOXHiL6PLmp+VmprKwIGB+Pr6AeDuXp4uXdpx5MghWrZsneV6Q4Z8btSej0992rRpwd69u2nfviMACxbM5bXX3JgzZwEqlQrAqC/Hjh3h0KEDjB8/kebN/70v/el/Z4enZ1WcnUtgbm7x3Evuf/11M+Hh91m5cp0hqX3jjbp06tSGdevWMnjwMAD69vU3rKPT6fDxqc8//5xj27YtDBgQQKlSrpQsWQqlUplle4GBn3DmzCkAqlatzsyZc577o8fOnds5c+Y006Z9l+1xt2//PqtWLWfVquWMGDEqw/KoqCg2bvyBHj160b//ACBt/0dFRbFs2RI6dOhsiItGo+GTTz6ladMWhvXTk+1Onbrx/vudAShZsiS9e3fnwoV/CA5Om1WvX9+XAwf2sXfvb4Zk29u7Ht7e9QDQ6/W8/nodkpKS2LhxnSTbQgghhBCi2Hklk+3sUipAbWfFV/viDIl2uhvRWr77M56Jje349WoyqYV8JWzDho0ylB0+fJDQ0BCuX79KfHy8ofz27ZvPTbaVSiXe3vUNn8uUKYulpSUPHjx4bh/Onj3DkiULuHTpIjEx0U+1dwuApKSk/13uHmBI6J7111/HsLKyolmzls9ty1ROnfobD49KRrPHarUD3t71OX36pKHsxo3rBAfP5+zZ00RGPjGU376d8TL5rIwePY7Y2Dju3r3N6tWhDB36KQsWhGBra5eh7pUrl5k5cwqtW7flP/9pgkaTvS+YpaUV3bt/wJIlC/noo34Zlp8/fxaNRpPhSd9Nmzbnt992cPv2LaN9kf6Dy7N8fP79fri5lQfgzTd9jOq4ubkbYg+QnJzMqlXL2blzGxER4Wg0/z73ICEhARsbm2yNUQghhBBCiJfBK5lsr21qnq16ZkqIiUtidANbbsVojRLuCg4qRjewJTI6iRVNst6e5f9yyhm+Zuizd/V6jllZWWFjY2OUkP3zzzlGjRpOo0b/4cMPP8LR0RmFQsGAAX1ITn7+y+ItLS0xNzcek7m5OSkpWc/gh4eHM3x4IFWrVmPkyNGUKFESc3NzRo4calgvNjYGnU5n9LqrZ8XEROPiUgKFwvSX22cmNjYWJyfnDOXOzs5cv34VSLvUfPjwQBwdHRk8eBiurmWwtLRg6tSJpKQ8f18+zd29AgA1atTE27senTu35eeff6Rnz15G9cLD7zNixBCqVavBF198leMxdejQmdWrQ1m9egXvvdfWaFlsbIxhfE9zcnIBMPqRJP17lRk7O3vDv9O/K0+XpZc//Z1ZsCCIzZt/pG9ff7y8qmFvb8/+/X8QGhpCSkqKJNtCCCGEEKJYeSWT7ZzcN52SpMECWNBCbbiUvIKDigUt1OhSktGlarK1PUtV/iWPmSWm+/b9jp2dHd98MxWlMu05eOHh9/OtD0ePbnJKjAAALf5JREFUHiIxMYFJk2Zgb5+WdGk0GqPkzc7OHqVSyaNHWT/wS6124PHjR+j1+gJJuNVqdaYPcXvy5An29mogbcb+wYMIpk2bRZUqnoY68fFxQKlctevs7ELJkqW4e/e2UXlUVBTDhwfi5OTE5MnTn3uZeVZsbGzo1u0DQkNDaNDgLaNlanXamCIjn1Cy5L99j4x8/L/lDoYyU+//vXt/o337jnz4YR9D2aFDB0zahhBCCCGEEEWFPI08G1KSNOhSklnQQk1jNwtDol1Qr//KjeTkJMzMzIwSpp07t+Vje8koFAqj5HDPnt/Qav+9GsDa2poaNWqxffuvRuVP8/auR1JSEnv27Mq3vj7t9dfrcO3aFW7dumEoi4mJ4a+//jQ8vCw5OQnAaLb/zJlTGZ5unjaTm72Z7oiIcMLD7xu9oishIYERI4aQmqphxoy5mV5enl2dOnXF3NyCtWtXGpVXq1YTMzMz9u7dbVS+Z89vODk54+bmnus2XyQ5ORkzs3/3oVarZffunfnWnhBCCCGEEIXplZzZzo30Ge5Jje0K9D3bueXjU59169Yya9Z0Gjduwtmzp9mxY2u+tZd+v+7kyV/Tvn1Hrl+/RljYqgyXFg8cOJjPPhvI0KGf8v77XbC3t+fSpQs4ODjSpk17fHzq4+vbkClTvuHu3TtUr16TmJgYfv99N998MwVIew3X1KnfMnv297zxxpt56vd777Vl3bo1jBw5FH//QYankatUKrp27QFAjRq1sLa24b//ncaHH/bh4cMHhIQEG80MA5QvXxGtVsu6dWupVet1bG1tcXevwIwZk3FwcKRq1erY2dlx69YNwsJW4+zsQps27Q3rf/XVSC5fvsjo0f9HePh9w5UIZmYKqlatmaNx2dra0aVLd5YtW2xU7ujoSKdO3VizZgUWFhbUqFGLw4cPsmvXdoYNG5nlvfSm4ONTn82bf6JiRQ8cHBz58cf1pKRk72GFQgghhBBCvGwk2c6BlCQNj5LiCrsb2eLr68egQYPZuHEdW7duplat2kyfPpsePTrmS3uVKlVmzJjxLF26iC++GEaVKp5MnDiNceOMn4hdu3YdgoKCWbx4AZMnT0CpVFGxogf+/oMMdSZOnM6yZYv5+edNLF26CGdnF6MHcun1erRaLXoT3ARvY2NLUFAwQUH/Zfr0yeh0WmrVqs38+YsNr/1ydnbh22+nMn/+bEaN+hw3N3dGjhzD6tWhRttq2LAR77/fhVWrlhMZ+YTatd9g3rxFVKtWg19++ZFNm9aTmpqCq2tpGjRoSO/efY1epXbs2NH/jX88zzpw4K8cj61Llx6sW7fG6OF4AAEBn2Fvb8/mzT8RGhpC6dJlGTFiNB06dMpxGzkxbNhIZsyYwqxZM7CysqJVqzY0btyEadMm5mu7QgghhBBCFAaF3hQZSyHRanU8eRKf6bLU1BQeP76Pi0sZzM1N/7qtosbMTJntJ1aLl4vENv8Vxt8LMzMlTk62REbGS3yLGYlt8SRxLb4ktsWXxLb4KszYOjvbolJl725suWdbCCGEEEIIIYQwMUm2hRBCCCGEEEIIE5NkWwghhBBCCCGEMDFJtoUQQgghhBBCCBOTZFsIIYQQQgghhDAxSbaFEEIIIYQQQggTk2RbCCGEEEIIIYQwMUm2hRBCCCGEEEIIE5NkWwghhBBCCCGEMDGzwu6AeD4/P+8X1hkzZjzt2rXPdRuXL19k377f+eCDj7Cyssr1dgrLsWNHCAqaxe3bt7C0tGT79t/x8/Pm008/o2fPXiZvLyQkmHr1GlCrVm2j8nnzZnPkyCEiIsJRKBS4u5ene/cPaNaspaHOrVs32LDhB06c+Iv79+/h7OxC/fq+fPzxIBwdHZ/b7qRJE9i2bQstW7Zm3LhvjJaNHv05sbGxzJu3yGTjzE+TJk3gwoXzrFy5rrC7IoQQQgghRL6QZLuIW7hwmdHngQP70rlzN5o1e9dQ9tpr5fLUxuXLl1i2bDGdOnV7KZPtyZO/oVKlynz++SgsLS3zvb1lyxZjbW2TIdlOTEygXbsOuLtXQKFQ8Pvvu5kw4St0Oj0tWqTF69ixo5w6dZJ27TpSuXIVIiLCWbJkIX//fZxly9ZgYWHxwvZ/+20H/fp9kue4CyGEEEIIIfKPJNs5YGamxNbemvjYRDQaXYG0WbNmrQxlpUqVzrT8VZSQkMDDhw/o338AtWu/Uah9GTlyjNHn+vV9uXHjGtu2bTYk282ataRjx64oFApDvXLl3Bg0qD+HDu3n7bebPreNcuXcSUlJZsWKpYwe/X8mH0NychKWli/fDy5CCCGEEEIUNZJs54CZlSW3Y3WUsrJEE5dY2N0x2Lp1Mz/8sJrbt2+hVjvQqlUbPv54ICqVCoDY2Fi+/34Ohw8fJCYmGkdHJ2rVep2vv57C1q2bmTz5awDatGkGQOnSZdiwYXOmbd28eYOlS4M5c+Y00dFRlClTlvfea0+3bj1RKv99BEBKSgrLly9h164dPHr0AEdHJ7y96/HVVxMMdc6ePU1ISDDnzp1Fr9dToUJFPvlkED4+DbI97vS+T536LVOnfkurVm2M2njaTz9t5IcfVhMefh8XlxK0adOe3r37Gfr96NEjFi2az99/n+Dx40eUKlWKJk2a0bevv2HGOf2y/u+/n8P3388BYO7chdStm/nl/mq1A/Hx8YbPDg6OGep4enr9r/2HLxyzubkZXbp0IyhoFn37+lO6dJks6169eoX582dz+vRJVCoVPj71CQwcTunSpQ11/Py8GTAgkNjYGLZv/5XExER27dpH585teestP8qVcyMsbDWxsbG8/fY7jBw5hps3r/Pf/07j8uVLVKzowahR/0elSpUN21y7dhW7d+/k9u2bmJtbUL16DQIDh+HuXv6F4xNCCCGEEKK4eCWT7SSNPtNycxWoFAp0ej0p2rQylRLMlQosLc1IRUHAzmjWtHPA0tKM5GQNyVo9+kw2l74eYKijUIClSpGxch6Eha1iwYIgunfvSWDgUG7cuMGiRd+j0+kYNGgwAEFB/+Xo0UMMHDiY0qXL8PjxI44cOQSAr68fH33Un9DQEL77LghbWzssLMyzbO/hwwe4u1egefNW2NjYcOXKJUJCgklMTKBfv08M9caO/YLjx4/Rq1dfatSoRVRUJH/8sdew/PTpk3z22SBq1KjFl1+Oxd7engsXzhMREZ7tsfv6+jFr1nyGDQvgo4/64+vrh5OTU6Z1N2wIY/bsmXTu3I233mrEmTOnWLZsMXFxcQQGDgUgOjoKtdqBwYOHYW9vz+3bt1i6dBGPHz9izJjxQNpl/c9eyl+xYkVDO3q9Hq1WS2JiIgcP7uPYsaMZ7q9+1unTJwEoX77ic+ula9OmAytWLGXFiqV88cVXmdaJiAgnIMCf114rx7hx35KSksyiRQsYPPgTQkPXYmNj+9S+WUv16rUYNWocWq3GUH7gwD4qVqzEyJGjuXfvLkFBszAzM+fcudN06/YBzs7OLFgQxLhxX7Jq1XrDjxYPH0bQqVNXXF1Lk5AQz08/bWTQoH6sXbsJtdohW2MUQgghhBDiZfdKJts9dqdmWv6tjxk1nRWEJ0DAgbQ6w+pY0KKSNakomHAgjrhUPRMOxDHBzw4rc3OWnIjn1+uaDNvq5KHkwyppu3fwgVQeJkFtFwUTvLNOZHMqISGekJBF9OzZm4CAwWg0Onx8GmBubkZQ0Cx69uyFg4Mj//xzjmbN3qVVqzaGddMf2uXk5GS499fLq9oLH9Ll7V0Pb+96QFpi+frrdUhKSmLjxnWGZPvYsSMcOnSA8eMn0rz5v/eWP/3vBQvm8tprbsyZs8AwA1+vXvZmtNM5OTlRtWp1IO2+9awurddqtSxfvoSmTVswdOhIQ1sajYawsFX06tUHBwdHKlWqbEi8AWrVqo2VlTWTJo1n+PAvsbKyMrSR1aX8f/31J8OGBQCgUqkYNuwLmjRpluUYkpOTmTdvDp6eXob9+iKWlpb06NGL4OD59OnzMaVKuWaos27dGrRaDbNmzTMkuJ6eVfnwwy5s3bqZzp27G+ra2zswefIMo0vb002d+h3m5mnf2b//Ps7mzT8yc+ZcGjR4CwCdTs+XXw7j6tUrVKniCcCQIZ8b1tdqtfj41KdNmxbs3bub9u07ZmuMQgghhBBCvOxeyWQ7J971tCU8XkfAzmjiUtOmsE9EaOj5SzTzW6gJ8Lbn1+uRhdK3M2dOk5iYQJMmTdFoNIb7yL2965OcnMy1a1d544038fSsyrZtW3BxKUGDBr54eFR+wZazlpyczKpVy9m5cxsREeFoNP/+0JCQkICNjQ1//XUMKysro6dwPy0pKYlz584yYECAIdHOTzdv3iAqKop33jFOet95pzkrVy7j/Plz+Po2RK/Xs379Wn755Ufu3btHSkqyoe69e3eytd9q1KjJkiUriIuL4+jRw8yePQMzMxVt2nTItP7MmVO4f/8eCxeGZJrsZqVDh86sWhXKqlXLGT78ywzLT506Sd263kYzyeXLV6By5SqcPn3KKNlu0OCtTNuuU6euIdEGcHMrj1Kp5M03fZ4qcwfgwYMIQ7J99uwZlixZwKVLF4mJiTbUvX37VrbHJ4QQQgghxMvulUy21zbNfHbZ/H95X2mbf+skxCZQysaKNe0cmHAgjhMRGuq6mvG1nx1m6EmITch0e6qn3mAe5GduuIzclKKjowDo1+/DTJc/eBABwLBhX6BWB/PDD6v4/vs5lCrlSq9efXn//c45bnPBgiA2b/6Rvn398fKqhr29Pfv3/0FoaAgpKSnY2NgQExONi0uJLJPH2NgYdDodJUqUzHH7uREbGwuAk5OzUbmzs7OhP5A2Gzx//hx69uxN3bre2Nvb888/5/nvf6eRkpKSrbZsbGwNs+3e3vXQajUEBc2iVau2GX5YWLToe3bu3Mb06bNz/AOIlZUV3bp9wLJli+ndu38mY44xJL9Pc3JyMUqA4d/98Cw7Ozujz2ZmZlhaWhol4On/Tv9hIjw8nOHDA6latRojR46mRImSmJubM3LkUKMfL4QQQgghhCjuXslk28rs+VmvUqHAKn3P6PQkxSViaWnGBD87ev4Szdd+dqhSU0hK1qDMxvZMfZ92Ont7NQCTJs2gbNkyaLXGT0gvU6YskJY0ffbZ53z22edcvXqF9evX8t13U/HwqJTjJ3jv3fsb7dt35MMP+xjKDh06YFRHrXbg8eNH6PX6TBNuOzt7lEplth4IZgpqddp+iow0vgLhyZMnwL/7ce/e3TRs2JiBAwMNdW7cuJ6ntr28qrFu3VqioiJxcSlhKN+wIYyVK5cxZsx46tf3zdW2O3Xqwtq1K1mzJjTDMrVaTWTkkwzlkZGPcXN79kFlpvt+Hj16iMTEBCZNmoG9vT0AGo0mQ4IvhBBCCCFEcad8cRUBkJyswRw981uoMUNPcnLG+7QLWs2ar2NlZcXDhxFUq1adqlWN/8vsydeVKlVmyJDhwL+JpJmZ8ezk8yQnJxvqQ9o9ubt37zSq4+1dj6SkJPbs2ZXpNqytralRoxbbt/+KVqvN1ljzwt29PI6OTuzd+5tR+Z49uzA3N6d69RpA2muvnp61Bdi5c1uG7ZmZmWV7lvb06ZPY2toaxWLXru3MmfMdAwYEGN1Hn1M2NrZ07dqDn3/elOGHhNdfr8Px48eIiYkxlN26dYOrV6/w+uu1n92UySQnJ6NQKDAz+/d3vD17fiuQOAshhBBCCFGUvJIz27mlSUrG7X/v2S4K7O3t6d9/IN9/H8SjRw+pXbsuKpWKe/fusH//PiZNmo6VlRWDBvWjUaMmeHhUQqVSsn37r5ibmxtmtStUqADApk3radTobaysrIxe5fQ0H5/6bN78ExUreuDg4MiPP64nJSU1Qx1f34ZMmfINd+/eoXr1msTExPD777v55pspAAwcOJjPPhvI0KGf8v77XbC3t+fSpQs4ODjSpk17ALp160Dp0mWYM2dBnvaTSqWiT5/+zJ49EycnZ3x9G3Lu3BnWrFlBly49DImwj0991q8PY+PGH3BzK8+OHVu5c+dOhu2VL1+RAwf2Ubv2G1hbW+PuXp579+6xYEEQTZo0pUyZsiQmJnDw4AG2bPmJAQMCDMnn338fZ9KkCdSt60OdOm9y9uwZw3ZLlSqV6cPOnqdz5+6Eha3i7NnT1KlT11DetWtPfv11M8OHB9K7dz9SUpJZvHgBrq6lad26bS72Yvak3889efLXtG/fkevXrxEWtgo7O/t8a1MIIYQQQoiiSJLtHNBodERHxr+4YgHq0eNDSpYsybp1a1i/PgwzMzNee60cb73VyJDg1apVmx07fuXevXsolQo8PCozbdosKlRIe9WUp2dV+vX7hC1bfmbNmhWUKuWa5Xu2hw0byYwZU5g1awZWVla0atWGxo2bMG3aRKN6EydOZ9myxfz88yaWLl2Es7MLPj71Dctr165DUFAwixcvYPLkCSiVKipW9MDff5ChjlarNdmMaOfO3TEzMyMsbA0//rgeF5cS9O3rT+/e/Qx1+vTxJyoqiiVLggF4++2mDB06gi+/HGa0reHDv2TOnJmMGDGE5ORk5s5dSIUKFbGzs2P58iU8efIYW1s7ypevwOTJM2jU6G3DuidO/IVGo+H48T85fvxPo+327etP//4DcjQuOzs7OnXqRmhoiFG5q2tp5s1bxPz5s/nmm7EolSp8fOoxePBwo9d+mVqlSpUZM2Y8S5cu4osvhlGliicTJ05j3LhR+damEEIIIYQQRZFCr8/sLdEvB61Wx5MnmSe/qakpPH58HxeXMpibWxRwzwqemZnS8DRyUbxIbPNfYfy9MDNT4uRkS2RkvMS3mJHYFk8S1+JLYlt8SWyLr8KMrbOzLSpV9u7Glnu2hRBCCCGEEEIIE5NkWwghhBBCCCGEMDFJtoUQQgghhBBCCBOTZFsIIYQQQgghhDAxSbaFEEIIIYQQQggTK/bJ9kv8sHUhRAGRvxNCCCGEEMLUim2yrVKpAEhJSS7kngghirr0vxMqlVkh90QIIYQQQhQXxfb/LJVKFdbWdsTFRQJgYWGJQqEo5F7lH51OgVYrs3PFkcQ2/+j1elJSkomLi8Ta2g6lstj+/iiEEEIIIQpYsU22AdRqZwBDwl2cKZVKdLqCfaG7KBgS2/xnbW1n+HshhBBCCCGEKRTrZFuhUODg4IK9vRNaraawu5NvVCoFDg42REcnyAxoMSOxzX8qlZnMaAshhBBCCJPLcbJ99epVJk6cyN9//42trS3t27dn6NChWFhYPHc9vV7P4sWLWbNmDU+ePKFatWqMHj2aOnXq5Lbv2aZUKlEqn9+/l5mZmRIrKysSE7VoNDIDWpxIbIUQQgghhHg55Wg6Jzo6mo8++ojU1FSCgoIYNmwY69atY+rUqS9cd/HixcydO5c+ffoQHBxMyZIl6devH7dv385154UQQgghhBBCiKIoRzPbYWFhxMfHM2/ePBwdHQHQarV8/fXXDBgwAFdX10zXS05OJjg4mH79+tGnTx8A3nzzTd59911CQkKYMGFCXsYghBBCCCGEEEIUKTma2d63bx++vr6GRBugVatW6HQ6Dh48mOV6J06cIC4ujlatWhnKLCwsaN68Ofv27ct5r4UQQgghhBBCiCIsRzPb165do1OnTkZlarWakiVLcu3ateeuB+Dh4WFUXqlSJUJDQ0lKSsLKyionXQFAqVTg7Gyb4/WKm/Q3mjk4WKOXZ2gVKxLb4ktiW3xJbIsniWvxJbEtviS2xVdhxlapzP7rpHOUbMfExKBWqzOUOzg4EB0d/dz1LCwssLS0NCpXq9Xo9Xqio6NzlWwrFApUquL77uyckicqF18S2+JLYlt8SWyLJ4lr8SWxLb4ktsVXUY9t0e6dEEIIIYQQQgjxEspRsq1Wq4mNjc1QHh0djYODw3PXS0lJITk52ag8Jibmf+/CznpdIYQQQgghhBDiZZOjZNvDwyPDvdmxsbE8fPgww/3Yz64HcP36daPya9euUbZs2VxdQi6EEEIIIYQQQhRVOUq2GzduzKFDh4iJiTGUbd++HaVSScOGDbNcr27dutjZ2bFt2zZDWWpqKjt37qRx48a56LYQQgghhBBCCFF05egBad27d2flypUEBAQwYMAAIiIimD59Ot27dzd6x/ZHH33EvXv32LVrFwCWlpYMGDCAoKAgnJ2d8fT0ZO3atURFRdG/f3/TjkgIIYQQQgghhChkOUq2HRwcCA0N5dtvvyUgIABbW1s6d+7MsGHDjOrpdDq0Wq1Rmb+/P3q9nqVLl/LkyROqVatGSEgIbm5ueR+FEEIIIYQQQghRhCj0ennrnBBCCCGEEEIIYUry6i8hhBBCCCGEEMLEJNkWQgghhBBCCCFMTJJtIYQQQgghhBDCxCTZFkIIIYQQQgghTEySbSGEEEIIIYQQwsQk2RZCCCGEEEIIIUwsR+/ZFgVvyZIlbNmyhTt37qDRaHBzc6Nbt2588MEHKBSKLNe7du0aq1at4siRI9y9excXFxcaNWrEZ599hrOzs6Hepk2bGD16dIb1/f39GTFiRL6MSaTJbWwBUlJSmDVrFr/88gvx8fG88cYbjBs3Dg8PD6N6V69eZeLEifz999/Y2trSvn17hg4dioWFRX4O7ZWm1WpZunQpv//+O1euXEGv1+Pl5cVnn32Gt7f3c9cNCgpi3rx5mS7r1q0b33zzzXPrTZgwgR49euR9ECJTeYntnTt3aNq0aYby2rVrs27dOqOyEydOMG3aNP755x9cXFzo0aMH/v7+L/y7IHIvL7E9ffo0a9eu5a+//uLBgwe4urrSsmVLBg0ahI2NjaGeHLeFIy+xBYiNjWXKlCn89ttvpKam0qhRI8aOHUupUqWM6slxWzgOHjzIpk2bOHXqFLdv3+aDDz7g//7v/164npxvi7bcxrUonmsl2S7iYmNjad26NVWqVMHS0pLDhw8zceJE4uLiGDhwYJbrHTp0iL/++otu3bpRtWpV7t27x9y5c/nzzz/5+eefMyRbS5Yswd7e3vDZ1dU138Yk0uQ2tgATJ05k69atjBo1CldXVxYuXEifPn349ddfDXGMjo7mo48+okKFCgQFBREREcHUqVNJSkrK1h8skTtJSUksWrSI999/H39/f5RKJevWraN3796EhITg6+ub5bpdunShUaNGRmXHjh1j5syZNG7c2KjcysqK0NBQozI3NzfTDURkkJfYphs+fDj169c3fLa1tTVafvPmTfr370/Dhg0ZOnQoFy9eZObMmahUKvr372/yMYk0eYnttm3buHnzJh9//DEVKlTgypUrzJ07l1OnTrFixQqjunLcFry8HrdDhw7lypUrTJgwAUtLS2bPno2/vz8bN27EzCztf6PluC08+/fv58KFC/j4+BAdHZ3t9eR8W7TlNq7pitS5Vi9eOsOHD9e3aNHiuXWePHmi1+l0RmXHjx/Xe3p66rdv324o27hxo97T01P/+PHjfOmryJnsxPb+/fv6atWq6cPCwgxlkZGR+jp16ugXLVpkKFu4cKG+Tp06+sjISENZWFiYvlq1avrw8HCT912k0Wg0+qioqAxl7777rn7AgAE53t6XX36p9/Hx0ScnJxvK5s6dq69Tp06e+ypyJi+xvX37tt7T01O/bdu259YbN26cvkmTJkbx/u677/Te3t5GZcK08hLbzM6fv/zyi97T01N/5swZQ5kct4UjL7E9ceKE3tPTU79//35D2dWrV/VeXl76X3/91VAmx23h0Wq1hn83adJE//XXX+d6W3K+LTpyG9eieK6Ve7ZfQk5OTqSmpr6wzrOXQVSvXh2ABw8e5FvfRN5kJ7YHDhxAp9Px7rvvGsocHR1p2LAh+/btM5Tt27cPX19fHB0dDWWtWrVCp9Nx8OBBk/ddpFGpVDg4OGQo8/LyyvGxl5yczK5du2jZsqVc+l8EmDK2Wdm3bx9NmzY1infr1q2JiYnh77//NkkbIqO8xPbpW7PSyfm26MhLbPft24daraZhw4aGMg8PD6pVq5bhfCvHbeFQKk2Tysj5tmgxVVyzUpDHrCTbLwmNRkNcXBy///47P/30E717987xNo4fPw5ApUqVMixr06YN1apVo2nTpgQHB6PVavPcZ5E9OY3ttWvXcHFxyfA/D5UqVeLatWtG9Z69h1utVlOyZEmjeiL/aTQaTp06lSEeL7J3717i4uJo06ZNhmVJSUk0aNCA6tWr07p16wz3IomCkdPYTpgwgWrVquHr68vYsWOJiooyLEtISOD+/fsZtuXh4YFCoZDjtoDl9riFf8+3z64rx23RkN3YXrt2jYoVK2aYvPDw8DAcj3LcFg9yvi1eitK5Vu7ZfgncvHmTFi1aGD4PGjSIPn365GgbycnJTJs2jerVqxvdn1SyZEkGDx5M7dq1USgU7Nmzh9mzZxMRESH39RaA3MQ2JibG6P76dGq12ui+lpiYGNRqdYZ6Dg4Oubr/ReTekiVLiIiIyPFxu2XLFlxdXfHx8TEqd3d3Z8SIEVSvXp3k5GQ2b97MuHHjiI2NlfsDC1h2Y2thYUGPHj3w8/NDrVZz6tQpFi5cyNmzZ1m/fj3m5ubExsYCZDhuLSwssLa2luO2gOX2uH3y5AlBQUE0bdqUChUqGMrluC06shvbrM63Dg4OnD17FkCO22JCzrfFQ1E810qyXcBiY2OzdVmZm5ub4dKGMmXKsGHDBhISEvjrr79YvHgxSqWSIUOGZLvd8ePHc+fOHcLCwox+oW3UqJHRAyL8/PywtLQkNDSUgQMHZnjapshaYcVW5L/cxDbdwYMHCQoK4tNPP6VmzZrZbjMmJoY//viDDz/8MMPlVO3btzf6/Pbbb5OamsqCBQvo3bs35ubm2W7nVVdQsS1VqhQTJkwwfK5Xrx5VqlRhwIAB7Nq1i9atW+eq/yJrhXHcpqamMnz4cACjeIMct6ZUGLEVBSMvsc0tOd/mv4KKa1E810qyXcC2b9/O2LFjX1hv69athsu9LSwsqFWrFgD169fHzs6OadOm0aNHD0qWLPnCbc2aNYvNmzezcOFCPD09X1i/VatWLF26lH/++UeS7RwoqNiq1Wri4uIylMfExBhdWq5Wqw2/3j0tOjo6wyXo4vlyE1uAc+fOMXjwYNq0aUNgYGCO2tyxYwcpKSm0bds2W/VbtWrFjh07uHXrVqa3iojMFUZs0/3nP//BxsaGc+fO0bp1a8MM2rPHbUpKComJiXLc5lBBx1av1zNmzBhOnz7NmjVrsnX+lOM2dwoqtmq1mvDw8AzlT59H5bg1rdzGNi/kfJv/CiOu6Qr7XCvJdgHr0qULXbp0ydM2atSogVar5e7duy9MtleuXElwcDBTp07N8IoDYVoFFVsPDw8ePXqUIWl+9h7tp+8pSxcbG8vDhw9zdQ/iqyw3sb158yb+/v688cYbTJw4McdtbtmyBQ8PD8ODlkT+KIzYZsXGxoYyZcpkOG6vX7+OXq+X4zaHCjq206ZNY9u2bSxevJiqVavmaF2RMwUVWw8PDw4fPoxerze6KvD69euGyQs5bk3LFP8vlVNyvs1/hRHXrBT0MSsPSHsJnThxAoVCQbly5Z5bb8uWLUyaNInhw4fToUOHbG9/69atqFQq+aNTCLITWz8/P5RKJTt37jSURUdHc+DAAaN3QzZu3JhDhw4RExNjKNu+fTtKpdLoyarC9B48eEC/fv0oU6YMc+fOzfFlZg8ePODPP//M9EEtWdm6dStqtRp3d/ecdlfkQF5j+7S9e/eSkJBguLoF0o7b3bt3G72VID22b7zxRp76Lp4vL7FdtGgRy5cvZ+rUqdl633o6OW4LRm5j27hxY6Kjozl8+LCh7Pr165w/fz7D+VaO25eTnG+Lv8I+18rMdhEWGxuLv78/7dq1o3z58mg0Go4ePcqKFSvo1q0bJUqUMNRt3rw5ZcuWJTQ0FIA///yTUaNG0aBBA+rVq8fJkycNdUuXLk3p0qUB6N+/P/Xr18fLywuA3bt3s27dOnr37p2tS9RF7uQltqVLl6Zz585Mnz4dpVKJq6srwcHB2Nvb0717d8N63bt3Z+XKlQQEBDBgwAAiIiKYPn063bt3x9XVtcDH/KpISkrC39+fyMhIvvrqKy5fvmxYZmFhYfQjVvXq1enQoQOTJ0822sbWrVvR6XRZXtLWsWNHOnTogIeHB0lJSWzevJmdO3cyZswYuX8sH+UltlOnTkWhUFCnTh3UajWnT58mODiYmjVr0qxZM8N6/fv3Z/PmzXz++ef06NGDS5cuERISwrBhw+R1NPkoL7HdvHkz3333He3ataNcuXJG51t3d3fDq8HkuC0ceYntG2+8gZ+fH2PGjOHLL7/E0tKSWbNm4eXlZfRwUzluC8/du3c5c+YMAImJidy6dYvt27cDGL0iVc63L5fcxrUonmsl2S7CLC0tqVixIsuXLyciIgIrKyvc3d35+uuvM8xUa7VadDqd4fPRo0dJTU3l8OHDRr/IAgQGBjJ48GAAKlasyMaNGwkPD0en01GhQgXGjBlDr1698n18r7K8xBZg7Nix2Nra8t133xEfH0/dunVZtmyZ0VNTHRwcCA0N5dtvvyUgIABbW1s6d+7MsGHDCmKIr6xHjx5x4cIFIO3p8k977bXX2LNnj+FzZrGFtP95f/3117P81dzd3Z3ly5fz6NEjFAoFnp6ezJgxg3bt2plwJOJZeYltpUqVWLt2LevWrSMpKQlXV1c6d+7MkCFDMDP791Rcvnx5QkJCmDp1Kp988gnOzs4MGTKEfv365fPoXm15ie3BgwcB+OWXX/jll1+M1p0yZQodO3YE5LgtLHn9mzx79mymTJnC//3f/6HRaPDz82Ps2LFy3BYRR48eZfTo0YbP+/fvZ//+/QBcvHjRUC7n25dLbuNaFM+1Cr1erzf5VoUQQgghhBBCiFeY3LMthBBCCCGEEEKYmCTbQgghhBBCCCGEiUmyLYQQQgghhBBCmJgk20IIIYQQQgghhIlJsi2EEEIIIYQQQpiYJNtCCCGEEEIIIYSJSbIthBBCCCGEEEKYmCTbQgghhBBCCCGEiUmyLYQQQhRT77zzDqNGjSrsbgghhBCvJEm2hRBCiOfYtGkTXl5enDlzprC78kpJTEwkKCiIo0ePFnZXhBBCiFwxK+wOCCGEECJ/bN++HYVCUdjdyJXExETmzZtHYGAg9evXL+zuCCGEEDkmybYQQgjxEtBoNOh0OiwsLLK9Tk7q5rfc9F8IIYR4mcll5EIIIYQJREREMHr0aN566y1q1qzJe++9x4YNG4zqpKSkMGfOHDp27Mibb75JnTp16NmzJ0eOHDGqd+fOHby8vAgJCWH58uU0a9aMWrVqcfXqVYKCgvDy8uLmzZuMGjUKb29v3nzzTUaPHk1iYqLRdp69Zzv9kvjjx48zZcoUGjRoQJ06dQgICODJkydG6+p0OoKCgvDz86N27dr06tWLK1euZOs+8Of1Pzv74M6dO/j6+gIwb948vLy88PLyIigoyFDn6tWrDBkyhHr16lGrVi06duzI7t27sxEpIYQQomDIzLYQQgiRR48ePaJr164oFAo++OADnJ2d2bdvH1999RVxcXH06dMHgLi4ONavX0+bNm3o0qUL8fHxbNiwgY8//pj169dTrVo1o+1u2rSJ5ORkunbtioWFBQ4ODoZlQ4cOpVy5cgwfPpzz58+zfv16nJ2dGTly5Av7O3HiRNRqNYGBgdy9e5fQ0FC++eYbZs+ebajz3XffsWTJEpo0aUKjRo24cOEC/fv3Jzk5Odv7JbP+Z2cfODs7M2HCBCZMmEDz5s1p3rw5AF5eXgBcvnyZHj164Orqir+/PzY2Nmzbto2AgACCgoIM9YUQQojCJMm2EEIIkUezZs1Cq9WyefNmnJycAOjRowfDhw9n3rx5dO/eHSsrKxwcHNizZ4/RpdRdu3alVatWrFy5ksmTJxttNzw8nF27duHs7JyhzWrVqhnVj4qKYsOGDdlKth0dHVm6dKnhfm6dTsfKlSuJjY3F3t6eR48eGWak58+fb1hv3rx5RrPLL5JZ/7Va7Qv3gY2NDS1btmTChAl4eXnRvn17o+1OmjSJMmXKsHHjRsN2evbsSY8ePZg5c6Yk20IIIYoEuYxcCCGEyAO9Xs/OnTt555130Ov1PHnyxPCfn58fsbGxnDt3DgCVSmVIDnU6HVFRUWg0GmrWrMn58+czbLtFixaZJtoA3bt3N/rs7e1NVFQUcXFxL+xz+iz80+tqtVru3r0LwOHDh9FoNPTs2dNovQ8//PCF235R/3O6D54VFRXFkSNHaNWqFXFxcYZ9HRkZiZ+fHzdu3CAiIiJH/RRCCCHyg8xsCyGEEHnw5MkTYmJi+OGHH/jhhx+yrJPuxx9/ZOnSpVy/fp3U1FRDebly5TKsl1lZurJlyxp9VqvVAERHR2NnZ/fcPme1bkxMDAD37t0DwN3d3aieo6Oj0aXsL5JV/3OyD55169Yt9Ho9c+bMYc6cOZnWefz4Ma6urtnupxBCCJEfJNkWQggh8kCn0wHQrl073n///UzrpN9r/PPPPzNq1CiaNWtG//79cXFxQaVSERwczO3btzOsZ2VllWW7SmXmF6fp9foX9jkv6+ZEZv3P6T54Vvr+7tevH40aNcq0zrM/EgghhBCFQZJtIYQQIg+cnZ2xtbVFp9Px1ltvPbfujh07cHNzY968eUaXcc+dOze/u5kj6TPft27dws3NzVAeGRlJdHR0nrad3X2Q1fvB0/tjbm7+wv0thBBCFCa5Z1sIIYTIA5VKRcuWLdmxYweXLl3KsPzpS8hVKhVgPIN86tQpTp48me/9zAlfX1/MzMxYu3atUfnq1avzvO3s7gNra2vg30vb07m4uFCvXj1++OEHHjx4kGH7z77CTAghhCgsMrMthBBCZMPGjRvZv39/hvLevXvz+eefc/ToUbp27UqXLl2oXLky0dHRnDt3jsOHD/Pnn38C8Pbbb7Nz504CAgJ4++23uXPnDmFhYVSuXJmEhISCHlKWSpQoQe/evVm6dCkDBw6kUaNGXLx4kX379uHk5JTlrHN2ZHcfWFlZUblyZbZt20aFChVwdHSkSpUqeHp6Mn78eHr27Enbtm3p2rUrbm5uPHr0iJMnTxIeHs4vv/xiit0ghBBC5Ikk20IIIUQ2PDvLm65jx46ULl2a9evXM3/+fHbt2sXatWtxdHSkcuXKjBgxwqjuo0eP+OGHHzhw4ACVK1dmxowZbN++3ZCQFxUjRozAysqK9evXc/jwYerUqUNISAg9e/Y0em1XTuVkH0ycOJFvv/2WKVOmkJqaSmBgIJ6enlSuXJmNGzcyb948fvzxR6KionB2dqZ69eoEBATkdehCCCGESSj0pn4aihBCCCGKpZiYGHx8fBg6dCiDBg0q7O4IIYQQRZrcsy2EEEKIDJKSkjKUhYaGAlCvXr2C7o4QQgjx0pHLyIUQQgiRwdatW/nxxx9p3LgxNjY2nDhxgi1btuDn58ebb75Z2N0TQgghijxJtoUQQgiRgZeXFyqViiVLlhAfH4+Liwu9e/dm6NChhd01IYQQ4qUg92wLIYQQQgghhBAmJvdsCyGEEEIIIYQQJibJthBCCCGEEEIIYWKSbAshhBBCCCGEECYmybYQQgghhBBCCGFikmwLIYQQQgghhBAmJsm2EEIIIYQQQghhYpJsCyGEEEIIIYQQJibJthBCCCGEEEIIYWL/D8hjm0jUahUyAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc={'figure.figsize':(12,8)})\n",
+    "ax = sns.lineplot(data=df_lr_exp, palette=colors[:6], markers=True)\n",
+    "ax.set(ylim=(0, 1.05))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "498ce8b7-2e23-445c-9b79-7c3dbc580a3c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "36a0ac8a-1ab6-4bbd-a48c-48f61b1bfbc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#4c72b0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dd8452;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#55a868;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c44e52;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8172b3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#937860;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#da8bc3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c8c8c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ccb974;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#64b5cd;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.2980392156862745, 0.4470588235294118, 0.6901960784313725),\n",
+       " (0.8666666666666667, 0.5176470588235295, 0.3215686274509804),\n",
+       " (0.3333333333333333, 0.6588235294117647, 0.40784313725490196),\n",
+       " (0.7686274509803922, 0.3058823529411765, 0.3215686274509804),\n",
+       " (0.5058823529411764, 0.4470588235294118, 0.7019607843137254),\n",
+       " (0.5764705882352941, 0.47058823529411764, 0.3764705882352941),\n",
+       " (0.8549019607843137, 0.5450980392156862, 0.7647058823529411),\n",
+       " (0.5490196078431373, 0.5490196078431373, 0.5490196078431373),\n",
+       " (0.8, 0.7254901960784313, 0.4549019607843137),\n",
+       " (0.39215686274509803, 0.7098039215686275, 0.803921568627451)]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sns.color_palette()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "e7805520-9756-4db1-89d8-65e6ddaac7be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# np.log2(df_experiments[\"Learning rate\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1103b810-e79b-40e7-80ad-bcd4cd4b5260",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}