{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "fluF3_oOgkWF" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2024-07-19T06:15:43.486544Z", "iopub.status.busy": "2024-07-19T06:15:43.486279Z", "iopub.status.idle": "2024-07-19T06:15:43.490392Z", "shell.execute_reply": "2024-07-19T06:15:43.489757Z" }, "id": "AJs7HHFmg1M9" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "jYysdyb-CaWM" }, "source": [ "# Simple audio recognition: Recognizing keywords" ] }, { "cell_type": "markdown", "metadata": { "id": "CNbqmZy0gbyE" }, "source": [ "
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ resizing (Resizing) │ (None, 32, 32, 1) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ normalization (Normalization) │ (None, 32, 32, 1) │ 3 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d (Conv2D) │ (None, 30, 30, 32) │ 320 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_1 (Conv2D) │ (None, 28, 28, 64) │ 18,496 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d (MaxPooling2D) │ (None, 14, 14, 64) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout (Dropout) │ (None, 14, 14, 64) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ flatten (Flatten) │ (None, 12544) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense (Dense) │ (None, 128) │ 1,605,760 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_1 (Dropout) │ (None, 128) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_1 (Dense) │ (None, 8) │ 1,032 │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n", "\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ resizing (\u001b[38;5;33mResizing\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ normalization (\u001b[38;5;33mNormalization\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m3\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m1,605,760\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m1,032\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Total params: 1,625,611 (6.20 MB)\n", "\n" ], "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,625,611\u001b[0m (6.20 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Trainable params: 1,625,608 (6.20 MB)\n", "\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,625,608\u001b[0m (6.20 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Non-trainable params: 3 (16.00 B)\n", "\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3\u001b[0m (16.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "input_shape = example_spectrograms.shape[1:]\n", "print('Input shape:', input_shape)\n", "num_labels = len(label_names)\n", "\n", "# Instantiate the `tf.keras.layers.Normalization` layer.\n", "norm_layer = layers.Normalization()\n", "# Fit the state of the layer to the spectrograms\n", "# with `Normalization.adapt`.\n", "norm_layer.adapt(data=train_spectrogram_ds.map(map_func=lambda spec, label: spec))\n", "\n", "model = models.Sequential([\n", " layers.Input(shape=input_shape),\n", " # Downsample the input.\n", " layers.Resizing(32, 32),\n", " # Normalize.\n", " norm_layer,\n", " layers.Conv2D(32, 3, activation='relu'),\n", " layers.Conv2D(64, 3, activation='relu'),\n", " layers.MaxPooling2D(),\n", " layers.Dropout(0.25),\n", " layers.Flatten(),\n", " layers.Dense(128, activation='relu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(num_labels),\n", "])\n", "\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "de52e5afa2f3" }, "source": [ "Configure the Keras model with the Adam optimizer and the cross-entropy loss:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2024-07-19T06:16:01.949502Z", "iopub.status.busy": "2024-07-19T06:16:01.948907Z", "iopub.status.idle": "2024-07-19T06:16:01.959249Z", "shell.execute_reply": "2024-07-19T06:16:01.958667Z" }, "id": "wFjj7-EmsTD-" }, "outputs": [], "source": [ "model.compile(\n", " optimizer=tf.keras.optimizers.Adam(),\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=['accuracy'],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "f42b9e3a4705" }, "source": [ "Train the model over 10 epochs for demonstration purposes:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2024-07-19T06:16:01.963000Z", "iopub.status.busy": "2024-07-19T06:16:01.962427Z", "iopub.status.idle": "2024-07-19T06:16:12.703262Z", "shell.execute_reply": "2024-07-19T06:16:12.702559Z" }, "id": "ttioPJVMcGtq" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1721369763.126953 159410 service.cc:146] XLA service 0x7fd828005cc0 initialized for platform CUDA (this does not guarantee that XLA will be used). 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 95/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8692 - loss: 0.3837" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8689 - loss: 0.3845 - val_accuracy: 0.8516 - val_loss: 0.4833\n" ] } ], "source": [ "EPOCHS = 10\n", "history = model.fit(\n", " train_spectrogram_ds,\n", " validation_data=val_spectrogram_ds,\n", " epochs=EPOCHS,\n", " callbacks=tf.keras.callbacks.EarlyStopping(verbose=1, patience=2),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "gjpCDeQ4mUfS" }, "source": [ "Let's plot the training and validation loss curves to check how your model has improved during training:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2024-07-19T06:16:12.707178Z", "iopub.status.busy": "2024-07-19T06:16:12.706914Z", "iopub.status.idle": "2024-07-19T06:16:12.985877Z", "shell.execute_reply": "2024-07-19T06:16:12.985267Z" }, "id": "nzhipg3Gu2AY" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Accuracy [%]')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fv344OzvTr18/3N3dr+En5ZhM1gsH0BvEZDL95yI55xszZgzvvvsux44dw9XV9bLXnTlzhqioKN577z0eeOCBS16TlZVFVlaWfT8lJYXIyEiSk5Px9fUt1Pu4Gr9vT2Tw1A34ebiwZkQHPFwLvwiAiIhIUcvMzCQuLo5KlSqVykZQaXClzyglJQU/P7/r1p6RoqXPT0REjKD2n+M6cOAAVapUYf369TRq1MjoOHZF1R4tkT0orVYrkyZNYsCAAVcsTgL4+/tTvXp19u7de9lr3Nzc8PX1zfe4ntrVLEdkoAfJZ3P4OfbodX1tERERERERERFxTDk5OSQkJPDSSy/RokULhypOFqUSWaBctmwZe/fuvWyPyPOlpaWxb98+wsPDr0Oyq+NkNjGwRTQAk1cduGhVKBERERERERERKXtWrlxJeHg469evZ8KECUbHKTaGFijT0tKIjY0lNjYWgLi4OGJjY+2L2owYMYKBAwde9Lwvv/yS5s2bU7du3YvODRs2jGXLlnHgwAFWrVpFz549cXJyol+/fsX6Xq7VnU0q4O5iZmdCKusPnP7vJ4iIiIiIiIiISKnWtm1brFYru3btol69ekbHKTaGFig3bNhATEwMMTExAAwZMoSYmBhGjhwJQHx8/EUrcCcnJ/Pjjz9etvfkkSNH6NevHzVq1KBPnz4EBQWxZs0aQkJCivfNXCN/T1d6xpQHYMqqA8aGERERERERERERuU6cjXzxf6rAlzN58uSLjvn5+ZGRkXHZ55TklTUHtoxmxrrDzN+WQEJyJmF+mpBWRERERERERERKtxI5B2VpVSvcl2aVAsmzWJm29qDRcURERERERERERIqdCpQOZlDLaABmrDtEVm6esWFERERERERERESKmQqUDuaWOqGE+bpzIi2beVvjjY4jIiIiIiIiIiJSrFSgdDAuTmbuaVERgMmrNMxbRERERERERERKNxUoHVDfZhVxdTKz5fAZYg+fMTqOiIhImRMdHc0HH3xQoGtNJhOzZ88u1jwiIiIiUrwK0/6ToqcCpQMK9nbjtvrhAExddcDYMCIiIiIiIiIiIsVIBUoHNbBVNAC//hXPibQsY8OIiIiIiIiIiIhDysvLw2KxGB3jmqhA6aAaRvrTINKf7DwL3647ZHQcERERG6sVstONeVitBYr4+eefExERcVEjrXv37tx///3s27eP7t27Exoaire3N02bNuX3338vsh/R1q1bad++PR4eHgQFBfHQQw+RlpZmP7906VKaNWuGl5cX/v7+tG7dmoMHbfNOb9myhXbt2uHj44Ovry+NGzdmw4YNRZZNREREpNDU/rvIe++9R7169fDy8iIyMpLHHnssX3sPYOXKlbRt2xZPT08CAgLo1KkTp0+fBsBisTB27FiqVq2Km5sbFStW5PXXXwdsbUWTycSZM2fs94qNjcVkMnHgwAEAJk+ejL+/P3PmzKF27dq4ublx6NAh1q9fz80330xwcDB+fn7cdNNNbNq0KV+uM2fO8PDDDxMaGoq7uzt169bl119/JT09HV9fX3744Yd818+ePRsvLy9SU1Ov+udVEM7Fene5JoNaRjHk8Bm+WXOIh2+qgouT6skiImKwnAx4I8KY137hGLh6/edld955J08++SRLliyhQ4cOAJw6dYr58+czb9480tLS6Nq1K6+//jpubm5MnTqVbt26sWvXLipWrHhNEdPT0+nUqRMtW7Zk/fr1JCUlMXjwYJ544gkmT55Mbm4uPXr04MEHH2TGjBlkZ2ezbt06TCYTAP379ycmJobx48fj5OREbGwsLi4u15RJRERE5Jqo/XcRs9nMRx99RKVKldi/fz+PPfYYw4cP59NPPwVsBcUOHTpw//338+GHH+Ls7MySJUvIy8sDYMSIEUycOJH333+fNm3aEB8fz86dOwuVISMjg7feeosvvviCoKAgypUrx/79+xk0aBDjxo3DarXy7rvv0rVrV/bs2YOPjw8Wi4UuXbqQmprKN998Q5UqVdi+fTtOTk54eXnRt29fvvrqK+644w776/yz7+PjU+ifU2GoQOnAbq0fzhvzdpCQksmi7Yl0rRdudCQRERGHFxAQQJcuXZg+fbq9gfrDDz8QHBxMu3btMJvNNGjQwH79//3f/zFr1izmzJnDE088cU2vPX36dDIzM5k6dSpeXrbG9Mcff0y3bt146623cHFxITk5mdtuu40qVaoAUKtWLfvzDx06xLPPPkvNmjUBqFat2jXlERERESkLrnf77+mnn7ZvR0dH89prr/HII4/YC5Rjx46lSZMm9n2AOnXqAJCamsqHH37Ixx9/zKBBgwCoUqUKbdq0KVSGnJwcPv3003zvq3379vmu+fzzz/H392fZsmXcdttt/P7776xbt44dO3ZQvXp1ACpXrmy/fvDgwbRq1Yr4+HjCw8NJSkpi3rx5RTra6HJUoHRgbs5O9GtWkXF/7GXyqgMqUIqIiPFcPG3fZBv12gXUv39/HnzwQT799FPc3NyYNm0affv2xWw2k5aWxqhRo5g7dy7x8fHk5uZy9uxZDh269ilVduzYQYMGDezFSYDWrVtjsVjYtWsXN954I/feey+dOnXi5ptvpmPHjvTp04fwcNvv+CFDhjB48GC+/vprOnbsyJ133mkvZIqIiIgYQu2/i/z++++MGTOGnTt3kpKSQm5uLpmZmWRkZODp6UlsbCx33nnnJZ+7Y8cOsrKy7IXUq+Xq6kr9+vXzHUtMTOSll15i6dKlJCUlkZeXR0ZGhv19xsbGUqFCBXtx8kLNmjWjTp06TJkyheeff55vvvmGqKgobrzxxmvKWhAaM+zg7m5eESeziXVxp9gRn2J0HBERKetMJtswGyMe54ZBF0S3bt2wWq3MnTuXw4cP8+eff9K/f38Ahg0bxqxZs3jjjTf4888/iY2NpV69emRnZxfXTy2fr776itWrV9OqVStmzpxJ9erVWbNmDQCjRo1i27Zt3Hrrrfzxxx/Url2bWbNmXZdcIiIiIpek9l8+Bw4c4LbbbqN+/fr8+OOPbNy4kU8++QTAfj8PD4/LPv9K58A2fBzAet78mzk5OZe8j+mCn8+gQYOIjY3lww8/ZNWqVcTGxhIUFFSgXP8YPHgwkydPBmzt1vvuu++i1ykOKlA6uHA/DzrXCQNg6uoDxoYREREpIdzd3enVqxfTpk1jxowZ1KhRg0aNGgG2CcvvvfdeevbsSb169QgLC7NPOH6tatWqxZYtW0hPT7cfW7lyJWazmRo1atiPxcTEMGLECFatWkXdunWZPn26/Vz16tV55plnWLhwIb169eKrr74qkmwiIiIipdn1av9t3LgRi8XCu+++S4sWLahevTrHjuXvYVq/fn0WL158yedXq1YNDw+Py54PCQkBID4+3n4sNja2QNlWrlzJU089RdeuXalTpw5ubm6cOHEiX64jR46we/fuy97jnnvu4eDBg3z00Uds377dPgy9uKlAWQIMbBkFwKzNR0nOuLhqLiIiIhfr378/c+fOZdKkSfZvz8HWKPzpp5+IjY1ly5Yt3H333Ret+Hgtr+nu7s6gQYP4+++/WbJkCU8++SQDBgwgNDSUuLg4RowYwerVqzl48CALFy5kz5491KpVi7Nnz/LEE0+wdOlSDh48yMqVK1m/fn2+OSpFRERE5PKuR/uvatWq5OTkMG7cOPbv38/XX3/NhAkT8l0zYsQI1q9fz2OPPcZff/3Fzp07GT9+PCdOnMDd3Z3nnnuO4cOHM3XqVPbt28eaNWv48ssv7fePjIxk1KhR7Nmzh7lz5/Luu+8WKFu1atX4+uuv2bFjB2vXrqV///75ek3edNNN3HjjjfTu3ZtFixYRFxfHb7/9xvz58+3XBAQE0KtXL5599lluueUWKlSocFU/p8JSgbIEaFYpkJphPmTmWPhuw2Gj44iIiJQI7du3JzAwkF27dnH33Xfbj7/33nsEBATQqlUrunXrRqdOnezfrl8rT09PFixYwKlTp2jatCl33HEHHTp04OOPP7af37lzJ71796Z69eo89NBDPP744zz88MM4OTlx8uRJBg4cSPXq1enTpw9dunRh9OjRRZJNREREpLS7Hu2/Bg0a8N577/HWW29Rt25dpk2bxpgxY/JdU716dRYuXMiWLVto1qwZLVu25Oeff8bZ2bYUzMsvv8zQoUMZOXIktWrV4q677iIpKQkAFxcXZsyYwc6dO6lfvz5vvfUWr732WoGyffnll5w+fZpGjRoxYMAAnnrqKcqVK5fvmh9//JGmTZvSr18/ateuzfDhw+2ri//jgQceIDs7m/vvv/+qfkZXw2Q9f1C7AJCSkoKfnx/Jycn4+voaHQeAGesOMeKnrUQGerB0WDuczMU//l9ERMq2zMxM4uLiqFSpEu7u7kbHkUu40mfkiO0ZKTh9fiIiYgS1/wTg66+/5plnnuHYsWO4urpe8dqiao+qB2UJ0aNhefw8XDh86ixLdyUZHUdEREREREREREqRjIwM9u3bx5tvvsnDDz/8n8XJoqQCZQnh4erEXU0jAZi86oCxYURERMqIadOm4e3tfclHnTp1jI4nIiIiIkWsLLf/xo4dS82aNQkLC2PEiBHX9bWdr+uryTW5p3kUE//cz597TrDveBpVQryNjiQiIlKq3X777TRv3vyS51xcXK5zGhEREREpbmW5/Tdq1ChGjRplyGurQFmCVAzypEPNcvy+I4mvVx9k1O2lu3IvIiJiNB8fH3x8fIyOISIiIiLXidp/xtAQ7xJmYMtoAH7YeIS0rFxjw4iISJmg9fQclz4bERERKQ5qY0hBFdXfFRUoS5g2VYOpHOJFWlYuP206YnQcEREpxf4ZwpKRkWFwErmcfz6b0j7cSERERK4Ptf+ksIqqPaoh3iWM2WxiYIsoRv2ynSmrDjCgRRQmk8noWCIiUgo5OTnh7+9PUlISAJ6envqd4yCsVisZGRkkJSXh7++Pk5OT0ZFERESkFFD7TwqqqNujKlCWQL0bV+DtBbvYdzydlXtP0qZasNGRRESklAoLCwOwN1LFsfj7+9s/IxEREZGioPafFEZRtUdVoCyBfNxduKNxBaasPsjkVQdUoBQRkWJjMpkIDw+nXLly5OTkGB1HzuPi4qKekyIiIlLk1P6TgirK9qgKlCXUgJbRTFl9kMU7Ezl8KoPIQE+jI4mISCnm5OSkYpiIiIhIGaL2n1xPWiSnhKpazpsbqgVjtcI3aw4aHUdEREREREREROSqqEBZgg1sGQ3At+sPczY7z9gwIiIiIiIiIiIiV0EFyhKsfc1yVAjwIPlsDnO2HDU6joiIiIiIiIiISKGpQFmCOZlNDGgRBcCUVQexWq0GJxIRERERERERESkcFShLuLuaRuLmbGZ7fAobDp42Oo6IiIiIiIiIiEihqEBZwvl7utKjYXkAJq86YGwYERERERERERFxOFarlVPp2Ww/lsKSXUl8u+4QuxNTjY5l52x0ALl2g1pFM3PDYRb8nUBCciZhfu5GRxIRERERERERkesgKzePpJQsElIySUjOJPHcnwkp57ZTMklMySI715LveS/fVpvqoT4Gpc5PBcpSoHaEL82iA1l34BTT1x5kyC01jI4kIiIiIiIiIiLXwGq1cjojx1Z0TM0k8fyiY3ImCSlZJKZkcio9u8D3DPRyJdTXnTBfN8IdqIObCpSlxMBWUbYC5bpDPN6+Km7OTkZHEhERERERERGRS7jaXo+X4+psJtTXjTBf93MFSHfC/M5t+9n2y/m6OWy9SAXKUqJTnTBCfd1ITMnit60J9Igpb3QkEREREREREZEypbh7PdqLjr7uhJ4rPIb6uhPg6YLJZCrGd1a8VKAsJVyczPRvHsV7i3YzZfUBFShFRERERERERIqQej0WHxUoS5F+zSoy7o89bD50hr+OnKF+BX+jI4mIiIiIiIiIOLTMnDyOp2ZxMj2bE6lZnEjLKvZej2G+7vgXd6/H3GxIjbc9Uo5d8Gc8tHwManUrvtcvBBUoS5EQHzdurRfO7NhjTF51gPf6NDQ6koiIiIiIiIjIdWW1WknNyuXEBUXH42nZnEyzbZ+wb2eTlpVb4Hu7OpvtBcZQP3dCfdyuf69HqxXOnv630Jh67NJ/Zpy48n2qdlCBUorHoFbRzI49xq9b4nmxay2CvN2MjiQiIiIiIiIick0sFiunM7I5kZZ9rsCYZd8+ed72idQsTqRnF3iY9T9cncwEe7sS7ONGkJercb0e83IgNeEyvR6PndtOgNyzBbufkxv4hIFvBPiEn/dnOIQ3LL73UUgqUJYyMRUDaFDBjy1Hkvl2/WEeb1fV6EgiIiIiIiIiIhfJybNw8lxh8Xhaln3b3vMxLYvjqbbi46n0LCzWwt3fy9WJYB83gr3dCPZ2Jcjbth1y3vY/RUkfN+fiLTxarZCZfPEw6wt7PaYfBwr4Rj0C8xccfSIu/tMzEErA4jkqUJZCA1tGM/T7LXyz5iAP31gZZyez0ZFEREREREREpAzIyM7lZFo2x88vNKbm7/H4z3by2ZxC39/f0+XfwuL5RcZz20HnbXu4XqfFZfJyIS3xguLj0XOFx/OO5WQU7H5ml/OKjud6PV7YA9InHFzci/d9XUcqUJZCt9YP5415O4hPzmTR9kS61As3OpKIiIiIiIiIlGAZ2bnEnUjn0MkMe/Hx+CWGWGdk5xXqvk5mE0Fe//RodCXkgiLjP0OuQ3zcCPRyxeV6d8LKTLn8IjP2Xo9JYC3gkHJ3/wL0egwCc9nqbKYCZSnk7uJE32aRfLJkH1NWH1CBUkRERERERET+U26ehSOnzxJ3Ip39J9KJO5HG/uPpxJ1IJz45s8D3cXM2X9zL0ceVIC+3c0Ou/z3u7+GC2VyEQ5AteZCdbuutmJ0OOWfP286A7AzIOXf8kscyICvl33kgs9MK9rpmZ/AOO6/XY/kLCo/nHq6eRfdeSxEVKEup/s2jmLBsP2v2n2JnQgo1w3yNjiQiIiIiIiIiBrNarZxIy2b/8TTiTtiKj/uO24qRh05lkJN3+fkPAzxdiA72ItTHnWAf13NDqm1zOv6zHeztiveV5nO0Ws8rGp6C9IIUDS9TSMxJP/fnecXIvKyi/6G5+eUfbn2p3o9eIWWu12NRUoGylIrw9+CW2qH89ncCU1YdZEyvekZHEhERERERESk9LHlwci/E/wUJWyBhKyTtBEsumMxgdgKT07lt83nb546bzbZ9k9O5YxduX3iPSxy/6Ni/98ixmEjOzON0Zh5nzlo4fTaHkxl5nMrI42yuFQtm8s49ojBR4dy2k4uZAG8PAn08CPLxINjHgxBfT0J8PfF2dwFTBuRm/ls0zMyA1P8oGp5/rKDzMF4zE7h6gYunrdeii+d5215XPubqDT6h/xYgXb2uU+aySwXKUmxQq2h++zuB2ZuP8nznmvh5uhgdSURERERERKTkyTkLidv/LUTG/wWJ2yD3rNHJLssFCD73yMd07uSVnD33SCr6XPk4u58rCJ4rJLp4XFBU/Kdo6HFBAdHrguLiecdcvWzXO7uXiNWrxUYFylKseaVAaob5sDMhle83HmbwDZWNjiQiIiIiIiLi2DJO2YqQCX+d6x35F5zYfelFUFw8IbQuhNeHsPq2bRcPsObZrrdc8Kc174JtywXX/rP973GrJZfUjCxOpJ7lZGomp9LOcir1LKfTs0jJyMRktWDG9nAy/bNtxQkLXi4mAjyc8Hd3wt/DCT83M77uTni7mnDGekGmC7Oel+n8Y1aLrfh3YSHRxeMSPRG9rlBc9LD1+hRBBcpSzWQyMbBlNC/M2srU1Qe5v3Wlop14VkRERERERKSksloh+ch5hchzRcnkw5e+3jP430JkWD0IbwCBlYusyJaamWOfE/KfhWn2n0gj7ng66VdYGdvdxUylYG8qB3tROcSLisFeVAr2onKwt0ZSSomhAmUp1yMmgjd/28GhUxks3Z1E+5qhRkcSERERERERub7ycuHknnPDs7fYCpEJW+Hs6UtfHxBtK0KGNfi3KOkTds1DhrNzLRw6lXGuEJl23gI16RxPvfziLmYTRAZ62guPlUK8qHyuEBnm667OSFLiqUBZynm6OtOnSSRfrIhjyqqDKlCKiIiIiIhI6ZadAUnb8xciE7fZFna5kNkZQmraCpDh53pGhtUDd7+rfnmr1UpiShb7j6ex394j0laMPHz6LHmWy6+SHeztZi88Vg7598+KgV64OmuFaCm9VKAsAwa0jOLLlXEs232c/cfTqBzibXQkERERERERkWuXcepcIfK8OSNP7rn0fJGu3ufNF1nPVpQsVwuc3a7qpS0WK0fPnGV3Yiq7E9PO/ZlK3Il0Mq4wJNvT1YlK9iKkt70gWSnEC193DcmWssnQAuXy5ct5++232bhxI/Hx8cyaNYsePXpc9vqlS5fSrl27i47Hx8cTFhZm3//kk094++23SUhIoEGDBowbN45mzZoVx1soEaKCvGhXoxx/7Exi6uqDjLq9jtGRRERERERERArOaoUzhy5YvGYrpBy59PVeIef1ijz3CKwM5sL3QrRarRxPzWJXYiq7ElLZk5jGrsRU9iSmXnZuSCeziYr2Idm24uM/w7NDfd0waXVpkXwMLVCmp6fToEED7r//fnr16lXg5+3atQtfX1/7frly5ezbM2fOZMiQIUyYMIHmzZvzwQcf0KlTJ3bt2pXvurJmUKto/tiZxI8bjzCsUw283dR5VkRERERERBxQXq5t1ezzV9FO2AqZZy59fUCl/IXI8HPzRV6FMxnZ7EpItfeK3HWuV+SZjJxLXu/iZKJKiDfVQ32oEeZDtXLeVCnnTcVAT1ycNCRbpKAMrVJ16dKFLl26FPp55cqVw9/f/5Ln3nvvPR588EHuu+8+ACZMmMDcuXOZNGkSzz///CWfk5WVRVbWv5PRpqSkFDqTo7uhajCVg73YfyKdWZuOMKBltNGRREREREREpKzLTrfND3l+MTJxO+RdYsEYswuUq2lbuCasnq0QGVoX3H0vvvY/pGXlsudc8XFXQhp7kmy9I5Mus1CN2QTRQV5UD/WhepgPNUJ9qB7qTXSwlwqRIkWgRHaja9iwIVlZWdStW5dRo0bRunVrALKzs9m4cSMjRoywX2s2m+nYsSOrV6++7P3GjBnD6NGjiz23kcxmEwNaRjH6l+1MWX2Qe1pEqUu5iIiIiIiIFD+r1bZAzdkzcHzHv8OzE/6Ck3svM1+kD4TVzb94TUgtcHYt1Etn5uSx73iavRD5zzyRR06fvexzyvt7UCPM51yvSG+qlfOhajlv3F2cCvnGRaSgSlSBMjw8nAkTJtCkSROysrL44osvaNu2LWvXrqVRo0acOHGCvLw8QkPzr1QdGhrKzp07L3vfESNGMGTIEPt+SkoKkZGRxfY+jHJH4wq8s2AXe5PSWLXvJK2rBhsdSURERERERByR1Qq5WZCdBlkpkJUGWann9lP/fVxx/9xzs9PAknv51/IOvWAV7fq2YduFmC8yJ8/CwZPp7Eo4Nyz73DDtAyfTudyi2SE+bud6QtoKkdVDfagW6qMp0UQMUKL+q6tRowY1atSw77dq1Yp9+/bx/vvv8/XXX1/1fd3c3HBzu7pVu0oSH3cXejWqwNdrDjJl1QEVKEVEREREREqb3KwCFhDPKx7a91Mh+7ziouXS8y5ePZNtoZp/hmf/M1TbJ/S/n3qOxWLlyOmz9rkh/5kvcv/xdLLzLtETE/DzcLEVIsO87QXJ6qE+BHgVrjemiBSfElWgvJRmzZqxYsUKAIKDg3FyciIxMTHfNYmJiflW+S7LBrWK4us1B/l9RyJHTmdQIcDT6EgiIiIiJUpeXh6jRo3im2++ISEhgYiICO69915eeukl+xQ6VquVV155hYkTJ3LmzBlat27N+PHjqVatmsHpRcQwVqttKLMlD6x5+bctFtufORmXLxbaC4wXHLuwh2ORFxUBV2/bw80H3M796epzwb43uPle+RoXrwL3irRarSSmZNl7Q/5TkNyTmMbZnEuvnO3p6kS1UB9qhP67aE2NUB9CfLRqtoijK/EFytjYWMLDwwFwdXWlcePGLF68mB49egBgsVhYvHgxTzzxhIEpHUfVcj60rhrEyr0n+XrNQUZ0qWV0JBEREZES5a233mL8+PFMmTKFOnXqsGHDBu677z78/Px46qmnABg7diwfffQRU6ZMoVKlSrz88st06tSJ7du34+7ubvA7EClhTu6DfX9AxskLinrninz2Y5bzjufZCoKXKgaefzzf8yyXuIfl3+LhRcVFyyXuce7+F90jD7jMOOPi4uJ1QfHQ51wB8cJjPlfed/Uu1FDrq3EyLYvdiefmiTyvIJmaeelh4a7OZqqEeNsKkfYFa3wo7++B2axCpEhJZGiBMi0tjb1799r34+LiiI2NJTAwkIoVKzJixAiOHj3K1KlTAfjggw+oVKkSderUITMzky+++II//viDhQsX2u8xZMgQBg0aRJMmTWjWrBkffPAB6enp9lW9BQa1jGbl3pPMXH+YZzpW10S/IiIiIoWwatUqunfvzq233gpAdHQ0M2bMYN26dYCt188HH3zASy+9RPfu3QGYOnUqoaGhzJ49m759+xqWXaREyM2GQ6thz0LYPd+2iEpZ4eL5H8XDy/VevERvRrPj/TsvNTPn30LkuaHZuxNTOZGWfcnrncwmooM8/12w5twK2lGBnjhr5WyRUsXQAuWGDRto166dff+fhWoGDRrE5MmTiY+P59ChQ/bz2dnZDB06lKNHj+Lp6Un9+vX5/fff893jrrvu4vjx44wcOZKEhAQaNmzI/PnzL1o4pyzrUCuU8v4eHD1zljmxx+jTtPQtCCQiIiJSXFq1asXnn3/O7t27qV69Olu2bGHFihW89957gO1L94SEBDp27Gh/jp+fH82bN2f16tWXLFBmZWWRlZVl309JSSn+NyLiSNJPwJ5FtoLkvj9sQ5b/YXaBqFYQVAVMZjA52YpvJrPtYXayHcu3bfp3236tk60nYL57nP+8y93PfME9Lrzfha9t/vf4JZ93hcylbBhynsXKX0fOsGz3cZbtPs6Ww2cuu2BNZKDHeQvW2P6sHOKFm7PjFVpFpOiZrFbrde5n7vhSUlLw8/MjOTkZX19fo+MUiwnL9vHmbzupHe7L3KfaaD4OERGRUqYstGeMYrFYeOGFFxg7dixOTk7k5eXx+uuvM2LECMDWw7J169YcO3bMPhURQJ8+fTCZTMycOfOie44aNYrRo0dfdFyfn5RaVisk/m0rSO5eAEc2kG8ItGcwVO9ke1RuB+7676CkSErJtBckV+w9wZmM/HNihvq65esNWSPUh6rlvPHSytkipU5h2qP6P0AZdVeTSN5ftJvt8SlsPHiaJtGBRkcSERERKRG+++47pk2bxvTp06lTpw6xsbE8/fTTREREMGjQoKu654gRI+yjicDWoI+M1CgXKWWyMyBuua0ouWchpBzNfz6sPlTvbHtExBT7vIdSNLJzLWw4eMpWlNx1nJ0JqfnO+7g7c0O1YG6qHsIN1UKI8PcwKKmIODIVKMuoAC9XujeM4LsNR5iy+qAKlCIiIiIF9Oyzz/L888/bh2rXq1ePgwcPMmbMGAYNGkRYWBgAiYmJ+XpQJiYm0rBhw0ve083NDTc3t2LPLnLdnTkMexbYeknGLYfczH/POXtAlXa2XpLVbgHfCONySqEcOpnBst1JLNt9nFX7TpKR/e+q2iYT1C/vx03VQ7ipRggNKvhrvkgR+U8qUJZhA1tG892GI/y2NZ7EW2sR6qsVJUVERET+S0ZGBuYLenY5OTlhsVgAqFSpEmFhYSxevNhekExJSWHt2rU8+uij1zuuyPVlybMN1/5n6HbStvzn/SLP9ZLsBNFtwEW96UqCjOxc1uw/ybJdtqHbB05m5Dsf7O3GjdX/7SUZ6OVqUFIRKalUoCzD6pb3o0lUABsOnmb62kM8c3N1oyOJiIiIOLxu3brx+uuvU7FiRerUqcPmzZt57733uP/++wEwmUw8/fTTvPbaa1SrVo1KlSrx8ssvExERQY8ePYwNL1Iczp6BfYttBck9i+DsqX/PmcxQodm5+SQ7Q7lapW4hmNLIarWyOzHN3ktyfdxpsvMs9vPOZhONowK4qUYIN1YLoXa4L2azPlcRuXoqUJZxg1pF2wqU6w7xeLuquDqr672IiIjIlYwbN46XX36Zxx57jKSkJCIiInj44YcZOXKk/Zrhw4eTnp7OQw89xJkzZ2jTpg3z58/H3V0jVqQUsFrhxJ5/e0keWg3Wf4f44u4HVTvaCpJVO4KnppMqCZIzclix9wTLdiexfPcJElIy852vEOBhG7ZdPYSWVYLwcXcxKKmIlEZaxfsSytKqlzl5Flq/+QdJqVl82Lch3RuWNzqSiIiIFIGy1J4pjfT5icPJzYKDK2H3Qlth8nRc/vPBNf7tJRnZHJzUF8bR5VmsbD2afG7YdhKxh89gOa864O5ipkXlIG6qHsKN1UOoHOyFSb1fRaQQtIq3FJiLk5n+zaN4//fdTFl1QAVKERERERGxSU20rba9ZwHsWwLZaf+ec3K1zSFZvbNtgZvASsbllAJLSslk+Z4TLNt9nBV7jnM6Iyff+WrlvO2L2zSNDsTdxcmgpCJS1qhAKfRrHsnHS/aw6dAZth5Jpl4FP6MjiYiIiIjI9WaxQMKWf3tJHtuU/7x3qK0YWb0zVG4Lbt6GxJSCy861sPHgaZbtti1usyM+Jd95Hzdn2lQLtveSjPDXokUiYgwVKIVyPu50rRfOz7HHmLL6AO/c2cDoSCIiIiIicj1kpUHcsnPzSS6EtIT85yNi/u0lGd4QzJqz3tEdOpnBsj3HWbbrOKv3nSA9Oy/f+foV/OwFyYaR/rg46TMVEeOpQCmAbbGcn2OPMWfLMUZ0qUmQt5vRkUREREREpDicPvBvL8kDf0Je9r/nXLygSjvbfJLVbgGfMMNiSsGczc5jzf6T9l6ScSfS850P9nblxmq2Ydttqgbr33oi4pBUoBQAYiL9qVfej61Hk5m54TCPta1qdCQRERERESkKeblwZN2/q24f35n/vH+UrZdk9U62eSWdVcByZFarlT1JaecWtznOugOnyM612M87m000igqwr7hdO9wXs1mL24iIY1OBUgAwmUwMahXNsO+38M3qgzx0Q2Wc1dVfRERERKRkyjgFexfbipJ7f4fMM/+eMzlBxRb/rrodXB20OrNDSz6bw8q9J1i26zjL9xwnPjkz3/ny/h7cVCOEG6uF0KpqEL7uLgYlFRG5OipQit1t9cN5Y94OjiVn8vuORDrXDTc6koiIiIiIFITVausZ+c9ckofXgPXfXnV4BEDVm21FyaodbPvisCwWK1uPJtuHbccePkOexWo/7+ZspkXlIPtcklVCvDCpyCwiJZgKlGLn7uJE36aRfLp0H1NWHVSBUkRERETE0aWfhJXvw/af4cyh/OfK1f63l2T5JuCkf/45suOpWSw/V5D8c89xTmfk5DtftZy3vSDZvFIg7i5OBiUVESl6+g0l+dzTIooJy/axev9JdiWkUiPMx+hIIiIiIiJyobxc2PAlLHkdMpNtx5zcoNKN54qSncC/orEZ5T+lZ+WyYFsCP206ysp9J7D+20kSHzdnWlcN5sbqIdxYPZgKAZ7GBRURKWYqUEo+Ef4e3FI7jPnbEpi6+gCv96xndCQRERERETnfviUwfwQc32HbD60LNz1nG7rt6mVsNvlPeRYrq/adYNamo8zflkBGdp79XN3yvrStXo4bq4cQU9EfF60LICJlhAqUcpFBraKZf+5bvOGda+LnoQmWRUREREQMdyoOFr4EO3+17XsEQvuXoNEgDd8uAXYlpPLTpiPMjj1KYkqW/Xh0kCc9YyrQM6Y8FYPUS1JEyib9FpOLtKgcSI1QH3YlpvLDxiM80KaS0ZFERERERMqurDRY8R6s+hjysmyrcDcdDG2fB89Ao9PJFSSlZjIn9hg/bTrK9vgU+3E/Dxe6NQinZ0wFGlX01wI3IlLmqUApFzGZTAxsFcWLs/7m69UHuK9VNGazfmGKiIiIiFxXViv89R38/gqkxtuOVboJOr8JobWNzSaXdTY7j4XbE5i1+Sh/7jlhX33bxclEuxrl6NWoAu1qhuDmrEVuRET+oQKlXFLPmPK8+dtODpzMYNnu47SrWc7oSCIiIiIiZcfRTfDbc3BknW3fPwo6vQE1bwX1tnM4FouVtXGn+GnTEX77O4G0rFz7uZiK/vSKKc9t9SMI8HI1MKWIiONSgVIuydPVmT5NIvlyRRxTVh9QgVJERERE5HpIS4LFo2HzNMAKLl5wwxBo+QS4uBudTi6wNymNWZuPMHvzMY6eOWs/XiHAg14x5ekRU57KId4GJhQRKRlUoJTLGtAiikkr41i66zhxJ9KpFKwVAUVEREREikVuNqydAMvGQnaq7Vj9u6DjKPCNMDSa5HcyLYtfthxj1uajbDmSbD/u4+7MrfXC6dWoAk2iAjRNlohIIahAKZcVHexF2+ohLNl1nK9XH2RkN81zIyIiIiJS5HYvhAUj4ORe235EDHQZC5HNjM0ldpk5efyxM4mfNh1h6a7j5J6bV9LJbKJt9RB6NipPx1qhuLtoXkkRkauhAqVc0aBW0SzZdZzvNxxm6C3V8XLTXxkRERERkSJxYg8seAH2LLTte5WDjq9Ag7vBbDY2m2C1Wtlw8DQ/bTrKr38dIzXz33kl65X3o1ej8nRrEEGwt5uBKUVESgdVm+SKbqwWQqVgL+JOpDNr81HuaRFldCQRERERkZItM9k2lHvtBLDkgtkFWjwCNw4Hd1+j05V5B06k89Pmo8zefJRDpzLsx8P93OkRU55eMeWpFupjYEIRkdJHBUojpcSDux+4ehqd5LLMZhMDWkTx6q/bmbr6AP2bV8SkVQNFRERERArPYoHYabZFcNKP245V62RbnTu4qrHZyrgzGdn8+lc8P206wqZDZ+zHvVyd6FIvnF6NytOiUpDmlRQRKSYqUBpl8zT47Tlo8Si0f9HoNFd0R5MKvLNwF7sT01i97yStqgYbHUlEREREpGQ5tBZ+Gw7xsbb9oKrQ+U2odrOhscqy7FwLS3YlMWvTUf7YmUR2ngUAswluqBZCr0bluaV2GB6umldSRKS4qUBpFDcf2+p8Kz+ABn0hqIrRiS7L192FXo3K882aQ0xZfUAFShERERGRgko5Botega3f2fbdfOGm4dDsYXB2NTZbGWS1Wtl8+AyzNh3ll7+OcSYjx36uVrgvvRuV5/YGEZTzdTcwpYhI2aMCpVFqdYMqHWDfYpj3LNzzIzjw0OlBLaP5Zs0hFm1P5MjpDCoEOO6wdBERERERw+Vkwupx8Od7kJMBmCDmHugwErzLGZ2uzDl8KoNZm48ya/NR4k6k24+X83GjR0x5esaUp1a45v8UETGKCpRGMZmg69vwaQtbkXLHHKjd3ehUl1Ut1IdWVYJYte8k09Ye4rnONY2OJCIiIiLieKxW2PkrLHgRzhy0HYtsbhvOXb6RsdnKmJTMHOb9Fc9Pm46y7sAp+3EPFyc61w2jZ0x5WlcNxknzSoqIGE4FSiMFVYHWT8PysTB/BFTtCK5eRqe6rEGtolm17yTfrjvE/zpUw91Fc7GIiIiIiNglbof5z0PcMtu+Tzjc/CrUu9OhR0uVJjl5FpbvPs5Pm4+yaHsi2bm2eSVNJmhVJYheMRXoVDcMbzf9U1hExJHo/8pGa/MM/PUtnDkEy8bCzaONTnRZHWqWo7y/B0fPnOWXLce4s0mk0ZFERERERIyXcQqWjoH1X4I1D5zcoNWTtra+m7fR6Uo9q9XK30dT+HHTEX7ZcoyT6dn2c9XKedOrUQV6xEQQ7udhYEoREbkSFSiN5uoJXcbCjL6w+mNoeDeE1DA61SU5O5m5p0UUb83fyZTVB7ijcQVM+iZYRERERMqqvFzYNBn+eB3OnhtCXKsb3Px/EFjJ0GhlwbEzZ5kde5SfNh1lb1Ka/Xiwtyu3NyhPr0blqRPhq3+ziIiUACpQOoIaXaB6F9j9G8wbBgPnOOwQkL5NI/ng9938fTSFTYdO0zgq0OhIIiIiIiLXX9yftuHciX/b9kNqQZc3oXJbQ2OVdmlZufy2NZ5Zm4+yev9JrFbbcVdnM7fUDqVXo/LcUC0EFyezsUFFRKRQVKB0FF3ehP1LIG45/P0j1LvD6ESXFODlyu0NIvh+4xGmrDqoAqWIiIiIlC1nDsHCl2D7z7Z9d39o9yI0uR+c9M+r4mC1Wlmx9wQ/bDzCgm0JZOZY7OeaVQqkd6PydKkXjq+7i4EpRUTkWug3qKMIiIYbhsKS120r/lW7Bdx9jU51SYNaRfP9xiPM2xrPS7fWopyvu9GRRERERESKV3YGrHgfVn0EuZlgMkPj+6D9S+CpL+2LQ57Fyvy/E/hkyV62x6fYj1cO9qJnTHl6xJQnMtDTwIQiIlJUVKB0JK2egi0z4NR+WPomdH7D6ESXVLe8H42jAth48DTT1x3i6Y7VjY4kIiIiIlI8rFbbCKdFIyHlqO1Y9A3Q+U0Iq2tstlIqO9fC7NijTFi6j/0n0gHwdHWid6MK9GpUnoaR/ppXUkSklFGB0pG4uEOXt2Fab1g7AWL6Q2gdo1Nd0qBW0Ww8eJppaw/xWNuquDprjhcRERERKWXit8Bvz8OhVbZ9v4pwy/9B7e4OO2d8SXY2O4+Z6w/x+fL9HEvOBMDPw4V7W0Vzb6toArxcDU4oIiLFRQVKR1Oto23lvx2/wNxhcN88h2z8dK4TRoiPG8dTs5i/LYHbG0QYHUlEREREpGikn4A//g82TgGs4OwBNwyBVk+Ci4fR6UqdlMwcvl59kEkr4jiZng1AiI8bD95QibubR+Htpn+2ioiUdvo/vSPq/CbsXWz7pnbLt9Cwn9GJLuLqbKZ/84p88Psepqw6oAKliIiIiJR8eTmwbqJtuqWsZNuxur3h5lfBr4Kx2UqhE2lZfLUyjqmrDpKalQtAhQAPHrmpCnc0roC7i5PBCUVE5HpRgdIR+VWAm4bD76Ng0ctQowt4+Bud6iJ3N6/IJ0v2svHgaf4+mkzd8n5GRxIRERERuTp7F8P8EXBil20/rD50eQuiWhmbqxQ6duYsny/fz7frD9lX5K5WzpvH2lWhW/0InJ00fZSISFmjAqWjavE4xE6HE7ttK3t3fdvoRBcp5+NOl7rhzNlyjCmrDvD2nQ2MjiQiIiIiUjgn98GCF2H3b7Z9zyDoMBJiBoBZPfiK0v7jaUxYto9Zm4+Sk2cFoH4FPx5vV5Wba4ViNjve1FYiInJ9qEDpqJxdoes7MPV2WP8FNOwPEQ2NTnWRQa2imbPlGD9vOcaIrrUI1MTVIiIiIlISZKXC8ndgzaeQlw1mZ2j2ENz0nEOOXirJth1L5tOl+5i3NR6rrS5Jy8pBPN6uKq2rBmlFbhERUYHSoVW+yTbnzd8/wtyh8MAiMDvWcIdGFf2pW96Xv4+mMHP9YR5tW8XoSCIiIiIil2exwF/f2qZTSku0HavSATqPgZAahkYrbTYcOMUnS/ayZNdx+7GOtcrxaNuqNI4KMDCZiIg4GhUoHd0tr8PuBXB0A8R+A40GGp0oH5PJxKCW0Tz7w198s+YgD95QSXPGiIiIiIhjOrIBfnvO1rYGCKhkK0xW7wzqxVckrFYry/ec4JMle1kXdwoAswluqx/Bo22rUCvc1+CEIiLiiFSgdHS+4dB2BCx8ERa9AjVvA89Ao1Pl061BBG/M28HRM2dZvDOJTnXCjI4kIiIiIvKv1AT4fTRsmW7bd/WGG5+FFo+Cs5ux2UoJi8XKgm0JfLJ0L38fTQHAxcnEHY0r8PCNVYgO9jI4oYiIODIVKEuC5g9D7DRI2g6LR0O3D41OlI+7ixN9m1Vk/NJ9TFl1QAVKEREREXEMVius/gSWjoHsNNuxBndDx1fAR23WopCTZ2FO7DE+XbqXfcfTAfBwceLu5hUZfEMlwv08DE4oIiIlgQqUJYGTC9z6LnzVBTZOgZiBUKGx0anyuadFFJ8t28eqfSfZk5hKtVAfoyOJiIiISFmWkwk/P2abzx2gfGPoMhYqNDE2VymRmZPH9xsOM2HZfo6eOQuAr7sz97aK5t7WlbR4poiIFIoKlCVFVCto0A+2zIC5Q+DBP8DsZHQqu/L+HtxcO5QF2xKZsvoAr/WoZ3QkERERESmr0k/At3fD4bW21bm7vAWN73e4BSdLotTMHKatPcQXf8ZxIi0LgGBvNwbfUIn+zSvi4+5icEIRESmJVKAsSW5+FXbOg/hY2PgVNB1sdKJ8BrWKZsG2RH7adJThnWviq8aJiIiIiFxvx3fD9Dvh9AFw84O7pkLltkanKvFOpWczeWUck1cdICUzF7B1Unjkpsrc2SQSdxfH6TwhIiIljwqUJYl3OWj/Evz2LCx+FWp1B+8Qo1PZtawcRPVQb3YnpvHDhiPc36aS0ZFEREREpCyJWw4z74HMZPCPgv7fQ0gNo1OVaAnJmUz8cz/T1x7ibE4eAFVCvHisbVVubxiBi5N6pYqIyLUz9LfJ8uXL6datGxEREZhMJmbPnn3F63/66SduvvlmQkJC8PX1pWXLlixYsCDfNaNGjcJkMuV71KxZsxjfxXXW9AEIq29rdP0+yug0+ZhMJga2jAZg6uoDWCxWYwOJiIiISNmxeRp83dPWTq7QDAYvVnHyGhw4kc6In/7ihrF/8OWKOM7m5FG3vC8T7mnEomduonfjCipOiohIkTH0N0p6ejoNGjTgk08+KdD1y5cv5+abb2bevHls3LiRdu3a0a1bNzZv3pzvujp16hAfH29/rFixojjiG8PsZFswByD2Gzi0xtg8F+gZUx4fd2cOnMxg+Z7jRscRERERkdLOYrGNLvr5MbDkQp1eMGiOQ400Kkl2JqTw1IzNtH93KTPWHSYnz0qzSoFMub8ZvzzRhs51wzGbTUbHFBGRUsbQId5dunShS5cuBb7+gw8+yLf/xhtv8PPPP/PLL78QExNjP+7s7ExYWFhRxXQ8kc0gZgBs/hrmDoWHloGTY4zW93Jz5s7GkUxaGceUVQdoW6Oc0ZFEREREpLTKOQuzH4NtP9n2bxgK7V7SYjhXYdOh03y6ZC+/70iyH2tXI4TH2lWlaXSggclERKQscIyq1lWyWCykpqYSGJj/F+aePXuIiIjA3d2dli1bMmbMGCpWrHjZ+2RlZZGVlWXfT0lJKbbMRabjaNjxCyT+DesnQotHjU5kN7BlFJNWxrF093EOnEgnOtjL6EgiIiIiUtqkn4AZ/eDIOjC7QLcPIaa/0alKFKvVysq9J/lkyV5W7z8JgMkEXeuF81jbKtSJ8DM4oYiIlBUl+qvFd955h7S0NPr06WM/1rx5cyZPnsz8+fMZP348cXFx3HDDDaSmpl72PmPGjMHPz8/+iIyMvB7xr41XEHR8xba95A1ITTA2z3mig71oWyMEqxW+XnPQ6DgiIiIiUtoc3wUT29uKk+5+MGCWipOFYLFYWbAtgR6frOSeL9eyev9JnM0m+jSpwOIhN/HJ3Y1UnBQRkevKZLVaHWIlE5PJxKxZs+jRo0eBrp8+fToPPvggP//8Mx07drzsdWfOnCEqKor33nuPBx544JLXXKoHZWRkJMnJyfj6+hbqfVxXljz4oiMc2wT1+kDviUYnsluyK4n7vlqPj7sza0Z0wMutRHfWFRERKXFSUlLw8/Nz/PaMXJI+vyvYvxRmDoSsZAiIhv4/QHA1o1OVCLl5Fn756xifLtnHnqQ0ANxdzPRtWpEHb6xMeX8PgxOKiEhpUpj2TImsGn377bcMHjyY77///orFSQB/f3+qV6/O3r17L3uNm5sbbm5uRR2z+P2zYM7E9rD1O2g0ECrdYHQqAG6qFkJ0kCcHTmbww8YjDGoVbXQkERERESnpNk2FX5+xLYYT2Rz6TgevYKNTObzMnDx+3HSECcv2cfjUWQB83JwZ2CqK+1pXIti7BP5bSERESpUSN8R7xowZ3HfffcyYMYNbb731P69PS0tj3759hIeHX4d0BijfCJrcb9ueNwzycozNc47ZbOLec0XJsfN3sv94mrGBRERERKTksljg91Ew50lbcbLuHTBwjoqT/yE9K5eJy/dz49glvDjrbw6fOkuQlyvPdqrByhHtebZTTRUnRUTEIRjagzItLS1fz8a4uDhiY2MJDAykYsWKjBgxgqNHjzJ16lTANqx70KBBfPjhhzRv3pyEBNu8ix4eHvj52eZIGTZsGN26dSMqKopjx47xyiuv4OTkRL9+/a7/G7xe2r8E22fD8Z2wZjy0fsroRADc0yKK3/5OYG3cKR6btonZj7fG3cXJ6FgiIiIiUpLknIVZj9jauwA3Dod2L9hWc5FLOpORzeRVB/hq5QGSz9o6MET4ufPQjZW5q2lFPFzVJhcREcdiaA/KDRs2EBMTQ0xMDABDhgwhJiaGkSNHAhAfH8+hQ4fs13/++efk5uby+OOPEx4ebn/873//s19z5MgR+vXrR40aNejTpw9BQUGsWbOGkJCQ6/vmrifPQLj5Vdv20jch+aixec5xdjIzrl8Mwd6u7ExI5ZWftxkdSURERERKkrQkmHybrThpdoEeE6D9iypOXkZSSiZvzNtBqzf/4IPf95B8NofKwV6MvaM+S59tx72tK6k4KSIiDslhFslxJCVyUnKLBb7qDIfXQu0e0GeK0YnsVu09Qf8v12K1wjt3NuCOxhWMjiQiIlLqlcj2jNjp8wOSdsL0O+HMIXD3h77TILqN0akc0uFTGUxYto/vNxwhO88CQO1wXx5vV5XOdcNwMqugKyIi119h2jMlbg5KuQyzGbq+Ayaz7RvmfX8YnciuVdVgnulYHYCXZm9lV0KqwYlERERExKHtWwJf3mwrTgZUgsG/qzh5GT9uPEKH95Yxbe0hsvMsNIkK4Kv7mjL3qTbcWj9cxUkRESkRVKAsTcLrQ7OHbNvznoXcLGPznOeJdlW5oVowmTkWHp22kfSsXKMjiYiIiIgj2jgFpt0BWSlQsSUMXgzB1YxO5XBy8yyM/mUbQ7/fQnauhRaVA5n5UAt+eLQV7WqUw6Rh8CIiUoKoQFnatHsBvEPh5F5YNc7oNHZms4kP7mpImK87+4+n88KsrWh2ARERERGxs1hg0Uj45SnbSt31+sDAn8EryOhkDudUejYDJ63jq5UHAHiqQzWmD25B88r6WYmISMmkAmVp4+4Ht7xm217+jm1YjIMI8nZj3N0xOJlN/Bx7jOnrHCebiIiIiBgoOwO+HwQrP7Tttx0BvT4HZzdjczmg7cdSuP3jFazadxIvVycm3NOYITdXx6yh3CIiUoKpQFka1bsTotpA7lmYP8LoNPk0jQ5keKcaAIyes52/jyYbnEhEREREDJWaCFNugx1zwMkVen4ObZ/XSt2X8MuWY/Qav5Ijp88SFeTJrMdb07lumNGxRERErpkKlKWRyQS3vgNmZ9j5K+xeaHSifB66sTIda5UjO8/CY9M2kZKZY3QkERERETFC4nb4oiMc3QgeAbYh3Q3uMjqVw8mzWHlr/k6enLGZzBwLN1YPYc7jbage6mN0NBERkSKhAmVpVa4WtHjUtv3bs5Bz1tg85zGZTLx7Z0MqBHhw6FQGw7//S/NRioiIiJQ1exfDpE6QfAgCq9gWw4lqZXQqh5N8NocHpqxn/NJ9ADx8U2W+urcpfp4uBicTEREpOipQlmY3PQc+4XD6wL/z+TgIP08XPrm7ES5OJuZvS2DSuQm+RURERKQM2DAJpt1pW6k7qjUM/h2CqhidyuHsSUylxycrWbrrOO4uZj7qF8OILrVw0nyTIiJSyqhAWZq5+UCnN2zbf74Hp/Ybm+cCDSL9eenW2gCMmbeDTYdOG5xIRERERIqVxQILXoRfnwFrHtS/CwbMAs9Ao5M5nIXbEujxyUriTqRT3t+DHx5pxe0NIoyOJSIiUiycC3LRkCFDCn3jl156icBANTQMV6cnbJoC+5fCb8/B3d851ITjA1tGsS7uFHO3xvPEtE3MfeoGArxcjY4lIiIiIkUtOwN+etA2RzpA2xfgpuEO1TZ1BBaLlY/+2MMHv+8BoEXlQD65uxFB3lrRXERESi+TtQCT/5nNZlq2bImra8EKRytWrGDXrl1Urlz5mgMaISUlBT8/P5KTk/H19TU6zrU7sQc+bQmWHOg7HWreanSifFIzc+g2bgUHTmbQrkYIXw5qilnDVkRERK5JqWvPlDGl7vNLTYAZfeHYZttK3d0/hfp3Gp3K4aRl5TJkZiwLtycCcG+raF68tRYuThr4JiIiJU9h2jMF6kEJMGvWLMqVK1ega318tJqcQwmuBq2ehBXvwW/PQ+V24OppdCo7H3cXPu3fmJ6frmTJruNMWL6Px9pWNTqWiIiIiBSFxG0wrQ+kHAGPQNsX5lEtjU7lcOJOpPPQ1A3sSUrD1dnM6z3qcmeTSKNjiYiIXBcF+iruq6++ws/Pr8A3/eyzzwgNDb3qUFIMbhwGfpG2VRL/fMfoNBepHeHLq93rAPDOgl2s2X/S4EQiIiIics32/A5fdrIVJ4Oq2hbDUXHyIkt3JXH7xyvYk5RGqK8b3z3cUsVJEREpUwpUoBw0aBBubgWf8+Tuu+/Gy8vrqkNJMXD1gs5v2rZXfmQb9u1g+jSJpFej8lis8NSMzRxPzTI6koiIiIhcrfVfwPQ+kJ0K0TfAA4u0UvcFrFYr45fu477J60nNzKVRRX9+eaINDSP9jY4mIiJyXRV6MpNBgwaxfPny4sgixa3mrVD1ZttclPOehf+efvS6MplMvNajLtXKeZOUmsXTMzeTZ3GsjCIiIiLyHyx5tpW65w61rdTd4G645yet1H2BjOxcnpyxmbfm78RqhX7NIpnxUAvK+bobHU1EROS6K3SBMjk5mY4dO1KtWjXeeOMNjh49Why5pDiYTNB1LDi5wf4lsH220Yku4unqzPh7GuHh4sTKvSf5cLHj9fQUERERkcvIToeZA2D1x7b99i9Bj0/BuWCLbZYVh09l0Hv8an79Kx5ns+1L+jG96uPm7GR0NBEREUMUukA5e/Zsjh49yqOPPsrMmTOJjo6mS5cu/PDDD+Tk5BRHRilKgZWhzTO27fkvQFaqsXkuoWo5H97oVReAcX/sYfnu4wYnEhEREZH/lBIPX3WFXXNtX4j3/hJufNb2JbnYrdp7gts/XsGO+BSCvV2Z/mAL7mkRZXQsERERQxW6QAkQEhLCkCFD2LJlC2vXrqVq1aoMGDCAiIgInnnmGfbsUa83h9bmaQiIhtRjsGys0WkuqWdMBfo1q4jVCk/PjCUhOdPoSCIiIiJyOQlb4YsOEB8LnkEw6Beod4fRqRyK1Wpl0oo4Bkxax+mMHOqV92POE21oVklD30VERK6qQPmP+Ph4Fi1axKJFi3BycqJr165s3bqV2rVr8/777xdVRilqLh7Q5Vxhcs2nkLTD2DyX8Uq32tQO9+VUejZPzthETp7F6EgiIiIicqHdC2FSZ0g5CkHVbCt1V2xudCqHkpmTx7Dv/+LVX7eTZ7HSK6Y83z/Skgh/D6OjiYiIOIRCFyhzcnL48ccfue2224iKiuL777/n6aef5tixY0yZMoXff/+d7777jldffbU48kpRqd4JatwKllyYO8zhFswBcHdx4tP+jfBxc2b9gdO8s3CX0ZFERERE5HzrJsKMuyA7zbZS9+BFtimFxC4++Sx3fbaaHzcdwcls4uXbavNunwa4u2i+SRERkX84F/YJ4eHhWCwW+vXrx7p162jYsOFF17Rr1w5/f/8iiCfFqvMY2PcHHFwBW7+H+n2MTnSR6GAvxt5Rn0enbeKzZftpGhVIx9qhRscSERERKdv+Wal77XjbfsN74Lb3tRjOBTYcOMUj32ziRFoW/p4ufHJ3I1pXDTY6loiIiMMpdA/K999/n2PHjvHJJ59csjgJ4O/vT1xc3LVmk+IWEAU3DrVtL3wJMpONzXMZXeqFc1/raACGfr+Fw6cyjA0kIiIiUpZlpcG3/f8tTnYYCd0/VnHyAtPWHqTfxDWcSMuiZpgPvzzRRsVJERGRyyh0gXLAgAG4u7sDcPjwYQ4fPlzkoeQ6avUUBFWFtERYMsboNJc1okstGkT6k3w2hyembyIrN8/oSCIiIiJlT8ox+KoL7P7NtlL3HV/BDUO1Uvd5snMtvDBrKy/O+pucPCu31g/np8daERnoaXQ0ERERh1XoAmVubi4vv/wyfn5+REdHEx0djZ+fHy+99BI5OTnFkVGKk7MbdH3btr3uM9sKjA7I1dnMJ3fH4OfhwpYjyYyZt9PoSCIiIiJlS/xfMLEDJPwFnsFw71yo28voVA4lKTWTuyeuYfraQ5hMMLxzDT7uF4Ona6Fn1hIRESlTCl2gfPLJJ/n8888ZO3YsmzdvZvPmzYwdO5Yvv/ySp556qjgySnGr0h5q9wCrBeYOBYtjrpZdIcCT9/o0AGDyqgPM/Sve4EQiIiIiZcTuBbaVulOPQXANeHAxRDY1OpVD2XL4DLePW8mGg6fxcXdm0r1NeaxtVUzqXSoiIvKfTFZr4ZZv9vPz49tvv6VLly75js+bN49+/fqRnOyY8xgWRkpKCn5+fiQnJ+Pr62t0nOsj+Sh83BRy0qH7pxDT3+hEl/XmbzuZsGwf3m7O/PJkGyoFexkdSURExOGUyfZMKeJQn9/az2D+87YvsyvdBH2mgoe/sZkczA8bj/DCrK1k51qoWs6bzwc0pnKIt9GxREREDFWY9kyhe1C6ubkRHR190fFKlSrh6qqJsUssv/LQ9jnb9qKRcPa0sXmuYNgt1WkWHUhaVi6PTdtEZo7moxQREZHr6+jRo9xzzz0EBQXh4eFBvXr12LBhg/281Wpl5MiRhIeH4+HhQceOHdmzZ4+Bia+CJQ/mDYffhtuKkzED4J4fVZw8T06ehdG/bGPY91vIzrXQsVYosx5rpeKkiIhIIRW6QPnEE0/wf//3f2RlZdmPZWVl8frrr/PEE08UaTi5zlo8BiE1IeMELP4/o9NclrOTmY/6xRDk5cqO+BRG/7LN6EgiIiJShpw+fZrWrVvj4uLCb7/9xvbt23n33XcJCAiwXzN27Fg++ugjJkyYwNq1a/Hy8qJTp05kZmYamLwQslJhRj/bHOUAHUfB7ePAycXQWI7kVHo2A79cx1crDwDwvw7V+HxAY3zc9TMSEREprEIP8e7ZsyeLFy/Gzc2NBg1s8wFu2bKF7OxsOnTokO/an376qeiSXkcONaTmeov7E6bcBpjgoSUQEWN0ostasecEAyatxWqF9/o0oFejCkZHEhERcRhluj1TzJ5//nlWrlzJn3/+ecnzVquViIgIhg4dyrBhwwBITk4mNDSUyZMn07dv3/98DUM/v+SjMP0uSNwKzu7Q8zOo0+P6ZnBw244l89DUjRw9cxYvVyfeu6shneqEGR1LRETEoRTrEG9/f3969+7NbbfdRmRkJJGRkdx222306tULPz+/fA8pgSrdAPXuBKwOvWAOQJtqwfyvQzUAXpz1N3sSUw1OJCIiImXBnDlzaNKkCXfeeSflypUjJiaGiRMn2s/HxcWRkJBAx44d7cf8/Pxo3rw5q1evvuQ9s7KySElJyfcwxLFY+KKDrTjpFWJbqVvFyXx+2XKM3uNXcfTMWaKDPJn1eGsVJ0VERK6Rc2Gf8NVXXxVHDnEkt7wGu+bD0Y2waQo0uc/oRJf1ZPtqbDhwmhV7T/DotE38/HhrvNwK/ddaREREpMD279/P+PHjGTJkCC+88ALr16/nqaeewtXVlUGDBpGQkABAaGhovueFhobaz11ozJgxjB49utizX9Gu3+CH+yEnwzbtz93fQUCUsZkcSJ7FytsLdjFh2T4Abqwewri+Mfh5aki3iIjItSp0D8p/HD9+nBUrVrBixQqOHz9elJnEaD5h0O4F2/bi0ZB+0tg8V+BkNvFB34aU83Fjb1IaL87aSiFnLRAREREpFIvFQqNGjXjjjTeIiYnhoYce4sEHH2TChAlXfc8RI0aQnJxsfxw+fLgIE/8HqxXWjLfNOZmTAZXbwQMLVZw8T3JGDvdPXm8vTj5yUxW+urepipMiIiJFpNBdzdLT03nyySeZOnUqlnPDf52cnBg4cCDjxo3D09OzyEOKAZo9BLHTIPFvWDzKNim6gwr2dmNcvxju/mIts2OP0bxyEP2aVTQ6loiIiBSzRo0aFep6k8nEnDlzKF++/DW9bnh4OLVr1853rFatWvz4448AhIXZhvsmJiYSHh5uvyYxMZGGDRte8p5ubm64ubldU66rkpcL85+H9eeGqDe+F7q+o8VwzrM7MZWHpm7gwMkM3F3MjL2jAbc3iDA6loiISKlS6ALlkCFDWLZsGb/88gutW7cGYMWKFTz11FMMHTqU8ePHF3lIMYCTM9z6LkzqBJumQsxAiGxqdKrLal45iGG31OCt+Tt5Zc426lfwo06E5kEVEREpzWJjYxk6dCje3t7/ea3VauXNN98kKyvrml+3devW7Nq1K9+x3bt3ExVl63FYqVIlwsLCWLx4sb0gmZKSwtq1a3n00Uev+fWLTFYqfH8f7F0EmODmV6HVk2AyGZ3MYSzYlsCQmbGkZ+dR3t+Dzwc2VhtTRESkGBR6Fe/g4GB++OEH2rZtm+/4kiVL6NOnT6kY7q1VL88z+zFbT8qw+vDQUjA7GZ3osiwWKw9O3cDinUlEB3ky58k2+Lrr238RESmbykJ7xmw2k5CQQLly5Qp0vY+PD1u2bKFy5crX9Lrr16+nVatWjB49mj59+rBu3ToefPBBPv/8c/r37w/AW2+9xZtvvsmUKVOoVKkSL7/8Mn/99Rfbt2/H3d39P1/junx+STvhi45gyYVen0Pt24vndUogi8XKh4v38OHiPQC0qBzIJ3c3IsjbgF6uIiIiJVSxruKdkZFx0YTfAOXKlSMjI6OwtxNH13E0uPtBwl+wYZLRaa7IbDbxbp8GlPf34MDJDJ7/8S/NRykiIlKKxcXFERISUuDrt2/fbu/leC2aNm3KrFmzmDFjBnXr1uX//u//+OCDD+zFSYDhw4fz5JNP8tBDD9G0aVPS0tKYP39+gYqT1025mnDX13DfXBUnz5OamcPD32y0Fyfvax3N1w80V3FSRESkGBW6B2WHDh0ICgpi6tSp9gbW2bNnGTRoEKdOneL3338vlqDXU1nocVAo6ybCvGHg5gdPbgDvgvVSMMrmQ6fp89lqcvKsjOpWm3tbVzI6koiIyHWn9kzJps/PGHEn0nlw6gb2JqXh6mzm9R51ubNJpNGxRERESqTCtGcKPQflBx98QOfOnalQoQINGjQAYMuWLbi7u7NgwYKrSyyOrcn9sPlriN8Ci0ZCz6tfofJ6iKkYwAtdazH6l+28Pm8HDSL9iakYYHQsERERuQ5yc3P57LPPWLp0KXl5ebRu3ZrHH3/csXouikNasiuJp2ZsJjUzlzBfdyYMaEzDSH+jY4mIiJQJhe5BCbZh3tOmTWPnzp2AbdXC/v374+HhUeQBjaBvrC/hyAbbHEVY4b7fIKqV0YmuyGq18ti0Tfz2dwLl/T2Y+1Qb/D1djY4lIiJy3ZTV9sxjjz3G7t276dWrFzk5OUydOpXq1aszY8YMo6MVSln9/IxgtVoZv2wfby/YhdUKjaMCGH9PI8r5qKgtIiJyLYqtB2VOTg41a9bk119/5cEHH7ymkFLCVGgCjQbCpikwdyg8vBycHHcBGpPJxFt31Gd7fAoHT2Yw9LstTBzYBLNZq1KKiIiUJrNmzaJnz572/YULF7Jr1y6cnGwL+3Xq1IkWLVoYFU8cXEZ2Ls/+8Bdz/4oHoF+zioy+vQ6uzoWeql9ERESuQaF+87q4uJCZmVlcWcTRdRwFHoGQtB3WfW50mv/k6+7CJ3c3wtXZzOKdSXz+536jI4mIiEgRmzRpEj169ODYsWMANGrUiEceeYT58+fzyy+/MHz4cJo2bWpwSnFEh09l0Hv8aub+FY+z2cTrPesyplc9FSdFREQMUOjfvo8//jhvvfUWubm5xZFHHJlnoK1ICbBkDKTEGxqnIOqW92NUtzoAvL1gF+viThmcSERERIrSL7/8Qr9+/Wjbti3jxo3j888/x9fXlxdffJGXX36ZyMhIpk+fbnRMcTCr9p7g9o9XsCM+hWBvN2Y81IL+za99hXcRERG5OoWeg7Jnz54sXrwYb29v6tWrh5eXV77zP/30U5EGNILm/LkCiwW+vBmOboC6d8AdXxqd6D9ZrVaemRnL7NhjhPq6MfepGwj2djM6loiISLEqa+2ZM2fOMHz4cLZs2cKECROIiYkxOtI1KWuf3/VitVqZtPIAb8zbQZ7FSv0Kfnw2oDHhfqVjLn0RERFHUpj2TKF7UPr7+9O7d286depEREQEfn5++R5SypnNcOu7YDLD3z/A/mVGJ/pPJpOJ13vWo2o5bxJTsnj621jyLIVeG0pEREQcmL+/P59//jlvv/02AwcO5Nlnn9XURJJPZk4eQ7/fwv/9up08i5Vejcrz3cMtVZwUERFxAFe1indpp2+sC2DuMFg/EYKrwyMrwdnxV8jek5jK7R+v5GxOHs90rM7/OlYzOpKIiEixKSvtmUOHDjFs2DB27NhB/fr1eeeddwgKCuL111/n22+/5YMPPqBLly5Gxyy0svL5XS/xyWd5+OuN/HUkGSeziRe71uK+1tGYTFpAUUREpLgUaw/K9u3bc+bMmUu+aPv27Qt7Oymp2r8InsFwYjes+cToNAVSLdSH13vWBeCDxbtZseeEwYlERETkWg0cOBCz2czbb79NuXLlePjhh3F1dWX06NHMnj2bMWPG0KdPH6NjioE2HDhFt3Er+etIMv6eLky9vxn3t6mk4qSIiIgDKXSBcunSpWRnZ190PDMzkz///LNIQkkJ4BEAt/yfbXvZWEg+YmyeAurVqAJ9m0ZitcL/vt1MYoqGfomIiJRkGzZs4PXXX6dz58689957/PXXX/ZztWrVYvny5XTs2NHAhGKkgyfTuefLtZxIy6JmmA+/PNGG1lWDjY4lIiIiF3Au6IXnN/a2b99OQkKCfT8vL4/58+dTvnz5ok0njq1BP9g0FQ6thvkj4K6vjU5UIKNur0Ps4TPsTEjlyembmf5gc5ydCl2rFxEREQfQuHFjRo4cyaBBg/j999+pV6/eRdc89NBDBiQTR/Da3B1k5lhoFh3I5Pub4ula4H/+iIiIyHVU4N/QDRs2xGQyYTKZLjmU28PDg3HjxhVpOHFwJhN0fQc+uxF2zIG9v0NVx++h4O7ixKf9G3H7xytZd+AU7y7azXOdaxodS0RERK7C1KlTGTp0KM888wwNGzbks88+MzqSOIjlu4+zaHsizmYTb/Sqq+KkiIiIAyvwb+m4uDisViuVK1dm3bp1hISE2M+5urpSrlw5nJyciiWkOLCwutD8YVjzKcx7Fh5bA85uRqf6T5VDvHmrd30en76J8Uv30TQ6gPY1Q42OJSIiIoUUFRXFDz/8YHQMcTA5eRZe/XU7AANbRlO1nI/BiURERORKCjyuNSoqiujoaCwWC02aNCEqKsr+CA8PV3GyLGs7ArzD4NR+WPmR0WkK7Nb64dzbKhqAZ2Zu4cjpDGMDiYiISKGkpKQU6vrU1NRiSiKOZurqg+xNSiPIy5X/daxmdBwRERH5D1c1zmHPnj0sWbKEpKQkLBZLvnMjR44skmBSgrj7QqfX4ccH4M93oP6dEBBtdKoCGdG1JpsPnWbLkWSemL6Z7x5uiauz5qMUEREpCQICAoiPj6dcuXIFur58+fLExsZSuXLlYk4mRjqRlsUHv+8GYFinGvh5uBicSERERP5LoQuUEydO5NFHHyU4OJiwsDBMJpP9nMlkKlSBcvny5bz99tts3LiR+Ph4Zs2aRY8ePa74nKVLlzJkyBC2bdtGZGQkL730Evfee2++az755BPefvttEhISaNCgAePGjaNZs2aFeZtSWHV7w8bJcOBP+O15uPtboxMViJuzEx/f3YhbP/qT2MNnGPPbDl7pVsfoWCIiIlIAVquVL774Am9v7wJdn5OTU8yJxBG8s2AXqZm51InwpU+TSKPjiIiISAEUukD52muv8frrr/Pcc89d84unp6fToEED7r//fnr16vWf18fFxXHrrbfyyCOPMG3aNBYvXszgwYMJDw+nU6dOAMycOZMhQ4YwYcIEmjdvzgcffECnTp3YtWtXgb9dl6vwz4I5E1rD7t9g129Qo4vRqQokMtCTd/s05MGpG/hq5QGaRQfSpV640bFERETkP1SsWJGJEycW+PqwsDBcXNSbrjTbeiSZmRsOAzD69jo4mU3/8QwRERFxBCar1WotzBN8fX2LZWiMyWT6zx6Uzz33HHPnzuXvv/+2H+vbty9nzpxh/vz5ADRv3pymTZvy8ccfA2CxWIiMjOTJJ5/k+eefL1CWlJQU/Pz8SE5OxtfX9+rfVFm0aCSs/BD8o+DxteDiYXSiAhszbwefLd+Pj5szvzzZhuhgL6MjiYiIXDW1Z0o2fX6FZ7VauXPCajYcPE33hhF82DfG6EgiIiJlWmHaM4WebO/OO+9k4cKFVx3uWqxevZqOHTvmO9apUydWr14NQHZ2Nhs3bsx3jdlspmPHjvZrLiUrK4uUlJR8D7lKNw4H3/Jw5iD8+Z7RaQplWKcaNIkKIDUrl8embSIzJ8/oSCIiIiJSQHO2HGPDwdN4uDjxfJeaRscRERGRQij0EO+qVavy8ssvs2bNGurVq3fRMJmnnnqqyMJdKCEhgdDQ0HzHQkNDSUlJ4ezZs5w+fZq8vLxLXrNz587L3nfMmDGMHj26WDKXOW7e0OkN+H4QrPwAGvSFoCpGpyoQFycz4+6O4daPVrA9PoVXf93OGz3rGR1LRERERP5DelYuY+bZ2vuPt6tCuF/JGcUjIiIiV1Gg/Pzzz/H29mbZsmUsW7Ys3zmTyVSsBcriMmLECIYMGWLfT0lJITJSE2pftdrdoUp72PcHzHsW7vnRNkdlCRDu58EHdzVk0FfrmL72EM2iA+kRU97oWCIiIiJyBZ8u3UtCSiaRgR4MvkGrtIuIiJQ0hS5QxsXFFUeOAgkLCyMxMTHfscTERHx9ffHw8MDJyQknJ6dLXhMWFnbZ+7q5ueHm5lYsmcukfxbM+bQF7FsMf8209aQsIW6sHsKT7avx0eI9vDBrK3XL+1K1nI/RsURERETkEg6dzGDin7Z/o7x0a23cXZwMTiQiIiKFVeg5KI3UsmVLFi9enO/YokWLaNmyJQCurq40btw43zUWi4XFixfbr5HrJKgKtP6fbXvWI7BmvLF5Cul/HarRqkoQGdl5PDZtExnZuUZHEhEREZFLeG3udrJzLbSpGswttUP/+wkiIiLicApcoKxduzanTp2y7z/22GOcOHHCvp+UlISnp2ehXjwtLY3Y2FhiY2MBW+/M2NhYDh06BNiGXg8cONB+/SOPPML+/fsZPnw4O3fu5NNPP+W7777jmWeesV8zZMgQJk6cyJQpU9ixYwePPvoo6enp3HfffYXKJkXgpueh8X2AFeY/bxvunVcyCn1OZhMf9o0hxMeN3YlpvDT7bwq54L2IiIhcR9HR0bz66qv2dqSUDX/uOc7C7Yk4mU280q02phIyrZCIiIjkV+AC5c6dO8nN/be49M033+Rb7dpqtZKZmVmoF9+wYQMxMTHExMQAtuJiTEwMI0eOBCA+Pj5fI7NSpUrMnTuXRYsW0aBBA959912++OILOnXqZL/mrrvu4p133mHkyJE0bNiQ2NhY5s+ff9HCOXIdODnDbe/Dzf9n21/3OXx7N2SlGZurgEJ83BjXLwazCX7adJTvNhw2OpKIiIhcxtNPP81PP/1E5cqVufnmm/n222/JysoyOpYUo5w8C6N/2Q7AgBZRVAvVlDwiIiIllclawG5hZrOZhIQEypUrB4CPjw9btmyhcmXbJNSJiYlERESQl5dXfGmvk5SUFPz8/EhOTsbX19foOKXD9p/hp4cgNxPC6sPd34FvuNGpCuSTJXt5e8Eu3JzNzHqsNbUj9HdCREQcX1ltz2zatInJkyczY8YM8vLyuPvuu7n//vtp1KiR0dEKpax+foUxaUUcr/66nUAvV5YMbYufp4vRkUREROQ8hWnPlKg5KKUEq90d7p0LXiGQ8Bd80QESthqdqkAevakKbWuEkJVr4fHpm0jNzDE6koiIiFxGo0aN+Oijjzh27BivvPIKX3zxBU2bNqVhw4ZMmjRJU7aUEifTsnj/990ADLulhoqTIiIiJVyBC5Qmk+miOV00x4sUSoUmMPh3CK4BKUdhUmfYs8joVP/JbDbxfp+GRPi5E3cined/3Kp/3IiIiDionJwcvvvuO26//XaGDh1KkyZN+OKLL+jduzcvvPAC/fv3NzqiFIF3Fu4mNTOXOhG+3NU00ug4IiIico2cC3qh1WqlQ4cOODvbnnL27Fm6deuGq6srQL75KUUuKyAaHlgA3w2EuOUw/S7oOhaaDjY62RUFeLky7u5G3PXZauZujaf5mkAGtow2OpaIiIics2nTJr766itmzJiB2Wxm4MCBvP/++9SsWdN+Tc+ePWnatKmBKaUo/H00mW/X2+apf6VbHZzM6jQhIiJS0hW4QPnKK6/k2+/evftF1/Tu3fvaE0np5xEA/X+EX5+G2GkwdyicirMtpmN23FkHGkcFMKJrLf7v1+3836/baVDBnwaR/kbHEhEREaBp06bcfPPNjB8/nh49euDicvGQ30qVKtG3b18D0klRsVqtjJqzDasVbm8QQbNKgUZHEhERkSJQ4EVyyhJNSn6dWK3w57vwx7lVvmveBr0mgqunsbmuwGq18sg3G1mwLZEKAR7MffIGzXkkIiIOqay1Zw4ePEhUVJTRMYpMWfv8Curn2KP879tYPFyc+GPYTYT7eRgdSURERC6jWBfJOXv2LBkZGfb9gwcP8sEHH7Bw4cLCJ5WyzWSCG4dB7y/ByRV2/gqTb4XURKOTXZbJZGLsHQ2oGOjJkdNnGfp9rOajFBERcQBJSUmsXbv2ouNr165lw4YNBiSSopaRncuYeTsBeKxtFRUnRURESpFCFyi7d+/O1KlTAThz5gzNmjXj3XffpXv37owfP77IA0oZUO8OGDgHPALh2Cb4oiMk7TA61WX5ebjwaf9GuDqZ+X1HEhP/3G90JBERkTLv8ccf5/DhwxcdP3r0KI8//rgBiaSojV+6j4SUTCoEePDgjZWNjiMiIiJFqNAFyk2bNnHDDTcA8MMPPxAWFsbBgweZOnUqH330UZEHlDIiqqVthe/AKpB8CL7sBPuWGJ3qsuqW92Nkt9oAvDV/F0t2JhmcSEREpGzbvn07jRo1uuh4TEwM27dvNyCRFKVDJzP4bLntS+GXbq2Fu4uTwYlERESkKBW6QJmRkYGPjw8ACxcupFevXpjNZlq0aMHBgweLPKCUIUFVbEXKiq0gKxmm3QGbvjY61WX1b16R2xtEkGexcv+U9byzYBe5eRajY4mIiJRJbm5uJCZePE1MfHw8zs4FXhdSHNTr87aTnWuhddUgOtUJMzqOiIiIFLFCFyirVq3K7NmzOXz4MAsWLOCWW24BbPP+aAJvuWaegTBwNtTrA5ZcmPME/D4aLI5X+LPNR1mf/s0rYrXCx0v2cvfEtcQnnzU6moiISJlzyy23MGLECJKTk+3Hzpw5wwsvvMDNN99sYDK5Viv3nmDBtkSczCZe6VYHk8lkdCQREREpYoUuUI4cOZJhw4YRHR1N8+bNadmyJWDrTRkTE1PkAaUMcnaDXp/DTc/Z9le8Bz8+ADmZxua6BHcXJ17vWY9x/WLwdnNm3YFTdP3wT5bs0pBvERGR6+mdd97h8OHDREVF0a5dO9q1a0elSpVISEjg3XffNTqeXKXcPAujf9kGwIAWUVQP9TE4kYiIiBQHk/UqliBOSEggPj6eBg0aYDbbapzr1q3D19eXmjVrFnnI660wy6BLMYudAXOeBEsORDaHvtPBK9joVJd04EQ6j0/fxLZjKQA8fFNlht1SAxenQn8PICIics3KYnsmPT2dadOmsWXLFjw8PKhfvz79+vXDxcXF6GiFVhY/v0uZvDKOUb9sJ8DThaXD2uHnWfI+SxERkbKqMO2ZqypQXvhif/zxBzVq1KBWrVrXciuHoQahg4n7E2b2h8xkCIiG/j9AcDWjU11SZk4eY+btYMpq23ysjaMC+KhfDOX9PQxOJiIiZY3aMyWbPj84mZZFu3eWkpKZy2s96nJPiyijI4mIiEghFKY9U+gZw/v06cONN97IE088wdmzZ2nSpAkHDhzAarXy7bff0rt376sOLnJJlW6AB363LZpz+gB80RH6ToPoNkYnu4i7ixOju9elReUghv/4FxsPnqbrh3/y7p0N6Fg71Oh4IiIipd727ds5dOgQ2dnZ+Y7ffvvtBiWSq/Xuot2kZOZSK9yXfs0qGh1HREREilGhC5TLly/nxRdfBGDWrFlYrVbOnDnDlClTeO2111SglOIRUh0GL4Zv+8GR9TC1B3T/GBr0NTrZJXWpF06dCD+enLGJLUeSGTx1A4PbVGJ455q4OmvIt4iISFHbv38/PXv2ZOvWrZhMJv4ZJPTPgip5eXlGxpNC2nYsmRnrDgEw+vY6OJm1MI6IiEhpVuhKSXJyMoGBgQDMnz+f3r174+npya233sqePXuKPKCInXcIDPoFavewzUk562FYMgaubZaCYlMxyJPvH2nF/a0rAfDFijju/Gw1h09lGJxMRESk9Pnf//5HpUqVSEpKwtPTk23btrF8+XKaNGnC0qVLjY4nhWC1Whk9ZztWK9xWP5xmlQKNjiQiIiLFrNAFysjISFavXk16ejrz58/nlltuAeD06dO4u7sXeUCRfFw84I6voM0ztv1lb9oKlblZxua6DFdnMyO71ebzAY3xdXdmy+EzdP3oT+b/nWB0NBERkVJl9erVvPrqqwQHB2M2mzGbzbRp04YxY8bw1FNPGR1PCuGXv+JZd+AU7i5mXuhaOua4FxERkSsrdIHy6aefpn///lSoUIGIiAjatm0L2IZ+16tXr6jziVzMbIaOo6Dbh2Bygr9mwtc9IeOU0cku65Y6Ycz73w3EVPQnNTOXR77ZyKg528jK1XAzERGRopCXl4ePjw8AwcHBHDt2DICoqCh27dplZDQphIzsXMbM2wHAY22rEqGFBkVERMqEQhcoH3vsMVavXs2kSZNYsWIFZrPtFpUrV+a1114r8oAil9X4XrjnB3DzhYMr4cub4dR+o1NdVoUAT757uCUP3VgZgMmrDnDH+NUcPJlucDIREZGSr27dumzZsgWA5s2bM3bsWFauXMmrr75K5cqVDU4nBTVh6T7ikzOpEOBhbzOJiIhI6WeyWq9+Ar8LJx8vLQqzDLo4gMTtML0PJB8GzyDoOwMqNjc61RX9sTORod9t4XRGDj5uzrzZuz631g83OpaIiJQiZa09s2DBAtLT0+nVqxd79+7ltttuY/fu3QQFBTFz5kzat29vdMRCKWufH8DhUxl0fG8ZWbkWxvdvRJd6ahuJiIiUZIVpz1zVcsJTp06lXr16eHh44OHhQf369fn666+vKqzINQutDYN/h/CGkHESpnSDv380OtUVta8Zyrz/3UCTqABSs3J5fPomXpq9lcwcDfkWERG5Gp06daJXr14AVK1alZ07d3LixAmSkpJKXHGyrHpj3g6yci20rBxE57phRscRERGR66jQBcr33nuPRx99lK5du/Ldd9/x3Xff0blzZx555BHef//94sgo8t98wuC+eVDjVsjLgh/uhz/fddgVvgHC/Tz49qEWPNa2CgDfrDlEr09XEXdCQ75FREQKIycnB2dnZ/7+++98xwMDA0vdSJ/SatXeE/z2dwJmE7xye219biIiImVMoQuU48aNY/z48bz11lvcfvvt3H777YwdO5ZPP/2Ujz76qDgyihSMqxfc9TW0eNy2v/hVmPMk5OUYm+sKnJ3MDO9ckyn3NyPIy5Xt8Snc9tGf/Bx71OhoIiIiJYaLiwsVK1YkL08jEUqi3DwLo3/ZDsCAFlHUDCsbQ9pFRETkX4UuUMbHx9OqVauLjrdq1Yr4+PgiCSVy1cxO0PkN6PoOmMyw+Wv4pjecPWN0siu6qXoI8/53A80rBZKencf/vo3l+R//0pBvERGRAnrxxRd54YUXOHXqlNFRpJCmrT3ErsRUAjxdeObm6kbHEREREQMUukBZtWpVvvvuu4uOz5w5k2rVqhVJKJFr1uxB6PctuHhB3DKY1AlOHzQ61RWF+rozbXBznmpfFZMJvl1/mO4fr2RvUprR0URERBzexx9/zPLly4mIiKBGjRo0atQo30Mc06n0bN5btBuAIbfUwN/T1eBEIiIiYgTnwj5h9OjR3HXXXSxfvpzWrVsDsHLlShYvXnzJwqWIYap3gvvn21b4Pr4TvuhoK1pWaGx0sstydjIz5JYaNKsUxNMzY9mVmEq3cSt4rUddejeuYHQ8ERERh9WjRw+jI8hVeHfhLpLP5lAzzIe7m1U0Oo6IiIgYxGS1Fn4VkU2bNvHee++xY8cOAGrVqsXQoUOJiYkp8oBGKMwy6FICJB+F6XdB4lZw9oDeE6FWN6NT/aek1Eye/jaWVftOAnBH4wq82r0Onq6F/l5BRETKILVnSray8PltP5bCbeP+xGKFbx9qQYvKQUZHEhERkSJUmPZMoYZ45+TkcP/99xMQEMA333zDxo0b2bhxI998802pKU5KKeRXHu7/DardArlnYeYAWDXOoVf4Bijn487XDzRnyM3VMZvgh41H6P7xSnYnphodTUREROSaWK1WRv2yDYsVbq0fruKkiIhIGVeoAqWLiws//vhjcWURKT5uPtB3BjQdDFhh4Uswdyjk5Rqd7IqczCae6lCNaYNbUM7HjT1Jadz+8Qq+W3+Yq+j8LCIiUmqZzWacnJwu+xDHMndrPOviTuHuYuaFrrWMjiMiIiIGK/RY0R49ejB79myeeeaZ4sgjUnycnG2rewdWhgUvwoYv4cwhuPMrWwHTgbWsEsS8/93AMzNj+XPPCYb/+Ber95/ktR518XLTkG8REZFZs2bl28/JyWHz5s1MmTKF0aNHG5RKLuVsdh5vzLVNFfXITVUo7+9hcCIRERExWqErG9WqVePVV19l5cqVNG7cGC8vr3znn3rqqSILJ1LkTCZo+Tj4R8GPg2HvIpjUGe7+zjYU3IEFe7sx5b5mjF+2j/cW7WbW5qNsOXyGT/o3olZ46ZybSkREpKC6d+9+0bE77riDOnXqMHPmTB544AEDUsmljF+2j2PJmZT39+DhG6sYHUdEREQcQKEXyalUqdLlb2YysX///msOZbSyMCm5AEc32RbPSU8Cn3C4eyaENzA6VYGsP3CKJ6dvJiElE1dnM6O61aFfs0hMJpPR0URExEGoPWOzf/9+6tevT1pamtFRCqW0fn5HTmfQ4d1lZOVa+LR/I7rWCzc6koiIiBSTwrRnCt2DMi4u7qqDiTiU8o3gwcUwrQ8c3wGTusAdk6BGZ6OT/aem0YHM+98NDP0uliW7jvPCrK2s3n+SN3rWxcfdxeh4IiIiDuHs2bN89NFHlC/v2KMkypI35u0gK9dCi8qBdKkbZnQcERERcRCFWiQnJSUFi8Vy0XGLxUJKSkqRhRK5bvwrwgMLoHI7yEmHb/vB2s+MTlUggV6ufDmoKSO61MTZbOKXLcfoNm4Ffx9NNjqaiIjIdRcQEEBgYKD9ERAQgI+PD5MmTeLtt982Op4Aq/adYN7WBMwmeKVbHY38EBEREbsC96CcNWsWzz33HLGxsXh6euY7d/bsWZo2bco777xDt27dijykSLFy94P+38PcIbBpKvw2HE7FQafXwezYq36azSYevqkKTaIDeWrGZg6czKDXp6t46bZaDGgRpYa/iIiUGe+//36+33tms5mQkBCaN29OQECAgckEIDfPwug52wHo3zxK82fL/7d35+FRlecbx++ZSTLZQxayQdiRfZEtbAIiAooLlaogbtTqTwQqRttCLeBSBVwoVSgodW1VKK0iWkUx7BoWQdlXWZIASQghK9lnfn8MDg4ESCDkZCbfz3WdC+adM2ee49vCw533nAMAgItK34Ny8ODBuuuuu/Tb3/62wvfffvttLVq0SF999VW1FmgET73nDy7Bbpe+nS1984zjdaubpRH/kHwCLvapWiP7dImeWrxN3+xOlyTd3CFaM0Z0VDCXfANAnUQ/4948bf7eTzqsqZ/uVIift1Y9NUChAT5GlwQAAK6yqvQzlb7Ee8eOHRowYMAF3+/Xr5+2b99e6SKBWsdkkvo+Id35rmSxSnu/kN65ScpLM7qySqnn76MF93fVlFvaytti0hfb0zTstbXalpptdGkAAFx177zzjhYvXnze+OLFi/Xee+8ZUBF+dqqgRK9+vU+S9NTgawgnAQDAeSodUJ46dUplZWUXfL+0tFSnTp2qlqIAQ7X7lfTg55J/uHR8q7TgBil9p9FVVYrJZNJDfZvqP4/2VsNQP6VkFWrEvO/09rpDquRiaQAA3NL06dMVERFx3nhkZKRefPFFAyrCz2Yt36ecwlK1jg7SqB6NjC4HAADUQpUOKJs0aaLvv//+gu9///33aty4cbUUBRgurof020QpvKWUmyq9NUQ68I3RVVVap7h6+t/vrtPQdtEqLbfruc936f/+uVk5p0uNLg0AgKsiOTlZTZs2PW+8cePGSk5ONqAiSNLu47n6YMMRSY4H43hZqvSMTgAAUEdUukO444479PTTTys9Pf2899LS0vTnP/9ZI0aMqNbiAEOFNZV+u1xqcp1Ukid9cJf0/TtGV1VpIX7emndvFz17Wzv5WMz6ele6bn5trX5IZqUzAMDzREZGatu2beeNb926VeHh4QZUBLvdrmeW7pTN7rg3dq/mzAMAAKhYpQPKSZMmKSgoSC1bttRjjz2mv/3tb/rb3/6msWPH6pprrlFgYKAmTZp0NWsFap5fqHTvx1KnUZK9XPp8ovT1FMlmM7qySjGZTHqgdxN9/FhvNQ7319HsQt05P0kL1hyUzcYl3wAAzzFq1Cj97ne/08qVK1VeXq7y8nKtWLFCjz/+uEaOHGl0eXXSF9vTtOFQlqxeZv3p5jZGlwMAAGqxSj/FW5JycnI0efJkLVq0yHm/yXr16mnkyJF64YUXFBoaetUKrUme9tREVAO7XVr9krTqzD2s2twm3fGm5O1nbF1VkFdUqkkfb9f/th2XJA1sHalX7+zEjeoBwEPVtX6mpKRE9913nxYvXiwvLy9Jks1m0/3336/58+fLx8e9/r5z9/krLCnXoFmrdTS7UI/f0FJP3HiN0SUBAIAaVpV+pkoB5c/sdrsyMzNlt9tVv359mUymyy62NnL3hhBX0dZF0tLxUnmJ1KCbNGqhFFjf6KoqzW6364MNyXru810qKbMpJsRXr4+6Vt2ahBldGgCgmtXVfmb//v368ccf5efnpw4dOrjtPdLdff5mf7NPs7/Zr9gQXyU+OUB+PhajSwIAADXsqgeUns7dG0JcZYe/lRaNlgpPSfUaSaP/I9VvZXRVVbLrWK7Gf7hFBzMLZDGb9OTga/Rov+Yymz3rhw0AUJfRz7g3d56/1FOndcOrq1VcZtOce67VLR1jjS4JAAAYoCr9TKXuQdmlSxfnJd2V0bdvXx09erTS+wNupUkf6aFvpLBmUnay9I8bpe3/cVwG7ibaxgZr6YS+Gt45VuU2u15atldj3t2kk/nFRpcGAMBlGTFihGbOnHne+EsvvaQ777zTgIrqrulf7FFxmU3xTcM0rEOM0eUAAAA3UKkVlGazWStWrFBYWOUuA+3du7e2bdumZs2aXXGBRnDnn1ijBhWclBbeI6Wsd7xu1EsaOkOK7WxoWVVht9v17+9TNPXTnSousykq2KrXRl6r+GY8ZRMA3F1d62fq16+vFStWqEOHDi7j27dv16BBg5Senm5QZZfHXecv6aeTGrVgvcwm6fMJ16ltrPvUDgAAqldV+hmvyh70hhtuUGWvBve0e1ICFQoIlx5YKn37N2ntLCk5SXpzgNTlPmngVLe4N6XJZNLd3Rupc1yoxn24RQcy8jVqwXo9MegaPXZ9C1m45BsA4Cby8/MrfBCOt7e3cnNzDaio7ikrt+nZz3ZKku6Jb0Q4CQAAKq1SAeWhQ4eqfOCGDRtW+TOA2/GySv3/IHW+R/rmGWn7YmnL+9LOJY7xHv8nedX+p4a2ig7S0vF9NGXJTv13S6peXb5PGw5l6a93d1b9IKvR5QEAcEkdOnTQokWLNHXqVJfxhQsXqm3btgZVVbd8tClFe9LyFOLnrSdvdK/7cwMAAGPxkJwKuOslNagFktdLX/5ROv6j43VYc2nodOmaIYaWVRX/2ZyqKUt2qLC0XBGBVr02srN6t4gwuiwAQBXVtX7ms88+0x133KF77rlHAwcOlCQlJibqo48+0uLFizV8+HBjC6wid5u/7NMlGvDKKmWfLtWzt7XTA72bGF0SAAAwWLU/JAdAJTXqKT28Urp9rhQQKWX9JH14l/SvEdKJfUZXVym/7tpQS8f30TVRgcrML9botzZo1vJ9Ki23GV0aAAAXdOutt2rJkiU6cOCAHnvsMT355JNKTU3VN99843bhpDuatXyfsk+XqlVUkEbHNzK6HAAA4GZYQVkBd/uJNWqpolxp7StS0t8lW6lk9pJ6PCL1/6PkV8/o6i6psKRcz362Uws3pUiSGob6adz1LTSiS0P5ePGzDQCo7ehnztqxY4fat29vdBlV4k7ztyctVzf/ba1sdunDh+PVuzlXXgAAAFZQArWDb7B043PSuA3SNTdJtjJp/d+l17tI378t2cqNrvCi/HwsmjGio/42srMiAn2UeqpQkz/ergEvr9T7SYdVVFq76wcA1G15eXl688031aNHD3Xq1MnocjyW3W7Xs0t3yWaXbmofTTgJAAAuS60IKOfOnasmTZrI19dX8fHx2rhx4wX3HTBggEwm03nbsGHDnPs8+OCD570/dOjQmjgV4HzhzaV7Fkr3fixFtJJOn5Q+f0J6o790eJ3R1V3S7Z0baO0fBmrKLW0VGWTVsZwiTf10p/q9tFJvrTukwhKCSgBA7bFmzRrdf//9iomJ0SuvvKKBAwdq/fr1Rpflsb7ckaakgydl9TLrTze3MbocAADgpqocUKakpCg1NdX5euPGjZo4caLefPPNyypg0aJFSkhI0LRp07RlyxZ16tRJQ4YMUUZGRoX7f/zxxzp+/Lhz27FjhywWi+68806X/YYOHeqy30cffXRZ9QHVpsUN0thvpaEzJd8QKX279O4w6d8PSKeOGF3dRfn5WPRQ36Za84fr9dzt7RQT4quMvGI9//kuXffSCr255icVFJcZXSYAoI5KS0vTjBkz1LJlS915550KCQlRcXGxlixZohkzZqh79+5Gl+iRikrL9cL/dkuS/q9fM8WF+RtcEQAAcFdVDijvuecerVy5UpKjGbzxxhu1ceNGPf3003ruueeqXMCsWbP08MMPa8yYMWrbtq3mz58vf39/vf322xXuHxYWpujoaOe2fPly+fv7nxdQWq1Wl/1CQ0OrXBtQ7SzeUs9HpQk/SN0ekkxmadcSaW4PacVfpJICoyu8KF9vi+7v1USrfj9AL/6qgxqG+ikzv0QvfrFHfWeu0NyVB5RXVGp0mQCAOuTWW29Vq1attG3bNs2ePVvHjh3T66+/bnRZdcIbqw/qaHahYkN8NXZAC6PLAQAAbqzKAeWOHTvUo0cPSdK///1vtW/fXt99950++OADvfvuu1U6VklJiTZv3qxBgwadLchs1qBBg5SUlFSpY7z11lsaOXKkAgICXMZXrVqlyMhItWrVSmPHjtXJkycveIzi4mLl5ua6bMBVFRAu3TJL+r+1UpPrpLIiac3L0uvdpG2LpVr+7Cqrl0X3xDfSyqcG6OVfd1STcH+dOl2ql7/aqz4zVmj2N/uUc5qgEgBw9X355Zd66KGH9Oyzz2rYsGGyWCw1XsOMGTNkMpk0ceJE51hRUZHGjRun8PBwBQYGasSIEUpPT6/x2q6Wo9mFmrf6gCRp8s1t5OdT8//dAQCA56hyQFlaWiqr1SpJ+uabb3TbbbdJklq3bq3jx49X6ViZmZkqLy9XVFSUy3hUVJTS0tIu+fmNGzdqx44d+u1vf+syPnToUL3//vtKTEzUzJkztXr1at10000qL6/4XnnTp09XSEiIc4uLi6vSeQCXLbq99MBn0l3/lOo1kvKOSR//Vnp7iHR0i9HVXZK3xaw7u8Xpm4T+mn13ZzWvH6DcojLN/ma/+s5coVe+2qtTBSVGlwkA8GDr1q1TXl6eunbtqvj4eM2ZM0eZmZk19v2bNm3SG2+8oY4dO7qMP/HEE/rss8+0ePFirV69WseOHdMdd9xRY3VdbS9+sVtFpTb1aBqmWzrGGF0OAABwc1UOKNu1a6f58+dr7dq1Wr58ufPhM8eOHVN4eHi1F3gxb731ljp06OBc0fmzkSNH6rbbblOHDh00fPhwff7559q0aZNWrVpV4XEmT56snJwc55aSklID1QNnmExS29ukcZukgVMk7wApZYO04HppyTgpr/avtvCymDX82gb6+on+mnPPtWoVFaS84jLNWXlAfWau0PQvdyszv9joMgEAHqhnz55asGCBjh8/rv/7v//TwoULFRsbK5vNpuXLlysvL++qfXd+fr5Gjx6tBQsWuNxOKCcnR2+99ZZmzZqlgQMHqmvXrnrnnXf03XffecQDe9YfPKn/bTsus0madmtbmUwmo0sCAABursoB5cyZM/XGG29owIABGjVqlDp16iRJWrp06XlB4aVERETIYrGcd7lLenq6oqOjL/rZgoICLVy4UA899NAlv6dZs2aKiIjQgQMHKnzfarUqODjYZQNqnLev1O8pacL3UseRjrEf/yW93kVaN1sqq/0Bn8Vs0i0dY/Xl49dp/r1d1TYmWKdLyvXG6oPqO3OFnv98lzJyi4wuEwDggQICAvSb3/xG69at0/bt2/Xkk09qxowZioyMdF7xU93GjRunYcOGudyuSJI2b96s0tJSl/HWrVurUaNGF7yNkbvccqjcZtezn+2SJI3s0UjtYkMMrggAAHiCKgeUAwYMUGZmpjIzM10eZPPII49o/vz5VTqWj4+PunbtqsTEROeYzWZTYmKievXqddHPLl68WMXFxbr33nsv+T2pqak6efKkYmK4/ARuIDhWuuMN6aFvpAZdpZJ86Ztp0tx4ac8Xtf7+lJJkNps0tH20/ve7vnrrgW7q1DBERaU2vbXukPq+tFLTPt2hY9mFRpcJAPBQrVq10ksvvaTU1FR99NFHV+U7Fi5cqC1btmj69OnnvZeWliYfHx/Vq1fPZfxitzFyl1sOfbQxWbuP5yrY10tPDW5ldDkAAMBDVDmgLCwsVHFxsfMyliNHjmj27Nnau3evIiMjq1xAQkKCFixYoPfee0+7d+/W2LFjVVBQoDFjxkiS7r//fk2ePPm8z7311lsaPnz4eZeV5+fn6/e//73Wr1+vw4cPKzExUbfffrtatGihIUOGVLk+wDBx3R0h5fD5UmCUdOqQtHCU9M9fSRm7ja6uUkwmk25oE6Ul4/rovd/0UNfGoSops+m9pCPq//JK/emT7UrJOm10mQAAD2WxWDR8+HAtXbq0Wo+bkpKixx9/XB988IF8fX2r5ZjucMuh7NMlevXrvZKkhBuvUViAj8EVAQAAT+FV1Q/cfvvtuuOOO/Too48qOztb8fHx8vb2VmZmpmbNmqWxY8dW6Xh33323Tpw4oalTpyotLU2dO3fWsmXLnA/OSU5OltnsmqPu3btX69at09dff33e8SwWi7Zt26b33ntP2dnZio2N1eDBg/X88887H+4DuA2zWeo8Smpzi7R2lpQ0Rzq4UprXR+r+W2nAJMk/zOgqL8lkMqn/NfXVr2WEkn46qddW7Nf6g1n6cEOy/r0pRXd0aaDHBrRQk4gAo0sFAOCSNm/erIyMDHXp0sU5Vl5erjVr1mjOnDn66quvVFJSouzsbJdVlBe7jZHVaq31vepfl+/TqdOluiYqUPf2bGx0OQAAwIOY7PaqXS8aERGh1atXq127dvrHP/6h119/XT/88IP++9//aurUqdq92z1Wdl1Mbm6uQkJClJOTw/0oUbtkHZK+/rO053PHa79Q6fqnpa5jJEuVf95gqI2HsvT6iv1au9/xpFWzSRreuYEeu76FWkQGGlwdALg/+pmrJy8vT0eOHHEZGzNmjFq3bq0//vGPiouLU/369fXRRx9pxIgRkhw/YG/durWSkpLUs2fPS35HbZu/vWl5uvm1tSq32fXBb+PVp0WE0SUBAIBarir9TJUTjdOnTysoKEiS9PXXX+uOO+6Q2WxWz549z2vUAFSzsKbSyA+kn1ZKyyZLJ3ZLXzwlff+2NHSG1Ky/0RVWWo+mYfrnQ/HaknxKryfu18q9J/TxD0f1yY9HdUvHWI2/voVaRQcZXSYAAOcJCgpS+/btXcYCAgIUHh7uHH/ooYeUkJCgsLAwBQcHa8KECerVq1elwsnaxm6369nPdqrcZtfQdtGEkwAAoNpV+R6ULVq00JIlS5SSkqKvvvpKgwcPliRlZGTUip/uAnVC8+ulR9dJN7/iWEWZsUt6/zZp4WjHKks30qVRqN4Z00NLx/fRjW2jZLdLn209piGz1+jRf27WzmM5RpcIAECV/fWvf9Utt9yiESNGqF+/foqOjtbHH39sdFmX5audafrup5Py8TLr6WFtjC4HAAB4oCpf4v2f//xH99xzj8rLyzVw4EAtX75ckuPJg2vWrNGXX355VQqtSbXtkhrgok5nSaumS5vekuzlksUq9R4v9U2QrO53qfSuY7mas3K/vtyR5nxg+aA2kZowsKU6xdUztDYAcCf0M+6ttsxfUWm5bnh1tY5mF2rCwBZ6kid3AwCASqpKP1PlgFKS0tLSdPz4cXXq1Mn5AJuNGzcqODhYrVu3vryqa5Ha0hACVZK+S1o2STq02vE6MFoa9IzU8W7Hw3bczL70PM1ZcUCfbzsm25k/pfpfU1+/u6GlujYONbY4AHAD9DPurbbM32uJ+zVr+T7FhPgq8cn+8vdxr3teAwAA41z1gPJnqampkqSGDRte7iFqpdrSEAJVZrdLe7+QvvqTdOqwY6xBN+mmmVLDboaWdrl+OpGvuSsP6NMfj6n8TFLZp0W4JgxsqZ7Nwg2uDgBqL/oZ91Yb5u9YdqEGvrpKRaU2/W1kZ93euYEhdQAAAPdUlX6mysuqbDabnnvuOYWEhKhx48Zq3Lix6tWrp+eff142m+2yiwZQDUwmqfUwadxGx+pJn0Dp6PfSP26QPnlUyj1udIVV1rx+oGbd1Vkrnuyvu7vFycts0rcHTmrkm+t11xtJ+vZApq7g5ywAAOACpn+5R0WlNnVvEqrbOsUaXQ4AAPBgVQ4on376ac2ZM0czZszQDz/8oB9++EEvvviiXn/9dU2ZMuVq1AigqrysUt8npAmbpc6jHWNbP5Je7yqtfVUqLTK2vsvQODxAM3/dUat+P0Cj4xvJx2LWxkNZGv2PDRox7zut2ptBUAkAQDXZeChLn209JpNJmnZrO5lMJqNLAgAAHqzKl3jHxsZq/vz5uu2221zGP/30Uz322GM6evRotRZohNpwSQ1QrY5ulr78o5S6yfG6XmNpyAtS61scqy7d0PGcQr2x+qA+2pis4jLH6u2ODUM0YWBLDWoTyT+kANR59DPuzcj5K7fZdcvr67T7eK5G9Wik6Xd0qNHvBwAAnuGqXuKdlZVV4YNwWrduraysrKoeDkBNaNBVemi5dMcCKShGyj4iLbpXev82KX2n0dVdlpgQPz1zWzut/eP1evi6pvLztmhbao4efv97DXttnb7cflw2GysqAQCoqoWbkrX7eK6CfL301OBrjC4HAADUAVUOKDt16qQ5c+acNz5nzhx16tSpWooCcBWYTFLHu6Tx30v9fi9ZrNKhNdL8vtL/npROu+cPGCKDfPX0sLZa98frNXZAcwX4WLTreK7GfrBFQ/+2Rku3nn24DgAAuLic06V65au9kqSEG69ReKDV4IoAAEBdUOVLvFevXq1hw4apUaNG6tWrlyQpKSlJKSkp+uKLL3TdddddlUJrEpdEoU44dVhaPlXa9anjtW896fo/Sd1+I1m8jazsipwqKNE73x7SO98eVl5xmSSpWf0Ajb++hW7rFCsvS5V/LgMAbol+xr0ZNX/PLN2pd787rJaRgfri8evkzd+bAADgMlWln6lyQClJx44d09y5c7Vnzx5JUps2bfTYY48pNtYznu5HQ4865dBaadkkKX2H43X91tLQ6VLzgcbWdYVyCkv13neH9da6Q8opLJUkNQ7317gBLfSrLg34BxcAj0c/496MmL+9aXm6+bW1KrfZ9a+H4tW3ZUSNfC8AAPBMVz2grEhqaqqee+45vfnmm9VxOEPR0KPOKS+TtrwnrfiLVHjmUu9WN0uD/yKFNze2tiuUV1Sqf64/on+sPaSsghJJUsNQP40d0Fy/7tpQVi+LwRUCwNVBP+Peanr+7Ha77n1rg749cFKD20bpzfu7XfXvBAAAns2QgHLr1q3q0qWLysvLq+NwhqKhR51VeEpa/ZK08U3JViaZvaVejznuWWkNMrq6K3K6pEwfrE/WG2sOKjO/WJIUE+KrR/s3193d4+TrTVAJwLPQz7i3mp6/ZTvS9Oi/NsvHy6xvnuivRuH+V/07AQCAZyOgvEI09KjzTux1XPb90wrHa/8IKS5eCm8mhTV3rKoMayYFxUpm97pUuqi0XB9tTNb81T8pPdcRVNYPsur/+jXT3d3jFOTrvvffBIBfop9xbzU5f0Wl5brxr6uVklWo8de30FNDWl3V7wMAAHVDVfoZrxqqCYA7qd9Kuvdjad9X0leTpayD0t7/nb+fl58U1tQRVoY1OxNcngkwg2IcTw6vZXy9LRrTp6lG9WikxZtTNX/VTzqaXai//G+3Zi3fp19d20D39Wqs1tH8Yx4AUDf8Y+1BpWQVKjrYV49d7963dgEAAO6JgBJAxUwmqdVQx8NyjqyTMg9IWT9JJ39y/HrqiFRWKGXscmzn8vI7E1r+ctXlzysvow0PL329LbqvZ2Pd3S1OH29J1T/WHdKBjHx9sCFZH2xIVo8mYbq3V2MNbRctHy/3WiUKAEBlHc8p1NyVP0mSJt/cWv4+/PMAAADUvEp3IHfcccdF38/Ozr7SWgDURl4+jpDy3Kd6l5dK2cmO1ZUnf3L8+nOAmZ18Jrzc6djO5R3gGl7+cvVlYGSNhpc+XmaN7NFId3ePU9LBk/rX+iP6ame6Nh7O0sbDWYoItGpUjziN6tFIsfX8aqwuAABqwvQv9qiwtFzdGofqtk6xRpcDAADqqEoHlCEhIZd8//7777/iggC4CYu3I1QMby61vNH1vfJSxwrLX4aWWWdCzOxkqbRASt/u2M7lE3jmsvFzVl2GN5cC6l+18NJkMql38wj1bh6htJwifbQxWR9tTFZGXrFeX3FAc1ce0I1to3Rfzybq0yJcplp4+ToAAFWx42iOlm49JpNJeua2dvzdBgAADFNtD8nxJNxUHriKykqk7COuoeXPv89OkXSRP5J8gipedRneXPIPr/bwsrTcpuW70vXPpCNKOnjSOd4sIkD39mysEV0bKsSPh+oAqJ3oZ9xbTcyf3W7Xpz8e04GMfB6MAwAAqp0hT/H2JDT0gEHKiqVTh11Dy5M/SVmHpJxLhJfWEMfKy1+Glj8Hmf5hVxxe7k/P07/WH9F/txxVfnGZJMnX26zhnR0P1WkXe/FV5gBQ0+hn3BvzBwAA3B0B5RWiIQRqodKiM+HlT+fc8/KglJt68c/6hvwitGzm+nv/sCqVkV9cpiU/HNW/1h/RnrQ853iXRvV0X6/Guql9jHy9LZdxggBQvehn3BvzBwAA3B0B5RWiIQTcTGmhI7x0WXV50LHlHr34Z/1Cz1wu3lJq1FNq2s/x+hIrLu12u74/ckrvJx3Rsh3HVVru+KM0LMBHd3eP0z09GikuzL+aThAAqo5+xr0xfwAAwN0RUF4hGkLAg5Sclk4dOn/VZdZPUt7xij8TFCs16Ss1vU5qcp0U2uSigWVGXpEWbUzRhxuTdTynSJJj94GtInVfr8bq17K+zGYePACgZtHPuDfmDwAAuDsCyitEQwjUESUFjvtbZv0kpe2QDq+TUjdJtlLX/ULiHIFlk+scoWW9RhUerqzcpsQ9GfrX+iNauz/TOd4ozF/39mykO7vGKTTA52qeEQA40c+4N+YPAAC4OwLKK0RDCNRhJael1I3SobWOwPLo95KtzHWfeo3PhpVNrpNCGpx3mJ9O5OuD9clavDlFeUWOz1u9zLq1U6zu69lYneLq1cDJAKjL6GfcG/MHAADcHQHlFaIhBOBUUiAlr5cO/xxYbpHs5a77hDY9E1b2c6y0DI5xvnW6pExLfzym95OOaNfxXOd4x4Yhuq9nY93aKZaH6gC4Kuhn3BvzBwAA3B0B5RWiIQRwQcV5jsDy0BpHYHn8R8luc90nvIXrCsvASNntdv2Qkq1/Jh3R/7YdV0m54zP1/L11V7c4jY5vpMbhATV/PgA8Fv2Me2P+AACAuyOgvEI0hAAqrShHOpJ0ZoXlWun4Nknn/LEa0epsWNmkr07ag/Tv71P1r/VHdDS70Llb/2vq676ejXV960hZeKgOgCtEP+PemD8AAODuCCivEA0hgMtWeOpsYHlorZS+/fx9IttKTa5TeZO+Wldyjd75IVer953Qz38aN6jnp9E9G+nubnEKD7TWbP0APAb9jHtj/gAAgLsjoLxCNIQAqs3pLOnIt2ceurNWyth1zg4mKaq9cmN66quClnrtQH2lFDpCSR+LWcM6xujeno3VpVE9mUysqgRQefQz7o35AwAA7o6A8grREAK4agoyHfeu/HmFZeZel7ftMik7pI1WlbTSZznNtcnWWnnyV9uYYN3fq7Fu6xwrfx8vg4oH4E7oZ9wb8wcAANwdAeUVoiEEUGPyM86GlYfXSSf3u7xtk1k77E31XXlbrbe10W6fdrq5a0vd27OxmtcPNKhoAO6Afsa9MX8AAMDdEVBeIRpCAIbJPX5mheWZp4RnHXR5u8xu1jZ7M623tVVudE917XuTru/QRF4Ws0EFA6it6GfcG/MHAADcHQHlFaIhBFBr5KQ6Lwm3H1orU/YRl7dL7BbtNrdUScPeahF/k0JbXSd5+xlULIDahH7GvTF/AADA3VWln+FGZgBQm4U0lDqNlDqNlEmSspOlQ2tVsG+Vyn5ao5CSNHWy75FS9kgpb6vU5K3CyGsV1GqATE37SQ27S96+Rp8FAAAAAAAXREAJAO6kXiPp2tEKuHa0ZLerOPOgdn77P+XuXqFWRVsVoyx5p2+U0jdKa16S3WKVKa6H1OQ6qWk/qWE3yeJt9FkAAAAAAODEJd4V4JIaAO5oR2q2lq39Vrm7V6mrfYd6mXcp0pTtupM12BFUthgktbjBEXgC8Ej0M+6N+QMAAO6OS7wBoA5q37Ce2o8appzCwfrv5lSNTDosZR1QL/Mu9TLvUj+v3QouzpH2fO7YJCnimrNhZeM+3L8SAAAAAFDjWEFZAX5iDcAT2O12fffTSf0z6YiW706XzVau9qbD6m/eqoFe29TJtF8W2c5+wMvXEVK2GOTYIlpKJpNxJwDgitDPuDfmDwAAuDue4n2FaAgBeJrjOYX6akeakg6e1IZDWco+XapgFai3eaf6m7fqeq9titZJ1w+FNJJaDHSElU37S778eQi4E/oZ98b8AQAAd0dAeYVoCAF4MpvNrt1puUr66aTWnwks84pK1dJ0VP3MW9XfvE3xlj2yqtT5GbvZS6aGPRyXgrcYJEV3lMxmA88CwKXQz7g35g8AALg7AsorREMIoC4pt9m181iOkn46qaSDJ7XpUJbKS06rp3m3+pu3qp95m5qbj7t8xh5QX6bmZ1ZXNh8oBUQYVD2AC6GfcW/MHwAAcHcElFeIhhBAXVZabtP2oznOFZabDmcpoixN/c3b1N+8Vb3NOxVoKnLub5dJiu0sU/MzqysbdpcsPIMNMBr9jHtj/gAAgLsjoLxCNIQAcFZJmU1bU7MdKyx/OqltySfUwbZH/c4Elu3MR1z2t/kEy9x8gONy8OY3SPXijCkcqOPoZ9wb8wcAANwdAeUVoiEEgAsrKi3XD8nZSjp4Uut/OqmUlEPqZd+q/pZtus68TWGmfJf9S8OukXerwY5LwRv3kbx9DaocqFvoZ9wb8wcAANwdAeUVoiEEgMorLCnX5iOnlHQwUxsOZKj82Fb11Y/qb9mma037ZTGd/WumzOKr8kZ9ZG012LHCMryFZDIZWD3guehn3BvzBwAA3B0B5RWiIQSAy1dQXKZNh7OUdPCktu8/rND073SdaZv6W7YpxpTlsu9p/wYytxwk3zZDpKb9JGuQQVUDnod+xr0xfwAAwN0RUF4hGkIAqD65RaXadChLSQcydWz/FjXISlI/01b1MO+R1VTm3K9cFuVEdJVf28HyazNYiuogmc0GVg64N/oZ98b8AQAAd0dAeYVoCAHg6sk+XaINh7L0/b5UFR1Yo2Y569XfvFXNzGku++V7hSm3QT/V63ST/FvdKAWEG1Qx4J7oZ9wb8wcAANxdVfoZrxqqCQAASVI9fx8NaRetIe2iJXXTyfxibTiUpSW7tsrr0Eq1Kdik3uYdCizLUuCRJdKRJbLJpLSANippcr0iu9wi/yY9JAt/hXkcW7lUeMrxe/9w7k8KAAAA1BGsoKwAP7EGAONk5BVpw4E0pW1frYCUVepcvFltzUdc9sk3BSo1tIfMLQcprvut8otoZEyxuDCbTSrKlk5nSadPOrbCX/z+9EnX906flAqzJZ1pS7wDpNAmFW/1GvE0+Eqgn3FvzB8AAHB3bneJ99y5c/Xyyy8rLS1NnTp10uuvv64ePXpUuO+7776rMWPGuIxZrVYVFRU5X9vtdk2bNk0LFixQdna2+vTpo3nz5qlly5aVqoeGEABqj7ScIv2wc5dyd36t8LS16lr2o0JN+S77JHs1VkZkXwW0vE6NGsQqIDBE8gmUfPwlnwBH2MWKy8tnt0vFuWcCxXNDxgsEjoVZkt12mV9okjOovJCg2AsHmIGRrL4U/Yy7Y/4AAIC7c6tLvBctWqSEhATNnz9f8fHxmj17toYMGaK9e/cqMjKyws8EBwdr7969ztemc/4R8tJLL+m1117Te++9p6ZNm2rKlCkaMmSIdu3aJV9fVlwAgDuJDvHVTb27SL27SJqk1JN52rJljUr3LVeDzO/U1rZPjcqOqNGxI9KxDy58IIvVEVb6BJ751f/sa2//Ct77+fdnAk6fCjbvAPd7kI/dLpWevvhKxorGbGWXPnZFrMGSf5jjkm2XraKxcMm3nmQvl7JTpFOHpVOHzvx6WDp1xPG6JF/KO+bYkr87/zu9/aV6jV1Dy7Cmv1h96Xd55wIAAADgqjB8BWV8fLy6d++uOXPmSJJsNpvi4uI0YcIETZo06bz93333XU2cOFHZ2dkVHs9utys2NlZPPvmknnrqKUlSTk6OoqKi9O6772rkyJHnfaa4uFjFxcXO17m5uYqLi+Mn1gBQy9ntdqUeO6rk77+Q5eAK1cvdJ+/y0/I3FctfRQpQkbxMl7uKr5K8/CoOL12Cz4u9F+galvoEOI5Z2eCztOjS4eLPYz9fYl1WdOnjVsQ74CLhYgVjfqGSl8/lfdeF2O2OczkvvDzsCDBzUy+9cjMo5iKrL6M8ZvUlK/DcG/MHAADcndusoCwpKdHmzZs1efJk55jZbNagQYOUlJR0wc/l5+ercePGstls6tKli1588UW1a9dOknTo0CGlpaVp0KBBzv1DQkIUHx+vpKSkCgPK6dOn69lnn63GMwMA1ASTyaS4Bg0V1+ARSY9IkrIKSrQtNVvbU3O0LTVbe1JPKi8vW/4qlr/JEVr6m4oVZCpW83omtawnNQ4yqWGATRHWUllKTztWGJbkSyUFFW+lBWdDsLJCx3Y6s3pPzrlq85zVnJLrpdalBZd3fItVCoiQ/MIqGTiG1Y6VhyaT44nuAeFSw67nv19WIuWknBNc/mIrzpXyjju25Ap6DS8/KbTxBe592dgxHwAAAACqlaEBZWZmpsrLyxUVFeUyHhUVpT179lT4mVatWuntt99Wx44dlZOTo1deeUW9e/fWzp071bBhQ6WlpTmPce4xf37vXJMnT1ZCQoLz9c8rKAEA7icswEcDWkVqQKuztwnJyC3SttQcbTuao+2p2dqWmqOTBSXSSTm2M3wsZrWJCVLHhvXUoVmIOjYMUYv6gfKynLOa0W53rEI8L7zMPxNuFvwi4Dwn7Cz95f4VvPez0jOvK5M/mr0qcfn0OWPe/h6zUtCFl48U3tyxnctudzwl/LyVl2e2nFRH2Hxij2OrSGDURVZfRrvfJf8AAABALWD4PSirqlevXurVq5fzde/evdWmTRu98cYbev755y/rmFarVVartbpKBADUMpHBvhrU1leD2jp+eGW323Usp8gZVm4/mqNtqTnKKSzV1tQcbU3NcX7Wz9uidrHB6tDQEVh2aFBPzSICZPb2c6woDIiovkJtNkdA5gw4zwk7S087Vm6eGzpagz0zbKxuJtOZ/2ZhUoMKVl+Wl1549WXWYak4R8pPd2wpG87/vMV68dWX1sCrdGIAAACAezM0oIyIiJDFYlF6errLeHp6uqKjoyt1DG9vb1177bU6cOCAJDk/l56erpiYGJdjdu7cuXoKBwC4NZPJpAb1/NSgnp+Gtnf8XWG325WcdfoXgWW2dhzNVX5xmb4/ckrfHznl/Hyg1UvtYoPVKa6eOjRwBJeNwvzPe2hblZnNv7iUu+IHxeEqsnhLYc0cW0UKT1340vHsFKm8WMrc59gqElDfNbSMf7R6A24AAADATRkaUPr4+Khr165KTEzU8OHDJTkekpOYmKjx48dX6hjl5eXavn27br75ZklS06ZNFR0drcTERGcgmZubqw0bNmjs2LFX4zQAAB7AZDKpcXiAGocH6NZOsZIkm82ug5kF2n70zErL1BztOJaj/OIybTiUpQ2HspyfD/HzPrPC8sxKy4b1FBvie+WhJWoPv1DHFnvt+e+Vlzke0HOhALPwlFRwwrGlbnJ8pvvDNVU5AAAAUKsZfol3QkKCHnjgAXXr1k09evTQ7NmzVVBQoDFjxkiS7r//fjVo0EDTp0+XJD333HPq2bOnWrRooezsbL388ss6cuSIfvvb30py/ANz4sSJ+stf/qKWLVuqadOmmjJlimJjY50hKAAAlWE2m9QiMlAtIgP1q2sbSpLKym06cCLfGVhuS83W7uN5yiks1dr9mVq7/+zDciICfdShgSOs7HgmuIwM9jXqdHA1WbzOroysSGG2lH3kFysuk6VAVskCAAAAUi0IKO+++26dOHFCU6dOVVpamjp37qxly5Y5H3KTnJws8y9uOH/q1Ck9/PDDSktLU2hoqLp27arvvvtObdu2de7zhz/8QQUFBXrkkUeUnZ2tvn37atmyZfL15R+FAIAr42Uxq3V0sFpHB+uubo4HqpWU2bQvPe/M5eHZ2pqSo33pecrML9HKvSe0cu8J5+ejg30d97NsEHLmvpb1FBbgY9TpoKb41XNsMZ2MrgQAAACodUx2u91udBG1TW5urkJCQpSTk6Pg4GCjywEAuKGi0nLtPp6r7UdztDXFEVweyMiXrYK/dRuG+jkfwNOxYYjaNwhRiJ93zRcNj0I/496YPwAA4O6q0s8YvoISAABP5Ott0bWNQnVto1Cpl2OsoLhMu47namtKtrYfdVwifjCzQKmnCpV6qlBfbE9zfr5pRMDZ+1k2cISWAVb+2gYAAADgefiXDgAANSTA6qXuTcLUvUmYcyy3qFQ7juacvafl0WylZBXqUGaBDmUWaOnWY5Ikk0lqUT9QHRqEqE1MsNrEBKt1TJAiAq1GnQ4AAAAAVAsCSgAADBTs663ezSPUu3mEc+xUQYm2Hc3R9tQzTw8/mqPjOUXan5Gv/Rn50g9HnfvWD7KeCSyD1CbaEVw2qx8gb4u5oq8DAAAAgFqHgBIAgFomNMBH/a+pr/7X1HeOZeQVaXtqjnYey9Xu447tSNZpncgr1om8E1qz7+yDeHwsZrWMClTraEdw2fbMistQHsYDAAAAoBYioAQAwA1EBvnqhja+uqFNlHOsoLhMe9PznIHl7uN52nM8VwUl5dp5LFc7j+W6HCMq2Hr28vBoR3DZNCJAXqy2BAAAAGAgAkoAANxUgNVLXRqFqkujUOeYzWZX6qlC7ToTWu5JcwSXyVmnlZ5brPTcE1q19+xqS6uXWddEBal1dJAzvGwTE6R6/qy2BAAAAFAzCCgBAPAgZrNJjcL91SjcX0PbRzvH84pKtS89T7uOn11xuTctT6dLyh1PFD+a43KcmBDfs/e2PBNcNgkPkMVsqulTAgAAAODhCCgBAKgDgny91bVxmLo2PvsEcZvNruSs02cvEU9zhJeppwp1PKdIx3OKtGJPhnN/X2+zWkUFuVwm3jomWCF+3kacEgAAAAAPQUAJAEAdZTab1CQiQE0iAnRThxjneG5RqfYczztzeXiudh3P0960XBWV2rQ1NUdbU11XWzao53feasvGYf4ys9oSAAAAQCUQUAIAABfBvt7q0TRMPZqeXW1ZbrPryMkC7f7FJeJ70vJ0NLvQuX2zO925v5+3Ra3O3Ney7ZngslV0kIJ8WW0JAAAAwBUBJQAAuCSL2aRm9QPVrH6ghnU8u9oy53Spdp9ZabnneJ52pznubVlYWq4fU7L1Y0q2y3HiwvzUJjpYrX8RXMaFstoSAAAAqMsIKAEAwGUL8fdWz2bh6tks3DlWVm7T4XNWW+4+nqe03CKlZBUqJatQX+86u9oywOfsass2McFqERmoyCCr6gdZFWj1kslEeAkAAAB4MgJKAABQrbwsZrWIDFK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