v1.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "2024-03-25 00:16:27.149970: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
                        "2024-03-25 00:16:27.174291: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
                        "2024-03-25 00:16:27.174314: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
                        "2024-03-25 00:16:27.174974: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
                        "2024-03-25 00:16:27.179340: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
                        "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
                        "2024-03-25 00:16:27.622029: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
                    ]
                }
            ],
            "source": [
                "import numpy as np\n",
                "import tensorflow as tf\n",
                "from keras.models import Sequential\n",
                "from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization\n",
                "from keras.losses import categorical_crossentropy\n",
                "from keras.optimizers import Adam\n",
                "import keras\n",
                "\n",
                "from settings import *\n",
                "\n",
                "from utils.general import clean_session, plot_model, split_dataset, scale_dataset, shape_dataset, print_dataset_info\n",
                "from utils.models.ocr import save_model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "import Datasets.OCR.EnglishFnt.loader as EnglishFnt"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "def create_model():\n",
                "    model = Sequential()\n",
                "    # Signle convolution to extract features\n",
                "\n",
                "    # CNN\n",
                "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',input_shape=INPUT_SHAPE))\n",
                "    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
                "    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
                "\n",
                "    # Pooling\n",
                "    model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding='same'))\n",
                "\n",
                "    # Drop some data points randomly (mainly for caligraphy reasons)\n",
                "    model.add(Dropout(0.25))\n",
                "\n",
                "    # Flattening before feeding to FCNN\n",
                "    model.add(Flatten())\n",
                "\n",
                "    # FCNN\n",
                "    # use softmax since we are doing classification (1 node for each possible output)\n",
                "    model.add(Dense(128, activation='relu'))\n",
                "    model.add(Dropout(0.5))\n",
                "    model.add(Dense(len(COMBINED_CHARS), activation='softmax'))\n",
                "\n",
                "    model.summary()\n",
                "\n",
                "    return model\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
                "def train_model(model, learning_rate, batch_size, epochs):\n",
                "    optimizer = Adam(learning_rate=learning_rate)\n",
                "    model.compile(loss=categorical_crossentropy, optimizer=optimizer, metrics=['accuracy'])\n",
                "\n",
                "    EX_data, EY_data = EnglishFnt.load_dataset()\n",
                "    X_data = EX_data\n",
                "    Y_data = EY_data\n",
                "\n",
                "    X_train, X_test, Y_train, Y_test = split_dataset(X_data, Y_data, test_percent=TEST_PERCENT, random_state=RANDOM_STATE, shuffle=True)\n",
                "    X_train, X_test = shape_dataset(X_train, X_test, input_shape=INPUT_SHAPE)\n",
                "    X_train, X_test = scale_dataset(X_train, X_test, batch_size=batch_size)\n",
                "\n",
                "    print_dataset_info(X_data, X_train, Y_train, X_test, Y_test)\n",
                "\n",
                "    history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs, validation_split=VALIDATION_PERCENT)\n",
                "\n",
                "    return history"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Model: \"sequential\"\n",
                        "_________________________________________________________________\n",
                        " Layer (type)                Output Shape              Param #   \n",
                        "=================================================================\n",
                        " conv2d (Conv2D)             (None, 98, 48, 32)        320       \n",
                        "                                                                 \n",
                        " conv2d_1 (Conv2D)           (None, 96, 46, 64)        18496     \n",
                        "                                                                 \n",
                        " conv2d_2 (Conv2D)           (None, 94, 44, 128)       73856     \n",
                        "                                                                 \n",
                        " max_pooling2d (MaxPooling2  (None, 47, 22, 128)       0         \n",
                        " D)                                                              \n",
                        "                                                                 \n",
                        " dropout (Dropout)           (None, 47, 22, 128)       0         \n",
                        "                                                                 \n",
                        " flatten (Flatten)           (None, 132352)            0         \n",
                        "                                                                 \n",
                        " dense (Dense)               (None, 128)               16941184  \n",
                        "                                                                 \n",
                        " dropout_1 (Dropout)         (None, 128)               0         \n",
                        "                                                                 \n",
                        " dense_1 (Dense)             (None, 35)                4515      \n",
                        "                                                                 \n",
                        "=================================================================\n",
                        "Total params: 17038371 (65.00 MB)\n",
                        "Trainable params: 17038371 (65.00 MB)\n",
                        "Non-trainable params: 0 (0.00 Byte)\n",
                        "_________________________________________________________________\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "2024-03-25 00:16:28.827078: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.854030: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.854202: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.854916: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.855011: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.855070: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.912293: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.912425: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.912498: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
                        "2024-03-25 00:16:28.912556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2093 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
                    ]
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                    "text": [
                        "Data samples: 106575\n",
                        "X_train shape: 85260\n",
                        "Y_train shape: 85260\n",
                        "X_test shape: 21315\n",
                        "Y_test shape: 21315\n",
                        "Epoch 1/15\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "2024-03-25 00:16:38.581813: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n",
                        "2024-03-25 00:16:38.997161: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8906\n",
                        "2024-03-25 00:16:39.041907: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
                        "2024-03-25 00:16:39.978834: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
                        "2024-03-25 00:16:40.260196: I external/local_xla/xla/service/service.cc:168] XLA service 0x718704d609c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
                        "2024-03-25 00:16:40.260213: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 3050 Ti Laptop GPU, Compute Capability 8.6\n",
                        "2024-03-25 00:16:40.263633: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
                        "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
                        "I0000 00:00:1711318600.313895  183330 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
                        "2024-03-25 00:16:41.592810: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.86GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
                        "2024-03-25 00:16:41.999898: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.86GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "1065/1066 [============================>.] - ETA: 0s - loss: 0.8187 - accuracy: 0.7671"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "2024-03-25 00:17:58.274712: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.40GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
                        "2024-03-25 00:17:58.606375: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.40GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "1066/1066 [==============================] - 90s 76ms/step - loss: 0.8185 - accuracy: 0.7672 - val_loss: 0.1940 - val_accuracy: 0.9451\n",
                        "Epoch 2/15\n",
                        "1066/1066 [==============================] - 73s 68ms/step - loss: 0.3384 - accuracy: 0.8946 - val_loss: 0.1290 - val_accuracy: 0.9601\n",
                        "Epoch 3/15\n",
                        "1066/1066 [==============================] - 69s 65ms/step - loss: 0.2503 - accuracy: 0.9179 - val_loss: 0.1045 - val_accuracy: 0.9669\n",
                        "Epoch 4/15\n",
                        "1066/1066 [==============================] - 74s 70ms/step - loss: 0.1999 - accuracy: 0.9324 - val_loss: 0.0926 - val_accuracy: 0.9704\n",
                        "Epoch 5/15\n",
                        "1066/1066 [==============================] - 68s 64ms/step - loss: 0.1768 - accuracy: 0.9387 - val_loss: 0.0844 - val_accuracy: 0.9731\n",
                        "Epoch 6/15\n",
                        "1066/1066 [==============================] - 63s 59ms/step - loss: 0.1499 - accuracy: 0.9479 - val_loss: 0.0752 - val_accuracy: 0.9750\n",
                        "Epoch 7/15\n",
                        "1066/1066 [==============================] - 68s 63ms/step - loss: 0.1310 - accuracy: 0.9546 - val_loss: 0.0709 - val_accuracy: 0.9778\n",
                        "Epoch 8/15\n",
                        "1066/1066 [==============================] - 64s 60ms/step - loss: 0.1197 - accuracy: 0.9573 - val_loss: 0.0716 - val_accuracy: 0.9773\n",
                        "Epoch 9/15\n",
                        "1066/1066 [==============================] - 62s 58ms/step - loss: 0.1104 - accuracy: 0.9608 - val_loss: 0.0693 - val_accuracy: 0.9788\n",
                        "Epoch 10/15\n",
                        "1066/1066 [==============================] - 62s 58ms/step - loss: 0.0971 - accuracy: 0.9651 - val_loss: 0.0666 - val_accuracy: 0.9788\n",
                        "Epoch 11/15\n",
                        "1066/1066 [==============================] - 62s 58ms/step - loss: 0.0897 - accuracy: 0.9673 - val_loss: 0.0656 - val_accuracy: 0.9810\n",
                        "Epoch 12/15\n",
                        "1066/1066 [==============================] - 64s 60ms/step - loss: 0.0838 - accuracy: 0.9698 - val_loss: 0.0701 - val_accuracy: 0.9797\n",
                        "Epoch 13/15\n",
                        "1066/1066 [==============================] - 64s 60ms/step - loss: 0.0791 - accuracy: 0.9720 - val_loss: 0.0672 - val_accuracy: 0.9806\n",
                        "Epoch 14/15\n",
                        "1066/1066 [==============================] - 63s 59ms/step - loss: 0.0747 - accuracy: 0.9726 - val_loss: 0.0668 - val_accuracy: 0.9807\n",
                        "Epoch 15/15\n",
                        "1066/1066 [==============================] - 63s 59ms/step - loss: 0.0692 - accuracy: 0.9749 - val_loss: 0.0723 - val_accuracy: 0.9814\n"
                    ]
                },
                {
                    "data": {
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JJ5/EYrFgsVhIT093Lxn7+OOPGTx4MD4+Pnz99dekpKRw/vnnEx0dTWBgICeffDIrV66s9Zy/X55msVj473//y4UXXoi/vz+9evXi/fffdz/+++VpNcvIPvnkE/r27UtgYCBjx44lMzPTfU1VVRW33noroaGhREREcPfddzNp0iQuuOCCo/oaPfvssyQmJmK32+nduzevvvpqra/frFmz6NatGz4+PnTu3Jlbb73V/fgzzzxDr1698PX1JTo6mksuueSoXvtoqdNIRESOTWUZFO2Fgj1QuBcK91Tf9kLBbtd9SW7ta2w+4BcKvqGH7n1D6h7zqz5++DF7AFgsrff+GmIYrgCsohgqiqrvi6HiIJQfrP15RfXnTgdguK7FOPQ87mPU8zh/8PgfXX/Y444KV81VZa77wz+uuTccLfafjKqylntuabf25peya38pabnFnNg11OxyRETavNJKB/1mftLqr/vrQ2PwtzcuYnjyySfZvn07/fv356GHHgJcnULp6ekA3HPPPcyZM4cePXoQFhbGrl27GDduHI888gg+Pj688sorjB8/nm3bttGtW7cGX+fBBx/k8ccf54knnuCpp57iqquuYufOnYSHh9d7fklJCXPmzOHVV1/FarVy9dVXc+edd7Jo0SIAHnvsMRYtWsSLL75I3759efLJJ3n33Xc5/fTTG/3f6Z133uG2225j3rx5JCUl8eGHHzJ58mS6du3K6aefzttvv82//vUvFi9ezPHHH09WVhY///wzAD/88AO33norr776KiNGjGD//v189dVXjX7tplBoJCIiDassPSwIqr7/fThUkte457L5gLMSDCc4yuFgtut2tKxeDYdMRwyeQlyhTa1Qp4H78sOCnt8HP4c/TuP/ouZ5LODtB16+h937g7dvA8f8Drv3q+daX/ANM/tNiQdKiAjgu7T9pOUWm12KiIg0k5CQEOx2O/7+/vUuEXvooYc488wz3Z+Hh4czYMAA9+cPP/ww77zzDu+//z7Tpk1r8HWuvfZarrjiCgAeffRR/v3vf7Nu3TrGjh1b7/mVlZUsWLCAxMREAKZNm+YOtQCeeuoppk+fzoUXXgjA008/zUcffXQU7xzmzJnDtddey9SpUwFITk5m7dq1zJkzh9NPP52MjAxiYmJISkrC29ubbt26MXToUAAyMjIICAjg3HPPJSgoiPj4eAYNGnRUr3+0FBqJiHRUFSVQlHmoG+jwDqGacKh0f+Oey9sfgrtAcGfXfchhH9cc96sODMqLoCwfSvNd92UFhz6udV9Q95izEpxVrs6l33cvmcke6OqAct+CDvs4EHwCXf+NbN7VF1iqu6WqO6ZqPnY3UDX0uOUYHge8fH4X7tSEOr8/5g82e9vo6JIOLyHSNdQ0XaGRiEij+Hnb+PWhMaa8bnMZMmRIrc8PHjzIrFmzWLZsGZmZmVRVVVFaWkpGRsYRn+fEE090fxwQEEBwcDD79u1r8Hx/f393YAQQGxvrPr+goIDs7Gx3gANgs9kYPHgwTqez0e9ty5Yt3HDDDbWOnXLKKTz55JMAXHrppcybN48ePXowduxYxo0bx/jx4/Hy8uLMM88kPj7e/djYsWPdy+9aikIjEZHmZBiuUKR0P5RU32o+LssHR2XNiY1cYsQfPH4US5QMBxzMqQ6FdkPpgca9J++A+kOgw8Mh39DGBwy+wa5baMOtxPUyDKgsaThQOmIAlX9oaZS3f+1A5/DAxyeo4fDHJ6j+67z9waoRgSItpXuk6wfhtLwSkysREfEMFoul0cvE2qrf74J25513smLFCubMmUPPnj3x8/PjkksuoaKi4ojP4+3tXetzi8VyxICnvvOPZlZTc4iLi2Pbtm2sXLmSFStWMHXqVJ544gm++OILgoKC2LBhA6tXr+bTTz9l5syZzJo1i++//57Q0NAWqcez/yWJiLQkR5UrbHAHP3m1QyD3/YHDHjvg6obxFO5A6LAwKOR3wZBvSNvoOLFYDoU2IV2O/vqqctfSNmvz/RVMRFpeTafRzjx1GomItCd2ux2Ho3GzFL/55huuvfZa97KwgwcPuucftZaQkBCio6P5/vvvGT16NAAOh4MNGzYwcODARj9P3759+eabb5g0aZL72DfffEO/fv3cn/v5+TF+/HjGjx/PzTffTJ8+fdi4cSMnnXQSXl5eJCUlkZSUxAMPPEBoaCifffYZF110UbO918MpNBKR5uV0uoYi56XA/pTq+zTXx/m7XOd42V3zbdz3Pq5lO3WO2Q/dH/5xQ/fuj33qfw2rzdUFVFIdAJXuh5IDvwuBDguGygqa/t/Byxf8wsE/3LUsq+beZuePlxTRyGVIjT122PMERNUOh3yC20Yg1Bq8fMyuQKRVzZ8/nyeeeIKsrCwGDBjAU089Vaul/vfmzZvHs88+S0ZGBpGRkVxyySXMnj0bX1/fVqy6rvhwV2iUX1JJfkkFof52U+sREZHmkZCQwHfffUd6ejqBgYENDqcG6NWrF0uXLmX8+PFYLBZmzJhxVEvCmsstt9zC7Nmz6dmzJ3369OGpp57iwIEDWI7i5+m77rqLyy67jEGDBpGUlMQHH3zA0qVL3bvBvfTSSzgcDoYNG4a/vz+vvfYafn5+xMfH8+GHH5Kamsro0aMJCwvjo48+wul00rt375Z6ywqNRKQJnE7XLJz9KbA/tToYqr4/kPbHOyRVethfi31CwD+sOgSKqA6Afh8I/e4xe8utKxYR+SNLliwhOTmZBQsWMGzYMObNm8eYMWPYtm0bnTp1qnP+66+/zj333MPChQsZMWIE27dv59prr8VisTB37lwT3sEhfnYbMcG+ZBWWkZZbzKBuCo1ERNqDO++8k0mTJtGvXz9KS0tJS0tr8Ny5c+dy3XXXMWLECCIjI7n77rspLCxsxWpd7r77brKyspg4cSI2m40bbriBMWPGYLM1vpP9ggsu4Mknn2TOnDncdtttdO/enRdffJHTTjsNgNDQUP7xj3+QnJyMw+HghBNO4IMPPiAiIoLQ0FCWLl3KrFmzKCsro1evXvzvf//j+OOPb6F3DBajtRfoeYDCwkJCQkIoKCggODjY7HJEzGEYUJR1WLdQTTCU6rqvKm34WqsXhMZDeA+ISITwRIjoAaEJrtkvVRWu3bMcla4lQ47yQ8fc9+Wu7cEdFXWP1bo//HkqGn4+R6XrcZ8gV7hTb+gT/rvHarqDlK+LtFX6nl2/YcOGcfLJJ/P0008D4HQ6iYuL45ZbbuGee+6pc/60adPYsmULq1atch+74447+O677/j6668b9Zot+bW4/D9rWJu6n39NGMCFg7o263OLiHiysrIy0tLS6N69u+mdoR2R0+mkb9++XHbZZTz88MNml1PLkf5tHM33bP0mJNKRGYZry/OaTiF3QFQdDFUeYeioxeYaZOwOhRJdIVF4D9dxm3fD14qISIupqKhg/fr1TJ8+3X3MarWSlJTEmjVr6r1mxIgRvPbaa6xbt46hQ4eSmprKRx99xDXXXNPg65SXl1NeXu7+vCX/4ts9MoC1qftJz9UwbBERMc/OnTv59NNPOfXUUykvL+fpp58mLS2NK6+80uzSWoxCI5H2zDBc83mKMuFgFhRmHgqH9qe6Zg1VHGz4eovVFQCFJ/6uayhRwZCINJphGOQVV1BS7qBbhJZutrTc3FwcDgfR0dG1jkdHR7N169Z6r7nyyivJzc1l5MiRGIZBVVUVN954I/fee2+DrzN79mwefPDBZq29IfERrrlG6RqGLSIiJrJarbz00kvceeedGIZB//79WblyJX379jW7tBaj0EjEEzmdUJLrCoOKsl2BUFH17WD2Ycez/3gnL4sVQuLqhkLhPVxLzLw0O0JEjqy0wsHeglL25peSmV/GnnzXx65jZezNL6W8ysmAriG8N22k2eVKPVavXs2jjz7KM888w7Bhw9ixYwe33XYbDz/8MDNmzKj3munTp5OcnOz+vLCwkLi4uBapL6EmNMpVaCQiIuaJi4vjm2++MbuMVqXQSKQtcVRB8b7Dwp+aICjLFQIVZbqOH9wHRuO2pwRcc3oCYyAoGsK61w6IwuK1q5SINMjpNMg5WO4OguoLhfYXV/zh81gsUOHQGMXWEBkZic1mIzs7u9bx7OxsYmJi6r1mxowZXHPNNVx//fUAnHDCCRQXF3PDDTdw3333YbVa61zj4+ODj0/rfP/oHlnTaaTlaSIiIq1JoZFIazAM11buB3YeWipWX2dQcQ7Q2F+qqrdPD4qGoFgIjIagmOr72EMfB0arW0hEGnSwvIq9+aWHgqDDg6GCUrIKyqhsRNgTYLfRJcyPzqGuW5dQPzqH+hIb4vo4OtgXu1fd4EGan91uZ/DgwaxatYoLLrgAcA3qXLVqFdOmTav3mpKSkjrBUM1OMG1hz5T46mWNBaWVHCiuICxA39dERERag0IjkebiqIT8DDiQ7tp2/kC6a2bQgZ2ujyuKGvc8Flt18BN9qDuovlAoIEq7eol0YIZhUOU0KK9yUl7poKz6vrzK6T7m/rjKQVmlk31FZXW6hQrLqv7wtWxWCzHBvnQO9XWHQq5gyBUKdQ71I9jXC4vF0grvXBojOTmZSZMmMWTIEIYOHcq8efMoLi5m8uTJAEycOJEuXbowe/ZsAMaPH8/cuXMZNGiQe3najBkzGD9+/FFtI9xSfL1txIb4kllQRlpesUIjERGRVqLfOEWORmn+7wKh9EMhUcFuMJxHuNgCwZ0PdQEFxdQfCvlHgNX8H9BF5NiVVTrYV1hOVmEZWYVllJRXuUOc8sragU55VXXIc/jH9Zx7+DnOZmoACfHzdodA9YVCnYJ88LKpS8iTTJgwgZycHGbOnElWVhYDBw5k+fLl7uHYGRkZtTqL7r//fiwWC/fffz979uwhKiqK8ePH88gjj5j1FupIiAggs6CM9NxiTuoWZnY5IiIiHYJCI5HDOR1QuOd3odBhIVFZ/pGv9/KDsAQI7+66D+t+6POQOPD2beE3ICKtpaiskqwCVxiUWVDm/jjrsI8bM+unudi9rPh4WfHxsrnuva34etnw8XYdt3vZiAy0Vy8bqx0KBfjox4H2aNq0aQ0uR1u9enWtz728vHjggQd44IEHWqGypkmIDGBNap7mGomIiLQi/ZQoHU/5wdodQocHRPkZf7zbWECn+kOhsARXt5CWZ4h4NKfTYH9JhTv8ySwsI7ugOhgqLHUfL65o3DB6Hy8rsSG+RAf7EuTr7Q5xDg933B97WfHxtuFbfe9zeBB0hOvsNitWq/6/R9q3hOq5RtpBTUREpPUoNJL2q6occrZB9mbI3gRZG2HfFtfuZEdi9XbtKFZfKBSWAPaAlq9dRFpEpcPJvqJysgpKySooJ7OglOzfdQplFzZu8DNAsK8XsSF+RIf4EhvsS0zIYbdgX2JDfAnx89asH5FmkODeQU2hkYiIuCQkJPDXv/6Vv/71r/U+fu2115Kfn8+7777bqnW1JwqNpH04uM8VCmVvgqxNrqAodxs4Gxjw6hfWQCjU3TV3SDOFRDxSpcNJZn4Zuw6UsGt/CbsPlNb6OOdgOY3ZCMpigchAH3eH0OH3MYeFQ/52fRsVaS3dq0OjtNxiDMNQGCsiItIK9NOueJaqCsjdXt09tLE6INpUvVV9PXxDILq/6xbTH6KPh/BE8Att1bJFpHk4nAb7isrYtb+UXftL2HWgOhiqDoUyC0r/cDi0t81CdPBh4U/1fWyIHzEhPsRUD3721uBnkTalW7g/FgsUlVVxoKSScO2gJiIi0uIUGknbVZx7qHsoe7MrIMrZ2sDMIQtEJB4WDlXfQrpqxpCIBzEMg7ziiupAqJTdB0rYtb/mvoQ9+aV/uHTM7mWla5gfcWH+xIW77rtWf9w51I9wf7vm/4h4IF9vG7HBvuwtKCMtt1ihkYhIQwwDKk3YNMDbv9G/e/3nP/9h1qxZ7N69u9Zunueffz4REREsXLiQlJQUkpOTWbt2LcXFxfTt25fZs2eTlJTU5BLLy8u56667WLx4MYWFhQwZMoR//etfnHzyyQAcOHCAadOm8emnn3Lw4EG6du3Kvffey+TJk6moqCA5OZm3336bAwcOEB0dzY033sj06dObXI8nUGgk5nNUQt6O6q6hjYeWlx3Mqv98n2BXx5A7IDoBOvXRrCERD1FQWlndGXSoS2jXYd1CpZVHHjBts1roHOrrCoXC/F0BUfihgCgy0EehkEg7lRAZwN6CMtJzixkcH2Z2OSIibVNlCTzaufVf9969jf6d7NJLL+WWW27h888/54wzzgBg//79LF++nI8++giAgwcPMm7cOB555BF8fHx45ZVXGD9+PNu2baNbt25NKvFvf/sbb7/9Ni+//DLx8fE8/vjjjBkzhh07dhAeHs6MGTP49ddf+fjjj4mMjGTHjh2UlpYC8O9//5v333+fN954g27durFr1y527drVpDo8iUIjaV0l+6u7hw4bTp2zFRwNbEsd3uN3y8v6Q2g3dQ+JtFGVDidZBWXszS8ls6CMvQWlZOaXkVlQyt78MnYfKKGwrIFZY9UsFogO8j2sS8iPruH+7s6hmGBfvLR0TKRDSogM4NuUPA3DFhHxcGFhYZx99tm8/vrr7tDorbfeIjIyktNPPx2AAQMGMGDAAPc1Dz/8MO+88w7vv/8+06ZNO+rXLC4u5tlnn+Wll17i7LPPBuD5559nxYoVvPDCC9x1111kZGQwaNAghgwZArgGbdfIyMigV69ejBw5EovFQnx8fFPfvkdRaCQt7+A+2PwObHwLdq+r/xx74O+6h/pDp37gE9i6tYpIgxxOg9yD5ezJrx0EZRaUsregjMz8xg+ajgiwVwdBfu6lY65QyJ/Oob74eGkYvYjU1T2iZgc1E5ZdiIh4Cm9/V9ePGa97FK666iqmTJnCM888g4+PD4sWLeLyyy93L1c7ePAgs2bNYtmyZWRmZlJVVUVpaSkZGRlNKi8lJYXKykpOOeWUQyV7ezN06FC2bNkCwE033cTFF1/Mhg0bOOuss7jgggsYMWIE4NqJ7cwzz6R3796MHTuWc889l7POOqtJtXgShUbSMkoPwJYPYNPbkPYlGM5Dj4Ul1NM9FA9WdQ6ImMUwDA6UVLI3v7TBLqHswjKq/mjKNGC3WYkJ8aVzqC+dQ/yIDXUNme4c6kvXMH+6hPoR4KNvPyJy9OIjXL+QpOeq00hEpEEWi0eM7hg/fjyGYbBs2TJOPvlkvvrqK/71r3+5H7/zzjtZsWIFc+bMoWfPnvj5+XHJJZdQUdHAKpVmcPbZZ7Nz504++ugjVqxYwRlnnMHNN9/MnDlzOOmkk0hLS+Pjjz9m5cqVXHbZZSQlJfHWW2+1WD1tgX5ql+ZTUQzbPnYFRTtW1l5y1mUw9L8Ejr8QgmPNq1Gkg3I6DVJzi9l1oKROl1Bm9XKy8irnHz6PzWohOsiH2FA/YkN86Vx9XxMKxYb4ERGgQdMi0jK6R1Z3GuUWYxgGFi1XFxHxWL6+vlx00UUsWrSIHTt20Lt3b0466ST349988w3XXnstF154IeDqPEpPT2/y6yUmJmK32/nmm2/cS8sqKyv5/vvv+etf/+o+LyoqikmTJjFp0iRGjRrFXXfdxZw5cwAIDg5mwoQJTJgwgUsuuYSxY8eyf/9+wsPDm1xXW6fQSI5NVQWkrHItPdv2Ue0p/Z36Qf+Lof9FrtlEItJq8ksq+HFXPj9m5PNjxgF+ysinqPzIs4QAIgN9qsOf2kFQ51BXQBQV6KN5QiJimrhwfywWKCqvIq+4gshAH7NLEhGRY3DVVVdx7rnnsnnzZq6++upaj/Xq1YulS5cyfvx4LBYLM2bMwOn84z9yNiQgIICbbrqJu+66i/DwcLp168bjjz9OSUkJf/nLXwCYOXMmgwcP5vjjj6e8vJwPP/yQvn37AjB37lxiY2MZNGgQVquVN998k5iYGEJDQ5tckydQaCRHz+mA9K9cQdGW96Gs4NBjofFwwiWurqLofubVKNKBOJwG27OL2JBxgB8z8tmQcYDUnLpLN/y8bcRH+Lu7gzqHHhYKhfgRHeKjWUIi0qb5etvoHOLHnvxSduYVKzQSEfFwf/7znwkPD2fbtm1ceeWVtR6bO3cu1113HSNGjCAyMpK7776bwsLCY3q9f/zjHzidTq655hqKiooYMmQIn3zyCWFhrh057XY706dPJz09HT8/P0aNGsXixYsBCAoK4vHHH+e3337DZrNx8skn89FHH7lnMLVXFsNozMjSjqWwsJCQkBAKCgoIDg42u5y2wTBg9w+w6S3XUOuD2YceC4xxLTs74RLXMjS1iou0qLyD5fy0K98dEv28K5/iirrb1PeIDGBQtzAGdQvlpG5hHBcdqC4haXf0PbvtaK2vxVX/Xcs3O/KYc+kALhnctcVeR0TEE5SVlZGWlkb37t3x9fU1uxxpQ470b+Novmer00gaZhiQvdk1o2jT25C/89BjvqHQ73xXUBR/CljVnSDSEiodTrZlHeoi+jHjQL27BgX6eDEwLpSTuoUyqFsYA+NCCQuwm1CxiEjLSogI4JsdeRqGLSIi0goUGkld+1Nh49uurqKcrYeOewdAn3GupWeJfwYv/UIq0tz2FZW5l5j9mJHPL7vzKausu3a7Z6dATqruIBrULYyenQKxafi0iHQA7mHYeQqNREREWppCI3EpzITNS11zivZuOHTcZodeZ7kGWh83Fuz+5tUo0s5UVDn5NbOQHzMOsKG6i2j3gdI65wX7ejGwW1itLqIQP28TKhYRMV98hEIjERGR1qLQqCMr2Q+/vgublkL610D1eCuLFbqf6lp61udc8As1sUiR9sEwDLIKy9xLzDZk5LNxTwEVv9vm3mKB3tFBDKoOiE7qFkqPyEBtYS8iUq17pOsPWOm5JRiGgUWzFEVERFqMQqOOprwItn7kWnqW8hk4D9uCO26Ya+nZ8RdAYCfTShTxdEVllWzPLmJrVhHbslz327OLyC+prHNumL+3a1h1XCgnxYdxYtcQgnzVRSQi0pC4cH+sFjhYXkXuwQqigrSDmoiI9reS32uufxMKjToKpxO+fRJWPwZVhy1/iTnBFRT1vwhCu5lXn4gHqnQ4Sc0pZmtWIdsOC4j25NddYgZgs1roHR3ESfGhDIoL46T4MBIi/PVXchGRo+DjZaNzqB+7D5SyM69YoZGIdGje3q4/NpaUlODn52dyNdKWlJS4Ns+p+TfSVAqNOoLSA/DOjbB9uevz8ETX0rP+l0DUcebWJuIBDMNgb0EZ27IK3d1D27KKSMk5SKWj/gQ/OtiH3jHB9IkJond0EL1jgujZKRBfb+00KCJyrBIiAth9oJS03GKGJISbXY6IiGlsNhuhoaHs27cPAH9//UGyozMMg5KSEvbt20doaCg227H9/qHQqL3bswHenAT5GWDzgXGPw0mTXINTRKSOgtLK6lDosIAou4iisqp6zw/08eK46MBDAVFMEH1iggj11+6CIiItJSHSn693aBi2iAhATEwMgDs4EgEIDQ11/9s4FgqN2ivDgPUvwsd3g6MCwhLgslcgdoDZlYm0CeVVDlL2FbMtu3b3UGZBWb3ne1kt9IgKqNM91DXMT3/NERFpZQk1O6jllphciYiI+SwWC7GxsXTq1InKyrozNKXj8fb2PuYOoxoKjdqjimL48Hb4ZYnr897nwAXPaBc06ZAKSivZmVfMzrwS0nOL2ZbtCofScoupcta/tKxziC+9Y4JqdQ8lRgVi97K2cvUiIlKf7pHVoZE6jURE3Gw2W7MFBSI1FBq1Nznb4Y1rIGcrWGyQ9ACMuFXL0aTdMgyDnIPlZOSVkJ5XQkZeMel5JezcX8LOvOJ6dyyrEeTr5Q6FagKi46KDCPHT7mUiIm1ZvLvTqBjDMNTxKSIi0kIUGrUnG9+C92+FymIIjIFLX4T4EWZXJXLMHE6DvfmlZOwvIT2vuDogcnUPZewvoaTCccTrIwN9SIjwp1uEP706BbmDotgQX/2iISLigbqF+2O1QHGFg5yD5XQK8jW7JBERkXZJoVF7UFUOn94P6/7j+jxhFFz8AgRFm1uXyFEor3Kwa3+peynZ4QHRrgMlDe5SBmC1QGyIHwmR/nQLDyAhwp/4CH/iIwLoFu5PgI/+r05EpD2xe1npEubHrv2lpOeWKDQSERFpIfpNytPlZ8Cb18Ke9a7PR90Bp90LNn1ppe0pLq86rFOohIz9xaTnugKivQWlGA3nQthtVrqG+5FQHQQlVIdC8RH+dA3z17whEZEOJiEiwBUa5RUztHu42eWIiIi0S0oWPNlvK2Hp9VB6AHxD4aL/wHFjzK5KpJYDxRUs35zFBz/vZW1qHg3MngYgwG6jW0SAeylZQkQA8eGuj2ND/LBZtZRMRERcEiIC+Oq3XNJzNQxbRESkpSg08kROB6yeDV/OAQzoPAgufRnC4s2uTASAwrJKPt2czQc/7+WbHbm1dikLD7C7O4VqAqL4CNeysshAu2YMiYhIoyRoBzUREZEWp9DI0xzMgbf/AmlfuD4/+XoY8yh4+Zhbl3R4xeVVrNySzQc/Z/Ll9hwqHE73Y/1igxk/oDPnnhhLXLi/iVWKiEh70T3S9f0kPbfE5EpERETaL4VGniRjrWt+UVEmePvD+H/DiZeaXZV0YGWVDj7fuo8PftnLZ1v3UVZ5KCjq1SnQHRT1iAo0sUoREWmP4iMOdRoZhqFOVRERkRag0MgTGAasmQ8rZoLhgMjj4LJXoVMfsyuTDqi8ysFX23P54Je9rPw1m+LDtrtPiPCvDoo60zsmyMQqRUSkvYsL88dqgZIKBzlF5XQK1g5qIiIizc307Ybmz59PQkICvr6+DBs2jHXr1jV4bmVlJQ899BCJiYn4+voyYMAAli9fXuucWbNmYbFYat369PHgcKWsAJZcDZ/e5wqM+l8MUz5XYCStqtLh5IvtOdz55s8M+ftKrn/lB977aS/FFQ66hPrxf6f24MNbRvL5nadxx1m9FRiJiEiLs3tZ6RrmWqKWpmHYIiIiLcLUTqMlS5aQnJzMggULGDZsGPPmzWPMmDFs27aNTp061Tn//vvv57XXXuP555+nT58+fPLJJ1x44YV8++23DBo0yH3e8ccfz8qVK92fe3l5aENV1kZ4YyLsTwWrN4yd7ZphpPZraQUOp8F3aXl88HMmyzdlcqCk0v1YdLAP406IZfyAzgyKC9WSABERMUVCZAAZ+0vYmVfCsB4RZpcjIiLS7piapsydO5cpU6YwefJkABYsWMCyZctYuHAh99xzT53zX331Ve677z7GjRsHwE033cTKlSv55z//yWuvveY+z8vLi5iYmNZ5Ey3lx9dg2R1QVQYhcXDZy9BlsNlVSTvndBpsyDjABz/v5aNNWeQUlbsfiwiwc/YJMYw/sTMnJ4RjtSooEhERcyVE+PMlkKYd1ERERFqEaaFRRUUF69evZ/r06e5jVquVpKQk1qxZU+815eXl+PrWXq/u5+fH119/XevYb7/9RufOnfH19WX48OHMnj2bbt26NVhLeXk55eWHfjkuLCxsyltqHpWl8NGdrtAIoNdZcOFz4B9uXk3SrhmGwS+7C/jg570s25hJZkGZ+7EQP2/O7h/DuSd25k89wvGymb6iVUREGmn+/Pk88cQTZGVlMWDAAJ566imGDh1a77mnnXYaX3zxRZ3j48aNY9myZS1dapMl1AzD1vI0ERGRFmFaaJSbm4vD4SA6OrrW8ejoaLZu3VrvNWPGjGHu3LmMHj2axMREVq1axdKlS3E4Dg3iHTZsGC+99BK9e/cmMzOTBx98kFGjRrFp0yaCguqfszJ79mwefPDB5ntzTZWX4lqOlr0JLFY4/T4YmQxW/aIuzcswDLZkFvHBL3tZ9ksmGfsPbVcc6OPFWf2iGT+gM6f0jMTupX9/IiKe5mhHACxdupSKigr353l5eQwYMIBLL23bu7R2j3SFRpppJCIi0jI8atjPk08+yZQpU+jTpw8Wi4XExEQmT57MwoUL3eecffbZ7o9PPPFEhg0bRnx8PG+88QZ/+ctf6n3e6dOnk5yc7P68sLCQuLi4lnsj9fn1fXjvZigvhIAouPgF6HFq69Yg7d6OfUV88HMmH/yyl9ScQz9g+3nbOKNvJ8YP6Mypx0Xh620zsUoRETlWRzsCIDy8dkfz4sWL8ff3b/OhUXyEaxD2zrwSDMPQjD0REZFmZlpoFBkZic1mIzs7u9bx7OzsBucRRUVF8e6771JWVkZeXh6dO3fmnnvuoUePHg2+TmhoKMcddxw7duxo8BwfHx98fHya9kaOlaMSVs6CNU+7Pu82HC55EYJjzalH2h3DMPg2JY9nVu/gmx157uN2Lyun945i/IDO/LlPJ/ztHpUhi4hIA5oyAuD3XnjhBS6//HICAgIaPKctLO+PC/fHZrVQWulgX1E50cG+f3yRiIiINJppvyXa7XYGDx7MqlWruOCCCwBwOp2sWrWKadOmHfFaX19funTpQmVlJW+//TaXXXZZg+cePHiQlJQUrrnmmuYsv3kU7oU3J8Outa7PR9wKZ8wEm7e5dUm74HQafPprNs+u3sHPuwsAsFktnHpcFOMHxJLUN5ogX/1bExFpb5oyAuBw69atY9OmTbzwwgtHPK8tLO/3tlnpGubHzrwS0nKLFRqJiIg0M1NbC5KTk5k0aRJDhgxh6NChzJs3j+LiYncr9cSJE+nSpQuzZ88G4LvvvmPPnj0MHDiQPXv2MGvWLJxOJ3/729/cz3nnnXcyfvx44uPj2bt3Lw888AA2m40rrrjClPfYoNTV8NZfoCQXfILhgmeh77lmVyXtQKXDyXs/7WXBFyns2HcQAB8vK5efHMeU0T3oGuZvcoUiItKWvfDCC5xwwgkNDs2u0SaW9+Mahr0zr4T03GL+1COi1V9fRESkPTM1NJowYQI5OTnMnDmTrKwsBg4cyPLly91/GcvIyMB62BDosrIy7r//flJTUwkMDGTcuHG8+uqrhIaGus/ZvXs3V1xxBXl5eURFRTFy5EjWrl1LVFRUa7+9+jmd8NU/4fNHAANiToDLXoHwhpfYiTRGaYWDxd9n8PyXqeyt3gEtyNeLicPjmXxKdyIDTVqCKSIiraopIwBqFBcXs3jxYh566KE/fB1Tl/cfJiHCny+A9LySPzxXREREjo7pQ0ymTZvW4HK01atX1/r81FNP5ddffz3i8y1evLi5Smt+Jfth6RTYsdL1+UkT4ezHwdvP3LrEoxWUVPLKmnRe/Dad/cWunW8iA334y8juXP2nblqCJiLSwRzLCIA333yT8vJyrr766laotHkkVO+glq4d1ERERJqd6aFRh2EY8PoE2L0OvPzgnH/CoKvMrko82L7CMv77dRqL1u6kuMIBQFy4H/83OpFLBnfVDmgiIh3Y0Y4AqPHCCy9wwQUXEBHhOcu83KFRnkIjERGR5qbQqLVYLHDWw/DBbXDxCxDT3+yKxEPtzCtmwRepvL1+NxUOJwB9YoK46bREzjkhFi+b9Q+eQURE2rujHQEAsG3bNr7++ms+/fRTM0pusu4Rh0Ijp9PAarWYXJGIiEj7YTEMwzC7iLamsLCQkJAQCgoKCA4Obt4ndzrAqg4QOXqb9xaw4ItUlv2yF2f1/2oHx4cx9bRE/tynExaLfkgWkY6nRb9ny1Ex62tR6XDSZ8ZyHE6DtdPPICZEO6iJiIgcydF8z1anUWtTYCRHaV3afp5ZvYPV23Lcx07rHcXU03oytHu4iZWJiIiYz9tmJS7Mj/S8EtJyixUaiYiINCOFRiJtkGEYfL5tH898nsIPOw8AYLXAuBNiuem0RI7vHGJyhSIiIm1HQmQA6XklpOcVMzzRc+YxiYiItHUKjUTakCqHk2UbM3l2dQpbs4oAsNusXDy4K/83uod72KeIiIgckhARAORoGLaIiEgzU2gk0gaUVTp4a/1u/vNlKhn7SwAIsNu46k/x/GVkd6KD1WovIiLSkIQIfwDScxUaiYiINCeFRiImKiqrZNF3GbzwdRo5ReUAhAfYmTwigYnDEwjx9za5QhERkbavphM3PbfE5EpERETaF4VGIibIPVjOi9+k8cqanRSVVQHQOcSXKaN7MOHkOPzt+p+miIhIY3WvCY3yinE6DaxW7SgqIiLSHPSbqUgr2n2ghOe/TGXJD7soq3QCkBgVwI2nJnL+wC7YvawmVygiIuJ5uoT64WW1UF7lJLuojNgQP7NLEhERaRcUGom0sH1FZXy6OZtPNmfxbUoeDqcBwICuIdx0Wk/O6hetv4iKiIgcAy+blbhwf9Jyi0nLLVZoJCIi0kwUGom0gF37S/hkcxbLN2WxPuMAhnHosVN6RjD1tJ6MSIzAYlFYJCIi0hwSIlyhUXpuCSMSza5GRESkfVBoJNJMduwrYvmmLD7elMXmvYW1HhsQF8rY42MY2z/GPXdBREREmk9CZABsyyE9TzuoiYiINBeFRiJNZBgGm/YUsnxzJss3ZZGSc+iHVKsFhnYPZ+zxMZx1fAydQ9UmLyIi0pISImp2UFNoJCIi0lwUGokcBYfTYEPGAZZvci0925Nf6n7M22ZhZM9IxvaPIalvNBGBPiZWKiIi0rEkHLaDmoiIiDQPhUYif6DS4WRNSh7LN2fx6eZscg+Wux/z87ZxWu8oxvaP4fQ+nQj29TaxUhERkY6re3Wn0c68EpxOQ5tMiIiINAOFRiL1KKt08OX2HJZvzmLlr9kUllW5Hwvy9SKpbzRj+8cwulcUfnabiZWKiIgIQOdQX7xtFsqrnGQWltFFS8NFRESOmUIjkWpFZZV8vi2H5ZsyWb0th5IKh/uxyEA7Z/ZzDbIe3iMCu5fVxEpFRETk97xsVuLC/EnNLWZnbrFCIxERkWag0Eg6tP3FFaz8NZvlm7P4+rdcKhxO92OdQ3wZ0z+Gs/vHMjg+DJva3EVERNq0hMgAUnOLScsrZkTPSLPLERER8XgKjaTDySoo49Nfs/h4YxbfpeXhNA491iMygLH9XR1FJ3QJwWJRUCQiIuIptIOaiIhI81JoJB3Gd6l5PLZ8Kxsy8msd7xcbzNj+MZzdP4aenQIVFImIiHiohEh/ANLzSkyuREREpH1QaCTtnsNp8PRnO3hy1XZ3V9Hg+DDGHh/DmONj6Bbhb26BIiIi0izUaSQiItK8FBpJu7avsIzbFv/EmtQ8AC4+qSt/G9ub6GBfkysTERGR5tY90hUa7dxfgtNpYNU8QhERkWOi0EjarS+255C85Cfyiivwt9v4+wX9ueikrmaXJSIiIi2kc6gf3jYLFVVO9haU0jVM3cQiIiLHQqGRtDuVDidzV2zn2dUpAPSJCWL+VSeRGBVocmUiIiLSkmxWC3Hh/qTmFLMzr0ShkYiIyDGyml2ASHPafaCECc+tcQdG1/wpnndvPkWBkYiISAfRvXquUZrmGomIiBwzdRpJu/Hp5izueusXCkorCfLx4rFLTmTcCbFmlyUiIiKtKCFSw7BFRESai0Ij8XjlVQ5mf7SVl75NB2BAXChPXzGIuHC1pIuIiHQ07tAoT6GRiIjIsVJoJB4tPbeYaf/bwKY9hQBMGdWdu8b0we6llZciIiIdUUKE649G6XklJlciIiLi+RQaicd6/+e93Lt0IwfLqwjz9+aflw3gz32izS5LRERETJRQPdMoI68Eh9PAZrWYXJGIiIjnUmgkHqe0wsGDH2xm8fe7ABiaEM6TVwwkNsTP5MpERETEbJ1D/bDbrFQ4nOzNL9VydRERkWOg0Eg8ym/ZRdz8+ga2Zx/EYoFbTu/JrWf0wsum5WgiIiICNquFbhH+7Nh3kPS8YoVGIiIix0ChkXgEwzB484fdzHx/E2WVTiIDfXjy8oGc0jPS7NJERESkjUlwh0YljOpldjUiIiKeS6GRtHkHy6u4/52NvPvTXgBG9Ypk7mUDiQryMbkyERERaYtq5hql52oHNRERkWOh0EjatE17Crjlfz+SlluMzWrhjrOO48bRiVg11FJEREQakBCp0EhERKQ5KDSSNskwDF5du5O/f7iFCoeTziG+/PuKQQxJCDe7NBEREWnjajqN0vIUGomIiBwLhUbS5hSUVPK3t3/mk83ZACT1jWbOpScS6m83uTIRERHxBAmRruHXu/aX4HAa2NShLCIi0iQKjaRN2ZBxgFte/5E9+aV42yxMP7svk09JwGLRD3siIiLSOJ1D/LB7WamocrI3v1Q7qImIiDSRQiNpE5xOg+e/SuWJT7ZR5TToFu7P01cO4sSuoWaXJiIiIh7GarUQH+7Pb/sOkpZbrNBIRESkiaxmFyCSd7Cc617+ntkfb6XKaXDuibF8eOtIBUYiIiLHYP78+SQkJODr68uwYcNYt27dEc/Pz8/n5ptvJjY2Fh8fH4477jg++uijVqq2+cVXzzXaqblGIiIiTaZOIzHV2tQ8blv8I9mF5fh4WZl13vFcfnKclqOJiIgcgyVLlpCcnMyCBQsYNmwY8+bNY8yYMWzbto1OnTrVOb+iooIzzzyTTp068dZbb9GlSxd27txJaGho6xffTLpXzzVKyy0xuRIRERHPpdBITOFwGjz12W/8e9VvOA1IjApg/lUn0Scm2OzSREREPN7cuXOZMmUKkydPBmDBggUsW7aMhQsXcs8999Q5f+HChezfv59vv/0Wb29vABISElqz5GaXEOnqNEpXp5GIiEiTaXmatLrswjKu/u93zFvpCowuHdyVD24ZqcBIRESkGVRUVLB+/XqSkpLcx6xWK0lJSaxZs6bea95//32GDx/OzTffTHR0NP379+fRRx/F4XA0+Drl5eUUFhbWurUl3auXp6XnKjQSERFpKnUaSav6YnsOyUt+Iq+4An+7jUcu7M+Fg7qaXZaIiEi7kZubi8PhIDo6utbx6Ohotm7dWu81qampfPbZZ1x11VV89NFH7Nixg6lTp1JZWckDDzxQ7zWzZ8/mwQcfbPb6m0t8dafRrgMlVDmceNn0t1IREZGjpe+e0mpeXbuTSQvXkVdcQd/YYD64ZaQCIxERkTbA6XTSqVMn/vOf/zB48GAmTJjAfffdx4IFCxq8Zvr06RQUFLhvu3btasWK/1hssC8+XlYqHQZ788vMLkdERMQjqdNIWsXqbft44L1NAFw5rBszz+2Hr7fN5KpERETan8jISGw2G9nZ2bWOZ2dnExMTU+81sbGxeHt7Y7Md+t7ct29fsrKyqKiowG6317nGx8cHHx+f5i2+GVmtFuIj/NmefZC0vGK6RfibXZKIiIjHUaeRtLjt2UVMe/1H9/yiRy7or8BIRESkhdjtdgYPHsyqVavcx5xOJ6tWrWL48OH1XnPKKaewY8cOnE6n+9j27duJjY2tNzDyFPGaayQiInJMFBpJi8o7WM51L33PwfIqhnYP55ELT8BisZhdloiISLuWnJzM888/z8svv8yWLVu46aabKC4udu+mNnHiRKZPn+4+/6abbmL//v3cdtttbN++nWXLlvHoo49y8803m/UWmkV37aAmIiJyTLQ8TVpMWaWDG15dz+4DpcRH+PPc1YOxeymnFBERaWkTJkwgJyeHmTNnkpWVxcCBA1m+fLl7OHZGRgZW66HvyXFxcXzyySfcfvvtnHjiiXTp0oXbbruNu+++26y30CwS1GkkIiJyTBQaSYswDIPpSzeyfucBgny9eGHSyYQFeG57u4iIiKeZNm0a06ZNq/ex1atX1zk2fPhw1q5d28JVta6ESNcco/S8EpMrERER8Uxq+5AWMf/zHbzz4x5sVgvPXjWYnp0CzS5JREREOpiaTqNd+0uocjj/4GwRERH5PYVG0uw+2pjJnE+3A/DgecczslekyRWJiIhIRxQT7IuPl5Uqp8Ge/FKzyxEREfE4Co2kWf28K5/kN34C4LpTunP1n+LNLUhEREQ6LKvV4u42StNcIxERkaOm0Eiazd78Uq5/5QfKKp2c3juK+87pa3ZJIiIi0sG55xopNBIRETlqCo2kWRSXV3H9yz+QU1RO7+gg/n3FIGxWi9lliYiISAfn3kFNw7BFRESOmkIjOWZOp8Ffl/zEr5mFRAba+e+kIQT5eptdloiIiAgJkTWhkTqNREREjpZCIzlmj32ylRW/ZmP3svLcNUOIC/c3uyQRERER4LBOIy1PExEROWoKjeSYvPH9Lp77IhWAJy45kcHxYSZXJCIiInJI9+pOo10HSql0OE2uRkRExLMoNJImW5OSx73vbATg1jN6cf7ALiZXJCIiIlJbpyAffL2tOJwGew6Uml2OiIiIR1FoJE2SllvMTYvWU+U0OPfEWG5P6mV2SSIiIiJ1WK0W9xK1NM01EhEROSoKjeSoFZRU8peXvie/pJIBcaHMuXQAFot2ShMREZG2SXONREREmkahkRyVSoeTqa+vJzW3mM4hvjw/cTC+3jazyxIRERFpUHyka5MOhUYiIiJHR6GRNJphGMx8bzPf7MjD327jhWtPplOQr9lliYiIiBxR95pOo7wSkysRERHxLAqNpNEWfpPO/9ZlYLHAvy8fRN/YYLNLEhEREflDCZE1oZE6jURERI6GQiNplM+2ZvP3Zb8CcN+4viT1iza5IhEREZHG6V4dGu0+UEqlw2lyNSIiIp5DoZH8oa1Zhdzy+o8YBlwxNI6/jOxudkkiIiIijdYpyAc/bxsOp8Gu/VqiJiIi0lgKjeSIcorK+ctLP1Bc4WB4jwgeOr+/dkoTERERj2KxWIiPcA3D3qm5RiIiIo2m0EgaVFbp4IZXf2BPfindIwN49uqT8Lbpn4yIiIh4npolamnaQU1ERKTRlABIvQzD4K63fuHHjHxC/Lx5YdIQQv3tZpclIiIi0iQahi0iInL0FBpJvZ5c9Rsf/LwXL6uFZ68+iR5RgWaXJCIiItJkCdXL09RpJCIi0nimh0bz588nISEBX19fhg0bxrp16xo8t7KykoceeojExER8fX0ZMGAAy5cvP6bnlLre/3kv81b+BsDfL+jPiMRIkysSEREROTYJEa5OI800EhERaTxTQ6MlS5aQnJzMAw88wIYNGxgwYABjxoxh37599Z5///3389xzz/HUU0/x66+/cuONN3LhhRfy448/Nvk5pbYNGQe4882fAZgyqjuXD+1mckUiIiIix65mptHuAyVUVDlNrkZERMQzmBoazZ07lylTpjB58mT69evHggUL8Pf3Z+HChfWe/+qrr3Lvvfcybtw4evTowU033cS4ceP45z//2eTnlEN2Hyjhhld+oKLKSVLfTtxzdl+zSxIRERFpFlFBPvjbbTgN2HVA3UYiIiKNYVpoVFFRwfr160lKSjpUjNVKUlISa9asqfea8vJyfH19ax3z8/Pj66+/bvJz1jxvYWFhrVtHc7C8iutf/oHcgxX0jQ3mycsHYbNazC5LREREpFlYLBbi3UvUNNdIRESkMUwLjXJzc3E4HERHR9c6Hh0dTVZWVr3XjBkzhrlz5/Lbb7/hdDpZsWIFS5cuJTMzs8nPCTB79mxCQkLct7i4uGN8d57F4TS49X8/sjWriKggH16YNIQAHy+zyxIRERFpVt0ja4Zhq9NIRESkMUwfhH00nnzySXr16kWfPn2w2+1MmzaNyZMnY7Ue29uYPn06BQUF7tuuXbuaqWLPMPujLXy2dR8+XlaenziEzqF+ZpckIiIi0uxqhmGnawc1ERGRRjEtNIqMjMRms5GdnV3reHZ2NjExMfVeExUVxbvvvktxcTE7d+5k69atBAYG0qNHjyY/J4CPjw/BwcG1bh3F699l8N+v0wD452UDGBgXam5BIiIiIi3EHRppeZqIiEijmBYa2e12Bg8ezKpVq9zHnE4nq1atYvjw4Ue81tfXly5dulBVVcXbb7/N+eeff8zP2RF9syOXme9tAiD5zOM498TOJlckIiIi0nISIhUaiYiIHA1TB9ckJyczadIkhgwZwtChQ5k3bx7FxcVMnjwZgIkTJ9KlSxdmz54NwHfffceePXsYOHAge/bsYdasWTidTv72t781+jnFJSXnIDe9tp4qp8H5Aztzy597ml2SiIiISItKqJ5ptOdAKRVVTuxeHjWpQUREpNWZGhpNmDCBnJwcZs6cSVZWFgMHDmT58uXuQdYZGRm15hWVlZVx//33k5qaSmBgIOPGjePVV18lNDS00c8pcKC4gr+89D2FZVWc1C2Uxy4+EYtFO6WJiIhI+xYV6EOA3UZxhYOM/SX07BRodkkiIiJtmsUwDMPsItqawsJCQkJCKCgoaHfzjSqqnExc+B1rU/fTJdSPd28+haggH7PLEhERaZL2/D3b03jK12Lck1/xa2Yh/504hKR++qOiiIh0PEfzPVs9uR2IYRjc/+5G1qbuJ9DHi4XXnqzASERERDqU7pprJCIi0mgKjTqQpRv28MYPu7Fa4KkrBtE7JsjskkRERERaVc1cI4VGIiIif0yhUQfy2bZ9APzfqYmc3qeTydWIiIiItL6EiOpOo9wSkysRERFp+xQadSCpOa6/qA2JDzO5EhERERFzJFQvT0vLVaeRiIjIH1Fo1EE4nQZpuQcBSIzSTiEiIiLt3fz580lISMDX15dhw4axbt26Bs996aWXsFgstW6+vr6tWG3rqek02ltQSnmVw+RqRERE2jaFRh3E3oJSyiqdeNssdA3zM7scERERaUFLliwhOTmZBx54gA0bNjBgwADGjBnDvn37GrwmODiYzMxM923nzp2tWHHriQy0E+jjhWHArv1aoiYiInIkTQqNPv/88+auQ1pYzdK0hIgAvGzKCkVERNqzuXPnMmXKFCZPnky/fv1YsGAB/v7+LFy4sMFrLBYLMTEx7lt0dPvcjt5isRAf4RqGnaa5RiIiIkfUpPRg7NixJCYm8ve//51du3Y1d03SAlJzXEvTekQFmFyJiIiItKSKigrWr19PUlKS+5jVaiUpKYk1a9Y0eN3BgweJj48nLi6O888/n82bNx/xdcrLyyksLKx18xQ1c412agc1ERGRI2pSaLRnzx6mTZvGW2+9RY8ePRgzZgxvvPEGFRUVzV2fNJOU6k6jHppnJCIi0q7l5ubicDjqdApFR0eTlZVV7zW9e/dm4cKFvPfee7z22ms4nU5GjBjB7t27G3yd2bNnExIS4r7FxcU16/toSd0jNAxbRESkMZoUGkVGRnL77bfz008/8d1333HccccxdepUOnfuzK233srPP//c3HXKMUrVEGwRERFpwPDhw5k4cSIDBw7k1FNPZenSpURFRfHcc881eM306dMpKChw3zyp+7ym0yhdnUYiIiJHdMzDbU466SSmT5/OtGnTOHjwIAsXLmTw4MGMGjXqD9uapfWk7KvpNNLyNBERkfYsMjISm81GdnZ2rePZ2dnExMQ06jm8vb0ZNGgQO3bsaPAcHx8fgoODa908RUL1TKN0zTQSERE5oiaHRpWVlbz11luMGzeO+Ph4PvnkE55++mmys7PZsWMH8fHxXHrppc1ZqzRRcXkVWYVlACRGqtNIRESkPbPb7QwePJhVq1a5jzmdTlatWsXw4cMb9RwOh4ONGzcSGxvbUmWaqqbTyLW7rMPkakRERNour6ZcdMstt/C///0PwzC45pprePzxx+nfv7/78YCAAObMmUPnzp2brVBpupr1+pGBdkL8vU2uRkRERFpacnIykyZNYsiQIQwdOpR58+ZRXFzM5MmTAZg4cSJdunRh9uzZADz00EP86U9/omfPnuTn5/PEE0+wc+dOrr/+ejPfRouJCLAT5ONFUXkVu/aX0Cs6yOySRERE2qQmhUa//vorTz31FBdddBE+Pj71nhMZGcnnn39+TMVJ80ip2TlNXUYiIiIdwoQJE8jJyWHmzJlkZWUxcOBAli9f7h6OnZGRgdV6qOH8wIEDTJkyhaysLMLCwhg8eDDffvst/fr1M+sttCiLxUJCZAAb9xSQllus0EhERKQBTQqNDm93bvCJvbw49dRTm/L00sxqdk5L7KR5RiIiIh3FtGnTmDZtWr2PrV69utbn//rXv/jXv/7VClW1HfER/mzcU6Bh2CIiIkfQpJlGs2fPZuHChXWOL1y4kMcee+yYi5LmpU4jERERkdq6u3dQ0zBsERGRhjQpNHruuefo06dPnePHH388CxYsOOaipHml5mjnNBEREZHDJURUh0a56jQSERFpSJNCo6ysrHp304iKiiIzM/OYi5Lm43QapOW6Oo0So9RpJCIiIgKQEOkPKDQSERE5kiaFRnFxcXzzzTd1jn/zzTfaMa2NcW0l68TbZqFrmJ/Z5YiIiIi0CTWdRnsLyiirdJhcjYiISNvUpEHYU6ZM4a9//SuVlZX8+c9/BlzDsf/2t79xxx13NGuBcmxqlqYlRATgZWtSRigiIiLS7oQH2Any9aKorIqM/SUcpx3URERE6mhSaHTXXXeRl5fH1KlTqaioAMDX15e7776b6dOnN2uBcmxSa4Zga56RiIiIiJvFYqF7ZAC/7C4gLbdYoZGIiEg9mhQaWSwWHnvsMWbMmMGWLVvw8/OjV69e+Pj4NHd9coxS3EOwNc9IRERE5HDxEa7QSHONRERE6tek0KhGYGAgJ598cnPVIi0gVUOwRUREROrVPaJ6GHaeQiMREZH6NDk0+uGHH3jjjTfIyMhwL1GrsXTp0mMuTJpHyr6aTiMtTxMRERE5XEKk6+ej9NwSkysRERFpm5o0GXnx4sWMGDGCLVu28M4771BZWcnmzZv57LPPCAkJae4apYmKy6vIKiwDIDFSnUYiIiIih3OHRuo0EhERqVeTQqNHH32Uf/3rX3zwwQfY7XaefPJJtm7dymWXXUa3bt2au0ZporTq9fmRgXZC/L1NrkZERESkbUmIcIVGmQVllFY4TK5GRESk7WlSaJSSksI555wDgN1up7i4GIvFwu23385//vOfZi1Qmi6lZuc0dRmJiIh4hJdffplly5a5P//b3/5GaGgoI0aMYOfOnSZW1j6F+XsT7Oua1pCxX0vUREREfq9JoVFYWBhFRUUAdOnShU2bNgGQn59PSYm+4bYVNTunJXbSPCMRERFP8Oijj+Ln5wfAmjVrmD9/Po8//jiRkZHcfvvtJlfX/lgsFrpXL1FL0w5qIiIidTRpEPbo0aNZsWIFJ5xwApdeeim33XYbn332GStWrOCMM85o7hqliVLVaSQiIuJRdu3aRc+ePQF49913ufjii7nhhhs45ZRTOO2008wtrp1KiAzg590FmmskIiJSjyaFRk8//TRlZa4By/fddx/e3t58++23XHzxxdx///3NWqA0XU2nkXZOExER8QyBgYHk5eXRrVs3Pv30U5KTkwHw9fWltLTU5Orap/iImh3UFBqJiIj83lGHRlVVVXz44YeMGTMGAKvVyj333NPshcmxcToN0nJdnUaJUeo0EhER8QRnnnkm119/PYMGDWL79u2MGzcOgM2bN5OQkGBuce1U90h/QDuoiYiI1OeoZxp5eXlx4403ujuNpG3aW1BKWaUTb5uFrmF+ZpcjIiIijTB//nyGDx9OTk4Ob7/9NhEREQCsX7+eK664wuTq2qcEd6eR5nKKiIj8XpOWpw0dOpSffvqJ+Pj45q5Hmklq9dK0+IgAvGxNmncuIiIirSw0NJSnn366zvEHH3zQhGo6hprQKKuwjNIKB352m8kViYiItB1NCo2mTp1KcnIyu3btYvDgwQQE1J6Zc+KJJzZLcdJ0NUOwEzXPSERExGMsX76cwMBARo4cCbg6j55//nn69evH/PnzCQsLM7nC9icswE6InzcFpZWk5xXTNzbY7JJERETajCa1oFx++eWkpaVx6623csoppzBw4EAGDRrkvhfzHRqCrXlGIiIinuKuu+6isLAQgI0bN3LHHXcwbtw40tLS3EOxpfklRLr+yLZTc41ERERqaVKnUVpaWnPXIc0sVUOwRUREPE5aWhr9+vUD4O233+bcc8/l0UcfZcOGDe6h2NL8ukf48/OufNI010hERKSWJoVGmmXU9qW6O420PE1ERMRT2O12SkpcwcXKlSuZOHEiAOHh4e4OJGl+8e5h2Oo0EhEROVyTQqNXXnnliI/X/IAj5iguryKzwLW7XWKkOo1EREQ8xciRI0lOTuaUU05h3bp1LFmyBIDt27fTtWtXk6trv7pXL09L0/I0ERGRWpoUGt122221Pq+srKSkpAS73Y6/v79CI5OlVf+VLDLQToi/t8nViIiISGM9/fTTTJ06lbfeeotnn32WLl26APDxxx8zduxYk6trvzTTSEREpH5NCo0OHDhQ59hvv/3GTTfdxF133XXMRcmxSaneOa2HuoxEREQ8Srdu3fjwww/rHP/Xv/5lQjUdR/fq5WnZheWUVFThb2/Sj8giIiLtTrN9R+zVqxf/+Mc/uPrqq9m6dWtzPa00QYrmGYmIiHgsh8PBu+++y5YtWwA4/vjjOe+887DZbCZX1n6F+HsT6u9Nfkkl6bkl9OscbHZJIiIibUKz/hnFy8uLvXv3NudTShOk5mjnNBEREU+0Y8cOxo0bx549e+jduzcAs2fPJi4ujmXLlpGYmGhyhe1XQkQAP5XkszOvWKGRiIhItSaFRu+//36tzw3DIDMzk6effppTTjmlWQqTplOnkYiIiGe69dZbSUxMZO3atYSHhwOQl5fH1Vdfza233sqyZctMrrD96h4ZwE+78jUMW0RE5DBNCo0uuOCCWp9bLBaioqL485//zD//+c/mqEuayOk0SMtVp5GIiIgn+uKLL2oFRgARERH84x//0B/mWlh8hD8A6bkKjURERGo0KTRyOp3NXYc0k8zCMsoqnXjbLHQN8zO7HBERETkKPj4+FBUV1Tl+8OBB7Ha7CRV1HN2rd1BLzy0xuRIREZG2w2p2AdK8Uva5uoziIwLwsunLKyIi4knOPfdcbrjhBr777jsMw8AwDNauXcuNN97IeeedZ3Z57VpC9Q5q6VqeJiIi4takVOHiiy/mscceq3P88ccf59JLLz3moqTpDg3B1jwjERERT/Pvf/+bxMREhg8fjq+vL76+vowYMYKePXsyb948s8tr12pCo31F5RSXV5lcjYiISNvQpOVpX375JbNmzapz/Oyzz9ZMI5MdGoKteUYiIiKeJjQ0lPfee48dO3awZcsWAPr27UvPnj1Nrqz9C/H3JszfmwMllaTnFXN85xCzSxIRETFdk0KjhtbVe3t7U1hYeMxFSdOlVg/B7hGpTiMRERFPkJycfMTHP//8c/fHc+fObelyOrSEyAAOZOSTnlui0EhERIQmhkYnnHACS5YsYebMmbWOL168mH79+jVLYdI0qdWdRomd1GkkIiLiCX788cdGnWexWFq4EukeEcCPGfmaayQiIlKtSaHRjBkzuOiii0hJSeHPf/4zAKtWreJ///sfb775ZrMWKI1XXF5FZkEZAImRCo1EREQ8weGdRGKuBPcOagqNREREoImDsMePH8+7777Ljh07mDp1KnfccQe7d+9m5cqVXHDBBc1cojRWWvUPOJGBdkL8vU2uRkRERMw0f/58EhIS8PX1ZdiwYaxbt65R1y1evBiLxdIhf6aLj/AHtIOaiIhIjSZ1GgGcc845nHPOOc1ZixyjlJyaeUbqMhIREenIlixZQnJyMgsWLGDYsGHMmzePMWPGsG3bNjp16tTgdenp6dx5552MGjWqFattO7pXdxql5ZaYXImIiEjb0KROo++//57vvvuuzvHvvvuOH3744ZiLkqY5tHOahmCLiIh0ZHPnzmXKlClMnjyZfv36sWDBAvz9/Vm4cGGD1zgcDq666ioefPBBevTo0YrVth01y9NyD5ZzsLzK5GpERETM16TQ6Oabb2bXrl11ju/Zs4ebb775mIuSpkmt7jRKjFKnkYiISEdVUVHB+vXrSUpKch+zWq0kJSWxZs2aBq976KGH6NSpE3/5y18a9Trl5eUUFhbWunm6YF9vIgJcOwRrrpGIiEgTQ6Nff/2Vk046qc7xQYMG8euvvx5zUdI06jQSERGR3NxcHA4H0dHRtY5HR0eTlZVV7zVff/01L7zwAs8//3yjX2f27NmEhIS4b3FxccdUd1uhuUYiIiKHNCk08vHxITs7u87xzMxMvLyaPCZJjoHTaZCWWz3TSJ1GIiIi0khFRUVcc801PP/880RGRjb6uunTp1NQUOC+1deF7om0g5qIiMghTUp4zjrrLKZPn857771HSEgIAPn5+dx7772ceeaZzVqgNE5mYRlllU68bRbiwvzMLkdERERMEhkZic1mq/MHvuzsbGJiYuqcn5KSQnp6OuPHj3cfczqdAHh5ebFt2zYSExPrXOfj44OPj08zV2++7hHVoVGehmGLiIg0KTSaM2cOo0ePJj4+nkGDBgHw008/ER0dzauvvtqsBUrjpOxzdRnFRwTgZWtSA5mIiIi0A3a7ncGDB7Nq1SouuOACwBUCrVq1imnTptU5v0+fPmzcuLHWsfvvv5+ioiKefPLJdrPsrLHi1WkkIiLi1qTQqEuXLvzyyy8sWrSIn3/+GT8/PyZPnswVV1yBt7d3c9cojXBoCLbmGYmIiHR0ycnJTJo0iSFDhjB06FDmzZtHcXExkydPBmDixIl06dKF2bNn4+vrS//+/WtdHxoaClDneEdwqNNIoZGIiEiTBxAFBAQwcuRIunXrRkVFBQAff/wxAOedd17zVCeNdmgItuYZiYiIdHQTJkwgJyeHmTNnkpWVxcCBA1m+fLl7OHZGRgZWqzqT65MQ6RqEnXuwgqKySoJ89QdRERHpuJoUGqWmpnLhhReyceNGLBYLhmFgsVjcjzscjmYrUBontWYIdqQ6jURERASmTZtW73I0gNWrVx/x2pdeeqn5C/IQQb7eRAbayT1Ywc68Evp3CTG7JBEREdM06U9Mt912G927d2ffvn34+/uzadMmvvjiC4YMGfKHP4RIy0it7jRK7KROIxEREZFjEV+9RC1Nc41ERKSDa1JotGbNGh566CEiIyOxWq3YbDZGjhzJ7NmzufXWW5u7RvkDxeVVZBaUAZAYqdBIRERE5FgkRGgYtoiICDQxNHI4HAQFBQGubV337t0LQHx8PNu2bWu+6qRRav4KFhFgJ8Rf6+5FREREjkX36rlG6XklJlciIiJiribNNOrfvz8///wz3bt3Z9iwYTz++OPY7Xb+85//0KNHj+auUf5AinvnNHUZiYiIiByrhEjtoCYiIgJNDI3uv/9+iotd30Qfeughzj33XEaNGkVERARLlixp1gLljx3aOU1DsEVERESOlZaniYiIuDQpNBozZoz74549e7J161b2799PWFhYrV3UpHWkqtNIREREpNnUdBrlFVdQWFZJsK+W/4uISMfUpJlG9QkPD1dgZBJ1GomIiIg0n0AfLyIDfQDYmau5RiIi0nE1W2gk5nA6DdJyXZ1GPdRpJCIiItIsEiJcw7DTNNdIREQ6MIVGHi6zsIyySifeNgtxYX5mlyMiIiLSLriHYWuukYiIdGCmh0bz588nISEBX19fhg0bxrp16454/rx58+jduzd+fn7ExcVx++23U1ZW5n581qxZWCyWWrc+ffq09NswTco+V5dRfEQAXjbTv5wiIiIi7UJ3hUYiIiJNG4TdXJYsWUJycjILFixg2LBhzJs3jzFjxrBt2zY6depU5/zXX3+de+65h4ULFzJixAi2b9/Otddei8ViYe7cue7zjj/+eFauXOn+3MvL1LfZomqGYPeI1DwjERERkebi3kFNy9NERKQDM7U1Ze7cuUyZMoXJkyfTr18/FixYgL+/PwsXLqz3/G+//ZZTTjmFK6+8koSEBM466yyuuOKKOt1JXl5exMTEuG+RkZGt8XZMkVr916/ETppnJCIiItJc4qtnGqXnaRC2iIh0XKaFRhUVFaxfv56kpKRDxVitJCUlsWbNmnqvGTFiBOvXr3eHRKmpqXz00UeMGzeu1nm//fYbnTt3pkePHlx11VVkZGQcsZby8nIKCwtr3TxFijqNRERERJpdzUyj/cUVFJRWmlyNiIiIOUwLjXJzc3E4HERHR9c6Hh0dTVZWVr3XXHnllTz00EOMHDkSb29vEhMTOe2007j33nvd5wwbNoyXXnqJ5cuX8+yzz5KWlsaoUaMoKipqsJbZs2cTEhLivsXFxTXPm2wFqTnqNBIRERFpboE+XkQF+QCwU0vURESkg/KoycmrV6/m0Ucf5ZlnnmHDhg0sXbqUZcuW8fDDD7vPOfvss7n00ks58cQTGTNmDB999BH5+fm88cYbDT7v9OnTKSgocN927drVGm/nmBWXV5FZ4BoCnhip0EhERESkOXWvnmuUpmHYIiLSQZk2IToyMhKbzUZ2dnat49nZ2cTExNR7zYwZM7jmmmu4/vrrATjhhBMoLi7mhhtu4L777sNqrZuBhYaGctxxx7Fjx44Ga/Hx8cHHx+cY3o05an6AiQiwE+LvbXI1IiIiIu1LfIQ/69L3k56ruUYiItIxmdZpZLfbGTx4MKtWrXIfczqdrFq1iuHDh9d7TUlJSZ1gyGazAWAYRr3XHDx4kJSUFGJjY5up8rajZp5RYpS6jERERESaW81cI+2gJiIiHZWpe9EnJyczadIkhgwZwtChQ5k3bx7FxcVMnjwZgIkTJ9KlSxdmz54NwPjx45k7dy6DBg1i2LBh7NixgxkzZjB+/Hh3eHTnnXcyfvx44uPj2bt3Lw888AA2m40rrrjCtPfZUlKq5xn1iNIQbBEREZHm1l2hkYiIdHCmhkYTJkwgJyeHmTNnkpWVxcCBA1m+fLl7OHZGRkatzqL7778fi8XC/fffz549e4iKimL8+PE88sgj7nN2797NFVdcQV5eHlFRUYwcOZK1a9cSFRXV6u+vpaXW7Jym0EhERESk2SVUzzRK10wjERHpoCxGQ+u6OrDCwkJCQkIoKCggODjY7HIadPaTX7Els5AXJg3hjL7Rf3yBiIhIO+Mp37M7gvb4tSgur+L4Bz4B4OeZZ2mGpIiItAtH8z3bo3ZPk0OcToO03JpOI800EhEREWluAT5edApybZaSpiVqIiLSASk08lCZhWWUVTrxtlmIC/MzuxwRERGRdql3TBAAz32R0uDGKyIiIu2VQiMPlbLP1WUUHxGAl01fRhEREZGWkHzmcXjbLHy8KYv/fJlqdjkiIiKtSmmDh3IPwY7UEGwRERGRljKoWxgPjD8egMeWb+XbHbkmVyQiItJ6FBp5qNTqXTwSO2mekYiIiEhLumpYNy4+qStOA275349kFpSaXZKIiEirUGjkoVLUaSQiIiLSKiwWC49c2J9+scHkFVdw02sbKK9ymF2WiIhIi1No5KFSc1ydRto5TURERKTl+XrbWHD1YEL8vPlpVz5//3CL2SWJiIi0OIVGHqi4vIrMgjIAEqPUaSQiIiLSGrpF+DPv8oFYLPDq2p28vX632SWJiIi0KIVGHiitep5RRICdUH+7ydWIiIiIdByn9+7EbWf0AuDedzayeW+ByRWJiIi0HIVGHqhmnlGilqaJiIiItLpb/9yL03tHUV7l5MbX1lNQUml2SSIiIi1CoZEHSnHPM9LSNBEREZHWZrVa+NeEgcSF+7Frfyl/XfIjTqdhdlkiIiLNTqGRB0qt2TlNoZGIiIiIKUL97Sy4ejA+XlY+35bDU5/tMLskERGRZqfQyAPV7Jym5WkiIiIi5jm+cwiPXngCAPNWbefzbftMrkhERKR5KTTyME6nQWpuTaeRQiMRERERM108uCtX/6kbhgG3/e9HMvJKzC5JRESk2Sg08jCZhWWUVTrxtlmIC/MzuxwRERGRDm/Guf0YGBdKYVkVN762nrJKh9kliYiINAuFRh4mZZ+ryyg+IgAvm758IiIiUr/58+eTkJCAr68vw4YNY926dQ2eu3TpUoYMGUJoaCgBAQEMHDiQV199tRWr9Ww+XjaevfokIgLs/JpZyH3vbMIwNBhbREQ8n1IHD+Megh2pIdgiIiJSvyVLlpCcnMwDDzzAhg0bGDBgAGPGjGHfvvpn7oSHh3PfffexZs0afvnlFyZPnszkyZP55JNPWrlyzxUb4sdTVw7CaoG3N+zm9XUZZpckIiJyzBQaeZjU3Ooh2J00z0hERETqN3fuXKZMmcLkyZPp168fCxYswN/fn4ULF9Z7/mmnncaFF15I3759SUxM5LbbbuPEE0/k66+/buXKPduIxEjuHtsHgFnvb+bHjAMmVyQiInJsFBp5mBR1GomIiMgRVFRUsH79epKSktzHrFYrSUlJrFmz5g+vNwyDVatWsW3bNkaPHt3geeXl5RQWFta6Cdwwugdjj4+h0mEwddEGcg+Wm12SiIhIkyk08jCpOa5OI+2cJiIiIvXJzc3F4XAQHR1d63h0dDRZWVkNXldQUEBgYCB2u51zzjmHp556ijPPPLPB82fPnk1ISIj7FhcX12zvwZNZLBaeuPREekQFkFlQxi2v/0iVw2l2WSIiIk2i0MiDFJdXkVlQBkBilDqNREREpPkEBQXx008/8f333/PII4+QnJzM6tWrGzx/+vTpFBQUuG+7du1qvWLbuCBfb567ejD+dhtrUvOY8+l2s0sSERFpEi+zC5DGS6ueZxQRYCfU325yNSIiItIWRUZGYrPZyM7OrnU8OzubmJiYBq+zWq307NkTgIEDB7JlyxZmz57NaaedVu/5Pj4++Pj4NFvd7U2v6CCeuGQAN7++gQVfpDAwLoSx/WPNLktEROSoqNPIg7jnGanLSERERBpgt9sZPHgwq1atch9zOp2sWrWK4cOHN/p5nE4n5eWax3MszjkxlimjugNw55u/uH+WExER8RQKjTxISvU8o0TNMxIREZEjSE5O5vnnn+fll19my5Yt3HTTTRQXFzN58mQAJk6cyPTp093nz549mxUrVpCamsqWLVv45z//yauvvsrVV19t1ltoN+4e24dh3cM5WF7Fja+up7i8yuySREREGk3L0zxIqjqNREREpBEmTJhATk4OM2fOJCsri4EDB7J8+XL3cOyMjAys1kN/OywuLmbq1Kns3r0bPz8/+vTpw2uvvcaECRPMegvthpfNytNXnsS5T33Fb/sO8re3f+HpKwZhsVjMLk1EROQPWQzDMMwuoq0pLCwkJCSEgoICgoODzS7HbdyTX/FrZiEvTBrCGX2j//gCERGRdq6tfs/uiPS1OLL1O/dz+X/WUukwuP+cvlw/qofZJYmISAd1NN+ztTzNQzidBqm5NZ1GWp4mIiIi4kkGx4cz49x+AMz+eCvfpeaZXJGIiMgfU2jkITILyyirdOJtsxAX5md2OSIiIiJylK75UzwXDuqCw2lw8+s/kl1YZnZJIiIiR6TQyEOk7HN1GcVHBOBl05dNRERExNNYLBYevfAE+sQEkXuwnKmLNlBR5TS7LBERkQYpffAQ7iHYkRqCLSIiIuKp/Ow2Flw9mCBfL9bvPMCjH20xuyQREZEGKTTyEKm5xYDmGYmIiIh4uoTIAOZNGAjAS9+m8+6Pe8wtSEREpAEKjTxESnWnUWKUOo1EREREPN0ZfaO59c89Abhn6S9sySw0uSIREZG6FBp5iNQcdRqJiIiItCe3JR3H6OOiKKt0ctNr6ykorTS7JBERkVoUGnmA4vIqMgtcu2uo00hERESkfbBZLTw5YSBdQv1Izyvhjjd+wuk0zC5LRETETaGRB0irnmcUEWAn1N9ucjUiIiIi0lzCAuwsuHowdi8rK7fs45nVO8wuSURExE2hkQeomWfUQ11GIiIiIu3OCV1D+Pv5/QH454rtfLk9x+SKREREXBQaeYCU6nlGiZpnJCIiItIuXXZyHFcM7YZhwK2Lf2TX/hKzSxIREVFo5AlS1WkkIiIi0u7NOq8fA7qGkF9SydRFGyirdJhdkoiIdHAKjTyAe+e0SHUaiYiIiLRXPl42nrl6MGH+3mzcU8Cs9zebXZKIiHRwCo3aOKfTIDXX1WmU2EmhkYiIiEh71iXUj6euOAmrBRZ/v4vF6zLMLklERDowhUZtXGZhGWWVTrxtFuLC/MwuR0RERERa2MhekdxxVm8AZr63mZ935ZtbkIiIdFgKjdq4mnlG8REBeNn05RIRERHpCKaelshZ/aKpcDiZumgDuw9oMLaIiLQ+pRBtXMq+6iHYkRqCLSIiItJRWCwW5lw2gO6RAezJL2Xck1/x8cZMs8sSEZEORqFRG5eaWz0EO0rzjEREREQ6kmBfb179y1AGdQulsKyKmxZt4N53NmpXNRERaTUKjdq4lOrlaYlR6jQSERER6Wi6hvnzxv8NZ+ppiVgs8Pp3GZz/9Ddszy4yuzQREekAFBq1cak56jQSERER6ci8bVb+NrYPr143jKggH7ZlF3He01/z+ncZGIZhdnkiItKOKTRqw4rLq8gsKAPUaSQiIiLS0Y3sFcnHt43i1OOiKKt0cu87G5n2+o8UlFaaXZqIiLRTCo3asLTqeUYRAXZC/e0mVyMiIiIiZosM9OHFa0/mvnF98bJaWLYxk3FPfsX6nQfMLk1ERNohhUZtWM08ox7qMhIRERGRalarhSmje/D2TSPoFu7PnvxSLntuDfM/34HTqeVqIiLSfBQatWE184wSNc9IRERERH5nQFwoy24dyfkDO+NwGjzxyTauWfgd+wrLzC5NRETaCYVGbZg6jURERETkSIJ8vZk3YSBPXHIift42vtmRx9lPfsXqbfvMLk1ERNoBhUZtmHvntEh1GomIiIhI/SwWC5cOieODW0bSNzaYvOIKrn3xex5Z9isVVU6zyxMREQ+m0KiNcjoNUnNdnUaJnRQaiYiIiMiR9ewUyDtTR3DtiAQAnv8qjUsWfEt69eYqIiIiR0uhURuVWVhGWaUTb5uFuDA/s8sREREREQ/g621j1nnH859rBhPq780vuws4599f8e6Pe8wuTUREPJBCozYqtXqeUXxEAF42fZlEREREpPHOOj6Gj28bxdDu4RRXOPjrkp+4442fKS6vMrs0ERHxIEoj2qiUfdVDsCM1BFtEREREjl5siB//m/In/prUC6sF3t6wm/FPfc3mvQVmlyYiIh5CoVEblVq99rxHlOYZiYiIiEjT2KwW/pp0HP+b8idign1JzS3mwvnf8uI3aRiGYXZ5IiLSxik0aqNSqpenJUap00hEREREjs2wHhF8fNsokvpGU+Fw8uAHvzLllR/YX1xhdmkiItKGKTRqo1Jz1GkkIiIiIs0nLMDO8xMH8+B5x2O3WVm5ZR/jnvyKtal5ZpcmIiJtlEKjNqi4vIrMgjJAnUYiIiIi0nwsFguTRiTwzs0j6BEVQFZhGVc+v5a5K7ZT5XCaXZ6IiLQxCo3aoLTqeUYRAXZC/e0mVyMiIiIi7c3xnUP48JaRXDq4K04D/r3qN658/jv25peaXZqIiLQhCo3aoJp5Rj3UZSQiIiIiLcTf7sUTlw7gycsHEujjxbr0/Zz95Fd8ujnL7NJERKSNUGjUBtXMM0rUPCMRERFpovnz55OQkICvry/Dhg1j3bp1DZ77/PPPM2rUKMLCwggLCyMpKemI50v7cv7ALiy7dSQndg2hoLSSG15dz8z3NlFW6TC7NBERMZlCozZInUYiIiJyLJYsWUJycjIPPPAAGzZsYMCAAYwZM4Z9+/bVe/7q1au54oor+Pzzz1mzZg1xcXGcddZZ7Nmzp5UrF7PERwTw1o0juGF0DwBeWbOTC+Z/w459RSZXJiIiZlJo1Aa5d06LVKeRiIiIHL25c+cyZcoUJk+eTL9+/ViwYAH+/v4sXLiw3vMXLVrE1KlTGThwIH369OG///0vTqeTVatWtXLlYia7l5V7x/XlpcknExFgZ2tWEeOf+obF6zI0JFtEpINSaNTGOJ0GqbmuTqPETgqNRERE5OhUVFSwfv16kpKS3MesVitJSUmsWbOmUc9RUlJCZWUl4eHhDZ5TXl5OYWFhrZu0D6f17sTHt41iZM9ISisd3LN0I3+avYr7393ImpQ8HE7D7BJFRKSVKDRqYzILyyirdOJtsxAX5md2OSIiIuJhcnNzcTgcREdH1zoeHR1NVlbjBhzffffddO7cuVbw9HuzZ88mJCTEfYuLizumuqVt6RTsyyvXDeWes/sQ6u9N7sEKXlubwRXPr2XYo6uY8e4m1qYqQBIRae8UGrUxqdXzjLqF++Nl05dHREREWtc//vEPFi9ezDvvvIOvr2+D502fPp2CggL3bdeuXS1T0M418M5NUFXRMs8vDbJaLdx4aiLf35fEy9cN5bIhXQnx8yb3YDmvrt3J5f9Zy59mr+KB9zaxLm0/TgVIIiLtjpfZBUhtKfuql6Zp5zQRERFpgsjISGw2G9nZ2bWOZ2dnExMTc8Rr58yZwz/+8Q9WrlzJiSeeeMRzfXx88PHxOeZ6j6j8ICy5CkryoDgHJrwK3urEbm3eNiunHhfFqcdF8fcLnHybksuyXzL5ZHMWOUXlvLxmJy+v2UmnIB/GnRDLOSfGMrhbGFarxezSRUTkGKmVpY1Jza0egq3QSERERJrAbrczePDgWkOsa4ZaDx8+vMHrHn/8cR5++GGWL1/OkCFDWqPUP+YTCBf+B7z8YMcKeO0SKNPsJDPZvayc1rsTT1w6gB/uP5MXrz2ZSwZ3JcjXi31F5bz0bTqXLljD8H+s4sEPNrN+pzqQREQ8mTqN2piandMSowJMrkREREQ8VXJyMpMmTWLIkCEMHTqUefPmUVxczOTJkwGYOHEiXbp0Yfbs2QA89thjzJw5k9dff52EhAT37KPAwEACA03+Q1avJLhmKSy6DHZ+Da+cD1e/Df4ND+mW1mH3snJ6n06c3qcTj1zYn2925PLhL5ms2JxNdmE5L36TzovfpBMb4svZ/V0dSIPiQtWBJCLiQUzvNJo/fz4JCQn4+voybNgw1q1bd8Tz582bR+/evfHz8yMuLo7bb7+dsrKyY3rOtiSleqaROo1ERESkqSZMmMCcOXOYOXMmAwcO5KeffmL58uXu4dgZGRlkZma6z3/22WepqKjgkksuITY21n2bM2eOWW+htvgRMOl98AuHvRvgpXOgqHFDvaV1+HjZ+HOfaOZeNpAfZiTx34lDuGhQFwJ9vMgsKGPhN2lc/Oy3jHzsM/7+4a/8mHEAw1AHkohIW2cxTPx/6yVLljBx4kQWLFjAsGHDmDdvHm+++Sbbtm2jU6dOdc5//fXXue6661i4cCEjRoxg+/btXHvttVx++eXMnTu3Sc9Zn8LCQkJCQigoKCA4OLhZ3/ORFJdXcfwDnwDw08wzCfW3t9pri4iIeCKzvmdLXa3ytdi3BV65AA5mQXgPmPgehHZrmdeSZlFW6eCr33JZ9steVvyaTXGFw/1Yl1A/xp0QwzkndmZA1xAsFnUgiYi0hqP5nm1qaDRs2DBOPvlknn76acC13j4uLo5bbrmFe+65p87506ZNY8uWLbXW6N9xxx189913fP311016zvqY9QPopj0FnPvU10QE2Fk/48xWe10RERFPpdCo7Wi1r8X+NHjlPMjPgOAuruAoslfLvZ40m7JKB19uz2HZxkxW1hMgnXuiawnbCV0UIImItKSj+Z5t2vK0iooK1q9fT1JS0qFirFaSkpJYs2ZNvdeMGDGC9evXu5ebpaam8tFHHzFu3LgmPydAeXk5hYWFtW5mOLQ0TfOMREREROoV3h2u+wQij4PCPbBwLGT+YnZV0gi+3jbOOj6GJy8fxPoZZ7Lg6sGMH9AZf7uNPfmlPPdlKuc9/Q2jn/ic2R9vYePuAi1hExExmWmDsHNzc3E4HO619TWio6PZunVrvddceeWV5ObmMnLkSAzDoKqqihtvvJF77723yc8JMHv2bB588MFjfEfHrmYIdo9IzTMSERERaVBwZ5j8Mbx6IWT9Ai+dC1e/BXFDza5MGsnX28bY/jGM7R9DaYWDL7bv48NfMlm1ZR+79pfy3BepPPdFKt3C/TnnxFguGdyVRM38FBFpdaYPwj4aq1ev5tFHH+WZZ55hw4YNLF26lGXLlvHwww8f0/NOnz6dgoIC923Xrl3NVPHRqek0SuykTiMRERGRIwqIhEkfQNyfoLzANesodbXZVUkT+NltjO0fy9NXnsSGGWfyzFUncc6Jsfh528jYX8Kzq1M4459fcNmCNby9fjelhy1rExGRlmVap1FkZCQ2m43s7Oxax7Ozs4mJian3mhkzZnDNNddw/fXXA3DCCSdQXFzMDTfcwH333dek5wTw8fHBx8fnGN/RsVOnkYiIiMhR8AuFa5bC4qsg9XNYdClc+jL0GWd2ZdJEfnYb406IZdwJsZRUVPH51hyWbtjN59v2sS59P+vS9zPr/c2cP6gzl5/cjf5dQswuWUSkXTOt08hutzN48OBaQ62dTierVq1i+PDh9V5TUlKC1Vq7ZJvNBoBhGE16zrbC6TRIy3WFRomdFBqJiIiINIo9AK5cAn3OBUcFLLkafnnT7KqkGfjbvTjnxFheuPZkvr3nDO4a05u4cD+Kyqt4bW0G5z71Nef8+yteXZNOQWml2eWKiLRLpi5PS05O5vnnn+fll19my5Yt3HTTTRQXFzN58mQAJk6cyPTp093njx8/nmeffZbFixeTlpbGihUrmDFjBuPHj3eHR3/0nG1VZmEZpZUOvG0W4sL8zC5HRERExHN4+bg6jE68HAwHLJ0CP7xodlXSjGJCfLn59J58cefpvH79MM4b0Bm7zcrmvYXMeG8zQx9ZSfKSn/guNU/Ds0VEmpFpy9MAJkyYQE5ODjNnziQrK4uBAweyfPly9yDrjIyMWp1F999/PxaLhfvvv589e/YQFRXF+PHjeeSRRxr9nG1VavU8o27h/njZPGrUlIiIiIj5bF5wwbPgEwjf/xc+/CuUF8Ept5pdmTQjq9XCiJ6RjOgZyYHiCt75cQ9Lvt/Ftuwilv64h6U/7qFHZACXnRzHxSd1JSrI/BEUIiKezGIoiq+jsLCQkJAQCgoKCA4ObpXXfOmbNGZ98Ctn9YvmPxOHtMprioiIeDozvmdL/drM18IwYNWD8PW/XJ+P/hucfi9YLObVJC3KMAx+2pXPku938f7PeympHpTtZbVwRt9OXH5yN0YfF4XNqn8DIiJwdN+zTe00kkNSq+cZ9dBWoiIiIiJNZ7FA0izwCYJVD8GXj0N5IYyZDVZ1c7dHFouFQd3CGNQtjPvP7ceyX/ay+Ptd/JiRzyebs/lkczaxIb5cOrgrlw6JIy7c3+ySRUQ8hkKjNsK9c1pUgMmViIiIiLQDo+4An2D46E74bgGUH4Tz/g1Wm9mVSQsK9PFiwsndmHByN7ZlFbHk+10s/XE3mQVl/PuzHTz1+Q5G9ozk8pO7kdSvEz5e+vcgInIkCo3aiJTqmUaJ6jQSERERaR5Dp4A9EN6bCj+9BhUH4aLnwctudmXSCnrHBDFzfD/uPrs3n27OZsn3u/h6Ry5f/ea6hQfYuWhQFyacHEev6CCzyxURaZMUGrUBxeVVZBaUAZCoTiMRERGR5jPwCrAHwFvXwa/vQkUxXPYK2LVEqaPw8bIxfkBnxg/oTEZeCW+u38UbP+wiu7Cc/36dxn+/TmNwfBgTTo7j3BNj8bfrVyQRkRpa2N0GpFXPM4oIsBPqr798iYiIiDSrfufBlYvByw92rIBFl0BZodlViQm6Rfhzx1m9+ebuP7Pw2iGc1S8am9XC+p0H+NtbvzD0kVVMX7qRn3flo/2CRETUadQm1CxN0zwjERERkRbSMwmueQdevwx2fgOvnAdXLwX/cLMrExN42az8uU80f+4Tzb7CMt7asJsl3+9iZ14J/1uXwf/WZdAnJojLT47jgkFd9IddEemw1GnUBriHYEdqnpGIiIhIi4kfDpM+AP8I2PsjvDgOirLMrkpM1inYl6mn9WT1nafxvyl/4oKBnbF7WdmaVcSsD35l6COruOq/a1nwRQqb9xbgdKoDSUQ6DnUatQHuIdid1GkkIiIi0qI6D4TJH8Mr50POFlg4Fia+B2HxZlcmJrNYLAxPjGB4YgQPllTy7k97+N+6DLZmFfHNjjy+2ZHHPz6GyEAfRvWKZFSvSEb2iqRTkK/ZpYuItBiFRm2AOo1EREREWlFUb7huObx8HhxIgxfPhmvehajjzK5M2ogQf28mjUhg4vB4UnOL+Wp7Dl/+lsualDxyD5bzzo97eOfHPQD0jQ1mdK9IRvWKYkhCGL7eNpOrFxFpPgqNTOZ0Gu5B2JppJCIiItJKwhJcwdErF0DuturgaCnEDjC7MmlDLBYLiVGBJEYFcu0p3SmvcrBhZz5f/pbDV7/lsGlPIVsyXbfnvkzF19vKsO4RjOoVyanHRdGzUyAWi8XstyEi0mQWQ9sC1FFYWEhISAgFBQUEBwe36GvtyS/llH98hrfNwq8PjcXbpjFTItL6HA4HlZWVZpchUoe3tzc2W8N/tW/N79lyZB77tSjOg9cuhMyfwScErnoTug0zuyrxEHkHy/l6Ry5fbs/lq99y2FdUXuvxmGBfRvWKZPRxUZzSM5LwAA3UFhHzHc33bHUamSy1ep5Rt3B/BUYi0uoMwyArK4v8/HyzSxFpUGhoKDExMfprvbSMgAjXcOzXJ0DGGnj1Arj8dUg83ezKxANEBPpw/sAunD+wC4ZhsD37IF/9lsMX23NYl7afrMIy3ly/mzfX78ZigRO6hLhCpF5RDOoWht1LP/+LSNum0MhkKfuqh2BHaZ6RiLS+msCoU6dO+Pv765dyaVMMw6CkpIR9+/YBEBsba3JF0m75hsDVS2HJ1ZCyCl6/DC59CfqcY3Zl4kEsFgu9Y4LoHRPE9aN6UFbp4Pv0/Xy5PYevfstla1YRv+wu4JfdBcz/PIUAu43hiRGMPi6KUb2iSIjQ92ERaXsUGpns/9u78/AoqnQN4G/vS5JOpxOykpBIwr5KgAs4ooKDC4yogCJKFBkvg6gBg4AODIoQ9l1kcEa4uDsqXkaGkUXgakSIYBzZQUPYQgJk6SSdTne66/5RnUo6G4kmXYG8v+c5T1edqq76qmg6J1/OOfWLNJ8Rk0ZE5Fsul0tKGAUHB8sdDlGtDAYDACA3NxehoaH1DlUj+k20RmDsB8CnE4HjW4GPngAeXA/0GCN3ZHSD0mtU+F2CmBACgByrHV+fFoexfXP6Kq6VOLDreC52HRcT422DDLi9QxvcnhCCAe1DEGjQyBk+EREAJo1kJz05jZNgE5GPVcxhZDQaZY6EqH4Vn1Gn08mkETUvtQ4YtRHY+hzw4/vAZ88AjmIgcYLckdFNIMykx6g+bTGqT1u43QKOZVvFCbVPXcX3WXm4kF+K9w+cw/sHzkGlVKBXtBm/SwjBbfEh6BRhgr+Ov7oRke/xm0dmP1/h8DQikhe7wlNLx88o+ZRKDTzwBqDzBw5uAL6YCtgLgYHPA0omLalpKJUKdIsKRLeoQEy+Ix4lZeU4kHlNmlD75yslOJSVj0NZ+Vi56zQAIMpsQHyoPxJC/cXXMH/EhwawRxIRNSsmjWRkc5Qju9AOAGjPnkZERERELYNSCdy7GNAFAF8vA3bNBQ78Fej2sDhcLbwHwGQmNSE/nRp3dQrDXZ3CAIhPWP7aMxfSwbN5uFJUhosFpbhYUIp9p654vTc0QIeEMH8khAZISaWEsAA+qY2ImgSTRjKqGJpm8dPCbOSXOhGRHGJjY5GcnIzk5OQG7b93717ceeedyM/Ph9lsbtbYiEhGCgUwZA7g1wbYuxAoygb2rxVLSAeg+xig+yjAEid3pHQTijIb8Gi/GDzaLwYAUGBz4ExuMU7nFuN0TjFO5xbhTG4xsgvtyC0qQ25RGdLOXPM6RrCfFu0rkkieRFJCqD/aBOjYg5OIGoxJIxlVDk1jLyMiooa644470KtXL6xcubJJjpeeng4/v4Z/Dw8cOBDZ2dkIDAxskvMTUQv3X38S5zQ6swv4z8fAqX8DV08Be14XS9t+Yu+jrg8CfiFyR0s3KbNRi8RYCxJjLV71RXanlEw6k1uM0zlFOHOlGOfzSnGtxIFrmXk4mJnn9R6TXi0lkOI9yaT4UH9EBuqZTCKiGpg0kpE0CXYI5zMiImpKgiDA5XJBrb7+j7k2bdo06tharRbh4eG/NrQbmsPhgFbLnrHUCql1QKf7xWK3Aie+EBNImfuACwfFsn0G0P4uMYHU8T5xTiSiZhag16B3TBB6xwR51dsc5fjlSglO5xZ5eiaJSaWsayWw2sul+ZKq8tOqEB8qzpMkDncTh7y1DTJAqWQyiai1UsodQGsm9TQKZU8jImoZBEGAzVHu8yIIQoPie/LJJ7Fv3z6sWrUKCoUCCoUCZ8+exd69e6FQKLB9+3b06dMHOp0O33zzDX7++Wc88MADCAsLg7+/P/r27Ytdu3Z5HTM2Ntar15JCocDf/vY3PPjggzAajUhISMDWrVul7RXnKigoAABs2rQJZrMZX375JTp37gx/f3/cc889yM7Olt5TXl6O559/HmazGcHBwZgxYwaSkpIwcuTIOq/12rVrGDt2LKKiomA0GtG9e3d88MEHXvu43W4sXrwY8fHx0Ol0iImJwfz586XtFy5cwNixY2GxWODn54fExEQcOHBAupfVz5+cnIw77rhDWr/jjjswZcoUJCcnIyQkBMOGDQMALF++HN27d4efnx+io6MxefJkFBcXex0rLS0Nd9xxB4xGI4KCgjBs2DDk5+dj8+bNCA4ORllZmdf+I0eOxBNPPFHn/SBqMfQmoNdjwPjPgWnHgWGpQGRvQHABZ3YCn/0RWJoAfDoROPUl4HLKHTG1QkatGt2iAvFg77Z46Z5OeGt8Ivak3IFjr92D7S/8DqvH9sbzd8Xj3m7hSAj1h1qpQInDhR8vFOLTwxewcPsJPP0/3+P2JXvQec6/cffyfXh6Uzpe/edRbEzLxFcncnAmtxh2p0vuSyWiZsaeRjJiTyMiamlKnS50mfOlz8977LVhMGqv/yNp1apVOHXqFLp164bXXnsNgNhT6OzZswCAmTNnYunSpbjlllsQFBSE8+fP47777sP8+fOh0+mwefNmjBgxAidPnkRMTEyd53n11VexePFiLFmyBGvWrMG4ceOQlZUFi8VS6/42mw1Lly7FO++8A6VSiccffxwpKSl47733AACLFi3Ce++9h40bN6Jz585YtWoVPv/8c9x55511xmC329GnTx/MmDEDJpMJ27ZtwxNPPIH27dujX79+AIBZs2bhrbfewooVK3DbbbchOzsbJ06cAAAUFxdj8ODBiIqKwtatWxEeHo7Dhw/D7XZf9z5X9T//8z/405/+hLS0NKlOqVRi9erViIuLwy+//ILJkyfjpZdewrp16wAAGRkZGDJkCCZMmIBVq1ZBrVZjz549cLlcGD16NJ5//nls3boVo0ePBgDk5uZi27Zt2LFjR6NiI5JdQDgwYLJYrp4BfvoH8NPHQN4vnuV/AMZgceha9zFAdD9OoE2y0mtU6BxhQucIk1e90+VG1rUSqVfSac9Qt1+ulKCs3C3VVadQAOEmPaItRrSzGBFjMSImWHxtF+yHIKOGQ96IbnBMGsnE7RaQedWTNOKcRkREDRIYGAitVguj0VjrELHXXnsNd999t7RusVjQs2dPaX3evHnYsmULtm7diilTptR5nieffBJjx44FACxYsACrV6/GwYMHcc8999S6v9PpxPr169G+fXsAwJQpU6SkFgCsWbMGs2bNwoMPPggAWLt2Lf71r3/Ve61RUVFISUmR1p977jl8+eWX+Pjjj9GvXz8UFRVh1apVWLt2LZKSkgAA7du3x2233QYAeP/993HlyhWkp6dLya74+Ph6z1mbhIQELF682Kuu6qThsbGxeP311zFp0iQpabR48WIkJiZK6wDQtWtXafmxxx7Dxo0bpaTRu+++i5iYGK9eTkQ3nJB44M5ZwB0zgYuHxeTRkU+BkitA+t/EYm4HdB8tltBOckdMJNGolIgPDUB8aADurVJf7nLjYkEpsq7ZcC5PLFnXSnAurxTnrpWgxOFCdqEd2YX2GnMnAUCATi0mlIKrJZQsfog066FWceALUUvHpJFMsq12lDpd0KgUiLYY5Q6HiAgAYNCocOy1YbKctykkJiZ6rRcXF2Pu3LnYtm0bsrOzUV5ejtLSUpw7d67e4/To0UNa9vPzg8lkQm5ubp37G41GKWEEABEREdL+hYWFyMnJkXoHAYBKpUKfPn3q7fXjcrmwYMECfPzxx7h48SIcDgfKyspgNIo/M44fP46ysjIMGTKk1vdnZGSgd+/edfaOaqg+ffrUqNu1axdSU1Nx4sQJWK1WlJeXw263w2azwWg0IiMjQ0oI1eaPf/wj+vbti4sXLyIqKgqbNm3Ck08+yb9G081BoQDa9hHL7+eL8x799A/g+D+Bgizg66ViCe9e+QQ2U6TcURPVSq1Sol2wH9oF1/wjtyAIyCtxICvPhvN5tsrE0jUbsvJKkGMtQ1FZOY5lW3Es21rj/SqlAlFmQ7VkUuVygF7ji0skoutg0kgmv3jmM4qxGKFhhp2IWgiFQtGgYWItVfWnoKWkpGDnzp1YunQp4uPjYTAYMGrUKDgcjnqPo9F4N1QVCkW9CZ7a9m/oPE11WbJkCVatWoWVK1dK8wclJydLsRsMhnrff73tSqWyRoxOZ825V6rf07Nnz2L48OH405/+hPnz58NiseCbb77B008/DYfDAaPReN1z9+7dGz179sTmzZvx+9//HkePHsW2bdvqfQ/RDUmlBuKHiOX+5cCp7cB//iHOfXT5J7HsnAPE3iZOoN35D4DBLHfURA2iUCgQ7K9DsL8Ot1abiBsA7E4Xzku9kyp7KlUUR7lbWsaZmse3+GkRbTEiNtiITuEmdIsyoWtkICx+fCADkS/duL8Z3OCk+YzacD4jIqLG0Gq1cLkaNvFmWloannzySWlYWHFxsTT/ka8EBgYiLCwM6enpuP322wGIvYgOHz6MXr161fm+tLQ0PPDAA3j88ccBiJNenzp1Cl26dAEgDhszGAzYvXs3Jk6cWOP9PXr0wN/+9jfk5eXV2tuoTZs2OHLkiFddRkZGjQRYdYcOHYLb7cayZcugVIp/9Pj4449rnHv37t149dVX6zzOxIkTsXLlSly8eBFDhw5FdHR0vecluuFpjUC3h8ViywOOfS4mkM59C5z9WizbXgQSfi8mkBKGARq93FET/Wp6jQoJYQFICAuosc3tFpBbVIasayU1eyrl2ZBX4pDKj+cL8L+4JL03MlCPLpGBUhKpW5QJ4SY9e6sSNRMmjWQiPTmNSSMiokaJjY3FgQMHcPbsWfj7+9c7/CohIQGfffYZRowYAYVCgdmzZzd6Iuim8NxzzyE1NRXx8fHo1KkT1qxZg/z8/HobuAkJCfjkk0/w7bffIigoCMuXL0dOTo6UNNLr9ZgxYwZeeuklaLVaDBo0CFeuXMHRo0fx9NNPY+zYsViwYAFGjhyJ1NRURERE4IcffkBkZCQGDBiAu+66C0uWLMHmzZsxYMAAvPvuuzhy5Ah69+5d77XEx8fD6XRizZo1GDFiBNLS0rB+/XqvfWbNmoXu3btj8uTJmDRpErRaLfbs2YPRo0cjJCQEgDivUUpKCt566y1s3rz5N95hqs0bb7yBJUuW4PLly+jZsyfWrFnjNUyyqqNHj2LOnDk4dOgQsrKysGLFCq+5q6iJGS1A4gSxFJwDfvpEHMKWeww48YVYdCax51GP0UDs7wBl0wzjJWoJlEoFwgP1CA/Uo/8twTW2F9mdOOdJJv18pQTHLllx9FIhzl6z4VKhHZcK7dh1PEfa3+KnRddIMYnUNdKEblGBaGcxQqlkIonot2LSSCaVPY04CTYRUWOkpKQgKSkJXbp0QWlpKTIzM+vcd/ny5ZgwYQIGDhyIkJAQzJgxA1ZrzXkVmtuMGTNw+fJljB8/HiqVCs888wyGDRsGlaruXwL//Oc/45dffsGwYcNgNBrxzDPPYOTIkSgsLJT2mT17NtRqNebMmYNLly4hIiICkyZNAiD2yNqxYwdefPFF3HfffSgvL0eXLl3wxhtvAACGDRuG2bNn46WXXoLdbseECRMwfvx4/PTTT/VeS8+ePbF8+XIsWrQIs2bNwu23347U1FSMHz9e2qdDhw7YsWMHXn75ZfTr1w8GgwH9+/eXJhcHxB5YDz/8MLZt24aRI0f+mttK9fjoo48wbdo0rF+/Hv3798fKlSsxbNgwnDx5EqGhoTX2t9lsuOWWWzB69GhMnTpVhohbMXMM8LtpYrl8xPPUtU8A6wUg412xGEOAiJ5AWBcg1FPadAQ09Q8FJbpRBeg1ngRQoFe91e7E8UtWHL1kxZFLhTh2yYrTucXIK3Hg69NX8fXpq9K+/jo1ukSY0MWTROoaaUJ8qD+nBiFqJIXwWydduAlZrVYEBgaisLAQJpPp+m/4FQak7kZ2oR2f/mkg+rSrOQaYiKi52e12ZGZmIi4uDno9h0D4ktvtRufOnTFmzBjMmzdP7nBkM2TIEHTt2hWrV6+ud7/6Pqu++Jl9I+rfvz/69u2LtWvXAhA/c9HR0Xjuuecwc+bMet8bGxuL5OTkRvc04r9FE3K7gXP7xSewHf0csBfU3EehBCy3VCaRKhJKllvYK4laFbvThZOXi6RE0tFLVpzItqKsvGbPYq1aiU7hAV69kjpHmKBvogdyEN0oGvMzmz2NZGBzlCO70A4AaM+eRkREN72srCzs2LEDgwcPRllZGdauXYvMzEw89thjcocmi/z8fOzduxd79+7FunXr5A7npuNwOHDo0CHMmjVLqlMqlRg6dCj279/fZOcpKytDWVmZtC5HL76bllIJxA4Sy72LgUsZQO5RIPc4kHNMXC7NB66dEcvxrZXvVeuBkA5AWFcgtDMQ6nk1RYpPdiO6yeg1KvSMNqNntFmqK3e58fOVEhy5WCglk45fsqKorBz/uVCI/1woBHAegPgUt/Zt/NAtMlDqldQl0gQTn95GBIBJI1lUDE2z+GlhNnL2fyKim51SqcSmTZuQkpICQRDQrVs37Nq1C507d5Y7NFn07t0b+fn5WLRoETp27Ch3ODedq1evwuVyISwszKs+LCwMJ06caLLzpKam1jvZOTURtQ6I6S+WCoIAFOeIcyDlHBOTSblHgdwTQHkpcPk/YqlKH1jZKym0c2VSycAe73TzUauU6BgegI7hAXi4j1jndgs4l2fDUc/8SEcuWXH0YiGulThwKqcYp3KK8dkPF6VjxFiM6BZlQpcIE9oGGcU5mEziPEzsmUStCZNGMqicBJu9jIiIWoPo6GikpaXJHUaL4esn2FHzmDVrFqZNmyatW61WPgXPVxQKICBcLO3vqqx3u4D8s54k0jEgx9M76doZwF4oDnk7V623WUCkZ2hblV5JnC+JbkJKpQKxIX6IDfHD/T0iAACCICDHWiYmkS6KyaSjl6y4WFAqPcntXz9drnGsIKMGYSY9IjyTeYebDIgI1CMssLIuQKfmE93opsCkkQykSbBD+OQ0IiIialohISFQqVTIycnxqs/JyUF4eHiTnUen00Gn0zXZ8agJKFVAcHuxdB5eWe+0A9dOe3olHatMKhWeB4ouieXMrsr9q8+XFNpJnLA7MBrwa8NhbnTTUCgqn+I2pHNl78z8EgeOZVtx5GIhTl4uQnahHZetdmQXlsLudCPf5kS+zYkTl4vqPLZRq/LqnRQhLRukumA/LZ/wRi0ek0YyqOhpxCenERERUVPTarXo06cPdu/eLT2Zzu12Y/fu3ZgyZYq8wZE8NHogvLtYqrIXikPaGjpfEgCodEBgW7GYo8VEUmDbyldTlHg+ohtYkJ8Wg+JDMCg+xKteEARYS8ulBNJlTzLpcqEd2YV25FjF18JSJ2wOF365UiJ1GKiNRqVAaEDVHkt6KYkl1hkQGqDjE99IVkwayaDii6N9G/Y0IiIioqY3bdo0JCUlITExEf369cPKlStRUlKCp556CgAwfvx4REVFITU1FYA4efaxY8ek5YsXLyIjIwP+/v6Ij4+X7TqomekDGzZf0tVTnl5JlwFXGZD3s1jq4hfqSShVJJOiqySaYsR5lNhbiW5ACoUCgUYNAo0adAwPqHO/UodLSixVJJIuVxRPkulKcRmcLgEXC0pxsaC03vMGGTUI9tch2E+LEH8dgv21CPareNWK2/y1CPHTwWTgsDhqWkwa+ZjbLSDzqmd4GnsaERERUTN45JFHcOXKFcyZMweXL19Gr1698O9//1uaHPvcuXNQKiv/cn3p0iX07t1bWl+6dCmWLl2KwYMHY+/evb4On+RU13xJAFDuEIeyFZwHCi94yrnK5YLz4kTcJbliuXio9nNojN69kwKjqySZ2orzLKn5sBi6cRm0KsSF+CEupO7f95wuN3KLyqRkUtUEU9VXp0uQhsOdacC5NSoFLH6VSaUQT7JJSix5JZx0MGg5qTfVj0kjH8u22lHqdEGjUiDaYpQ7HCIiIrpJTZkypc7haNUTQbGxsRAEwQdR0Q1NrQWCYsVSG0EAbHlir6TCKomlgiqJpZJcwGkTey9dPVXHiRRAQIT3MLiK8wbFiXUqPg6dbmwalRJRZgOizHVPOu92C8izOZBX4sDV4jJcK3bgWnEZrpU4cLXK8jXPtqKycjhd4uTeOdayBsVh1KqkBJJXQslfXLf4aRFk1CLQIPaw4gTfrQ+TRj72i2c+oxiLkWNTiYiIiOjmoVAAfsFiiexV+z5OO2C9KCaV6uqx5HJUTtB94WAt51GJiaOgWMAS551QCooFDObmukIin1IqFQjx1yHEX4cOYXUPh6tgd7qQV+LAtWIHrpZUJpnyKpJMVequFjvgcLlhc7hgyyvF+bz6h8hVUCkVMOnVMBu1MBk0MBs0MBs1CPQsmwwamD1JJrNRrKtIOOnU7NV0I2LSyMekJ6dxPiMiItnExsYiOTkZycnJcodCRNS6aPSVT3irjdsNlFzxJJA8PZYKzgH5WUD+WbG4yoCCLLFk7qt5DL257oSSKQpQ8VcgujnpNSpEmg2IrKf3UgVBEFBcVi4mkUrKPD2XqvZkEhNMeSUOFJQ6UGBzoqzcDZe7crhc4+NTwmzQwmzUSAknKblUJQkV6JWI0iJAr+ZT5mTEb0wfq3hyGifBJiIiIiKqRqkEAsLE0rZPze1uN1B8uTKBlJdZuZx/Vhz+Zi8AsjPEUuP4anEOpdoSSkGxgN7UPNdF1MIoFAoE6DUI0GsQW8/cS1XZnS4UljpRYHN6Xh0oLHV613nWC22OyuVSJwQBsDvduOwUJwNvXKyAv04Nk15MJJkMVZc1nmU1TIbqdeK+Bo2KQ+p+AyaNfKyypxEnwSYiooZzOp3QaDiHBxG1ckolYIoUS7uBNbeXFYs9kGpLKBVkiUPf8jPFUhuDpVpCKdbzxDeL+LQ5fSCgM4lxELUyeo0Keo0KYSZ9o97ndgsoKitHoc2JglKHV5KpavLJu058LXW6IAhAkb0cRfby6z5prjYalQImfUUySUwuVU8sBXrWpcRTxX56DbTq1v3/nUkjH6vsacSkERG1QIIgTlDqaxpjgx6/vGHDBsydOxcXLlzwevLTAw88gODgYLz99tv4+eefMW3aNHz33XcoKSlB586dkZqaiqFDhzY4nPT0dLz88sv44Ycf4HQ60atXL6xYsQK33nqrtE9BQQFmzJiBzz//HIWFhYiPj8fChQsxfPhwAEBaWhpeeeUVHDx4EDqdDv369cOHH36IoKCgWofH9erVCyNHjsTcuXMBiH8BXLduHbZv347du3dj+vTpmD17Np555hl89dVXuHz5MmJiYjB58mS88MILXvG//fbbWLZsGc6cOQOLxYKHH34Ya9euxYQJE5Cbm4svvvhC2tfpdEqPXn/66acbfI+IiFoknT8Q1lUs1bldQFG2dyKpamLJdhUozQMu5tX95DcAgEJMHFUkkeorBnPNOm0Ak07UqiiVCnFeI4MGMWjcw6DKyl2wlpbDaheTSNZSJ6z28irLntdq+xR69nO5BThdgjhheInjV8Xvr1MjyE8Di58OFqMGQX5aWIxa8dUzUbjFTwuLZ59AgwaqphxOJwgNaic3FyaNfMjmKEd2odgV75YQDk8johbIaQMWRPr+vC9fArTXT6aPHj0azz33HPbs2YMhQ4YAAPLy8vDvf/8b//rXvwAAxcXFuO+++zB//nzodDps3rwZI0aMwMmTJxETE9OgcIqKipCUlIQ1a9ZAEAQsW7YM9913H06fPo2AgAC43W7ce++9KCoqwrvvvov27dvj2LFjUKnECR4zMjIwZMgQTJgwAatWrYJarcaePXvgcrkadVvmzp2LhQsXYuXKlVCr1XC73Wjbti3+8Y9/IDg4GN9++y2eeeYZREREYMyYMQCAN998E9OmTcPChQtx7733orCwEGlpaQCAiRMn4vbbb0d2djYiIiIAAF988QVsNhseeeSRRsVGRHTDUaoqn8gWe1vN7WVF3gmliqRS4QXAXiiW8lIAAlBWKJbCXxOIQhwGJyWSzPUnnrT+4jndLvGXR8HlWa54dYuloq7qcm3b661ziUMAqx4HAqDSifNRqT1FYwDUOkDtea26XnU/td6zbhCfeMchQtRIOrUKbQJUaBOga/R7BUGAzeHyJJDExFLVZFNhLckmq73ck4RyoqisHABQXFaO4rLyBk8WrlAAZkO15JL0qkYbvRthqiJYlMUIEqwwCVboHXlQ2PLE5LUtDyi5CtiuietdHgBGrGr09TcVJo18qGJomsVP/MAQEVHjBAUF4d5778X7778vJY0++eQThISE4M477wQA9OzZEz179pTeM2/ePGzZsgVbt26t8/Hj1d11111e6xs2bIDZbMa+ffswfPhw7Nq1CwcPHsTx48fRoUMHAMAtt9wi7b948WIkJiZi3bp1Ul3XrrX81fs6HnvsMTz11FNeda+++qq0HBcXh/379+Pjjz+Wkkavv/46XnzxRa/eR3379gUADBw4EB07dsQ777yDl156CQCwceNGjB49Gv7+/GMGEbVyugAgvLtY6lJeBtitlUkke0GV5cLr15fbAQiV662Koo5kU9XkUi3JJo0nSaUxVnv187xW26Y1iu9jby5vbjdQZhU/m6UFlZ/T0oLKz2tty2XFgFor3m+t0XOP/Sr/DbzqPPe/4t+mYtnr1bOvqvmH3CsUCvjp1PDTqRGJ608OXp3LLaDILk76nVdShrwSJ/JLHMizOcTXEgcKSkrhLM6DUHIVytJr0DkKYFEUwVJmhcVRBEuBFRYUwaIoQpCiCMGwQq9o3CTiOdkXENbo6JsOk0Y+xKFpRNTiaYxirx85zttA48aNwx//+EesW7cOOp0O7733Hh599FFpuFpxcTHmzp2Lbdu2ITs7G+Xl5SgtLcW5c+cafI6cnBz8+c9/xt69e5GbmwuXywWbzSYdIyMjA23btpUSRtVlZGRg9OjRDT5fXRITE2vUvfHGG3j77bdx7tw5lJaWwuFwoFevXgCA3NxcXLp0SUqo1WbixInYsGEDXnrpJeTk5GD79u346quvfnOsREStgloH+LcRy6/htHt+cb9e0qlKKSsCFEpAoRITIdKySnxVKD31FXXVl5WV+9ZW57W9luMD4hPrnHYx6VVRal0vFRNrTs9redWeGZ4h8E4bgPzf+A/RAOqqyabqCSdjlaRGbduqLusBpcb7HipV4qTqVdcVnro691P/9kSWy1lHkqeg9vqqySG7FYDw287flJTqaomkaoklqc4o3kcoPJ9NhfeyQllt/XrbK9YrllHnviqFEmZBgNlegDip54+nVKyX5sPrvjawb4gDGuTDhDwhAFfcAchHAPIET/HU5wkm5CEAtxo6YFHT3flGY9LIh6RJsDk0jYhaKoWiQcPE5DRixAgIgoBt27ahb9+++Prrr7FixQppe0pKCnbu3ImlS5ciPj4eBoMBo0aNgsPR8HHsSUlJuHbtGlatWoV27dpBp9NhwIAB0jEMhvr/WnW97UqlEoLg3XBzOmv+1cnPz/vf4sMPP0RKSgqWLVuGAQMGICAgAEuWLMGBAwcadF4AGD9+PGbOnIn9+/fj22+/RVxcHH73u99d931ERNQENJ4eNP6hckfiG4IgTj7uLG18sklat3uSTaWeYqt8ddi866omqSqOX+qDBFVjVE0uKdWVSbvqyaWKZYXC07utoGnmnVQbqsy1Za5l2VxlPi6zOE+Yywk4Sjz3vKTKvS8R73vFssNWyz427/3d4pAvuMsrh3neDPRmwC8EMAYDxhDAaKm2Hgz4BUvrWq0fwhQKhAGIc7qQb3PgWrED+TaxB5PYo0ns2dQ9KlDWS2PSyId+uconpxER/VZ6vR4PPfQQ3nvvPZw5cwYdO3b0mqA6LS0NTz75JB588EEAYs+js2fPNuocaWlpWLduHe677z4AwPnz53H16lVpe48ePXDhwgWcOnWq1t5GPXr0wO7du72GklXVpk0bZGdnS+tWqxWZmXU8yadaXAMHDsTkyZOlup9//llaDggIQGxsLHbv3i0N16suODgYI0eOxMaNG7F///4aw9+IiIiajELhGY7W+PlofhW3W0wcVU8uSYkNWy3baqurtr/gEpMcbrf4Kq1XzP3kWa7YVh/BBbhcQOOmOfSmM3mSPIE1kzx1JX8qln31b1GXckctCabS+pNOFXNwAZ5lwbMuVFuubVst2xu7r1dCyFOqJoQMQYDq16dW9BoVIgINiAhs/BA6X2DSyIdm3dsJD90ahVtCmDQiIvotxo0bh+HDh+Po0aN4/PHHvbYlJCTgs88+w4gRI6BQKDB79my43e5GHT8hIQHvvPMOEhMTYbVaMX36dK9ePIMHD8btt9+Ohx9+GMuXL0d8fDxOnDgBhUKBe+65B7NmzUL37t0xefJkTJo0CVqtFnv27MHo0aMREhKCu+66C5s2bcKIESNgNpsxZ84caRLt68W1efNmfPnll4iLi8M777yD9PR0xMXFSfvMnTsXkyZNQmhoqDRZd1paGp577jlpn4kTJ2L48OFwuVxISkpq1L0hIiJqsZRKsce0nL2mKxIP0iTjFQmlausVE5BXTTZVJKaqJ6V0AVV6/ph+U4JCdmqtWAxBckdCDXQDf9puPJFmAyLNLTN7SER0I7nrrrtgsVhw8uRJPPbYY17bli9fjgkTJmDgwIEICQnBjBkzYLVaG3X8v//973jmmWdw6623Ijo6GgsWLEBKSorXPp9++ilSUlIwduxYlJSUID4+HgsXLgQAdOjQATt27MDLL7+Mfv36wWAwoH///hg7diwAYNasWcjMzMTw4cMRGBiIefPmNain0X//93/jhx9+wCOPPAKFQoGxY8di8uTJ2L59u7RPUlIS7HY7VqxYgZSUFISEhGDUqFFexxk6dCgiIiLQtWtXREbK8LQ8IiKim5VCUTmnEdFNQCFUn1SBYLVaERgYiMLCQphMJrnDISJqFna7HZmZmYiLi4Ner5c7HPKh4uJiREVFYePGjXjooYfkDue66vus8md2y8F/CyIiohtDY35ms6cRERFRK+F2u3H16lUsW7YMZrMZf/jDH+QOiYiIiIhaMCaNiIiIWolz584hLi4Obdu2xaZNm6BWsxlARERERHVja5GIiKiViI2NBUelExEREVFDKeUOgIiIiIiIiIiIWh4mjYiIWjn2PKGWjp9RIiIiInkwaURE1EppNBoAgM1mkzkSovpVfEYrPrNERERE5Buc04iIqJVSqVQwm83Izc0FABiNRigUCpmjIqokCAJsNhtyc3NhNpuhUqnkDomIiIioVWHSiIioFQsPDwcAKXFE1BKZzWbps0pEREREvsOkERFRK6ZQKBAREYHQ0FA4nU65wyGqQaPRsIcRERERkUyYNCIiIqhUKv5iTkREREREXjgRNhERERERERER1cCkERERERERERER1cCkERERERERERER1cA5jWohCAIAwGq1yhwJERER1afiZ3XFz26SD9tPREREN4bGtJ+YNKpFUVERACA6OlrmSIiIiKghioqKEBgYKHcYrRrbT0RERDeWhrSfFAL/NFeD2+3GpUuXEBAQAIVC0WTHtVqtiI6Oxvnz52EymZrsuDeK1n79AO8Br5/Xz+vn9Tf19QuCgKKiIkRGRkKp5Kh7ObH91Dx4/bx+Xn/rvX6A94DXL3/7iT2NaqFUKtG2bdtmO77JZGqVH/gKrf36Ad4DXj+vn9fP629K7GHUMrD91Lx4/bx+Xn/rvX6A94DXL1/7iX+SIyIiIiIiIiKiGpg0IiIiIiIiIiKiGpg08iGdToe//OUv0Ol0cocii9Z+/QDvAa+f18/r5/W31uunX6+1f3Z4/bx+Xn/rvX6A94DXL//1cyJsIiIiIiIiIiKqgT2NiIiIiIiIiIioBiaNiIiIiIiIiIioBiaNiIiIiIiIiIioBiaNiIiIiIiIiIioBiaNfOiNN95AbGws9Ho9+vfvj4MHD8odkk+kpqaib9++CAgIQGhoKEaOHImTJ0/KHZZsFi5cCIVCgeTkZLlD8ZmLFy/i8ccfR3BwMAwGA7p3747vv/9e7rB8wuVyYfbs2YiLi4PBYED79u0xb9483KzPIPi///s/jBgxApGRkVAoFPj888+9tguCgDlz5iAiIgIGgwFDhw7F6dOn5Qm2GdR3/U6nEzNmzED37t3h5+eHyMhIjB8/HpcuXZIv4GZwvc9AVZMmTYJCocDKlSt9Fh/deNh+YvsJYPuJ7Se2n9h+unnbTy297cSkkY989NFHmDZtGv7yl7/g8OHD6NmzJ4YNG4bc3Fy5Q2t2+/btw7PPPovvvvsOO3fuhNPpxO9//3uUlJTIHZrPpaen469//St69Oghdyg+k5+fj0GDBkGj0WD79u04duwYli1bhqCgILlD84lFixbhzTffxNq1a3H8+HEsWrQIixcvxpo1a+QOrVmUlJSgZ8+eeOONN2rdvnjxYqxevRrr16/HgQMH4Ofnh2HDhsFut/s40uZR3/XbbDYcPnwYs2fPxuHDh/HZZ5/h5MmT+MMf/iBDpM3nep+BClu2bMF3332HyMhIH0VGNyK2n9h+Ath+YvuJ7Se2n27u9lOLbzsJ5BP9+vUTnn32WWnd5XIJkZGRQmpqqoxRySM3N1cAIOzbt0/uUHyqqKhISEhIEHbu3CkMHjxYeOGFF+QOySdmzJgh3HbbbXKHIZv7779fmDBhglfdQw89JIwbN06miHwHgLBlyxZp3e12C+Hh4cKSJUukuoKCAkGn0wkffPCBDBE2r+rXX5uDBw8KAISsrCzfBOVjdd2DCxcuCFFRUcKRI0eEdu3aCStWrPB5bHRjYPupEttPbD+1Jmw/bZHW2X6q6WZuP7XEthN7GvmAw+HAoUOHMHToUKlOqVRi6NCh2L9/v4yRyaOwsBAAYLFYZI7Et5599lncf//9Xp+D1mDr1q1ITEzE6NGjERoait69e+Ott96SOyyfGThwIHbv3o1Tp04BAH788Ud88803uPfee2WOzPcyMzNx+fJlr/8DgYGB6N+/f6v8LgTE70OFQgGz2Sx3KD7jdrvxxBNPYPr06ejatavc4VALxvaTN7af2H5i+4ntpwpsP7Wu9pPcbSe1z8/YCl29ehUulwthYWFe9WFhYThx4oRMUcnD7XYjOTkZgwYNQrdu3eQOx2c+/PBDHD58GOnp6XKH4nO//PIL3nzzTUybNg0vv/wy0tPT8fzzz0Or1SIpKUnu8JrdzJkzYbVa0alTJ6hUKrhcLsyfPx/jxo2TOzSfu3z5MgDU+l1Ysa01sdvtmDFjBsaOHQuTySR3OD6zaNEiqNVqPP/883KHQi0c20+V2H5i+4ntJ7af2H4Stcb2k9xtJyaNyKeeffZZHDlyBN98843cofjM+fPn8cILL2Dnzp3Q6/Vyh+NzbrcbiYmJWLBgAQCgd+/eOHLkCNavX98qGj0ff/wx3nvvPbz//vvo2rUrMjIykJycjMjIyFZx/VQ7p9OJMWPGQBAEvPnmm3KH4zOHDh3CqlWrcPjwYSgUCrnDIbphsP3E9hPbT2w/UetsP7WEthOHp/lASEgIVCoVcnJyvOpzcnIQHh4uU1S+N2XKFHzxxRfYs2cP2rZtK3c4PnPo0CHk5ubi1ltvhVqthlqtxr59+7B69Wqo1Wq4XC65Q2xWERER6NKli1dd586dce7cOZki8q3p06dj5syZePTRR9G9e3c88cQTmDp1KlJTU+UOzecqvu9a+3dhRYMnKysLO3fubDV/JQOAr7/+Grm5uYiJiZG+D7OysvDiiy8iNjZW7vCohWH7ScT2E9tPFdh+Yvupqtb2Xdha208toe3EpJEPaLVa9OnTB7t375bq3G43du/ejQEDBsgYmW8IgoApU6Zgy5Yt+OqrrxAXFyd3SD41ZMgQ/PTTT8jIyJBKYmIixo0bh4yMDKhUKrlDbFaDBg2q8YjgU6dOoV27djJF5Fs2mw1KpfdXrUqlgtvtliki+cTFxSE8PNzru9BqteLAgQOt4rsQqGzwnD59Grt27UJwcLDcIfnUE088gf/85z9e34eRkZGYPn06vvzyS7nDoxaG7Se2n9h+YvupKraf2H5qje2nltB24vA0H5k2bRqSkpKQmJiIfv36YeXKlSgpKcFTTz0ld2jN7tlnn8X777+P//3f/0VAQIA09jYwMBAGg0Hm6JpfQEBAjfkH/Pz8EBwc3CrmJZg6dSoGDhyIBQsWYMyYMTh48CA2bNiADRs2yB2aT4wYMQLz589HTEwMunbtih9++AHLly/HhAkT5A6tWRQXF+PMmTPSemZmJjIyMmCxWBATE4Pk5GS8/vrrSEhIQFxcHGbPno3IyEiMHDlSvqCbUH3XHxERgVGjRuHw4cP44osv4HK5pO9Di8UCrVYrV9hN6nqfgeoNPY1Gg/DwcHTs2NHXodINgO0ntp+qYvuJ7Se2n9h+uhnbTy2+7eSz57SRsGbNGiEmJkbQarVCv379hO+++07ukHwCQK1l48aNcocmm9b0yFhBEIR//vOfQrdu3QSdTid06tRJ2LBhg9wh+YzVahVeeOEFISYmRtDr9cItt9wivPLKK0JZWZncoTWLPXv21Pr/PSkpSRAE8bGxs2fPFsLCwgSdTicMGTJEOHnypLxBN6H6rj8zM7PO78M9e/bIHXqTud5noDpfPzaWbjxsP7H9VIHtJ7af2H5i++lmbD+19LaTQhAEoSmTUEREREREREREdOPjnEZERERERERERFQDk0ZERERERERERFQDk0ZERERERERERFQDk0ZERERERERERFQDk0ZERERERERERFQDk0ZERERERERERFQDk0ZERERERERERFQDk0ZERERERERERFQDk0ZERNXs3bsXCoUCBQUFcodCRERE1OKx7UR082LSiIiIiIiIiIiIamDSiIiIiIiIiIiIamDSiIhaHLfbjdTUVMTFxcFgMKBnz5745JNPAFR2f962bRt69OgBvV6P//qv/8KRI0e8jvHpp5+ia9eu0Ol0iI2NxbJly7y2l5WVYcaMGYiOjoZOp0N8fDz+/ve/e+1z6NAhJCYmwmg0YuDAgTh58qS07ccff8Sdd96JgIAAmEwm9OnTB99//30z3REiIiKiurHtRETNhUkjImpxUlNTsXnzZqxfvx5Hjx7F1KlT8fjjj2Pfvn3SPtOnT8eyZcuQnp6ONm3aYMSIEXA6nQDEBsuYMWPw6KOP4qeffsLcuXMxe/ZsbNq0SXr/+PHj8cEHH2D16tU4fvw4/vrXv8Lf398rjldeeQXLli3D999/D7VajQkTJkjbxo0bh7Zt2yI9PR2HDh3CzJkzodFomvfGEBEREdWCbSciajYCEVELYrfbBaPRKHz77bde9U8//bQwduxYYc+ePQIA4cMPP5S2Xbt2TTAYDMJHH30kCIIgPPbYY8Ldd9/t9f7p06cLXbp0EQRBEE6ePCkAEHbu3FlrDBXn2LVrl1S3bds2AYBQWloqCIIgBAQECJs2bfrtF0xERET0G7DtRETNiT2NiKhFOXPmDGw2G+6++274+/tLZfPmzfj555+l/QYMGCAtWywWdOzYEcePHwcAHD9+HIMGDfI67qBBg3D69Gm4XC5kZGRApVJh8ODB9cbSo0cPaTkiIgIAkJubCwCYNm0aJk6ciKFDh2LhwoVesRERERH5CttORNScmDQiohaluLgYALBt2zZkZGRI5dixY9LY/N/KYDA0aL+qXaYVCgUAcc4AAJg7dy6OHj2K+++/H1999RW6dOmCLVu2NEl8RERERA3FthMRNScmjYioRenSpQt0Oh3OnTuH+Ph4rxIdHS3t991330nL+fn5OHXqFDp37gwA6Ny5M9LS0ryOm5aWhg4dOkClUqF79+5wu91e4/x/jQ4dOmDq1KnYsWMHHnroIWzcuPE3HY+IiIiosdh2IqLmpJY7ACKiqgICApCSkoKpU6fC7XbjtttuQ2FhIdLS0mAymdCuXTsAwGuvvYbg4GCEhYXhlVdeQUhICEaOHAkAePHFF9G3b1/MmzcPjzzyCPbv34+1a9di3bp1AIDY2FgkJSVhwoQJWL16NXr27ImsrCzk5uZizJgx142xtLQU06dPx6hRoxAXF4cLFy4gPT0dDz/8cLPdFyIiIqLasO1ERM1K7kmViIiqc7vdwsqVK4WOHTsKGo1GaNOmjTBs2DBh37590kSL//znP4WuXbsKWq1W6Nevn/Djjz96HeOTTz4RunTpImg0GiEmJkZYsmSJ1/bS0lJh6tSpQkREhKDVaoX4+Hjh7bffFgShcjLH/Px8af8ffvhBACBkZmYKZWVlwqOPPipER0cLWq1WiIyMFKZMmSJN9EhERETkS2w7EVFzUQiCIMiZtCIiaoy9e/fizjvvRH5+Psxms9zhEBEREbVobDsR0W/BOY2IiIiIiIiIiKgGJo2IiIiIiIiIiKgGDk8jIiIiIiIiIqIa2NOIiIiIiIiIiIhqYNKIiIiIiIiIiIhqYNKIiIiIiIiIiIhqYNKIiIiIiIiIiIhqYNKIiIiIiIiIiIhqYNKIiIiIiIiIiIhqYNKIiIiIiIiIiIhqYNKIiIiIiIiIiIhq+H8p6x40Y+BBTwAAAABJRU5ErkJggg==",
                        "text/plain": [
                            "<Figure size 1400x500 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Saved model v1 to disk\n"
                    ]
                }
            ],
            "source": [
                "model = create_model()\n",
                "history = train_model(model, learning_rate=0.001, batch_size=64, epochs=15)\n",
                "plot_model(history, version=1, save=True)\n",
                "save_model(model, version=1)"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "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": "3.11.8"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}