From 90b77b873ed2888f8328e9c8b2183c0f6e2a6777 Mon Sep 17 00:00:00 2001
From: Paul Balanca <paulb@graphcore.ai>
Date: Mon, 22 Jan 2024 17:08:03 +0000
Subject: [PATCH 1/4] MNIST experiments data & plots

---
 .../mnist/mnist_classifier_from_scratch.py    | 42 +++++++++++++++++--
 experiments/mnist/mnist_experiments.json      | 16 +++++++
 2 files changed, 54 insertions(+), 4 deletions(-)
 create mode 100644 experiments/mnist/mnist_experiments.json

diff --git a/experiments/mnist/mnist_classifier_from_scratch.py b/experiments/mnist/mnist_classifier_from_scratch.py
index 5c5c719..8ac4ec8 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
@@ -76,13 +77,28 @@ 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: 0.001, 0.002, 0.004, 0.008, 0.016
+
     layer_sizes = [784, 1024, 1024, 10]
     param_scale = 1.0
     step_size = 0.001
     num_epochs = 10
     batch_size = 128
 
+    use_autoscale = True
     training_dtype = np.float16
     scale_dtype = np.float32
 
@@ -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..ea078d6
--- /dev/null
+++ b/experiments/mnist/mnist_experiments.json
@@ -0,0 +1,16 @@
+[
+    [
+        [
+            1.0,
+            0.001,
+            10,
+            true,
+            "float16",
+            "float32"
+        ],
+        [
+            0.9698333144187927,
+            0.9395999908447266
+        ]
+    ]
+]

From ce2c4c0bf5530bda9aca3d279e890666a00c86d8 Mon Sep 17 00:00:00 2001
From: Paul Balanca <paulb@graphcore.ai>
Date: Mon, 22 Jan 2024 17:57:21 +0000
Subject: [PATCH 2/4] Additional MNIST experiment results.

---
 .../mnist/mnist_classifier_from_scratch.py    |   6 +-
 experiments/mnist/mnist_experiments.json      | 476 ++++++++++++++++++
 2 files changed, 479 insertions(+), 3 deletions(-)

diff --git a/experiments/mnist/mnist_classifier_from_scratch.py b/experiments/mnist/mnist_classifier_from_scratch.py
index 8ac4ec8..3f52905 100644
--- a/experiments/mnist/mnist_classifier_from_scratch.py
+++ b/experiments/mnist/mnist_classifier_from_scratch.py
@@ -90,7 +90,7 @@ def update_experiments_json(filename, config, results):
 
 if __name__ == "__main__":
     # Param scales: 0.5, 1, 2, 4, 8
-    # Step size: 0.001, 0.002, 0.004, 0.008, 0.016
+    # 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
@@ -98,8 +98,8 @@ def update_experiments_json(filename, config, results):
     num_epochs = 10
     batch_size = 128
 
-    use_autoscale = True
-    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()
diff --git a/experiments/mnist/mnist_experiments.json b/experiments/mnist/mnist_experiments.json
index ea078d6..5d4be58 100644
--- a/experiments/mnist/mnist_experiments.json
+++ b/experiments/mnist/mnist_experiments.json
@@ -12,5 +12,481 @@
             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,
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+            "float16",
+            "float32"
+        ],
+        [
+            0.09871666878461838,
+            0.09799999743700027
+        ]
+    ],
+    [
+        [
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+            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
+        ]
     ]
 ]

From a6537b7092833085272e0eca31b737092f8167fc Mon Sep 17 00:00:00 2001
From: Paul Balanca <paulb@graphcore.ai>
Date: Tue, 23 Jan 2024 12:24:59 +0000
Subject: [PATCH 3/4] wip

---
 .../mnist/mnist_experiments_plot.ipynb        | 1084 +++++++++++++++++
 1 file changed, 1084 insertions(+)
 create mode 100644 experiments/mnist/mnist_experiments_plot.ipynb

diff --git a/experiments/mnist/mnist_experiments_plot.ipynb b/experiments/mnist/mnist_experiments_plot.ipynb
new file mode 100644
index 0000000..103474e
--- /dev/null
+++ b/experiments/mnist/mnist_experiments_plot.ipynb
@@ -0,0 +1,1084 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 138,
+   "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": 139,
+   "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": 139,
+     "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": 142,
+   "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": 141,
+   "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": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_fp16_autoscale_exp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "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": 136,
+     "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": null,
+   "id": "3285658f-3977-4d22-8cbe-45b2470b6198",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f56416a-7cc3-4504-bbf8-e8090541335f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "6b531345-1ee3-45cc-ad38-fb2603667dea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sns.set_style(\"darkgrid\")\n",
+    "p = sns.lineplot(data=df_scale_exp)\n",
+    "# p = 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": 145,
+   "id": "818eb73f-30d9-4e11-a78a-d50b37a2d182",
+   "metadata": {},
+   "outputs": [],
+   "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\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "9d6e5bf6-9a48-4991-9524-c3c0c0ba8ae4",
+   "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>Learning rate</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>0.0005</th>\n",
+       "      <td>0.937717</td>\n",
+       "      <td>0.9183</td>\n",
+       "      <td>0.938200</td>\n",
+       "      <td>0.9165</td>\n",
+       "      <td>0.952150</td>\n",
+       "      <td>0.9300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0010</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>0.0020</th>\n",
+       "      <td>0.985217</td>\n",
+       "      <td>0.9462</td>\n",
+       "      <td>0.987867</td>\n",
+       "      <td>0.9483</td>\n",
+       "      <td>0.988667</td>\n",
+       "      <td>0.9471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0040</th>\n",
+       "      <td>0.996567</td>\n",
+       "      <td>0.9516</td>\n",
+       "      <td>0.995967</td>\n",
+       "      <td>0.9513</td>\n",
+       "      <td>0.995417</td>\n",
+       "      <td>0.9505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0080</th>\n",
+       "      <td>0.998717</td>\n",
+       "      <td>0.9507</td>\n",
+       "      <td>0.998517</td>\n",
+       "      <td>0.9536</td>\n",
+       "      <td>0.999200</td>\n",
+       "      <td>0.9513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0160</th>\n",
+       "      <td>0.998817</td>\n",
+       "      <td>0.9509</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "      <td>0.998233</td>\n",
+       "      <td>0.9510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0300</th>\n",
+       "      <td>0.988783</td>\n",
+       "      <td>0.9355</td>\n",
+       "      <td>0.098717</td>\n",
+       "      <td>0.0980</td>\n",
+       "      <td>0.943250</td>\n",
+       "      <td>0.8904</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Train acc. float16 AutoScale  Test acc. float16 AutoScale  \\\n",
+       "Learning rate                                                              \n",
+       "0.0005                             0.937717                       0.9183   \n",
+       "0.0010                             0.969833                       0.9396   \n",
+       "0.0020                             0.985217                       0.9462   \n",
+       "0.0040                             0.996567                       0.9516   \n",
+       "0.0080                             0.998717                       0.9507   \n",
+       "0.0160                             0.998817                       0.9509   \n",
+       "0.0300                             0.988783                       0.9355   \n",
+       "\n",
+       "               Train acc. float16 Normal  Test acc. float16 Normal  \\\n",
+       "Learning rate                                                        \n",
+       "0.0005                          0.938200                    0.9165   \n",
+       "0.0010                          0.968150                    0.9383   \n",
+       "0.0020                          0.987867                    0.9483   \n",
+       "0.0040                          0.995967                    0.9513   \n",
+       "0.0080                          0.998517                    0.9536   \n",
+       "0.0160                          0.098717                    0.0980   \n",
+       "0.0300                          0.098717                    0.0980   \n",
+       "\n",
+       "               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": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_lr_exp = pd.concat([df_fp16_autoscale_lrexp, df_fp16_noscale_lrexp, df_fp32_noscale_lrexp], axis=1)\n",
+    "df_lr_exp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "840cfe4d-1650-4d51-b008-4f13fe65a130",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = sns.lineplot(data=df_lr_exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36a0ac8a-1ab6-4bbd-a48c-48f61b1bfbc0",
+   "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
+}

From d9f88d29b571fb373b739b6cf0d0b267aecfddcd Mon Sep 17 00:00:00 2001
From: Paul Balanca <paulb@graphcore.ai>
Date: Tue, 23 Jan 2024 17:44:36 +0000
Subject: [PATCH 4/4] wip

---
 .../mnist/mnist_experiments_plot.ipynb        | 287 +++++++++++-------
 1 file changed, 169 insertions(+), 118 deletions(-)

diff --git a/experiments/mnist/mnist_experiments_plot.ipynb b/experiments/mnist/mnist_experiments_plot.ipynb
index 103474e..904406a 100644
--- a/experiments/mnist/mnist_experiments_plot.ipynb
+++ b/experiments/mnist/mnist_experiments_plot.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "cbad259b-8b2c-415b-a5d1-7d9a2694b9e4",
    "metadata": {},
    "outputs": [],
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 87,
    "id": "7ce8ae78-2f0e-46f3-9ed5-375a496a821f",
    "metadata": {},
    "outputs": [],
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 88,
    "id": "0be79d98-5c22-4597-a91c-3e1a3334bde1",
    "metadata": {},
    "outputs": [
@@ -547,7 +547,7 @@
        "34    float32       0.998433     0.9527  "
       ]
      },
-     "execution_count": 139,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -560,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 89,
    "id": "a1392b70-a863-4629-ba8a-fb3300455e5a",
    "metadata": {},
    "outputs": [],
@@ -588,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 90,
    "id": "2845cf78-81dc-4102-a1b9-9e2b5d66e9c3",
    "metadata": {},
    "outputs": [
@@ -662,7 +662,7 @@
        "8.0                                    0.999000                       0.9533"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -673,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 91,
    "id": "68b639f4-b95c-4da0-86ea-542f522f005c",
    "metadata": {},
    "outputs": [
@@ -791,7 +791,7 @@
        "8.0                                 0.998433                    0.9527  "
       ]
      },
-     "execution_count": 136,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -803,11 +803,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "id": "3285658f-3977-4d22-8cbe-45b2470b6198",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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",
@@ -819,15 +846,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 125,
    "id": "6b531345-1ee3-45cc-ad38-fb2603667dea",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "[(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": {},
@@ -835,9 +872,13 @@
     }
    ],
    "source": [
-    "# sns.set_style(\"darkgrid\")\n",
-    "p = sns.lineplot(data=df_scale_exp)\n",
-    "# p = sns.lineplot(data=df_fp32_noscale_exp[[\"Training acc.\", \"Test acc.\"]])"
+    "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.\"]])"
    ]
   },
   {
@@ -850,37 +891,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 116,
    "id": "818eb73f-30d9-4e11-a78a-d50b37a2d182",
    "metadata": {},
-   "outputs": [],
-   "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\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "9d6e5bf6-9a48-4991-9524-c3c0c0ba8ae4",
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -903,10 +916,6 @@
        "  <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",
@@ -914,73 +923,41 @@
        "      <th>Learning rate</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>0.0005</th>\n",
-       "      <td>0.937717</td>\n",
-       "      <td>0.9183</td>\n",
-       "      <td>0.938200</td>\n",
-       "      <td>0.9165</td>\n",
        "      <td>0.952150</td>\n",
        "      <td>0.9300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.0010</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>0.0020</th>\n",
-       "      <td>0.985217</td>\n",
-       "      <td>0.9462</td>\n",
-       "      <td>0.987867</td>\n",
-       "      <td>0.9483</td>\n",
        "      <td>0.988667</td>\n",
        "      <td>0.9471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.0040</th>\n",
-       "      <td>0.996567</td>\n",
-       "      <td>0.9516</td>\n",
-       "      <td>0.995967</td>\n",
-       "      <td>0.9513</td>\n",
        "      <td>0.995417</td>\n",
        "      <td>0.9505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.0080</th>\n",
-       "      <td>0.998717</td>\n",
-       "      <td>0.9507</td>\n",
-       "      <td>0.998517</td>\n",
-       "      <td>0.9536</td>\n",
        "      <td>0.999200</td>\n",
        "      <td>0.9513</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.0160</th>\n",
-       "      <td>0.998817</td>\n",
-       "      <td>0.9509</td>\n",
-       "      <td>0.098717</td>\n",
-       "      <td>0.0980</td>\n",
        "      <td>0.998233</td>\n",
        "      <td>0.9510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.0300</th>\n",
-       "      <td>0.988783</td>\n",
-       "      <td>0.9355</td>\n",
-       "      <td>0.098717</td>\n",
-       "      <td>0.0980</td>\n",
        "      <td>0.943250</td>\n",
        "      <td>0.8904</td>\n",
        "    </tr>\n",
@@ -989,58 +966,79 @@
        "</div>"
       ],
       "text/plain": [
-       "               Train acc. float16 AutoScale  Test acc. float16 AutoScale  \\\n",
-       "Learning rate                                                              \n",
-       "0.0005                             0.937717                       0.9183   \n",
-       "0.0010                             0.969833                       0.9396   \n",
-       "0.0020                             0.985217                       0.9462   \n",
-       "0.0040                             0.996567                       0.9516   \n",
-       "0.0080                             0.998717                       0.9507   \n",
-       "0.0160                             0.998817                       0.9509   \n",
-       "0.0300                             0.988783                       0.9355   \n",
-       "\n",
-       "               Train acc. float16 Normal  Test acc. float16 Normal  \\\n",
-       "Learning rate                                                        \n",
-       "0.0005                          0.938200                    0.9165   \n",
-       "0.0010                          0.968150                    0.9383   \n",
-       "0.0020                          0.987867                    0.9483   \n",
-       "0.0040                          0.995967                    0.9513   \n",
-       "0.0080                          0.998517                    0.9536   \n",
-       "0.0160                          0.098717                    0.0980   \n",
-       "0.0300                          0.098717                    0.0980   \n",
-       "\n",
-       "               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  "
+       "               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": 147,
+     "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"
+    "df_lr_exp.index = np.log10(df_lr_exp.index)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 123,
    "id": "840cfe4d-1650-4d51-b008-4f13fe65a130",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "[(0.0, 1.05)]"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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qB92+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": {},
@@ -1048,14 +1046,67 @@
     }
    ],
    "source": [
-    "p = sns.lineplot(data=df_lr_exp)"
+    "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": []
   }