study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/4) Feature - Compare experiments - compare experiments across backends.ipynb

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    "<a href=\"https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/4)%20Feature%20-%20Compare%20experiments%20-%20compare%20experiments%20across%20backends.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    "# Goals\n",
    "\n",
    "### 1. Understand how you can compare experiments on same data\n",
    "\n",
    "### 2. Understand where to look for comparison data - logs, charts, graphs, etc when comparing experiments\n",
    "\n",
    "\n",
    "### 3. Steps\n",
    "    - You will use 3 backends - keras, mxnet, and pytorch\n",
    "    - Train three classifiers by keeping all parameters same and changing backends\n",
    "    - You will keep the model as resnet50\n",
    "    - Compare the three experiments"
   ]
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    "### Why compare experiments\n",
    "\n",
    "    - One usually runs mulitple experiments on the same data by changing hyper-params, base model, etc\n",
    "    \n",
    "    - Hence this compare exxperiments feature helps to see which combination og params works the best for that data"
   ]
  },
  {
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    "# Table of Contents\n",
    "\n",
    "\n",
    "## [Install](#0)\n",
    "\n",
    "\n",
    "## [Train a classifier using Mxnet backend](#1)\n",
    "\n",
    "\n",
    "## [Train a classifier using Pytorch backend](#2)\n",
    "\n",
    "\n",
    "## [Train a classifier using Keras backend](#3)\n",
    "\n",
    "\n",
    "## [Compare experiments](#4)\n",
    "\n",
    "\n",
    "## [Visualize and study comparison metrics](#5)"
   ]
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   "source": [
    "<a id='0'></a>\n",
    "# Install Monk"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Using pip (Recommended)\n",
    "\n",
    "  - colab (gpu) \n",
    "      - All bakcends: `pip install -U monk-colab`\n",
    "      \n",
    "\n",
    "  - kaggle (gpu) \n",
    "      - All backends: `pip install -U monk-kaggle`\n",
    "      \n",
    "\n",
    "  - cuda 10.2\t\n",
    "      - All backends: `pip install -U monk-cuda102`\n",
    "      - Gluon bakcned: `pip install -U monk-gluon-cuda102`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda102`\n",
    "      - Keras backend: `pip install -U monk-keras-cuda102`\n",
    "      \n",
    "\n",
    "  - cuda 10.1\t\n",
    "      - All backend: `pip install -U monk-cuda101`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda101`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda101`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda101`\n",
    "      \n",
    "\n",
    "  - cuda 10.0\t\n",
    "      - All backend: `pip install -U monk-cuda100`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda100`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda100`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda100`\n",
    "      \n",
    "\n",
    "  - cuda 9.2\t\n",
    "      - All backend: `pip install -U monk-cuda92`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda92`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda92`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda92`\n",
    "      \n",
    "\n",
    "  - cuda 9.0\t\n",
    "      - All backend: `pip install -U monk-cuda90`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cuda90`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cuda90`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cuda90`\n",
    "      \n",
    "\n",
    "  - cpu \t\t\n",
    "      - All backend: `pip install -U monk-cpu`\n",
    "\t  - Gluon bakcned: `pip install -U monk-gluon-cpu`\n",
    "\t  - Pytorch backend: `pip install -U monk-pytorch-cpu`\n",
    "\t  - Keras backend: `pip install -U monk-keras-cpu`"
   ]
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  {
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    "## Install Monk Manually (Not recommended)\n",
    " \n",
    "### Step 1: Clone the library\n",
    " - git clone https://github.com/Tessellate-Imaging/monk_v1.git\n",
    " \n",
    " \n",
    " \n",
    " \n",
    "### Step 2: Install requirements \n",
    " - Linux\n",
    "     - Cuda 9.0\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu90.txt`\n",
    "     - Cuda 9.2\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu92.txt`\n",
    "     - Cuda 10.0\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu100.txt`\n",
    "     - Cuda 10.1\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu101.txt`\n",
    "     - Cuda 10.2\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cu102.txt`\n",
    "     - CPU (Non gpu system)\n",
    "         - `cd monk_v1/installation/Linux && pip install -r requirements_cpu.txt`\n",
    " \n",
    " \n",
    "  - Windows\n",
    "     - Cuda 9.0 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu90.txt`\n",
    "     - Cuda 9.2 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu92.txt`\n",
    "     - Cuda 10.0 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu100.txt`\n",
    "     - Cuda 10.1 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu101.txt`\n",
    "     - Cuda 10.2 (Experimental support)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cu102.txt`\n",
    "     - CPU (Non gpu system)\n",
    "         - `cd monk_v1/installation/Windows && pip install -r requirements_cpu.txt`\n",
    "         \n",
    "         \n",
    "  - Mac\n",
    "     - CPU (Non gpu system)\n",
    "         - `cd monk_v1/installation/Mac && pip install -r requirements_cpu.txt`\n",
    "         \n",
    "         \n",
    "  - Misc\n",
    "      - Colab (GPU)\n",
    "          - `cd monk_v1/installation/Misc && pip install -r requirements_colab.txt`\n",
    "      - Kaggle (GPU)\n",
    "          - `cd monk_v1/installation/Misc && pip install -r requirements_kaggle.txt`\n",
    " \n",
    " \n",
    " \n",
    "### Step 3: Add to system path (Required for every terminal or kernel run)\n",
    " - `import sys`\n",
    " - `sys.path.append(\"monk_v1/\");`"
   ]
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   "metadata": {},
   "source": [
    "## Dataset - Malarial cell images\n",
    "    - Credits: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1mMEtGIK8UZNCrErXRJR-kutNTaN1zxjC' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1mMEtGIK8UZNCrErXRJR-kutNTaN1zxjC\" -O malaria_cell.zip && rm -rf /tmp/cookies.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "! unzip -qq malaria_cell.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using gluon backend \n",
    "\n",
    "# When installed using pip\n",
    "from monk.gluon_prototype import prototype\n",
    "\n",
    "\n",
    "# When installed manually (Uncomment the following)\n",
    "#import os\n",
    "#import sys\n",
    "#sys.path.append(\"monk_v1/\");\n",
    "#sys.path.append(\"monk_v1/monk/\");\n",
    "#from monk.gluon_prototype import prototype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a>\n",
    "# Train a classifier using Mxnet Backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing experiments\n",
    "        - Provide project name\n",
    "        - Provide experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mxnet Version: 1.5.1\n",
      "\n",
      "Experiment Details\n",
      "    Project: Malaria-Cell\n",
      "    Experiment: exp-gluon\n",
      "    Dir: /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-gluon/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf = prototype(verbose=1);\n",
    "gtf.Prototype(\"Malaria-Cell\", \"exp-gluon\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------Malaria-Cell\n",
    "                        |\n",
    "                        |\n",
    "                        |-----exp-gluon\n",
    "                                    |\n",
    "                                    |-----experiment-state.json\n",
    "                                    |\n",
    "                                    |-----output\n",
    "                                            |\n",
    "                                            |------logs (All training logs and graphs saved here)\n",
    "                                            |\n",
    "                                            |------models (all trained models saved here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Details\n",
      "    Train path:     malaria_cell\n",
      "    Val path:       None\n",
      "    CSV train path: None\n",
      "    CSV val path:   None\n",
      "    Label Type:     single\n",
      "\n",
      "Dataset Params\n",
      "    Input Size:   224\n",
      "    Batch Size:   4\n",
      "    Data Shuffle: True\n",
      "    Processors:   8\n",
      "    Train-val split:   0.7\n",
      "\n",
      "Pre-Composed Train Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Pre-Composed Val Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Dataset Numbers\n",
      "    Num train images: 1411\n",
      "    Num val images:   605\n",
      "    Num classes:      2\n",
      "\n",
      "Model Params\n",
      "    Model name:           resnet50_v2\n",
      "    Use Gpu:              True\n",
      "    Use pretrained:       True\n",
      "    Freeze base network:  True\n",
      "\n",
      "Model Details\n",
      "    Loading pretrained model\n",
      "    Model Loaded on device\n",
      "        Model name:                           resnet50_v2\n",
      "        Num of potentially trainable layers:  105\n",
      "        Num of actual trainable layers:       1\n",
      "\n",
      "Optimizer\n",
      "    Name:          sgd\n",
      "    Learning rate: 0.01\n",
      "    Params:        {'lr': 0.01, 'momentum': 0, 'weight_decay': 0, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n",
      "\n",
      "\n",
      "\n",
      "Learning rate scheduler\n",
      "    Name:   steplr\n",
      "    Params: {'step_size': 1, 'gamma': 0.98, 'last_epoch': -1}\n",
      "\n",
      "Loss\n",
      "    Name:          softmaxcrossentropy\n",
      "    Params:        {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n",
      "\n",
      "Training params\n",
      "    Num Epochs: 5\n",
      "\n",
      "Display params\n",
      "    Display progress:          True\n",
      "    Display progress realtime: True\n",
      "    Save Training logs:        True\n",
      "    Save Intermediate models:  True\n",
      "    Intermediate model prefix: intermediate_model_\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk\n",
      "  warnings.warn(msg)\n",
      "/home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/system/imports.py:160: UserWarning: ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "gtf.Default(dataset_path=\"malaria_cell\", \n",
    "            model_name=\"resnet50_v2\", \n",
    "            num_epochs=5);\n",
    "\n",
    "#Read the summary generated once you run this cell. "
   ]
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     "text": [
      "Training Start\n",
      "    Epoch 1/5\n",
      "    ----------\n"
     ]
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     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.01\n",
      "    [Epoch 1] Train-acc: 0.701, Train-loss: 0.669 | Val-acc: 0.666116, Val-loss: 0.805, | time: 6.4 sec\n",
      "\n",
      "    Epoch 2/5\n",
      "    ----------\n"
     ]
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     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.0098\n",
      "    [Epoch 2] Train-acc: 0.787, Train-loss: 0.499 | Val-acc: 0.636364, Val-loss: 1.085, | time: 6.3 sec\n",
      "\n",
      "    Epoch 3/5\n",
      "    ----------\n"
     ]
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     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.009604\n",
      "    [Epoch 3] Train-acc: 0.776, Train-loss: 0.531 | Val-acc: 0.730579, Val-loss: 0.588, | time: 6.5 sec\n",
      "\n",
      "    Epoch 4/5\n",
      "    ----------\n"
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     "text": [
      "\n",
      "\n",
      "\n",
      "    curr_lr - 0.009411919999999999\n",
      "    [Epoch 4] Train-acc: 0.773, Train-loss: 0.553 | Val-acc: 0.727273, Val-loss: 0.691, | time: 6.3 sec\n",
      "\n",
      "    Epoch 5/5\n",
      "    ----------\n"
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       "model_id": "28c125f2ce294a53af2c8c577f0d41a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.009223681599999999\n",
      "    [Epoch 5] Train-acc: 0.796, Train-loss: 0.529 | Val-acc: 0.823140, Val-loss: 0.434, | time: 6.5 sec\n",
      "\n",
      "    Training completed in: 0m 30s\n",
      "    Best val Acc:          0.823140\n",
      "\n",
      "Training End\n",
      "\n",
      "Training Outputs\n",
      "    Model Dir:   /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-gluon/output/models/\n",
      "    Log Dir:     /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-gluon/output/logs/\n",
      "    Final model: final\n",
      "    Best model:  best_model\n",
      "    Log 1 - Validation accuracy history log: val_acc_history.npy\n",
      "    Log 2 - Validation loss history log:     val_loss_history.npy\n",
      "    Log 3 - Training accuracy history log:   train_acc_history.npy\n",
      "    Log 4 - Training loss history log:       train_loss_history.npy\n",
      "    Log 5 - Training curve:                  train_loss_history.npy\n",
      "    Log 6 - Validation curve:                train_loss_history.npy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Start Training\n",
    "gtf.Train();\n",
    "\n",
    "#Read the training summary generated once you run the cell and training is completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2'></a>\n",
    "# Train a classifier using Pytorch Backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using pytorch backend \n",
    "\n",
    "# When installed using pip\n",
    "from monk.pytorch_prototype import prototype\n",
    "\n",
    "\n",
    "# When installed manually (Uncomment the following)\n",
    "#import os\n",
    "#import sys\n",
    "#sys.path.append(\"monk_v1/\");\n",
    "#sys.path.append(\"monk_v1/monk/\");\n",
    "#from monk.pytorch_prototype import prototype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing experiments\n",
    "        - Provide project name\n",
    "        - Provide experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pytorch Version: 1.4.0\n",
      "\n",
      "Experiment Details\n",
      "    Project: Malaria-Cell\n",
      "    Experiment: exp-pytorch\n",
      "    Dir: /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-pytorch/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf = prototype(verbose=1);\n",
    "gtf.Prototype(\"Malaria-Cell\", \"exp-pytorch\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------Malaria-Cell\n",
    "                        |\n",
    "                        |\n",
    "                        |-----exp-pytorch\n",
    "                                    |\n",
    "                                    |-----experiment-state.json\n",
    "                                    |\n",
    "                                    |-----output\n",
    "                                            |\n",
    "                                            |------logs (All training logs and graphs saved here)\n",
    "                                            |\n",
    "                                            |------models (all trained models saved here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Details\n",
      "    Train path:     malaria_cell\n",
      "    Val path:       None\n",
      "    CSV train path: None\n",
      "    CSV val path:   None\n",
      "    Label Type:     single\n",
      "\n",
      "Dataset Params\n",
      "    Input Size:  224\n",
      "    Batch Size:  4\n",
      "    Data Shuffle: True\n",
      "    Processors:   8\n",
      "    Train-val split:   0.7\n",
      "\n",
      "Pre-Composed Train Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Pre-Composed Val Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\n",
      "\n",
      "Dataset Numbers\n",
      "    Num train images: 1411\n",
      "    Num val images:   605\n",
      "    Num classes:      2\n",
      "\n",
      "Model Params\n",
      "    Model name:           resnet50\n",
      "    Use Gpu:              True\n",
      "    Use pretrained:       True\n",
      "    Freeze base network:  True\n",
      "\n",
      "Model Details\n",
      "    Loading pretrained model\n",
      "    Model Loaded on device\n",
      "        Model name:                           resnet50\n",
      "        Num layers in model:  107\n",
      "        Num trainable layers: 1\n",
      "\n",
      "Optimizer\n",
      "    Name:          sgd\n",
      "    Learning rate: 0.01\n",
      "    Params:        {'lr': 0.01, 'momentum': 0, 'weight_decay': 0, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n",
      "\n",
      "\n",
      "\n",
      "Learning rate scheduler\n",
      "    Name:   reduceonplateaulr\n",
      "    Params: {'mode': 'min', 'factor': 0.1, 'patience': 1, 'verbose': True, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 0, 'epsilon': 1e-08}\n",
      "\n",
      "Loss\n",
      "    Name:          softmaxcrossentropy\n",
      "    Params:        {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n",
      "\n",
      "Training params\n",
      "    Num Epochs: 5\n",
      "\n",
      "Display params\n",
      "    Display progress:          True\n",
      "    Display progress realtime: True\n",
      "    Save Training logs:        True\n",
      "    Save Intermediate models:  True\n",
      "    Intermediate model prefix: intermediate_model_\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf.Default(dataset_path=\"malaria_cell\", \n",
    "            model_name=\"resnet50\", \n",
    "            num_epochs=5);\n",
    "\n",
    "#Read the summary generated once you run this cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Start\n",
      "    Epoch 1/5\n",
      "    ----------\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c480090fdaba4935b6cc46c5419d5c55",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.01\n",
      "    [Epoch 1] Train-acc: 0.686, Train-loss: 0.765 | Val-acc: 0.831405, Val-loss: 0.390, | time: 10.0 sec\n",
      "\n",
      "    Epoch 2/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc19195eed3946c6b3a8efb5958638b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dd26176b46c45f984ba5061e6f66761",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.01\n",
      "    [Epoch 2] Train-acc: 0.793, Train-loss: 0.555 | Val-acc: 0.923967, Val-loss: 0.210, | time: 10.0 sec\n",
      "\n",
      "    Epoch 3/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3763c8afd23420099190aa303007c2f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c04f43f9eeb244d59d6b6b8f0efec852",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.01\n",
      "    [Epoch 3] Train-acc: 0.779, Train-loss: 0.576 | Val-acc: 0.753719, Val-loss: 0.640, | time: 9.8 sec\n",
      "\n",
      "    Epoch 4/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8ac2561d8ad438995bca491170db562",
       "version_major": 2,
       "version_minor": 0
      },
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       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95274a8ffa004814914af69d9ecc61d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch     4: reducing learning rate of group 0 to 1.0000e-03.\n",
      "\n",
      "\n",
      "    curr_lr - 0.001\n",
      "    [Epoch 4] Train-acc: 0.811, Train-loss: 0.567 | Val-acc: 0.841322, Val-loss: 0.382, | time: 9.8 sec\n",
      "\n",
      "    Epoch 5/5\n",
      "    ----------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bf44687c4c64fe0b57c8f41091365f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=353.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a758f5e6f2c41c9a9753eb7c711dbfc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=152.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "    curr_lr - 0.001\n",
      "    [Epoch 5] Train-acc: 0.843, Train-loss: 0.374 | Val-acc: 0.919008, Val-loss: 0.216, | time: 9.9 sec\n",
      "\n",
      "    Training completed in: 0m 47s\n",
      "    Best val Acc:          0.923967\n",
      "\n",
      "Training End\n",
      "\n",
      "Training Outputs\n",
      "    Model Dir:   /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-pytorch/output/models/\n",
      "    Log Dir:     /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-pytorch/output/logs/\n",
      "    Final model: final\n",
      "    Best model:  best_model\n",
      "    Log 1 - Validation accuracy history log: val_acc_history.npy\n",
      "    Log 2 - Validation loss history log:     val_loss_history.npy\n",
      "    Log 3 - Training accuracy history log:   train_acc_history.npy\n",
      "    Log 4 - Training loss history log:       train_loss_history.npy\n",
      "    Log 5 - Training curve:                  train_loss_history.npy\n",
      "    Log 6 - Validation curve:                train_loss_history.npy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Start Training\n",
    "gtf.Train();\n",
    "\n",
    "#Read the training summary generated once you run the cell and training is completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3'></a>\n",
    "# Train a classifier using keras backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Using keras backend \n",
    "\n",
    "# When installed using pip\n",
    "from monk.keras_prototype import prototype\n",
    "\n",
    "\n",
    "# When installed manually (Uncomment the following)\n",
    "#import os\n",
    "#import sys\n",
    "#sys.path.append(\"monk_v1/\");\n",
    "#sys.path.append(\"monk_v1/monk/\");\n",
    "#from monk.keras_prototype import prototype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing experiments\n",
    "        - Provide project name\n",
    "        - Provide experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras Version: 2.3.0\n",
      "Tensorflow Version: 2.0.0\n",
      "\n",
      "Experiment Details\n",
      "    Project: Malaria-Cell\n",
      "    Experiment: exp-keras\n",
      "    Dir: /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-keras/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf = prototype(verbose=1);\n",
    "gtf.Prototype(\"Malaria-Cell\", \"exp-keras\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------Malaria-Cell\n",
    "                        |\n",
    "                        |\n",
    "                        |-----exp-keras\n",
    "                                    |\n",
    "                                    |-----experiment-state.json\n",
    "                                    |\n",
    "                                    |-----output\n",
    "                                            |\n",
    "                                            |------logs (All training logs and graphs saved here)\n",
    "                                            |\n",
    "                                            |------models (all trained models saved here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Details\n",
      "    Train path:     malaria_cell\n",
      "    Val path:       None\n",
      "    CSV train path: None\n",
      "    CSV val path:   None\n",
      "    Label Type:     single\n",
      "\n",
      "Dataset Params\n",
      "    Input Size:   224\n",
      "    Batch Size:   4\n",
      "    Data Shuffle: True\n",
      "    Processors:   8\n",
      "    Train-val split:   0.7\n",
      "\n",
      "Found 1412 images belonging to 2 classes.\n",
      "Found 604 images belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ubuntu/.virtualenvs/monk_pip_test/lib/python3.6/site-packages/monk/tf_keras_1/models/common.py:224: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-Composed Train Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'MeanSubtraction': {'mean': [0.485, 0.456, 0.406]}}]\n",
      "\n",
      "Pre-Composed Val Transforms\n",
      "[{'RandomHorizontalFlip': {'p': 0.8}}, {'MeanSubtraction': {'mean': [0.485, 0.456, 0.406]}}]\n",
      "\n",
      "Dataset Numbers\n",
      "    Num train images: 1412\n",
      "    Num val images:   604\n",
      "    Num classes:      2\n",
      "\n",
      "Model Params\n",
      "    Model name:           resnet50\n",
      "    Use Gpu:              True\n",
      "    Gpu Memory Fraction:  0.6\n",
      "    Use pretrained:       True\n",
      "    Freeze base network:  True\n",
      "\n",
      "Model Details\n",
      "    Loading pretrained model\n",
      "    Model Loaded on device\n",
      "        Model name:                           resnet50\n",
      "        Num layers in model:  108\n",
      "        Num trainable layers: 2\n",
      "\n",
      "Optimizer\n",
      "    Name:          sgd\n",
      "    Learning rate: 0.0001\n",
      "    Params:        {'lr': 0.0001, 'momentum': 0.9, 'weight_decay': 0, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n",
      "\n",
      "\n",
      "\n",
      "Learning rate scheduler\n",
      "    Name:   reduceonplateaulr\n",
      "    Params: {'mode': 'min', 'factor': 0.1, 'patience': 1, 'verbose': True, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 0, 'epsilon': 1e-08}\n",
      "\n",
      "Loss\n",
      "    Name:          crossentropy\n",
      "    Params:        {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n",
      "\n",
      "Training params\n",
      "    Num Epochs: 5\n",
      "\n",
      "Display params\n",
      "    Display progress:          True\n",
      "    Display progress realtime: True\n",
      "    Save Training logs:        True\n",
      "    Save Intermediate models:  True\n",
      "    Intermediate model prefix: intermediate_model_\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gtf.Default(dataset_path=\"malaria_cell\", \n",
    "            model_name=\"resnet50\", \n",
    "            num_epochs=5);\n",
    "\n",
    "#Read the summary generated once you run this cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Start\n",
      "Epoch 1/5\n",
      "353/353 [==============================] - 11s 32ms/step - loss: 0.5183 - accuracy: 0.7443 - val_loss: 1.1040 - val_accuracy: 0.7301\n",
      "\n",
      "Epoch 00001: saving model to workspace/Malaria-Cell/exp-keras/output/models/resume_state.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.10396, saving model to workspace/Malaria-Cell/exp-keras/output/models/best_model.h5\n",
      "\n",
      "Epoch 00001: saving model to workspace/Malaria-Cell/exp-keras/output/models/intermediate_model_01.h5\n",
      "Epoch 2/5\n",
      "353/353 [==============================] - 7s 21ms/step - loss: 0.3726 - accuracy: 0.8357 - val_loss: 0.5781 - val_accuracy: 0.7583\n",
      "\n",
      "Epoch 00002: saving model to workspace/Malaria-Cell/exp-keras/output/models/resume_state.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.10396 to 0.57811, saving model to workspace/Malaria-Cell/exp-keras/output/models/best_model.h5\n",
      "\n",
      "Epoch 00002: saving model to workspace/Malaria-Cell/exp-keras/output/models/intermediate_model_02.h5\n",
      "Epoch 3/5\n",
      "353/353 [==============================] - 7s 21ms/step - loss: 0.3512 - accuracy: 0.8435 - val_loss: 0.9290 - val_accuracy: 0.7285\n",
      "\n",
      "Epoch 00003: saving model to workspace/Malaria-Cell/exp-keras/output/models/resume_state.h5\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.57811\n",
      "\n",
      "Epoch 00003: saving model to workspace/Malaria-Cell/exp-keras/output/models/intermediate_model_03.h5\n",
      "\n",
      "Epoch 00003: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
      "Epoch 4/5\n",
      "353/353 [==============================] - 7s 21ms/step - loss: 0.3041 - accuracy: 0.8690 - val_loss: 0.9377 - val_accuracy: 0.6970\n",
      "\n",
      "Epoch 00004: saving model to workspace/Malaria-Cell/exp-keras/output/models/resume_state.h5\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.57811\n",
      "\n",
      "Epoch 00004: saving model to workspace/Malaria-Cell/exp-keras/output/models/intermediate_model_04.h5\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n",
      "Epoch 5/5\n",
      "353/353 [==============================] - 7s 21ms/step - loss: 0.2943 - accuracy: 0.8768 - val_loss: 0.2580 - val_accuracy: 0.7136\n",
      "\n",
      "Epoch 00005: saving model to workspace/Malaria-Cell/exp-keras/output/models/resume_state.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.57811 to 0.25800, saving model to workspace/Malaria-Cell/exp-keras/output/models/best_model.h5\n",
      "\n",
      "Epoch 00005: saving model to workspace/Malaria-Cell/exp-keras/output/models/intermediate_model_05.h5\n",
      "    Training completed in: 0m 41s\n",
      "    Best val Acc:          0.758278\n",
      "\n",
      "Training End\n",
      "\n",
      "Training Outputs\n",
      "    Model Dir:   /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-keras/output/models/\n",
      "    Log Dir:     /home/ubuntu/Desktop/monk_pip_test/monk_v1/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/workspace/Malaria-Cell/exp-keras/output/logs/\n",
      "    Final model: final\n",
      "    Best model:  best_model\n",
      "    Log 1 - Validation accuracy history log: val_acc_history.npy\n",
      "    Log 2 - Validation loss history log:     val_loss_history.npy\n",
      "    Log 3 - Training accuracy history log:   train_acc_history.npy\n",
      "    Log 4 - Training loss history log:       train_loss_history.npy\n",
      "    Log 5 - Training curve:                  train_loss_history.npy\n",
      "    Log 6 - Validation curve:                train_loss_history.npy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Start Training\n",
    "gtf.Train();\n",
    "\n",
    "#Read the training summary generated once you run the cell and training is completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4'></a>\n",
    "# Compare experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Invoke the comparison class\n",
    "from monk.compare_prototype import compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and managing comparison experiments\n",
    "        - Provide project name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison: - Sample-Comparison-2\n"
     ]
    }
   ],
   "source": [
    "# Create a project \n",
    "gtf = compare(verbose=1);\n",
    "gtf.Comparison(\"Sample-Comparison-2\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This creates files and directories as per the following structure\n",
    "    \n",
    "    workspace\n",
    "        |\n",
    "        |--------comparison\n",
    "                        |\n",
    "                        |\n",
    "                        |-----Sample-Comparison-2\n",
    "                                    |\n",
    "                                    |------stats_best_val_acc.png\n",
    "                                    |------stats_max_gpu_usage.png\n",
    "                                    |------stats_training_time.png\n",
    "                                    |------train_accuracy.png\n",
    "                                    |------train_loss.png\n",
    "                                    |------val_accuracy.png\n",
    "                                    |------val_loss.png\n",
    "                                    \n",
    "                        |\n",
    "                        |-----comparison.csv (Contains necessary details of all experiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the experiments\n",
    "        - First argument - Project name\n",
    "        - Second argument - Experiment name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project - Malaria-Cell, Experiment - exp-gluon added\n",
      "Project - Malaria-Cell, Experiment - exp-pytorch added\n",
      "Project - Malaria-Cell, Experiment - exp-keras added\n"
     ]
    }
   ],
   "source": [
    "gtf.Add_Experiment(\"Malaria-Cell\", \"exp-gluon\");\n",
    "gtf.Add_Experiment(\"Malaria-Cell\", \"exp-pytorch\");\n",
    "gtf.Add_Experiment(\"Malaria-Cell\", \"exp-keras\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating statistics...\n",
      "Generated\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gtf.Generate_Statistics();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5'></a>\n",
    "# Visualize and study comparison metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Accuracy Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-2/train_accuracy.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loss Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AANFCAI2YsNYGJV0n6QNJX0n6P2vtF8aYCcaY0ZVKL5T0smV+AmqhfbJLM0f5NPJIt+P6O5tCOuNtv7YXhaPcGQAAAAAAQPNkyPUQT/r162cXL14c6zYQY6Gw1e8WlOjxL0sd149KNfrfCK9ObOkcVAMAAAAAgO8ZY5ZYa/vFug80TeyABhB33C6jf52SpL+dlKTqU6GljYVWw97ya9bWYNR7AwAAAAAAaE4IoAHEJWOMftsjSVPO8MrrsNF5X4k06r1ivbTWeZc0AAAAAAAA6o4AGkBc+3F2gqb/yKfW3uprpWHp8tkB3ft5iRhHBAAAAAAAUP8IoAHEvZPaujVnTLKOSXMayCFNWFKiK2cHVBIihAYAAAAAAKhPBNAAmoWj01yaMyZZg9o6v+y9+HVQ575frLwAITQAAAAAAEB9IYAG0Gy08hq9O9Knnx2d4Lg+a2tIp73l18aCcJQ7AwAAAAAAiE8E0ACaFW+C0fOneXRLr0TH9S/3hjVkql+f54ai3BkAAAAAAED8IYAG0Oy4jNGf+3v06KkeuR3GQm/3Ww1/2693Ngaj3xwAAAAAAEAcIYAG0GxdcUKi3hzhVarDZuiioHT+jGI9/mVp9BsDAAAAAACIEwTQAJq1EZ0S9PG5Ph2RUn0rdNhKN8wL6A+fBhS2HE4IAAAAAABwuAigATR7PVq5NWe0TzktnV8SH1xRql98WCx/kBAaAAAAAADgcBBAA4CkjqkufXSuT2cc4XZcf3N9SCPe8SvXTwgNAAAAAABQWwTQAFAuLcnof2d7Nf74BMf1T3eGNWRakdbmhaPcGQAAAAAAQNNEAA0AlSS6jB4b7NGf+yc5rq/Ltxo6rUjztoei3BkAAAAAAEDTQwANAFUYY3RLryS9cJpHSQ6vknsC0tnv+PXqt6XRbw4AAAAAAKAJIYAGgAh+fkyi3h3pU6an+lpJWLroo4AmLiuRtcyFBgAAAAAAcEIADQA1GNzerdmjk9W5hXFcv+OzEv36k4CCYUJoAAAAAACAqgigAeAQjs9wae4Ynwa0cX7JfGZ1UD/+oFgFJYTQAAAAAAAAlRFAA0AttPG59MGPfBrT2e24Pn1zSKe/5deW/eEodwYAAAAAANB4EUADQC0lJxhNGe7V9d0THddX7Alr8FS/Vu4ORbkzAAAAAACAxokAGgAOg9tl9I+BHv1zYJJcDmOht+y3Ou0tv2ZsDka/OQAAAAAAgEaGABoAfoBfd0/S/53pVXJC9bWCUmnM+8V6bnVp9BsDAAAAAABoRAigAeAHOveoBM0c5VNbX/Wt0CErXTM3oLs/C8haDicEAAAAAADNEwE00MhYaxUuLIx1G6ilvlluzRnjU9cMh3kcku5fVqpLPw4oECKEBgAAAAAAzQ8BNNDI5F19tbZnZGiry6UdnTuraPLkWLeEQ+jcwqVZo5M1tL3bcf3lb4P60bt+7SkmhAYAAAAAAM0LATTQiBQ8+KD8Tz0lhUKStQpt3Ki8q64ihG4CMj1Gb5/j1bhjHIZCS5q7Payh04q0Lj8c5c4AAAAAAABihwAaaCRsMKiC226rvlBUpPybb45+QzhsSW6jZ4Z59Mc+iY7ra/dZDZnq16KdoSh3BgAAAAAAEBsE0EAjUfiPf0iBgONaeNs27f/3vznMrgkwxujOvh49NdSjBIex0LnFVme97dfU9cHoNwcAAAAAABBlBNBAI+EbO1byeCKu7/vNb7Rn5EiFtm2LYlf4oS4+LlFvneNVmsNmaH9I+vnMYj28siT6jQEAAAAAAEQRATTQSCR07qz0p56SEp3HN0hS4P33lZuTI/+bb0axM/xQpx+RoFmjfToytfpWaCvp5oUl+t38gEJhdrYDAAAAAID4RAANNCIpv/ylMp57Tq527SLWhHfv1t6f/ER5l1+ucEFBFLvDD9GtpVtzxvjUu7Xzy+2jX5TqZzOLtb+UEBoAAAAAAMQfAmigkUkeN07ttm1Tu/37lfzrX0esK3r2WeX26qWSBQui2B1+iPbJLs0c5dM5ndyO629vDOnMt/3aXhSOcmcAAAAAAAANiwAaaKRcycnK+Pe/1fKdd+Rq29axJrRunXadeqry775btrQ0yh3icKQmGr12lldXnZDguL5kV1hDpvr11V5CaAAAAAAAED8IoIFGzjtypLJWrpR3zBjngnBYhRMmaNeppyr49dfRbQ6HJcFl9PAgj+4/KclxfWOh1bBpRZq1NRjlzgAAAAAAABoGATTQBLizspT55ptKf/ppmZQUx5rSRYuU26uX9j/1lKxlnnBjZYzRjT2SNGW4Vx6HiRx5JdKo94o1+Wt2tAMAAAAAgKaPABpoIowxSrn8cmUtW6bEk05yrLFFRdp31VXae955Cu3cGeUOcTh+0iVBH/zIp9be6mulYWn8rID++nkJv0wAAAAAAABNGgE00MQkHHOMWn/yiVrcc4/kdj7UrnjaNOXm5Kj4nXei2xwOy8C2bs0enaxj0ozj+p+WlOjqOQGVhgmhAQAAAABA00QADTRBJiFBLe6+W60/+UTuo492rAnv3Kk9o0Yp79prFS4qinKHqK1j0l2aMyZZp7R1fjl+fm1Qo98v1r4SQmgAAAAAAND0EEADTVjSyScra9kyJV9xRcSaov/8R7v69FHJ4sVR7AyHo5XX6L2RPp3fJcFx/aMtIQ2b5temwnCUOwMAAAAAAKgbAmigiXOlpirjqaeU+eabcrVu7VgTXLNGuwYOVMFf/yobCkW5Q9SGN8HoxdM9uqlnouP6l3vDGvw/v5bu4t8fAAAAAABoOgiggTjhO+88Za1cKc855zgXBIMquOMO7R46VMH166PbHGrFZYzuHeDRv0/1yO0wFnq732r4W369uykY/eYAAAAAAAB+AAJoII6427VTy3feUfqjj0per2NNybx5yu3ZU0XPPy9rmSvcGF15QqLeGOFVqsNm6P1B6afTi/XEl6XRbwwAAAAAAOAwEUADccYYo5Rrr1XW558rsU8fxxpbUKC8Sy/V3p/9TOHdu6PcIWrj7E4J+uhcnzokV98KHbbS9fMC+sOnAYX5JQIAAAAAAGjECKCBOJV4wglqvWCBUm+7TTIO8xwkFb/2mnb26KHiGTOi3B1qo2crt+aM8al7S+eX6gdXlGrch8XyBwmhAQAAAABA40QADcQxk5SktL/+Va1mz5b7qKMca8Jbt2rPWWdp3403yhYXR7lDHEqnVJc+Pten4Ue4HdffWB/SOe/6tauYEBoAAAAAADQ+BNBAM+AZPFhZy5fLd/HFEWv2/+tfyu3XT6XLl0exM9RGWpLR1LO9uuz4BMf1BTvCGjK1SF/vC0e5MwAAAAAAgJoRQAPNhCs9XZnPP6/MV16Rycx0rAl+8YVyBwxQ4cSJsmHCzMYk0WX0n8Ee/alfkuP6t/lWQ6cWaf72UJQ7AwAAAAAAiIwAGmhmfD/7mdqsWKGk4cOdC0pKlH/zzdp9xhkKffdddJtDjYwx+kPvJE06zaMkh1fv3QHp7Hf9eu3b0ug3BwAAAAAA4IAAGmiG3B07qtX06Ur75z+lJOcdtSUff6ydPXrI//LLUe4OhzL2mES9M9KnTE/1tUBIGvdRQBOXl8ha5kIDAAAAAIDYIoAGminjcin1xhuVtXixEnJyHGtsXp72jh2rvRddpHBeXnQbRI2GtHdr1uhkdW5hHNfvWFSi38wLKBgmhAYAAAAAALFDAA00c4k5OcpatEgpv/tdxBr/5MnK7dlTgdmzo9gZDqVrhktzRvvUP8v5pfypr4L6yfRiFZQQQgMAAAAAgNgggAYg4/Uq/YEH1GrmTLmOOMKxJrRpk3afdpry//AH2ZKSKHeISNomuzR9lE+jj3I7rn/wXUhnvO3X1v0cKgkAAAAAAKKPABpABc/w4WqzYoW8P/uZc4G1Kvzb35R70kkq/fLL6DaHiJITjF4+w6vfdE90XF+2O6zBU/1atScU5c4AAAAAAEBzRwAN4CCuli2V+fLLynjxRZm0NMea4LJlyu3bV4WPPMJBd42E22U0caBHDwxMktNU6M37rYZN82vm5mDUewMAAAAAAM0XATSAaowxSr7oImUtX66kwYOdi4qLlX/99dpzzjkKbdsW3QYR0XXdk/R/Z3rlc5jIUVAqjXm/WJPWlEa/MQAAAAAA0CwRQAOIKKFzZ7X6+GO1uO8+KSHBsSbwwQfKzcmR/403otwdIhndOUEzz/Wpja/6Xuigla6eE9A9iwPsXgcAAAAAAA2OABpAjYzbrRZ/+INaf/qpErp2dawJ796tvT/9qfIuv1zhgoIodwgn/bLcmjPap64ZTgM5pPuWluqyWQEFQoTQAAAAAACg4RBAA6iVpD591HrJEqVcd13EmqJnn1Vur14qWbAgip0hkuw0l2aNTtaQ9s4v9VO+CWrUe37tDRBCAwAAAACAhkEADaDWXMnJSn/kEbV891252rZ1rAmtW6ddp56q/Lvuki1l1nCsZXqM3j7Hp18c4zxCZc62sIZOK9L6/HCUOwMAAAAAAM0BATSAw+Y95xxlrVwp73nnOReEwyr885+1a9AgBdeujWpvqM7jNnp2mEe39050XF+TZzVkml+f7QxFuTMAAAAAABDvCKAB/CDurCxlvvGG0p9+WiYlxbGm9LPPlNu7t/Y/+SQH3sWYMUZ39/PoySEeJTiMhd7ptzrzbb+mbghGvzkAAAAAABC3CKAB/GDGGKVcfrmyli1T4kknOdbYoiLtu/pq7RkzRqGdO6PcIaq65PhETTvHqzSHzdD+kPTzGcV6ZFVJ9BsDAAAAAABxiQAaQJ0lHHOMWn/yiVrcc4/kdjvWBN56S7k5OSp+553oNodqhh+RoI9H+9QppfpWaCvppgUl+t38gEJhdq0DAAAAAIC6IYAGUC9MQoJa3H23Ws+bJ/cxxzjWhHfu1J5Ro5R37bUKFxVFuUNU1r2lW3PG+NSrlfOXgUe/KNWFM4tVFCSEBgAAAAAAPxwBNIB6lXTSScpaulTJV14ZsaboP/9Rbu/eKlm8OIqdoaoOKS59eK5PZ3dy3rU+bWNIZ77t146icJQ7AwAAAAAA8YIAGkC9c6WmKuPJJ5X5v//J1bq1Y01o7VrtGjhQBffeKxsKRblDHJCaaPT6WV5deUKC4/ri3LAGT/Xrq72E0AAAAAAA4PARQANoML4xY5S1cqU855zjXBAMquCPf9TuoUMVXL8+us2hQoLL6JFBHv11QJLj+sZCq2HTijRnG78oAAAAAAAAh4cAGkCDcrdrp5bvvKP0Rx+VvF7HmpJ585Tbs6eKnn9e1jJzOBaMMfp9zyT9d7hXHoeJHHkl0sh3/ZryTWn0mwMAAAAAAE0WATSABmeMUcq11ypr6VIl9unjWGMLCpR36aXa+7OfKbx7d5Q7xAE/7ZKgD37kUytP9bXSsHTpxwHd93kJvygAAAAAAAC1QgANIGoSu3ZV6wULlHr77ZIxjjXFr72mnTk5Kp4+Pcrd4YCBbd2aPSZZR6c5/zu6Z0mJrpkTUGmYEBoAAAAAANSMABpAVJmkJKXde69azZ4t91FHOdaEt23TnhEjtO+3v5X1+6PcISTp2HSX5oxJ1sC2zl8mJq0NavT7xdpXQggNAAAAAAAiI4AGEBOewYOVtXy5fBdfHLFm/0MPKbd/f5UuXx7FznBAa6/R+yN9+ml2guP6R1tCOv0tv74rDEe5MwAAAAAA0FQQQAOIGVd6ujKff16Zr7wik5npWBP84gvlDhigwokTZcMEndHmTTB6abhHv++R6Li+ak9Yg6f6tWxXKMqdAQAAAACApoAAGkDM+X72M7VZsUJJw4c7F5SUKP/mm7V7+HCFvvsuus1BLmP015M8emSQRy6HsdDbiqxOf8uv9zYFo98cAAAAAABo1AigATQK7o4d1Wr6dKX985+Sx+NYUzJrlnbm5Mj/8stR7g6SdNWJiXrjLK9SHCZy7A9KP51erKe+Ko1+YwAAAAAAoNEigAbQaBiXS6k33qiszz5TQk6OY43dt097x47V3nHjFM7Li26D0DlHJuijc31qn1x9K3TIStd9EtDtnwYUthxOCAAAAAAACKABNEKJOTnKWrRIKb//fcQa/3//q9wePRSYNSt6jUGS1Ku1W3PH+NQt0/lLyAMrSnXRhwEVBwmhAQAAAABo7gigATRKxutV+sSJajVzplxHHOFYE/ruO+0+/XTl33qrbCAQ5Q6bt06pLn082qfTj3A7rr++Pqiz3/VrVzEhNAAAAAAAzRkBNIBGzTN8uNqsXCnvz37mXGCtCv/+d+WefLJKv/gius01c+lJRtPO9uqS4xyGQktasCOsoVOL9M2+cJQ7AwAAAAAAjQUBNIBGz5WZqcyXX1bGiy/KpKU51gSXLVNu374qfPhh2TCBZ7QkuoyeGOLRPX2THNe/ybcaMrVIC3aEotwZAAAAAABoDAigATQJxhglX3SRslasUNKQIc5FgYDyb7hBe0aOVGjr1ug22IwZY3RbnyQ9N8yjRIevKrsD0oh3/Hp9XTD6zQEAAAAAgJgigAbQpCQcdZRaffSRWtx/v5SY6FgT+OAD7czJkf+NN6LcXfP2i2MT9c45XmU4bIYOhKRffFisB5aXyFrmQgMAAAAA0FwQQANocozbrRa33qrWCxcqoWtXxxq7Z4/2/vSn2jt+vMIFBVHusPka2iFBs0Yn66hU47h++6ISXT8voGCYEBoAAAAAgOaAABpAk5XUp49aL1milOuui1jjf+455fbqpZL586PYWfN2QqZLc8f41C/L+UvMk18F9dPpxSosJYQGAAAAACDeEUADaNJcyclKf+QRtXz3XbnatXOsCa1bp12DByv/rrtkS0uj3GHz1DbZpek/8unco9yO6+9/F9Lwt/zaup8DIwEAAAAAiGcE0ADigvecc5S1YoW8553nXBAOq/DPf9auQYMUXLs2qr01VymJRq+c4dWvuznP6l62O6whU/36Yk8oyp0BAAAAAIBoIYAGEDfcWVnKfOMNpT/9tExKimNN6WefKbd3b+1/8kkOw4sCt8von6d4NHFgkpymQn+332rYNL8+3BKMem8AAAAAAKDhEUADiCvGGKVcfrmyli9X4sknO9bYoiLtu/pq7Rk9WqGdO6PcYfP0m+5JeuVMr3wOEznyS6XR7xXr+TWMRwEAAAAAIN4QQAOISwlHH63Wc+eqxZ/+JLmd5xAH3n5buTk5Kn777Sh31zyN6ZygGaN8auOrvhc6aKWr5gT0p8UBdqYDAAAAABBHCKABxC2TkKAWd92l1vPmyX3MMY414Z07tefcc5X3q18pvH9/lDtsfvq3cWv2aJ+OS3cayCH9dWmpxs8KqCRECA0AAAAAQDwggAYQ95JOOklZS5cq+corI9YUPf64cvv0Uclnn0Wxs+apS5pLs0cna0h75y9B//0mqFHvFWtvgBAaAAAAAICmjgAaQLPgSk1VxpNPKvN//5OrdWvHmtDatdp1yikq+MtfZIMciteQWnqN3j7Hp7HHJDiuz94W0rBpRdpQEI5yZwAAAAAAoD4RQANoVnxjxihr5Up5Ro50LggGVXDnndo1dKiC69ZFt7lmxuM2em6YR7f1TnRcX51nNXiqX4tzQ1HuDAAAAAAA1BcCaADNjrtdO7V8+22lP/aYjM/nWFM6f75ye/ZU0aRJHIrXgIwxuqefR48P9sjtMBZ6p9/qjLf8mraBHekAAAAAADRFBNAAmiVjjFJ+9Su1/vxzJfbp41hjCwuVd9ll2nvBBQrv3h3lDpuXy7omaurZXrVw2AztD0k/m1GsR1eVRL8xAAAAAABQJwTQAJq1xK5d1XrBAqXefrvkcn5JLH79de3MyVHx9OlR7q55ObNjgmaN9qljSvWt0FbS7xaU6KYFAYXC7EgHAAAAAKCpIIBGzBhjzjbGrDHGfGOM+UOEmp8ZY740xnxhjPlvtHtE82CSkpR2771qNXu23J07O9aEt23TnhEjtO+GG2T9/ug22Ix0b+nW3DE+9Wzl/OXpkVWlunBmsYqChNAAAAAAADQFBNCICWOMW9Kjks6RdKKkscaYE6vUHCvpNkmDrLXdJP022n2iefGceqqyli2T7+KLI9bsf/hh5fbrp9Jly6LXWDPTIcWlD0f5NKKT23F92saQznrbr53+cJQ7AwAAAAAAh4sAGrEyQNI31tp11toSSS9LGlOl5kpJj1pr90qStXZnlHtEM+RKT1fm888r8//+TyYz07Em+OWXyh0wQIX/+IdsKBTlDpuHFklGb5zl1RVdExzXP8sNa/BUv1bnEUIDAAAAANCYEUAjVo6Q9F2ljzeXX6vsOEnHGWPmGWMWGmPOjlp3aPZ8F1ygNitXKumMM5wLSkuVf8st2n3GGQpu2hTd5pqJBJfRv0/16N4BSY7rGwqshk0r0txt/BIAAAAAAIDGigAajVmCpGMlDZM0VtJTxpiMqkXGmKuMMYuNMYtzc3Oj2yHimvuII9Tqgw+U9uCDksfjWFMya5Zye/RQ0ZQpUe6ueTDG6KaeSZp8ukceh4kcewPSyHf9evmb0ug3BwAAAAAADokAGrGyRVKnSh93LL9W2WZJ06y1pdba9ZLWqiyQPoi19klrbT9rbb+srKwGaxjNk3G5lPrb3yrrs8+UkJPjWGP37VPeL36hvb/4hcJ5edFtsJk4/+hEvTfSp5YOvwcoCUuXfBzQ35aWyFoOJwQAAAAAoDEhgEasfCbpWGNMtjEmSdKFkqZVqfmfynY/yxjTWmUjOdZFsUegQmJOjrI++0wpN90UscY/ZYpye/RQYNas6DXWjAxq59acMcnqkmYc1+9aXKJfzQ2oNEwIDQAAAABAY0EAjZiw1gYlXSfpA0lfSfo/a+0XxpgJxpjR5WUfSNptjPlS0seSbrbW7o5Nx4BkPB6l/+MfavXhh3IdUXVkeZnQd99p9+mna98tt8gGAlHuMP4dm+7SnNHJOrmN85ev59YENeb9YuWXEEIDAAAAANAYGP5cGfGkX79+dvHixbFuA81AeO9e5f3qVyp+5ZWINQk9eypz8mQldusWxc6aB3/QavysYr2x3vkAwu4tXZo6wquOqfyeFQAAAADqyhizxFrbL9Z9oGniJ3MA+AFcmZnKnDJFGS+9JJOW5lgTXL5cuX37qvDhh2XD4Sh3GN98CUaTh3v1ux6Jjuur9oQ1eKpfy3c7B9QAAAAAACA6CKAB4Acyxih53DhlrVihpCFDnIsCAeXfcIP2nHOOQlu3RrfBOOcyRved5NFDgzxyOYyF3lpkdfpbfr3/XTD6zQEAAAAAAEkE0ABQZwlHHaVWH32kFvffLyU678gNTJ+unTk58r/+epS7i3/XnJio18/yKiWh+lphqfSTD4r19Fel0W8MAAAAAAAQQANAfTBut1rcequyPv1UCSec4Fhj9+zR3vPP197LLlM4Pz/KHca3kUcm6MNzfWrnq74VOmSlX38S0B2LAgpz7gEAAAAAAFFFAA0A9Sixd29lLVmilOuui1jjnzRJub16KTBvXhQ7i3+9W7s19zyfTsx0/tI2cXmpLv4ooOIgITQAAAAAANFCAA0A9cz4fEp/5BG1fO89udq1c6wJrV+v3UOGKP/OO2VLGQ9RX45MdWnWaJ9O6+B2XH91XVDnvOvX7mJCaAAAAAAAooEAGgAaiPfss5W1cqW8553nXBAOq/Avf9GuU05RcO3aqPYWz9KTjKad7dXFxzkMhZY0f0dYQ6YW6Zt94Sh3BgAAAABA80MADQANyN26tTLfeEMZzzwjk5LiWFO6eLFye/fW/ieekGVGcb1Ichs9OcSju/smOa5/k281dFqRFu4IRbkzAAAAAACaFwJoAGhgxhgljx+vrOXLlXjyyY41tqhI+665RntGj1Zox44odxifjDG6vU+Snh3mUaLDV7tdxdKId/x6c30w+s0BAAAAANBMEEADQJQkHH20Ws+dqxYTJkhu5xnFgbffVm5OjorfeivK3cWvcccm6u1zvEp32AxdHJLGzizWgytK2H0OAAAAAEADIIAGgCgyCQlqceedaj1vntzHHONYE87N1Z7Ro5V3zTUK798f5Q7j07AOCZo9OllHpZpqa1bSHz4t0Q3zSxQME0IDAAAAAFCfCKABIAaSTjpJWUuXKvmqqyLWFD3xhHJ791bJZ59FsbP4dUKmS3PG+NS3tfOXvie+LNX504tVWEoIDQAAAABAfSGABoAYcaWmKuOJJ9Ry6lS5Wrd2rAl9/bV2DRyogr/8RTbIrOK6apfs0oxRPo06ynkEynvfhXTG235tKwpHuTMAAAAAAOITATQAxJh39GhlrVwpz8iRzgWhkAruvFO7hgxR8Ntvo9tcHEpJNPq/M7y6tlui4/rSXWENmerXl3tCUe4MAAAAAID4QwANAI2Au107tXz7baU/9piMz+dYU7pggXJ79VLRc89xYF4duV1GD57i0T9OTlL1qdDSpkKrodP8+mgLu84BAAAAAKgLAmgAaCSMMUr51a+UtXSpEvv2dayxhYXKGz9ee88/X+Hdu6PcYfy5PidJL5/hlddhIkd+qXTue8V6cW1p9BsDAAAAACBOEEADQCOTcPzxaj1/vlLvuENyOb9MF7/xhnbm5Kj4gw+i3F38OS87QTNG+ZTlrb4XOmilK2YH9OclAXadA6gX1lo9t7pEnSfvl/epQmVP3q8nvyxRUdAqzOsMAAAA4pDhB2rEk379+tnFixfHug2g3gQ++UR5v/ylQhs2RKxJuf56pd1/f8TRHaiddflhjXnfr7X7nL8ujjsmQY8P8SjJ7TS0A0BzFgxb7Sq2yvVb7fBb5RZb7fSXfbyz/J8D17butwrW8O23zy0lJ0jJCUa+8rfJCZKv/K3Tx5Xrkt011yYnSImusr+6AQAAqC1jzBJrbb9Y94GmiQAacYUAGvEonJ+vfddfL//zz0esSTjxRGVOnqzEXr2i11gc2lNs9bMZfs3dHnZcH9berZfP9CrTQ3ADxDNrrQpLpZ3FVUJkv9XOKuFybnFYu4ulpvQdtds4h9wHhdXuqmu1/bj8fXfZvH0AABAfCKBRFwTQiCsE0Ihn/ldfVd7VV8vu3etckJioFn/5i1J//3sZt8NQY9RKIGR15eyAXvnW+QDCrhlGU8/2qXMLplgBTUkwbLW7PDzeWXm3coRg2R+KdcdNn8d9cCDtO+Ru7urB96F2c3vd7OYGACAaCKBRFwTQiCsE0Ih3oS1btPfSS1Uyc2bEmqShQ5XxwgtKOPLIKHYWX8LW6k+LS3T/MucDCNv6jN4c4VXfLIJ+IFastdofVMXu5ANjLqoGyzua6C5l1F5ygsOO7kOMIqlp97bz2BJCbgBA80YAjboggEZcIYBGc2DDYe1/5BHl33qrFAg41pj0dKU/9piSf/GLKHcXX55dXarrPgko5PClMjlBevF0r0YdlRD9xoA4dWCXcm7x9yFyxfzkIlttJEY87VJOMKpxNjRiKyHi2JIqo0cSJJ+bsSUAgPhDAI26IIBGXCGARnNSumqV9o4bp+CKFRFrfGPHKv3RR+XKzIxiZ/Flxuagxs4sVoHDZmiXkR4YmKRruyVFvzGgiSgsjTw/ufL1XH/ZQX5N9TvTVh4py2fUxmeU5XWpzYH3fUZtvEZtko2yvGXX3tlYqrsWl+q7QqtOqUYT+idp7DGJClsrf1AqCkpFQauioOQvf1sULF8LVfm46nrF7SR/hNpS5zH3aASqji059CiSQ48pqRyIJyeUPQZjSwAAh4sAGnVBAI24QgCN5sYGAsr/4x+1/4EHpAiv5+5OnZTx/PPynHZalLuLHyt2h3TeB8Xast/5c3xDTqLuPylJLn6gRzMQClvtDhwcIO+oNP6i6kiMIudx6o2ex62y4PhAiFwRLn9/rW3529Ze06RGNJSGq4bVtkro7RBsRwizqwXkldb4KaNxMqo6bqQWY0ocZnMztgQAmhcCaNQFATTiCgE0mqvARx9p7yWXKLx5s3OBMUq56Sal/fnPMh5PdJuLE1v2h3Xe+8Vascd56+B5nd167jSvkhP4gRtNz/7SSsFxlSA5t/jgsLkp71JueWCXsteojc91ULDcxntw0NwikV2idWGtVSBUZTd3qGrQHenjmtbKRq8c+Lg4jsawxJvDGVsScUzJIQ+lFL/8BYAoIYBGXRBAI64QQKM5C+/dq7xf/UrFr7wSsSahZ09lvvSSErt3j2Jn8aOgxOoXHxZr+mbnxGNAG5deP8urNj5XlDsDDnZgl3LlA/mcguUdRU17l3KSSxW7kCt2JFcJkttUusaOzPgTClsVVw266zC2xGltf1COZwGgcfC6ncPsg8Jqt9Pu7tqMOCl7m+TiF1IAQACNuiCARlwhgEZzZ62V/7//1b5rr5XNz3cu8niU9re/KeU3v5FxEZQermDY6vp5AT2z2jmxy25hNPVsn47P4HOL+nVgl3LlA/oqz1U+sEM5t7hsl3K4iX6Ll+lRlR3JroNGX1QeiZHGLmVESWn4ULu3awq2HW4bcgrEY/0sEUl9jC35PDek/1sX1K5iHTT7HQCaCgJo1AUBNOIKATRQJrhxo/Iuvlglc+ZErPGceaYyJk2Su0OHKHYWH6y1emB5qe74rMRxPdMjvXamT6e2d0e5MzQllXcpOwXLOyqNv8j1l+3CbIqSXDp4jrK3+kzltuUH9LX2GiW5CZTRPFlbtpv7oPC6HseWHLgeYGxJo5Dslh4b4iGEBtBkEECjLgigEVcIoIHv2VBIhQ88oII//lEqLXWsMS1bKuOJJ+Q7//wodxcfXv22VONnBVTiMBY6ySU9PdSjn/ODZbNSFKxyCF8Nc5Wb+i7lg3ckuyqC5YqRGOXBMruUgcYlFD44kK5p93bEj0PVA/CqtYwtObSOKUbf/iIl1m0AQK0QQKMuCKARVwiggepKly7V3nHjFPzqq4g1vksvVfpDD8mVlhbFzuLDvO0hnT/drz0B5/U/90/SzT0TCeCaqFDYak9AyvWHq+1Irhws72jiu5QTK89SrjrqosrHWexSBnAI1lqVhqvP5q5t8N2cxpYsvyBZXRnbBaAJIIBGXRBAI64QQAPOrN+v/Ftv1f5HHolY487OVsaLL8ozaFAUO4sPa/PCGv2+X+sLnL+mjj8+QQ+f6uEAtEbiwC7lysFxbnGVncvF319vqruUM5JUfUeyz1Ux/qJy0JyexC5lAE3PgbEltRtTUvVQylrUln/s9JdO9aVFovTcaV6de1RCwz0IANQDAmjUBQE04goBNFCz4g8+UN6llyq8fbtzgcul1NtuU4u775ZJZHTE4cj1W/10ul+f7nT+KfXMjm79d7hXaUmEfPUtbK12F5ftUq66I3lnlR3LucVWhc4TaRq9xAOzlB12JLf1VZ+rzC5lAKgfwbDDDu3QwaNHIoXXRUGrL/eEtDC35l9o3tknSbf3SZSLXwYCaKQIoFEXBNCIKwTQwKGFdu3SvquuUvGbb0asSezXT5kvvaSE44+PYmdNnz9oddmsYr253vmEp5yWLv1vhFcdU/lT20PxV52lXFxlrnKlYDkedilXO5jP5yoLlStdz2CXMgA0WVO+KdVNCwLaVRy5ZtRRbj03jF9WA2icCKBRFwTQiCsE0EDtWGvlnzRJ+66/Xraw0LHG+HxKe+ABJV9zDaHXYQhbq9s+LdG/Vjpvs+2QbPS/s73q2cod5c5iK2wPzFK22uEPH7QjuWIMxoGdy014l3KCUfX5ycnf71quGIlRHix72KUMAM3KV3vDOn+6X9/kO/8cfly60atn+ZgLDaDRIYBGXRBAI64QQAOHJ/jtt9r7y1+qdMGCiDWeH/1IGc88I3fbtlHsrOn7zxcl+t2CEseduamJ0pThXp3VqWnPezywSzm32GpH0fdzkyt2Lhd/HyznFluFmui3HOlJOmjMxfc7lV3Vdi6zSxkAcCh5AatLPy7We985/8UUc6EBNEYE0KgLAmjEFQJo4PDZYFCF992ngj/9SQo5/yDkyspSxjPPyHvuuVHurml7Z2NQF31UrKJg9TW3kR451aPLuzaeWduVdynvLN+lvCPC+Itcv1VBE9+lXHV+cuUD+yoHy+xSBgDUt7C1+vOSEv11aeQvpsyFBtCYEECjLgigEVcIoIEfrmTRIu0dN06hb76JWJN81VVK++c/5UpJiWJnTdvnuSH9+INibfc7f729pVei/tQvqcF+uCwOfj8vufJhfE5zlZvyLuW0xCqjLyodxtfG5zpoJEamh13KAIDGYer6oMbPLo44emrUUW49O8yrdOZCA4gxAmjUBQE04goBNFA34cJC5f/+9yp68smINe5jj1XmSy8pacCAKHbWtG0sCGvM+8X6Ki/suP6zoxP01BCPvAmH/uEybK32BlQeHIcPOpCvcpB8YK5yU92l7K48S9nrHCy3rbSLuTafOwAAGiPmQgNoCgigURcE0IgrBNBA/Sh+6y3lXX65wrm5zgVut1rcfbdSb7tNJoH5hLWRF7D6+cxizdrqPOZEktr6jMYe49aJmW7HYHlnHOxSrr47+ft5ypVHYmR4xJ8cAwCajbyA1WWzivXuJuZCA2icCKBRFwTQiCsE0ED9Ce3YobzLL1fgnXci1iQOHKjMF19UwtFHR7GzpqskZHXt3IBe/NphKHQTdGCXcpY38vzkyqMv2KUMAEBktZkL/cc+ibqjT8ON7gKASAigURcE0IgrBNBA/bLWquiJJ5T/u9/J+v2ONSY1VekPPSTfZZcxV7cWrLX669JSTVhSEutWHLVIPHj0RdVguU2yq2Lncia7lAEAqHdTNwQ1fhZzoQE0LgTQqAsCaMQVAmigYQTXrNHeceNUumRJxBrvT36i9CeekLt16yh21nS9tLZUl88ONPjjuI0qgmTH+clVrvvYpQwAwGGxJSUK79yp0I4dCm/frvCOHQpt367w9u0V10rXrpXduVMKhyWPRwk9e8pz0klydeggd/v2FW/dHTrIZGZqdZ7VBTP8+nofc6EBNA4E0KgLAmjEFQJooOHY0lIV/OlPKrzvvrIfnhy42rVTxqRJ8o4YEeXumqZOLxZqZ/Hh367FgVnKVQ7nqxwst/G52KUMAMAPZEMhhXNzDwqRQ+XhctVrds+e+n1wj0fu9u1VeOTRuuGsezSjTS/HMuZCA4gmAmjUBQE04goBNNDwAvPmKe+Xv1Ro/fqINSm/+Y3S/vY3GZ8vip01PVO+KdU1cwIqrnTekMtIA7KMBrRJOChcrjwSI5ldygAAHDYbDiu8e/f3IXLl3cpVQ+bcXKkR/KwcNkb/Ovd2PTLqDxFrblz6gm7a8ZES2reTu8qOaleHDnK1asWYNAB1RgCNuiCARlwhgAaiI5yfr3033CD/pEkRaxJOOEGZkycrsXfv6DXWBE35plR3fVai7wqtOqUaTeifpLHHJMa6LQAAmgRrrWxeXsXIi8qBcrVrO3dKodCh77QRmt5rlH43/knt97ZwXD9j2Tt64LmrlObPr76YmFgWRpeP+Djw1jGodjHSA4AzAmjUBQE04goBNBBd/tdeU95VV8nu3etckJioFn/+s1JvuknG7Y5ucwAAoEmy1soWFBw06iJisLxjh1TSOA/2rW/ftDteV107RevbHeu43mX7Wj352FgdvX3tD3uAhATHoLpqYO1q3ZqgGmiGCKBRFwTQiCsE0ED0hbZsUd5llykwY0bEmqQhQ5TxwgtKOOqoKHYGAAAak/D+/dVnKB8ImKuEzdbvj3W7tWeMXK1by9WunVxt28pd+W27dipdtUr7H3pIKq508ENSkrw/+Ync7doptG2bwlu3Vry1RUURHyrfl6Ybxz+tj3qe47ie6s/XA89epbOWv1Pfz/J7CQlytWtXcWhixKA6K4ugGogjBNCoCwJoxBUCaCA2bDis/Y88ovxbb5UCAccak5am9MceU/K4cVHuDgAANBRbXFwWHDsczlf1mi0sjHW7h8W0bCl327ZlYWv528rBcsW1rCyZhJoPAiyaPFkFd9yh0KZNch95pFrce6/j90QHdn+Htm5VeNs2x7el27brnz3G6eERv4v4eNe/dZ9uePs+uWL5877bXT2orjL2w92+vVxt2vCXckATQACNuiCARlwhgAZiq3TVKu0dN07BFSsi1vguvFDpjz0mV2ZmFDsDAAC1ZUtLFd650/FwvqrX7L59sW73sJi0tIPC44oQueq1Nm1kPJ5Yt1uj/31VoCsWWBWEnHcZn/HNbP3z2SvVYte2KHd2mFyusmC/ht3UFUH1IYJ+AA2HABp1QQCNuEIADcSeDQSU/8c/av8DD0Q8Pd7VsaMyX3hBntNOi3J3AAA0TzYUUjg396AZyk7zlcPbtyu8e3es2z0sJjm5eohcvlu5arBsfL5Yt1uvvtob1gUz/Pp6n/P3XMelG/3foLCO3b9NoQg7qg+8tQUFUe7+MLlccrVpc+igum1bgmqgARBAoy4IoBFXCKCBxiPw8cfae/HFCm/e7FxgjFJ+/3ul/eUvjX6HEQAAjZENhxXes6faDGXHucq7dknhcKxbrj2P5+BZyjUEy67U1Fh3G1P7Sqwu/bhY724KOa63SJSeHebV6M41h7LhwsKyMLrSTGrHoDo/vyGeRv0xJmJQfVBg3batTGJirLsFmgwCaNQFATTiCgE00LiE9+7Vvmuvlf/llyPWJPTooczJk5XYvXsUOwMAoHGy1srm5TnOUK52bccOKeQcOjZKCQmOh/Q5XTNpaTLGxLrjJiNsrf7yeYnu/bw0Ys0dfRL1xz5JctXx8xrev792QXVjH89ijFxZWRU7pyMG1e3aEVQDIoBG3RBAI64QQAONU9F//6t9114b+QcRj0dp99+vlOuv57R0AEDcOXCwnNMM5WrXduyQSkpi3XLtlY9FcDycr8o1k5nJ1/kGNnVDUJfPKlZBhBz6R0e69dxpXqUnNXy4Hy4q+j6orhJOVw6v7d69Dd5LXR0qqHa1by93u3YySUmxbhVoMATQqAsCaMQVAmig8Qpu3Ki8Sy5RyezZEWuSzjhDmZMmyX3EEVHsDACAHyZcVFRthvKB+cpVg2Xr98e63cPiat3aMUSues3VqpWM2x3rdlFJbeZCv3qWT10zGscvA6zfr9D27TXOpw5t2ya7Z0+sWz0kV+vW38+irhpYH7jWrh3j59AkEUCjLgigEVcIoIHGzYZCKnzgARX88Y9SqfPWHJOZqYwnn5Tv/POj3B0AAGWH6VY7nM/pwL7t22ULC2Pd7mExmZk1H9J34FpWFiMHmrj6mgvdmNji4rL/92oY+xHetq1JHKLpatWq5vnU5W8JqtGYEECjLgigEVcIoIGmoXTpUu296CIFv/wyYo3vkkuU/vDDcqWlRbEzAEA8sqWlCu/c6Xg4X9VrNi8v1u0eFtOixUHhsdOBfe527eRq04Ywq5mJ5lzoxsQGAgcF1ZEC6/CuXbFu9ZBMy5Y1j/04EFR7vbFuFc0AATTqggAacYUAGmg6rN+v/D/8QfsffjhijbtzZ2W8+KI8p54axc4AAE2BDYUU3rUr8izlSu83hR2RlRmfT6727asfzld1p3LbtnIlJ8e6XTRyh5oLPfJItyZFaS50Y2JLSmoXVOfmxrrVQzKZmc5jP6oG1T5frFtFE0YAjboggEZcIYAGmp7iDz5Q3qWXKrx9u3OBy6XUP/xBLe6+m4NdACDO2XBY4T17ajdXOTdXCodj3XLtJSV9Pze5arBc5ZpJTZWJox2piL1DzYU+Nt3o1TN9OiGzccyFbkxsSUnZa0+VoLpqYB3euTPWrR6Sycg4ZFDtat+eX2zBEQE06oIAGnGFABpomkK7dmnf1Ver+I03ItYk9u2rjJdeUmLXrlHsDABQV9Za2by86jOUnYLlnTulYDDWLddeQkJZcOxwOF/VayY9nVAZMbWvxOqyj4v1Tg1zoZ8Z5tWYJjQXujGxpaXOQXXVXdU7d0qNPIcx6ekHBdWRDlR0paTEulVEEQE06oIAGnVmjEmR5LfWho0xx0nqKuk9a23kYWMNhAAaaLqstfJPmqR9118f8VAn4/Mp7YEHlHzNNfwQDwAxZK2VLSyMeDhf1WsqKYl1y7XncsmVleUYIle95mrZUsbFjlE0HWFrde/nJfpLDXOhb++dqDv7xtdc6MbEBoNlr401jP2oCKob+V95mBYtDjmf2tWhg1ypqbFuFfWAABp1QQCNOjPGLJE0WFKmpHmSPpNUYq0dF+1eCKCBpi/47bfa+8tfqnTBgog1npEjlfHss3K3bRvFzgAg/oWLir6foewwS7nygX3W7491u4fF1br19zOUq85Srhwst24t43bHul2gQU3bENR45kI3ajYYLDs89VBB9Y4djT+oTk095HxqV4cOcrVoEetWUQMCaNQFATTqzBjzubW2jzHmN5J81tq/G2OWWWt7RbsXAmggPthgUIX33aeCP/1JCjn/magrK0sZTz8t7+jRUe4OAJoWGwiUBcdVAmWnA/tsQUGs2z0sJiPD8XC+asFymzYyiYmxbhdoVFbnhXXBdL/WMhe6SbOhULWg2jGw3rEj4vfVjYVJSTnkfGp3hw4yLVrw15AxQACNuiCARp0ZY5ZKulbSg5Iut9Z+YYxZaa3NiXYvBNBAfClZtEh7L7pIoa+/jliTfOWVSvvnP/nTPgDNii0tVTg31/FwvqrXbF5erNs9LKZFi4iH8x0ULLdpI+P1xrpdoEljLnTzYUMhhXNzKwLpiIH19u2NP6hOTo48n7pyUJ2WRlBdjwigURcE0KgzY8xQSb+XNM9a+zdjTBdJv7XWXh/tXgiggfgT3r9f+b//vYqeeCJijfuYY5T50ktKOumkKHYGAPXLhkIK79rlPFe5SrAc3rUr1u0eFuPzVZulXC1YPhAuJyfHul2gWWEuNCqz4XDtg+pGfmis8fmqBdVOgTWHxNYOATTqggAa9coY45KUaq3Nj8XjE0AD8av4rbeUd/nlCufmOhe43Wpx111Kvf12mQR26QBoXGwwqNB33ym0bp2KpkxR8auvyubnS16vTFaWTElJ2etbI5/jeZCkpO/nJleeq+xwcJ9JTeWHe6CRYy40DocNh8t+aVpl1MdBgfW2bQpt2yaVRv7lRqPg9R40izpiUJ2R0ay/lhFAoy4IoFFnxpj/SrpGUkhlBxCmSXrIWvuPaPdCAA3Et9COHcq7/HIF3nknYk3iyScr86WXlHD00VHsDEBzZ62V3btXwXXrFFq3rtrb0KZNjf5PmiVJbrfzDOXKwXL5+839B3EgHjEXGvXNhsMK79lT83zq8reNPqj2eA459sPdvr1My5Zx+fWRABp1QQCNOjtw4KAxZpykPpL+IGmJtbZHtHshgAbin7VWRU88ofzf/U7W73esMSkpSn/4Yfkuuywuv/kDEBu2pEShjRsjhsw2PyZ/AHZoxsiVlXXQIX0HBcuVrrlatpRxESwBzdmh5kKnJkrPMhca9cxaK7tnT81jPw4E1SUlsW63Zh6PTGpq2cG+JSUyLVoo6Zxz1PLll5v0zyYE0KgLAmjUmTHmC0m9JP1X0r+ttbONMcuttT2j3QsBNNB8BNes0d6LLlJpDf/Pe3/8Y6U/+aTcrVtHsTMATZW1tuxgP6cdzOvWKbR5s9SIvnd2tWpVbZZy5UC54lrr1owmAnBYmAuNxurAXxzVKqgOBGLd7kEyXnpJyePGxbqNH4wAGnVBAI06M8ZcL+lWScsl/UjSkZJestYOjnYvBNBA82JLS1UwYYIK//rXiHNTXe3aKeO55+Q9++wodwegMbJ+v4IbNlQLl4Pr1im0fr3s/v0x7c9kZFSfoex0cF+bNjKJiTHtFUD8e2tjUJd9HHku9DmdyuZCZ3gIodG4WGtl8/Iiz6euFFiruDgqPbmPOkptN2yIymM1BAJo1AUBNBqEMSbBWhv1I3EJoIHmKTBvnvJ++UuF1q+PWJNy3XVK+/vfZXy+KHYGINpsOKzw9u2OO5iD69crvHVrzHpzZWVJLVoovHHjwfOgvV61uP9+pV59tYzXG7P+AMDJoeZCH5Nm9NpZzIVG02Stld2375BBdXjr1ojj/2rNGHVoSocNV0EAjboggEadGWPSJd0taUj5pdmSJlhr90W7FwJooPkK5+dr3w03yD9pUsSahBNOUObkyUrs3Tt6jQGod+HCQoXWr3fcwRxcvz5qO5mq8XiUkJ0td3a23F26KKFLl+/fZmfL1aKFJKlo8mQV3HGHQps2yX3kkWpx771N+k9yAcS/fSVW42cV6+2NzIVG82Stlc3Pdzw8sVpQXVTkeB/sgEZzRgCNOjPGvC5plaTnyy/9UlJPa+1Pot0LATQA/2uvKe/qq2X37HEuSExUiz//Wak33STjdke3OQC1YkMhhbZsOThcLt/BHFq3TuGdO2PWm6t9e7mzsw8Ol8vfutq35wA/AHErbK3++nmp/vx55APgmAuN5s5aq/3PPKP83/zm4F+IezzKeOaZJv0LZwJo1AUBNOrMGLPMWtvrUNeigQAagCSFtmxR3mWXKTBjRsSapCFDlPHCC0o46qgodgbggPC+fdUP+zuwq3nDBqk08sFXDckkJzvvYO7SRe7OneVKTo5JXwDQWDAXGji0ePxrJwJo1AUBNOrMGLNA0s3W2k/KPx4kaaK1dmC0eyGABnCADYe1/5FHlH/rrRFPwDZpaUp/7DH5fvELGXbqAPXKlpYq9N13jjuYg+vWRf4rhYZmjFxHHOG4g9ndpUvZ4X68HgBAjZgLDTQ/BNCoCwJo1JkxpqekFySll1/aK+kSa+2KaPdCAA2gqtIvvtDeceMUXL48Yo335z9Xxn/+I1dmZhQ7A5o2a63snj3OO5jXrVNo06aDD9qLItOihWO4nNCli9xHHSXj8cSkLwCIJ8yFBpoXAmjUBQE06o0xJk2SrLX5xpjfWmv/Fe0eCKABOLGBgPLvvFP7J06UInzdc3XsqMznn5fn9NOj3B3QeNlAQKGNGx13MIfWrZPNz49NY2633J06RQyZTcuW7GIGgCiozVzo23on6i7mQgNNHgE06oIAGg3CGLPJWntktB83HgLoYDAol8slF4cYAfUu8PHHyrvkEoW++y5iTcrvf6+0e+9lhySaBWutwjt3Ou9gXrdOoc2bI/7SpqGZzMyIYzLcnTrJJCbGpC8AQHWHmgt9die3nmcuNNCkEUCjLgig0SCMMd9ZaztF+3HjIYCe9tZUfbflO4XDYbVIbaGBJ5+i4487PtZtAXEjnJenfddeK/+UKRFrEnr0UOZLLykxJyeKnQENw/r93+9cLn970C7moqLYNJaQIHfnzs4hc3a2XBkZsekLAPCDrM4L62cz/FqTx1xoIB4RQKMuCKDRINgB/cMs/nyxFiycf9A1t9ut4aedQQgN1LOi//5X+669VnbfPucCj0dp99+vlOuvl+EvEtCI2XBY4W3bHHcwB9etU3jbtpj15srKct7B3KWL3EccIeN2x6w3AED9yy+xuuxQc6GHejUmm7nQQFNDAI26IIDGD2aMKZDk9B+QkeSz1kb9u4qmHEBba/WfJx9TyOHAJo/Hoysuu5KxHEA9C27apLyLL1bJ7NkRa5KGD1fmpElyd+wYxc6Ag4ULCsrCZYcdzMH166VAIDaNeTxKyM6uFi5X7GJOTY1NXwCAmAlbq/uWlmrCEuZCA/GEABp1QQCNuNKUA+g1a1dr+szpEdezsrI0bMhpate2XRS7AuKfDYW0/5//VP4dd0ilzoMLTWamMp54Qr4LLohyd2gubCik0JYt1cPl8rfh3NyY9eZq3756uFz+1tWuHX8hAABw9Hb5XOh85kIDcYEAGnVBAI240pQD6B07dui1N19VOByuse7EE7rplJNPkc/ni1JnQPNQumyZ9o4bp+CXX0as8V18sdIfeUSutLQodoZ4Ec7LO2hExkFvN26M+AuQhmaSk513MHfpooTOnWX4egMA+IHW5IV1AXOhgbhAAI26IIBGXGnKAbQkrV7zlT78+MNDhtBej1cDTz5FJ55wImM5gHpk/X7l/+EP2v/wwxFr3J07K+OFF+QZPDiKnaEpsKWlCn33neMO5uC6dbJ798amMWPk7tjRcQezu0sXubKyZPgTaABAA2EuNBAfCKBRFwTQiCtNPYCWpDVr12j+gnkq3F94yNo2bdpq2OBhatu2bRQ6A5qP4unTlXfppZEPb3O5lPqHP6jF3XfLJCVFtznEjLVWds8e5x3M69Yp9N13ksMc/2gwaWmO4XJCly5yH3mkjMcTk74AAJCYCw3EAwJo1AUBNOJKPATQlW3btk2z5n6sXbt21VjX/cTuGnjyKfJ6vVHqDIh/4d27lXfVVSp+442INYl9+yrjpZeU2LVrFDtDQ7KBgEIbNzqHzOvXy+bnx6Yxt1vuI4+MGDKbzEx2MQMAGj3mQgNNFwE06oIAGnEl3gJoSQqHw1r5xUot/HSBSkoi7xjwer065eRBOvGEEwkhgHpirZV/0iTtu/562ULnv0owPp/SJk5U8q9+xf97TYC1VuGdOyOOyQhv2SLF6Hsj07Kl8w7m7Gy5O3WSSUyMSV8AANSnQ82FPjrN6LUzvTqxpTvKnQGoCQE06oIAGnElHgPoA4qKijRvwSdavWZ1jXXt2rbT0CHD1CarTZQ6A+JfcN067f3lL1U6f37EGs855yjj2Wflbtcuip3BSbioSKENGxx3MIfWrZMtKopNY4mJcnfuXD1kzs6WOztbroyM2PQFAECU1WYu9DNDvTqPudBAo0EAjboggEZciecA+oAtW7do9pxZ2r1nd8QaY4y6d8vRyQNOZiwHUE9sMKjC++9XwT33RJzz62rdWulPPy3fmDHRba6ZseGwwtu2RRyTEXF2dxS42rRxHJPhzs6W+4gjZNzs5gIAQKrdXOg/9CqbC+128VdmQKwRQKMuCKARV5pDAC2VjeVYsXK5Fi5aqNLSCAPUJPl8Pg0aOEhdjz+B0QBAPSlZtEh7L7pIoa+/jliTfOWVSvvnP+VKTY1iZ/ElXFCg0Pr11Q/6W79ewfXrpUAgNo15vWU7lp3GZGRn8+8cAIDD9M7GoC5lLjTQ6BFAoy4IoBFXmksAfcD+/fs1b/4nWvP1mhrr2rdrr6FDhimrdVaUOgPiW3j/fuXfdJOKHn88Yo37mGOU+dJLSjrppCh21nTYUEihzZurh8vl74dzc2PWm6tDh+o7mMtHZbjatZNxuWLWGwAA8Yi50EDjRwCNuiCARlxpbgH0AZu3bNbsObO0Z++eiDXGGPXo3kMnDThZHo8nit0B8av4rbeUd/nlkcNSt1st7rpLqbffLpPQ/GYYhvPyHGcwB9etU2jjRqmGv+BoSCYlJeKYjITOnWV8vpj0BQBAc5ZfYjV+VrHeYi400CgRQKMuCKARV5prAC1JoVBIy1cu16LPPq1xLEeyL1mDTjlVxx93PGM5gHoQ2rFDeVdcocDbb0esSTz5ZGW++KISjjkmip01PFtaqtCmTY47mIPr1snu3RubxoyRu1Mnxx3M7i5d5MrK4vUPAIBGiLnQQONFAI26IIBGXGnOAfQBhYWF+mT+XH39TeT5tJLUoX0HDR0yTK1btY5SZ0D8staq6MknlX/jjbJ+v2ONSUlR2kMPKXn8+CYTflprFd69uyxcXrdOwco7mNetU2jTJikcjklvJi1NCUcf/X24XDlkPuoomaSkmPQFAADqjrnQQONDAI26IIBGXCGA/t53m7/T7DmztDcv8g5EY4x69uilk/oPUFISYzmAugquWaO9F12k0hpeh7znnaf0p56Su3Xj+OWPDQQU2rChYkRG1UP/bEFBbBpzu+U+6ijHw/4SunSRycxsMkE+AAA4fMyFBhoXAmjUBQE04goB9MFCoZCWLV+qRYsXKRgMRqxLSU7RqYNO1bHHHEegA9SRLS1VwYQJKvzrXyPuDna1a6eMZ5+V95xzGr4faxXescNxB3Nw3TqFt2yRYvS9gKtVK+cdzF26yN2pU7Ocmw0AAL7HXGig8SCARl0QQCNmjDFnS3pIklvS09ba+6usXyrpH5K2lF/6t7X26ZrukwDaWUFBgT6ZP1fffPtNjXUdj+iooYOHqmXLVlHqDIhfJfPna+9FFym0fn3EmuRf/1ppf/+7XMnJdXqscFFR2QF/DjuYQ+vXyxYV1en+f7CkJLk7d6522F9Cdrbc2dlypafHpi8AANBk1GYu9K29EnU3c6GBBkUAjboggEZMGGPcktZKOlPSZkmfSRprrf2yUs2lkvpZa6+r7f0SQNds06aNmj13tvL25UWscblc6tWjl/r3H6CkRGaoAnURzs/Xvt/+Vv7nnotYk3DCCcp46SUl9ekTscaGwwpv3eq4gzm0bp3C27c3RPu14mrbNuKYDFeHDjJu/iwWAADU3aHmQo/o5NYLzIUGGgwBNOqCABoxYYwZKOkea+2I8o9vkyRr7X2Vai4VAXS9C4WC+nzZUi1e8lnNYzlSUjR40BAdc/QxjOUA6sj/+uvKu+oq2T17aqxztW8v789/roROnb7fwbxunYIbNkiBQHSarcrrdd7BXB40u1JSYtMXAABodpgLDcQOATTqggAaMWGMOV/S2dbaK8o//qWkkyqHzeUB9H2SclW2W/pGa+13Nd0vAXTt5Rfka+4nc7Ru/boa6zp17KShg4cpMzMzSp0B8Sm0davyLr1UgRkzYt1KNa4jjqi2g/nA+6527fglFAAAaDTyS6wun1WsaRHmQqckSM8M8+rHzIUG6hUBNOqCABoxUcsAupWkQmttwBhztaSfW2tPd7ivqyRdJUlHHnlk340bN0blOcSLDRs3aM7c2dqXvy9ijcvlUu9efdS/b38lJiZGsTsgvthwWPv//W/l33JLVHc0m9RU5x3MXboooXNnGa83ar0AAADUVdha3V8+FzpSosFcaKB+EUCjLgigERO1GcFRpd4taY+1tsYTq9gB/cMEg0F9vuxzLV7ymUIh550EkpSamqrBg4bo6C5HsyMSqIPSL77Q3nHjFFy+vH7u0OWSu1Ong0LmA3OY3V26yNW6Nf/PAgCAuMNcaCB6CKBRFwTQiAljTILKxmoMl7RFZYcQ/sJa+0WlmvbW2m3l7/9Y0q3W2pNrul8C6LrZt2+f5s6bo/Ub1tdYd2SnIzV08DBlZGREpzEgDtlAQNvbtJHNz69VvUlPV8LRRzuOyXAfeaRMEoeGAgCA5mdt+Vzo1cyFBhoUATTqggAaMWOMGSnpX5Lckp611t5rjJkgabG1dpox5j5JoyUFJe2R9Ctr7eqa7pMAun6s37BOcz6Zo/wagjGXy6W+vfuqb59+jOUAfqCiyZOVd/nlB4/jcLmUOGyYvGedddBcZhdz2AEAABwxFxpoeATQqAsCaMQVAuj6EwwGtfjzxfp86ZIax3KktUjT4FOHKLtzNn/iD/wARZMnq+COOxTatEnuI49Ui3vvVfK4cbFuCwAAoElhLjTQsAigURcE0IgrBND1L29fnubMna2Nm2o+3LHzUZ015NQhSk/PiE5jAAAAAFBFbeZCP3+aV5nMhQYOCwE06oIAGnGFALphWGu1bv06zf1kjgoKCyLWud3uirEcCQn8eRsAAACA6DvUXOgu5XOhuzEXGqg1AmjUBQE04goBdMMqLS2tGMsRDocj1qWlpWnIqUOV3Tk7it0BAAAAQBnmQgP1iwAadUEAjbhCAB0de/P2avacWfpu83c11mV3ztbgU4coPS09Sp0BAAAAQJmwtfrb0lL9qYa50Lf0StQ9zIUGDokAGnVBAI24QgAdPdZafbvuW839ZI4K9xdGrHO73erft7969+rDWA4AAAAAUffupqAu+Yi50EBdEECjLgigEVcIoKOvpLREny3+TMuWL61xLEd6erqGDh6qo47sHL3mAAAAAEDMhQbqigAadUEAjbhCAB07e/bs0ey5s7R5y+Ya647ucrQGDxqiFi1aRKcxAAAAAJBUUGJ1+exiTd3AXGjgcBFAoy4IoBFXCKBjy1qrr7/5Wp/Mn6v9+/dHrEtISKgYy+F2s8MAAAAAQHQwFxr4YQigURcE0IgrBNCNQ0lJiRYtXqTlK5bVOJYjIyNTQwcP1ZGdjoxidwAAAACaO+ZCA4eHABp1QQCNuEIA3bjs3rNbs+fM0patW2qsO+boYzR40GClpjKWAwAAAEB0MBcaqD0CaNQFATTiCgF042Ot1dqv1+qT+XNVVFQUsS4xIVED+g9Qzx69GMsBAAAAICpqMxf66aFe/aQLc6HRvBFAoy4IoBFXCKAbr5KSgD797FMtX7FcNb3uZGZmaujgYerUsVMUuwMAAADQXDEXGjg0AmjUBQE04goBdOO3a9cuzZo7S9u2ba2x7thjjtOpg05VakpqdBoDAAAA0Ky9uymoSz8u1r4S5/WzOrr1wunMhUbzRACNuiCARlwhgG4arLVavWa15i34RH6/P2JdYmKiTup/knrk9GQsBwAAAIAG9/W+sC6YXqyv8pwPU2cuNJorAmjUBQE04goBdNMSCAS0cNFCrVy1osaxHK1attLQIcN0RIcjotgdAAAAgOaIudBAdQTQqAsCaMQVAuimKXdXrmbN/ljbd2yvse74447XoFNOVUpySpQ6AwAAANAcha3V35eV6p7FzIUGJAJo1A0BNOIKAXTTZa3VV6u/0rwFn6i4uDhiXVJSkk7qf7J65PSQy+WKYocAAAAAmhvmQgNlCKBRFwTQiCsE0E1fcXGxFn66QCu/WFljXatWrTVsyDB1aN8hSp0BAAAAaI6YCw0QQKNuCKARVwig48fOnTs1a87H2rFzR411XY8/QYMGDlJycnKUOgMAAADQ3DAXGs0dATTqggAacYUAOr5Ya/XlV19o/sL5hxzLMfCkgereLYexHAAAAAAaRG3mQt/cM1F/6sdcaMQfAmjUBQE04goBdHzyF/u1YOECffHlqhrrslpnaeiQYWrfrn2UOgMAAADQ3BxqLvSZHd164TSvWnoJoRE/CKBRFwTQiCsE0PFt+47tmj1nlnbm7qyx7sSuJ+qUgafI52MsBwAAAID6d6i50NktjF47y6vuzIVGnCCARl0QQCOuEEDHv3A4rC++/EILPp2vQCAQsc7j8WjgSaeo24ndGMsBAAAAoN4VlFhdMbtY/6thLvRTQ736KXOhEQcIoFEXBNCIKwTQzYffX6T5C+fry6++rLGuTVYbDRtymtq2bRulzgAAAAA0F7Z8LvTdzIVGnCOARl0QQCOuEEA3P9u2b9PsObOUuyu3xrpuJ3bXwJMHyuf1RakzAAAAAM3Fe5uCuoS50IhjBNCoCwJoxBUC6OYpHA5r1RcrteDTBSopifAdnySv16tTTj5FJ57QTcbwjR8AAACA+sNcaMQzAmjUBQE04goBdPNWVFSkeQvmafWar2qsa9u2rYYNPk1t2rSJUmcAAAAAmgPmQiNeEUCjLgigEVcIoCFJW7dt1aw5s7R7964a63K65+jkAQPl9Xqj1BkAAACAeMdcaMQjAmjUBQE04goBNA4Ih8NauWqFFi5aeMixHIMGnqoTup7AWA4AAAAA9Ya50IgnBNCoCwJoxBUCaFS1f/9+zVvwidasXVNjXbu27TRs6GnKap0Vpc4AAAAAxDvmQiNeEECjLgigEVcIoBHJli2bNWvuLO3ZsydijTFGOd176OQBJ8vj8USvOQAAgP9v786j5MoOOs//buwRuWdGpPatSkutUi2pfbWNwQwce2bAdtljY/rMYGBwt7vbzAz00AxjGKB7ulnczYExDNNgGwwY3Jhu3KbatpRaS0ttKpVKpX0rSRmR+xKZGcudPyIUuSg36WVsL76fc3QUefMq7416FarIX938PQCuNV8vdMQn/eG+oH78cX+JdwYsHAE0nCCAhqsQQGMumUxGb559Q6+cekWpVGrWeZFwRLt37damjU9QywEAAADAsYX0Qv/8Fr++SC80KhQBNJwggIarEEBjIYaGh3T02BG9e/HdOectW7ZcB/YdULQtWqKdAQAAAHAzeqFRrQig4QQBNFyFABoP4+atmzrUeVC9fb2zzjHGaMuzW7R923YFAtRyAAAAAHDmUn9WH315VG/30guN6kEADScIoOEqBNB4WJlMRq+/+bpOnTqpVHqOWo5IRHt27dXGDRup5QAAAADgyOC41U91juqbV+mFRnUggIYTBNBwFQJoPKrBwUEdOXZYly5fmnPeiuUrtH/fAbW1tpVoZwAAAADciF5oVBMCaDhBAA1XIYCGUzdu3tChwwfV19c36xyPx6Mtm5/Tto5tCgQCJdsbAAAAAPf5LzfT+sz3RtVHLzQqGAE0nCCAhqsQQGMxZDJpvfb6azp15pTS6fSs8+rq6rRn115tWL+BWg4AAAAAj4xeaFQ6Amg4QQANVyGAxmIaHBxU55FOXbl6ec55K1eu0v69+9Xa0lqinQEAAABwG3qhUckIoOEEATRchQAaxXDt+jV1Hj6k/oH+Wed4PB49t+V5bevYJr+fN4QAAAAAHt5CeqG/sNmvX91KLzRKiwAaThBAw1UIoFEs6XRar73+qk6dOaVMZuYTCZJUX1+vvbv36fHHHqeWAwAAAMAjma8X+gdWePWV99MLjdIhgIYTBNBwFQJoFFv/QL8OH+nU1WtX55y3etVq7du7Xy3NLSXaGQAAAAA3WUgv9F99MKRn2+iFRvERQMMJAmi4CgE0SuXqtavqPHJIAwMDs87xeDx64bkX1PHiVmo5AAAAADy0oZTVTx0a1d/QC40yI4CGEwTQcBUCaJRSOp3WmVdP68xrZ+as5Wiob9DePfv02LrHqOUAAAAA8FCstfq/30jpl0/RC43yIYCGEwTQcBUCaJRDf3+fDh3u1PUb1+act2b1Wu3fu09NTc0l2RcAAAAA96AXGuVEAA0nCKDhKgTQKBdrra5eu6LOI50aHBycdZ7X69ULz7+ojhc65PP5SrhDAAAAANWOXmiUCwE0nCCAhqsQQKPcUqlUoZYjm535TaEkNTY2at+e/Vq3dl0JdwcAAACg2tELjXIggIYTBNBwFQJoVIrevl51Hj6kGzdvzDlv3dp12rtnn5oam0q0MwAAAADVjl5olBoBNJwggIarEECjklhrdfnKZR0+2qmhoaFZ53m9XnW80KEXnn+RWg4AAAAAC0YvNEqFABpOEEDDVQigUYlSqZROnT6p1954bc5ajqbGJu3bu19r16wt3eYAAAAAVLX5eqHXNhh9g15oOEQADScIoOEqBNCoZD29PTp0+JBu3bo557zH1j2mvXv2qbGhsUQ7AwAAAFDNFtIL/eV9QX2UXmg8IgJoOEEADVchgEals9bq0uWLOnz0sIaHh2ed5/P51PHiVr3w3PPyeqnlAAAAADA3a63+zRsp/cs5eqH/+Wa/fo1eaDwCAmg4QQANVyGARrUYHx/XqdMn9fqbr89Zy9Hc1Kz9e/dr9eo1JdwdAAAAgGr1nZtp/cQcvdAfWOHVV+mFxkMigIYTBNBwFQJoVJuenm4d7Dyo2+/dnnPe+sfXa8+uvWpoaCjRzgAAAABUq0v9WX3s5VGdoxcai4QAGk4QQMNVCKBRjay1unjpXR05ekTDI3PXcmzr2Kbntjwvr5c3igAAAABmRy80FhMBNJwggIarEECjmo2Pj+mVU6/ojTff0Fx/N7c0t2j/vgNatXJVCXcHAAAAoNrQC43FQgANJwig4SoE0HCDRHdChzoP6r077805b8P6Ddqza6/q6+tLtDMAAAAA1YheaDhFAA0nCKDhKgTQcAtrrS68e0FHjx3RSHJk1nl+v1/btm7Xlme3UMsBAAAAYFYL6YX+qw+GtJleaMyAABpOEEDDVQig4TZjY2N65eQJvfnWm3PWcrS2tGr/vgNauWJlCXcHAAAAoJrM1wsd9kpf3h/Ux+iFxjQE0HCCABquQgANt4on4jrUeVB37t6Zc96mDZu0e9ce1dXVlWhnAAAAAKrJQnuhf3VrQD56oZFHAA0nCKDhKgTQcDNrrd65cF5Hjx9VMpmcdZ7f79eObTu0+dkt8ng8JdwhAAAAgGoxXy/0+/O90G30QkME0HCGABquQgCNWjA6OqoTJ0/orXNn56zlaGtt0/59B7Ri+YoS7g4AAABAtbg8kNVH/4FeaMyPABpOEEDDVQigUUu64l062Pl93bt3b855T2x6Qrt37lEkEinRzgAAAABUi6GU1WcPjemvr6Zn/Dy90JAIoOEMATRchQAatcZaq7fPv61jJ45qdHR01nmBQEA7tu/Us08/Sy0HAAAAgCmstfq3b6T0S/RCYxYE0HCCABquQgCNWpUcTerEieN66+235pwXjUZ1YO/7tGzZshLtDAAAAEC1+IebaX2aXmjMgAAaThBAw1UIoFHr7t27p4Od31dXvGvOeU8+8ZR27dhFLQcAAACAKeiFxkwIoOEEATRchQAakLLZrN4+f07HThzT2NjYrPOCwaB2bt+pp596hloOAAAAAAX0QmM6Amg4QQANVyGABiYkk0kdO3FMb58/N+e89li79u87oKVLlpZoZwAAAAAq3UJ6of/ZZr9+jV7omkAADScIoOEqBNDAg+7evaODnQcVT8TnnPf0U09r5/ZdCofDJdoZAAAAgEpHLzQkAmg4QwANVyGABmaWzWb11ttv6cQrx+es5QgFQ9q5Y5eefuppGcMbSAAAAADz90KvqTf6xg/SC+1mBNBwggAarkIADcxtZGREx04c1fl3zs85r719iQ7sO6Al7UtKtDMAAAAAlWwoZfXTnWP6xhV6oWsRATScIICGqxBAAwvz3p33dKjzoBLdiTnnPfP0s9q5fadCoVCJdgYAAACgUllr9W/fTOlfnhpXdpY4iV5odyKAhhME0HAVAmhg4bLZrM6+9aZOnDyh8fFZCt0khUIh7d65W08+8RS1HAAAAAD08q1cL3TvLO1+9EK7DwE0nCCAhqsQQAMPb3hkWEePHdWFd9+Zc97SJUt1YN8BxWLtJdoZAAAAgEp1eSCrj708qrd66IWuBQTQcIIAGq5CAA08utvv3dahzoPq7umedY4xplDLEQwGS7g7AAAAAJVmOGX1WXqhawIBNJwggIarEEADzmQyGb351pt65eQJpVKpWeeFw2Ht3rlHT2x6gloOAAAAoIbRC10bCKDhBAE0XIUAGlgcQ8NDOnrsiN69+O6c85YtW64Dew8oGo2WaGcAAAAAKhG90O5GAA0nCKDhKgTQwOK6dfumDnYeVG9v76xzjDHa/OwWbd+6nVoOAAAAoIYtpBf6r34wpC30QlcdAmg4QQANVyGABhZfJpPRG2++oZOnXlEqPXstRyQc0e5de7Rp4yZqOQAAAIAatZBe6P9nX1AfX08vdDUhgIYTBNBwFQJooHiGhgZ1+OgRXbp8cc55y5cv14G971NbW1uJdgYAAACgkiykF/qfPuvX/7WNXuhqQQANJwig4SoE0EDx3bh5Q4cOH1Jf39y1HFs2P6ftW7crEAiUcHcAAAAAKsV8vdDvW+7V1z5AL3Q1IICGEwTQcBUCaKA0MpmMXnvjNZ06fVLp9Mw/WidJdXV12rNrrzas30AtBwAAAFCDrgxk9VF6oaseATScIICGqxBAA6U1ODiow0c7dfnK5TnnrVyxUvv37ldrK7UcAAAAQK2hF7r6EUDDCQJouAoBNFAe129c06HDh9Tf3z/rHI/Ho+e2PK+tHVsV8FPLAQAAANQSeqGrGwE0nCCAhqsQQAPlk8mk9eprr+n0q6fmreXYu3uf1j++nloOAAAAoMbQC12dCKDhBAE0XIUAGii/gYEBdR7t1NWrV+act2rlKu3fe0AtLS0l2hkAAACASkAvdPUhgIYTBNBwFQJooHJcvXZVnUcOaWBgYNY5Ho9Hzz/3gra+uFV+P31vAAAAQK1YSC/0H+wL6iV6oSsCATScIICGqxBAA5UlnU7rzGtndObV08pkMrPOa6hv0N49+/TYuseo5QAAAABqxEJ6oT//rF+/Ti902RFAwwkCaLgKATRQmfr7+9V55JCuXb8257zVq9do/579am5uLsm+AAAAAJTfQnqhv/qBkKL0QpcNATScIICGqxBAA5XtytUr6jxySIODg7PO8Xg8evH5F/XiCx3UcgAAAAA1gl7oykYADScIoOEqBNBA5UulUoVajmx25jeXktTY0Fio5QAAAADgfsMpq5/uHNNf0QtdcQig4QQBNFyFABqoHn19fTp0+KBu3Lwx57y1a9Zq3579ampqKs3GAAAAAJSNtVa/9WZKv0QvdEUhgIYTBNBwFQJooLpYa3Xl6hUdPtKpwaHZazm8Xq9efKFDLz7/onw+Xwl3CAAAAKAc/uuttD5FL3TFIICGEwTQcBUCaKA6pVIpnT5zSq++/urctRyNjdq/94DWrllbus0BAAAAKIsrA1l97OVRnaUXuuwIoOEEATRchQAaqG69vb06dPigbt66Oee8dese077d+9TY2FiajQEAAAAoC3qhKwMBNJwggIarEEAD1c9aq0uXL+nw0U4NDw/POs/n86njha164fnn5fVSywEAAAC4Fb3Q5UcADSc85d4Aapcx5kPGmAvGmEvGmF+YY96PGWOsMYa/6IAaYIzRhvUb9KlPflovPP+iPJ6Z/1OVTqd14uRxfe3rX9P1G9dLvEsAAAAApWKM0Re2BPR3HwqpJTjznN89m9KPfHtUiVEOWgKVhhPQKAtjjFfSu5I+KOmWpFOSPmGtfXvavAZJ/1lSQNLnrLVzHm/mBDTgPj09PTp0+KBu3b4157zHH3tce3fvU0NDQ2k2BgAAAKDkFtIL/ZcfDOm5KL3Qi4kT0HCCE9Aol22SLllrr1hrxyV9XdJHZpj3q5L+laTRUm4OQOVobW3Vf/vh/04/9MEPqS5SN+u8y1cu66t//hWdfvW0MplMCXcIAAAAoFQea/To0IfD+uhjM9fwXR+yOvCtpL5+KVXinQGYDQE0ymWFpMl3GbuVHyswxrwgaZW19j+XcmMAKo8xRhs3bNSnPvkpPbfleRkzc69bOp3W8RPH9Gd/8We6cfNGiXcJAAAAoBTq/EZfeX9Qv74toJkqn5MZ6TPfH9P/cnxM6dlKowGUDAE0KpIxxiPptyR9YQFzP2uMOW2MOR2Px4u/OQBlEwgEtXf3Xn3iY5/U8uXLZ53X19erv/27/6hvf+fbGhoaLOEOAQAAAJTC5F7o1ll6ob/0Fr3QQCWgAxplYYzZKelXrLU/lP/4FyXJWvsb+Y+bJF2WNJT/I0sl9Uj68Fw90HRAA7XDWqt3L17QkaNHNJIcmXWe3+fXtq3btGXzc/J66YEDAAAA3ObqQFYfpRe6qOiAhhME0CgLY4xPuZsQfkDSbeVuQvhJa+25WeYflPTz3IQQwHRjY2N65dQrevPsG5rrv2ktLS06sO+AVq5YVcLdAQAAACiF4ZTVzxwe019eTs/4+bBX+oN9Qb203l/inbkDATScoIIDZWGtTUv6nKTvSDov6S+tteeMMV80xny4vLsDUE2CwaD27dmnj3/0JS1bumzWeb29vfrm335T33n5v2hoeGjWeQAAAACqT53f6E/fF9RvbKcXGqg0nICGq3ACGqht1lq9c+EdHT1+RMlkctZ5fr9f27ft0OZnNlPLAQAAALjMf72V1qe/N6qesZk/f2C5V1/7QEjR0Mw3N8eDOAENJwig4SoE0ACkXC3HiZPHdfats3PWcrS1tmn/vgNasXxFCXcHAAAAoNjohV5cBNBwggAarkIADWCyeLxLBzsP6u69u3PO27Rxk3bv2qO6SF2JdgYAAACg2ObrhQ7le6E/QS/0vAig4QQBNFyFABrAdNZanX/nbR09flSjo6OzzgsEAtqxbYeefWazPB5ukQAAAAC4gbVWv302pf/95Lhmq37+J8/49RvbA/LNVB4NSQTQcIYAGq5CAA1gNqOjozr+ynG9de7snPOibVHt33dAy5ctL9HOAAAAABTbvL3Qy7z66gdCioUJoWdCAA0nCKDhKgTQAOZzr+ueDnYeVFfXvTnnPfnEk9q1Y7cikUiJdgYAAACgmK4OZPWxl0f15iy90KvzvdDP0wv9AAJoOEEADVchgAawENZanTt/TsePH9Po2Oy1HMFgUDu27dAzTz9LLQcAAADgAiNpq5/pHNNf0Av9UAig4QQBNFyFABrAw0gmkzr+yjGde/vcnPNi0ZgO7DugpUuXlWhnAAAAAIrFWqvfOZvSv6AXesEIoOEEATRchQAawKO4e++uDnZ+X/F4fM55Tz35tHbt2KVwOFyinQEAAAAolu/eTutT36UXeiEIoOEEATRchQAawKPKZrM69/ZbOv7KcY2NzfIOVLlajl07dumpJ5+mlgMAAACocvRCLwwBNJwggIarEEADcCqZHNGx48f09jtvzzmvPdauA/vepyVLlpRoZwAAAACKgV7o+RFAwwkCaLgKATSAxXLnzh0d7Py+Et2JOec989Qz2rFjp8IhajkAAACAarWQXuh//Ixfv1mjvdAE0HCCABquQgANYDFls1mdfeusTpw8rvHx8VnnhUKhQi2HMbX3ZhQAAABwi/l6ofcv8+prNdgLTQANJwig4SoE0ACKYWRkREePH9E7F96Zc96SJUt0YN/71B5rL9HOAAAAACw2eqEfRAANJwig4SoE0ACK6fZ7t3Wo86C6e7rnnPfsM89qx7adCoVCJdoZAAAAgMW0kF7o398b1Cc31EYvNAE0nCCAhqsQQAMotmw2qzfPvqETJ08olUrNOi8cDmv3zt16YtOT1HIAAAAAVchaq989m9Iv0gtNAA1HCKDhKgTQAEpleHhYR44d0bsXL8w5b9nSZdq/74Bi0ViJdgYAAABgMX0v3wvdXcO90ATQcIIAGq5CAA2g1G7dvqVDnQfV09sz6xxjjDY/s1nbt+1QMBgs4e4AAAAALIZrg7le6De6a7MXmgAaThBAw1UIoAGUQyaT0Rtn39DJU6/MWcsRCUe0e9dubdr4BLUcAAAAQJWp5V5oAmg4QQANVyGABlBOQ0NDOnLssC5eujjnvOXLlmv/vgOKtkVLtDMAAAAAi6FWe6EJoOEEATRchQAaQCW4eeumDnUeVG9f76xzjDHasnmLtm/drkCAWg4AAACgmtRaLzQBNJwggIarEEADqBSZTEavv/GaTp4+qXR65h/Rk6RIJKI9u/Zq44aN1HIAAAAAVWS+XmhJWho2+s0dAX1ifXXXchBAwwkCaLgKATSASjM4OKgjxw7r0uVL884NhUJ69pnNemLjE4pEwvL7A4TSAAAAQAWbrxdakgIe6cv7g1UdQhNAwwkCaLgKATSASnXjxnUdOnxIff19C/4zXq9XkUhE4XBEkXBYkUhEkXD+40hEkUi48HEoFCKsBgAAAMpgIb3QzQHp3mfqS7uxRUQADScIoOEqBNAAKlkmk9arr7+m02dOzVnL8Sg8Ho/CobDCk0LpXGAdnhRYRxQOhxUOheX1ehd1fQAAAKDWfe92Wj/896Mzfs5IGv0pAmjUJl+5NwAAQK3wen3a+uJWbdqwSYePdurK1SuL9rWz2ayGR4Y1PDK8oPmhUCgfVE86WR2ZOGk9EWBH5PPxdgEAAACYz/tX+LQ8YvTeyIOHPVfV89OKqF18RwkAQIk1NjbqR374R/VH/98fKplMlmUPo6OjGh0dlXrnn+v3+x+s/wiH84F1ZNLnwgoE6K0GAABA7fr17QH9bOeYkpmJsYhP+uLWQPk2BZQZATQAAGWyd/c+fe/gdx+o4wgGg0qn08pkMrP8ydJKpVLq7+9Xf3//vHO9Xu/U09SFx7nO6sknq+mtBgAAgNvcv9HgL58a180hq5V1Rr+6LVDVNyAEnCKABgCgTDZt3CRJOn7imAaHBtVQ36CdO3Zp08ZNstZqfHxcI8kRJZNJjYyM5B6PjGgk/3EymRsbGRlRKpUq87PJyWQyGhwa1ODQ4LxzjTG5CpDwRD/1xONJN1mMROitBgAAQNX4xHo/gTMwCQE0AABltGnjpkIQPZkxRsFgUMFgUC3NLfN+nXQ6PRFS50PpkZHklJD6fpg9OjrzjVFKzVqb3+eI1D3//FAwVLjJYiGknuFkdSRCbzUAAAAAVAq+OwMAwAV8Pp8aGxvV2Ng479xMJqPkaPLB09T5j+8/TiaTGkmOyNoHb6JSDqNjoxodG1Vv7/zF1X6/f1pQne+sntRXff8xvdUAAAAAUDwE0AAA1Biv16v6unrV19XPO9daq9HR0Uknq5MPnrRO3g+zRyqrtzrVr/6BhfVWT67/mBJQTztpHQqFCasBAAAA4CEQQAMAgFnd72kOh8OS2uaca61VKjWeC6kLp6gfPFldib3VQ0NDGhoamnfuxD+P/E0WC33V0+pA8iE2vdUAAAAAah0BNAAAWBTGGAUCQQUCQTU3N887P51OTwuqk1NutDg5sK7E3uoF1FbneqsfCKrzdSDTbr7o93OjGgAAAADuQwANAADKwufzqbGhUY0N8/dWZ7PZQif19MC68HFyRMmRCu2t7lt4b/XkfupwZNpJ63xoTW81AAAAgGpBAA0AACqex+NRXV2d6urq5p1rrdXo2Gihl3pk5MH6j8lhdrX3Vs92svp+mB0KheTxeErwDAAAAADgQQTQAADAVYwxCofCCofCal1Qb3WqEEZPDqcLAfakmyyOj4+X6FnM7aF7q0PhidPU909W3w+tpwXW9FYDAAAAWEwE0AAAoGbleqsDCgQCam5qnnd+Op3OBdRTTlPnQurJVSAjIxXWW53f10J6q4PB4NR+6kI4nbvJ4kQ1CL3VAAAAAOZHAA0AALBAPp9PDQ0NamhomHduNptVcjQfTM9wmnrKaetkUtlstgTPYH5jY2MaGxtbWG+1z6/wtFB6ppPV9FYDAAAAtYsAGgAAoAg8Ho/qInWqizxMb/X0GytOOlk9KbBOp9MleAbzS6VTSg2kNDAwMO9cj8cz6WR17jT19PqP+4/prQYAAADcgwAaAACgzKb2VrfOOXdyb/XkwDp3kvr+44kbL1ZKb3U2m9XQ8JCGhhfWWx0KhR4IrKd8PCnEprcaAAAAqFwE0AAAAFXkkXurJ5+snnSaeiK8TiqZTBb/CSyAtbawn+6e+Zurg8Hg1I7qKfUf4UmBNb3VAAAAQKkRQAMAALjYI/dWT7rZYjJ/qnricWX2Vvc9am91ODzj42AwSG81AAAA4BABNAAAACQ9fG/12NjYpL7qEY2MJKdVgUzUhFRrb/XkbuqJ+o/wtCoQeqsBAACA2RBAAwAA4KHd72kOhULSPL3VkjSeGs+dnp4ppJ502rrSequHh4c1PDw879zJvdXhWeo/wuFwYdzr5W04AAAAagPvfAEAAFB0AX9AgaaAmhbQW53JpAtB9eS+6vv1HyPJ5JQqkEowubdaC+itvs/n8+nxdY/r6aeeVjQaUzAYLOIuAQAAgNIjgAYAAEBF8Xofrrd6dHR00mnq+yerk9PqQSqrt/q+dDqtCxcv6MLFC5KkpqYmxaLtao/FFIu1KxaNKRwOl3mXAAAAwKMjgAYAAEDV8ng8uY7mSGTeuYXe6llOU08+bT0yUp7e6v7+fvX39+vS5YuFsYb6BsUmBdKxWEx1kTpukAgAAICqQAANAACAmjClt7plob3VD1aBTL/xYjI5orGxsaLte3BoUINDg7py9UphLBKOTAml22PtamhoIJQGAABAxSGABgAAAGYw0VvdNO/cTCadv5nizCH1/VPXPb09stY63ttIckTXb1zX9RvXC2PBYFCxWLvaoxPBdHNzM6E0AAAAyooAGgAAAHDI6/Wpvr5B9fVz91ZfePeCvnfwu1PqPe6fzHZ6Q8WxsTHdunVTt27dLIz5/X5Fo7GJUDoWU2tLqzwej6O1AAAAgIUigAYAAABKZNPGTZKk4yeOaXBoUA31Ddq5Y5c2bdyk8fExxRMJxeNdiifiisfjjk9Mp1Ip3bnznu7cea8w5vV6FW2LFgLpWDSmttY2+Xx8awAAAIDFZxbjRwCBStHR0WFPnz5d7m0AAAAsilQqpe6ebsXjXeqKxxVPdKm7u1vZbHZR1/F4PGptaS2E0u3RdkWjUfn9/kVdBwAAVCdjzBlrbUe594HqxDEHAAAAoEL5/X4tXbJUS5csLYxlMhn19HTnA+m44vEuJboTU2o9HlY2m1WiO6FEd0Ln35kYb2lpUXu0fcoND4PBoJOnBAAAgBpDAA0AAABUEa/Xmz+p3F4Yy2az6uvrLZySjufD6fHxcUdr9fb2qre3VxcuXiiMNTY2qj0fRsdi7WqPxRQORxytAwAAAPeiggOuQgUHAABAjrVW/QP9uU7pfCDdFe/S6Ojooq9VX1c/5ZR0eyymurp6GWMWfS0AAFB6VHDACU5AAwAAAC5kjFFzU7Oam5q1Yf1GSblQemhoSPFEvlM6f8PD4eFhR2sNDQ9paHhIV69dLYyFw+Epp6Rj0XY1NjYSSgMAANQYAmgAAACgRhhj1NDQoIaGBj227vHC+MjIyAOh9MDAgKO1ksmkbty8oRs3bxTGAoHAlPqOWDSm5uZmeTweR2sBAACgchFAAwAAADUuEolozeq1WrN6bWFsdHQ0f5PDeCGc7uvrdbTO+Pi4bt2+pVu3bxXGfD5fPpDOnZKOxWJqbWmV1+t1tBYAAAAqAwE0AAAAgAeEQiGtWrlKq1auKoyNj48r0Z2YEkr39HTLyX1l0um07ty9ozt37xTGPB6Pom3RQn1HNBpTtC0qn49vXwAAAKoN7+AAAAAALEggENDyZcu1fNnywlg6nVZ3d/eUCo9Ed0LZbPaR18lms+qKd6kr3qVz+TFjjFpbWic6pWPtirZFFQgEHD4rAAAAFBMBNAAAAIBH5vP5tGTJEi1ZsqQwlslk1NPbo0QiPhFKJxJKpVOPvI61Vt093eru6dY7F84XxpubWwo3OczVeMQUCoUcPScAAAAsHuPkx+WAStPR0WFPnz5d7m0AAABgmmw2q77+vlx9R/5Gh13xLo2Pjy/6Wo0NjVM6pdtj7YpEIou+DgAAtcIYc8Za21HufaA6cQIaAAAAQNF5PB61trSqtaVVmzZukpQ71TwwOFAIpbsSud+TyaSjtQYGBzQwOKDLVy4Xxurq6vI3O2xXLJoLpevr62WMcbQWAAAA5kYADQAAAKAsjDFqamxSU2OT1j++XlIulB4eHp7SKR2PxzU0PORoreHhYQ0PD+va9WuFsVAoVAil7/dKNzU2EUoDAAAsIgJoAAAAABXDGKP6+nrV19dr3drHCuPJ5Iji8XjhlHQ8Hlf/QL+jtUZHR3Xz1k3dvHWzMBYIBPKhdK7Coz0WU3Nzizwej6O1AAAAahUBNAAAAICKFw5HtHr1Gq1evaYwNjY2pvikQLor0aXe3l5H64yPj+v2e7d1+73bhTGfz6doW3RKKN3a2iav1+toLQAAgFpAAA0AAACgKgWDQa1csVIrV6wsjKVSKSW6E1NC6Z6eHmWz2UdeJ51O6+69u7p7725hzOPxqK2trRBIx6IxtbVF5ff7HT0nAAAAtyGABgAAAOAafr9fy5Yu07KlywpjmUxa3d096kp0FW54mOhOKJPJPPI62Ww2/7Xievt8bswYo5aWlolQOtauWDSqQCDo9GkBAABULQJoAAAAAK7m9frU3t6u9vb2wlg2m1Vvb6+64l0TNR6JuFKp1COvY61VT0+Penp6dOHddwrjzU3NufqOWHuhXzocCjt6TgAAANWCABoAAABAzblfodHW1qYn9aSkXIDc199XOCV9/4aHY2Njjtbq6+9TX3+fLl66WBhrqG+YEkq3x9pVV1fnaB0AAIBKRAANAAAAAMpXaDS3qKW5RRs3bJSUC6UHBwcLp6S78uH0SHLE0VqDQ4MaHBrUlatXCmORSGRafUdMDQ0NMsY4WgsAAKCcCKABAAAAYBbGGDU2NqqxsVGPP/Z4YXx4eHhqfUc8rsGhQUdrjYyM6PqNa7p+41phLBgMKhZrV3s0H0rHYmpuaiaUBgAAVYMAGgAAAAAeUl1dndbVrdO6tesKY8nRZK6+Y1Io3dff52idsbEx3bp1U7du3SyM+f3+fJf0RH1HS0uLPB6Po7UAAACKgQAaAAAAABZBOBTW6lWrtXrV6sLY+PiY4olEob4jkYirp7dH1tpHXieVSum9O+/pvTvvFca8Xq+ibdHCKen2aLva2lrl9fItHwAAKC/ejQA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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-2/train_loss.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation Accuracy Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k+FBnj7L2WH26tuD97gxKgyf5NXNwkuolEUIDAAAAZcEPO6K6Pz2kL9YVfcVizEqtj3GW8KyAsoMAGkCZMWPGjEMal5WVVeTt+9ebBopTzFq9+WNE9ywMamcw/rgWNYye7+FTzwaHt9h0GKPXenm1KTemeVsK7p5Yu8fq/CkBTTk7SUmuQwu0AQAAABx9v2TH9GBGSO+viShRfYFAVPrw57CGt+HKalRObJ8CAOAwrNwZ01lf+vWPWfHDZ49DGpXqUfr5yYcdPv8hyWX0Sd8knVi9cMi88PeYrpweUIwyWgAAAECJ25wb081zgmr3ca7eO0j47DLSw53curG1u8TmB5Q17IAGAOAQBCJWj2WG9O+lYYXj9xhUzwYOPdfdp5ZHoU5zHZ/R+P5J6jk+VzsOCLs/z4rq7gUhPXZa0U0LAQAAABxdO4NW45aG9PzysPwH6Q/euIr0+Gk+1fJKl30bUFo9Z7muAQ0cCZ75AAAcxPSNeU0G12TH39tQyyv961SvLm9x8CaDh6N5DYc+6Zuk/l/5FTog+H76+7CaVjP6R2su5QMAAACKy96w1X+Wh/XEspB2hxKPreGWrm7l1oOdPHI58l4XvNvHp/StMfpLodIigAYAII5tAat/zg/qndWJmwxeepJLj53mVd2k4qnJ3O1Yp17r5dXfvi1c8+O2eSE1qerQ2cfzJx0AAAA4moJRq1dXhvVYZlhb/InL39XxSXd18Oi6Vm75DujV0quhi/AZlRo1oIESYIzRZZddtu/zSCSiunXrauDAgQnPmzFjxkHHHGjTpk264IILDnuOb7/9ttq0aaO2bdsqJSVF48aNSzj+gQce2Dfmyiuv1CeffHLYj/lnNW3aVNu2bSuxx0PlY63V2z+F1e6jvQnD5xOrG00c4NPrZ/iKLXz+w0UnuvVQp8I7nWM275K+xVsPcg0gAAAAgEMSjVm981NYbT/K1W3zQgnD5+pu6f6OHq26uIpubuspFD4DYAc0UCKqVKmi5cuXy+/3KykpSVOnTlWjRo2O+uNEIhE1bNjwsMPgiRMn6umnn9aUKVPUsGFDBYNBvf3220d9fqUtEonI5eLXHhL7aVdMN80Oasbm+IGu2yHd3s6tf6Z4lFSCC8yR7d36ZU9Mr68qGIrnRqRzJwc0a0iSmlTlvWUAAADgz7DWanxWVPenB7VqV+Idzz6n9I/Wbt3R3qPaPkJnIBFepQL7yViSoQ0bfy1w24aNvypjScYR3/eAAQP01VdfSZLef/99DR06dN+xhQsXqkuXLkpJSVHXrl31448/Fjo/3pg333xTgwYNUu/evdWnTx9lZWWpTZs2kqSsrCz16NFDqampSk1N1dy5c4uc26OPPqpx48apYcO8a4K8Xq+uvfZaSdLPP/+s/v37q2PHjurRo4dWrVp1yF/z1q1bdf7556tTp07q1KmT5syZI0kaPHjwvoD7pZde0qWXXipJ6tWrl2655RZ16NBBbdq00cKFCxPev9/v11/+8he98sor2rt3r4YNG6bOnTsrJSVF48ePL/L7k5OToz59+ig1NVVt27bdN27v3r06++yz1b59e7Vp00YffvjhIX+dqBiCUauHF4fU8dPchOFz1/oOLTw3SWM6eUs0fJbyrqZ4tptXZzV2Fjr2m99q8KSAdocSL5QBAAAAFPbNxoi6j/fr4mmBhOGz00jXtHRpxcXJ+tepXsJn4BCwFRCVxnMvPHtE58+dN+egY2664ea4xy655BKNHTtWAwcO1LJlyzRs2DDNmjVLktSyZUvNmjVLLpdL06ZN0z333KNPP/20wPmJxixevFjLli1TrVq1lJWVte+cevXqaerUqfL5fFq9erWGDh2q9PT0QnNbvny5OnbsWOS8r7vuOr344otq3ry5FixYoBtuuEHffvtt3K9z9OjRSktL06BBg3TLLbdoxIgR6t69u9avX69+/fpp5cqVevnll9WtWzc1a9ZMTzzxhObPn7/v/NzcXGVmZmrmzJkaNmyYli9fXuTj5OTk6JJLLtHll1+uyy+/XPfcc4969+6t119/Xbt27VLnzp115plnFvr+RCIRffbZZ6pevbq2bdum0047TYMGDdKkSZPUsGHDfW8S7N69O+7XiIpn1uaobpwd0I8JFpo1PdIjp3p11ckuOY5ik8HD5XYYvdfHp95f+PX9joJdCVfsjOniqQFN6O+Tx8lCGAAAADiYBVuiGp0e0oxNiUvaGUkXn+jSqI4enVSD/ZzA4SCABkpIu3btlJWVpffff18DBgwocGz37t264oortHr1ahljFA6HC52faMxZZ52lWrVqFTonHA5r+PDhyszMlNPp1E8//XRYc87JydHcuXN14YUX7rstGCzcBG1/Y8eO3ffxtGnTtGLFin2fZ2dnKycnR/Xr19fYsWN1xhln6LPPPisw9z92hvfs2VPZ2dnatWuXatasWehxBg8erDvvvHPf7ukpU6ZowoQJ++pSBwIBrV+/XlLB74+1Vvfcc49mzpwph8OhjRs3asuWLWrbtq1uv/123XXXXRo4cKB69OhxON8qlFM7Alb3LAzqjR8TNxm8+ESX/n2aR/WTy8ZCs7rH6PN+PvUY79em3IKh+fRNUQ2fHdRLPb0ypRiUAwAAAGXZDzuiuj89pC/WHbyXyoAmTo1N86ht7cJXIgI4OAJooAQNGjRII0eO1IwZM7R9+/Z9t48aNWpfGJuVlaVevXoVOjfRmCpVqhT5eE899ZTq16+vpUuXKhaLyefzSZLuvffefTt9MzMz1bp1a2VkZKh3794Fzo/FYqpZs6YyMzP/1Ncbi8U0f/78fY+7v++//161a9fWpk2bCtx+YGBmjFG/fv20ZcsWpaWl6dVXX5UkdevWTZMmTdJf//pXGWNkrdWnn36qk08+ucD5CxYsKPD9effdd7V161ZlZGTI7XaradOmCgQCatGihRYvXqyvv/5a9913n/r06aPRo0f/qa8bZZ+1Vh/8HNEd80LaGoi/67lptbySF/2OK3t/LhtXdejz/nk7oXMOeM/qrZ8ialbNobtTCzctBAAAACqzX7JjejAjpPfXRHSw4nU9jnVobCevuh5L8AwcibKxlQuoJIYNG6b7779fbdu2LXD77t279zUlfPPNN4s891DGFHVOgwYN5HA49M477ygazXtn9+GHH1ZmZua+YPnuu+/WHXfcod9++02SFAqF9Oqrr6p69epq1qyZPv74Y0l5od3SpUsP+evt27evnnvuuX2f//F4Cxcu1MSJE7VkyRKNGzdOa9eu3Tfmj9rLs2fPVo0aNVSjRg1NnjxZmZmZ+8JnKW+n9THHHKMbb7xRktSvXz8999xzsjZvCbFkyZK435N69erJ7XZr+vTpWrdunSRp06ZNSk5O1mWXXaY77rhDixcvPuSvE+XLz9kxnT0xoCunB+OGzy4j3dHerSUXJJfJ8PkP7Ws79W4fn4qqtvFARkjvrS58NQUAAABQGW3OjenmOUG1+zhX7x0kfE6p49AX/X2aOjCJ8Bk4CgiggRLUuHFj3Xxz4TrRd955p+6++26lpKQoEim6FMChjDnQDTfcoLfeekvt27fXqlWr4u6UHjBggIYPH64zzzxTrVu3VmpqqrKzsyXl7Rh+7bXX1L59e7Vu3Xpf0754Ro8erQkTJkiSnn32WaWnp6tdu3Y65ZRT9OKLLyoYDOraa6/V66+/roYNG+qJJ57QsGHD9gXHPp9PKSkpuv766/Xaa68lfKxnnnlGfr9fd955p0aNGqVwOKx27dqpdevWGjVqVJHnXHrppUpPT1fbtm319ttvq2XLlpLydmR37txZHTp00JgxY3TfffclfGyUP6Go1eOZIaV+kqtvNsa/zO7Ueg4tOC9JD3X2KrmEmwz+Gf2Pc+nZbt4ij103M6iZCRoqAgAAABXdzqDVvQuDavVBrl5aEVY4Fn9sixp5/VbmDklS3+NclLQDjhLzR+gDVARpaWn2wCZ7K1euVKtWrQ7p/IwlGapfr54aNzpu320bNv6qLb//ro4pRTfpw9HTq1cvjRs3TmlpaaU9lSN2OM87FL+5v0V14+ygVuyMv9qs7pYe6uzVta1Kt8ngn3XvwqDGLS2847mmR5oxKFmtjuE9ZwAAAFQeOWGr55eH9cSykHaHEo89rorRfR09uqy5Sy5H+XstUBKMMRnW2vL/Yh2louxeVwyUgqJC5saNjisQSAMoP3bl73Z4dVXiqwbOb+bSuC4eNaxSfkPaBzt5lLXH6pNfCn6tu0LS4El+zRqcVGaaKAIAAADFJRi1enVlWP/KDOt3f+JNl3V80l0dPLqulVu+cnD1I1BeEUADKDNmzJhR2lNABWFtXhB7+7yQtiRYdDapavR0V6/OPr78/zl0GKPXTvdq496Y5m0puNN7XY7V+VMCmjIwqVyUFQEAAAAOVzRm9d6aiB7MCGldTuLgubpbGtHOo5vauFXNw/oYKG7l/xU3AAD7WZsd0y1zg5r8a/zax04j3dTGrdEdParirjgLTp/L6JO+Seo5Plc/ZxdcdC/aGtOV0wN6v49PTi4rBAAAQAVhrdX4rKjuTw9q1a7EwbPPKd3Q2q2R7T2q7WNNDJQUAmhUCtZamgegxFBbv3SEY1bPfB/WQxkh+RP03etYx6EXenjVoU7F7GZdx2c0vn+STh+fq+3BgsfGZ0V198KQHj+t6KaFAAAAQHnyzcaIRi8KKX1rgs6CytuActXJLt2T6lGjclx2DyivCKBR4fl8Pm3fvl21a9cmhEaxs9Zq+/bt8vl8pT2VSmXh71HdMCuo73fEX3hWdUtj0jz6xynuCr8DuHkNhz7pm6T+X/sVPCCMf+b7sJpWM7qhtad0JgcAAAAcoQVbohqdHtKMTQl2nkgyki4+0aVRHT06qQbBM1BaCKBR4TVu3FgbNmzQ1q1bS3sqqCR8Pp8aN25c2tOoFLJDVqMWhfTSirAS7Tsf3NSpJ7t41bhq5Vl0dj3WqddP9+rSb4OFjt0+L6QmVR0aWAFqXwMAAKDy+GFHVPenh/TFusTBsyQNaOLU2DSP2taumFc+AuUJrzxR4bndbjVr1qy0pwHgKLLW6vOsqG6bG9Sm3PjRc+MqRk919WpQ08r55+6CE93KyrG6d2GowO0xK/3t24C+GZik1LosyAEAAFC2/ZId04MZIb2/JpJw44kk9TjWobGdvOp6LOtcoKyonK/IAQDl1vqcmG6dE9RX6+PvenCYvOYiD3T0VPqu1re3c2ttdkyvrooUuD03Ip07OaCZg5N0fLXKszMcAAAA5cfm3JgeXRLWayvDihwkeU6p49DYNI/Oauyk/CZQxhBAAwDKhUjM6vkfwhqTHtLeSPxxHWrnNRnsyM5eSZIxRs9082p9jtWUDQVD+9/8VkMmBzT9nCTV9LJIBwAAQNmwI2D1xLKQnl8eTthgXJJa1DAak+bVkGZOOQiegTKJABoAUOYt3hrVDbODWrItfpPBZJd0f0ePhrdxy1XBmwweLpfD6L0+PvX+wq9lBzRqXLEzpounBfRFf588Tr5vAAAAKD05Yav/LA/ryWUh7Q4lHntcFaP7Onp0WXMX63+gjCOABgCUWXtCVmMyQnr+h7BiCS65G9DEqae7eiklkUA1j9Hn/X3qMd6vjXsLfjNnbIrqxtlBvdzTy+WKAAAAKHHBqNWrK8P6V2ZYv/sT19qo45Pu6uDRda3c8rlYuwLlAQE0AKBMmpAV0Yi5QW3YG38B2iA5r8ngkKbUeTsUjao49Hk/n874wq+ccMFjb/8UUbNqDt2T6imdyQEAAKDSicas3lsT0YMZIa3LSRw8V3dLI9p5dFMbd6Xv8wKUNwTQAIAyZePemEbMDWp8Vvxib0bSdae49WAnj2qw+Dws7Wo79V4fn86dHFD0gDX+mIyQmlYz+mtzd+lMDgAAAJWCtVbjs6K6Pz2oVbsSB88+Z16D8ZHtPartY+0PlEcE0ACAMiEas3pxRVj3p4e0Jxx/XNtaDj3f3atT69Nk8M/qd5xLz3bz6sbZwULHrpsZVKMqRqc3ZIkAAACAo8taq282RjV6UUgZCfq7SJLLSFee7NI9qR41qkKpPaA84ycYxc4Y098Y86MxZo0x5p9FHD/eGPONMWaZMWaGMabxfseuMMaszv/vipKdOYCSsnR7VD0n+HXbvPjhc5JTerizR/POTSJ8PgquaeXWHe0L73QOx6SLpga0cmfiFwQAAADA4ViwJar+XwV09sRAwvDZSLrkRJeWXpis53v4CJ+BCsBYm/hSB+BIGGOckn6SdJakDZIWSRpqrV2x35iPJX1prX3LGNNb0lXW2r8ZY2pJSpeUJslKypDU0Vq7M97jpaWl2fT09OL7ggAcVXvDVg8uDunZ78OFykHsr29jp57t5lWz6iw+j6aYtbr826A+/iVS6NjxVY1mDU5S/WS+5wAAAPjzftgR1f3pIX2xLn6JvT8MaOLU2DSP2tZmw0lZY4zJsNamlfY8UD5xfS2KW2dJa6y1v0iSMeYDSYMlrdhvzCmSbsv/eLqkz/M/7idpqrV2R/65UyX1l/R+8U8bQHGbuD6iW+YEEzYbqZ9kNK6LRxee4KLJYDFwGKNXT/dq496Y5m4puAtlXY7VeVMCmjowScl0FwcAAMBh+iU7pgczQnp/TUQH2/rYs4FDYzt51YUrHYEKiW1NKG6NJP263+cb8m/b31JJ5+V/fK6kasaY2od4LoByZnNuTH+dFtCQyYGE4fM1LfMuu7voRDfhczHyuYw+7pukk6oX/h6nb43pim8Disa4WgoAAACHZnNuTDfPCartR7l67yDhc0odh77o79OUs5MIn4EKjAAaZcFISacbY5ZIOl3SRkkHvzYnnzHmOmNMujEmfevWrcU1RwBHKGatXloRVruPcvXp2sIlH/7QqqZD089J0vM9fDrGS/BcEur4jMb3T1Jtb+FjE9ZFddeCUMlPCgAAAOXKjoDVvQuDavVBrl5aEVYkQfLcoobR+318mjskSX2P42pHoKKjBAeK20ZJx+33eeP82/ax1m5S/g5oY0xVSedba3cZYzZK6nXAuTMOfABr7cuSXpbyakAfxbkDOEqW74jqhllBLfg9frMRr1O6N8WjEe3c8jhZgJa0k2o49Gm/JPX7yq/gAW8BPrc8rKbVjIa38ZTO5AAAAFBm5YSt/rM8rCeXhbT7IPsWjqtidF9Hjy5r7pLLwZofqCwIoFHcFklqboxpprzg+RJJf91/gDGmjqQd1tqYpLslvZ5/aLKkR4wxx+R/3jf/OIByIjdi9cjikJ5alngHxBkNnfpPd69OqsGFOaWpS32n3ujl01+/CRQ6NnJeSE2qOjSoKUsHAAAASMGo1asrw/pXZli/+xPvBavrM7orxa1rW7rlo78IUOnwKhLFylobMcYMV16Y7JT0urX2B2PMWEnp1toJytvl/KgxxkqaKenG/HN3GGMeVF6ILUlj/2hICKDsm7ohoptmB7V2T/zFaB2f9O/TvBp6EpfdlRXnn+DSI3s8umdhwe0rVtLl3wY07ZwkpdWlPh8AAEBlFY1ZvbsmoocyQgl7ukhSdbc0op1HN7Vxq5qH9T5QWRlrqViAiiMtLc2mp6eX9jSASm1Lbkx3zg/pg5/j13mWpCtbuPTIqV7V9rEQLWustbppTlCvrCz8b1g/yWjm4CQ1rcZudQAAgMrEWqvxWVHdnx7Uql2JsySfU7qhtVsj23tY71cQxpgMa21aac8D5RM7oAEAR0XMWr3xY0T3LAhqV4Laby1qGD3fw6eeDdhFW1YZY/R0V6/W51hN/rVgQegtfqvBk/z6blCyatIkEgAAoMKz1uqbjVGNXhRSxrb4PV0kyWWkK0926Z5UjxpVYcMCgDwE0ACAI7ZyZ0w3zgpozpb4C1KPQ7qrg0d3dHDLS5PBMs/lMHq3t099vvRr6faC/66rdlldPDWgL/7io2EkAABABbZgS17wPGNzNOE4I+niE10a1dFDXxcAhRBAAwD+tEDE6l+ZIY1bGlY4wWaI0xs49Vx3r06uyWK0PKnmMfqsn089x/u1YW/ByyxnbI7qH7OCevV0L/W7AQAAKpgfdkR1f3pIX6xLHDxL0oAmTo1N86htba5wBFA0AmgAwJ/y7ca8JoNrsuPXf6vllR47zau/NafJYHnVqIpDn/fz6Ywv/NoTLnjs/1ZH1KyaQ/d19JTO5AAAAHBU/ZId04MZIb2/JqKDdQzr2cChsZ286lKf4BlAYgTQAIDDstVv9c8FQf3f6sRNBi9r7tK/TvWqbhLBc3nXtrZT75/p0+BJAUUPeCXy4OKQmlYzuqyFu3QmBwAAgCO2OTemRxaH9PqqiCIHSZ5T6jg0Ns2jsxo72WQC4JAQQAMADom1Vu+sjuif84PaHow/7sTqRv/p7lXvRvyJqUjOauzSf7p79Y9Zhf/xr58VVOOqRr0a8m8OAABQnuwIWD2xLKTnl4flP0i1jRY1jMakeXVuM4JnAIeHV4oAgIP6cVdMN80O6rsEzUfcDumO9m7d1cEjn4sFaUU0rKVbWXtieiyzYC2OcEy6aGpA3w1KVqtjqPMNAABQ1uWErf6zPKwnl4W0O5R47HFVjO7r6NFlzV1yOVjnAzh8BNAAgLiCUatxS8P615KQQgmaDHar79DzPXyEj5XAA2keZe2x+vDngiVYdoekwZP8mjk4Sccm8zwAAAAoi4JRq1dXhvWvzLB+9yeutVHXZ3RXilvXtnSzwQTAESGABgAUaebmqG6cFdBPu+MvTGt6pEdP9erKk11ycBlepeAwRq+c7tXGvTHN/q3guxLrcqzOmxzQ1IFJquLm+QAAAFBWRGNW766J6KGMkNblJA6eq7ulEe08uqmNW9U8rOkAHDkCaABAATsCVncvCOrNnxI3GbzkRJceP82j+ux2rXS8TqOPzkrS6RNytfqANygytsV0xfSAPjzTJyeXaAIAAJQqa63GZ0V1f3pQq3YlDp59TumG1m6NbO9RbR/rOABHDwE0AEBS3uL0/TUR3Tk/pK2B+IvTptWMnuvmVd/j+BNSmdX2GY3vn6Se43O1LVDw2BfrorpzQUhPdPGWzuQAAAAqOWutvtkY1ehFIWVsS1BLT5LLSFee7NI9qR41qsLmEgBHH+kBAEBrdsd005ygvt0Yv8mgy0gj2rl1T6pHydSAg6QTqzv0ad8k9f3Kr+ABT53/LA+raTWjm9p4SmdyAAAAldSCLXnB84wEDcQlyUi6+ESXRnX06KQaBM8Aig8BNABUYqGo1ZPLwnpkSahQgLi/0+o59HwPr9rUcpbc5FAunFbfqTd7+TT0m0ChY3fMC+n4qg4NaspyAwAAoLj9sCOq0ekhfbkucfAsSWc3cWpMmkdta7O+B1D8eEUIAJXU3N+iunF2UCt2xr8kr7pbeqizV9e2oskg4jvvBJcezfHo7gWhArdbSZd/G9C0c5KUVpcXNwAAAMXhl+yYHswI6f01ESWu8iz1bODQ2E5edanP2gxAySGABoBKZmfQ6t6FQb22KnGTwQtOcGlcF48a0GQQh2BEW7fWZsf08sqCzyt/VDp3ckAzByWpWXWeSwAAAEfL5tyYHlkc0uurIoocJHlOqePQ2DSPzmrslGFjCYASRgANAJWEtVYf/xLRyHkhbfHHX6EeX9XomW5e/aUJfyJw6IwxeqqrV+tzrCb9WvCyz9/9VkMm+zVjULKO8fKCBwAA4EjsCFg9sSyk55eH5T9ItY0WNYzGpHl1bjOCZwClh3QBACqBtdkx3TwnqCkb4q9QnUa6ua1bo1I9quJmcYrD53IYvdvHpz5f+JW5vWBpl1W7rC6eGtCXf/HJ4+T5BQAAcLhywlb/WR7Wk8tC2h1KPPa4Kkb3dfTosuYuuRysvQCULgJoAKjAwjGrZ74P66GMUMLdEWl1HXqhh1ftaUKCI1TVbfRZP596jPdrw96CO+2/2xzV9TODeq2Xlx04AAAAhygYtXp1ZVj/ygzr9wRXMkpSXZ/RXSluXdfKLS9v+gMoIwigAaCCWvh7VDfMCur7HfGbDFZzS2PSPLr+FLec7IzAUdKwikPj+/vUa4Jfe8IFj727JqJm1Y1GdfSWzuQAAADKiWjM6t01ET2UEdK6nMTBc3W3dFs7j4a3cauah3U9gLKFABoAKpjdIavRi0J6aUU4YRfswU2derKLV42r0hgOR1+bWk59cKZPgycFCjXFeWhxWE2rOfS3Fu7SmRwAAEAZZq3V51lRPZAe1KpdiYNnn1O6obVbI9t7VNtH8AygbCKABoAKwlqrz9ZGddu8oDbnxl+oNq6S1yxuUFP+BKB4ndnYpf909+r6WcFCx66fGVSjKka9G/E8BAAAkPLW899sjGr0opAytsW/ilGSXEa68mSX7kn1qFEVNpQAKNt41QcAFcD6nJhumRPU1+vjF3p2mLzdEQ909HBZHkrMVS3dytoT078yC9biiFjpkmkBzTgnSafUovY4AACo3BZsiWrUopC+25ygcYskI+niE10a1dGjk2oQPAMoHwigAaAci8TyOmGPzQhpbyT+uJQ6Dj3f3auOdQn6UPIeSPMoa4/VBz8XfJLuDkmDJwc0a3CSjk3mBRQAAKh8ftgR1ej0kL5clzh4lqSzmzg1Js2jtjQOB1DOEEADQDmVsTWvyWDm9viX51VxSfeneXRja7dcNBlEKTHG6OXTvdqwN6bZvxV8vq7PsTp3ckDTBiapipvnKAAAqBx+yY7pwYyQ3l8TSdi3RZJ6NnBobCevutQneAZQPhFAA0A5sydk9UBGSC/8EFYswWr17CZOPd3NqyY0GUQZ4HUafXxWkk6fkKufdhd84i7eFtPfvg3o47N8cvJGCQAAqMA258b0yOKQXl8VKdSo+UApdRwam+bRWY2dMoY1EoDyiwAaAMqRCVkRjZgb1Ia98VerDZLzmgwOacpCFWVLLZ/R+P5J6jner62Bgs/hr9ZHdfu8kJ7q6uF5CwAAKpwdAasnloX0/PKw/AepttGihtGYNK/ObcZ6HkDFQAANAOXAhpyYRswNakKC2nBG0vWnuDWmk0c1aDKIMuqE6g592tenvl/5FTjg6fzfFWGdUN3o5rae0pkcAADAUZYTzuvZ8uSykHaHEo9tUtXovlSPLm3uonwegAqFABoAyrBozOq/K8K6Pz2knHD8cW1rOfRCD68616MuHMq+U+s79eYZPg2dFihU8/DO+SEdX9Whwc1YogAAgPIrGLV6ZWVYj2WG9bs/ca2Nuj6ju1Lcuq6VW14nwTOAiodXdwBQRmVuy2symLEtfpPBJKc0qqNHN7d1y80uCZQj5zZz6V+nenTXgoJbgaykK6YHNKVKEm+oAACAcicas3p3TUQPZYS0Lidx8FzdLd3WzqPhbdyqxhWMACowAmgAKGP2hq3GZoT03PKwognWrH0bO/VsN6+aVafJIMqnW9q6tXaP1YsrCm7v90el8yYHNGtwEs9vAABQLlhr9XlWVA+kB7VqV+Lg2eeUbmjt1sj2HtX2ETwDqPgIoAGgDPl6fUS3zAlqfYLdEvWTjJ7o4tEFJ7hoSoJyzZi85/L6nJi+Xl+wIPTWgNXgyX59NyhZx3h5ngMAgLLJWqtvNkY1elEo4ZWLkuQy0lUtXbo7xaNGVXiTHUDlQQANAGXApr0xjZwX0qdrIwnHXdPSpYc7e1WTQA4VhMth9E5vn8780q8lB7xo+3GX1cVTA/riLz7qIQIAgDJnwZaoRi0K6bvN8RuFS3nNwi8+0aVRHT06qQbBM4DKhwAaAEpRzFq9vDKiUQuDyk7QZPCUY/KaDHapT01cVDxV3Uaf9fOpx+d+/bq34O7/7zZHdf3MoF7v5WXHPwAAKBOW74jq/vSQvlyXOHiWpLObODUmzaO2tVnHA6i8CKABoJQs35HXZHDB7/Ev1fM5pXtSPBrRzi0PO0BRgTVIdmh8f596TfAXejPmvTURNatuNLqjt3QmBwAAIOmX7JjGZoT0wZqIEld5lno2cGhsJzaQAIBEAA0AJS43YvXw4pCeXhZWJMHKtU+jvCaDXKaHyqJ1Lac+OMunQRMDhX42Hl4cVtNqDl3ewl06kwMAAJXWpr0xPbokpNdXRRKu3yUppY5DY9M8Oquxk6u3ACAfATQAlKApv0Z005ygsvbEX7nW9Rn9u4tHl5xIk0FUPn0aufR8D6/+PjNY6Ng/ZgbVuIpR70YsXwAAQPHbEbAatzSkF34Iy3+QahstahiNSfPq3GYEzwBwIF7BAUAJ2JIb0x3zQ/rw58RNBq862aVHOntVy8eiFZXXlSe7lbUnpkeXFKzFEbHSxVMDmjEoSa1rcTkrAAAoHjlhq/8sD+vJZSHtDiUe26Sq0X2pHl3a3CWXgzU8ABSFABoAilHMWr3xY0T3LAhqV4LF68k1jZ7v7lOPBoRqgCTd39GjtdlWHxzwpk12WBoyOaCZg5PUIJnyNAAA4OgJRq1eWRnWY5lh/e5PXGujrs/orhS3rmvllpdeLQCQEAE0ABSTlTtjunFWQHO2xG8y6HFId6d4dHt7Fq7A/owxevl0rzbujWnWbwV/htbnWJ07OaBpA5NU1c3PDQAAODLRmNW7ayJ6KCOkdTmJg+fqbum2dh7d1NbNOgQADhEBNAAcZf6I1WOZIY1bGlY4fvasXg2ceq67Vy1qsosTKIrXafTRWUk6fUKuftpd8MXgkm0x/e3bgD45yycnl7sCAIA/wVqrz7OieiA9qFW7EgfPPqd0Q2u3Rrb3qDbl8gDgsBBAA8BR9O3GiIbPDurn7PgL2Npe6bHTvLqsOU0GgYOp5TMa3z9JPcf7tTVQ8Ofq6/VR3TYvpKe7evhZAgAAh8xaq282RjV6UUgZ2xLsGJHkMtJVLV26O8WjRlXYOAIAfwYBNAAcBVv9VnfND+rdNYmbDP6tuUv/Os2rOuyaAA7ZCdUd+l8/n8760q/AAR3oX1wR1gnVjW5p6ymdyQEAgHJlwZaoRi0K6bvN0YTjjKSLT3RpVEePTqpB8AwAR4IAGgCOgLVWb/8U0T8XBLUjGH/cSdWN/tPdqzMa8WsX+DM613PqrTN8umRaQAdeX3DX/JCOr+rQkGb8fAEAgKIt3xHV/ekhfbkucfAsSWc3cWpMmkdta9MgHACOBl6pAcCf9OOumIbPDmjm5viX7bkd0h3t3bqrg0c+F7uegSMxpJlLj53m0Z3zQwVut5KumB7Q1CpJ6lyPF4oAAOD/+yU7prEZIX2wJlLoTewD9Wzg0NhOXnWpz3oCAI4mAmgAOEzBqNXjmSE9nhlWKEHJuO7HOvSf7j61OoZL9oCj5eY2bq3NtvrvinCB2wNR6bzJAc0cnKQTqvMzBwBAZbdpb0yPLgnp9VURRQ6SPKfWcWhsJ4/ObOSkrwQAFAMCaAA4DDM3R3XjrIB+2h1/FVvTIz16qldXnuySgwUscFQZY/REF4/W58T01fqCl9BuDVgNnuTXd4OSVYs66wAAVEo7Albjlob0wg9h+Q9SbaNFDaMxaV6d24zgGQCKEwE0AByC7QGruxcE9dZPiZsMDj3JpcdO9ah+MjswgeLidBi909unM7/0a/EBnet/2m110VS/vhqQJK+TF5IAAFQWOWGr/ywP68llIe0OJR7bpKrRfakeXdrcJZeD9QIAFDcCaABIwFqr99ZEdOf8oLYF4o9rVs3oue5endWYX6tASajiNvpfP596jvdrfU7BKxJm/RbTdd8F9eYZXnYzAQBQwQWjVq+sDOuxzLB+9yeutVHXZ3RXilvXtXLzRjUAlCCSEgCIY/XumG6aHdT0TfGv3XMZ6bb2bt2T4lESTQaBEtUg2aHx/Xw6fYJf2QVLQuuDnyNqVt3ogTRv6UwOAAAUq0gsb6PIQxkhrctJHDxXd0u3tfPoprZuVXWzZgeAkkYADQAHCEWtnlwW1iNLQgomqBt3Wj2Hnu/hVZtadMkGSssptZz68CyfzpkYKNRg6NElYTWt5tCVJ7tLZ3IAAOCos9bq86yo7k8P6sddiYNnn1O6obVbI9t7VJv+EABQagigAWA/c36L6sZZQa3cFYs7poZHeqiTV9e0oskgUBb0buTSf3t6de13wULHbpwVVOMqRmdSHgcAgHLNWqtvNkY1elFIGdvir9WlvKsUr2rp0j0pHjWsQm8WAChtvBoDAEk7g1b3LgzqtVWJmwxecIJL47p41IAmg0CZcnkLt7L2xPTw4oK1OCJWGjotoOmDkrhaAQCAcmrBlqhGLQrpu80JLk+UZCRdcpJL96V6dFIN1usAUFYQQAOo1Ky1+ujniEbODyVsWnJ8VaNnunn1lyb82gTKqlGpHmVlW727puAbSdlhacikgGYOTmIXFAAA5cjyHVHdnx7Sl+sSB8+SdHYTp8akedS2Nm84A0BZQ5ICoNJamx3TTXOCmroh/oLWaaRb2rp1X6pHVWhYApRpxhi92NOrDXttoR1Sv+61OndyQN+ck0TzIQAAyrifs2N6MCOkD9ZElLjKs9SzgUNjO3nVpT7BMwCUVQTQACqdcMzqmWVhPbQ4JH+CzRSd6uY1GWzPLgqg3PA4jT48y6fTJ+QWakyUuT2my74J6JO+PrkchNAAAJQ1m/bG9MiSkN5YFSnUXPhAqXUcGtvJozMbOWXoywIAZRoBNIBKZcGWqG6YHdTyHfEbl1RzS2M7efT3Vm45CamAcucYr9H4fknqOcFfqLTOxF+jum1eSM909fBiFQCAMmJHwGrc0pBe+CGccIOIJLWoYTQmzatzmxE8A0B5QQANoFLYHbK6b2FQr6xMfBnfkKZOPdnVq0bUiQXKtWbVHfpfX5/O+tJf6IXsSyvCOqGa0a3tPKUzOQAAIEnKCVs9931YTy4LKTuceGyTqkb3pXp0aXMXVzIBQDlDAA2gQrPW6rO1Ud02L6jNufGj58ZVjJ7u5tU5x/NrEagoOtVz6q0zfLp4WqDQG0//XBDS8dUcOrcZP/MAAJS0YNTqlZVhPZYZTtgIXJLq+ozuSnHrulZueZ0EzwBQHvGqC0CFtW5PTLfMCWrir/Gv43MYaXhrt0Z39KiahwUtUNEMbubS46d5dMf8UIHbraQrpwfUMDlJp9K0CACAEhGJWb23JqKHMkJal5M4eK7ulm5r59FNbd00EAaAco4AGkCFE4lZPbc8rLEZIeVG4o9LqePQC929Sq1L+ARUZDe1cWvtHqsXfih4bW8gKp0/JaDvBifpxOqU3QEAoLhYa/V5VlT3pwcLNQk+kM8p3djardvbe1TbR/AMABUBATSACiV9a1Q3zApq6fb4TQaruKQH0jy6obWb+nFAJWCM0bjTPFq3J6av1he8ImJrwGrwJL9mDkpWLV7kAgBwVFlrNW1jVKMXhbR4W/z1uSS5jHRVS5fuSfGoIf1YAKBCIYAGUCHsCVndnx7Sf1eEFUuwqeLsJk493c2rJlVZ1AKVidNh9E5vn8780l/oBfDq3VYXTvXr6wFJ1JYEAOAoWbAlqlGLQvpuc/xyeJJkJF1ykkv3pXp0Ug3W6ABQERFAAyj3xmdFNGJuUBv3xk+eGyYbPdXVq8FNnTKGgAmojKq4jT7r51PP8f5CdSdn/xbTtd8F9eYZXjn4HQEAwJ+2fEdU96eH9OW6xMGzlLc5ZEyaR21rUxIPACoyAmgA5davOTHdNjeoCQkWt0bS9ae4NbaTR9VpMghUescmO/R5P596feHX7oJ9CfXhzxE1q2Y0ppO3dCYHAEA59nN2TA9mhPTBmogSV3mWejZwaGwnr7rQCBgAKgUCaADlTjRm9cKKsB5IDyknHH9cu1oOvdDDq071WNgC+P9OqeXUh2f6NHBiQJEDXiH/KzOsptUcuqqlu3QmBwBAObNpb0yPLAnpjVWRQn9XD5Rax6GxnTw6sxFXJQJAZUIADaBcydyW12QwI0ETkySnNKqjRze3dctNk0EARTijkUsv9vTqmu+ChY7dODuoxlWNzmrMMgkAgHh2BKzGLQ3phR/C8h+k2sbJNY3GpHk1hHJ4AFAp8coKQLmQE7YamxHSc8sTNxnsd5xTz3bzqmk1GpgASOxvLdzK2hPTQ4sLXkoRtdLQaQHNGJSkNrW4ggIAgP3lhK2e+z6sJ5eFlJ3gakRJalLV6L5Ujy5t7pKLjSEAUGkRQAMo875aF9Gtc4NanxM/eT42yeiJLh6df4KLXRUADtl9qR6tzbZ6d02kwO17wtLgSQHNGpykhlV4QwsAgGDU6pWVYT2WGdbv/sS1Nur6jO5Kceu6Vm55nazNAaCyI4AGUGZt2hvT7fOC+t/axNf0XdvKpYc6eVXTy+IWwOExxujFnl5t3Gs1Y3PB3zUb9loNmRzQNwOTVI0mpgCASioSs3p3dUQPLQ4l3BAiSdXd0m3tPLqprVtV3fztBADkIYAGUOZEY1avrIpo1MJgwsv6Tjkmr8kg3bMBHAmP0+iDs3zqNSFXq3YVfGG9dHtMl30b0Kd9fVw6DACoVKy1+jwrqvvTg/pxV+Lg2eeUbmzt1u3tPart4+8lAKAgAmgAZcr326O6YXZQC3+P32TQ58y7bP6Wtm55uKQPwFFwjNdofP8k9RjvL3RZ8aRfo7p1blDPdfNS4gcAUOFZazVtY1SjF4W0OEHjb0lyGemqli7dk+KhZBUAIC4CaABlQm7E6uHFIT29LKxIgg0WfRo59Vx3r06szgIXwNHVtJpDn/Xz6cwv/PIfUPnnlZURnVDdodvaeUpncgAAlIAFW6IatSik7zYnLoFnJF1ykkujOnpYlwMADooAGkCpm/xrRDfNDmpdgppydX1G47p4dPGJNBkEUHzS6jr1dm+fLpoa0IG/ke5eENLxVR06/wSWTwCAimX5jqjuTw/py3WJg2dJOruJU2PSPGpbmzJ4AIBDwysoAKXmt9yY7pgf0kc/RxKOG3aySw939qoW9eQAlIBBTV0a18Wj2+eFCh27akZADaskUXseAFAh/Jwd04MZIX2wJlLojdcD9Wzg0NhO9F8BABw+AmgAJS5mrV5fFdG9C4PaVTjf2efkmkbPd/epRwMWuQBK1vA2Hq3dY/Wf5QU7oQaj0gVT/PpuULJOqsElxwCA8mnT3pgeWRLSG6siCcvfSVJqHYfGdvLozEZOrkQEAPwpBNAAStSKHVHdODuouVviNzTxOqV/dvDo9vZueWkyCKCUPH6qR+v2xPTFAZcjbwtIgyf5NXNwsmpzZQYAoBzZEbAatzSkF34IF+p3cKCTaxqNSfNqSFOCZwDAkSGABlAi/BGrR5eE9MTSxE0GezXIazLYoiY7CwGULqfD6K0zfOr7lV/pWwu+abYm2+rCKX59PSBJPhcvygEAZVtO2Oq578N6cllI2eHEY5tUNbov1aNLm7vkcvA3DgBw5Eh4UOyMMf2NMT8aY9YYY/5ZxPEmxpjpxpglxphlxpgB+bc3Ncb4jTGZ+f+9WPKzx9HwzcaIOn6aq8cy44fPtb3Sq6d7NelsH+EzgDKjitvof319Or5q4Rfgc7bEdO3MoGL2YFUzAQAoHcGo1X+Wh9Tqw1w9kJE4fK7rM3qii0fLL0rWFSe7CZ8BAEcNO6BRrIwxTknPSzpL0gZJi4wxE6y1K/Ybdp+kj6y1/zXGnCLpa0lN84/9bK3tUIJTxlG01W915/yg3luTuMng5S1cevRUr+pwKTuAMqh+skPj+yep14TcQnXrP/o5oqbVjB7s5C2dyQEAUIRIzOrd1RE9tDik9TmJ3yit7pZua+fRTW3dqupmPQ4AOPoIoFHcOktaY639RZKMMR9IGixp/wDaSqqe/3ENSZtKdIY46qy1euuniO5eENSOYPxxJ1U3er6HV70a8qsIQNnW6hiHPjzLp4ETAwofUML+8cywmlZz6OqW7tKZHAAA+ay1+jwrqvvTg/pxV+Lg2eeUbmzt1u3tPfQ0AAAUK1IfFLdGkn7d7/MNkk49YMwDkqYYY26SVEXSmfsda2aMWSIpW9J91tpZxThXHAWrdsU0fFZAs36L32TQ7ZDu7ODWne091E4FUG70aujSiz28uvq7wu+s3TQ7qCZVjc5qzNIKAFDyrLWatjGq0YtCWrwt/jpcklxGuqqlS/ekeNSwCqXvAADFj1dJKAuGSnrTWvuEMaaLpHeMMW0kbZbUxFq73RjTUdLnxpjW1trs/U82xlwn6TpJatKkSUnPHfkCEat/Lw3p8cywQgnWvN2Pdeg/3X1qdQyLXQDlz2Ut3MraY/Xg4oK1OKJWGjotoOnnJKltbWcpzQ4AUBnN35IXPH+3OZpwnJF0yUkujero0YnVWYsDAEoOATSK20ZJx+33eeP82/Z3taT+kmStnWeM8UmqY639XVIw//YMY8zPklpISt//ZGvty5JelqS0tDQ6QZWC7zZFdOPsoFbvjv/tP8YrPdrZqytOdslh2PUMoPy6N9WttXti+r/VBevb7wlLQyYHNHNwkhqxowwAUMyW78gLnr9anzh4lqSBxzv1QEcPb5ICAEoFATSK2yJJzY0xzZQXPF8i6a8HjFkvqY+kN40xrST5JG01xtSVtMNaGzXGnCCpuaRfSm7qOJjtAau7FwT11k+JmwwOPcmlx0/zqF4SgQyA8s8Yo//28GpDjtWMA3abbdhrde7kgL4ZmKRqHt5sAwAcfT9nx/RgRkgfrInoYLtvTm/g1NhOHp1Wn+AZAFB6CKBRrKy1EWPMcEmTJTklvW6t/cEYM1ZSurV2gqTbJb1ijBmhvIaEV1prrTGmp6SxxpiwpJik6621O0rpS8F+rM3rqn3XgqC2BeKPO6G60XPdvDqTmqgAKhiP0+jDs3zqNcGvlbsK1h1auj2mS78N6H99fXI5CKEBAEfHpr0xPbIkpDdWRRQ5SPKcWsehsZ08OrORU4arDwEApcxYS8UCVBxpaWk2PT394APxp63eHdNNs4Oavin+pX4uI93e3q27UzxKoskggAosa09MPcf7tcVfeD11bSuXnuvm5YU/AOCI7AhYjVsa0gs/hOU/SLWNk2sajUnzakhTgmcAR5cxJsNam1ba80D5xLZEAIckFLV6YmlYj2aGFEyw8O1S36Hnu3vVuhaX+QGo+JpWc+izfj6d+aVfuQdUI3plZUTNqjl0e3tP6UwOAFCu5YStnvs+rCeXhZQdTjy2SVWj+1I9urS5i6tvAABlDgE0gIOa81tUN8wKaNWu+FdM1PBID3f26uqWNBkEULl0rOvU2719unBKoFAtznsWhtS0mkPnn8CSCwBwaIJRq1dWhvVYZli/F3GFzf7q+oz+meLWta3c8jpZgwMAyiZeDQGIa2fQ6p4FQb3+Y+Imgxee4NK4Lh4dm0yTQQCV0znHu/REF49umxcqdOyqGQE1rJKkLjSAAgAkEInl9Vl5aHFI63MSB8/V3dJt7Ty6qa1bVd0EzwCAso0AGkAh1lp99HNEI+eHEu66OL6q0bPdvep/HL9KAODGNh6t3WP13PKC10kHo9L5k/2aOThZJ9XgjToAQEHWWn22NqoHMoL6McEVh5Lkc0o3tnZrZHuPavkIngEA5QOpEYACfsmO6eY5QU3dEL/Qs9NIt7Z1695Uj6qw4wIA9nnsVI/W7YlpwrqCv0O3B6VBk/JC6DoEBgAA5QXP0zZGNXpRSIu3xRKOdRnpqpYu3ZPiUcMqvJkJAChfCKABSJLCMaunl4X10OKQAgmaDHaq69ALPbxqV5tLyQHgQE6H0Vu9fTrrS7/StxYME37Otrpwil8TByTJ5yKEBoDKbP6WqEYtCmrm5sTBs5F0yUkujero0YnVCZ4BAOUTATQAzd8S1Q2zgvphZ/wFcDW39GAnj65r5ZaTztoAEFeyy+h/fX3qMd6vdQfU8Jy7JaZrvgvq7d5eGrYCQCW0fEfejuev1ifY8ZFv4PFOjUnzqE0tNn4AAMo3AmigEtsVtBq1KKhXVkaUqNrcuc2ceqKLV4243A8ADkn9ZIfG909Srwm52nVAX8KPf4moaTWjhzp7S2dyAIASNW5pSA2TjaZsiOqDNYnX3ZJ0egOnxnby6DSa1wIAKggCaKASstbqf2ujum1uUL8laDJ4XBWjp7t5NfB4flUAwOFqdYxDH53l09kTAwofcIHJv5eG1bSaQ9e0cpfO5AAAJSInbLVse1T3Ljz4jufUOg6N7eTRmY2cMlwlAwCoQEiVgEoma09Mt84JauKv8RfBDiMNb+3W/WkeVaXJIAD8aac3dOnlnl5dNSNY6NjNc4I6rqpRv+NYjgFARWOt1Se/RHTXgpA27k285/nkmkZj0rwa0pTgGQBQMfGKB6gkIjGrZ5eH9WBGSLmR+ONS6+Q1GUypwyV/AHA0/LW5W1l7rMZkFKzFEbXSX78JaPo5STR2BYAK5IcdUY2YG9J3mxPvem5S1ei+VI8ube6Six4rAIAKjAAaqATSt+Y1GVy6PX6TwSou6YE0j25o7WYBDABH2d0pbq3dE9PbPxV8BzAnLA2ZHNDMQUlqXJU6+wBQnu0OWT2YEdILP4QVTbDp2Ui6/hSXHjvNK6+TdTcAoOLjlQ5QgWWHrEbMDar75/6E4fPA451aemGybm7rIXwGgGJgjNHz3b06o2Hhnc4b91oNmRxQduhgbakAAGVRzFq9/VNYbT7K1XPL44fPbod0T4pbn/Xz6eNfIpq35eB1oQEAqAjYAQ1UUOPXRjRiXjBhzblGVYye6uLV4Gb8KgCA4uZxGn1wpk9nfOHXip0F3xT8fkdMf/0moM/6+eTmjUAAKDeWbIvq1jlBzf89/mYPSTqtvkNv9PLphOp5e8De7eNT+taYejUsiVkCAFC6jLXstkHFkZaWZtPT00t7GqXq15yYRswN6ot18XdUGEn/aO3WmDSPqnsIOgCgJK3bE1PP8X795i+8BhvW0qUXuntpQgUAZdz2gNXoRUG9tiqiRK+oT6xu9EQXr/7ShA0fAMo3Y0yGtTattOeB8om/gkAFEY1ZvbAirAfSQ8oJxx/XrlZek8FO9Wh4BQCl4fhqDn3Wz6c+X/oLNYV9fVVEJ1ZzaGQHT+lMDgCQUDRm9dqqiO5PD2pHMP64ZJd0d4pHt7R1U+cZAFDpEUADFcCSbXlNBhdvi3/pX7JLGt3Ro5va0GQQAEpbal2n3unt04VTA4odsHXu3kUhHV/N6MIT3aUzOQBAkeZvieqWOUFlJuitIkkXnODSv0716DiaywIAIIkAGijXcsJWYzNCem55uFCAsb/+xzn1TDevmlZjEQwAZcXA4116ootHI+aGCh27+rugGlZxqNuxXK0CAKXtt9yY7lsY0jurIwnHtarp0FNdPTqjES+zAQDYH38ZgXLqy3UR3TonqF8TNBk8Nsnoya5endfMST1RACiDbmjt0dpsq2eXF6ydFIxKF0zxa+bgZDWvwZuHAFAawjGr//4Q1oMZIWUnKHFX3S2N6ujRP1q7aSQLAEARCKCBcmbT3phumxfUZ2sTNxm8tpVLD3byqqaXRTAAlGX/OtWjdTkxjc8q+Ht9R1AaPCkvhK7j43c5AJSkGZsiGjE3pBU7E5fb+Ftzlx7q7NGxybxZCABAPATQQDkRjVm9vDKsUYtC2pNgB0brY/KaDJ5Wn8u2AaA8cDqM3jzDp75f+rVoa8Gg4+dsqwum+DVpQJJ8LkJoAChuG3Ji+ueCkD7+JXG5jQ61HXq6m1ddWHMDAHBQvE0LlAPLtkfV6wu/bp0bP3z2OaWHOnm04LwkwmcAKGeSXUaf9vWpabXCIfO8LTFd/V1QMZug2D8A4IgEo1aPZ4bU9uPchOHzMV7puW5ezR2SRPgMAMAhYgc0UIbtDVs9tDikZ74PK5ogdzizkVPPdffqhOq8pwQA5VX9ZIfG90/S6eNzteuAvoSf/BJR02pGD3f2ls7kAKACm/RrRLfPDWpNdvwFt5F0dUuXxnTyUhYJAIDDRAANlFGTf43optlBrcuJvxCul2T079M8uvhEF00GAaACaFnToY/7JmnA136FDyg7Om5pWE2rOXRtK3fpTA4AKphfsmO6Y35QX66L31tFkk6t59DTXb1KrcuOZwAA/gwCaKAMGbc0pBOqGf1vbfSgdeeGtXTp4U5e1WIHBgBUKD0bOPXK6V5dOT1Y6Ngtc4I6rqpR/+NYwgHAn5UbsRq3NKRxS8MKJsie6yUZPdzZo8uau+RgswcAAH8ar16AMiJmrbbkWt23MKREVT5b1jR6vrtP3RuwAwMAKqqhJ7mVlW31QEbBWhxRK136TUDfnpOk9rX5OwAAh8NaqwlZUd0xP/FVhk4j/aO1W6NSParpJXgGAOBIEUADZcS/loT17PI4HQYleZ3S3R08ur29Wx4nC2EAqOj+meLW2j0xvfVTwSticsLSkEkBzRqcpMZVqf0PAIfix10x3TY3qGkbE5fb6NnAoae6etWmFm/yAQBwtPCqBSgjrmnlUq04vaV6NXQq/bxk3Z3qIXwGgErCGKPne3jVu1HhEGRTrtWQyQFlhxJdMwMA2BOyumdBUB0/zU0YPjeqYvR/vb2acnYS4TMAAEcZATRQRtRLcujRUwsm0EbSHe3cmjTApxY1+XEFgMrG7TD64EyfTjmm8N+A73fE9NdvAgrHCKEB4EDWWn2wJqx2H+fqiWXhQo1d/+B2SCPbu7XswmRdeKKbxt4AABQDEi2gDDm+qpErf817eQuXPjzLpzd+Cuu7zYkvFQQAVFw1PEbj+/t0bFLhUGTqhqhunh2UtYTQAPCH77dHddaXfl0xPahNufF/P57V2KmM85P1cGevqroJngEAKC7UgAbKkIxtMb3Y06smVY1Ob5j341nD41P61ph6NSzlyQEASk2Tqg593t+nPl/4tbdgSWi9/mNEJ1R36I4OntKZHACUEbuCVmMzQnpxRVjRBO/LHV/VaFwXr8453smOZwAASoBhxwwqkrS0NJuenl7a0wAAoFh8tS6iC6YGVFTVjXd6e3XRie6SnxQAlLKYtXr7p4juWxjS1kD817c+p3RH+7ym3kkugmcAOBzGmAxrbVppzwPlEyU4AAAAyomzj3fpqa5Fd6y9ekZQc36jZBOAyiVja1Q9x/v195nBhOHzoOOdyrwgWfd19BA+AwBQwgigAQAAypHrT3Hr1raFdzqHYtIFU/z6aVecTlsAUIFsC1j9Y1ZA3T73a9HW+L/3TqpuNKG/Tx/3TVKz6rz8BQCgNPAXGAAAoJx59FSPzm3mLHT7jqA0eLJfW/2UWANQMUVjVi+uCKvNR3v1+qqI4v22q+KSHu7k0eILktXvOFofAQBQmgigAQAAyhmHMXqjl0+n1iu8lPsl2+qCKX75I4TQACqWub9F1eVzv26ZE9TOYPxxF53o0rILkzWyg0deJ+U2AAAobQTQAAAA5VCSy+iTvj41q1Y4XJn/e0zDZgQUo9k0gApgc25Mw6YHdMYXfi3dHr/cRutjHJpytk/v9PapcVVe6gIAUFbwVxkAAKCcqpfk0Pj+STqmiL6E/1sb1b0LQyU/KQA4SsIxq6eWhdT2o1y9uyYSd1x1tzSui0cLzkvS6Q0ptwGg7LCxmGyM/hwAATQAAEA5dnJNhz4+K0meIlZ1Ty4L66UV4ZKfFAAcoW83RpT2aa7+uSCkPQl+jV3RwqXlFyXrpjYeuR2U2wBQ+vY8/riC06crvHKltvXoodxXX1Vw+nTtefzx0p4aUGp4exgAAKCc69HAqVdO9+qK6YWLot46N6gmVY3+0oRlH4Cyb31OTHfND+p/a6MJx6XUceiZrl6dWr9wQ1YAKE3ulBTtGDhQNhSSIhHtzsyU8flU65NPSntqQKlhBzQAAEAFcMlJbo1J8xS6PWalS78JKHNb4jAHAEpTIGL1ryUhtfsoN2H4XMsrPd/dqzmDkwifAZQ5ocWLlX3nnbK5uVIkv3RQbq5cLVrI06tXqc4NKE0E0AAAABXEXR3curJF4Z3OeyPSkMkB/ZpDDUIAZc/X6yNK+TRX96eH5I+TPRtJ17VyaflFVXRNK7eclNsAUIZYv1/Zd9+tbZ07K5KZWeh4eP58+T/4oOQnBpQRXIsJAABQQRhj9J8eXq3fa/XtxoIpzuZcq8GTApo+KEk1PAQ3AErfz9kxjZwX1NfrE1+hcVo9h57u5lVKHXY8Ayh7grNna9fVVyv6009xxzgaNpSzceMSnBVQtrADGgAAoAJxO4w+ONOn1scUXub9sDOmodMCCsdsKcwMAPLkRqweSA+qw8e5CcPn+klGr53u1fRBSYTPAMqc2J492jV8uLb36BE/fHY45Lv4Yim/HjRQWRFAAwAAVDA1PEaf9/epQXLhnc7fbIzq5tlBWUsIDaBkWWv12dqI2n+cq0eXhBWKUxXIaaRb2rr1/UXJuqyFWw7DVRsAypbAxIna2rq1cp9/Pu4YV7t2qrNggWp98IGO+egjhRYtKsEZAmULJTgAAAAqoCZVHfqsn099vvBr7wEbbl7/MaJm1R26s0PhpoUAUBxW7ozptnnBQuWBDtSrgVNPdfXolFrseAZQ9sS2b9fuESPkf+ed+IM8HlUbNUpV77pLxu2WJHnPOEPeM84ooVkCZQ8BNAAAQAWVUsep/+vj0/lTAjqw6saoRSEdX9Xo4pPcpTM5AJVCdsjq4cUh/Wd5WJEEF140rmL02KkenX+CS4YdzwDKGGutAp98ot3Dhyv2++9xx7lPO001X3tN7lNOKcHZAWUfJTgAAAAqsAFNXHqqq7fIY9d8F9TszYl3IwLAn2Gt1Xurw2r7Ua6e/j5++OxxSHd1cGvZhcm64EQ34TOAMie6ebN2nneedl50Udzw2SQnq/rTT6vO7NmEz0AR2AENAABQwV1/iltZe2J6alm4wO2hmHThVL9mDErWyTXZlwDg6Fi6PaoRc4KasyVOked8/Y5z6okuXjWvwe8fAGWPtVb+N97Q7ttuk929O+44z5lnqubLL8vVrFkJzg4oX/hLDwAAUAk80tmjc5sVrqm6IygNnuTXVj9NCQEcmZ1Bq1vmBHXaZ/6E4XPTakaf9vVpfD8f4TOAMinyyy/a3revdl19ddzw2dSooZqvvabaU6YQPgMHwV97AACASsBhjN7o5dOp9Qov/9busTp/il/+RAVaASCOmLV6fVVYbT7aqxdXhAvVnP+Dzynd39GjpRcka+Dx1HoGUPbYaFQ5Tz+trW3bKjRtWtxxviFDVG/FCiUPG8bvMuAQEEADAABUEkkuo0/7JqlZtcIvlBb8HtNVMwKKWUJoAIdu0e9R9Rjv1z9mBbUtEH/ckKZOLb0wWfekeuRzEdYAKHvCK1ZoW/fuyh4xQjY3t8gxjnr1dMzHH+uY//1PzoYNS3iGQPlFAA0AAFCJ1E0ymtA/SbWK6Ev42dqo7lkYKvlJASh3tvqtrp8ZUPfxfqVvjV9uo0UNoy//4tOHZyWpaTVefgIoe2wopD0PPqitKSkKz58fd1zS5Zer3ooVSrrgAnY9A4eJJoQAAACVTIuaDn18VpL+8rVfoQNyo6eWhdW0mkPXn+IunckBKNMiMauXV4Y1Jj2kXQner6rqlu5J8eimNm55nAQ1AMqmUHq6dl19tSLLlsUd42zSRDVeekm+/v1LcGZAxcJb0AAAAJVQ9wZOvXp6EdugJY2YG9TX6yMlPCMAZd2szVGd+plfI+YmDp8vOdGlZRcm6/b2HsJnAGWS9fu1+847te3UUxOGz8k33qi6y5cTPgNHiB3QAAAAldTFJ7m1Lsdq1KKCSVLMSpd9E9A35yQppY6zlGYHoKzYtDemexaG9P6axG9Mta3l0FNdverRgN8bAMqu4Hffadc11yi6Zk3cMc4WLVTz1Vfl7dGjBGcGVFzsgAYAAKjE7mjv1rCTC+9J2BuRzp0c0Pqc+LVdAVRsoajVE0tDavtxbsLwuYZHeqqrR/PPTSJ8BlBmxbKztesf/9D2Xr3ih89Op6refbfqLV1K+AwcReyABgAAqMSMMXq2u1frc6ymbYwWOLY512rIpICmD0pSDQ+X0QOVybQNEY2YG9RPu23CcVed7NLYTh7VS2JvE4CyK/DVV9p1/fWKbdgQd4yrQwfVfO01eVJTS3BmQOXAKgEAAKCSczuM3j/Tpza1Ci8Nf9gZ0yXTAgrHEodQACqGdXtiuniqX2dPDCQMnzvWcWjW4CS92NNH+AygzIpu26adl12mHQMHxg+fvV5Ve+QR1V24kPAZKCasFAAAAKDqHqPP+/nUMLnwTudvN0Y1fFZQ1hJCAxVVIGL1yOKQ2n2cq8+zonHH1fZK/+3h1ewhSepcj3IbAMoma638H3ygra1ayf/uu3HHubt2Vd3MTFW7+24Zt7sEZwhULgTQAAAAkCQdV9Whz/v7VLWI119v/hTRY5nhkp8UgGJlrdWX6yLq8EmuxmSEFIiTPTuMdP0pbi2/qIqGtXTLYSjLA6Bsim7cqJ1Dhmjn0KGKbdtW5BhTpYpqPPec6syaJXfLliU8Q6DyoQY0AAAA9mlf26n/6+3TeVMCOrDqxv3pIR1fzWjoSewQAiqC1btjGjkvqEm/xt/xLEld6zv0dDev2tdmxzOAsstaq9xXX1X2yJGy2dlxx3n79VONl16S6/jjS3B2QOXGDmgAAAAU8JcmLj3T1Vvkseu+C2rm5sRhFYCybW/YatSioFI/yU0YPh+bZPRGL6++PSeJ8BlAmRb5+Wdt79NHu6+7Lm74bI45RjXffFO1Jk4kfAZKGAE0AAAACrnuFLdua1d4p3MoJl001a9Vu2KlMCsAR8Jaq09/iajdx7l6PDOsUJwfY5eRbm3r1vcXJeuvzd0ylNsAUEbZaFQ5Tz6prW3bKjR9etxxvgsuUL0VK5R8xRX8TgNKASU4AAAAUKSHO3uUtSem/60tuENyZ1AaPMmvWYOTVC+J/QxAebBiR1Qj5oY04yBXMJzR0KmnunrV6hh+tgGUbeHly7Xr6qsVXrgw7hhH/fqq8cILSjrvvBKcGYADsaoAAABAkRzG6PVePp1Wr/CSMWuP1flTAsqN2CLOBFBW7A5Z3TEvqLT/+ROGz8dVMfrgTJ8mDvARPgMo02wopD1jxmhramrC8DnpqqtUb+VKwmegDGAHNAAAAOJKchl90jdJPSfk6pfsgmHzwt9junJ6QO/38cnp4HJWoCyJWav3Vkd0z8KQtvjjv1HkcUi3t3frjvYeVXHzcwygbAstXKhdV1+tyPLlccc4jz9eNV5+Wb6+fUtwZgAS4a1tAAAAJFQ3yWh8vyTVKqIv4fisqO5eGCr5SQGIK3NbVL2/8Ovq74IJw+cBTZxackGyHkjzEj4DKNNiubnaffvt2talS/zw2RhVuflm1V2+nPAZKGPYAQ0AAICDalHToU/6Jqn/V/5Cjcue+T6sptWMbmjtKZ3JAZAk7QhY3Z8e1CsrI0pUHOeE6kbjTvPq7ON5OQig7AtOn65d11yj6C+/xB3jatlSNV97TZ6uXUtwZgAOFTugAQAAcEi6HevUa72K2AYt6fZ5IX21LlLCMwIgSdGY1asrw2rz0V69nCB8TnJKY9I8WnJ+MuEzgDIvtnu3dl13nbb37h0/fHa5VPXee1V3yRLCZ6AMY9UBAACAQ3bRiW6t22N136KCZTdiVrrs24C+GZik1LrOUpodUPks2BLVrXODWrwtlnDcec2ceuw0r5pUZQ8SgLIv8MUX2nX99Ypt2hR3jDs1VTVfe03uDh1KbmIA/hRWHwAAADgsI9u7Naxl4X0MuRHp3MkBrc9JHIQBOHJbcmO69ruAek7wJwyfT65p9PUAn94/M4nwGUCZF926VTuHDtWOQYPih89er6r961+qs2AB4TNQTrADGgAAAIfFGKNnu3n1a47V1A3RAsd+81sNnhTQjEFJquGhqRlwtEViVv9dEdbY9JCyw/HHVXNL96Z6dGNrtzxOfhYBlG3WWvnfe0/Zt9yi2Pbtccd5evRQzVdflatFixKcHYAjxVvgAAAAOGxuh9F7fXxqW6vwcnLFzpgunhpQKJqoDRqAw/Xdpog6/8+vkfMSh8+XnuTS9xcla0Q7D+EzgDIv+uuv2nHOOdp12WVxw2dTtapqvPCCas+YQfgMlEME0AAAAPhTqnuMPu/nU8PkwgHX9E1RDZ8dlLWE0MCR2pAT09++DajvVwH9sDN+uY12tRz69pwkvX6GTw2SeakHoGyzsZj2vviifm/dWsGvvoo7zvuXv6juDz+oyj/+IePgdxtQHlGCAwAAAH9a46oOfd7fp95f+JVzwI7Mt36KqFk1h+5O9ZTO5IByLhi1eu77sB5ZEtLeSPxxNT3SmE5eXdPSJZeDHc8Ayr7I6tXade21Cn33XdwxplYt1XjmGSVdeqmM4XcbUJ7x1hEAAACOSPvaTr3bx6eirvR/ICOk91YnqBUAoEhTfo2o46e5undR/PDZSBrW0qXlF1XR9ae4CZ8BlHk2ElHOv/+t39u1Sxg++y66SPVWrlTyZZcRPgMVAAE0ip0xpr8x5kdjzBpjzD+LON7EGDPdGLPEGLPMGDNgv2N355/3ozGmX8nOHAAAHKr+x7n0bDdvkceumxnUzM3RIo8BKGhtdkwXTvHrnEkBrd4dv4RNp7oOzR6SpP/28KluEuEMgLIvvGyZtnXpouw775QCgSLHOBo00DGffaZaH34oZ716JTxDAMWFABrFyhjjlPS8pL9IOkXSUGPMKQcMu0/SR9baFEmXSHoh/9xT8j9vLam/pBfy7w8AAJRB17Rya2R7d6HbwzHpwil+rUxQuxao7PwRq4cyQurwSa4mrIv/hk1dn9FLPb2aOThJaXVZGgMo+2wwqOzRo7W1Y0eF09Pjjku++mrVW7FCSUOGlNzkAJQIakCjuHWWtMZa+4skGWM+kDRY0or9xlhJ1fM/riFpU/7HgyV9YK0NSlprjFmTf3/zSmLiAADg8D3YyaOsPVaf/FKwZsCukDR4kl+zBiepPs3RgH2stZqwLqo75gW1Lif+jmeHka4/xa3RHT06xsuOZwDlQ2jePO26+mpFVq6MO8bZrJlqvvKKvH36lODMAJQkVv8obo0k/brf5xvyb9vfA5IuM8ZskPS1pJsO41wAAFCGOIzRa6d71aV+4WXmuhyr86cElBuJH7IBlclPu2IaNCmgi6YGEobP3Y91aOG5SXqqq5fwGUC5ENu7V7tvvVXbunWLHz4boyojRqju998TPgMVHAE0yoKhkt601jaWNEDSO8aYQ35uGmOuM8akG2PSt27dWmyTBAAAh8bnMvqkb5JOrF44KFu0NaYrpwcUjRFCo/LKCVvduzCo1E9zNWVD/HIbDZON3jrDq2kDk9S2NuU2AJQPwWnTtLVNG+195hnJFv333nXKKaozd65qPPmkHFWqlPAMAZQ0AmgUt42Sjtvv88b5t+3vakkfSZK1dp4kn6Q6h3iurLUvW2vTrLVpdevWPYpTBwAAf1Ydn9H4/kmqXURfwvFZUf1zQajkJwWUMmutPvo5rHYf52rc0rDCccqiu4x0ezu3ll2YrEtOcssYdj0DKPtiu3Zp19VXa/tZZymalVX0IJdLVUePVt3Fi+U57bQSnR+A0kMAjeK2SFJzY0wzY4xHeU0FJxwwZr2kPpJkjGmlvAB6a/64S4wxXmNMM0nNJS0ssZkDAIAj0ryGQ5/0TZK3iI2bzy4P6/nlhNCoPJbviKrvV3797dugNu6NfwVAn0ZOZVyQrEdO9aqah+AZQPng//xz/X7KKcp9/fW4Y9xpaaqbkaHqY8bIeIt4hxpAhUUAjWJlrY1IGi5psqSVkj6y1v5gjBlrjBmUP+x2SdcaY5ZKel/SlTbPD8rbGb1C0iRJN1pr41+jCAAAypyuxzr1+ulFv8gcOT+kL9ZFijwGVBS7gla3zwuq8//8mrk5zpZnScdXNfroTJ+++otPLWvyMg1A+RDdskU7LrpIO889V7HNm4se5POp+r//rTrz5sndrl3JThBAmWBsnHo8QHmUlpZm09PTS3saAADgAOMyQ7p3UeEdz8kuadrAJHWsS31bVCwxa/V/qyO6d2FIv/vjv+byOqWR7d0a2d6jZBc7ngGUD9Za+d95R7tvvVV258644zynn66ar74q10knleDsUByMMRnW2rTSngfKJ1dpTwAAAAAV3+3t3fplT0yvrSq44zk3Ip07OaBZg5N0fDV2faJiWLw1qlvnBrXg9/g7niVp4PFO/fs0r06oznMfQPkRWb9eu//+dwUnTYo7xlSrpurjxin5mmtkHPyOAyo7fgsAAACg2Blj9Gw3r/o2LrzTeYvfavCkgHYFuTIP5du2gNUNswLq+rk/Yfh8YnWjz/v59GnfJMJnAOWGjcW09/nntbV164Ths/fss1VvxQpVue46wmcAkgigAQAAUEJcDqP3+vjUrlbhJejKXTFdPC2gUJQQGuVPNGb18oqw2n60V6+tiijeszjZJT3YyaMlFyTrL024GBVA+RH58UdtP/107R4+XDYnp8gxjjp1VPO991Triy/kbNy4hGcIoCwjgAYAAECJqeYx+qyfT42qFK51O2NTVDfMCooeJShP5m2Jquvnft00J6gdwfjjLjjBpWUXJuvODh55ndR6BlA+2HBYe/71L/3evr1Cs2fHHZc0dKjqrlih5KFDZQy/4wAUxNvuAAAAKFGNqzr0eT+fzvjCr5xwwWPvrI6oWXWH7k31lM7kgEP0W25M9y4M6f9WRxKOa1XToae6enRGI156AShfwkuWaNfVVyu8ZEncMY5GjVTzv/+V75xzSnBmAMobdkADAACgxLWr7dR7fXwqaiPo2IyQ/u+ncOEDQBkQjlk9831IbT7KTRg+V3dL/z7No0XnJxE+AyhXbCCg7Hvv1dZOnRKGz8l//7vq/fAD4TOAg2IlBAAAgFLR7ziXnu3m1Y2zC9ctuH5WUI2rGvVqyHIVZcf0jRGNmBvSyl3xGwxK0t+au/RQZ4+OTWa/D4DyJThnjnZffbUiP/4Yd4zzxBNV85VX5D3jjBKcGYDyjBURAAAASs01rdy6o7270O3hmHTR1IBW7kwc9AEl4decmC79JqD+XwcShs8dajs0Y1CSXu3lI3wGUK7EcnK0++abtb1Hj/jhs8OhKiNHqu6yZYTPAA4LW0pwSIwx50j6ylrLq0AAAHBUje3kUdYeq49/KVjOYHdIGjzJr1mDk1SfMA+lIBi1eub7sB5dElJuglLPx3ilsWleXd3SJaeD5lsAypfAlCnafd11iq5bF3eMq00b1XztNXk6dy7BmQGoKFjJ41BdLGm1MeZxY0zL0p4MAACoOBzG6NXTvepav/DSdF2O1XlTAsqN2FKYGSqziesjSvkkV6MWxQ+fjaRrWrq0/KIquu4UN+EzgHIltmOHdl51lXb06xc/fHa7VW3MGNXNyCB8BvCnEUDjkFhrL5OUIulnSW8aY+YZY64zxlQr5akBAIAKwOcy+rhvkk6qXjjAS98a0xXfBhSNEUKj+P2SHdN5k/0aMjmgn7PjP+dOrefQ3CFJer6HT3V8BM8Ayhf/p5/q91NOkf/NN+OOcZ96quouWaJqo0fLeDwlNzkAFQ4BNA6ZtTZb0ieSPpDUQNK5khYbY24q1YkBAIAKoY7PaHz/JNX2Fj42YV1Udy0IlfykUGnkRqzGpAfV4ZNcfbU+GndcvSSjV073asagJKXWdZbgDAHgyEU3b9aO88/XzgsuUGzLliLHmKQkVX/ySdWZM0fu1q1LeIYAKiJqQOOQGGMGSbpK0kmS3pbU2Vr7uzEmWdIKSc+V5vwAAEDFcFINhz7tl6R+X/kVPCADfG55WE2rGQ1vwy4sHD3WWo3PiuqO+UGtz4m/49lppH+0dmtUqkc1vex4BlC+/L/27jw+7qre//j7zD7JZM9MAl0pu6ylFQQVRURcUK54FdyuCpRdQEBAxQuCIiKyytYW1J/LVfG6oF5FBL3gFZCWrZSyFCjdMzPZt9nP74+ZTpOmE9JkZpJMXs/Hg0eT75zM+YRp0sk7n/kca60Gf/QjdX/pS7JdXQXXed7zHtUvWybXggXlKw5AxSOAxlh9TNJN1tpHhl601g4YY06bpJoAAEAFOrLFqR+826dPPRQbcdsljyU0N+DQR+bzNBYT92JXRhf9M66HNhXueJako3dz6OajvDqgkY5nANNPat06dZ9xhuIPPlhwjamtVe33vqeq006TMfySDUBxMYIDY3WVpH9te8cY4zfGzJcka+1Dk1QTAACoUB9b4NK1h4/sdLaS/uPhmFZERg8MgdH0Jqy+8kRci341MGr4PKva6Cfv8eovH/ITPgOYdmwmo77bblPkwANHDZ99H/mIQi+8oOrTTyd8BlASBNAYq/skZYa8n85dAwAAKImLDnZryf4jO50H09JJD8S0rjezk48CCrPW6udrkzrovgHd+FxSqQITN9wO6cuHuPXcx6v08T3dBDIApp3kmjWKvvOd6jn/fNn+/p2ucQSDavj5z9Xw29/KOWtWmSsEMJMQQGOsXNba/Mk/ubcZwAgAAErGGKObj/Lq+DkjO0/bBq1O/POguuKFZ/YCQ61qT+u9fxjU5/4W15aBwn9vjpvt1FMfq9I3D/cq4CZ4BjC92GRSvd/6liKHHqrkP/9ZcJ3/M59R8IUX5D/5ZH7JBqDkCKAxVpHcQYSSJGPMiZKik1gPAACYAVwOo5++x6dDmkY+bX2xy+rkB2NKpAmhUVhn3OpL/4zr8N8M6h9bC3fNzwsY3XecT79/v0/71PNjEoDpJ/HUU4q89a3qveIKKZHY6RrH7Nlq/OMf1fDjH8vZ3FzmCgHMVDyzwlidJemrxpj1xpgNki6TdOYk1wQAAGaAGo/Rb473aXb1yA6tv29J6+xH47KWEBrDZazVD15M6sBf9uuO1UllCvwV8Tmlrx/m0bMfr9JH5rvoBAQw7djBQfVcfrmihx+u1LPPFlxXdfbZCq1eLd8HP1jG6gBA4vhwjIm19lVJbzPGBHLv901ySQAAYAaZVe3Qb4/36ZjfD6o3Ofy2n7yS0h41Dl2xiOlgyFoRSevC/4vrycjoc8I/Ms+p69/m1R619OUAmJ7ijz6qrtNPV/rllwuuce69t+qXL5f36KPLWBkAbEcAjTEzxnxI0gGSfNs6Q6y1V09qUQAAYMY4qMmp/3qvTyf+OaYdp25c81RC82qMPruPe3KKw5QQGbT6zyfj+sFLKY3WE79XrdGNR3l1/Bx+HAIwPWV6etTzla9o4I47Ci9yOBS45BLVXHWVjN9fvuIAYAc848KYGGPuklQl6RhJyyX9u6R/TWpRAABgxjlutku3vcOrcx6Nj7jtrEfiml1tdMwsnuLONKmM1bI1SV21IqGunY89lSRVu6SvLvToiwe55XUyagPA9BT705/UfeaZSm/YUHCN6+CDVX/PPfIsXlzGygBg53itGcbqKGvtf0jqtNZ+Q9KRkvaZ5JoAAMAMdNp+bl166MhO55SVTv5rTGs6Rx+7gMryf1vTOvI3g7rwn6OHzyfv6dKqT1TpkkM9hM8ApqVMe7s6/+M/1PHBDxYOnz0e1XzzmwquWEH4DGDKoD0EYxXL/TlgjNldUruk3SaxHgAAMIN9Y7FH63qtfvlqatj17oR04p8H9ciJfrVW0WtRybYMZPTVJxL62drUqOsOaHDopqM8etfu/OgDYHqy1ip2333qPu88ZSKRguvcRx6p+nvukXv//ctYHQC8OZ6VY6x+b4ypl/RdSU9JWifpZ5NZEAAAmLkcxmjZ0V69vWXk09k3+qxOeiCm/uRoU4AxXSUzVjc9l9CBvxwYNXyudUvfO9Kjf53kJ3wGMG2lN29W50knqfPkkwuGz6aqSrW33KLmRx8lfAYwJfFMDG/KGOOQ9JC1tkvSfxtj/iDJZ63tntzKAADATOZzGd33Pr+O/t2A1vYMD5tXRjP6j7/F9Mv3+uR0MG6hUjy0KaWL/hnXi12j/3Lhc/u4dM1bPWqhCx7ANGWt1cC996rn4otluwv/6O1573tVv3SpXHvsUcbqAGDX8IwMb8pam5F0+5D344TPAABgKmjyGf3u/X41+0be9oc30vry46MMBca0sb4vo1P+OqgP/k9s1PB5YbNDj3zEr6Xv8hE+A5i2Uq+9pvbjjlP36acXDJ9Nfb3q771XTX/5C+EzgCmPZ2UYq4eMMR8zxtBCBAAAppS96hz67/f55XWOvO321Und9jwh9HQVS1ld93RCB/9yQL95PV1wXaNXuv0dXv3fiX4d0bKTvwgAMA3YdFp9N9+syEEHKfHQQwXX+T76UYVeeEFVX/iC+BEdwHTACA6M1ZmSLpKUMsbEJBlJ1lpbO7llAQAASG9rceqH7/bpkw/FRtz25ccSmhtw6MT5PPWdTv74RkoXPxbX672FO56NpCX7u3TVYq+afIQwAKav5OrV6jrtNCWfeKLgGkcopLrbb5fvYx8jeAYwrdABjTGx1tZYax3WWo+1tjb3PuEzAACYMk5a4NK3j/CMuG4lfe7hmJ4MF+6gxdSxtjujf/vzoE76S2zU8PnIFoce+6hft73DR/gMYNqyiYR6r7lGkYULRw2f/Z/7nEJr1sj/7/9O+Axg2qENBGNijDl6Z9ettY+UuxYAAIBCvnSQW6/3ZLR0TWrY9cG0dNJfYnrkI37tUUsPxlTUn7S6/pmEbnwuqUSm8LoWv9G3j/DoU3u5CGEATGuJJ59U12mnKbVqVcE1zrlzVbd0qXzHH1/GygCguAigMVZfHvK2T9LhklZKes/klAMAADCSMUY3HeXV+j6rP28Y3vEcHrQ68YFB/e9HqtTgJbicKqy1+s3raV36eFwb+gt3PDuNdN6Bbn3tMI/qPDx+AKavzMCAeq+6Sv3f+56UKfwbt+rzzlPNtdfKUVNTxuoAoPgIoDEm1toPD33fGDNH0s2TUw0AAEBhLofRT4/16djfD+qZ9uE/2L/UZXXygzH9/gM+eZ2EmJNtTWdGX/pnXH/bPPp4lHfv7tRNR3r0lkYOGAQwvcX//nd1LVmi9Nq1Bde49t1XdcuXy/uOd5SxMgAoHV5/iPHaKGn/yS4CAABgZwJuo98c79Ps6pEh8/9uSevsR+KytnC3LUqrJ2F12eNxLf7vgVHD59nVRj871qc/f9BH+AxgWst0d6vrrLPUfswxhcNnp1OBr3xFwWeeIXwGUFHogMaYGGNuU/YMHyn7i4tDJT01aQUBAAC8id2rHfrd+3169/2D6k0Ov+2na1Pao9bo64u8k1PcDGWt1c/WpvTVJxLaOlj4FwAeh/Slg9267FCPqt10qgOY3mJ//KO6zjxTmU2bCq5xHXqoGu69V+6FC8tYGQCUBwE0xmrFkLdTkv7LWvt/k1UMAADAWBzY6NTP3+vTiX+OKbVD3vnNp5KaX+PQZ/dxT05xM8yz7Wld+H9x/bNtlBMGJR0/x6nvHenV3nW8WBPA9JaORNRz4YUa/NnPCi/yelVz5ZUKXHKJjJt/jwBUJgJojNWvJMWstWlJMsY4jTFV1tqBSa4LAABgVO+d7dL33+HVWY/GR9x21iNxzao2es8snhaXSkfM6qqVCS1bk1RmlKkn82uMvnekVx+a65QxdD0DmL6stYr94hfq/uIXlYlGC67zvP3tqlu+XO799itjdQBQfrQVYKwekuQf8r5f0l8nqRYAAIBd8oX93Lr80JGdZSkrnfLXmF7oGP0QPOy6dMbqnheTOvCX/br7hcLhs88pXbnIo2f/vUonzHMRPgOY1tKbNqnjxBPV+clPFgyfTXW16m67TU2PPEL4DGBGoNUDY+Wz1vZte8da22eMqZrMggAAAHbFVYs9Wtdr9fNXU8OudyekEx+I6dET/Wqtoj+jGP4Vzo7bWBkdfdzGv8136jtv82p+Df/fAUxvNpPRwPLl6vnyl2V7egqu8x5/vOruvluuefPKWB0ATC6e6WGs+o0xh217xxizSNLgJNYDAACwS4wxWvour97ROvIp8Po+q48+EFN/cpQZEXhT4cGMzvzfmN75u8FRw+d96oz++AGffnGcn/AZwLSXWrtW7cceq+4zzywYPpuGBtX/6Edq/NOfCJ8BzDh0QGOsLpR0nzFmsyQjqVXSyZNaEQAAwC7yOo3uO86vd90/oJe7h4fNT0Uz+uzDMd13nE9OB2MgdkUqY3X3C0l9Y2VC3YnC6wJu6asLPfrigW55nPw/BjC92XRa/TffrN6vf112sHB/lu/f/1113/++nC0tZawOAKYOAmiMibX2SWPMfpL2zV16yVqbnMyaAAAAxqPRZ/S79/t19O8GFYkND6H/uD6tix9L6KajPMwiHqNHt6R14T/jer5j9HEbn9zLpWsP92j3ajqeAUx/yeefV9eppyr55JMF1zhaW1V3++3yn3RSGSsDgKmHZ38YE2PMuZKqrbXPW2uflxQwxpwz2XUBAACMx4Jah/77fT75nCNvu/OFpG57nt+zv5lN/Rl97uGY3vuHwVHD54MaHfrrCX798Bgf4TOAac8mEuq56ipFDjts1PDZ/4UvKPTCC4TPACACaIzdEmtt17Z3rLWdkpZMXjkAAAATc0SLUz88xqed9Tlf+nhCv3s9tZNbkEhb3fBsQgf9cmDEgY5D1Xmkm47y6PGP+vXO3XaS9APANJN44glFDjtMfd/4hpTc+S8qnfPnq/Evf1HDvffK0dBQ5goBYGoigMZYOc2Q16EaY5ySPJNYDwAAwIR9dA+Xvn3EyKc0VtLn/hbTv8Lp8hc1hT24MaVF/z2gr/0rof5R8vkv7OvS85+o0jkHeORinjaAaS7T36/uiy5S9MgjlVq9eueLjFH1+ecruGqVfMcdV94CAWCKYwY0xurPkn5hjLk79/6Zkv40ifUAAAAUxYUHufV6b/YQvaEG09JJD8T0yIl+Laid2X0b63ozuvTxuH63bvRAflGzQ7e83au3huh4BlAZ4g8/rK4lS5R+7bWCa1z77af6e+6R56ijylgZAEwfM/uZNHbFZZIelnRW7r9VkvyTWhEAAEARGGN045EefWDOyNA0ErM68c+D6tjhsMKZYjBl9a2nEjrkvoFRw+cmr3TnO736x7/5CZ8BVIRMV5e6lixR+7HHFg6fXS4FrrhCwWeeIXwGgFEQQGNMrLUZSU9IWifpcEnvkbRmMmsCAAAoFpfD6CfH+nRo08inxy93W53810HF0zMnhLbW6vdvpHTorwZ09cqEYgWyZ4eRznqLW89/olqn7ueWwzBuA8D0F7v/foUPOEADy5cXXONetEjBFStUe801Ml5vGasDgOmHABqjMsbsY4y50hjzoqTbJK2XJGvtMdba709udQAAAMUTcBv95nif5lSPDFEf2ZLRmY/EZW3lh9CvdGd04gMx/ftfYlrXW/jzfXuLQ0981K9b3u5Vo4/gGcD0lw6H1XHKKeo48URlNm/e+SKfTzXf+Y6aH39c7kMOKW+BADBNMQMab+ZFSY9KOsFau1aSjDFfmtySAAAASmP3aod++36fjrl/UD3DR0Lrv9amtEeN0ZWLK7PTrT9pdd0zCd38XFKJTOF1rX6jbx/h0Sf3csnQ8QygAlhrNfizn6nngguUaW8vuM7zzneqfvlyufbZp4zVAcD0Rwc03sxJkrZI+psxZpkx5lhJ/KQBAAAq1oGNTv38OJ9cO3nGc+3TSf3opeTIG6Yxa63uezWpg+8b0PXPFA6fXSZ7YOOqT1TpU3u7CZ8BVIT0hg3qOOEEdX3mMwXDZxMIqO6OO9T0978TPgPAOBBAY1TW2t9aa0+RtJ+kv0m6UFLIGHOnMeZ9k1ocAABAiRw7y6Xb37nzTudzHo3roU2pMldUGqs70jr+jzF95uG4NvYXHrdxzO5OrfhYlb7zNq9qPQTPAKY/m8mo/847FT7gAMX/538KrvN+4AMKrl6t6rPPlnEQoQDAePDdE2Nire231v7MWvthSbMlPS3pskkuCwAAoGQ+v69bX1noHnE9ZaVTHoxpdUeBk/mmge6E1Zcfi+utvx7U/24p/HnMqTb6+Xt9+tMHfdq/gR8dAFSG1CuvqP2YY9R9zjmyvb07XeNoalL9T36ixj/+Ua65c8tcIQBUFp5FYpdZazuttUuttcdOdi0AAACldOUij07Zc+SxKT1J6cQ/x7S5f5RhyVNQxlr9+OWkDvrlgG59Pql0gaZnj0P6ykK3nv14lT66B7OeAVQGm0qp9/rrFT74YCUeeaTgOt/JJyv4wguq+vSn+f4HAEXAIYQAAABAAcYYLX2XV5v6M3p06/CweUO/1Ul/iemvJ/gVcE/9gOLpaFoX/l9cj4dHD80/ONep777Nq73q6FUBUDmSzz6rrtNOU3LlyoJrHLvtpro775T/xBPLWBkAVD6eVQIAAACj8DqNfnmcX/vUjQyZn45m9JmHYkplCs9PnmztMavz/hHTkb8ZHDV8XlBr9Ov3+fSb4/2EzwAqho3H1fP1ryuyePGo4XPV6acr9MILhM8AUAI8swQAAADeRKPP6Hfv9yvoGxlC/2lDWhc9lpC1UyuETmeslq1J6sBf9mvZmpQKVed3Slcv9ujpj1XpQ/N4gSSAypF47DFFFi5U3ze/KaV2fnisc4891PTXv6p+2TI56uvLWyAAzBAE0AAAAMAYLKh16NfH++Rzjrzt7heSumVVsvxFFfB4W1pv/92gzvtHXB3xwutO2sOp5z5RpcsWeuRzTf0xIgAwFpm+PnVfeKGib3+7UmvW7HyRw6Hqiy5ScNUqeY/leCMAKCVaHAAAAIAxOjzk1I+O8emUv8ZGdBRf/kRC82oc+ugek/cUu20goyueTOj/vbzzTr9t9qs3uukor94zix8HAFSW2IMPqvuMM5Ret67gGtdb3qL6e++V54gjylcYAMxgdEADAAAAu+Df9nDpO2/zjLhuJX3+bzH9K5wue03JjNWtqxI68JcDo4bPNW7pO0d4tOJjVYTPACpKprNTXaedpo73va9w+OxyKXDllQo+9RThMwCUEc86AQAAgF10/oFuvd5jdecLw8duxNLSSQ/E9MiJfi2oLU+vx/9uTulL/0xodWfhAwYl6dN7ufStIzzarYoeFACVZfA3v1H3Oecos3VrwTXuxYtVf++9ch90UBkrAwBIdEADAAAAu8wYo+8d6dGH5o4cCB2JWZ3450F1xEp7KOHGvow+81BM7/tjbNTw+eBGhx7+sF/3HuMjfAZQUdJtber4xCfUedJJhcNnn0+1N9yg5sceI3wGgEnCM1AAAABgHJwOox+/x6fDmkc+pX652+oTDw4qni5+CB1PW333mYQOum9A971WeNxGvUe65e1ePf5Rv97eupOTEwFgmrLWauD//T+F999fsfvuK7jO8+53K7RqlQIXXyzj4gXgADBZCKABAACAcap2G/36eJ/mBsyI2x7dmtEZ/xuXtcULoR/YkNJhvxrQFU8mNFAgezaSTt3Ppec/Ua2z3uKW0zGyNgCYrlJvvKGOD3xAXZ/7nGxn507XmNpa1d19t5oeekiuvfYqc4UAgB3xK0AAACZBOp3SP/75f3I6HEpn0orF4qqtrc2+n07rbUccKWMIjYDpYLcqh353vE/vun9QPcNHQuvnr6a0R63RVYu9E9rj9Z6Mvvx4XL9/Y/QDDt8adOjmt3u1OEjHM4DKYjMZDdx5p3ouv1y2r6/gOu8JJ6j+zjvlnD27jNUBAEZDAA0AQIklEnFFolFFIhFFohFFoxF1dHYokyk8s/WFNS8oGAwpFAqpJRRSMBhSoDpQxqoB7Iq3NDr1i+N8+vCfYkrt0PD87aeTml/j0Of3de/y/Q6mrG54NqHvPptUfJTsOegz+ubhHv3HPi45+OUVgAqTeukldZ1+uhL/+EfBNY7mZtXeeqv8p5zCL/EBYIohgAYAoIj6+/sViUZyYXNY0WhU3T3du3w/A4MDemP9Or2xfl3+WnVVtUK5MDoUDKkl1KKqqqoiVg9gIt4zy6U7j/Zqyf/GR9x27qNxza42eu/ssT39ttbq/jfS+vJjcb3RV3iEh8NIZ73Frf9c5FGDl8AFQGWxyaT6vvc99V51lRQf+b11G/+nPqXam2+WMxgsX3EAgDEjgAYAYBysteru7lYkGh7S2RzVwOBAyfbsH+jX6+te1+vrXs9fq66uVijUopZgNphuCYXk9xNKA5PlP/Zx6/WejK59evgsjpSVPvnXmP72Eb8ObBx9PMZLXRld/FhcD24cfdzGO1sduukorw5qYtwGgMqTfPppdZ12mpJPP11wjWPWLNXfead8H/5wGSsDAOwqU8xDUYDJtnjxYrtixYrJLgNAhUmn0+roaM92NkcjikSjikYjSiaTb/7Bk6AmUJPvlG4JhhQMheT3+Se7LGDGsNbq1L/H9bO1I08JnFNt9MiJfu1ePfIs8L6k1bVPJXTr80klC0/o0e5VRtcd4dEn9nTxMnMAFcfGYuq9+mr1XX+9lC78i7iqM89U7Xe+I0ddXRmrA2YuY8xKa+3iya4D0xMd0AAADLFtXnM0HzZH1NEx+rzm8fJ5fUokE9prr721e+tuemP9G1r3xjpJ2QBrvHr7etXb16tXX3s1f622plahUHZ0x7YRHj6fb6KfAoCdMMborqO92tif0SNbhn/v2NBv9dEHYnrow34F3Nnw2Fqr+15L6bLHE9o8UPhr32WkCw5y6ysLParxEDwDqDzx//s/dZ92mlIvvVRwjXPPPVW/fLm87353+QoDAEwIHdCoKHRAA9gV/QP9ww4GjEQj6u7e9XnNY1FfV6/m5qCCwaCCzc0KNoe05qU1agmFNHvWnPy6jZs2aOvWrZo3d57aImFFwmGFI2FF26NFD8HrauvyYztCoZCCzSF5vd6i7gHMZJ1xq3fdP6CXukY+3/7AHKd+9T6fXuzK6Ev/jI8Iqnd07CynbjzKq/3qR3ZOA8B0l+ntVe9Xv6r+22+XCmUUDoeqL7pINd/4hhycgQGUHR3QmAgCaFQUAmgAO2OtVXdP94iweWCg+POaHQ6HmhqbFGwO5gPn5qZmeTyeCd1vOp1StL1dkUhYbeGwwpG2knRm19fVDx/fEQzK4yGUBsbr9Z6Mjr5/UOHBkc+5FwcdejqaUXqUp+PzAkbffZtXH5nvZNwGgIoUe+ABdZ9xhtLr1xdc4zroINXfc488b31rGSsDMBQBNCaCABoVhQAaQDqdVkdnx4iwuRTzmt1ut4LNwWFhc2NDo5zO8hwIlkqlFG2PKpzrlG6LhNXR0T6h8R0701DfoFAw2yUdCrWoublZHvfEAnVgJnkynNZxfxjU4OhnCg7jdUqXHOLWJYd4VOUieAZQeTIdHeq+6CIN/uhHhRe53aq54goFLr9cZoK/zAcwMQTQmAgCaFQUAmhgZkkkEoq2RxWJbA+a2zvaSzKvuaqqakTYXFdbN+U6ElOplKLRiNoiYYXDYUUiYXV0dhQ9lG5saMzPlA4Fs6G02+0u6h5AJfnd6ymd/NeYxvKVeMI8p777Nq8W1DJuA0BlGvzVr9R97rnKhMMF17iPOEL199wj9wEHlLEyAIUQQGMiCKBRUQiggco1MDCgSDSc62zOHhLY1d1Vkr3q6uq2B825sLm6qroke5VDMplUJBpRODdPOhxpU2dnZ1H3MMbsEEqH1NwclMvFecfANreuSujLjycK3r5nrdH3jvTqA3P5ugFQmdJbtqj7vPMU+/WvC64xVVWq+da3VP3FL8qU6VVlAN4cATQmggAaFYUAGpj+rLXq6elWJJoNmrd1N/cP9Bd9L4fDocbGRgWbQwo2N+cC5+YZMfM4kUhkQ+lcp3Q4ElZXV/FD6abGpu3jO4ItampqIpTGjGWt1UWPJXTH6uEjgapc0lcWenTBQW55nVPrVRUAUAzWWg3+8Ifqvugi2a6ugus8xx6r+qVL5VqwoHzFARgTAmhMBAE0KgoBNDC9bJvXHN0WNkfDikajSiQKdwiOl9vt3t7R3NysYHNQjY1NZZvXPB3E4/ERndLd3d1F3WPbIY1Dx3c0NfE4YOZIZ6w+9VBMv12XlpHkcUjL3+3VJ/ZkhA2AypRat07dZ5yh+IMPFlxj6upU+73vqerUU6fceDMAWQTQmAhakFBSxpj3S7pFklPScmvtdTvcfpOkY3LvVkkKWWvrc7elJa3K3bbeWvuRshQNoCQSyYSi0eiwgwHb20s0r9lfpebg0LA5pLq6qTevearxer2aPWu2Zs+anb8Wj8dzYXRY4XCbwpGwenp6xr1HJpPJdbdHtFqrJWVD6eam5iGd0iF+OYCK9ejWtP6xNa373+/TkS1OPRVN69MPxRTyG717d56aA6gcNp1W/+23q/erX5XtL/xKNt+JJ6rujjvk3H33MlYHACgnOqBRMsYYp6SXJR0naaOkJyV90lr7QoH1X5S00Fp7au79PmttYFf2pAMamBqy85q3B82RaERdo7zcciLqausUDAbV3JSd1RxsDqq6evrOa54OYrHYiFC6t7e3qHs4nc5sKD2kU7qxsVEOB4eyYXq74dmEFgcdw8Lmv29OaUUko0sO8UxiZQBQPMk1a9R12mlKPvZYwTWOYFB13/++fB//OE0CwDRABzQmggAaJWOMOVLSVdba43Pvf0WSrLXfLrD+n5KutNY+mHufABqY4rLzmntGhM39o3S5jJfD4VBjQ+OwsLm5qVleb+XPa54OBgcHh82TDkfa1NfXV9Q9nE6ngs3B/DzpUDCkhoYGQmkAAKYIm0yq7/rr1Xv11dIoI9X8n/mM6m6+WY6mpjJWB2AiCKAxEbzOD6U0S9KGIe9vlHTEzhYaY+ZJ2kPSw0Mu+4wxKySlJF1nrf1tieoEMAbpdFqdnZ0jwuaSzWtuah4WNjc1Nsrp5J+tqcrv92ve3HmaN3de/trAwMCITumJ/HIinU5ra9tWbW3bmr/mcrmyofSQgw7r6+sJpQEAKLPEypXqOvVUpZ57ruAa55w5qrvrLvk++MEyVgYAmGz8JI+p4hRJv7LWpodcm2et3WSMWSDpYWPMKmvtqzt+oDHmDElnSNLcuXPLUy1Q4ZLJZHZec3tEkUg2cG7vaFc6nX7zD95Ffr9fwebg9gMCg0HV19XzUswKUFVVpfnz5mv+vPn5a/39/cMC6XA4rIHBgXHvkUqltGXrFm3ZuiV/ze1yKxgc3ildX8/fKQAASsEODqr3G99Q3w03SKM8V6w65xzVfvvbctTWlrE6AMBUQACNUtokac6Q92fnru3MKZLOHXrBWrsp9+drxpi/S1ooaUQAba1dKmmplB3BMeGqgRlmcHBAkUhEkfZo9s9oRF1dnSXZq7a2NncwYC5wDgZVXVVNMDiDVFdXa4/qPbTH/D0kZce45EPpIcH04ODguPdIppLavGWzNm/ZnL/mdrtzs6RDCoVaFAqFVFfLwZQAAExE/JFH1HX66Uq/8krBNc6991b98uXyHn10GSsDAEwlBNAopScl7W2M2UPZ4PkUSZ/acZExZj9JDZIeG3KtQdKAtTZujGmW9HZJ15elaqBCWWvV09uT7WyORhSJhEs6r7mhoTEXNjfnA2fmNWNHxhgFAgEFAgEt2GOBpOzf1b6+vuGd0pGwYrHYuPdJJpPatHmTNm3e/ntQj8ezPZAOBhUKtqi2tpZQGgCAN5Hp6VHPV76igTvuKLzI6VTgkktUc+WVMn5/+YoDAEw5BNAoGWttyhhznqQHJDkl3WutXW2MuVrSCmvt/bmlp0j6uR1+Iub+ku42xmQkOZSdAf1COesHprNMJqOOzo7ts5ojEUXbo4rH40Xfy+1yq7m5Wc25oDnYHFJjY6NcLv6JwfgYY1RTU6OamhrtuWBPSdlQure3d0QoPZG/04lEQhs3bdTGTRvz17xe7/BO6WBINTU1hNIAAOTE/ud/1H3WWUpv2FBwjeuQQ1R/zz3yLFpUxsoAAFOVGZ75AdPb4sWL7YoVKya7DKCsksmk2tuj+UMBI5EyzmtuDqquro4D3zAprLXq6elRONKmcCSSD6aLfTCmz+cbFkiHgiEFAgFCaQDAjJKORtXzpS9p8Cc/KbzI41HNf/6nApdeKuN2l684ACVnjFlprV082XVgeqI9DQCmkcHBQUWikWGdzV3dXSrFLxNra2vV3JSd07wtbK6uZl4zpg5jjOrq6lRXV6e999pHUjaU7u7pVjg8vFM6mUyOe59YLKb1G9Zr/Yb1+Wt+v39EKM3XBwCgEllrFbvvPnWfd54ykUjBde4jj1T9PffIvf/+ZawOADAdEEADwBRkrVVvX2/+UMBoLmzu6+8r+l7GGDU2NOYPBdwWNjOvGdORMUb1dfWqr6vXPntvD6W7urpyndJhhcNhRSIRJVPjD6UHBwf1xvo39Mb6N/LXqqqqRhx0WF1VPeHPCQCAyZLevFnd55yj2O9+V3CNqa5Wzbe/repzzpFxOstYHQBgumAEByoKIzgwHWUyGXV2dQ4Pm6ORksxrdrlcam7KHQqYC5ubGpuY14wZJ5PJbA+lw9ku6Ug0olQqVdR9qqurR3RKV1VVFXUPAACKzVqrgXvvVc/FF8t2dxdc5z3uONUtXSrX/PnlKw7ApGAEByaCABoVhQAaU10ymVR7R3s+bI5EI2pvj5ZkXrPP58t3MweD2bnN9XX1zGsGCshkMurs7MyN7cgG05FopOhfn4FAIBdGZ7ukQ8GQ/H5/UfcAAGC8Uq+9pq4lS5R4+OGCa0x9vepuukn+z32O8VPADEEAjYmg5Q0ASmQwNqhoNDokbA6rq6tE85pratXc3JwPm7PzmjkkDdgVDodDTU1Nampq0v77ZedXZjIZdXR0DBvfEZ3gL436+vrU19en115/LX+tpqZm+PiOYEg+n2/CnxMAAGNl02n133qrer/2NdnBwYLrfB/9qOpuv13O3XYrY3UAgOmMDmhUFDqgMRmsterr61MkGh7W2dzXV+J5zc3N2c7mpiBBFVBG6XRaHR3t+UA6HMmG0plMpqj71NbWjhjfwWx2AEApJFevVtdppyn5xBMF1zhaWlR3++3yf+xjZawMwFRBBzQmgg5oANgF2bmxndmQOT+zOapYPFb0vbbNa25u3n4wYFMT85qByeZ0OhUMhhQMhnTAW7LX0umU2tuHd0q3d7RPKJTu6elRT0+P1r66Nn+trq4uO7ojGMqN7wjK4yGUBgCMj00k1Hfdder95jelZOHDef2f+5zqbrxRjsbGMlYHAKgUpBgAUEAqlVK0PZo9FDAXNrd3tBf9kDJp+7zmoWFzfT3zmoHpwul0ZQPhUCh/LZVKqb29PT9Pui0SVkdH+4TG8HR3d6u7u1uvrH05f62+vn7YPOlgc1Aej2dCnw8AoPIlnnxSXaedptSqVQXXOOfOVd3SpfIdf3wZKwMAVBoCaACQFIvF8qMzormwubOrsyTzmmtqaoYFzcHmoAIB5jUDlcblcqmlpUUtLS35a6lUStFoRG2RsCK5TumOzo4Jfa/p6upSV1eXXn7lpfy1hoaGYQcdBpuDcrvdE/p8AACVITMwoN4rr1T/jTdKhV6pY4yqzz1XNddeK0dNTXkLBABUHAJoADPK9nnN22c1R6MR9fb2Fn0vY4waGhoUbAqqObg9bGZeMzBzuVwutbbuptbW7Qc3JZNJRaNRhSNtagtng+mOzo4J7dPZ2anOzk699HI2lN72/Whop3RzUzOhNADMMPG//11dp5+u9KuvFlzj2ndf1S1fLu873lHGygAAlYwAGkDFys5r7hoRNsdipZnX3NTUpGBTMHswYHNQzU3NzGsG8Kbcbrd222037bbb9lA6kUwoEokoHAkrkhvf0dXVOe49rLXq6OhQR0eHXnxpjaTcoaaNTUPmSYf4vgUAFSrT3a2eSy/VwNKlhRc5nQpceqlq/vM/ZWiYAAAUET9hAKgI22atbg+bw2pvL828Zq/Xu318RnDbvOYG5jUDKBqP26NZu8/SrN1n5a8lEnFFItnxHeFcp3RXd9e497DWqr09qvb2qNa8+IIkyeFwqLGxcdhBh81NTXI6ecoIANNV7A9/UNdZZymzaVPBNe6FC1V/zz1yL1xYxsoAADOFKcV8U2CyLF682K5YsWKyy0CJxWIxRdu3HwwYiUbU2Vmiec2BGjU3Nw8Jm0PMawYwZcTj8WyXdCSstnBY4Uibenp6irqHw+FQU1PTsPEdTY1NcjqdRd0HAFBc6UhEPRdcoMH/+q/Ci7xe1Vx1lQIXXyzDWCYAozDGrLTWLp7sOjA90c4CYMqy1qq/PzevObJthEZUPb3FDVek7EvR6+vrFWwO5cPm5uZm+X3+ou8FAMXi9Xo1Z/YczZk9J38tFospHAnng+lwODyh75uZTCb7PTgS0epso7QcDoeam4NqCYYUzHVKNzY0EkoDwBRgrdXgz3+unvPPVyYaLbjO8453qH75crn23beM1QEAZiICaABTQiaTUVd3l6LR4Z3NpZjX7HQ61dTUPGyMRlNjE4dxAagIPp9Pc+fM1dw5c/PXBgcH86F0ONcp3dfXN+49MpmMwuE2hcNt+WtOp1PNzc0KBVvUEsoG040NjYwnAoAySm/cqK6zz1b8D38ouMYEAqq97jpVnX22DN+jAQBlQAANoOxSqZTaO7LzmqPbOpvboyWd19w8JGxuYF4zgBnG7/dr3tx5mjd3Xv7awMDA9lA6ElY43Kb+/v5x75FOp9XW1qa2tjatyl1zuVy5773Z0R0toRAz8wGgBGwmo4Hly9Xz5S/LjjKKyXv88aq7+2655s0ruAYAgGIjgAZQUvF4fPvBgJGIotGIOjo7SjKvOVAdyI3O2B421wRqmNcMADtRVVWl+fPma/68+flr/QP9uQ7pbCAdjoQ1MDAw7j1SqZS2bN2iLVu35K+5XW41B4eP72iob+B7NQCMU2rtWnUtWaLE3/9ecI1paFDdzTfL/9nP8v0WAFB2BNAAiiI7r7l/eNjcHin6YVjbNNQ35A4F3B44+/3MawaAiaiuqtYe8/fQHvP3yF/r6+8bEkpnx3cMDg6Oe49kKqktWzZry5bN+Wtut1vBYHDY+I76unpCEgAYhU2l1H/zzer5+telUcbW+T7+cdXddpucLS1lrA4AgO0IoAHsMmuturq68mFzNPfnRAKJQpxOp5oam/Jhc7A5qKamZuY1A0CZBKoDCuwR0II9FkjK/hvQ19c3bHRHOBKe0Mz+ZDKpzZs3a/Pm7aG0x+NRaFuXdG58R21tHaE0AEhKrlqlrtNOU/LJJwuucbS2qu6OO+T/6EfLWBkAACMRQAMYVTqdUnt7hyLRcC5sjioajSqZShZ9L6/Xq+am5iFhc0gNDcwKBYCpxBijmpoa1dTUaM8Fe0rKhtK9fb0jxnfE4/Fx75NIJLRx00Zt3LQxf83r9Q4LpIPBkGpragmlAcwYNh5X77XXqu/aa6VRzk+pOvVU1d5wgxwNDWWsDgCAnSOABpAXj8fz3cyRaFSRaESdnR3KZDJF3ytQHVBzc7OCwVC+s7mmhnnNADAdGWNUW1Or2ppa7bXnXpKyoXRPb8/2UDoXTCcSiXHvE4/HtXHjBm3cuCF/zef1KRQa2indokAgwL8nACpO4okn1HXaaUqtXl1wjXP+fNUvWybve99bxsoAABgdATQwA1lr1T/Qnz8UcNsojVLNa66vb8gfCpgNm5vl91eVZC8AwNRgjFFdbZ3qauu09157S8r++9Pd0z2iUzqZHP+ramLxmNZvWK/1G9bnr/l8PoVCLfmDDltCIVVXE0oDmJ4y/f3q/frX1X/zzVKhg7yNUfX556vmm9+UIxAoa30AALwZAmigwllr1dXdNSJsLsW8ZofDoeam5uyhgNsOCGReMwAgxxij+rp61dfVa5+995G0/d+pbQcchiNhRSKRiYXSsZjWr39D69e/kb9W5a/KdkmHto/vCFQT0gCY2uIPPaSuJUuUfv31gmtc+++v+nvukefII8tYGQAAY2dsod+gAtPQ4sWL7YoVKya7jEmTTqfU3tGRDZvbI7k/oxP6Ib4Qj8eTH52xLXBuqG+Q0+ks+l4AgJklk8moq6tr2OiOSDSi1CjzTsejuqpaoVAof9hhS6hFVVW8QgfA5Mt0danny1/WwPLlhRe5XAp85Suq+drXZLze8hUHYEYyxqy01i6e7DowPRFAo6LMpAA6kYhn5zQPCZs7SjSvubq6ekTYzKFPAIByymQy6uzqHDa+IxKNKJ1OF3WfQHVAwVBILblu6VAwxNgoAGU1+Lvfqfvss5XZsqXgGveiRaq/5x65DzmkjJUBmMkIoDERBNCoKJUaQPf39+dHZ0Sj2bC5u6e7JHvV19fnw+ZtgTPdYACAqSiTyaijs2P7+I5wWNH2aNFD6ZpATf6gw5ZgSMFQSH6ff1z31Xv99fK89a3yHH20Mu3tMh6P4v/8p1LPPqvA5Zfzy11gBkuHw+o+/3zFfvGLwot8PtVefbWqv/QlGRcTNQGUDwE0JoIAGhVlugfQK59aoerqgJxOR7a7ORpR29atiifiRd/L4XCoqalpSNgcUlNzkzxuT9H3AgCgXNLpdC6UblM4ElE43KZoe7TorxCqranNd0iHQi0KNgfl8/ne9OMG779fnSefLLlcUl/fyAUej4zbnf3T45Hcbpltb+9w29Dbd7p+tNt2vK+he+be39nHjljP6C1gwqy1GvzpT9V9wQWyHR0F13mOPlr1y5bJtc8+ZawOALIIoDER/MoUmCL6+vv0xJNPFL1rS8rOa25ublawOZQPnBsamNcMAKg8Tqcz/2/dAblr6XRK7e0duZnS2U7p9o72CYXSPb096unt0dpX1+av1dXWDQmlQwo2h+TNzWW11ir2q1+p+4tflGKxwnecSMgmElJ/v6ZFm4jDMWqwPSLM3pUAfSJhfKHb3G4Zh2Oy/68Bean169V91lmK/+lPBdeYmhrVXn+9qs44g7+/AIBpiQAamCKqq6rlcDgmHEBXV1Xn5zRv+wG8tpZ5zQCAmcvpdGWD4VBI0oGSpFQqpfb29mwgHQnnQ+mJvDqwu6db3T3demXtK/lr9XX1CgYCCjz0sGr+9GfVd3XJPdFPaCrJZKR4XDYenx6BuSS5XDsNycfVDV6qAH3o+y4Xz+MqkM1kNHD33eq59FLZnb0aIsf7gQ+o/u675Zwzp4zVAQBQXATQwBRhjFGwOajNWzaP+WPq6+pHhM3MawYA4M25XC61tLSopaUlfy2VSinaHs2N78iG0h2dHRMKpbu6u9TV3SXtt0/2v0xG/q4uNa99TQ3r16tx3RtK+nzqnrW79v3rw0X4zPCmUinZVEoaHJw+oXkZgu5RR628STf7iNt4ld2oUi+/rK7TT1fi0UcLrnE0Nan2llvk/9Sn+AUEAGDaI4AGppBCAbTD4VBTY3Zec3MubG5uapbHw7xmAACKxeVyqbWlVa0trflryWRS0Wh02PiOjs7CM1rflMOhwcZGbTi8URsOz41RtFbV4YgGmprUuG6dGtatV004LMNZLdgmmZRNJqfvaJZdmUc+1nEqYwjQx7xPmUaz2FRKfTfeqN4rrxx1FI//lFNUe8stcoZCJa8JAIByIIAGphBHrlukqbFJs2fNltPl1OoXVuv9x71fc+fOm+TqAACYedxut3bbbTfttttu+WuJZCIbSoezgXQ4ElZnV+f4NzFG/S0hvdYS0mtHv0OS5HG7FapvUKiuXqFAjUL+KlU5HLLbZkQnk9k/d3g/fy2ZHNNtI+5rtNt2cl8iJMfOTMfRLE7n2A7efLMO9J10iscfe0yu+fOV/Ne/lHzqqYIlOHbfXfV33infRz5Sxk8cAIDSI4AGphCf16t/+8hHNWf29hlv8+bOU1s4TAANAMAU4XF7tPtuu2v33XbPX0sk4tr0t79p449+qHavV51z56ivZfzdi4lkUhsjYW2MhPPXagI12bEhoVa1tMxXKBiS2z25E6VtOr09sB4aeo8h6B5zgL7j+l0JyXdyG7BT6bTs4GDJRrMk3uT2qiVLVHv99XLU15dgdwAAJpeZyEw7YKpZvHixXbFixWSXAQAAZpBMd7d6vvIVDdx557DrSZ9PnXNmq3PeXHXNmaPugw5Qr9dbtH2NMWpsbFJrPpRuUWNDoxxlGCUwXVlrszOgJ9op/iZB96hh/Gjh+s460lOpyf7fhhJyLlig+mXL5H3Peya7FAAYlTFmpbV28WTXgemJDmgAAABgnAZ/9zt1n3OOMptHnuHgjsUUemWtdpNR/RX/Ke+73qXXXn9NDz70F+21596KxWLavGWTYqPMgh2NtVbt7VG1t0e1+oXV2T1dboVCIbW0tKol1KLWlhYFAjUT+hwriTEmOz7B7Zaqqye7nDGxmcz2ULpU41R2oSP9TfdhNMvYOByqvvBC1Vx9tRzT5O8iAADjRQANAAAA7KL0li3q/uIXFfvv/y68yOVS4NJLVfP1r8v4fJKkzq5OfegDH9LsWdvHbb2y9hW9vu41VVcH1Na2VeFIWMlxjopIppLatHmTNm3elL9WXV2tllBLPpRuCYXk8RSvExulZRwOyeuVKWL3fKkVHM0yhk7x8c4jn+jc87JyOlV7660KnHNOefcFAGCSMIIDFYURHAAAoJRsJqOBe+5Rz5e/LNvdXXCd+/DDVb9smdwHH7zLe2QyGXV2dqotvFVb29rUFt6q9vZ2FfN5e2ND47Au6cbGJjlzhyEDM82w0SzFGrUyZH3qlVcUu/9++T70Ifk/+UmZ6mp1feYzavjlL+U95pjJ/vQBYEwYwYGJIIBGRSGABgAApZJ66SV1nXGGEo88UnCNqa5Wzbe+perzzpMpYqCbTCYVjoTVFm5TW9tWtYXb1NvbW7T7d7lcCjYHh4TSraqpqcmOrAAwIb3XXy/PW986LGyO/+1vSjz5pGouvXQSKwOAsSOAxkQQQKOiEEADAIBis4mE+r77XfVec40Ujxdc5/3gB1V3xx1yzZtXlrr6B/oVDrdlu6RzoXSiiKME/H5/fnRHa6hFoVCLfLlRIgAAYGYhgMZEEECjohBAAwCAYko88YS6Tj9dqeefL7jGEQyq9pZb5D/llEntGLbWqqurS1vbtqotvFVtbW2KtkeVyWSKtkd9fb1aQq1qaWlRa6hVzc1Ncjo5VgYAgEpHAI2JIIBGRSGABgAAxZDp7VXvFVeo/7bbpFGeL/s//3nV3XCDHE1NZaxu7FKplKLRSG6WdLZTurun8OzqXeVwOBQMBtUSalVrS4taQq2qq6tjdAcAABWGABoTQbsCAAAAMETsj39U99lnK71hQ8E1zgULVH/33fK+971lrGzXuVwutbbuptbW3fLXBgcHh82Sbgu3KRaLjev+M5mM2tra1NbWpudWZa/5vD6FWlrys6RbQi3y+/3F+HQAAAAwDRFAAwAAAJLS4bB6LrhAgz//eeFFTqeqL7pINVddJUdVVfmKKyK/36/58+Zr/rz5krKjO3p6uod1SUeiEaXT6XHdfywe0/r1b2j9+jfy12pra7d3Sbe0KtgclMvFjyIAAAAzAc/6AAAAMKNZazX4ox+p++KLZTs6Cq5zL1youuXL5TnssDJWV3rGGNXV1auurl777rOvJCmdTivaHs13SW9ta1NXV+e49+jp6VFPT49eWfuypOzojuam5vwhhy2hFjU0NDC6AwAAoAIRQAMAAGDGSr36qrrOPFOJhx4quMb4/aq5+mpVX3ihzAzp2nU6ndlwONSSvxaPx3Md0m1qC2/V1ratGhwcHNf9ZzIZhSNhhSNhrVqdnd3h8XgUCrWodVso3dKi6qrqonw+AAAAmDwz4xk0AAAAMIRNpdR3443qu+oq2VFCVM9736v6u++Wa8GCMlY3NXm9Xs2dM1dz58yVlO0c7+3rzc2A3qqt4TZFImGlUqlx3X8ikdDGjRu0ceP22ds1gRq15A43bGlpUSgYktvtLsrnAwAAgPIggAYAAMCMknjqKXWffrqSTz9dcI1pbFTdTTfJ/9nPMhaiAGOMamtqVVtTq7332ltStrO5vaM9H0q3hdvU3tE+7j16+3rV29erta+uze/Z2NiUnSWdC6UbGxrlcDiK8jkBAACg+Iy1drJrAIpm8eLFdsWKFZNdBgAAmIIyAwPqvfJK9d94o5TJFFzn/9SnVHvTTXKGQmWsrnIlEgmFI9nRHVtz4zv6+/uLdv9ul1uhUCg/S7q1pUWBQE3R7h8AAEjGmJXW2sWTXQemJzqgAQAAUPFiDz6o7jPPVPr11wuucc6dq7q77pLvAx8oY2WVz+PxaPasOZo9a07+Wl9fX+5ww60Kh9vUFm5TMpkc1/0nU0lt2rxJmzZvyl+rrq4edsBhSygkj8c74c8FAAAAu44AGgAAABUrHY2q56KLNPjjHxde5HCo+vzzVXPNNXIEAuUrbgYLBAIKBALac8GekrKjOzq7OrOzpHNd0u3t7RrvqzX7+/v12uuv6bXXX8tfa2xoHNYl3djYJKfTWZTPBwAAAIUxggMVhREcAABAyh6QN/izn6nnwguViUYLrnMddJDqly+X5/DDy1gdxiKZTCoSjWhr29bsTOnwVvX29hbt/l0ul4LNwSGhdKtqamqY+Q0AwE4wggMTQQc0AAAAKkpq3Tp1n3224n/+c+FFXq9qrrxSgUsukXG7y1ccxsztdmv33XbX7rvtnr82MDCgtnCuSzp3yGEikRjX/adSKW3ZukVbtm7JX/P7/fnRHa2hFoVCLfL5fBP+XAAAAGYyOqBRUeiABgBg5rLptPpvvVW9V1whOzBQcJ3n3e9W/d13y7XPPmWsDqVgrVVXV1e2SzqcDaWj7VFlRjlkclfV19erJdSqlpYWtYZa1dzczOgOAMCMQwc0JoIAGhWFABoAgJkp+eyz6lqyRMknnyy4xtTXq/aGG1R16qmMWahgqVRK0WgkN0s6G0p393QX7f4dDoeCwaBaQq1qbWlRS6hVdXV1/J0CAFQ0AmhMBCM4AAAAMG3ZwUH1XnON+r77XSmVKrjO9/GPq+7WW+VsbS1jdZgMLpdLra27qbV1t/y1wdhgbo709tEdsVhsXPefyWSy99XWpudWZa/5vD6FWlrys6RbQi3y+/3F+HQAANNcMplUJpOR1+ud7FKASUMADQAAgGkp/re/qeuMM5Reu7bgGsesWaq/4w75PvKRMlaGqcbv82v+vPmaP2++pOzojp6e7mFd0pFoROl0elz3H4vHtH79G1q//o38tdra2u1d0i2tCjYH5XLx4xcAVJJUKqW+/j719fWqt7dPff19en3d65Ks0qm0+vr6FIvHtNeeeykUatGihYsmu2RgUvAMCAAAANNKprNTPV/+sgbuuafwImNUdfbZqv32t+WorS1fcZgWjDGqq6tXXV299t1nX0lSOp1WtD26vUu6rU2dXZ3j3qOnp0c9PT16Ze3LkrKjO5qbmvOHHLaEWtTQ0MDoDgCYotLplPr6+rcHzH3ZP/v6+tTX16fevt4xv5rmtddf10EHHlTiioGpiwAaAAAA04K1VrFf/UrdX/yiMm1tBde59t9f9cuXy3PUUWWsDtOd0+nMhsOhFunAgyVJ8Xg8F0i3qS28VVvbtmpwcHBc95/JZBSOhBWOhLVqdXZ2h8fjUSjUotZtoXRLi6qrqov2OQEAdi6dTqt/oD8XJvfmAuXhAfPAYOEDjXdVXV2tZs+aU7T7A6YbAmgAAABMeekNG9R17rmK//73hRe53ar52tcUuPxyGeYsogi8Xq/mzpmruXPmSsr+EqS3rzc3Azo7SzocCSs1yvzx0SQSCW3cuEEbN27IX6sJ1Kgld7hhS0uLQsGQ3G53UT4fAJgJMpmM+vtz4XL/ts7l4eFy/0B/WWtKJpJl3Q+YagigAQAAMGXZTEYDd96pnssvl+3rK7jO8/a3q27ZMrn337+M1WGmMcaotqZWtTW12nuvvSVlg472jvZhoXR7R/u49+jt61VvX6/Wvro2v2djY1N2lnQulG5saJTD4SjK5wQA00kmk9HA4EA+UC4ULltrJ7vUYTKZjKy1jF3CjEUADQAAgCkpuXq1upYsUfKxxwquMTU1qr3+elWdcYYMgRwmgcPhULA5qGBzUAcecKCkbGdzOBLOB9Jb27aqv3983XbWWrW3R9XeHtXqF1ZLktwut0KhUH6WdGtLiwKBmqJ9TgAwGay12XA5d5jfzuYu9w/0K5PJTHapw1RXVysQCKgmUKNAIKBUKqWXXn5JR73tKC3YY4E6Ojv1wIN/1qbNGxnDgRnLTLXfCgETsXjxYrtixYrJLgMAAEyAjcfVe+216vv2t6Vk4Zes+k48UXW33y7nrFllrA4Yn76+vnwYHQ63qS3cpuQof793VXV19bADDltCIXk8jKIBMDVYazU4OLhDsLxD93J/35QLl6uqqhQI1ChQHVBNIJB9OxDIB85VVVVyOp3DPmbl0yvVEgoNC5s3btqgtnBYixYuKvenUDTGmJXW2sWTXQemJwJoVBQCaAAAprf4P/6h7iVLlHrxxYJrHK2tqvv+9+U76SReyoppK5PJqLOrM9sl3damreGtam9vL+rLxhsbGod1STc2No0ISgBgoqy1isVjwwLlHTuX+/r7lE6nJ7vUYfx+fy5MrsmGy9XbA+aaQI2qq6v5njkEATQmggAaFYUAGgCA6SnT3a2eyy/XwF13jbquaskS1V5/vRz19eUpDCijZDKpSDSitrat2trWprbwVvX29hbt/l0ul4LNwSGhdKtqamr4RQ6Agqy1isfjuc7l7aFy7w6dy+M9jLVUfD7f9rEY1YF80Jy9FlB1dUAuF1NpdwUBNCaCrzYAAABMqsHf/lbd556rzObNBdc499lH9UuXyvuud5WxMqC83G63dt9td+2+2+75awMDA2oL57qkczOlE4nEuO4/lUppy9Yt2rJ1S/6a3+/Pj+5oDbUoFGqRz+eb8OcCYHpIJOLDwuSdHeqXTBVvXFAxeL3efLfyzsZiBAKEy8BUQwc0Kgod0AAATB/pzZvV/cUvKvbrXxde5HIpcNllqrniChlCMUDWWnV1daktnOuSbtuqaHu0qHNT6+vr1RJqVUtLi1pDrWpubuZl6MA0lEgmdjIWY3jn8nh/oVUqHo9ne7fyTuYuBwIBedyeyS5zRqIDGhNBAI2KQgANAMDUZzMZDSxfrp5LL5Xt7i64zn344apfvlzugw4qY3XA9JNKpRSNRnKHHGZHd3SP8rW1qxwOh4LBoFpCrWptyXZL19XWMboDmETJZHLIWIydzF3u71M8Hp/sModxu93ZELl6e6fysBnMgQCHp05hBNCYCAJoVBQCaAAAprbUSy+p64wzlHjkkYJrTHW1aq69VtXnnitD1yUwLoOxQYW3BdK50R2xWKxo9+/z+hRqacnPkm4Jtcjv9xft/oGZLJVKDZu53NvXp/6+PvUOGYsRixfv67kYXC5XwQP9to3G8Hg8/OJqGiOAxkQwFAcAAAAlZxMJ9X33u+q95hpplI4s7wc/qLo77pBr3rwyVgdUHr/Pr3lz52ve3PmSsqM7enq6t3dJt21VJBpROp0e1/3H4jGtX/+G1q9/I3+ttrZ2WJd0sDnIHFZgB+l0uvCBfrnrg4ODk13mME6nMx8iV+9kLEZNoEZer5dwGUBBPBsAAABASSWeeEJdp5+u1PPPF1zjCAZVe8st8p9yCj/AAiVgjFFdXb3q6uq1z977SsoGYe3t7doa3prtkm5rU2dX57j36OnpUU9Pj15Z+7Kk7OiO5qbm/CGHLaEWNTQ08DWOipVOp9U/0D/q3OWBwYHJLnMYh8OxPUiu3vlYDJ/Pz9ctgAlhBAcqCiM4AACYOjK9ver92tfU//3vS6M85/R//vOqu+EGOZqaylgdgJ2Jx+NqC7epLTdLuq2traiBmcfjUSjUotZtoXRLi6qrqot2/0CpZDIZDQz0D5+zPHQsRn+f+vv7J7vMYRwOh6qrqwse6FcTqJHfT7iMsWEEByaCDmgAAAAUXeyPf1T32WcrvWFDwTXOBQtUf/fd8r73vWWsDMBovF6v5s6Zq7lz5krKju7o6+vT1twc6ba2rQpHwkqlUuO6/0QioY0bN2jjxu3fG2oCNWppaVFLKBtIh4Ihud3uonw+wFhkMhkNDA6M2rncP9CvqdTAZ4zJhsvVw7uVtwXM28Jlh8Mx2aUCAAE0AAAAiifd1qaeCy/U4M9/XniR06nAxRcrcOWVclRVla84ALvMGKOamhrV1NRo7732lpQN69o72od1Sbd3tI97j96+XvX29Wrtq2vzezY2NmVnSedC6caGRoI0jIu1VoODg0MO8Bs5d7m/v1+ZTGaySx0m27mcDZKzIfPQcDmgqqpqviYATBuM4EBFYQQHAACTw1qrwR/+UN0XXyzbWXiGrPuww1S/fLncCxeWsToApZZIJBSOhLOzpMNt2tq2tajjCNwut0KhUH6WdGtLiwKBmqLdP6Yna61iscFh3cr5t/u3j8mYauFyVVXVDp3Lw8diVFVVyel0TnaZwDCM4MBE0AENAACACUm9+qq6zjxTiYceKrjG+P2queYaVV9wgYyLp6BApfF4PJo9a7Zmz5qdv9bX15cf29EWblNbuE3JZHJc959MJbVp8yZt2rwpf626unrYAYctoZA8Hu+EPxdMDdZaxePxwp3LubnL6XR6sksdxu/3b+9W3snc5UB1gHAZwIzDs38AAACMi02l1Hfjjeq98kopFiu4znvccaq76y65FiwoY3UAJtu2wG3PBXtKyo7u6OzqzI7uaNuqreGtam9vH/dc3f7+fr32+mt67fXX8tcaGxqHdUk3NjYR9k1B1lolEokh4fKQ7uX+7QHzeGeNl4rP58uHyDVDQ+VcJ3N1dUAufskKACMwggMVhREcAACUR2LlSnWdfrpSzzxTcI1pbFTdTTfJ/9nPyhhTvuIATBvJZFKRaCQbSLe1KRxuU09vT9Hu3+VyKdgcHBJKt6qmpobvSSWWSMSHdSv39vWpv69ve+Dc3zfubvhS8Xq9BQ/02xY6czgmZjJGcGAi+NUcSs4Y835Jt0hySlpurb1uh9tvknRM7t0qSSFrbX3uts9JuiJ32zettT8qS9EAAGCnMv396r3ySvXfdJM0ykxN/6c/rdqbbpIzGCxjdQCmG7fbrd13212777Z7/trAwED+cMOtufEdiURiXPefSqW0ZesWbdm6JX/N7/fnR3e0hloUCrXI5/NN+HOZKZLJ5Jt2Lo/38SoVj8eTD5F3Nnc5EAjI4/ZMdpkAULHogEZJGWOckl6WdJykjZKelPRJa+0LBdZ/UdJCa+2pxphGSSskLZZkJa2UtMhaW/BkIzqgAQAondhf/qLus85S+vXXC65xzpunujvvlO8DHyhjZQAqmbVWXV1dagtnu6Tbwm2KRiNFPViuvr5eLaFWtbS0qDXUqubm5hk5uiOVSuU6lwvMXe7vUzwen+wyh3G73MMO8KsObJ+7vK2TmdngwMTRAY2JoAMapXa4pLXW2tckyRjzc0knStppAC3pk5KuzL19vKQHrbUduY99UNL7Jf1XSSsGAADDpKNR9Vx0kQZ//OPCixwOVZ9/vmquuUaOQKB8xQGoeMYYNTQ0qKGhQfvtu7+kbFAabY/mR3e0hbequ7t73Ht0dXWpq6tLL738oiTJ4XAoGAyqJdSq1pZst3Rdbd20Ht2RTqeGhMlDw+XeXPdyn2KjzPOfDC6Xa9QD/WoCNfJ4PNP6cQGAmYAAGqU2S9KGIe9vlHTEzhYaY+ZJ2kPSw6N87KwS1AgAAHbCWqvBn/1MPRdeqEw0WnCd66CDVL98uTyHH17G6gDMZC6XS60trWptadUhuWuDsUGFw23ZQDoXSo83UM1kMrnDEtv03KrsNZ/Xp1BLS36WdEuoRX6/vzif0ASl0+l8iNw3pFu5d0jQPDg4ONllDuN0OnMjMXY+d7kmUCOv10u4DAAVgAAaU8kpkn5lrU3vygcZY86QdIYkzZ07txR1AQAw46TWrVP3WWcp/sADhRd5vaq58koFLrlEhoOZAEwyv8+veXPna97c+ZKyv0Tr6enZPrqjbasi0YjS6V36cSMvFo9p/fo3tH79G/lrtbW1w7qkg81BuVzF/TE7k8mov79/1LnLAwMDRd1zohwOx/Yu5erhYzECubd9Pj/hMgDMEATQKLVNkuYMeX927trOnCLp3B0+9t07fOzfd/wga+1SSUul7Azo8ZcKAABsOq3+W29V7xVXyI4SaHje/W7VL10q1957l7E6ABg7Y4zq6upUV1enffbeV1K2U7i9vV1bc4cctrVtVWdXwSNm3lRPT496enr0ytqXJWWD1+am5vwhhy2hFjU0NBQMWjOZjAYG+ofPWc4Fy9sC54GBAU2ls5scDoeqq6sLHuhXE6iR30+4DADYjkMIUVLGGJeyhxAeq2yg/KSkT1lrV++wbj9Jf5a0h839pcwdQrhS0mG5ZU8pewhhR6H9OIQQAIDxSz77rLpOP13JUf4tNfX1qr3hBlWdeirhAoCKEI/H1RbePrajra1NA4PF6yh2Op1qaGjUvDlzlclk1NvXp/aOqPr7+5VMJqdUuGyMUXVV9Q6jMIYHzFX+KjkcjskuFUCZcQghJoIOaJSUtTZljDlP0gOSnJLutdauNsZcLWmFtfb+3NJTJP3cDnn2Za3tMMZco2xoLUlXjxY+AwCA8bGDg+q9+mr1ffe70igvTfd9/OOqu/VWOVtby1gdAJSW1+vV3DlzNXdOdpyftVZ9fX3a2rY1F0xvVTgSViqVGtf9p9NpRaMRRaORYpY9LtXVuXC5OtupHAgMn8FcVVVNuAwAKDo6oFFR6IAGAGDXxP/2N3WdcYbSa9cWXOOYPVv1d9wh34c/XMbKAGDqyGQyau9ozwfSbW1tau9on+yyhqnyVxU80C8QCKi6qlpOp3OyywQwTdEBjYmgAxoAAGAGynR2queSSzRw772FFxmjqnPOUe2118pRW1u+4gBginE4HAo2BxVsDurAtxwoSUokEgpHwsNC6b7+vpLs7/f7853LO5u7HKgOEC4DAKYsAmgAAIAZxFqr2H33qfv885Vpayu4zvWWt6h+2TJ5jjqqjNUBwPTh8Xg0e9ZszZ41O3+tr78vf7hhW7hNbeE2JZPJUe/H5/PlguXsWIzqIXOXawIBVVcH5HLxozsAYPriXzEAAIAZIr1hg7rOPVfx3/++8CKPRzVf+5oCl10m4/WWrzgAqACB6oACCwLac8GekrKjO158aY0e+ccjmj9vDzU1NiqRTOr51at09Dvfpb0W7CW32z3JVQMAUFoE0AAAABXOZjIauPNO9Vx+uWxf4ZeHe97+dtUtWyb3/vuXsToAqFwOh0ODsZhO+OAJmj1rTv76vLlz1RYOEz4DAGYEAmgAAIAKlly9Wl1Llij52GMF15iaGtVef72qzjhDxuEoY3UAUPkWLVw04trsWXOGBdIAAFQyAmgAAIAKZONx9X7rW+q77jpplPmjvhNPVN3tt8s5a1YZqwMAAAAwUxBAAwAAVJj4P/6h7iVLlHrxxYJrHK2tqvv+9+U76SQZY8pYHQAAAICZhNdYAgAAVIhMd7e6zj5b7e9856jhc9UZZyi0Zo38H/sY4TMAAACAkqIDGgAAoAIM/uY36j7vPGU2by64xrnPPqpftkzeo48uY2UAAAAAZjI6oAEAAKax9ObN6vjYx9R50kmFw2eXS4ErrlDo2WcJnwEAAACUFR3QAAAA05DNZDSwfLl6Lr1Utru74Dr3EUeoftkyuQ86qIzVAQAAAEAWATQAAMA0k3rpJXUtWaLEo48WXGOqq1Vz7bWqPvdcGaezjNUBAAAAwHYE0AAAANOETSTUd/316r3mGimRKLjO+8EPqu7OO+WaO7eM1QEAAADASATQAAAA00Di8cfVtWSJUs8/X3CNIxhU3a23ynfyyTLGlLE6AAAAANg5DiEEAACYwjK9veo+/3xFjzpq1PDZ//nPK7RmjfynnEL4DAAAAGDKoAMaAABgior94Q/qPuccpTdsKLjGuWCB6u++W973vreMlQEAAADA2NABDQAAMMWk29rUccop6vjwhwuHz06nApdequCqVYTPAAAAAKYsOqABAACmCGutBn/4Q3VffLFsZ2fBde7DDlP98uVyL1xYxuoAAAAAYNcRQAMAAEwBqbVr1XXmmUo8/HDBNcbvV80116j6ggtkXDyNAwAAADD18ZMLAADAJLLJpPpuvFG9V10lxWIF13mPO051d90l14IF5SsOAAAAACaIABoAAGCSJFauVNfppyv1zDMF1ziamlR7003yf+YzMsaUrzgAAAAAKAIOIQQAACizTH+/ui++WNHDDx81fPZ/+tMKrlmjqs9+lvAZAAAAwLREBzQAAEAZxf7yF3WfeabS69YVXOOcN091d90l3/vfX77CAAAAAKAE6IAGAAAog3Q0qs7/+A91HH984fDZ4VD1l76k4PPPEz4DAAAAqAh0QAMAAJSQtVaDP/uZei68UJlotOA618EHq375cnne+tYyVgcAAAAApUUADQAAUCKpdevUfdZZij/wQOFFXq9qrrpKgYsvlnG7y1ccAAAAAJQBATQAAECR2VRK/bfeqt6vf112YKDgOs+73636pUvl2nvvMlYHAAAAAOVDAA0AAFBEyWefVdfppyu5YkXBNaa+XrU33KCqU0+VMaaM1QEAAABAeXEIIQAAQBHYwUH1fOUriixaNGr47PvEJxRas0bVp51G+AwAAACg4tEBDQAAMEHxhx9W15lnKr12bcE1jtmzVX/HHfJ9+MNlrAwAAAAAJhcd0AAAAOOU6ehQ12mnqf3YYwuHz8ao+rzzFFq9mvAZAAAAwIxDBzQAAMAustYqdt996v7iF5UJhwuuc73lLapfvlyeI48sY3UAAAAAMHUQQAMAAOyC9IYN6jrnHMX/8IfCizwe1XztawpcfrmMx1O+4gAAAABgiiGABgAAGAObTmvgzjvV85WvyPb1FVznecc7VLd0qdz771/G6gAAAABgaiKABgAAeBPJ1avVdfrpSj7+eME1prZWtd/5jqrOOEPGwTEbAAAAACARQAMAABRk43H1futb6rvuOimZLLjO92//prrvf1/OWbPKWB0AAAAATH0E0AAAADsRf/RRdS9ZotRLLxVc42htVd3tt8t/0kllrAwAAAAApg9eHwoAADBEprtbXWedpfajjx41fK464wyF1qwhfAYAAACAUdABDQAAkDP4m9+o+9xzldmypeAa5z77qH7ZMnmPPrqMlQEAAADA9EQHNAAAmPHSmzer46ST1HnSSYXDZ5dLgSuuUOjZZwmfAQAAAGCM6IAGAAAzls1kNLBsmXouvVS2p6fgOvcRR6h+2TK5DzqojNUBAAAAwPRHAA0AAGak5IsvqvuMM5R49NGCa0wgoJprr1X1OefIOJ1lrA4AAAAAKgMBNAAAmFFsIqG+73xHvd/8ppRIFFzn/dCHVHfHHXLNnVvG6gAAAACgshBAAwCAGSPx+OPqOv10pVavLrjGEQqp7tZb5fvEJ2SMKWN1AAAAAFB5OIQQAABUvExvr7rPP1/Ro44aNXz2f+ELCq1ZI//JJxM+AwAAAEAR0AENAAAqWuwPf1D3OecovWFDwTXOPfdU/d13y3vssWWsDAAAAAAqHx3QAACgIqXb2tRxyinq+PCHC4fPTqcCl12m4HPPET4DAAAAQAnQAQ0AACqKtVaDP/iBui+5RLazs+A696JFql+2TO6FC8tYHQAAAADMLATQAACgYqTWrlXXmWcq8fDDBdcYv18111yj6gsukHHxVAgAAAAASomfugAAwLRnk0n1fe976v3GN6RYrOA67/vep7q77pJrjz3KWB0AAAAAzFwE0MAU0nX22Uq9+qp8J5wg96JFch96qJL/+pcSTz6pmksvnezyAGBKSqxYoa7TT1fq2WcLrnE0Nan25pvl//SnZYwpY3UAAAAAMLMRQANTiI3FlHjwQSUefDB7wRjJ4ZDn2GPlqKqSe9EiuQ45RI6qqsktFACmgEx/v3r/8z/Vf/PNUiZTcJ3/M59R7Y03yhkMlq84AAAAAIAkAmhgSrEdHTtcsFI6rcRf/qLEX/6SveZ0yvWWt8i9eLE8ixdnO6UPOUTG5yt/wQAwSWIPPKDus85Set26gmuc8+ap7u675Tv++PIVBgAAAAAYhgAamEISK1a8+aJ0WqlVq5RatUqDP/hB9prLJdcBB2QD6W2h9MEHy3i9pS0YAMosHY2q50tf0uBPflJ4kcOh6gsuUM3VV8sRCJSvOAAAAADACATQwBSR3rJFmc2bx/fBqZRSzz6bnX96zz3Za2633AcdlA2jt4XSBx0k4/EUr2gAKBNrrQZ/+lP1XHihMu3tBde5DjlE9cuWyfPWt5axOgAAAABAIcZaO9k1AEWzePFiu2IsXcRTkI3F1Hfbber7xjfkOuwwZdavV/qNN4q7iccj98EH50Npz6JFch14oIzbXdx9AKCIUq+/ru6zz1b8gQcKL/L5VHPllQpcfDHf0wAAAIAiM8astNYunuw6MD3RAQ1MEcbnk6xV4+9/L+8xx0jKvtR84N57FX/4YTmqqpRcsULpDRvGv0kioeSKFUquWCHdfXf2mtcr9yGHyL1oUX6mtOstbyHAATDpbCql/ltvVe/Xvy47MFBwneeYY1R/991y7b13GasDAAAAAIwFHdCoKNO5A3qs0uGwkitXZv9bsUKJFSuU2bSpuJv4fHIfeujwUHr//WVc/M4KQHkkn3lGXaefruTKlQXXmPp61X3ve/J/4QsyxpSxOgAAAGBmoQMaE0EAjYoyEwLonUlv3ZoPpRO5DufMli1F3cP4/XItXCjPkJnSrv32k3E6i7oPgJnNDg6q9xvfUN8NN0jpdMF1vk98QnW33CJna2sZqwMAAABmJgJoTAQBNCrKTA2gdya9efP2QDrXLZ1payvqHqa6Wu6FC4cddOjaZx9CaQDjEn/4YXWdcYbSr75acI1j9mzV33GHfB/+cBkrAwAAAGY2AmhMBAE0KgoBdGHWWmU2b853SOdD6UikqPuYQCAbSi9enD/o0Ln33jIOR1H3AVA5Mh0d6r7kEg3+4AeFFxmj6nPPVc23viVHbW35igMAAABAAI0JIYBGRSGA3jXWWmU2bhwZSre3F3UfU1Mj92GHZQPpXKe0c889CaWBGc5aq9gvf6nu889XJhwuuM51wAGqX7ZMniOPLGN1AAAAALYhgMZEEECjohBAT5y1Vun164cF0okVK2Q7O4u6j6mrGxlKL1jAQWLADJFav17d55yj+B//WHiRx6OaK65Q4LLLZDye8hUHAAAAYBgCaEwEATQqCgF0aVhrlV63Lh9Kb5srbbu6irqPaWiQ+7DDsoH0tlB6/nxCaaCC2HRa/Xfcod6vflW2r6/gOs873qG6Zcvk3m+/MlYHAAAAYGcIoDERBNCoKATQ5WOtVfq117Id0rlO6eTKlbI9PUXdxzQ2yrPtkMNtofTcuYTSwDSUfP55dS1ZouTjjxdcY2prVXv99apasoQxPQAAAMAUQQCNiSCARkUhgJ5cNpNR+tVX8x3S+VB6lC7H8XA0N8s9JJT2LFokx+zZhNLAFGVjMfVee636rrtOSiYLrvN99KOqu+02OWfNKmN1AAAAAN4MATQmggAaFYUAeuqxmYzSr7wyPJR+6inZ/v6i7uMIhfKh9LaZ0o7ddyeUBiZZ/NFH1b1kiVIvvVRwjWO33VT3/e/Lf9JJZawMAAAAwFgRQGMiCKBRUQigpwebTiv18svDDjpMPv207MBAUfdxtLbmx3bkDzrcbbei7gFg5zLd3eq57DIN3H33qOuqzjxTtdddJ0d9fXkKAwAAALDLCKAxEQTQqCgE0NOXTaeVevHF4QcdPv20FIsVdR/H7rvnw+j8TOmWlqLuAcx0g7/+tbrPO0+ZLVsKrnHtu6/qli6V9+ijy1gZAAAAgPEggMZEEECjohBAVxabSim1Zs3wgw6feUaKx4u6j2P27JGhdDBY1D2AmSC9ebO6zztPsd/8pvAil0uByy9Xzde+JuPzla84AAAAAONGAI2JIIBGRSGArnw2mVTqhReGz5R+9lkpkSjqPs65c/NhdP6gw6amou4BVAqbyWhg6VL1XHaZbE9PwXXuI45Q/bJlch90UBmrAwAAADBRBNCYCAJoVBQC6JnJJhJKrV49PJR+7jkpmSzqPs7580fMlHY0NBR1D2C6Sb74orqXLFHiH/8ouMYEAqq59lpVn3OOjNNZxuoAAAAAFAMBNCaCABoVhQAa29h4XMnnnx9+0OGqVVIqVdR9nAsWbO+SXrxY7sMO4zA1zAg2kVDfd76j3m9+c9RXIHhPOEH1d9wh55w5ZawOAAAAQDERQGMiCKBRUQigMRobiym5atWwgw5Tzz8vpdNF3ce5117bu6S3hdK1tUXdA5hMicceU9eSJUqtXl1wjSMUUt2tt8r3iU/IGFPG6gAAAAAUGwE0JoIAGhWFABq7yg4OKvncc8MOOkytXi1lMkXdx7nPPvJsO+Rw8WK5Fy6Uo6amqHsApZbp7VXvV7+q/ttvl0Z5/lB16qmq/e535WhsLGN1AAAAAEqFABoTQQCNikIAjWLIDAwo9eyzw2ZKp9asKW4obYxc++6bH9/hXrxY7kMPlSMQKN4eQBHF/vAHdZ19tjIbNxZc49xzT9UvXSrve95TxsoAAAAAlBoBNCaCABoVhQAapZLp71fymWeGzZROvfjiqF2gu8wYufbff9hMadehh8pRVVW8PYBdlG5rU/f55yv2y18WXuR0KnDJJaq58koZv798xQEAAAAoCwJoTAQBNErKGPN+SbdIckpabq29bidrPiHpKklW0rPW2k/lrqclrcotW2+t/cib7UcAjXLK9PaODKVfeqm4mzgccr3lLcNnSh9yCCEfSs5aq8Ef/EDdl1wi29lZcJ170SLVL18u96GHlq84AAAAAGVFAI2JIIBGyRhjnJJelnScpI2SnpT0SWvtC0PW7C3pl5LeY63tNMaErLXh3G191tpdmkdAAI3JlunpUfLpp4cddJh+5ZXibuJ0ynXAAcND6YMPlvH5irsPZqzU2rXqOvNMJR5+uOAaU1WlmmuuUfX558u4XGWsDgAAAEC5EUBjIgigUTLGmCMlXWWtPT73/lckyVr77SFrrpf0srV2+U4+ngAaFSHT1ZUPpbcddJh+9dXibuJyyXXggcMPOjzoIBmvt7j7oKLZZFJ93/ueer/xDSkWK7jO+773qe6uu+TaY48yVgcAAABgshBAYyJoWUIpzZK0Ycj7GyUdscOafSTJGPN/yo7puMpa++fcbT5jzApJKUnXWWt/W9pygdJw1NfLe8wx8h5zTP5aprNTyaeeGnbQYfr118e/SSql1DPPKPXMM9I992S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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-2/val_accuracy.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation loss curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-2/val_loss.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training time curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YopUF6Kp6aZL/m+RGVfXxqnpEkqcluXtVfSDJ3RbHAAAAAADsAses6g/q7gcd4qm7rmoGAAAAAABWZ91bcAAAAAAAsEsJ0AAAAAAAjBCgAQAAAAAYIUADAAAAADBCgAYAAAAAYIQADQAAAADACAEaAAAAAIARAjQAAAAAACMEaAAAAAAARgjQAAAAAACMEKABAAAAABghQAMAAAAAMEKABgAAAABghAANAAAAAMAIARoAAAAAgBECNAAAAAAAIwRoAAAAAABGCNAAAAAAAIwQoAEAAAAAGCFAAwAAAAAwQoAGAAAAAGCEAA0AAAAAwAgBGgAAAACAEQI0AAAAAAAjBGgAAAAAAEYI0AAAAAAAjBCgAQAAAAAYIUADAAAAADBCgAYAAAAAYIQADQAAAADACAEaAAAAAIARAjQAAAAAACMEaAAAAAAARgjQAAAAAACMEKABAAAAABghQAMAAAAAMEKABgAAAABghAANAAAAAMAIARoAAAAAgBECNAAAAAAAIwRoAAAAAABGCNAAAAAAAIwQoAEAAAAAGCFAAwAAAAAwQoAGAAAAAGCEAA0AAAAAwAgBGgAAAACAEQI0AAAAAAAjBGgAAAAAAEYI0AAAAAAAjBCgAQAAAAAYIUADAAAAADBCgAYAAAAAYIQADQAAAADACAEaAAAAAIARAjQAAAAAACMEaAAAAAAARgjQAAAAAACMEKABAAAAABghQAMAAAAAMEKABgAAAABghAANAAAAAMAIARoAAAAAgBECNAAAAAAAIwRoAAAAAABGCNAAAAAAAIwQoAEAAAAAGCFAAwAAAAAwQoAGAAAAAGCEAA0AAAAAwAgBGgAAAACAEQI0AAAAAAAjBGgAAAAAAEYI0AAAAAAAjBCgAQAAAAAYIUADAAAAADBCgAYAAAAAYIQADQAAAADACAEaAAAAAIARAjQAAAAAACMEaAAAAAAARgjQAAAAAACMEKABAAAAABghQAMAAAAAMEKABgAAAABghAANAAAAAMAIARoAAAAAgBECNAAAAAAAIwRoAAAAAABGCNAAAAAAAIwQoAEAAAAAGCFAAwAAAAAwQoAGAAAAAGCEAA0AAAAAwAgBGgAAAACAEQI0AAAAAAAjBGgAAAAAAEYI0AAAAAAAjBCgAQAAAAAYIUADAAAAADBCgAYAAAAAYIQADQAAAADACAEaAAAAAIARAjQAAAAAACMEaAAAAAAARgjQAAAAAACMEKABAAAAABghQAMAAAAAMEKABgAAAABghAANAAAAAMAIARoAAAAAgBGXigBdVfeoqvdX1Qer6gnrngcAAAAAgEtu7QG6qo5O8ttJ7pnkxkkeVFU3Xu9UAAAAAABcUmsP0Eluk+SD3f2h7v56kpclue+aZwIAAAAA4BK6NAToayY5e9PxxxfnAAAAAADYwY5Z9wBHoqpOTnLy4vArVfX+dc4DLMXVk3x23UPAMjx23QMAO5F1kF3DOghskTWQXWOPr4HXOdILLw0B+hNJvmvT8bUW576pu09JcsoqhwJmVdXp3X3SuucAgHWwDgKwV1kDYe+5NGzB8TdJblBV162qY5P8ZJLT1jwTAAAAAACX0NrvgO7uC6rqUUn+LMnRSX6vu9+z5rEAAAAAALiE1h6gk6S7X5vkteueA1gp2+oAsJdZBwHYq6yBsMdUd697BgAAAAAAdqFLwx7QAAAAAADsQgI0AAAAAAAjBGgAAAAAAEYI0MCoqvIBEwDsWdZBAPYy6yCQCNDAvJPWPQAArJF1EIC9zDoICNDAuM+sewAAWCPrIAB7mXUQSHX3umcAdqmqunKS7u4vr3sWAFg16yAAe5l1ENjPHdDA0lXVravqzCTvSnJmVb2zqm617rkAYBWsgwDsZdZB4EDugAaWrqreleQXuvsvFsffn+R3uvum650MAOZZBwHYy6yDwIHcAQ1MuHD/XzaSpLvfkuSCNc4DAKtkHQRgL7MOAt/CHdDA0lXVc5JcLslLk3SSByb5WpKXJEl3v21twwHAMOsgAHuZdRA4kAANLF1VvekwT3d332VlwwDAilkHAdjLrIPAgQRoAAAAAABG2AMaWLqqenFVXWXT8XWq6o3rnAkAVsU6CMBeZh0EDiRAAxPekuStVXWvqvq5JK9P8pz1jgQAK2MdBGAvsw4C38IWHMCIqvr+JG9K8tkkt+juT615JABYGesgAHuZdRDYzB3QwNJV1UOT/F6Sn07y+0leW1U3W+tQALAi1kEA9jLrIHAgd0ADS1dV/yPJyd39mcXxbZKc0t03X+dcALAK1kEA9jLrIHAgARoYU1WX7+6vLh4f291fX/dMALAq1kEA9jLrILCfLTiApauq21fVe5O8b3F8s/jQCQD2COsgAHuZdRA4kAANTHhOkh9O8rkk6e53JrnTOgcCgBV6TqyDAOxdz4l1ENhEgAZGdPfZB5y6cC2DAMAaWAcB2Musg8Bmx6x7AGBXOruq7pCkq+oySR6d5Kw1zwQAq2IdBGAvsw4C38KHEAJLV1VXT/LcJHdLUkn+Z5JHd/fn1joYAKyAdRCAvcw6CBxIgAYAAAAAYIQ9oIERVfWjhzsGgN3MOgjAXmYdBDYToIEpt76YYwDYzayDAOxl1kHgm2zBAQAAa1JVd0zy5CTXycYHhFeS7u7rrXMuAABYFgEaWJqq+vHDPd/dr1zVLACwattZB6vqfUkek+SMJBduutYHNQGwo3g/CBzKMeseANhV7n2Y5zqJv3AAsJttZx38Yne/bmgeAFgl7weBg3IHNAAArFhV3XLx8AFJjs7Gm/Lz9z/f3W9bx1wAALBsAjSwNFX12MM9393POsxrj05yfDb9ZkZ3f2x50wHArK2sg1X1psNf2ndZ2mAAsAKX5P0gsLvZggNYpitt50VV9S+TPCnJp5NctDjdSW66pLkAYBWOeB3s7jtPDgIAa7Dl94NVdf0kH+/u86vqB7PxHvBF3f2F5Y4GrJM7oIG1q6oPJrmtD1wCYK+pqt9I8oz9b7Sr6juTPK67/+1aBwOAFaiqdyQ5KcmJSV6b5NVJbtLd91rjWMCSHbXuAYDdp6puWFVvrKp3L45vWlWHeyN9dpIvrmY6AJi1xXXwnpvv8uruzyfxphuAHWuL6+BF3X1Bkh9L8lvd/a+TnLCqWYHVEKCBCb+b5IlJvpEk3f2uJD95mOs/lOTNVfXEqnrs/q8VzAkAE7ayDh5dVd+x/6CqLpfkOw5xLQDsBFtZB79RVQ9K8rAkr1mcu8z4hMBK2QMamHD57v7rqtp87oLDXP+xxdexiy8A2Mm2sg7+QZI3VtULF8cPT3Lq5HAAMGwr6+DDkzwyyX/o7g9X1XWTvHh6QGC1BGhgwmcXHybRSVJV909yzqEu7u5fW1x3xcXxV1YxJAAMOaJ1sDbemf9hkncmudvi9FO6+89WNSgADDji94Pd/d4k/2rT8YeTPH0VQwKr40MIgaWrquslOSXJHZJ8PsmHkzykuz9yiOu/Nxv/yn3VxanPJvnp7n7P/LQAsFxbWQer6szu/r7VTggAc7a4Dt4gyVOT3DjJZfef7+7rrWRYYCUEaGBMVV0hyVHd/eWLue4vk/yb7n7T4vgHk/xGd99hfEgAGHIk62BVnZrked39N6ubDADmHeE6+JYkT0ry7CT3zsaWHEd196+uZkpgFQRoYGkWHxz4xe5+wQHnH5HkSt39nEO87p3dfbOLOwcAl2bbWQer6n1JvjvJR5Ocl6SSdHffdH5iAFieba6DZ3T3rTb/RtD+cysZGlgJARpYmqo6I8ntuvsbB5w/Nsnph3ozXVWvSvK2/OOHTTwkya26+8cm5wWAZdrOOlhV1znY9+ruj85MCQAztrkO/mWS70/yR0n+V5JPJHlad99oBSMDK3LUugcAdpVjDvzLRpJ099ezcUfXofxMkn1JXrn42rc4BwA7yZbXwUVoPi4bv3Z87yTHic8A7FDbeT/46CSXz8YHEd4qGzcjPWxsQmAtjln3AMCuclRVHd/dn958sqqOP9yLuvvz2fTJxwCwQ215HayqRyf5uWz8A2ySvKSqTunu3xqcEwAmbGkdrKqjkzywu38pyVeysf8zsAsJ0MAy/WaSP6mqx2VjS41k41+xfzPJMw/1oqp6U5Jv2w+ou+8yMSQADNnOOviIJLft7vOSpKqenuT/JhGgAdhptrQOdveFVfX9K5wPWBMBGlia7n5RVZ2b5N8n+d5sROX3JPnV7n7dYV76S5seXzbJP09ywdigADBgm+tgJblw0/GFOfy2VQBwqbTNdfDtVXVakv+WjQ/j3f+9XnmI64EdyIcQApdKVfXX3X2bdc8BAJOq6rHZ2OvyVYtT90tyanc/e21DAcCKVNULD3K6u9tnAsEuIkADo6rqbd19y4u55qqbDo/Kxq9p/SeffAzATneE6+Atk+z/FeS/6O63z08GAPOOZB0Edj9bcADTjuTXiM/Ixq9nVTa23vhwNvbEBICd7rDrYFW9uLsfmn/cK3PzOQDY6S5uHbxhkucnOb67v7eqbprkPt396yuZDlgJARqY9icXd0F3X3cVgwDAGlzcOniTzQdVdXQ2fhMIAHaDi1sHfzfJv07yX5Kku99VVX+YRICGXcQWHMDaVNWPH+55HzwBwG5VVU9M8itJLpfkq/tPJ/l6klO6+4nrmg0AVqWq/qa7b11Vb+/uWyzOvaO7b77m0YAlcgc0sHRVdbskv5XknyU5NsnRSc7r7isfcOm9D/Ly/VtxdBIBGoAd50jWwe5+apKnVtVTxWYAdpMtvB9Mks9W1fWz8f4vVXX/JOesalZgNQRoYMLzkvxkkv+W5KQkP53khgde1N0PT5Kqelz+MTxn8fiLVXXz7n7HKgYGgCU6onVw4UZVda8kf9rdF61oPgCYtJV18EnZ2H7je6rqE9n4PKCXr2JIYHWOWvcAwO7U3R9McnR3X9jdL0xyj8Ncfqskj0xyQpJrJPn5xfW/W1W/PD4sACzZFtbB30ny4CQfqKqnVdWNVjYkAAzZwjr4n5M8Jsm+JN+T5LeTPGQ1UwKr4g5oYMJXq+rYJO+oqmdk41eoDvcPXtdKcsvu/kqSVNWTsvFhFXdKckaSZwzPCwDLdMTrYHe/IckbquoqSR60eHx2Nj6U6SXd/Y1VDQ0AS7KV94P3z8ad0j+V5P/Lxt3SP7SSKYGVcQc0MOGh2djn61FJzkvyXUn++WGu/ydJzt90/I0kx3f3PxxwHgB2gi2tg1V1tSQPT/KzSd6e5LlJbpnk9eOTAsDyHfE62N0fysY/wL4qGzH6h7v7iyuaE1iR6u51zwDscVX175L8WJJXL07dO8lpSf5jklO6+8Hrmg0AJlXVq5LcKMmLk7ywuz+16bnTu/uktQ0HAEOq6swsPnhw4Z8k+WIWNyB1903XMRcwQ4AGlqaqXtHdDzjIXyaSHP4vEVV1UpI7Lg7/T3efPjQmAIzYzjpYVfdMcpNsrIEXJXlLkud399em5wWAZdrKOlhV1znc9+rujw6MCKyJAA0sTVWd0N3nHOovE/4SAcButp11sKpekeRLSf5gceqnkhzX3T8xNykALJ/3g8ChCNDAUlXV0Une0N13XvcsALBqW10Hq+q93X3jizsHADuB94PAwfgQQmCpuvvCJBdV1VXWPQsArNo21sG3VdXt9h9U1W2T2IYKgB3J+0HgYI5Z9wDArvSVJGdW1euz8anHSZLu/lfrGwkAVmYr6+CtkvxlVX1scXztJO/fv3+mD2ECYAfyfhD4FgI0MOGViy8A2Iu2sg7eY3IQAFgD7weBb2EPaAAAAAAARrgDGli6qrpBkqcmuXGSy+4/393XW9tQALAi1kEA9jLrIHAgH0IITHhhkucnuSDJnZO8KMlL1joRAKyOdRCAvcw6CHwLW3AAS1dVZ3T3rarqzO7+vs3n1j0bAEyzDgKwl1kHgQPZggOYcH5VHZXkA1X1qCSfSHLFNc8EAKtiHQRgL7MOAt/CHdDA0lXVrZOcleS4JE9JcpUkz+juv1rnXACwCtZBAPYy6yBwIAEaAAAAAIARtuAAlqaq/jjJIf9Vq7vvs8JxAGClrIMA7GXWQeBQBGhgmZ657gEAYI2sgwDsZdZB4KBswQEAAAAAwAh3QANLV1U3SPLUJDdOctn957v7emsbCgBWxDoIwF5mHQQOdNS6BwB2pRcmeX6SC5LcOcmLkrxkrRMBwOpYBwHYy6yDwLewBQewdFV1RnffqqrO7O7v23xu3bMBwDTrIAB7mXUQOJAtOIAJ51fVUUk+UFWPSvKJJFdc80wAsCrWQQD2Musg8C3cAQ0sXVXdOslZSY5L8pQkV0nyjO7+q3XOBQCrYB0EYC+zDgIHEqABAAAAABhhCw5gaarqtMM93933WdUsALBq1kEA9jLrIHAoAjSwTLdPcnaSlyZ5a5Ja7zgAsFLWQQD2MusgcFC24ACWpqqOTnL3JA9KctMkf5Lkpd39nrUOBgArYB0EYC+zDgKHctS6BwB2j+6+sLv/tLsfluR2ST6Y5M2LTz4GgF3NOgjAXmYdBA7FFhzAUlXVdyT5kWz8q/eJSf5TkletcyYAWBXrIAB7mXUQOBhbcABLU1UvSvK9SV6b5GXd/e41jwQAK2MdBGAvsw4ChyJAA0tTVRclOW9xuPn/uVSS7u4rr34qAFgN6yAAe5l1EDgUARoAAAAAgBE+hBAAAAAAgBECNAAAAAAAIwRoAAAAAABGCNAAAAAAAIwQoAEAAAAAGCFAAwAAAAAwQoAGAAAAAGCEAA0AAAAAwAgBGgAAAACAEf8PBG+nB3JSCm4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-2/stats_training_time.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max GPU usages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-2/stats_max_gpu_usage.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Validation accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"workspace/comparison/Sample-Comparison-2/stats_best_val_acc.png\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goals\n",
    "\n",
    "### 1. Understand how you can compare experiments on same data\n",
    "\n",
    "### 2. Understand where to look for comparison data - logs, charts, graphs, etc when comparing experiments\n",
    "\n",
    "\n",
    "### 3. Steps completed\n",
    "    - Train three classifiers by keeping all parameters same and changing backends\n",
    "    - Compare the three experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}