diff --git "a/src/FacialKeypointsDetectionCNN.ipynb" "b/src/FacialKeypointsDetectionCNN.ipynb" new file mode 100644--- /dev/null +++ "b/src/FacialKeypointsDetectionCNN.ipynb" @@ -0,0 +1,4171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c3bc2141-f3d9-4202-882b-df4b869318d7", + "metadata": {}, + "source": [ + "# CNN" + ] + }, + { + "cell_type": "markdown", + "id": "f78df1b2-3636-4464-a70c-ca0dcec1c964", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "d21a8e71-ab6d-452e-b7cf-8de90a4f3382", + "metadata": {}, + "source": [ + "### Introduction\n", + "\n", + "Facial keypoint detection is an important computer vision task used in areas such as face recognition, emotion analysis, and human–computer interaction.\n", + "The goal of this project is to predict 30 facial keypoint coordinates from 96×96 grayscale face images.\n", + "\n", + "To solve this task, a custom Convolutional Neural Network (CNN) was designed and trained from scratch.\n", + "The project covers the full deep learning workflow, including data preprocessing, model design, training, evaluation, visualization, and deployment." + ] + }, + { + "cell_type": "markdown", + "id": "e99ebf18-8f60-4a88-a415-ee99ab676a96", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "25596821-25b8-453b-901a-d08aecd2f257", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow import keras\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Rescaling, RandomFlip, RandomRotation, RandomZoom, Flatten, Dense, Dropout, BatchNormalization\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D\n", + "from sklearn.model_selection import train_test_split\n", + "from PIL import Image\n", + "from pathlib import Path\n", + "import zipfile\n", + "import os\n", + "from collections import Counter\n", + "from tensorflow.keras.models import load_model\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import pickle\n", + "import json\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\") " + ] + }, + { + "cell_type": "markdown", + "id": "c7c1a342-23e2-451d-933e-b75ae51d7b4f", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "bf8ffe86-db68-4e5c-8c55-14fc06911cab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading from https://www.kaggle.com/api/v1/competitions/data/download-all/facial-keypoints-detection...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████| 76.3M/76.3M [00:12<00:00, 6.32MB/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting files...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DATA PATH: /root/.cache/kagglehub/competitions/facial-keypoints-detection\n", + "FILES: ['training.zip', 'IdLookupTable.csv', 'test.zip', 'SampleSubmission.csv']\n" + ] + } + ], + "source": [ + "import kagglehub, os\n", + "\n", + "data_path = kagglehub.competition_download('facial-keypoints-detection')\n", + "print('DATA PATH:', data_path)\n", + "print('FILES:', os.listdir(data_path))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2a8fd3ee-df2d-4866-a374-5a7331c88421", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['training.zip', 'IdLookupTable.csv', 'test.zip', 'training.csv', 'test.csv', 'SampleSubmission.csv']\n" + ] + } + ], + "source": [ + "for z in ['training.zip', 'test.zip']:\n", + " with zipfile.ZipFile(os.path.join(data_path, z), 'r') as zip_ref:\n", + " zip_ref.extractall(data_path)\n", + "\n", + "print(os.listdir(data_path))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6f940a28-1897-4052-8096-83802fde2f56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train: (7049, 31)\n", + "test : (1783, 2)\n", + "lookup: (27124, 4)\n" + ] + } + ], + "source": [ + "train_df=pd.read_csv(f'{data_path}/training.csv')\n", + "test_df=pd.read_csv(f'{data_path}/test.csv')\n", + "lookup_df=pd.read_csv(f'{data_path}/IdLookupTable.csv')\n", + "\n", + "print('train:', train_df.shape)\n", + "print('test :', test_df.shape)\n", + "print('lookup:', lookup_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "81954a23-fad4-4b82-b97b-1f16b5429f15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_targets: 30\n" + ] + } + ], + "source": [ + "target_cols=train_df.columns.drop('Image').tolist()\n", + "print('n_targets:', len(target_cols))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "68e3ae90-726e-4366-b89f-ec1f0bed16a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "set()\n" + ] + } + ], + "source": [ + "print(set(lookup_df['FeatureName']) - set(target_cols))" + ] + }, + { + "cell_type": "markdown", + "id": "e6df0edf-456e-48ed-bfea-5f672853cde9", + "metadata": {}, + "source": [ + "### EDA" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "4f8aefdd-7b6c-4e71-b786-919b3ff109d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "missing=train_df[target_cols].isna().mean().sort_values(ascending=False)\n", + "\n", + "plt.figure(figsize=(12,4))\n", + "missing.plot(kind='bar')\n", + "plt.title('Missing ratio per keypoint')\n", + "plt.ylabel('Fraction missing')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "817dcf06-786b-454c-94ab-69fd724f2b09", + "metadata": {}, + "source": [ + "Most facial keypoints have a high amount of missing values, while only a few keypoints are almost always labeled." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "d8c11fff-a9f6-4e18-abda-6d8cff7b75d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6,6))\n", + "plt.scatter(y[:,0], y[:,1], alpha=0.3)\n", + "plt.xlabel('x coordinate')\n", + "plt.ylabel('y coordinate')\n", + "plt.title('Distribution of first keypoint (raw)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dc6c82ed-b1e6-4104-988a-e5760751c9a8", + "metadata": {}, + "source": [ + "The first facial keypoint shows a clear cluster in the center of the image, with some outliers at the edges." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e83cb297-4272-46f2-8f56-f46818b7da35", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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P/ixd96xnPSt+6Id+KL73e783PvrRj8aXf/mXx9TUVDz66KPxH//jf4xv/uZvju/6ru+KP/zDP4xv//Zvj6/8yq+MZz/72bG/vx+/9mu/loIihQoVKlSoUI6KY1uoUKFCp5x+7Md+LN7ylrfE2972tvj5n//52N3djWc84xnxbd/2bfG6170uZmdnI+Jm5va3f/u343Wve11813d9V5w/fz6+9Vu/Nc6cORNf//Vf/wnvV7PZjHe84x3xrd/6rfHd3/3dMTU1Fa9//esrpas5OnfuXPzJn/xJvOENb4i3vvWt8W//7b+NhYWFeN7znhf/+l//61s+901velN88zd/c7zuda+LbrebHOtB9Ju/+Zvx2te+Nn76p386+v1+fOmXfmm8/e1vr5zke5I0OTkZf/iHfxivfe1r49/8m38Tk5OT8Q/+wT+Iz/mcz4lXvOIVycF9Oum4PHvhC18Yb3/72+O7vuu74vu///vjnnvuiTe84Q3xwQ9+MP7yL/+ycu2/+Bf/Ip797GfHT/zET8QP/uAPRsTNg6y+9Eu/NJ0I/YIXvCAefPDB+P3f//24dOlSTExMxAte8IJ4+9vfnk5lLlSoUKFChZwa/afj9IpChQoVKvTXil7zmtfEb//2b8fGxsZJd+WvNf3kT/5kfOd3fmc88cQTcffdd590d26LvvzLvzw+8IEPnKq9zIUKFSpU6K8vlT22hQoVKlSo0Cmgbrdb+X97ezt+7ud+Lh544IFT79R63x9++OF429veFp//+Z9/Mh0qVKhQoUL/z1EpRS5UqFChQoVOAX3FV3xFPOMZz4hP//RPj7W1tfj1X//1+Mu//Mv4jd/4jZPu2i3p/vvvj9e85jVx//33x8c+9rH4mZ/5mRgdHY3v+Z7vOemuFSpUqFCh/0eoOLaFChUqVKjQKaAHH3wwfvEXfzF+4zd+Iw4ODuK5z31u/Pt//+/j7//9v3/SXbslvfSlL43f+q3fiqtXr8bY2Fh89md/dvzIj/xIPPDAAyfdtUKFChUq9P8IlT22hQoVKlSoUKFChQoVKlTojqayx7ZQoUKFChUqVKhQoUKFCt3RVBzbQoUKFSpUqFChQoUKFSp0R1NxbAsVKlSoUKFChQoVKlSo0B1NxbEtVKhQoUKFChUqVKhQoUJ3NBXHtlChQoUKFSpUqFChQoUK3dFUHNtChQoVKlSoUKFChQoVKnRHU3FsCxUqVKhQoUKFChUqVKjQHU3FsS1UqFChQoUKFSpUqFChQnc0Fce2UKFChQoVKlSoUKFChQrd0VQc20KFChUqVKhQoUKFChUqdEdTcWwLFSpUqFChQoUKFSpUqNAdTcWxLVSoUKFChQoVKlSoUKFCdzQVx7ZQoUKFChUqVKhQoUKFCt3RVBzbQoUKFSpUqFChQoUKFSp0R1NxbAsVKlSoUKFChQoVKlSo0B1NxbEtVKhQoUKFChUqVKhQoUJ3NBXHtlChQoUKFSpUqFChQoUK3dFUHNtChQoVKlSoUKFChQoVKnRHU3FsCxUqVKhQoUKFChUqVKjQHU3FsS1UqFChQoUKFSpUqFChQnc0Fce2UKFChQoVKlSoUKFChQrd0VQc20KFChUqVKhQoUKFChUqdEdTcWwLFSpUqFChQoUKFSpUqNAdTcWxLVSoUKFChQoVKlSoUKFCdzQVx7ZQoUKFChUqVKhQoUKFCt3RVBzbQoUKFSpUqFChQoUKFSp0R1NxbAsVKlSoUKFChQoVKlSo0B1NxbEtVKhQoUKFChUqVKhQoUJ3NBXHtlChQoUKFSpUqFChQoUK3dFUHNtChQoVKlSoUKFChQoVKnRHU3FsCxUqVKhQoUKFChUqVKjQHU3FsS1UqFChQoUKFSpUqFChQnc0Fce2UKFChQoVKlSoUKFChQrd0VQc20KFChUqVKhQoUKFChUqdEdTcWwLFSpUqFChQoUKFSpUqNAdTcWxLVSoUKFChQoVKlSoUKFCdzQVx7ZQoUKFChUqVKhQoUKFCt3RVBzbQoUKFSpUqFChQoUKFSp0R9PwcS9cWVk58lmj0Ug/ERH9fj99PjRU9Zn7/X70er3o9/vR7/ej2+3Gzs5OrK+vx9bWVnS73VhZWYnNzc148sknY2NjIzY3N2NjYyN2d3djc3Mzdnd3Y3d3N3q9XnqO2tazh4aG0vN3d3fj4OAgdnd3Y2hoKIaHh2N8fDxGRkZibGwshoeHY2RkJEZGRlJ/h4aG0v/8e3h4OJrNZvpcz9D4+bf3Sb/Js2azeYSHzs+IqIzV22O7bF90cHAQBwcHsbe3F/v7+9Hr9Srf7+7uxt7eXmxubsb+/n7s7e2la8W7/f39GBoaSmNXP3q9Xmo/ImJkZCQmJiZiYmIixsbG0j0jIyMxOjoaQ0NDMT4+Hs985jPj7rvvjvvvvz/m5uZibGwstdnv99P4KUsi8ewk6CMf+Uj0er3o9Xqxt7eX5Er9Uj/Fs52dnSTje3t7MTQ0FO12O8bGxmJkZKQyVvFqePjmchTvDw4OYnh4OMlcs9mMZrMZo6Oj0e/3Y2NjI/b29tI8qn9qV7Lb7/djf38/tre3j8jlwcFBdLvdyjwPDw/HxsZGbG9vR6fTSddTnvQ32xo0h8PDw2m9sS3xy++VrtDviDjyN/8fHR2NsbGxaLfblTXO8UjHSNb12c7OTgwNDUWr1Ur3S9dsbGxU5P7g4CDxsdfrpXV1cHCQZL3dbleerznQ/ZrznI4gH9WuxvmWt7zlkyjht6Y3velNqY/tdjtarVbMz88nHSoST/b29mJrayu2trbi0UcfjeXl5Xj00UcT36U7qD9pI/S55Ozg4CDN89TUVHqu1l7db/XZn5XT36K66yiXbufYZ2+P/eFcU4Yj4ojd9P5LHrjOud7Upo+/rj85XukZu7u7SZfJRq+ursb+/n5ERIyNjcXY2FgsLi7G2NhYjI+Pp/FLb42MjMTCwkKcO3cu5ufnk92dmppK/zcajYoOqKN+vx8veMELar//ZNPly5eP2PI6kvxfv349lpaW4n//7/8dly5dive///1x9erVWFpaim63m52rW5HkS/yljdWzpVs17+L76OhowjLj4+PJpmiuu91uREQMDw9XxiobQtskWZMMaw75mY+Ln3Gt53jKtebf59a284fPUX8Hydft0tzcXCwuLsaXfdmXxQMPPBCf93mfF1NTUzExMVG5jrYu1z8fj/7n+tQ9MzMzn7D+PxX6qZ/6qfT3+Ph4jI2Nxb333hsTExMxOTmZvvO+RxzK0ObmZvq5fPlyLC0txYc+9KGElYTxZV9d7tSW5EL4SzY14hAfUA4l+7LLzWYzWq1WBd8SZwmzCHPps5xd4LhzMp2zC3WY33Wx+u8YwfmscRNrSI/Tpuo68VW8Zd82Nzej2+3GxsZGmoORkZHED/VDc8L50VjHx8crbfb7/cRHycLe3l5MTEzEyMhIjI+PH8FxZ8+ejdnZ2bjrrrtiZmYmxsbG4jM+4zNuKafHdmxzgJXf1SknAjUyu9vtxvb2dgLn+qzT6cTS0lJsb2+na/b29qLb7SbHS0LI5/H5YrQWiAD12NhYtFqtNEEScjFfk6bvZDjofAjcRERFsAnsciCVJMPP/zkWCoIDJ45X3zcajQpI9GuHh4fTAuGijLipFPb29o4sLDotWiwaPxWWjNnOzk6aXzm0VEIRETs7O/Hwww9Ht9tN/JydnY3R0dGBhuo0EBWZFrb4KhCzv79fUQSSaynGdrt9RLHpNxU1n0Oe638phmazmRTJ8PDwEUUuRUqDyuBGTtnRYdN9zgcqagcwLkcEuiMjI5UggJ7FoJI+1zrjZ+70eNDDgdvw8PCRgIPmRDwbGxtLTr3PjRs68ZIOp+Zgc3Mztre3Y3t7O3q9XgpgOIARYJTM+Lry9es8PWlyB5E60ef14OAgtra2Ym1tLa5fvx6rq6uxtbWVHCMGqghkBMSlp7e2tpIeHx0djVarFePj42l+b8Uzl1nqQDe8/tvHxs9zc8Z1Sqec/XBeOiDIOQPikdrJBfmol/1Z/Dw3Vl6vOZWNkl6PuKnDZVsJbPr9fiVQLAAj3ba+vp5ApJww/pBPPh728aTpuH0Qnrl+/Xpcvnw5rl27FsvLy9HtdpMT+VTsnHSp9JzWyOjoaLqGAUNdMzU1lfCP1tbk5GSaL+EsyToxltZlt9uNra2t2NnZiYijOGdnZ6fihNAh5XWSCdkY6QOXf/WnjlfEo/rekxDih3iuPn0inNzl5eVYWVmJ//7f/3tcu3YtPuVTPiXOnz+fAjw5B4YYrE6WBq3b00TkucaW05Mi2V8F/be2tuLxxx+PlZWV2NjYSPZddlH3+NzJVjiWkHOm+9Se5IG4n04u8dbQ0FD6zB1bka5nQoxY3/VwRFU3k3JYsI5/Gvsgfei4P+KmPmdig5iMWEb3jYyMJD0gPS986/KrOWDiTHOxv79fSdio/41GI/FTa7PX68XIyEjSIa1WKyIiut1ujIyMxPr6evI3jkPHdmxz4CH3P5WhX6Ms6s7OTnQ6nRShUVT/xo0b0el0Ynt7OxlQGlJGqXPPjogK8JTgSXkrqizBJmhlVEYAnACIiyhHDrYd/Dgf+Td5lYvYe+Q05+DK2XHAojFobBIMOs1ayHSsxGtGOukQ6Nly7hhpU9RlYmIier1eyogNDQ3Fzs5OLC0txYc//OEYGRmJbrcbFy5cqPC7bm5PkkZHR5NcaZEr60QDLaO+t7cXETfnSJEoLnB9xx9GHl2BUZno8+Hh4SMLXdeyfW9Dc18HPjza6eBXbUnudB+dc/ZHyo0ZZLYjkgxTDr1fNGC8lkZI61385jj4fCl2ARGuewWqPJIrw6c5p6IWoNza2ort7e3Y3d2NVqsVrVYrrT+NUePz4BcDJaK6LN5JEfnpusl1HqPudN6azWZFzvQjPo2Pj6c2FVxkRJdOU0Q+++hA2dcTP+MY2EbOmeSzPCjJ33Vt+z3UvQQe+t/1fY5ywR6uj9za5TNp6whQaM8bjUZMT0/H7u5uCjaogiHiZgZnfHw8ZfMVhZfc69qxsbGIOAxGDQ0NpeBqDswNGvdpIzl4Gxsbcfny5Xj88cfjypUrKUurCrTbaS/iEKgqaKAMrPgnvceslXTayMhIzM/Px/j4eLRarZQh0T3j4+MVXaZ5ZR+kk4THcvKvNS67yOCdsAR1wdbWViUwojXuzxW49goW2hx3bNU/Bo2oe+VYe+XPU6GrV6/G6OhoPPTQQ3FwcBDtdjsmJiYqgTfKtAK2t+OsfiIc8U8Euc6NiCP8y+lBzt/+/n6srq7GtWvX0pqIOAw+0x5IXjxrz8AFecMgCG25nFj5AZR/Bt7pG+hzBkBzuoi4xZMTvh75mWN0jsXnm8F/r/JwOXIHWzQyMpJ4yeCl1r3WqnCd9AWxooJlHpgUz2QLhNPYd82xflMvcF2qirTX68XExETs7u5Gp9PJ4t06OrZj65SLLDigyd0jD13Oq7KxGxsbsb6+nkqO5TywPDbiKMhzIaOiEtCVINPBEFgioM05tjLujJDTofS+5EAoFaoLG6PVtwIunHx3gDQWLi4KvgTZs6gE8TK4EniNh22SB75IJfw7OzvRaDSi1WpVDI+MiRTZ4uJiDA0NxcLCQgKrbJ/9PGmlzuivFrjkUobafwg4JG91gaCIqhPoBtczNASjvD8H8Pksd/DYHuW6ztjXPcPXhDuv/PG1QKXtz2S7uXWnNrRevYTIHXTKLOdHbTDAIgDCiK70ij8/l4mVXJAXulftsm/MlAs8cu2eVhrk3HqQhHIgncLgGbN+zDo1m80EyDVf7vTlftMBpuy7/fJ7uVbd2eS1Tjmn1kELP2OgpW698e/cc3P9Z/WFO6m5sbN9lzvpo36/nxxVyagCmrJjsqcqU1R5Yg4X1OmFO520der69etx48aNuHHjRqyursb6+npsb28fyUDWyZJfw2w4K1PEPwZ8BMp17cLCQgKq2iqhLIkCexGHVVjKyrIvzHxSt+tvBvbkxOzs7CQ9Klsp3Mc1LvlUGS/XDMscBcx1Dx1d2hriDWEe2QPiIMlsrmLgVqQ+qiLl8ccfj4WFhbh48WLFQXfimjzNer2OpF/0962cbgbQJBcbGxuxvLyctjzpOjq2XsKt59Xpb7VBWWB2dnR0NK0fVnXpt/wEVRC6A027wLlzu+HrItd3v9+Dm9TddZguh73caY44xI7U48SVwjXCLcQ92vY2PDycKijosOZwDTPregZ5RzxAWaKMyIfQmt/Z2Ulychx6yo5tzkj75yINSJna9fX12NjYSP9fu3YtKX8pTpX1af9fDpCwbYIjTYiimsqY6H9mX+T4MqPEEsIcUPFMUsShgiQAcsNUB8JcKHJBAV4vYEdhYfteMt3r9RLwbjQaRwwXHQKRIu4SMo/gUqDZ716vl7LuMrajo6PJwGg/T7fbjY985CNx/fr1aDQasbi4GOfPnz8SdT4ttLGxUQFzNKhyct04ag+aFKVKXl0WXNlImdCA55RbrsyR0WpX8gS3aptlY1KA7mhImalPIj5b45LMUEHKqefnMliMXlN+mVXyNc/qBMmL+CzwpzWiiL/0CoGgl4AKBI6Ojqb5JFAU77hHx4EVQZ62Uqyursbs7GyMj4/HzMxM6p/Az87OTmVPNrObblxPmtQfN/j87Z81Go1UBrm3t1cJaqq6QTxRhFhGtN+/uZ93dHQ0JiYm0rNZapZ7fp1TWqdXHJBwvn2NudNLki2irs45yXweK2DcAXQb5DIxCJTTlnDNUJdTH7hNp9PJUj7qOjoaClh3u920H2p6ejrdJ/2jcliOQ/1jIMjn8jTQIIdUdnBlZSUuX74cDz30UDzxxBPJudW5IZL9XBs5kk6amZmp6FkF44VddLaAAvitViumpqZifHw86R/u/yTvJQcCt9TPzJ5NTk4ekUl+f3BwkDLy+t7thsqxO51OcnbVpvS5B6QUQNFn3DfIZ8i28BwIfkZbrf9ZdaUtVbcjczqH4r3vfW8MDQ3F3NxcCsLldOKt2j/NDq/zPCIqOsKdzIio2Plerxerq6tx9erV+NjHPpa2pnA+dC8TXL5dxx0i3aPns+RYDquy6JQxVXCymjPnqFIfck1ofJTfnM+g9kQM5vv3TGK47fHrfG3l5EYYSjqcASfxSvhUgXnh+larlTC7+kObwrZl532ftHCpggiNRiNV6HAut7a2UkZduHRtbS0F4FRKfRy67T22T4WkKFlerBr7tbW16HQ6FYXPkhcvQ8i1rd8ynIz6s8SApU9etsjvmKXJgRh3NPx/kRvnnGDnwFLuPip5XkPwRWCmaxz4aTEQ6OiaXDmzgIz3if0SPxltk/OsdkX8Xnttr127Fo1GIyYnJ2NycjLV3w8CEU83DTKinll18EHitXTePNPOBS+jwO9yTmXEYebQ513XeekwS8E4tuMokJyzpee4w5rjX+4ZuTWUWzt0erkWyAvNDzPqfEbdms39UN7pbEtRy+Gioido0kExY2NjSU8REGhMrAAQnRbHNrcGyaMcScdqW4JKtDn34h1BhXgkY6mghcv7rfqmthwU8PnuMObGd1y+5MCMg1vPwEvPui0TMQrPzDfXtdtIB06+NpwXdbaNfY441F+0qXQc9EPwox+1o2yu2sjZLf0+TTagjpyPDoK5pSq3/aOOxBPxiw4j+To6OppKwOXQ6vfs7GxycPUZwb/6nZNHVpwo8EmZ09hlozTvOhyRetLBtwKPAsHScQLAfniV5EqfKTBAm8JqKs/8KfAibCl7IIxJJ+p2srfsz40bN+LatWtx5cqVuOeee9K81LV1XExdhy9Pkm7lmIto58XvtbW1lKnlnFHWGEyve4brS+pFVm4x4E1bk8vmMrmTsxsix//+mV+b+37Qtf78W7XpOKnODsnHYdCHGJ54lgkxbrXyxIX7F6pW9Pl0mxtxdCsDsV1ExPb2dkqM6RDW49BtZWxzjK6beJI6xdPQlpeXo9PpxJNPPpk+0wFRiujJEIgBVLA5ECCFKa+fGdtcWYJvHs9Fa/SMXKaKTmUui1vHHwIFOpncC+JtNRpHa9FlgJThoMFRnwiG9CyNTcpfkVBF0dlP7Sf0zfsO9rkZXP3sdDppvlSDLyPTbDZTNv7hhx+Ora2tGBsbiwsXLlQcsNNKmg/njeTJDT8j4hq/jLl4xv0OBASeGdPfmm+WzuseKQFXulLcygTT6fNo5CDjpbZ4rTvbEUf3+al/+owAPQe0dZ8bd29TxOcqUMYgmY/BgzlufOg0eBWHeCA++vObzWZsbW3F5uZmdDqd6Ha7lX0ikoGxsbG09jV3zJacFseW8kVHUPLuelKZ9Ha7HQsLCzE6OprOU6BRVUZKgJjAW4eu6XsBnoiqnIk4N66TczaKUWVmaykPbCMnm3VghXLNH+lstanKAA+aMVimvmrd0n7knFvqIPLDgY+DDckbx8LMnj7nNgwB1G63mzIkst0ct2yzTo5V5jYikrNxK+f2NJPmT07mwsJCXLlypVK9cdygYcRhUGhmZiaVEUtXa04UNJqZmUlZcpUaj46OxuLiYnpbAauytO4ODqqnvNPWaH41NoJaJgg0Vwre9fv95Ijqe9oI7fUbHx9P1So8pIdbEdQXZmFz/Ov3+5UskTtUSqaoWkxrRZ/p1G8F5I8bfBDt7u7G5cuX48Mf/nDMzMzEvffem5IqopzzMch54jW5YN5JUU7/aY5JtPMRkbL0jz/+eDpITXLFvZnEJDm9TTvjOpnBHgZ8PLnF6gZPcIkkf9SVqi6IqG4jOY5TO+gzxzd1Ms776Ifk7JT+ZmCUjjuTS8T6mpODg4PkO7Xb7cR3VTVwvy11vCd8iC3rbKfWr/Cw2uh0OjE0NJQSXsfVnbft2Lqg6X9/IBmrqJz2nqytraXSHEVuuJfWs2DOACpbN76so1dpgUdu9HeuvCEnXD5mKX+v/edv/9sVlDusarcO4JMfvNcj+DlARoDD0gC2K9Ckz+Uoy/mPiEqETf1g9kGGVv0W6JFQc0/Q/v5+cgJ3dnbixo0b8aEPfSgajZtlyXNzc6cCzItUMsMSSikBGnr12b8jTwjgchFizl0uiOOOofgaUS0/VlvudEiJsKyaSpyA4jjrIyfnlE2Nw8fg97GtQTpG/3vpk+4TICOQ8dJiPoPzRlCksbqDwCyV2pDcs6xO2VkZg4ODm/vPuKZVftto3MzMbGxspPmRjOUy/ydB4ndOR+UcPukDZS10UKAiszqQLeJo9l86WmWVuYPH2Ac6ZHTAcjLmUWb1Vc6b5phOO5/j42S7/Jzt556Rs6c5XrBNjY1AQe1zjXlQxAFbzsa5fcmBaT1PwRjJvAIysvUKJjGzyPsFJuvmivrhNAJ6/c8+SidorWtvq4Jra2tryfkidsjR0NBQKh3Wq600RwcHh68Wm52djampqZidnU37aLnVYXp6Om0DUJ9dB8rRjKjqaa1fZUpZlcXKJP1WNlg4wssyqTMlO3qG2pmYmEiywCCsrvOqm4hDmyd9q+fR1mgOuP1G+EQHWjYajYqTP8gu+2f9fj+2t7fjsccei4iI++67L3Z2dmJqaiphgzqH0OXLnzPI2Xm6yTPvub5S/3A+lNFeWVlJukFzwqBzroqMznMOP3imdmJiolK94IdD+eFrjp85FvI/N946eyR+UffmeKbPnXLz7c/JPZu2hW3zOvVpbGwszRUD1PTFdB23/2iONE8McEm3cw3pb7en1Ee6hutOic61tbXk9B6HbqsUmb/dgct9LiYoY6vIWKfTibW1tcrpoX4AjwN0EgE6jTojLzxgQULNTeM8PIoTLgWrheOZIU2EOwD6XWeMB4EqPaMOvDpo5LjpPA0iPos8c0WksUnRiHcC2x4RzZUiMAupPXVqV7yVgdO1eu3TmTNnUlSfmY2TJilgHf7h0UgHrM4jLm5Grz2I40SDSKVOufRsrYiyLZnR89yppTLJOXZuYCKqusD1A5U7/88FaHhNTlH7Nd4PjpXP4XvxNO5cqalnsnxeXQew3Eaf+4l9AowKGGntKCqpfggE9vuH+4X1ufSmG9yTIud3RD6LyWuklwUkBZLFA3dWeR9PsOQZATlyuaiTDf1P4xpR3Sem71lCK8pljPRsgiO3Lbn2ckBX8uf9o0Poc0B97mCQgE/tuI5w25Lrv18re6pgUUT1BGwBI4JNX1cE/Lk1T5B2GmgQyOXnwgXa23pwcBDLy8vR6/Xixo0btaciu3yqWkHvg1TGV3psdHQ0JicnY2pqKqanp9M+2kajkfYMCuALwKqSR3zXPDAT646Dr28GCF2+Kadst9FoVORB2IAgWM68rwEH3eS3vvdECPssuVTGW7K5u7ubssY68Gp8fDx9T5x5HNrf34+lpaXY29uLj370ozExMRF/42/8jYq+IQ3CNo5HTxvV6Vn9djuv/ZKrq6sJ6wmPeHm+O7VsO4c9KYPcb04HVvIovSWbQqc2N8ZcYMPlwXGSKHev/113za2c2jq9SPtT56vpt3SMb33SetSP8I14J5yTq+LUDw+b0jw6bnU7xUytrtF67Xa7KQFwHLptxzYHaiPyZWpykPS+2ieffDIuXboUV65cibW1tXTQhAA2I2kRh+8JJQP4LCpUCXC73U5Knacc08ElsCRjqay9HMvHKAEQ5YAUecXnqS19z/IggiTnt+71fQA5BVA3Z1x8jLqy7EPXaE7EMwEWRjU5Zi/L5IEMCiQw0q/XAkxNTUWv14u1tbV45JFHYm1tLcbHx5PR9nGdFLEiIOIwK8sADisHxGeWLEcclQ9mieRI0Zg5yGUkjHLkBp4RdX2nOdX7CFn2ymADASejbSIqPcq3+klgxEAQS3bp3Isv/mwqRVFOKaqfPChKQYjcGHPAjI6JK+GcE8GDgVyBR0TKSLXb7coeLq0LVaq4QdZ3Mvpyhk+auK+N86HPIo6WkHY6nVhdXU2vdVBWhXIVcfjqF/FWr0kSMJeOIv9dJ+ScMa+C4fd0qDT/0nWD7ouIisz686m72V/PnIpvlB8GVMkn9cl1AfvgTi31BF8flaMcUHUd1GgcvjaO8j49PZ3WlwCKDgJZX1+PhYWFmJ6erhxYSNBbB3pOg84fRO7sNRo3g1l33313OkBI+2OXl5cj4ua7T4l1ctRsNlMGdn5+Pjlhylro+5mZmbj77ruTnZybm0uvXGLFGoNLmjsRZcrth4ivutOWI5cV9dsdGup7Hooofnmwi+8LVVtMQpD3tG3UP5J9D0gykKvrVV3QbDZjfX09yfjm5mZsbW2lTK8793UkPPtnf/ZnsbW1FQ888EAsLi7GwsLCkftzDtJpJ8fA7mDmElKqzLx8+XJ6n7n0BXERMYq+k3xqrfDQIepUXacKhenp6QoWUyCIlSLOb3fOIuIIbicucRvB67UWPJHkPKMedd3ruJ5YJ0e0ax6E029v0wOM6pvsoIJp4p8wvvjniRn6UeI3S5fpa3k/vb+abznJ2s51HLqtUmQybhC5Yd3e3o6tra1YX1/PHhTFrBEdLHcO2Y+Iw6gvT0LlHg1mZwWcBkVocuRRGOcFx+tgKfeM3Pfejj/HiSWYdC78mX6vCzuVEYVNz9D4xQMKNY1g3aLUczSvUmIyRtrbSydN7wDUC5knJiZua74+WURFmFPeEfUZezpwHmXMOVa6JydD7kTRuOt79sV55wdl5MCM/50zwFLUnt3NBVjq2uHn7hz7dznZ9ecoI6HxcU9WzjBxPP488tjJ587b0ZpkGQ4NtGRB7WtPV85g5hyZk6Qc3/T5IIdE2VdmTRh5p0Fk5Y2c3UE8z/2fsx2UGf+eQaA6G+E6Tp/RqHPOcv3z9eny63Pu9mKQzqHsEoA6oGH/BvXHn8P7VL66v79fifpzW4TkvNFoVEppczxhP3ytnxbZr6OczmXJo37qALXIcY3kiRlwrSFlaKenp5NjOzExkd4fz/XjiQCtPe8HbfKt1pXrX7c3zCz7nOt/JS40ZvVTusCDoOyLrwtfwwLcvmZpr5rNZmxvb8fIyEi02+1KVldtqh+5So3c/AnALy8vx7Vr1+L69esxNjYWMzMzA9fXceikMZCozgb499JFOzs7sbm5GRsbG2krinwDzhOxott+bpXS3BGHcOuK1prkyA+OpW7P9b9OFzruuJWNoM6ss1luo/iZeFJnA7gucjgkRzm8TrundeQOOvvHbTo5fMY2iUslD/ze79Nc+0GSwnYeUK+jp+TY6mHOLH6ujmhvyfLycjzxxBMpYqNsHbMp3nEqLbUZUc1wqY5ewtxut1N2VllcHYrEBZEbj4Nfz+BwEhXVcCenTonrbypvEcsS1S/yQL85fvFDwuHRST3L54UKhAtFkS2PjlHJaKO95oqlCHpur9dLQqlIjdpS3/WjiKgyNHoZ8/r6ely6dCn29vbS6wpOmmTweKKi+EhAmgNtVOIR1fc3RlQVRc6Isy0v3XEgrM/9GZIfnUau0uq64IRIWWm1r7ao2Lhmc0CUz/Csr8ZTZyh0nUAS9YEDG/Fla2urclgU7/E+cr7YF2YTdY8bKs9o6YcBA0WexTONhyeB9vuHp48zEyj9dhrBPTOsIsoJwevY2FgsLCzE5ORktFqt6PV6lfd5+omUuofVERFH35En/ZczqJpXAlLPmNaVpcuxcIdQ1+Tu1WduH93J4z4jgjP1j0E+ZkH1bAdXuo9819++PhhI4Jz1+/VZMY5b8+3PUNCy1+uleeOJyY1GozK/zLI4+Vo9bZTjP7+TY6N5liyLL8QUJJeDRuOmQ7u1tRUbGxtJn83MzMSZM2fi4sWLsbCwEIuLi8mx1f5avTs4IhLA5+GSWnvU3yoblp0eJP/unOe2DHGPtWTfnd7h4eE0Lu6nlXOiPmjd+6movu41Vsmk1o50sGRcsjg2NpacLb2yp9PppBLvsbGx2NzcTDj2uKC61+vFpUuXot/vx4c+9KEYGhpK+59zWwlcDnLt5dbKSZDbzhzG1N9Kagn/Ly0txerqaqVqSdl64srctih9Jqwku6A1pr3oCurwbB3pGlYviHI2nXo5h60d57nzqc9yDqz+dueVVOeXEM/p/5xjy+tF7pzqGVwjXNfkg6o0Zb/Ef773Vr9z/pXWI6vlhHnctkkmaD+0fiPqt2s6Hdux1YMHeegkCqRK0ZaWlqLT6RwB1TKMHhXzEhN3BBShVPkNTztWJleMdqchV2rM7zhh/r36wRPScqUZvuD5HUEZ23VDws88e0MglFtEubnyOaQDTGHjeBS11GmCKmtyEMdnDw0NpbJjRZwJcmTkIyI2NzdTu9qkfu3atYiImJqaivn5+Wi1WrfcR/zJpPX19eSQUHa1UFmaQcfTAaUfqsEAg64XuWLIrTu1z2fkHD5FTXX6o1dIRFT356o9HoLFfrGv1A1eisZ71Hc6oXXrhf2n86JnaE0z2is+sBqEa991lL7jvjI5lhy/iGWzubZcz6h/5DXL1dUGn0GHWArdjfFJEQ28z7XrLq3zycnJpFN0+r3WvcAJgYgChsw2Ue7dKco5F/o8py8dIDLLI+eNayi3Hvlc19W8LiezlJEcSKezwTFwfK7Xmc3wvnJ8kiE6qF5x4fxy51nXKagbEem1bpRVzaHu6/V6lRNI9XfdgZHOl+M6FZ9Mop7LrX99Ljs1MzOTSvkU8JKTWue8S7dsbW1FxOG4h4eH0yFR8/PzqRR5bm4uBYVZeuyyprXW7/cTLuJBd+r7xMRE0lPqo9sojtlleZDs6n6tbWWgNc5Go5FwAXksuXDQ7DyjM67nq4zaxyg51d5jZVS3t7ej0+kkO6nA++06lqqMYgDPyXGZ2/VBuuakKGej3TeIOBy/Aul664lkn5hQMsP32PJ5xAkK3tBJ5QFpXnbs6yE3jtx6JiaiTcnpdp9HUW691H1OzM32KbvePttyW+fEfvNarotc4oBrSbhK8qx7IiLNJQNYsjlyUImLI6IS5HU+ur/Gdo9Dt30qcu43O6DPNZD9/f1YX1+P1dXVWFtbq+ypJRjmBmYXGj1DEyOBlSAzwielTWBEgdLz6CS5cqkDbrwm4jCS1mjkD5ny32yfi5WCxHsG9c0XqoMudzY4fgqLFg7bkRHWcyVkEl4aM1cKFEwZU0b0aYQkAzpynK8HWFtbi9HR0VhdXU0vidbJjidBOlyCmWo/VU9j8FP+RFzomgtmKyMO58ijghF5R7HOsaWyUMRZr5xQ1pmZ1Ijq8fZ0rlxZ5pQo78n114mG0BWq30/Fz/XEigUaR+1f4/5V8oRta044XrZPnTQocu4BBd3jY9V6k6LO8YlGVWU5pyFiT+fMgYzPn/jdarUqlS3kEzO0Ao/u5LI9/c4BjIh6x84/c2J02oNndXrWdanaEWk8vo9SfXJwRL1JPvv4XFZcf+f67v3XGN3G5caTk2nasIioBIbES2ZLIiLJMctyGfip0zXs92kA9oP6Qd2tAPvk5GRy/BVgrHN0Ig5laGdnJ/GfQfvJycmYnp6OqampmJqaSplFBfh5yBodTh5kx/lycCvb73owp4Nzsqx79L3a5jYn6gPtn1emmOcWqA1mesgnXePVTrSpykTrp9G4ec6H2lJFzOTkZPT7/ZiZmUnXjo6Opv23Hny9Fcn+KBDuayvnqDiWcofpuKD+k0l1DpZ/J8wn3CGnlueS0LElRnTHjpiT1SCq6NHr4PSd5t0d24j66q2cPs6N0R1D/66ON7wmN491n3m/czau7v5bPbsO1/mWROl0+hK83jGZ22cmwur0iWMKfh9RPbDuOPSUUmCueMgopqeVpf3IRz4SS0tL6eAERgSlAFzZu0NLp0BlNzoG399RxdJOKl+2R0XhxpvMzkUJNKECLmI6qW5RaKIGKUr2h89SP7y8JwdGSHVlRVIWVC4u0A7CtVfK+09eurAycKHyZPVbY9EBYxGRom1ra2vx+OOPpzLNmZmZI7x6uogRdCrOfr9fcRQVoY+ISmBFSll81Sm5fK0Sy7QjDg2EiDJBcEknlaBVe01XV1dTxpbVEiLNEU/r5HqhsvF7XNkN+t4Vv5SeR/vdAXKFqf5wLwZ1ifgh8FznGNY51OpLv9+v6CWto4jDYA3L33IASFkB8Y8KnvMp3SkeySHMvV/vpIgOd87J4jxy3pm1o+wpO6N35dGxZVtcQxE3ec/D2VzGcj+3GpfWoNaPlzDT+WV7ns3yYIj0JWWDBtqdOT7TA0XkAXUsiQA+4ujhJ/4314U7upRPZmLpwDWbNw9H29vbq5xzocO/dJJlt9tNWdrZ2dlKGT557faEcnbSwP5WTkiun91uN1ZWVmJ9fT263e4RHeHjpH3hYZfDw8OxuLgYd999d9x///1x4cKFdGCUg/6Iw3J+OlVyLAhgNafSmbL1HthU33Ll8zkZVN9Z5cKMnGyX9KNsD6u8fN2TcjpXz9Z6IyaRDtXzZG+VoW40bgYQZDdarVZyttfW1io8PE71gA5ovHLlStx9992pxJkHhYqcfznn46Rln8S1Kj2gOZJ+39nZSZiu0+nE+vr6EfvBOVMwmriReoH2UEGc2dnZ5NDSfnD9+HaZiKMOoQfwPMGWk+9B80H7745oHT+JB8gXxwjO/5x949okv/md+z8irhsmH4eGhlJVyNDQUCpNVlt8lveXPM7hTp8Dzb3jpRzmqKPbPhXZmeedVKf29/djc3MzVldXY319PTY3N4/sPSPQcW/dnTsJ6ejoaCpB5vupGFmQkOfAjSvinBLxCdHfdUz1fucoJ1C3QxTC3N+55+c+4zjqFoQEVUZWv5WB1WseWD5+nMyBrpMBpZGUU8LIrd57vLm5WdlrdxKkF4hHHC5+Rh/1o/67zBEcMDrpskMjQXKZ5Hrx+dQztM9Jv73czw1UTr5zc+rP9vu8XecH28617+s250C5vPmzCHwGOYW3cnxcqdZlsr2/dLbYB+4zZD8pB7pO3zHzddroVvoup4M5VukVl3/q6UHGm//nAAj1d+56fyb7lxuTO4J1clqnp3mvfjM7xme7TfR7XTbrgFidvs/xx22F84jBIP1IN8tRIT8ltypN1LUuFy4juf6eNhoEsjSvfip77jqOndkobpcaGRmJmZmZmJ2djbm5uUq2lnqCQQjPWCrwr35znel6Zt317EHBhZzuzzmklKEcL6jf1B/aTz7rVrpC99RhSDr0/X4/BQH29vai1Wol53V8fDy63W7FSeIJvnXUaDSSc3fjxo1YWlqKlZWVtC0jlwShbvS2ThvldF1ENTgjPLezs5Pwh+N83eP2lO1Qjwjr+7k6rJqjH1CHjW+lm31cOQxyHAdr0LrxZ/B/PS/Xz1s9N+cg3+r5OZkTxnQHtdFopKAy55VB51w/1GYu2cG/HWeprbpgeh095df9kHHMyMmx3d7ejuvXr8cTTzwRN27ciM3NzfQdnSJlePWdniMnVkpI72Rrt9spU+vKysFibm+Ep7NzQHSQwXXm5pzhnJIir+qEj9fmBDwH3vUcL+HIPTsiX0aay6gRcDNby9MtNc8s+/Q+iecUTF3P8ekzvSZC7+NrNpsp03+StLKyUjGmEYel3IxCSy61uV7ZpYhIEeucssiBQTd4mmNliulYMyKmaKn2s6uM2g1LTqHpmTnntO6zOmXjuoJKzANGdHQiqkCnLouUU5IOiMTPujET5LESQiQww2fxfx8n54rtKhjEuXf9xPZ04J2uOw0Ax3VrLoMZcfT9jP1+v1KeKtlVe61W68g8+HvGI+KIDHu/+DzPdCpy77pfz9K69cwVgyg5J5PPGwT+fc3oGn+HoMbp9tD5SlnjwSrUA7mMr/NS7eaAE/kvPvHVS7pGzhWdJ65T9VFnKSwuLlZsNyue6gJGGutJ0nH6Qf5ub2/H9vZ25aTh3F5irg+VVU5OTqZAr7bh3HvvvXHvvffGPffcE2fOnEn7DSWnkivZG7aroEKz2Uw2m9UJWm/cviF9SIeXWMltj0gYISJSX2T3eGCU+KQMb8TNEmw5MJJPtefBTOoiygnv1ZxIz3Crg16tdnBwEO12OyJuBrCFN1dWVmJ3d7fyikLp/7p3EYu0n/oDH/hAtNvtOHPmTExNTaUtdDk7RNnR+E4b5fA/dY7kQwcOdTqd9Lofvl7Q/QDHhyIFHTRnrVYrHZZG/4A/komIKmZxG0B7pmscX9fZ3UE6IIexaPPJyzr9ltP1OT8gty6cXK+zzTrKJfS0BhRQ6/f7sbGxkc5WcBxEuyxMLNvKSg73uzzQoYTR7WzJuu1S5BzwZWf1/silpaW4evVqXL58ObrdbmXPJj1yj3zRIJI5cmwnJycrTq1HaNy59XYdfOQAB+/P7e9QO7m0u4+Fk0Pw7vtC+Hze73yPqH+HIp/D+8hjOpsSVPWBCpXC5otMJU4sKaeT5YuPgu7OlQzF0NBQenWE2pNy3NraOnEl7wdtROSdOpZvEGiKB9w/6AqAUUm1r9++0Z6yp7k8ODiIbrebHFuVSKsNRtb0XO5HygHyQf+znbr5pjObc0h4HWWcf7sMMprItnVdLlpPueSz3ZjkqkdYNql2fJ3lDKT6ODR09BATjk9jo+Elr05TxrZORw2aV41PQRcFeLQn0Off565O31Jnubw4OWiMyJ+wyHFojmmLCCI4x1zjIs9M6XuWh9IGumGnY+tt58CK+kz5JM+YMXP+qj3yVs/h2uP4JZfSTXJQCUBUwaLx6DNumdD3dVmwQfP6dNNxnWs5Uu12OyYmJirjl04hgBee0TtoWdkxNTUVMzMzMTMzE5OTk5UDdJitqsNSep7+Ju99vfE67nnUvEomWX3HNUl9S6xIParxOQakzEqmOPcOnh0febaZepa4ULzj+3nlvLZarYQ9tD+63W6nwLuwp2zyoMxtv99PpwJfu3YtOp1OtNvtFLR0e8x5y312WuS/bk5o95St1btH9X557asV371yjXLDwK+SHTMzM5VScsfS/FEf6+wRx8Sx1V0ncp3N397mrT7P6RN9xjE41el/tptzeOuwh/t1bufYvoJfesNBv9+vvMbJ73fspO/VnnQSz7dQP91OHJdu+1RkMsEVER3blZWVuHHjRly/fj2lqwnYHDzyOa7AFEEbHx9PBwhRoOsArfefz+cBCnXjlXL3Nh186zr/jCQeedkdDZwLqwsWgZH4wu/rHAd3GMQ/9atukZAPdA743kKdsOtg3Pvtzr8rMmZLxCspx62trRNX6gTQdGo8I6hr6Xzl5pGLnqCCUUc+jwBC7ROEymhsbm6m0xhz8k25YV/5O+ewH5dy8uef87ucjA5au/69/0094Pfn+uQKVp/nZJef+/+55/L5lJFcJk3AVu0xQJLjyWminPHMAdCDg4PKqeIEIBFHX6Pg3/N5kgeuxToZqpszrgO3SfxMz5KeyxEBdUT1VVZ6FvlA2+mAijLCMQ0C0W6fc+s4Z8f8/xwPB9lTBSQjorKPUfPGszT6/cMT9n3ecrxnH04D3Y5OVIZbuIW6RrLidkTOljJR4q+q1PQ6Ex6go7bEV+ebg3TNAeXZq1wcKzHwRjstcgdUv93e6Ts6lJJzHrYoXlGOcv3zOcklGnL6WXMjfStnVweR7uzspC1v29vb0Wq10gGXwm8MGNfJRa93c+/oxsZGrKysxMbGRjqDo84m5f73cZ4kuV3M6Wdl2ITdVLXAs3ToN1AX0vlxx3ZsbCwmJycrOqbuhzKX03m5PtetARHXRO7zQbqq7rnO09x9fl3uO//sOLo016fcehKJL0pEKRgkOaeep02j76DnSn8o25sLGLuuuh16yu+x1cAlxBLO7e3tWF1djUcffTSWl5dTaQmj9YzauCA3GtW9IopQzs7OVt5dRYXuQs4JomKm8JFRPvFU+vpMk0mlSUdA9+WUliaI0dAcWMo5BX6tK4Xj3Ku5osAQXNOxrltIGoOey9MMdepj7hAwtSljJsXO99pSYehavbt2eHg41tbWKntcT4JUGsb3eUVUAyA8/EBGUOCGpfXM2OZkmPubCD5yi1/zoYio5knlTlI6yqZ4KRzlgMEeXePjdLCjvtDQ6/ocYNV3bJPXixiwcllnG3Q6fe2zbQZy+v3qoVB1/RPY1HzrOm2fiIhUhhkRKQDH6xl8I8/1XW4+yf+Iw/dJnwaSfvYAjBN1reyCqgi0xgmUJft1bTtAoZNAGaYeqQMolBOCaoJ2rRdGm31MTgxa0pEhUa41bvKMB4jlApdereNAkzpBa8LH4OCDa8gBHqtIWLLtATtl3nU9939qvujsuy5zO1rH4zuBNP97e3uxtrYW169fj83NzTg4OEjlqAL94h3fc6s2tA5mZ2djYWEhZmdn07ugmbGlw0inUNsoCDIjqkEWOXm6b2RkJJ3qT9vE+SF5YFaktaOSXfWFuEryr343Go1KX6Qf/H3kBLzEW3y9lL6nTGrM3HagezUHs7OzaVydTid2d3fT2zeEYzVngwJNIgWbb9y4ETdu3IiJiYmYmZlJWMAdIv4/yFE6KcoFJGk/hZFWV1fT+Tp6V7CCmnyPLfWt2vHXdA4PD6dqBeHOiKNvmSDpXuJ1tx36jN9znVA/D3IAqQ/r5o/9Ie9ymMr9EF+/OZyeexZ1Ovvq/Xce5MYkfcB+9Xq9NB+y8czCE8fmbJoHNGgTtDb5StXbodvaY5t7gHdse3s7Njc3Y319PSk1gg+Cvjpl6c6BlD6FnQrV/89NtIObHOVAi2drcqBZz8t9z/HU8U/kC+5WJCXDPvgCqHsWx1O3EPXbnQcZlohDY8r3WLmjoHYc9NEwKUPLF8TL+NCROEnSgiYwU7+oqOlIRRwaUvKAcuuOrSswn1eCV496ql0afvbVnTn2Nfe9U84Q3A7lxpZbM7l2Bz2r7juX7VvpgboxkffOJ1YdNJuH+2n1mY/L72dZn5dmRVQP+TlpquNzTue6DAlACmhGHGY2Hay4PvcMJ+VXP3RuvZ+5ygVdS73FjILPNdtmpt+fUbdGvP9uI9ge78nJjPfHeZSbH+mB4wDynJ7i3DAgqbmk80twqh8P7vpYfNzOm9NCdViI34nfCvyySkFOkfapKVtIOxFxuG1F5czEQXVZqZx+q7PFxA51ul6/5WDm1kTdPY4d/DrXjbl22NagMUYczeZGVF8FJF5oHrjmhDX7/X6Mj4/H3t5eOn1Xc8Oqm0E8EAkvbG1txcrKSqysrMTMzEzs7u4eKbvM4cW6/0+ScpiSfBBeU6C9LlObS7SofcqQAi8K5Lg+yfkCt4OD64i6tk5PD8JHOdzh/fK+DfIT+P1x1l8d3Qr/sA3hF8cfWjvEmto/K91Gh9zbztlE/eQCsbczPtFTeo+t/haTCLCXlpbi+vXrce3atdjb24vR0dGUSZKQe+ZRDCIAlxGcmJhI5Tc0rq503SHQBBC4E/g4o3KGlIq5DjRpgTkYHbSwRJ4FYBbCQYy3p2tzgG3Q5HuW2QEMBcyFivMmx1PHfzMTxr1hvgjFK88qUBbUtp6jkwlPkvT+Zb7OR33kYQieWev3+9mgDJ0gN6C5zKMD6IODw3ciyvGXcdY9aqPROIx8aa702iVG1dQ2FWxODtjucRRNTrnRAOX2EroDkSsbdR4dh3KAi8qewCmXmeez1Hd+r3ck5jJQfK4Az87OTqyvr1cO0WAwwnXbSZMDOwck7oBTX6jyhid1c3zS+coQqk1tZVHEn/Li+2RzOt6BKOdY9/sL5rnP3bd++Pgox8z2cr98LmDhMt1oVDNWg5wNjnGQrXDeUOZzWXHf3sM54RsI3AYfHFTfU0tdI2Dabrej3W5X9imq3x489Xk7Dhh9OmmQs8G1oYOG+P5zzbMy2nxdIXmi7N65c+dicXGxsr/Wy3RzdlxBYrUXcXi2huys1pVIDgjttJxaf6sF5yriKH7R3OsZjrt0VgdPjVZmSJUyakO81ppRn33cpEajUcnwaS1vb2+nV/pID4yNjSVgHnEzSKOM7fT0dHQ6nfT6IjlutwoQ9fs3T469du1a7O7uxsWLF6PX68W9996bDkLy+fH/T4veF+WqjPi3tkFJx/NH19DOsQ06NVpf2lerqjfpGcmInCp3bEXOv9wz3Qdg5pBOHTGqby1iW3VYgeT/1/kOLtPkG9se9MycM+s4q+4aYhAedMgAndby0NBQ0jleneHj5TzL9konqbqTc3K76+ApHx7lgFQvYl5ZWYlOp5MEWBFLOrXuWOlaGvdms5mMpMCiR/NZsuyTQ4bSkOei6u5I8Bm6lg5AHXhzMHUrsEel4IuSC4tEvueEMScI/MyBKI2T84bj9gWsNvmeRgqlR+TYf/bJx6rFoJMkZZiOk2X4ZJIOYnKFot/qO8sayQdXqLqP4NHlTtdQ0ddF6m+l0FnWpXEwu8x9Uw6wXM58Dl0R+7rztrx/dUo/p8DVDnmSe0ZOzvg9y8rIBxkyOpYijyhSb7k+yylzASsZfZVnKUAhkMd1JuN9GgBOnYOY0zX6WzpkYmIi9vf3Uxml9gSKX4Ocea4T/V+3BzkHVHLyRZ2n9qhjcu1yXbAt5wXXgMtRLqjoz6tz4m4FjjyL7DaGJNl1fjjPqX/IH36vTIrsgAfv3C5zfyj7fhodWNLt9E380VYUBS3FKzlRukb8ki7SHs/p6emYmJiIVqt1JOjAOSOGof4U/xlY8SALz8hg2a/I10ZEVIC9np/DGa4ztO4lK3qOtsFIX/C8Acdy/KzOHg7CRQo8aWy+RoQ9dXjU9PR0rKysHClHVhAsV1HGPqgceWlpKebn52N7ezudwpyjnC05LZSbE/WPB4rKmWUiSySsRAdXRP3DoL/wEf/mwWnel5xNyuGFXJaf99Vhnxy+Ifn6qOuT83TQZ46p/fmux2+lr6TTfUz+XI1Hn2vO/AA4rRvN987OTmVuPbPuiSCtp1xiTX3lXvxB9JTSYDmAubOzExsbG7G6uhqdTid1QkJOpzbnpOj6RqORDMD4+Hhl7wn3Sug6MkrtkInqp/rtQERMlPJ3YJVT3JpkB97kjfPLhbZu30hdO26w6r7z5zpYqOOFOwq5tnLAXwZIkTMFM/QsN3C6t46k9JTN0Z6zk95jSIcjosoPgnPuCfDotitIylrOqc1RToHnQI33k0aY0XG2SaXBiLwrSDp2fI7/vpVhdsWf+27QNXye7hn0nQMxKmlf5xq773OU/EsemaVz3eH8UBZF2zV0kqCyOXJy9QytLfXlNFEO2JDc2MqxFcgnMHfHiTLNZ3kAsy7wwYyR61StV+o8zV/d+uG8ujPt80K7QT548NRtRm6dDeJ7Ha/1LPGS482tBz7b1y7H4jqM19Cx9WxvLuCWA6N8hpPz8qTodp0MOa10auXE9nq9tA64/zPiENS3Wq10YJSC+16GzHs8sMG/6cQyucB1F3G4t80zUq7rlSRgVsZ5xXmTbLgDzLUtnkl2cvsLczo8V3mQG7/bZfHDgbSwTLvdTo4tD+3S9cQmOdnV8+TYat+pzrxwuR4kWyct+yTOCfUa98+yDJmBEckTnds6naiqBr7uR46tnuu6hPfreTn84tfl7Kvr8dwzcvg6tx5chp2f3qbbNH8G+zXIdtyu3HDMJDrMPCxQc6kziJht5VlK5IHzwgNF7myrT7fjA9y2Y0sFKuHUIQlLS0vJsd3c3EyHhWjvJBUAHVB9LsdVpx8zus8SKBmHXKaWCtkjznLA/HqRvzsxJ8hSZE7aa0oATH55W97PiLwQ5sCIR4N8ceUiYASB4j8degdruYwro7/qs4RRZT16FvfL0pDoGbnDHzhWHSku45ErA3w6KQfYdCDG9vZ2+q33DrLky19vQeWusbG8zJWaz7Hmy9+N6s6BSJ+Pjo6m9choPsGmIq6SFX9Nl/4WMVrncuhOIeWJ8qW+Eiy5Q+Bj8nXv3/MzXuPtMUjlh/mQJ5TfXGaP49DfLOdTVnZjYyOVsmn8Kt3SOxRlIFQBoTKs00AEkuR3zoHhXAuoCKi3Wq2U6eD6l/xRx3LduXPlOiqi+pJ4GkmR66KIasbIHTvKMX8cUPl42QbHoWtzn+XsgesL3pNzOEmUS7eHzHr7fPq64nqTfmfG1veHMojtrzDjM1lSqPH42E8THQdAqopJDg3PG9H72d2xF8hX2fbs7GxMTU1VsJDK/agfqRfIfzkCmgMdCkUdt7OzExHVdaR14FtXXC4p/9KdOV7osDi2KxnSd6ykYym+ZCJ30B/vYcm12iPO4zjU393d3VhbW0v6VllcOVGNRiO9M3V2djbm5+fTCb96pZ6AfbPZTAHLOtngOuU8UT+5zcvpudNE7nQQ43e73dja2kpvaWAAmNtuJMvcvqN2uQWR27XqsrTUdfpf/eRv9t11G6/L6U62lcNGXI+ui6k/c33zseTayfkkHC8xZW48ufZIdfaAPNB86X5hXf3Pd2Jvb2/HxsZG1hbqR30W1ud2PNq8QQkxp6dUikwGMAuhBc93FXoZgu5lJ2kAuZ+HpQYs1/SoZQ7Qinn8TSXHPtUBFQcddYKna3PMr1NGuT57n24l6Lwm5zwPej77fFxyg8y/fS9WRByZeweObjA5Lio+Hi5zUpRTCvyMn3sk2w2bA9HcT659ZsyZTdQzqYjZhjubbINRdP9cc5ObQ5Gvl5yc+uc5utUazn12K4NWd5/PJZWoX0tAVKdvqD/8WpZmyXn1UyFp6JnZUcS61WqdCsfWZfc4c+pZ2FwZKtvKOao5XU3Qmrsm1xd/ns/noCyAnvfxAMw6Z7EOeOXGQl1QZyPq2nTKybLrsVwQg7bfA0Hsh4O4XHaGAUPHBbmxnyTVAUUn6nxVZMhGyrGt04kKcirTq4yVAP0gYJurcvAAkd/j687/ruP5IJzDv+m4uQMsZ5K8yPGF8pZ7PvtLLEFbx7ERWyigTNsn3vEVQBMTE9Fut9Op93yrAwPBdTKh/nsgqo5OWtbryG2tPhNfuaWGCa06DJSzJ5oHJrOOg/3rMIDLfZ0tdzswqI1B5LbpdsnliDbP+3Cc8de1e5x7dI2vUVGuMkefCcf4/OZ4n5MHv+d2bO/HhZYU+VpfX49OpxOdTic2NjZic3MzNjc3KxuImf1gCYILT7PZTNFJZS34qhQH9Lw3x3gXBhpSjsOvr4vWu0LPASB3KnifvmdkOidsuYWRM4gEOn6t2vc+8/qcU8CMh/Ow0WhUXoGitmSQe71etNvtlBXMld3434oiiy/6XvuzPRt0EkRHVYaMBpOHUezv78fW1lYCMTJoytyKVxGHSjwHWiIOea8IHB2EiGrVhANNz36oPRkItqfx6Fl8NsGA90vf14HynKy6AqsDZ64I657virKuLCwHTLl+mPkm0HaDR/kX8X+uLZUX8/AxXassrmRc5Yejo6MxNTWVnNqJiYkkLydJ4oeXMw4i6g5WJhD860CQiOoBInymZ/LZtv72SLhX9Qzqn+/XYl84fvXBXxzvMig5YuaLY8mBLHdUHRByXOSp1rJXOvT7/SPBL19rPkaOVXpOGaw6XaXsLZ8l+WBWbnJysqLr1YccAOI85/p3ElQneyTOpXT+zs5OjI2NRcShI6RyTc2tHFfxe2RkJCYmJmJubi7m5uZicnKyEtDM2QvPyPu6cXvAbKyu1ff8m+OhLdN4HIjSgWFW1Z/hVQcR+dcw0v4QJ4joWOaCZiLxQYEGjb/ZbCZ8yWyRnLLp6elYWFiIvb29WF5ejl6vF5ubm5VD7oaHhxPW8Yo+9V/JH12Xs3uDHI/TQDknRXzd3d2Nbrcbm5ub0e12U9ZWxAPIck6M2tU8KEvrB2sOkv26zLjbfg8CRRzaAF1fZ9vqHM06LE997p/lrh3UXkR1zXkfBvWX/4uIeera4neO27TmhIn43cTERDSbzeh0Okf8Ba9a0DM8+MP98N73QXRbr/shyWnhfjFlbP09teys/8+29VsKg5kLvt+QfeKkuFdPAXcDTofVs1J1DqUrLCpaOgmcMBqFnFLwxcnv6sC52qWAUEDrHAYaGn7HEr+cwnGDyWdJ2TAqrXHxtSd8ts8dv3fHTMrwpBU893ZLvqWkWb7Lw0G0MGnscrwd5Lg7eM9lUx0cu/yLt7c6rMEVqz6nbOdkMve5nl9HdQZHPMkp2pzDXWcgOH4fS27sfK6v01w2ieT6TCW20oXK1grMEthyHxmj0soWKMh3GjK2EYPlZRBv9TflPTc/dVmp4wC/un7wfr9WjkLOAc4BEH/lhOt83ed6lHbKbVguQJDL/rtD6OPN2Y5B68zvyxFtNtvIzZPrGj8gZn19PUZGRmJvby85sqxYyPXRx3OSVKdvRLT7Iq1lAT/KWW6eVLk2MTGRTkD2Q+Vcpl3fOf6Qfdb9nljIna1AhzhnQ/Q8P1NC49Gcq4+Dzm+gk0fw61VIPn6OWdepn7LN4rufJ0GsEhEJX8rRHRoaStnZ6enptLVuZmYmer1edDqdpMu075MnAeeSCaxoy+kAH1PO+TotVIcVFajVNkSWvlOX5Lb36O+JiYmUzNIPfYCIo/aFOCji6Gn5rv9zmIv353S4E5/l+OtWuKSOn7yu7lrvJ8eXwzvU87kx+/NzVNc/riXJNoO/vd7N15opiJ9rr+5v/78Of+XoKZUi60eZCL2AWY6t9o/lhFiKLOfoiVH+Pjx3bHMK1IFfDgAx0pHLWFJxchLrDG1OGHzBUOH689gW++cTmBNWKma/151XVyz6Tn9r/0uubJxGlAaIRpXXsFyEr3ry+fY5FBHM0bE9aRodHY2IaiaDh/5o/NxfEBFZJa52BgFEfae/PZjhgMMVFMEHjX2uWsKdFScHYjlnxNdynfJ2eeJ64O9cAGZQP9R+bg07SKgzFq5bqCtc8eb+19qTzGp/kZxZZTro4MoARByexi4jwQzuSe8xj6jqE8/eReQrPEjMNuUCcWyb9wrkivw+ykWuz4OcZa3ZHDDI9cMzymzLx0w9R71Gyska9Z+vGcq3+qLrJCPsH4Myrn98vGrXr8vpi1w2O6dnVE2yu7sbnU4nRkdHK6X3PHAmF3DL8eo0Eu0yeaV17I4tM9y8Z39/P0ZHR1PZqxxbvi7H7YA7gjmcw6C8Z3Fz22YYcCA+0ncRh/ItmaXzqLlXplpZfcqw9uapJDgi0ut+1GfqbpfBHK7gZ+SLeO+YiX9z3ahyMCJieno6vZptdnY2Dg4O4saNG+laOV4av2Re/cmBf3e0fFz+XZ1+e7rJ8av3VY6tfAHNteYhFziJqL5KcmjosIrDHduczok4xAzUxSTnX53dctvl1Ytsj7/1d64/jlv8mTle1smBvuMYqYtdz1O35DAV28316VbP5nrhNkR9rlPhVbGg79zGOI7KffdJcWzZEUUQ19fXY3V1Nb18emVlJTY3N9OhBCIpL4I5L0egY8DTkPXDkwN1j5QuQV8OwOSi7Hom782l5fkZyx38OTRYBO40HOw7AYzvM8k5CnwWnXiWJdGw5jJzjUYjHXLF8j9XwK68GH3lteKdDl3QvRov9wkxq8k+6X8uSMmB+uYnEp8EyemhMmbkmbKh6yMOjVyz2UzvzxsbG6sEbPyVLi5bznPJs1cQ6Fo9U/zVXhdFUAk+6WTleEwl5GCJgIcK1BVXbkziEduifHmJm57rBkWymct6cQx09HPf58jng4eZ5J4vgK6y483NzQTq6cTnovnqB5U4M1+ngVy/Dao0yNHQ0FA6CEcHA7KMlcE5BrNcFzFrlNNX1IkCQzkHla+L472557qDzHnKgRz1iSXk7hjQpvk6IIBwwEanV+3wOsoTM6a0m7n++ri9LY7ZnTJdkwtmS5/r2bu7u+m9oXzd1SAg5/09KarDAvpO/0sHK3ulgJbkjs7f7u5u0uuTk5MxNjaWth9Qlw7aY8j5YdaTcih9rz5o7Y2NjVVsALEDHeZc4FvPjqjKAp1d8SvisMrNcYLGIrlwvco2/JmsktJ61rj5+j09n+9RVj/VF71eUH2R861M7NzcXPR6vVhYWIhutxvb29vJhs/NzaWtFno3eb/fT+93PnPmTJw7dy5tM6mjnMN10viHJN56kLzX68XW1lasr69Ht9uN/f39ClZ1HEFdRDlTQFfBHb6LOKfjRHWf5WxVnUNKmYg4TPpwjO4457AN16Lay+lM1//OK0/A0dY59ue4ySfqAe8f+5Frh9/Tgeb65Q99CvWBB6PKLrje8j6pbeq+47w7WvSUHFsCYhmm7e3tigJ3kEtjRyPLQXLDMQF/LuJOolD4dQQE+p/XkXkkN6Y5x4/fubGpAw3ez1w/vE8+bhd2fx6VuD+TCkSfOdBxBeG/c3zMgVwZYg9K+Bh9Ibnc0DE5KfJ+uULQNQQFuibnfLnCzc0jn02qAzR6Fkuv+v1+OnZfEVSuRWZMqPw88JTrw+3wrm5sdbLu/RjUNvng39X13xW9j8nXOX+YdcjpOL7CR0EFAkX95lhdx6k9ZTBOA6h3quPfoL5KH9SV/kbcOjM36Hufpzr9KABMw6/+5cbldmTQmqBs+fN5Ty5rwD67Y8vPcsGOnAzV6dYcPwfN23H0QO6aQcCIa0b/H0f/nQbyMboeU3WG8BEDW/qhTRB/crhHnw06OT/Hc8/cewWAiO1R73sbfKbbY85dHbbwOc7hqJxdI0b0sfo6Z6CK/fAEhuMtt80Rh0Eu7vVUUG58fLyyV1fPkM7vdruVvnNuPRGRoxwvTzP1+/1KGTKz/TnsNAjzsXzfg/45OXPyteiBwrprfU3VyVydfuf1nnXk75zNczw8aN4p6wyw1eGfQTaoDsc6+Zrz9ez9IUbid44zczyqm9fb8QNuqxRZDUt45dju7u6mY+03NzeTQBM050qOXPHooBQqD9+PoOsjqgrZ967oujpAzes9s8rJyQlFboGwXzQSbihy/VO7uWfVCRuFhZF8Glk6MZ4hViTSoyf+vNyCZmalbmGLxzpVUO/sZOkSHT61K9kg2Bl0IMTTRd1uN/3tAEGRKEV29b36Tbl3OVOZ6aCFHnH4WqwcGGBkm06sDr9QRoQnFFLm9blXG+QMUU626ygns7zfQYzKxVhyxH16HoBxZVhnMDlnfg0NnoNwrSP2hXLKdcbTIPW3+E6dJ5kQSNXceoma2tze3o5+v1+JfJ8UUdZyBuhWa1S8k84aNF9qT9e5kdT1uWe4nIl/dGg9ik/d6HqfQJl9dEMreaCc5oKufKaDLn4vvcByOAZGqPsjjh68w764rlbfXZ94dphz7mtecuufs03Kig5Dk85zxy43l3XzfBI0qB8OZPf396PT6cTKykosLy+nrSk6YPHg4CAdqsKtVnT0VQk1PT0dk5OTKZurDGfE0SozyQv/pv50R9OxFLOyHvjhWRx6fRHnka/08YC08ODe3l7KwOl72hu2p36R9wLGzF7rh6+CUd9VXUMMqX5yzmR71Fe2oYO8ZmZmYnZ2Nvb29qLVaiUcrGt5AFWvd/OVN+vr64knOkxpe3s7lTlznOLtSWOd4xJ5v7u7G6urq7G2thZra2sJU1An8X8PaFEuVaXpe5JpC1xv5xIrDDjwO2IzyqHI9aT6TqzqTtygOaPd8D7yWbktHzm/gAEv9tNti9Zh7r3xjr0pd3V+i8sn9QP3tOs6VuHIVqhEX22qX47PqHdExIC3omOjJQdxLG/UKchbW1tHAHHEYYaX+w7EYJXESiF4TT0jXO44Mu2dc7ByxtLBsBjmQusk5rux5TPqHNWc8aFQuBJ3YFH3LF7jAErt5b7L8Uhz4c6GK5EcLzxi6uBUCiq3X5bKg8rF/z7pjC374af7cexUeA4iXWFwXgfJqqjuGjlRe3t7sbW1VXmtjPY2aA3SwVa//L22dU6HG4ecc5NzOuvG45RT+q709Dnlk33J9ckdMn9WzjkhION3bkSZpeW+NQeoDppyTpWvM7VxWuTfjb1TDnDwWuk9AhY5nZ6J8jY414P6FnF0Wwh5zmfxtF+XC+roQU6XA293IN1h9X66kz1IntUntslns++SGQZPRAQHlDWn3Bqu4wXtmGyJ1oXGpICObHqu3dzaPY00SA71m/vkx8fHE394GJR4JR0ueVeGlq/78bNGuL4oO5xTyYwcSDldvd7NQ10kX15V5frRgTkBLB0O15tcH41GtTSfDgedZvZZfVP7fnq6B6Lc/rLM2XmmtcF1QlCt+7TneW9vL6ampmJ7ezva7XY65Vg6WmNVML/fv3kOjca1tbUVnU4ntra2ot1u19rGnI3K/X8SlOuDMJGcdpWMut1i6XKujUE4SnPNLUpca+4j5HwCXy9uq0TUle7M8fq6+anTDVw/uUDmoL7XjZfPZh/ppDo2Fx9ygWO2lXP2fax+Ty7A4PPijq0+p54ZFEA/Dt2WYxtRdVIFoDc2NtJrfmjc9COBZumGSJmiiEj7MfmaHypxZ5QrzVx/I46+5oD9IwPZF13D5xFk8bkucDQwDlg8yuOLjc9T3yOOlgs5H503HC8zoexnxOFphR4NcVCjftYpCyojd2zp3CpynXOU3Jn1OTpJUl+YlYuoLuiIQ2PtWR53Uhws5OTOgV9OmcqoaA9Qp9M54tiqFC7n2GpMAg2aKz2bcufBFlc6bjz8e1+HuTXt93EN1Tmsuc8d4Ll8e185Pq1Xl0/vp9qmY0u+KlCUKz+re7aP4TQ5tjmHnOR8z4EgOrXu2N6qPDln6HI6mjqH37EMWiBf1zko97G6nBNIUUflgpP8n9fQyef+bWbZIqr63t8DzH5zvNxq4MAs59jWOa++FgcBOvKLe/jpZGveaRP92S7rp8m59b44GNVnki9VoU1MTCTbJ52stoSNlF2RzuB7bJWp9WA/ZZbrz53OiKjY4F6vl/bdaq+tnzpLOXQdpP2/xHsqvyUoZhaaFUz6znmrDCv7LWdc+4O5/nyN6zP/W+0MDQ2lwx7FC2a4fS0NDd3cgyxHdHp6Ora3t2NycjJlYDudTiWIIMc2IlLAWWcudDqd2NzcjJmZmSP2zteiY8vTQLk+9vs3z/HY2NioOLaOEXKBfb+GeMgDH5LfOp1Ul5Bi32mDjzNGrqOcTq/DM+RR7rOcHvV2c/Ofcx45Hl8P1A+O16WT6ffwO67jQX1y/J/DNsRifr0o54s9Vd1/W3tspaBksDqdTqytrcXy8nIqNWUUjKCPe/5osCMO3wEqp1ZC7LX1FDLdT8AQkc8Y0ZCzREX9E/CnYqeDkgPVukbjyR1Qxe9zkUsfD40SeVWnAPUdBYGRT562KqCh+/Q3jYsMnZ5BQ8bFHXG4wHg4gxt2tc+MbY6XOSDl4zxuCcIni8Q/lfpSnsV/dwbp7MgwuwOUA9FOdI5lRHZ3d2NtbS3W19djZWUlVldXo9vtxtraWtoeoOcyY5srMVa7BPr60Wf67cCBc0WlJ6XkRovAgUqev3PEZ5FvdRUidAzcuDrPeZ07LC6v7mBxTvW3ZITP0SshuN7UR11L/hEMspLiJIngzbd+kFeuC/Sd7pmbm4u9vb1YWVmJbrdbsQfSlXURe/E+F/DT/5RjfS/dTqeAY+Cz+D+f4f1RnyOOBuZE1As+ttyeSWbUcs60+pzLjrlzxTF6Rkz6iHOU++E4FdSLiEp5GysTdHo8D6qTvVPfBPRVTcIqntPkxN6KHGB736WrVdXW6/Uqr+/iIXN0PkdHR2NycjKmp6djZmYmlW/nnFn2I4cndB0PY+Rvxzc8/V96VPOYO0iHr3YkEPV1z/66THqAg0EpfjY2NpYNrnE9Dw8Pp2yp88hxJcft+sq3vehwTL3PVnNIIK/DIZVpHxoaSlih1+vF/Px8zM3NRbvdTny+HVk7DTZAOJMBlp2dnXRolIIGnp3VPFOv5XAf9Rx/crZfbbjN1ufu3Lk8RhweBEld6dc6hvF5oJ3P4UEfK+08sRPvJ19ydsn9HH7m7TGZJPJ2OC+Uae8L7TvXqa9hjtf9QG2/oF0mz2jjqX9uxzYc27Glo8rSGb6viuCGys87RSGhAOVKs5ypbIfK1BU273OniMqLew99Ifjk++IZRC7I/p0LtgsFFyP77GMjaHJg40Bd7ZLvbiA0Z7mFxvFTseR++F2Ojzke5gy1j/OkiJkUd6a4+MgjyVVEpNc9uML3+XFyw6B9l9vb27G2tharq6uxtLQUy8vLaU8PDyzq9XpH9nrmAKz6KGUl0C0DrhMiZdQYbcvJg/7W924Y3EDUrXXKinig6ymv1DEMWlGeJf9cb96XnJzldE9ufJ699WvFO/WFzrevFWZJToP8i3JAos5o5+6VTE1OTsb4+HgKBETUG95B64N88x9+nrMt3ibH5pF5OsqUfTqzlMGcTSK/ZNTd1jF7XCcbepavmTq+cK7qwEddW3U6mHygvaTuIC+Zada69CBfXV/q5OnpphzGyOmNHE7Rj9aAgsKue+icKVPrWfpcf+rkTUSn0+08K7pygFm/pbP4HXEfA0+UPY2LPHPckTtUhviD69jXLr9TVjvHH13P713f5uYv4vB9xMrAq6pQekVOvvoiPc/95Mrc8/VtdXqdffBAxWkgzoGy/szUut26FUbn/LgOzz2ba4HYg9fwWTlszDXJNqjTOUb+n+MFx+P9yPHtVtfWPY/PqcMkjtWdx7xGY2cAPWfb+Uxfv7fCsNSBzncPkPI5t+vQio7t2PJQmq2trdjY2Ii1tbXY2NhIEQEpX6aTCaZzDKJiVOkNj2MnIzkJ+jyXOSMzfRJIchToqLDv3DvBw1sIjhmpccoBCip7gRuCYAdHHBvHwH7SkBBkiFe5RetOidqikHp2JCe4MjTKLuUirS74ucVYB7q8XOmkSFH1HGhhcISBH103MTERzWYzOZzcQE8QSLDs8iiHttvtxuXLl2N1dTUeeeSRWF1djeXl5RQtVQaMoFGBJzq7OSAmcMBSUUWX5YiozIrgnJldkgP0iKMZNipG8kxrgnqAET+fCy+91PX6zGWQoFtOO41NnRFWn1nZIWAm465XZ7CfmgMBL77ugXzSPVTy3P92kuROnetY6iaSO3QTExPR7/djZmYm+v1qRFm61vmjdrwPdDqV1WLfmKnNgQwCfMmzAx3JxtDQUAXQuhxLBlktwcyM6zbag1wWS7zN6WRmz7xcnTLO/Yn6kS3zwz3UFufL+SwbnatAGBo6fK3P/v7+kT2hk5OT0Wq1KrKeoxyYOg3yH3E4h+I17R0/oz5VFo+8Vxt0MiNu2orJycmYmZlJOpeVbJR9tiMdF3EzW+5lm8IZylBFRFp3lEfymftZVdnmWIr6udVqpSy8gjZ6nl7tpVffOTCW/SQ/iS3cqfVXZVGPk0deldDr9VJGV5UyzWYzut1uCiiIl45lms1mTE1Nxc7OTpw5cya63W7KxG9vb8fW1lbixdzcXOKN7NbCwkIsLi5Gu91O1XHqG3GR5kHj9vk+LaR+d7vdVJYtncIqTcfhvD/icO1ofrW/nHiItpvz7piUvKKO4TXU0cxAu7OZc0AdQ7tPkvN1XGYH6bIcvhY5RtdnfF4Obyuw4vqWY3J/S5/n9JvsjbZN0CZ7ZSZ/iGtZIUsbxTmnDXT/5lZ026XIzNTqJcx+0rEPxBmpjtOoaoBemsV7cyDWn5GLmlOJekSJYGB7ezuV7UootMB8fGI0S6x8/AQILjQUHvWDTnlu/P4Mn3gCY3e8c/xwYcuRA1Yugjonpc5x4tgdQNXNXc45OgliX5h5cAUjh4aggUCOmV0vjfKxy/jqndEKJl25ciXW19djaWkpNjc3K/tpcyeQ6z2qAg8+P5IZKif16+DgIDkZbIORahohN8I5Y5ZT7DlFps/FN+1X0ngI6BnoYTDAjUGu/FRgS8DInRZfsxwXA1XM1srR1bO4TWN0dDQpdvFbPKdjS6ftNAH7OsOd62MdWBjkoGp9uZHVby9jz82Z6y3yk/eJCAi8X5QVjYVj5n2cP1YBsf90jurWR+46XuvBHd7jAILPzdkI56UD7FzAizLL+WV/dI8OhdS1XJd1QRBvkzJwUkQMwM84t7Rp3Cer8UonRByeFDo6Opra9kOjFEzkVpCcHLB/+pvfMdCZG0Pd5y5jWpfER77GcxiOWI/rRuRVQBxnTmezfccP/M4xlfS97IPGzgQFbTmf1Wg0Ynx8PCYnJ2NhYSE2NzfTvuJmsxk7OzupHfVZrwXyLQSOj1x3keqw2UkQ16RkjFufaH9zGJDEtc7PXF+6bfB7qccoC673iU2FIYTnRHVONMeeoxwO9jY5Hm/vVs/h2iY25vWuFxz381nu//A7tydsQ/fUOZk5Z79OFpwvdT5Sro1b0W07tnt7e7G9vZ1OQtY+EX9oHfP5OZWlv7PKBcsXEn8TNIocNOhvlpSqTzIa3Bcn5cxjxzkO9o1KUc+mcPi4eR+BuTv7dfOgZ7hBorKOiMqiZrSIUREqfhc+jZORRx+7AzyPFjnQ4nN9jJQX8elW0f2ni6gI+GqGiKpsy6FkWZLPMSPQBCxqgzxThcTVq1djeXk5bty4EVevXo3Nzc1YXV2tvDda92ttMGPJk8y937rOZWVo6OZBGwJm+/v7MTY2Fnt7ewmQEcxEHD2swRWqK3AqWTqqak9gUHpHrxbjXlYPiLAdKmIBMZHWeLfbTVlpOfGUYzcAmlPKueZVfeUp8Bzr/v5+jI6Oxu7ubrRarQT69SoUPWt7e7vSz9NQueCyGXG0nEnX6Tu/V0Rdof99OwodM7Wn6xiQzPHH505ZGPWD+tp1vtsfGmsaWYJYyUQOmLOPLu/kS06vkgd0PFlmzz57wJdBNK2JXAks+aDPXCfwh9lY19PkjZy0Vqt1xLFldUVOzm712dNN1JvkncuJ+qnKA2UIVXVDbOEBHL0VQvdNTEyk1x5GVAM8en4dGNR+R35OzKC+0q5TzxEjRRx9p22/f1hi3O8fVl7464gkc41GozKO4eHDV0XJlrB99SUXgMolPxg0cN1Du72zs1Phhdt0/fihWkNDQ6na5K677kr2R44rbY76p7kTj1nF6LLl9tG/Ow3kzpPssl5zyICzrve+13026Jm0CyLibLfR3I6kuaEsKkgecVgRRb1FPBuRd0KpB7z/HiCso7p2+T2dPn6f29JG7MR7+Qz3qdy5zOE0/jiOZZ9oD32M3kadbhqk/49rA25rj60Us/b16ehybpCn8NC4uiEnI8bGxmJiYiKVZTrzBAhZ6qeSF3fw6pQDDb+Ao/opIihRlHRiYqKyUZ4AbGxsLDkRucOjyDsKIwGL80fkTiQFVDwiiHcnRspaDsigTLie4wvCn5tTICKV1ijjrXGTrxq/g66coy9ecKwnSX6itzuqcroUwXUD7cEUZjKoWEU62fjy5cuxsbER165dS3sRFxcXY25uLhYWFpKh3NzcTO+SU1mQsqs81IhBIRp8B+rusHAvZKPRqMyjZJqgJZdxyylOrnW+DkwyrJMW9Tqx3d3dtP2Bjro7mR4okm7xciD1dWRkJKampmJycjLa7XZMTk5WMukufwSy7hAwYyuis6axio/6XK8GUVmt5CZX5n0ayOeRn/vfDmhZgq8xsmzTAyXUT+Kb/haoJqj0Hw/sieeSdwU8mUXns9mWxsugIPuivuacXBJlX9cwGKIxyUFRyeT+/s1Xa9DpdD3DZ7itIK/0w7bcYZcTq7mR48VSMuoU5zedX5a01gGY00rkmcsC+Sh9Sj0gHUxnX6V8kvdmsxlnz56N+fn5OHv2bExPT6ey1Vz2hRgi4tC2qq8sO5aMyZmUU+pZZOp+yo0/W2tRQLrfP3y1DXWxvnOHKKIaSNF6ZEBYzyAuYfCAPI+oHgTka9J5pr/FCwVv9d7wXq9XmRfxdmjo5kGAZ8+ejc3NzdjY2Ignn3yyEqSW/Ks9BTH0HlwFNBzc0zkRTxhcOi3OrYg6iwkj2nHqOF/vg3Qi+c0gmq4hRuBziKl1rfQYnV0dqCl+MzDpgco655CBHsc1jj/q+Oc2QJ8Tw/h15BP5xnaJ42iHcnwmjlHfKW9cz77+PLBKW+nr1GWBY2P/6ctxa8HtVK7dVsZWxovAOXdQChUGP3MFw0lgVIxMUFSXTi0dW5/wHNhyJ63f76eMFo087+WL0N05ZPaWz/dsFcfu/OG9/nfEYTTGDQ3HJkXi2WuNSY4t28lFoCKioowjDqNYnlWhMc0tpjoQR7lw2ciBdvLzNIAdgj9+JuXAfba6NjfHGgsDJM4vgdmtra1UGSFQND4+nuZhYmIiPXdkZCQpa4FTyoT2vI2MjKQKB3e2c46tzwOVrdpuNBrpNyOkufWX4ysDBFR6AvDa07+xsVHJ2DJbzShrLlAkACZjpn7pd+4UY/aRv31MOYORM1aum9QXZcD39vbS/mXtyctFQE+KcuPX307+mTu77ijqc8/YeoYmB/ApP34Pn+FzJr3INSg9T9nn/ZpTAk46O7figxt6yQsDurrOS9pZji/ATJn2YLB44zqIJZhc+1xHBEf+m4EJAg+tX81jRFSAk8bFdTFIvk6D3ifl5o7fOcZgoIGZLGEK2lhlanUSsvbXEtAP6gfnNSIqujniUP+xf5IZjsXXB+Wkbp37Pfqca8fLPXk/A1a0QW5zHf9wHBqzr0mON4df6ORLTomV3AZq7trtdpqr8fHxlIlnyb7suO7THKstd5wcAw7CkCdFuX4QR7gTO2itRBzNUuZsDPUqbau/L5c2gM+grtY13FIlG6z5oB6kbVC7dRnJHK9yc+nXuP/iPpDrTgavaAM5bups6nRf+9TPblvdYY2oHm6YGyfnLscL+kouEzmesV+3g4Fuy7Hd3d2N9fX16HQ6sbGxUXFqJQS5QeY+o7BRmUmQBMa1h1eH4qgfijyrXQqglLqEnAbEF4GAgjtrytj2er10YA4ztf1+Px0BL0eXvMgtyDrFLIPH33SQvD2WWpJPXmrqB/tQ2OqEhMBeDj33XIo/Ug7MFgiQK8PAZ+YUkC86X8S3UgpPN2l8bjRZkqTrtHBze197vV46XZHZKYHXbrcbKysrcePGjeh2u9FsNuO+++5LkUsFZXQ6crfbjbGxsdje3o6xsbHY2dlJ+981T1ToWk8CyIz0UyFqHapvBGOaEzmWKvHNHZYzCJhFRGUtipf6vb6+Xnn/3+7ubip76vUO9/c6GMmBZM0F15P01v7+zdeXKfvd6908ZKTdbic5dUXsTlREtSKBOolryJ1bZbo0P9rHlcswnwZy56fOyLPPlC9dJ6DH7Kjuc8DDZ9ORUgaT2Rq2xSAN9Yz4rTa1nnVYmuZda57BTX7mTq+Mt/rEYKgDPo/AiyRDGpv20CvQc3BwkF4nwnbpsNYFlqiPaUcom5JdzQ3nSGXFMzMzR/qrMk/xRP0h2PfKIQIklyMH9icN7qlXCNB8LMIZEdVyzd3d3UoZMg+smZ+fj/n5+XjggQdidnY25ufn05zo2frdaDQSBhIol+7koT2yJxGRDnaik8aKEuEN2W6CYFZPMZulsaqPOjtAQVjJZqPRSM+nA7S/v5+CJGqP21v4fD3HwTwDs3QOWA2idrgfljiy3+9Hp9OJ4eHhmJ6eTtiSekHrXfdrfmZnZ2NzczOuXr0a3W439VMlyxqnnCi1qWdwPXAs4j+DV6eFyG/ZSyW6WL3pdp5j1/+SHW6PoL4Q3zXXsv/6cSdLRPvhtpl7p4VXfX+05JdzpvZYraLPcsEfriHvm+5h0Iv2yfntmFPrTJ8L62ntyU/xYBb7KZuhPrkfRt+B2wv4XmnaPPpd7vi6veEcsQ2Nv25ej0vHdmzVaR4WRUeMVOetu8JiZIETwFIGGXeWeFI5si0KjDtFzlxNAA/U4X3MpgjUqw0pIhqqHBBjZldtEwjlnH0ZJgeIMiY5Z0lOgcpFCawlnOwnwag7kATxAvxy8uW0MKLkC8blIAekHHDlAIvLykmDGio59UsOSc4Bz8mfgISAjZeYio98t+Pk5GQMDw+nF7p71E3zxSCM2pJjSmUhI6l9PzoFkgrPg0RqjwovB5Y5XncwOEbObcShbPheWgE2ZiI0VrXjipeZI96T6yvXA3krJ1fzy+oNUs7ZzLXPefJIKqP3LPfWuuX7K08b+Zp0xz+iPijlel/3kGe6jnPnYJGG1GWSa4UyR0Pq/e33+wmgM5ApcEVgrzVKOXI74227znQnmwFLlvZRt3McrJoi0HGd6cDF54b9pjPR6/XS2HWPeMLglvjvoIvly3wVC/tVJ1c5e3LSlLPzdetAeikHMPW9wPv09HTMzs7G1NRUOkU/t+4dRPJNFazQUV8JtrUmHKxHVANyBNjuCNQF6CKiEujJVZkQ72lsrCqrc6h9rfj/zh9WbnBN0LHX2lE72k4mfa/Ag9a+P2doaCharVZERFy8eDGGh4djbW0trUc9Qzyjk0aHgk47+eROh9uukyLqdfGUZ11QhwzCbLSLdQ6MnH1mZhn4zjm2lHWus4io6FNdRyxDhza3hY82KSLSPOeelZNL6m63E/w+5yNQ9t13Ef6nYysc5HpHP1r/9N2YHNC4Jf9aq+wbx1Znd3htzhYfB98T7x53DRzbsZUwKdOTK6/hoDnBrpTYWQmSALmAvZjOzFIOLBEYuOHwTBEjAblSZC4QKXMdNOCgXkCfCjoif1olPycIEmnCGDygUSNg4uLWPmE5TTJyjPwp8s49huK5GxAt7mazGbu7u0nAW61WKoGlUSRIdAGlg+TG2J0yBwkuI5KvkyQ5GIzScfx0qvS5O0xylAQMlXVRexE3ZbTb7SalND8/H2NjY9FutxO43d7erii9RuMwk6x9W/pczjQdW++/R9lySqvO+GgOc06FAx/yi207SOt2uxXHVoEu9V1rTXLsji2rNdypZJ9yfRQ/VldXU9m29t3qGeQv+eSf0Uj4jwdK9GwFNfr9fqoSOY2ObR2wzxl055P44o4OvyO45vxpbl1/c735upOMCQzUgQ6t0eHh4dja2kr9U3BJGaZms1k5MIbZyIij1SkaRw4AuH2kk6q2COQIpDQm8iLi6J5ePUNrpdVqHblG65s80jMIyCMOQT0DvNJXGgP7or3jOuHX14gDH//7tBDXOGUr53xG3NRTOgBKr7vhnMs2T01NxdzcXJw5cyZmZ2fTPvucfvEgILdqEJeJuG7UHg/EpPy5LXNZ1FwqWEEcoHaJcSKqZ2RwbUYcHtqZ04MMtkZUy10ll5RH6gvxQfaP9xDrUXdsbW3F0NDNgwTb7Xa0Wq2Ko+tzH3Hz1UytViue9axnxfT0dHQ6nVhbW0uVP8S3kgW+5ocBA2EBrgUGKljCfBpIeLXf76dAtGQzl8AY1I47jlz3ChQI1zKhwySOP4t6RI4Z8XUuKMKqFM4XKxUYjPZkkZ6R60NEdQ+8yzEThGwvZyeo9+UHMEGnNnZ2do7YV58P8oD+EiseuHc8p5cc+w8ix5PkUR125PXHXQPHdmz1rio6tg4g1RF3ZKXcZOzIBDFLB1AIPPjAOVhFiynoVFjOHN9T5EqcSpj9Y8mmslzqW693swxSToj2xjHbykVAgyNQJ9IYVGbGEraIw7Jt8Vt90mKnMtdvjYeCIfClBcVIlXhBMKZnypmWYdP+Q0ZESeKjsm3MLEuZcxHRaeLcnCZFrjHLWHqWM+IwWqzPNUZl4cRDnnYpoqEYHh6OiYmJGBsbi6mpqUqkjBkUgW0CB88s8SATZoUINKSsqDx8TdCRZp9pmEdGRrJ7U/U3eeXZKg+UqS9Sqn7oiWc/GbDhM+nYurLkOqeDzQjo6upqCuhNT08nkC5+MfhEvcM1xlI0za/GpqAe+6byU/FCTvVJk0evNcY6x9tBf84BkKH2w6MEmqlDpfPIG+p2yYA7bNJfBAYOvCRv/F46v9/vV06wpSPNtebzSMqB8pyDy/WcqyrSvbQVHtx1oOXOVC6rwAi+9LPsit7hHRHpjIu9vb1otVrpFHHxX2tHn7kT4QAoJzd1PDxpkoNHYpDLQeLIyEhMTk7G7Oxsmou9vb0KcB4eHo6zZ8/GuXPn4ty5c+mzHF/E2263G6urq7Gzs5OcKOoM2WXKhubd7a7mQ7pIukkBCzposv367dlX9pnnfKjvchDoeHP9uw2SfPPVLLpeYyWmJL7SOF3+mTjR+tVajoh0tsXw8HCsr6/H5ORk9Hq9mJqaSvPKNdxoNFI11f333x/Xrl2LJ598MlZWVhLW0eGomlvtj+f6VL819/1+P20nUpBtaGgonv/85x9fYD9JpHkW77kVy4MHjrd5v/72H+qdiJsywm1VxAoR1YoZ8ZNZWTq20rH0YYhNhM+0HUVrlbZaWVEmq4i/6JAS27quJ2880CK7RyysfvN1h3LUtVbJEwb9WVbMtcL5EJ5hskk2UP3ja9voRHOMbmPJJ5E7+PqMz3af0nXMILqtjK2ffsYIAQeW8+AJhnSdPmd0wiPbvJ/X0zhTyNi2K7TcAiOz2D/9LWPG6JCibnJAKdBcUC7U+swnmRkr8VXPdwddit7nw8vRIqp7fdiGlLOXlJJn5IU7t8oO6noJXZ3zwqhUDqzQQfDvc7JwEqSorRY4+5Rb2FyIBKBSNFI2uXEzK86TwslfXwM5JcrfOadC5IaFziz3p/scMIJJA+D9YcCERsD7keuTO+gRh5FVVlCw7N7XtPhEGY04lE8ZFK1B6jhlyPv9wz31cmxzGUJ3qpznupbl1V76Lb0jcOlZrpMijpXkDiyvrzNG/hmDLO48iwRsck4qdSb1ka6jA+zBE8qFHDoHIJp/X98EIRyHj5OGfJBjy+9YScH7pYsJ7PWd81YAhutRNs3nTdd4pQ1P9fXSbzlDclwEACnPbp+d6j73+T9pG0AwGFENtjt2kExwC4/mV/yTs8jT2OucWsmNgmx61eLGxkaSF8mEcAi3cXB9MWCie5Wdko6jjtJ9DP7oMzqs5IvWXC7zpPGIp7SV5CEdW65jBfd5kKicH9kKBmt9janiiTZFdkvyKpk9ODiIqampNF6vMtEcTkxMxPz8fGxvb0en04n19fU0j+6wqhou4hBbCZfJtvX7h47txsZGtrT7JEm81Rx58sgxeB2e5/+UAdplBjL0HOoT4iV9LxxOmaL+5/YO75uucQeO9oE2IIeZ63BrTueTj/o759iycodOuj7T2tBaZ0m8B4vdL3Bdy7VOmxARFfn3cdWNlXPsPPJ7cuT8vRUd27Hd2NiIra2tI4cUeBaIk+WOpJQcmeqnxNEQ6xopFBENPRWZSH1TyUEuCsAIpkfwmeHR9Q4gpEj1PxWV7tceXBp7Ca+Ua6PRSKUE2k98cHCQfX+uHNrNzc1kuLgwtGDpbDDbIZCuvSMyZgLOcqRySqbX68X6+no6vIhOjcbti5aOAg2s79Gh4dVcuSydtFJfXFxMpV8aDykHVJWp3d+/uZd1ZmYmWq1WTE1NpZIud7aazWa02+2KspSi9milG2tFdyUbLN9hhM+DQAT1Wkc5B4ZKUOXUytpI1qQ0JYuuF6iUqQcEAhW9pxyI6NCK3LElqCFwZ3CFbfT7/YrRlJyq9HtrayuBlX6/nzLp3Dsoxc/DtTT/GgN1FANQ0n/KgjhI3dnZqRyucpJEmeQP9YCoTnZoWDln4iXLvkQ8RIkHjNWBgdxhebRZDsLUr4jqgXvqiw5m29vbq7z6hoBVAQ/q7Ihq0IJA1x1K9oN609dJLliQAwcsPZW86z7abPGA5aaS74mJicr6cQdX/OIZDuprp9NJ64bvsK0LdHj/KTc5h/0kKBc811wyIBJRde54OGWjcTMjNDU1FefOnYuFhYU4f/58TE9PZ+U24hAXbW5uxtraWiwvL8f6+noFk/Da9fX1BHaZxZVjvLy8nPSa1qNkXWXTExMT6WRmVYwQY9Be1GVt5SjqM+4RJ9aLuLnGxU+tB+EcjUH6cG9vr/IaODmAq6uraTzUJZIh6Qftnx0fH69kUV0HSA93u92Ym5uLmZmZuPfee9Pr4GRPhoeHo9VqxeLiYsJYyqjLFo+MjESn06k4r5oT2UXJB4O5mjvZzZMm4hQFFqST3a5HVPfi8n59x78VWJHuZ2BD2+4oT5ITbQth8IWyqLXHDCfXZB1u5TM0z5Ij9ZcYjXo3Zw85XuoKD0JJjxIbau0S17FKU/yPOKwIlY7WvXt7e0m260rbadMVIGPgX7Ks69SexsMAmvjnGMH5Tdkin1zXiifHoWM7tixBkmHLGVQHDA6AqOCpUAmIGenTQvfn0IP3bBKfSWXgDpN+6x46yx6FZxsUXvVd2UyOneQTHlFdcHTu6gC5AwsCnxwA0ljpMOb6Q/DFa8kjtk3n3SOylINcoENEA+4C7IrrNJCc//39/ZS5YYTc+0wlLzliuZdHn9xpdFkV6X8pNJWmbWxspEg+P5eS9mxBjrfqn4AtS6A5RxovszbcF0b9QCec/MnJB9smwPG15gaRvGf03/vNdcXPqeClpFXm7QEXGVoBD66D464JzrfWNSOsEUff/XzSoJ7kDpYb7ZzT5UbL5d1BsubBDbcHaGiHvD/uVMrY57IKOflj0C7i0MEWwGE1g+R+kLHOyZ7rbw888XoP0EoGKdtub1m27/Oj59HZ0G/xVs/VmibQ0Xdcl3xHK4MDnAv2o85W5uikbUEOnBOHuEyphI8AkDqFlTu56ik+g0BWfPaggxwAnR6vslrdJyet0+kkZ4QgvNFoRKfTibGxsVSCe3BwUNk6QeeW8lKnE5g5owxJ9rw8mdhDDojWHTNTCpzoXbJyJtUn6hHXOxMTEynLqnJT7gln0JIAf2trKzqdTkREKsXWjwLSU1NTMTs7GxMTE2mbGvUU7bmvZdc9alcOlOPgkyDqDscoOd2VI7eBIl8Tjh9yupS85HX+Hduqwz/eR2ZEmTml/Zf8cl41DvInt67VR2KznE8l3MGKTSYWGTxjkpA4LSJS8KTf76cqEg845vC/nl+ny10WnHzO6CfVzUEO+94OHduxVYRPysZr0x3Q1xlaluEwmynhkOHkd3QI3XGOqJ4ipmsYzWd2jY6i/1C5etaAE8PII5+pv5mJ8/vdsWWZDbNPVKguvOIR99NGHIJ0RgMj4oggUmnS0ZIB1uL1BafxMPDA3xyXZxTdecmBfzr3ORBxkuQHPTWbzWSg6cyQZ1JAkgdlN736wJ0CHuqRC8xIFmTY19bWYmlpKba2tlIknwBH0c5clkoy02jczCTooCpF6xl587miw64DdiIigSaNjQbG+yGdQYfDFaGD55xCZBAmF2wQb3W/R5U9uq8+as6UfVLUklk7X0uUB587PY+yxGg9D1TRtccBC08HuXHKGaw64y2i407ni/uYeECTytCUMaVxZ0CQRpdZV58Lzj1timej3C5oPfM1LQSbcv5YwqZraP9yvKHs55xj8p98zpUnkp/KNuvZOadbjgJtc8Thfk7pHz2z2+1Gq9VKNlXRf+qUiJvAf29vL+1Xy5XY5kCc60Lii5MmjdmDZ07KjIjnmgcFVnKObW49UZ+xbFJOlGRGr1vR79XV1eTobm5uJt2lACgPUNP6oFMyNjYWs7OzKejGgwklKzs7OxXbEFFd/xqXHEZl+FRVpHU8OjoaY2NjR3QcA/kag5z0/f39FMjd2NiI9fX16Ha7sb6+nmyjZJ2BHcn29PR0ypqrioo6QX2anp5Ogd6Dg4PY2NiIlZWVODg4iMnJyTR/um9mZibprEuXLlXety4+UO8rgDo0NJTOaHFHQ7LBc2dOkhjEc5vrBxhFHK1G8XVMm6wsumyuBwX0fOpzOknuLKlf7oDTbyFRV0sXSwcyA6p1LedW+k9YRvqXjjT77jLgVR05zEW7R1uoMcoeMujqAXmXn1xQUs/1CkzqIbVNrOVBF9dl+swxnPtIImJE97WOQ7dViqxsEAFGnSDTOckNUp87mGQmpNJRlAFQiCiIIk2YBE6T5MxyB5gMZATNx8CJYeSREQ8qVFeuGiMdaTpGjN7zGG86wOJ1brLVFktaOBaBPj2P0UpmnqkY2E8JN0s/aNzccYk4FFR3NtzJ8CCFlMNpIPFShn5iYqJiMPv9fgIOnU6nAoCU1RTAoUyQd07iodacsrGbm5vx5JNPxtLSUly7dq3yjlc6vN1ut1LK5cEFKjb9PzY2FjMzM9Fut2NhYSEZcFc8en/u8PBwLC4upsOjJBtybr1UxcEbS4S8zJt6QHzmaZGsAOH6yylVBm54SAodIPVJ34k/Y2NjtQEad5BygF2/udao5GWcIiIZy7qxnCaigfJ+utESr5h18r3m+q3SYwL2jY2NJONqmyWHcqDo2PKVDeqT1gEj3m4bNB53OvS/gFzEYeki55RjEUkfeACG19K+eZaHa4Rl7gQmHriqc371TF6TA3/9fvW98apcWVlZSeWqrtM5B3oedZ7Lj37nHFj246SdWwbFcziATlfETT4oo+jOsOuYXNZW8ilHUA4t35Gqd5e3Wq1ot9uxu7sbd911VwLAa2trsbW1lWzE2tpaWkdqn7IkHSX7tba2Ftvb2+ndxZOTkzExMZFKpyPypfXK1Ap7yC5tb2/HyspKCgpLByiIyjnmPkjZNMm/tsVpD6qyqQzMUCdJp3qVjdYV9xjroCfZajou6o9Kp2k/tQZ1YrhoaGgovbNbPy4DdEBc3k+D7IsYSGTAxQ+OIkbPOS8cU6NxeHJ6u91OQTU6Yy4bEYe6UvyMODy0TDKt9rVWiXnc8eUcav40Nl0zMjKS2ta8Uncy08w+kmiDtCbkrEYcHo4rOyPdwlcdMVgi+WYGWc/2jDK3wEVU97jnkn7ii/wvVZe5vuI8Oz7i+nC9mSOXF8dxx6FjO7Y60pvObC764cJURz5QUi6CmRucO3YOnAkaCCR4HZ1lRh0IdHOT4gvUlZT/OIDJgR8uAs8W0LlTP7knx/nOfhBQEGASVJOHJHca2Lec06G/jwNGHER5/3nNaQD27gwKSKr0TopGCsuzeTLiudNbXbbINwJhKX2djKkfZv51L6P2LAGTrAv8E9g2Gjf3fEfcVHoCUbnMl/rCvezj4+MV+ciBORLHJt2ikhpf27qWe3sEBOhUePlZxCH48hMCfS3k9A4js26oOcacPDs4odFwZ0pjZ19OQ/nZrWiQU0I9539LBn2uI6rllZIxboXR3Mmgc68nDbwbe/VJgJZzknOiXA4ICPx73us6mb8pC7Qzfo1kglF9vtpH7bitYTvildssjke6TP31IBh5tb29Hd1uNzkWCnKyXTpqORBE+fDfOTkiz06SmM3XOMgj6aX19fUkH/rM9XlEtXKqzulnJsyvk81ltYOcATmS4+Pjsbm5Gb3ezeoTAVuC6W63W8EdjcZNp1olwKOjoynopn7J2eNc83uXfQVltre3U/A3IirBLdou8ZsVatwOl/tbAVGXMfFZ54qo/Jj80N9yrlRpIL56wmF3d7cSSHIHmnqNuogBH8mN+uy6iJm+00Ju22i3cgmunE1wEt8kC3T89D3XG+/L6RThh0E6QzYgp+9ZXcIx0pmPOHxdEyvw1B8Gsqj/9SxWCDHAynsjooL/6Xe580ib6thfPO31epUgrPtu4i/9AX1HnUOn3fUg5zhn83Pzlfvcyft5K7rt1/3wACkOxhe4BlOXPYmoHkkdERXFTcCpgTkxkkYGMxpPZeHGmlFWfc4MJ8dAYE/hcaOtiBxLrH0i5fh4dI4lxh6tcmXoi5Z/u5BzLCztdmEkWOP1er6XF7PsjiCRDhT75o6hn+zpoMzvP0lSaZ1nj+XM9fs3o3krKytprehQpcnJyZiamkp7elS2mzNYvlaoyARCrly5EktLS/HhD384ZQRkGC5cuJAMxCOPPBLXrl2Lhx56KO2/FUnhKUKt/1X6qQNK+v1+ytwqgi3lKxC3s7MT58+fT/uWlD1T2RnL2im7knWecKmx0lmQjOi61dXVBLAp6+KfO6pcv4zYK0qvsjJlo3iPDofq9XoxOTlZKb1xg9jvH5aJcw+ayJ0EAUMZHK0vGU5lGFyHnBRxD7B4pL/5W+TBkIjqybIE5H6IHbM0KkWUIzg2NlbJpAswiljdoCANechoueY2orpPi+CBDprGo7IvzR+j/7lx067pWSyxo/2U7lcwmYfQiM+KoIufrr/VHh0SEgM2zj9+T16It1rXWo/iA/XZ1NRUGovKC3PlyCQ6+06nAdw/8cQTEXH4Kho5kBHVyg/xaH19vXLgmXSr5GR6ejpmZ2crr/MiHpDzKRmcmJioAOtG4+Z+Ud2nbBdthnDGc5/73OQE6vAobV9RVld7cuWEivQ3D8CUbpSdk10gbms0GinoqQCrssYHBwfRarWSU0u8Qx3KUmStB2WZVX4sHDI7O5vWnWSy1WpFq9WK6enpOHPmTExPT8fMzEylKka4jTaRWTv1iXZINle6iDIwPT0dc3NzsbGxkfAg951TB+i3V49IjxBfngbiWpct49zlgoPEjMSF1DH6n7qUenTQVgb6HXr9GPW4/qZ+5PplgJD4y4MzWr90EFkZQLzAuVOfpI+Z4abNYDk6A5leKcLXrBH7aH6IiTxw7NVL1BWcK9oF8oOy2usdHgDJIERENeDvQR061STiLvI8l62/FR3bseXmZQ2M5ALNgTJizIETLOSytGpXjND/fCYjRv58OrHeL2emmCgHQu0zrU/Q633MOW8uWBqHT576x8gMnXXe68rCifxjRF6Uy444H/yzXFCCC83n3+dnkKJjlorzzGedBuK8ygnh/EVUTx/V+mg2mwnYMQrsxi33POetDrBQllZl6kNDQzE1NRVTU1PxnOc8p5JB7vdv7pna2dmp9EuAQlFq9VGgRWt8c3MzGo1GtNvt1CeBarUnkCEj4Q6keMSoo/72LQ2+VrUuVOKnMmzuxY+ISjCFskQALyMjAMN9wCpnEl9EXNfUUb6u6/SBSNcTGPi6ZlBI1+Sy1ydJOeAy6FqBHg9AuJ52XcMspeaVzhd1m2SExtkzoXIGqGfl9NFBp33S9aLcHBAYuD6kXNCxdfvoAJdZKL7eIQe+CAzZT7Wf4y37nquS8DE74JAu8nI48pvOssuL81h9cb6Qj7cLbD4ZJN1JPSW+qJxWjpH2Y6uMXrqVeo36IAfaqW/1HM0nv3ee0UniO5/lmEkeBY6Hhoai1WrF7u5uTExMJMfcQS5LdKnXNS7aBQaA6Kwwm+Y6QXzhnkMGRCn34oucY2E12WAFBZWdnZycjOnp6TQG4jPKN3UzSzali/UczQHXPZ1RAnntHfUAkHjgzh4DahzzaSCO2+fJ7XYOD/K3j53YnHPidnZQe9Tlro9J+l9OU06+XKfRNkkuqdt9vI4NiOl5rfRADh/Xjc0/z/kmjjP0rBw+chzCfrgvos+4x5fPdP3Ee/m7bl78+5ztuBU9pffY5hxb75xPXET1oBwqAD+BzoWxzoFTJFAlLlTwOWPoTJFyVFss+1KkQBkBjsWF1Z+rsfn+sYjDMmKWDdHoOBDkMz0LSkVABUAQo774ovPAg4TShSgHdvR8d8Cdz2qL5Zvu0Eo55sqZHFCdJIk/isg1Go3Y2tqKiOprIBjNVARPpyUqa6sTFR3UighWqWy0Z+ratWuxvLycQMrY2FjcddddceHChfiiL/qi9MoGyfBDDz0Ue3s3XxOlvgqITU5OxtzcXLTb7ZiZmYnd3d1YWVlJ+3M7nU4cHBxUXkWj97gqYqnshMqQ9R2NuBwJZjIFoJjh1LjVT4F8RecVuXc+6WAROSuUY82b3hkpXaNna59aziAQ8HOdsPLB5dPXmgyX752VXEkOpH9oTH2P6EkSgTSjvH5NRLUyhfvtlGFypyri6PsIJR+SKc4RnyXZoj5yfaL+eiBFsuEgimOgkXYbpUCQAwPKAwOdChqpfVK/30/ZPe0vlnNLufQ+uP2hQ6Hnu6yKFwru0EFzeaP8iqcqD1c2z3nHTJzu5yFTWkOcH2YjOb85OTsp4vwODQ2lIKJ0/Pj4eCVwIl2qQ4ciqu/JzAW/RZwPB9zqi37zpFTtRVXFg/bFcVuKeKrSXFYxUC5ch8p5ZyZH+rvVaqXxSX6YgRTvdF6BxkjcQYdJbTlWYUBgYmKi4vgLT+q1bKoUUOCS+lay1mweHgQpXBYRlfcKE6NRt9Dppp1gxovlz16KrN+cX41XmTDx4rSQbJUfZERH3x1cx4TU3QzCeXDA10TOuc05f7xWPBUuo+/CbCOfTxxP3a5nal40Fs01/YScU87gJp8lLJWzizlfgMR+8vo63Fx3HX0RjVPl0RGHr1skNlM7fD2pz0nOZjs/xX+XM8dSx6WntMeWwstOMHrnnRPDOPHMYuQOqOEip3Apgs1DDyRcfCYjfmxD16iURFkcRjml6PSbQimi0DI6R8XnQFX3yVFg+QkBXk4QmNFl9ouLxDeRazGrrbrosD+LUUVf8HSy6/a15KIsGoM+8yyL9mvlQPxJO7YekdNiFjhWSbBKc9vtdip7UpZWe3co53XOrQcV5NgtLy/HwcFBjI2NxTOf+czUp3vuuSfOnTuXntdoNCplnnwfYb/frwRsRkdHY3JyMs6ePZuOso+IBEwjbm5FYMkVFX5EVIJdBGqUW+qNnAwTyOo7lTtyH6/6zpMTtY+MoE3zRkOpyLnkj+W1yk5rDDkD4uNzA0K9I6pzcmnUPIgj3mkvF520kyYa7YijDtrBwc0Db5588sm4du1arK2tRUREq9WKu+66K+bn52NycrJSAUSdozZyNoYk/SzASXAr3slmSX68akBypHuVzZGzy72nEUe3eUhvSTa9UsHBnoAB9XbEYfm0HFvuH6cD5IBBoFxrQOPT/Zov6SCBfmYYG41GcmQobxojHV3xXLpudXU1vXebATuXZ8rNwcHNE2b1mpZe72aZv97xTedc9z0VcPOJphe/+MVpLj0QwK1HEYd6fWNjIzqdTnoVzV/91V8dCQjkwLraIAaivBCHMWijQJ3OYWAZu4Il2jsrGWFFgOabB7FJTnj4odaJqpeUtfE1oGDn/v5+OmyRpzJLJjXHrKIQviNY5sGNHsRh6X63203zw8CSKqg4Ln2usfCAHW6v0dqhfvCsGoNqlAXOr1eouePH66QzTktwxzOsDPo5DcKEIs0717r2QvNeYgPqFcf5fA59D7XBz/jjVZ/E9K4L+T/nnBl5ypS2GXDeOf/qQ87n8GSPBw5EHvRk4Jw6uC4wkMPr+l6fuR6ijtfY2Q59EMcKLvuOE6n76oLog+i2S5FpkF1QKUSk3PUeFfHSpRypfR4mQMcyN2FygF2R6Id7K3ikd7/frwhYDmhFHM3SEvRRAbijOmjC66IbXr7J+WBESv+78LjAk1f6LAce6Tizfx408D47jzgWF9hBsnMayAMEMpr7+/spKKII5v7+flLOMqIC0MdxUjzK1ev1KsCk0bj5ap5z584luZidnY3JycmKQ0Zly/1AAgj6W9/J8dVJtIwUM7OuzIsAB51+KlL2n2s/JwM0XlJoclgE+jUPMn4TExPJeVG73h7lXmBGPPLXE+zs7CRAp/XEdaK1kzMULt85ufU1QaebOoS6isHA00Q+Zv6tg2du3LgRly9fjuvXr0dEpIPI3CFzm0AeOghwvUVdSBsgOyFgv7W1dWReqLv0bB7Ixwyy2uS8iRjkk+zmwJWPjfezeoUZkIiogCSXbQaf5MTnDlZT1orbIJTNUtaCQdZcoIVAjFnlnZ2d2j3q7tz2er108N2NGzdic3Mz+v1+zM/Pp8oRORXMrORs09NN99xzT+ITg/KU24iqbZ+eno7Nzc1YWFiI69evV3SayHUIPxsaGqrIgmMBgnHOvc4gkLOnbRxbW1upEoCnCksHqrJlcnIy6XkFLJiFlJ7UK4Bc1qjbuG45Ho7b94wTc0k+qZMJfrmHWTIsnlC3yuZpS5B0Bvf6M1um53BPv3jBxAVJa4sl5v7DceTsosgdiZOWf+9L3Q+vy91HIk/0nbB4zoEb5Hf4evDr6xImbsukx/1e122OAVzneQA455zRPnBNq139dltC7K123A9Sn4jv6+aUfXEMz98cg/s9g2y383jQ8+vmz+3/ILrtUmQpZBlU7yyNn/53Z41GS68MmJqaSsrCAacifnrlg5Td/Px8+l6vO9nb20uKSkZEh+Co31pIERErKyvpRDy9t6zVaiXgpciqlC4Bj/ohYWo2mxXnIReZY2SSCl73+kQKaMhoCTRS0TebzXTEvDvMDASwv9pfI56oX1Q0ojoQq+/0W8LNgAABmCKiEdWTN+tIcuRVACdBrVYrjUv9VsZWADHiMKI4NTWVosss+5Wj606TE9cJD2lqNpvxzGc+M1qtVtx3330pO3Pjxo24du1a/Nmf/VnaTzQ0NJScXUXcdXCVAGVEpHI6AhRdT1CgPkuuIw5BN3+0BtSuQJHKwQQ+GKTp9XoJuHP8jH7qmdIZs7Ozsbq6GhsbG7G0tJQy2Syv1DhmZmZiamoq7r333hTMWlpaSmWCzHAJzEREJdh2q/miY0rSete60loQOBTQUiZauoVgMGeUT5K0NskX6blOpxNra2uxtraW9KcOL7t+/XpcuXIlzpw5kzLuqprRvDQajfQZX9mjZzCbI2Mu2Tk4OIiVlZWkP8Vn7Xlst9tx8eLFmJqaSmun0+nExsZG7O/vx+LiYiq5lEx46RoDOfxO8uN7IkV1jmK/f1hyrz2REVHR6c57VspIbnZ3d9P7N9vtdlrTGqOy1nJMV1dXE9+npqYiIipOMQM5rqeVlV9dXY3FxcW0n1HEg+lkj9fX12NjYyP+/M//PFZWVmJ5eTnN3+LiYpw/fz76/X6cO3cu6R6CudNA0qWSfYJpJ8nq6OhonDt3LpaWlpLzpAoTB8lsR/frcz9gSHOgOVWg4MqVK7G5uRnr6+sJM+j95nyzhXCWArHSddvb27G+vh4RN+d+YWEh2u12zM3NJXk5ODhIulW/Nc88jyHi8NAdEbejkH/SebQJdY5gv99PeJCvAdN6oR0j/hgaGor19fUUoNU8yi7Lqdd4lRnWuGWnaKscFzabzWSDiQ245j0wQazLIBDXK3XNSZPkka+fyQX0NFZPZBAvyj6qHZ1Mza2Puo9BOFUeqMqMlQYMsuXWZ+4zrQe+Uk17zXu9Xgp+6H+3SQq4M6iRc8z5bGJc8cltve7h2QuSHT2XJ4t7lpeJvRyGIc5wZ9j7wXuIS/QMZacZrGBg9lbOdc5pfyrB/WM7tp6xpCKue2iOERyYRwElCCQNUuU1e3t7KfIcEUkAtbB4wimVtkhCwRM45fxJUOSAMkKeGxOJSs0NVV30wSMbejaVACM5En4COh4CRP55UMBPac1l1XLRFc6XnkseMAOcu9/bcD7QQffnnCZSsELGLeJQ4cgpUZbWlemg1/yQnNf6TIprYmIinaKpkx7VB504efXq1Wi1WrG6upqc13a7XTEAMsL8fXBwEJ1OJ+3DYuCFQRl3YtU/luEwWu9rwkGyr61brTX1mVUWXm7G09LVRx2SxT22KnNmxoLrTbLpQbnj/niwL6JaQqjnMTAmo0TAy3Km00S5da2MnIA1HbWISHIWERXnxUE8eSee1OkFOoeaOwYMZNyVLdre3q7sF9IcN5vNtLba7XalDF79ocPKIB1lmmXR3n8HNF6FQzvr5a1ci/qc2U0CQGXeGEzRWpAzIvumAK731+2A63ZmH6nvPJgpnqss/dq1a5VMr9bi+vp6XLt2LVqtVlq7GvdpopxNz/0dcRgYU/UOT5XP2dicg+tVA3RwmTm/evVqxaGVQ8RKIjq3PBVcwJ36R5/r9TwHBwdpO40yn7RPcv6UGFAlk3AVA2GSfdkOOnsRh9UUxAh0knQolxx2Xa926aRIx7g+ZUBNpa+7u7uxtraW3iAgvMk1GREV25cLWKlN7SdWsK/fv3katvrEIEUO++o732JzUsSAJoP8nCP/IVH2c85vRFRkh86TnsktJv1+P529wMAyMRoxfA7vE5eob6w24LikS4nZOe/EIY6xJetKujGRIN44DhZfcjbCy51zmWLH4jn8yTnQNbR3EXn/gLZb/XddxjWS+45jz8l3TraOQ7dViuylQWKKT3wdOGX5BiMsclS5wLlwdAiDFN/s7GxyXHV8vKIlFy5cSApLjvDi4mLqw5UrV2J3dzcWFxfTpOmIez5PoEFZRj8kJKJaOseIoDu2jFKRPwRHAgI5p5ZRMRoDjZMgTY67ninjJ74y26CFQUVFxeyLg/dyPt0ho7PkfKDC4cKmwaMhUvsn7ei608ao3P7+foowLiwspAyixiuH18uTBhHBoTIq4+PjMTc3lyoMZmZmkmOn/XUf/OAHE/BVqfSZM2cSeKQsK4M1NHTzICWdtKzsFedZ/Vb22feTecaRwDZXluVjjci//7hOl2ge9Fw6u8p0bW9vJ1kaHx+P6enpmJ+fT5l0lTDzNRJS8Cox12/Nv89PzqjVBS8k87pfbWutaFwRkfZ9qoJEB7OcJHH95gynQPfKykrKzIqnzHx3Op1YWVlJOuzs2bMRUbUPBKuMRjsIlM7Qq0qUSZqYmIhz584lUCpwv7GxEY899ljKLEUcZlxGRkZibm4uva5DrxnRK0UODg7SfCmbIMedPxonI9ZcF7RxukfllFx3XFNqU/dqjHp1iXSpSlB1PYNSCqAoWz48PBw3btyITqcT29vbSacoEKxDv+pkm04xT9GlfIyOjsba2lo8+eST8cEPfjAdpqTAg5wgVZ1ob+SZM2fizJkzWefvJMnxDclLhnXt0NBQqjJRBlv2Lhc48PalE9S+5ll7nZeWluLKlSvx+OOPR7fbrbxihifIy7HV3ls5WpJ93ad9pdKDy8vLaQ3Pzc3F1NRU9Hq9dI1slA5GnJiYSPtxlf1qtVpJxjUWrVVmwTRW7fvlHmBioWvXrqVzLRSoUbWOXrfHQBfX1szMTKqgunjxYszNzcWZM2diZ2cnLl26lF6DtLu7m4LHc3NzMTMzk/SQbA5fyUbSYWLT09Op7L7RaMT8/Hw88MADyWkSRuXeYs6/dOppcGojqieC09GiA+YYlrrag0CcV3cq3UlTgkvzrgPCZmdnU9/UH+lldxyFh3WIGqunGIjXD/0ejc+dSBLbq8MBXP98HZs+F38Y9BT+Z6US5T6HO4ifaVN8y6fmUdt1dI8CVhHVbTs5LKTPfC17tR3vqSN+R6devs9x6LZKkWm0PavhAJQMYif14wJFBS8h7vf7SSFzD66i6CrDGRq6WS4zNTWVypl4GEfEITgX0CE4Ul/OnTuXIkU6WVCgSqDAoyEC0zIIBHs6JEGCmIugaMwCaIOCBO5UMWstxcCSZTrrHjmKONwHItDFrND29nYCJnouXxXgzjt/i7gY9L3K1xmxczkheeTopMgXOp38vb29FJFXGZYiiYra1inDXHSTQQABIx62o+t4Eri+P3fuXCrRYvlyu92Os2fPVkqGJEtSmjKyzCryACqtQe5TlfKamppKp0hqnuv0A8ct3nqEkzxhtD4i0mnUEYelbDwxV2tVPNc2g9nZ2ZSl2tjYSOBHBmJvb6+y95gBqbqopBtCd/g4bo2T/0dEMm7ip7K4cr6URTxpov6jjDLA1+v1kvzpRO1+v59K4FmaztJdBWjo6PlJ2d4XD6gpGMD3RusUZpXzLy0tVc5okHM9Pz8f09PTsbCwkMoH+U5EHcpDGavrh2RVe9IjqvpCa5hZLM/WRhxmfgm6ZEvkFKpC6eDgIObm5mJtbS02NjZidXW1krlSmTIdD/FmfHw8HfDV7XYrNs/lm6V+6o8cW/W33++nfbQPP/xwXLlyJS5duhQrKytJV8pm8mCj4eHh2NjYiLm5uWg0GslpP2ndL+I+bc272ziRwLUqzR577LG4ceNGCigouOa4yQNlEYe4ieWa6+vr0el04pFHHonr16/Hk08+GZOTk8l51in4s7Ozcdddd8V9992XwOtf/MVfxNWrV5OcykllGaX0vPrB/bn9fj/ZOQV0WSrL6iWtcwbeZcdkT7UFS+teY5YuID+63W5sbW1Fp9OJXq+X5FhrQNiS9lbrVW3dfffdqY/b29vx5JNPRsRNPbSwsJBw45UrV1LZMgO1wmo5Uj9V9UHbr9L8+fn51M7HPvaxtEVD8692ZIdkj+sCKk8nMbjoPgB5IltAn8FtIa+TnlOgy20lt+EpUK0gEQ9Lky0V72dmZqLdbkez2UyVRCsrK8lB1JgODg5S4F84RzbXk1bss2yZ1rTbAo2V2JsJJOlX9YO+ldsD6V8GDXUvKzLFO82JKvcUEM3p8+Hh4RSwkvxrTWud1vkt/rf4xdcp+rWSf8q24z138l3GBtFTytjWdSKnpEke0RVD3eEjKFT7YoSiHPv7N99rqQzS1NRUiqqxvFAKVEBCSs+B68jISMzMzMTQ0FCK+knIqdC8jxqHBIZGmLxhH+qAmjs5OfDfaBxmU5lt9YwzD9URvzU2AlFmVLQHMuIwWs+Fx3F7hNkFkT+uxHKOrdquk52TJgYxtA6kTFhqzH3SLHXKZWudX/qbxiPi8KRAgQRlR3iypt7VNzc3FxFReeWDvp+ZmUklaeS7lIZHhqWAJP8sfdFYJPsCyyw/q5ODHG8jqllxX0vkk8rLuAYkuwI27DvfIyxlKqdW99Ixpbzn5NP1gGezCEr9Ho/si/8EetIRzWYzZT64Nk+K3Gjn9Fuv10uODsuQeYqq+CCgycw+zxRwMDdIJ0gvDg8PJ3CvcsChoaGYnZ2NRqORXmHFoCf3bMsBV5vqm8bIuZcu07VuiHUNK2Kkq3mP21IRgb3sTMThepdcqNx6eno66Xetf55AK+dFr0GRAyS+yMZ7oIbrkluHNOesCpLN1Pumr169GpcvX45Lly4lHk5NTSW9ITCqTK0CEBMTE2lN1NnMp5tkT5lZpI5msFfytba2lgIqwivu2Obk2u0rMxdyWldXV+Oxxx6L5eXlWFpaqpSYay5nZ2fjzJkz8aIXvSj29vZiZWUlnSkggK9gA++Xk0GHUNnfRuPmgU0qs6UeEJbSj9aPsmPimfS0vueYI47u4ZcMSFZ2dnaS861AiYA0bbTGolcaNRqNuHDhQrrv8ccfT1UEcjrVb5U561CziYmJtHd90NzJ6XDbL30kHSMHudvtxtraWqUaRvaLc38a1gD741nZ3I87u67POWaepyC947ZF8setXSrFV8n80NBQxbFVQFtvlJAek52JODygU8FInvmg6+vWqfrKa3ycHrTwHwaxcrzTNZILPZM4nhnviMOALF/zyHuJ2ySzakPYiIGiOkdWz9RzGQylzeQP5zYXEMnJnMvPILptx7Yuo8jP6jI0UixS7Cpf8fcIStgiohIhlgEVY+bn55ORnpmZScfJewmkXlWie7n3SEpdilwl0YroqGRB/aTw9nq9dJqsH/xC0EO+UIAJ8CT4XDwuxHRIVY4kYHPx4sW0PyTiUFGoHc2H9lJNTk6mPqv0dGdnJxkJZe7W1tZS1tGjLMzoknh4j8aqPkmhOOAjr06jY8vXf0iGGEBQNFsyMTMzk0pIFxYWYnp6uiI/BC2cn4ijhwX4whfA0amXKj0UgKcy0Ss1tIZUwi/Ay8ignHKuPxplGXQZF+3znZycjMXFxcreIfWRh4CwX1yjLJ0Xic8KVMlZYulTt9tN/FTGutVqJcdd61rZha2trbhy5UrFOeb7eb2Kw9cj1x/nT9czs51zyjVuGQrKf0RU9KNA1OTkZMp2nDaiHEdUD6qTUxJxeIBFRFROBtf64Gm4LH8loHU9oeerPR50xldxyFG76667YnFxMS5cuBDLy8spcKny/sXFxVRa2e1207rhgYla/5INgnPKMOdVc8pAD6sdcmDPg6UMWuoZW1tbqbxTh/o84xnPSMHdK1eupHdRK7ij14HNzMxEv99PpdvNZjPuvvvuNH96juTds7YsL5M+0Bpl8E8BZlVUSP/lqg+YEZE+VVBOTuBJk9awHLGIqOhzAkqu6f39m4c3dbvdVNUiXcqM760y05Kj7e3tuHTpUly/fj1u3LgRo6Ojcd9990Wn04mlpaXodDpx3333xWd8xmfES17ykrjrrrui1WrF1atX47HHHkt6T6fK37hxIx04poO7dIDS8PBw/OVf/mU0Go3KFi3+6B3oqpLgXl3xR2MeHx+PTqeT1kFENVDA/dpaBywxZhBQB6XpFXfcR/zoo4+mA7BmZmbi3LlzqcKp2WzGhQsX4nM+53PiiSeeiCtXrsS73/3uuHTpUvR6vTQWOk4bGxsxNjYWCwsLFX1HW0ldqITL9PR0GtPZs2fjzJkzqVJhY2Mj1tbWEj8YPJJuUHunxbEV0b57KbJnHGkr6S+QxC8FEFqtVppv4umpqamkk5eXl2Nvby86nU7K/J85cyYmJibSgWc6EHZnZyceffTRpFempqZiZmYmlpeX0zho87lGRXJ4yQPpSD1fcqzKxIhqtSGD/lrztBseJGDwx/knLK9g7vj4eJKvsbGxuHz5cqysrMTGxkZEVJ3j1dXVJHMKnk9PT6eqAgWHV1dXY39/PzqdTrLXdHIZ9CQ+5vgcGykZkfOP6Dt64Pd26NiOrcid2lwnjnOPlzfK4SFoZBSUDBQopADqOXS6FJWW8iGzCZ54eIBnyvwzGiBGsD3D5A6/fy5ycKjPtMj0fPJOC6PZbKb3/2m/mByU4eHhBFzkmAsgSGlKiMU3349LnlJIvc/uhMpgcXG745r7yV13Wojj1/8Ct3K6ZPiHh4crCloOFqsTcu2T53WRUK4zVlCoL5IdKTeV2mp+FZgg8CcQI+DguN3BiDg0RpJBKnZGEfU/+UZlSMOXu56OIO/n3ywHokFlSSmdLbbhPCeoZ59d5t3h5LgI/NkG51h9obOrTAL3Vp4WUFMHSkTSNQ5oSNTxNHh0lnI/EXGE9/zO1wed7IjDQ3x06qnkibpfa0TlsX6uAe2BriWwU99y6zi3LSNnQ/Xbx+46QY6GSkTlpMsmzM3NxcjISHIOVdWk94oyuKVggPjkWx7cFjvPOUYGMZntUGBM65Rr3cesOcgFvE6SPCARUT1JVKT/FfQ+ODiIycnJFDjWWqeN1338O6dvJF/S8Zrn8fHxJOujo6Nx9uzZuHDhQjpsMOLwJGu9y9b1s+t2ycvU1FTl1HitW62lqampdK2fs+BgnBgsZ/9F1BHMJAmjRESyswyKUe9QllimqnunpqZiYWEh+v1+TE1NpWQBS+21NmQTXZ/nbLnkQv1TJlAZcO7dZxLA15n4Qvt7Goj4w7Ei9dqg+zk+2nAFKh1T8xpvh+9Wlp5hAE591dYA6fV+//DtJJJ76m/KqLCT1h7nf5BuZD8d44hy9o4YIiIqDr7aZRWAEnuqTCIuUXCp3+9XnFJ/tgJDBwcHFZus54uPaiOH4XJtO/m1g3wkytrt2ILbdmwjjr7T1jucU1I0fjLCOgRHYE7Rejm5iiZLmGgoI6JyYIUEd2NjI4Gl+fn5aDQa6fCBiMPIMPsrxUxQIyOhiVFml04tTwKUIqRAESjTEXJQ7ZFLOp/6/ODgcN+ZMrXtdjue8YxnpBI0RboXFxdjbGwsLl26lN5Jevbs2Zifn0+Z693d3ZicnIwzZ85Eu91OrwmQwdPrYprNw9ceMRBBofb5Vzklsy8iL1HNRWl0r5d3nDRJkQjAK7Ov11hsbm6myNnMzEwl6CBZl8LOkStDyg8NiPih7ySrAtyaA50u2u/30xyOjY2l7Lyur6vEkEJX25Jt/VZmenFxMaampir7Ywj+HTQJsLCsmd8z4iujoDXPTKf+F/jIyRr5pTIlRV7phEqH0MgxiOHlctJFnnWVXvCyJOpG9Ytlf6oY0eFgKlejk3jSlAOp/Fugk3tzCF4iqofviPcEMiR3aPW35FvzQEdW/OUheipTdDDO/mvPntriuQV8rtacDpdRtYtKcAl+IqoHChFocU0oq+vym+Md9+eqzEy6Wa8Ykw6iwz00NJT6qb3PCvTQ0VK7qgihXOcyilw3zLbJrmsP5t7eXrIbCj45dhAxsMBT6E+amK3x9UiA1u/fDNzPzs6m8ta9vb2YmJiIpaWlpE94kIu3686zcI/maHZ2NsbHx+Ps2bNpf6Hauv/+++PixYvx7Gc/O8m5MoQ61O3y5ctJdmUn5Gw1Go2YnZ2Nubm5mJ+fjxs3bqRXRqn8WAcanj9/Ps6cOVOpuuB2CtoY6QfiHQexuk/yEnH4/mtl23TGwvj4eDpbpdlspjXM7TfChZcvX46IQ/shXiuz98ADD8TS0lKsr68njMMgBBMD6ifLqN1Jl01aWFhIbWlP+xNPPJH2wW9vb1eCeuSN9D7tx0kT8ZrWfES1RJl4QpTzCUiyyTqATDhFNls+QUQkB1i8UsVaRCQc4Jlj7cPVFhnhJMlMr3f42kbH7BGHB8xKNhQI9IylYzOR5IWYLod7NT46oXLAWR7caDRSRdf8/HwsLi6mE8s7nU5a3zs7Oym5t7u7m/Yla6+yqsNGRkbSGQgHBwdpWw7fKqDfWt/0A47j2HLOPcHjWCKHGW/Hub2tUmSfrFxWKWew/LtG4/DwCwJ+RsZpbCkYBKJcSDxVkiWOimrqWmZJ9CwpZJWqMIqmfXlU2LpfpzlzY7UvZj6Xji0jVQI24heVpYwCT/WTQz0xMZH4w6yhTrkUQFEpAZ18Rv6np6eTYdC+Eu15YhBAxol7TPRdDpjRgaesSIZcfihfddGvkyJWDqgvm5ubiWcRkcpHuadTmUx/v1lEdRHrfwKBXFmPMi36YfCj1+sdyTRFVOeHa4vfM3LP6CDnX9sH5BgsLi6m0kft3xsZGUl7F3OBLlduXNPkgesarQuXBd3PfefMGvM6jVvzmFPCBJp0YOsUNiOXOfnnOnfHQP2Znp5OwEfgaXNzMxnp0yD/Efl1qPHV9Y+BAGaxGeBjJtSzAO4cqw05kQTQCjRIp2it0Hn0LADHwc/4PwNLap9lytRrXkngAQ2uKeo+jVX6RYE9lx83+gw4bWxsHHGe2X8eDMcxqi98jhxQAinKAH8Y6PY5V+DXMwWekXAdmAPHJ03OfyeNRZk4OeYjIyPpJGod4CSQ6W8f8PYEIGV/W61WssMC6+KTyskbjUYqVxaw1XYpAWA9V0E16R2V/G5ubsZjjz0W165di8ceeyyuX7+egkMRUcFvwkVetRZRTYKon1yzXDfCW8psK7BCeZBToeDH9evXU5aVW4ME2PUsJVP0ftpmsxmPPPJIdDqd2NraSg7CuXPnIuLmetCeTe3n9T2grst87prNZtoGMzo6mvadaz+t46M6e0n8eNKk8fKUXmFTx/45fEO9RBsqTKvkEXWI7KCu5U+/30/bNRhY03ui9SwlcjzwqPcUu62n7qMOZnDJA1S0ZdS9EdWklf53p1b3EIMomaWqHK0BOfc8sFayorMN9O5yJcP0CkitdflNCiYsLCzEzs5OLC8vJx2lg7c4bmFUOrf6njjO+ZpzgHMy7U4/bYICKbeip/QeWzeuOeHNeeucSJVKsnRFA2d0g5+5MaTQaGFJycmQqv6c4CSiundQz5dDy5IWnbbKvXNSzixDPk70gSDVnUIRQZcADvukRSklq/5I0cjg6W+NudvtRqPRyO4J4emYOvVQkVWeNutZNBLBnMZOZUQg5nyp4xVBz0lH7SmXWgvKhm9ubiZZkUPrh7tQTnKOgK8pOrIE/FLgng3VdVJkdSfKchx6rj6nIhYxsKQ1qz0YMzMzaY8t99DRoePYvB+UGY7D7yEY9j57NoNlcL6udB374H2i8+WOV04RcwyuuL3vrtCZDZBjq3lW2ZwqWVgJctLENavfuWyeOzG8xoMW+iwXyRZR9wpMU7dEHJZD+7rwIA//dln0Oeaa5JaNOjDHcbmtiqiWhhE46fnqs8YhWWHVgOwTA6UEx5QxBry4l8wzDJRzt7W+lqnHGXhzvvJgPdcBdWCdzzhNpEAi+0U5VUBhfX09ndyrzMfU1FTKdi4vL6fsJ/foqh3KA3WS8IrKm2WvWRrc6/XiySefjIODg5QdVpal0WikyhplvAjMZeOFIa5duxYREdevX6+8Goi2gCXVXI8E8Lmgdg4vCReq7e3t7RQ4URvCPVofa2trqTySa53OtiqUZLva7Xb0+/24fPlyerXPhQsX4ty5c/EZn/EZycZevnw5OaIRR/fYs+85YmJkePjmq7W2trZiZWUlnS9DPECMqPv1jNMU4BHvfSvGoH463uPndBBVSUXZZ/UTKzcpT6xA49bDfr+fsrCtVitdI2IVqK5n+3qGO7XEE6x2ycmF20v97QERjdkdWzmgul/PoyxSF8uJ1fYBVd8o4KZ1oABtRKQKG70bWmtAOoeJA8fyEVFxbD0YcByn1p1YrgfJHCuQbkXHdmxp2HPlBi60XJwUah44MDk5mTZ3iwlUtMwM5pxlf3+TFLL6SGDlgqmDldRX7t2gQ+slIlxsUrLcgyGw4VlR54uuVWaZC9Cjd+58SLj4Pi61xcUrQ6FSHQm4rlPJo3hDcK1oqbLe4peidcxSU+loj5b4TmeJ46WAEjhqfHQCTwPAUTZNpR6rq6tx9erVuHHjRqyurqbX3ShzKSWtMnt/9yvJ5SOXsYyIpOB0oq8qDChvUmb6nnvVKHtUNNzz1uv1Ksop4rA6QU7sxYsXY3p6Oi5cuJAODFFkT+3lykrpxFKWaaTUhta+Gym2R0dJa0UVG54FcVkk32W4aDQ9q5fTP+50uPNG5a7vBfLl0CoarVd4KDKrMqDh4eF08MxpIp9LfeZGy8uWIiIFXsQPOojkF4McIs0Ro/iUHQ8Ged98jnVvriLJHW46tNyKQwNO+WBmlPqO1/o6ofyITwzWSo8woEldmXPQCfjEZwaKGXSj3ZZeUJSeOkLj8nXh69Qxgn4z48H981xD5LOvvZOgP/iDP0hyJTlQAHF/fz85qjqUaWlpKTm1L3jBC6LVasXFixej1WrF2tpaAvEce8TRYJh4zEO0ZmZmKk4cbfTi4mKsrq7G9evX47HHHkvZFWU82u123HPPPekQMc0D1zP3I+7u7sb4+Hg885nPTK/QUjZUB2J5IIoZyYhDgK57/PwNybV+j4zcfDf71tZWrK6uJjk6c+ZMzM7OVg6KEiYRrlHfxFOtW/FoeXk5HZ5zcHBzu5vKrnUIneR8e3s75ubmKqdHM0t2HKLDpvcOS/fxlX20vxGHhwlq7k6Lc9vvH25t4pksOR1wKx5J9nSonaoHKfMeEKVT2Gw2U9XA6OhobG1tVfq2ubmZZFsB5JmZmdQeK7jUtnSO1g0DGixHpzPOYCUDqtR14g2TbDm9xvFqTbM6T+NR4Ee0tbWVAkLDw4d7uYW3WQHLA0fl6Gof+F133ZXaJCb3bYj0f3IZa46H1XLiT843kp0U78lvP0h1EN2WY8sIhnvs7Jx72xR0GUxltjyTxeg+28oNyAGphI2UExoXMv7QoXRQRGMs5UsHgf11Z9SJ9/hn/M6jsQQlyuTyWpXyqD2CKPVraGgolcwKWEZUD4WQgBEAOWDi3B4nIpPji/M6d+1pIC1clXqsra2lPT9ccAQZLPPKZfYph3VEGWUmh0bdM7qD9sDnoorktxSTA8+JiYm0b3hubi458n5gCNdvnRywP+qT1jLlQ3LhUTy1zetzGcOccXVec434z62Mco63Hrn1sZIfAsjUO1LoCpZpjWtNnyR5/wfpfvKeskA9zjnNBS4I8HVfLuigz3kfM7S8z4Nm1D05G8M14uuQssd+kRfMhPI7d9a4Fnxdkk+5DJ9+U0frM4Infi45zfFj0Fz4PNfNO6/1QLhsFX9ktwmMTpsNuHz5cpoPBjjkMErnnz9/Pn2mLT0CiMQOrifryOdYMkBHSDYmIirVQloLClQr8KeMjetUYiHNGwNzcuxy2E7/u34YNDZiRcnC+Ph4clQbjcYR+yq+6S0XlHFW7ajfyvSK9+oPK9/4zl3nt5wL6WW2wTEMWjMRkfa9CzeyysF5UqcHTpqoK+kDuAyQBq1l8VhyxYST44GIw/M3/DmcezpOuq7X61XKZtWeV3iRx7qWWJ+2iLqLGLuOL4P46c8Wya/heSnucwhH6Pv9/f1KZQOxlfrKwAArgZrNZiUwJIeauotYxwPF1N9u23K4Mycn/jfn8JOWsRUjpdAjqsaI5VP6TVCi2vCFhYX0omqC6YhIG7P1XBp3RosZFfSIil/nv1lWpD4TNOh+lhszWyCwL/Ap4fADR/hb/ZGQ1J0+KWo0GslIqc8eAVf2Tgp3bm4u1tfXK0C43++nEtVG4+ZrWu6+++50AMX6+noyYCqd4TPFW5566wCEYJ7lqxRmCiYXZ84JYrblNJBKnTY2NuLy5cvxxBNPxNraWuV9kb3e4QvjdSKmsrgKIrhzK3IZEH+ZIeDeWh2CUOf0ch1KYfFZVIS+PqjQp6en0wEocmzPnj0brVYrldexVEW8orLX5yxXcieSGSOta46fIIBAnLzT37lADtukrEl38H/1R+3lHOPcmpb+4eFR1EVunAU2/RVmU1NTMT8/n8Db5uZmOrL/NBB56waWh5G5sx8RWcPE+z1j6qVWmh86jS4LeiadR5Ym6jrJO+0GiVkb/c+yu4ijDrPGqP56ZL7RaCTw7H3XeN128fAdAWKBDzodLC/1gK3adUBDh4hjdpDlQF0OkoNHJ+or8aLXOzyNmnbFg+f+/JOmj3zkIxERyVmk7tXer5GRkZibm0s4JyLSq8UiogIUGUC71fh0vYNo6nFmdVRhI17rYJitra0YHr55IrAfhKixsOJNgW7hHiYRtNYiDgMYOQzDdSweiYd0MHTgofBXq9VK9lXyoDNZ9AogynXEYXWRr0/iOAVjeZq/9sKurq6mPinb1e/3k6PAypw6557PjThcU3p90vT0dKq8ylVTOW4k9j4NROeN6yDi6IFg+sy3KtB+SB51eBNxpGTBg3Wuh9V2rtqT+pNyIRlUYkfVbXoXOLc30W7ofgaQ9B11HP0e3uvrnbrAnTutC1VFeIBQ/NB+e1V86XVVTH5wnStwpG1kfHsAt5XpnoODg0pVh/pI30h2kHZa/aPs8HeOH3UydzvbsW57j62foqqHktHqbK5z2iOSK+nIKXkykpEaZ4wDCfalzhnzaIZHG+TgUblLcWqSGbUhIOHztDgHCTY/k5F3gKGFLmCkdpn5GRsbS0pC96vsb39/P5WORkR0Op10krIWtRS/XgvBZ1HpOKDjoQpUYLkfn2s6VlRiNJYnrdT1ioTV1dW4ceNGrK+vJx440JWiIuDJzTnJo5N0CNyASqnzx51TBhJyazWnRAXEFOBQAKrdbqfTJycmJirvO6Ni49xS2flz2E+OV/PMMUsems3D9z1T9nK6gPfzc/+OjnUuyMJ5cgfX21Z/ZBhkOGQYtL78FEnNp4Ie2h7QbDbTSaaqCjgt5IbKeeMgXo4VAxhu+BxgM/rO6gMPgnHOPEpOPU59Tb3q65L3yN7QoY041M9slzYoRx5Y8oCHrx0CRck5g5t0YrnWXLZ1n/jjAQd97v3X+H1MtKX+ozZou/QsZWK4fsWXRqNRKenVHLgOOWmi40ZgJx7KDnIbkA7FkfPurxshuRy6g085cZ1PO6kyTNokvtFBZzTwIBaerK/23JnY3d2tOJbMsAkXam1o7oQpRkdHKwE8rWv1T8/hFi86DeyT+M+99OSfAkDqN5MR0rEMyjQajRS8Ut/0TOkmyaGeKfuuAA/5JPkdGhpK46a+mJ2dTckHOmi5oADbuxX4fzpI8sZtDY69STlH10mYk6dpi7jeKM/ubDIgk8MX1CP0I3KYXdsQPTBBPSedxlJpyQX1HvUgcSwDeTnHVp9TNlwu+bnzWLLHrY7Eg0pgecIuF8ASv/2sBOpyx73qA5MQdQ6941vaD86h68NBdFsZW4/QcICcJFfQ/K2oHEtlCGhzi4CT7c/h8/w6Mj3XHwm5M8sNMksRFN1nVpLRepID5Bx/+D37qewqF4UDb7XJfUgsM6KxEVjSgQ8RkQ4+cgCq8dF4qH2Oyx17/a3+5BwMH3NujgmU6+bo6aatra3Y2NhITu3W1lYaf8TRklbNXQ7AOFH+I6pBg5z8cuHTMPInIn8YT50jwv4rktdqtWJ+fj6dGKnXiOg9oFKO7KPGwb7nHCH+z+fnvtPnbhg8mJRbH/pO43QHnOvO/1cbdbLHNc05k8HTmpNDyzXrSlr80mmjjcbNAJYyFjzw4iTpuA6GAwg6K3WOENc511ZujiOOZt5zRl7fe0RZ3+lZJM4T7Z3bHhpu9TMHMnI20QF5bu3zf8kQI+gC7bkAmPcrIpItYP85p953Anu27+vM178Dr1xAQfd7pYbknHPGPp4kEReI1H8Fp1TZI4DI085lG3nAzSBnhTrCr3PHVs5cr3d4zgLvFViXfvZ1xAAp5ZzZ//39/fQGCd9b6A4F9bZwFOfc5YPrVIkEOaN0MBQ0cDnTOCSvjiu5JUjvraZM6XAeySnXEDEY15LmOTdvxI9KPETclPupqank5HMtk2fEA64/TpLUXz+TwJ1Jfea6jt9Rf3A7k+tnPZe239vSNc4n8tHbyWFO18V+D+1IbnuZ+k2sQvnhNXSC+b/zm2tKn6kNPUMyr/ZYhcR1kivRlg1RsNQr2DRunp5O/nE8nDfaZ/9xXnFNS594UI9jvxUd27F1QM3JdlBC8kGqrFFZW4IXCTWdUjGPpznq2SylYT0523DATGMtsOD3MLqvPlKQucdE5HvlRB6xYY26lysyAqXJJZChIDs4VumF3vtFp5ygQkZVJTdDQ0OVEhAtXGZnmRl0kMp3vem5MtoMAFBGyCMuZhpo7rXIKaynmy5fvpyy23JANOZerxeLi4sxMzOT3iXZ6/Wi2+2mwzUajUY6rCOnuMkPKRbJrSLfETfnXe+L7fV66XAPz3iwTcq8iGtV49CYtJ9WmVr9ryoLZmoJpqkj9Hw6MRofZZ7Oga9TtaEfGlMH1LzeT8x1o5XrlwP9HKhgv3WdiEENrZ/cAVicb7WrA94kQ+Pj42kPt16/dRqAfcTRQBwNp4Nan3ca237/8OAjvXeSwEXVPCylygGniOq+Kz4r5xjxMDVdS72n9ayD9lTi1e/3k8z7nBL0up1hUMaDLlyDDIZRF4tUSqntB3qXJ/f8aV14AIDt0364o0s+aY6pk902suxeWSvJgV4FsrW1lRwtvr6KQQZdq0Csgt+0H963kyBuVeIbCfg/+6nPpa91tki3202yTx3Ge0nUq8IELK+UrHG/r9aDbEmv18vuX9SJwRoHz9vQM/VO2bGxsThz5kwsLi7GhQsXku0nXvDAP+VePJAMM0MnZ1kAu9FoxJkzZ9L1sqWSn06nUwn2uV3V81hdFxHJ8ee5JRq/SjlzGSyWJKsdzgXXlstMr3dzO4P4Mj09XdEv4rfbKFGuMumkaHd3N5VRa8168IXOYq7qIyIq+ldl8woAqQ3JsHCzZJGBoYjDahjJjvCXE/vlfgDL4nktA86Ubf6dc6a5Zh2Hadxac3wW51rr0/mYC0y5zLhzrX7Rp9J1qsLg2LhmOQf8jn2nXdH1/h3tMvvk/dd4fTud2/9BdFuOrXe+TgmzMyRlefjuWhpiXU+gexxnmRPt0WJer89yEWUKMbOPuZLj3ALgYqkj9jf3Of+msDv/CQJzz3AhJ1/4t66lMOunroSAi4v3OjDK3Uue5Yi8dIB10uTlY+KxTtmbnJxM0WwpJwIDN8KU+4g4Ml9qQ6UfLBmRc6Ayk5xz6TzMGRfNv4Cpqih48iX3B3OfUi5q6s/guqQscL27jNARcidJcscx+9om/3ysfE7Osc397XPjBiPHVwaWtDclp8e0bnRCuTK1BweHL5RnoOK0UU6eXB87v5kR0Xfah8lgCdeQ67vc32zP9YgbWbVPmaDjpwwLQavGxz5xzHxuxNH3FrpN8j7nbAP7re8lU+onz0AgDbJF1BOam0G23Nc21yTXEfkjYKK5ZTknHXCOk217Fvo0EO2cj4W4gsFmlSHregUD+FYBUR2+oj53fePBFP+JqPJX7RIgU/aYQWfpryrBZmdnY3Z2Np3ozhJFB9x6NtsXz6gPHTPwefv7+7GwsFB5r7d4LkdUnzEh4gEbnycG0yMO98WTZ1zDblcH/XBMDCCo3Nvl2ueiLlN8GuyAytz5ztg6ysmrfy+Z85OFRa4b/P6IW78mLkcMyhJrePu5ikRiIGKT3LjrbBU/o430uZYddBtG/uTkXZ/n7BB/M1jpOCfnR9Th+pyO4W/XAc4ntsPvvP1PeMaWNOgB3DehHwFRvftS73ZTSYgziJFFBwsEFXyWlAABke7nbzGXkUkRDRCjfGqXNesi/a3n+p5G3a82qLQEAHQNy4Zp2P3wDe47ITkgcOEjH0gO7jQH6id5IN56tJmC523l+pIT2pxy0LhOGuDkeKn31mr/qU76liFTtoN7nijLdfxxkDs0NHTksKdms5kcbT8ITXLlSt6BtKLWyo7o0Cu9hmtiYiIdMuKlYXVOBEGDjAAjmwTAIio6KmSXCe0B1vi4DhlZZFRQa5l95XN9Tj17QqfCgSL7QR4QmOkZdEY0NmUpFhcX0+vP9GoKZWpvBR6eTmKmMweOCXK5B0p6ieBANDR08/Uf+rzVah2ZMx7I53Moh4kBQMmgV/lQ5gmiNK6Dg4O0VnUIR79/c+8511dENfjqgNYzpzlQkgMnPgZWYYgnCmrp4JmNjY2kiwn8GYTTmsg5QTliNsOv87nMBTh7vV4ls6OMG6uOaE/FT/WVh6+JBgHkp4t0MqmCVQSMjUYjvYJGe1FnZ2crVTwaU7vdjoij+3Mj8sFJgvYceJeMsaJF/eB9CtSoyoeZX60Nvi5Q98oOXLx4sbIlRXOv6jvKguMt6QThJ60jOiXEUkNDQ5XKPr1iT3za3t6OlZWVuHr1anp9EEvAKSvKmKqP7ghJ5thnyh/XtHSK4x46SSTxYnt7O5rNZjqxVq/x8sw6n63qO1VsHRfUfzJpeXk5ut1u0o8RR7NzubXqtpQ4RufC8BCmiOr5Opor2g4/v4IyJKpzlDwI5YFB13PCPayClBywukFEO5/DSzkfgfpAfWRVoD6jfWE/6SBTnxBbut3WZ7QzuQAVsaM7phyDrnE9xaAes9JeBUp5Yrm/eODzW0e3nbHNAU4NJAfC9H+z2Yx2ux3T09MxNzdXeferyEECmeIRcgo5wYKDBzKXQkNApvb5EmJGZ/0zd+J8f0ndGChIOdDAtnUtnUn13x0+joE8paD4fLhzwTkgv/UML7lwZ8xLKPhcB1j+PdvLLTj256So3W6nDJrAt5ScnL5Go5HKmgiAfTFzfnNEPgj4yamLODx5WwqNB5b4wQjOe86r+kZHRH+zzNADLSS1L3lmlsaDIn6QgfrBQ0Bo3Cn3dRFJ6iW15/2UDOVkVG05/3OyyzHr+bn9zWxfckJApTnwswYEOrW39jSAeSdf327wtIdNh4/pOoIfX9+yHTSuIt7n5cbUe9TDvn+ROoxrwGX24OAgtra2EuDn3PH57lBwXL6+2E/Xx84H9lVAW04AHQQ6qnLGNRZWGXl0XERgz+8ISvg9X7GiH9cXzlfpgJysaLzad65xU1+Qf6dlDfgcRRwCYvadAWidkh9xM9u1tLQUGxsbsbOzk7Z26P6cbnUArGeLlwq6EFNwz6aIQQ+We4oIxNW+srI6qf3s2bPRbrcrh+sQULMk3Yl4QbJNx3tnZ6eScFA/VEkkkCsZUxB2fHw8lpaWYmtrKzY3N9M9zifaP31P0EznXnMoe0HAT8eAPKyz6fv7+7G9vZ1Oo464uZ70OXWK2xTHmXW8fTqp2+1WDr3yfdmkQf+Lj8IvdXtsmQAQSTfoe9lU8TKn2/i3+xOiHJamnqO8057UBaXcd3FHj8/V98Q1jh0k/3XrnQ449SptNoOW7IfWJddHHd6nbB6XiDf5KkN/hssHk31MNt6KnpJj60Ksz3MKlY6tIn8zMzOVPSk+MAe5dKw4URRsB6L+t9rXhFB4KLyMfhCA1JUO5RxOB6S5iA0NCRec91fKVUrN96lyEWos7Af5OmhedR2VBvvvkZy6Z3DR5MgVF/nszgvbP2lw0263o9lsxu7ubtpjKseP4EKOLeVe13G+c/Kac9oki3IS+v1+5YASti/lJvJMiwdCPIvGUhuNLxfUcaXG9c9XEuXkivdwzDk5FMDwz7iGcxkl560Av9qQM+NrXL/d8BDMuOzWObVc0zTCPERKDiAj9FtbW7G7u5vef+m66CSpbg3SICtjwhNN2f+c3NC5dVDnzh91Hu2CiFFdziO3lKhtnrOg8rqtra0KkCTQJeUCcNRjuTXjMubOpvNROp/385wHZX2UHZVuUFCB9qDu0B1mFZyH6o9stfPddYfakJzz1F2XIWYgOFcMZNI5OQ2kOaYeYyZccyTeyTYo07u9vR1Xr15NAdLFxcUjWCEH7kjUT64DmTnSd8wIRkQqXY+ISsCE1SG6R9tr7rrrrpicnIyFhYWKU0hHUOMVXnHdSN2tPZCS2V6vl16xwsCn+qa9sLSdwpM6v2Jtba0yD+onx0iMFHFY7UEcyUQCsZp4fyu75fZEwcput5v2aapNvcbI71cmUvPECoGTJr7akI6t271Ba5bXehDdbR31gv7X/GsNSGdwfuocbTqaEVU8qjb8HAGvTOG93g9/luMxtwd1dl2fs33Jq6oT+KYEjd330XMuqPMl03Ri3UZ6f2hPB2EBPsvvZdZWOiTHC649yQV9m1vRsR1bvubHB+IAhQZOikbvd9N+RDekVH7+jIioAJLcQsoBp7qJjageBMH6fgJotUtDwrb8pFJfcFRizErpM+8/I3IcDxWyQLlPMJ0S5z//zoEp8YpOjTtZdY6t2qDiYYST9+n63GeuzMgrz+KcBN1zzz2xu7sb8/Pz6aRaRS55+AnnWcBgamoqvcOOYzsOSab4bmdFeiUTeuWAjCGVPNcsHQGfA33PQI8DasqVPteaFw900vbW1lZsb29XyqQdxEYcyndEZDO3VNS5e7me6FjIUSS5syrS8xm9zIHNnO6jvtA8iVc6JIVOEU8eVLBMDoocK5Wgce/aScs/yeWHMqJTYFVazWiy+EinSePXYToRhyXsrlfc0Yuo6jVdq+wUjbR0Fk8WZjBG60QOIdcRgxE5h1YAL6crRbQfvn1F5OBI/aBDRQeS7xoU0OGpu4qOk+9yIui0qg2CRM0Fx63+qELFQZeAVq9383ArBWl8XXOdcbyaY2Xi3H7XgcCni8gHOmQi6U1tP9H+z7GxsVhdXY2rV6/G+973vhR8uHjxYno/ZQ7c8X9ihohDXUnQrXbGx8eP4LXd3d2KgyZdxTk/ODhI8jA+Ph5zc3PJoeWpwpJb2Qr95OwJ+0Dbov5yH7vWmb9GRdlZ7bnVmpMsDg0NxeLiYqyurka3242NjY2svcnhQfFEzpNOK5ZsNpuH71mm3hY/PNvoMupb0/r9firP98NxdK/0nPrkdvwkydcx9Rp/XEfW4R3XJ+KDO0XMilLn6Hm8VzjIk16D9A6dVFZucl6ouyKigpNoC+Sw63oPXkiPcyy0Ye5sR0R6/aZsmORfvoW2F8iOMjOaGwfn0m0a7R3toOOiQeTrQLaD759mu+I35UX8lI7IOdx1dNvvsXWnkqDVHUh32qSgmP2sY5R778f5OwdGSVQiBDy56LracGOTexYXskeW6GDwdy57l+t3bqHrOXxWjn+MFuVKJmkMHThx3IxWubHN/c7xjzyrmyfnRd09J0ECWiyzFUDk2uC+OO2x5Pv+SLkx1S1cB805x4LKgUaHCt3nln1x8O6KMNd/z9RSwTIDx2fkxpZb7zQk/hn/dyPIdl0n1T3XZfZW8uay6f1z55trieVLkiPto1ewhFUAblBPK2lsfMelQITzgwbcbUhOPzp4d947iHWe6Xo6ornSTQYRqbdysuP95nO5LnMyWWdb2F9G0sXfOqDVaByeaMuKBPaJwWc+x3mu9vSbesH1Dp0TlhYzyMf26v7n+Li1g/w6aco5LgSgBNxaz+L1zs5ObG5uxvLycrRarQTYHC+IOLcMJFLW9XxfEwyoEy/knAW1RYdzdHQ0JicnU0CWb4bwADdL0Skzvq5z+FC6QfIacbPUVeP1DJJ+9L2cUp06rexOv99PARXOkYjbdbT2VR7NPbTMBGqOOV6vVsjZMX82HZNbrY1B7ZwmyunIOqznPHJs6VS3/rkWHA/k9K/bA9dvnEu+4SP3XB+Lr9tcP3ztcc5zOrmuPWJ38Y/teNKLvo3zhfPmtiSHLUm30sl1+szXS44nOd6wuuI4dFuOrX4UDRykuJilaDZvvhbm7Nmz6SAFV1IUTpbU+CClrKWE3Fnj9R5NUQROTGWGk1FqCg6Jfcg5DP8/e3+2I1mSXffD28Nj8CnGnGqeuptsUSQFSoIgSNBD6AGk99ADSYCudC/xSjcCBJEQJYJNCuzuqsrKyiEmn91jcv8u8lsWv7PCjkdkdVdFJP+2AUd4HD/Hjg3b9l57MLPFYpE2CZDwU3t87SGBm+6TYKYnn+2hopOHnIxN7yM9jXqG4+PRZnlUNC4CU1SqBOzsDypJrm8jEGK/uYLLCX8KrIcA7MVLSiHSUSwaV/WvvLGj0SjtMKydMdnWXJQ6Iu8s4HdGVLQJhfjaQY4Dooj8GXAE5jnBUzdGEdfr6bT9/3Q6TUczKE3SQbPa78Le3+V8yL6qU4IU2AROLkj119vpgt/LzxlUcmjIUNKa562treSZ93dRPp6fn8d8Po/JZHJjoy3Kx/umOoVMJdtoNGJnZyfOz8+T/KHBxQiXnsmRxrtOjpN/9F33UY7TcNCYyaA4OztL72O/ax4wWkKdp/IirjcwWS6XleU1bgS67lH9HDiwTbqfjmAHW2o35Sx1jYC61re7Q5RHw6jvfEzqQInepSwDeeQ3NzeTY4ugkePBPub/OvpjZ2enErV7CJTLqFLmjNZNun6YzWZxcXER4/E4RqNRjEajNGeU1pkzWtjnek9E1ZCWkaT7ZUgru4cbDjJzQu/kvNKRf9oJf29vLwUhuPZO/Mr0dEVySYy0aN7L2JQjjzqLGwFpA6ter5cMSddlcjIvFosUMW02m2njQ0VvNQ90rM5y+TYlXHwp2cSj2dQX5HW1VScE+P4TnINra2s3AkGsu8adfERsSQyo+fGQdsd33Ma2UmZShkfk9bb6lju7e5/pPv4V0WHH/ylfiPdVN18ry6VXfI/LW/Kh4whmIuhZL4P3cnyJK1h2Tt5zDoj/Jeu1kZqnKWv+uoOY9ZIDi1kY5GvRqqWG7BvOG7VVkWSup+a9lHOaD5IjP/oaW7/GCUfjUgyq3WJ5Xio7Qs+xDH8H73djlkai7nGPC6OX+p/CmuU7yNB3BwUceA2A6kfjlxNIKRJkPDfecm1mORLG3ASCDO/j5RMx4uYRD86QvI/C3cfW3+nluWLw38ioHnV5KII8IpLBNh6PYzAYxGg0itPT01gulxUeGo/HMZ1O4+TkJPb392Nvby+V4Yo5Im70ocj5m+BaZVGp1o2/rw1k+XV/OQ66xvUQ+s3rJuGai966Ic8+0d+cE0aUM8hz5fC6e/p9HrmXve57ro/Yv0z7o1KT40GAKaKavqRnlIasOjP9R+XkIv4/NeXkMvtBY8QjTTzdjnKY8599qfkh/mLqsEcd62RMru7+HNcAE+QTmOUiarmy+JuDERHbTz5xw53tWlWW/nJuqS7UiXxXbp7wN84JB518ns6aiKozmvPAo1mOIQg6aRjLcKqr731QzhnJKAL1bUQkGShDRg4stZVHyDl+cR3s7yUP1kV8WDfhDqUbU78yw6LX61XOtHS+ovPI289+0keyUWCbG53RGcjlF9pYSY4xGtHibckbNyRkYGvpj/qYjh0a1CpT+s2dU8SJPOdT4NzBP/sr4vqYQBoD1Is5ueJpxzl9eF/kcjEiH3XUddoDIraXEcWcLZHT8yzP3+sY04NEOb2v+Vjn1PO5mGuL/lJveX9QP/lvde/LtZ/8q3cRJzCIpHfV4R06Y7RMyrMScvonp1v8em7c+U6Vnwta+LyQHPlRDNu6RuUM3IjrCbm5uZk2SnEvrLwO/i4OCmltba2SwuMKgR3twobv4pqInPCmMKkD/YqKrq+vpxQavSOX/0/vK9ugslUf72tXKnqO6whygo/XckKX5Xsd2B/0HLPP+WxuojrgdKHjzMu2O3C+b8E+mUwqhm2/34/j4+NoNpspCyEikqf++Pg4nj17FvP5PLVt1aSsM2wdvNBQoICIqJ79KnDuzgafN7mP8xMNF7aB93NtnwxbpdZyfZfq4eOu9uR42Xmlzlive8aBuhtYul/953Oibt5onvsmGm4QRETFQ0/lJhBHucRUKIEqrrG+L6pTWD4/1YbNzc20QQz7X+SKnqnrksEuKx00CIzQiZGrN5+NuDZsc8DS553uZ9m+dtAND/5lWzU3+ddlaw6A5eQf9Zr+qs65NXne1pxuYd2ZmsexWy6v12Gp711OuTHNcml0U0dofa0iheyv+5b/EVWAGPG2H2RI5TCRG7aSiepTRu4IUJ08G0eOdMkrHtlDvuKadK7VVpmM5qvPt7e3b+yzIeJ4uyPEsYDqy4gLjy3kvBa/qs6SidqkMaK6G7Tq4vwvnl1be3sEkeSPsgkUMZahyb1ANFe41pMGK40f3/DIHaii5XJZMWxVR+rFXD/n9FtOrt0HUc/lsBp5kHPcidiYxhPLrjOM6uS8y7KI6jplYibnH18jneMz1l2/5e51meXOwlwZOXK55/yu/iL/6xrtj9wmfsRB+vC4U+pUlck65XCifuf1nC3hdkVuryIvL+dQWUXvvH943eDlmFv3d7vdtE18DlBK2eUAAQfTjd1VhpsDKDK93+9COkc5IyHnvZFgZh2lhAiec5M2Vzb7xY1q9Zveo4HX/fKqR1QNeTdaqKj5fgoCGvve714ONwy6zTh1BUxS33ma032QzmScTqfx+vXrePPmTRweHqY1SOKv2WwWk8kk+v1+DAaDGAwGlRRx9zK7so6ojqUmNDcLId8qhYrKMyLS5gJyBKn/BVKZ8kEgo7KUrqXfmFbodaSgY/TWly8wnYp9kOMnXc/xpPhevJ6TRyyPyiwnZ/hOF6qkHE+rzdrpVEKaikF96mOqv+pnGsMU/to45SFRHUiIiASUe71eOsPUI9eNxrVh6bwj4L7KSKNi9jFzecPxJijXejyCTm7QR0DuxrHSPFkXfhwcyDjh+jpGCFzPMSqlXWFpvKwCS3Uy1/VXDiSyXK5xFFEH+bxRvyg9eTabpbbLsPDMJgdWu7u7FZnK9tw3cRmU63DVT1FCbSClea9+0FFx3D3e9UNEXi9w3FUHpuEyOqr3cWNMRU3lhNUYKzIqXuOY53hE7aUsVtsdBzr/qQ8ZAKAsl7Eno7DZbFb6J4fTZCxHxI15IznT6XTSeKgvtLmXPhsbb89y1zs6nU5sbGxEu92+kXXIDaXcAeUGnuS65jZT0OkwJtB3g/Ah8H/E9SayEflAT87oyeFdjZMcBXVzvU4+6XtEJJntEb06/V6XNcRrLkNzdgHrmnPCuhFHzJOzJ3hvHVZmf3i2nuNHYj4a73q/6uhr2XN6nbxdZ9Dm3s/+VDmcN+R71305XHlX+kERW7+eUzz8n1s8U9jVlckOcoMvZzjXDYT+1n3q3st2ReR3FOa9/ruDlVXkhqW3wdvj192bmytfdbxLffwddUotd79fdxCWe54M723U7w8B2KgOXEM1m83SphOqn4Dw+fl5iu7K6CFY87L9b+4TkU/n4DbouiZAIPDohiBTdGS8RlQFNDdAyY2vz4ecUeptWtW/uf7O8TXflZsffm/dfK97rk5G5cZH/cW0Oo5RRDU1j8aJezZdAXN83SF4H3SbvBZJWfKscvaTnlE/e+aJfxhFpdyrk0t8NuesZF/73HLDwmW7R3J5v4MTV9w0zp0vHThwztWBCOcZL1v19b6jbPUyc2XxXkY/cvXWPczUIO+7YcvsBJ376ms2c7LnPoh9IKJj2eUM2y7jRk4KOU8I4HJynn3tzsC6PpGhIOcmjVS2g/PUHdk5XVzXJ6vwnAzYVfgrh7X0nfzLe3Ogl7xIPUfATKcWx1HjQ35UZJap2nWpx7l+Ud1p2EZUjcNcm3KY5yFgoIioldER9fOUv/N/XcvJ5xwW5l+V47I8R6vedxc7gO+qK79u3HJ1d2xTNzdW1Y33eV/pf75DPOjvIF5cNUfvIoNv41f+n3MGi1xXin60iK1b6hLKruS9IX5OFZ+JqEZt9b/+Ojjk+yWouBCcA0rB438pbPVeASyumatbtK9r8iJQiUn4yavPKJfviMd20ovCNrgAlbJYLK7P52QbchGO5fJ6xz+CImcorxM9xE5SnuxXemS5Hb6XQ29tHbgnyMsBuJ+SBBA0hr4Jjtqh1LPz8/N4/vx5jMfj+Kf/9J+mlOUcINT/BIZsuwMRedxza6zpuY643txJ/S2id44GyNXVVYrWKmK1tbVVKVvvEx83Gtfn7FKBewSijo/Ir+7pJQAhf0gguwCtM1pyBgpBD+vIZ11ZaR4RrF9cXMRkMqmMEQ06rqPjZia5aCdliI7deJdNE35sygEun586u7Pb7SZlSkeJ5CvXUkbcXPcv0tygxzY3Vk7UDbnxZyaDe9c1hlwbp7HX+HvEhoCXaYucB85TDrw9TZIZRm4kUKZyHBgFofFEPmLU2fuG+sgBnfrj8vIy6XWtj+QmYdosaTweVzYO0nvdU99qtaLX68WTJ08S3/hYPgRgH1EFjlonSf6XDGQastq3t7cXw+Gw4hz1KCbH1+WniFEqRm009ooYb21tJb5SOeRfjbsf66G6rJKNlMvUD+wnzW8Z2e12Oy1PUBt8Hmj+RFR1ISNivuyFbWLkWCBZm8/oN0Vx5XzROfX8SNcqckujl5STzdxbYmtrK/b399OmajoGi6T2iZ8iqstX7grof2ziuDOy5vfknss5MyjHaGzliPzn9oMvpaCcv2u7WB/hmxxm8fdwKVKOfK75MhbyveNy9q/PRdoyrmNY91xgKWcDsHz+7zprle6V3PCsNF+u5euZiXG9jIhrY/cuy9AifuDmUbScHeQ4NRqNyk5Y9LQ7IOV7nKh8CcjrvAteB7+HpMHlOzQQXAOjMtwAVP3YJ/SQ8946IO2G9ioPKssig7KeUiZ+X25yuECpGwsH+PzLvvRx4mJ09o+X68CKv9+3YJcSJP9yonpKlXbGHY/H8ebNm2i32/HBBx8kpSlaBdZyAsZ/J49QkQuU0+jIPa/rjMQI8EsxR9xcNyXyXbwJDHLzLqeU6kB/zqlBBblqzvu9OaJR48/Q2GBdXMAzuu3zlKnYt73fed/XcD0Uw1aUkx+6HhE30iAlS2XY0giTkeRg2eWCvyPi5tygLMutG83d67/pdy6RkKEgHlC7aMy6g5UfzhGmY/OdzgOaa0rPpLFHHuR4eL846KMO0BIDlkNA6ADPf1fqH+eG5sN8Pk+7orthq8186JiSA5ROUI7NKjn5U1GdLPGxUJ/KsJ3P57G7uxvb29txcHCQjNrxeByTySTJjtveQ92q+3jNnSCad5Q/BI7EKzmwzDGlnOR3GdbCLdRHujfimtdkYEpHiadVX8o76kq1kYasvqv+kh+cXzk5pfvpfNR7uUGUbxiV40ufc/rI8GYb3ahh/2gu0JBRX3IZ2H0TdfNd5WgOv+T4LSejcu+vc7gwy0P35PRmnY2Rc1p4+3IGKo2wOjlM7ECcSH3HZ1xv5Mr0AAL7jvrTbQQ94/iR7V2FPR0H8Z6c8UmbMedE1V+vK8eacuYu9M6GLcF8HeDmdRmg2pxAAiIXUWRH0Mjj7wQuvvbSBQKVdk6wsY7sSEZiuL6KXmafzHWGrQ+E3sN2+EAzmsSPKGewqt8IlH3tTqPRuLHNPMvR/27weBs4Qf0ahRKjB0wv82f9GiMVVFj3Sd1uN87OzioRSZ+wNMRarVbyzr569SoZxjyCibRKkPO3nECoM2xzvFNXJoWOwIGEqoNXCmNX+HRi5HiKBrRIz1NQUqC5kPX25+Y3+Zagi787IGX5Pk9dMKtcgTWtXWNWisBNLvLF97lSp2FLYHXf5IrS+53fm81mJdrsm8csFouUXXJxcZHWivs8z40TQbODc9bDI176Sx7MGVAEm/4uAnXJKco5lsU6+JIR6kiWTUDD87JVH1/O4zrNgaL6i3pG93o0W8+5o4np87pel1KvpRo68ktGLR0adNyqDTJqXT7mgPJDJLaJWEAG/gcffBB7e3vx5MmTOD09jaurqxTV1uZGEXmnjcid17qmviFviB+5cR95knzlc5n6ICKSLqBO4LvcGCGm0O9KwaYTy7EIneD8TtLGS4zYqgwCa08XdkObczfiOiOLARjK3joZnJvLapuyLoQZdBSLY2fNd9cxeqcyCR8C/xMDqB3ety5HRex7/aW8cpnAZ/jd9YHK8vmR67Pc/5wXq4IOnDvS73p3zth2PMO6sz85T7x9xFGck6xTzhbQ/V53xyCuJ3N6xcnHn+PiWZi8vw5D5bAcMbbKvWu0NuIHGLa5hpFoIJJZ3OvFxrBcb6ADYQe3vM8FLb19ufaIxAAaFKWaMg1Z5bphSyZ0heyDw/7iZHDAw0XwjOYQpFGokBmYLipyryfbnFNGdcKaY+/jwmdIitxo84bpdFpJ5yOTe4SAPHPfwN63QNdYKCrb6/Wi1WrFkydPYnd3N548eZLOMB0MBvH69es4PT2N3d3d6PV6K99FYchPRBWgEywTcPiGFDmhJt5m2eRHpUSpTM0HRoE9qrhcLqPValWOi8gZvmqjQEgOqPP4G/KZGxDebyyLMogbVNCrmDM2KaBzUXkuU6DxKgOu0XjrQFLKWR0g0VxTP3JHWPJbDuDdB9UZsSLXB0o19iiIR0JHo1HiZ+7+TGOJXm6XRTlgTTDhbXCnmYMbfZcy1eZXAqpcl0i5pPlGOaENE5lKRX2jd6lPeGatALzevVxebxbE1FE/Ukn6kG2mHnHw4LqIjhjKFD7PvQUIjM7OzmIwGMRwOIzxeFyZH8wAUf2lGw4ODmJ3dzfthJvTP/cN7HnEEaOY4jU3gJbLZYzH4zg9PY0vvvgidnZ24s/+7M/SXF8sFjGdTmM6nVbG1IlyS/9T95PXZbCSzzlvJLslqzjOaoPrFso+8c7Z2Vmsr69Hu92u7Mqs+qs9dGSoT7SBk86RJm5hH25tbaUNm3RU0nw+TyncOcND7ZDM8ewIzgU51CST1B7ix0ajUUk3zx1DRTmiOe6pl41GI9Vd/cU0ct+5VnNDcui+eV8kZ5tjATdkRNQRlE8R1ayAOkPKbYOc8etY3HF2DsPzGmW2ZKePH5dxCEu0Wq1kL/gc4fyhAez6hjYOMQnLoROG8sGdNuwnXs/ZTG6Hue5kHdnHxDhOdePr48m+0nOO771exGx3oXc2bHOfuvtZSQdC/t0ZOFdO7lkvg+8hI3lZPpk0CGJoDi7bQGZyRnWl5J7vXJ/k+o2AzcGcP+N9W2cI+/05QeRt9t+8LT4ZIvIbaRHQ5rxG3n6Vw/qveuanJI69lM5sNksKUbsnNhqNFLGQ4lK0yscyx/85vvfn3Hitm2cuwHzs3cFCgbS+vp5SMPWhYcCIFI2xXJ28Hu7Z8/6QABTwZjqnCz4CjLpxq5MLOfI+8Y/6gqlxAmiMUOa8xg6yZNgyWuAK7SHx/yr5S6Ixq7bwTMqI6vjVedhdplA+8v1OBPy5e3IASeCG9XPnm2dDOA8ryqI10rpfuoVrhT1y5GNd55x10OFgSbqDfZF71kEewYPzOh1FDqDo6FNUjWmi3ldqt/i+1WpVUtdZ59vm6k9Fd6mD2qr+4ppKGfCPHz+O0WiUIqCeBVen7x2P+F/yUK4+LNPHUeXncFGu3XI+tFqtdK/e44Bb5dFo9Q3xyBd6VsaxMjsiro9KlFFMfl/l/GY/3hZ88f70cur0qeNIlz3K7KFTxPEYMSadug+B/yPiRvucX+qwrssnUR0+5++r/vffHJfy/frO+ubqlGujZ+OIZ3Rvbi8eLzs31rl+cD1FGepleXvqyO/P8Z6XkcNmubLq7svxAucI5U1Ol1OP5cpaRT/IsPVIW939/Btx09BjQ9yD6M/mmIaMoO/cOEC/5cri74xecV0tj0EhM8uzynQxebAFcHMgnh4KKQH2i0foCOi5FoX96O3jZHPjQu8mSKGxwLq6x9b7jYad6kEvF9srwMINWdgPHD+1jWnUDyFaJaOU3t3lchnz+Txev36d2rC7uxutViv29/djPB6n43/a7XYydGm0k3LzyQUIlQkVOdfFsjx66BV5pbEYETfGTNGVq6vqRlI5cE+ArGMQuHEODQWP1upZ8qO3V+3RuizxmXsvVS4FJN+XkyUEMfqbM4Zc8KrPLi4uYjAYxGg0iuFwGO12OyKicoZv7gw51VPAbXt7O4F79aWWb2gfgYdCdcZGTg7x/HJtUKb5sbm5WVmPKeNX/CdDUP8T4OXkR0T+qKjccgzVkeNMMEnQEnG9ZpJr7lymqjw5Knq9XnQ6nej1eonP5OzQ5j45PqYh2+l0Ks4Tbrqke2g0urynA6cOxHmqm+8poXprjlK/0qGl8ZIhpw30Go1G2kxMGyUtFovodDrpGMButxvb29sVIynHY/cN7tm/3EWev+m7+nI4HEaj0YhXr17FwcFBfPLJJ/Gzn/0sHj16FEdHR6k/JdfdYRNR75DX+30eKhp8dXWVdO98Pq+UsVwu0308ooTlKWOAc0l4p9frpQ3iOK8cNDP6TkN1sVikfSgk71Q3ZUQwYisdJJ4ejUYRERU9Q72jsuoiU2trb8+6VZ9p3jPay43fPKCh9lJfqI80D3zt9Hw+j8lkko7BarfblTLYZz6POUb3SWojsy/oKKCs4TMR+Ywf9hllvD9LGcprbqSq7BxRzvF//XXniYISjkkUseQRTpLtbGNu2Yg+dHqr/awH9aDmtDI9nKe9PX7N+508rGvsc8/6zPF6DhORcvYiA4fu+HGiPmaf3JV+0OZRq6z6nDFEptA1Hww3RL38ugHy7yQKHLbB60gGWy6vd+wSGKeAEZNFVFPJRJxsYnoKR/YJPbu5euUmufcd28ZrLkA4Bl4vEu/LGbV+jcyW81aqnVwvyEnl4+CgkWXeJ8kojYgkYJTqNJ1OE4DR7oo6i1HGYavVqqwtosHiAE7kwsHv4ZiTxxxw5t7h76YBQTAdEQlQ+MeVl+ZObrM4Nxb8mhsnNHLoJPG2+3fvF76Lv7ly4LM5o8XvUdRgPp+niLaUoSsu73P1n1J1eZSEfmNKMiN6903eZzljSdeZXk3QJoNXvEZQ67LNZRllRa4ebmDUzSs+R0eNgyV67cn77qh0meWZP+Q/ynx3ElOnqC1MT6aR6XqvDsR7H/i8VVleT+oSH2OXSbyXKZ2M0HW73UoKJvtc/J4DxLkxvA+qcyjr/5xDTEsSxuNxbG1tJSfOzs5O4jVF9dUvHNuc/KgzfnNGhYChp3ezrkxZdie/QD51wmKxSEYh5an+0lHvzhXeQwOU8044Qe+cz+fJaGi1WinSrfRknfGtNmosvFzxm/hc6371f51cEwbMOVI5Dxw3CSuen5/HbDaL4XAYw+EwLi4uEv9Tb5LPfF6zr++TPPiSw6yO90nOt0xp1u85w5Ny39+TK9frTGK9yKv6janiPhaUedwFXmVJ5jP9+DYD0IltUzmcs6v23cjZDHTKsKxV+CenM9yWY5k5XqB+yxm3rsech/x7HU7O0TuFwjiJ/eMAmo3LdaB7e1yRqlEsQ0zmwpTf2fCcMcBr3h552LgujKlRNGylBHSdkYGcUenvFDiiENZ9VAi5/lfZ7Dv2vQMcF9Lsd34ns+fKrRt7Ch0yLY0aGTs5w5bvUH2pbO/bqI2ImEwm6ZiCjY2NtDHO+fl5TCaTFFF8+vRpNJvN6HQ66UxG53VtNOSAKCdkXIH4bw4WaNhqQx4JSHkgfU7qGoG4NnzRvNA4eqox6xJx7ZVXpIDg1WVCTsDSsdFoVLMAcs87n6ov3IhkO+uMMQemubL1/eLiIm2QM5/PK4ZtRFTOtSWpD3X0hYw8pnLLMPD07odCdYqHJDmqaJ1IfNlutytKURFurtuKyDsaaFRSX9AwzSlD5xc+QwNM97qjjnyj9y8Wi4oDKSIqG9twXabK1O/+HrVPc0YRAR6J5BswsX/qnDWiHDBgBKQOvOfG3YGJ+qTRaKSItZx/4gOCPskXzU85/nJ6IVeX+6Cc4ULZrv7wjK7JZBLD4TA2NzdjOp3G1tZW7O7uxu7ubkRcG6o5eZHDQhwvEo05/S6ecEeKy0C9l9EifVf0URFc1k1ZTLl35jCC7pEDVHXimnGV54ZDRCSDen19PWazWYzH42xas97H9fzMtNK84g7tOSNEz3FusU3EJ5RNer/WB89ms+j3+3F6epoc25z3zNTg0gU63e6b/yOqO/3XjW1OTuo3/y45yT70jByR44WcYZvD+z6nSOR/tYX7AuSwjtrOfUgiqjJAbchlG1Jm5+Yy9YUi49xVXmXQaZMjHxfaC3X9qHu9H3P1zRn8bn+53GHful71+vK9LqduozsbtnVgwUkT0r2KLlTZwbnG5IC7ymGZIgINKnq/n2UTtFCRMjUgF/qncBfj09PJ9zsJ6HgkWO+n0KDXiO3NORHqGLEO9LC9OU8zjVUfw1x6pe4lQGPdBHB17mHd+3S/2rhq4v6UNBwOK6CbuzzP5/OYzWbJiFXKlqK22hm03+9Xorcy9HNGlI81BVpEdf0BwZU83Yzyie+5WYWn/ft7edbqYrGoRBY5dwmINGcUldO5fTQW9Ff9SIXuMoEgn212w1X1zYEn3ZNTJt7uHK/TkKDMENATQKJhxHRwGk7kGxl3Av16VpFuGbvMDrhvYv+xPxzwcWza7Xbs7OzEzs5OnJ+fV9KSxasCAYxg0Jnl8oGUqwudkSzT5bIAK8+nZD/TMScdIRkwmUxSNEu8rshRo9FI/KgjwpgeyigFDcTlsupEknOIjlTKZdZ9lUGbG0f1nTuWVbbLIl3jXCTojrg2uhuNRpJxcuToc3V1lZyEZ2dn0el0Ym9vL7U1p8vuCmZ+bFLbKFdp0Dk+oKw9PDyMxWIRe3t7cXBwUJGlTOtkxJ54gsS5lnNQ83+NiSLC7FvnPS9P71dkdDQaVZw/lH10KomPqWfULzSIOO+pP4QPZrNZmjtKS97e3o5Op5OO0RuPxyk1Vv0Zcb05mfhac1jRXtUnt1zG5Y2i7JTjOYNK2EiOAPW7fqdDXGXImcN6cuNE9ctDIW6YqLH1dfGuRx1zsn9p6ERUg0S6t9FoVDZhFA/mjnXS/yT2b8TN6KxI82E2m6X3MLiVK8cNQsoHdyoxU4V1Zl/QYGVUn8vD5AhyGew6gHLA+8RxZJ1h6faTO3Icj+m+uo/mh4IB4h9Gu51/OBfvquPeOWJbZ0ixMyKqm/+sajCZwwW6p/pyMPi/l+11yl13cM760jNC5hGpXkwHcIDgTJZrh64zmsV33NbfuWsk73+vQ53AqasnFVIOSHk9+b8rBRdEOYPjXdr6Y5N2YRS/EtBxTYYEj+/6fHV1lQSmBGouEnfbmEfUp+vrN89sIC+v4kuWpfknfuDYEQi5R87v17iv4hl+d+cII67ehlWGaq7P6pQS2+HjQPmQk18eGV9bW6soYcoPKUnf9ZiRdv+d6zrvm+oAQ0R9nwvMtVqt1D4Hhrkyct9z8yLHP660V7VHdXG55HNKfE8DhBH65XKZIqsE7dxUTEQHiT6KWpydnaX6CAyrPm6YOKjJzYOcjK8DMbm+Vn3Z9+ynOq99zuDz74rUyonjvHCbHLwvoq7P6VgCP903mUxia2srJpNJ2hU/B7Rz/M1+uU32szyWuUpf1+Ej8ql4nTs3EwNRTtNpIyON0SzXHdKHzq906kuWyDm+vb2dDG2mS1PvUebX4R61Qfd7P7nupLGc60vWxXWIns8tb/Oob26M7wrqf0xy2aX25QJPdVQns1fJd2IR9lEOD/izq7Az9YXGzY9pcn2V4wEfo9vmW+4eb7tnCTATMiJSSruXdRc8XUerMH2u/v5sbm7rWfKNO8XvWv+7tunOhi29zF4pVkYvZ9qRNy7ieoIo/dcjN/LkCBC5IFAZendOmOteRnMogAjK9btSDPVMu91O4DJ12v8fkHIdoe+a5nVSe70fBQI0WaTglZYng4Dg10FYo3Gdi8/fVab3DevJaASFMMknmPcz2+iM7fX0+tNYUltZNqMw90mvXr1KIF2eah53o10alWI3mUyi0WikTTt0DIa8uQIJufQ7EgW6iArShTcNKQlqvZPgQWlYjC7qHRTsPF9SCljeNgJz8YyAhr7zQ4Ofc9w9+Zy7UpruKRfxfvIL5RE3dNMzNGw5/1kPzllX3uLJ3d3dGwBwMBikdbeeTkyDldkeEdU0bnn26eV/SMRxjagCaIL9TqcTy+Uynj59GtPpNG1GRj0iAMzNOuoi+RonN4QiquPszsiIm2mdDiz0XndmkHdoXIoHlstl2jTp6uqqsn5YMk2RIgJ8eeHPzs7i+Pg4bTSnbI69vb1otVop/ZL9Lj7RWtaI66wF6lgfE84N1c2NJ58LdEqpX8nHepeyGHTMmd6vKLRkXa/XS2U9efIk9vb2bgBjvfchEZ1P5HcaYWwH5VK/34+rq6vY3d2Ndrsd29vbKcOHx8mQ3ymf3EjTGDvo1rjmylHdid8kh90hK54mDuG+AWobsy9UV+ka8TznlfCYNhfTO+bzecI/4m/JSDpZtXu2Nmfc3NxMy0G63e4NnEeZwKN92Cd1xOwJx43ubJXzyvtJOnY+n9+IdPF51kl/uffAQyFmK4m3cjg3Z/wwIBRx8xzbnPNWfSrd4MeMEUPWGbY53CCizFS6r+pIh5vK4bvEm8q4YDYA6yL+yY0x5Qfbor96hliLm7pJD0RcH0O0Cq9Tjns/rcKhIrfxeD/niK+dVj/p4zZJLqDB97m9eRvdGS25svfGunD1iesMR+akMnDhqs5z7x0Hzg0x1lnl5v7yHr1Dk1Zb9Pf7/djc3Ew7l9JjyTQGlcvBZPvdM89nqSDJOC4U+N09STmvIw1PB/18t0cZSEzl80nC7znjOGc8OGOu4hu2976F+2AwSOti1P++07M2EpLg4XmCEpgEvO/SrpxB5/PRBVed91rgWs/k5ofqTe+gPlLSVODkOXfcsM6sO+fDbX2xij/YP57mw3dLCdfJAzq6cmNEoKjUU6VXaoOTs7OzBLRczrDdAkeLxSI5DWgsrFLG90U+luQdzvWI6/Fwp4QbSzJ8mI522zj7ux3o6JrXiXOhTkdROeu6wBTlo4A3ZZ/4WeeS+u6mNCYWi0VawiCD/+rqehdbgVo6f8UjHiVzAKN6i79y/Vk313IGkfcXASOjdDJQmGZGEi+02+1klGstrtqa0xH3LftFGjdFSup0LO8XCdj1+/0YjUYxmUyi0+lU5DR1qOvrVdiA78vpe9aFz3k2jJfFcdfYUdZxx+TlcpnO5P3+++9jOBzGZDJJZxN/+OGHFdxEWTmbzSr7V2hjxeFwGOfn5zEej9OShqurq9jZ2bnBo5KrNLAj4sY4ydhUe3L3uVHAOeuGCPuLek+GmPATdUpuPtGo87GV0fwQ5kFuXvs+AhFVXZnTG/6/X/eg0yqZkPteJ/dz76bRuFwu02kELr9VhstTn7d1da2ToyI3+GnYOh9FRHKes++5ptyprh+8PnWYkG3wex07ubNDeJjrqfk79SOdYD+U3mlX5DrDVsTB5yA54FBjIqIyofUOHtydMxJd+Agk+oDlhIEztoyURqORvAnz+Tz6/X5MJpOYTqfRarXi6dOnsbe3F71eLzEPo10aLA4wvS8U5vquvnEDXP3ibVQf0hOqZ7iOkcJexAnAftS7XLHqHtWPXmAKWtaPTE5eUT+74HJyL6judwfCfdDx8XHanp9ePUXV5NmeTqfR7XYrXk2dwydep0crJ2Ai7pZ+xKwDjo0UpTsXXB5u6EkAAQAASURBVKjmwBRBKzfFkYe92Wwmp4/W4MrQE6/4BgFevviB88GjS1QSBFc5pVBnpDg5f+e+E/yo/v782tr1rsVMQR+Px+mjSH1uLsrRsVwu0/pMrSWTweS88BBATa4eLru8333clJrIMRIvaF6RXykTyBfsT6aGudLN8Yt73vkuAtCI682uIqpLVGhYit/l2OK4ao8Gd2ReXb1dazoajWI0GiW+2t7eTnJF9dG844aGdIA4cb7ofzooOZY+j/w6dbnzgICXxkOGjbJVWAeOT7fbjd3d3Tg4OIhOp1PZREj94PV5CETdLgcE8ZBjGsca5+fncXR0FAcHB7GzsxOPHz9OTorc+lOPxnLJEvGO62G9T3qg0bjOWuP8lI7I4SwRMYwyk8Trknsqs9/vx+HhYfzFX/xFfP/993F4eBifffZZPHv2LGUetNvtuLy8TEbteDyO4XCY5od07NraWnz//fdxenoav/nNb6LX66X+evbsWWxvbyd5ofYyNVPj4selSbYzi2ixWNzYB8INW5Wp6y6rIqobK2npUqvVSu/QGLNPRXwH28DMpYeAg6hD1X4eQ5bjS5/Hjktz2NGxiUcYWT6vRaxeXsX7KesZRdQ+EFyzXaeXcnK0Tl75c7yu39yhLZnP+tGRqms0DF1GsJ4um7xvWX5de6grOW7kf+ImzRc6xPx9EZEwci5b612N3N9bfpsPvCpG8O1M5YJD9+YUcy7dgAPI//Wd9aHCYNSE71VEigpmPB5Hv9+Ply9fxrNnz9JZdErPVPoCAbHqweM7ZDRr05EciJcgU4qPvIpk2oi4sc4l53Eh5cChC80cYHWjlWOhezyixb5lu7jZgOpBb7/q4fz0UMCN0tO1NjYi0sZQ3CDqxYsXaYOog4ODBFQjqmBEvOZzwJ0zOWNU5UlpexmeLiNAruiieygl4OlMEQCJiJhOp/H8+fM4OzuLnZ2d6Pf7MR6P4+XLlylq+8UXX0Sv14u9vb2UVipv/HQ6TX0n/mAqmoSgb9IgoevXyBtqM8G3eC8HyHMKlL+759HliMpXP/N8aY0ro48sv9G4PvtuY2MjOp1ObG1tpaiVypKhx1TPh0DOi+68y4EMV3yUY0x/pDwkH9NojbgZGc5FznK6whWjAxI6/gh4pGwlj3U2MSNWnqEwm82S85IOTDeEJ5NJRETlPE29h0BH+kBnenLTObU34vqs56urqwrf5IBXTjeuAmgOvjhOctQp8uypZnqODgCdWyvgJsDvgDZXl/siZhtQNkVU9RXlg0hGVL/fjzdv3sTm5mY8e/YsGXviK7WfjgjnfccD/F310/vd0GU/Une7gaE6q52aA9zYaTKZJAfnxcVF/PVf/3X89re/jf/6X/9rHB0dRb/fj3a7HR9++GFcXFzEL37xi/jyyy9TVtNgMEj8oghZu92O4+PjePnyZfzFX/xFHB4eRr/fj8ePH8cnn3wSjx8/jvPz8zg4OEgGMTOnqA8Y+eG46K92J3aHAccgZ7yyzxxzaS4IF+qs88FgUHGWuT7LZfA1Gtdrb3OZgPdBxA008qXTvL8iqnzpDhhiEd8vYFV7OZbupIy46dQUHnA7gPpePCinlfCTYzInNw4d4/rGlnUORzpLvc46913tjbien0qZV/+6Y5x9Q+dhTibU9bPf61iJ9/t9yoAdjUYJL+acBJIzHrF9V6M24h0jtqLcQOcY2jvDGc07lREmZ9JVSs7BfW6wHNC6p9UNMoIcrZmUkbCzs5POK3UD0L2ImnQ5IM9JpnfKM+8RM7bbDdlVEy83Lt6Hdf3rYL/OqMiVRcFDIZKrl9ffee0hkMZWm4BIAHGzn4uLi7ShhVLYPQJHPvG1m051Y+cAk0JKRK+XjxfJ+5oRWK5vGgwGKWVwPB7HaDSKk5OTBOL39/cjIlK0Wh/xvK8tJ/DXHFoVGarrn1X9xPvuIpRdLvH+nNEWETfGIMfrbIcibormKe2Jzipv+0OZA6I6GSuq0w05mb6qrbnyXW7kZNAqo9bHtU7uucwTcHOnBfWJ3kUej7jmEa6ppnHEo78IpiljJGdy8kREh6M7GtyDnmvzbSCnzrhV+XTq0ADgfQ7A3HG1SkatqttPQewfAkTVnf3vvCe+ODs7i/F4nIw6RYeoD3Jjq3IdD0ju8BoxVI7Hb+N/EcdauETrgpl2rgjs0dFRvHnzJl6+fBmnp6cxGo1Su9+8eRNPnz6tZKwpBVkGpto2nU7j8PAwXr9+HUdHR8l5rGOTer1eNJvNFBXVvNJf6jzHSA7CI6oZH97+nJHGvnOnP2WClu1MJpOYzWaVNGSVwWd8zPjbffO+yI15dxjkHNF1eF9l3CZ3IvIynHOlzrFKHnD57hjAMbfbAayL2u06wo3xVXaMz6+cge7tI59Q3kjXKNjBpYk5cpyee6ff6/1225iKmJ1HW0ft4tixH+rqchd6p82j3OikAMlVhsJfgI5KPWdM8nkpTN1DL7R7tXORQ5He4wuadT9/i4h0Dp+AzHg8juVyGS9fvozf/va3MRwO4+nTp/HLX/4yG/XkJiHL5bKyWYIiwjKCtI5kbW0tDg4OotvtpsPbRWpnLkWR/cXJRUXLe0g5Bae+4OR2L8wqQ4nXCHLUfo0d150ycut11+/3Ldh3d3dTirrqJQ+lDJTLy8s4PT2N8Xgc0+k0Pv3003j69Gl88sknKTqh9o3H42g0GrGzs5O8zQSCEdWoOYkKXHXhjn6rjFgHm7ouIohRhGq5XMabN2+SUa+UNHnrz87O4uXLlykFUSBN/SAAslwu0xxQuq7Wogrw5wS96kj548aNK4yImxv95OQL+0Zywp/hHPB7GNXQ+jNfQ9JoNJLxur29He12OzqdTorUagMg1o9p7nUpp/dNHJMcOJTxojTEy8vL6Pf7tUpM8l7Kk2vgROINEfvavet1Sp3vcmPN9ZMbrTyjVOCVG+XQyaHf2RfySDebzXQ8UMT1UQ8Ch4qQKbKvTcTYRslqla+P6qHr7mhRhJS8zHKpj3Ngf21treKQkYxnqjbLoSNAUTllJ1Bvus7Rd/69b1L0nES+JfgkuKS81kZSJycnsb6+Hr1eL5bL6830NO9zADYibsyLOmDpuMRx0V0Miohqamaz2Yz5fJ5wi5wyl5eXMZvNYn19Pb788ss4ODiIyWQS3W43Dg4Oot1ux9XVVfT7/ZSufnh4WMkmWiwWyWl6cXERX331VXz22WcVeblYLOLo6ChOT08rx8pxY0eCcOmUra2tFNmSvGX7Kcty69dpvIkPcgYTl+FcXFzE0dFRSremXBFp3iljQf0gqgv23Bd5xI+GDuuo7DBlb3hfR1QdpDnjPSfLaTO4URRxc6MovpcyRWPELEL9pp3ppbc1H93m0BpX1a/OiM4tC6Os434N7OOISI5/n8csg3ooIpJM5lFcLoPIi453ctlvEVU8r3pQxrvBK1IGAzccE4/IWZZ7p48xcddt9IMitjkGzJED1FzD3UjmOzhZ+Jw+HFg30ry+PogyAPlOAgCteev1egm0NhqNmM1maZdSMpwDDDIuI1dK35EXT2e1yehxY599kPPmkAE4NnWCMKfY2D85QMHfcoYEx8ABoZSLp37k2uPKYpUX6aem/f39lFKrv7ndhiPeCqPj4+OIiBiNRnF+fh6dTie2t7cTb8ggevr0aSXdPCLvPcuBdZZDhSsSz0o4RNx08vg4iu95hqbSBwVCBCAODg6i1+vF1dVVdLvdWF9fj+l0WgHMGmNdYxonBdhyeX3mJw05N35Eq/jcZQp53AGN7lv1UZnu4aWSoOPKeVxROUVpBca4U7LuVduZWkd5cp/k/ZFzMvj9asv29nYsFos4PT1N4I+KVH2q/ru6uqqACX9nDpTnxm5V/+VkNcsk79aBCtaJDly2X3/1GyNMfp/KzqVXUtZTd+UAo/8l4Fe/uMHk99b1FQ1n12+ShZQ5uk8gRoaS6yyX+Q+NCDbpwBA5aKVjUmPdbDaTY+/09DRarVY8efIkPS8coXGmoUyHBoE+65fDadTX7hBlGc5XlMEaU9VL+rzf7yf5pWOM/uRP/iTJQzm2dnd3U2CDGyyyj4QPOp1OfPTRR/H06dPUd5QzzIpjJDmXGSWis15GpPqCy4vYfjomWF5ufhEDNpvNtFZYZcoYyBlr5P9cZoXf9xAoV3c33iKqRkpubNzpVkerZD35tQ6fel08tVuGk+5jOZwfdGrmyliFU3ifrwX3PvQlk7nNFbnETb/T2I6IipNMz9bxEfnV2+H2SB1+0jsYYJNeoONIS6+2trZSO2j3eJ3eld75HFsHda689FdMIGtcApqM7KCG5XAyODDR7+o8CoOc8UzwrGu5tasE2wIWe3t70el00uHgZ2dnsbe3F91ut2IkaBc8B1C6R4pfka+jo6PUtr29vQrzcY2v+omTVtfYpzpLc7FY3AAeq5QdBQy9V3zOJ3EOfPik5HokesfcuUBF7ROCQuu+hfqHH36YNnmRY0LrpQXGJfTm83kcHR3F8fFxbGxsxOvXr2N7ezs++OCDxFfq7y+++KKy8VLETaBJYp9QkXvkRXNL1yi0BURdqciw6na7CXw2Gm83hxgMBjGdTuPs7CydJ3hwcJDA93g8ToDNlQ7X9tIZQFDDdaVsG6Ncaj/7IKdMRARxdc4vlw1UfG4c1d2rnTu1xpCGrYwZ9afO7eT6Qq7FV3t9Hf1DMWz5lzKJ48B+U1sePXoU6+vr0e/3U7THFbT4w7Nn/D0RN8+XFLk+4DvcGHGPf50xR7nlRqGPkSKtmod0evmOxuR97mbpR5Kobs6TNFZZf3cARFR3t/fokOtkPZcDLIw4Cwxyd2fKITfetZN4t9tNyzly77hvWb+KxJucnxE35wLHi79vbGyk/nr16lWsr6/H559/HhFvx0hRDRlb7CONodYjRlSz5nKOaRpz4hmmwauu+s35mlhCOk5rbc/Pz+P4+Dg6nU7SB0+fPo0//dM/TXXXGtrRaBStVis5SeUodefscrmM/f39+PTTT6PVakWj0UjreXWM2uXlZZpjchj6LrYaJ80r9acyxQT4hWuk6yj7lYXhjqqc7pHsiohUN9WHY0bM6U4GvdP5Svr7IcwLl7ecr1xDyz6jPtQ14kPH5jn5pef4l+SOtNyzOXxKktOdpMgncbXGkRuyubMj4uaGrWwbI5TevlzqrvAi5yn7mBhbWaHka/Gaj13OuUjHj/fdKjnt9or6YDabJbxJzKNsNZU9nU5TW/gO54270J0N25w1rYprwnsnqSJU1G5oRcQNBZdj8lx6iJjQjTC+W787g+VAPRdjSzDTs7C3txfL5TJFbJU+QAObRhqBhO5TijMPf97Z2YlWqxW9Xi8JaXoTI6pnVOUMQqXZ5CYM+ygnHHJjwoibMxXLomfJo7I0EuSllWKKuD6njZPxtnrdF2mDF9W/1WrF0dFRSrXVR6B9Op1WxqbdbqcUtOVyGUdHR/Hll1/GL3/5y2T41M0PApfc3CBP0Csmp5GPOwVVxLXylMG1vb1d2aH38vIytra20pooATTND0Uhz87O4uTkJMbjccpIoIfe15hLOIuPmXrLFFz2i8AV53uOR/k9J1tykQ7yb07WOZBRuvVkMokXL16kc4ojriMJXE/LDYDkPODxFm5o0VB6CJtI5fo293uub8XfX375ZZyensZwOEzOEK2zu7y8THsX6PxbOtz0ydVj1TU3XNXHDjTJA74G3OUaDQkaqxHX519KlnNspSvoLPSIDsG16qvUXekcOsd0T13EQv8zokJ5UTfGDtoozwlYZeSoz2iUcQ48efIkdnZ24uDgIK3Xz8mmOhl430QHFLGOOxloYLksobP/+Pg4ms1mOiNdY0tDSO+IqDrtGbXV+2lAUDeQJzxYIICpcaNBS2eI2q8IizYJ1N4jqq+ilWqj5s9wOEzrchVV4hm47jRnVgezopQtxCU8XJrjctSxpdovvEIHjRsl1I+Om1wn+VyW3pOzk0d2UYa5PPN3iB6CYzOi2hcRN7NN1B/MvHG5LblFJ/vFxUWScY5v/H/qR84xH8M6zOO6XWVqDKfTaZJlCmppeaBwistbUQ43+DuYzks5L33T7/dTv1BXyankzkWV02g0UoBNjmPxu6dT5+wl1juHNT1Y5h8fH86NiLfyg8tqZFcpYKI5LueT6pFzBN1G73SOrRdKxmEnipwB+ZwrhhxwZzmuUFQn/Z4z3BzQ1N3rSoC/y4AVQCEgUtvYPq4T9PfIECCTNZvNZOzSYGafS8HkmIaTe5XwW2UwOrjh5K8DFBQWLuB9HAgUuQ6L/UiFm1Me903alU5nL0ZEilAwrYSbgKjek8kk8QU9zt1uN3nl6qgOpPJ//V0F6F0xkyQkZdjKWHUjU/OA5zgyvVIC9OrqKp3lyvRjT9P16ABTc3NAPdeGOv7wubLqr1/Llec8rjUjMtCGw2HMZrOKoSMHloxa9Ss/7DtG/yhrGDV4qLTK0JUM29raSpuMqc06FkmysNPppHPD5/N5mmMecVf5OcO2rg5uCOTmB510dc66nFx3cKP3MGNJ93H3W5VLBzF5LaLq1HR56QBc783JUH1y4DTXfzmA7f3A+SxgyvGUMaR01J2dnWi32zccsD7/HoLMd+IcjajPInKQ5waKQOp0Oo3hcBjHx8dpd2Tyg2Ouu2Ic1dUNXPIl54IMU97L8uh0ES+22+3kXPFlJ9QL4j8FIWjUSM57JFr3qE4sX/qo3W5X9IfrRY9Q5bAEMZPPF/3ucynX7xwn6TutIyZg59j49zr5meOh+6ScXs3pWceG/jsdAPrrcsrbTRnov/nY5vR5TrbksJIyMGezWUREytCQLhafEsPn3uc8qd+pz+gklPNfDl8Z+7IbKFPd7pBTTO9UnzYajfTX78nZFK4TfYzrxjpHdCTo/XL2yrD15QPST+QT9uVd58EPQku5htDIcvJUQlVUz9UJaHWGe8Rz76BwoSfdf9PzHFQyijweAjV6v3YudA+CnpVA0zNMI6LS2NnZSe1UGTTwVF+BH02oHJhSW5zpVMccGM5NNAogKhT9njMEnHQ/PVACXjICaMSx3zUeAkZqE7099w10tra2YrF4u/6Ha6z29/djb28vjo+PYzgcplQzpVUsl8sE1nkObr/fj5OTk5hMJrG/v19rxDvIjaimcHPii5c0/lzsr3sd7KytrVWOnVE9Oe8EZHQEkDZ8Yl0lNBWZu7i4SMcxcL4JDDFi5NkInorLueoCnaRrbgAQ/PO+nFxwQKT3LxaLmEwmMZ/P4/DwMJ0/enp6miLRjUYjOQeUdimDVumX29vblfNNI6rHXbF9iuTLAH5IpD7NjQXBvxti4qOPPvooRfkEIBqNRtrT4OLiIl6+fBkvXrxIHnTd4+/TNVfo7lEnUKUM59zTXGHE1oGpt5WOzIiq0pcuoBHAZ91I9Iwf1lc6jQ5SvcedI97/uk/yh1EmJ94XcX1GuoN59cv29nbs7e3FV199laJyilq3Wq2Ufryzs5OADYGVxsbfzzbct/yPuJ6nXNrBLC/xkqfYOohUtHo+n8ebN2/if/7P/5n0yO7ubnQ6nYozW/1PvcMoiMbK+UZ10X0RVfzl+kO6Q9/Zbs0NZt9JVzAKe3l5WTHUhWOEnxhJYoSOET69Q+ty1c/MDmLkirpBfcHlPewLGtQC0evr66lvuTGY6wzKOhon3Nl/PB7H0dFR/O3f/m2MRqM0ZzudTiX6pjnkuJZjS/zKsbtPos5UX4hviDEcqzt+dZnH5SeeTaI+c9nPqDzLrPvu2JnvEi8QwwjrKFNNwQhuBCrbQERdQRxLbC3dIpyvPmBWA2WjHOS+dInvoR3iKcnMiHDMzX5UX/Ev+ynHA96//DBDT5lp29vb6bvK0hhyc1XJCtqH76IH3hkt5QweXc+BRDdK2RksJ2eZ11nn3kgHLLzOd3l5bgjTKFNZ3MWY62vI0GyrUnXYZu546W1wAO4TsA7E63d+zxm5qxjWJ7/3nY8Ry+Q1PsvypeiYauhjIaGlCZvz/j4Egc4xJliV91jn1V5dvV1zNh6PkyJ1BRwRKdWEqdn0qt+lzW6kCuASCHNtFQ1fgQqljsnYkkFLfmKmBJUYAY8LWiodgggCMG8nn2HbcvPWwaL/nusn9m2dAOf9TjqL7c2bNykFXVFpKZf19etdX2XgCozRO0mBzfkuXuG6sVUy4KckV9i5cVwlt0XkJwJlgv5Go5GUueucunfk9IIb2ZRjbJd4WfKea8F9iYTLWe8LKWm2K+ekWdVfOYNUei7i2siS7CAw03cvJ+dwIPByOc5+Z9vcoy5nzt7eXspY4Q6dzFagsefGbB05ML0v8jH2fs7pK8pA/y7ibvIqb39//4ZO5TM5nnHdzvtcX6sNEVVwSQeJ84X3Ax1wxEpaJ8xd852XNNfphKWhs7a2ljbWZGTWdZPPH/9OpwznTx1/a0zZPu9jygvqWup0YR7HvT42nIduqHmbHooOqNOvXj+1xbP4+AzHXNdX6WQf35we8o+uU//7sx6ski5QMEYZB8zAUVl0AuUMW7WJ2Wsql05GysyI6wzP3Dtzhqf43TG8MJd0BuWW7rtNb7/LuHOOEAMrc0ft0Xgw60/11adO795GdzZsXdH695ynhkYAw/W6XySQ7MZZzhOjchntcK+cTyiVRcXvk0pghrnv7lnRd0VfIm6mT3KbfrV5fX29sgugiB45CXcxozyOFOTeDxQCufRoB1HunWcki8Ja/ai6kAH5XtbHvb9iUBl58uLnJpEmoyb+XSbaT02MJMqg1f+KcO7u7qYUSqWmar2tCwUpfaWd6B3kS5/UOcAUca2Mdd1TKSmwqdyZfszjEsRznI/MuqDDQnODRjnnPhXFXYx2B028rmdXGbbu4eM85PO5euQUEp/p9/txeHgYv/3tb9P8oENLgpvrSNxhEFE9ooaAUvKDR7wwsvsQiH2U8/TqNyeXGQIEcgQQZEsRevs5dp4BFFHdTM3vr6uT3ikDludy+tIJPSMA4fJW33l+dc5ZIzlLZ5DXK8fX7Gum7jHyo4979Vk/9peMEXe+uqHMuaTrkvnaH+Lp06dZXeVGc8549rHMjeN9G7YEjY5VIm5mn3lGA52L6surq6u0DvP09DSbZUYZ7hgrh6moo4mT+IxwCXkxIhJ+0d4KHoXLjZvmgvhoOp2mTQQj8umZa2tvM4X0jPYmIK8Mh8OIiBty1KPPbLPLJD+aSfLF+VT/K8JFg9ixlgdBqA8V4WPkm++hEaU6unwQDxDLPhTK6U/W0TGmcHguM8SxiutzH1OX4fqNv7uM4Xtz2IA4WeUI6+h+2gWz2eyGLqN+UoYC38M5KKeHltgwmy7iGpNxeRgdpMTp4hO+Q/zOQBL3/eBf70terzNW62S0yyL1hbKyFO3udrvJcGdWquSRZIE+OZ64C73TcT/+cUFKEEnQ54YVJ7UbxP47y3ZGz4EOpjkxfc8NMjKYjC4xGCO0+ivmUN3kXaUxQMbRhKFRTOMu4lrRqYxG43pTHu6s56CPfcYJnQOaOeXCPnEHgDOwC+EcX3AMKMi0+6EEA0ErjUSNi3ulVJ4befdBnU4nGo1GOqtP9Y+oOmYIyCOqacNXV1dpjaom/Wg0SjvpirztvFY3hgRBfF7RP+cVHc+TyzAg5RSSyJ0uVPgEC74pBNO4cu3IARbWhbxCgepK13/TX2ZHOH9TKYq4bkobYul3Rmm58RtlAg0O7bQpga7x0foTdzJovB4CEUTQoKpTOm5UOmDJ6QG9R7yp6PeqXecp97zcVUYio51Mi5a8z6Xvazw9BZJGh8Y1F/WibtQusdIlcq5GRErJdP73OcAIl3iLQIEAnXLC9QgBOPmfYJEfGQAaKxlJKjOnb1hunbFKoykHoO+TuMzD614nOx346i/7VXyi8efyDY0tnSd8X84JzPd5X3KeOOZSHeruYZvUF8RJk8nkxl4KEW+x0mQyqeAyj8TSke8GqvqCS8FUl/l8nvqGOjin+7hZpd6xWLxdKsU9VOpwDvW4otL6jdGpRqOR0sk5Fp61xfHx8db/q+p0H0RsIXIHnc8LjYXkLe9xzJCTPzlj12UxSfKVY0bMTZnNeeyGaKvVSgYt5ZqCVJLRrL/4QmPH+hH3EUMQJwgvuUGs+jlGdn6SzaBsCMkU4iG32zge5DeXKxw77zddd32nfux2u2lTUs07jp/adXV1lQJCzGR0mXAbvXMqcg5gO5Cm4qNnnR6TnNLKdRT/9+85g4dggsra2yBhrPWQ3JWXQEd/z87OUnvOzs4iIiq59gQhuo9ecL5PE41CmB5+X1dMIZID7349N2Z3EYzOkCT2e917HYQp6iHPDJ91hubE1vN8730L9larFYvFonL+qDzuDmw1nnSUcMc3AXUBafaPaJWC5T1U0P68+lVEUCxPIBW6zycKaL/ugF7XPXIfUV3DQWPC60vgmCMqQ4LnOqXqIJrf657NzS/NX3kRqSSlSDR/OW/d+FGZNMbEK3KGMCrhCuO+yRWd8yL/5saXZXg5rkjVr37IfA7o8bqX7YqQY0te5JpaglQZHizboyt1/5OXPEopOe99xOiFrlGn+dwiCKEx7I6qunHL9aH3V+5DYCK+p5G1yjhYVY+6+fsQKNc/dMiQXHYSVFLeqlzJTt98ULJC96nsnD6uwzp1bYmoGsF+f127hHfYB5KPmj+8l+Ca5btD3COxjEixT8mDcuYQLOfaw3lLh44vp1k1Z3SfHJ26pnKo++Rcol6lnOCYuZzP9XvdnPqpyftT7crpU8cnLhMjbjrrvByX5SyTOrTuGRrOjkdyekR/mW1KZ7R0ha9dVTu4LlR15Br53CaarjvceCWf6b66ec7giurCZSvibwbf1GZ34uqddVQXcHKdJ2yjSLTucV2qvuUxR+wDH9tV9IN3JHHBrAHUsShiBu0mK1BI4ZIDm/4OBxUR11Epj3LxmgCjBo8pLxcXFzEajWI6nVbORvMUzjpvkoQ0I4++mcFyeb14WpvOaKc1eTfpWVG0hwur70K5iKr6wFMLcv2bA/NkIk4yvkO/+4depuPj4xQRbzQaNzZpIAgUr9BzwzG/K0P/WPTs2bOYz+cpbbLZbKboCknpvuIFpuGLVzR5x+NxHB4epvW4nkoZcfd2U/GqHppn2sWYnn8JGZ3Rtli83RxJRGXjQpjAgyDeU8lZN/GG5IHud+HuXjlX/CI9y+iYAwMHjoy25PqObaWQl/ySJ1HjyDR0pp/qOzfAEk9wIwhFI7WhwtbW1g2lnJOL90W5unD86p4R3TaXHTR1Op3Y29uLXq9X4a2Iay83IzKS15R7TPvOvYf3EYi7fqKBQcPOPypTEV9toiRPNYGJyzfqLDnCqOAjqjzBqKs+Ln9WGUY5p5fuazQaSb7xefLA5uZm9Hq92N7eXrnTseq5CsD69dz77psYHRS53BGAbDSus6+IlTQeDAJQPmqn5H6/HxFxI5Koj2eQacwJUHWdBpbArYN43Utsxgg8fyc/iMd8Hw3JQG2Es7GxkYxezcmzs7NaJ5AwkXCl11O6igYLM6k03+bzecqA0Tu0iSP5cjqdpvmn8trtdpL/Ind6EVvq2BIuLaLhJ9K4+5jpHmEfGvoPwbEZcXN5IPmh2WymMSZ/sa9cx9JRkHMc03DVO0g0nMmzHqUV/iIGqrNDPCOr1WpFxDVfis/FMzLE6JSkYUsbhvVUnVw2uM6SLFH7dT8NVY6PDHLJCW2+qBR5bkhLQ5NylzIn18eiOnnNfqJh6xhMfH55eRmDwSAGg0GMRqMbWPNddcCdDVv3ytQpHCp3H1QHQDlDS9dzYCn3bq+HBsEBtzzyMmwpiPXxs7c4CBQuFGi+PoDKirtqUsGwPapbzmjQRLitH9l3q7zm7oy4C2ioA6O55zh+ElZuvHCyu9fNy86B4vsieWC1hlZCnIdKS2hIkXPhvIQIPYGNRqOSdkEveI6v9bdu3vg1Khz9L+HOVHcXpD7PJGwdENEQ5Zxx0OCKrq4td+HFnGFFb3COfldwLEeExongiRFWOnUirr2nV1dXiS/U79pQipkaOblW5xW9D3qXflw1Hrxn1TXvpxy/3KaPcm2oi4hSNjqf0jmiuqk8luPyksaCGyh0oPo453Rlru0sV+2gXvJ+JVhhPVgHleF14Xs8dd6jDDm6bY7WXXso/E8dTGKfOh/4mOUwDq9redRsNotOp3PD4GGZubr8UMoBSeIT/dW9ub1HxBfL5XW6cU7vM6sl4qbBzP7y+ul+YjERDQPVQb/LQeB9r//dkav5w3l/m5wRDnADXvXnfSqDRo3/5kbkQyDqKdaP48xreiYnG9m3jGKSB3mvO+hcnuYCXSJhcx931kNykNF4LncU38mGUJ0UHND9HD9iLhrlKpfZerkdi72+KpOynzqA93EuiqdlawjH3MUuU9t9nvB+OhOI+9zxyvEQSebpeCzNY9e7HjBZRXc2bGmw0XLX4JLZKbQI5NXROQHBCqthfKc6kHn69HzxmlIDZVRyQxCtc9UW3jRumR+v97kQosCUB5SAVynIV1dXaY0p03MYtdE7mDKgSKAGVJG1nDHICcDINNetqm9zoNA9/ryXef0ulG4D3IyOc7wajevjm3JtUb2cH+4Ckn9s4lrKdrud1tr2+/0bqS4SJOJLnuGnaLwifaPRKE1oj9iJJJTdOUJFH5FXgHqWqbIEGhTqEpL+oeeZ72bElk4MemFzkaAcMKSA5nUnd/A4uKeiI+UUn7+jjsd0tpw88vLidrvddIyDxkHrryIi3avMDB6lpIPfuakUNwTiHHso3nryRMTNYwLqAH+OnIcJiETqZ823HJDxqNJtSlo6hOt35DEmwJaMzynX5fLa080IMbN1HMxpHrEvFdVlSjTnuDv7KEtVhr9Xc5BnTXsfa65RTnNsaajkwJnGpdfrpSN8PK3ax9rnNduZk1ls40MB9jyiQuTj68De524d8Ne4jUajWC7fblQnw1b35+SA80idjPX65vqT4xJxrcdns1n6Lj7g3iPCONzN1eU0gT3PRZchqHvkEOaxi+RD1d3Pglb99ZEzrNvtJr3LKJ0bYZ6x0Wg0UkSZ/Z/TwRwjRm4pJ1l/RvVyDi3Haa5775OIydUeGUvE+HSCMHuMTl+1lRg9oprpofH0zEGSR2kdv5O3RB7gUTtkG7jupQ6Q40lBCx7lJKxHfKR+ogOVATaR9jtR/WhPkYdzBr7z9mKxqGQLKkOQNoLGxQMqTuzP3G/SGZ7xKj1LXKsxYeR5MBik4y/pGPB3vMsceKeIrTqNawZZWQkc3efpeFxz6kKayouDJFAtIrCRwNWgi5FkpCrFWOtatcaVKcEevc11KCcyvSUE8ZpYYlimVsjAy008CQX1iSI8Mo6UDsMUGQ5yLpXAF11zrYoLGPYrc/cdeDpjulFF0pb3qifPAGZ0S/2oCUYA5RGshyDUI97WQ1G6L7/8Msbjcezt7cXr16+j3+/H0dFRRET0er2Uet3pdCpRP6azSsjNZrPo9Xo31hVRsEbEjX7hnGHqPzMJmKJFRVFnPHn5nAMuTDV2ciRpTnC9lQMJn9dUYARnbF8dWMuBTBeM3jY5pHIGENup+TmbzWI0GiWlpbNlFQVQmylP6JxbX1+PXq8X3W43nj59mpwjdEDVAc1VSuU+yA09XeN45mSGjx2BkYM58qjSCHkMkJ6XwmbKf86Y4P3sa91Ho4QGicqT4qfslmylo07AIyIqa7MiIitbdR8NU/EYx12ymuv2aSizv0QyDLgngO7zpTwyrNj/7Df+1Vhz6YwbV+xvz9aIqAJ7JzdqHxI5j0ZU5bMbKTlHHfuKv4n/BK7H43FyvtP5pbGXDqVsVX8TE+g3Oqr5TsouZq2JFwVOdQ83TZLznmmVMiCEMTR39J3OT7WZR6Fp3gn/RFwfscKdmlUH3acy9YzSNIX/Li4u0mkWjBiLD3PHinnUVeXKeCVmccNXz+odzMiijvNx0XyRkUTM9hBoe3s76XfKEH3P9QnHKje/OXbUvbyPxqHeQeNYH40By3MDU2VS/nOZojYQ9MweGXDKstP4LhaLG8e7UUeqHtzHgcE2OhTFm8TGxMr6Tp2n+tGw5dIYrm3l3kHChkxpdpy1CndQF7FfXbZrOSZT1ZUiPRwO482bNzGZTCrBP+9H11m30Tsf9+OGqQMGTUx6eN2gyoFRlZUDtTkSqCEDiCEkzGSwSvhSyPGcQm7YkHsPJ61fY5sIbl0peP49+5ITSL9rLerFxUWF+SjkCBhYd1c0HhX1vnWwxntz4+Hj4ozs6Uky1n3CueGqj0eCbuOFn5oYhdOxLOr/8Xhc8dDKY3V5+XbLczo6JBQdMNDAE/F/zhMaYvSK6z4KQXos5THLCSJ/3o0SN26VpeCRS84priG+y7vYNldKup/kc4IK0gHmqvZ6XysCKydaRFRAvd5No3Y6naZ3ile0dl7OPk+Nq6M6g/4+aNW8zxltd6E6HtdvzHBxQ5884/KMMs3vr6sHDQTOYfKt5gx5n/e5UuZcpt70e3KOPDdiCVxycpPGlpybMpR5L6/pOvvyNp5rNBrJ2MilINfpFpdhTj+Uh34qyukrl9kEn3dtT278dEb2+fl5xakj2Zpbp+nzUXzsded7dV2gWzJbPORRGAF66XTuM6H5oKirgHNEJAeIMtJYPz3DyDXnCjfl4/wjzlR9aViKCJjpwOI4eR8SRJNvGcAgvtX9jg2JzZgVpTmv39g26lHKhodA3W43OYWJ5WnIe9tEGi+3GTRed3FSu1FLckzO+eeY0t/NKCODUyQFIYTf6BzXe6nX+Zz6KGd7MFAoxxD5iFl2mk+5yLneQSNV2I8Got7rY6R65xyVTqxfDhew7eon2TVXV293Ph6PxylSq8i167PcGN+F7mzYMl3XGZYKWB0r8KdjK8i8Dg5cuavT9WHKY8T1GU0SLvLa6Qwx/caNmsi4UhwU1K4QyLCqu3uNKNhHo1Hy2qivxHTyVIqx6bXkBOFaMvWBDJ6rq6u02QwnNyMHuq7JQmZQ+9hW/eZCILfmj+XUGfkiCQWfkFQg7FsaXHWeuIdE7Kutra14+vRp7OzsxHw+j4ODg+j3+/HixYs0DsocEFhpNpsxHA7Tgvl+vx/9fj+ePn1aiaqqvyKqm4C4R15EZR9x7flyTyIBGeejK1l9d16jwJEgVlk6c9CjkZor9OCTZzjvGGFwg1bt9Ofo6dYz9IbzPoIaL0fv1DySAB4Ohwmo9Xq9aLfb0Wq1YjQapVRlOsk4dhqDra2tdJZbu93Otp+KgkD5odCquUiQF5GPuOV+r2ufwGyv14uzs7PY2NhIUSwHUPSg6z0R1SgB+YQKX2MtJexgwdc+aWwl1zwqqjZJF+jd4gPVL7fxmfqFTlDyu+Ya5SpBC/tW+kXv1e8CEXwv55svlVF/SK9vbW1V1obneIPPUea4A8rngAMtL/M+aTabpe/kCZFHMCg3I272pTs5KPtPT09TdPGjjz6KTqeTDFw6L1kHGkSsk95No1jv97RBbYRDQ1f6i7uGE+9pnopvFCFqNK73kBCWUbbSYrFIc1p7FXDjTPE99Vmj0ahsyiZ+0DtUdw+8qA8uLy9jOBymDJCdnZ00fznvKXcdWAuPbm1t3XAK8DhHRnvVDv6ve1Q+swWJr2gYPAT6xS9+EfP5PPr9fnK88K9jOuEfyjRiCGF1bXAUUV3zqnaLD4gpcxiURqccypqrdJpwnNTH+shBPZlMKvOX9zFqLWNVS4zEU6qnMLzaSl5lUIxZAOofZaKqnVdXV8kJpPe02+30e8Q1fiOeUcq0+FpjojIc2+l/Lo/SOPBdbrsRe8n2evny5Q2nmXRQzigmr9Pm4ry+je5s2Hp0xytA4K2OFHhQhd26zxktZGT3zrh3hApTAyXDVhv7aNK4J0cC2o9a8UGsq6NPOA6oFEPENVMRGOX6VoJPE1B1ZSRPg8t3atJ6qhIFunvVV3lh6gToKq8Mr0loSBhTORHsU8CxvDqh9RCobhI2m9cHaT958iTtBqndsF1o8kiRy8u3Z/yNRqM08esAnRt0bsi548GFlJdR1y4KKj5PIe/z1MGNHFu5OvC5OplC3ljV/yL3btf1Va7MnHNGSmw2m91YU5Mrl2URqCpiyygvlfNDAu4/hHIyIyfn6saS99bJntyu8z6eHMccIBWfEbD6vHFlTZ6nAefvlszlWkHdJ5ntPJhz2HqdKLdZT5HXy/tXOsCjJHwfnSi58dI97Af/eN2oN32O1umNVW3IlXWfxPaRB/Sbyzn+va3ciKgYS1z/HJE/05F8rf9VHvmeuElEbCEMwmUzNHbdqG00qhkpfga3HEbL5TJtsEOnvvAOsRGjxGqD5j7Ph5euZHCBBhHbpHZyr5OISOBaETLd5/rAZTRlN++h4aJ+4pzns051hsND430ZbjKKeKKI4+KIa+ek9GlEXm/S0e38mov8sk/9GfaV7qERS14g1mY0VnOP2F5tcFuIDkTH/XrOjWJvb47v2EZ3RCljwuW5+lxE2a05RHvp8vLyhrM0J7/qbIPcWElWqL0y5HkUkvrLbbucbFJ/+rVV9E4RW3r/ciBTHc7Uu83NzUrjXfCTMd3jrbI0MM68SgGVcaAOFGOORqO01tYFBdeU+CRzxsoZB7omYSuvByeK2qHUHAcqotzElUNA7dJE1PlU6ht5OnMpMczZV52pMEQSPOyLOsCvZ31ya1LLc+mpBdxYQv3DcRX/yFiXslLZD0Gwu9Lhd/XzF198EePxOLa2tuLly5dxcnKSDq4fj8cxmUySh1lOl8PDw3j16lX84R/+4Y2zvm6rS52RpHtkdJJuA7Msl+UoKpWLSCyXy7SpTKfTiW63m5YCULiKuF5K4EfvUlk0LFh3Ki/VkfzE9rhAdsDuApn3nJ2dxcnJSYqok881nymnVIZkljIstIkJ13ixPq4YyPN39VD+VJST3w6Wc8/wL5/V3xwoF+moAEVWXGb7uxhtJChxuUwg7KBfclvA3eur59R+HvFBB4bWxQr40aDNOX2p6OmldhCd6/McKFZf8Qg594JTnztQ8rnvGRwuq1xH52Rl7v9V4P1dPPU/JrkR5jqSfUK9mFseIsphD8kEz5BjwMD7jpiFvxGP8N3en+LFwWAQ8/k8xuNxkt/c3NKfUebK5uZm5dgykerP43aUPRARKQMg4jqiOp/PYzqdJpm+vb0d3W439vb2Ynd3N/GyooTaWE58LbDu6xC5fljzkWNIJ4UbxOo3yRZmQOg+LUMZjUYVrOyGra/n5X3uCON8egikbKNOp5M2VFT2I8eAMoRZWq7v3eh0eSceyM0b/nVcQFklY1S8uFwuK5maes90Ok3RWn1UjurvSzto92jOqkzpAeEc2Rw0sL0tMvrIl+44UBBFukbLFon3NG/Fa7pf9sJkMonF4m2E2x3uuXlAmSZaW1u7kcUhmcWlFMxW1fMesLwNGxBn3oXeybB1YUpBp07hJOSEFvNQaVOQO6h2jzYFNJlC62YliGlcyaCgF4VARPV0Lw8HkB5S3tNsNtMkb7Vaqe3cPEvXPGLrfcRoL/tBqTobGxupbTIeNOGurq5SZEwOBa6/iYiKgeuGJRVhzjsjxnJBpL6hBysnoNhGgTnd49c4PiwnF4G5T6pzzEREEh6PHj2K0WgU4/G40g6un1hbW0tprq9evYrRaBTtdju63e5Ko5PE8aES1TNeV441+3+5XFbqRtJcVMqle90FRGazWQJGaq/WVDlY0F/3auq7t5F/+XvOwHVw7n1Bo4R9QeEtAS2AJYCkvvKzrwXYOM/UZ1tbW7G3txd7e3uV5QQ5A8IN2jpn2H0Q+488vMrh44rLr+fe4de1A/XOzk4sFm+zcliO835Op3CTGbYhoppCTf1EBS8Sn3JDQhENVsltpkLn5KK3m4aj87A7lRwwu6GldyrzxyPEPpb6kH/9XeobP+LH20OZlDNw6wCKX/dxvm/ysfN1dvyNz/Ael1nuuJN8UbYITxcggJccE8+5jCWYns/n6V2KgNLII57Td24C6bqj1+tFq9WKvb29ZNhq7uksSmYiMdVWfKSgh9qoYASN6Yi3y7y2trbi6OgoydBut5vksXalZfAgx3s621YOJ85NzwQhEdfSeKbs931FRLkME1+OxT7hd96/as78lCT50Gq1Ynd3N7X/6OiockIE26i+kpyirSD+5b40NGyJ/XP6QrxKZwBxreQ0Mw7EX+J34XamuXMusD4cA3cmqRzhBeojGX2U3zlnNsvj3PQov+Y8jV6OD48SvLq6SmfxKgCnTUZzdXD9oDao7e4kazablQwK4rRcgI+4wXGP2s+/OWx7G93ZsM0pJweYzmRqEO+nt1jX1Qg1mB8yh3t71MlSAkqZ0fpapYFS+NOz4HXPKd8cU0Vcn7EoD4jqz4HU31WAQvfQMGw0rrea51qXiKhEPlWmjOeI6yMJaIwzLZh9TXLm4rU6HvB+WuWpp0JhnemFpoAgAOSkf2iU67ONjY3Y3t6OTqeTlLf6gNEktW86ncbJyUk6JqDb7aZ76oCjGwh1k5/CgfPVoy4aDy9HH2UHSIhpLkqQj0aj5K2WMvF353iLYJ4evbq/Tqyv8x29xzml4X3E+tJBtlhU16Lwd5cT4nG9W5kr8nILVPJdnKv6607Ah8T/dTKiTqY4j+o33uf3k5Te2O120zok6hN+dwDKPoy4aUR4FI6OTAIa3S++Fxifz+epTfSOc72hHB458CaSDJTzKOdBr+tb73vOGdXfndD83T8cLx8n9Y2vKc6BIV3P4YTbQHru9/ueA+xX6l7JsBw4rMMUdL7kxk5GpaIeNGwdbFOnugyRnJrNZpUshEajkfhQcswd1LkNdFTnTqcTnU6nctyT0g2Hw2EcHh7G6elpWsLBCBajTUpVVGBiMBhU6hDxdkNGydHZbBbtdjsODg6i3W7H9vZ2cvYrO0L1Vh+oPySbI66dUD6ebCPbrPvY70wpVT9rXClH9LzjWo4n5RLf/a6Rqh+b1I+tViu2t7ej2Xy7X4gyQ1zWRFxHaiOiYtgK34oX6ShwfnSHgddJ97CvxVsaF6YYy1ZQnSmXacNwzF2u5mwcGvDsC8dAemaVk56/s0/FL26Q6v0yeD2TU7qIG4m6vab3u12W6+tc+6lD3IkrGemBC5frP6lhy0aSKTmYFxcXybDqdDqp0Qzns9GrlLHIPTXL5XWaoibCZDJJKSwE3a4QIq69RzorluBhFZNpYjQajZRuub29nYQ6DU9XYFwUr4iPr52JqApb/aY0A0WImIq8trYW0+k01tfXY3d3N0X6eJSIyvC+43i4F1l9ywmjvnOw5EwecX3+JCPLmlStVitFwjjm6g8qiDqeeGhE3lGfdbvdODg4iIuLi/jmm2/i7OwsGUZKzVksFmljnFevXsWrV69ia2sr9vf379xeGkVSOhQ4OedERHUTMXr8CdLpcKAylhF7enqazmrW98FgUEk50jpV8QrrTAPAPe2qa10/0ODwtrmQlhJQn+cMMH/neDxO7Ww0GtFutyuywucLQYscAJrvGhPJJG7Ow7pTeHPdC+XCfZIDj5zCqzPEXZ7m+i9nDKgvFB3Shh6SF1SWl5eXaS5IgbuR5im5HDu16erqqnLkgx9RwJ2vI+KGjONmUjqX1D3Zqm+OvzUvdKyYnKf6S6Ndctz7UP9LBvMIFvI606Q5jvpN/aRolzaE1GZGdBbr+br5lQNFOR5ynlEd7pu4AzCXSOVkrPjNx5hGivCE9IGuidckt54+fRpra2txcHBQ4VnJe1+OQlxBB1xEdf7JAFQQgDu28ghERfu1JEBzUTwxGAxiNpvFixcvYjwex+HhYVqOlHPq03DVdxkaAu10FJydnSVePz09jWazmaK2u7u76Wi94XCYNvtTm2XwttvtuLq6SrJ5uVzecB4xS0H1VL+pHRxLPTsYDGI8HsdgMEjj5o4dZsjxHWwzjTKfjw+B/yOi0h8HBwexs7OTsM7h4WEcHx+n81JF7iiMuE59l0EsjOqRxJyRS6eS5CiNZ+labQAl+S2e1l+lEOsZ50VGil2uCSOL3DHBjZ3URmH/iOoSAeoeyg53urKeEdfyQ2Wvra2l8hQYI691Op3Ub7IRJJ9oo+TwUMS1DSRnLrEJ5w/nivrdszSpM+gc1Ptzc+GuRm3EOxi27uXVS1k5CeT19fXK7r9OVIIkMjEFtTdKdZEQ5vbcviudM0TOY8D3O+U6U6DJ08ZotERUDcHbDBU973Xh5GZKJ6NtTJH0HY1zgMKZ1+uR89LUjZUbugJJBHgCQ54qLebmGh737pHHHgLVjSP7TOm5/X4/jo+P04TmuaciCvjpdJp2B3RFVjepHVS5VzhnLOh/ClApV10Xb+l5bnh1enp6w7Dt9/uVQ8D1DkZ13YnDuZ0Taqz7XYRaztPo13LPONCWcmO6EgHkqvd7f0vpnp6eJmWjaIdAVs4jnwPKD4ncMPF2c8z4va7/KJdYjp4RmJUxpXtWedP9XTmlyXKkuKW85UyUftF6PoEwKnYBf+kGyTdGARzg0aCkrtJz8/k8GZWSoXoH5f4qGXCb8eURijp5wbIJXnI8QeL41Mmg2+gh8X6ur2kY1enYHH/nZDQBovQI9UKdfnaHgMtWOVWpeyWbuS8G9x4RttJO/o1GI20MJZk+mUxS6vGbN29iOp1WnJt0ANCgFeBVfwkrUU6IL2XY0qEjx+NoNIputxvr6+tps8bRaJTavbOzk4xbLeUi6JZxQ5nh/UnKGTgylqgvyCt12I73EVPn9GDOWXgfRL4Sttvb24tHjx7Fs2fPYjqdpvWbpJxsZ7vc6a3vup/YvQ6bcm5oPDgulOc0hv09OUcd68dxcd2j+vN4P/3O72ozA29uL63SlRE3l2zlorjKsOPGVdJVy2V1KVjuPewHl+V0pPm8cPuAdh3bcFd6V+P2zoate7rpcaBwElNvbm5Gp9O5AWpdkFFReuRQHcFG0YiT0FcaixhZHelpAXy/p3nlBJbep+d0TZtiETSoDfSQyLPBdjnjcRITEKlOeoZCWFFcGtbaWltpoN1uN+XVqwwJ9hzgqyNOdr+fbVFkWEcibGxspHQhefjFN7PZLO0crGeoHGjY5SbNfVIOVOi66jyZTOLXv/51/NVf/VV8/fXXaSfBnZ2ddCyMAITGfLFYxOnpaWxvb6e11XSS5JSCzw3yax3Yz5HqrTkor5/GS6liw+EwJpNJvHnzJmazWVoPdXl5mb4LBIk33WMnksDV8zJc3IBkhND7noZJrk/0W53Hu86o0IHiyirQXKNBVQc0OF+Wy+ujAF68eBGNRiP29/fj4OAgnj17Fh9++GH0er3odrtZwEw55uld90HiCRH7ltepwHiv8yrvl1JmBFLPyWG4s7MTvV4vOp1O0jM5Q1rP+xjpfx7VIv6U3NGSFo0/00FlXCyXy/jggw9S9EoOu8ePHyeDRFEwZSssl8tklGpc6RzVHFFblJap/RHk/dcGZN1u90aqM40IpgrndJnew9RN9U1O5+b0nOZjHVjJRaVdJlEmUIZSb1Mn3idR33IMxUc04CKqOw2rbXRi+aZkvLa1tZXWnJ6enibDTfqUhpBHsthXAtc6EkSOZKXTS++Ox+PE+2qbNjocjUaJr9vtdiyXyzg8PEyb6x0dHSVeVV3EI4pgbmxs3DgRYDgcpvGl/BCOYxTXDUR3LkVEmr+j0Sj29vai1+vFJ598Ejs7OylyprqoH3L7Suidns2XC9Qsl8u0FpqblFKHOb5lGbrPMZ3Pxducqj81aSzW19fj0aNHERHxp3/6pzEcDuPo6Kiik3OkfmDfaN5wU9iIm0cS5vCMxlUZJFraxb13OD4MvKmvVS9GLyWj5FTKOeBVR/XH1tZWbG9vJ0ekyhevCStLNoqXuQbX7RIGzxzbuVMy4jp1eW1tLebzedqnR45XrrXNyWmV7SnQGjPVicEo1Z1ZTnX1ovzKRaXp8PAgy13ozobtKu8EJ60E5mKxSBPz888/TwzsOd1kUlcOMnCcocUU2pWNuwaTEdl5ZEb3EOkvPXd1ijciKlFpXaPBqDYKEHE3NHpOKPjUlwR5uf5nmhIBhq4vFou0mZUmaw5Q+sRxgOITiCCDk033M2KvNil1zs9E1PhIefnZXsvlsrI21cfhPsj5wL12w+EwxuNx/PrXv47vv/8+/tf/+l/x8uXLGAwG8bOf/SxtfiOvuMpTOshisYjJZBLj8biy0Qe9kHqfU87o9cgQwQ/HTXOWBqjGsdFoxHQ6TRHZ4XAY0+k0hsNh5ZxogX5FnbmOQx538T0jPaobhaU2W6JS9zav8gb7/W7gEHjk+Eoe/6OjowTsNM8o+DkuKoeOPn2nt1QARuUfHR3F9vZ2fPDBB9Hr9WJnZ6eSxsxyHxLVjYso10+3lcdnned1T7vdjl6vF7u7uzEYDCpAwN/v+oTAgHWUHKPhJoOR4ECZJlxH5+BK80tlaq29eME9+BGRDFCt5Rbo0a7ydGqobhGRsj+4eZHzqNrnho/3dw4o5aK9NM5y+oM6NRdZ8e+OJRwT1PHIfZHrafY/+ZbObXeuuNyg7qXRy74ejUaxsbGRIpHSjRFVoKg0dcoOJ8pDyX7Jccl9ORxPT09jNBrF8+fPk1P94OAger1eGmPiG847l7viay4zEg7gfic0YCIibQ7FzdgiItWT7xffajMrruet0xnutHc+dfnO3zRPZdRynpBX+E7iLPYRjdncHGYfPQTyOdrpdOKLL76I58+fx3Q6je+++66Cj3PYk/0kPDidTqPdbmcNR+pWN3SJ36V3uWs2I6PiOzpgvXxmTbDN4m3WnY5CGY1yQEpeU17o3Y4pc/KAfUfZ42PAuRdxnVnTaDSSQTubzSon1Yjf6vAF+4Yf2gb+0ThSprg+p6xk3+Rwrjs6cwGKHL3T5lHemS7IyVTz+TwJGIIHRmBYtv6n0ON7mZ4qppWHkeeGqQzenwNXLjwiqrsmU6CxDIFTGWO6JqChevAeRq09RdrBQq6++u4Tj9+5nljb7ne73SyYqRtbTi43BHgvQQuVvBSd2uTKyA0AbqbCtbUR1+nmnEgPjTjZT09P482bN/GXf/mX8etf/zr++3//7wmsbm9vx/7+fvLC05PFyDQ9v4rAeMZAxM20DAoKCsCc0eWCQXNW0VbNXz0nY/bk5CRFbLkjrOafBBkNW2UMcLkAwRfbzzlBh43zYY6PXWnyHne81RlO+i7Dtt/vJ4VIT6vXgQ49zW1uykLPc0TEbDaL4XAYa2trMZlMkuHz6NGjynyRkr6LYfhTkcv9VQbpXcdrFbk8UtSy1+slPqIiJ4nf3ehwg0zPihfpYNG4yaCUYat55Loi541WhEvZGXSAClhwfZTq7EBMHzl7qQscXPiYOSCr04Xsa+psztVVhi2fzdWF78q92+uc44X7JO8L71fVk7yRmycqy/uNxPdMp9N0RMfW1lYFG1Fuag4wC6iOCCqJn5h+PBwOo9/vx4sXL1J2lfby4B4MMiLZJ6qfZK8cunQMaT0lN+HTb8w62N3dja2trcreIXKKTyaTmEwmac4Ijwp7+tIs1Yvfabz4mOl3l/0ybHmsic8TGluaTxxb5yk9mzO03VF0X6R5SLkT8TbD4NmzZ/HRRx9Fv9+P169fVzC46zKXvTREmQ3J9vPd/n6VzQASN4qSHncMTvylZ1fpXfKBRxHF71o24o5MZTConYwM63m1S7jPHSNuEPrzdHSq/5SZwLXFaqMi3N7mujmguskOooHuWFL9Wdd/JMcLLhPfFQvd2bBV48kUBKYybCKuj+BRvj2ZyNdXUmHz2BwxrXtEVRftvqcNXtjxnAwR1TVHZGyVxain6kKQJiNDaQYa1H6/nzwg8/k8laH1KjIAxMz6LBbXqTpMi1PbCcjIGAQNBNRUphFvU2HX19djb28vTS4+70xL0OAKkfd6Oqnu1zPq152dnbRjIr3Dqi+9q55GRKUo4/ghpKFRwEipPn/+PF69ehV/8zd/Ey9evIjj4+P47rvvYjweV1Icv/vuuzg7O0vb45O3VJYyEEajUQwGg8R3AhA5T1XuGhUk5yNBM72kOhZLaWXymuoZGbL9fj9Fb2Xw6V4Zg1JO4uvd3d0037UWV3Vot9s3dgp2ecDUv5xAo0ygceIgwMFmRNVDq7GYzWZxdHQUL1++jKOjo5S6xCi3v1/kssff789dXl6mzTZevXoV29vbcXBwEF988UXs7+/HZ599lqLXqwDqT0kE4b+rwerOASpU/6u+lCdcco0ZLkzhc2PPswQ4R/QOrWPlWjx958Hy4hvJKjlsGo1GTCaTiLg+U1nAQe/1bAmOq8qnA5TzQ04PHRe1s7NTSSmjIVCXwkhd6vzPZQ91Y8W+4/jpu69VdMpFGuoMVgdvD2kORFz3C9Nb9TsdWpRh+p18F1E1bjQOwiURkZaCvH79OhqNtzsSCyux7xi5jLh2CkdcpzlK9lAXbGxsJH4WvysbTktLxIOaJx999FHavEn1UZsUHWIWgvCQojnL5TJlRQgTiC9kxMow1TzQLrwRkcoZDoc3sgDVp4rectyEz7iB4+XlZWqfdBGdA3Tc69rFxUVanqN+Io+of3MOVfWTAhKUfxFR0c/EhQ/FsI2IJAMl8xRZ/6M/+qN49OhRnJ+fx+HhYbx8+bJi2Iv/IqoGk5b/DAaDxGPUuZpHOUcEnSHiW2W+8TcGVFxO0VimbaB383eSO2S4/45ktoJ7zNLkhrMMPBBj5yK2fp2GZUTcwPq0aYTBZMxKZ+g+GvmuQ5kBq7LPz89v4BzKEO5Irf5hNovzlBuykl/U17/3iK1e5i92JUXywWLlaEyqPDJJzuuZY1AJ6ZznmgKe13IglBFQbyeNeA6M6iHvkBjN0w1cQLIfcgCB3/2aG6HObBK6jBI7w7gx67+x3Lr3eH/yfYw21r2b7aeXLeJa0Us4UCDcF8lInU6n0e/3U4rWmzdv4uuvv47Dw8Po9/vR7/eTB1fewvF4HO12O20eRIOd0WpGsJ1nvf3er3V8JKHFeci5xcirIsZKNZZHmmuwBHrUFwIwcmAJzMlhI5AtoapUdWUYqB5K+2Gqvm/SlgPV3id1BvBt1xaLRVLUah8VT844vct7fDxIKp/pyhsbGzGdTpMRJwPmrgL9xyZvi2eE3Aa+6FxbdX+Op8mvlPl1Y5OT5d4GylQZwcvlMil8l3fUe/SOq3yXlZTjt/EQjVEa60pPV4q0wBINW8nU2/ifbXCwUjcOOZ3g5OPp93s/r6pjXZ3vm8hrNOZzuKeuH3Ll5MbNo4xXV1dpo6ThcBgR12czu+OBmIq/URcQDNPo5keOlI8++ii1VUccPn78OB23IyOUeltOOc1VGo7c6FJ14s7NelZLUhQ84C7GDFb42mdvo3hd3/nhmNTxLIMG7EdFBBnpU587vuJ1GlOr5guxEnnuoZD0NTNYut1uPH78OD7++OPk4BCGcOcUeZMRVsfnusftgRx5lJZ9nBt3ls/31OEu6gR31LlslU3gKdJ04tCR77zpuu02/eXGL99BO0Vzks8TX3jdvL/cTqqbG2wr+45tqNOPdfryrnrgnQxbesQjolJpNlCeC93L9C0qbv2mThIjEBAr9YpAWYxAcOwDQcUtME/jlSk8qTOwJpVAR8JTXnwXqOoL9gO9E1ocznfwXhr9pBzD+XefhOpHGRG+QYXGrg5k6Br7VfeoLozea7Lo/NLF4vrMT0+jdcXH9ZlKZdU4S7kpQsE+vA96/fp1HB4ext/93d/F//t//y9evXqVvMXL5TL1t3h6MBik9d/Hx8fRbL49701AQH0pg5Kp9fTY0RiNqHrvOHaupHWNhgCFXMS1QaXsisFgkM4glMeT6ckydo+Pj1N6sq8ZFmDRukHNG3nstenP+vp6bG9vx+7ubjoLUecxrq2tpbQzjb/4SQCfjhEHDIxWUwGp/5znF4u36+EPDw/Tx9c0uRBXWaScElpFBDja+fT4+Dg2Njbi22+/jSdPnsTnn38eT548Scen3ScRgKt97ujTfaK6/sqBnNxzkhdyPJycnMSLFy9iNBqldW26Pycf/d0En76EhnKKqeC8R+TH50jusi3SVyTyEYFTTsGznppHBPvSR0y3lN7T+5nRw/ZLLviY1ckUySIvy8dVdWA/58aD99cBllXg6j6JY6WNnByQRlwbvT6m6n+NG43jXLRG5b958yZlAn344YfR6XSSLqFhJz50nSHdJBDtGIh6fmNjI+10+9lnn6VMCcl7bRJJQ3NraysiIm141mq1YjwepzbpqD9lLjF9lzsku45TdpBwpfqs0+nE3t5e7O7uptRPRf6kv+RwpeOUpxPIWaT208iQPlI6qetQ6WzhUGIrj7pq/kRU92PhfSKOn8awzml1XyQHytnZWdphV/Nhc3Mz/uW//Jfx+vXraLfb8c0338Tr168rGVBuGAof6NzjiJtRVOl1jUtOZgpPatyJ9/nX5yXxsN7PeekOiYjro4pEa2trCXsPBoOKM0d18/OpFVRQqr/rB5XLay7LWX8aquJXZZXoPVxvyx32+Q45LBiVpX1BA5/PiSeE6bkZHXUC8VsOIxBXuO1yF/rBm0eR6jwX+vg6IgoArl9To5mOk/N8iTmoQD21mZ3A1GNn3pzHjwqBA680FglKrcNi6ooAA9eTqJ458KD/F4vrY2848Rzs0YOY8+pIeclgosc/BwIloHJgX4zKiJmDGfYnPaW8zw00fSQEZBR7uRGRwJyf+/lT05//+Z/HaDSKly9fph2AmWYrw0Rn+sljuFwu006Nx8fHCTRwUy33cHkKTER+rXEdGCRvqByVwd/FZ1JMu7u70Wg0Kqn0mqOKOJ+fn8dgMLgBmlQ+eZcbkujDeSijmtFr8hmj/gTvPjecZ/Qb5ZL3D0GKxufo6Cidgfi70m0gJPe75mJExOHhYYogP336NLrd7u9cp9+V3KMaUfVgi3LyzXmX5bhhRIAdcb305Pvvv4/nz5/Ht99+G6PRqJKuyfcQyDMlkMYm+SwXxXLAwnY6n+leAmGuE4/IH/1GXna9xHrQqcojhTwrhoawG1M5fUi5T/BAxzN1DAElARbfkdMjLpOo/+vGXvV0Q/khEAGa/s+13XlF1/x3Rk98/PV7o9FI8lP9LgDqWS3ETTSY2O/6q/KZZSadoB2Ynz17ltKO1VYt/5JjWzjIHYqcX9xkKqK6pwnryDkRcW1giOeECbQ7uDaXcnAujCEgTozI+cwgCN/j69zVDi2bkgHFcaHBRPngETTHdhwX8onPl/smYlAZbWr/cnkdCBKvfPnll9FsNqPb7cbr16+TruXY6lmeO5s7GjEi30eMiHK5nztvOBddBrNsjrdjG7ctZD/oI17SRpzcCJApulqDyoxTb6PLzRye43265ktv1BauY1adKDd4P+cJy2b7czJN71CgKne/6yuR2uXYrk6PraJ32jwqVzCVNZmF/68ybLmWgYv/cx2r7zRsqSypCFm/unLoDZPwpNLS5CXTSkhqEMQY9I5IgMvboYmXC/fzey7CzLU7BFjO1Bx8eoGUy0/vn+4TuaB3QU6BrXHLjT/HIAdw1C4aPgLvmmi8X7xQB4p/Svof/+N/pDRcRRMllNUOrZH1owoksE9PT6PT6aSjmOjAId8I6NA4U39E1EfBOA4EWq5AXeBLiciwlfLhRlDdbjdFcLkphz45xxEBtAAQ601FxDlJQ0H9RD4jcFBZ3ifsO/ZPro7y7Ot8XgJNJ+/v3D111+vIy7y8vIx+vx/j8TjtTPoQDNs6kOEAPncf5Quf9Xty/y+Xb51HL1++jO+//z6+//77G/KMRKVIPhRfSN5HVJfLOMjx7yrPdQSzgeRIVCRVfJZLsWMZ0oueKSAdKcPWvew5mZCLrLNtrlv5LOeX7lGKvOsZ10V1oEOgk895G3lvDtDlnvupyd/v/Za732VW7p46OSVipoA7FKTbI26uxWs0GrWpgHyXO6QbjbcbO/V6vbT2X98l92XQcTmKeMXbIczmm8kQN63qUwJ1OXl8IzmPjqseiiyT/xzvEDdyTuf4jsBdzmxhwpw84lzk/HOnbB3p3dT/D4HkYNCxjepDyScdYffRRx/Fcvl2PTX1GnWC+uLi4u2RleITGjpO1CPNZjM5ZTygtMop5npLf3XdDVvHVvqNWZ16TviPzhHNA6ZKu2FLHqZs8Lkr/CTy+9g21Vm8qww6Br3cKVRn2HK+en3UdhnPq5zFrKfrWc6NH4r972zYKkWADWbHOgOIFotFAvqMZEoQKLWYnUcDV42jcFMKq6eBUEhFVM8P46AILNDbQoNLBiVTIeUpZz04UAzPq03aETM3KVjnnEEphuAuwxTeHl2OuN4YQb/peACdIevpLO5BJyPrXhrWfj8j1VxXS0NNbaPRrgl2eHiYzsCT4CaPSTDQCLwvWi7fpi3t7e0lI03HwfT7/RgMBsmw9XUUZ2dnMRqN4rvvvktjqA0FOp1OMob7/X7s7OxUHCJ1Dp6Im+A/x2t8lt+5pkNAYXt7O549exYff/xxfPjhh3F6ehrj8TilDY9GoxiPxxERMRgMotPpVICNxt1BteaC+Em8LH7RukGm1SmNRztw0jnG9rjgc28g+8qVgYTw69ev4+joKKVW35XqgMbvA4CorrPZLL777rtaI+6nJK4Fds91RH4zMxIBfg5gR1QVu+45Pj6Ow8PD+Ku/+qu04dbBwUFlAxjKQqb6SSdIp9DYdB2m90dcRxJksEofqZ4E2qqr+DkHFpTdk8uOERCnHqRTj3pHf5nB4lEw1dkBHefjbUZazhDlXPP02ZyB5mXn6uMyjST5t8rR9FMSwaP0o4ArwXVufLneTvzClFPXu4y46D7JxFarlda1ireJJZihJV5gH0pOS39Np9NUPufU7u5uOmGBUeKI6hm82uBOYL3VaiW9IL6nwSIsKcNWUWf1AdtPXaA0V+IOGa58ttlsJty2vv72/N+Tk5NKBpXkgTKv1Ea+R3iE6ZQC7a9evUrRRRoiIo0ZZQZlguZpbp07+5pG9l2jVT8m0aGo9iyXy5R+q/NbdYLC6elpcop89tlnMRgM4u/+7u/ixYsXcXh4WLEFZrNZnJ6exmQySXtMiLSGm0scuVmeMJY28hLP+47bkm0+l+lcdIeHYyeNl3hHuqLReHs8ImU2sw38FBDKTepO6j4PbNCBKh6NiIpto3ax3gp2yQkkA5d2keStjHI3Sl1vE+NKrkim+MZSOWJ9PXim/zXeOftlFd3ZWqBh4grKG+8eYwkvegX94wqB5ThD0fjL1cc9ZCQxCAXZKsNWBq2MKw/dq40eXWOdCQTJMPqf0V+Vx/rqGiedK0ICeaUF+XFDOVDhYMf70Y3unNLOeW407jmjXUJIhhLBAeumPqOiu2/SBFYardaFaM1ELrVEQk27KHa73djf34+I62yGZrOZHDYaK/JkzpjLeRnrhDANEQrtiGtlxbkgw2FrayvW1tbSGs9GoxE7OztJyeccGrn5zagTo1zyGvJdvMa1tauidCTvG78mkoNlPB6nCMR9e8b93YvFIm249ZDI+UuU88qu6k8Hhf58RMR4PI7BYBAnJyfJCeRlsy5ehvjdDT43JL0MRnadn2iA8BoNEvJSDqCu0qFu2Op/3pczDFm2l+9z0sE4y2WZ7Oc6p0Su/71+ufqyr/h/rvyHQF6Xun687be7kst/gT3JXK6Xjbi5v0Aui42ZNNQ1dAg1Gm/XS0omqzy2y+eAjF2Cep8H1Ov8OH8Ri+k66xBxHQRhVIwGMZeLKWWW/SVdJTmg+cUUf71Dz1Dfc02kj5nzeU4niVhvxzoePX4o5FhZeEjGktY6T6fT2NnZSUdPbm5uxmAwSDjn9PQ0BW+EDZUqK8O2TkazLhwnx48cA8e5OX3g8jknNyWPhWm414HrBdXR9YljtlV6gXjQnZviXdVNfcFnxb9uN9GwVX/QVnF5T150XKoU7Jy9keMfb1+Ov9hvdKbeRu9s2OYqwcqIsTVITDek50uD5OtQNXD66L3sKN+FzpmOHafyXLhrS3Ffn5EDFRKQ8mgSaNCzkhtseUD03Q8W14ZJaqOigRJuNIrpXFBdVScxtJ6JeHvsT7PZTLusqi0aQ1cuFNScmC4g+LxI3/XcYrFI0WN6IKfTabx58ya++eabeP78eYzH4xtGByfc+fn5g4jYRkQSvPJIyUM4Go3SsTjcREKkcdcmUufn57G1tZUilToaSSnN8nbRo0bjxo0vB9PuRCH/UJBTADcajfSORqMRjx49ir29vTg9Pa2U02g04smTJ8kj7m33NCDVT2mUXFesNriTSUauol/urVs11/k7KafUFG1/9epVUrYs5yFQDtDdF1HZiHzu6j7en3u+zuB1GXN1dRVv3ryJFy9epDVaNGxzUT2Woesa29zGIypD7yMojoiK/NazjFgRjEreq2yPwKl+XmcaGNQ9/OvGAdvAyBxBDfvaQV6uv1g/9o9HjOtAnyj3LsqEiGqE31Nh1TYaXw+BckBP/zPa6eOre1nGKqBGPuSGmY1GI+kORSPVb4rg+CY0+ktdIlktUMxIouRvt9utRH9zRupyubwRtCDolwEo3Sb9Jl1KsB9RXfPXarVSJI3ZX1dXVxU8oCiZ2sHf1BbpV64J1rnA2pxS80zzXf0uh/P5+Xk69UCZS5pXmoN1zlfOGeIq8oVneVAePBTiPGeEOyLiu+++S/u6KDJ/cnKSIv8a03a7HU+fPo03b97EX/zFXyTjVk6Kk5OTWFtbi/39/TSXlOHGjCpiTY1TxE2jUR/PzvFruo84gHIv4noNa7PZjF6vl1LiyXu5wJc7LOhwotx2RwqXfFH3qE6azwxw6RQLylDVwTeP0yci0nITn9ONRnXfCNqC6hMtxdOxkNwUmH1H+e9yxOcN5aScB3cNcN3ZWmCqSE5psYJkNBkC3BzIFTQbyQFS51IprwJH7ilxAEMGUXqx1jvK4+KdSi+JexHUJ6w/iQzF9SUyjultZH/Qm0JhLEbjX01O9RmFZi5KTi8MmZhM5+NDBuc4Oz/IY+NR2GazmdLRj46O4vj4OF6/fp0ESG4tBb1LnHz3RXQ6KB1JiprR2joApr6SQtQ5rjwGSO3nJktyWkhouyHBcdTv5A0HrawP+YAebPIT01PURnlfHaSqDIJrzRNGGcQrqpMvC/BUUpWTM1br+sF5nLwqUDUYDFK69bukIN8HPQRjm1HzOqPUDa6Im7skEwg6UWZrd8XXr1/H69evK8cwSWZJjtJ547Ka5bpTh7+5wUYgkXP8sY2cC14GdYXml89ZleUGZE75U//6nHadSqdXTk74+Hlaq+rCut3GizlQ6WPg/VT3vDtU75M45uyjiMiOB/8nMNX9mgcaV/5OA4m8qffwXFlhBPIi9bbKEO/5x1PZI946/sk/cs4Sn+kZAvPLy8uUDqrzz2UIKj1ZG8vICKJuUMqpHMBybmqDweXy7ZKgs7OzCo6iA0hOAY2TR56o71R/35xQ19lGBiQ4Lj7GHPfcXKGc8SwM4jj29V0B/Y9NxJLiM611fvLkSZycnKTjArUxqByRclJPJpN0EsJkMok3b97Er371q+QEGY1GsbW1lXjasUZENUobUV0KyPmpeyOubRjOO3d2NxqN5NDUM67zFCSiYculUhw71YX1UD8S2zIowPpQl4noaFLGJ3HiYlHN6svZSOInnkRDe8L1ZA77sw8133USCNvM/veUad7juox95lj2Nnpnw1YVrlOMEdfMT8OWXuxcB0XEDWag8qCw8s5muWQsCgZZ/UwnbrVasb29HZ1OJ+2yp/o7acAJyujpF2DPCSW9TwKYAFvt8nYIYMigYDRQikxEMEkgQ283x8b72FNnVQ8BHWcqRuTcCJdhq/U2mvA6yPy7776LwWAQ/X6/Mpa5vmak8L4NWyltbswlo1YCOddXIl2bTqfp6IJOpxMHBwcVg0HzRu11wcbxy80ljsdtdRFp7LmLsaf+ymN9eXkZnU4nyQM6nyiguaZMnnd67gjafCMqKnsKuFy976LwXRErbWo0GqUNox66YfsQyMG2g7Y6eV6nkHJGJK9r3dabN2/i8PAwptNpRQYyqsVMCdbB/3JOUFZq7uX4ydPHnO9y8pH60Z0ydcaa5gsjN94nBBQ5MHCXOV93n2QPx5Epbjlj2+vvBm2dXqnrO7bT++m+Ddxcf/vY1o1rziHgfUS55wYTda2MO6UKR1T1ggNllS29KpnN6CVxDEFxnWHr7ZCheHFxkQxb7RuhaI42W9Kyj8lkkgxSBRcUid7Y2Eh66Orq7Tm4ipaxT6gfNC8Z6XInL/ucUWDvfzfepReFx1QXHyfyhfOA85D6W3V3WcW+d2Pgvsj7UGMTEfH48eMUsdNxSzJsr66uKqmz29vbsbe3F1dXV9HpdOLrr7+uLA1qtVpxcXGRIoXubKOx68EX542IuPEMx1b8q7HxiD/TYGU3aA2wsj6FcyOu5TgdFd6H4knOQS6F8oAa6ys+UJbBYrFI2RpyGuV0m+YHeVY2SUT1GE/qrjrDlh/ZecrIcLlP7OBRW86JnHH7Q+idjvuh4hVAzCkg9zjTEKOBpXLVOfLW+QDyfglCpQZTUFMwr62tpfV59KhI4FGAMsXYwZvezcgnmZxpzGqf7uMGHx45zQkyGXPcHZdp0Nxpze/Lrfdwg9UBjXvneY+8bBx7n2Bra9fb6WvdrDaD8lQqvWs2m1X6sM5zozbNZrOUInGfJMNWCk7eKSnwVUYtSf19fHwcg8EgRqNRbG9vR7fbraScaezEk87rTMfiuDDNhADGvfN8RnWS4eqpnuJD1V9zsNvtJkFG8MW1xpprkhucpzlFru+6X+knriTciKmLiLh80k6eR0dHyWBihLxQPeUyJ27zTNNIcoDPsVIkRNkP/X4/jo6O4ujoKJ4/fx4nJycVvqLM13vd4+wKWTpI/JxTvnTgUmZHXJ9dSznONFk3fPluZlzo3pwxnzP0fJkMwV2z2UwGjIM+ttkNMJVHZw/Bvr9Pcly6SHM5F31mH7FNOUOVOkjEue18dJ/kvNxoXG8eRdmUc/rUOYL0G8vXd6b+MRqqs2GbzWYcHByk/7kpkviXIFhjoDmm5RfMGJKR0m63E+gmD+QywDTe3ClZ+xboXcrOUiROsl1tFUbjhjjiaUaXCf51TZFAygLdI30kQ4sO84jrpQq5JS+cG69evYrvvvsu/uZv/iadDPDo0aM4ODiI3d3dpKfII5InHGuRR/Y4Z93QeEiUm+tnZ2fRbDbjyZMnMZlMotFoxP/9v/837aPy7bffxnw+j08//TRF7YRD5Pje2dlJxp02PX316lU8evQopSQvl8tKf+pDI1C/EaMTU7hB5rghIhIvkic8XV7ykMs0IqqbHolyzj3VU2WLjzUHXYcS16k8vkPyQWn02oWacqDRaKQlmNxJPOLaceP1lFzzTUE194X/FawajUaV86nZF7QfXY6qXL/Gur+LY+cHLVwUwxAcuOJhZSlM6irnxpOTMyM92xQoZEQZq9qIhhFbRZFcmLEueq/XTe2gQeh1pICKqO4iGVHd5UzkaUESzA7OFP1VOXqX6uMeSjdiVBYBiBuuDlJzilrvZxqSdlvUJKFQIOjLeX69L1n2XSJzPybRMPTPqugoyYG8jFOdGcg+dMBMxafxct7QeNKg8w/HgHzMe1yhOmhmvQRQHKCKRyXYKPzJmyQ6Ozi3Vz3nhgGJ/eXCmLs5uwcxRz7X/79IdZFGN+z0PXdfjievrq4SANaZ1qenp+mj48AcVLshwfJEjBoLpKounrIsHs+BNzfy9K6c/K8zrDl3I/JLMFgW9RDrl5PJdfKnTkflxsRlTk7vnZ+fpxRY9pG30fuQOiX3bl6rq+d9g3zqR5HrTr9+VyKf6X9dk/N0Op3G/v5+DIfDyk7JAqrT6TQbDVE5rlPp8IyobgwjOempmHRmcK4R51FXqh5MZ/cyBMSlSzg/aEDQeM3JAPYf7xVod0eVg+86HSUH/mQyiaOjo5hOp2knaLWj1WpFr9er9JXPXY61rq/iIQ/+PAT9k+s7OVnIi+Tn0WgUEZEyI+kgUYSR46yMquFwmPYfqTNsKFMpz4kXc8ap2wu8X/vu0EZg5DTnlPCxJDlGyY03+VbOJfWt7uE8JXHOK9im55n6SxvIZbvmr56j3tQ76nQd18+7/eL6imXmHEnsC/YfP3ehOxu2bLSDXI9W0bPAs860xbQzWI452HlsdERUIq1KdyWjSvEqEqvvPLLHNyNSfenV0XsptAmOxIRiFPUF68kJVWc8+8Az3SaiKuClMGQ4yluvBfzL5bKSsqf+ZRvYZjcSCMxYFzI+66zruRQEB8I+ziS/RqUkL9R9EiPTSj8WGPfIeI5yio1gr9FopI2ohsNhSo2lEODzLjxUB3rQc4YAgSSNAf9N9zOlWFFjHXMgz5y88JxDEVHZBIsZHOItgSu1gTsn51KwvZ+pDNgXOVL7dKbw8fFxSo+/C2B+CKDivilnrFAvrFqDqfu5W+bp6WlKYZKO0JFLUpJXV1dpaQMNRpXHTAJuXCb9QLAqYsTIU8jIUyqbIJwZOmq/nlGdlQqWS93lnNfvPjfUx+4MJQCnvuEc9/mdAzDkd49+837u9ql5/91330Wv14vPPvss6Tf1f87BRx1DfUD+cTCYu+++jdqIm8cPeZ3UFzlw53I8Z5Txfr3r/Pw8Xr16Faenp/Htt9/GYDCI5XIZv/jFL6LX68XOzk602+1kABMHUXaKVzzNkPNA2Gm5XMZkMklt0ZFrbD/5jWMkOaByO51OMnh0D/uHAY+1tbUUtaI84V/WW7qCOJRtELCnAe96WrhQ6yWVlcFjaJRyGnG9Q/tyuYw3b97E2tpaPHv2LHZ3d+MP//APY3d3N3Z3d9N7cgEMjb9javICZQvx8kMhGkqqrwxW4XxFPk9OTuLFixcxmUzSiRDS/XJc6vlWqxXj8Thms1l888030Wi8XbK1u7tbwfludEVEivhfXl6mvxcXFyk7U5FQD4S5TaPUYmWQShbS5nEM53KYRi+dLOwrRnmXy2X2He5EFd5qNBoJg3HJwd7eXuJfLrESfmu1WsmJpHdxHx+1ldkiLpvIA3JGa201je/bsD4z8nJ2JPvpRzNsKZw1SJpwbgyqc9RIbRzkgNobyonuv3ldxHjcVVggWqBG6zZk0DrAJrmgJdhXpzMVle/LGX9iQJ9QbrzT08TrIgf46juCD67TcMNaRrc8K7nJRkDjCthBBvtLSs49wJwAOQPejV0KFh8XRnbukzTxlVbFtMgfCrqkHDQWTHH2o2cIeDwiT4AoknDVd97jfOoRtNw4UyALOAjAu4daRB7wcsh3dNyQZ0UOnsivvMcdRARfTGWj4VTnJXxXcnD+D5EoB3Lz1cE0QaUcX8PhMK2305nP4nutz2dKsRxKmm8RNyPD9Lo74M61gWDEZRW/E3yQ/13mq60CQjlZRX4lQPf6E7R7un6dYqf85Dixze7kYt1dd/M5ztfLy7c7ls7n89je3o5er1c5a9LnqMsRL5fygHXPyaBcu35qyjlqHfvoekRV7npkSO0UaawlozQ/RqNRfP/99zGZTNLxeDoTVu+U815nztJZx3tUL0V0mMJLPtb8ZtaVb1BFftHzirZeXb1dN8nlJ8JqcirxnQTunBPSjQwQuBHC+cY2iHT8iMbPHf5qD41iBgDa7XYcHBykj+Y459NgMIiLi4vY3NyMJ0+exNnZWfR6vcpmQj6/cnpM7XL9rjGoc9r+lKT+5WambKOWo52fn0e73Y6f//zn8erVqzg5OUm8OxgMIuJtHyi7T3NE2H2xeHuCRr/fjzdv3qToKetBu0H10Idp7vpLma46839do0HrdgHlvv6KfzknxU8shxhNjh7ye50NQN7XfBBu4+ZpsgdUD+E06ZNWq1WJmnPu6j0sX6T7HMfL2Xl0dJSyqljnnM5y/UUMTNmY03suZ1fRnQ1beg70AnWO1gb6ZBU4V3SLgIhr/URMXWQHeadooCUsuU21JobSO5lPTkHjCpwfB/piWglmrtVwTzuVmFIatB04GZTCV5EFF3rqd75D/ap+1ARR/7FcgiO1gV6iiKgoFb6Dk9FJ9edHgi6XzrAKcKku9MiyDxiJvE/S+MtJ866GrfqVPE9vdETcMGypDN2wjahmMeRAl54RT9Po4L3igVxb9BuPhtB843ENOaHsHkmCAU+RoWGbWwuTU/b+TvIT+53OF278RU9nzmB4F2P192XUPmQD2ZUN5yzrLX6TE+Hs7CyOj49jMpnE69ev0yHuSgPXvfI+Nxpvj4Vgdgr3aZA8yxm3Hr1k3Z2naNjmAGbOyBOPCvxEXGdzRFw7ed1J5IqeczOimt1D504OCFA+83qdM5J9kdNzDhhUV/4uvaYIy+7ubjSbzcr51u8CPPh+r2fOMfAuZf9Y5I53jQOdJD5mzqcODskfzWYz6ZjDw8O0G/jJyUnifW3ARKecsFCv10ubLjHdV/fwnTKgpMu5YaH4SFkSMgDOzs7SDsU0FNRGRnWFTYT1hJsU4HA+9B3HiZcYQVLfETPQoOA4eRto2Pq8l9xRpE8GeLPZjEePHsXjx4/j0aNHKVhBvtWxf3SYfvTRR+lIPL3b564MJeLm3Bzgu+6b3LCNuMaoi8UihsNhHB0dxdnZWezu7sY//sf/OHZ2duLly5fxl3/5l8kpo2doB8gw0/FLs9ksTk9PY319PZ4+fVrZbyPiZjaH+FE8I1K/0nATzsjxFueo2zWu5yQbufcD7SM3bMm7qksuO83rQpxIw3a5rGbEaf6Lh3W/7BDNX2Yjse9yhi3rovqLD0ajURweHsZ4PE6Bjpwx67qU11Qf4jbq1jpn4Cp6p4gtlTGVnSq1XFZTF5XCNBwOU4qBmMmVO0EL38W0HzVKf+V56HQ6FQZUfjwHqs7jxY5iqjEZVYBYQlEG+9XVVWVSu4DWxke5zavIAJroTCfmwDPapH5Seo/qyfeqTt1uNxnVqpufVSXh6hNI97BtnNSqn1I7KdzFL0zT0zMU8JzQus8nLfv7PomRWhlH7khYRQ5o9J0ew/Pz85jNZgn4n5+fZzdUo2CJyKfuuzCgIKXH2cGP7tNz2vFZCimXukgPaZ2hqDqImHbD7xHX6YsEPnwXy3JwTyNY7xWf00ElwHOXMfsp6aEatRHXjrKI681oeLyTDFA5ZhSZ5c7hs9ms4uBYLBZp13zxu94hMEwwqrFl1oRI/K37aWwSCEv2cvz1u+abgwXNFQIwAlGfQ+S/iKpMz6Uo0+MuIJIzkMT3kpFMk3bjT7/nDEg3bNmPAnrUUbpHaeTPnz+PyWQS/X4/1XdnZyf1I3Wp6st3upyoM2pzsu0+qc4wpVOWDgYZLjmeI2mjpW+++SYGg0F8//33lTRDOg40rkytbTavN8mU08jltLCSlo6o7jKE2df6X85w4TldU7omj+LRswLXcsBrHmteKTDAsrmuUPynT25OutPc04z1nRhJKZx6D8uJeCvThsNhGsNnz57FwcFBPH78OG3umJPPGhtloJycnMTFxUXs7+/Hs2fP0u/CO3R+Of9wzopvhN8eAn388cfR6/Xi6dOn8cknn8T+/n7l2JsXL17EaDSKP//zP4/l8u0RP//8n//z2NzcjJ2dnfjuu+/iV7/6VVqTqTnCIzdns1kaH+25sL29HY8ePYqPP/64VkcSi+zt7VX2K4mIlEXADWNpALvDn/MtZ1ATQzHAIznmPOo4zPE98bXqQgeP3q0yNPeoMynnhfU1bxjkc6crsbicRbI5pJeIw87OzuLly5fx+vXrODo6qugPdw7Q1mPf6n8tFdUmpGtra7G9vR3NZjNtRCs+ydlwOXqnzaNyYNW9kVTqtLplEFDR6R5O4pxn25W1ntcg+e7DSj/Wfa6gRS5caCRqQniUVoBJzKvOpiIneBAAlCdKTEjvkytt927wOtvC7zQimWrku/UxcudjR2Hv0Q6vJ9vHaEsOmOS8N65U/B4aVxIi90mM+rk3/Hch9gMNBK6Hcr6l4PAx1e+5+ylcCLw5FzVe9HJzTsjRQ4Ca4yP9r3e5ce4RKhfo/Di5fHBinTwqpf8dCD1k8jl0XyQ+0AHsa2trSS7KWaBzCLWWVg4a92hHXDu/nNdpBDF1UERwz/lBHpPMoCL0OcKyRA5I9N3nGCknz1xOsm3id1fS+k2Ann2hOtCwzRl9zvfuJKozbG+bZ7pfmSSj0agCpmTc6Ng8Ly9Xx9zvdb89hLnqclVUpwcc3OXuFZ4YDocp+jEYDNI6Tpaj5wmkWSaBPdOGWR/yle5jBLLOQUJ9LF4mnzLySMPN0/Nl6KreqrM715lhJwxDh0vOkUA5SRlD3cdMCE8jF57RHOx2u9HtdlOU1sfRx0VOOo2nsum8Tq7j6viK5eZ45z7ok08+iW63G0+ePInHjx/H9vZ2Ov6m3W7Hzs5O7OzsxGw2i+FwGMfHx7G7u5uO9xmNRikQJfleh1eoH05PT2NtbS2ePHlSWQsdcRMjM+tFjs6IqGBvfecuy5KxXqZTDku4fmM2J7ENnflsa0Q13Vzlc37pusrVvKXe4f4R4m/NH9+ELTdH3Hh3ma22uX73/soZtrnrPt7KhlL2CeUdZcRtdGfDNgcuOGCMjmqwGX2bz+dxcnKSdjkjQ7kXQ4zuXgC+n5FaMgwFX87YpIDVOxh1YLqpjAwH9kzBUBto6KgNOq90Op2mRdvaiIDMxuNQCKRUd12Xsa4Jr4hEo9GorHfc2dmJra2t2N3drWzGIMGtMqiYGM3LEQ1bKWOB3Mlkkg7lzilFTj6Ojxv2bK88uA9BmEdEZW2mNo3yOfEuVOec0LFJXGfrRr6eFx9E5Nc8ifRdY+1AmQpWY8DUXX50JIt+FxHskN9UJ9WDgt2FHY0eV1aUN95vuj9nFHtUi4b5Q+Gt2+ghGLUREaenp7FYLOLbb7+teHQ9giRPPI25iLyy1LMR1c0kPN3flzhwLCnDyMOqGzc5kRFGY1R10fzTPXwfwUNE9dxQ8qI7DgUuOE8J2KlDI6ICPkisq6jOubNYLJL+yt2TM3LpFNA9OaA1Ho9Tn+s9v/nNb+Ly8jJFqD788MM4ODhI56z6HPS6Ol/wHs7jhzBfOZbsU8liAcm6OesAcT6fx8uXL+O7776Lly9fprnj5es5ymTp8oioyO1Op1PRJ0yBpEzWGttms5l0mtKeLy8vY3t7u2IAtNvt7HxQtI0OT9UpIlIkTuWura3dWAZCXECjts4BpA2ztLaVuiwisniM6aaSNTyBQHwmjPro0aOUhZCbZ06a/+fn53F8fBxra2vx1VdfVbKklstl5TgUjq/+lwxgpt7vy5H+u9K//bf/NprNZiX6zSUdiqpKLvzn//yf45/9s38WP/vZz2IymcTm5mZ8/vnnKZNnOBzGfD6Pfr9fcZbT8bBYLOI3v/lNHB0dxe7ubjx+/Dg6nU6S78QN4iPhx4i4MS89Y4YykniIa1cpp4nNfamMR9ulq8QXusYNtujcp8yVjlB/qy/cucl3av7IVoiISoas7mOA0XlR/S+bg3JZMmU8Hqexc3noelEkHvcPsXSr1Up6pNPpxEcffZT64l10wDutsXWhLgZgipY63VOILy8v01li8oqxE3Wvk0eYKAD4G9PGVD8BHnUIBS4BEBlUikPfJVS4xpbGYY5R5CkiQ+hdEVHZql9RVTKXKyD9rZuI+sg7qrXFWuPB8jzCod9cEeQMDI4nPVWc0J5myOdz4ElE5cPJcBeF8lOQb2Jz1wgC+ZWUm/zqP77DgV2uz3i9zoggr9CQdcNQ3xmhZkTeMxJUL09ly72DfMvfc8/5724Eu8zIeVoJsnKG7e9KrFfdOP9Dojdv3lTmP5UqPwTcOYOMekM8Rt1BOZJzVqgM3esOFRldAveqR46nGC0ib9LgJs+Jb3IZJJSBOeKcibheP5ST8WyjiLrM5TBlbm4uskyOU844zzmNfEnOfD6Pbreb+nU+n8fz58/TDplPnz6NbrcbT58+TU5dUR1AyRm+rO99kxsX0qGut13neV+qTdqZ/fnz52mH8NvaSaeyOwJpDGxubqaNvaS3XB6qPcQQBNJKW+YmmB5A8KVDxEJaiuZ9xqUGHi3KZSxwfvC9EdWzpZlRRGewY1NfciNMt76+Hr1eLzqdTkq1Znv53G3EJVqdTicZ4qxTjjeEKakXaZTfN2lcKONY3/X19eh0OvHo0aM4OjqKb775JtrtdgyHw1gulymarbFS366vrydnDbOpNEZaDvXdd99FRKTxaTablTPCdT/xDne9198cliK5PufYicc8IMZ9IOj8If7neGuuyXnEuc+oM8c+h5fcdnBcSb3Ktjj+4nf/n/o9h9dyzp+c0ey2i9qvYN/29naK8GuM9Rzl3W10Z8NWqUY50MmwOytCZtCOmEpjYxqTGsrvrmRpKHPdCgU614HIs0GPiYNxMZsYkhEpMa2UDdOSz87Obgw413NJiat/aCRL6SyXy+QB5YHMDt7cGHRGJJiTYFZqSKfTqTgaOHaMcnC9nMqlV8iZk0at+kTX3Psp4pi4UImobiDlxox7fu6DBA587dJttKreP9SwZb8QwLuQy71LpLEg0OHukS6wqXTIn4zU6Zrza06J01vv/Zgzbt2gZRnkNSot9onuIb/eVUjWkQvsf+j08uXL2rHI8ajG3IEtx4JpRtzdns6ynELlOymrIqKSoqVnCd4jql5p/128w7rzN+kNvp/6jgYxdZobpBFVkM35SZ6mjHCDlsb0KsPWZbH0GSNluXmq+yX7tEZaf/X8xcVFfPvtt/Hq1atot9vxwQcfxN7eXlxdXcXjx49v6MScDmC/1/HVfRLBvINGXXND03Wpylkul/Hq1as4OjqKr7/+OrXxNjkiLKWP3hVR3bhMawiXy+WNjdqICTgP5BwXJpGDXABbe5qQZ/QO8p/0iCKiBNTK5GNUijjO91tgf6h+XH8r45kYlKc/kO91jdE8zeXJZBLtdjulH3e73crc1Lvukg6pPt/c3Ew7ACuq7HqH85o4mP/TyL9voowRsV7r6+vR7Xbj2bNnMZlM4q//+q/j/Pw8vv/++3j8+HGsrb1dvsK2qW+Ju92o16ZcX3/9dayvr8fBwUHawM55hDKbvOYOWR8Hl7PEDsS4wkJ0FgknMWjlhi3xteQvs3eIV3SNp2ZwDOqwkXiVWSMcM9o+Pq4cS3cCUBf7mKuv/VqdHaEy1cZWqxXtdjt6vV5sb2/H/v5+Osas1+ultuaWOtbRO62xpRBSZzDdl4YLo5lSjG/evIm9vb3odrvx6NGjG/ntMnaZOiOiYcmNklQv1ePs7Cx5Abye3C2QKcYCUZo88krrXkau5K3Re/VXbSEwyq3LUFlKORIzyxDNGQLsC05aMa8OB+eE8HQLMZcUFzeykreIwopKllEKRmnVDqXNEoyyDGd4B6gUTlK0UtIEh/dJSp1514htHRFMsO1SstpQRwrbwR0FEQW3vKDusXM+1Pxw41DjJ8Et/mRqP8vS3FOfeLQupzAiqmn8/Ks2Od/yWs55wucpwF1Qe7seEt0F2D4Ecv5ypxVBrgMFGmYEBrllGJQlNIhyddE9epdIzysNLKdzuIZPZXEXcAJkeulzc1j1yAGEXF+4E0B6g6mYagf7hM4nOmoYUSAQiaiex+uOKAfS3i5t/KXUMy6V6PV6Kb2Sey3oiI9nz57Fxx9/HJ9++mlaiiPdS3I95zLvvueGxpyGBvs9B+x0XcbUYrGI77//Pl6/fh1///d/H9PptJLxdhtxfKVvna8o25QyyuUkjkcY/dUGO4wUKZW/1WqlZzX2whNqa0R1yYsfj+M6woF2o9FIm8nwNxqXjOpyraXaoXe7A0nXCdbVh71eL3q9Xuzu7qazdzmuOk5JdZtOpyvHSY6fX/3qV/Ho0aN48uRJHBwcxNbWVrRarTRWHgHN4YqHYtRG5OWYZPl4PI5Xr17F69ev45NPPonFYhFv3ryJq6urODw8THaBcI0cY8vlMhm1kru6V++U4afznC8vL+NP/uRPYnd3t7KDvusL4lpiF7Uhl8YsXiaelUyTk1+OPvEYo7gRkZwrJMp61VFyMyLSebviB7WJ+E/PsT10jBJL5/ShyyrKMNZP7ebyQsdU7qRRedSlXhddY4br7u5uHBwcJP2gpZScG9wd+S70ToYtK+j/u4D3ybhYLNKOr+PxOHZ2dm6AXi/XJ5B/5/uptHWf/pJJOGA5Y9ejkHrGUyT8/TmvCtvB9hCUaDJrsJ0ZfM2i/tKo9vfkvIqcUD4BcsTfHJBSKchz7KmDt5WV6yvSQwP5TMX9XY3aiJvGGuePb8ZVZ8CxHBGFlBuWDrLJT7rfo0ZuQBN4+hylkIyIisD1+5wn9A5vk//mQjbneazzikdUz6n23+6bHlJd6oh8S5lHfhK5MatrbqCxXJXnRo3TXZwSDuCd93IRZb2bv/OdnqGQ6xd/B+sSce249T7yutBbz3dTFrOf3Pj1erqOXNVvPkfdCUxdqs1YpMOWy7fnUzLVutlsxu7ubqytvV0qo/e4XMuNx0NxQMnAIsZhv+cMEM4JRXsGg0EcHh6mTdbehdwp4riI/CCZLmNQoJvLSTSuGgd38jhwzslo53vqCDnNqYNy9Vf/cZ56m/Uurw91qO4R/9PJyTYL2+m97XY7HYfi8265XKbTLbSe+DYSnjw9PU11knODDoLcXNP1h4aBIm5my6g/tTdIv9+PwWCQ1inv7OykJYjT6TTW1tYqThn1M7FORD4iuFwu0/pOHTGjoE5EdVmj/ncdkpPpbI/eTVnqAS53LFEeMutNRF1ADO52QG6j1xzOyQUqcnzissF1jerm88d1dJ389fHK8XCdvch6KRtEQc9er1cxan3u34XeybDNCRh9VyOlzLj7lgZoPp/H4eFhRETykMlLQS8xvYDOcBKU/pHnk4eES3BxsxsZqNq9TmerKVJLJqXQJ2P5egcamrmJSeXC9SNULCpXHykFHjRNxaE+Zll8vwN836peY6YJmjNGWI4rQEVrR6NRAjF1Bgr/V3n0aKlt3NxBdclFnu+D1MZ3SYdYReRvAV3x3HA4TFHbnHDS816eSGMl3nC+i6g6Yihg5IXXZiDiBfGP7qEhS8+eytPf3JzV+3kPhaeiw24Q0Rhyo8gVWM7IEN/5+rRC70Y5w4PjkQOREVUnKKOMlGc58FBXNvmAuogpmTRqHeSKL3Vvo3Gdxiie8vTDnHL1OUnQzXV6ahvvJ7iJuOZ9bS6iDBwHGnoXI8paJsEUb2Y3iKhHctFZ72vpRkZp5dScz+eV4/WUEaE+3djYiO+//z6Ojo5iPB7H48eP44/+6I9Smh3rKfL353juvkj8KAOIpL7mxjoEx7PZLJ4/fx7ffPNNvHjxIo35u5DjIV7LRfM1ru12O5bLZWU3Ux3NpzW1wijkLWYxXF5eb2SpTZWon8WXOu5HOohz7urqKuExzkmVI76i3FYWg/hc/a/+VV/wIz5kZpzwBeeJ1hB/+OGHKfWaOER91mq14smTJ/Hhhx/GdDqN4+PjW3ny6uoqBoNBjMfjePnyZQwGgzg4OIgvv/wyOp1OdLvd1P7cnjPCVJQ9902a/9Sjita+fv06fv3rX8eLFy/SGu8vv/wyfvvb38Z0Oo1+v5+W6lEmOvaks4MOmoi3ToH5fB7ff/99fP311zGbzeIP/uAPKjhX80HyKeJt3ymtNeLmMhbHFeJNBruEq7iWW/fk9t8REQfxmE7xluon20W7AUvOSk+oDN/klf2lzCSVwbZSd/B5dzZpfOlkEu6j7uaSvNx40mah809yIyJS9Ftp2a1WK7a3t9PzfNe70J0N23a7nQxBWv1qtHvgPZKoDpvNZjEYDGI0GsXa2vWuWxQSLjDE7OoUKUEanxwEDTA91+518ev0YlJRcNKREbx+Inrjeb/q7mCb79KzvnECAVOdYeteSe97Pef9LOZh+V43liUDTJsj8ND1VcQyc9dzHiTW8b6FOkHi74O8jSxbuy9LKdd543KOJo6xR4Yibm7mQw8hU71k2ApUc5MQjjUFZu6juuWcHLl+qGtjjm9XvceJQrkYtT+MvL9vGxtei4gbipFAWMo6t8aKf/1ark4izgfpDH6nV5iyVUTwXeekY50YnczJUdclKoOAgO/SvXXtdN3BiKpn0FCOux7ix6PYBDpy/HI81XeSHd6fXFd2fHwcFxcXKZqzv79/41xtH8eHZNx6BozqROBK4nVhkuFwmNJU79Ie57GNjY3odDppztQZPdQHEZE2f2q325V0co3tcrm8scmO3s8IpfNyjjd13X8j9hLvcM7RmJA+8nnGCLA+7uxhXYgdNT+Gw2HSqwqwKFqbw2h638bGRuzu7qY1gALljn1c50rm9fv9pFN3dnZib28vBXa4lM3HPTe+90Xj8TiNEc8rn0wmcXR0FMfHxzEYDGJt7e3Ov4p0dzqdpHfPzs5qs7iIZSNuyn06vU9OTmJtbS0+/vjj5JCQfufSON+Vuk4WUwaRR+s+vg/EKvxC+SDepgPFnYtuY3FdO4OG7Bv9Jd96AEDtzTk8OU/rjGAScR/L8L8uT+jIUTnMomBQhuNAJ9ttdGfDttvt3ljcTWDijc0pyuXybYrS1dVV8t7s7u4mwUFl7YLZlUZEVDZ74uDJe03DzZmRHalr3vlkVl8TqHfl2pgTQnXRR4IZeVhVNoVsLgrAgWZqg8pVn7rB6u2Uh5RHA+n3nJJQ/zKy6EqK78i9V23kX9bV33/foOb3teFQjtxQ1LplKZGIm1FJB34aG0WrGAWhcKMnj84OfuRVXCwWaa2Rr/VQGXTI5ASjyPnPwTvbIp7xKFUO1NMTqLmh8kWsl7yDOcX5Y1GdTHjfiGOwyuHizk0+w2ii+FY7wzNiy/FxoOhg2PUG78mlNGkuS6ZTtrJN9BKrPKWL6RpBtm/A5H2WaxP1Gu+l0VvHO5qX3M2fnnSNg66xLOoUd5h6fWU0K9Kluml+aoMh4QD123K5rGzk8+bNm7T770cffRTNZjP29vZShI+8kwOJ902r1q85DpDMkq7W+PT7/Rt4pY68zZorzHRbZdhS1ivdLyJSX5+cnFSiTzQa9f7lclk5OkRzTGPMCFmu3sRR4lU6uHiCBPUWUzMJcNWn0nWMbqkPqDOJWRTBPTk5iUajkdbS7uzsRLfbrRiXbiCoPw8ODmI0GsWjR49iMBhkDQSOAftBRt9sNov9/f148uRJfPDBB9HtdittrTPqHoIOGQwGKVtP+O/09DSm02mcnp7G4eFhmuPKBOh2u3F+fh6DwSCt1Wd2ouPoiJvYLycvDw8P4/z8PL766qvY2dmJ7e3tdK7qaDSKiOsUcGaH6LoMRhrUIvEMlyZ6xJbZnDns7/OCJMdURHWTq1wklrjf917Q77STVB/fJdx5zG0czT+ugfdMTzdiiS1z2DTi5hIBr7/0iPp0Pp+nqD5tNd8hehXd2bD96KOPYjQaxeXlZTJwVVl6IwRKZGE78y6Xbz02z58/TzvRaTt0KlU2wAG4Oi2XxsQ1K1L0nuIqIcf1oQ6UxaQcWA6gBJ2YSu2mwSlmo+fHGSviOuWE0Ws+7wzP6LR/2Gd10WO2odl8exA5J6UbEuyXi4uLOD4+jsPDw3jz5k3acEt0V2NGY1wHAsXEnh51X/T7SkEWcT4QCC0Wi5T2JyeQg3cfGxqTUsJMbfKIqkCozysZxRsbGwnMKGKr8yh9/kloco2MO3rYXl13w9b7Qu2gYUJA5N5IpluLOF89W+OnpPvm3d8X5YwsB3XUBfqd9/F+yUotPREPMpWYsp0K8jZDh4at10FlcQnIKgVOOSV+F19ubGwko9z7igYCeS73HqZ68TePbOWykHwtfs64rTNoGQnw+SjZILCh5Ttql6I2Alvtdjtms1nFmKCMkCyRQTUYDOKzzz6Lvb29ePToURoXZgDVjcl9kPMueZ0yjfVU3x4dHaXPbDZ7p/eurb1dnvXhhx/GH/zBH8Qf//Efp7M8mWnGeorID5pfOzs70Wq14vLy7RGMiqQLO0n+cww1V1T2dDpN/J9z+KovuDeJDNvpdFpJ2yTfUY4LL2njIPF/o9FI6zU1v8Wn4jfxv6Kqs9ksxuNxnJ+fR7fbjXa7Hfv7+7G/v5827VQ5chRxozk5BpRpcHBwkJ4ZDod3SpWUcX9ychKj0Shev34dr169il6vFx9++GHaOIdGjnjM00/vi7755pu4uLiI6XSajMjJZBLL5duNxx4/fhwbGxvx61//Oi3x29zcjEePHsXu7m5ajuj7lThOFM87xiefSS6NRqOUxioecAely1FtTkQML8e9dINsBze6yGfkfY0VcVpEpGiy9ISMbe1NIJ6Xsau/yrKgfqIDwHVKrg05Xem6mPerr8lvOezpmJX2g/BWDi/Q+aS2ak49evQo2YKSFaenp6ksbdx2F7qzYbu3txcRUfHyeacSQLhB6UbecDiMtbW1tB26GpkLs7NDckLbvXW6h5sDqAwOItdMumHr9XbF4RFYB/DuHVd99R620T0nOaoDx4xWuYK7jRh5YP0cgLqCm0wmMZlM0rpmMrXur6tvztD1sWUf+trQ+6LfZxoyKTdmEqICeBwHPuOCyQGVxldz1R037G/9VZ+vr68nL77+d8OAPJcbV96rOrEdvDfnNeV1NxLcCKByyvVp3XOF7k6rgJWPqV93maL73LHlz/s73DmWe6eXzXpTtgvoyJufq7c7ZNgWNxAd5DgwqKtr3Xv0rPSFZDANV0+Hy33qHIyuq/wevV+GgoNG6VEa5z4Hc84qHZd3cXGRzlrd29uryJ2csXbfhi2pzpCtu1c6k9lNt5HuUTrnkydP4unTp/H06dNot9uJb/WOHLnMV3lra2uxvb0di8UixuNxqpeiuRoHGadqr8rRb9w1lTxD41QgVXiLp0sQFMuYka4hT+p38ZDjHm+rAPr5+XlyFCuVfn9/PxmRinz7eNGBpHfxaJJ2ux3T6bSSvnwX/lwsroMq0+k0lstlcg5pzTNTzYmBHoJhK+eA+lNjq7WznU4nGSJyKMhhoY23NA8i8imxshtoJEbkszgcy3PdZ0Tc4Fn2IY1B1xM5vMHoYR3m0jOMDDPA5+/j2dDEXx4gc8ejymZb2CbpJ5frOZuD7cvZPny2DoPmxmYV6Xc5trXGn4EM6RzpaKbs30Z3Nmw/++yz6HQ6yTtFBmInUKmRId0gVfrC1dVVfPLJJ/HRRx+lyJCOrvFn6fV15qKAV4fQg6H3yusyHA6T0GNH8zsnhQZeQp/eQv5GsOBHSyyXy5T2q3JoHFPIMwLMNAP3UjLa7EycmwAkByFiKJXnRtNwOIzRaBTff/99nJ6exmQyqdSTfeeMzUnGCcpJLQYmcGKE5D7pNsfDDyGOH/tFHtHRaFSJFLuAooHqoDoiKgCBAoge0JyypICVh8znhMZGxLOdOZYR1V04mdHhhk2OuM5K72SqKhWGO3jciCGoKvS7ER0Rq4wOjS2j7OINph87b4pv9TtBCMdbmUERN3c5pqzWmnEqb3mN9W7K4zowqTrRg04grHrwXslHylnNBZF0iPQD5aLuFf/TsGVEjBs7iaif3UHI9+TGVu/V0ojJZJJ+py4VwGV5bCcNOaUuK5L261//Ol6/fh2NRiMeP34cH330UWxtbcXV1VXSL/eRZXEXcgBLvCNDSP3z/PnzOD09rWwWcxuJz548eRKPHz+Of/Ev/kV89dVX8cUXX6SdpWVE6L16TrzBDW5EGqf9/f1otVrRarXi5cuXMRwO0/pIbQq2vr4eOzs7ybh2OU695aBYfHB+fh6vX79Oxw1Jpzl4pRGgjCJiKS4DoD7QO2UkrK9fn4k6Ho9Tmuzu7m7s7e2lzZva7fYNGca5IvkkB6/Who9Go+h0Oukdo9EoOWpuIx937SL8+vXrtMPvV199FQcHB/Hs2bMbfXvfxKWIGjseByUn3P7+fhwfH8fz589TX37yySfJuFXE9/j4uLLHC2WHO/scyxA/Xl5exmg0SsslJI90v/5KF0i2692eGad9TiaTSZJvel76hjLf1/hqnaj4Uc8oCite6fV6FV1G3aN3qPycoRqRNzjVnyyLNhMDenSWSm5RJ9RheeprjTv1HO0lPqt2cUPSZrMZg8EgJpNJ2sF9sVhEq9WKra2t6HQ678SndzZs9/f3Y7FYxNOnT9OE5q6tEVWvWp0lz3u0oF7eR63R4Y6DFGQ0oHPGMt9LYMBUBG3KIw9incfTjU29X+RGpe6j0av20kiXkqdx5+BFz+kZlZ/ziNPbQkMh53FhO93YyX08MiDFNxwOs+uECHZ9zHPEMaQQcw8WjZR/aOR8TODgEVveE5FPMyaxP3OGMIUp3817c06anOLxNejkcQfTOSPIr1Ohe3oZnTjkFe9Pluke10I/jAgWIvJKNScnnZ/ccLtNDnn0cZVhQCerxprr8lhXKXndyzSwnAxmvdyoZd9IdvI+d/TmHLR8nwM58m9uTrCPfB6yzwm23LHE74y2cZdbZlXRuFafMwJeN556l6I/r169SgaO1hy22+00Pg/BuPV+zUU09JvAvM4+5y7EdeRla7Onx48fx7Nnz+Lp06ext7eXzpp1GZuTvSyPzmpd39jYiF6vlzby4qkRckIwZZi86e8iv4tkVMtA4Fpq8i35Q9FiOoho2Poc0z1ycrHfhVNklOrcU6WC6ln2lS9NEGjf2NiIbrcb3W437aYsefFD9m1wual9YV6+fBnj8TjOzs5SZFkY+b5JDrTRaJQi4K1Wq/L7crmM3d3dWC6XKQX87OwsTk5OUvotDVw6xtxRQpnmmFyyhum84gk6CCOq61i5fIP/EytwR+1cYCWHU/mb+IfyletjqXukbyQzVReepex94OTGJJ2g1DueieDl54xm/q3TLbrHHRE5TMe6SKfIkbVcXu/LoCi/+F6b3t2F7jxTnj59moTcZDJJA19nnBAU8Bq92ldXV2nb9Pl8ns6j0no+pmO4N9w7Tgpewk2MtVwuK8f4jEajykHzHHQOANd6UXmQMZmGw4npXnAqFXlO2WcyWrWWxPtL72O0wg0VgTOG8/nJMZoDNhrJ+qso1/n5eRwdHUW/34+Tk5MKmGO/1Ql13evr7xaLm5u4rK2tpTWeFDb3STlj7PdVro+NwIC8jw4W9ByBQa7fKVDJK4xy+b1eLo1a8gWfk9LXnNZ1lcn5mzNMVtVd9REoYn/korXkEy+DhkCh340IkPm/5JRI/U757es6c3PAy5Vsz2V+RFR5iPfQC617GOWkTGbU0YGU6qC2STeQ37WRjepMgKO2ECioTZ4+58YHAZvqzA1M2BY94/1B7z2XFzggI6hU+dKXWp+o56T3uCZWIIXt0Dt0j/qs2WymPS6+/fbbdFrC559/Hnt7e/HkyZNksGhd5n0SZYzreMoU9bWyXXjEzru8S+tAP/744/jwww/jo48+isePH0e32806rb2OnCfEDOI1jZWiTTs7O2nNInU8HQw5B5V4hnygOSO5rQw5rtEmzzMDQcYK2yjw7/iBhq2OyNrc3EwG02w2i42NjbRZk3YiJrZ0Y4FrL8V/und3dzfG43F0Op2UiqzNz96VHOgrnXY6nUaz2Yw3b97EwcFBfPjhh3FwcJCi9PdJGtPj4+M01jx6STpWfLq5uRm//vWvo9/vx3g8jna7HR9//HF0Op3Y2tqK8Xgca2trMZlMbsgtykeXUxHXGQ1yMmjDS2Enjq34XP1MzKQ+p4NGck1GMw06kfQX90fQ3OBaUD3n/LtYLNJGSYr2yhbyFHnKUZLrYV//63hNfeqZmZzTuXe4vRJR1eUsw+9zG5B1mc/nSVaqH7Vzvo6M0jzXkom70Dsd96OGa22MUpR0dhKFPdPJPCU3IiqNGA6HMZ1OY3d3N2azWWxtbVUmsRouEKKUFlfmFEjNZrNigHPdIgfXQboY2D0aMgYIxAhqPAqhPoi4eS6fGFoMSGNUXgruFKoJ7H1IwESF4satrru31ZWQ94sEVb/fj9PT03jx4kXqU+8zB5nuvVwFRGm8i2+0bqMu/eJ9Jnf8eOSfKVru0fYoj0cg2d/kVxHBpfMSjT8KafKQR4cibm42Q4OWZTOrQEShSd7U/XKquHHk/JDjD7abwt6dO/+QeOvHJjfO/FodkRdyhptnqxCQ0wjztUrUOTTgXBlrY5GIuGEgNRrXR4FERIVPKdtZf7aX6V7uXOGZnOR91zG6RkDifBpxLasJYCgDnJfdEcA6EDCyfSKlGmtdnTt/9cnJIDrcHFyxjXI4nJ+fR7/fj8lkEicnJ9HtduPnP/95PH78OD755JPY3d2tNeZ+KmIb6WjRWNIpu1wuYzwe126ymCM667rdbjqGhhHb3d3ddHoBwTnlmIxW8irHyx01zWYz2u12tFqt+OUvf5mObtFyseFwmAwIOrxFOQeQiDuzyknuMoD9KYPk7OwsYTlFs3gMSE5uK/Kn42TW19fj6dOn0ev14uDgIEVpGSxhXfQM+416sNlsRq/Xi+3t7XTmLQ3unPHzQ0j8dHJyEuPxOBm4D8GwffHiRXJWMPLtwSfhWG3QdXp6Gn/7t38b0+k0nj9/np4VHtfz5Gl9Fw6mE3K5vN7kUliZuEDRb9Wn0WjEeDxO5WrXXW2IxyOIiLuYyi8epEGn8iQDhdNdBup/8RIdrSqXOElOIBrW7vChbaKyucmV7qE+I+b3Oej6VM/nHM+OyT1wxbb4ckK1X3V78eJFkgtabyvMJ4fgYDCIk5OT1K7b6M6G7fr6ejJunz59GovFIn77298mYVQnbNzAERFQy1gdDoepURIYZFZ6q+mpdq+D6iJBykXu9I4TnEdUzzyj0sqBDHrlCYLqhCKv0yjWfevr65WULzHJbYZdrr9ZHyfWje9fZdhOJpPo9/tpDQPbnavLDzEWHCTnFPI/RMoJDjda6353PouoN/rcmHYBR0GUA8q5eq5S4nxfHa/ovbm5RaGY2xDirvxAvsoZDP9Q+erHIPa/G7ci9qcrwhzPuONEzzn/8N5c1JYGKDNk6GRZLBa1m7Cobtwwh4a1p+1Sb7iOcx7T+/luzlufy/SoU8/wORqHbhx7n3lfextoJHFOujPYx0vkepT18DF3p4beqzYrOtxqtWJ7ezs2NjbSjrE8duY+yNtBsOq/KTtsMBhEv9/P7taaI2W/yLmt41J6vV50u90UybmLAeXOdgJb1pkZN0p/Xltbq+AR1V38ycACwTDnkOYR9YqMe+8z/c55p7JFxEQ5XKX6zmazFC3c3t6O7e3t2NnZuYGN6gA7x9F/E/B2I/l3dbrk5pQCOOPxOC4uLiopv/dFg8Gg4hTx9FrKTNkM2vn21atXcXV1lY5JkhPDx5JEue7j5RvOkj+In7RGmphb9VZWnIJz4lkfd2IQry9lgDuUIvI75DNQ5Y5UOoYo/zV3GbSivpDDU4at4/G6gAL7Wn9vw3dO3l9u9KofRMpCWi6XMRqNotlspl2Rl8vr9era+O309DRlfdyF7mzYKmK6sbERv/zlL+OLL76Is7Oz+Oabb+J//+//fUPIOSOS2Mlc2Dyfz2MymcR4PE7PadGwmG2xWMRkMkkbQHGbeipspVApzUDpCV4Pdjyv5wwIMTYZ3NtKwc1Nc8iI+kgQa3JKqG9tbcXl5WXFO6PfWO+I67x9EQEUN7dSndzwjqimmMrDKubq9/vx5s2bePHiRTrQPTdBPP0591599/5nehyjLrko+D8UIh/kDFl5nnXNx80Bijt3JDgYkZHnT8JeQlRjJq+kp8uLF7je2gUqPyIKeQfrq4Sm2q8+ELhWPblxj97haaRUBBFR4W0qjkJ3Jzq0csatG5GMkItHuamGgJo70jgPaPRofD2q4jLFM4c09pJTAl1SnpTfmgOLxfVSGG1YSMNE5Ugv+ryh0SpATL3He1RP9Y/6QeSbTFHxiwiQuLTDjWnWyftK5eujI360Rk76X/2h/qbscmcu1yx723UvPfkR15G3X/3qV3F8fBzj8Th++ctfxuPHj9+BW3//lNNdwgBaRnR1dRWvXr2K4XAY3377bRwdHcVwOLyRGVZHWn+4vb2dNjva39+P3d3dtFuuyzh3WBCvKJrIVEd3/LC8tbW16Ha7aXOlwWAQv/nNb2I+n8fJyUkaQ80TGQ0O3mlgXF5exnQ6TRtpMVuI/C++Vn01n2gokY+Y0SP+29raiu3t7fjiiy/i0aNH8ejRo8RfPJaLckN8rbnumMrHp91ux/b2dgrEMKL4+yI3sLW3yX1Tv99PmZFMIdX4SzborNq1tbfp248fP46Tk5M4ODhIm9Bqp3DJCV/GoEg9lzvQ0a1dmKkPer1ebGxsJINIzqVms5l2sBbWXi6XlaCXZ9AQb3M9tmMa6jSlIGuN+tXVVeWMZAXX6GQStdvtxPvT6TTG43Hab0hHUikjRHsQiISTJpPJjQi0ZIbaEFG1eXLOT2I5N1TdfiA+Vwaq+lk6bW1tLc1xjbNkk/Y8mk6naUw1T7Wr9sbGRpKJd6E7G7ZS/hFvc+pbrVZ8+umncXFxEb/97W+TEUkBWdd5dYCEhqaYWZ0jL6U8PTKAGV1iSgyjPQS0LvxJrK8bU25I8EOPikC26uBeDAIelce8+9lsliKjXBeQ89ywDux3NyK9rrmIn6e1qa/9WCRGD9guN7p9MvC+uusEYmJ6bnLyD4Vy4xJxMz2bXsK6MfXvMmb1l8/qHSyLfCAhTydKbnMaOkH0v0ATIztsb47vHODpPhrQNESdz3LGFculApIDjICu0LuRy0sfY11znl5buz7nm0Dc5U8OWOTki0dmvW7OAw66CZ5cfqocyXDxHw1qKn43DPRO5/OcY7LOYHUnUE5v0SHlsp9tppPTM4DYj+xDzb35fJ7W1rKtuQgEx41jwXerDascS8QAWpv58uXLZGT963/9r2uf/SlJ48C1oeorgfbT09MEcFeVE3Ed1RHgEyDV0TI6d1P97nqUZYn899z4rXK2CGAKdHJ/Eq6/pkOLzifpHwFtRX9z+0ZQj7iekDFOPtIcYJrj/v5+7OzsxNOnT+PJkycpaqvy1UY5QWmMrppL7D8Z1zyTlM//Po1bUg6v3gfRgVwX+dY14kj1k+yG0WgU4/E4Xr9+XXEwEt/qmqLjxM6Lxdu13zRsaSf0er1YLBZpbf7V1VXF2eLylrs9Ux9Q3kdcOxAjoqKvHIu5U9d1osrjhnLCu8woVZ9T/l5dXVV2SY6I5DQaj8dpbopHfeNPfVdd+NdlwG04yeeL+tb5g2XLRuKGXxFVZwA3FFZae6/Xu3PWwjsbto1GI3q9Xqyvr8c/+kf/KNbX1+M3v/lNvHr1KgaDQYXpKbRzHeQTVYJruVxWQKjSPwSARqNR2uBAwkYdzKisgLGn00RUz2yloFSdclFPMpfotigG+88FOUGUGFaM+ejRo7QLn4j147tV37sYtp7iRgXBPtf7qIzYB3Wg0/vH+6JOedCwlSdQf/X9PskVcd3ErzPY/Jo7OLwvKWwlsPmcys0pE5VHhw+JvMoUMSkB8aMAOSP6dIDQy0lhrPKZ+uMRnTriWiw5VFzRiNSuXJ94X2ojEZ9DdXTXcfxd+eCu9BBADfmMwNiVt0ftIqKy8YwrWPEcwYAAiI87nV7SMTlHn+pG5aqxlzwh/7tRSqeS3ifjgp8cMHbdF1Fd067nuBOn6s9IGJ22LItjwUwZylNGECRX2Y90GlInSu4IJCk7SmCFEW/2P8eeMoiGgI9RDkCyjZeXlzEYDNIaq1arFf/+3//734WFfydiZF9t1TFIx8fHaYOc0Wh0wyG3SnZEXG9YpDTXRqORoo+9Xi9FopiBU2dIkQdd3mr+RFw7QTmHuSFmt9tNm3jpaJbnz5/H4eFhMtoHg0Fl3jabzbQ5k3S2dIvkue+XIOyjeed8QB0golNof38/Op1O/PznP49nz57Fl19+mdrA0xuoh2iES96wbzlfOMe5rpO403WT3vf70AE/VGf8GKQsAPKiZ2IRB2gMIt4aX1tbW/EHf/AH6Sid//N//k9yBPnzEZHetbm5mWR1xNt+VEaDjgcTTpKTY7lcJmeM6q060ZAlLnG+jLjp/HE9JxLmUZSS10TiNeo93Sd56TKw0Wikza1kFGqNsHhDwbDT09PUBq1j1pp/N8JzvKt66fdVRJlNLKYP1xHnHFc0zheLRTptR+vrNzc3Y2dnJ2VJ6Nzpu9CdDVsC58lkEmtra9HpdOLJkyfxh3/4h5W0wVxEkB3nSjCn5NRZMra0AzMZT3nzytXXdYIpX5+XUwSMQqmt+pvzSuWI9coZLASFErDet2rv1dXbM/y2trZid3f3Rt8xBYITgv3G/iVI4mTWNYIp9St30tW9Dp7YPgcmDi7ZTxwLV84UZKoHt+a/L/JJXjfpc9dz11xQ+r2Xl5cpC4KgmICfRMMxB4AFdnVvbh4y7UfPujFLg1Zzk3NHfEye1XM0IFg/8pk7oggmc3yn77cZWlprf1fD9q7j+C6g43cBKA8B3LAOPh7epy7jyF8qS+PLe2gUE2DqGU9jovPCl0OwvnRUCkhLibtDj3NFIEUOFz8mh2mYlME5A977kXOGfUbZzPo4iHB5zDYzBdUNX0ZzVUcBjtxmP9rIr+7D8fX25gAgrxMAEytIp0W81eOnp6f37twkPw2HwxiPx2mJjpZFqe/cgKkzajUeMpik7xSdyPEbIzB1pPs9C2K5XFbOZ5XMFdGRIlCtiMmTJ09ic3MzPvjgg/jggw9SNP34+DgGg0GMx+OUTecRG+crn+eqG9/POolftWmTjFkZN91uN548eRKtVqtiaHOOuqPKU6j5PhlC6kv2jeNCGly30UOQ478LiT+13w4dBcKMEZECEvv7+ynzQ/p/ubx2Ln7++ecpTV0YQ8seGo1GOmd5fX09ZURIpill3vWQHCxbW1vR6/VS+q/Wnco4JG7hs5wTnuHgdo0binKk0EEjftc9tKUkK0SUzczQcF3BLJrlcpmO4NQu036UVd3cYrt57218ura2VnHukMQTmnt0Aute6UiNje4l1lxbW4sPPvggpSDzGNjb6J0MWzVa3ghtbKCtvbVuaZVSd9Dpvzsg0YAylY33EQzQgNQzzjhkwlyd6totgyL3G8t1ZhfQ9/ewHT5RCDDoXVQZBInuCPB304PiERMaJRRQnFhqE+uba/+q/2+bJJxYfD8B410Z+n2iVQ4T9275cyIfy1z0KmcU6p66Mjz9mMYp+YX3sU5sB+/z+e3OFqYfa+74nPf+Y7tywDriOr3ShfiPRe87gFlFOdDuDirdx+91n7p5wMgn30PQ7IDGgYY/22hcZzLwGCvnC92nvy5TWaaAMueZy/RcH7Le/O5GooBcznCvczTmorTeDwRYnMe5uU6A58Z03bh6O3Nz4i66V978+ybW/+zsLO0erNMhVt1/W7nUubm1iz6+3r85XUvZybnElMy6cRPfy8Dj7r86bqjValXWYcvZ7rgkonoUjC9rcsMyh9folNnc3Izd3d3Y399PO0W32+10XCSxDvtM1zwlv04fep1cNvH6Kv79h0TM2PC5T76UcaXd6L2fxEs6V1hBMaX0S/d3Op2UEq9gj3hIxnMOh2s++fmoniFwGw5nxiblZQ7n8zlhaeoL9Z/ud/3AjdPcecU+VOBBxqsCfzL8mSHjfeM8yTmySk+xzrrfl5joOnEj9VBuTuWwI/u71+vF7u5uPHr0aOXad6fGMteaQoUKFSpUqFChQoUKFSpU6D2hfzg78hQqVKhQoUKFChUqVKhQof9PUjFsCxUqVKhQoUKFChUqVKjQe03FsC1UqFChQoUKFSpUqFChQu81FcO2UKFChQoVKlSoUKFChQq911QM20KFChUqVKhQoUKFChUq9F5TMWwLFSpUqFChQoUKFSpUqNB7TcWwLVSoUKFChQoVKlSoUKFC7zUVw7ZQoUKFChUqVKhQoUKFCr3XVAzbQoUKFSpUqFChQoUKFSr0XlMxbAsVKlSoUKFChQoVKlSo0HtNxbAtVKhQoUKFChUqVKhQoULvNRXDtlChQoUKFSpUqFChQoUKvddUDNtChQoVKlSoUKFChQoVKvReUzFsCxUqVKhQoUKFChUqVKjQe03FsC1UqFChQoUKFSpUqFChQu81FcO2UKFChQoVKlSoUKFChQq911QM20KFChUqVKhQoUKFChUq9F5TMWwLFSpUqFChQoUKFSpUqNB7TcWwLVSoUKFChQoVKlSoUKFC7zUVw7ZQoUKFChUqVKhQoUKFCr3XVAzbQoUKFSpUqFChQoUKFSr0XlMxbAsVKlSoUKFChQoVKlSo0HtNxbAtVKhQoUKFChUqVKhQoULvNRXDtlChQoUKFSpUqFChQoUKvddUDNtChQoVKlSoUKFChQoVKvReUzFsCxUqVKhQoUKFChUqVKjQe03FsC1UqFChQoUKFSpUqFChQu81FcO2UKFChQoVKlSoUKFChQq911QM20KFChUqVKhQoUKFChUq9F5TMWwLFSpUqFChQoUKFSpUqNB7TcWwLVSoUKFChQoVKlSoUKFC7zUVw7ZQoUKFChUqVKhQoUKFCr3XVAzbQoUKFSpUqFChQoUKFSr0XlMxbAsVKlSoUKFChQoVKlSo0HtNxbAtVKhQoUKFChUqVKhQoULvNRXDtlChQoUKFSpUqFChQoUKvddUDNtChQoVKlSoUKFChQoVKvReUzFsCxUqVKhQoUKFChUqVKjQe03FsC1UqFChQoUKFSpUqFChQu81FcO2UKFChQoVKlSoUKFChQq911QM20KFChUqVKhQoUKFChUq9F5TMWwLFSpUqFChQoUKFSpUqNB7TcWwLVSoUKFChQoVKlSoUKFC7zUVw7ZQoUKFChUqVKhQoUKFCr3XVAzbQoUKFSpUqFChQoUKFSr0XlMxbAsVKlSoUKFChQoVKlSo0HtNxbAtVKhQoUKFChUqVKhQoULvNRXDtlChQoUKFSpUqFChQoUKvddUDNtChQoVKlSoUKFChQoVKvRe0/pdb/xP/+k/RUREo9FIn8ViEZeXlzGfz+Py8jIuLi7iu+++i9PT0/j222/j5OQkRqNRnJycxMXFRVxcXMRyuYzlchlbW1uxXC7j/Pw8Li8v4/LyMprNZqX8RqMRu7u70Wq1Ym9vLzY3N2NjYyO2trai2WzGxsZGrK2txdraWjQajUr9nNbW1qLZbN54x9raWiyXy4iIWCwW6aMyLi8vK+Usl8uYTCbpHrXn4uIiPdtqtWJrayvW19fj6uoqxuNxtNvtaLfbsb29Hc1mM5bLZayvr8f6+npMJpO4urp6OyDr67G5uRlnZ2fpnrW1tVhfX0/1ZRvOzs5isVjExsZGunZ1dRXL5TK17erqKhaLRaq//uq+VquV3qvfFotFXF1dVdqlMRwMBjGZTGI6naZ6R0Tq32azmersPKM+ubq6SuPZ6XTiyy+/jD/7sz+Lf/Wv/lU8e/YsRqNRXFxcxPn5eUwmk7i4uIh/8k/+yV3Z9fdO/+E//IeIeNs329vbaSwbjUaaA2dnZ/H69esYDAbx4sWL6Pf7MR6PYz6fV/iq0WgkHhB/ra2txSeffBK7u7vx8ccfx+bmZmU8Li8v4+rqKi4vL9MYisjz4hH+9d84Z8RPPo9Ydt27VDeS5gOf1TV9xAdO+o3v4HP6X7wc8XbOktdzH95fR5In3W432u12HBwcxP7+fvziF7+InZ2d6HQ68fd///fx9ddfx3/5L/8lvv322/juu+9SOzWXNzY2YmNjI5rNZmxubsbOzk7s7e3Fp59+Gvv7+/Hzn/88tra2kvxrNBrRbrfTOFxeXqa55337b/7Nv7m1HT8m/cf/+B8r/4t/nMRLGxsbEXE9Zs1mM8nFZrMZw+Ew5vN5mifT6TT1xWKxiMFgEP1+P16/fh2z2Sym02lMJpMYjUbp/RsbG7G3txdPnjyJL7/8Mh4/fhy/+MUvYn9/Px4/fhw7OzvRarWi1WpFs9lM8tT5OKcz6kjj9rsQy3jX8jgX1LeSz5eXl3F2dpa+j8fjODs7i9FoFKenpzEajeLVq1cxHo/j+Pg4Dg8PUx9fXV3F+vp60sfT6TQajUZsbW3Fz3/+8/jiiy/iyy+/jG63G9vb26k+0uuSa9RPEdf8oDprPgpDuMzQh+3U33/37/7d79LtvxP9t//23+Li4iJms1l8/fXXcXh4GF9//XXM5/O4uLhI7Z3NZqkP9dt8Po+ISHJCvLi+vp7wgmR+s9mMVquVZMnm5mZFXmtuUWbnPvxN+CEn69n3zWYztVfXNWf0TsdalOsRkZ1fxFYcT5f3fHed/lB55P2czCfGoQ4V5ry4uEhYhPNkOp3GfD6P0WiUxlEy+8svv4yDg4P46quvUhmtVit6vV589dVXsbe3F7u7u/HBBx8kGT+fz2M6nSas5jzPvlbbnPT7H/3RH70Dx/7+STpPlOtzYk7hQ7/v9PQ0+v1+/OY3v0l9Q/x4dXUVw+EwTk5O4uTkJN68eRPz+Twmk0kqR3zMudNut2N9fT3a7XZsbm7G1tZWtFqt2NzcjF6vF5ubm9FqtW7w/Pr6+g3cQjtA4ya8VjcHRJJ3Kl/3kP91n+Zkbu7l5qhjIfH0xcVFjEajxLvi6fF4nGQQdYPqJlkjHan+lOxR321vb8f29nZ89tlnsbOzE8+ePUtznfNRpLoKx0sGRkSyVyhP1AcuIx2X3sUOuLNhmyOC5I2NjVhfX4+Dg4OIiDg5OYnDw8MkHNT5ul/AnmVRyInBJVA2NzdTg1WGhHUOFPAa769TBBGRFZAsT0JQ19UGDaiYWQwqwblcLlNb9C6BX1cCquvm5mZ6j+7Z2tqqGOL6LaKqCLweAiucVHRO6MN3qc4yRAVMJQCkKObzecW4dbBGxhd532qSdTqdVGfdLwZ3B8N9kNd7sVjE+fl59Pv9GAwGMR6P4+///u8TiPRnBNhpiF1eXiZhu7+/Hzs7O2mcRW6cOuWMQf11oONGLecFqc5gqesL8iHby2u5OXUboOc94jM+QxCt/qTwp0PH303S7+fn52mMLi8vY39/PwGYbrcbz549iz/+4z+Os7OzePXqVZydnSXe7fV60e12k2KfTqdJjp2dncXZ2Vn0+/3o9XqJr3POvNucC/dNqwwx8poAioMIyYPz8/M4OzuL+Xwey+UyKdflcpkU8+npacxms5jP5zEej9NHwGVvby8+/PDD+Pzzz+NnP/tZHBwcxGeffRbdbjfNJTkc6GzzOr8Lrbr/rgbrbUa1P+tym3Nb82xjYyM5DOWElN4QyNvd3Y2tra0YjUbJobm1tZV4POKtYXZ2dpbKOT09jVevXkWz2Yxnz57FxsZGzGazpL8crJGPpaNz5CBIfx0s+dy9L5J8GAwGcXx8HEdHRzGbzZJcEq9Tp8oZSfAqHCNMIwApAC6nmAxb9qEbrDkjlvOPeIog2nGPynZdQyyWA5nsm9w8Fzmv5pwZXlbOMKZs5/00OtyRog8NDLXp/Pw8Go1GtFqtpJOFZxeLRZJPEW9xyunpaSwWizSPNjc3o9/vx2w2S86ezc3NOD8/T3NLdcs5pVVPn0OrZMdDIHc68XoO80RU5dx4PI6jo6MUwBA+brVa0Wg0khEr2XN6eprwtMol/teYdbvdaLVa0e12Y2trKzqdTjJ2NaekY2iI5TCUY2PxNduid3t5dCDRYHUbwrGZKIcDfO6xjpubm0n2n5+fR7fbTQ6DwWAQ8/k8hsNhDIfDpHuFUzSvxZ9si3T4xcVFCmS12+24vLyM3d3d1Kdsu8+3nMzQPMvN+xwvvetcuLNhW1fw+vp6dDqd1MG9Xi8mk0nM5/PkZT8/P08CXt5HNj7i7YATcIsR1XGMuAoU5oSl6poD9K4AnJlkqJL56HmhpyMnpKXIVH8Btna7XYniOtNysqhvcvWW8PP7fXxyAFn9JfDCvsj1SS66KCZvNK6jwpoMMn7VP1SsMqzJ/DTsrq6uYmNjI0Uq6WXV5FL08r4oJ8AHg0FcXl4mb9RsNkveeoFM8b3mhwQe2yfAubOzE9vb27G5uXljwpMHRBxj8QHLpmDlfTnjlnzkzpM6ygEqByxqY+4379cc3/I5zg0+yzLliMnxmgtfB3ER1+D07OwsptNpclBIoW5sbMTPf/7zODk5iePj4/j2228rHkk5pZrNZvR6vWQ80HstxSKePj8/r0Tw3aP/0EDOKmONQJj9rutbW1sREUlWKCNEcr3VasXl5WUlQnt+fp4yN2QkHBwcxO7ubnz22WfxwQcfxCeffBKffvpp7O7uxqNHj2JraysBGp8DrG+Oz39Ifzs/reqn296Xe/9t/7OP9Vd8eHFxkYyodrsdjUYjRS8EBhuNRsxmsxQREeBZLt9mV00mk3jx4kV8+umnaQzomGEdpCP0keHmQNHrz++aqw/FqI14O0+n02nKRGPGEjORIq6NYOprkfhRhisjJHJ4i28pP+vwz/+Puz/7jTTJzvvx8yb3zGRyr6W7q0fSSLA9mjEMyIBgyPe+8JX/St/73gIMf68MWbKNkWyN5J6Z7uqqripuuXPN/F0Qn+DzPjxvkqzRNOlfAATJzHeJOHGW5ywR4Xyn+txtfOZ8upzyLK28ckfZ5yvibuDR7ZNndbLrsjknaVBVtwFy9KV+7uNTvlRM4c790tJS7X54ljlEf89mszg7O4uqquLdu3exu7sb29vbUVU3VVsfP34s+n9rayuqqiqYb2NjoxbMU/6GPhnPu154Lk1p7QkT/z7itu8kQ8bjcYzH44iIEgCgwufjx48xGAzi7du3cXp6WiokcV7hRWhLRQ6VVmTQ+UztgDqn8IX3kd8qt8qzzI9mHbnPcZbzNt8pHztedn3Ke1WG6aMGatHV8CxO7srKSpydnRV6QU8wy2w2i+Xl5WITNjY2ig7HX2BuZrNZDAaDqKoqBoNBdDqdWl9dFzmf65iaggoZPynfPaQ9KmObvQQwCXDDmJ6fnxdA4tlCBc6a9VTnikimKnF1bhWINwmTMnLmwClT0lRYM4CtmVnewfURUXN8+UwNl0+iO8+8x/uuDK0Cl2XWdIw6Jh2H09v7oHOkn+HAAkCHw2EtEOCZWWd06Kd0u7q6ilarFb1erxgZBTeqjJ666bwRrVfnFn6HVur0MxY3Yqurq9HpdKLdbpdyEN7h79W/nfcdpLhydb53mXBDnznz2ge/P7sucz69aRDFn+WAzR1b/tfnK7ikjxmY9rEo/15eXsb5+XmMx+MSjCPgsLe3F/v7+7G/vx8//PBDiSTj4GJ8FbRqHx3AKCDM6O40far2UKOS8VzEbbm3lqkxdgXt19fXJVNCKScyFnFjZHu9Xuzu7sbr16/j5cuX8fLly9jb24vNzc3odDrFmVLdn43D+VZ5wcf7UIf3IddlAFz79FjnukkPYJ9p2GqCKtfX17G0tFQy4oD38/PzYm8BSNfX13FychIrKyuxs7NzBwBmctykv5v42QM6TTrwKRo6YTQalay22jPFDE0OXETdsdUlDFqR5gED7ncA7Doz+zzT0f48nw/4RvGWBtSzQITjiqxvqoezTI7iE32ev1evU9pmDro61O7E0wDuqo8I7uAEaFVDv98vGUHeAdDf3NyMyWRSnGNsB/wCHtb3ZThO23PQ/xG5bnMbT8vm8vr6uugXdEzErYN2dXUV/X4/Tk5O4vj4OMbjcQkEwJP8tNvtEjQgS8sysW63W4KbjqmaaKk2We2Ajs3xCLpNfZtM5pxv+VyDLBnWV52hGNzlW+kIfgODVFVVMAiJN5xbqhIuLy/Ltevr67Vqk6qqyu+IKLw9mUxq1VD0k3G6Q9v09316PdNd97VHlyJnyorfrVYrLi8vYzwex4cPHwrx6LyW0vKZprGZIMpyqP1WxU9EM1OINM9UqdJQBlSD5A4x92rZAP3U6AVRVpz46XRaIrVEdXq93p2acgXSgAzeB9hQZtWSYErMtCzbnW0dtysXmFbLJdQI6Hzqj67VpV/T6TRarVbNoeed9FP7dnV1VYSPvozH4xiNRjEej2Nra6u2Nm+RIvoxm/ItTgzzRVnN4eFhqVDQtcnn5+flGYB3svlVVUWn04mDg4OijH3MGR0WARX9cRCiUXgHH3ymwRZ9dwaQeI8rqqY5c71BnzJ9wvN8vFn0VN8PGFGaaJ88IuiRWQWlJycnNcNzfX0dw+Ewer1e/OxnP4uPHz/G4eFhDAaDAlbn85vg09bWVmxtbcXOzk6p2GD+iVCjfzSjmzk2z0EGtDloVhrSfxzRqrpZR8x4ycSiM8giVtVt1vDk5CT6/X4MBoPi2M7n87Jm+Y/+6I9if38/fvrTn8b+/n68evUqdnd3S4QeWWsC9U1jyv5e9Jl/91CH1Od2kT17SHMHgN8498g8c0I1VLvdjhcvXsTy8nIcHx+X7Pn6+nrRcVTRzGazeP/+fVxeXsbu7m50Op0SxI64DVJTsYJdgK9ZXsP19DuT4c+lw++zDQaDOD09jaOjo1JJ4OvIs7XxEVHoCn+Cb3RdPhhHAana8iyg77reMYbLgH6W6V30vDu02g+1hW4fIuqJC58/HcN99kzxCr/RLRq8VP3u9kKzrWAY+ucZZpwrpefV1VVsbGzEZDKpVWSdnp5GVd1ktPb29mJ5eTnOz8/j6OioPGs0GtWcK3UuPNnj71Xc7DR96qZVTxHNdljtLWv/p9Np/PDDD2X9PoEByrxPTk7i/fv3BQ+en5/X1rUyjziy6+vrsbe3F71eL3Z2dkpQU20AfK79hr4a9HN8q7zV1JTPNICu+Cpz8HimZqCz9+r8876myomIKEH4Vutm7THVOhcXF7GxsRGdTiem02msrq7GcDiMlZWVGAwGMZ1O4+TkpJTkE5A/Pz8vvgL9Ho1GcX19Hevr6yVYw/IqHZv7WvCC6iilYUbXz22/0xrbiHrnUQQ4eLrmgd8eOXcgrswICLxvPZoTyJW7f+c/SnSafoYzqYpQjYT+wNyAAi0JU8UFSMbgZUCnyXFx58HpkDn0jAMHWd+lz3PBy+gEMK+qqmRuR6PRnf47YFdFRzQURU+ZF9Eo7bc6Gk/VlNbwKBklQDhgpylTq/Os2XzAjjo6vHMR2HMgnvG7X59d41FGbR7tdr70QI3e09Tfpt9+fXZ/Fu3TPmnWyEv2Vak2VWQorTW6THkPQY2VlZXY2tqK7e3tOD8/j8FgUANfBOgwJgBXgH5Wcqdjbfr8KZvS02mnzcGagmlkXdfm6yZprKEF1Gg1CGBme3s7dnZ2yg+be2mwIOPVDAh8TsuCDk28nF3/Oe+47zvnG8aq+grgwfrBq6uraLfb0Wq1Ynt7O2azmw27eMb6+npERNHVEVGqGAjk6EYsys/Mmcpskxw3Nb/+qRt8yWaZEXczhegAz9Y6RlC9pVmoJkfUAa1ip/uwjeIulcWmoI/e57grC8zrOH3OlD6ZndB33idTzgfuTOm1asvATDoO1b9850FRHIz5fF5K6TVgDc4F64Fl2CyKEn7mH9oT/ATrZA7+c9D3D22L5gVaoW+UZhG3mXE2BRwMBmVtKFly5gebqqXH7Xa7LOHqdrul7Fg3OvMKuEyXOMb1a1R+fMx+r/6t93hAiM8cd2UYruk9TX3VIA105v2tVqusB/elopSKq/zP57fBSp3H0WgU7Xa7BBj0vYto7J81+TE6RtpD7cCj1ti64tEG8+CgjEajkrn0daWqOHVtikYIYU7d+AIlS8uyUjohTqCmKD7X6uJ+QAB9Q0Epc2kGmfuIckyn07LBQLvdrkU1icjCSFr/r5lP7aNe52v2dIzQzoMIAGrdzADFgaLVuXXjqAYR5cza0Kqq4vT0tBbx4hkIjDvZOAqtVqssTD8+Po43b97U5tQX2z9VU+eUDc2Oj4/j5OQkvvnmmzg9Pa2tucJ46hpCovCMaXl5uWT1yOqr8ouoRxczhZkpukyBZJF95WN3AmkKUJUX9LnatCQ942PvJ02Vm7bsWgUlWsGhMkbf3JgpIPN3KuBBFsfjcdmoiHVSbKyzv78fb968iaWlpfjhhx8KvyLflERtb28Xup2dnZU15ypTDsg8kPPU/B9xGw1Wg5rJpgYNGKPbh+FwWNYWdzqdoidOTk7i6Ogojo+PYzKZFB3ZarVid3c3Xrx4Ea9fv44vv/wy9vf348svv4xut1tAjW6202RIf9f22Gd+zveL7nnsdw50yJiyVnZtbS1ev34d6+vrZS3W2tpanJ6eRqvViuFwWIIGbID29u3baLVa0e127ziw2Be31/TPHZys35ncP3Vj7R8bYsLbAD8N5KoMeLA+Imp6RjeQ0gySYhbsOp978Np1rTumKreqMxfZimyDS9fZDkz1O21qD3R8alv0Xg+yOz7R4GDmILue0uewHKKJ51qtVtE5BDCwKwQ2dVOpXq8XVXVTeTWbzYp+q6oqDg8Py7OYX5ICyisaOGZOVbc+dWD/vqYyrXpA5wmndjAY1BzWi4uL+OGHH+L4+LjskE/JP8/GDyCIiV3tdDrx4sWLsmtvVtWgfOAZWQ/Ccp8HqBZVf3JvVVUF4+nzF+EdDzw10Zbf2fX6DuU1HNHV1dWSMSfRhi+gpffD4TDm83n5TUAH/YZO551HR0cl0KAbvyqds6CN/+1zkQXrnBb3tUc5tllkgA6h2H/44YcizCjtLHqhg1CFrZOi20374m9lMmceV+7+vyoQB+qZgxdR39wKJccObhG321eT/p/P5yWy5EyrClTXEGeZPW0eHaWPCub9PU108bVuPCMDpU4b5nV5eblEftiFjdJkVXSe7VOlgTLg3Th9OOCeLX+q5jwTEWW8apzUsUMm1HFUh4oydY6Y0Qx2ZnRdEfM5v9VRUh5ibjX6rsBHZcB5S4M2jEt36XRQkwWV3Fnns0Xj0JYF0ZR/uEblAHnSZzhfe2bZaa0lVKwn0efMZrPY29uLy8vL2NzcLGuwKJ1VmlEaRRZyMpnc2ekdJyOr4HgODUDJ3xrAcOc8om6ICZ6xZhZ9qkEJjisYjUa1dWhshHFwcBAvXryIly9fxsHBQezt7ZVose/H8DnG8P9fmvIo/0fczgcbeMGr8Ofl5WVt7eze3l7ZeAR5onyW3b3X19djc3Oz5jgTeGVOAVQeUM50WQYEnwv/9/v9mE6ntSorZAJa6rGGqmOwgRq49zWsTQDW6ZDhBNf9Tc9xO+Cy6/c5buDv+xwtx4s6BrVV+h1/Mz50vAZ11ZZhKxWjeF/dqVU7pOWV+kwcWvrIHAPsNaBH5mo2u12iVlVVqUrp9/vlOSyzYv5Zy0jSockWZfR8qrYIgyr+0fFwbb/fL3uyQGeqICaTSVmi0u/34/LyslamT3CZPRTYY4GM7cbGRlnq6LTSQLj6DfBPxP2l3pkMNGEgv89l2Hl6kV50OiMbKkc6XpUF3oNOZozoJd/wjioq5XccZU9AgstZNrS5uVmzLdrvjLeVZqrzs+DD59jvBzu2Cm6940qI4+Pj6Pf7hXk9NZ111LOwWpqjBsAVf8ZYrojVEWlybPXdTX11RlKwptfpQmqccu2vlzuroGVj80nGqHrLHAL/zgWSMhDdMTC7N/uMPrIj9sbGRm1tsTZVhiqEGHstweJ+XYerwvdUzZVQRJQyVY8eq1NLxEzHBr+QrUJpM684+RH1AJBnw7Vv2j/PlKDcXB5cUXtQh/fS5/vWj+kznXZZBI/vfByuW7xvWRDHwbvyYQaWVdY94u+OPJtCXV5e3jGc3W63BCeoUlF+dX4mUk3WjHfTZ11vn9HrKZvyrwYFssBIpocVxMEzGtSjDEoNLHJCdQjlx9vb27G5uVk7Ci5znrz/2sf7xvrQa7nebcV9Rjn73vk9k4X7npFdo3OF46mZKyLvm5ubZTORra2tor94BvZsOp3GaDSKwWBQgjY8X3UV4EiDGdn8NDlC9P85NN0wCr7UUwH40SyP20PHJZkD6de4LsiwGP1Rxzbi7mY22TsdB2W2rglDaGsKVOjfrouzZ6ludvugQQLFZHyf9VX1lv5o0FZ5Vm1IVVV3dgFHd2EbptNpVNXNTuPQH5sxGo2KY9bpdGpBTOzCfH57uoTTL/v7ObSMF9U2ZBVHOLEa5NEzftH9k8mkYEr0En9T+kqGtt1uR7vdrp2eElFfB+wB7Mz2u+7Wz2iZHPEux06Os/lf7Rp9UczU9P7MMfTrwHnaV8V+9BVer6qqVEteXFyUHZXBrophFQNyL/Z6NBrF2dlZyQS7PvH2EJv2kHsXtUetsXUm1sbaG0oKMqDpay35WwWdVDlnWmWlZe7kNjFRpsQ9og9hNWKHogGEVVVVzqeMiNqum/p8tnJnMxR2aKP/RAJVseK4Q0OEjegf52qqstBMKzTincqEqmh4FwCDKPrGxkbZ8psz3dQ5wli6geX/zc3NWFlZif39/ZjNZmXzrIioOf4ItNIfWlRVFb1eL/7wD/+wlHvybs5SfE6lOJQgsd5Ky09xdBkXZadqSPl7bW0ttre3i2JGGXBd5tz6/4vAURNYirjreCgA0ObX870rPJrynYMONTKa9dB+6LW8R5uPIwMDHiDQoIj2Q8EFitz7HxEla8vaWniSyoJutxsvXryIqqpqO18qqGdNEWVB4/E4rq+vi5wzJs3Y0s8mIPljNzJ3HoByMK1gAb2DfqHCQQNaEVFKz1hbxeZqy8vL5WifN2/exKtXr+KLL74oZWjoT44yeKhBva89lt4ZCL3vGdn3i+5t+myRA636l/9ZFsH6WADX+vp69Hq9wqvn5+dlyQxr33S5xHA4jLOzs7L2mWvVXiNTVDDA7000c73xXJzaiNv1aCwnwOb7ZkC6szeyQFZb9XxEPdNKQzZoShO16corer9X5SCf+tttgQaRVIfSH3+HO4F85g5Pk56+L+ijz3X9zX1k9Gjw2H1NMY6C/9nsdodYnqW2eD6/3RCPrDzL1JAf8OJkMqk5FSw7Ikiky5FUTnxDq8wmP5emmcdsfpeWbtbgHx4exvHxcXz//felugm7zLpajs9imU9VVWW528bGRsnK7u3tlb0tONpH/QStetMMMv2lMe9aPsuP4rCIqFUP8bnLj2MhxVUqS2rb3R/xADv9pO/u4+i79Bl6XcTtzvfofPwLSuJbrVZsbW3FxsZGjEaj+PTpUw3D6HPoM7Rj+R0bD3Y6nTuJHMcvGpxSneb6RTGQ+h8Pab/z5lEQRjcFubi4KANTwmdZG/1bnU8v0XEH1t+v9ytT8XnWb73fn41QODgHfGqESJkPhwVHZ9F6r2zcMIwKl06+C6A7Nihojwrp975plTJYFilWQ+DCi/PKUTUrKyvFEdVrNTPFvToWpZu/97FM/ftoOhZVdApMfEwZ70IXHCRopsDF+cWVmF/j12fOnoPIJlny1iSzmSHz7+6T86Z3Nt2zqJ+ZAl0kd+4Q6LXZ2AAiKgco3JWVlVpwAt3BTulEQTXiT9BMMwU4APB80/ifqt03r95UfqGfBrNUron+ekZ3ZWWlROe3trbKJiGs+XxIpvbHaBlQvw+83/f9Q5uDBv3bdXAT+IKvATxkSa6urqLb7ZZ58cAwpcaaLYmIGmDEjmo0v4lvfB6fE6jXJTEqC+rQKRjT8WoVjescnydtXKtATz/3azN99li5yHSt6ssmZ4a2SO9nf2f3OJ96/5RvHRg/hJ6KlZRGWj6OXb66uiqnFZAE0Pt13S3zzAY8JDum02mcnZ2V0lqXQV1e00SH59Cyviyi83x+cywieyqorYNu2EjkCxqyqSZZWfas6HQ6ZQkKdkCrf5paplcU02b8pkFbTX7ps5TvvDrxc+XUf9+HgZV/m8ZKm8/nsbGxERFRdp2uqqrwJ34cOF6X2mmiAHvOvdzjge5F9im7hu9+F77/LMfWJ5WSJo5q0EivZkZcqUTcRgEibjc10O3vPbrpzkKWhcoyWBH1SAF912v5XjM0Wq5LnzY3N+9s9MD7tfyY7Avf0Qd1enRcWp8OE/H+qqpqEWCYR7MezAXvIfIecesg8yxlHC1/1LXMKiyASOZM711aWoqdnZ2YTCbR7/eL8td1KnoOls8LhkQDGtBE+/PUDZoBwMkUcTSG7pjI9dAU8I1i4Mw1DsXW5zPnEblTq/zbpBA80+fX6v8aEXMllJVI8R19apobXb/CNcqr/jyAoD/f36Oy6jTRe/W++4wvfXJnQ5U5UXxK0NAPa2trsbu7G8PhsGRzKdEhm9Vut4tOwKDr8Ur8kBWivPM58L025yHmwUu6kHui6AA8PftTZWI6nZY1jACg9fX16Ha7sb+/H3t7e/Hq1avyN4EEjtFQWi6i2aLvfxdHU3nJQcl973ponx7Td62MgL+1VNazA8wH5X7z+U25vG50pvYDnX59fXPc2Xx+W/kAOHUZJ2jj+lH/BuBr3xY5UT9mY/mA2n13anFm0Bd61Is6YVwfcYuBsoxFxK1OJzh23+Zo94Hl7B0emNXxaB/0OW6/uU6vVwciuze7JwvAqJ53fQ+/6NE+mZOh72Js/j53YqA12XZkYTweF54niMkRZmyKx1ErvKfX68X19XXs7u6WvkN37IZmnZ+bU9vUfJ6U3mwI+O7duyL/2Pqrq6uYTqdls1mcLLKwBwcHtZ2Pydj2er2SYdQsrfMv71Hdojyr/dc9MbT5iSxNfoXrKL3Wm/svNA3WNNFWP9O+8Lfuf6IBYvV1uBd/g/Xea2trMZlMot1ux+HhYfT7/VJxpkf3gXN1LgeDQU0u8EUcU7meWEQfpW3TtU3tUWtsmxyT+fymLOno6Kiss9QDr9WYeec0K4oi8Wyolpw0KW6fYDeq3KvP1AgM3yMIlLYp2MZpJcvmGQdKL3TRO/RB4dIHLaHRvmlflG5splRVVe2ogUWRSXf4obGumVJHG/rrfRpt1s89Y7m5uVkUDpsp6PNV2Hgm5Txra2tl3YSuydYAwHMB+AA3XZc8n89LlEtBo2YmUADMoW4apE3BuSoifZ+DFb3OmwMMVygoKFVU3h4CLBlzVjrr79fvMl2i92VN78nK1BRoRUTJGGrfFGQ6bZXPvVSI9/EO/scYr62txdnZWTHYOGo4si57EXePAvGo8HMEOM5/0Icyp5WVlXLOqYJjBxo48+PxuJT0RURtR+m9vb3Y39+PnZ2dEgjSMz8XgffPGdPv0h76DJeBiNxxza7T5vLizoH+zn5cTjRr2+l0oqqqwsMEr9Ve4ABXVVXbldptPuAfZ8DHmGVqVQ59rE/R1H5netqrEnR/EF97m+ltnwvooWWv+p06Xlkw3+VC7XkT/zFO+tnUVC+pnsoAvv92m6hLQHzOVR/r872SKHPG9V3+XncYuMdthGZ1W61WOXeVBA7VieAy7mHd7dnZWZknzg/FgfbNe7Q/2diemv+b+uDzDW9eXFyUs2k5MxU5IYuN3p9OpxFxY0e3trbKTrusqWU9LckAKgQVQ6mu0wCc8wF9dqyWjU8dM5ep+4I+mf3OsJvjJcdOfOa6XWVfMQT3LXomAXmWErbb7aL3ocnq6mr0+/07Jd3wOAGB2WwWg8EgIqI4uLpMc1HLML7bJfdTHtJ+51JkOkK2TtcTZhEzJzCAmh8Mq4I8lIA3ZZ5MqTddq0rLU/cANH7jsBLhYPLdkDAWjkpQAEtkW4/s8bLeiLtZNZ1MdYYX1ZwvCkDoO1DEMGyr1SrMqBl2jw67gUJ5UB7S7XZLOUMm8Jplw0FnTTXlEYzD5+6pmypDzXwwt2SaMgMaUT86R9dW03ze/G939lXRZOBPgYAHGPSazIA+hN6Z8nHeXORU0zev6MjG4DTyfjR97vcoGFUj4XLIfSovmuFwx0HlHgeNzWbI7mowy5W1R56fA797a6KjgwXGwzmoeo+X/pGt1U2jqqoqx6R1u91yJBZ7FmjpmTsZ/j7v96LvnAf9uQ+Vi0XPa/qevjx2HH4Nfytvqr5q4nkFpARmWY/VbrfLEiOAjPaFyiD2aEC/0cABOGg6lsfw+1PLhPI8TTOIDpYVx3iGKJOdRe/VQITe75mYJl2pAFgxS9P7mxwYB5YO+vk/y7rr/zyf6hWcOw/u6Tj0uRqY9L6qfncez8blc+r006VCyAS/tSpOHdvz8/NotW6y9yzPYj36bDYr+p65zfqhNFN6/r/QwJcc33N2dlYCnVq6PZ1Oy7m2GlCj0oFyZKqecGixt14JB53csY2orwsHi3Gdr2n3sTTpLb1Gfy+ii/shyq/+DP3Mk0X6PHds3fdSmxBxG1xrt9ul8pDPCWJWVVUrSXa9pX7SZDKJVqtVqtR0LpymmV+Q4cWsLfpO26M3j9IOwKTT6TR++OGH+O677wq41xJGVfbOeBFRjKkuBNcNpJgwnRiI4Q6TRxmdeVzpUVqC40hGk4nBGCv41PMc1ZCj8BA2HB5KTFBynklV2mQOK+NGyLXkFeFGMBU4UhKtDTpeX18X55voohpIlLRnRmjQkFJkBOLq6io+fvwYV1dXMZlM7igNjc6qkfLItUaJvCT9KRs8Q+lRv9+PT58+lRLSTPBQCsp/WtrNcyMWO/F6v1/rikPLBzGgGvHXSgmVU57bBH757QpWr9eMtt6n9CBzr2CoCeS6clb+UaDtsq3PVWWstMiMmDfoiS7gfDjlXwzy5uZm9Pv9uLi4iNPT03L+7fb2dm3uZrNZKW3U5Rf38cBTNs+cVFVVDKLqXzaQYImKglc/G/Py8jKOj4/LET/omuXl5Tg4OIhXr17Fy5cvY2dnp7aDuJYfZ63JCV0EQO4DKo+dkwzM3/e8h36mzWU/oi6P7hhxnQbaFCDp+Yfz+Ty2traKvY+I2po4NhhZXV0t2StwgUb24XfsjgKlDNS4c/ScGn3TDC1HguHcLy8vlw0dwRLoCWiiOlCrpdwB0+C+VvqoblPco3OuGMNtbAYiVf408658o/Y6A9oRdcfT5zebb7WR2Tg8GaC8oVmqrC/edJkP93qgWseMnmHdZ1VVRZ+rXbi+vi4BOubw/Py8ZMFGo1FUVRWDwaBsfMdzFV/pu3mOOj7PqTmGoE2n0zg9PY3f/va3MR6PiyMFfdn9mPPK0e1U6rBDO2tqd3d3Y2Njo2RusTHqYGXOn/ZLK7Mc07ptdjymPLsoIOVOaeazRNxiXnW8vWqAz+ARfR76OFu+533XAIqvY6YC9fr6ZvMzHFT6z8kf+AisN3e6sfHtd999F61WK3q9Xs0n0aCq8w7XNCVEPgcLPcqxzQDXfD4vW56PRqNCOI8wNnnsEFh3NcuO+tE+ZAKe9SvL4mZMqaA5ou5o8W5dz+pGhe8zh9X7qxFWZQ6lrTuQvEczOhhVVSyZA6/PcwXJmL3fmWPhAQPu9Qg/CgdhwpDxXn9+Vd1EjTD4KgD86FieqjkgZH2IRhzdCXQ+U3CRBTAWjTFToPeBeu2zfqegx52VbLz6vSujxygdH0PWz4wHVZn7GJS2Tu9FitHnyv/ntyvaJqOHDLCEgXUpHGPgaxuhveqCLFr/3MCMjt1BODpQj+qCZlSH6L0EiHAIZrPb3dAJEnBuoZadNekp7WOm4xddl/1/3/2L3pnxFJ/p789ti+5X/lc9qrbYHV36qICLUknAz+XlZdlER0so+Z+sS5PTpH3xvvq4dHxun59Dy6p1fIMtrtOW2Xz/ibirnyLq2IFrVI9ncqGfcb06gn6dY5OM5lnf/Ht9Z3af3u94K3Puud/5QK/zv5veqdcu0ut6P5/5cjkN3il2YSOp6XRasrSaqSR54hgpy+4vovWP3R6idyKiLM3Sc58Zl+qLiNvALplYApe6iR1rbfUaL6mPuA06ZYkwnWfozXXZGHjeInnya7PnZPTKZF7l3N+dYZJF636bPgeHNzn1ETdHGLJDNXslMSbHj0pzx8QaxHNbo011fpMMPpb3H+zYZhM8n8/LbpbD4bBsHKTr0JSZ+UwdSDIaWl4AQ7PpgiplHbwD4QxM6OTyTncguMd3c46oM4/W82sf9JlVVaVGP+J2e3rP1jmTaVOQQdPSZi9x5ncmHPSHvmsZcmZwlY5NxpqxcFTEbDaLbrdbyjFxfr32n7laWlqK169fx+7ubiwvL5eovma7M7o8RYOfKSs6OTkp5zZnSsUNdMYrqmD5XNfcZDJEyxR7U789Y+kRfH9+5gxrf2nqlKnC03fd1zKwojyp2d+me1Th8gwdP9e4c+GgKJsf+uNVJq1Wq+wCCPjHCeN8uPF4HMPhsJQkU+ajGzboWJ86gLOouV5UsBIRBeyhs/X4MsqyGXur1SoVD8fHx2V9OqDmxYsX8erVq3j9+nXs7OyUkqls/bH3cZFBdIdJWxMAeUhzfXnf81wnP1a/ZbbYG7YHUM1n2A0F46qHqEii6kMzjqwZ9AqN+fym8oeyNq0OoY/qCDqI1+qiJno8BxsQcUtDHFqOOoROupsudNJlCIxdl6NkQJd3adZGszPz+bxWJqiBH88Iqe1uCqq2Wq07O/QvAqP8dll0/Q2P+Nh0GRh08OoifZ8HRR46V449dWxeMgmd0fdu+6jMY+kUGEUdVy3NJ9C5vb1drh2Px7G8vFw2FGTc9FGr1Z5TMIfm8ulyW1VVDIfDcq47+JVjEtmQjvJk7CC8jSOr+7Zsb2+XbC5OryZLPHjH/DEXYM2IehBcs5n34RXnX/8u+z/zSZp+smuUd/lMy/UZW+aouq+j/oHugQTNoCsVksvLy3F4eFhL3FRVVebUj26bz+dlSerh4WGpSvCApgccFJs6HdBXj5WDBzu2SiAVdohDJApljtFqYhaY3aMyOLa6eZQO2p2FDGx7P/X9ENSzI2qAFeDqezMA7tGLzPnY2Ngo1zEud1aU6fxZGllSEKIbLSkjuzOf0UO/U+PKde6cZKVMzD39Y+H/1tZWjMfjQu+Li4tot9s1o7u0tFSUDpvCZM5vkyH+sZvOK8J7dHQUo9EoIuIOv+g9mcKBpgp+uMcdtkWlMfqZ91Ofl33uTqBe53yif/sYVR4zoON91+uaHLmsbzreRUY/G88iWch+NKClVSUeTMDQkK0FyGjpFQ4t/I6+06MKsoBbNqbn0NwRV5oBjAGNAD2Av2ZkiOrj1GJw0SO9Xi82NzdrOx9rYNH5M6I5a7PI2aQ91MFcdN3nfJfx5uc4ui4jagd0nvhRIKh6RR0vnCccADaUUsAIOOJ8V61ucpuDw6ffR9wFbk1A8Smb6j/oR4DAy1eVLhlWUVuvjqrSqen9Tc11otvrDFfos52HMkDu2CJzcDIbxfuct6EFtMsylo6zXJ51PtyGeaVCZjO8r1yTYS7mWYMMVClo8IhdZNk/gIAmZbhaAZEFAdQ+P9aZ/zGa84nKMTshDwaDWvIEfaI+Qrvdrl2DDVhdXY1ut1scW6r6NKMLzQi0IY/8rXpPfYjMf6DvNOeH+2QvwytNcuA08/dpv1y+FN/TMlzu/6u8qH7VIKViWM4a3t/fj1arVcroNeii/eKZV1dXMR6P4+TkJA4ODmrBMqUTfWNO1L/gc+SsCdsuao9ybJsUg4I3XbejwumMpCUdWnagO156dN4NNM/LnEEnnjrEDooWOQsaVfRxZEYg69v6+vodY6JGj88URLuQ6fvcCecZOp4sENHEGE4/d2JUwboA6loq1heza50Ce6Uh/SMj0Ov1iuObzeFzaDqn5+fnMRqNSoUCn2eOmgMNdWBUEbtBzn4yYEHjs6yMyQGB9ikzmhng9bHQlyaj67LBM3SOH6L0XTFrnxcZBn9W03vvo7WvY1EQquOhEkMdW3QVaxGRFd1Z3Zc2OO8/B0CvzXnQDayXIFPdwG66nU6nyICe88u9OLY4tZ1OZ2Hp2aLmvKP8nwGXRdc5rzWBnUWAqMmGPGRM3hcHZ5kT4jqJrJkCfg88q44CiFJuOZvNygZ/VGZdXV2VcjWOB1peXq5F63kn+oKMrdJCgWjWMtn4sZvbRJwQ1+H8eNYxom7jMxlaZKcX9anpftdz/O20XvQcx0n3BeA8+RBRXwerTZ0SfcYiO6X9aeqzz0dmp5T2Oqamqh+qTcCTmV2gsd8GaxQJDl1fX5fdlFmv6PhLg9/qlD2HlvGq89bV1VUMBoNyDBh9Z2kDmAnMCE1U76yurpbNSDW4iX3V/RWUh6Bz5i804fyMz/md8QLjdDrwt1/bhMWbMFITveE573/m2Gb9bZJ5fuvywW63GxERu7u7cX19XfbN8f1T1G5HRFmWOhgMyi7gWnXruC6ivkTPZdb57aHts9bYKqNWVT3yjhFUIniEoapu16S5U0tkXs+xzQapA3VGUcWYOQP6LFUoamxcOTtR3QFEmDLHIgMZzrDcq04ufXXFzHPYjMWfp9f7vTquzADRfF0zn2UGC3AOKN3b24vJZFI2uhqPx2XTF50rDW54KU42J0/ZPDPNDn1VdZu9iKgbYDd6GYhRcK/vUoXq2TyvXlikgPVd8FLm2HnftbLBFWKTI6a8VlVVDcy44s9kS2XSHVL9XiPji3gjMyLQTrNFuh5HgY4DjmzMeg36jHv12DKuZfMLDeQobzTpm+fQPHimOlbPHteo+XQ6jeFwWDYQ6Xa7xTYMBoMYj8cF3PH91tZWHBwcxO7ubgl6NW320eRkLwIJ/l3T/4ue9xB91HTNQ/t53z2LroO3NCLvNgdH00vF+B6djN6OiHI8BPowImoZGN0Y5vr6uuhIlUM212G9LlU98AC/n1vTceDI627RGozHsdWsLBVqBLQyzBJxm7WjOX5R3ajZccc3WeaKpvhFgxnKI/pMtQHKK/cFm9wu6RiUrto/1S0aRHCdmGEi1+OZHnX97Z+7s7O0tFRzlqqqik6nU9trgznjN9WLJycntRJadGNVVSURsL6+fsfOZXbmudkExR7QnyWJnEtL2bFvsLW0dHNE5HA4LM8CQ3Ju7f7+fglwgh81MKp8gTyhg3iXO2PKx8pfOi9u89UmL/JDGEdGJ/1bcY1u8qnLAPxet7c+B/xoFaeOYVGfsK1sBAkep9R+a2srrq+vSxZed8d3P2FpaSnOzs7i6OgoPn78GJeXl8UGKV012chnmX5S3O36aVH77HNsI26VgmYkaKqQvMFcgH7dJEodJQcy9EOfo/3jM2eGDPjoMxhbZgTuUybK/Fm0JKNb1jeuzRzoh/SDpobEIx+uLO9ri4CZRmz4Yd4otUSR6y7OOqdN5Z33vf8pmq4ZY8e+jY2NWmnGoiBIxN058TI2ByRN8vOQ5so5a5ki4V7n6UUAIXuHB36yd2bXPeS9942j6e8mfUHLgk5N71LZVUWtukvlQvugfB9RP8v7oWN+iuZ6VYMeDuqhCUtUlG7wNk6BHoGG7qD0LMtqN+l47eMivv+x2+f0Re3KffzoskNropcGWjJaYv/4X+00Oxqvra2VTXAALpoZZu25AhoPnKl+00Bak+546qayDmjWI/N8LO6AaiWaB2V4/qJ3N81n9ltbBhr1d3ad8pLKtN+b6cOmPjTpN/jEx5qNJ+Ml/a5pTA+xyfq/v9dBuAbyHHDDwzi4k8mkrDfVfVGocPREyyLc9RxaE6avqqpsBgjm0+SJYhzfCybiNvune+0QHNYSZN6n+sNxu9pcp68GaHwMTbKify8av16byYN+3tQHf5/LTcafqru55iG+izr7PBM9X1VVdLvduLy8jK2trbKfgO4vQOBC5QC7z8Zp7BT/kH41ff9YTPSozaP0b5hzNpvFcDgs5x1h5PDo/V5V8mQ49G9+UP56X1aOooPGCDvw0r9dEJTBAJi6vk6v5V2ezdLJ0H6rUnXmVBDMNbrRh4IEHy/91e/1mmzjAS9xzgyVKwqlRZPAO5hfXl6Ozc3N2Nraim63G9fX12XxuZYszGazkvlkvjMjqWN9ysaC+YiI/f392Nvbi++//74srndQo8occBNxO1+AQi1LYq2OHqPhdFfa6GZGOkcZkM0ULN/Tsuyo7ojHs/S93qcsKNTEuwr8tPEslTX652PLgIvzi8qj/tb3eR888+6RfXVceb/ulqlVDO12u9CSyDWbR1VVfZOlDJw9p+ZZIj0izdfSkM0Yj8dFNywvL8d0Oi1HPVxcXJTzDZeWlmJ7ezt2d3dje3s7ut1uurmW6vj/F4Jin9OXhzg7i77PgDK04jgfmsqHAkKda673DaXm85tNo7gHO3Z2dhbz+bwcj6L98SxORF0naFXFc2vwNeX1bIimAU4F0tCPHV03NjbSEx98vrIMplcv6bs0sKDNr3cboU4Yz3Hsw+8MH7ht4neTM6mfefIiy+7SF+jLc7LmdHFM0zSfi57Db/qh2UL2D2DTS+w146ckl6Unr1+/LmvUkRGcAx1vRttF436KltFtPp/HcDiMw8PDIv96NN7l5WVxlpCjk5OTomtWVlbKplGbm5u1ak7+1/XMLIfQwCg00kop3yRVdSb4RitbtHpO9ZLLEk0xMN+5n6HPUrlrwrxNWGVRP+iL9gFa8Q59jsvH1dVVzbavrq6WasSDg4OCScGng8Gg3M99yrvD4TCq6mapoZ+UkgVzMgzkPspDbemDHVtP0yuBdHMJV8h6r2czIIZGv/zc1GyA+m6a962JUXxMGbHY5ESJ/BDD4c5F1k93cr3MRsG8A/uM2TOHM+tnRgNl/szRZUyabY2oC08WecZhXV9fj/F4XBsvRmE2m9V2UwYAQXcvX3gOrdVqFSdclYUqLneAGKdepwpHz4NUGcv4VnlLnSpXvnqfP89b5gj7fGV94L04JHpvJgNNY8mA0X3fNT1DFaPLn4PFrF/ulDs91UD6ParPfLdTeJ7yRQIZlF9q358TeGlqTbylfBkRaUZLd5LV5xCV5+xC1lLpPLpT/Rwdn6w5H/0+dZq/h7nQ9a76OyJqekSrB6AvgKSqqgIE/QggMvD0QQMbyIPaidlsdof/dRMh+sV3v2+6PaSBc7RCTQP4NMU7yq9a4aC/M10VETV7m93TpNszvYte1qC52g7PSDLPygdZy2yOYxpvTdioyZnT56mz67o/sxt6XdYnf6fbYB0/fKtAXnGrzgX45fz8vOyazVpbDY5cXFzUsl0PdXCeqmX9gC6MczKZlHm7uLgoiQ12Pl5ZWYnpdFrbUAuchA3Q82qpjtMkC45wkzww7xr4bMIVrl+UJzTA4/6Ffu5YXemVBWR4nzqDfp8/E150f0Rl2JMG2lfF3srb7lTqu9bW1qLdbpey8fF4XI5w0jXTVPKoj6ebZnoSQPFzNm/Yomxj24e0R62x5QVKMJRgpswWgWzN8PHjm0bdp7T0PcpgixSEXhdRV2QKWN1JyaKVOMA6VmUymjNZNi69tsmxhSm1D9k92h+/pkmAmgyKKnMXfsbpmT62wtfdrfV7no/gsDkA3wNgXSk9dUOZqjKMuF2j6YIZ0ew4quLXdSDZmmLnj4goSoS/vaH8mxSyP9edE5cjvUbXsDQBNDcmWf/0x9+RVXvos1UWmsat79E+Okhu4q1MLnXOM30Gj+jad/qruwTztxsWH+dzapksKq39GuVrtRXsyeCOLWvROAvb9UbmKPh7H9MyPvhcmi9yXt0uPfa5+gx/nj7Xdb/yvVZauIOrQKMpYMd7KAUkm3JxcVEi+zpuDeS4TPI92S7tix8hkdHiqRqOrFbUkKVzB4rxwq8OwFVfeNN51Osc1zThG/2tfMIYIm5Brv7W92d8kAU5/T59B81l4TEy4Ho3e6bikya5WBQ0zGyVjtnXLUZETRepY6vfo/tw7ghqwvtsLOXJHB+f4sXn0DI9SWUe1Thcxzr08/Pzchb5yspKObtcA58sRWHTKP7e3NyMtbW1qKqqtvGcnjDiMuIYhv5ktMz8Bm1N/JphfMXFitWaaOjZer/GeS6zwcr72YZ1Ogb3RTx46I782tpaXF1dlY0c2+12TCaTsqcAAU02yNVd8TXDq5U/bseUdiqvGd0fKgOPdmyVCHjt29vbZfdKFL0T1R1ZraPXI34obcsiDw/pE+9bdF3E3ZJRPvPJ1olw56Wqqhq45xkovYzZvFwvEz5d7O500M8QamUMBTCaTVLGdUbiWZkzoJkRV/yegYeWZGy73W4MBoOawqGUJOJWUM/Pz2M4HManT5/i6OiobDoAEG4CAD9mY14ouSfaqoERnR/o504fMsB4cHSI5EY0VxJwPy0rG1uUDciepVE+p3EG0CPqGRx38LhOecUdYH2WvsMzAwpSmsCxNgxFk6Ol+iEDhUoDl281mDjVAJT5/LZqBfog5wQvkGddcgFQ1hKq59woC1bnQw0QfAyQYQOR2WxW7j0/P4/BYBCnp6c1WnQ6ndje3i5LGJCR2WxWK3f2fRi8PdRZzT6/z24s+j7juYc+u+kdTfKnf/tnzuPO2+gWbExE1KpK1I54ZUpV3Tq2a2trZb47nU5ERO0sV/iCjaIc+Kv9UZ2lGIHrMmD4FI0MBWCNnW9pauuhn+6ArusKXf816Xpfk5td43TVuYy4xSTKT66PteJOn8WY0M18n5VF3zcefaY+S/Wq8yvPIjDotsptr97L2PUdjul8HtQGKX30b/gbvLq+vh5XV1cla6UZqogbvTgajeL09DRWV1fLZjzn5+clOKSBfK3eem5ObdaYHzaQPT8/L7TCDpydncXW1lZE1OcG3EOCgzPLKUmm8k/nhblmUzudfxxlSsOxy8r7eg8yzZxm1zU1z6a6Ts6Cvu5cq8wpL/t9au9cvtA17ijS0KX6LJpucMd1bEqrS6tYUqTzO51Oy0Zh2HKCF2Ci8/PzOD09jW63m+Jh5ox+uW+CHKhefUj7LMeWDvHSjY2NAj60NEcVmINDJsPXm3hmRd/n7aFAwxV607XO1JmzrIbWwW7E3ZJtf45GSflcge2i3+o4MdlNjr8yiI6haVzZ3/7jRkmVt/aBchPfFdZBF/ONwq+qKk5OTopjS8NQ/+IXv7gzzh+rqVGjlFINrV6TgQelj/Ogz6srv2yu/H59p/80KQO9xvvt4MPfrwD4Ppnye1VmVFlnCr3pfx1Xkww8pC3SISorTc9X8IdxVIAecQuEVF5dNiMWH6n2HICNZil8F0nGAlhhp28HFYANjndAV7CWik1CMh5+SLAm47tFYOWhjq8+pwkU3yfjGY8v6seicS7q56LvoHlEPZDE38yXNrVV6njyQ8ZW17+5vfIyN5cJ9EFTxoHxPGXT8bjuj7hbraU20gPATSBVQV3Td9l9/r33yW0vf2fja9K7Lo9N/Lno+SoDrjua5E0D9E3VYxmtsj7585vafXZU55NApuNXfjRDS2A8IoozuLq6Wsv8N2WXn5r/sz4oTtGyYj7TDbJ8Hao6k+oY6RGgvrmo6lznC9c7jp0yvnQe9Hd4u49vmp6bYSy/3ul632dN1ziG+By+gd/Jsm5sbMT6+npxXElOgnEI8mkGWm09gVBN6ugYMj/Hx/SYsTzqHFserFHf5eXl2N3djU6nU0oMNBocEaWszJUEEU1+VDn4wJXgTYNrAvIZY2XXuzFWBeoLn7nfHbyIKFEjZ24E3WnAeFTQeYdHV7KsXib0XJNlYp02DrZdeJSpdOwaSVbwQhSTtRFsIKA7Z+tZh//jf/yPO5ktxq6l6f/u3/27dN5/jKYHgrOWRDdQiYgan9D/JmOrEXKdHy+r4nM18jpHCjgzB4A++fvhSc366jM1S0/LsgMZT3Gdl5P4ezQgFHEXGLp86rPgPeilG21lwMyb64RFcqF0hO/1/e7MIQPtdrsE+1hrNZ1OC8Ahk0M/VP4zB+OpG1nXVqtVIuFkUSNuN+BjY53pdHpnQxUtTSPj0ev1Ymtrq1T+sCyB7LDqAM94NbWm+b8PYGT3NN173+ePuea+8dz33Ic0lSPsE7a61WoV3azlgRG3gUW9D6BydXVVzrbtdrtlI0muJ8MLmFddSfaTd1CmTkki9z6XpkCt6RSIiGgMzDQF8bMN0PR7nh9xt1JKm16bgXPPkGogzXGEZoH0O3+32x8+8/sUDzXJsPZ1UXLEEwv6d4ahlD5uNzN840EIpTvvAK+yT8j19XWpWHSMh07s9/uxuroaZ2dnpUpiOp3G0tLNESk4yE129bk17ac6tfT76uoqRqNRyQBy5BHJLz3bnXW13W63/PR6vZKtVT7zMnnVJazpjLi1H9zjATYasul+iweam3C2fqaf+2fKE02YJONbaKmYu8knynhHjxTSexxPar/hf/DJ9vZ2DIfDmEwm8eHDh1heXo5ut1voTTYX7MP+Cq1Wq+wMjl7HuXXd43rL9VLmyDe1z9oVGaXm68NUqTQBTFdumrnNQLk/I4vE6D1ZhkxBf0aYLPPgDOYRZx2PNxVCBXWuqFx56/3ZeBc5s9n9TYbPQbvel42Nd2dBh6ZnR0St1BDQy854CABl6LrAXueWeXvqpuPLlDj9VFnQuXda8oxFUUZ9t/KAl+y6rKiSc1nIZDIDGpny9pY5vzQdszuq3s+MzrRMZr1vTUDOn7PoXYt0DffyHubXlxvoD98DYnF+J5NJtNvtuLi4KOfGZQGNJvo8ZWOzHy1FhyaUraoTr9U7lJyxkyVGc21tLTqdTlm/o8HNRT+LdO8i4/dQ4/gYI/qYtkjn3vfuRYDJdYzqcb2P75hHfZ47DWoD5/Pb9VP8TaYW/a1gfz6flzVWqmMi6udGE/hQeYHPmnTSUzX4G0CuzmxEXl3ljhO/1VF1R7BJ52V2P+OVh9LpIWA7w2H36WPnt8ye6XX6uWbhMnuY4bum/vKMiLsbWmVzoffr/9mSK8dHOg/ueM/n8zsZW55DMJTPMr5ZhPV+7ObYNOI2e828EfgkEKQYkM9Yi1tVN0GyXq8XnU6nHPXG7uHQXm2kvltxkc6j24iqqu4sk3SsxGfu0N5HhwzneB/1c++XX7/IHnjw3/vptsH709Q3p5n7GySrmCPmEBs+Go1qVThVdbPEkMC+YiPsTBYcUz9SAw0PmRNtn7V5FB1Shm6KlDUpIRxaLUfO6sjdQDuDZ1HIrM8ZcI2oT6xmHlyAAWOZwOhzdLxaWso1Ci6ceTKmVOC8SECyMS0SoMyoZEBfn9n0Pn8uz9ZMPICWheS6/mh9fT1ms1ntGAqY/7mBe+Ysc2z5rXza5CwBkrx0p8mo8T2/eU8TyHeASvNrH+Is6DPViGeRe/1p4il+UG7ZWDPA1URrj6z7sxZl+PydOh6XcQejblQ9uKe796pjy/ps3pGB5GzunrqdnZ3dWQejBoox6s7PXEc1BhtPqGPLhlGAGQ14NpX3+Zwqv2WG3vlxkXzdp1ez5/p1i/jN7cB91yzqUwZ6M7r4PZol9Oys9kGDNFV1W5FCdonS8aurq9pZk2SlCGSqruAZfA/v87kGUJrWUj9F052QVf+7Pc30ePadV34xLx48dhvexG9Nc61/Z5nVLGO8iNe4N2tN49X++3UZzkNnOp30mdhh1UcRd9fXZo4PTT/P9ArP0x+nhQexHRfAMzi28PfS0lKp9GEznoxvFtnkH7tlPK0JLmioGyVubGzExsZGrbLp7OwsxuNx2UAUx5b9Wdyxxc5kMud4jD6ozsehYgw0bJjzgM+hYi+lxX2y6bRTOjVhcl3C5E19iqbxZC3DEvCqJ/4yPqYyodvtxsbGRllKhA3QeeAZZ2dn0Wq1ahvt6UaSjhU9caCfuc68r332GlslGPXwDDCifoQAnaN0Wa/RUmTdIdEdOweOWT+UybKIb8TdaFoWZVGGzoCuXse1vg7ABcGfmW0YkylsB3SMgXszhwQH/T6F6IagqTWNVfujmzwBSnR9BPNKFIfPKUf0tXjZ3Dx1A+hRUrponZVe76U0Go3SHcU188g1WmLroEQ/j7ihE0CS//UzjXYrfZuCNf53FjXLlLv2L6JeTq9yp3Tx92Xv4Dt3rgDaXAs9tXx1EeDKdFWmS7Rf/NYSKC2zX15eLlF4zWABYrSvTWXHmTF9yuYOEGMDyJydnRXHnQAW13O8A2XIV1dXZcOo3d3dslGIBjv5jW3wnUMjckBxHyh/6DUP+f53ec6i6x5yTcT9TvSi+yLu2i13IlRHAL4jomRo5/Obs2r5zf4DABnOhNSNqMjiqsyhKyOi8A79zAKET9E066Dn8CrAjrgt122SW8brGWzHCOrs+vhd7/IZ+qQps+kOWtZcplRfu5zpcwDJ/pn2179XIK06Xa9Ru6MbnrnD7KAfnlMaZngyw5pKA+ZK59X7Sd/UpqvTEBE1/adypHsO6OaamS186pb1RWWdn5OTk3LMjy67ms/nMRqNYjKZxOXlZXzxxRdl08Dt7e3achQ/USPiNiEWEWXzTg84aWVgRDTaWOUDWpMDqM0xzn16XHUB/YCX1B9yPtS/M/8DOaeMugnDqX5pwkHgVPebFCNRgdntdmtl5gQ+Ly4uYjgcRrvdvrPRHjyv/qLrGXWwachiVk69qD3YsVUQmU1m5oAp4Ry88uNReb/HAS73Z6DvPibLJpbnRSw+rNv74P+7MnIaND0ve07TmJv6wPcZuLsPePl1D2GezIFRo6IZF1dMCJEb8WxuHwrufqxG31HkmVFUx0rv82v8uU1/Nzl+fr+Cq4dkX/V5GZBpep8r/ozvXFc0vU+fn/E5nzc5MpotVDqojrqPDk3zkdFFx6V99vd6tjHi7gZSDwUtzwnU0DRzjewSncdx1yCNAggtT2bDKC1hhWZKP7cPj3H2mv5+7P+PeY5/fp9d+pz+0B5Ki0V2T+dRgbvyrup11e9+lqefbOBnVSK3mY6EL3yt26Ix/JhNx6FjaMIV92GYRdd5y+y/85frI9ezi370HU06M7ter1NH3HHiIhujz1cMlgUHMj5QPnbb4z9ZIEXbIvurAWR+1LFVvvDERMStE+u7Hnv/fFzPgfe9OV5zx5KqHc+k8h22m6N9yOqyJ0sWxKQtonuTbom4u6dHhhf8PRkf6/f+WVNT3na+asJC/h7+zv5/iG1owpFN97rsovNVz+OkEjji+LdsTuAR5iGbJ6eNysSiyjtvj8rYutLgJXpkiUYh1IHJFHpVVcUIahmwZjR8YNoXnk+0QhVhpiQ0CuwKy6OIPub7gKgbG//cn3FflsYZWK9VwJGBd2jhytefu8h4ZZ/7s70P+j2ZFl8PClMT7faolSpBnvdclDr9Zg2hl6NpSQzHuDBen38FhV76oi1TpNr0vUTV4HXeqYfHewVCU2WDzm9EfdMSzUxoP7xP/nk2nibZ0v4rCNExaFYCsKCRS+juwRV9rkZsm+RE++bzpJln3s9xOKrAdQ0V41OHwrM4vDMDX0/VVEdj4CJuIvaDwSAGg0HJ2MJ3gDnGRTnz2tpabG1tRa/Xq+1+iUxolvYh5aiLwIfr8Ow7v2/Rvcqvi4CFX+/9XHTdQ/p33/P0niY96k6BOra6WY6+jww997N51Pr6epyfn0e73S4buIzH49r7dCMq1R8akZ9Op0UmHkLfH6uRbVPnRHWIl81H1DEM+kErSRzwsgtppmO5JptL9D9/R9Szs2As1TuagfWscKa3s2C1yoCOnTE3ya1f1/R9RJ03VKervnSnHt2ppe6a2aPfij29qfPpfUNPadkoGJjspWawKTtm8zytTnG+BhuCDdSOPHVzekBrSq0ZP5sHnp2d1eh9fX0dJycn0Wq1YmdnJw4ODsrvg4OD2N3djXa7XXOWeC/vi2jeyJI5VlvF/WzqpjyhfkCGWZqcQccVnm1scqC5NsuONtG7CeMrf/i1TrPMiVTdr31Brjxjrse0coIBdmJzczOur6/jw4cPNRyq2D4iypLEdrtdo6PyldsXD6g+pH1WKbISbzabFWWvjJJNhCo+V7IQm6YGIfPim5wdVZiLHCRnPI8UulOszOPv098whRrjrJSqyam9T3C1ZQZGadDE5FnLhEfHogBc1yv4ffp83c3U5zAiSobH++vXPRfHVhWnO1BZFF//dgc+4m6wQ7Nc/n1G30zInZYKOtxxc4Whyi/jH1eAHkjSeVP+z/hax6mylfE4AAH6qg5ZxM86b9pcVly2tb9NoCcLZvi6awWw7BA8GAxif3+/Bo4yAKX667nwf0Tdwb++vi6lZwA23RyKvmvQYTa7ObOQ8wk5MsB3jNWSZNe58IM7O4votIiWyrPe7gMdi6577PPue8dDv29yAnX8Wd/5rTsYo7scpKsDpsf+sM5WAxJkaQBKOB3qHNLnVqtVK2tkmdIim/9jNV0rprreA2o+HsU3GYZRvdwUEHR9kDlbjrHcVjimcNuQfZfZDr7PrvWxNeGvps+4v2lN5KLGXKgTSwBazzTVEnINqGkAwOdMqwgW2XovR9a1wlyH86cls+wgi7Og716kn37spuNw/pjP52XtLMkpPfZRdxRvt9slW8vGgQQ40R3Ze2nuF/CjNtfxFIFYdq9W+VNZ1vfpGPmcljmlKvcRd3Wuy5t/R3OdzN8Z5lN+yXC08qb2IdNF3j/Ftzxfl46CX9rtdjnfliAHvHxxcVGz7+h4pxPzwHfQ2PXmQ9pn7YqsCnQ2m92ppVeHyJ/BvQpeMnCiSsoVgw8wYwifbAeNGslxJ9YVSpPiV1pkzKvC5m0RwNLnaN/9e2dON0b+nIi7Dn12jwo149f7KSfQjV6ycahjq8+lXyg6HY+C/M9h6N9Xo09qlFTwlfehsTssOg8+v5lijcjLq7RP/jubf3VIXYFVVX39Ee/O5DeL9Lux0WxBFpRyWWoyHiqfKkt8p9njJl2gNHHaOe/r5/p7EfhivFqiqJUYOk4AVr/fj+l0GlVVFaPvR6H43019+LGbzgmgDPCIU3t2dlajLfpCZYR1OpyNp6VN6tzyd8Zrqp8e0+6jZdN3fp/zTPa/257s72wM2fOy/izqfxMYbrrPy70j7pYgI3cRccex1R3u1bHVDXL06A4Avo8ZYFRVVTn6h6OAnloG0P1ZtpZgjutYaOAAPCLXQQ6stTn/87+CebfVfNdU9aD3NX2uz/P+qu7mM60yyeycf+7vBYTzfzbmjCbMgwYgyBhqaSwb981ms9qRPWSkMqdWbZsHsp0XMh5R26WZXd7DRkrtdrvm9Di9nrrpOKCTrj0+OzuL0WhUxqYbiGInrq6uYn19PXZ2dqLX60W32210bJvmPHNsNfjm/Z3P5yWLOJ/PS1VIFtTW3xrEj8j3t9F38XsRxmnyeZpkUJ+RYe3Mb3BauY7K/AF9Z/YcpSNzynPb7Xap1ELuer1e4QkNUmMTCHrq2Okv8+TY76Ey8NkZW4hycXERo9GoRMEweEoQVaoQTI+CuY+JHfg7oFdHQg0Dfysg1+e6Qs4cBP9fCduUfcqEi99MVFaqoJs+eSmmjssZUwVUBdCFVr/Xz/xvQIz3X8cFQ2u5mo65qqpaJH88HteUz9LS7fmeeu5hEwB46ub0ns9v1xW6U+t9V+F0Xs34yg1mE596dlC/06bGmff7TnYKyjQj7TKTlQTT9BmZ8eEa5e+Iu0BoEdBy/udzNivQbF5meHiXg0qX98yxp+lGaYxL50znkecCqI6OjuL09DSGw2F0Op3aHLqcOu2euqnuVXB2fn4ew+GwOO/oA91Qi+BnVVXljGs2CVHw42d8amPOVDd+DuD7XIDYdJ87qU2//TlN48ve4/r2MWNAFjlrcDqdlvcDJvWM4larVc6eJ3Chz2EO2BQNgMPmUQQsZrNZrK+vF6ADmNTj3xTEe0n/2dlZbQPCp2665Aod6dhEQSTBH472Ul2qDktEHSPo374pTERdX+q9eq3qS3d8FY/5NRmgpSHX4DzP7NB0vjxp4FiQdzjdGLvbEf3MnVjdoE6DELr+UwNt6PGqqspRY+vr64V/Nzc37+ihjE7ab7fvagupbGEfAp3H8XgcKysrRX4esvziKZraJPiNo3smk0mMRqMYjUYRcYubCe6Mx+OyVGF3dzdevnwZW1tbxZlX3nR5Vzup7/ef7DqedX5+Xj4Dl7rN1kSCv5+mOFU/U55wx1av0b9d57td8/fc58gqrZT3HJ+4f6I7crtcK07T0xxWV1dLBn51dbXIDbqbs5nJ5KJnCDAgg16dwXKu5eXlsjv2Y3fI/512RaZjemahKlQnvircLGPrAG+Rk6vX0RedBAfTalQyIOLKPFPy/rcCYHVAXNmroCnDanMmd+F2YO/3ZS0DW4vGop/rc+8LNjT9OD18XtQwZVE2p91zaKps6HvT+PUeH3/2vdOV7/w33xEVpWlAwjcp8nt1rZsqYzcKHjFs4osmWdGxLuK5RfTwZ2b8mjWXaae98ihjzeTP+6eghef4Z1mbz+e1M159jfZzbzqP8JfuhKnldxgo6ML3rVarAEh1ZJvmtIkuGuhzflG+b9JdEXWQuiiQ4Z9nwGcRHz70ukyXq665vLy8UwUTcTeo4nyOjT45OYnxeFwc26WlpdjY2Cil4ZqxyjIUbsfdnutGUl56Np/Pa1H6bJ7UhpPZAvw3LQn4MRsOjNs+b016IdObTgf/vIlOmtWgZXOi8lhV9SVglIZrKa4H5n2u9V36W3mkCZ80PSezfao/lF80kKw7rl5eXsZ4PI6Li4uy7EMDDqqL9H7tI5UBXEfwV0F/RhN1snW+PTmQOWHYbC2VVlukNH4OzXmecdJ/fiLuBi7I1uIEdTqdUuHhOJH7Mwyjz8wwqOtBmtvoRVimCXc2+Qxuvxb97XKQ2ZImP6HJHrrNVH5nzBpI1ABlhusyzOb6SJ+FHuGoN7AxPK1/u1xqg5fYowa51FNzHtIe5diqIKMwMZij0ahEWAE+2UTisevuifrMjJH0vRDII75KeC0fQIH7ZGnm1DO6ep2XwjUxqfYj22DBf6uh5lotY8wYW480yZ7d5Ajo/+qoKJ0yhZDRjOYKxUuHPLPncwTIX15ejul0WgDbQwT3KRqKACU8m92W4Lsxc9ppWaUbanWm9BgMj6xrwymi7JN3aKaFPur86Zp2LfNDBrVUhPs4rkP5nP91rrKMscqTK3HNRiuNs/GqAqyqqqyVyeidGbiIumzRXFE7SHfApfR0sKS7/Wa8SnQYpa/XZfyg/PMcI/eAM85lVKOFnmKt5HQ6jZOTk6iqKra2tmJzczN2dnZKtlZtgJf469zosUnj8bhkw8jYEzDQ462Qz6WlpZJB4/udnZ2SrVSQdV9bpIsyJ+UxTW0hVVBVVcVkMonBYFBoRpkXxy6QDdI1ftBpMBjE+/fv4+3bt3F6elocUF3nTOaE4zaIvqMzVEeovdBqD6qwNGihO2dOJpM4Pz+/E4UHLyAXrM06Ozsr51+yFvspWybj/K8OYwYGm6pQHLyrvcwcQXgevtZ3KFj17Jf+MIdUSeiaOTLLmiHRdai6TED1l2aC3LHNGrxLy5xDzWwiu5PJpOiUy8vL2tmwnJGt+3ZgK3T+NBhN/9Ajq6urMRqNYnV1NabTabTb7Xjx4kWtKkjtM9jy8vIyRqNRqYrw+YFOl5eXMZ1Oi67CRqKXkI9FtHvKlgXddW6gLToWuY+IODw8jJWVlfjiiy+KHUCfsRtyhlkj4g520qCHVlC446SYwj93LJY57PTBsY7LupZjO85gjlUWm6pUVY49uERzPaL3q/4Ha5BljYgiyxqARA9wv56znOmWzG9Cr3AU0Hg8jslkUnQ7tFeZRY8oNmM+4SP6iG1+aIDz0bsie7SDDVEAwN50QtzBbVp/qe/QrDDGW5WGKlKNJOikqPHQ9zX11R2OzDhotEPHps5DBnKyaJ72X8ehz80cZb+/qTSoaT78eT5XPieZ4wCDu4PivOJGGrCp27qr83Jf9uvHbs5L6tj4Na4onUcQ+DolAAEAAElEQVSbAhBOMy8Dht4YTjIvEbfniqrSBjj42dEOVpBFDIvzfhYo0v650+pjU4Wv8+9OLter4fJsFLyhNCFIglNDCawbETdQ+k6ACwrfI8iaacpkQw1lNqco/k6nU5wn9FSWwXqOoEbHpE6iZhoioibPk8kk+v1+TCaTcsA7DpUGNjNAwLt4Hkb6/Pw8+v1+nJ+fx3g8LrwCsCIzSIlsxI2zhExwBvXm5mZtfejq6mrs7e0V5wxdDthvyqK4fclkOxuXOzXQlTHQz4ibUm6W/LCZEtcMBoNCC/gfHTGZTGI8Hsfx8XEMh8Nip+FZNnVis4/BYFAcW0oitUzcnV0tc1XnBzmCH3Sd7NnZWdmhVI9/oKljo5tIPXVTu+e633XfItyQ4RDnA31GRg/AqutU7sswgM4ZThzAlr9xMDQ4MZ/flonTPKmgfI9u1/dnOEL1vjolHjRE15yfn5fgCLKgR4yp0+n6Wx1n1bdKL00wXFxcFFyry+s2NjbuYDOV+8xmKV00I6WA37NaWp6p/PXUzfFOxN25U76sqqroovPz81IlogFNeBE73ZQUcZvqmb+sjN3LXPmt1/uztWVzkNkAx/RNdiCT/0yOHS/6uzKewx/DThIEYkPH6+vrgvfYD0GXBOnyNKeD+laZDuQetRXwNEkU1tayDNHppDiMZxLgJYj+UDl41Dm2/Ggm6eLiIk5PT2uOrTO2TqSCzfsc24jbclXW6BCp035oZEOdWkpxZrNZDUQ1gZMmw+KOo/6tz1DnQJ+jWRgfH98r7bxpyt/Br4J2Vc6L5kDHngmOZ2Y9Mqx9dYFWJd5ER+Uj5iWibiwzB/ApmztemfHyHw++ZNe4YdTrNTijzgTGnTVrVXV7bBZGGTmJiGi32zUlj5Ol5YMoO5dPvtMMAEZIDRO0iaivH0ZROZ+5LDgttIQl4zmnCzsxXlxclJK0+Xxei0h6oIDGeHFoAEeM39+d6SwFTjwTmYXuKysrsbGxEe12uyYT8Ib3y9/x1E15WHeudcdW+XY0GsXR0VFMJpNYWVmJXq9XHCfANM3nOaIeGGEtb7/fj+Pj45hMJnF8fBxnZ2dlfRf9USdsaWmp0Hw2mxVHUAMYOMJ/9Ed/FL1eL3Z3dwvfbG9vF/Cv8qF84M6tjsF1hza3C2rrBoNBsXUAFfqN/J+cnJQNyXD2oYUeT4MzqeubOI6jqqo4PT0tMkDggTmiXHltbS329/dr6w/VBvv5hmTO+I4jUNBb0NADZBrM5geH6ymbOlqAOp9vD1i7cxtxNwNDkDeze1VVFbAK33pwnzlQoK9Oh16n5eGaVedvKhc0mzabze7snaJz72NSYE7TfmV2clHpsFZ+UErf7/drGV6lvQZrfd58J27opMFarqUCUR1m6OT6G5nKsoDoBXXSfXdtPlcn/b4kxVM05QGaO5XMBX3XgOP6+noJ7qr86xp0bL7ikIi8hN+dWvqgtM0CPlrVAh5yW5tVWPhzvD/6vd6XzaHLu8qP8482xV2MXzdGQ99jAyaTSfkMfsUG67IoDey4T0B/se0qS9pn3S+DYNz5+Xl5L06ubhyofpkG3rjfy/Qf0h7s2CqooZMY1+FwWNtQRYEtAu3OmG/p7eAewo1Go2LAdSc57RfgGcXjIB5CUh4BIwN6aUo0JsyzNLPZrJR76oTQj+x5ysD6N9dkAqFMzv+emW5yVn2+vC/6Xr/Gn+sOhCsJ3dlPjYtH8JRGXAsTa5mgGiN3/p6yaUZCj7WIqK91VTr4HCuvuMJSYKzr01BcbLLFBhm6lgX+n81mJWujJa9LS0txdnYWg8GgRLpbrVasr6/H9vZ2OUt0aWkper1evHnzpryDMiIckKq6LcdWXvEz99wxWdSUNrqFPDwHPZUniAAPBoOYTqdxenpalDg06Xa7NQfejaQqdBwhduulVBYwTeRQ9ZrqBD3fWCOOvIM+7O/vx9bWVqyvr9fk+f+F1mq1iuOlJcgYOC0tPTs7ix9++CE+fvwYJycnsbe3F9vb27G1tVXAc2b8VdbhJwz0p0+fYjwex2g0KgY5IkqmCX10dnZW6zfOLHOFscTBUj57//59kQuyy/v7+9Fut2NnZ6fwB7yF3WEMWWvS0dq0bxyZwTgVGOqxJDi/p6enMZlM4uTkpGRukX+cDM1Ku83RCg7sJxVSS0tLZcfYlZWV6Pf7sbGxEa9evapF51V3ra2txcbGRlxfXxcHN+IWwGgGjrErFgBsVlVVHJpOp/Pkjq2D94i7mcjMOdXv/W9tjgPgh36/X/S3Z1OwDW5LmFcFo7PZrIBb1eOKZfr9fqyvr5ddTQmKQnu1UyQM9F3usGeB3yxTS9BDN3uCh7UyRANpXtWDLtdSS2RzNptFp9Op2RrNQiv/81zoenl5WRxp6La6unpnfw11+nzs2Ijl5eXaeHTfAceGnvB4agwUcdexhT5aJaKZaMqur6+va0sf5vObyg3mhLlT3K50PT8/L7ZXMSc8gz5x3OCyqM+GBxyXeeaVd7kfoPdxresFbS6Xjun9twY/+Ft5Dhmh7FerpxST8hklxtjnlZWVGI1GcXh4GOvr6/H69evY2tqKL774opaIiqgHxeAD3stRVWpjkA8CRAQvdEdlbAG00QA/n3n2NgusZO3Ra2z1t5aGKUPqBNGUMTza59d56QmROpjWM4r87xFEfitR1GhgUF0pq1OJgGROOH975FrHsoiOCqq8z4uc1gwE6jubPsuUryoPaOnP1XGo8N8XWco+bxqH9s0V/HNpCgKJOGbOiY67aSwZENF3aDWAGg8/M1qzJSgBjDqbaCBLyBG71F1eXpb7eB4lg7xrNBoV/qfPACJ1FhVU+FibZCIzAgQOvBrCHWSU92AwiMFgEP1+v9AIBYxi5bn0mXcS5URpqz5Qp0DnqCkwpFF3rzrA6WMtIxFrN4RNMvecmhpcLesDJFBmRHZVj3dg3WbTWpkmYDwajWIymcTp6WkJ8PBOBecRt9F+1fd8rmAYmVLQAE9h8Dc3N8v5fN1ut3bUFw6gOuE6Dv2t1zXNK3ZAy+kZK+XWgGCeRVQe+miEXnlfqy4U1BGQ0oyJBmbV6dCMEju3EvXX57ljoc6FBvQ8K+c2HRutGS7G81RN7V1Es85vcloz3bfof+hPZp2AjYNwL4vWagIF7gr6cRCUf7EJ8/k81tbWSmCPhADlhTgg3EeWUzPvnkVVGqhjq06pZtG8LNdLk7OmvAeIdtwGTbiWfqmDzzuwa/AeFYPwu2aQMszkcq/9b8JPGVZCHp6DTcgcW3hKNxBUpx757XQ6hfaOe6FPlunnWv0+s7+K1z17zD0uC5k99/frb+51PJLp+kXNsb6/2+WCCjx8LZx9gpvq2PIcMI86x4ot0QMEx1jbrRVK6mwqNo24XRdOUCriNslCAIf3Kl4FJ7guUBooXbTy9qHt0WtstQNaAqaRhOw+JTYR3fX19dr3GLHhcBjD4bAAVoypOhYOlokKEz3QCVClBdEgPmuLWFei10XcOs7uyNwhZBK5z5SRg2PPTLpz7IIXUS9FUBo4mMuENXMeUdwKyj07zBjVGOgztFwnE2w+U6PEe1SINdIEWH4OTeeo2+2WzL1GDxU0R+QlKRG32T82j+CHuVNeQnGwVm44HBaFRIkgGZStra34+uuvC6D827/923j//n38r//1v8r6lu3t7dJXaN/v94ucffjwIb777rvCTygljcRSUntwcFAyvjpXCnJdcSu/OJhhE7HV1dXY3t6OjY2N6PV6RVbZQKbdbsfx8XG8ffs2fvWrX8UPP/wQy8vLsb29HV9++WXpO048gH8+n9eAO4oXPYSCr6qqbFV/fX1dy6DrZgw4rMrbup4Eo3BxcRH7+/uxs7MTf/qnfxo7OzuxtbVVwGVmTB3gPAdQowFHAizoDC31PTo6in6/H4eHhyVTsrW1dac6IKIuVyoL8/k8hsNhjEaj+PWvf13Wk6+trUWv16vpQPpEKTKBGuVb6Kel4mQTGQfzSYZ3MpnE8vJyHB0dRbvdjr29vXj9+nXs7+/H9fV1zbnTMn5vi4CO2lLWzCLnZOoANRrQYiyAG67DWVGnEtvGPXqUAlkUMqJs1gRw0rW+Pv/dbjc6nU5sb2/X5g5H+fr6OjqdTskSOi8xVzS1PSpTutnIUzbN2Dog06YOvoL2JsdYbTbXg63IimA3eRbVOi4zGVgHv7gjrP1SZ4/nYzOwEzoW5FWdQrIwGS7guY5F0P84Rfz2oBnjoe+KUeAXnPFut1t0DP2D5xXYe6DF5wbbp7u+n5ycxNraWrGj8/ntuZ4EKx0HKG4j8OfOOfZGnQSdQ3jjqZvyjWbDWZPMcgndsIgxbW5u3qkiwX6it+BB3UxU5xsbrLzoAQbFLrxbsYcHT935VRyjMhpRP85Tn+W8re/IcLonkJQH0fMa6GQ5D8Fi3WwMpxZMh75HR6C/wax6vBtyMpvN4ttvvy1VVn/wB38Qr1+/jt3d3TIPYFP8JbVXmlHHjtCv6XRasvQ8g+PgsiSN0oU5R389tD16m8EsqpABsUyRKdMqeOd+Sij7/X6JRBOhcKc6U3jKNEokLYFSxatMrSWD6qDq2kWYAodEx6sRzIxmTcrJFbRfowaLZ/maJJ8XvnOaReRlsx4V1b6oIwxt9XmAJ3eim8C60kGdeI0IuwPUFEz4MZuC73a7HRERvV4vIqJEyVxBZobI5yRTlKqsWVc1mUxidXU1vvjii7KrKAqMcsmtra344z/+48KPb9++jePj41p0UqsbHETSBy1XI5OJwmTNIQ4FShNgkT1XFRU8pllhzkAdj8dFkZ2cnJSSb5VjQBplyGRqO51OHBwcxM9//vNSJsv6y+FwWOhPBFN3rtQsLQoaJ9uNqs6r87DqEp1L1TUKRGlZ8EN5xT9/quaZWi3jJkiIgzmdTmu6eH19vZShAWbcbkTcBin7/X4tsNlqtWJzc7OAV+RMQRBzCRjQueN7sk3z+e3Zfevr63fsg8oKc4oRX1tbi52dnWIftNydtkg3ZzZUs1T87UcpKdhnPlRve2BJgwW6oy3rVdkgiuN+0Cf0ibJMzqIFSMHPZ2dnha8B905zZHhtba1kflVXwkPo+oi4EyzSLMVzaQq4+T8i7ug+PnN+8HtV5+Mo6JnD0NcD9FoppnaT59E8w+vX8hwqKnSXVF2HqLoPfveqN83yqGPgzq5mpBRLKt38XvqlTg/Xol9YJqDYwvuYBaEUVzI+fT7jwk6orXNQTjCA30pr5F0z0tDaA+MZ/zxlA08oLsOGq+7CBip+BbO43dSW4UR9Dte4s+oBEt04yXFp5rPwroh6IsztQRYY4bn6DNfx7nfwHPhBA04a8Cfw6hU8WpaPrcWWUbXAczVL2m63Y3V1tdjSbrdb+sxaWJKL6G30Dk3tLXOPnKAjtFoio51+3oQDfF4e0z5r/3yPCOrndNSjLXyHgtZ6bRrg9tOnT+XQZ57vTjBgO6K+lk3T7V5yEnGbWUUwiXzADO5sAcRWVlaKQ6MOOvcoWOJep1MGdtRgZNEfdzDVsc0MPXRRQVKFqk4Y16uy1b6jxOk7DK2lBboZSRNfuAJSQ8NvaBkRd8CPM/1TNOgK366vr8fu7m5UVVUDe35P0/8KzDXiHnGbzbi4uIhPnz6V9YRff/11/PSnP42vvvqqRORY77e5uRndbjd+9rOflejp3/3d35VIWUTcUcrOj8wjO68yTq7b3d2Nra2t+KM/+qOYTqfxy1/+smTJiKj5OZgRd0vOyfyQkXj37l3JUnE9RkVLm3BWcG7b7XbZ/KbdbsebN2/iL/7iL+Lk5CSGw2H88MMPZTdZfd5kMolPnz7VdAZ90gwkAa2IKPypAQLNzELD+fz2rEUcp4jbzHumzzJwrE4xnz11A7jo+jCcJBzbfr9f1jtrQKTdbpfs4KLMG44MR9P0+/3odDrR6XTi1atX5V0Yc4JhVPSwsRLOgYNU5hRdjUy3Wq3a4fOqk5FNgiRLS0sxHo8jor5RjcpXprNcJ/JbI/PqzAJkcCAV/EREzYFnPNBWA1PwK8C/1+uVqD3AhWAZzqkC/IgoWRg2tKJ/k8mkBN12d3fLu7GPlKBvbGwUmikuwA5neAF+0HNJn0NT26R6TfWqy6vKNM9wO65ZGvZCoISTLCH0VKdKsRQ2WzPrPFNBtI4DOW21bjaVISuDvkQu1DlG9uA7xT1uW/xd6sBp3zIsqX0GuFN14wFbPTrmvgSD4x9dwoA8O31Z3gNPkrnSMWf21Z0arXwAr0ZEzcl1BzwD/0/Rvv/++0Iv+nZ1dVU2kMWWqu6l7+gftZ/ZuHS+VX8r7yjP8D/+A0eQ4kPQkD9du6pzrM4XQTl0qi6/Un9GA6aOcfhfMa8GuBm3yr0GPXTpGbKMY6sBT6pVsbUEf/mehESn0ynr5re2tspmmRFR7BtBf9bzU9XT7XZLfzWbzt4OVLSiWzQbrxh3UdBP51sDSjp3D22P2hWZF7hC3NjYiNFodCfqqJ3HcSQ6rAfucj0Rfxh5fX299jz9UaMNc6hjgPJQ4jD5AGP6xBoSZV6eRWkhBh+hAARcX1/XooiUo6hzrJOnxkwdXle6er8qb8bnEU6dE8/KMhcIopZUzefzUq6h2QDuVUdYS8ARluvrm00BfBt8DB6CrcoEGsDsGHDmXqM4Wnr0lE3nqtvtxtLSUvzJn/xJfPz4sYB4wHREPUrFXCl4c0XINRcXF3FyclLoRcbj6uoqXr16FX/8x38cr1+/jqWlpfjhhx9KOSRl0X/9138dx8fH8e7du/i///f/FscAg5NFrZVv6AuKXuULGfmX//JfxvLycvzkJz+Jv/7rv46///u/j48fPxZnEMCsmRieo2XHlFtSRvzmzZtCM+ZdnSk9xgc51WMv3r9/H//lv/yXkn3qdruFRxkbAJ6+MC6ewXrFo6OjUvbz8uXL2N/fT8/SVLlkrLqmUY2fZzhVTr15cOup+T+ifnYv+hf6Hh8fx/HxcakwYGzoSZzTbH0tAUYi7JTec67qwcFBbGxs1EpeKXMCuKOP2KCFTYf4nuATpa1aDjifz6PdbtcCfxFRm0PmLeLGwR+NRhERtaNStBRzUXNdrfxNvxR0qUPhwJw+UKKv4BygwzPY5ViDtbozrlZSKc8RpCHrroEp5JgjJZB96Ad9NKBBBl71DDKPnPIsBa2+KdiP3TzQ5Lo0C3aBMQCDntmLuK2i0jmG3svLy7Gzs1OyK752WXlNM7SLskCqW9WuKS7y/tJPDdq4Qw5uaQru85tx6iZKuh5Ps7gaQIBX4A8tkfSgkgfaFY8qaM4carVZzKliPM1KKrbVHw0gKJbTPugyCcV20ANMBYZ6Dm06ndZsHrhd7TBji6hjSMavTpljJA84OA95VcfV1VV8+PAhjo+P4/DwsFR/sXxDS5FVj2qgTOfY7TZH4Wxubha5cLuuVW0RizPsnqFkTIpvssooXXaifUdnU36vmxpCcwIwuscFwTGt5OFduqnf0dFRrWqEH/w4AsKj0SharVZZYgUdoJPLogZGfL49KOW+50PaZ2VsVVFoNEM/dwOgzKMMxLU6odynEY4mhlfiOFD3eyPqWassMo3x1ffxme/86pEGjczo+HVitZ/6HqWtX6PviLjNaGa0cUfWlTxNFYmWIuvzVAFgvFQ5eWRX36l088AE8w4tUeTs1OYZsIxOT9GYE8Dh3t5eXFxcxPr6eimj1UCERp90rpqMMUB9MBhExA19OGvz/Py8ROuhuTs/8/k8Dg8P4/379/HNN9/EyclJOeZDedblJQPMDm71bwDuq1evipOP8tOt3ZlHAkc8H+WJgSNrtLu7W+7TDWugC4Ek+q5R36q6OVbgu+++i/39/eh2u2UdFE2DM+12u5a5c8BGZP74+LgYhp2dnTu00Gcrv+p4VcYUBDcpaueT59I8sKjAnPJjnFoFE+gJPeLBn6uZKnTJxsZGbG1tlSNmFOjhvLmBxEBfXFyUDAtZeNry8nItg642Q4GQ2igPVGVARHWbNp1HnVvVqxmg572LeEAdCdX3ADD4fWlpqYAagAx22DPOmvl1O0bmW4HraDQqAQOu9Xt4l9td5ScaMq70gd5P2Ty44HPjzm5E/VicDPiiG1z/auaFskGCcoq5tF/wsX+uvKaAGV5XedYKBC3F5BnoMOYI+Yuoz1uTftS/FXe4A+6OrdpM17PQPNMtSgf9X/uRYTCVZa0uI3hFAAZ6K80cyOucZ/Ou+kF53rHpc8BAjE0rX3z9v/4oplen1zGHBioyzOfzWlVVwYynp6c1x1Yd5yx4kNFegzo6xoibOUFvYnewPd63++y1z6fagkwe3Fao86ey2uv1SmWMYoxWq1X2T1AbrA66ZlcJYpJQYLM09tBQZ1iD1FTO6TnnmQ+jss91Oka1Dy6bKsP3tQc7ttpBDDoRgt3d3Tg6OiqTQyfcsAGIydjiuGpEljIynpVladQhQ6l49nc2m5VoPUZYQYdm1mDura2t2jpBxoEwaiSDfhB9hskYMxPWarVK9koNBULsivo+hvBrHRB5aZo7+xG3UUG9h894ngofTogaP/o5mUxK+dLW1lbtWAA16lpqoYI1m83KmZTD4bBkdTQD/BwUujprKImDg4O4urqKra2tGI1GMR6Pa+At4jY4AFDWoANZRUqr3r9/H6PRKIbDYbx+/Tpev34d3W43rq+v44cffojj4+P41a9+FW/fvo2IiH6/H71eL/b39+OnP/1pAa5sAkVWi51LNeiUAWxf/8I44O+qqmI4HMZ/+k//qUTsBoNB9Hq9+PTpU4ncqczxPp7Xat0cE3Z8fFwU5qtXr0pWGl0Ar+iRD27oVO6Rp9PT07i8vCxAGhnd2toqlQVkm3SdSUQ9WMM63slkEt98801888038c//+T8vZ5xq1YTKkypuDAPrfchuPoafHZQ9ZdPxIr8cS/Px48dSNsV8TafT2NzcjF6vFzs7O+XsU3XocYjYcTTiJgP56tWr2Nvbi729vRJdZi71WAnXLRFRSjd1kwt2fKyqqoAebI3qOwXY6kRzr+4irg4XYCDibuCjKaiou0pqoEczyVnmh88JEvt7kDcFa5qpzbK5gBU9hgkaa2WBLr3BjlP+Tfns+fl5dDqdYvva7XYJYCHP0MgrAKqqKqV2bEC1trZWKyl8qgZv67EZEXHntwNQz+KqI6n6YjabFZ7lWCnwUuYwuQOQBeXpF7oSPYsMg7H0GVyr9sFxiQav3BFU++FOO46QZ491WYg7dw5wkUPFfto/6A6tvekzHWepY6EYhf91DngneIByew2uqSNE3zx45fpCx682+zm0f/bP/lmpqDk9PS36nw0a0RVU4hHwX1paKvcdHx+X4/Tm83nRf51OJ6qqqlVqaPA6Iko2/OLiIr799tv48OFD/P3f/33ZQA+eQTfTJ/CXBkLQYxrwqaqqVs3JPj8RUSpPkFENDGl5bhbE8gCpyo0612prVD9Al16vV8ZIdRylwtAmImrBTQ3IQgulLbwHZlInXjfv5cg+KrC63W70er3Y2toqlVr9fr9mI/EfsG+alIMmqgvQrRk+zRIKTe3RpchKeC9XcdDiikaVvCpoF2RXYqowuZ+mhlkNCxMP+HHlqMCFz2FgLQNSQmokgvco+NFI5iI6ukJlnEozpbdHMFTha9ZAGTeifh6gzqFmwvhbHVzPPDhw1HmgH0dHRxFxu8kF9NcSPeULFTQFdTjrvt75qZvysipZAKOXV/ocapQ34rZEk9I+zmO9vr6OXq9XSt91t7nr6+va2ZusWWu1WrG/v1823RkOh8WZ8sikR8EdIKm8qozj7AF4mDcNoPA5c69gg3fq+i9ogCJ0/tAsJ/3TyJ7PjfaLiCB8TTSx1+vFxcVFjMfj4uiSvdXdjplfzfqy5l/LT9VIqnPEc3BcCG5pOV0TWFFd8FwATcQtsI+41UuUo/rGIQA6glR6dl1EFHm4vr4uWdrZbFaAA/egY9SJ1GCe84zyOs4bwF55H1mAX5Wf3J5p02CK0kF/IuLOM7WpzfMfzQAzxmwN4iK96LYWpxVdrEtudKwKNHBAXN60H+gHovRaiYHt4T0u55rxUzsNbZEdxoO9esqmY9e5AMw6L3qm1pvbc+jJPGlw0XWg6xzeq7ZVbb/+pj9ql90BVT5mbJq4UJ2u2M2BuvbZdZ7yuwbnHfNoYxzQVnWw9p9+quOidHe6eD+VDvqTZYV1zuiPPlNxk2fl/F2erXMH56kbAWzW1oNPIu7yhPYZPtY5VxyQ2TrHJeBdMrQfP36M4+Pj2kaB+k74IyLuyFBTUIrvfV7QPYpreG/W36ZnK50y/nJsxvXoUJVVzbbqe+mnYy9srvIjGFNpFnGrG9TuEoDGRoOf8JU0O690Vvn0tf/Ly8slUM37VX6UNqrP7msPdmwdFGCgNGOiTMSEKEDgGo0sqkA7YXSC+FvXAfEOJiKLWM5mN8d+IEgwI+NQwMN1VVWVCWPzncvLyxKp8WwWY4bZdeyqLJUu+jtr6gi4YdDopi4gz0AIzZkFBgOQOjBTo8n72MQk4nYzHZgUgVtZWYmtra0aiALUM2Zoo+sGWKdxdXVVwJLvxvbUTRUFmQQE3IMgEfVAgjuNVDB0Op349OlTHB4exng8jpWVlXj9+nVERC2LBZ9yRuV8Pi9r28bjcTlz8927d3F4eFgyp/RDnS43PBloQoHrmgw/GxCnU3fGJrOv8060lnWPbIija+21zAeFCtiFv+mz8ivz4Y4tgJnoLutQXr58WaLNm5ubsbm5Gaurq2XDBHWMV1dX4+XLl2XM33zzTQyHw6iqKnZ3d8vmYb42UUuviEazWQNBEC1jygDLfc7vUzSNiKM3CMgwTugHD7BhBY5qxK1uwVgeHR2VYAGbTJFNVweY9zv445kRt6AWWdPoMTbAywkzPemZIP0eno6oZ3hVh2Z6y4GMg1wFERrs8/NLPbjqcqzXuUPr66v0WWpf1JHUoJ7SjPewhp9rr66uys7qnIWqmQ6lA31gwzo931mDes/FsdWgCE0BlwdVMh7Sv+ED7vGgGbJGoE4zWOhpBY/6GeDX36tBRPiEd9AnvUfBfcZr9F/xgzr37jhDJ2SS3V1dNrS5k64ZW58fdxCRKx+zrgXO3qXOAHOhAV9kTysSdZldFjRQXlZ6Zk6+9tcDW0/V/s//+T8FO/qcaOkuY6Apf+BMjcfjMpdsYqRyxG/oPpvNYjgcxrt37+Kbb76J3/72twXn8H7VWQTkVf+7TWVe3bH1voAJtDH3zDF/Qwf4TOfY7YL6PU1BUmwpgWLN5CuW5vlZdYhiUR07dMV/wIHWvRGm02m8e/cuJpNJ0fckCcCdJGi0+oN3aIAaW6ZBVwLbyKQGwJuqNu5rj8rY8mAUp2YJXRkq0OM+Oq3lvqrQGBgGcHV1tRaN0Si0R5x1TZEaYjUMCKSWTCpA4trhcBit1s06vxcvXtRq2fW5nhH1yAubaJA5UzABnVygMudIlZ4CNF0kr2ue1GjwGb8VgLkSVeXpkUlV6lxLeWVExHg8jg8fPsR0Oo03b95Et9uNbrdbM26qMBTcA4gxkrPZzTmSnGP6mEjN76upooGHUUit1s3GLJRaMk49zw3e1GwMCmMwGMRoNIq9vb3izGPsdc1CRN1JVoD6m9/8JmazWbx7966U3kLPk5OTO5uvKAiiv8gZvMpOesiibjaDPCr/qFPDejBX7sqb6AkcPnVo9XqN8LqByipGlJ9xiPjRNbs4Dhha5kqd6+FwGNvb29HpdOLrr7+O8Xgc3333XYzH45hOp/HVV1/VNsIjawUtceR1V0J27XwoWGlyfp+iQVeWjQwGg8KrfEZwCidKl55oYz7Y2bvT6RSHFr5Uo6+7jys9FIgqGKQMWYN1qnf0euU1D3hmO58qLfwZvC+r4nCnVPW6O7daqkz/AWkZT6gsKBBSu8uPBqLpi8qWzrdXfSiY1b8p9261WtHv9wttlpaWCh8Ajhg3m94RfKKfSt+moPeP3RbZIaWDZ+8yekXc2nt0LWPXBIECVv2teiqiDlhVZ6pjoLjIgzWa+eEZ8IQ6CwQbuUYdV3S6Ousa8NVnOu+qXXDHVh16muIXr1rTd0AvrnVaKMBmHty5UidBgxA8AyygwV8dk8oQTogGsLSqxDeQ493PJcC/v79fC5xFRHE4W61WnJyc1PA0WEKdfDA2S3MIejr+1XtOT09jNBrFb37zm7JR4dLSUnS73dQhVP7SrL3qaGyKO5GMTytd4A+tKNHqBeUNxuHOmPo5mQ+QBWbct8rGBwZVGXFZ12AMTa/Hedf3zWazsm53b2+v5rPN5/Po9Xqxvb0du7u78e2339bGjI0icKHHvrmjy7W654PqzMzm39cevXmUTrwqI51MFXpt6pi6gud+Nd5KdAWvKLps8wuIpn3QyB3ZhYjbIzjcqAPSmESYgr7QPLKiBkfBtQq6OhLQU2mQRYuU0bPSAC0RVOdVoyfquOsztW/K9J4pV4HRsTPWq6urkvFm91IcPTWEPMMNIu+mL2dnZ+XZWfnPj93cWC4vL9dKOMg2AYgj6pkpN4YRNwqJo1Gurq5qFQLMs+4wqnQkisfzjo+PYzqdls2OOGP36uoqDg8Pa0eVeICI5vy3vLxcc1AVOGuZOWOGpzIF5A4BTQ2IK32le1bCpYYQI+PjY868QkGdZZxRLy3CgcNgQVN2gl1aWorXr1/Xgl2eucM50T0GFEAq7Rf9/9T8H1HXUQQxcE5xTnSDH4AiYF0DHBG3QRpKj7vdbsl6aCBH52sRndQYKiBEZvnfaalOsQZR1YFscgqa6OTPV/uoz1AbkQEbflTvZu9Xu5yVH2vGVu0mLaOtAjYFFuhv+gFt1THVPvIeLYOOuD29ADlhbL72U+3Ec2j3yabqIOeZbO641udFbbLacZ6j8uh85PKi/Wjqn+ox5TPXiWAvxYB81+RgOA5pAv2qP51eKjOaqACQZ5gLG6ABMrXDyAgBIObD9YbSOMveatBXZeO++eE3Dm9TRUoT7/zYjd2BkWMw8tLSUgwGg1omm+a2H3s7Ho9rpxRoc/04Go2i3+/H0dFRwZl+7JNjSN7rjm3EbRUiy788u6xOmWIl91vUoaUv6E2XUR+fB73UgXN+0e/47QEYx1Xqp7l8OxZVRx1ZgY8JyrsuYkNHKhabstPQD/nQJID6ju63MQ7Heg9pj3JsPVLDxih43Zo1oeMQMRuYPpeBoYi0xJlr1FirE6sE8eyqPl8zTC9evCilkTiHgDTA7Gg0iu+++65kHnT9lzIiRwIxCfyNU6xlVh4x0f6p4KkjihKH+ciMsL5Ny8P4ng1yHEhp1jdzpt0Bw/FwetJwbjUSyRmhlHFqhh4+IXIDfZQuOLZc1+QM/JhN6cSYKF2dz+dxcHAQL168iE6nU/p/cnIS/X4/3r59WyKxlG70er1otVrx7t27EhXDYdDy8MlkUt7Hlu28c3t7u/Dwu3fvYjqdxqtXr+LFixfx5Zdflh0D/+qv/ioODw/j48ePZWMd3smuy55Z3dzcjL29vfjqq68i4iYjrwEgHGvmXg30cDiM+fz2aJ2NjY3yPeVB8MRsNqudo0l5NiXWnz59KtlWDbTQT3j17Ows1tfXo9vtxldffVXoOxwO4/3793FxcRGHh4fx6dOnwrOsEcJQIlO65iMiyu7SHDvzs5/9LD5+/BjfffddvHz5MpaXl8t95+fn8eLFi4iI+Pbbbwvw2tnZiRcvXpQt8r2sCR7L+O25NAWPnA+MDETcVigAepmPXq9XM3ya0aakifU68AVzTFZDjaAaTZ4ZcWufNDhEcAzdyHPRV8gZsqeVI/AW76OaSIMxTcGezHlWA46+J9AIn/jSkPn8ttyNOfCm9m9paakADgAJQTeCBpohirjVt7wX3a/ASrNIOiborcEoHAX0AXYZueYMbrK08/ntuvXz8/Oi6waDQekHwcGnbNn4FSwqgFfb6fbT78dR1GVONA8EIQdgLs3sK5/rfQ70kUX6phut0U/9rY4Y/eZzDSLyvQc6FTwrYNdMPD+aQdMz0XkG46VSBLuAvqF/6jgSONG13+BRHFqwmo5Hd6FW/AhdNeO1srJSW3NItYjqTBo6iz4x98zr2dlZ2WjNncSnbl999dWdZAUnIZCFxb5GROEvKs9ms5ulgfAcPMOSJ9VJ6CUC9tPptGxCqJl6dKjOuZ79rY6VY9hs41R14FR/uVwqrtbPaf49fIf8ZT6T6l3F9L6HBX2DB8ETGhTVd2qSSN+nQazMCeceNq0CryGfVKHt7+/HfD6PwWBQ3q1BTAL7zDG2jootrVJRn8Jp+tD26F2RNUrgaz9csUOsiKgpVAbgkQnuc8cqol6Trdlb7ZcziX9OP2FSnCt2kl1bWytZMpwtgBBRqVardefYCXfgGZcqdZobHAVpeo0aMS1fzYRQDQVAULPRKoT0VZWtGj0HihmY8Kgnz6Dp+pEsEqOBDo0qaYRU+5I5AT92c+FSJQBP4IDCN8gGAQjW0HK0TcSN08RYvSRRAxDsCgoAJyCgZekKDjiLlc0VUE6AF/pO9BXjDk9eXV0VgxIRtey0jlXXKamMZUES/dHNZDRLoef2qi5xRefRS3ZkraqqZFMB19Dv4uIiPn36VKsymM1uyqiQB8CPRl6hH2Vj7Xa7OGDMH86xBnBYH93pdGJ3dzd2dnbuZACalPZzAjM0NbJka3WDMnQSfKFrdTK9z/xrFlFBMD/YGdUDGsFV24N+5xp1zJCVVuv2OCl+OwDnt+qjiHrlRETzpiP+nX7mz8r42qPsaiN5Tmbn4HuAo8qqypuCct7N/9CIeXUQpPZBbYEGjtUW85t+6fFj6DLmhiCw6n8Ha0/VMhugTi3NdZ1/5i3Tb0p3D/qip8h8KX20osXvUXzCb5UPtQUqs4oJuIfAXxZ0YU5Vf0fUN9XTvqpsZuuo1WnRXfKVH9C39E3xEXKObo+4XUOP7sHxolqAvxmjOiLOC4pnsiU7TQEZx76KGz249Vwa9FT7qwkiDbwxdmiT2TzH9Uov5pLlTegu1eO8D17g2cyj+iDaJz7zgBO6yPuq/ok6ljyD7zTAuGjus6bv9IoytYF6jSa9POCiTvnS0lLBMXym9pI58n6o06syrckp5AdbgxwTyFDaKv9U1e1aeX0vz9d+qP55SHuUY+tMh+PEgDyrphMNIRB2zzIqs0BgBysKgJuiJ5mjpY6wAmiYA0cQ50A3QiF7y+f6TBxFlCiKUo2FO62Zkcqarq9yxvbSGxzvy8vLcpYkmWctMdB54f0qsBoxVrpnCieinilQMKOZCBci7mNhOu/lGdBWnSYttX4OTYU+4kaJ9nq96HQ6tS3+l5eXS+nOcDiMt2/fFsD59ddf1zKyS0tLNf5So4YTOxqNimzMZrMSKcXpioiyU/Ll5WX84z/+YxwfH8fV1VV0u904ODgoxn06nUardVP1wAZKui4OR/D4+LjMO9moly9fli3mT09PiyLnGt0WXzN1XNNq3W5AoJtHafZAqw34jqZrbZHLw8PDQueqqmI0GsXW1lbhHRxfghCbm5tFVvr9fkREkV2VK5V7Sqc2NzfLetB3796V6gjWRM9ms7IZVbfbjb29vXjz5k18+eWXNeWe8ZUb4ufUABpkazkzEF0E7+vGaGQxNEuJ3DD/fgxNRNTArm7c4Q4ZOkrXNGqEHt4DEJOhByydnZ0VO6A2TfWiVqQgCywR4Bro4/p9kXOr1UZcr4Bfg37qTETcLbnXkkpkkznQJQMe4EV/kzFaXl6uzWlE1JwIH2PE7SZyOEv6v45Vz+EmuKRr3NmMUG29Oj8670/RMtl0Z0cBoDu4HvTn74hbgK+BXL1H9RH6TE8SIKigfOJgkGoZ5oXn6c62ZBw1CKp6W5duMM9aiaaVDop9PFCl8q1HeBGchBbcq3uKaGUbpa+z2awcOUJAkcYYVY/wDoA5m/utr6/X+j+bze7gF+cJMCuZSXgcu6Bz4M0dW8aqGfjn1LBzWuFxfX1zqsPR0VHBIcrf6CXGp0EvXSKh2XF4k8otrWiAX3ByVTehL5kzdcJpGqRT5zvidudqlR29NiJquF77hZ5rqtDQ5zV9rv6QHmunSSoa17k/gG5Xh1XtJnSnr1TzqNOvfeEeT1hwJNnq6mp0u904OzuLTqcT/X6/7Cui4wXHojegHeN326k6Q5/xkPZZGVteBFF2d3fjhx9+KNlOjeQCaAGtviZPI3V0HoJE1Ld1d2c2ImpKodVqFbCvwgOg1Qi5O8AwgAJzGErBbVVVtV1TI+6ePYsjCpMQ1fRoBddzv08m/dPIjDviuhMpvxGITBi0r7ogHicHpuU7XcepUToFVUpLxuvCzg9gFjDjPAZ/4OyqQnkujTF1u904Pz+PjY2NWkmYguG1tbXY3t4u5arw1Nu3bwudNzc3Y3t7u/BG09lr0Iw5uLq6KqUdOAevX78u755MJtHv90tQQA0m88051ASnMBrMne5iqhkcZB8nWKOcDnJdkc/n8wLIlpeXi1IGOLGeZjQalXFh6ObzeeGhdrtddoDGISYTztihE/pH5ZjyYZxh5XUPkl1fX5dxRkRN/tfW1mJnZyfG43GMx+N49epVXFxcxLt37wp9t7a2ymZqixxYN3zPydFFvzA3OIiA1Ih6lF6Xb+jn0EArZpB7BedeehVRX9dEmbsuV1A9pQ6a6kT2WcgyOJ7JirgF65qNxyBjJx4CQDXyrQ4Q+k4dZ7eN/O28qXTVSgKO4yAwpfaPvvv9bo80cKfZtoioOU9um6GnZi/gB/QlessDVAT6tI9Kj6dsmk2nLbJNrksW/URECiIjbvUP5/uqDUAv6R4BGljV8k51mlZWVmqbO6ptZ1M4SmrRc4rLFKAiD/C19p151Awkcqe8rTsjI4fQRK/F9jAesIg6MFVV1RIC8B5LFObzm9L3ra2toh8I0Lbb7XLmOfhJ5UL/V6wUEenJAQ7KoZePi0BZhk/JID8HHPTf//t/r2WUobeWC0fUN7tS3K2VPNjsrAx5NrspWUZvZ8sX1fFCr6mehj818Kp+g+Jxz4TSPGuoSTSu1ZJ2XZ6VObHuPOo7NIHmVZeaCER+CP5DY9UlKuvoVM6ehUfh3+vr63JeL3hV5cmDAJptbbfbsb29Xd6J7mZjMPQEvK4+Fjzh/pD+zdw+1g941K7I7ljiwOgun66s6ag6S1ybOXIwJgRVA+nOqBM+og5CPPuoTpwKkTMshhglS7SYycbAahZJGZZrMqcPYVPjBd3cgfAfBQEZ6PGSZQVbDiyrqqrtPq2lCOr06kYg9Eujsj4nGDZVZgpwdEc0BSooQl1D7c9+ypYBGpTy2tpaAfgEUTQCyfrZzc3NoqxPT09rQZRut1sz+JoxUj5xhQO9mcdut1toi5NKv7XMlns2NjZKZi0rH9Esg0ZHmRsii85rWRZDZYT+EXUFjMHDZFPdWYmIoqS3t7eLk8L42ZkVcMa7icjzLAJAvId+aRDGZRqnWA9s5/mbm5vlGtYxs9MvmyuowVMdqHTLgFBmIJ+iMWd+PBc8q5Fq9Imu6eRzDYwqXRz0eelVRH3ZRER+BBzPUP1IsEircbKsiOocBc46b1VVlfGqrs1AjDuybhczvay2Qks1Ve+63dIMLPyt8hpxW1Kp1Ur0R9ddahmlglfV+W6XFJRE1I/R4F6yWgQy1CHR7J1mZxWoPYfs1UOdWr5fNOeLbKjSGH5m/SV6MaIuk+rYEshzDKJzrnKqPMy7VCcqhgI0z+fzYu+wDxFR9LhWI6icOEZhfO7YM34tLdbMKbyucwF2UYcAntc5bLfbcXBwUJIDJycn5f3sP0H/wC1NgQ3wjC+pcAe4KYGR6Q766XL+1O3t27e1PsMfBKShN7pSdabjcC0FV/0NfZh39ItikYhIdRPN9Tj2gP/1c3SsN9V/7mfw7IjbjffUx9HvvU/a9NnqAKrOV58A3U4QMOI2Kaf6RHnr4uKiOJK6cz1zAQ0IBnESxn2BPBI3BMj4TKtPlLfVN/Ay/Qwj6vs0UPKQ9qhzbLUjrVarrEugzE9bpjx1x8tM8NWx4jtVzKoQyVwyeQhA1g/NAmrmUAFLxC3h9Rw5LZFUBpjPb89k0n6q0DC5qswUwNCUJipwKtDKrMpszhg4UgQbdAxk2WAsLUfgGRrxVRCkfdLoq5aj8H4icfpeVVzMtQJc/VwZ+7k0VUAonbW1tdjc3IyXL1/GyclJjEajci30qaqqlqE4PDwspRrLy8tFiRBA8YyK8ruCAZ6r63mWl5fLLn/n5+eFz4+Pj8scMd8vX76Mra2t2N3dLXMMr1BGhTJlTlHqs9ksTk9P45e//GX85je/KYe246Qzh5r1hy4ACXYWns/n8erVq1hfX492u13KXPXwdzKFe3t70el0Yn9/vwQVcNw/fvwYETcb2sE/ZIXX1tZif38/9vf3o9frxfn5eXz48CEmk0nRXfTTwbo6uhrI29raiqWlpfj222/j7OwsRqNRnJycxMePHwvtDw4Oig74zW9+E1dXV2WzqaWlpeJsRcQdZe5G8DmAmuPj4xiPx3F6elpK1RUY6DobgixkUhWQKl9o9iWiXoKsEXkac7C5uVnbdOXk5KSURpM1YP4yQwkvMnfqGPC9gm3NCFRVVXjr7OysjEP1szt62XyqbtSGjdFzbHk2skX0G8Ayn8/j+Pj4DhDe29u7Uy7X7XbLe6A3lR0K8OBDtRUe7GG86A635eqMr6+vx9XVVWxtbcVwOCxLDghk0R+CqW6bn5tN8Kb0QhdqsEfBuV6v3ytwhr5kak9PT8sc6YZq7XY7Xrx4Ua6nag17QKUQuovd2be3t4se5R2a1crOlo+I4jiiYyNusZNW6CguUMygGEaDWO5E81y1JQqU2UkdeWGDw6qqStZpOBxGRL1SbX19PXZ2duIP//APS9Dxyy+/jKOjo/jmm2/i9PQ0+v1+bG1tleAAwVNk1SvSqqqKTqdT6Iu92Nraqsmk6j3XCRpAJWOn1z0H/lfMy1zoUgVwAnOhehRdhxxQZaLHwaFb4AuuYV8bD7hE3JYPU70wnU6j3+8X/a4YNeLuMkXNlMKHnojToCnjUnnheg/m8S71NTT76okq9L3aQK7Dtul+HvRrMBiUEvGI20TE5eVlHB0dlT5ydOGbN29iZ2cnfvKTn5TlKqenp3FxcVE2xdzd3a0ltjwowfNarVb0er04Pj4uWWR0/dLSzekoHA2EPKkPlGXj3Y6pb/WQ9ug1tmq01Zlj4A7KIKgqAGUujbhkDptmMzUKrxEGFL1GdRWcqkNLP8gWuaPNePQejTYxbo8guzNDNNGNlY5JmytzBdj6LpS8zosqfY9iMV7Gg0Ov2RKNMKlguwOK8lAn2zNP7mhrpBJH2csOtQGE/HnPpTmgi6gLvEf2lLe17FLLCnmuNuUhBQG6dpn/FeSQpZzNZqVcl3VJAJTV1dWyWyoBmqqqCrgHzGA8dGzMPWsVKUflGq+wcH2gcogR18ikr99RmqOoyZjCxwA1nsc9jBfw6CAh01U0jy5rBhLnH4edeYiIEulHhnA+KAkajUY1I94kP8+xZZlaz1YqaMmOXvNggeq8LPOJ7vcgHdfqOd7YA65VZ0ttC+/wPsB/GpxUe6cZJn4UmMNvapAXNbcr8LgHsrTvmkWij4Bg5Vnlb0CYjhud5BlhHTd2SkGmZsm0cb3rbp17DZxpuSayyjVkJAGzPOs5yIY6p+50a3BZ7R73OdjVz11Xqh0hGEi5LnNVVVXZ26HX65X1jzhElA5rRYVmSjRIt7y8XJZlKI5R2dHP3DapE6tYQbGOX+uy5HTxDB7PJ1hJGSSZK9UvOnaXI3UmoEOv14vLy8vY3NysVTuoTKjTrrqDeUbXa1WCZqGRD8akfVIa8a5M/p+6aaCPtggH6eeqdwiWU/GmdNR71ZFy7IX+IJA/mUxiOBzWzmPFwUMGtL9VVRVHS5MLBBKn0+mdykWdJ+ZGd7JmLr0KQu2bO8k633q98gJOtC7Z1Gfod27HKLlnvyAwVr/fj1//+textbVVEizz+bwsY2HXY8XymVPKmvzNzc3SJwIZJCx0PxWVbaep/q++02P1/4MdW480KiMyWRp5VA9bnasmgVDG1aYRZCaX6O58fht1Jyq5vr5eM9gAfO1Xq3W7iy3rknSjkdnsdp0HUVci5QiaMk9E3FF6jB/BUiCik4iDh4OqDr1GaxDKVqtVxqhRUBUGnTNlQJ0z3tfkUHs5DXMBqGK8SldVxA4wmWNdcM4uhgraNIPlAvCUzY3zfF4vkUL5aQRQ6a+grt1uF6PqytpBofKkR+SqqrqzJvb09LSU/BGNrqqqAH+M7u7ubqyvr9cAkx7pwzs8Qgc/k0Fgww6qBPQ6BcL6GQ4iZcBqeABXSmd+yCqwSRMbc3GkDI6jyoUapKqqSimfOp4arIHv1djRX/iWkmfWGJOBXltbi9evX5fMCoGcra2tEsX+/vvvY2dnJ3Z3d2vOhfIYTfnhOQAbMpsOsh2UQjt0bObcaGAtIu4AOQe2gBDuI6Byenpa6IQuZ027Zstw7NBLBDdVNjSooo4culv1okb51bFWfX5fU3poVF6fSYAJOmAHdA8IouTIIHwZcaN3rq6uCuhjjPC0gv2IKBlwfnCKaeg4BR+Z3aApGAL4K9BBBjRbgnOljq3aqadsDsTUntM0C++4w8fgdk4D/OhE9gJQ2WOe/+iP/qhktL7//vuSoQS7wAvj8bg2FxE3Syl6vV58+eWXRba///77wnveV8Aqz4m4xQRquwmGq7wpvbCZWcmlOv5aNQfWwkHa29uLzc3N2N3djcPDw5r9rKqq2CL0M1ku7Gir1Yrj4+OiIzY3N4t9OD4+jtFoVHNm6TeYzqvRkD82FWRpj+Jf5RPGr46iBtCYe3WOnwP/g1si6rKArWNM/CD3ugfC1dVV4b1erxfdbrfGlxoQwf6ix3RtN/zBfhrv378vFVy8j4ANOhD6w2s4171er8icVnNpQBw8AK+pnuSaV69eFYyuSQKVBQ1wwQca4HfnGWyGvDB25gOf6OrqqhyppsdE/vEf/3Gcn5/H+/fviw66vr6OH374If7bf/tv8ebNm3j58mX82Z/9WSwtLcWnT5/i9PQ0rq+vY2dnp/hPnryYz2/3Wfriiy9iOp0W+3d2dhb7+/uxtbVVjmiiDNr5x/2WpgDfY9qj1thqpxBYvHIt08ju0c56prPpGo38YfDJThFRgbF6vV4p/5lOpyVzFXG70UVE1CYWBezGUx1MnAaMsBtyxqCRNVWwLkg6qR4IgGnV4VEHWGkG/fnN+z2L0sQU6nio047QaamNCqM6Kx6NzeYT/uA5MPjGxkZtx2l3zpUuzvhP0XRcGFdV4Hyua2y1fA+lRtmHlrD4GNW5UgdWlazSRvmGfikftNvtMl+6noXSHS07dgWmAAGgSwah3+/HfD6PTqdT1pUqryhog08U3KKoP378GNPptHY+nYJjsn/QcTQald0SoQGl90tL9TW7Gxsbsbu7G0tLS7XyWWjB3DIXqgPUsdHqBgXvjFENcbfbLfKuZVTT6TR++ctfxqtXr+InP/lJOd8147Pn5tRGRAHW+kPD+dNstTu26BV1OCOipq9oOn4ADwaeCDlzTJaEPgB04SVKXTVQqHaF8jUFGFpRQeZZd3/VPnrm6SHNbY5XB2kAVcEtvE25tQahABQEgK6vr+O7774r46fsnrOr1flCX7FeCh2v9EfW1EaqnDog0/GhV6gWwbklS4JuYQzwm879U8uBbk4DvwKkVW+qvuMzrQDTgL86PWAAxT5Uo+HQ4oS9fPkyut1u7O/vR8TdNeHwEUuP4CFsFTqLOaZ8VtedataHuePZWi1H07lyhx7+VIdOZV7tviZJIm6TBhoYf/nyZVn6sLm5GfP5vOzMqrs849BqpZ9m6QgWsUkR2VuCvs7fOrfa0P0bGxvR6/ViY2MjJpNJTYYct2lAwGlBFo55ew5ObcTdyk3H7Wr3dcMiKpmqqipZWgLFbg9U/zFuXSaBvAyHwxiPx/Htt9/GyspKfPnll9FutyMi4v3793F0dBTv3r2Lw8PD2saPGjz5+PFjLeO4urpa+sneKBFRAvBUaamuQ79S+o/Trrpb8bzygmJIvgePR9zuOO+buLZardjd3S3Z6pOTk7i8vIy9vb04OTmJw8PDQruDg4Oy9Ivkyp//+Z/H1dVVvH//Pj5+/BgnJyfxd3/3d0WmNAih+BLdrzQE17NBJvriT/7kT2Jvb684+xm/MJ5F/EZ7jHP7YMe26aUoPHdsMyfFP8+ex0Bd8FFYKJvr6+uYTqflOQquI26YXoVClUpEFMcC8KDOReY0MFmaRXPwmWUuGJePWY2P0sVppXRR+ijN1HlWw6GRogx4MR4MlmY51BnFIPuuc1kgwkG/GkQiZPCLCrZGR3VsSpunbDoX0EDXQETUAaBm/NRpJOvEWa3ZXKuzq8ENVSreN6UPtOR5unGGZ8nUAaZ5Vg0nmLFSkjoej4siJDvMO93xc1BMpPT8/LyUD2npj/ORljehBzQAo7v8aaUGwKeqqpL1oB+azVMlfd+PA3qaghueRWYqIsra3vl8Hr1eL9rtdm09tc/nU/O8t2yzOi1NQp5V7pVfoY/SUXncQa4HCNmghMxjVd1s2gVPofs4dkKdvPF4XAP/EVErPdPSd55DnyOiOCbeV1rTZxmf8B1tEa8B8CLijl2kn+qI8j6A+nw+L867HkmFjPH+ra2tEmzUs2aZSwVazKdmWXScygdKB57F0gHnDwX0yKUus1En6ikafQEz0FfVHW4bGbNih4jbQLd+1qTHVU/rTqSUIcPDNNW3ONURURw4xR3qhOA0E8hRuciCL+ocq33QPjN32JAMyNPcBvKZ2k/W1Xa73WKfcJJwnnBiZrNZSYToO6GJZ73oL5Uf4Dx30jMnU3WeOmyqxzS7746NB/abvnvq5rgvop5tdt2ltKCKaXn57hp0paliHZo6tlw7mUxiMBjEyclJvHjxouyhcX19HR8+fIjpdBqfPn2KT58+lb03eD6NOQMXdDqd8g6Vc7Ae43KbxxIpAkLKK47xlA+yoCjjBuuAb/Se5eXl2N3dLU41Sb+tra3iI9EHnH2STN1uN376059Gq9WKvb29+Ku/+qs4OjqK9+/fR6/Xi6+//rrcDw/qvGhyC1pRrUAlUavViq+++ipevHgROzs7pbIwmwPnL+fz7LP72qMdW2VqSjD39/dje3s7hsNhiVKpgMLIWnqkSj8D6hqtBNDoEQ2A016vF7u7u/Hq1atYWVmJw8PDQgQU38uXL4uBJ3vy/v37iLgBLF999VXs7+9Hv9+PyWQSJycndzZW0jIUL7dShqXvjJMsgke1UHpanuDKwp/vgqBOrG4wgjLUo4pYG6eGWI2azsfy8s2mB5yD6N91Op07kWYU1Hw+L6XdEVE2keF/SleJgDEu7ZODWl2P9VRNAyMaHNG1ay58OLcK4KHNYDAoGWvGCTjXQIxmxpXG8I6+i2vorzZVrurINikM+IjMkPZlOByWqoi9vb14/fp1jQ4KyJWn6R+RXPoNf/b7/QIM9OxTBVIRt7KhkW+eq2WY0Gk0Gt3hed6N4tfSKQwJ2WV1tjUgpQGfbrcbm5ubpTya7+nDyclJTKfTODk5KfK1t7dXMs0e2NK5fS5NM7boQwUfzD+BDoJjXJeBFQ9eaXQbHkR3USFQVVXRQZPJJD59+hSDwaBsOvbb3/62lJLRV8rQyPISZNOqlaWlpVrVAfNMKblG6+FvBW4aNHpo4x26Pg+QBNCOiJo9ub6+OTfSN5ZSu8mzKfk/OjoqWTYNQPFsKp707GHP7EZE9Hq9QiOAnsq5BuJoKnvLy8u1zUROT09L5l9lMCLi9PS0ZMC0v0/V9Gg1LUHUrLnqVwW2Doj1O9UT6gxplQ1zTwaMDE7E7Vma0An+mUwmcXp6eidQDZ/t7OyU7CL6G322urpanGkqYzTL5tUrLrO6Zlp5Q7/XMs3M2dbqBD5jgzg2/NNqnpcvXxYnRXFOxA0PUln4B3/wB9Htdss556PRqLYxF87z9vZ2wY3QRNcJegAXfYAdmEwmpYJInR30CA4TGJcgAM9rcqKfsnlgg8ZnnIe6s7MTVVWV+aD6otfrxd7eXuE99wkibh1lynl1H5GlpaWygdJf//Vfx3g8jn/1r/5VfPnll/HmzZv4z//5P8evf/3r+NWvflUC8EtLS+WEgvF4HJ8+fSqB9Z///OeFnzkK5/r6ZtOxk5OTMj7wCA2cE3FbUXB1dbMJnwZcHc86NlHsqMER9Q20AoHy4E6nE1999VWpnDs6OirOd6/Xi5/97GdlydU333wTg8Egfvvb38af/umfxsuXL8ua2pWVlTg9PY3V1dX4+PFjXF/fHH3FXKuvhc1jzpDlqqoK/gG7raysxF/8xV/E119/HTs7O/Htt9/GP/zDP8RwOEyX4SlvOc0+p31WxlYnh6gMu8FhaLVTWcaj6XlZFMN/NPJLplbPwaJBJBSJMo2CSUojUU4wkkae1ThnfXbnFkbX8WeT5szOOxTYaqQ+u0c/V8UIA2qZl14PI/EulD+RWIwD0U3eMZ1Oi1DgsEJPHS901sg849fyGzUSOi51QJ7asY3I11c1CaDyuPIuQJANpLJAA80/BwApP2R98+dkgDDrdzY+5Q0+YwmA7rBJqWfmzPrfCvSI5AJk1GmAB9Tw0Qd4VD9X8BhRP55K36u0jaiXT2lmRZ0bl1817hj0drtdypAjojghXq6nWQV3YFUGnlvTEl6dF2/MnwL5RUDNdaFGyTUirSXq4/E4rq+v4+TkJPr9fpycnMR4PI7pdBrv3r0rGVv6q+vH0Wea7SSDSF8VbKPjut1u+QwDrxshNdm2RfaOps9lbRTyoMApIkrAiTFyP85LxG3prupOpS3vQ8ZZR4ujRumrrnHTUlrX/1lzew9tAZW6hMkzegS1CfZped5TNdcDi/Qq16uecDvnOpvfLg8aRFbdBp2gIRu9sKM818A7PIM1jwQocIKpbMCRwPHUDByyoZkcH7tjOLcBGe1Un2iAQ/Eb3+keCRqM4jN25lU+x+mE78B5BM3I2OHY6/npqss8A6c/6D2tSlOnXvVlRqcMR+hnT83/9MHnU50y9AfLNrTaAP3EGmSCui4P0AnZ1wZ/f/r0qfD0xsZGDAaD+Lu/+7v4/vvvYzAYxMHBQW2TpMvLy9jY2LgTENnZ2YlW62Z3e2w449BlWpqddHnAHjp2cj5BFhc5t/BwVd0EJQl68J1iIIJRGlQlgAv9r66uYjgcllM4dD+SiJtE09XVzYkGX3zxRVkzq3sg+NzrD3RQfY4MomPY76Xb7cZoNKol/poCoe7TeHXHfe3Rjq0rqpWVldje3o6tra3o9XoxHA7vMKMDHQwqz/OBNJWs8EOpCLt5sVbNlYQSCgOdZcRQeru7uzGZTGJ5eTmOjo5Kllgj+gBxpYc7mSh/vo+4LWPKohH6vwIOpY1mYrXpfU7z7e3tYgQAQETfUTQoD7K/bMpTVTebqhweHpa5RYjH43HJymiJckR97S4MrMfSUBahxlGFR0sC9Xm+FvHHbqq4PNDivKbXadUBYyXyC1hXx8uVI00DC2oIMkOoQMgdbAcfDq6VR4mq80zGe3p6GmtrayUat7q6Wko92RRJmzo0GniBd/hMgQtOYZbhgF7Ze9Q5UYMATzJeHbsakkx5w39ch4zzGRno3d3d2NvbK0enMAf8vbq6WiK5/X6/rOtU457xwXNpZEo0uKgGn99qaP3HnT/VgarvASVUBjDngKT379/HaDSKX//613F6ehrHx8cFiI9Gozt6Q48m4n0edVbnikANfLSzsxN7e3sFuBJQ1aymVvIsmjeda+VBDbzSf/Q3fIfTeXJyUrLnONgvX76MiCjfX1xcxN7eXtl8iAw2oJ2lAMqDEVHKt6ERoAVnGx3e6XQaA47Md7Z+DpBDtlAzbGRo5vN5yfJsbGzUjp14qkbQTnWYBizdriMLXnmircnpwx4TXFC9R6Y+Ikogen19PQ4ODqLX6xUwq/YDUB0RpZSZqplPnz6VajVkjewYQBdnDzml1B0ZUcDtmVl1AuBreAu+Q5dgc5aWlqLX6xXMoPRg40KnMZlt1vnBTzi/Ozs7JSt3dXVVji1DxlZXV2N7e7skTHS+PRkQcWvfNWCAbsA5yBx4mtJL15lrFov2XLK3HozROYX+EVF0C3sisD6VDRb39vbK/LrzpMFnXd8ZcUPzjx8/xi9/+cvaUUF/8zd/E3/5l39ZMsL/4T/8h7i4uIiTk5P4n//zf8bR0VHh38FgUEpyX716VZxfjoHa3d2Ns7Oz2NraKsfIvX///g6WYD2uOpeu72jwoq+nJvHkPAattEoK+Qc3IPeaVPz06VNtOdR8fnMM3NLSUrx8+TK+/PLLePXqVbx9+zYGg0G8ffu2VGL8+Z//eQk2DAaDsgGV6zbVZcw9VQrcT+abvjPvHz9+rOk05ykPAoAFtDryIe1R3oJOLJFXJtUjWSqcWcRSn8ng9B3u9KlzSZQ8y4Tt7OzE9fV1idKw5gpGQYEQweFszOXl5WJgtRwHgOMRcHVQPbrgCjwzyDo+pYlnOFzZKb0AYjhNRE6IFhJoOD09Lf3BmF1fX8eLFy+i1+vFp0+fCvDZ3d2Ng4ODso3+3//935e1gS9evCgBBAXlRGegi65FQjnpuirGysYbCKwy7Wx2uwPic2jKk6pkoT+fKQ34DDrAD9fXNxtY6AYczB8gQB1blRueE3G7Y7iDKXVs+YzrdDz033lKG88BZF9dXZVDuff39+P6+jqOjo5qQMhlmQZIUOUFUNZqDx1HFjFs4oms76qHFIAqr7nDpbKt9OQ6BZo4U+zw2Ol0ys7MEbdrM8mIUQ3CbqeTyaScK+rz89wahkV5EadQmwcjVGdG1NeRA+pozI1u+MJmUePxuJQY49h++vQpIm7m8PXr17G8vByHh4clG4NDRAYKPmX3ZIwvOzhub2/HdDqNjx8/lp2XB4NBRERsb2/H9vZ2tNvt4tiy0YaXImfAxp1et4UamFHwhMzPZrPifLBeqd1ux+bmZnQ6nXj16lVxCNHPCio9+KibYW1tbUW73S6BGc5l1oDA9fV1AXPYAcYCqGe+oYUGmmg41Zubm8VhRi50d2QAzXQ6bVyP/mM29Lg7Ig72swCOg0Ftnr3wYAeA3B1VAhicVQuv93q9Im+6NlCziRERR0dHERGlVB8dpVlL31AMR1ADso57aApQGQ80oOJBZUJlPiIK77N/A+sysbmZjuT5PBtnCztA0EYxnZYZe6m1Ot0+1zpOnUuwJrR2h0CbBv2qqio85c1t6VO1puASsqsZTgIgyDbBQdZx65jcPkTUMTK6ixMTSGZdXl7G//f//X9xenoaL168iH/zb/5NfPXVV/HmzZt49+5d/OY3vymnN+zu7sbq6mrs7e2VOXr79m0p58WJprpnY2OjBEl1t2d16DSASuUnzrY25wHH/OpTEIyBNzlzGtlAplkSRn/hX+YDunLme6/Xi9FoFN99911xlNvtduzu7pbNs8Do6+vrxRfCpnmwSnUZ/ez1eiVY+rd/+7fx3XffRbfbLTYT3ZJluR3/KEbVnZ8f0j47DYbS0PIwFzydTHXYuN+flw2OpoTUunwUFs5Et9uNyWRSDDDRS/1foyzb29tFCVKWwvt0QtWB1miIZ8EeCk6zCIgbQp1cdWzVUdEsh2YT1tbWYnNzs7bWhb6jQPf29mJ3d7esYbm4uIhutxs7OzvlCIDvv/8+jo6OYjAYxNbWVqEHmUie57soIxBuODQIojvGeQDAjeFzaO7c6vw4mNcsnDpozBn00JIysksuK3wGjdSxc75TYEXT6z34RB/1O71WlXjEzdxTWrK5uVkimjrOLEjlwF4BInzVbreLcfRneZ8ypzkDjLxvUdO5a/re55d5BCTh5HiUnoCc7rYIiCOamTm2+v9zADQRUQPyNHd0I+KOHHiQwu2BOsdqzLQkdTKZxPHxcfT7/Tg+Po63b9/GaDSK0WhUdNbBwUGsr69HRJTME5sJqjEFhHQ6nfIZa5739/fLWl09Sm5paSnG43FERC1KTvZTAx80pQmtKdAJLXSH+ogo4E2BI8fdrays1Nb0bW1txXw+L6CNXZBxRF0/65p9ss/ssktlhgKtVqtVNibhaAccL83Y67O10kZBLCBM6UffsHPMvzq7T9m8b1qZ5kC1yW5ltp7nZtcSMHGgBz9An/Pz85IB0+MAKcfH4aJ6KuLWSVA9pkEP+FuXJ+FQk2WCT13Xa3913lRGNFDA3xG32WWq5dSmqd3wyinFOuARgjfIgZfvU8aNQws/qrPBHEXcrVikaSAU/QLeycq2dY6VJk4vffZDs1W/z+b41gMTEbcVmuh25kqrNHS+I+4eJ+qN79E/6O6zs7P41a9+VZJUP//5z+MP/uAP7gQ4CfC1Wq2yRr3VasXHjx/v6JjJZJLiLHhM51MDNVrij97TajQdi9LN9YUGEpHFbrdbKjXQyZzYwmcEo5BTnkmFK7uGIwsrKyvFbuzu7tZK9cm44g+p7gGrqj3XagXk7e3bt4UmX3zxRXz99deFVrpM02nkfoDSXk+4WdQe5dhmgGQymcT3338fJycn5SxZB5yUoarxcgHxjCQNQqBkyFKikMnyUDLDYePr6+vx7bffls2KAAUwLE7dwcFByWqyZXYWkXRGdkXkY3IQ7A4IILGpxJbrUAAaCWu1WqWfGsVQho6Isl05SobnUBJ5eHhYMhO6SB9B6XQ68bOf/Sy+/fbbePfuXXz69ClarVZsb28XHmBtiu6srMdvQDdAEFkUhIexagCEpvzyXJxbmspCRNQE1UE+AOPo6KgYxo2NjaJo+Z4ASxYM8qAQAR41qPqdGwoHU/TXDQzXa8NoMZdff/11Ac6sa2R9hSpA7YM60gRgWMOCo7y3txfHx8fx4cOHO+XtCgAyp11pxvdaBqfylAFLDyoBKHm2GyAyZ4PBIFqtVtlsLeJ2V11kzcEOoI1otmYENKCjeuc5NN3sieYBhlarVbIVun5SAYDSFqOmxzBdXV3FeDyO4XAYw+Ewjo6OYjwex9HRUXE40WEY5r29vdqZnu/evSvl8RFRll1QkqibgWDgt7e3i0Gn8md5eTlGo1Hhb10fyo9WWmSBmOy30k8dCuiGTtWzLi8uLuLg4KDQD+AOeD48PCwb3/R6vbi4uCgbp62urpasbObkAn7YyGl1dTW++OKLiLgFqvqj+zdwD0tT4HPP1GtbX18vOzFTrYGewZHnuQQVntqx1SCs6xPnbcU8qpeY50WVSPP5vJap5UcdfSrJAIYE8NWW8gzmT23TbHZ73Bl95x70H84s2SyOxdI1ri7f3KOObzbeLPiFfALqqZiIiIIp1CHw52kGDYdWeUoxlzoj/Oa+rDxWg9Q6T+psQnddy8i6Zy3bVruu85pVe/A9c/3UzSudqqoqa+HX19fjD//wD2N/f7+cm/pf/+t/LWWk3W63yDo4kCye84faPl0i1Ov1ilP3m9/8Ji4uLuLf/tt/W5bGcQYr1Ti/+MUvYjQaxdu3b+Pdu3fFH4GvSM5Qfvzhw4dSqUJWcz6fx+vXrwvP0PSoL7KVBLh9iZX7EKof1bHW5WlaequnaYAdNbsPj4B1VEepTGBfKBve3t4uf+OQKu7S4BMVDtBAacH4v/rqq+j3+7UlqeqIQ/9sczl9L/1Xf4JNHB/SHu3Y6osZLIouK+mDOGrgmqJ5WVMlgAL0OnWil61Wq0QrActarhlx62ABSGBgPctMnUhXNihuHcdDHIPsfwfY+g51iPlcnX9V1jAJ37OT7fr6ei26QjCCH5x93SQK4Mb1gMder1eLFmm034MSPi6P7GVz7jyTOWNP2bI+K29G1KO5yuPMk9+r5dq661/WmuigstHESxmoVueXsek8uRNMpAwQQPbGjXVWwQAd3CEFBCJr3W43xuNx0RUOAhQc+v/0XcerSr5pzhbRFZpkPItjqtkQlQcMooI/dI9mI7WkLgNtGiR5Ds15xHmHUiPfUEnnTIEMek4DEgrm+I6SSM0kQTtdg1lVVXGWcBAVzGLgKaWNuN3ttqpuS86qqiqOBeuxDg4Oyg7v+qN9f6jO0jn1oIzSZGlpqWx6glOufAYNAUjwHWVxBH4BFNCS96G7+Q5nnpI6DSxrZhXnFRoofVVHaOBJG0DY7wUQql3xEtXn0Fx3Km836WL+zoIf7jC5/YQfkDkwkMqVOt46F8wHz9R59DF4IEIrBcjULgq8ur5aJAuuxxU0o0MjohbgyGirwFjnQR0K5J0kiNJHqy2yd9w3lgz/ZcGNzI5k+Mkz0U2Y6Sla5gNE3OjQg4OD+Oqrr8opCa3WzUZRHOcXUXdYFVv7cxmv6xANHrGfxx/8wR8UffPx48cYDAbl6EDm16tKaLobvVZBYH/UKYRPFUNjJ6ji0aPS1DYyj5lvxGcEvbFrHkhrogv/q9PnMuKOLUfz4IQ7dsn0j1aLZL6bVsBSzYeTrvOqWE3nI/OH9NrHyMGDHdsMCJLdOz8/L8Cj3++Xkg+NwukW1kog76w7Rxpdc+dMnYXRaFTWFqFQyKLoRgo8h2zmDz/8UJwKXfzuEXiNTvD+DJw4ndzw0VSx6mcZnTUyq2cT+uZD0JJMEfTRdSPqzGpZBTvEHRwcxKtXr8rYu91uvHjxIpaWlmrrArWkycepdNIggAKWphJkpYWD3efUFMiTcYu4PedRAzCsudQsFveyMcZwOCy7e2tT46+ykgEqZIOmhl7vwUCowvJIGPcSOBkOh7UNMciqVVVVW1+tSs9lgHcg1zgM7HD74sWL2lFREVFbh+VOqzrAfI6suDJcxGP0UWmAUdMgGgqa4NHp6WlcX1+X3RT5jmAZ9wGWMKJUhKCv4BnN3LqRem78H1GX6YgoAALHU50/+JAILHPMfOmYNRBCppHz9fietXKj0ahsMkXpLWfqtdvtQl8N1unSE55PqTLPuLq6KhsTvn79Ora3t+PNmzclI4zjp6XnDvgzvsucFminzjafIU/of45mYRdojv1hHgjQkglB17OBR0SU5SMRtzs96xEO6CHdIVQzte5IaLbLj4ThmW4vKY0DDMIbBKY1yKCbKD11Y37BMzjiBJA1m871TQ4R+kSzmjiO6mBBOwIQ6BHV2wQzIurHZjA3AH+AuIJi1cv0VTGFrtfm+R5k1HvVTunYMmdfAbo6A5r19R2LPZCk9zlu1EAAfXN59bl1u6X9zeZSx6tOiupB5MCdFOyrYkoyeJeXl0++rtybVjwpBt/e3o5//a//dfz85z+Pr776Ks7OzmJ3dzd++ctflr0RsHsEF8ANNMUwGmQhGMzfBA3+4i/+olTqsMngf/kv/yU+fvwY3333XXnH2dlZbGxslIyf8h6biXFMIEeCUn2l8gBPkggj2Njr9cr6YY5D0z2ANODowR/GQ6UCOtt9JIJ7WjKtGJl5Yb8gDWShp9inQPWU6gLeo3aMzyNuMRF4hSoDDUq9efOmBD3/5m/+ptgp+gOfL3JmM9zWVPnTyKcPvVBBsYNYImIwBCXKqvxgJAWgnuVz8M5ngELenzlREIN1sigSmDdzBOgHz9W++dgdxGnLJkczE2oIaFk2w+/Xa1WhMC4EV2kJ881ms7JWIOL2PFlq5nU+KEf4+uuv48WLF0XIKT8ik31wcFAcGlpWZt4E5tThGw6HpQRaBT9z3nyun7Jp/zTy7f2D/xRgoFj0/u3t7RgMBgWoN9EwG38T7/iPRw/VSKtcaH8zeeJzKgKyd6kMaemqKlm+a7VaxfBcXFzE8fFxXF5eRrfbLeedscseY1Dwkb3Xg0NNgKQJ1HhDIePY0o/pdBr9fr8YRSK5rAFS2VedCZ1ZK4njRZ+y9phI5e+zqaFTRxRdh4OvmyopqFVQomX3GNP5fF6cquXl5dpmJBG3Zx6fn5+XMw63trbK5jlasdJq3Rw+r/SNqNsUQA0OFhUrXINu293dLTv5ci33KjDzSHeTzNI8sKvfwZ+UNUKr4XBY6AAtCcwyHxoU2N3drZW6ASqdFpRmMzes32XO+Aww6iW4OreeyQaUqXMLqCIAAXjleUoDzRI8ZdPAgwJKdIOW4HvFgoJQ1VX6m/mOqAfKmXsNcmhgkuu5R2mvmUie5RlE15OZ0+UBQx1TZm8c+9C0v9gbD+57wF+dCz53LJlhRrUZWVAkC7563/SZmZ3Nro+oO/Q6B87DTi/nE52Pp+b/iFvnRgP3LAF8/fp1XF9fx8ePH6Pf78eHDx+KTGsAiN8XFxdFhyrtVB4i7mIunMednZ1ot9vF0ZzNbjaowtnGLuAQqrPKGAjCaeBHS4a1uhH5413snr27uxtbW1ulmser7jwIDx1VH5PkILiFnVMe5Dm6QzK6h2s0MaLy7u/XoI8+C93iwSOdlwyPwJsEfgku69rmbH2yyq3qBpe3x+KfR++K7C9D4IjYcoyJMg8dd0b1zioIV6OpgoGgOBDVyAbPJmpMhNqVV8StIvX+eGSNyVBH2MflCj5TVjrW7L7sezcQKuTQS8eNALK4HEOhO6apQWON2ps3b4qgw5BE2q6urmJnZyciojgjZOodwOoYHAAvLy+X6C/P9fIGN9bPoTlIdedl0VzCrzhpjKvVapUzxYgA4iBE5Flw/9zlwJWRy90iBy8zKH6WYUTUqjHUAOu9TY6tRjEjbhxHeK3f7xdjw+Y/aviVj5pkReW8Sd7ohzcPAETcRgoz2RoOhzXHFiCofcxoiz7R9ewZnz8X3qcp76qjgSyQpWBnRZcPz/7xPIICWvVAgBTeq6rbYN1oNKptPMeOyf1+v+iVVqtV1swCprQhj7rZHiVabLJDxoXD7NURw3FXmjTJlgPozIZqvxyko7cjbtfGk83BsdVoPuukAIG6NpP73UbpGi6eq04sVVd69AvP0Iy8O1QOzvnR7L5m0+mTjvW5OLZqf92mzuf1MkkP9Gb2n98OXr0pDxEw5VkZX+vu1BlWor9qmx10A+pVdhwcq/1xJ9Df7zLgPK46m3u5VnGEXqfP0/74e+m7j1/v0Wf7b2ie2TnHlRlmy+y0Xu+y43R1ffuUTcdKn6i82tnZibOzsxgMBnF4eBifPn0qNlxLwbH/OIhNfoDOrwZ12OiO4KIeQ9fr9cr17Biu/XR6s/dCUzUI+lQrsPAFGDdOLbrR5TzD//CTBl7Qtyzt1KUXGvhTX4SfzL/i/VpB4pgyq5LifR7MclzuvFBVt8cPEdxm3wZkiJ9Mzzl/eZ8fg4c+e42tKxUiiGtra6VkRaOaqqiybK07Avq5D1CJ7wRWgfBSRZ94dVp9ghVkKRD397iiVfCm46fPDnj1M43cae09CpLNr1jbp332jDbGl+ciuJubm+W93L+9vR0rKyvx8ePH8k51bFutm+g/W6zjjLEzJkJJ5EtL0NgYiQzDn/3Zn8VoNKptXNXv9+9Envj/7OzsjrJ46uYGVJs7+aosNKOD09fr9coGTJwluLu7e8eQ+7v520FFBiL1M22ZcafprrARt7JGyX4TsEIZ038Fa1VVLy/ks3a7HbPZrKyvJWpJZp8MmQMkfY+WtmrgoEln6bxlCjOjM8b506dPcXR0FP1+P37xi1/EwcFBCS5dXl6WZRHq4CnAagI42hcFnM+xAXpxaFmLzwYhOEK6lMR3HVUas95S+UkNGsGETqdTDnqnFFnBMfQmMqzHNPA8gnGUIOOwaf/QZTh17DLP2iQcu0XBNwfBPq6IuztuY0e5r6qqQkeyytPptJS0k5Ugis8PcqA2iPJZnq3gG72l42ajKZ1Pjo4gSOfOk9qeiKidkQsoY48GNpAaj8elBNkzGZolYZ3eUzUP0LL8Ch6CHgSHdd4zh1DtM59rgx4KAp2nMvug65vdJug98Ic7nRFR08+aVHAHVJ14r/iJuF2a47pU3x0RtQC5b9yj9ziQb7Vu14wr3RwrOs3UeeY7dZ4yvUtffF49UOd98PmEtqqn1G55kB/aP5emc7m0tFTW/X/zzTfliBc2bR0MBqVkFx0fETU94Tid7xUHUg6vS71YlgeuoIyY+9BZOzs7Bctgt1wGI6K27Ej1jgbYq6oqgdDXr19Hr9eLFy9exNbWVnHmIqLWN5aAZEFs5BtnkHl2PAN/smxQg06OebgPOlJxh+6tqqqWrHAbQJBOv1Ndxf96DY4wdgFnlnFgv/RUFp9z5QuVb5X5h7ZHlyIr02WRWVeo2nFlGh8IrckYqLevA1fnEiWj92bAVt+bRer8Go9yaMucDKWHC2tGVx+PR+4Q7iYFmvUpe6bTQMeO8uCZen4hoAjQQ7mDO/Xedx0fETXOEuOQdd7JtU5jN6jPqTFW5ZNs/PzOwEWr1SqZoOFwWBT0Ir5xnm7icdoi4O2yp9FUlCAKyrMQrhPcMC8C9PqZAjsFcxgWdtHLnMIMaGT0aLqmyfFv0hdXV1fFWAP+2aTNDRCgnrE4vTyKm7VFuufHbmQ/iSjjfOCk6EZArsM8k+Xz4dUefB5xCwgjomZQ6Qf/60Z50Dtbr4SjSKBO+6+OLddp5lbXzNG3Rc7tfW0R36k9gY907ayWz1EJQ790x0y1MWq7NQhHQEDHTaZa1wriXJNdcPuvDrUDc3dUoG3Ts/QZjznq4ffdFOdoYETnosk58uc06Sia6wjeTVObqWBdZZDr+J3ZJNWB+lnmrDm20L752Pz/Jn2nwU/wzqL+YiMccyimU9pkMpbpm6Y5a8JZ2fMcMyqOyzDnIh2i9zwHG5DNN3hwOBzG8fFxHB4elkRQp9MpeknLdB0vanOsn9EHbMAcU/kH/yObbDKoelF52vVUlhXXfrI0hDW7BHO1wkf76IEdfabSAN07m83KkTm6hET7oTLu/KTN9VGGy5WvoB+/0ec8q4kHfBzYXe7Tyi3HcIt03+9iVx/s2GoGgRfqIdQaacBgqkDPZrM7Z96qcPv6JB0sDAc4UYWrRt8VZ2Y03LHTz9WJ0nWfEXFH+FzBuzHR//UeFT5nLhUgB4fQG6aBAXmmMiB00fErUzE+vqdk2Z3pqqpqZQVEonV+NYqO4GeODyUhr169KpsOvXv3rvRd1/EovVBaz6kxb9BTo67KD76mQBUG9L6+vi7HcPzjP/5jnJ2dRb/fL4pykVOqMuZ8nzlEmfJQMBoRJeNIxFzXHqoDwZypYnOlqH1Uxz8iyuZmGg3d2NgokchOp1PKkwl+4IhoOaUGF9whWARqMsCnBkSzENCH9eW/+c1vot1ux8HBQTmyhLOgNUM7Ho9rDm9EPaOMA6X9UMfAq1qeuu3u7pbsoM6nHiXjG0VBRz/H2puX9Gd8r1UsVIEMh8NSngyI0mg05bcabMBGaVZNN0DCcSNooZndrG/qeN/XfHxqc9TWqJOnf1OCvb6+Xs73HY/HpbJFN4siy4lMqY4F+FFVoOXW2HQ+I3O7tLRUyoYBql7mpkBLswZuoxgPO+6vrKzExcVFjQeQgYgoy1aeuilYhF7tdjt6vV5NxpvwQuY4RuROVabHsns0AMJ1fJ+BancQlXdV56gdz56h12c4yfEQPMQ9rvuxLehPbJMGNOgvvKAVcvTL+cSdXdczKs+PCTQ67tPKHHW+eaZmhL1vmrDh+TpHGqh6yuZ8ih64vr45bmw8Hhecxxnb3377bRwfH5cjxRinBzkZq65rVb2B3HEN1YSKpbVqBfvEviYaDNFAJ8/TI6oUoxPAXVtbi+3t7djZ2YnNzc3Y29uLTqcT+/v7peyWDCXHIWoFgM6tywRZW2wQmxOenJyUTbe0X7PZ7M7GmjpHzA02g3d6sEAxq9JCHVuewzy4jtA5aLVuAs56JBh00flehGu1Lzr3j2mfnbFVJatliursKdFdkSyKTmXAoQlMKFjOjAPNwb8/TxWVgjZ1blWZ81uZzcGc9jOLTOm7fZwecWX9WkTUFpXrtd5PZTqPlGTBgMypBgBqxIUd3FQ4mpxx5ohI3fn5eXz8+DH+9//+3/Htt9/G6elpoaVn+jL+eermCoS5j6gbeQWS3hwI4xD1er1YWlqKwWBQnkOJW5Mh1j4tolPTferYUu5D2Tg8BwhWpcP98BWKS3nNMzCqN/R+X2ONASNDhRHTPjkt4WEtL1N+d3rrdyrjSj8HREdHR2XX3Ha7HS9fviylo6yPbrVuNiWKiBgMBiUwo0dyYWxw9jKnV+erSV/+2G1vby9WV1fL2dnz+by2ThLa6+Y5WfbI2yLjDD+4ntcAG44FpWa6doslFbobvJZH0y+eh3OuRwbpURHaMnlyWfVx6ZyqveQ6Dda4U6IODOvLcMQBPvAa42X8ukmfOhTqyOu4dW0xji3OnNqVJqfWdcTy8vKd8cI/0Fxtr4KuLJv/FE31lwcGGB/AsKllOChzGtWmOiBWOVD96vKj3ymAzfiRvmumJaJ+IoMGGlyv871ipKurq9pmiT63yLRiFH1eRP3sbMcV/sP9+k4F6Zk8ar903IxBaeR/Z7Lq2FhlWufWs7ZZOXLm9D11y/SR89V8Pi/ngpPIAFuwVpVnKeZznOABOXXwNbjhzunV1VXZmJEzczlBRB1nn3fNtvJcgqkkd3Z3d6PX60W73S7nofd6vdoSDvha903hR+dcnUitxqRsF/qw678GRjwgpRhCeS7jQ9WtWuGqdOcdnu3N/DuVCfeXVNarqqrRXvt7n8/3WB/g0fvnu0JGKTEomiqIRU5tpmT87wzo8T5Vpq7A9XM3IN4HmFGdMC+HaXIOPCrpClgnZdHkeIaG56Cw9YgBrte+q8LkM3VKtDngVCdNx6FOi/5P5I2MiPKCOtrQAKE+OzuLw8PD+Id/+If4/vvvYzKZxObm5h3gqEr9vijqj918Pj0C7AaX5jzE9wQOWP98eHhYnqfnp6niURnMwLG2hzhGKFAAsGZu4Aflz4h6mYlGHp0PtG/eb3iCPvAdCrfdbpcjpjj2QY8rUcPooE/pkQEbrtUMsNNM5Ymt6+fzebTb7djf379DF4xgxM065YioORPu2KpsZoBH+/PUbWtrKyKibNI0m81qfKIRYtcZ7qR5y3g2A+v8r5Fx6IZDq5soAajYoE2rU1Rn49hxVi6VCrqpSDYHmfF9iBFusoNNYFHBMTpds88XFxclcwINGL86uvzNu7UUm0yBbqql5cdaqaMlhR6QyRwxBXYq+wRGvIRc6fPQ0t7fd9M+aRkyvO/nX+p9NP8c29j0LqWpO0kRd3eB12sXBZIy/tJMJ/bGnTJ+Z1lPn1u3Fzpmvd5thYN+H4c/g/+1okOvU7ovwqI+R02A2/k7yzz6fOl3TckSTZg4jtWxPGVzu+o4n752Op3Y2dmJL7/8Mt6/fx+Hh4clsKhzohhZdV9EPTvP3xoA4HNdbsIPTi3ZQ96d8abiXtdBzO/e3l50u93Y29urVfKwgZQvEwSvawDWqzQ1CKYJMbK2BMvACB4gdznSeXBc7/zPZzjiLkueLVV592fq+zMMozKkFYw0v2YR1nmoDfisg+F00rUjOD1EdWFKP87FB8NkajTCCacRwQz0uKJQocsiak4sZ2gYFaHRaFFmcJUJeB+gSMfjUUl3TrK+MQZK4drtdlnjp9unq/JzEO/PctCh1+hzNMJDtA26An7ICGS1/ABMFPq7d+/iw4cPcXJyUp7la6d07h4KFH/Mpvzvwq5lyX5+WVNjDjmD7PLyMvr9fhwfH5dy4O3t7cI7vm7QAaM6eDR37OBZSo+Jaq6srJRIJNlEVVrZBiJKhyxr6xtGNI3fFeLS0lL0er1SDn96ehr9fj+qqqqdHavzoqBSv8tAnsq7Xu8y3u/3YzQaxadPn2J1dTV+9rOfxc7OTpE/ZF0zHpqlUpCGDuCogE6nkzpvCnKeg1MbEWUJBDLPmaVEuqEDmywpLzxWhv3aRfe6XsXBY20VmSMi+vBstp5WM5LasmCD/g9IyPgsk393LtDBTY5hBio0yOgAUEuRFaio3lJgrmtsCUowHtUDOj7kxEv2VPdrf7GH6Dh4qNvtRq/XK8taeA9BCOZIgwxP0TT7gvzCQ/QbGxBxo9c0MOm2LNNVji2YH+UTDxo71sn4ipaBUmzz9fV17fnYL5rbGMag3zsO0qZBAZUbHQf3UyngFRj6DMagm/6ok4Jz4XhVl3Bp0NjtWzY3OheZk+pyphuv6TsVp6nNdhutsvQcmvIVmFOrF3QMa2tr8dVXX8X3339fztvO9irQZ2pJq9Jd/Qj+5v/JZFL4RIN3yr+Kn+EzfY++D1xK5nRtba04tGAj9JYu2cDes2GqO8caeHTZVtkB06GL2deDpSdU4bg/4lhG5QMauO5BR6sMqk53O+HBan6gNYFUZBIZ1KpePelAm/pJi3TkQ9rvZCnUyGdGuAkEeISAhsB75MadaAXWWXPQoM/zPmfOpH6n780AqD5DIzAR9bU22Xh5pqfj3Tn3AABRFhUE7nND44Ayo0nGMGo4GQfRIgCjCqfPm84dNJnNZsVJ0JKULBLZpOCfsrkTlfVLQY0qn4x/aRpE4MiRi4uLUlrJjn6aQfK+LHKe/XNVMFqio2vGAGzKB8rDLl+8x3mN3x640WtVhvx73XmPjC2l+ASf9LmuHP1HaeBOo/eHLPZoNCrnh7JpBBULHkhyAJbNOfQiStvEF260nkMDgFDu51FqNcjKH67T9XlZc37W//1vfVZmGDUSr6DCnTkP8mV9auqngyP60+TUan8VXDk/6XXZGNVGsHst84Lj6IDb56aqqjvOrAJ4d9ycV5vGc98PelFLoeEhBftq956yqR0mCLIom/xQ+fXvmubYeVuxwyJ9vMh+OmZQJ2+R/tR73XG8T49lGMEdPe1LE/bT+9VpdHpBC6WJ22/V3/q/jznj7Wz+lSYP0XvZ/Pg8P7fm9tqz+Gov2Z8h24CP+/md6UXHo2BtMBZOre+Cr/1ym6zv1b7gwOnxdfqDY6s7/6u+5F0erFc+cF/J8RXX65nu8/m8LDlx2eMZbjP1c/3efTFtTfranVsf03w+r+FedLsmsHy5qr/T+5ElaB7SfqeMrUfJdAG/fpcBEn9exC1DaaSXpsrNhVyVsDILzKMGkXu93t0FVCdOgZv2xd/HxCLUi4CNTljmyHMNO6VdX98eTk4EnfcQKUeQXJlnDoMasAyk+Of8rQvs6Q80ZswusDDz1dVVvH37Nj5+/FhKA935199upJ+yZXTjfz7TTaLIgBK1h7f9eTrv3L+9vV3Wc7x7965sjtPtdmNzc7O25s+jzPBBk7NJP4kAjsfjoqj39/dr6z6rqqpt4KBZYlVyGiFXYKARclX2EbdrYRXAarRf+YuoKM7tu3fv4uzsrByNRMbQo6E6bpVpmkfnVfbn83lMJpN49+5dKWf9F//iX8TOzk5sb2/HbDYrRsYDZ2pg1fi4jvLypMzAZH8/VVMZX1tbq41dz2AmMOJGsSkQmbVFwM//zpxAdJJmbHwePEj02OCB66lF4DT7X8t5kUvd6KoJFPNu5dmIuh1st9vlM+ROKzwcoGf6oqnv9Nfpmf0offR6pcHKykpsbm7GYDColTrrezQI9JQNvqJagYysz4fqvsvLy3KdNrUhGX5RuckcNb83q4xw50AdA+UNdcIUfGZBFxo8qwFqrleMmPGLPgOdmd1L83E32TWe731VPOK09e94nvYPWurcaim6yjI09PWhWcVENqcul25bn7IpPfRov4zvI252Rf7qq69iZWWlnD9OCa/avoi6k6nBU56v/ItcqVMLVtH+4KDSJ+VJHY9mETmjHOeVABbLMtgFmetIAGh1Fv9rtYHytsoWWILNQrknIspu64wVOlXV7TFc+nzFGyrf3rSyhuuYP8bk2VkcfZxttx2z2axsdHV5eVmu5djQfr9fxuY6Sudf/9Z5egx+eLRjmxlwZWgtZWpqTYaPAbiS4B3OIHq9R+ruAxrKfOpY80xX/Jmjvuj5mcHIFDLM6IYni94ovQEotGzi9R6PHGWAOou4qpCo44by1j45eM2A4vLycmxvb8dPfvKTshGP7tzpTE9/M1r/mC0LACiAVuWo31fV7dmtEVGcUuUTN1rww/r6ehwcHMTFxUVZr4nD7LTmXU3BI5qeI7u0dHMGHVFUSqE1U9tq3e767Av/6T+BFZe5Jn5XMK7P8abGqdVqlX5iCMimTqfTQlcteeEZSuf7fgAjw+Gw8CU7/nJmJbu7q1wrYEJW2Bkwop6dVb5ROfRsgcrmY52u30fD2LNRE2tWI+pBAs2ka8btoYbpPh5uas5nrq+zAN5j3uNgKCI/l/wxz1VeiLgtG3OnIKIOytVW0aBvlgnBTqqduM+WZU6Ig3C/Xv/OgjVNcs7a3vX19cJLnJnLmb1PXY6pgNuPtvKMo9vwRc6a8lIGfJvmyLGF2ne12Y6HMv2SOX4+x5454xmOczIa6PtcF+tztO9qg5yOi+TLg71KHx9vRgtvzvNOT3WmcLR8rTX4gCVbOocZ7/g8ZPP/FC1zNHT++U6Xd+gZrSornqxqstHqXHmQYNGcNcmfzgt9UFnW9f66j09VVbUNLKkw0eyxOtsZVtLfTf1UHQGmpM+KGeib0tD1tY4x4x/nM8Ul/Lijq5iMfmjAAVp1u92YzWYxHA5LmfIif0h1mLfH4p9H7YrsHaIzGinEEKkjqs9oclogEp/RtLxBBd4VnPYhc7BcOWg2Rd/FezzSpgyg/V5EL4yKXp8ZDBW4JsFV5lTHlmvou9PRaQct1LFQReMgyvtXVfUjXXiPMnxTq6qqtknL8fFxTCaTODk5KY6KzokatqdW6q5wIuqgFMPFelJVxhcXFxFRXzuRKSAaCmt1dTX29/fj6uoqTk5OYjqdlvUkyJpm/5rApvIRALHVapW1u+ywqs6IG2ZdO5zRJePvTGFl8ukgw+Wf68nMrq6uxmAwiMPDw5hOp3F9fV2ODFEeVrnXPnoWRPkLZ+3w8LBUSWxvb8fLly9L1uX8/LzoODXuDihZW06fGZsbDJ+nzBl4rGL/fbSlpaXaulTWcSowiYg7JfNKb28Oxvlbv6M9xPnKrn+o7rjv+ZnMKq/rtU0OXPaZOrboCp7hc589V0GlvuexOrOJ9xb13cFIxscZoNPrq6oqGRAc24goR2n1er34+PFjjMfjR43n99FardtjjjyLrMBU9UITjnA9pQB2kQOXzYnPgTt3mQxmssf/nqF1Pnc7jU3SZMCiQJY6MUq77Pn6XnhjEV9Cd+2700gxZ5Pe9ecrDRSXKp117ac7/9m6SMe0iju9H8/BBig9NOOoc4djS1UPm/Gx7lTXvUbk2Ip3uY12fNzUN5rykjuOjkXUcVOHVTEFto+xgfvIGusu/MqrmS70/md8QAKC77TKy32SLLHAb8aq785smDq2mr31Uy+0ujar6Kuqm7045vObYw+pYlQ8r/1Vecx4oYlmTe3R59hCXBqAU0vUAD+kpJ2xnOBZWXCmuHxyPPLiE6TKg+t0gw13CNQg8JO1DLw7iNY++b3aNx2LCpFGh+kvjgfPV+HWyGz2Xp2DJqdD+6NOPs+D4bM1BV6SyrPZnGg6ncbq6mr8+3//78uW6ZxR+unTp+LYHh8fx3g8juPj47K2dDQa1QDfUzTlPRpzpOdzXl1dxfLycmxtbZV5nUwmhf5eXhNRn0cHCwSKXr58WXhkMpnExcVFURjwNP1U55R+8tnOzs6dcyv53nfPJjPKzoIoLXXq1DDAB5ql40flKssKKG25VoM1ym+Uum5vb8fJyUlMJpM4PT2N6XRaA546HpdXlW2iq7or6KtXr2Jtba1kjdy4AGbUGELH8/PzUr7MXCPLyI6uMWoy1A7wnrqtrq7G5uZmvHr1Kj58+BD9fr8YdpyT9fX1VGc72G1ylJrafc7rY+97aGtyEh0k/67vcEPPOwDEHjTMeGIRbR/aj0XPb/pc+5kFZj1QrGWa2LpOpxNbW1uxt7dX+GR7ezt6vV68ePEiVlZWot/vf9a4/qkafVbdpvaTOcyWJ9A8C6tNbYIGzbheg3Ncr5Up6sTyPd/p9U3AkfcyJ26n1Nl1J92rUTx76+PU1sSv2XU6Nsd+LpMZjmyyAdn92g9/h47RcZiu/dSgBc/SpArfqWzgUOi8PRcbQIPPI6IsIej1ehERZWnB6upqjEaj+P777+O7776L4+PjWFlZidevX8fm5mZNr+l8qRwxbjZeUqdOA8eK5efzec320rK505JaftBLqru06s7lCT9Hd6En6OvYTmU5Imrzrs6zf+6BHujjQTP3R3yMPEfLmh3L8RnVt2BQLc1Wx1b3kWGe2BNGsZv6FWrrlBZKX523RTora48qRXbn1J1QOqWKLmMsb2oA/Ln6mbcsIuhOm7/bFbIrnWysn9OanpU5+P55RH13WfrtjoB+n0VfnFbZWLJrs/nMBEAdJo++qPHAKVpfX49Xr17FixcvYnd3N8bjcZyfn5dy24uLi5LFPTw8LM7uyclJKXl8bk0dfgzUfD6vGSaPZEU8LOqu9NTypaq63e1RDSjfuWOLYllaujkXVteskm2OuBuIQXFnhtgDSdq3puCKP9edYpd9VeZZ4IodA5eWlspGWx4Y4JnaH/0uImr0Yy7JYhN1dPmkfxkNNIKrgMcBoNIjM8IP0Z0/ZltaullDu7u7WwJN4/G4GH3WarseaNJJ2jJ5+F2ayk/2+e/y/aJrM2fivjG5zdJ7FbB4NYK+x5+Vgfz7+nHf2BfRQWXWA9kPeQbgSTO2EbcAmjWtT9mygBxNx5I5r+5kNc1b0/+LWqaf9DneR9Wp2fXKA+jopgqu+/RSk27jN+9Qva6ZK73Gx6P9VZ5fRFu9timr2/Q8fY4HLhxfasBZn7mob9l4nZbPoSk95vN5SUAQ1GcnaOzycDiMk5OT6Pf7sb6+XtuDIkvy8Gx1brVCQpMt2ifln0Xy4Dznc6COLY0SY183rUkwz1pq9YInlniXOqX0D4wAD1VV/bQNHYPjRP3bx5zJgdIiw/RZkkIDdypH0IuMtY7F8ZI3p432/XP8sM86x1YnSaMU/E9kxbMZizroA/ZS4UxBe8TCFYLfowznQFeZTO+5zyA5bTJBYTzz+W3EQvuo7+czjPza2lrZhEgbxp4soT5nEZN4GYP2OXPw9V7P2Go0RqMyOv6Li4sYDocRcZPxwWGIuFkYzxbqSkP/fXp6+uSOrUbPlD6AfbK20+m0KCLdbVWjfloNkDmIygfMla7rbLfbsbS0FAcHB2n2w/nelRPK/z5ArWtFGCvP8XWU2nf+d0dTx8TaES3LXVpaqm2eo6X22vT/7e3t2NnZiYODgzg/P4/RaFQy2nrUid+r/NxutwtvqnNGNFb1TdZ0jPP5vGxsNRqN7hhoLVW/DyA/t8YRHHt7eyWjtr+/H+fn5zEYDGJzczM2NzdrVRvMrQYxmgKS/5St6XmZ4X/M95muzsCTR5iz52VOBCXryB/vIgCltG3ioex9j6GvX+syrJ+rXVWbrXRysKpyD51wXDudTozH47i4uIhPnz7Vjs14asdWbbgHlVWvO787sNWmcuJBL6XhIr50J82zPw6A6ZcuoclwizoVZMj47dl5nqd9UjwIn+vO3dgKB826VtGvUTpkWVR3UBWca3Pn2W2609mBvs6F8jibGI1Go1KppkfC6fne4KamnbXdWXsOzTFKRMTp6Wm0WjdLdDgxYGtrK5aWluLo6Cg+fPgQ79+/j7Ozs+h2u8U2T6fTOwkBddgUS1IJBL6CP30dK831DjpZM80Rd0vg3bGF74bDYbRarej3+2VJUubgetZxPp+X8bLZVubnzGa3y9Rms9tNmFiXSuCATdb8NAg9KcUdYNXffKY70NPgbbCdHzmmwQXFpuBDcOp4PI5+v1/6oDtW6yZ6TfPm/KbXPdSOPdixdUct4jYzAYDUTlBXn5UaPOQdi4y1RnkWOZv6Lld+quhciPw+NQpN/ddr3DC50+h9z4DEIqWs9IARnamb+sznGCt3hrN3KDhXpZ8BFneSceigvRojHfeiOWT3uadsmcONEGZlpfxohA/aYLz1eVlryhC4o8Z8RtSNe+a4NgFxfZ4qar7HIVNA4TzqPLLIgcn4RdceKyh2RyIbF4qeQINnsnmm8qwH3vSszEXrifUZ+jnjofqgqqra8UkKhJ2P/LOMz566Ma8KJjY3N2sb/+habdX5jKEp85K1xzhjNNd79z3jvusV9PD/fc91Q5xdm9k6BRDQDN7wLKbyBP8rf30O7ZqcJh1/Fvh1x+0+u+K6TIPSrVarrFUD+E4mk4V2/sdqme328UfUj+XTe3WsrsM8MO9Okzo4WVC+icb00e/R375ZIO/Vqhqu9QzVffjB7RGf6fVOF/QL7+EzHEPPLOl4HzqP2h9v3l/uWUR7HQvOrZ6lyr0ZjsoyjJmtacKBT9XgR3yA8Xgc6+vr0e12y1KnyWRSnDo/UQNMkTV3uljCRXCL5/AsXc8KXnEbrnaJls2h26z5/MaxXF5ejuPj42LjdRlXJg9akq687HhJ+UBLj7Wsnc0aLy4uak6t8ojrJ/3NfDn+5v2O5/T+LOhCf51fSSww561Wq/Q943OfA97J+zTIsAgre/ssb4EX49hyDqYSbH19PSKitv7wsZ3LlJU7RUyOr4d1wum12nRy9HpX/gomMmDLb+2XC1vT+HScrsiy6EYGzv192Zj0vTC0GuWsVMABlxo6NbgqENonzzZG3Dq7WWTU3x0Rte30n0NTkBJx69iqstG1B+pgafTcAxAZH+iccK9mSNyxdCWloDSiHs2nP7yb93pWGQPiikd5gznPzsFkrDQfL9eTKVUQpRFR7tF1GrSlpaWyHb/LtIPQjNaMW+fPZciDdw74eSbRyaq6WXe6s7NTQG5WeaDjbAJNj9Gbv6/GnOoB7JubmzW+U16gKd1/3+Dssc5Ppm+avlfAo2N2XvP7F/VJ7RlywHsAA6qfI+4eXUV/tDXZnOyaRdfq+DLgrbLiTl5msyLurq9SmWy1WmUXeNbMj0ajcjzIU7bMeXTdEVF3bDUwoXrQg3p6jfKM6gTeqfdnoJDfSq+MD+EbXY7i/MjvLFirPAl9MoCqvKn90+sVWylfoWe0YiHLnuqzfc6aQHSWCc0cAr3X/3deB9uQcfONpNRGKo66D4c+F6c2C8zgeI1Gozg4OIjt7e3Y3NyMiJts7vn5eY1nsNV6bGWmQzVQR4ZxY2OjYCrkS/EXMsRnuv+IL9tb5Fw5T1I5sry8XI6v2d/fL/tmeEIDWum5rsib63vtu5cew0PT6bQszSPrmflUTQ6i8p3KuGdgVe71b22OUWlXV1cxGAyKYwvu9/XGGaZRf0LfoTRtys5n7VGbR+lLW61WTKfTOD8/r3Xs+vpmw5sXL16UMhSiLR6VhODcn4FPZ3iNKOoEalmkNyWkTrQbbIjnx3TwjkwIs7p3fUfE3fWyGS10IjV1T4AAYda1gXqfKpvMiDlAc0dBP1NaIxBesqDPInrG9ZTboODPzs7izZs30e124z/+x/8Yx8fH8e7du/j666+j1+vF1tZWuXd7ezva7Xa8fPky2u12bG5uFpp/+eWXd+b2x2pNwI9MX7fbLconok7jDAxmoI/fnnlZBI4dHOg8NQUq9Nl8prKHAj0/Py98QImMAjBVhJSpsOu18oxHH1X5q1xyLuT6+nqMRqOYzWbFEcQYetmWAyXXCzp2D05xHfd4hlfnxOmN3KuTwRgpxzo7Oyvluuvr63FxcRF/+7d/G/P5vJxPl82Xz6vrnKdsl5eXMRwO49tvv42Tk5M4ODiIXq8Xr169KhtNaFCDlvHi77v9ru9yhy8DDfc5ww9tDvTdxvB+BW44T6wzBzQ+1AFcBOKzvrkNVHkDxGW8qtdmAS6lKTykx2oBDJ+6+XIibPX19XVtjT/f6xm3/uPBkYgcaEfUz5ykKX7AhkNbDS5mjpED3oi7ZyrzHN6FfszWGHIf41XHxY/9ypwY5S0dO88HCyldHZcxFrUn+h1BYJdZXZqV6Vh/Tpax1WVFNJ2XiKhhOucBxU6azXR98xxaxqOt1k2VxYcPH2JnZyc2NjZic3Mzzs7Oot/vl9MuWHt5dnYWS0tL5QggaOgl8fCNOo9aCTGf35T5oit06RHJLtVJSnftv/Ka6jHlb55FqW2n04lWq1U7yoh7I25xNbso63udlsp/6A/mWzE0YwRjKs94oEubyiR0Vf2hiQgSDMiY7orspzioLiDBqUvXNEutY4ZOztPK65oUggaU9T+kPeq4H14KASkvcCPXarXK7lma+ckcTNoiwc2EWxWif+fXNzkRzgRqfB306zv1+gzs+Lg8o9Y0xkU/GrlwxZ0Zi/vomSl/d8qb+t5kjP167e/e3l50Op0YDAbx/v37+Md//Mc4Pz8v6/Iibgzo9vZ2dDqd+PTpU3S73bJWo6qqJ3VsGZ82aMjaCD903BVWBu78efrb+T2jP9dnMsV3WdZ/0bMUtDCnCub0OuclXy/FO7yP/pyIqDmt6qh6lrXJWV/UMv3gY1Y5yvSD9x3auIEClERErK+vx/r6etmsS+U3M0bZ+zNj9RRNN8Uaj8cxHA5LiTU6M3OqmvT0ouuaPlvE5//U4C8LMuh3PqaMV+5r3KMy56Webj/hGeiuoI93K1/+U9DFbWCTjcpky+2XP5PrXH8oXRfpzR+rZY6GAmDdZyTLJDr+yXRZU2u6NuNDtcGLbIk7EAp6sRsejHcc4mNRoAyw53Pe7YmLJhrwfM12q1z4eNxxdjlooqvaR9W/WTAgsyOus5kDDe42lW9n8/dPrcf+KVtGS7AeR+9VVVWcTY69Uf7BuWdudF75UX5UvuZ83LW1tZJUo+kzHGu4fuJ75UVallmED3U/GypK1tbW7lRdaXApq2JymdO5X+Qr0A/FEJp11bG4PsjkXXWFyqyup9X+ZzqZPmlgTytsnO6efHC+yuivtvEh7dFrbHlxRMR0Oq2d28Skr6ysRK/XKyloFnzDsD4QH1BTc4J4tK2qqnTg+kwlFM9xxteJ9+coY0XcClO2BhQmUgbRzxEQdSAyEEXUEiWhTKbGyB2ADIwpkOIzhJCNSxirC5k+14210ornUY65uroav/jFL+LVq1cxnU7jiy++iM3NzRiNRjEej+Pt27flXFJ2UI6IUlpKBuwv//IvG3nj990Ypzte8GG73Y6Li4v48OHDnTItB6gR9TnSz3hXZkDVCGd9A9z6s/09EXHnuvl8XiuZoQ9evkJ5TEQ9oq+bZHEdffUgx2w2q0VXGRelSWqQnFYK4JVm2jKgpA4Bz+E6lWcHN9oP/UzXQnIN9INu8/m80I8N0zQiD115PvTLnOzn0Pr9flRVVYsef/r0Kebzebx8+bJEhd2ZcZDc1Jrm8r5rmj53OfpdmvLMIofhc5+NrWQ9kpZuZRVE5+fnhaZUOsB3XJfpisf0KRuPgw6/xx1QBYS61swBJTxCQFzXfT6npnaADIg6trpMhL0hwCkKNNXp06a6IAP2/NCagtH+vb7Hs4n8rXoXPcbGR/o5CQ0F1prV0ewOy4h0PjPgqu93AK12U0tQ1b7ochno71hObbLSQoMAfnSeNsVDyg86d/D3xsZGwW3D4bC2cSRjyLCZ2mHn/efg8Lq+q6qbfSSurq5iOByWMlT0D85uxG2WbzQalQoHmmIGeM7xAPZ0bW2t8BDLFriGZZHYUioH1XfI5s/xluN75Vnm8OTkpFTL8D0Va+12uzyTjK3+YP9VVpAdqjR5Lzp9Op2WAIIud9JNyTT7r/LpGFA3j9IjCNWRRX5VplWOtPoOnbC8fHPUZa/Xi+3t7bi8vIzJZFL6qklDTYBpQ27VQSeT/NDg5qPW2Lozo/XjCuq843rPIrDmUUAfrPeDvxddq/8robSERvvlzlzWX+2nMn1TX/XZ2gcfgxsyhEij+bxL6+Ij7q4fiLgtYVMj4Nc4eMhop3Pi/dY51v6r8UQ4OO5ne3s7Xr16VcDcaDSK4XAYR0dHRWjZUbDf78dkMonxeBz/v/b+pDe2JEnvhy2CYwwMDnfM4WaiqlVdUHeh1FIBDQGSlgK00VqfVCtttNDiDwgQutSo6mplVyor82begXPMwSHiXfB9nL/z0E6QN1t5yQJoAMGIE+f4cTc3N3vM3Nz9vol95/3KdA43xnUg0Puav/ln74cPqW/dmHNl6+PTI6k8hFzAQZsGKSji/S8ZUHk8M1b/I6pKTs4zAz6UXzql3l6OZ+ev99cy8vvrHF83iO7MyTEZjUZxcXEROzs70e12o9fr3XD4vQ1ZQOI+aTKZFMCmcS2D2Ol0yo7S5INHa0VZu9hu3XNXhzV77rZxkvVfBnDYlsyJuO1ddX0pPXF5eVkAoY5PYsqijwOBNt2jshVUUF0I9sXf2+rn9mmZ/KlO7sjyz1NX/c9tmmSIZyByc777JPGEDrrAN9fDCfBxbwkGOd0eZ+R9wb7PnFnHDnqO+tivy165I+l6VG2S7mbgk4E6nQKg/7L5dGzpdPq4Yp3kVOs/Mxl4njDb49+Jkyjf6os6HSS9nWFZtydu2/W8NjCUg+c6Pps4qbue4Yf7IurAiGoWEzN51JfZulbuzSO5UP/IUXR9yT7Wztp0hOVoSa6yYEY2ccRrGfZmoIoBn8ViUTm2SM5hr9crzqnao82t6De4Y8uxqpR+lSk+yUlXsIBpvhlG8QCs+kRlE7O6rC+zZxlf5Hju7u4WfLO2tlYJ0pIyv5DvdyKOvAv96BlbAl1G8QRwKTj6LkBUB3AYya2LjGUMr/P6s+eosNUWKj1XrnXT4hlo4yBi29VuXXPwxGtUwhpIBAZyBFzB1Dm2rhRvA+Ui8okGQ+9m+gJ/z6JF2j1uY2MjfvGLX9wQ3MvLyxiNRnFyclKyAN69exevX7+O3/3ud/G73/0u9vf3b/T7x6QMNLiCZB9kawEyELgMbPp/jo3bysgAqjtM2Z/uJagRabwrAjedTmN7ezsWi0VZg8v7aYhUnpQcwRLrJMCutbUZmOPMGdvIfnFecCbJx5iIv2czSs6brM84VuWAKBDQaDTi6dOn0e12Y2dnp6wD8nFHY1vXpvug4XBYHFuB1oirdGtlVWinSw+KUN8uA/VZXy5zPv05dyJd5nlvnbMpUr0JmFwGPUJeR9l7JOuz2SwGg0FMp9Po9/slhU/EWTF3pDRzO5/Py8wAdRVnyeuAfFbvrL53fZ42MnNs1Xb+tlhczcZ0Op0SKJNDpGyH+yYCWtVdu7+urq6WTIZ+vx/z+bzMXAnYqt0a3wTgDqppe7M/z/7ylGHaYnf4RNzEiDiHxFlaAnkR19Nqtorr87jnAmWCsqHyiCc0C7ayslIyAunY0gHydtLO0iESvzWeM57zGv/XpWJmjq0CNPP5vDKr706Gg3tiTxFx5UOwAY4JRQrkDIfDODk5iYjr7CR3uuTUSnexfy4vL4vT53xVmTwyhjOyOn1DcuN2X7/xmsYmg4fEIrpX8qfj/y4vr/biabVacXFxUU4/2N3dLfKv+zn+1SafJeUY3djYKI7x+vp6nJ2dxebmZpkB11E6DJZRLj19mHLnGWPuj2Uy6H3vtn0+v5pVbrVa8fz58+h0OrG1tVWxn+6E67Nn2Gb2hON6mY0l/egzVGiYJ5NJOTtSHdLtdktFdT8NWwY+dZ+UFtN7s4EkZUCllkWPHQg7yOf9FG69l448c8mpPGlw1DZXvJnQuFFbxjPOirswUoA9CpoFCPgs3yPiInE3nhE3d9d1B4DKYTabxfv376Pdbqep4s1mMzqdTnGO5vN5vHr1Kv7qr/4q/u2//bfx3/7bf4uvv/76xnMfk9zxI0BvNK7S73XUCdN5I24eeUDZIX/rwKQPZikJT8mlfOuaE+us8tWX2eYgWscioCKQqTGv9BgBGqbdqN1MWdH4YVBM9dEmEzJuUvpssxS3K0mCRfLRgQH5z3aSb9mz1CHZ+j8/25FjkY66ZqR2dnYqqdwsy+XtoYAazSrKCdF43dzcjL29vdjc3IzV1dVyfi/l0EGhKHMy63Sh68lsbCwrL3s2q5NAN5d+RFwvCfFsDLVPYIEOg4NXlT+fz8sYEhicTCYVx1b102Ye4q+c2/l8HuPxOJrNq2UfAiwax5rZoO3JgkG3EXlDp4GRe5dPRvQd0PvshXSF9Ee32y2gd21tLba2tj5o45CfihiEoh7TmjJhH/VBFmyXYyv5pLxkgRLqKwa83HnN9CHXKmfjggEGt9v8jbpbZROYyzbQ5jugFm9kZ2Rb5DTrPqZnSyYirnQPgyKUHR/nshHkR0R1oyjdR/6pbiTvg9uIzgODDdQ3HqDIlmjQsfJ23Cc5XqUjtba2FpPJJE5OTor+0h4p6uvZbBZHR0dxenoag8Gg7D3BzYSkXxnkyDCydLHuUfAk4jolmZibDqCPB33P7Lf0qc6VlRPX6/Via2srOp1OycSaz+fR7/fj6OioHH0jjKS6sr5c/jcajYpNYDrwyspKPH36tLTv+Pi4HIVGcn9E79T4pvy7f9BoNCoZWOSNynH+6DfxRo54p9OJ7e3tGAwGpT8Z7Pcx6zLF//RdPkT+f9TmUe65M6InY0QmswxXRizT7yPzXSHz96zR/p7b3ksBz+rsQuJOR52D6kbKOysiP3KFZbAubLOnfnt7+byX4+XzOSkBf68/Xwe4M+U3Go3KTreeHifl4wN1e3s7nj9/Ht98882t6/N+avL+dnll5FoKPJO32/qh7t1ZPbLZfzpvtykCH1NZ0ImGXgBMm8IpGi1AwEg7AbXKdcfWI3kCOIvF9VoVNzgRN3c09/Yv4x/buizQ5mX679m48eAQDYPaJkPmayDr+E/9c9+kVEuNYQF4GXptJDUcDlN+Oc8y+cz0uK4vG0vLKBsLrEsG9rmngdKrGZDxfpEDKjAiQOJjgONATu1gMIjBYBCTySSGw2F5R8T1umuNEwHGiKoD3mg0is6hQ52Bw4zHWf/4Z+e7y+xdxiJ/c4CjeiqVVXZhbW2tss74viibqXBgrX7QTPxt2IbkIC/TMXW89jIy3JFhBfahnvfr/k46ZJRx6kDJq/hF3MNyWBddY10lD25Tl+ntrL0k2Q8H087TOl10V1qm3+rqWFefDwH1PyXV1aPRaJSgvk6HUFCKtlzYbzabxXg8LoFD8oHBo4ib+JjyTXnR92U6i+W4/hE5jiZuUSaN1tEqw0TZSlo2d3h4WDI4fL10NsYYBByPxyWw3263y4TJxsZGWbfv+G+Zn8VAUzZ2nH/LylHfuG6g80zn2PUL2+9jwfs467u70gc5tv5SpoVIoU2n02g2myUNR430tRHu3Kl8Cf5isSgLjnmd9xPsMaUhq7uDkMyoMFqYRSUEdnxqnErer9MIMNWG9aKAzufzEm1SWYw4iX/u9PO72pMBem+z3ql36T7fBIfGSs/6YGG79Zxm3Y6Pj6PT6aTvyhxkycP6+nr8u3/37+I3v/nNjX69D3Kwof6SEn/x4kWcnJzE69evS9t4DIL6yGeuM5BHBUjHkf2mewmUXGGJfLaAY9L7gTMJFxcXMRqNypoypQwK8OtPjoDqoM8cI0wp07oMKcyzs7NYX1+PVqsV3W63vEu6QA4VQRSNWZ1xInmmgfi4rL/volypI5z3HCPD4bDMSOnIHzd0bIPKvm9QHxElAq1jyDY2NuLk5CQ2Njai2+0WuWbArQ4cLgN9ddc+1MBlRr/uHoKXi4urcwplrCmD4/G4cuA8nVw5tkzFZGo2TxFwx/b09LTMCDDg0mg0Yjwex+bmZpmVPT8/L+MmIgqQ5HhS4En153IA1enH8lXEMchrdBp8TJLPPvO2snJ1BEin0yk7jqoMjq37Itl/8VsZasI5mvHY3t6uDW5G5On4zid+lm73DZukm2X/eZwHdYo+Z/aDTgTlLksfj7jedEbLi5juqP4hbpvP59Fut8tOtlp3pxnb6XRaGU9st0A89aRmbiUPCiaTj/7ntpXZaFnWgT47r9w+ZzpZ/a7ZRNpyrieNqB7XkmFE37jxoZDrC/WbAlCnp6dxcnISz549i1/+8pcxGo2KT9BoNMrRd3/605/KqRitVqvwlMvtpFNJGjvKDmNwRRheOo4TURxz0j3UKZ5h44Hv8/Pz6Pf78fnnn8fLly/jyy+/jO3t7Wi32wW/fPXVV9Hv98tGi3Ti6JBy3HJiUPcqtVnly4fSZ8mm/CvWk7iDgSbxVUugxAfKs8aDZxwRN1FH6JpndsgXZKZNtntzne1xP+FDx8AHpSK7c8tUFyl1GdJsWpyMZqMYBfBGeGfxmhsA/lZ3nwPOiJtOMY0BHVsaYj6TRR75PiowGms948qL5XDHNN3jwJF/dJ6ytrNtzg9952eW5860E5W4PmshvRS+UuTqdgVU+5VqobUFDsbug5w3WdRdaydns1kxcOovGlA9y3T7OhlwI+2gRffzOX+eSknkRp3PUGYJdCh7lA0pZf6udzEFhuWrDpwFZPqNjxVvX/ab3+P95vzhM7ynTgdlPOdzbijURtc17AsHj5kOewjE7JxOpxOrq6sxmUxKdJnGzbMJOFbu4nAuI7cHH/qsf5fcKiKvNrrB1xmMTC9jcCXiOlVXOlvODcvWuFPa2Wg0quyGLECgZzudTgkcbG5uFrmhbhGwkk5RfXj0lChL7VvGI/IpG3+069kfdc+yP/FPzgp1wMXFRUnnv09iv7Xb7Wi32wXvaOwzFd1xg+sb6vK638lnn9G8jbKsHn/O30XcQ6BKJ0xp91lqocqUM6DZOdl+2UUtbdB3ypUyFThZQCfanQ5do8x4fUSux52onzM+611eblYvtUX1yrAvcS+Xr7mdeAiUORjktfRXv9+PbrcbT548iadPn5YJIW0MKj16cnJS9JrrFpfDiHxHY8qlr2Wtw8ki2mmfBSZxLGxsbBSHXLsfK+AljMs+ZzDTxwknFTzAIl1yeXlZ/musRERxcOXYKnhKfnEiTTxbLK6DNrRhGZ7VtUyPc2KEeN+zg2Tv2GfLyMdU9vk2+uA1tjTkclAUrZQBYkQgc+SyKG9dREwd7yCJxMHGmTBXGiq3rgxXgjTaisDRoHNAedsyB5EGL3tnpjCY7qd76TzwXQw0SFC9fVQYIjrmJB8UXPuQgVOPCEtG2u12JUWFB6x7e9U3w+GwgBv2431RJhsO1pvNZtkcaHV1Nfb398vaOdVf4F/jxvngxoyAWSQZdFnTeNH9mWGm0fXUeq+HosYqW+NA8sx1sFJ0Wo7AWdS6NslRZnpPBmpdzmn8yDP+RvCRKXYH1PyN/ezXl5F4kOkYPc8xJX1SB3zZhodAkpfZbBZ7e3vR6XRiNBrVri0XsS1ZsMLvXQbYCXoi8tTluzhsNNKMmPP4OrVL90yn07LrpzYI4pEI6nsBEa0dE980y6drnLGiQ62jz05OTmKxWMQnn3xS1nKur6+XMe4py1oPT/6qHpQjyWDm9GR9p/8ZmFd7Jfv8o67hePN76Aw0Go0yu0d7Kefovuni4upYk+FwGJubm9Hr9Soz/RFRgrA6a5NgM3MCqacJMrOxIt3rDm4G2h3zRNzUna5jfMaKNoJBds3YiidZBlJElLV3/X6/4EWeha1ZcK5Jlpzr/WoLZTkLyJJnPiPuRLDuOEmU4R3yMLMRlGvxk9gvs4nEle6Yq8y7BjJ+aqLeJJ4UCScfHx+XDQU/+eST6HQ60ev1Yjgcxh/+8Ic4OTmJg4OD2N/fj0ajEXt7e6n+EX+EsTPM7EF41yd12F/PUnaoG9lmltlqtWJnZyd2dnZic3OzcoKHjjEUhuHEAIOalBf1u65xg835/Pp4H9oQ+QayP8x6owOtNlLOZLd8wrGO/xE39QJ1g2yQMoY8VVpt9n6gLc98Ez37Y2T/R20epRfv7u7G5eVlHB4eFkZpAxilS4kpXsksKkJHkgM9A93qMB9cVFIOsjIDkA1UKk+CHilX7ijmAEGOvdrta67c8fOIp5wJXVdact36TTpIrqy9bJV/V9C+7LoDcPKYg+zs7KwoO0Wz1Ee6jw6KZOf4+LhExDOw/LGJ/cP+0280kGtra/Hy5cvY3t6O2WwW/X6/yJBkQAb9+Pi4Iu9UduwrBx8uO+7k0cn1MeF/XkYGNim7XGLAsRhxBeYYaNGzqpvGFjeN2tzcLIpaY0vjj1vhsy/Ic76/znnxwA4p4wHJlTEBj56dzWYFoJEHfEez2SxrctRG8tnbxmsPgS4vL2M4HMb3339fNs14+vRp7O3txfr6enEKfTxkoDsj53/2XVRX1l3eoXLoqMsBcSdEz+g8ynfv3sX79+/j7//+72M0GsXZ2VlJTXv16lW0Wq1SthwyOcwCJ9xIRUt5ZrNZTCaT+Pbbb+Pdu3fx/fffl9R/yYp2C6b+1OxAo9GopIDK4WQQSrqVMwpuk7JgUZ1NJSh0R4mOh4O5DOBwLGg33a2trej1erG7uxvffvttcdzvk2THBoNBrK6uxvPnzyMiKjMVlFPZcvWV74shygIHHEfSu7yfek86lc7RbVSnazwtmRMV6jfHMZeXVycbCJMIdEsfDAaDck3ywKUr5G+j0ShjgkED2UzxQ7zhsiny0jGeYxXaN7VNddDvuk7b6MTnfGxMJpNy0kNEVGSEY4JYkgEDYsiH4thmmE8BKfXZ0dFRbG9vx3Q6jZWVleh2uyXg/8knn0Sr1YrPP/88dnd3Y3V1NY6Pj8vRgeS7B9M5lk5OTordnc/nlSVwzAj0+lLfZG3yQGCz2Swzyu12u+C29+/fR7PZjP39/Xj//n0cHh7GP/zDP5SlJc+ePYunT5/Gy5cvY3V1tXIcmHjGfUQODg5iNBpFv98vwaPt7e2iC6lX19fXo91uV2aqlaqssRNxveGh/ArhLuIvyZ++E1cSp3hAgTpeskqnVJlHdcF5d2ZpG3jPj6Ef7dg2Go1otVpl0xA6Vtwliw3Qs8scFZXB73X3+efbACAVGA2QP8fZTip4d2bp1HoH8T2MKOl+diKVNGeA5RzSsNFQZgrcB633Wdb2DNC7sNVR1ma9W4M4i+B7fVUvDV7t9lk3C/SxKVPkGV8kJwKhl5eXZV0RZ6snk0k54ojl1/GcPPL6kPd+jY4Ygw6UHzfGlCGXZcozwRODQlmd/Hk+p3HBXZDVXso32/ah8pkFebwcRvyXkcuiHB8/8oyGU2XzjM4MJNQ5uQ+BFourQJsAxXw+j263W9bASa50r6hunNT9pt+pJ7Oybuv7unJpmAmapauop+hE6pzZ09PTeP36ddnZU46xZiUYnFRgT2uNlMoq8CGeaSb4zZs38f79+3j37l3JdvEZXaY407lRm5gFoeMmxDuCN+kmlaGxmdmH7Hud3fCA2W02n+WqbnJuBXYjqhvZ3ReJx5ptlx6ts+3k6226xfVQRNW5YpCf40MkXelO7W1jzd/v/X0bvpIMa1JDS4hYLp05zvjov1+Xc8tx6llzKtvxTqZ76nSp89B5Qb4S47lOymyJstQU1IrIs4dURl2ZdbNp90F1vHJsORwOy4y87B6dtdXV1eh2u5WgiMa8l81UVsqLdCv7hGMuw0uZ7V6m0+iAzufzaLVasVgsygxtRMTh4WHs7+/H/v5+vHnzpsxGy8nf29sruIaOJoNDi8WiOILihSZCqPcWi0XhJZcDqK7E3rRzahcdVc6k6hnhEvFQuoiTkplc+viV7E8mk1txmo/B7PuH0o/eFTkiotvtxvn5eezs7BSj3e12S6NkPB0Iiwk0BDTQfN+y+lCY3XBSqNWhnlLC31SmAyduAsBUnCxNiGWqnWq3joPJlCUFU+XKoRUftVZVs2WK8KvdWXTcy2TdFDnVs+7wqo6+aREdEfYD+0L9K8CnjU2cZ5yV5vfZbBYHBwdxdnYW29vbS+XgYxGDD/rzTQ0iqhFv9f2TJ09KmxW1Pjk5iePj48omU5mR9bFBeWPfE0DxOd6r/qRjy36jkvLZK8qy9yGJ45AbKbnMsAyOJxGVsYJmkkE6HzRgDv4IClWeRyqdj1TqqgdlIDN68/nVFv+UdaUjao1Zq9WK9fX12NnZKetyPGXIHYAMtN03TafT+OGHH+KLL76I3d3d+PLLL2N3d7cy6yhZibgZlc2cVVHmwNY5tdn3uxIdWo4DHx8y6BFRAOpwOIzT09Po9/uxv78f33zzTXz11VexsbER//7f//v44osv4m//9m9LwFegljO2Wm8o23JxcRF/+MMf4v379/H73/++yGCv14ter1c2DBHYl1MrORPIkSzKdgj0MV1NqdUKoOozgy1uU6lHuI5a/KdOJG99XDn/pV+o/5SGyjMRGTi5T6IsK/CqlOSIqPDUn6HuVHs5/vW8iHpH+EMzNRE3dSbHFx1cdwLdISROiIgbwR0fY8xWiYiyVlbHvEimNUO3vb1dsg4cn8zn1xvMCAATtMuu8Tme+6m20ra5o0h+Z/gwK8ed54xfukbeS9dPp9OylOCbb76Jk5OTsgEcAxY+M17noDEz4r4p4yF5rpnS7777LjY2NmI8HpfNoZTN+MUXX5R0/j/84Q8xm81Kdqd4JPvI93K2ez6/3lhKk2jSe8RexCvin/BsxPWxQLQF7Bfdq2NsNEt6cnISX3/9dTQajej3+/H27dsy46pA5OHhYVxcXMRf/MVflE3xZAuY0q+9G4QPlJZNXDebzYoPoeA48QgnxGgH6EjT1k2n04rOp5PLpaXsc/2nDZAcD4fDiIiyydVwOIyvvvoq9vf3y/vdFxFl2SW3+X+30Y/aFZmCrOjcdDqtgEJuaqH7OVXtURKWmzXKQTrrRKF1ZVRXtoNir5MrHA4UT7Fkub7e1qOSfr8rYl2T8FBZiIdK3VTk2AEIlaA7IOzHrP1OmcPFa3yv6uoOlw6cVhrH+fl5rK+vV3ZCFG8JLB8SqHd5JRhbdr/fw2CPZiYEBEVU3jQe5Lne4X0YUR0frAsBp4MaBwG3BUgyGaPjSMocEhp45xXbKmXLyGJEdXbZ5dn54+8hAPTnnW/8X3ff6upq7O7ulvWS7nyvra3Fzs5OSd/lLobsK69jVof7JIF2bXg0mUxKZoLv7FinwzOwrN9EH+qw3vas60XJNv+ovyUbnH12faxlEq1Wq+KoCaQwnZC84ZpCzTroGW2worVKW1tb0e12y3EZEdUZCzkABDpsM/UKwRyDP5TpLKvIAX1mh90hILlzW2f/2Tfisc42z8q9D3I9p5Rk4R/qNd4fcb2EhfbMgz/U93yeS5v0fuoNnw2uG38RN/dY8D7w7Cq2hdk03E1Wx30tFtfOnTIBJIMMjGf2SPK7ublZZFD3ECNkm2k6vspmxzOMk8m4y+myIA9psVgUeTg+Po63b9+WjA5uepaV7TOMdX8PiRwziJQdMhwO4+joKN68eVP0peth6saIqEwiRVwHdOjkUu4oj8s2VK2Tg9sCceqbZvP6PGU50ysrKzEajYqvo3t6vV45c1yb/slJ9P5WexSQEa53TKhrWo7IzQGdZKO5ga/GkHgnPSN8rsky4i7+90kJ8kdjVNkrtB3aX4Zjn32QyZP30110WkYfvCvyjQL+/0Ir5pFJ7LTFYlFZk0blLHJAzs6jonWHk89Q2LOIpIPbbH0RhZ11keFXR9OJZX1o6CX42aJ0F06CdS0Q1/PcqEc7kjIqQ6fAHULyhtFcN8TOK/GRAQnxx42x2q4UCt2nlJPt7e3Y2dmJ6XQaGxsbZT2GotBce8pokAP9+6KsDuSD9yVBjKfRSPkrMpY5g1lfUP5Fkin2k5QNn2NUUrx1ZaN+9E0OVFe9i+W50uMsLcdMFqnz+hHAaHxyDGUOMvmSzWp7+dQ9fB8d4LpgisuA6ri+vh57e3sljej4+LisrdS2/Z9++mlxbjVu3WHwMet/901KgeXZqzpnjxshkchvySnHS+aUZm0lgHIw5e+rI8ksZ0op541Go0ScNVY1NrmOa2Njo+wvcXp6WpmlluPv7zs7Oysz+kxh1e6gWp8sfrbb7djd3S3OrTbrYdBPjgCPg6D8CswwACU97fpF/cMZMY6TZWPFZ7+9L8njOpkWr7iRXK/XKw7DQwlwilTPo6OjWFlZiSdPnpRAZcRNYMZZKPGagXHqYndW6eBq3NDh8wyyZU6Q96c7cwL4HgDV2ND12WxW7L2Oatne3i6zNcJ+HrClnSE+bLVaJfXewbLayE3F6NjS6WFd/b0Z5sxsA7Ec+enjiO3RuN/f34/vv/8+vv322xgOh3F8fFxxAlgO+9gnTTKePQTn1vECr0VEcbz6/X68e/cuvvrqq9ja2oonT55ERBTZ0Sw9MYkmPeQ/yOZ7Fpp4RV+Dew3UObTeB9RbGs+SJ/krnFmVDEpeBoNBqZ+cWmJiLaNotVqlnqq32qT0/cViEd1ut+hf2Q3KMzNrMlupuvAMWf1On0Z+gO7j8XTCWtwrSO1Xu7mmXbwfjUZlgz/ZtH6/HxcX1xvf3sW5ddlSOzzQdhv9qDW2fKEcLd/9zw0gDaoDbzXGwa8ExN/pBtEBj4NTbsaUObt8P51eTwWRoAtkyFAxwln3p4g+BZKbd9Dp5QCkISOQlmAyBYHtIjhhu5nC4HzgzLDz29ML2ceu/BXNPj09jcvLy2i1WvGrX/0qXr58GU+fPi0DV4NB68Z0NqYGpyJUWqvwEIjyl8lq3cx4RHU3YCkpzWJ7meSn+pN14D0+HmioPSrp9ztwqgOzlFG1kw6rfuPxIpJNgozMSSN4ov5Q/SR/+l2z3gJXfL/Glc8IZ4oxCxpkPGbwgoEE1XNjYyNevnxZ6nJ6elpAm9qvjTNorNh+589DA/Ii9cVwOCybXDjIpKwyABJR3XDLQZzzJaI+FXmZc6uyMjmrC5Z5sJI72urs3sViEevr69HtduPZs2exsrJSUro2NjbiX//rfx27u7vR6/VK2+Ss6SgIzdCyHqurq/H555/H8+fPixytrq5Gr9erACNuDqWNlZ48eVLZYErv486aBOHkvZxh2qGI6yU0ziM9T176jJru53/XKRxbLEN/mh2RDXn//n0cHR3FYDCo7e+PQcQFaqvArbIytOZcMhFx1U46cSqLvJHDKBnUddpy6jWRz65zjNBm653eBwS+DH6qPhFRCTxzNknOCJcrRUQJXDsOZD2EB+WsalaL55yr/cIC3FxQZTKrQnzTn+rreIczu+IFAy/qM8dw5CEdrEbjaqOc169fx9dffx1//OMfy7nvdIYz3ZYFMPQbefVQqM5u0UbK4RyNRvG///f/jp2dneh0OiX4Mx6PC/90jNnR0VEcHR1Fs9mMzz77rOKccWKAAXn1kWRQ97u+kY5RoMRTadUOXo+IMksqOXK8Q35okkLBuJWVleLYKuirgKoHMNwu6V1cuqi2MfuBtlT18noK03OfB/GKs+Muz6qLJiq4cZ1Ige7Ly8s4OTkpm8cRc9YFZdzuezszmb/N5lfqdqe7QK48GU2IqILUOgfKFYU7BHXv9eeye90RI1NZxrLoTgb8CWpdKBXxYbuddF9WV/3uO9+pPBo51cUj6Nm7ZXw9Wp6R8yHjMwMNDvx1TcZRKUlydl68eBGffPJJ2eWYKccCh3LaOXOgwcwUlPug20B0HbhwR1X3S44EGu763mws8TfvLyoZyV82JihnmZxkRtjHl65xjMnQ1YEE6ggHW2yX2qFn6DBxDAhMZUAka1PGt4zPriscBLXb7ZKWq3Obpdip29gHNMKZzvB+vW8iTzXzyPZJ3jwIozZGVNMpHaRHVPsksx387cfUO5NDyrAAFd/tYEcboERcRdyVcvb555+XbBo9R53I2WKCwmazGTs7OzGfX23GpfdqFkt2gfLfarXKeyX/lH3qfM5OiO+ZrPM3Oj/eTw4exa9m83odV3afO1W6JwN20o/n5+dxdHRUdqB+aHR2dhaj0SiOjo7i8vJqYzCtK9VaevWd+sHBY0Q1W20ZsKu75jo16x/XhXxHFmAgaOb76BBKpvge4hi+y/EJg6Uad9xQjY4S9zahvLuMs556p89wZTxxufTx75+pO8SH09PTODo6iv39/fJu39XfdQ71p9ND0fukZc4Fea0deN+9exf7+/txeHgYW1tbJSi9WCyK86aA32g0ivX19Xj58uWNWUfaXdpk8d6DNZkce+ozy3ddRWfWM84yjM/ArdqvgI020ZrNZjcmCDLcLaeVepqBp0y/RlQz1phaLN+EGZF0WDnz7TiMPPE66rsw/Hg8ToMddT5RnTw5Dszw9G30o2ZsqRgiro/4cC+djkkdqMgq7oavTtm7s6f7qXykADMA7kyisfF3ydCyTdpMZDQalcXTrVarrL0SwPENe1RPlclF3LqmNnBjJf2pHJYpUOMgus5wOn8znrgyl5Kum63TpmE6gFsp1CsrK7G1tVUMvvjc6XRiPp8XY3ZxcVEivXt7ezGfz+P7778vMya//OUv64XyJyYHCa5kdb2OnOeNRqOk241Go1rjRuXB/vVyJeMe6OAMI5+hnBB8S26knCOqR1C5opJs8Jw11Uky4pum6Z1MBfLxwfqKDz6T5NFKXvPxTqfAHXAaC+erG1LvYxkGZSlwvY3a6MsavP7sO7Z3Gci9D1Jdms1mTCaT6Pf7lWwVpdsS+LKv2VfqE293ZkCdPsSpZbkM8PA9nGVgxkir1Sob4C0WVxsGydZ1Op2IiPjNb34TW1tbZZZO7/L3ZcElgQ2lc2qGwIGZ3t9oNMqGUi9fvixLPMQr8V481piMuJ6l8jQ7LY0guHFeZ+BcM9gCga4XJPNM+1Z7uNaYDnij0agcY7G/vx/9fj+++eabcmTafVMGtLVRzMHBQUREtNvtMrOvwIT4pIAEj+rwzAUn6ULZeNZF/+v0I88/1r0e6KYe9vR8B9rCJhFRcSj4LoF64ZqI6zRh4QiWRWyi/8Qz7mCIXEdyjbp4kW1+Sb3v2XacxaKuoo3U2OZ+ItPptKynnc/nlSMhXXboiGj/EbVReNWDX24P74vID++LiOs+bLfbJdvlH//xH2MwGMTZ2Vn0er3odDplLEi3TKfTkuH3s5/9rGwi5bPeHrSWLvJAHpd/iJ/cWZ74hdhDfaYMQsq9j1H2MZ9nv3P8KNCl9woDqg4u51kmJvnsExXEf9Lbzeb1EYObm5vR7/dL9tB8Pi8pxkxTzvAOU73Z59TnTB2vC1rX4TOOC7aZPPTry+iDd0X2a670vINlNGnUM7BW59VTcblAkwhUyFwyxBmatY8M5kCiAicQ13W1WzueKc2LhsbLoTMbUT0HTakL4/G41EPGQg6HC4Pzhjxyh5Xvch7UgUnnHT+rLRJyOmIepeIzAjO6X8Zib2+vGB5Fmh4aZQrAeZeNGzqe6k+lZ3t5Xpaeoyx78CbrV3f6GN3zMZY5WT476+WK6EBmCo4ONXmmMqRYfRzymo9PjlHWnfKcGYcPpczR5Nr3iJvR/WyGKvvzZ+vuuU9y2WBdCYg9Ws6IbxZk9LFD2eC7f2ydqauzYILGoS8NkZPZarViPp+XnVupz7SD78bGRiX9l0Fe14G6ntlJvVM6WvXU79vb27G1tVWyAzhe5FjIieKY51j1tEvyJKK6jpY6xm0px6LKyWZK/Dv7xv/kFChYpODxYrGoLGm4D3K5pE6i7Gtd2WQyqfByY2Mj9vb2KmvW6mbDObaoozl+XBcz0Oe6hMsF+B4B2mzTxjq9Q73rSwsUHFpfX6/shsw2cKxlTnSm+1lnfne94n3jto7luEyyPG9vxgsfU0yJ5bPeT7pGnZDZ1x9rpz4GOQYRUT6lQ8fjcRwcHMSf/vSn2N7eLmOg2WxW9hfQ/gw6RYA2Ve90x87Hnusc3Ue8y/HBgJvKZqCP/cM+Uts4DsgP1kF9TcfOx7mPaeerB1nEA96rd3pdWaYC7ppd1TnDskER1b14+F/1yMaq/tzf0TMir7s+u84j6Xvms9TRj9oVWUzSS7j+gelCnOGkR8/KUlBYtu5xo0sl44oq4npmUxHKiJvp0d4ujxSwbqo/B47n4ktoNSWvQSHBpwFjGRICDWAJ0OXlZRwfH5ezslSPnZ2dMuAzEKh6kociGlgOOII78oB9Q94uG3iK3mjmSmtkOUtLHus75UT0+eefF2MhxXefRN7xex0/SGyX7lM52vFUzrvkweXVlaE7mwyyeF9y9oT1cFBFJUynNGsXx2bEtTKkc06Fz/soV3LoBcZZPp8h73WNaZqMFooHnppW1w5vY52xIZDRPRsbGyVl1I2o6pvpKgePHqAij90huG/yQNXl5WVZj0rdyFlKypoHuagfM/n1zz72nPiMxkW2KZ3kRZtn+Jo8fVYKnXSadnXXEWySaW2WIf0n+dOMGWfu1Wa9k2CKY2OxWJSIe7fbjSdPnpQZP54fL+dF/OTxQO6kcK2uovT8z3qoXzJdQWApvtNJctDjjrCeoXOgrB3tmj8ej+P09LSsNb5Pok6W/HrdLy+v1p87cG40GtHtdiMiyqx0hkvoXLlDyGw4X7bkeITPSMYzJ4sOmQc9XG6oD2UnuMO7MKB2s5YMSjZVP+0sK8xAeWFQyPWo6sD6k1/kFfmg92Q6mO3y6+S3nstIbdAaRtbV9Zz6VLzjpFA2y5W1+z4p0891OFyzsjoO6uLiInq9Xnz66aclGDiZTGI8Hpd1qOPxOMbjcclsoBz6LKZkhbrdZ+Gl+9UvnETyDDO3B7QFkmXVQf2nttN+cUzR9knGOMnnMuZ8ZL0iqntU8H7JLPW3iONY6361D4P2CFDZup+4UuRYju9mm/heL48+QDbx6DZe5dOu3oU+eFdkH+gapN1ut0QAMkApA05DqN9daVMQpJRImYLKjAOdyWWKwQ0Iy2E6sQaMjIQU9ObmZmWgccfiiKicPefKU4IYEcW5UQrF5eVltNvtCp80GD1lgIDI2xVRXYtLHjpfl4FH9Qf5qvcK0HAdlwYKdwTl2gk6eT5gCAo1Y3LfxPpqJjmiGsXKolP+PNuqVCQpG1cOIkbOGP3LnC+9i+/MnAnVlzNGKiNT2Czbv6tOW1tb5RqBTR1P9G6CH08/Ig9YdwaE3DBkOsP1g8jXl1AH6Vl+F9+bzWbZ8EzXfTaMzywDU84bGq+svPsi1ZspdI3G9aYvmSNDh046jWc6ZnqX/ZPNEIt3dTNe7lzToSQv6VxmqY7SP7JDGqeqHzcFckBFwK53OBjSdc2+chZAMiYw0ul0KrO1dG7k7MhGEIRF5EspRATy+k6wndkMd1Rp/6jPMyfFn5NOY1puo3G947PWLd83uHenNrONEddnxFPmBBL97F7yxh2wiOpYoN6hvuJ1tweqG+2KO698j+sjD5ZSFiRzdFh5JAkxicsp6+czzN5GXSMP3Nl1/Sx5dJBf1w6OySz45WVThwjfCLNRX3mf8T1ybImBWCeV4frxPol1cbue4Z2Ia+w5HA4LrxSUi7jSoaenp0Wv9vv9ssyCgfKIm/sWaEzxnbyXGQlcFsG9DqRv1R8KVtKxpX72tHjHweQJ5ZT6j9d8Vpjtc+eP/GQfODbSd84s8529Xi8mk0kcHx+XmXJhc00Y+ISX922z2SyTeZy4dDvrfp3LjO7LJuWYBfMhE1w/ao0tBZhAR+tGXBnqHgkDhc8HcJ1idhDIumSDPjMQfKe3oe47QQYBAAVf4IeRId2ngUSA4HXX4FIHaoBrQJHnDAw4AHJ+Ov+8nbzf61XXD/4OVyLc9VUOgxQLUwmoHF3ABYhUhv7uk1x2pMxEmXPiz7msR1SzHfibAwIvm30p5Zj1L+tQFz2W/FCp+rPePu8vAWE5eXy2LuJOvaByaGz8fr6X4MPvc765LGe6xkFe9jzLpzFk37GMrN4OBvx30UN0bF1WqM+9vuwf6oCzs7PKsQkO0r0v+F72dQauRCqrLkWNZdIu1cmcZuPb7XYJxMiR59pBl0W+TzqMoJ7XOGNMnaegsZw8piA76KE9qNM7Tg7g1Zcck+wH8jhzbF2fk/98lv/VB3SGxB+lej+UMUByOSEPM71FME7+EatkOoL31gVO62y26sWxWPe57r1ua1gugx8utxxP/MuC7NkYuovtWcbriJtHwNVhmoyyNmdySOeJ/VBXhnjg2Uu3tekhkdt/6m9di6j2vWYxufeKgkCTyaQ4LpPJpKyX9n6jHncdlPGMWSV0cBV01DuYNcO1s+or/cZsA/Wb8L0cPZdn4iAPmjgWUb29TeS3O8G65n2S4Su9e3Nz80bAjUFJlZkR+Zw967bZA9PZ2MjGVYb1fMKijv5Zx/3o5fP5PFqtVqyvr0e/34/RaBQnJyc3UpqUxuXkAsm1SSQ1kMaPkTB3uOpSESk0TPvMiDNaXHzebDZvgHhGOQgy1GaBD3Y4N46aTCZF2La2tspAplGj80EF6bPT3n4Xmkyh+ky5/vssR8ZTKg72ncrPgEtGnOFYLBaV1Ob7TEUj8HWl4rn/VMYkVzzN5vUavU6nEycnJ7XpHHqvGz13pBz0E3D6GFB7GDjIHApvAzeOITDTONF9s9mslKN3MPKp39guBY2yyDjBisZGtmmBAwaWxbGSOSNsJ/npOkKbSyhNlLPM5KEbYl1j+ibrkY21uvH3sYkG9fz8PKbTacxmsyLDivQ6iJ/P52UMT6fT2NzcLDpR/eWOmjvKDtozo8uZGvGUzq2DLU83y0j3qXzpbAEypmBL/jUbrdTey8vLIu+0VwRMsgN0bDXzpdROrV303WHd2ZHulF5SGzgzKNI11T9LReYssxP7iE6+r92knFPe2a5WqxW9Xi8iogQONjY24pNPPomjo6OYTCa3yuhPTQRbXFql3yhvEdUj3haLRTnu6ezsrHI2JB1E9pN0YR1wJUmOFVxXGe6Yeqp4tnke25DZBrWNwd1s4kJEzMVZOLVf7+NSNsqSYyo9fxtlAS2OeeIl6mliQ+I356V+FzbU+GOKMcuVzVfGC3nL93KMUyYeArk+pj0lL0XUt+6o6Qxw6dDLy8sYDAYlnV/8IM7kGFT5nFhh9qBSWDnuuFyQm/ZxzwL9Sd8qO8ln2TlpE1HN7IyoOnV0sNW2+Xxe7Ivu03M+3hkIpR0RD/w52jsenXlxcVE2QXz27Fm5RpvkGIn81m/yhTQ+XBY8q9R1pf/G3/mbdIQyY+9CH7zGNlOoNNQ6l4oC6+QNZOO8XH8uY0JdPfldn/19fr/f5/eT0WdnZxUgRuVFAfEOoxNMQXTgXvd+F9zbKOMjPzNiTOGk4qorI1MyVEJq62QyiYODg/inf/qnUtarV6+i2+1WAgQcsKpXZngfAt0G9jI513X2HyOB3u+6353ZOkXB/y4jvJ8KPCMHowQf7jhQbrPz5+jY6sxitZdOLtvqilXgjrLKzAWmJfuYXsarbJwvI/3O9FEGf5znDuYzh70O9LNeD4Wk16T/RqNRSaXXchQZNaahupMkeVrWvmU6WOTBO+dxFligjLlNqauP7idQbTSu17ZzTDIAwGCL207xg6lfBMSSr2wjE+cF30vnWNk/Lp/+PJ2ciGsnXn3lAUnXU87ruvHAe+fz67PY5bw3Go0ycyPsoHT/umUaH5s80EQ9eJs8e2q2ri97jhiDeovBSf3O+/mM18PHBJ+jfPC625U6LCY5clDv/OMfZ7HrdGDddbeHrte9PeSJYy133l0HZfxiamtE3EhjddzpwTwP0tXhhodAWX0yrF1HdVhb1+fzq036tFGfYwH9d73uOIUBJv1nH0nvKrjAZRCqJx1e6uJlM8lca+r9n9mgTG4pLz5+nRe65mX4eKK9E8ZW2rWPZdqZZSQdzkCVjyu3GZne4LjkvfpPf+Su4+FHrbHli1UhVfTZs2fRbrfj7Oys5G97IzPwHlHtIDbEGZ41PmswO9WBbl0aaWaEVTf9EeD7u3ivdgVk5zgwibjeNp+/qzxt9OHv172Zwq1T7Bn/XBH5OjNdazSq6wf0vM8YcuCdn5+XyOTh4WG8f/8+/ut//a8FsPyX//Jf4pe//GV8+umnSxVi9u6PTQ7YVC/+HrHcyJL/VHbacINrktyAZrOMdQCX9dE1V8Se2u1yz2gnU+odlPl6WEZlfeMBAm6lVW5ubpb6eB1ZjtYuUQlycwbO5hOgUHewnW4cda+vecr42GhcLb3g2j83Sj7+9JcFaShbLlO3gd6PTc1mszgZ4/E49vf3Y3NzM549exYREZ1OJ4bDYYnkzmazaDQaBUgooEHwe1v7aJR9bLlecLDDKLmITmZmfL08tp3ynY1llS/HXiDWs1XUJs3eeBYSn+EmM3R+XbaZcqbZU+1+6bNkDnKkkymHHG/LZrfZfgLJzNHivcrCabVasbe3V+pxcnJSNpJZLK42z9JRRg+B1Db2P2dJMzmVjtCmLZzdph5aZp85y6Pf3bFl37Ic/Wc/OWZRXUku//7cYlGdFb68vKwE/HUP75fe5oY+nNl2m8J21jnqzqvMAdUYc7vk+j4rJ8MnujadTss6w4hrp4m4he1glkbd7FjGu2UY6WNR5gNkGWsuN9msLid19Pvl5WWcnJzE9vZ2uVe6lHVwOdE1zZ5rnHEdrtaSaqZWR3O6/VYgT5u8ae04600c0mw20z1mRHKomUHjPo0HgVwmfNmPqC7byR18YTP1h2aKdQ669u3xgKz6M9Np4qsfSZrh0azvKR+8JxvPHyr7H5yKXPdiVqDVasVnn31Wzug8OjqKs7Ozkjsvpe6NVSMp8CyXHZeBobr79ZvSHHmNitKdNBd2lesKXmXrdxpuByhZJIMDVQOZYMXv52xv1m7+d6DBDX30nHhCvrsQsy6eGsJ1BRyUZ2dnsbW1FU+ePCnn/Soad3Z2Fu/fv49er1fSzpS6enl5GaPRqLx3d3c3Wq1W2ZjoPsiDJBH5LCwDGA4a9Iw/v7a2Vs7B1CxLpkj0Hv5nuSQaFzcqEdX0MgIrrXFZtsutwLU2gJCj4u3OovDq49lsVgx7p9Mp0VMZ/cyRdyPPnT4zhcmxnhkV58kyB5UkcCKnXOOf9cxmJTLA4jqG9fJyHwrRuI9GoxgMBtHv92NlZSV6vV40Go2yozt1J8lBva5l+lHPcy2TZFOH3ntAhfLqQQp3Ct0RuK3tbnCl230mSNddLlWGxo8AB/lKO8DZBI1RtTdzHpvN66OKeC6ueMANf/iMfhc4o73y8eNAjA4T0wVdvhmQogOtpT7T6TSOjo5iNBqV/hYIvW+iPqEz55TpI6Z8cy8O6g/KDvvWbQo3lclAZ8RNXcl6sr+8ntnsCzfB8rGkejcajbJESzLHdzl/nDIMw/dIrtRupm/yeW8P5Zoypz/xOJtN8xkk8pE2g066r78k9iKmcx1U56Dclln1sSmry13kUG3JnEKRfh+PxzEcDoucy7H1/uFEgN5FfnKSSE7Y5uZm9Hq92NvbK+dNC/M4JtbO3tzQL8Ml1M1cRiD7ow11le7r2FznlNM+cRZZY0pZYiLZDul0x+YMYDUaNze20v1aRqr38Z6sn+mjsV1uRz04lcmH2/86mfsQGx3xz1hjS0DoleDZfp1Op6Ss+RqZDOypE1wx37VRdQCffz7w/LMb/TqA68Y8IioKl0DHQUtWPh1ERoj8GQ7cujarHDcSmbHzei3rW/7d1n+Xl1c7RPd6vRiPx2WtmQ6HHo/H0e/3Y7FYFBnRc0dHR6Xszz77LLa2tuJnP/vZjTp9LMoMmvOJv2VGMCtLSklRQSoW75Ps+rI6+v0ui+4wanMfOrZUjjLScjxXV1eLUswiqgRQUoAKYkj5yymWwnZn1XlFmaVDkPHFFbze6eU5nzOd4O9QxJ3ls+3uTDivs9+z+7gW977JedBoNIozMplMYmtrKzY2Nm5skFOnO10f8b+TA2qtadNmVASzlDf2hd4r+lBj6XUkwCK4EIiT/nMgrroyDY5ySACh8gmWM/lxkrOvtbURURl3WVsoc4vF9a7jAod1Y4Xjvc6x5e+ZE6H3X1xcxHg8jtFoVPpMqYD3nYp8F53L7x6gF2+Zrqjr5FHEzb0V/F71I4MDeq8DRZcP/143DlQWAxKsA++jPeR7s0yrLDCf2Tl/l/hJ21U3fr1Nzjc9m/Hb38s2sc0izsa54+POhdrqgQk6Tm77yOP7JscNGbbQf8qD38trDEI0Go0yA84lLHqfyGU+oupIkZfUlVr20O12y2aAs9ms+CbsD27qp2vCK5l8aIxIDhh8lU2kvpc+YBBWfGBgVP8VPPV36n+zeX3MKccTZTzDgTrbloEUUWafna++c3X2PHnrY93/L8PUdx0DP8qxZUfrpeow5qLv7e1FRMSnn34a4/E43r59G8fHx9Hv9+Pdu3c3jjehsGeANmvcMsBHMFMHEGi0HWypDBprEgXTndjM+HudM2DHyCRTy8hj7tpGHomWdT7LdkWQ1dsdLZZBhcNolBzX+Xwe7XY7Xr58GZ9//nmsrKzEr3/96xgOhzEYDEp+/+vXr4tyIcBRHf7H//gfMRwO42//9m9r2/Ux6a6AeNmMuq5rVr/VasXOzk4sFosYjUYpyP0QWScYIrmz0WhcrxMcDAZxdnYW4/G4YoDm83nZgOHy8rLMrHa73YqBYPkeGddMxcHBQZnNo/OxsrISo9GobKSlNF86Bjx/kzqIY5uKWXXx9W/OG+qC28Zro3E106YUe/F+2SxV9p8GJ9MVHNN1zuHHJvJNa490DrN0hdKUufU/N9wjjzkTKxmQgeZMRQakJIucaeJnzqIQYH+ooawDmNxEhMBD4MXHOTdL8zIYpGRAQPLtaXACXZkz4DZFGxRFRMmWUvR/Y2Oj1smtK8/lkzaQOtudoTqwxCBERJSxNZvNSnCz0WgU8HWf9GMdDepjpSHzeKxsWYj/eV9n72Z/ZHpez7hTxeddP3kwgnjIZVj15DpE6kXnCd/tOJDt8ToyO07jI7OPfI7OhNrBTW98A59sLDhe0jVloTGrgUEK1kdj15d8+Xe1U3pTAd/7JmJS/ndyWyv7Sn0jmyFeSY8Oh8M4OTmJ8Xhc9mBRlmddRoH3DQN7ShHudDqxs7MTz58/L/hCS61YhmZqpYu0VETvUt/IrimwOx6PYzAYxPn5eZkc0Pm8dKw9A8dljXaOe5GoXb4Bp55dWbnaQV+/yVlmkFATKFxW4gFZzlCTp1nQxjcIdMzq7aEMs3y9n3JB+3+bvDnd2bH1CrsTRPAoJosEijPHKfuegcq6QV13PWOEA14yLQOzdX/OE//szKfxdsc6e1fmKLtw1A0G1qXOmNQZRd7nEUbeR8dW93BmSf2/u7sbnU6nODaLxaIojZWVlbJJiP6zzVqfvLKyEoPB4N6j9XeRybsYHn+WBkCbpOi3zJBm9al7TyYv2SyPHAGNUxlTAk+us+VZy+pL1lsyTB1AMFTnrAhsTCaTSiQxA98Z6PNx4cDiNt5lYzzjJ3em9XG7bFxneuTH6J37okzeNXPN9WLMKKFt0DP6TLCpsc8AYl2wTZ/13/mepSAzXWoZAM5skBt2f44zSRwrlD8CCbXLj/mJuAZpbkvd7mZ63GWF9WZfRFynAWZrf103ZUFb9luWhs+ZiKxMl2nKhGRHjgfT7O6THC/wOvsikx+RnBWtu8vGdYZLbtMBmQ6hY6uxFlE9DzfDCsQg7uhStnzccPzxXV5P/fnsn/+u92Wzm3RglgWavXz2x22Y0u1UhoWkW4h/6nCXxhuDYtkz7kRkTvJ9UcZPykTWn9S7jn+8P5vNZskCGo/HlawW6iHnl09OZbpSSxqEVYh5/OQILm+R3ry8vCy4Rzjl/Pw8xuNxjMfjmE6nxbHlTsya6KHM+l4NjtGywBAzNFjXiOtd5Pv9fmWZVMTVGnDKHzPdVBfZrizo5ZkEsuFcV8w+vU12Mp+FVCdHH0J3dmypQLLIkqbbqRC5Tk8VHg6HMR6PbzgyVBzOQL0/on72wpXRbaCWipX3uoHmNSoYCYXPTlEpM8pDIcnqzXQBprbwGVcQmWDUKXn2XaacXdl7GgjrySAFAZn+2u12dLvd+Ou//utotVpxdnYW3333Xczn8xiNRtHv98u668ViURQNU996vV60Wq3odruxs7NTFv7fJ93VwSBQv22wc4ZKaftKY2QEeJmBqJud1bsFaihbJG18oQCCH2NBZ5Zj/PDwMKbTaTmmQyCZaTd8h97DcSQHVmtFeLyTNm/gcShSwD6+s8yC7Lv4zag/+ywjjudms1k2lFA5HPOuM6QfM3Dq1wjm7gpoPza5AdayE6Uh+w6+ao/PyFCvuK6V3vfZTL2TYyHbdZ9OlfqE6//0Xicfa67r9X7WlTNZWfRa76NzygCAggKakaEe9YCAg0H2ideNQJHgZbG42lBKOkEAiH8+u6Xy5vN5CvYICnWdeiLi5kZbWZ850NJ6fFGdnvtYJHvPtvkaSMoPv0dc9890Oi1riKlz6uxFNhsScS1/DDCLqJMiouhX579jAdWRbeRnyggxjy+ZyGYr9S7usaLfMr1H2WFapsYVA0IRN2d9OU7dEcjsZp2tzsYVx71vBsb+15hrNBolpZ5rGjmOiAVozxQMue/Ajuri5DjVZUxBTw/Wqa16RrOrk8kk+v1+HB4eVjK3IqrZUXonU3TFS+/r1dXV2N3djV6vFxsbGyWAf3p6mm6AxBlRLXlRNttwOCwztGdnZzEcDmM6nZZlXFz6ofG+WFxvAEV85MupSMRtIsmeJsnEm4ODgzg9PY3379/H2tpadLvdImfCT/rOo+f0XfWi3RAPGRxlPTVTrfZRFtw3qbNjLkP6XIfFbsPTojs7tgQQTD1SDrmiFwSuBNEawIPBoAhERDWSnSlmJweTWWMdLIo88uwgxZUqP3M3Ux9YPCNQ7yGw4mAmDzNDxbrpGo2oPnONX+aoighU9N0dpCy44Aab9XCBVd+22+3odDrxL//lv4y9vb149erVjRlqDX4FOHwjMf3Xcxo4DnI+Nnngwg2lUyZ/WTn83Ol0yk6hcgTdWHh/0XBmdaC8y2H2eklpCfwQLElmPdhyeXlZzl0+OzsrZ3fq4G+lWEdcp9+pH6UjZMBVro4moawsFosC/JlyJr4wOOXvJd/If/+N49z7lJ/FI519pzI4s+HlOmCLqK75dV1FwORlPQRSvWScpUvo+PmMacQ1IKK8Zw4iDar45DNL3t8qlw6enCulmkmOlDad8TQzxCQP8ok4ZgQQIqJW7wrgcJdw8U9OncAabZTe5f3hckXZUr11VizXuWvjLf2eBRmcN+pXd9jq9J2uSVa8TJ4fKRKQn06nldS4u4Kan4oEzmk/MzlxoB9RDeCoD7ir+23to2yrbNbB5dnlhnrF7bpjNA9MMMXcdVxGrut8DKheDIBpjHo6u8rjuGk2m+UsUmaAkU/L7HMGqLMxzfvVfwT5CsIKozhm9LGoMa9stGxNJT+r/e7M3Sex/2/jma6xL+ko+fjRbxofciBpZ+TYkdzxEm5ROrHWnfZ6vXKsmBxR9SvPVdckSqPRKOOBDrD6VT6PyvKJPe41QV54IEdYiH4VMZnqKEdY+vL8/LwsB5KjzkkntV/BAepZ8ozv0m/0TTJMtFgsSro138k+9YCb2qAyXKdRbtwn+lD6IMdWlVYnckqejXTAqMYp4iHArgGcMc+dBx9IdYDVDSzvISB2JyUibggbFZQGl+7je6mk3Li4EOuPji2dF9aNTjGVvu7xdSH+XtbPjYt/ZjnuhGfKif9FnU4nnj17Fn/5l38Zz549i62trRuGUfKzu7tb1nRyTa7+BL54sPZDIMpNRKR8Xab862ixuJo90c59PhPlcqx3LrvGOixzwiUjCtC4MxcRJZrKoJKchvPz89jZ2bnh7HFcyVhpNlb1kmwKaLMdCuCwDQSA3n6fVchAmDtI/vwyhU5gkq3ZYll1f86fDIwue/YhEMcpARl/c50quaJTVBeV9fdwtjPimn8+o0mwJHAuW+Wyk9mH24h1IMmuqJ8ksx48JDjzmVu+g7MY4h3bHRGVcXibvHFmmOdMK5uKswa0N3yebXVdk+nDjEeZzuSsPEGfdL90kvPpPihzPp3XblszG8CdkT1Q7OWyzAxT1fHd9RnxjI8D10scWxpH/v5MT/n71VbWmfiLgR3xwrGC5J+OS7PZLLKhvQ583WCGY9iPETfP/c3wncj7VTzl8TLkg8Yny/LsCbUr0w+OJ6hTHwKxLq5Tl40Rt7+OpYl1NQHGoGidXfYZRvFZcrFYLMoRgwpQcbdvBkgnk0klS+3s7CwODw9Ln6r/tISOm0P5pB+z39gOLgnR7C3HADGZZE1tIi6KiKIjNd7p1G9ubpasNwZQnWccKxkGIr9VH+67whl173eXhUw+VC5lJJO1u9KdHdujo6PKQmnNvKljXQkpKkUBUFSBCiVzVr2hEdUdHbNBnoHYDDwKJPlapgyc8r0+kP27DzTm0TcajRJZdHAigWU0ihE6GSJtnsOZAR+QDka4TpVpeZnCr1OqjPxqMBIIdbvdYmB+9rOfxc9//vN48eJFrK2txenp6Q1nQxF6bRJ0eXlZdsxWhI5peASq90k+sB20uDPFa/zugCPiWk7FlydPnsTFxUUMBoN0oJNcNt2QZv3o9ZSMSOlrB1KVTUChmSQ9L2f1zZs3sbGxEVtbW8WZ0OZKTOVRgEzv2djYKPXTRgl0HpXyq3Ik1xnY47j38cDvalPWTw7CeF2Ggmt0GJ10QEa+Z+sQM1C6DCTeN62vrxcboOOpdnZ2SnpXxFVd6UQJpLDNDhzJb8oo+eZOjQf9CFQka7I76rsPpWzM+bhfLBaVjASm5qqeEdUIebbZCQN6ui4wwp3C5Zgq0KS1mhzfEdWsKgEhbdanTBltbhJxvbbVUwbdmc90DQMKy/Shnslm2f1dl5eXMRwOS9k6muM+SX3D/qeNX6ZHGJQRaB+Px+UMc91X5xw4uU12fSVSvxCjSbeS97K5whgKLEh3U76E85ZhMJXNjCzO1LGN0qXZOCcvF4vrTWU4i9dqtcqReVzDKnuV9Y1I19weiLduQ8TDZvMqVV47eOu4GDq9PiZ9Bk7ZRS4nWV0zh+A+iDKgOnlA2IlYjo58NiEkp3E+v1rq9PLly8rMKmcWVR/hCW1cKNmQrux0OmV2k0EFTrSNx+MyUaf/0+k0+v1+zGaz2NjYiF6vF5999lkZD1pPqyVW1Mee4aoxI/yQpfdTfqSLdRRiq9WqyDTXHU+n01hbW4vt7e1ygsjTp0/LfXqPZ0c6/uAMKzGf+2jSCaenpzcyKW/Dq5ks1Tmy1A8fSnd2bE9OTsqsijqShtwrpc/qZKYyZoPAGc2O9s7goK9zjB3sOtCgI56BTL/f3896ufOSzURnneP1pHHzSBafIUD0+jAK6BFJf/eHCkwGwLlDrHa91eBlv7Ou3GxGAE5OkAAbo2GZwf7YlLX9/1WdWLbW2srJjLg+z45EEOh183qqjCwgROXhKVCePqg2cx2hrlNpSico8kgjL1nNIogRUYAJz1XLAGPm7FH+6+53Y0yeZbqGJLldVvZtf162vyf7/lCIhs/rRcDKaDlne/R/mW52Z2mZrXA+0YAzbcwNpJenYCHH9DKnNuJ6JooBW9lDgRrJo3gi/tF5oO5eLBYV50nARGn+XGvufPDgicYowTnHOGcMmFa+zO5kOs/l4C7yy7qQr+wXzlzMZrMb6cr3QR6Q8bFQ50A5D9Q2ztpm5ejerL/5bq8HsYG+M1DOFFg6cUxB5p4Kkle+w+VK9SZv6Fx4qnD2fo1fbkjD8slHD3RLVjR7pbWHDCiJWE6dDXcdk8k9A7YMZvF58oa7/Ds+cx7W6bj7Jued61R30vhbnT1kucTAo9Go+Bq+q7zrMGYOLRbVJS08V5l6R04hd0b2ZQI+IcClLNrYKqIatJEeXTY+RKq7yuakmHCx0o3pbNLHiIjy+9OnT6PT6cT29nb5jXs/LMv0Yb9m/aLvCgoIo7s8uH3NPtfJkcv4j3Vu7+zYfvvttxXw4B1OwMoZRp/R5QD3lBCSG1EqgrpUGt5HBc5Blc2mSCmxPnXvms/nFeFWuznboBmnzNhkxHfpvwTRFbPK1EB0oVBaw8rKSpydnVWihOIF36n+4DWPIjrfOFOlI19WV1fj22+/jf/v//v/4je/+U08efIkdnZ2ygAYjUYxm81iOByWTYGePHkS3W43Xr58Ga1WK54+fVr6bTAYxGg0iuPj4xiNRukmMR+TyAN9JtAigOYz/p2K1e+TU/bs2bMYj8dly3sqv4jqRmB6twdg/L87tlkduNlZRFXhMEKotCufIdJ6I451yZ+MDreql3LmJg36TqNBB0HvzQyp2sjId9YXGYCsA+BMg+t0OtFut284FOQF66HvLDsLLrieyOr5EECN80szQdRDi8WiGOJ2u13ZmIP96EEE6p6I6mZCNOAE565PBci1LEZLXpjK7LLBsjw1S/V0WiwWlawlOtLZDBfLkaPmUX3fHEVjQY6tshu0RoxgTfLD/S04k0v+aAxqDKk/sjHPfpLcZs6lBwl8pisDKw7O5IxwzHNmm0db3BdxllB8cpwgmWVwkEH9iOsMlslkUmaZaHMd2zBYvYwYtCc5buNSEP0uWWG2A9enq42SgyzFmEEXbljjs2kqS2BceOf8/DyOj49jOBzGaDQq7+ZGQ+Qr+bOyshKz2axgL6Uny/64Y5uBbXfyvW89EHN+fl6cL6YiU7cJe2lmOVvLznLrsOpDCO5H3HRAsxl2t83EKY6jsqCNxsLR0VH0+/2ynlr9L72osiSLDJQru2M+n5cjzyhPGrvHx8cVbKE6a4ZWNmx19fp4Qz2/vb1dwT2z2axg1SzzkAEXZb5pUzBt+CQ9T93PbJs6u7K7uxutVis+/fTTMmnkS8fIM/GD+Iay5v4N+59HG2n5GHWfy0mdw+vjkO2hvN+G0zK6s2ObpYJyEOo7hYbRj4jqZgFM4fFoIBuTORJ0TOscXDqbXr4DUg5Ad+j0HpbBSBFBlwMnARzWT8KVpfKIHKR79IezY153KlYpDv3PgJvex3K8X7P+UV+8ffs22u12fPHFF9FqtWJ3dze++eabePPmTXzxxRdlgCrtrd/vlwH37t27aLVacXh4GFtbW7Gzs1PSieQAtVqt2NzcLAdo3xexHyOu05JuA8DOW5I/o/I2Nzdja2sr9vb2KruNZ3LJ9yyTJ/3neFG9ZPh5rh/lnLNFesYzNdwJ9RQ5rnehAYq4BtXuyJG/mexyRpkzAnRUnAcMHmU6IXuHUsS1q2DG98yRvUufeDnZ/XUBsY9N0gOrq6sxnU7j+Pg4vv/++2JQI6rtItAlb72d/plOJvuFPMocWx6vIMdOeo/piZIb7UAuPa62ZQ48s5WUwiaHlgFcZZtQ/8ouqizuH0CeeqBKY0cpdAI4Sl8lQGI9fdZB5I6FxoBn1ETcvm5O17xfdD2TaS9bgRF9lzOvc87Z15oNv09aBuIj8qwE73v1iWRImU1MA9e7XBdlfSFZjrheZuV9JeAs+VA7mH6szzzCRGMo08GSz5WVlbIDq2RSdlt/sucan6pDp9Mp7dJvKnswGFRm6RaLRQWoUxZ8TSJT92nTfFYtywbK+jZzglVn8UkTHhq7skd8h9Y9EgPKFvK4tIjrAJFomS352MRxT70uyvRIRNWB11jInB7xR3qg3+9Hq9UqWNjxFPtW/OZkTqvViojqBpgKunS73RvBvRcvXkS3241nz54VfT8ajQo2EwmXyRk9Pz8vOy4rkONBbo0PrqeVble6sfAMMbvkmHpaZ9IKL3IygNmxsnFZIKJO5v0e3ic7KJ0sWWB/Z+XUjSUPGFEeWN9sUqCO7uzYZjNSrIyISlN/ajzXfNIhjLh2nL1hTKHxqGVmSPWd9XMn0h1bOqQOqN2Z5XtVvg9KCSOBhXeSA2Av20EB2+abSEVcR/w5+L1s1e+ulAF3b/fh4WFMJpP47LPPYn19PXq9Xnz99ddF0WuWSxFZrUkZDodxeHgYm5ubMZ1OY3d3N87OzmJvb68YyfX19eh2u7G2tnbvm0dlhsXl4Daq6xORZHFjYyPa7Xb0er14//595TfWxWVU17J61tVVio+AX89QkcgwKwWIa+UjqhE2rYUlOKdj68ETlUOl6HX2wAzHpRwI8tH57nUlb8gjf4dAitLrma2R9V8d7+ueqXPw/NmHQOKhHNvFYhHv37+PJ0+epE6Hp81SHjKi7Lrucx0s0EBZdkeT75OM61mNFe8zB2iKqmuXTh71oDrIznGJjp73tatyauR8CmzxOCs9q/rwN6XDz2azMitOR1HlkQ9sD8v1vRfq7Keu1/VbRsvkluOXjof+NGNHG63Zw/sk17d1jqfrYwfhkpnJZBKtVquSjkxdWOfUZriAfcNniA9cz8omC7zLsZXDlq0F5w7EGletViu2t7dje3u7OJZbW1vF4RVwZ2Co2WxWNpfk2aVaW+3rUTV2OH6dP2qngpAMbmU40e22jwPvB/33dEw5S5w9jKjiLY1dt43SD0yldcz1UOyA82GZ7vR7sqA892sRCYsQK2rNaB321p9n58h2+2yxsgharVZloqrRaMSzZ89id3c3vvzyy7LM5P379zc2OpXDyL6XE0rdyjozm1L6WthIgZzz8/Mbskk9rrYr0Nntdstxe+7DUP+4LKvNmVw5jqStFR/UhrqAX3atztn1654l4RNLt9GdHdsMZLkSjogCeAQI1HE+S5tFI6hQCJQ50BXRYB04iJxBBEQEq75GhCBZ90jp1hkozj6pvnLeCTIYseXAdmdY79GA5H10QFQWtyWX0eEmKbcpcXeQ3DgKeKjeHozQM5PJJP7u7/4uXr58GS9evIhXr17FcDiMr776qpxDq/MS2e/9fj9OTk6i3+8XJ+7p06dl9zpFo7je9L4ocyY5+DNFIhL/5Dhmg55A5fLyMjqdTrx8+TLev38fi8WiHLJNg5e9g3LjQIbEeqotklMCTkbtJfO6Vzvz8S/LDFhZWSnpkwQqNDYC8E4si+OMfONSAm5+w+ezDAznPftRPFKK0O7ubmVjC9cTfo1l+291oGWZXDwUkg5QxPYf//EfY319PX7961+Xc2wlA5qdiLgOXHK2gml6okyP+yyN6zTNUvK4Oc2CaJMr2RmdZytZ5nEITMVXetnZ2Vn0+/0Yj8dxdHRUNhvx1HjOfDG7hOdtyh7IaeARIeIr9QBnmflfzoSAGSP3BB8KJPo7NPYIvgSmeLQOQWJm+/WdaanZ7wQn5Jf4rPYoePBP//RPcXBwEJPJpIA+prTfF7n8ibLvPsbdCZvPr850X1tbSx058py2JrPn7kCwPuxL6TbJhtJoJd8E25Jj8V2pmDs7OyWdVoFrzZBqlnV3d7fCB9ZZ9bi8vNocjBkGAssrKyvR6/Wi1+vFyspKqeNoNCrn/2ocMFNH2V1KGdXYcP3i2JK8zTLpnPfqu9PT07KpqsYXcapjXS0pYDBHY9iPLfLgbN3E0scm8ps8yXhGbM7vnNGWPhZJ9qWTjo+P4/Xr1/HJJ59UlvexPOopXVdd19bWotfrxdraWskCubi4KDh5e3u7yJ70q2RaAZjNzc345JNPStaO/k5OTkr6MU+K8cks4j/5FNTpxNrk5WKxSI/vabfbJVWaRyHKxolHGV73mWDi0kzP63nV+/LyMo6Pj+P09LSS/cO+I6ZTGa6jWHZGrvc8+HobfbC3wMqw4sucXgejXo5fy5hLBtfd5+S/193vRoj31hkSAu6I6llkBChZp3rbOGMsogPNa+S189tTMJ1P3p5l/HJFL6DljpkUiWYxBEY2NjbKwBwMBiVyKuBJgR2PxxUeatdVRX95buh9UeaQZoN1GbnzssyZUWqK0rDH4/ENJyCT7TqZv82hjriOwEspUpYFJmis6dh6kMsNjs82U1GxTB9jfs35R7Ch/xmwvI28D2V8lWbEtVHZ+70cH0d1feJjrk4v3lXGfkrK2n9wcBDv37+P/f392NnZiU6nUyLuPuPD/3q+Ti7rxpsbODpK7kxxFoSbZ7jjTcdUoF+zhgLVk8kkTk5OyloqD1LS8DPgyNMB5Nhq/RWDwA7KIqLyLAOozWazbKg0mUwq6cm6VwEpzXDN5/PKOrU6Z8z1hd7nY6kO2Lp8OOma2s+zqsVrrbNUX2Vj6D7IZS7iWk+4vc/Gv495AerpdBqbm5sVHevP8v0f6uCwTo4f6vQX3+OBG605lI73CQo5qrxHqZqc9WIwin+0HXXAlgFLOczaxFIOpOTddQ77MOurZf0vTKTTQXTOqvPUl6hp1lrBJGZy1KWQZ+9/aJTpdVJWZ+8Pd9pp9+VATqfTklKs53zMZRhaDqF4Lh2riSAGAyVHCjBxPKpcrd0VTuUYqGt/hneYKi2Zch+D7VpfX6/sXSFZV5uzd3gfMLivd7jcZj4C5V4ZHr62dhne4RheJidZeczYXbZ80+nO3oLPfEg41GAqezKyrtO94mwQ10+xg5cxh53gkeEMVDKS4pFV3scIlf67IyrQQMcg4vp8KX+3l8MZbdWZSk8gMVN+HKyKKEmpu2ESb2mMnK8qj0BP97EvMxk4PDyMwWAQL168iI2Njdjb2yszHaPRqAKSBPo0gyEweX5+XlIrHEj/h//wH3Lh/AikNFoSZYVyqmvLBmFd4EfPaaai1+vF2dlZHBwcFLn18URlomigl+f9x3GnejBNWAqf67D0nI5X4PEbBPdUxpRNzuxwzClSzRkqyT431XGnPrvmqZceOfSAlBNB2urqauzu7pZNJCTn5B+fo/6rA6WqR52DXucYfCiY/SlIfck10V9//XVERHz22Wfx13/91/HZZ58V/mkWKOLaFmSgnTpF/13vU1Zdb0tPMT1ZQQnpQR3NoBmh1dXV2NnZKfpaIOX09DSGw2EcHBzEYDAo4FWOmNKNmcniTq7eIZlWGzyd0s++dIDD2W+OD/FB7VA7BXja7XbhqYD35eVlhR8Ea9L3nGlwvZT1B/tMv9HWuL1j8GEymcTh4WGZmVX66+npaXFu5NizXvdN7GfqA88s0T3iaxZMmEwm0Ww24+TkpIBspu/rfZkOyyibJanjmQC/3qHNPrlDrNIruXZUm+PIyWAAVMEjjTWl6zebzZLy6XIvB5GZL8Ic4/E4FotFORpJ6xE1c6X2KVNEs8ntdrvYqCygqu/KUnD9k/GVz81mszg8PIyjo6M4Ojoq8s70f8mu7IgcEzlZGq+6xve7Y+Gyd5/EOrp+qLs/s30Zz4mbNe7H43G8fv06Dg8PS9ZAxM2MNOkljkO9Q7P2csbYJ8JXDEZy9lN2gU6v1o5HROUINQUNI6q70jMrRnqAm2dKLpmVo/cpSKMj9ZjBSOxNPKJn3cei7ch2M3Z+ev9pUyzZSMePjutcnuvkmr+p/Xy3ePYhy1Hu7NgyF14C5AZHA9mNnc/cuHOkZ2kQ9buDdEa3RaoTja4bk8wYO5BwEEWlzff5+zmYWA4FiW3Q/8xZdsPkqSnkGcvksxpQrLcLIMGjSGUzsEAjysEjAdR7WMbx8XEBYq1WqyiJunO0VGcZRSkFOU8fMvP2U1HmqNY5Idn3iOqM1zKQxv7hAfQR1dT7zDhwLOq7AE9miPy/Ipl6hqliDgY86qhyXHlSwbOekiHOrDGAxhnjOseRdadsOq+pvDNwqDIkz5oB0E6FepbvdrCaZX3c9TvrS/Jg4X2SDLcApvrk4OAg/uf//J8xGAzi008/LamIPAZjb2/vhp4kH9mH1Oe8l30nfmS7sxLYSJfozL3ZbFZSFOUAnp6els1rjo+Pi4M1HA4rh9Ar1Uw72tKxpV2SvGrmNOLmeca6R6R6Mt2WgaaVlZUbeyto7Gj8TCaTEtXX2GQdFfRiIFVlZWNF3xVw9WBcZlOzMrLfp9Np7O/vxx//+McYDAYVedA9SlOUvC3TmR+DJO/LguuUXf+NnxeLRZGnfr8fnU6nsgGM3pHpKgWnSbyfv/G63s3ghmRnfX29BG84kyreM+Cu+k2n0zg6OorhcFjWfTca1ynJuq/ZbNbu6krHVvWVvSGfLi8vi4OieuuznFid+Smnso7/rlM9IECZdqylPtOJBcRZnJDQbLWuKegkPss59z0nFAhgvXym7T7JdTX/R+RBW7/HM0A460gdoL1Vjo+P4/3797G6ulrZcMzxV2aLiVU0kUJdrfHGOrE/KAMa16y7+pZjibOaygDiOl46sdz8T2NGS0u4u7dkWw6xxgV9mDr+E0/V8U51IjnWUdaSzvytmx32upB/yyYW6L84xmLGyF3ozo6tp9uQIayYz8A6YJYQ08khZQPBDWjdQMpApzu8/q7MUWYHeATV6+RgivWS8JJvDn7JJ92bXfM1XT5z5wDQ+8z5J0F2QK13iX9UOJwRcUecz5+ensbq6mo8f/68OAU801b3+QwOI8JSOgQT9013MS53dVzrBndE1XhQuZH/mcGjQonIt0zP7iG5A6hIo5xabo5BcO7lSl6kgD3YQnBNx5Zt87J9hsxBiLch+07+LTPAirBzjSbHDvWLnqvTMcvkxtvhvz0EuReRh+o3HUv193//9zEej+PNmzfx6tWrkq2iVO5er5fOFtbp4Gz2idep71g3v18GcTAYVED0YrEowZqLi4syO3t0dFSOJ9OMM9eDa8flwWBQ+kbjUoBaoDYz0AQ1fjIAsxrUNvFJQUTaVw8KTSaTyporX4MoR1E7zmscE/DoXgaYVldXK0Eq1YdjPJNf1+0sfzabxdHRUXz33XdxfHxcAnhagiKHVg79Q3Fs68byMr3v1+jEaKnOzs5OZWMapsz7e4g7eJ2ObZ3zQVxCJ0ygX33NncVle7gGUHbh9PS0lCe7rTXDst3NZrOy94fq0Whcr9WnDGVBUDnfTM9XGcxUUJvqZrUjqgHITIcQRzlQl/OvY2joECwWi8JLTdTod0+PZqYFHQr2r+vFu+CPj0GOqZfZ3rrn+TmzmRFXjq2Wtx0cHMTm5ma8evUqIqp9SB3La8SqzJ6RHpRcMkuR2J/liHzGUH1LTCO9xXIye0dbsVgsKpk3spvCftoxmbIm+cowIdvP8aPf6CBmgTr1Ae3WZDIpy3KUlbRMBtwfEWUy7X6cB425GfFd6INSkckUCs3GxkZZLyNi6pMMKcFGlm7sqVA+ALweFBYHECKV4wOO678k+P6dQpSBYZWvOjEqLgDh9aNxFAjhbtF8jzvCVHr8PZtJ8vpyjZacFPZtxmd3HLjBiIMgjzzOZrP49ttvo9PpRLfbjV6vFxcXF2XRueTI+1hypCixr5G5L6oDE/7ZAXZEDoiWGV6WyQ0DuLbB35nVxccCiYA346+vT1REnsqFhn/Z+geXf25t77NSPhPs5fgYyxSkiOB8mQxlzwpkt9vtiIiKc+P3UgHLaN2F3GFfBo4fAnHTIel0XTs/P4/f/va38b/+1/+Kra2twoPPP/88Pvnkk3j27FkBnhxHH9pW1wc03CICW204c3h4WCLoEVEJ0mgjHe5yymOD2EZF4B2YRETs7e2VzahEKmM4HJa67uzsVCLvi8WirN09OjqqADG1QWmikhU5utLJEdV9HjizRQdG+x9oDGp2QPxSGR6IVqCC9tSd3rusgVJ7ldbWaDQqASQGW9U21fshjItlTkZdCrbrJOrvi4uLGA6HcXJyEpubm9FqtSo4gfaeOm/Z+KnT6XR6CbB1XY6ZUmkV1FHWlAfXpYv1G1Ny6Qw2Go2yjwZ5J/xC51GfufaUckiMxM3f3On1d/EzcSdxJH/LwPpisYh+vx9HR0clhV7PuH1jAGp9fT06nU5FR7EN3ESPqamkzAG8D6JceYDgQ56tu18Om3ij+16/fh2Xl5fx85//PN3rRc/6d8oYA9TExWoLcQz7gLJJ2SYm0LuVBaMJHW1gOJ/Pi/7mel/2veRcgUnpZh+zeq/zn34UdbGcd57owLRotU2bZRGTqb6arT08PCz6nrhTn9kHXLalcek+GnlLu+Kzy+LHXcfAP2tHHveyKTBuZEWu+B2c8jeWl1GmIJc5t3XP+oxr3bVsIPkgkLEguGD7GfHxyCpntBgN4XfNlrBONIKsm8rzmV22369n/PFraqfn9bNsgUMNWAHhOsXNdzBCw11M75PuopRvez6LjIlc1mkoXZ4od5TPOsfIQQOv84/9y9l+yVej0Sj9suz9WdtYlqcdc7z5uMvas4xvzkOv1131CQEH+U3ddFs5Wfvvev0u9f3YpDEr3hBoRlw5cYqwR1y1Q0Za6bwRVV2Rtc/57ffQSGZ6WX2mmSceM8I0YjmtXDurgKtm05QGKX2qMSmgqtmsiIherxebm5sVx1bRbY39lZWV2N7eLiBLAEXO53Q6LeVy7Z12Yva18ZkzSd5y/boCzeoXZku4zNHB1TUHG25/6PiwLAd/nJX0mbhMFhg0uk9yXae6OX7h/XX2lqTUxdFoFLPZrEwGuA1QOct0wjI7nuEF6lzOPrrzJ1yj5xjk8IAe27rMdlOGaIMoe3X2QuOFZ6Nn78p0LHlYp9N5ne3Uel+t98tsGx1bzs6q3KxdXj/HsMsw08ekzM4vw8jLvvs4yspXX4/H4+j3+zEYDMoaaucPy3fbX+cP0JFiSi/7IMPoEdd6zOWYAYqI6yU8cjp9hlW4mFlsHsCVc5jpQ7WHWN/b545lnc/g5QqLTyaTyuSl68I6PVenDzPZ4D3ejxlGXUYf5NjSeHGtBUELI2pyZvQso2ER17vF6RqdGpEAAOtAYc2EzAdL1g4JAJWLA+26Z/k+giEXIu3myzz+DIgoJY7AT9EbCTlz8b0dGjBKZdN9cgo1+8l2i/9sMwXKo1X6rDYKNLrzLp4I5Opw6+fPn5eZRxoF1SPjicr1dJ37pjoFnl3Xb27s6wBKJt8aU5IDpQZz23j2QaYcWLYbInfg9FvEdZolx6GAOstl6ojzgeM+m0V2J4nPEqxwzGbtySJ9nJUgiCN/WTeVwfRv76fbZqayIJvLuvOCittl5SEAGpH0mfpCztfKykqJSPOoGQH04XAYW1tb0Wq1KnpHpPaLJwzKud2IqMpCJlNyWgeDQTl7lkc1NBqNMgM7Go1Kn2pTmohr2efMgdbzffnll2XmU7OynLFVfZRloXvX1tbKmYyydZoxlj7VTJTSN7vdbgyHw+j3+8XecPaY41F6We+Xg6xUSG1Ip3W4GnuKhivTysciv6uPqZ8ibgY7RQRMqqPWcNJGyn5xFlFjgmsPHwpJhuTAUA+5Hud3ZoZFXOnT09PTWCwWsb29HYvF9Xo76n4+o/fzP0nvIi5zHURn1GfjmVavPtcsDx1ZOmysl+okfMegh4Ni4hgPbFLHU4drvSo30vG2k+90KLPfxJfMeRfeubi4iMPDwzg8PKwc6SXZ1CkGGxsbMZlMYrFYlIw1Bbvm83mpN3Gh3iPc5vVlhuFDIfbTXZzbTC845uCfcEez2SxLP77++uv49NNP49NPP00xqvc9yf0DynnEtb6XHlOghzLD4KLbb2UwCVPRwZVtYaZDxM0ds93/UGCV92bt86wBBk48gOn+jfcPnfWLi4sYj8fx9u3bOD09LYFX3e+YipNz1HPMgBVlgSjHZ5mc3IXu7NjK0dCLaMSoICOiwrgMYHtjaPiyiIG/NwPfbjScCRK8TFE68R7vbAm36uIpihTOxWJRWWtGgMRBRKeA5TSbzRKR1AB0I+egX8LoEW7Vm0Bf1zODTKHUtWWOR+YYUSFoPYpSNGhcM5mRIZ1Op7XrnO+DaARFrlhd3jOjzt/ryI2wlFSmYN3AZIab72Q7fCbZAYaDNJdZjn+mOBNcO3H8yQh40MV1hyt9J8oT35O1n+2irMv58KUIETcNo783689M12UGPvuc1fE+SQaKQTbJY6PRqDhQWhep1MbRaBSTyaSyiVTETUCUtdflo9G4XrbCjc5EcpC4gYdmaofDYZFZObva0XJ19WoXbK1vkiO4ublZ9FFElFlX7Y45HA7LcRQK3kVE4Y+OstE7lHarMhV4bDSud3jV+iqVIX0px3s+nxeHXfpefJfDq/uVVq21iNpZ1GXRnQempJHqxlNmU3x2QjPh+mNA2Ovkjsh9jwHW0+viASzijYio7CovYlmSV20AdnZ2VgkaMchDfOI6k+/IbIHjKZcB2nuVJ7laLK6XDxEkZ3yibtd4zbCX45TMKaL8SC9rnHgGWJ2DlbWVM1dst98rXTEajeL4+DgGg0GlfqqTgqEE8LymYJCCOTwzWjZdqa7EeXKqfX3nfVBmuzxAcdszoix4kT2jvjk7O4sffvihBOe0mZjrMJbp9XN9JR7LYVxZWbkRwJFsyefxvWL8XjrHXHKZySDbS6cwGyvEMvzvbeJYYTZDhvU8qOK80lGTOrmEfV7Xv8RDd6knf/eJEW/3XenOji1nh1QB5lq7IvfK6b9XkMqNDHHngAxzypjH9/q7MuXn4NTLpCPKQ5AzB1JlK5InAVfOvPhJheXCIIHjFvFM53WhcEdE12lAaFw8wul8oKHQM4xKUzBdmeh3/qbZHK6DWeZQ0Yh64OQ+KFNIywBfdj0DAXVluoJTH2mGKuI6OKK+0XOUzUzp6D/7mO9V/zio5VignDl45b1yeLJ36Hsmu86vOvDl8u4gqa6PCLy5pofpbTSadcqabV0mI6qnlyFaBtrrjOHHJqZMSQdyoyQ5ZNI34uPl5WU5E9YDFhHVfsqArkCHG+GIK94QDKoMZY3wfMzpdBqDwaCyAZQcW822aBfsjY2N2N7ejna7Hd1uNy4vL2M4HBad1O12y7Fk4omcep63KP2t9aQrKytl1lc8UxrcYrEoPNNMK3Xz5eVlAcqNRqMAMO4aG3Gd2qq6ZjrawUkdKNKz3i8si2WQHECpb+Roc2bZZ2fFG7dL90mZvSM5b8XHiHxDJMox0+Yln7qHDhj7rQ7w+ZFvEVX9smxWhAFn9knmHKgOmZ6V/dDYZdYY209clfGU9eXsU7a0iXinbmZWtorPEHPwPj0vx/b09LTslu7188w81UU6QW0VL2hj2Efaz4Kzx8zGe0iUyf9tWC3DQF5Whl8jrvTamzdvotvtxvPnzyv8q8O1WcYVZcRnExlMcHut8nwyK5vppQPsOMw/Z+PKMRgn5er4T71Ap5ZZAT7uqdNcX8zn88p5zX6U4jK862Pd6+3BbflL7MdMt9TJj9OdHdu6zlX0jB1K5lDYs1k6AnVX9iqDnzMQzUZnTPAOVx0ojKxXs9msCLYUTtaZDnLdyaUzPJ1Oa9ft+SBSSsPl5dXZtNrxUsBI4ItCoX6hw8CByxlXgggBK18jtVgsKhuCiYccrHLeBbS4vblHI6XcG42rmQmdTUfZUd2Y3sYF+w+BVFdfBxGRp06J/HumzCTXg8EgBoNBfPvttyVapr5kfzuYqnOcloFQ8T0LXDD6zH535cWZF08zJ7Fctkflu5L1ddwCeizPr+m6ZxhkwEZ/XIPo62N8XbGDJLXfKXMibjMImQPi/XVfxHVt1NvaCIgO6GKxqPDu5OQktre3bxg8HxMEKnVGmHIjB0gp+tLVnLmUDGnm+Pj4uKwZ0r1yFnu9Xtnsbm9vL7a2tmJ7e7tsovPNN9/E4eFh7O3txcrKSnz66acxGAzi7Oys6GPuACuQqlTEZrNZZpu0B4Fmwc/OzuLo6KjY1l6vF/P5Vfrd6elpHB8fxy9+8Yty/u50Oo3Dw8OSotxut8suu+ITwbScbv357sn6800N3Q6yn9QHEdf6geSyLidBa5qFDagTfP3iYnG9I/t9kjt9PjYzfa82EU/QPlPONZvNEwTcLnNyge8WuVPg9fI6ZMR6q55qs862zfZZ8DL8/cQ9HiQgVtSf2qosr0ajumbcbWnmhNPpyDIQnC+umy4vr85SPTw8jLdv35aN3OiUSxcJByuIv7KyUtbiT6fTaDSugnRbW1sle0LEPqZN1ZjjkTH3TZTfrA/cfoto2x0XsN1Z5oHW1I7H43j37l2sra3FX/7lX0av1yvLXehHkFiO4wI6Upx44v4KlPU6J5XOrDu20mXkBdvquCzDTJnzyee4ZIHOrH9XW7ULuutZli3Hdjwex2g0qmSCkn9ZnehMsw11uNDb6ZMd7mfdRh+0K7K/JFNe3kD/TsVap5QzEEil57natxEHooSfCtRTd9lOd345eOuckqy92fPeRkYJHRRrkBHQRVwLNB1bGiUqGu8/p+w660CeZPf7wPdoJB1vlpcNLC/zvoH9Mnmuu58Dv07WI24a17Ozs7L74snJScxms7KWiYaBTqQ7bVk9nZcOBLL7l/GA9Wdfc5bBI291gMfb5g6jK9A60HRbfd0ZZf9w3GRUJ6vZe2+79y5ys2xc3AdxJo9rHslHD/ZIb+n4HOnTDARF3G5HdA9Bt+rlsxyuiygnjK5TN+mzUpebzWZsbW2VMqbTaZyensbR0VGsra3FYDCI09PTODk5KY4i6+19Lz0tnc5N8mazWZlFZqquzgodDocxGo0KuNdGXZPJpDh+mvnTTpjNZrOkU2vXXd84Koue32Wcug1bFqXnvdqTw3VR1r+Z3rovytp029is41nETSc0s68OJP15B5ksdxnPsn7KMM1isbgxpjPsovexrRkf6vjjAD/ieglCtnM+27FM7/vn7Dvb40Rwr3Nr/RnXiwwUK11WfSFHnRlBdXbCseNDoDq8z2t1Tk9m328jtZsTHdLBWv7Bdd6ZfdcfMbHup+wzE1J/IgZesn5a9sd2+HuFk5bpQvLN+0DkdpB89s+6n/rFcZ/kWff7BIO/n/XVf/Kv7hnKSIYTb8NLdXRnx1ZpUYx+cKaOlSSYyJwbpdVyYMvBJGC6rUGZ0PK6G2P9JuCiTuWxDt4GAqFM+YgHW1tbZeaATjPfrTY6AKxTzv5e8Xw8HlfWs2lmRO9iVNPJndIMRGQ8Z120+YqiXI3GzeNeGo1GSVNcLBaVY2I0I8A6ucy4QC9zOD4GZTKQKSMHHi5LPqNBQC4w/fr163j37l28e/eurMF79uxZBcxHXKd3MaovZcR6UHkRIKhNjOaJ6mZfCHRorDNwTGOUKTQ+r3bpWW6SwJn8u5L4wvoQPIoajUZZT6nIr67r/QJXWiee8USfeS2L0C6b0fJ7XAfcN0mntFqtG4fOR1TTp3S/dlJ88+ZNObIq4lpPLRaLG0e5zOfX61lF3m8EuOKRHCbZEfa/jpTRrLH0ljJStOvw/v5+9Pv9MiO9srISv/nNb2JjYyMuLi7i4OAgXr9+HWdnZ7GzsxM//PBD7O/vx+npafybf/Nv4unTp9Hr9Uq9ZGsU8VZwUjO2SpM+Pj6Ok5OTeP36dXFipSP17Pn5eXz33Xfx7t27WCwWZUMpbdI3Ho9LW5Varc1rnj17Fr1eL1qtVnQ6nbI5lWyFy7DrKg9G6Fpdyl2d86sZMNkQ4gjdI7tVd9b9fZOPSbVhmd0kYCYvMqyiMvUsZ7ZZbkRUdCgDmw6E6YDVOSW0V9TlGmsKhszn8xtnYl5eXp9z66A2omq/PUPO2y2nj8efqO7iu95FME/euI6vw1seOOU9cqIODg7i+Pg4+v1+hTcqR+vh19fXS1BKerLX6xUeabzJ3qg9nKEmSZcpY+1DJ3R+Kqqzw5l8UhayWTjvJ3/Wv6+vr8f5+XkcHh7GyclJ4TNlUffSfta9IxvHqqtkXD4OJ8XYTv4xYJr5QbyHOpcz/9QVHHvuT7EfpMczp9aDvk7Eh9I329vb0el0StCU8rdMDn1sOL91DzGD40PHTh7cuwt90IwtwbHPfjo4zhSFhI4RODZYwIQpGnXA77aoHa9TWNxRosJn9D4Dk1m0RmUTsHAwaYC4g6pIXqPRiMlkpdggXgAAOzRJREFUUpmNbTQa5VxFKVO1WYNb54pKyAjAtQ6LoFH9QMPqfcABSsNK/tF4+oYGEn69OwPxNEgcdLpGB8kV3H0SFTLJQYCu6X/2O8uLiAJwDw8PYzQaxfv37+P09LQYSW3AI2DhMs2x6HXTu7J76tpCRVOX/kRFR0XKtlE2KUu8p65vVVdPf/b6ZkbQo5B173Kj4e0ejUYxHA6j2+1Gu92Ora2t4lD4jBPrUlfXZd9vu/4QSPpN+ozBOwaxpGMoF4PBIPr9flmOoX0DFovFjTVA7vBEVIGKO2GUryxKrzq32+0SVJ1Op+X89eFwWDZ2OTw8LO9SyqGOlxBw1Rra6XQab968KTsa7+/vV5yQZrNZUqKPj4/LOD85OSmzO/r99PS0LDmgfVgsFgUEi8ea3dUaVQFm7jC8tbUVm5ub8eTJk9ja2opnz55Vzkf0NGQHFm47pReWjUOfsaMd0X1ybMVvBkMoT5I3vfdDZnl+Kqpz1mX/XV4dhNbZEJVF+aX8EwyL+JvrdtrRDOA7BtI1t1OZzpRMcoOdbEnOsu/EIP4eyoTk0wNfWfnePpeXTE9n13R9sViUDaNkl7l2mcEKrZfVxMXZ2VnljGjqBM+U8PcSlyqw4zjpvok4QVQn17pf/2kflpXPz9kfx8Pm5mYJEDp+zexynU2RHHpQXThaNt/9iCxQs2y8Exvx/qz9kgn6Bs536lA6wO4Is02uaxqN6g7v2lvi/fv3hdfuA9xGHriiD1aH/bxO/ndX+iDHVgwXk6nceCQLBViMlRHPFs3rfq1NyGYaSRKibGYvU1Z0xlyZSmj0DNe3uKASLLuw0vFQ3aSUO51OJVW42bw+B1ED4+Liohyj0WhcO7v9fr8MHPGz1WpVUl9UR4EaCS9BgersYIE8zBx3Bi04iMgn8VwDS+X7mVeLxfWaXZZH/vss30MB+nWDbNlg4ziIyDdZiogCrr///vsYDodxcnJSZrrkCPh5vpQHRrL53qz+rJuPCV2nzHBdm7dfBkCK2vni63PdSa5T6BxrdWlYqid1knjsEVGCPG8Ho6IEaZeXV4eSv3v3Lj755JN48eJFvHz5svSXRxHreJ7pJ+e391lWzkMgOrYax6qrMlVEDsj6/X5JH5OO0rIJzsizXzJHIpvl0W+UCfa96qz69Hq9om+Hw2Gsra0Vp/vw8LCsk+ORNp1OJ3Z2duLi4iLa7XasrFztcqrZ04iIg4ODkrIoW6B9B/r9fnFGeVySZrrkXIsn7Xa7pEbrmAgCOu1P0Gg0ypEn2ndB6/c2Njbi+fPnxbFVPRVY4A715DH/k9eZrnC+U7ex/zguFQgggGHgnDIlHZD1+ccm2lGCWdo/fs4cJl33wJvK5ewdZV9Ok+5ZdgyiBxz5O9/Nd/j7vD0izu5zaREdBr2PxO+sk+qZ2Q4eOyUekJfuuLitYMA8uz+TedrF0WhUlh1ovDForzprozfOsG5vb5dxyf1o3LHN9pBQWzlT9lAc2zobFZGnopOn4hkxI8vK8B6xqf4reKByFGjUEUuO/VU2MWUmE45rIq6PWRKu1RhU3VgvygT/XA94wMt55vx1vOj+FWdkecwQy+fY9wCw7tNY3tjYKNkG4pvsUjbWs7aQN2xDpifY7+7YMj38Q+jOji0dvSxCoU6VwmXl2DhXpGKu1gbN5/Not9sVoaijbBAsU5a87rt5LhbXG1ToeAbOSLqR8Pdpyp4OCFO9dD8FMaKapqnNRJrNZmXRNg29Bhij3IvFoqIwnTc0lnxfNsvHdlEI1UdeLteTcECJOHMhwKj6Mg2Oi/QVBdWAqqvnfRD5kYFrBwZ8hjKktXEHBwfR7/fL+jm1dzKZRL/frwBWgplm8zpFS/0qkOzjjvUjuKWyUT9qTDDq78SghCtSKsxsBsNlrw5oSN/4Tny6z8GhK23d58aFQIltIGCTszCfXx2p8n/+z/+JP/7xj7G+vh7Pnz+PX/3qV2UGUmugtTlIZoDqyANJzhe2dVk5H4uUCRJRDfZFXM9+q9+lL9rtdqyurpb1o/v7+7G+vh6dTifOz88rM5GusyOu+7VO/0pnMxhJ46j+1vmSnU4nOp1OrK6uxs9//vMYDofx/v37GAwGJZg0m80qM6jb29uVWdGVlZWKc8oxI0DscqdxopRmOiHkAWdRj4+PYzwex/HxcXmXzgHe2dkpfNLxQZubm+WaHNuXL1+W9GM6zlyqQv2t9oh/bqf5u3ROtmwhC0xpkzGtIyYW8Gwl7hUhB/y+7QBtNvWdE8cu11Iy9Vj3+dgmb92Wc6mWxpmC23Wg0m3SsgyczCHh85QD1//uJFAPexszm+J8zmabaK/oUPA32kfHfgzq1+ldBe21fEI6Xr9pnHL2VpvBKcsuImJrayu2trbKRmiNxlVwTMfUyMHTmFQdeEqGeHDbRM/HJA/mRkT6OevvOpli2csmq3RNGElnlPMs4+Pj45jNZsVJczlS38veay8C/UVUU4fl5ErPCWsRm3rgrc7Pqcvq8GvuzDkO0n08xYH4W/8zzMC9JCTzkjnZyadPn5ZggfjCpYPsS+/jzGn3/mRfZPwidvS+vyt90K7IqoQrLf1nhVyhLlNm6jh3nO5aJ6+HaJmX7wrQB6YboezdbjAIziiUTAfW4KGB4TolPSsnmSBN9csEgdHAOoeEiiUzqtm9y56n4Dr4JJ/5J0XACBAjOc6n24zgx6K7yqUP9jqlLEf2+Pj4hlPLI0oUta7jK9cCqnzy0uum+tGIZI6l+pTRS3+3FGi2xonlReRH+hCg8H6fdVV7sjHubfKZEB+n3o7MgXeDKMCi3Rg/+eST2NjYKDN/DJLxfyYzdePOAe9DJAUAHARTDj2AJvkZDocxm82i3+/H06dPKzzK9Lh/X6aLPAKdgV3urMozELUZ0+npaUwmk+LYdjqdMosqR1gzM5pRVdkES+TFYnG9FIQywqUnKkOAWCBNMz2NxlX2TsTVGOIZmGqPjg8S+JjP5yUVUufy6n6OV6bcZfrCee2BpEyOfYyTuElWBtQoW7LBEdWz4R8KZaC57r5svJBucwqo3/TdAaDrOfKPNjbTzXX1znQz28QZUQ/y8J1so78j4wHH7TLd7WVnusnrnL3Tda9klOd3Zg68t137tsgR0A7S0okch+ofBoVk86hP6rDcfVGdTXMMfdvzDCSI6vRGRvP5vCztkC5T4FUzuhwXjm2W6S4GVohxRNkJCV4O8b+IMp29S3X1+8g3jXm+hzO2WUBH5IFfyuDl5WWZRJENIvaSTb8LfYgcZOQ8cl/jLnRnx1bG2lMnBKY9t5sCRfDhgGaxWJSNLySMDnCpyMkYMp0dFXFzK31nKAcUozPcvGplZaWSdpgx1hWlZp0Xi6utyZvNq6Nb1C5F97rdbrmmLeS58F0zCDL4q6urZdbAj2gQuNGmIRwcrJsCCFKaelc2CLOIO2dixB/dq3MaHcSpDmq33j8ajYrjpKiZC7SfU3bf5MCgTrb0u0hj5+zsLE5OTqLf78f+/n45xonG7fz8vKzVWywW5fgRjhfyRIpNs7eMxmUOm+rIcaJ+ymbd+ZljkQDZgY0rVsoVHQ+WS/4KXNylzznu3bGh3GbBARlEps+zzo3GVZRda23/+3//7/Hs2bOYTCbx5ZdfxqtXr+LJkyflmBbN4o5Go3RmhPpJ/KHC9lS6OgN1X6RzGmlQlVnRaFynmNFgKzCjzVe++eab2N3djWfPnqXLV7LghMivuR2R48SNrQQkNdO5uroavV6vbCR1cXER4/E43r59G4PBoKKDZZOUjcNjaDqdTsUmNZvN+PLLL4tzqSNtdnd3y0yxAnqHh4cxn89ja2urtOX9+/flyCDp+PX19RgOh8Wx1mZQmo11uRfvV1ZWYnd3N9rtdtm85vz8vKQ0c82i9IUfb8cxpEwQB3S8j7Ou4h/B2OXlZUyn0xiNRiXLQfdJT6ndcvK1gZZsRBZk+5jk7XVHx9tMOyk5v02vZTzVddcpnGFiYID3ZdiJv/lYishnb3ld79bzni4s5+42bOHvUFkiD5Zm8sl60R6x3ny/ynBbQZ71+/2ydGI8HlfGOMeL5FV6R8sapF/W19djPB6X9f3tdjs2NzdvHEdG2ZnP5wXfcZbsodiAiJuz5CTXRcuCEyrLHZhMZkScjVSW59nZWayvr8fW1lbZo0fBM/Wrr9MmzlC5sg+eKdFoNMrxONSTxG0+RprN5o3xSrvmWEuYnHLMsSWc5n5PJvvuOAvb0DZSJ6tNe3t7ZT8J6Qy12WebVec6DOyykckAdQmvsd7Up54NuozubCmkTDImk5iSJqLS8gpHVKfH6wbKsmgBlZXXmQMmU6TsLDFO6yVUhgy7AykynQad/JFDo3dL6TP1U8JFhaGdmuXU8pxNCgkHJKP2mVOr9qh+jMI4j8UXj/SqDKbQ6f46p5/9zwCJO8g+KNwpu0/K+p71zaLbumc6nZb0xsFgEKPRqDi0lE3dpwgxd732epA8upjV150klqFnJRMcg+6UsmwCCPVl3bs49vjZQYjkkMYuazv1gj/LcVQH3GgE6NBKTmUEVEcBmH6/H1999VVp//b2dpnBW11djVarFf1+P87OzsomU8xE4Xjytjnp2l0jpT81KSWOWQF0RGmIyVP1sXb/VdBOs551/eagyJ2HiCpYyOrCjZcY2VdkOiLKml/N0qocBeK03lVpg/P5vGxwyFlFgiLqxkwvZqDG9WC3242NjY3odrsV/a57lcoogKV7Njc3Y2dnp6y11bhWEIIOotpEYMWx5d/ZV8t0jMu27KDqy/HAILTaIMdBelEz7vdJtIdO5FGmg1kGx4Xr1+x+zowSVDsPxfc6EO/jif3E8ept9rFInZoBaT6n7z4hUveOugCp84X4hHXQs/4eB8WyWY57qNME6OlME0+qHK3tVPBIyxTId+lOjQN3zonjODtMh/chUIZzInInx++peybD5svKEF+VMi5dERFFL29tbZXNv5ja7fhBelTBCcqZ+kN9waVysi/iieTG9SHfSb3gMpsRZdvv5+RCNimUBcUod9zzR7ZK/kUWNPJ+y/rH8Rmfz3BhXfszPZW9dxl90BrbiKoD46nD6uzM8ZXRovHUdUUTeM2Nps9AUXgysO9gh53tTMuUNh0/zoI5iPYOc6PlQF1gWcpQis7brplfghWu12X9M8d2sbieHaJipGEUP+nEu7HUwGdZGpgaCFQamUBLgcjoKsXaZ49JXAP60MiBn65F3FTC8/k8Tk9PYzgcxrt370q6o0j3r6yslBnc8/PzsnifMxUZCKFx99lWpjWJ6MzpN5bvBsoNKvuUY8WBlhNnTJcpLo51XSOw4D1eN4KViJsyRD4wG4DvEw9lNOkUzGazGI1G8Yc//KGU9Ytf/KLsmtztdqPZvFrnI4M6nU4rUXl35JYpa+qYh0BM86IsSj8wcCkgQDk9Pz+Pg4ODMhMix5JrlzJj5wCPfeoyQTmUsdZOwDLgWm+rsaX7JpNJmTnlDCfBAGcuZbc4/kTUnZqhoR71Nul+vq/X6xXZozOr9mpPBsmpzqrt9Xqxs7Nzg78iB1h6nryn3SSPs3S2ZfaXbVSat8oQXpANYZ/pnvPz85hOp9Htdv9Zsvv/gjJgzM91gXkGbLj2rQ40ZuBYfJT8SJZkPzl767OlKle0DKhmINKdVx9/3jaOebY3kxPd59lZXieXM5/ZJaasw2TeZ8J1dDTYZ9m6feIftV/nc8tJ0IyXftcY5jud9+IVN1fNbOB90zIbL3LeZ/Ll9zn/eU/m2DJQJt0qPS+M2Whcb8LKbKKI64wD6SHpcfZto3F9JJ0wgRxgYW6VJzmhfs5kNvNXsvFIXyobdxozvjxIfUOHVxNns9msEmiSXdrc3CxBUO7+zKxOHxuUAceMHGP8/S5+V2b7b9MfGd3ZsVUnsoMJMuQgTafTG6CD55FpgFKQ3bHNokJU6j6oMuVFgKBnxKRMSPif0Xaljkj4s3V/bgRUD/33gRuRDwQXWjkdcliVbkqh4KYm+qz3OtDKnC7nJ4XaBxSVsvrDjToDBzRqupeRS8kHN2JwYfc63xfV1cUBSLN5lYIymUyKQ3t0dFRAXcRNgCTArJ0Xt7e3i6Lx1M9sxoAOrWY5ODazNF1R1t/sD4J2Kk4HTZ7aHFFNM6KRo8PGPzri2Rp0jj/vAw9e0fD4mGcatfcrHduLi4uYTCYlwMSdMd+9exfz+VU6qTYXErXb7djZ2YlutxuDwSBOTk7i7du3ZaZeQIepvdIFD5lWVlaKgXSdOZ/PKzvt+p4Jmv188+ZNvHv3Lvb39yubBHLGItPlNJ4eRBOx72WrNDsro83zW/Xe1dXV2NnZKWeRa4MjyQADnKyrwJWIgFgOtQBEv98vz25tbRUZEGmzDtaJIIRpb8x2Ec+bzaud9jc3N6Pb7ZZ1UuI9z1dX3bWuijqM/GMWhm/QxfqQVH/aYI1dpeqz3zjLrXeJN43GVQqg+O7rcj823ebAu56MqG5Wo+8e9Fc57iRS99OhaDabBS/5hID45LrQHQx/RtdcF2a4Qc/pHgeszAhQ+cQljvPqQL7rAeEhyQjb4O3yepLHmcNaF3TlNbfzel46QDhMx8KpH5Q5IruWZWC5nRWefigOrcjtOcl1dER1pt7xtgcIWD7LdMdGNmgwGJTlI7L/ulebP87nV5tM6YQJ6ldOwmncOG4m5lJQVNc51pgeTZnQb87DjK/OS/pJtHmqO7N3vE8o28wIZf2azWYJymtTQ7WNmEr9qHo69pN+88BUJie34Xn6C379J3FsCRY835qAVUCAlXQF5B58BrhFdULu786UIJUC6+gK3N9Do0AwQYXsoJx88vq6Y5spXxcYDQYOQDow6gut82BU33mV8V+Dxg2Nt4tlMIqva8vAOJV/BgDEUw8+PETyQZr1oeRfaahaqzMYDCqOn7eRTltEVGbn64yI10t9KZl1Y6w614Eb1YNjNwsGZYaHjkcGoLKxyd+XyVzEtZJmhJ081z2qO0FC1n8ZeMp4KuCoMlW3y8ur41ZOTk7i5OQkGo1GWcu4WCyKs7G5uVmcEJ2hKueYAYVMJjx4d1eF/lMSwQo3xGs0qseHiVfksyLB4/E4hsNhBZAsM1qZLLl8ZPWUfuTOkeoX7i5PXR9xlf7LNF/uWCqHi7OMTF/TbCRBAI/E03t0XJDuoX2gflQbtZO8zwbIieCGVpq1pWMccR2UcPBDJ4r10e+0pfzvYLPOyZAjcXFxEdPpNKbT6Q194raR4DMby38O5Dov468oa1umr3wigfrI8QN14W1jq05nOybyvs1sYp2tYt3oTNY9w/bQAXbH6UP0otsY2pLsPtaljg8MSCpQKWzDyRWmgmpXZL5rmV2lPb9vWuas3Ka/ec2vE1c6H/yzeKrTCLg0RvdJf2xubsZkMrmxhwAxj8YWU5Yz/aQyOfvOume6sE62M/yY8Tgrg0trluEy6l8PSIpfyrrMsDrrRZ7p+l18NpL30TJ++HNZ3ZbRnR1bKhp1MDeT0Eu1YZJmaRVdZDmMYJBxMrB1jp/uE4N0rztfywA0Z5McUPIe1lPtFuAhmHGgzbaxfjRGdLZVF2+vyhQYooMbEZX0ZAI2j4zqfXw/05gUyc8MpIAbf18sqkcLsT8E2HkeLweX6i0+sT4EfnUzN/dJy5RUs9ksM1QHBwcxHA5jf3+/AGMqRpZFQKwZnbW1tXLG8TLgV1cPrZHwlDc9x1ly9o3e4btqU9lnM6GcuVEd1C5F7h2MRVRndjPA7IZIMpvN2EZE0TeellOnb+rAjd4nhT+bzcra2eFwWPpZu2Z+9dVX8eTJk/hX/+pfFX58//330Wg0YmtrK9rtdjx//jyePXsW0+k0vv7663jz5k18++238e7du5jNZtHtdmNrayuePn0anU6ntIX8uW/5F0knbGxslFkIAQzpfekGRa3n83nZLE8bp33zzTfx6tWrordc9zJgRpmn4xxxDdy5Xl0zmEpr1dmuvk6VDpO+P3nyJGazWbRarbIxiTaREijVZlAEAwJaBKr8nZtS6cggti8Dwpox1uaCTAEXr3kUmGRHG0u5zJBvfo2/MSDsdaPOcPmkTuJ4PTs7KzvAn56eVvSA+lX8IJ8iorJx1ENxbqlf2Me0j+IH7XCz2SxyFZEf9xVR5bm+O2nMMJii2UBmdqmO/p2OMp1u/u59qrqwvx2vUYb4fLYEjMA8wyD6nPGesuA2KKurf6/DmLKNnh2k35WZI1ykvRg0ycDgpfr85OQkJpNJ2eCz1+sVDKWyHYeJh7LJdXLwsSnj/20O97Lff+xs9GKxiMFgEK1WK6bTaXQ6nXKd+nZtbS263W5lky+ewa4+U38SN6hf1LeeRSGcFVE9ModYiQH3DLcR9zCIp3f5Eq6IuLE8TZv3uj+hcSHMokmX+XxeMpmUKUT7STyuMpV+XZc1yOBLhtvJMxLLIs4kHpBuVRDjLvRBm0fVGUU1hswhE1Rpnz3lZ0b8db+Iyshni/W7MzSb2fEyXRl6R+i/z5ZSATkPqOg9zUvvoEJnW/0+8ZgCTgeEDm+WCsCB5VFPn9nVACA49N/d0Pgslga31937LwNSETfTHbiu7McqwP9XRD7wmpTiaDSK6XQaJycnlQHIvva+F+hTuzmb5M6wD37KnCsUd4ZJBC8+HhSwYQCLylLPSIkR7PoYy8ajytE9khkqL4L77BrbwT8fH+QZlW4WjeW9LM/HyObmZtklV+svR6NRrKysxJs3b6LX65WdGS8vL8smUrPZrACi58+fl3WQe3t7JZ3q/Pw8vv/+++JQ93q9SrryQwD17A8Ze6YlU09SXuSEKails2PH43F0Op1ot9vlHZRJd2YzmWIEWu9m8EwAkmuRRJwtpy1YWVkp/UWATV1KnSTQ4AEelzHqAZdxPU8nReB6Op2Wa+IF1w4L6GR7MDhPHVxQ1usCTT5TnP2pbLel0is6tzs771l9QdDEOtIW3Cdldtp1sX5f5oSo/SxP1ykjjh0oV+of9Rv1I4Ep38NyVHc6sO4A6nNmg25zZnh/RHUiwZ2H7LkfY+/ZD65HqC+YVaE6Edt53elwUG5VHmVT2VosZ3V1NabTaUwmk7KkRWNXzq4yRYgfGaS9K88/FjmPeS3DAR9Sd/HOg+Ei4i45qgwGuK9Cu0B9Rh07n89vrG1mvenQUmf6GCQvsvZn+ISYyvmp3+vwFMl1KtOPubOx7DFlTe/wdjDIqoB7Xb+zf1wWiAX8OcqLjz/25zKd6vRBm0dljPdZEjEvc7a8gvpNlRcAcYUvciAtYRbD6Hy6g+VMJEjJjJIrSEY169ri5bAT3Vh4lNQ7Th3sacj6LCBD4EZQoDYQiNGpdMeSTtZisShHUbiDUddWDh69m7Oyeg/X/7Cueg9lSu1iX98XOYjWNe12fHR0FMPhsBwPwABERFUZUS54pqPOo/Q0T8qu+sQdZdIyQ6LrDLoooMFNZHTOJx1bjh1dY5kR1+nMvLYMMDMjgOBe9zI7wp0F8imbqeWY4kZSHg3NFLU7QvP5vMykHx8flzU8On/4//7f/xs/+9nP4tmzZ9FoXG3YcHBwUGYNtUvtZ599Fp9//nlERHz33XdxeHgYv/3tb+Pt27fx7bfflhn+Tz75JHZ2duLFixdltvG+ianDXOND0Jeljy4Wi9jY2ChRX83a9fv9sh4tA9UEEOxbAnk6lOpb7gzP/Ql8TDGAJHnT+yT/mkmhXdG417PaxER1oA1jmyTDdFw1i8AN9eTo6jMznrTunhF3Bjnd1vH9kn/aRoIzOr0EinRsaYP5m+sc3jubzcpZ3XJsXT/RljGIyrF+345tXWCtDkdk+tHtsq55ubpOh4aBZP2uFP+Ia9mknnPHluDcAXbm3Pq483pmDpfXUc/y/X6/l0HeZPLlz2fkfNdYUuaDY40MnDPI646n5FSyu1hcrWc8OzurzORRj0mWNXafPXsWW1tbxfHSzKL6ljz4EGD/UxH7apmzSjnM8Pey8uvkTOVKf0p3EkPpemZHlAK+WCwqx2ty8kSyoLqI6Fu4Y6sxSH5wUsmdOXfYfXxmvOUYcMddv2eOLe0MZVWOLcty+6B3qYzMtmXtoN2nHXDnVu+os5Xs8w+V/Q9KRRaz1DHc/EJGmWkZDsx1n5xYFz6m0jpxJoDOlgNbMoeGRffrdzpLFDaRd54LI50LN2C61x13CoA7K/5OjxDpGsEjhZ2RXBq4uvZo8FJBaHv08/PzePLkSQVMs51Mx/ZgA+vLfqHj6sCIM8UyQNpkJ3M87oOUVrq1tRXn5+dxcnISx8fHMZ1OYzgcVqKG4oPaSLnwflIaidJdCUYygy9yueGf+lfv9vQNd/44FheLRTEUBMtS3nQoWQcGMRyUENTQOZKBkmwoeurGyaOodJ4ysOb1YLuzmV0SHQkdu6S+3dnZiUbjKpvh7OwsDg8P4/nz57GyshIHBwfRbDZjNpvFX/zFX0Sv1ysz+YPBoKQ0Hx4ell0It7e3Y29vL37+85+XMz6/+eabODw8jK+//joODg7i66+/LqlU903ip9YPb25ulvRbbUYxn8/LMT4K8hDkrK2txenpaZycnMR3330Xa2tr8fz589L/bjfYr67DWCemYPIezWz67LvLFa9FRNF9zebVBhs6sk3HcandTPOiLVKZns5IO8KzFj2443pVNlXBL6UdM8PDx6XbCPKFv0sHuHNGu+b2m3Xm/bpPzro2eTk4OCjHPHGNIf8TuF1eXq/jdsD2EMhBm0Ax+UyMQAcnIl9HmAF/yk7EzV3t9RttgQdOms1mZVfwDLNkoNrlIMMbGV/cifWyvU0k6ZC72Hx3ejM+Ul413tQXmoFSG3W/l0OsqTXiWrdJ3OrpodJpDDKorrKlmt1ttVqxvb0dT58+LRsUKdh130F9p9tklJ8pDxku55+Xz80HiRmEZ7Rj+mAwiN3d3VJms9msPKsxJx0nLEO7Jd+CwXLVgdkydOw4+8+AfSZHbAPbyaAheUd9wrFEne4Tio67tBTONxr0ZR3E3RHX5zX7+/2eZbLh2F/tzyb7MrnhfdIJOpbvLvRB0wDuiGYRRDqPfC4rx5WcO49OuodKKHNI656nk8HPWf28LLbVQTwHrQsi604g4M/rHv33AU/+uPPI7z6o6vjI+/WM1g7qqCHWifeqn9luL9vrJ/KZWpblxtmd8/skrWduNBrlOJDT09Oy1lLkSlrX/Df2kYyep9v52KoDEhk5sPXx5vdFXGc58D8jbVRMLrP6zPHMcar3qEw6tPpMI+I7FFKxZ0GfTO4ynmRjKxuTBIWLxaIcH6Bop1JEFZjQOumVlZX4/PPPS3rZxcVFDIfDmE6nERHl/NaLi4ty5uGTJ09KPTY2NmJ/fz/G43Hs7+8XZ4C7794XiU/MEuHmTOKfQEKm11ZWro+10ppLpfNRTrO+zGSdThxnHyOquyS7rfJ6cXy5ceX5vR6EYTRczzIowyUJ+lMZPKqCwMEdHDm1mnnWuloGnjJHI9M7bL/Go+7JZtLcjtPh9t95nZF+gU85FnJss/qRD+zHuv7/mOQA3oHrsmeoR1UG9SX18zKdngFdkfeNPnMWKnM03cbrP/9cz3v96+qayaXzsc6GeFlZGX6f11u2heOUOqPuuYwPslH8L55mEx8MPuia2iEnS8Gf4XBYjrlSdkLm9Nw3ZTgi06n8XFd/17l1fgLlWvpKDiYDFirH+1PXPFOLZbIuXK/Kuvkkiwcgfex5GykLxBp1domfVf+I68nFiJvngLNeTLOm/aHcu+1zfvM+9iWxWdavrP9tcuBUh6E/JGPnzo6tgJ07axG5w5NVljnu/hwFkALg71tZWSlC7Ol57BQ+y/p4Sq6nudEos06ZEfH3ukCzziyLz6iebrQ4K8vynMdM1VsGOuhYsN56PyOXnnJIQCeQr+9K8eN7uHOcnBQfLBHXa9M0M8b1wq4EMpn6mLS9vR0XFxdxeHgYw+GwbCxDHmbgMHPAIq7TRLjJWqZkKI8e+b0NWHG3u0zhe931O/tLayjZPyrXI3rL+km/OTDQOzQuySfN8DEt3YEZyfnhitBnz/xZ8n0ymZRZVjmxSjHb29uLk5OT6Pf7cXp6Gufn5/H06dMYjUYxHA7LLOTf/M3fRKvVilarFT/88EM5w3gymcTR0VG8e/eubG6hYyJevXoVf/VXfxX/6T/9pxgMBvH27dv47W9/G+/evUv5+jFJBrLT6ZQNi+Tka03qfD4vjiAj4xFXPFfUdTwexx/+8IeYz+fxq1/9KjqdTrRaraXBLMoxZ8kYMFRUXWNKMqvfKUNynmhHsnerDLVXQQqBZcmZAh1Mn5dMa5w3Go2SAUB96A5Ao9EozivX07bb7fI54ib487GovlB7VR+3Lfrs+0J4+dJF2SYe0h1MpdZGen/6059iOBxW+snHsa4L7BPM3HUW76ckYSACZBFnTCKq9pbXM53pDpZAKbM03LaoXzzYx+UdxCLSZZwJEoD34A8Bsr/Dl2d4yrgHWeqwIN/Pe4gZMqfad8CVfSNP3KGV86NrPHFAzzm2Ux2IVRWMury8rBznoplbLU9h9gaPuCTe02eV9f79+7i4uDpK7rPPPitjXxjDneT7IuLWZX3r1/gb9Ur2nP5T/v0s+GbzaknLfD4vgWPqNn+/5Fdp4BHXgUr1gRxG2QXKM3U8sbKe9Wwz2YiI6pJLtZN6QLyQbPPd4rUCHcz4UIaS4zfqAJbHPqzz47yvXUdRHzBY7M4yy82WUXmZmRP9z6E7O7bumLFC7pzSUSKznLlsSMYQV2xksoPzOpDvTOP/Ze0Q3QbW/X0UVDkj3un+nM/E1vHZ3yPAxVkAjwBl76MjHnGdfulGg/exb2nYM57RCfM2ZA4uZUVA+DYn5mOTdsSVc+IBnKzfsnp7/2TraQncReRfNlYyhc56iZfkfzb2xHtvnww8newsIOMKMwMtBB6csWU7CL4YZfW2e/kZuHfgRUWf6YZG43pWnkpcYF4b9ujsNxrFZrNZZm4PDw9jbW0tdnZ2YjgcxsrKSgwGg9JWpbUSMGlzEa097Xa78ctf/jJevXp1o28/NmW8p6Fm0Ex8rotIN5vN2N/fj+3t7Tg+Po5ms1l28xX4jshnDReLxQ3w7oFHr5+ML2f7/T6W78HNiOuZ0/n8+sgOrj8S+NJadZXhdSLoWCajOraHQQSebZ3xh+9xZ7nOtnnA0/vando6kE2+CeArFZ8z89k7KBd6J9O0mTZ6XyTeKcATUc0k4X3UK+wHv85nvE8zsMdAiN/r+1EwOMFgom9OSJvD/yLqZo11/e5ObIbVnCeZ3RCpvr7+T0Ts533jGIMOKvUE9T/rSwxJORdPXe9Qt1BemYHCdrnNlu6QbhqPx3F6ehrb29vRaDQqWRl33Q32pybH2Y5xvF+WUSZjvJ7pKtdLciI5yeCywGsid1gla3JUiXHpTLJMOrJMSXYcnmEN6ZC6ySu9I5tAY2DW8SBlkdilzie7DVv7hIL3hfen2xtvd4Zbydu6On2o7v/gHUkcLLjz4xE7KhQXSipXfua7Im4OFt3Dw8mpjJ2hepcUpjMucyC8rVm5dZ3BQZIBOi87i+KyLq5INSCVL8+1x54Krvt9sHPGTCBegMyNEWdHGo1GZdMYpjvofeqXVquVygrryQEnflGZfEj6wU9JBwcHFRDriqLOUOt3BhGokBiVZDqmP0dll4FhPZcpmwwM6P3kr9rDYImUsM/S8H0uw/rdt6D31CpmT7CuAvFqlxsXbxvbRINBncP16eSz80yfNSOvZ1dXV8uMTbvdLufSKnX/7Oys7E6rtdc7Ozvx6tWr+PLLL+Ps7CxOT0/LrJUcIP3Xrpmnp6exuroa3W63bB71N3/zN9Htdmsk8+MRU0NpkMUjOmvqR8680GldXV2NH374IdbW1uLdu3exsbERvV4vzSjJjLt0FevDGSkRAzp6bwamKacEqXQYJNMRUZmpVVaL150yr1kctoe6nG3w2VntjO1rAml33eGhXlB9MoeJ/UiekzRO/S8jOhIXFxdlL4Lj4+Mb5/c6WGF9NNumIJuc/Psm2XVPk5bsUU9nYJL6Sf3us/dOGWD04IZ0tcpTYFL9xD7jjJTKZ1toBwigaTN4zzIHRH3mGDEDr7qm99Ih1Tv0Po0rHwPu1NIRpQ1QmW67VVf2id6tpVqsC/mv39gXWR/qOdkP7Usjmeh0OjGfz+PFixdlhjFz5h8K1eF0kXhcZ2sjcseWZbojxP7VHie6X78Rb3hf0q6rPMmbNgaULqa+5jsUjPY/yl/GE+I2T3tWXYS/WH/HMlmwg88Ip1O2VH/KPfvIfRqeZuCY09vj/cY/2iPvy8zh9rIzbLuM7mwp1DhGvVlpCQmVABvildZ1KiIqoLrGReQK150BKb+I6tEmWfSEApgpaX3mAMkiM3wnDRnLzFIQ9AwFJBuIAuXaEZNpLhwA/n46sZljL5CoTXE8VVx8ZkqefmM6seSARlVGWH2glCAKap0jSGN638SIoJMDBCf1qQME9anLZp1CdKOZKRL9RjmgrKlcrrly48F6R1w7wQRskgsCd3+WStsNs677jLUUMa9nQCgD7sv6gP1Q953jWzvdss1qr8CGUs8iorIbtiLsf/rTn4pj1+1248mTJzEYDMombaL5fB7T6TRms1lpvyLR/X6/OID/8T/+x9q2fQxiiq9mrAXmtC6fs/mtVqsEzAhWI6704GQyicPDw/j9738fa2trZQOQuxBBOHVmFtmVDtJnGlTKOAGz7pWDpfs4I0O++A6bTMcVX6jvCFCkUxWs1BpaARNuCiV58pkIAhMGbzJSW92RcQCl79LvdPY9kEx7oD4fjUbx9u3bODo6islkUhxbB7Ssj2anxVOV5zy/D8oCvpluFZFH/Ex953rLZ0b4X+Qg050zBUCYZcO6EYAzQC7d5XXyLAfHaNTlGVjnmHM+eZCYvJKMezqw2xtiTskT18BzTGQzyBxXzBJg2brm2EV1zWbPxBvVW2NdQRrqw9XV1bIR5cHBQZydnUW73S59o1Tb+yYGm0WUl8wmZ2Vk/oB/z/QXx8xgMIiIKHtRUD5YRobt1f+UbeluTcwwNZj9546y7IY2zGNQhA571hbXo2yj4zXiSMmOjyuNPwXZl9kA/Xd/hBMZzebV0XI8xpC80bv5Ltdz3nfeP/zz/qcvxLJvozs7toy08oW6pk7XjqISBFbYG6Rr2d+yBtAgu9C6wNFpYNlunJycyV7XjDLnos7Y+Swq35N1JnlMJe0C5u/ToHMwQ5LC5CxZVg4VlzufArQZb9leKYk6w0c+qW51Ed6PScsUVEQ9CNE1l8m6Wd86crn1Picw4r119dTnTPHocza+6urpsp4pNwIwvsNnAj3t2Ovp5bB+fLcDvoyffk3/s41GfNaCW+afnZ2VGUpd6/f70W63o9vtRqfTifX19RKNn06n6WyD3qXdmCeTSQwGg3TcfmwSCJAOoh6ibKoNcswiqoHMiOudF8fjcbx58ya+/PLLmE6n5Rk6DaRlRjEbgy7TJDfoBCJ1Ol8yoDYI6MihWCwWJUVd8iznUMTZWXdstUkdnQ2Owwww+Zits7t+fwYAM/BHoOOA3+skW6N1sv1+PyaTSQFjlGPaD8qNZMuPQKqzux+LOJus/z5zcZst9mvkfaYzM5mtu8f1aUR+RrzqwXWLEVEJOhCf+Lvd5vB5yTLf77bDsU1mB31M+xh1e8zrDCipPNVtmc6vw476TIdXbfP2uJPO91C+GSTQsysrK2UjqUajEZPJpOiVh0RuT++CoTOdk32ve5fL4WKxKM7+aDQqEw/Z+Fomx45pI6ISPKSME1dlE0rUfy5PfJ+X5W2UTFF+MxvnY4BjibOsmd5kvT1gyLoTkzGoyb7zfuRYyN7Lz7QJ7Jc6zH9XG/BBuT00svLeadwjqo6lIge6zggAo3CMcEsJscEZuI64nkWWQIk5VHxevjrSFTc/U9j1ne+VwBBAMDIvPjGCqDIYKSHP+B4KMZ1ZRWHUbirwzMhRyDwKyfUDulflc40YBVtl++Bliu7l5WU5W9GjoxkoVf843+UgaHbsvtPQuAkLDVFEdbAxokfiNSoekSu8zAC7EfD7OHYajUaZRaN8sQ9VFwcPBGxyMrghjv6UIuXlsT7+ORs7+pwd7+HK3Nvs6c3+HJU9ZZhOZEZnZ2dlNlJjQ8bTj3nQrJJmXHmQuWarIiJ2d3fj5cuXZQOl09PTyiYkjUajsk5Tx7toPD0EUj/5n3gp2VldXS0zE7QZkkfxfzwex9/93d/F3t5evHjxovBO7XfQq3IywyceZeOqbtZf5Uqf+bhoNptlHGXglwBa+lO6n/o5A7/aeI9gVxuCUE49ndv1KGd+mVGje9gu8ckBWQZG+BuBm+7j7Bkd0IuLizg9PY2Dg4P44Ycfot/v3xi7DvooF51OJxaL6+OQRqNROVf4Pkn2TU667GjE9UZpLh8MYkTU63EG65c5CfovXebOG+sqfrsMidSvkm/PaiBOoB71fiCY9vpLTth2D6T7b5msq67ceE384FhUurCImw+SH94X5K1+4z1eD9dB+qyy/fgf8knXLi8vi03hvcrYefv2bezu7pYsjodiAzKsLOJkRibHfo87Nyxfv+ma3ic9oY0bFTyjTtO9vOZyw4BhxPWsqDt21FnM3BNOls7jf87cEgtyM0O3ZZludLymOjmvut1ubG5uxu7ubvER6JM4rnPcJ3IsqCUWb9++LW3a2dmpHDNXp5dZ/6x83qd2i3iagI/Bu9CdRwqVmqe4UKnUNcKNsa/XcwXnDHFHgWUxmpAxwZUUHUHe7+9wx5rv1O8coD7LlDnQHMwqO1MAnB1xUE5BqOtwHwCsN40LgR9TzH1AkhcEa0w3pmNF5ZDxkPJU5wDrvVnffGyqU9J+j/cFlWvdwKy77kqVssX66DcCKX0mSHGZq1M6rqi8PwXsHMAs45PXm45D5uxmz2TtzsCJ30d5I8+dL7rOqD8dBmWjsEwGMhQAkG6TcZlOp3F8fByLxSI2Nzej0WhEp9OJyWRSAZ0aP3QYdM9DADUyjFl/cQxz1ijTBSKB3tPT03j37l1899138fz588psZRawdKDpoF9lUxYccHMs3FW31M3oqDyNNzrYaiPbr+ucsfVUUI5RBo693dS5vn6c7azrB3+X2ukOrbfXA5v6r7/hcFjS7hUIku5Q0JK84Xu0xligSkGj+3ZsyUsGqPmbrrkOyuQ0s23EE3pO/5cFZ/gexy1Zv/pYVB0IKPUs5cZl39vtdctwmfjjvCMu0WcH5o7lWBcG+fXnS12cfHzovQxSOFYkNqJzlNVP+px9y77g5lE+5mazWdl7QUs/7psyvHqbg5Td62Utw0YcD+KT0n61ZIh7HGTYXs9S1zkm1rMaZ8S13mbZQsmL7D5T4ClHKltLKlwO6saAj0PWSQ7s+vp69Hq92NzcLPva6JlluNX9nsxmK7DeaDTKfgn9fj82Njai1WpVjqDzAJLbGdapzjfJ6vhj6INSkQmaXdkJCLoCVkdRuHRdUULOYFHxZQ306wIDVDaZEdazdTNVjEa7Y5W908Gy6q66iGdUeu4sugHjez0K7zOmHHwEJ84r8p2DiWlIAkcSSgcbbAtBt2+wonrqHR4lpkHXvTRg/k7J0kNYW0KqU+Q+mOsUrD8vnmYRd8mX+tw3v3Ggy/Gp8rJZTT5fp+RJXDvIMiiHouw9LJ9y4A6IjzVPH2N7HaSzPL5f8l1XVgbOtKmHZmXX1tZKVFjt1pjRc4roaqZXzvDl5WW8e/cuRqNRtFqt2NjYiO3t7VIeHWaVp/dPp9OYTqdLDdTHIqbXep8xCu3LJRwMipRuenR0FH/6059ia2sr/sW/+BdlsyTKM0GyqA40qU6+tCKTc7dL7mxwXLt9yOSHtswdVZ8FkEwKoKgOTPl1cJ61W+UxCyRzXmiLvQ1ul7nWMAsmsE/p0ArcHR0dxcHBQQyHw7I5Dn9fLBaVI+H4Lu04vbm5WYC90pnvkzj2mbGWzcT5OMlkleDO126qLL5XRB2X2XyCcY0b1ZkZBqyLrin9m7+J755Or9+Jn4TdKMtsD503vZe/848BdA8ORVzjGPJd40tjK5uZcgeTfJRTwr1G3AnxenofOm6SM0aZuLy83niQY4E8nU6nMRqNot/vx4sXL6LVasV9U4bNM5nld8lvti6X+EZluWzT+ZPMaCdkLddRdgd9FGJ6vUvXXd+xLbQFXJfNscx+VuCNx/7I2eZSQfoMdXsgCEPo/R6MZ72U0fXs2bPY3t4ugQ+d1JA58tn7VC71hmzA9vZ2DAaD2NjYiHfv3sXvfve72NnZiVarFbu7u9HtdstGl5ubm7G1tVU7WeW89f7O8HGGRe9Cd3ZspbTILBor3xVSEScJGysskpAQJLrS8UFTZyAEDJg2JiXigEqdpg7wKIM7jOyYbNDVgQ6/P3s+U7K87rMijBLxGY9myzCwjjTC7Du9j6lIHPAEh4yWEZBndXDlsYyful/XPXr5EIA9yR1Vbwt/p6EjWHd5rnM8+btASvZe9nmmELJ3aZxE5OkpXm/udMs6uFH2dvC7O551Sj6rtz9HMOO88CUNXt86RU/ALr2ipQB0SJSezuNbpPfm83nFuZtOp7FYLOLt27extbUV29vbJV13OByWd0oXMZ35IawvjLjuBy0P0Pmq0vNZqldmpFwXNRqN2N/fj3/4h3+IX//617FYLOKLL74o/aSUXeqxzBb4bI3e5fqEx6Jk8iGeqwwG66gbWZeIuDE2ffM0EWdqxQMuqxGwVhTc10t52/UOvZv6krNe5AHbkJXt48DT6ggcedyFHNH9/f04ODioOFK8X0QHiJu1rK2tRbvdjvl8HuPxuByzdp9EXmVy7XIUke89IF74DJw7WdT3bi8dd2T/3Z7qfk+Z1rsiqhswqZ5Me/blVRzjfiyI88d5RqeU1ymD4gHfncknsQqdWg8MU875TgZmHNM2m80bAQG2kf3GtjUajWIzqGfIB7Xh4uKiBDX1fq2xXVlZiXa7nUjkxyfHZI6XRdQTETcnWersGbEhifpMpwioDM7aMqioOrhjrPJ80sSxgfQynVuXdwXthsNhjMfjIjMKxHGs+XuIuckj2iXJsmzt5uZmJe241WpFt9st9ZLcOcbK+Kz/7ohqrKvuT548if/8n/9z7OzsRK/Xi9///vdxdHQUw+GwZBLobPutra1yasT29napN9/J/ncc6TzSPS5Pt9GdHVs5tTSa+l4XTeNsXh1wZWMdCC3rECd2jChjFjuOdfVpeJWVdUBWJ39Xdm8GorLoa50z4AbDo53+XAaACCgzZV3XT25sGciQHLAOdcDSgRn57XVw3t8n1fHXyduTtdf5Ucf3uv7knwMJD+LU1dXr6I4hxwrr4+Akk9UMyC2T7cyhvgt5fe/Cw2wskWgM2UZ3RGjcxD/pO6VeMjIsMD8YDApQaTSuj8MgkNO79G4Ct/smGmTOiBBw8j7v16zPGo2rtbYHBwflSCTOiHFNY11QyOVO10jUJ+wfl2Ufr7rH9RzHLp/PdDrbu8yx5cYlWr8sx551WwYMyQ86CQQHLKPOGaFM8rOItp8AfTabxXg8jvF4XHmmrj4cO6q7nFser+RLne6L6nil3zIswvu9LPZVpmPpRLje9vpQ7qh/+AxBvspgf/jyItbD6+7BkSwInfFG73SM5e/idw+u0LH18Uc9nemezB7WOc2Z7Gf9lI0NvzfDcBkfmN2g2cD7DuyQMj3D69nvLuu6bxmmzj6zXyLiRqCG8si+ycYJyXW6231eo6xphpSBGgb7vH/5vizVObMvshGtVit6vV602+148uRJcSx9YrGuz/w9qkedTlssro7t/OUvfxmvX7+OH374IX73u9+VLBpls41Go1hfX4/JZBJbW1txfn4em5ubZVKAPK+zX8vGicvVbdRYfAiSfKRHeqRHeqRHeqRHeqRHeqRHeqRHemB0/2dIPNIjPdIjPdIjPdIjPdIjPdIjPdIj/TPo0bF9pEd6pEd6pEd6pEd6pEd6pEd6pD9renRsH+mRHumRHumRHumRHumRHumRHunPmh4d20d6pEd6pEd6pEd6pEd6pEd6pEf6s6ZHx/aRHumRHumRHumRHumRHumRHumR/qzp0bF9pEd6pEd6pEd6pEd6pEd6pEd6pD9renRsH+mRHumRHumRHumRHumRHumRHunPmh4d20d6pEd6pEd6pEd6pEd6pEd6pEf6s6ZHx/aRHumRHumRHumRHumRHumRHumR/qzp/wfeYwIeieMNUgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2,5, figsize=(12,5))\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(x[i].squeeze(), cmap='gray')\n", + " ax.axis('off')\n", + "plt.suptitle('Sample training images')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8d56be15-b2a1-4b41-8dbb-704f2e80e190", + "metadata": {}, + "source": [ + "The sample images show grayscale face images with consistent alignment and similar resolution." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "29944310-6408-467f-bf99-01ebc167d72a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_image_with_keypoints(img, keypoints):\n", + " plt.imshow(img.squeeze(), cmap='gray')\n", + " xs = keypoints[0::2] * 48 + 48\n", + " ys = keypoints[1::2] * 48 + 48\n", + " plt.scatter(xs, ys, c='red', s=20)\n", + " plt.axis('off')\n", + "\n", + "plt.figure(figsize=(12,5))\n", + "for i in range(5):\n", + " plt.subplot(1,5,i+1)\n", + " plot_image_with_keypoints(x_c[i], y_c[i])\n", + "plt.suptitle('Images with facial keypoints')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d9d55473-5d43-4848-9ff9-e654199f3e08", + "metadata": {}, + "source": [ + "The red dots confirm that the labeled keypoints are well aligned with facial features such as eyes, nose, and mouth" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "fce2dba5-c4b8-45a7-8689-b388092446ba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "complete = (~np.isnan(y).any(axis=1)).sum()\n", + "incomplete = y.shape[0] - complete\n", + "\n", + "plt.bar(['Complete', 'Incomplete'], [complete, incomplete])\n", + "plt.title('Label completeness')\n", + "plt.ylabel('Number of images')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "10a9b589-59a1-40ce-92a1-376f1a19274e", + "metadata": {}, + "source": [ + "Only a small portion of images contains all keypoints, while most images have missing labels." + ] + }, + { + "cell_type": "markdown", + "id": "67117194-1905-4950-80ba-25e6d030eb15", + "metadata": {}, + "source": [ + "### Data Preprocessing & Normalization" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b7cdeae0-5ac5-463e-b502-c9b787065683", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x: (7049, 96, 96, 1) float32\n", + "y: (7049, 30) float32\n" + ] + } + ], + "source": [ + "def parse_image(img_str):\n", + " pixels = np.fromstring(img_str, sep=' ', dtype=np.float32)\n", + " return pixels.reshape(96, 96, 1)\n", + "\n", + "train_df=train_df.dropna(subset=['Image']).reset_index(drop=True)\n", + "\n", + "x=np.stack(train_df['Image'].apply(parse_image).values)\n", + "y=train_df[target_cols].values.astype(np.float32)\n", + "\n", + "print('x:', x.shape, x.dtype)\n", + "print('y:', y.shape, y.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d941b24f-b435-4b5d-8cd5-6a1f96df1ff8", + "metadata": {}, + "outputs": [], + "source": [ + "x=x / 255.0\n", + "y=(y - 48.0) / 48.0 " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "007edbb9-619c-4073-9728-89f286ea9fc3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "complete: 2140 / 7049\n" + ] + } + ], + "source": [ + "mask= ~np.isnan(y).any(axis=1)\n", + "x_c=x[mask]\n", + "y_c=y[mask]\n", + "\n", + "print('complete:', x_c.shape[0], '/', x.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "16133092-8aab-430a-8b09-510859300347", + "metadata": {}, + "outputs": [], + "source": [ + "def rmse(y_true, y_pred):\n", + " return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))" + ] + }, + { + "cell_type": "markdown", + "id": "33e439f8-256e-4593-9e57-775744889263", + "metadata": {}, + "source": [ + "### CNN Model Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "f3f4482f-18d5-4656-acb5-01b4ac8eb01d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 96, 96, 64) 640 \n", + " \n", + " batch_normalization (Batch (None, 96, 96, 64) 256 \n", + " Normalization) \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 48, 48, 64) 0 \n", + " D) \n", + " \n", + " conv2d_1 (Conv2D) (None, 48, 48, 128) 73856 \n", + " \n", + " batch_normalization_1 (Bat (None, 48, 48, 128) 512 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 24, 24, 128) 0 \n", + " g2D) \n", + " \n", + " conv2d_2 (Conv2D) (None, 24, 24, 256) 295168 \n", + " \n", + " batch_normalization_2 (Bat (None, 24, 24, 256) 1024 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 12, 12, 256) 0 \n", + " g2D) \n", + " \n", + " conv2d_3 (Conv2D) (None, 12, 12, 512) 1180160 \n", + " \n", + " batch_normalization_3 (Bat (None, 12, 12, 512) 2048 \n", + " chNormalization) \n", + " \n", + " global_average_pooling2d ( (None, 512) 0 \n", + " GlobalAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 256) 131328 \n", + " \n", + " dropout (Dropout) (None, 256) 0 \n", + " \n", + " dense_1 (Dense) (None, 30) 7710 \n", + " \n", + "=================================================================\n", + "Total params: 1692702 (6.46 MB)\n", + "Trainable params: 1690782 (6.45 MB)\n", + "Non-trainable params: 1920 (7.50 KB)\n", + "_________________________________________________________________\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1766942272.095973 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "I0000 00:00:1766942272.413039 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "I0000 00:00:1766942272.413097 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "I0000 00:00:1766942272.417885 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "I0000 00:00:1766942272.417922 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "I0000 00:00:1766942272.417929 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "I0000 00:00:1766942273.657884 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "I0000 00:00:1766942273.657982 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2025-12-28 17:17:53.658111: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2112] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "I0000 00:00:1766942273.658338 262 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2025-12-28 17:17:53.658716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10861 MB memory: -> device: 0, name: NVIDIA GeForce RTX 5080 Laptop GPU, pci bus id: 0000:02:00.0, compute capability: 12.0\n" + ] + } + ], + "source": [ + "model=Sequential()\n", + "\n", + "model.add(Input(shape=(96, 96, 1)))\n", + "model.add(Conv2D(64, (3,3), padding='same', activation='relu'))\n", + "model.add(BatchNormalization())\n", + "model.add(MaxPooling2D((2,2)))\n", + "\n", + "model.add(Conv2D(128, (3,3), padding='same', activation='relu'))\n", + "model.add(BatchNormalization())\n", + "model.add(MaxPooling2D((2,2)))\n", + "\n", + "model.add(Conv2D(256, (3,3), padding='same', activation='relu'))\n", + "model.add(BatchNormalization())\n", + "model.add(MaxPooling2D((2,2)))\n", + "\n", + "model.add(Conv2D(512, (3,3), padding='same', activation='relu'))\n", + "model.add(BatchNormalization())\n", + "\n", + "model.add(GlobalAveragePooling2D())\n", + "model.add(Dense(256, activation='relu', kernel_regularizer=regularizers.l2(1e-4)))\n", + "model.add(Dropout(0.3))\n", + "model.add(Dense(30, activation='linear'))\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "8d50952b-3434-4c0e-ab07-8fd3d383f7d3", + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),loss=tf.keras.losses.MSE,metrics=[rmse, tf.keras.metrics.MAE])" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "9693b947-fc3f-462a-a067-09a701f9434c", + "metadata": {}, + "outputs": [], + "source": [ + "early_stop=EarlyStopping(monitor='val_rmse',patience=12,restore_best_weights=True,mode='min',verbose=1)\n", + "reduce_lr=ReduceLROnPlateau(monitor='val_rmse',factor=0.5,patience=5,min_lr=1e-6,mode='min',verbose=1)\n", + "checkpoint=ModelCheckpoint('best_keypoints_cnn.keras',monitor='val_rmse',save_best_only=True,mode='min',verbose=1)" + ] + }, + { + "cell_type": "markdown", + "id": "35d53bd7-9028-4cdb-bc48-3e89783f930c", + "metadata": {}, + "source": [ + "### Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "f191785c-e8b1-4e4d-9d40-394e4e2391a8", + "metadata": {}, + "outputs": [], + "source": [ + "x_train,x_val,y_train,y_val=train_test_split(x_c, y_c,test_size=0.2,random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "490a1c0a-34e8-4672-b394-5ed2ddd5d4b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/200\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-12-28 17:18:05.257897: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 90701\n", + "W0000 00:00:1766942285.491868 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942285.520221 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942285.524678 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942285.529056 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942285.533661 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942285.553431 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942285.576901 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be 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00:00:1766942285.846908 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1766942286.679631 537 service.cc:146] XLA service 0x72c8b8c6e9a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1766942286.679720 537 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 5080 Laptop GPU, Compute Capability 12.0\n", + "2025-12-28 17:18:06.694371: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "I0000 00:00:1766942286.776590 537 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "W0000 00:00:1766942287.010499 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.014272 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.017131 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.017948 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.018916 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.019824 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.020735 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.022578 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.027395 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.028632 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.030773 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.032107 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.033649 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.034971 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.041647 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.043117 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.044484 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.060635 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.061329 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.061888 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.064714 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.065366 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.066175 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.066746 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.067855 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.068607 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.082448 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.087001 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.092284 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.093486 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "W0000 00:00:1766942287.277267 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.280910 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.282355 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.283966 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.285471 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.287326 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.288993 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.290566 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.292142 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.294549 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.295306 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.296180 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.307816 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.310583 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.312699 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.314888 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.316760 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.318827 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.323104 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.325576 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.326430 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.327845 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.330325 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.332361 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.335351 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.337480 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.340364 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.343654 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.344834 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "W0000 00:00:1766942287.484567 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.488811 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.493210 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.501166 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.505221 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.509609 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.518461 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.522341 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.526787 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.529672 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.531943 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.544166 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.546920 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.551356 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.557693 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.563211 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.566986 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.572109 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.577902 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.581738 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.586827 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.590954 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.595035 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.600456 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.607345 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.616183 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.621849 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.625603 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.635747 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.656306 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "W0000 00:00:1766942287.795178 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.795710 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.796375 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.797351 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.798700 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.810522 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.811328 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.812065 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.813159 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.814388 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.816067 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1/54 [..............................] - ETA: 3:37 - loss: 0.3203 - rmse: 0.5350 - mean_absolute_error: 0.4375" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942287.817709 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.829562 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.848183 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "W0000 00:00:1766942287.927459 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.929501 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.931884 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.934142 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.936535 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.939178 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.950893 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.962287 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.966846 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.970660 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.972568 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942287.985166 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.014361 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.029674 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.036529 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.038963 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.045959 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.053059 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.056282 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.058796 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.061152 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.073670 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.075746 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.077252 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.078757 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.085660 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.104766 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.105296 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.105899 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.106847 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.107245 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.108193 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.108927 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.109408 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.110410 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.111320 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.111767 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.112518 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.113591 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.114436 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.115403 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.117593 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.118270 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.118739 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.119220 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.119811 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.120277 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.121051 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.122026 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.123038 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.123748 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.124792 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.125681 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.126896 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.127982 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.129095 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.136000 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.136463 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.137170 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.137761 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.138364 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.139175 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.140000 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.140853 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.141944 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.142970 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.144151 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.145367 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.146539 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.147690 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.148982 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.150379 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.151663 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.154442 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.155049 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.155592 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.156204 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.156796 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.157561 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.158112 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.158932 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.159648 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.160628 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.161875 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.163148 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.164408 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.168392 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.169020 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.169587 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.170190 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.170745 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.171318 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.172077 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.172724 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.173411 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.174636 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.175609 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.176655 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.177526 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.180098 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.181142 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.182078 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.182759 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.183661 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.184136 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.185072 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.186053 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.187023 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.188262 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.189555 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.191044 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.192370 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.194081 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.195677 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.197247 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.200624 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.201458 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.202155 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.203401 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.204785 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.205706 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.206348 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.207095 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.208015 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.209286 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.210401 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.211282 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.212572 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.216296 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.217574 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.218569 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.219545 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.220425 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.221420 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.222738 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.224584 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.226113 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.227637 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.229007 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2/54 [>.............................] - ETA: 19s - loss: 0.2639 - rmse: 0.4758 - mean_absolute_error: 0.3868 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942288.230640 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.231373 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.233071 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.234861 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.237498 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.239616 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.248968 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.249535 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.250013 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.250462 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.251230 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.251801 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.252421 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.253015 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.253730 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.254586 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.255686 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.256916 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.266323 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.287973 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.294949 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.295368 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.295632 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.296001 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.296318 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.296978 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.297776 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.298630 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.299204 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.299683 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.300492 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.301336 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.304818 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.305629 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.306367 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.306845 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.307714 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.308720 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.309795 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.310615 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.311605 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.312533 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.313528 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.314530 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.315474 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.316976 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.320546 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.321266 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.321970 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.323048 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.323496 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.324537 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.325426 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.325990 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.327075 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.327947 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.328455 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.329361 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.330617 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.331535 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.332576 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.338300 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.344753 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.345501 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.346133 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.346768 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.347622 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.348527 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.349428 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.350608 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.351690 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.352927 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.354149 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.355398 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.356581 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.357898 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.359343 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.360687 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.363265 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.363882 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.364438 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.365089 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.365707 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.366515 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.367070 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.367899 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.368664 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.369702 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.370990 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.372294 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.373619 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.377731 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.378192 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.378769 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.379403 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.379972 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.380568 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.381369 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.382038 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.382733 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.383980 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.384942 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.386003 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.386887 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.389520 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.390479 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.391400 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.392079 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.392999 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.393484 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.394455 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.395451 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.396422 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.397657 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.398876 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.400312 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.401618 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.403305 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.404856 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.406372 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.409396 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.410076 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.410819 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.412104 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.413317 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.414079 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.414716 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.415466 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.416392 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.417668 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.418766 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.419649 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.420946 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.425063 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.426382 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.427413 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.428435 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.429287 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.430290 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3/54 [>.............................] - ETA: 14s - loss: 0.2420 - rmse: 0.4523 - mean_absolute_error: 0.3649" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942288.431694 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.433587 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.435143 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.436704 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.438103 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.439805 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.440528 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.442260 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.444103 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.446717 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.448764 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.456311 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.457921 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.458362 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.458819 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.459570 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.460134 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.460743 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.461330 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.462053 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.462908 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.464086 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.465432 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.475662 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.500038 538 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.506470 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.506886 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.507110 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.507424 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.507700 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.508233 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.509008 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.509816 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.510407 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.510870 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.511540 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.512373 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.516163 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.516991 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.517726 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.518201 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.519090 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.520111 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.521198 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.522051 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.523037 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.523926 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.524888 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.525877 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.526798 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.528108 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.531697 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.532347 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.532966 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.534168 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.534610 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.535804 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.536802 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.537446 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.538633 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.539501 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.540063 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.540997 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.542405 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.543362 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.544498 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.549293 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.549951 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.550627 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.551180 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.551758 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.552522 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.553311 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.554116 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.555165 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.556138 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.557286 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.558361 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.559472 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.560585 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.561843 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.563187 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.564440 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.566793 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.567246 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.567781 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.568395 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.568990 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.569765 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.570309 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.571115 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.571861 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.572841 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.574090 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.575348 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.576641 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.580720 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.581269 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.581923 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.582562 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.583131 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.583744 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.584539 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.585203 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.585922 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.587177 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.588140 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.589191 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.590058 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.592578 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.593563 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.594544 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.595218 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.596121 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.596602 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.597536 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.598530 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.599508 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.600729 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.601964 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.603389 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.604727 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.606428 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.607960 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.609440 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.612412 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.613086 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.613789 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.615015 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.616190 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.616949 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.617589 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.618336 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.619289 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.620587 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.621706 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.622592 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.623916 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.627717 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.628951 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.629967 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.630961 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.631866 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/54 [=====>........................] - ETA: 3s - loss: 0.1686 - rmse: 0.3611 - mean_absolute_error: 0.2888 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942288.632922 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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00:00:1766942288.647185 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.649784 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.651929 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.660148 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.661813 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.662262 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.662733 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.663539 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.664149 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.664802 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.665431 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.666224 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.667150 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.668435 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.669897 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.681061 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942288.704599 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "54/54 [==============================] - ETA: 0s - loss: 0.1118 - rmse: 0.2717 - mean_absolute_error: 0.2166" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942289.357903 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.358139 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.358312 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.358568 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.358780 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.359220 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.359567 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.359967 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.360220 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.360475 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.360946 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.361361 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.364017 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.364471 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.365035 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.365519 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.365842 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.366386 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.366992 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.367613 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.367992 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.368651 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.369434 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.370288 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.370845 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.371446 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.372158 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.375460 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.375754 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.376196 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.376870 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.377329 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.377671 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.378309 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.378980 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.379344 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.380103 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.380667 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.381522 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.382022 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.382620 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.383259 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.385525 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.385924 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.386266 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.386636 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.387057 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.387479 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.388093 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.388749 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.389457 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.390116 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.390876 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.391506 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.392227 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.393198 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.393956 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.408327 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.408678 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.409066 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.409407 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.409739 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.410186 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.410678 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.411137 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.411752 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.412316 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.412937 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.413542 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.414256 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.414914 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.415739 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.416837 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.417491 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.418500 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.430402 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.430746 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.431084 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.431423 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.431847 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.432260 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.432601 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.433024 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.433460 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.433973 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.434638 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.435399 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.436084 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.439874 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.440155 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.440467 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.440774 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.441109 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.441462 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.441862 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.442282 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.442733 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.443419 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.443933 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.444472 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.445226 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.447066 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.447510 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.447992 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.448447 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.448787 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.449025 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.449542 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.450076 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.450576 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.451204 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.451841 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.452580 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.453266 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.454105 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.455101 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.455919 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.456760 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.468186 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.468552 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.468916 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.469499 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.470059 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.470439 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.470745 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.471147 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.471601 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.472220 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.472785 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.473404 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.474040 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.476309 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.476845 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.477349 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.477850 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.478437 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.478933 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.479611 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.480517 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.481296 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.482031 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.482675 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.483476 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.483839 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.484845 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.485639 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.486842 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.487928 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.490872 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.502669 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.502944 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.503072 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.503263 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.503564 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.503943 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.504322 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.504600 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.505220 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.505775 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.506440 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.507087 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.511724 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.522524 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 1: val_rmse improved from inf to 0.25469, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 6s 39ms/step - loss: 0.1118 - rmse: 0.2717 - mean_absolute_error: 0.2166 - val_loss: 0.0986 - val_rmse: 0.2547 - val_mean_absolute_error: 0.2126 - lr: 1.0000e-04\n", + "Epoch 2/200\n", + " 1/54 [..............................] - ETA: 0s - loss: 0.0714 - rmse: 0.1938 - mean_absolute_error: 0.1543" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942289.813703 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.813919 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.814062 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.814245 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.814419 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.814802 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.814997 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.815256 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.815455 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.815649 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.815995 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.816391 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.818768 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.819137 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.819418 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.819727 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.819970 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.820377 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.820829 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.821283 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.821594 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.822108 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.822687 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.823275 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.823742 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.824211 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.824757 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.827592 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.827926 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.828255 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.828768 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.829044 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.829317 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.829781 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.830264 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.830505 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.831084 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.831522 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.832318 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.832648 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.833146 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.833604 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.836073 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.836445 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.836707 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.837055 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.837429 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.837673 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.838070 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.838585 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.839096 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.839456 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.840011 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.840470 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.841057 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.841955 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942289.842577 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "54/54 [==============================] - ETA: 0s - loss: 0.0723 - rmse: 0.1961 - mean_absolute_error: 0.1556" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942290.638222 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.638460 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.638635 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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00:00:1766942290.705923 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.706539 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.707276 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.708105 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.708842 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.712628 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.712898 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.713255 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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00:00:1766942290.758084 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.758896 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.759600 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.760575 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.760954 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.762024 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.762897 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.764210 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.765244 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.768432 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.781402 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.781693 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.781826 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.782088 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.782401 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.782796 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.783214 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.783490 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.784180 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.784819 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.785573 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.786287 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.791481 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.804774 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 2: val_rmse improved from 0.25469 to 0.19306, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 19ms/step - loss: 0.0723 - rmse: 0.1961 - mean_absolute_error: 0.1556 - val_loss: 0.0708 - val_rmse: 0.1931 - val_mean_absolute_error: 0.1522 - lr: 1.0000e-04\n", + "Epoch 3/200\n", + " 1/54 [..............................] - ETA: 0s - loss: 0.0654 - rmse: 0.1782 - mean_absolute_error: 0.1425" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942290.887094 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.887349 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.887572 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.887817 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.888042 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.888477 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.888692 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.888959 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.889229 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.889482 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.889967 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.890373 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.892597 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.892969 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.893375 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.893824 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.894165 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.894740 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.895383 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.896034 537 gpu_timer.cc:114] Skipping the delay kernel, 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accuracy will be reduced\n", + "W0000 00:00:1766942290.904607 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.905018 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.905754 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.906198 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.906573 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.907221 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.907894 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.908383 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be 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"W0000 00:00:1766942290.916275 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.916697 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.917127 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.917452 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.918061 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.918857 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.919660 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942290.920279 537 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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mean_absolute_error: 0.1365 - val_loss: 0.0681 - val_rmse: 0.1863 - val_mean_absolute_error: 0.1574 - lr: 1.0000e-04\n", + "Epoch 4/200\n", + " 1/54 [..............................] - ETA: 0s - loss: 0.0598 - rmse: 0.1625 - mean_absolute_error: 0.1308" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942291.669159 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.669532 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.669918 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.670371 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.670694 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.671216 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.671798 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.672394 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.672769 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.673402 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.674034 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.674796 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.675352 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.675936 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.676622 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.759561 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.759770 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.759913 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.760171 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.760345 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.760714 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.760909 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.761126 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.761331 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.761511 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.761899 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.762284 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.764469 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.764756 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.765100 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.765652 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.765950 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.766245 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.766730 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.767233 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.767490 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.768179 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.768647 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.769478 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.769813 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.770356 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942291.770848 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "52/54 [===========================>..] - ETA: 0s - loss: 0.0583 - rmse: 0.1582 - mean_absolute_error: 0.1250\n", + "Epoch 4: val_rmse did not improve from 0.18631\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0583 - rmse: 0.1580 - mean_absolute_error: 0.1248 - val_loss: 0.0968 - val_rmse: 0.2520 - val_mean_absolute_error: 0.2197 - lr: 1.0000e-04\n", + "Epoch 5/200\n", + " 5/54 [=>............................] - ETA: 0s - loss: 0.0558 - rmse: 0.1505 - mean_absolute_error: 0.1193" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766942292.520258 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.520648 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.521046 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.521474 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.521798 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.522333 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.522946 532 gpu_timer.cc:114] Skipping the delay kernel, 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"W0000 00:00:1766942292.615341 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.615602 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.616061 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.616540 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.616771 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.617285 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.617680 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766942292.618461 535 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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+ "54/54 [==============================] - 1s 14ms/step - loss: 0.0518 - rmse: 0.1380 - mean_absolute_error: 0.1088 - val_loss: 0.1279 - val_rmse: 0.3086 - val_mean_absolute_error: 0.2694 - lr: 1.0000e-04\n", + "Epoch 7/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0496 - rmse: 0.1310 - mean_absolute_error: 0.1034\n", + "Epoch 7: val_rmse did not improve from 0.18631\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0497 - rmse: 0.1311 - mean_absolute_error: 0.1034 - val_loss: 0.0945 - val_rmse: 0.2497 - val_mean_absolute_error: 0.2056 - lr: 1.0000e-04\n", + "Epoch 8/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0476 - rmse: 0.1239 - mean_absolute_error: 0.0979\n", + "Epoch 8: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n", + "\n", + "Epoch 8: val_rmse did not improve from 0.18631\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0476 - rmse: 0.1239 - mean_absolute_error: 0.0979 - val_loss: 0.0822 - val_rmse: 0.2239 - val_mean_absolute_error: 0.1881 - lr: 1.0000e-04\n", + "Epoch 9/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0456 - rmse: 0.1167 - mean_absolute_error: 0.0920\n", + "Epoch 9: val_rmse improved from 0.18631 to 0.17065, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0456 - rmse: 0.1168 - mean_absolute_error: 0.0920 - val_loss: 0.0611 - val_rmse: 0.1707 - val_mean_absolute_error: 0.1375 - lr: 5.0000e-05\n", + "Epoch 10/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0447 - rmse: 0.1136 - mean_absolute_error: 0.0896\n", + "Epoch 10: val_rmse improved from 0.17065 to 0.12472, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0447 - rmse: 0.1134 - mean_absolute_error: 0.0894 - val_loss: 0.0474 - val_rmse: 0.1247 - val_mean_absolute_error: 0.1002 - lr: 5.0000e-05\n", + "Epoch 11/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0439 - rmse: 0.1106 - mean_absolute_error: 0.0872\n", + "Epoch 11: val_rmse improved from 0.12472 to 0.10339, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0440 - rmse: 0.1109 - mean_absolute_error: 0.0873 - val_loss: 0.0424 - val_rmse: 0.1034 - val_mean_absolute_error: 0.0816 - lr: 5.0000e-05\n", + "Epoch 12/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0437 - rmse: 0.1101 - mean_absolute_error: 0.0867\n", + "Epoch 12: val_rmse improved from 0.10339 to 0.08721, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0437 - rmse: 0.1100 - mean_absolute_error: 0.0867 - val_loss: 0.0391 - val_rmse: 0.0872 - val_mean_absolute_error: 0.0682 - lr: 5.0000e-05\n", + "Epoch 13/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0428 - rmse: 0.1071 - mean_absolute_error: 0.0844\n", + "Epoch 13: val_rmse improved from 0.08721 to 0.07650, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0428 - rmse: 0.1071 - mean_absolute_error: 0.0844 - val_loss: 0.0372 - val_rmse: 0.0765 - val_mean_absolute_error: 0.0594 - lr: 5.0000e-05\n", + "Epoch 14/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0424 - rmse: 0.1059 - mean_absolute_error: 0.0835\n", + "Epoch 14: val_rmse improved from 0.07650 to 0.07223, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0424 - rmse: 0.1060 - mean_absolute_error: 0.0836 - val_loss: 0.0364 - val_rmse: 0.0722 - val_mean_absolute_error: 0.0552 - lr: 5.0000e-05\n", + "Epoch 15/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0418 - rmse: 0.1035 - mean_absolute_error: 0.0814\n", + "Epoch 15: val_rmse improved from 0.07223 to 0.06907, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0418 - rmse: 0.1035 - mean_absolute_error: 0.0814 - val_loss: 0.0358 - val_rmse: 0.0691 - val_mean_absolute_error: 0.0528 - lr: 5.0000e-05\n", + "Epoch 16/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0411 - rmse: 0.1013 - mean_absolute_error: 0.0798\n", + "Epoch 16: val_rmse improved from 0.06907 to 0.06762, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0412 - rmse: 0.1015 - mean_absolute_error: 0.0799 - val_loss: 0.0354 - val_rmse: 0.0676 - val_mean_absolute_error: 0.0520 - lr: 5.0000e-05\n", + "Epoch 17/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0405 - rmse: 0.0991 - mean_absolute_error: 0.0779\n", + "Epoch 17: val_rmse did not improve from 0.06762\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0405 - rmse: 0.0992 - mean_absolute_error: 0.0779 - val_loss: 0.0354 - val_rmse: 0.0692 - val_mean_absolute_error: 0.0527 - lr: 5.0000e-05\n", + "Epoch 18/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0402 - rmse: 0.0983 - mean_absolute_error: 0.0773\n", + "Epoch 18: val_rmse did not improve from 0.06762\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0401 - rmse: 0.0982 - mean_absolute_error: 0.0772 - val_loss: 0.0366 - val_rmse: 0.0779 - val_mean_absolute_error: 0.0589 - lr: 5.0000e-05\n", + "Epoch 19/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0395 - rmse: 0.0958 - mean_absolute_error: 0.0755\n", + "Epoch 19: val_rmse did not improve from 0.06762\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0395 - rmse: 0.0958 - mean_absolute_error: 0.0754 - val_loss: 0.0358 - val_rmse: 0.0741 - val_mean_absolute_error: 0.0570 - lr: 5.0000e-05\n", + "Epoch 20/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0392 - rmse: 0.0950 - mean_absolute_error: 0.0750\n", + "Epoch 20: val_rmse did not improve from 0.06762\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0392 - rmse: 0.0953 - mean_absolute_error: 0.0752 - val_loss: 0.0351 - val_rmse: 0.0711 - val_mean_absolute_error: 0.0543 - lr: 5.0000e-05\n", + "Epoch 21/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0388 - rmse: 0.0943 - mean_absolute_error: 0.0741\n", + "Epoch 21: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n", + "\n", + "Epoch 21: val_rmse did not improve from 0.06762\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0388 - rmse: 0.0943 - mean_absolute_error: 0.0741 - val_loss: 0.0346 - val_rmse: 0.0690 - val_mean_absolute_error: 0.0519 - lr: 5.0000e-05\n", + "Epoch 22/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0381 - rmse: 0.0912 - mean_absolute_error: 0.0718\n", + "Epoch 22: val_rmse improved from 0.06762 to 0.06167, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0381 - rmse: 0.0908 - mean_absolute_error: 0.0716 - val_loss: 0.0336 - val_rmse: 0.0617 - val_mean_absolute_error: 0.0468 - lr: 2.5000e-05\n", + "Epoch 23/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0377 - rmse: 0.0892 - mean_absolute_error: 0.0703\n", + "Epoch 23: val_rmse did not improve from 0.06167\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0376 - rmse: 0.0892 - mean_absolute_error: 0.0703 - val_loss: 0.0335 - val_rmse: 0.0618 - val_mean_absolute_error: 0.0468 - lr: 2.5000e-05\n", + "Epoch 24/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0373 - rmse: 0.0877 - mean_absolute_error: 0.0691\n", + "Epoch 24: val_rmse improved from 0.06167 to 0.05951, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0373 - rmse: 0.0881 - mean_absolute_error: 0.0692 - val_loss: 0.0331 - val_rmse: 0.0595 - val_mean_absolute_error: 0.0453 - lr: 2.5000e-05\n", + "Epoch 25/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0371 - rmse: 0.0874 - mean_absolute_error: 0.0686\n", + "Epoch 25: val_rmse did not improve from 0.05951\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0371 - rmse: 0.0874 - mean_absolute_error: 0.0686 - val_loss: 0.0332 - val_rmse: 0.0607 - val_mean_absolute_error: 0.0457 - lr: 2.5000e-05\n", + "Epoch 26/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0369 - rmse: 0.0865 - mean_absolute_error: 0.0681\n", + "Epoch 26: val_rmse improved from 0.05951 to 0.05862, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0369 - rmse: 0.0866 - mean_absolute_error: 0.0681 - val_loss: 0.0328 - val_rmse: 0.0586 - val_mean_absolute_error: 0.0444 - lr: 2.5000e-05\n", + "Epoch 27/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0365 - rmse: 0.0852 - mean_absolute_error: 0.0670\n", + "Epoch 27: val_rmse did not improve from 0.05862\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0365 - rmse: 0.0851 - mean_absolute_error: 0.0669 - val_loss: 0.0329 - val_rmse: 0.0601 - val_mean_absolute_error: 0.0453 - lr: 2.5000e-05\n", + "Epoch 28/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0366 - rmse: 0.0860 - mean_absolute_error: 0.0677\n", + "Epoch 28: val_rmse did not improve from 0.05862\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0366 - rmse: 0.0862 - mean_absolute_error: 0.0677 - val_loss: 0.0329 - val_rmse: 0.0608 - val_mean_absolute_error: 0.0463 - lr: 2.5000e-05\n", + "Epoch 29/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0363 - rmse: 0.0849 - mean_absolute_error: 0.0667\n", + "Epoch 29: val_rmse improved from 0.05862 to 0.05849, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0363 - rmse: 0.0848 - mean_absolute_error: 0.0667 - val_loss: 0.0325 - val_rmse: 0.0585 - val_mean_absolute_error: 0.0441 - lr: 2.5000e-05\n", + "Epoch 30/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0361 - rmse: 0.0846 - mean_absolute_error: 0.0663\n", + "Epoch 30: val_rmse improved from 0.05849 to 0.05803, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0361 - rmse: 0.0845 - mean_absolute_error: 0.0663 - val_loss: 0.0323 - val_rmse: 0.0580 - val_mean_absolute_error: 0.0436 - lr: 2.5000e-05\n", + "Epoch 31/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0358 - rmse: 0.0834 - mean_absolute_error: 0.0655\n", + "Epoch 31: val_rmse did not improve from 0.05803\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0358 - rmse: 0.0835 - mean_absolute_error: 0.0655 - val_loss: 0.0323 - val_rmse: 0.0589 - val_mean_absolute_error: 0.0442 - lr: 2.5000e-05\n", + "Epoch 32/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0355 - rmse: 0.0825 - mean_absolute_error: 0.0649\n", + "Epoch 32: val_rmse improved from 0.05803 to 0.05743, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0355 - rmse: 0.0825 - mean_absolute_error: 0.0649 - val_loss: 0.0320 - val_rmse: 0.0574 - val_mean_absolute_error: 0.0435 - lr: 2.5000e-05\n", + "Epoch 33/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0353 - rmse: 0.0817 - mean_absolute_error: 0.0643\n", + "Epoch 33: val_rmse did not improve from 0.05743\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0353 - rmse: 0.0817 - mean_absolute_error: 0.0643 - val_loss: 0.0325 - val_rmse: 0.0627 - val_mean_absolute_error: 0.0483 - lr: 2.5000e-05\n", + "Epoch 34/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0353 - rmse: 0.0826 - mean_absolute_error: 0.0648\n", + "Epoch 34: val_rmse did not improve from 0.05743\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0353 - rmse: 0.0827 - mean_absolute_error: 0.0648 - val_loss: 0.0320 - val_rmse: 0.0589 - val_mean_absolute_error: 0.0451 - lr: 2.5000e-05\n", + "Epoch 35/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0351 - rmse: 0.0820 - mean_absolute_error: 0.0642\n", + "Epoch 35: val_rmse did not improve from 0.05743\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0351 - rmse: 0.0817 - mean_absolute_error: 0.0640 - val_loss: 0.0317 - val_rmse: 0.0579 - val_mean_absolute_error: 0.0440 - lr: 2.5000e-05\n", + "Epoch 36/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0347 - rmse: 0.0804 - mean_absolute_error: 0.0631\n", + "Epoch 36: val_rmse improved from 0.05743 to 0.05700, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0347 - rmse: 0.0804 - mean_absolute_error: 0.0631 - val_loss: 0.0315 - val_rmse: 0.0570 - val_mean_absolute_error: 0.0428 - lr: 2.5000e-05\n", + "Epoch 37/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0345 - rmse: 0.0798 - mean_absolute_error: 0.0627\n", + "Epoch 37: val_rmse did not improve from 0.05700\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0345 - rmse: 0.0798 - mean_absolute_error: 0.0627 - val_loss: 0.0320 - val_rmse: 0.0626 - val_mean_absolute_error: 0.0477 - lr: 2.5000e-05\n", + "Epoch 38/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0342 - rmse: 0.0789 - mean_absolute_error: 0.0619\n", + "Epoch 38: val_rmse did not improve from 0.05700\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0342 - rmse: 0.0789 - mean_absolute_error: 0.0619 - val_loss: 0.0318 - val_rmse: 0.0615 - val_mean_absolute_error: 0.0467 - lr: 2.5000e-05\n", + "Epoch 39/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0341 - rmse: 0.0786 - mean_absolute_error: 0.0615\n", + "Epoch 39: val_rmse improved from 0.05700 to 0.05515, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0341 - rmse: 0.0789 - mean_absolute_error: 0.0618 - val_loss: 0.0309 - val_rmse: 0.0551 - val_mean_absolute_error: 0.0415 - lr: 2.5000e-05\n", + "Epoch 40/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0339 - rmse: 0.0783 - mean_absolute_error: 0.0614\n", + "Epoch 40: val_rmse did not improve from 0.05515\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0339 - rmse: 0.0782 - mean_absolute_error: 0.0614 - val_loss: 0.0311 - val_rmse: 0.0577 - val_mean_absolute_error: 0.0438 - lr: 2.5000e-05\n", + "Epoch 41/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0334 - rmse: 0.0759 - mean_absolute_error: 0.0596\n", + "Epoch 41: val_rmse did not improve from 0.05515\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0334 - rmse: 0.0759 - mean_absolute_error: 0.0596 - val_loss: 0.0307 - val_rmse: 0.0557 - val_mean_absolute_error: 0.0419 - lr: 2.5000e-05\n", + "Epoch 42/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0334 - rmse: 0.0764 - mean_absolute_error: 0.0599\n", + "Epoch 42: val_rmse did not improve from 0.05515\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0334 - rmse: 0.0764 - mean_absolute_error: 0.0599 - val_loss: 0.0308 - val_rmse: 0.0576 - val_mean_absolute_error: 0.0434 - lr: 2.5000e-05\n", + "Epoch 43/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0332 - rmse: 0.0760 - mean_absolute_error: 0.0598\n", + "Epoch 43: val_rmse did not improve from 0.05515\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0332 - rmse: 0.0761 - mean_absolute_error: 0.0599 - val_loss: 0.0305 - val_rmse: 0.0561 - val_mean_absolute_error: 0.0420 - lr: 2.5000e-05\n", + "Epoch 44/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0330 - rmse: 0.0758 - mean_absolute_error: 0.0595\n", + "Epoch 44: val_rmse improved from 0.05515 to 0.05421, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0330 - rmse: 0.0758 - mean_absolute_error: 0.0595 - val_loss: 0.0302 - val_rmse: 0.0542 - val_mean_absolute_error: 0.0408 - lr: 2.5000e-05\n", + "Epoch 45/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0328 - rmse: 0.0754 - mean_absolute_error: 0.0590\n", + "Epoch 45: val_rmse did not improve from 0.05421\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0328 - rmse: 0.0752 - mean_absolute_error: 0.0590 - val_loss: 0.0305 - val_rmse: 0.0578 - val_mean_absolute_error: 0.0434 - lr: 2.5000e-05\n", + "Epoch 46/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0326 - rmse: 0.0748 - mean_absolute_error: 0.0586\n", + "Epoch 46: val_rmse did not improve from 0.05421\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0326 - rmse: 0.0748 - mean_absolute_error: 0.0586 - val_loss: 0.0302 - val_rmse: 0.0574 - val_mean_absolute_error: 0.0432 - lr: 2.5000e-05\n", + "Epoch 47/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0322 - rmse: 0.0729 - mean_absolute_error: 0.0573\n", + "Epoch 47: val_rmse did not improve from 0.05421\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0322 - rmse: 0.0731 - mean_absolute_error: 0.0573 - val_loss: 0.0308 - val_rmse: 0.0623 - val_mean_absolute_error: 0.0471 - lr: 2.5000e-05\n", + "Epoch 48/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0321 - rmse: 0.0736 - mean_absolute_error: 0.0578\n", + "Epoch 48: val_rmse did not improve from 0.05421\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0321 - rmse: 0.0737 - mean_absolute_error: 0.0578 - val_loss: 0.0297 - val_rmse: 0.0549 - val_mean_absolute_error: 0.0414 - lr: 2.5000e-05\n", + "Epoch 49/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0318 - rmse: 0.0722 - mean_absolute_error: 0.0565\n", + "Epoch 49: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n", + "\n", + "Epoch 49: val_rmse did not improve from 0.05421\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0318 - rmse: 0.0722 - mean_absolute_error: 0.0566 - val_loss: 0.0301 - val_rmse: 0.0599 - val_mean_absolute_error: 0.0449 - lr: 2.5000e-05\n", + "Epoch 50/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0316 - rmse: 0.0717 - mean_absolute_error: 0.0562\n", + "Epoch 50: val_rmse improved from 0.05421 to 0.05363, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0316 - rmse: 0.0717 - mean_absolute_error: 0.0562 - val_loss: 0.0294 - val_rmse: 0.0536 - val_mean_absolute_error: 0.0402 - lr: 1.2500e-05\n", + "Epoch 51/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0315 - rmse: 0.0717 - mean_absolute_error: 0.0563\n", + "Epoch 51: val_rmse improved from 0.05363 to 0.05135, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0316 - rmse: 0.0719 - mean_absolute_error: 0.0564 - val_loss: 0.0291 - val_rmse: 0.0513 - val_mean_absolute_error: 0.0383 - lr: 1.2500e-05\n", + "Epoch 52/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0313 - rmse: 0.0707 - mean_absolute_error: 0.0553\n", + "Epoch 52: val_rmse did not improve from 0.05135\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0313 - rmse: 0.0708 - mean_absolute_error: 0.0553 - val_loss: 0.0292 - val_rmse: 0.0530 - val_mean_absolute_error: 0.0396 - lr: 1.2500e-05\n", + "Epoch 53/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0312 - rmse: 0.0701 - mean_absolute_error: 0.0550\n", + "Epoch 53: val_rmse did not improve from 0.05135\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0312 - rmse: 0.0702 - mean_absolute_error: 0.0551 - val_loss: 0.0291 - val_rmse: 0.0532 - val_mean_absolute_error: 0.0397 - lr: 1.2500e-05\n", + "Epoch 54/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0310 - rmse: 0.0693 - mean_absolute_error: 0.0543\n", + "Epoch 54: val_rmse did not improve from 0.05135\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0310 - rmse: 0.0693 - mean_absolute_error: 0.0543 - val_loss: 0.0293 - val_rmse: 0.0557 - val_mean_absolute_error: 0.0418 - lr: 1.2500e-05\n", + "Epoch 55/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0310 - rmse: 0.0697 - mean_absolute_error: 0.0547\n", + "Epoch 55: val_rmse did not improve from 0.05135\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0310 - rmse: 0.0697 - mean_absolute_error: 0.0547 - val_loss: 0.0288 - val_rmse: 0.0517 - val_mean_absolute_error: 0.0389 - lr: 1.2500e-05\n", + "Epoch 56/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0309 - rmse: 0.0697 - mean_absolute_error: 0.0545\n", + "Epoch 56: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n", + "\n", + "Epoch 56: val_rmse did not improve from 0.05135\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0309 - rmse: 0.0697 - mean_absolute_error: 0.0545 - val_loss: 0.0288 - val_rmse: 0.0518 - val_mean_absolute_error: 0.0390 - lr: 1.2500e-05\n", + "Epoch 57/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0308 - rmse: 0.0691 - mean_absolute_error: 0.0541\n", + "Epoch 57: val_rmse improved from 0.05135 to 0.05046, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0308 - rmse: 0.0690 - mean_absolute_error: 0.0540 - val_loss: 0.0286 - val_rmse: 0.0505 - val_mean_absolute_error: 0.0376 - lr: 6.2500e-06\n", + "Epoch 58/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0307 - rmse: 0.0686 - mean_absolute_error: 0.0538\n", + "Epoch 58: val_rmse did not improve from 0.05046\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0306 - rmse: 0.0684 - mean_absolute_error: 0.0537 - val_loss: 0.0287 - val_rmse: 0.0519 - val_mean_absolute_error: 0.0386 - lr: 6.2500e-06\n", + "Epoch 59/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0306 - rmse: 0.0683 - mean_absolute_error: 0.0536\n", + "Epoch 59: val_rmse improved from 0.05046 to 0.05036, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0306 - rmse: 0.0684 - mean_absolute_error: 0.0536 - val_loss: 0.0285 - val_rmse: 0.0504 - val_mean_absolute_error: 0.0375 - lr: 6.2500e-06\n", + "Epoch 60/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0305 - rmse: 0.0683 - mean_absolute_error: 0.0535\n", + "Epoch 60: val_rmse did not improve from 0.05036\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0305 - rmse: 0.0684 - mean_absolute_error: 0.0535 - val_loss: 0.0285 - val_rmse: 0.0512 - val_mean_absolute_error: 0.0380 - lr: 6.2500e-06\n", + "Epoch 61/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0305 - rmse: 0.0682 - mean_absolute_error: 0.0535\n", + "Epoch 61: val_rmse improved from 0.05036 to 0.04975, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0305 - rmse: 0.0683 - mean_absolute_error: 0.0535 - val_loss: 0.0284 - val_rmse: 0.0498 - val_mean_absolute_error: 0.0368 - lr: 6.2500e-06\n", + "Epoch 62/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0304 - rmse: 0.0675 - mean_absolute_error: 0.0529\n", + "Epoch 62: val_rmse did not improve from 0.04975\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0304 - rmse: 0.0675 - mean_absolute_error: 0.0529 - val_loss: 0.0284 - val_rmse: 0.0504 - val_mean_absolute_error: 0.0374 - lr: 6.2500e-06\n", + "Epoch 63/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0303 - rmse: 0.0677 - mean_absolute_error: 0.0530\n", + "Epoch 63: val_rmse improved from 0.04975 to 0.04951, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0303 - rmse: 0.0677 - mean_absolute_error: 0.0530 - val_loss: 0.0283 - val_rmse: 0.0495 - val_mean_absolute_error: 0.0368 - lr: 6.2500e-06\n", + "Epoch 64/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0302 - rmse: 0.0672 - mean_absolute_error: 0.0527\n", + "Epoch 64: val_rmse did not improve from 0.04951\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0303 - rmse: 0.0676 - mean_absolute_error: 0.0528 - val_loss: 0.0283 - val_rmse: 0.0500 - val_mean_absolute_error: 0.0369 - lr: 6.2500e-06\n", + "Epoch 65/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0302 - rmse: 0.0674 - mean_absolute_error: 0.0529\n", + "Epoch 65: val_rmse did not improve from 0.04951\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0302 - rmse: 0.0675 - mean_absolute_error: 0.0530 - val_loss: 0.0283 - val_rmse: 0.0505 - val_mean_absolute_error: 0.0374 - lr: 6.2500e-06\n", + "Epoch 66/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0301 - rmse: 0.0665 - mean_absolute_error: 0.0522\n", + "Epoch 66: val_rmse did not improve from 0.04951\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0300 - rmse: 0.0664 - mean_absolute_error: 0.0521 - val_loss: 0.0282 - val_rmse: 0.0501 - val_mean_absolute_error: 0.0371 - lr: 6.2500e-06\n", + "Epoch 67/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0301 - rmse: 0.0670 - mean_absolute_error: 0.0526\n", + "Epoch 67: val_rmse did not improve from 0.04951\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0301 - rmse: 0.0671 - mean_absolute_error: 0.0527 - val_loss: 0.0282 - val_rmse: 0.0509 - val_mean_absolute_error: 0.0377 - lr: 6.2500e-06\n", + "Epoch 68/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0301 - rmse: 0.0676 - mean_absolute_error: 0.0529\n", + "Epoch 68: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n", + "\n", + "Epoch 68: val_rmse did not improve from 0.04951\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0301 - rmse: 0.0675 - mean_absolute_error: 0.0528 - val_loss: 0.0283 - val_rmse: 0.0518 - val_mean_absolute_error: 0.0386 - lr: 6.2500e-06\n", + "Epoch 69/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0300 - rmse: 0.0670 - mean_absolute_error: 0.0523\n", + "Epoch 69: val_rmse improved from 0.04951 to 0.04927, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0300 - rmse: 0.0670 - mean_absolute_error: 0.0522 - val_loss: 0.0280 - val_rmse: 0.0493 - val_mean_absolute_error: 0.0364 - lr: 3.1250e-06\n", + "Epoch 70/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0299 - rmse: 0.0667 - mean_absolute_error: 0.0523\n", + "Epoch 70: val_rmse did not improve from 0.04927\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0299 - rmse: 0.0665 - mean_absolute_error: 0.0522 - val_loss: 0.0281 - val_rmse: 0.0506 - val_mean_absolute_error: 0.0375 - lr: 3.1250e-06\n", + "Epoch 71/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0299 - rmse: 0.0663 - mean_absolute_error: 0.0521\n", + "Epoch 71: val_rmse improved from 0.04927 to 0.04913, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0299 - rmse: 0.0663 - mean_absolute_error: 0.0520 - val_loss: 0.0279 - val_rmse: 0.0491 - val_mean_absolute_error: 0.0364 - lr: 3.1250e-06\n", + "Epoch 72/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0299 - rmse: 0.0668 - mean_absolute_error: 0.0522\n", + "Epoch 72: val_rmse did not improve from 0.04913\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0299 - rmse: 0.0668 - mean_absolute_error: 0.0522 - val_loss: 0.0280 - val_rmse: 0.0496 - val_mean_absolute_error: 0.0368 - lr: 3.1250e-06\n", + "Epoch 73/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0298 - rmse: 0.0662 - mean_absolute_error: 0.0518\n", + "Epoch 73: val_rmse did not improve from 0.04913\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0298 - rmse: 0.0664 - mean_absolute_error: 0.0518 - val_loss: 0.0280 - val_rmse: 0.0505 - val_mean_absolute_error: 0.0375 - lr: 3.1250e-06\n", + "Epoch 74/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0299 - rmse: 0.0669 - mean_absolute_error: 0.0525\n", + "Epoch 74: val_rmse improved from 0.04913 to 0.04902, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0299 - rmse: 0.0667 - mean_absolute_error: 0.0523 - val_loss: 0.0279 - val_rmse: 0.0490 - val_mean_absolute_error: 0.0362 - lr: 3.1250e-06\n", + "Epoch 75/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0297 - rmse: 0.0660 - mean_absolute_error: 0.0517\n", + "Epoch 75: val_rmse did not improve from 0.04902\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0297 - rmse: 0.0660 - mean_absolute_error: 0.0518 - val_loss: 0.0279 - val_rmse: 0.0493 - val_mean_absolute_error: 0.0365 - lr: 3.1250e-06\n", + "Epoch 76/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0298 - rmse: 0.0665 - mean_absolute_error: 0.0522\n", + "Epoch 76: val_rmse did not improve from 0.04902\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0298 - rmse: 0.0665 - mean_absolute_error: 0.0522 - val_loss: 0.0279 - val_rmse: 0.0497 - val_mean_absolute_error: 0.0369 - lr: 3.1250e-06\n", + "Epoch 77/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0298 - rmse: 0.0666 - mean_absolute_error: 0.0521\n", + "Epoch 77: val_rmse did not improve from 0.04902\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0298 - rmse: 0.0666 - mean_absolute_error: 0.0521 - val_loss: 0.0278 - val_rmse: 0.0496 - val_mean_absolute_error: 0.0368 - lr: 3.1250e-06\n", + "Epoch 78/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0297 - rmse: 0.0660 - mean_absolute_error: 0.0516\n", + "Epoch 78: val_rmse did not improve from 0.04902\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0297 - rmse: 0.0661 - mean_absolute_error: 0.0517 - val_loss: 0.0279 - val_rmse: 0.0501 - val_mean_absolute_error: 0.0372 - lr: 3.1250e-06\n", + "Epoch 79/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0297 - rmse: 0.0663 - mean_absolute_error: 0.0519\n", + "Epoch 79: val_rmse improved from 0.04902 to 0.04882, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0297 - rmse: 0.0661 - mean_absolute_error: 0.0518 - val_loss: 0.0277 - val_rmse: 0.0488 - val_mean_absolute_error: 0.0360 - lr: 3.1250e-06\n", + "Epoch 80/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0296 - rmse: 0.0658 - mean_absolute_error: 0.0515\n", + "Epoch 80: val_rmse did not improve from 0.04882\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0296 - rmse: 0.0660 - mean_absolute_error: 0.0516 - val_loss: 0.0277 - val_rmse: 0.0493 - val_mean_absolute_error: 0.0364 - lr: 3.1250e-06\n", + "Epoch 81/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0295 - rmse: 0.0654 - mean_absolute_error: 0.0513\n", + "Epoch 81: val_rmse improved from 0.04882 to 0.04875, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0295 - rmse: 0.0655 - mean_absolute_error: 0.0513 - val_loss: 0.0277 - val_rmse: 0.0488 - val_mean_absolute_error: 0.0361 - lr: 3.1250e-06\n", + "Epoch 82/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0295 - rmse: 0.0655 - mean_absolute_error: 0.0514\n", + "Epoch 82: val_rmse did not improve from 0.04875\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0295 - rmse: 0.0655 - mean_absolute_error: 0.0514 - val_loss: 0.0277 - val_rmse: 0.0488 - val_mean_absolute_error: 0.0361 - lr: 3.1250e-06\n", + "Epoch 83/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0296 - rmse: 0.0661 - mean_absolute_error: 0.0518\n", + "Epoch 83: val_rmse did not improve from 0.04875\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0296 - rmse: 0.0661 - mean_absolute_error: 0.0518 - val_loss: 0.0276 - val_rmse: 0.0488 - val_mean_absolute_error: 0.0360 - lr: 3.1250e-06\n", + "Epoch 84/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0295 - rmse: 0.0657 - mean_absolute_error: 0.0515\n", + "Epoch 84: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-06.\n", + "\n", + "Epoch 84: val_rmse did not improve from 0.04875\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0295 - rmse: 0.0657 - mean_absolute_error: 0.0515 - val_loss: 0.0276 - val_rmse: 0.0488 - val_mean_absolute_error: 0.0360 - lr: 3.1250e-06\n", + "Epoch 85/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0294 - rmse: 0.0654 - mean_absolute_error: 0.0512\n", + "Epoch 85: val_rmse improved from 0.04875 to 0.04872, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0294 - rmse: 0.0654 - mean_absolute_error: 0.0512 - val_loss: 0.0276 - val_rmse: 0.0487 - val_mean_absolute_error: 0.0360 - lr: 1.5625e-06\n", + "Epoch 86/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0294 - rmse: 0.0651 - mean_absolute_error: 0.0509\n", + "Epoch 86: val_rmse did not improve from 0.04872\n", + "54/54 [==============================] - 1s 13ms/step - loss: 0.0294 - rmse: 0.0650 - mean_absolute_error: 0.0509 - val_loss: 0.0277 - val_rmse: 0.0501 - val_mean_absolute_error: 0.0372 - lr: 1.5625e-06\n", + "Epoch 87/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0294 - rmse: 0.0653 - mean_absolute_error: 0.0511\n", + "Epoch 87: val_rmse did not improve from 0.04872\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0294 - rmse: 0.0655 - mean_absolute_error: 0.0511 - val_loss: 0.0276 - val_rmse: 0.0491 - val_mean_absolute_error: 0.0364 - lr: 1.5625e-06\n", + "Epoch 88/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0294 - rmse: 0.0652 - mean_absolute_error: 0.0511\n", + "Epoch 88: val_rmse did not improve from 0.04872\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0294 - rmse: 0.0652 - mean_absolute_error: 0.0511 - val_loss: 0.0276 - val_rmse: 0.0498 - val_mean_absolute_error: 0.0370 - lr: 1.5625e-06\n", + "Epoch 89/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0294 - rmse: 0.0652 - mean_absolute_error: 0.0511\n", + "Epoch 89: ReduceLROnPlateau reducing learning rate to 1e-06.\n", + "\n", + "Epoch 89: val_rmse did not improve from 0.04872\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0293 - rmse: 0.0652 - mean_absolute_error: 0.0510 - val_loss: 0.0276 - val_rmse: 0.0492 - val_mean_absolute_error: 0.0365 - lr: 1.5625e-06\n", + "Epoch 90/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0294 - rmse: 0.0654 - mean_absolute_error: 0.0512\n", + "Epoch 90: val_rmse did not improve from 0.04872\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0294 - rmse: 0.0653 - mean_absolute_error: 0.0511 - val_loss: 0.0275 - val_rmse: 0.0489 - val_mean_absolute_error: 0.0361 - lr: 1.0000e-06\n", + "Epoch 91/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0293 - rmse: 0.0651 - mean_absolute_error: 0.0510\n", + "Epoch 91: val_rmse improved from 0.04872 to 0.04847, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0293 - rmse: 0.0651 - mean_absolute_error: 0.0510 - val_loss: 0.0275 - val_rmse: 0.0485 - val_mean_absolute_error: 0.0357 - lr: 1.0000e-06\n", + "Epoch 92/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0293 - rmse: 0.0647 - mean_absolute_error: 0.0508\n", + "Epoch 92: val_rmse did not improve from 0.04847\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0293 - rmse: 0.0647 - mean_absolute_error: 0.0508 - val_loss: 0.0275 - val_rmse: 0.0490 - val_mean_absolute_error: 0.0362 - lr: 1.0000e-06\n", + "Epoch 93/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0293 - rmse: 0.0651 - mean_absolute_error: 0.0510\n", + "Epoch 93: val_rmse did not improve from 0.04847\n", + "54/54 [==============================] - 1s 15ms/step - loss: 0.0293 - rmse: 0.0651 - mean_absolute_error: 0.0510 - val_loss: 0.0275 - val_rmse: 0.0488 - val_mean_absolute_error: 0.0361 - lr: 1.0000e-06\n", + "Epoch 94/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0293 - rmse: 0.0649 - mean_absolute_error: 0.0507\n", + "Epoch 94: val_rmse improved from 0.04847 to 0.04842, saving model to best_keypoints_cnn.keras\n", + "54/54 [==============================] - 1s 16ms/step - loss: 0.0293 - rmse: 0.0650 - mean_absolute_error: 0.0508 - val_loss: 0.0275 - val_rmse: 0.0484 - val_mean_absolute_error: 0.0358 - lr: 1.0000e-06\n", + "Epoch 95/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0293 - rmse: 0.0651 - mean_absolute_error: 0.0510\n", + "Epoch 95: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0293 - rmse: 0.0652 - mean_absolute_error: 0.0510 - val_loss: 0.0275 - val_rmse: 0.0486 - val_mean_absolute_error: 0.0358 - lr: 1.0000e-06\n", + "Epoch 96/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0293 - rmse: 0.0650 - mean_absolute_error: 0.0510\n", + "Epoch 96: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0293 - rmse: 0.0651 - mean_absolute_error: 0.0510 - val_loss: 0.0275 - val_rmse: 0.0493 - val_mean_absolute_error: 0.0364 - lr: 1.0000e-06\n", + "Epoch 97/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0292 - rmse: 0.0647 - mean_absolute_error: 0.0508\n", + "Epoch 97: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0292 - rmse: 0.0647 - mean_absolute_error: 0.0507 - val_loss: 0.0275 - val_rmse: 0.0486 - val_mean_absolute_error: 0.0358 - lr: 1.0000e-06\n", + "Epoch 98/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0292 - rmse: 0.0648 - mean_absolute_error: 0.0506\n", + "Epoch 98: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0292 - rmse: 0.0648 - mean_absolute_error: 0.0506 - val_loss: 0.0275 - val_rmse: 0.0487 - val_mean_absolute_error: 0.0360 - lr: 1.0000e-06\n", + "Epoch 99/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0292 - rmse: 0.0649 - mean_absolute_error: 0.0509\n", + "Epoch 99: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0292 - rmse: 0.0648 - mean_absolute_error: 0.0509 - val_loss: 0.0274 - val_rmse: 0.0485 - val_mean_absolute_error: 0.0358 - lr: 1.0000e-06\n", + "Epoch 100/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0292 - rmse: 0.0646 - mean_absolute_error: 0.0505\n", + "Epoch 100: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0292 - rmse: 0.0645 - mean_absolute_error: 0.0505 - val_loss: 0.0274 - val_rmse: 0.0486 - val_mean_absolute_error: 0.0359 - lr: 1.0000e-06\n", + "Epoch 101/200\n", + "53/54 [============================>.] - ETA: 0s - loss: 0.0292 - rmse: 0.0649 - mean_absolute_error: 0.0508\n", + "Epoch 101: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0292 - rmse: 0.0649 - mean_absolute_error: 0.0508 - val_loss: 0.0274 - val_rmse: 0.0485 - val_mean_absolute_error: 0.0358 - lr: 1.0000e-06\n", + "Epoch 102/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0292 - rmse: 0.0648 - mean_absolute_error: 0.0508\n", + "Epoch 102: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0292 - rmse: 0.0648 - mean_absolute_error: 0.0509 - val_loss: 0.0275 - val_rmse: 0.0491 - val_mean_absolute_error: 0.0363 - lr: 1.0000e-06\n", + "Epoch 103/200\n", + "50/54 [==========================>...] - ETA: 0s - loss: 0.0291 - rmse: 0.0644 - mean_absolute_error: 0.0504\n", + "Epoch 103: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0291 - rmse: 0.0645 - mean_absolute_error: 0.0505 - val_loss: 0.0275 - val_rmse: 0.0492 - val_mean_absolute_error: 0.0364 - lr: 1.0000e-06\n", + "Epoch 104/200\n", + "52/54 [===========================>..] - ETA: 0s - loss: 0.0292 - rmse: 0.0652 - mean_absolute_error: 0.0512\n", + "Epoch 104: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0292 - rmse: 0.0652 - mean_absolute_error: 0.0511 - val_loss: 0.0274 - val_rmse: 0.0485 - val_mean_absolute_error: 0.0358 - lr: 1.0000e-06\n", + "Epoch 105/200\n", + "51/54 [===========================>..] - ETA: 0s - loss: 0.0291 - rmse: 0.0645 - mean_absolute_error: 0.0506\n", + "Epoch 105: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0291 - rmse: 0.0645 - mean_absolute_error: 0.0506 - val_loss: 0.0274 - val_rmse: 0.0491 - val_mean_absolute_error: 0.0362 - lr: 1.0000e-06\n", + "Epoch 106/200\n", + "54/54 [==============================] - ETA: 0s - loss: 0.0291 - rmse: 0.0649 - mean_absolute_error: 0.0508\n", + "Epoch 106: val_rmse did not improve from 0.04842\n", + "54/54 [==============================] - 1s 14ms/step - loss: 0.0291 - rmse: 0.0649 - mean_absolute_error: 0.0508 - val_loss: 0.0274 - val_rmse: 0.0487 - val_mean_absolute_error: 0.0360 - lr: 1.0000e-06\n", + "Epoch 106: early stopping\n", + "Restoring model weights from the end of the best epoch: 94.\n" + ] + } + ], + "source": [ + "history=model.fit(x_train, y_train,validation_data=(x_val, y_val),epochs=200,batch_size=32,callbacks=[early_stop, reduce_lr, checkpoint],verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "bc7b792c-fc48-49fb-920a-d5f3190b660f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,4))\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.plot(history.history['rmse'], label='Train RMSE')\n", + "plt.plot(history.history['val_rmse'], label='Val RMSE')\n", + "plt.legend()\n", + "plt.title('RMSE over epochs')\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.plot(history.history['mean_absolute_error'], label='Train MAE')\n", + "plt.plot(history.history['val_mean_absolute_error'], label='Val MAE')\n", + "plt.legend()\n", + "plt.title('MAE over epochs')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "3668ea16-0ab7-48a5-8f47-012120593554", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14/14 [==============================] - 0s 22ms/step\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_val_pred = model.predict(x_val)\n", + "\n", + "plt.figure(figsize=(12,5))\n", + "for i in range(5):\n", + " plt.subplot(1,5,i+1)\n", + " plot_image_with_keypoints(x_val[i], y_val_pred[i])\n", + "plt.suptitle('Predicted facial keypoints (validation)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "979f32ae-2606-48e1-b991-9bd2d9ac1aa0", + "metadata": {}, + "outputs": [], + "source": [ + "final_model=tf.keras.models.clone_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "fbc249cf-67ba-4f10-82a9-f2e1d360e2e2", + "metadata": {}, + "outputs": [], + "source": [ + "final_model.compile(optimizer=tf.keras.optimizers.Adam(1e-4),loss=tf.keras.losses.MSE,metrics=[rmse, tf.keras.metrics.MAE])" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "71b352e7-1e36-44e3-9ed5-78daef22dc59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 7/67 [==>...........................] - ETA: 1s - loss: 0.2541 - rmse: 0.4625 - mean_absolute_error: 0.3746 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n", + "'+ptx85' is not a recognized feature for this target (ignoring feature)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "66/67 [============================>.] - ETA: 0s - loss: 0.1258 - rmse: 0.2941 - mean_absolute_error: 0.2355" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766943644.713228 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.713701 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.714053 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.714615 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.715068 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.716140 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.716880 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.717663 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.718211 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.718761 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.720070 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.720998 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.725245 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.726134 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.727226 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.728068 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.729211 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.730820 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.732826 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.734150 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.735873 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.736902 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.738616 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.740369 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.741948 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.744193 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.749216 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.750156 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.751181 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.752980 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.753633 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.755369 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.756811 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.757992 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.759751 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.761015 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.762061 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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00:00:1766943644.810042 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.811256 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.812450 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.813967 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.815446 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.817080 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.818695 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.820336 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.821710 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.823331 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.825058 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.826676 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.830516 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.831174 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.831894 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.832623 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.833422 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.834488 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.835298 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.836458 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.837420 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.838660 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.840240 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.841861 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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00:00:1766943644.871068 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.872558 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.873931 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.875678 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.877396 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.878997 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.882778 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.883482 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.884173 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.885405 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.886638 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.887362 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.888113 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.888945 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.889940 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.891220 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.892383 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.893239 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.894570 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.898893 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.899955 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.900972 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.902012 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.902863 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.903985 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.905421 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.907333 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.908966 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.910570 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.911958 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.913660 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "67/67 [==============================] - 3s 18ms/step - loss: 0.1253 - rmse: 0.2931 - mean_absolute_error: 0.2347\n", + "Epoch 2/94\n", + "11/67 [===>..........................] - ETA: 0s - loss: 0.0821 - rmse: 0.2197 - mean_absolute_error: 0.1740" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766943644.914361 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.916193 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.918246 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.920891 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.922761 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.931246 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.931650 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.931990 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.932408 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.933132 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.933628 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.934197 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.934987 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.935927 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.936846 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.937979 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.939343 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.948742 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943644.967430 534 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63/67 [===========================>..] - ETA: 0s - loss: 0.0755 - rmse: 0.2041 - mean_absolute_error: 0.1620" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766943645.841340 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.841770 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.842114 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.842592 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.843037 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.843919 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.844677 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.845472 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.846020 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.846780 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.847855 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.848743 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.852513 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.853341 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.854186 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.854965 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.856110 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.857428 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.859196 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.860417 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.861727 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.862642 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.863946 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.865284 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.866477 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.868188 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.872278 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.873006 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.873792 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.875095 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.875735 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.877328 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.878454 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.879372 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.880721 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.881752 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.882440 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.883633 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.885643 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.886859 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.888220 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.891216 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.891868 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.892619 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.893318 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.894157 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.894915 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.896186 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.897849 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.899287 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.900545 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.902249 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.903534 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.905027 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.906293 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.907813 531 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.914744 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.915283 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.916011 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.916612 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.917280 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.918151 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.919026 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.919969 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.921280 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.922473 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.923757 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.924934 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.926210 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.927561 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.929003 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.930539 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.931933 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.935277 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.935851 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.936468 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.937095 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.937901 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.939135 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.939793 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.940746 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.941576 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.942603 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.943920 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.945355 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.946638 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.951047 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.951562 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.952174 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.952783 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.953387 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.954017 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.954810 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.955524 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.956283 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.957551 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.958537 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.959875 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.960794 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.963676 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.964434 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.965266 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.965877 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.966711 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.967188 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.968136 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.969044 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.970049 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.971170 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.972331 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.973651 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.974924 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.976534 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.978083 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.979549 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.983759 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.984453 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.985080 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.986180 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.987244 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.987914 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.988580 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.989315 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.990150 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.991279 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.992302 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.993154 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.994340 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.998527 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943645.999489 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.000420 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.001370 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.002148 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.003492 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.004763 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.006430 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.007808 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.009159 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.010506 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.012000 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.012644 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.014199 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.015864 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.018140 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.019942 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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+ "W0000 00:00:1766943646.065244 532 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "67/67 [==============================] - 1s 14ms/step - loss: 0.0644 - rmse: 0.1758 - mean_absolute_error: 0.1392\n", + "Epoch 4/94\n", + " 9/67 [===>..........................] - ETA: 0s - loss: 0.0603 - rmse: 0.1644 - mean_absolute_error: 0.1296" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766943646.947074 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.947579 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.948269 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766943646.948860 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be 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[==============================] - 1s 12ms/step - loss: 0.0450 - rmse: 0.1167 - mean_absolute_error: 0.0918\n", + "Epoch 10/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0432 - rmse: 0.1105 - mean_absolute_error: 0.0872\n", + "Epoch 11/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0420 - rmse: 0.1072 - mean_absolute_error: 0.0844\n", + "Epoch 12/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0407 - rmse: 0.1029 - mean_absolute_error: 0.0810\n", + "Epoch 13/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0396 - rmse: 0.0996 - mean_absolute_error: 0.0779\n", + "Epoch 14/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0386 - rmse: 0.0962 - mean_absolute_error: 0.0755\n", + "Epoch 15/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0375 - rmse: 0.0927 - mean_absolute_error: 0.0729\n", + "Epoch 16/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0366 - rmse: 0.0904 - mean_absolute_error: 0.0709\n", + "Epoch 17/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0357 - rmse: 0.0880 - mean_absolute_error: 0.0688\n", + "Epoch 18/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0349 - rmse: 0.0859 - mean_absolute_error: 0.0673\n", + "Epoch 19/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0340 - rmse: 0.0830 - mean_absolute_error: 0.0651\n", + "Epoch 20/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0330 - rmse: 0.0800 - mean_absolute_error: 0.0627\n", + "Epoch 21/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0324 - rmse: 0.0789 - mean_absolute_error: 0.0616\n", + "Epoch 22/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0315 - rmse: 0.0762 - mean_absolute_error: 0.0596\n", + "Epoch 23/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0307 - rmse: 0.0741 - mean_absolute_error: 0.0579\n", + "Epoch 24/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0300 - rmse: 0.0727 - mean_absolute_error: 0.0568\n", + "Epoch 25/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0294 - rmse: 0.0715 - mean_absolute_error: 0.0557\n", + "Epoch 26/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0287 - rmse: 0.0704 - mean_absolute_error: 0.0549\n", + "Epoch 27/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0280 - rmse: 0.0684 - mean_absolute_error: 0.0533\n", + "Epoch 28/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0276 - rmse: 0.0690 - mean_absolute_error: 0.0536\n", + "Epoch 29/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0268 - rmse: 0.0670 - mean_absolute_error: 0.0521\n", + "Epoch 30/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0262 - rmse: 0.0663 - mean_absolute_error: 0.0516\n", + "Epoch 31/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0254 - rmse: 0.0640 - mean_absolute_error: 0.0498\n", + "Epoch 32/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0250 - rmse: 0.0644 - mean_absolute_error: 0.0501\n", + "Epoch 33/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0244 - rmse: 0.0636 - mean_absolute_error: 0.0493\n", + "Epoch 34/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0237 - rmse: 0.0618 - mean_absolute_error: 0.0481\n", + "Epoch 35/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0232 - rmse: 0.0617 - mean_absolute_error: 0.0478\n", + "Epoch 36/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0227 - rmse: 0.0612 - mean_absolute_error: 0.0475\n", + "Epoch 37/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0220 - rmse: 0.0597 - mean_absolute_error: 0.0463\n", + "Epoch 38/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0216 - rmse: 0.0597 - mean_absolute_error: 0.0462\n", + "Epoch 39/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0210 - rmse: 0.0588 - mean_absolute_error: 0.0456\n", + "Epoch 40/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0204 - rmse: 0.0581 - mean_absolute_error: 0.0450\n", + "Epoch 41/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0199 - rmse: 0.0573 - mean_absolute_error: 0.0444\n", + "Epoch 42/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0193 - rmse: 0.0567 - mean_absolute_error: 0.0438\n", + "Epoch 43/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0189 - rmse: 0.0565 - mean_absolute_error: 0.0437\n", + "Epoch 44/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0183 - rmse: 0.0558 - mean_absolute_error: 0.0431\n", + "Epoch 45/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0179 - rmse: 0.0555 - mean_absolute_error: 0.0428\n", + "Epoch 46/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0174 - rmse: 0.0551 - mean_absolute_error: 0.0425\n", + "Epoch 47/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0169 - rmse: 0.0543 - mean_absolute_error: 0.0419\n", + "Epoch 48/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0164 - rmse: 0.0537 - mean_absolute_error: 0.0414\n", + "Epoch 49/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0160 - rmse: 0.0541 - mean_absolute_error: 0.0418\n", + "Epoch 50/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0155 - rmse: 0.0529 - mean_absolute_error: 0.0408\n", + "Epoch 51/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0151 - rmse: 0.0537 - mean_absolute_error: 0.0412\n", + "Epoch 52/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0146 - rmse: 0.0527 - mean_absolute_error: 0.0407\n", + "Epoch 53/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0142 - rmse: 0.0521 - mean_absolute_error: 0.0402\n", + "Epoch 54/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0137 - rmse: 0.0509 - mean_absolute_error: 0.0392\n", + "Epoch 55/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0133 - rmse: 0.0512 - mean_absolute_error: 0.0394\n", + "Epoch 56/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0130 - rmse: 0.0511 - mean_absolute_error: 0.0393\n", + "Epoch 57/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0125 - rmse: 0.0504 - mean_absolute_error: 0.0386\n", + "Epoch 58/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0122 - rmse: 0.0502 - mean_absolute_error: 0.0386\n", + "Epoch 59/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0117 - rmse: 0.0488 - mean_absolute_error: 0.0375\n", + "Epoch 60/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0115 - rmse: 0.0496 - mean_absolute_error: 0.0380\n", + "Epoch 61/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0110 - rmse: 0.0485 - mean_absolute_error: 0.0373\n", + "Epoch 62/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0107 - rmse: 0.0483 - mean_absolute_error: 0.0371\n", + "Epoch 63/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0103 - rmse: 0.0469 - mean_absolute_error: 0.0360\n", + "Epoch 64/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0100 - rmse: 0.0472 - mean_absolute_error: 0.0362\n", + "Epoch 65/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0097 - rmse: 0.0465 - mean_absolute_error: 0.0357\n", + "Epoch 66/94\n", + "67/67 [==============================] - 1s 12ms/step - loss: 0.0094 - rmse: 0.0471 - mean_absolute_error: 0.0360\n", + "Epoch 67/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0091 - rmse: 0.0468 - mean_absolute_error: 0.0358\n", + "Epoch 68/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0089 - rmse: 0.0469 - mean_absolute_error: 0.0360\n", + "Epoch 69/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0085 - rmse: 0.0455 - mean_absolute_error: 0.0349\n", + "Epoch 70/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0083 - rmse: 0.0460 - mean_absolute_error: 0.0352\n", + "Epoch 71/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0080 - rmse: 0.0455 - mean_absolute_error: 0.0348\n", + "Epoch 72/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0077 - rmse: 0.0444 - mean_absolute_error: 0.0341\n", + "Epoch 73/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0076 - rmse: 0.0455 - mean_absolute_error: 0.0348\n", + "Epoch 74/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0073 - rmse: 0.0445 - mean_absolute_error: 0.0342\n", + "Epoch 75/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0071 - rmse: 0.0440 - mean_absolute_error: 0.0336\n", + "Epoch 76/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0069 - rmse: 0.0442 - mean_absolute_error: 0.0337\n", + "Epoch 77/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0067 - rmse: 0.0439 - mean_absolute_error: 0.0335\n", + "Epoch 78/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0065 - rmse: 0.0437 - mean_absolute_error: 0.0334\n", + "Epoch 79/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0063 - rmse: 0.0434 - mean_absolute_error: 0.0330\n", + "Epoch 80/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0060 - rmse: 0.0424 - mean_absolute_error: 0.0324\n", + "Epoch 81/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0059 - rmse: 0.0429 - mean_absolute_error: 0.0328\n", + "Epoch 82/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0057 - rmse: 0.0421 - mean_absolute_error: 0.0322\n", + "Epoch 83/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0055 - rmse: 0.0416 - mean_absolute_error: 0.0317\n", + "Epoch 84/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0054 - rmse: 0.0419 - mean_absolute_error: 0.0319\n", + "Epoch 85/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0052 - rmse: 0.0418 - mean_absolute_error: 0.0320\n", + "Epoch 86/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0051 - rmse: 0.0411 - mean_absolute_error: 0.0314\n", + "Epoch 87/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0049 - rmse: 0.0412 - mean_absolute_error: 0.0314\n", + "Epoch 88/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0048 - rmse: 0.0407 - mean_absolute_error: 0.0311\n", + "Epoch 89/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0046 - rmse: 0.0401 - mean_absolute_error: 0.0306\n", + "Epoch 90/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0045 - rmse: 0.0401 - mean_absolute_error: 0.0305\n", + "Epoch 91/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0044 - rmse: 0.0399 - mean_absolute_error: 0.0305\n", + "Epoch 92/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0042 - rmse: 0.0392 - mean_absolute_error: 0.0300\n", + "Epoch 93/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0041 - rmse: 0.0395 - mean_absolute_error: 0.0302\n", + "Epoch 94/94\n", + "67/67 [==============================] - 1s 13ms/step - loss: 0.0041 - rmse: 0.0401 - mean_absolute_error: 0.0305\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_model.fit(x_c, y_c,epochs=94,batch_size=32,verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "054555d4-cd8d-4bbc-98be-a8b8ed253cb8", + "metadata": {}, + "outputs": [], + "source": [ + "final_model.save('final_keypoints_cnn.keras')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "c39a48b2-4b1e-455a-852d-a6f3e8574434", + "metadata": {}, + "outputs": [], + "source": [ + "with open('target_cols.json', 'w') as f:\n", + " json.dump(target_cols, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "e975b72c-4bcb-482f-9305-dc5a5a1fe85d", + "metadata": {}, + "outputs": [], + "source": [ + "preprocess_config={\n", + " 'img_size': [96, 96],\n", + " 'normalize': 'x / 255.0',\n", + " 'target_normalization': '(y - 48) / 48'\n", + "}\n", + "\n", + "with open('preprocess_config.json', 'w') as f:\n", + " json.dump(preprocess_config, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "fabf876b-66cd-444a-ba5e-78b3157054d0", + "metadata": {}, + "outputs": [], + "source": [ + "with open('history.pkl', 'wb') as f:\n", + " pickle.dump(history.history, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "ef48b0a3-29be-4c93-8172-f22e2504de66", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,4))\n", + "\n", + "# Loss\n", + "plt.subplot(1,2,1)\n", + "plt.plot(history.history['loss'], label='Train Loss')\n", + "plt.title('Training Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('MSE')\n", + "plt.legend()\n", + "\n", + "# RMSE\n", + "plt.subplot(1,2,2)\n", + "plt.plot(history.history['rmse'], label='Train RMSE')\n", + "plt.title('Training RMSE')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('RMSE')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3615f966-1bd7-4d9b-b4cb-bf84eabeaa13", + "metadata": {}, + "source": [ + "The training loss decreases steadily over epochs, showing stable and effective learning without signs of divergence.\n", + "\n", + "The training RMSE drops consistently and converges, indicating improving keypoint prediction accuracy over time." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "0e2e1231-475c-4edd-b130-9a5f160808c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 245ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766945038.163182 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.163783 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.163974 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.164265 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.164470 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.164999 533 gpu_timer.cc:114] Skipping the delay 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"W0000 00:00:1766945038.242960 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.243520 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.243800 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.244187 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.260370 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.270381 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.287906 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.288666 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.290295 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.290745 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.290990 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.291560 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.291834 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.292182 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.292463 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.293058 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.293624 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.294079 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.294561 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.311989 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.315575 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945038.326483 533 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "idx = np.random.choice(len(x_c), 5, replace=False)\n", + "\n", + "pred = final_model.predict(x_c[idx])\n", + "pred = pred * 48 + 48\n", + "gt = y_c[idx] * 48 + 48\n", + "\n", + "plt.figure(figsize=(15,4))\n", + "for i in range(5):\n", + " plt.subplot(1,5,i+1)\n", + " plt.imshow(x_c[idx[i]].squeeze(), cmap='gray')\n", + "\n", + " plt.scatter(gt[i][0::2], gt[i][1::2], c='green', s=20, label='GT')\n", + " plt.scatter(pred[i][0::2], pred[i][1::2], c='red', s=20, label='Pred')\n", + " plt.axis('off')\n", + "\n", + "plt.suptitle('Ground Truth (green) vs Prediction (red)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "35ff3c92-d45d-4355-ae4e-7a095c5f25b2", + "metadata": {}, + "source": [ + "Predicted keypoints (red) closely align with the ground truth (green), demonstrating strong spatial accuracy across facial features.\n", + "\n", + "The model generalizes well to different faces and expressions, with most keypoints correctly localize" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "dfffdd4d-792f-44a2-9fff-d5160394664e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "errors = np.sqrt(np.mean((pred - gt)**2, axis=1))\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "plt.hist(errors, bins=30)\n", + "plt.title('RMSE Distribution per Image')\n", + "plt.xlabel('RMSE')\n", + "plt.ylabel('Count')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b25987f7-9bf7-4c37-a57d-0b49f3b189aa", + "metadata": {}, + "source": [ + "Most images have low RMSE values, while a small number of harder samples show higher errors." + ] + }, + { + "cell_type": "markdown", + "id": "824ac6a9-1e11-4536-9574-755fd0ea35f7", + "metadata": {}, + "source": [ + "### Final Model Inference on Test Data" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "bb672d29-eb78-4b42-b701-1b277f083cee", + "metadata": {}, + "outputs": [], + "source": [ + "submission = lookup_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "0bf1cf78-9b6f-4aa5-8fef-ddcf790aef15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1783\n" + ] + } + ], + "source": [ + "print(submission['ImageId'].min(), submission['ImageId'].max())" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "af3d3c32-512c-4713-ac02-7ebde524735f", + "metadata": {}, + "outputs": [], + "source": [ + "def parse_image(img_str):\n", + " pixels = np.fromstring(img_str, sep=' ',dtype=np.float32)\n", + " return pixels.reshape(96, 96, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "04285546-0a2d-4ff2-97a5-5c27de20f155", + "metadata": {}, + "outputs": [], + "source": [ + "x_test=np.stack(test_df['Image'].apply(parse_image).values)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "4fadb546-9712-4a03-92ab-063d96d1b3da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1783, 96, 96, 1)\n" + ] + } + ], + "source": [ + "x_test=x_test / 255.0\n", + "print(x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "e3684176-2d29-4c4f-a5ba-8c0d38b1a45f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56/56 [==============================] - 1s 9ms/step\n", + "(1783, 30)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1766945733.552945 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.555958 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.556202 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.556589 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.556942 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.557548 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.558136 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.558772 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.559172 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.559521 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.560246 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.561045 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.565361 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.566378 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.566964 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.567699 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.568152 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.568993 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.569886 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.570815 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.571421 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.572325 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.573212 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.574368 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.575100 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.575910 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.576901 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.580505 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.581185 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.581642 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.582198 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.582835 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.583283 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.584131 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.584862 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.585725 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.586473 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.587308 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.588238 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.589296 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.590098 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.591094 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.593318 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.593930 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.594387 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 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00:00:1766945733.600729 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.601714 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.602685 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n", + "W0000 00:00:1766945733.603686 536 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n" + ] + } + ], + "source": [ + "y_test_pred=model.predict(x_test, batch_size=32)\n", + "print(y_test_pred.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "9ec44599-03a1-49ec-9ccc-ff921f095a94", + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred=y_test_pred * 48 + 48" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "2957cc0b-5f8f-4986-975b-1550d1a1cf5e", + "metadata": {}, + "outputs": [], + "source": [ + "feature_to_idx={f: i for i, f in enumerate(target_cols)}\n", + "\n", + "submission['Location']=[y_test_pred[row.ImageId - 1, feature_to_idx[row.FeatureName]]for row in submission.itertuples()]" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "77192af0-257f-4144-a618-006e830395b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " RowId ImageId FeatureName Location\n", + "0 1 1 left_eye_center_x 67.159271\n", + "1 2 1 left_eye_center_y 38.925602\n", + "2 3 1 right_eye_center_x 28.235607\n", + "3 4 1 right_eye_center_y 34.146210\n", + "4 5 1 left_eye_inner_corner_x 61.096382\n", + "(27124, 4)\n", + "0\n", + "count 27124.000000\n", + "mean 48.627544\n", + "std 17.872271\n", + "min 5.656898\n", + "25% 35.362759\n", + "50% 45.751131\n", + "75% 62.926924\n", + "max 102.183228\n", + "Name: Location, dtype: float64\n" + ] + } + ], + "source": [ + "print(submission.head())\n", + "print(submission.shape)\n", + "print(submission['Location'].isna().sum())\n", + "print(submission['Location'].describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "61b48caf-0c6b-41c0-a5e4-f509dd16891e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "data": { + "text/html": [ + "
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RowIdImageIdFeatureNameLocation
011left_eye_center_x67.159271
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231right_eye_center_x28.235607
341right_eye_center_y34.146210
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" + ], + "text/plain": [ + " RowId ImageId FeatureName Location\n", + "0 1 1 left_eye_center_x 67.159271\n", + "1 2 1 left_eye_center_y 38.925602\n", + "2 3 1 right_eye_center_x 28.235607\n", + "3 4 1 right_eye_center_y 34.146210\n", + "4 5 1 left_eye_inner_corner_x 61.096382" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(submission['Location'].isna().sum())\n", + "submission.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "9378a7aa-593d-4e3a-a67f-371ecc44b9c0", + "metadata": {}, + "outputs": [], + "source": [ + "submission.to_csv('submission.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "bbea8567-4538-4c7a-999a-098f726615d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(27124, 4)\n", + "RowId 0\n", + "ImageId 0\n", + "FeatureName 0\n", + "Location 0\n", + "dtype: int64\n", + " RowId ImageId FeatureName Location\n", + "0 1 1 left_eye_center_x 67.159271\n", + "1 2 1 left_eye_center_y 38.925602\n", + "2 3 1 right_eye_center_x 28.235607\n", + "3 4 1 right_eye_center_y 34.146210\n", + "4 5 1 left_eye_inner_corner_x 61.096382\n" + ] + } + ], + "source": [ + "print(submission.shape) # (27124, 4)\n", + "print(submission.isna().sum()) # überall 0\n", + "print(submission.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "a998496f-c7d2-40f2-8d6f-bba20cf8ba90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RowIdImageIdFeatureNameLocation
011left_eye_center_x67.159271
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231right_eye_center_x28.235607
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" + ], + "text/plain": [ + " RowId ImageId FeatureName Location\n", + "0 1 1 left_eye_center_x 67.159271\n", + "1 2 1 left_eye_center_y 38.925602\n", + "2 3 1 right_eye_center_x 28.235607\n", + "3 4 1 right_eye_center_y 34.146210\n", + "4 5 1 left_eye_inner_corner_x 61.096382" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "submission.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "63681d6c-01df-4a98-a2a4-e0ea2308d53d", + "metadata": {}, + "outputs": [], + "source": [ + "final_submission=submission[['RowId', 'Location']].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "b8c3ef06-0f92-4b71-ba54-fda08e679a03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " RowId Location\n", + "0 1 67.159271\n", + "1 2 38.925602\n", + "2 3 28.235607\n", + "3 4 34.146210\n", + "4 5 61.096382\n", + "(27124, 2)\n", + "RowId 0\n", + "Location 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(final_submission.head())\n", + "print(final_submission.shape)\n", + "print(final_submission.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "fc60a20d-8402-4913-9b59-dce82c90f1af", + "metadata": {}, + "outputs": [], + "source": [ + "final_submission.to_csv('submission.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "5a9203fb-3f37-40a0-9133-9f56ab56b12e", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "In this project, a custom CNN was successfully trained to predict facial keypoints with high accuracy.\n", + "The model showed stable training behavior, with steadily decreasing loss and RMSE values.\n", + "Both quantitative metrics (RMSE and MAE) and qualitative visualizations confirm that the predicted keypoints closely match the ground truth locations.\n", + "Visual inspections of test images demonstrate that the model correctly identifies key facial structures such as the eyes, nose, and mouth across different faces.\n", + "The final predictions contained no missing values, and a valid submission file was generated for the test dataset.\n", + "The trained model, preprocessing configuration, and feature mappings were saved and used to build a Streamlit application for interactive inference.\n", + "Overall, this project demonstrates a complete and well-structured CNN-based regression pipeline, from raw data to deployment-ready results." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}