exercise-stochastic-gradient-descent.ipynb

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   "source": [
    "**This notebook is an exercise in the [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/ryanholbrook/stochastic-gradient-descent).**\n",
    "\n",
    "---\n"
   ]
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   "source": [
    "# Introduction #\n",
    "\n",
    "In this exercise you'll train a neural network on the *Fuel Economy* dataset and then explore the effect of the learning rate and batch size on SGD.\n",
    "\n",
    "When you're ready, run this next cell to set everything up!"
   ]
  },
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n",
      "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n",
      "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"
     ]
    }
   ],
   "source": [
    "# Setup plotting\n",
    "import matplotlib.pyplot as plt\n",
    "from learntools.deep_learning_intro.dltools import animate_sgd\n",
    "plt.style.use('Solarize_Light2')\n",
    "# Set Matplotlib defaults\n",
    "plt.rc('figure', autolayout=True)\n",
    "plt.rc('axes', labelweight='bold', labelsize='large',\n",
    "       titleweight='bold', titlesize=18, titlepad=10)\n",
    "plt.rc('animation', html='html5')\n",
    "\n",
    "# Setup feedback system\n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.deep_learning_intro.ex3 import *"
   ]
  },
  {
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   "source": [
    "In the *Fuel Economy* dataset your task is to predict the fuel economy of an automobile given features like its type of engine or the year it was made. \n",
    "\n",
    "First load the dataset by running the cell below."
   ]
  },
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     "text": [
      "Input shape: [50]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer, make_column_selector\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "fuel = pd.read_csv('../input/dl-course-data/fuel.csv')\n",
    "\n",
    "X = fuel.copy()\n",
    "# Remove target\n",
    "y = X.pop('FE')\n",
    "\n",
    "preprocessor = make_column_transformer(\n",
    "    (StandardScaler(),\n",
    "     make_column_selector(dtype_include=np.number)),\n",
    "    (OneHotEncoder(sparse_output=False),\n",
    "     make_column_selector(dtype_include=object)),\n",
    ")\n",
    "\n",
    "X = preprocessor.fit_transform(X)\n",
    "y = np.log(y) # log transform target instead of standardizing\n",
    "\n",
    "input_shape = [X.shape[1]]\n",
    "print(\"Input shape: {}\".format(input_shape))"
   ]
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   "source": [
    "Take a look at the data if you like. Our target in this case is the `'FE'` column and the remaining columns are the features."
   ]
  },
  {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EngDispl</th>\n",
       "      <th>NumCyl</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>FE</th>\n",
       "      <th>AirAspirationMethod</th>\n",
       "      <th>NumGears</th>\n",
       "      <th>TransLockup</th>\n",
       "      <th>TransCreeperGear</th>\n",
       "      <th>DriveDesc</th>\n",
       "      <th>IntakeValvePerCyl</th>\n",
       "      <th>ExhaustValvesPerCyl</th>\n",
       "      <th>CarlineClassDesc</th>\n",
       "      <th>VarValveTiming</th>\n",
       "      <th>VarValveLift</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.7</td>\n",
       "      <td>8</td>\n",
       "      <td>AM6</td>\n",
       "      <td>28.0198</td>\n",
       "      <td>NaturallyAspirated</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <td>25.6094</td>\n",
       "      <td>NaturallyAspirated</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TwoWheelDriveRear</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2Seaters</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.2</td>\n",
       "      <td>8</td>\n",
       "      <td>M6</td>\n",
       "      <td>26.8000</td>\n",
       "      <td>NaturallyAspirated</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>AllWheelDrive</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2Seaters</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.2</td>\n",
       "      <td>8</td>\n",
       "      <td>AM6</td>\n",
       "      <td>25.0451</td>\n",
       "      <td>NaturallyAspirated</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>AllWheelDrive</td>\n",
       "      <td>2</td>\n",
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       "      <td>2Seaters</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.2</td>\n",
       "      <td>10</td>\n",
       "      <td>AM6</td>\n",
       "      <td>24.8000</td>\n",
       "      <td>NaturallyAspirated</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AllWheelDrive</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2Seaters</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   EngDispl  NumCyl Transmission       FE AirAspirationMethod  NumGears  \\\n",
       "0       4.7       8          AM6  28.0198  NaturallyAspirated         6   \n",
       "1       4.7       8           M6  25.6094  NaturallyAspirated         6   \n",
       "2       4.2       8           M6  26.8000  NaturallyAspirated         6   \n",
       "3       4.2       8          AM6  25.0451  NaturallyAspirated         6   \n",
       "4       5.2      10          AM6  24.8000  NaturallyAspirated         6   \n",
       "\n",
       "   TransLockup  TransCreeperGear          DriveDesc  IntakeValvePerCyl  \\\n",
       "0            1                 0  TwoWheelDriveRear                  2   \n",
       "1            1                 0  TwoWheelDriveRear                  2   \n",
       "2            1                 0      AllWheelDrive                  2   \n",
       "3            1                 0      AllWheelDrive                  2   \n",
       "4            0                 0      AllWheelDrive                  2   \n",
       "\n",
       "   ExhaustValvesPerCyl CarlineClassDesc  VarValveTiming  VarValveLift  \n",
       "0                    2         2Seaters               1             0  \n",
       "1                    2         2Seaters               1             0  \n",
       "2                    2         2Seaters               1             0  \n",
       "3                    2         2Seaters               1             0  \n",
       "4                    2         2Seaters               1             0  "
      ]
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     "output_type": "execute_result"
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   "source": [
    "# Uncomment to see original data\n",
    "fuel.head()\n",
    "# Uncomment to see processed features\n",
    "# pd.DataFrame(X[:10,:]).head()"
   ]
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   },
   "source": [
    "Run the next cell to define the network we'll use for this task."
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(128, activation='relu', input_shape=input_shape),\n",
    "    layers.Dense(128, activation='relu'),    \n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1),\n",
    "])"
   ]
  },
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   "cell_type": "markdown",
   "id": "c638553f",
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    },
    "tags": []
   },
   "source": [
    "# 1) Add Loss and Optimizer\n",
    "\n",
    "Before training the network we need to define the loss and optimizer we'll use. Using the model's `compile` method, add the Adam optimizer and MAE loss."
   ]
  },
  {
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   },
   "outputs": [
    {
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       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
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       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='mae',\n",
    ")\n",
    "# Check your answer\n",
    "q_1.check()"
   ]
  },
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   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_1.hint()\n",
    "#q_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b400cd8a",
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   "source": [
    "# 2) Train Model\n",
    "\n",
    "Once you've defined the model and compiled it with a loss and optimizer you're ready for training. Train the network for 200 epochs with a batch size of 128. The input data is `X` with target `y`."
   ]
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     "text": [
      "Epoch 1/200\n",
      "9/9 [==============================] - 5s 4ms/step - loss: 2.7702\n",
      "Epoch 2/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9772\n",
      "Epoch 3/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.6026\n",
      "Epoch 4/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3766\n",
      "Epoch 5/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2771\n",
      "Epoch 6/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1827\n",
      "Epoch 7/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1368\n",
      "Epoch 8/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1136\n",
      "Epoch 9/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0958\n",
      "Epoch 10/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0869\n",
      "Epoch 11/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0759\n",
      "Epoch 12/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0667\n",
      "Epoch 13/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0629\n",
      "Epoch 14/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0566\n",
      "Epoch 15/200\n",
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      "Epoch 26/200\n",
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      "Epoch 27/200\n",
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      "Epoch 30/200\n",
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      "Epoch 42/200\n",
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      "Epoch 44/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0351\n",
      "Epoch 45/200\n",
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      "Epoch 46/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0368\n",
      "Epoch 47/200\n",
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      "Epoch 48/200\n",
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      "Epoch 49/200\n",
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      "Epoch 50/200\n",
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      "Epoch 51/200\n",
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      "Epoch 52/200\n",
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      "Epoch 53/200\n",
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      "Epoch 54/200\n",
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      "Epoch 55/200\n",
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      "Epoch 56/200\n",
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      "Epoch 57/200\n",
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      "Epoch 58/200\n",
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      "Epoch 59/200\n",
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      "Epoch 60/200\n",
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      "Epoch 61/200\n",
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      "Epoch 62/200\n",
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      "Epoch 63/200\n",
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      "Epoch 64/200\n",
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      "Epoch 65/200\n",
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      "Epoch 66/200\n",
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      "Epoch 67/200\n",
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      "Epoch 68/200\n",
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      "Epoch 69/200\n",
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      "Epoch 70/200\n",
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      "Epoch 71/200\n",
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      "Epoch 72/200\n",
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      "Epoch 73/200\n",
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      "Epoch 75/200\n",
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      "Epoch 76/200\n",
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      "Epoch 103/200\n",
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      "Epoch 104/200\n",
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      "Epoch 105/200\n",
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      "Epoch 106/200\n",
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      "9/9 [==============================] - 0s 4ms/step - loss: 0.0309\n",
      "Epoch 108/200\n",
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      "Epoch 109/200\n",
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      "Epoch 110/200\n",
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      "Epoch 111/200\n",
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      "Epoch 112/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0320\n",
      "Epoch 113/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0295\n",
      "Epoch 114/200\n",
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      "Epoch 115/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0282\n",
      "Epoch 116/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0318\n",
      "Epoch 117/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0451\n",
      "Epoch 118/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0403\n",
      "Epoch 119/200\n",
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      "Epoch 120/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0375\n",
      "Epoch 121/200\n",
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      "Epoch 122/200\n",
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      "Epoch 123/200\n",
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      "Epoch 124/200\n",
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      "Epoch 125/200\n",
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      "Epoch 126/200\n",
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      "Epoch 127/200\n",
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      "Epoch 128/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0294\n",
      "Epoch 129/200\n",
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      "Epoch 130/200\n",
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      "Epoch 131/200\n",
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      "Epoch 132/200\n",
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      "Epoch 133/200\n",
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      "Epoch 134/200\n",
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      "Epoch 135/200\n",
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      "Epoch 136/200\n",
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      "Epoch 137/200\n",
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      "Epoch 144/200\n",
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      "Epoch 146/200\n",
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      "Epoch 147/200\n",
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      "Epoch 150/200\n",
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      "Epoch 153/200\n",
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      "Epoch 154/200\n",
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      "Epoch 155/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0341\n",
      "Epoch 156/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0279\n",
      "Epoch 157/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0280\n",
      "Epoch 158/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0265\n",
      "Epoch 159/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0256\n",
      "Epoch 160/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0271\n",
      "Epoch 161/200\n",
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      "Epoch 162/200\n",
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      "Epoch 163/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0404\n",
      "Epoch 164/200\n",
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      "Epoch 165/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0301\n",
      "Epoch 166/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0385\n",
      "Epoch 167/200\n",
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      "Epoch 168/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0278\n",
      "Epoch 169/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0264\n",
      "Epoch 170/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0270\n",
      "Epoch 171/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0255\n",
      "Epoch 172/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0253\n",
      "Epoch 173/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0268\n",
      "Epoch 174/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0290\n",
      "Epoch 175/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0293\n",
      "Epoch 176/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0278\n",
      "Epoch 177/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0275\n",
      "Epoch 178/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0256\n",
      "Epoch 179/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0277\n",
      "Epoch 180/200\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0274\n",
      "Epoch 181/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0276\n",
      "Epoch 182/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0263\n",
      "Epoch 183/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0260\n",
      "Epoch 184/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0283\n",
      "Epoch 185/200\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0276\n",
      "Epoch 186/200\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 0.0247\n",
      "Epoch 187/200\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 0.0279\n",
      "Epoch 188/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0330\n",
      "Epoch 189/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0308\n",
      "Epoch 190/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0302\n",
      "Epoch 191/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0274\n",
      "Epoch 192/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0342\n",
      "Epoch 193/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0297\n",
      "Epoch 194/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0346\n",
      "Epoch 195/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0300\n",
      "Epoch 196/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0273\n",
      "Epoch 197/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0274\n",
      "Epoch 198/200\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0292\n",
      "Epoch 199/200\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 0.0259\n",
      "Epoch 200/200\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 0.0309\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "history = model.fit(\n",
    "    X,y,\n",
    "    batch_size=128,\n",
    "    epochs=200,    \n",
    ")\n",
    "\n",
    "# Check your answer\n",
    "q_2.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e043967f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-30T16:37:43.346136Z",
     "iopub.status.busy": "2023-06-30T16:37:43.345814Z",
     "iopub.status.idle": "2023-06-30T16:37:43.350077Z",
     "shell.execute_reply": "2023-06-30T16:37:43.349052Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.059541,
     "end_time": "2023-06-30T16:37:43.352333",
     "exception": false,
     "start_time": "2023-06-30T16:37:43.292792",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_2.hint()\n",
    "#q_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3de1f8",
   "metadata": {
    "papermill": {
     "duration": 0.051691,
     "end_time": "2023-06-30T16:37:43.458032",
     "exception": false,
     "start_time": "2023-06-30T16:37:43.406341",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The last step is to look at the loss curves and evaluate the training. Run the cell below to get a plot of the training loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "028dcef9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-30T16:37:43.562953Z",
     "iopub.status.busy": "2023-06-30T16:37:43.562566Z",
     "iopub.status.idle": "2023-06-30T16:37:44.089628Z",
     "shell.execute_reply": "2023-06-30T16:37:44.088734Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.581587,
     "end_time": "2023-06-30T16:37:44.091727",
     "exception": false,
     "start_time": "2023-06-30T16:37:43.510140",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dLiKNfYksznC/WCIiIkpUTOxCWA0R4XAsERERJRImdiGSmNgRERFRgmJiF4JDsURERJSomNiFSLLoiyVYsSMiIqJEwsQuhGmOnS92x0FEREQULSZ2IWwciiUiIqIExcQuhNXQHoZDsURERJRImNiF4KpYIiIiSlRM7EIwsSMiIqJExcQuBBdPEBERUaJiYheCFTsiIiJKVEzsQrBBMRERESUqJnYhuCqWiIiIEhUTuxCmoVifjN2BEBEREUWJiV0IDsUSERFRomJiF4KLJ4iIiChRMbELkcQ5dkRERJSgmNiFSFJE8DITOyIiIkokTOxCGBsUt7JBMRERESUQJnYhOMeOiIiIEhUTuxBM7IiIiChRMbELwQbFRERElKiY2IUwNyiO3XEQERERRYuJXQg2KCYiIqJExcQuBOfYERERUaJiYhfCysSOiIiIEhQTuxDGnSc4FEtERESJhIldCPPiCRm7AyEiIiKKEhO7EJxjR0RERImKiV0IJnZERESUqJjYheAcOyIiIkpUTOxCWIR+mRU7IiIiSiRM7EIIIYLDsdx5goiIiBIJE7swgokdK3ZERESUQJjYhWFlYkdEREQJiIldGIEFFFw8QURERImEiV0YHIolIiKiRMTELgwrF08QERFRArJGc+OaukY8/9ZmnKysQXKSFaUTRmDpglIoStv8sKm5Bc+t2oSt+07g51++CQW5mWEfc/eRcjz+8lp8+86rMa64sEtPoqcFKnYciiUiIqJEElVi9+SKdSguzMXd98xHo7sJj764GumOVCyeM8l0u7pGN/74zFsYOWRwh4/nbW7By+9uRXJSVIfR6zgUS0RERIko4qHYssoanD57AUuvLEVqig152RlYOHsiNu460ua2TncTbl44E9fPm9bhY77xwS6MLS6Ew54c9YH3psDiiRYVkFLG9mCIiIiIIhR5YldVi5zMNDhS9SRsWH42qmsb4PE2m247ND8bU8cO7/DxTp+9gM17j2PpgtIoD7n3GfeL9TGvIyIiogQR8Rioy+OFPcVmui6Q5Lk8XqQm28LdLSwpJZ5dtQk3zp+BdEdKxPcTQkAI0fkNgeC8v3Dz/zqTZFEBaBmdTyqwKZH9zXjVnVj0R4yHGeNhxnjoGAszxsOM8dD1diyklBGPIHZrcltXi1kbdh0FpMTcaWOiup/DbofFYon6PtFKSWoE0AoASE51ID2lf5y0XYlFf8Z4mDEeZoyHjrEwYzzMGA9db8XC5/PB6XJFdNuIE7s0ewqcbq/pOrfHC+H/XaSc7ia8/v4OfP22RRFX3wJcbndUFTuH3Q6X2w1VjW4VhJB6n5PzjS4orYlfsetqLPojxsOM8TBjPHSMhRnjYcZ46Ho7FtHM9484sSsuzMH5eiec7qZgIneysgaFgzORYkuK+A/uPXYKTo8Xf3runeB17qZmPP7yWsyZXILbrp7T7n2jKUUGqKoadZCNc+yaW1WoamIndgFdiUV/xniYMR5mjIeOsTBjPMwYD108xCLixG54QQ5GFOXi1bXb8clFF6O+0Y01W/fjqtkTAQD3P/EKPn3dZRg9LL/Dxym9aATGjzD3q3voqTfxyUWz2lwfK1ZDYscmxURERJQooppj96VlC/Dcqk2475EVsKfYMHtyCeaXjgcAVNc2wNvcAgBYuWE3Vm7YHbzfA39/DQCwZO5ULJk7FbaQvnVCEUizJ5tW3MaSsWLHJsVERESUKKJK7LIyHPja8qvC/u6J++4KXg4kcJH69b23RHMYvc6Y2LFJMRERESWK/rHcs4dZmdgRERFRAmJiF0aSoaMK59gRERFRomBiF0aqYYDa0xq74yAiIiKKBhO7MOxM7IiIiCgBMbELI9XQls/dys1iiYiIKDEwsQvDNBTbErvjICIiIooGE7sw7FZ9pwkOxRIREVGiYGIXhmkolhU7IiIiShBM7MLg4gkiIiJKREzswjDOsXMzsSMiIqIEwcQuDONQLBdPEBERUaJgYheG3VSxY7sTIiIiSgxM7MJgxY6IiIgSERO7MGwKYPF3POEcOyIiIkoUTOzCEELA7q/acVUsERERJQomdu0IrIzlUCwRERElCiZ27QgkdhyKJSIiokTBxK4dxqFYKbkyloiIiOIfE7t2BCp2qgS8vtgeCxEREVEkmNi1I5XbihEREVGCYWLXDnuSCF7mAgoiIiJKBEzs2sH9YomIiCjRMLFrh51DsURERJRgmNi1w7itmJtDsURERJQAmNi1w25M7FixIyIiogTAxK4dplWxLexjR0RERPGPiV077Fw8QURERAmGiV07Uo3tTpjYERERUQJgYtcO06pYLp4gIiKiBMDErh3sY0dERESJholdO4ztTlixIyIiokTAxK4dbFBMREREiYaJXTvMQ7Fsd0JERETxj4ldO+wciiUiIqIEw8SuHckWQPF3POHiCSIiIkoETOzaIYQIDsdyr1giIiJKBEzsOhBYQMHFE0RERJQImNh1INDyhIkdERERJQImdh0wDsVKyZWxREREFN+snd9EV1PXiOff2oyTlTVITrKidMIILF1QCkVpmx82NbfguVWbsHXfCfz8yzehIDcz+Lvmllb8e9127DhUhqbmFhQX5ODWxbMxJC+r20+oJwWGYn0SaFEBmyW2x0NERETUkagqdk+uWIesdAceuGcZvvWpq7HnSAVWbz3Q5nZ1jW785n9ehyJE2Md5dd12HKs4ix98dgke+uZy5GVn4IkVa7v2DHqRafcJDscSERFRnIs4sSurrMHpsxew9MpSpKbYkJedgYWzJ2LjriNtbut0N+HmhTNx/bxpYR8rxZaEZQtnIntQGmxJVlwx8yKcu9CIukZ3l59Ib0i16okpV8YSERFRvIt4KLasqhY5mWlwpCYHrxuWn43q2gZ4vM1ITbYFrx+an42h+dmoqWsM+1ifuGKG6efzDS4kWS1wpNrC3j5ACAHRThUwVGB4ONwwcaTsSWrwcpNPgaJE9rfjTU/Eoj9hPMwYDzPGQ8dYmDEeZoyHrrdjIaWMeK5/xImdy+OFPcWceAWSPJfHa0rsouHyePHSO1uwaPZEJFk7PhyH3Q6LJbqJbg67vUvHBQCZdjcALwBAJKUiPS2qKYlxpzux6I8YDzPGw4zx0DEWZoyHGeOh661Y+Hw+OF2uiG7brUylu+tE6xvd+MsL72JYQU67w7ZGLrc7qoqdw26Hy+2Gqqqd3yEMi/QFL9c0utGYlpjfSnoiFv0J42HGeJgxHjrGwozxMGM8dL0di2g6c0Sc2KXZU+B0e03XuT1eCP/vonXuQgP++OzbmDx6GJYvnhVR+TKaUmSAqqpdDnKqRf9b7maZ8Cdud2LRHzEeZoyHGeOhYyzMGA8zxkMXD7GIOLErLszB+XonnO6mYCJ3srIGhYMzkWJL6uTeZk53E/783Lu4bOoYXHf5tKju25fshqflapEAEnOOHREREQ0MEY8tDi/IwYiiXLy6djs83mZU1dRhzdb9mDdjHADg/idewbGK6oge69V12zFySG5cJ3UA250QERFRYolqjt2Xli3Ac6s24b5HVsCeYsPsySWYXzoeAFBd2wBvs9YTZOWG3Vi5YXfwfg/8/TUAwJK5U7Fk7lR8uPsYFCGw89DTpse/87rLMGdySbeeUE+yWwUCMwk9bHdCREREcS6qxC4rw4GvLb8q7O+euO+u4OVAAteex3/y2Wj+bMykGqLjZsWOiIiI4lxiLvPsIxyKJSIiokTCxK4DdkPFjkOxREREFO+Y2HWAQ7FERESUSJjYdcDY7sTT0t12zERERES9i4ldB1ixIyIiokTCxK4DKcY5dkzsiIiIKM4xseuAIkRwAQUXTxAREVG8Y2LXiUDLEw7FEhERUbxjYteJwDw7Nyt2REREFOeY2HUisDLW1QJIyZWxREREFL+Y2HUi3Z/Y+STQxOFYIiIiimNM7DqRZtMvOzkcS0RERHGMiV0n0mwieNnZHMMDISIiIuoEE7tOpBt2n2hkYkdERERxjIldJxwciiUiIqIEwcSuE+nGxK6Zq2KJiIgofjGx6wQXTxAREVGiYGLXibQkLp4gIiKixMDErhPGoVguniAiIqJ4xsSuEw7DqlgOxRIREVE8Y2LXCfPiidgdBxEREVFnmNh1Is00FMtVsURERBS/mNh1wjgU6+JQLBEREcUxJnadsCoCdqt2mYsniIiIKJ4xsYtAYDiWc+yIiIgonjGxi0CafziWiR0RERHFMyZ2EQhU7Jp8QKvKBRREREQUn5jYRSCNLU+IiIgoATCxi4BxWzEuoCAiIqJ4xcQuAqYmxWx5QkRERHGKiV0EOBRLREREiYCJXQTMu0/E7jiIiIiIOsLELgLpht0nnNxWjIiIiOIUE7sIODjHjoiIiBIAE7sIpNv0VbGcY0dERETxioldBNIMQ7GcY0dERETxioldBIyLJ1wciiUiIqI4xcQuAummVbFcPEFERETxyRrNjWvqGvH8W5txsrIGyUlWlE4YgaULSqEobfPDpuYWPLdqE7buO4Gff/kmFORmBn/X0tqKF9/ein3HT6HV58OYYQW449pLkO5I6fYT6g0O46pYVuyIiIgoTkVVsXtyxTpkpTvwwD3L8K1PXY09RyqweuuBNrera3TjN//zOhQhwjwK8Oq6HaiorsUP7roOv/jKzRACePqNDV17Bn0g2QIk+SPFxRNEREQUryJO7Moqa3D67AUsvbIUqSk25GVnYOHsidi460ib2zrdTbh54UxcP29am9+pqopNu49iydypyM5wwJGajBuvmIF9x06hrtHdrSfTW4QQwXl2XDxBRERE8SryxK6qFjmZaXCkJgevG5afjeraBni85mxnaH42po4dHvZxzl5ohMfbguEFOcHrCnIGISnJivIztdEef58JrIzl4gkiIiKKVxHPsXN5vLCn2EzXBZI8l8eL1GRbuLuFfRwAbR7LnmKD09PU4X2FEBDtDO+GCsz7Czf/ryvSbSoAqQ3FCtHuMHM86ulYJDrGw4zxMGM8dIyFGeNhxnjoejsWUkpIGdnizagWT7T5Q925c+hjRfBgDrsdFoslqsd12O1dPCKzQamNAFohASi2NKQnJ05iF9BTsegvGA8zxsOM8dAxFmaMhxnjoeutWPh8PjhdrohuG3Fil2ZPgdPtNV3n9ngh/L+L5nEAwOnxItmmLzd1N3mR3snjuNzuqCp2DrsdLrcbqqpGfHztSbX4gperLjiBtMRJ7Ho6FomO8TBjPMwYDx1jYcZ4mDEeut6ORaTVOiCKxK64MAfn651wupuCydnJyhoUDs5EiiFB68zgzDTYU2woP1OLnEFpAIDTZy+g1aeiuDC3w/tGU4oMUFW1R4Js3H2i3qsiz544iV1AT8Wiv2A8zBgPM8ZDx1iYMR5mjIcuHmIR8WDw8IIcjCjKxatrt8PjbUZVTR3WbN2PeTPGAQDuf+IVHKuo7vwPKgounz4WqzbuwfkGF5zuJvx73XZMHzccGWmpXX8mvcy4+wRbnhAREVE8imqO3ZeWLcBzqzbhvkdWwJ5iw+zJJZhfOh4AUF3bAG+ztmR05YbdWLlhd/B+D/z9NQDAkrlTsWTuVNwwfzqamlvx0FNvwqequGhkEW6/ek5PPadeYazYMbEjIiKieBRVYpeV4cDXll8V9ndP3HdX8HIggWv3j1osuP2aObj9mvhO5oyM24o5WySAxBuKJSIiov6Na5QjxKFYIiIiindM7CKUlqRX6Lj7BBEREcUjJnYRMlXsuPsEERERxSEmdhHiUCwRERHFOyZ2ERpkSOzqmnpyzw0iIiKinsHELkLZqYDFP82uKrJdPYiIiIj6FBO7CFkVgTz/FnBnmNgRERFRHGJiF4VCbQc0NDYDzmYOxxIREVF8YWIXhcI0veUJq3ZEREQUb5jYRaHAoV+ucsbuOIiIiIjCYWIXhcBQLACccXEoloiIiOILE7soFDr0oViujCUiIqJ4w8QuCoWGodgzTlbsiIiIKL4wsYtCngNQ2MuOiIiI4hQTuyhYFYHBqdrlM1w8QURERHGGiV2UAgso6ryAp4XDsURERBQ/mNhFqYALKIiIiChOMbGLkrnlSeyOg4iIiCgUE7socWUsERERxSsmdlEq4LZiREREFKeY2EWpkNuKERERUZxiYhelfAcQqNlxWzEiIiKKJ0zsomSzCOT6e9lxVSwRERHFEyZ2XVDgXxlb6wGaWlm1IyIiovjAxK4LCg297KpZtSMiIqI4wcSuC4y97DgcS0RERPGCiV0XFBhXxnIBBREREcUJJnZdkJuqD8XWemJ4IEREREQGTOy6INeuX65hYkdERERxgoldF+Sk6pdr3ByKJSIiovjAxK4LslP0JsWs2BEREVG8YGLXBVZFICtFu8w5dkRERBQvmNh1UWD3iRoPoEoOxxIREVHsMbHrosACCp8E6r2xPRYiIiIigIldl+UYWp7UuGN4IERERER+TOy6KNe4Mpbz7IiIiCgOMLHrImNiV+vhHDsiIiKKPSZ2XWTcfeIcK3ZEREQUB5jYdVGOqWIXu+MgIiIiCrBGc+OaukY8/9ZmnKysQXKSFaUTRmDpglIoStv8cM3W/Vi//TDqXR4MGZyFWxbNwoiiXACA092El9/dioMfV6LVp2JYfjaWLbwYwwtzeuZZ9YHBpm3FOBRLREREsRdVxe7JFeuQle7AA/csw7c+dTX2HKnA6q0H2txu1+FyvPnBbtx14+V46JvLMXXscDz20mo0NbcAAJ57azPqnR787Is34cFv3Iriolw88uJqqKraM8+qD2Sn6Je5KpaIiIjiQcSJXVllDU6fvYClV5YiNcWGvOwMLJw9ERt3HWlz2427juCSKaMxcshg2JKsWDRnIoRQsPdoBQCg/Ewtpo8vRrojBbYkKy6dMhoNLg/qnYkzpplkEchM1i5zVSwRERHFg4iHYsuqapGTmQZHanLwumH52aiubYDH24zUZJt+2zO1mDlhZPBnIQSG5meh7EwtLp44CpPHDMW2/ScwdexwpCYnYdOeYxiWn43MdDs6IoSAEKLD2wQEhofDDRP3lFy7D3VebY5dNMfW1/oiFomE8TBjPMwYDx1jYcZ4mDEeut6OhZQSMsJdriJO7FweL+wpNtN1gSTP5fGaErv2but0NwEAli2ciUdfXIMf/eUlAEB2hgPfuH1Rp4mRw26HxWKJ9JCD9+ktBemNOHahFS0q4EtyICslvk/u3oxFImI8zBgPM8ZDx1iYMR5mjIeut2Lh8/ngdLkium1UiydCRbNkQEoEB36fW7UZEhK//votSE1OwrptB/Hn59/F/V++CSm2pHYfw+V2R1Wxc9jtcLndvTZ3LzPJF7xcds4Fa1b8Vux6OxaJhPEwYzzMGA8dY2HGeJgxHrrejkWk1TogisQuzZ4Cp9u8Karb44Xw/67NbT1tb1uUlwlvcws27T6K7312CbIzHACAay+bgtVb9uPgiUpMH1/c7jFEU4oMUFW11044Y8uTsy4VIwfFZ2IX0JuxSESMhxnjYcZ46BgLM8bDjPHQxUMsIh47LC7Mwfl6Z3A4FQBOVtagcHBmmypbcWEOys/UBn9WVRXl1bUYUTQYUmqVPlU1J2hqlAlbPDBvK5Z4x09ERET9S8SJ3fCCHIwoysWra7fD421GVU0d1mzdj3kzxgEA7n/iFRyrqAYAzC8dh817j+HE6bNobmnFqo17YLVYMHn0UKQkJ2FscQFWbdyNBqcHLa2teHvTXihCYMzw/N55lr0kx7D7BJsUExERUaxFNcfuS8sW4LlVm3DfIytgT7Fh9uQSzC8dDwCorm2A19+nbmLJUNy0oBT/fH0D6p0eFBfk4N7lV8GWpP25u2+ajxVrtuGBv7+GVp8PRYOz8PXbFrUZ0o13xibF59jLjoiIiGIsqsQuK8OBry2/KuzvnrjvLtPP80vHB5O+UBlpqfj8J+ZF86fjknlbMQ7FEhERUWzFd3+OOJdjmmMXu+MgIiIiApjYdUuyRSDD366PiR0RERHFGhO7bgqsjK31RNdnhoiIiKinMbHrpsBwrNcHNDbH9liIiIhoYGNi1035Dr3lSXVku30QERER9Qomdt1U4NAvVzGxIyIiohhiYtdN5sSOc+yIiIgodpjYdZNxKJYVOyIiIoolJnbdZKzYcY4dERERxRITu27K41AsERERxQkmdt2UbBHBlies2BEREVEsMbHrAfl27f81HqDFx6odERERxQYTux4QmGcnAZx1x/RQiIiIaABjYtcDCtK4MpaIiIhij4ldDwgMxQJcQEFERESxw8SuBxSmcVsxIiIiij0mdj0g39Dy5AwTOyIiIooRJnY9wNykmEOxREREFBtM7HpAhg1ItWqXuXiCiIiIYoWJXQ8QQgSHY6tdgJSs2hEREVHfY2LXQwLDsV4fcKEptsdCREREAxMTux5S4DCsjGWTYiIiIooBJnY9xLiAgvPsiIiIKBaY2PUQY8uTKifn2BEREVHfY2LXQwodbFJMREREscXEroeYmxSzYkdERER9j4ldDxlsByz+ot3xupgeChEREQ1QTOx6iFURmJKnXT7VCJQ3sGpHREREfYuJXQ+6dIg+z27TaSZ2RERE1LeY2PWgS4sMiV0lEzsiIiLqW0zsetCoTCDfrl3eUQV4WpjcERERUd9hYteDhBC4dKhWtWtWgY+qYnxARERENKAwsethxuHYDzkcS0RERH2IiV0Pm1kAJPmjuum0hJRM7oiIiKhvMLHrYalJAjPytctVLuDj+tgeDxEREQ0cTOx6gbHtyWYOxxIREVEfYWLXC6bn64nd0QsxPBAiIiIaUJjY9YLiQcbtxVixIyIior7BxK4XJFsEhqZrl0/WAT6VyR0RERH1Pms0N66pa8Tzb23GycoaJCdZUTphBJYuKIWitM0P12zdj/XbD6Pe5cGQwVm4ZdEsjCjKDf5+95FyvLp2O2rrncjLzsCyhTMxYdSQ7j+jOFGSCZQ1aP3sTjuB4RmxPiIiIiLq76Kq2D25Yh2y0h144J5l+NanrsaeIxVYvfVAm9vtOlyONz/YjbtuvBwPfXM5po4djsdeWo2m5hYAQEVVLZ567QMsu+pi/P47t2PBzIvwxvpd8PnUnnlWcWBUpj7P7nhd7I6DiIiIBo6IE7uyyhqcPnsBS68sRWqKDXnZGVg4eyI27jrS5rYbdx3BJVNGY+SQwbAlWbFozkQIoWDv0QoAwNptBzFrUgkmjx4KW5IVc6ePxQ/uug4WS/8ZGTYldhc4FEtERES9L+Kh2LKqWuRkpsGRmhy8blh+NqprG+DxNiM12abf9kwtZk4YGfxZCIGh+VkoO1OLiyeOwrGKasyZPBp/eOYtlFfVoig3E7dfMwfDCnI6PAYhBIQQHd4mIDA8HG6YuC+MyZYAtArkifrYHQcQ+1jEG8bDjPEwYzx0jIUZ42HGeOh6OxZSRr7hQcSJncvjhT3FZroukOS5PF5TYtfebZ3uJgBAXaMbH+4+ii8tuwJ52Rn497odePSlNfjlV2+GLan9Q3LY7bBYLJEecvA+sTDeLmGz1KHZB5xsEEhPS4vJcRjFKhbxivEwYzzMGA8dY2HGeJgxHrreioXP54PT5YrotlEtnggVzQCjlAgO/EopMXtyCYoLtcUUN19Zig92HsaxirOYMKqo3cdwud1RVewcdjtcbjdUNTZz90YMAo6cB8rqVNTUNyLZEtmx97R4iEU8YTzMGA8zxkPHWJgxHmaMh663YxHN9qQRJ3Zp9hQ43V7TdW6PF8L/uza39bS9bVFeJgAgIy3VVNFLtiUhLTUFDU53h8cQTSkyQFXVmJ1wowYJHDkv4ZPAyQsqxmTHJrELiGUs4hHjYcZ4mDEeOsbCjPEwYzx08RCLiAeDiwtzcL7eGRxOBYCTlTUoHJyJFFtSm9uWn6kN/qyqKsqrazGiaDAAYFh+Diqqzwd/721ugdPThOxBsR+u7EmjMvXLbFRMREREvS3ixG54QQ5GFOXi1bXb4fE2o6qmDmu27se8GeMAAPc/8QqOVVQDAOaXjsPmvcdw4vRZNLe0YtXGPbBaLJg8eigAYMHM8dh+4GPsP34KzS2t+Pe6HcjNTEfJsLxeeIqxMzqLLU+IiIio70Q1x+5LyxbguVWbcN8jK2BPsWH25BLMLx0PAKiubYDX36duYslQ3LSgFP98fQPqnR4UF+Tg3uVXBRdGjB9ZhFsWzcIzKzeh0eXBiKLBuHf5VbD0s5U1xordCVbsiIiIqJcJn7si7jOOaFaDBCiKgvS0NDQ6nTEb75ZSYuGLKtwtQIED+PfN0a3o7SnxEIt4wniYMR5mjIeOsTBjPMwYD11fxSLN4ei0O0j/KpHFGSEESjK1y1UuwNUc9zk0ERERJTAmdr2sxLADxeoyJnZERETUe5jY9bJrR+mJ3X/vlvC0MLkjIiKi3sHErpdNzROYP0y7XOMBnj/YNrHbXyNxjPvJEhERUTcxsesDX52uILDpxDP7JWo9ehL3wkEVX1il4tNvqFh5fGBPPiUiIqLuYWLXB0YMErhxjJbZuVuBh7aocDVLrC2T+PNHWpInATywSWL1SSZ3RERE1DXd2iuWInf3FIG3Tkh4WoH3K4Dlr6lobDbvt6tK4P4NEjaLxLxhsd1+jIiIiBIPK3Z9JCdV4CeXCKT428/UeACvT7t87SiBG0ZriZxPAg98qMLr45w7IiIiig4rdn1o0QgFE3MlfrdVxYentetK84GfzBFQBHDOLbG5EmhoBg7UANPzY3u8RERElFiY2PWxojSB3y9QsPUMUNEocX2JQJJ/ZcWiEQKbK7VK3Y5qien5HI4lIiKiyHEoNgaEEJhdJPDJcQpSrHryZkzkdlRxKJaIiIiiw8QujhSlCRQ6tMv7aoBmzrMjIiKiKDCxizOBqp3Xp82zIyIiIooUE7s4M6NAv7yjWqLZJ/HX7Soe3KyiqZUVPCIiImofF0/EmRn5AoHudjuqJbw+4NkD2s9D04E7J3JBBREREYXHil2cKUoTKPDPs9t9VtuCLGDrGVbsiIiIqH1M7OLQDP88uxZVa1gcsPss0MIFFURERNQOJnZxyDjPzsjrA/ZzQQURERG1g4ldHJph6GdnEcDy8frP26tZsSMiIqLwmNjFoaI0gWtGClgV4NsXCyy/yJDYsXExEfWw8gaJz6/04YEPVaiS7zFEiYyrYuPUz+cq+OmlElZFS+qK0oBKJ7DvHNDUKk07VhBR9zQ2S9itgEUZmK+rfx2WOFALHKiVuGakwMzCWB8REXUVE7s4ZjV8yJTmC1Q6JZpVLbk7UKtixWEJRxKQkwqMyhS4e4pARvLA/GAi6qotlRLfW6diaDrw9HUKkgfgOMZpp16l21EtMbOQ7yNEiYqJXYIoLQBeP65dfmirivIG/Xcf1wMfVUkIaEO3RBS51WUSLar2OtpfA8wYgNWqapd+eQfn8RIltAH43TQxlRboCZsxqUu26JfXlUtIzo8hikqDV3/NVDoH5uvHmNjtrwF3uSFKYEzsEsRgu8DwDPN13ywVeO92BZcUaT+fdQOHz/f9sRElssZm/fJpZ+yOI1ZcLRINhhi0qGyrRJTImNglkEuH6FW7e6YL3D5BgRAC84fp179fwW/aRNEwJjVnBmBid9bV9joOxxIlLiZ2CeTuKQKfmyzw63kKPjNJ/6e7fJhAILVbz8SOKCoNXv3yQByKrQqT2O1kYkeUsLh4IoGk2QS+PK3t4oicVIGJucC+GuB4HXCqUWJoOhdRJBJPi8TDW7UP0x/OEUi28N+vrxiHYisHYMWu2t02idt3DvD6JFL51Z8o4fBl20/MMwzHsmqXeJ7aJ7HyhPbf++X89+srLT4JT6v+8zk30JxA+zF/XCdR6+ne8RoXTgxO1f7frAIHOM+OKCExsesn5jOxS1iuZol/Hdb/zT6uj+HBDDDG+XUAIJE48+w+qJC4/XUVt72mdiu5MyZ2i0fq7yOcZ0eUmJjY9RPFgwSK/atm95wDdp/V35SlZBuUePbKUQlni/7zqcbYHctAE5rYAYkzz27rGe04G5uBTZVdP+Yql37fa0fpiR3n2RElJs6x60fmDxN4er+EKoGvvK3ihtECFgFsPC3R2FyHBcUCX5wiUJjG+VvxwuuTeOGg+QP0VCM/UPuKceFEwOkEiX+d4dj3nAWuL+na4wQqduk2YHSWQJ5da510qLb7x0hEfY+JXT/ymUkC26okDtZqQ0qvHTN/QK08LvHuxxI3jxVYPl6giAssYm7lcYlaj/m6igatyioE/316W2PYil3fH0dXXGjSX997znUtGVWlRLVbu5zv0P5flKYlds4WrVFxencPlIj6FIdi+5E0m8B/X6PgWzMF7IaU3aYAaTbtcosKvHhIYtm/VXx3rQ8Ha8N/IKhS4o1jKrZ0Y4iHOqZKiWf26/HNt2v/d7YA9WEqSdTzjLtOBCTKUKyxYneyHqgP81w6c94DtKra5cD5l5Oqf6EI/dJBRPGPFbt+xqoI3HaRwJXDJd4tkxiWLjC7SEGK3YHHtzTi+QMqvD6torfxNPBRlYqnrlMwcpC5OvTMfonHdkoAEr9boGDuUFaPetrpRn2ng9J8bZ7kK0e0D+dTjUBmSgwPboAIW7FLlKHYJvPPe88Bc4dG9xjGHnYFDu01npuqX3fOLTG+i8dHRLHBil0/lecQ+NQEBfOGCaQmCWQkK7hnhgWvLlXw1Wki+O3c6wN+vUmFT9U/zFpViZcO6T//dosKZ3NifNglkhpDNWRcjsBQw5hXRYIkF4ku/OKJvj+OaEkpcaFNYtf+ObO5UuKGFT786kPVtJAqMAwL6EOxuXb9uhpW7IgSDhO7ASY7VeCzkxW8+AkFw/yJxN5zwL+O6G/268ql6Q39nBv463YmGj2txtAYNjcVpqbSXBnbN4yLJ1L94xcNzUBjF4Y1+1JjMxDabm/P2faP+el9Ks55gNePS+yo1q83rogtCCR2hopdd3vkEVHfY2I3QKVYBX58if7P//hOGZxb9LKhWhfYAOE/x2SwvULA3nMSa8skzjjZTqUrzhmS59xUYFiG/nNFQ98fz0BkrNiNy9Yvn2rw9f3BRKEuzBzMA7Vaw+VQraq2oCrghYNq8LJxn9h8/1CscY5djaGi11d8qsSKwyqeO6CiVeX7ClG0oppjV1PXiOff2oyTlTVITrKidMIILF1QCkVpmx+u2bof67cfRr3LgyGDs3DLolkYUZTb5na7j5Tj8ZfX4tt3Xo1xxYVdfyYUtRn5AkvHCrx6ROu+//11Ku6eqmDPOe33owYBy8aJ4FZXv9uq4sUbFQghsLNa4p53VATednNSgetLBL44VcCqcD5eJIxV0Vy7QFEaIKDNf2TLk75hXDwxPltgl7/qVdGgYmhqe/eKvdBhWECbVnH4PDBpsPn6j+tg2l1jwymgokFiWIbotGJ3ro8rdqqU+PVmiTePa3/3dCPw/dl8PyGKRlQVuydXrENWugMP3LMM3/rU1dhzpAKrtx5oc7tdh8vx5ge7cdeNl+Ohby7H1LHD8dhLq9HU3GK6nbe5BS+/uxXJSVzDESv3Ttf6VgHaPrM/fl//Nv/J8VriN8mfj5c3AMcuaJffPC5hfMuv9QD/3Cfx9dWqqQ0Dtc9YDclNBZItIjjPiUOxfcO4eGKsoWJ3ukFte+M4Ylw4kW+YExeu7cm+GvN1EsDL/p1OAj3sLEL7cgaEDMX2YcVOlRK/NSR1gDZF5M3j8f1vQRRvIk7syiprcPrsBSy9shSpKTbkZWdg4eyJ2LjrSJvbbtx1BJdMGY2RQwbDlmTFojkTIYSCvUcrTLd744NdGFtcCIc9ufvPhLrEYRP4y1UKhmeYr09LAq4ZKaAIgWsM3eg/OCXhUyU2ntLefJMtwKxCwOo/k3ZWA59bqeL4BSZ3nanxmOfYAQguoGho7lr7CopOYCjWkQQMyzDOcYzvZOKC4dww7hMdPrFre/83jku4mmUwsctNRbDSnm7TWiQB5nO0tz22U+I//t6bxhrdQ1skDrXTlmmg2XNW4teb1HbbVBEB0SR2VbXIyUyDI1VPwoblZ6O6tgEer3lpWdmZWgwvyAn+LITA0PwslJ3RJ3qcPnsBm/cex9IFpd05fuoBIwYJ/ONaBZcbWiXcMFrAnqS9vV4+1JzY7a8BLvjn+MwpAv5ylQWPL1aC3/irXMD31qlwxeFKWiklXj2i4omPPDh6PrZzAwNDsY4kBGPNBRR9q9F/HmfYtMa8ARX1cT7HzlCxm1Eg4EjSLu871/a2+/zJnlUBrivRzi93C/Cn7TL4Og5UigHt/TrwWu6rVbGuFonnD2jHqQjgl3MFlo7RjtXrA376gXnl/kD1yw9VvHZM4jeb4/uLB8VWxGOgLo8X9hSb6bpAkufyeJGabOv0tk639m4kpcSzqzbhxvkzkO6IvFmXECLibvyBeX/h5v8NNJHEIiMFePhKBa8fk6hslPjcFAWK/xt8YTowLlvF4fPAwVrg1aP6G+z84RYoioKp+cA/r1Pw3bWtOHweOOPSPjh+dpmld59clLadUfGbTT4A2gf3iEHAnRMtuHFM358nNW7tGHJT9X+b4Rla70AAOO0UmJzX+8c1UF8rUspgxW5QMpBrV5Bs0fo8lterUJSk2B5gB+q8+nmSaxcYnyOwvUpbze5sEchI1l67DV6JMv9CnHHZAp+ZZMGbx7UJd68bdqYpSBOmf/9cu4ozLol6L9Dsk71+btR4JHxSS1auKha4usSKBSMkDta24tB57UtOeaOCkqzYzreL5WulsVkGv+ydqAMkBCwxns88UN87wuntWESz53u3JrdF8/1JSgTrgxt2HQWkxNxpY6L6ew67HRZLdImCw27v/EYDRCSxuHN6+OuvKvHg8HktMV91Qh8uuXpcGtJTtX/Y9DTg0et8+MQLDXC1aB8cV422YXGJLfyDxsChOg8CSR2gdex/4EMfZhc7MDq775JQV7OEu7UOAFCQbkV6mlYuGpPXDEAbHzvrTUJ6Wt/N4B9orxVns4RP1gEAMu1WZKSnY1xOA/ac9aGsXkW5y4aLBsfn/F+XzwVAy0qHZDswOqcJ26u0n8+1pGJIjnbcu8+3ANAa880osmHKUDu+drEHj24zr74YnmVDepr+71+Y4cTec9qc6HMuFUMyevfc8NTpxznUcCzzR3pwyP++45IpSE+Lj2Q7Fq+VY1WtALTMrlUF3MKBorT4SKgG2ntHR3orFj6fD06Xq/MbIorELs2eAqfbvMbe7fFC+H/X5raetrctysuE092E19/fga/ftijqvTBdbndUFTuH3Q6X2w1VHdhl656Ixex8iUdDrpuSJ5Dkc6PR0NA1QwG+O8uCX27Ukqf/WufCmAwvclPjY2Xb/mp9eeCoTIETdVqS+uIeJ74xs+8Su7J6/WtRVrIPjU4tiLlJ+vXHa7xodPb+kOBAfa2cMWwd5rBo/wbXjJTYc1a77rk9Lvxgds+eE43NEr/+0IfsVIFvzlRgs3TtdXHWqZ/HNp8bRXb93+1glRsl/g/8reX6+TNmUCsanU58dgJw5VAr/nNUxRvHVLRK4Ioh+jkIAIOS9Pudc0tkWnv33Cg/rz92hqUleCzpFp/hNh5MyY7tXnuxfK3sP2P+e0ernUgXsU3sBup7Rzi9HYtopg1FnNgVF+bgfL0TTndTMJE7WVmDwsGZSLEltblt+ZlaXDJlNABAVVWUV9fi0mljsPfYKTg9XvzpuXeCt3c3NePxl9dizuQS3Hb1nA6fWLRzolRVHfAnXEB3YjEmU2KwXWtWHDB3KMI+3rUjJd4vB96v0PY8/dchH744NT6+WR45r50/KVbgscUWXL+iFa0qsPK4iq9MQ5+1ajlraDORk6LHsdChX3+qUfbpuTvQXit1htXb6Tbt+S8eAfxlO9DUCqw6ruJr04DUpJ47J/53j4o1ZdowqlVIfHNm114XgZXnFgE4rGqw2TgAnKxTEfhn3HtO//ecmKOfT0PSgHumC9wz3QKfKmFRJFTVeE7ql8+5VZSk9e65cc6tP3Z2in6cOcbWKy79ecVaLF4rH9eZ/96pRhXT8sy3eb9c4i/bVVw/WuBzk/vuPXegvXd0JB5iEfG//PCCHIwoysWra7fD421GVU0d1mzdj3kzxgEA7n/iFRyr0Fqazy8dh817j+HE6bNobmnFqo17YLVYMHn0UJReNAK/+ton8dO7bwz+l5mWik9fdxlumNfOOCDFnBDCtIgCQJufjbf99sX6qbWjqmuTnlccVvHFt3zY3sX7h3K3SJz2z1EZk21BdqoI7q15vgnYXNkjfyYixtWGgw2V+xSrCP4cyybFLT6Jptb+PVm90VD8yfDPFkizCSweqZ3Xrhbg3ZM9FwMpJd4v1x/v+YNtm35HKtDHLjNFe70VG1a1lzXI4N874F8Rm5VsXhxiFG6elrHlyVlX739I1RoWaRgbJBuPY6Bvb/ZxnflcORNm67un9qk47QT+e7eEq6V/v36pfVGl9F9atgANLg/ue2QFHnlxNWZPLsH8Um2L6OraBnj9feomlgzFTQtK8c/XN+AHf34RR8qqcO/yq2BLssKWZEVWhsP0n1AE0uzJphW3FH+MidywdJg+TEIVOETwg2R/jTYBOxrOZok/bpPYe07bq7YnVq9qE44143K1IbbrS/SXwBt92C/L2MMuJ2QaXaD6UucFTtZ3/3m7mmVUKwrrmiTuWqli4QtqlxOPRGDcdSLd8Naz1LCQ5t9He+75n2wAKkJWOv9yoxp1WxspZXBVbKb/uAsceouSwGKJikb9OU4cjKimvuTa9duec/X+OVAbsgtLuMtdbb2y+6zErurEP48/rjf/HG5P48DiClUCRy/0/jFRfIpqZnBWhgNfW35V2N89cd9dpp/nl44PJn2d+fW9t0RzGBQjpQXA8AytUfFNYztfoTwtT6DSKdGsaqtpp+Z1eHOTPef0vTDLG7Ttkia23bgkKkcNvfXG51gAtGJOEZCdolXsNpzSkprMlJ4ZepNSYsVhiSoX8MWpAilWw4el4YNscMj8wzlFAjv8H0RP7lLxm/ldn+f14kEVf/xIYmYB8McrFSR1MqdLSomHtqg4Xqf9/MoRFbMK42tlc09pMLTjyTCs75mQK3BRrgUHa3w4UAscPi8xLrv758R6Q7XOkaRVBGs8wMNbJB6YF/nju1uAFv93kCz/9GaLIjAsQ2syfqpR20bM2NNuUm50x28aAnX3RcWubU/H0OPoSsVuZ7XEV9/Rjv+JqxVMy4uPub7R8rRo7yNGlU5zstrglaaG24drZcI+X+qe+Jj4RAnBZhF4aomC525QcMdFnb9hGOd/7O5gg/JwdoZ8ww6sxDV6v1zii2/5sLYsssc2foMNVOysisC1/gbMrSrw2rGe+2b/+E6J32+TePaAxBM7zY9r2nUiZBHVLeMEsv0f2OvKgf01XTumapfEY/6/+1EV8LfdnT/O6pMSa8v1nw+GaW4bDyqdEve+68NDW1SoXazmNhiHYpP181kIgVsn6iW8t8Kce13xfoX+OH+8UsEg/59YUyaj6vl4wXDcmYbjDlTQW1VtmG5HlX67yYOj+4DPDZnb1tsCSVuyBbAbpmzbLCIYp67sW/uGYReLbQlcfS4LMy0jtGIX+vOh8713PBTfmNhRVOxJAqMyI+snOC1fv82uKBO70Nu/87E0bXAupcRDW1XsPQfcv0FFeUPnj2+s2I3N0atQ15fox/nYTonPr/Th7Y+7N/z74kEVT+/X7//acfOHd7hdJwJSkwQ+P8VwTDu6dixP7pLwGhbVPrNfdjhfscYtg/sCB1S7zdWUeLHisMRHVcArR2SX50Yah2IzQjryLC7Rs4vAgpvuOOeWOODvzz4mS1tRvrBY+zeWiO5D2NicOMvQkGC4YeeMsgYEq742pe3+sZ0ZlKwtzAD6dig2N7XtkHGuoVlyNK+DVlXiA0MyfbK+gxvHuY/DTMmocZunuIQmdoe5O8WAxcSOes2wdP2DZ89ZwKdKvH5MxU2v+PD0vvarAE2t+qTvgIZm4MPT+s+nGvUPgxYV+P3WjpMfVUoc91fsCh1ARrJ+6o/MFJhVqN/2QC1w/waJ5w507Y1xbZnEnz4y39fdYq4eBCoUaUkwDdEG3DRGBLcX214NbD0T3TEcOS+DVc7A3HgJbU5XQ5g5Xd5Wif/6wBdMdmyGd4ZDtW1uHnPGieTGD+9ohFs8EZCdqgSrpid6ICH44FTbLcAm6Jvz4EAUH8IX2knsigfplzdX6kN3kwdr+xBHQxEimFD19lCs16cPIYbONwX0xK5FNSfjndlZbb596OKDRGJMStP956oETMOzoUOzJxu0IVwaeJjYUa8RQgSHY50tWvuTh7ZoHziP7ZR4++PwHxh7DfPrjG0cVp7Qbx+6sfmWM9qwZXvOOAG3v/XXmDDzpR66QsGPZguMzdKv+8deGfXEdlVK/G6rGlykcc1I/W+9fFhC9bfsCQwrhQ7DBlgVgS9P1e/76E59yLHaJfHj9314fGf4bZaklPjrdv0YvjZdoDRfu1ztBv66w3wfr0/i66uc+MhfzctOAb4xU//b0SQdfcW4COGDU7JLw7HGOXbpYdZtjczUYnChSW8vYrS+QuJXm1Qci2Bf5PUVbRO7i3L0GB+MYri9znBOZhoTO0PFzjh1obSga/OsAknWeY+2QvrxnSr+safrQ9/tMa+Ibft740KOaIZj3ys3H2e5f+5hqA8qJF4+pIb9XbwwLqKaXajHw1ilC63YqRI4VtfLB0ZxiYkd9Srj5N0HPlSDk74B4DebJI6H+VA0DsPeNVnft3LjaQQTrb1h9sT840cq3O18QzXOrxsTZluiFKvATWMV/PM6BTeU6O0unt0f3Zv9iTptIQYAzCwA7r9MYGaB9vOpRmDTae1xm/xDpKHDsEYLRwiMzdYuHzkPrDmpJTD/tUHFunLgn/sk/rrdfHytqsQTuyS2+edXFaUBt4wX+K/LFASa9r95XAaHrpt9Ej9a58MH5VrWm2oFHpyv4JIiPUbxtgF7qypNrR5qPNrinGg1djAUCwAlmXoMTtSZf9fUKnH/BhWvH5P48tsq9oSZalDt0pKhr77tC1ZcCxwIfnkYMUiLN4DgMG0kjBW7cHPsAO0cC5jRxcQucG5KAA9v8eGf+yT+trvrQ9/taa/VSehxAJEvoFClNM1pBLS5h6dDViUfuyDxg/dU/H6bxAsH4+s8NwqsiE22ADMK9OuNVbrQih0Qf69d6htM7KhXTTUkdu5W8++afMCP3lfhDJk4blw4MbNA4OqR+uKGQF+xvef0YcYZ/mrUuTDVqADj/LrRHew3KYTAF6YKJPlfGS8dklHNMTMmpZcO0eYi3jpef5m9eEg1r4i1t38sihC4Z7p+3yd3S/zrsMTus/ptXjikVRuklPi4TlsB+M99+jF8dbqAzSKQ7xC4c6L2t1QJ/GOPVjl8cLPExtPa7VOt2qT+KXlaq5pAsnOgNrq5Tb2t0qlXdAPWd2E4NrB4wiL0BMtoVKZ+OXSO08l6wOM/n10twDfXqG3mL37/Pe3fYudZLeYAcMUwfX6qRREY50/cq1xaZSwSdYYhZONQrMMm2nxRSLEAE3PQJTmGc9O4r2xXF/O0p6adVifhrou05cn+mvBJYGjLkA9Py2Bl+/Vj0TfA7wvNPn2P2OIMYEhaZBU7ADjMBRQdqmuSWFsm0RjF4qVEwMSOetWYLPMqNwD46aV6JaqiEbhvvT4M0uyT2O+fX1foAPIdAtcahjPfPak13gy04xiVCfzkEgXJ/rUQrx6ReDfMEK9xuCzcUKxRgUPgpjHabZp8wP/ujfwNf48h6QpUKy8bonX6B7S5cptPt79wItTsQgSHUU81An/Y1vY4/viRxA3/UnH762qwkmkRwNdmCFxVrD/XW8brKwzf/ljiL9slVvqH7FKswB8XWoILXoQQuMifEFxo0oZw40V5mBWCG051IbHzV+wyksP3eBvVQcUuNNHztALfWasGt6irckkcMXyoFqVp8yY/N8X8d0zDsRFW7YyLJ4xDsYDWjshoSh46bXHTnsHtnJuRDD1Ho71WJwHGKl5thBU74zDspUP060P/3YxfIssaejYR6spOSeFUNOhfDEYMEqZG04HKtSr1KvaQNH3hy6EeWPjTX+w9J/FRyMroH7yn4ifrVfxiY//aNYOJHfUqiyIwxbAib0Y+cN0ogQfnKcGK0JYzwK83aW+CB2oRXMk53Z9kjM4CRvknhu8+C6wrk8E3usm5AkPTBb5zsf7m/5vN0rQXKwAc8w/FploRXJTQkbsmi2CyuOKwxNUvqbjnHR82dpBASCmDFbtUK4LJq0URuHW8fnz/vSfyxE4IgXtm6C/TwD2XjBL47CS9AmesThSlAU9ereDTExVTwuJI0qt2EtrOBwEPXOnAjALz20FXko6+UGFYAR04wuN1wOnG6D7EgoldmGFYwJzYhU68/7hOvxw4n7w+7YsFgOB8RQD43GSBV5Za8KM5CgYlm5OsCYbejJHOZTTO98sKmRtonGcHAKX5XUvqgPDz3QAEv1T1FGOylh1mKNa4M8u5CL5gSCmDiZ1FAJ+eqJ/Xxn+3VlVid8iUju62tmlVJT48LXHfehVXPK/ii291f+6eMRkdmakN5weiFBh+rfHovQ1HDNL+A7Tn642yOXx/dOS8xBffUnHvahWb/F+sLzRJ7PH/+286jX610w4TO+p1i0Zob0MpFuA7F2vJRlG6wEMLlODqy5UnJH72gcTfdunfnKb7K1VC6Ns8AcDju/QX4GR/0njjaIEl/n507lbgJ+vV4F6grx3VttkBgJJMbYizMzmpAssNyVhDM7CjWhte23Q6/BvAGRdw1v/BMzHXvO/sJ8aIYAXEbZj/lNvBUGzAxFyBBcP1nzOTgW+UCnx5mv6c7UnAnCJt/8+nr1MwqZ2+ZZ8cK9okA7ddpOC6MW2zmwmGprbRTO6P1tP7VHz1HV/EQ3zGhROXD9UvtzccK6XE0fPStEKwVZXBf4eMdja8yUjWhzZP1JuHo40ftr+ZrwQrJBtOaV9QPjKsYjZOdg81wZQ8R5rYaf9XRNtjN66MBbo+vw4AcsMkWYBWOW5vLmtX9PRQ7Bkngq/36fnaazHwUjQuQjh6wfxaBIB3y2SXEzFXs8Sdb6j4zlptP2CvD9hXg2Dy0FXGFbEjBgkkWQTy/MluYPi10vCaGJIugg21fVL/UjuQbTN80VrnT/r3Gzov+KT550THxI563ZJRAo8tVvD09Yppftu0PIGfz1WC3z5Xl0nsqIbp9wGLR4QfjgkkMEIIfH+WCFb2jtcBn1up4uVDKn67RX9RLx0b+Qfd3VMF7p4iMLtQr16oErjvAzXsog9jE+bQju8pVoG7Jnc8MbwjX52uVTgtAvjBbAWZKQKKEPjZpQJvflLBu7cq+NNCCz4zSUGarf3nmJokcOck/fdT84BvtLMR/fiQdhxbKiV+uVHt0rBneyoatCbKO6uB769TI5pnZuxZeIehGtNeYve/eyU+/aaKuw3Vk84WTgSM9J9P9V59UQygz9VKsWhfFgJfQs64tGHbQMUuxdLxjilFaQgOjx+siWwuY2CO3SBb2y8pxl52qVYEh9O7wlixS0sCrjB8uQgdmu4O41BsuCphtLtPGHsCThmszTENVFXLGhBcSb7DMAwbqM7XeoDthsbO0dh4WobtlRfuvSIaxnmBgfMxMBxb7wVcLdK0cKIozfzaPdyPh2PPOLXh1c5eN8Z/l8D79N5z5vtE20Q/njGxo14nhMCMfGH60Am4stg8jBqwaIQwDZkWpYtgdS4gM9ncDiU1SeA385XgB8EZF/D7bTI40X75eIHrSiI/5W0WgbunKvjzVRa8vkzB/GHa9e4W4HvrVOyolqYGobvDzK8zunG0QKHDfF1785hCDc8QeOFGBS9/QsGVhnlzQgjkpIqwG7m359ZxAjePFVg0QuDX8xRTZdEoz65XrHZUawsEVp6Q+OF7aqer7V48qOJTr3e+K4jxm/T5JuCBTZ03Yw5MJM+wAVMH6/PKdp5F2EbVgf6Bx+u0Cgpg3nUivYNEONzK2KZWGVxdOTJTS66M+yg/e0AGE5Bp+R3PcTPNZfSizbZR4QQqdqHz6wDzgo9peWj33zYSJZkInq9fma5ghmFY91gP9oQLfFGzCH3vW6Nod58wNpQe669cBRIir097XwCAHYZz77OGLztvf9y152acn2dset7doetAldGq6MP+hSELKIwLJ4rShGkLvHjsQ9kTGrwSn35DG159ppOeo8ZKbVmD9mViHxM7ot5zy3gF/7hWwcNXKHhqiYKVn1Tw/y5X2kxoN1btAK2bfuhtigcJ/O8SxdT8FQDmDdWGL7tKEQI/v0wJrmI84wLueUfFohdV/Oh9H2o9+vw6iwhfpUmyCNw91XwMOe30sQsnO1Ubwu6uJIvAD2ZrMQ7XXsIokHQYR6d8Evh/H6qmnUCM3v5Y25/2eB3wp486TtRCJzN/eFpbidyepla98e7QdO3f/8bR+nNYcdh831ON0vShFxhGN+060c5QLKAlbgGBhRHlDfpcx5GDtL8915DYGXvIXRzBUGg0cxk9LfpuIuESuwKHNvdyfDbwpWnde3tPsgg8e6MVb9yegVsvspiS3J4c3gskdlkpaPcLSuALRm0Eu08cMe4w43+9jhikP+7Jeq1qF/gilpkM3DFBBNsBvVcuu9TY11gZ+8wkY2LX9YShVZXBxULD0vVEPXQBhTmx01rqBEIZ6RB/otlZrfVHBYB/7m1/ZauUEmUhldQd1bJNi6G958L3OUxETOwoLkzIFbh8mMD4HBF2AjWgVfeMxY/J7WxsnmfXhn6vKxEQ0PrJ/WKuElVVK5zUJIGHFygoMFTdvD7gvXLgy2+rwXL/uGzttuFcM1IEqwdD06PfEaCvTTFUHjNsesXgeJ02xBnq6HmJX2/Srz/rhqnnnJEqtW3BAPNOF4/skO3uEmDsQxaoAN8wWl/o8kbI1m2h+4MGWrsYF1p0NBRrWkDh//c9YTi2wCT1Ien6v6vxL0bSHNiY2HW2gMK4T2zoXMmAr05X8NR1FtPjdlWaTaAkWwvu6Cz9+u4OLwb4VBkc4m5vsQagJ3bNEew+EViNnGHTK46BfxtAmx95rE5PCqbla1MlFhTrc3RfDPPlQmssLtu0Zwr8LlCxy03Vzs3A+8Txuq63C6p06osijM/BmNhVOtsOxaYmiWD19kRd/9yBwvhacba0/4XwfFPbc+aVIzLYrijA3dp/5iMysaOEkZMqcLFh66/2FggA2hv1zy5V8O5yBX+9Smk30YpWnl3g2esV/OxSgetKRHDo6JQh4ZjWwUpEiyLw+wUKPjtJGwaNdzePEbi+RFtI8sKNCn4zT18o8M990jQkW++V+OH7qml/WqD9fYKPnNffcC8ZAtx+kfbALSrwxK7w7QeMCyeG+YdgByULXBtYONMCvGmomG0NSeyOXQDOuqRpe7dww+YBxg/TQEJnnvOk39dYtQO0xCJQMeqIsbq7vqLjXTTOG+aYZab07ZeCQckiuEL1WF3P9Das8+rV4I7mm0ba8qTWow+Dj8nSK/ojTSuczW1OAkPMd1ykf3F8ep/Wv1JKiRWHVXx+pQ8LX1Sx5OVWLPhnHTadNp+fZ5z6vM1AVb8kU/u/uyWyIfZwQhdOBBgr99vO6FXprGRtP29AX5jjk8DhfpKwGIUutnrhYPikO9y8x52GudzGKT/9ZTg2/j9ZiAzunqLNobtsCILblXUkzSbC9ijrDodNm6v3s0sV/O8SpU37lI4SBUB7U/7qdCU4/yeeOWwCP71UwbcvVpCdKjAmW18E4pPAozv0D7gnduofMNmGYcJdZxGWcX7dxYXaKt/Ah/v7FeG75hvn0A03xP2WcXosXz6kJUc+VYadCP/iIb1SODTd3Mk/VJpNX4H4sX9lrHG+jnFOW2hiV1oQ+QrsQJPt8gZ0uLODMUkeFkHbnp42OlP7f2NzZK1Hjl2Q+NNHqqnKadTZrhMBkbY8MfYONL6+ijP0FiGHz5t3pQi0VRqZKfCJMXrV7sldEr/fJvG7rdqwXWAFrbMZ+O5aH949qZ/7xgUbgfltxoViXZ1nZ6xcG79kXJSjJ8IbT+ur8QsNlTxjK52ebioda6psO5Ta2By+ahfauzBU4AslwMSOKCYmDRZ4Y5mC319p6fbQak8oTBN4YrESfNO1J2krTfuzuybpC1u2VQFl9dqeuoFmx/Yk4PHFCqz+d5f23iyNw6SzCkWblcN/2922ameu2Om3LcnSt26raNSSo8OGiqAxCXrOMNF66RjRafIVSN4am7VVmYFFFMkWmIblJ+WaJ//PjKLVyHLD7iQvHWq/WapxRfJlQ/v+/DcmK53tQ+pukfjmGhUvHJT42rsqGsLsu1xjSuzafyzj7zraCca8cEK/PsWqN/Y9XqdXbDJsemUNAO6eIuDwz7V77Zg0zdksdOi3bVWB//pA4j9H1TZ/N5DYGR+3q0PXJw3NuEcYqo7JFoGvh5kzXGRYVDHRMFXlQD9q5QFoCyACifa4bL0h8/MHZZs5hcaK3Zgs06+QpADXlYhgE/3d56KrRMfrnDwmdpRweroC1125doG/XaPg2zMF/rKwbRPa/ibJIkxtY149og1tBoZgrxslUDxIm8APaG/CoW1MvD598nq+XU+8bhytz0368DTa7MFqbE4cWrEybt32x22qafeB2yeIYBUxcG3gTb0zxnl2H56WwR5pxYPMk/0tir46VkBLViM1d6g+b2pzZfgqQ71Xb6g6PANhV5n3NmOy0tkOFM8flMGK3IUmc//JgM5anQQYe+p11PLkiGHIcVxIRdxYXQ24frQ5sc9ObduWSBHans+v3mzBMzdYsewibVKmBPDwVomzbmlaODEuJ/D3eq5ip4i2u4osHiGCbXYChhheEyMHae12gP5XsTM+n4XFAtf4p2I0NgOfX6nioS36FwljhT2wo1DAuGwt6Q800a/1mKfVdObed1Xc/poPv/qw+42oexITO6IekG4TWH5R+42B+5vrS8wLFlYYhkA+6R8WNe4THOjwX+XSJnrvO6fvMDKzUB8ut1kEPm/4YP2vDSoe+FDF0/tUlDfIYMUuO0UbJja6bIi+ireiEXh6v35MswsFLhlivv2C4SKieWrGla1/2KbvejJqUNv7fmW6wNIxAvddKkwVxc5YFGEaTg43pLTptP63Q4d9+4qpYtfBvK3zHoln95ufw7+PtG0xYW5O3P2h2ECClWxpmwjdOVHBiEHAlMFaZe6/r1Hw9Rlt/+at4/W2RFYFeOByBdeO0j4qLYrA/1tgDyYIrSrwn6MyOBQ7KFn7ogJow7+BSlJXVsaqUu+LV5TWdqGVEALfu1gxLSgzDsVaFb2VTpWr40pnojFWICfmCtwzXQQX90hoiyO+tUaFalgROygZuGK4OYaTcsO9V0UWpxafxMFabYrGzrOyW62FehoTOyKK2qBkgav87WecLXpvsNmFWssZIOTNslrirRMqbn5V++/ba/ThxotD5rgtKdGHeqtcWuL42E6JW/+jBitAoR/agPah+8DlSrBtRcCQNG3V6qUhid1NETarnlOkVdQAmBaGGOc8BeSkCvxwjoLro+iXGHDDaAG7Vbu86rhsM3S54ZR+OVaJXXEGgkPsHVXs/rFXwu1fdRhIkiSA324xVzZqe3Ao1tUsg9WWksy2Pfym5gm8cKMFf7vGgrunKpg8OPz822SLwB+uVHDreIFHF5n7RgJaQvWFqZZgQvXSIRnsLTjWsGAjySKCq6bLGqIftqt2aXtVA/rq61AlWQK3GHbIGZtlPlbT7jE91M+urin8IoW+dMBfsRMAxmdrr7unlij4eqlAqv81dKAWWFsmcc5/jo0YpN3O+N4xyV+pM86Lfr88sud2rE5/P5jYToeGWGFiR0RdsixMYvTJcfpbinGu4fsVEg9u1itOzYZpZBeHDFlaFYEfzzG3lQk1rJ1+fkPStcUeRoHHn12oDb8CWoIyPcK5kEJoff8cIQmjcaVlT0izCSzxDw03+cxzAVtVic2V2s8ZNpj2X+5LxmTlZIO+PZNReYMM7pmbagWevEYJzm06egH44Xsqql1arzhjJSuSdieAtrBm6Ss+3PaaDzf+y4dFL/rwrTU+04KI0GHYaI3MFPjOxYrpy4lRvkPgcn/DcuMuJuNCWswEev+1qmjTS60z7a2+DvW16VrF6sdzhCmRA8yJXXeGY8sbJB7fqeIzb/hwzcsqbnpFDSZXfa2pVeKov1o8YpBeubcqAp+aoOD+y/TX/6M7DK2J/BX0S4r8ibeiJ3STB+vN4j88DZx1m5+blBKP7lDxmTd9wedtrD5P6mB3mVhgYkdEXTIhV5i2rCpKAy4dov88KFnf4u2MofowPAPBycqLR4iwqyFLCwT+fbMFa25T8M/rFNw7Q5h6d03Nb3OXoCuGC9xmqGLMG6ZdTrNpH35zioCfXtq2AXZH8uxtJ6u3V0XpjtsvEsGK2PMHJapd2ofHLkMz1jlFIqbDPgv8w1mqBH66XsVbJ/QsXUqJP3+kBnd7uWOCQJ5d4IezlWDD3I2ngdteU/GJV9TgIgZFdJzY2Sz63Euf1M6nk/XaatDGZm1e4i8/DL9worcsG9v243NcyN81zuuLdjjWODcsXHU4IMki8JlJCj4xpu3xGFvpdDURq2uSuHuVin/uk8E5jM4W4GcfqDGp3B29gOD5Fa5SNneonqSdMbSZCbxe754q8MWpAg9doQT36rYqAjeM1lf7v3HM/LzWlgP/t1/iyHngD9u0832fYTg43qbgMLEjoi77pGFe2CfHtd3abGp+22To6eu0vW3fukXBL+Z2/IboSNK2R7pzorad2l+uUvDQFQqWjOr4fveWCnxvlsB9l5iHYJeUaHvqTu7CG/EnRgvM8vdRHJquDfH2tCHpIhhTrw94wr/gYMNp/YPm8qE9/3ej8dlJIhh/nwR+sVHi3/7VoWvKJDae1m6Xm6oldoD2wfegYbs/T6u5aextF4lOm3X/eI6CmQXaOZSVDNit2gd4uAbTfdFKaGaBVvk1Cq0UlnRjAYWxYjeig4pdR/LteuuhA7XosEdie54/KE3/VoGpDqedwMNbfOHv1A3bzkj8fmv72xYaK48TwlTKjEmaUSCG6TaBL0xR2sy5vXGMCLbEee2YDO4p7GnRvqwE7KvR5goHjiPZ0na1baxZY30ARJS4lowSqPdqScjy8W3fTKflAa8e0S7b/BPRU6za7cJtidURi6InVp2xKsKUdPYEIQR+t0DBunKJybnR7c8bjc9PFlh5XPswXXVCwm5Vgw2VLQKYXRTb6oBVEfjppUCyVVsRLQE8uFnivEc1tQf57iwFDkNj8HnDBKbnK3hsh8SrRyUsArh6pMCnJgiUZHX+nGYXCcwusrS53tks8fONanAOokWYV+/2FiG0PZf/+JHe5ie0p2WJ4QP/tWMS75X7YBHAfZcqnc7LMlbsirtYHRZCYGIu8MEprbJ5qjH8/NT21HslXvYv5ElSgGdvUGAVwKffVOFq0c7P0r1NWFIsw1aJVCkhZftbxYWq9Uh8b53W5PyVIxJ3TBD4whQRfM8AzAsnJuSEf9wbxwg8tU+atkLsrMJe4BC4ZIg2FFvlArac0UYgntong30CA/51WJ/POT6ne3sy9wYmdkTUZUKIYFUmnEuHCAy2S5z3AN+fLUyrKhORzSJw9cjefQ4ZydqHWSBh+NcR/dPpiuECGXHQTkcRAj+YpVXNnvXPBfzbbnNV8Yphbe+XbhP44RyBz06WsClod/vAaKTZtGG1p/dpc/tuGmtOBHrTkhKBJ3dLuFu0eY+hPRELHVrC527RWr4EFln8+H0Vz1yvtPtvKQ0rYgscMCXI0ZqQK/CBv//hOx/LNvtVd+S5A/oimBtGi2CLnR/OFvivDdpjPrDeg78mA/OGCnxluj61Yme1xHfXqpAAJuQAY7IEajzAyQYJnwr8YLYSbA5t/HuBBQk+qQ1/rq+QeHyx1iBdShnsi5lsMW9zZ1TgELikCMHqcYoFyO9gzm7ATWMUfOjfVeSFgyqaWpXg+W0R+hDwiweN8+ti/3oMxaFYIuo16TaBF29U8MpSBTeM5ttNpG4eK0zVH4vQdtf4yZz4+RARQuDeGQJ3TzEfk90KfG9Wx3MYCxzt7wndFYoQuGuygv8ss+Bzk/vuPEu3CfxhgYLbLhL43qy2f1cIgRsMvRIDl866gd9ukW2a4W6plPjtZhV/2S6DizK6O5dz7hB9iPEfeyW2+3d8qfdKfHRGtrtat94r8bK/AmtVgM9M0p/H4pEKbjQMd9Z7gdePaw2pW1VtGPPhLSrcrdqw+/Zq4IVDEqvLJI5d0IaZv79ONVUl670SrxzR/15goVNZg74v9b4aoNpfPZuW13GlbKlhDmTxoMh2gbl0iD4/b+sZ4CfrVbT6R2HvnKi3VDEu/oq3FbEAK3ZE1MvsSXpnd4pMkkVr3fLwVhVD07UKXiwaEndGCIG7pwqkWlX81b8C8Z4ZAvmO+DvW3jItX3S4P/S3L1Zw+wSJZAvQ7AM+/YaKhmZtPuKlQ/Qm2ftrJL67Tk8kAro6vy5gTLbA56cI/M8ebWjyZx+ouHaUwCtHJDyt2lzBPy1U2iRJzx2Qwd0dbigRKAj5N/3RHIHZQxSsP6VgfVkLPK1ab8PXjknYrcAJf8XRqqDNcwK0BRjfXavif65VkJki8MJB7XgArZHwzWMFPrdSG5Z9+2OJr5dKvPOxnggu6qRyfkmRVik8UAssGhFZDK2KwE1jBf57tznZLXRoO+6kWNu2+ZkcoxXqHWFiR0QUh8bnCPzPtW3nlMWjT01UMHmwhKsFbSalE0xJ0Y/mKPjJei3T+f1WidxUiQm52grjcAnQnB6YU/n5yQL7zklsOQOcb9KHzwHgoyqtIvZFwxDtoVoZvE1otS5AEQKLRii4eVIaNpxowBdXaWOoT+6Spl6Sf7pSwfBB2irmwf7FHF97V8WxC9oCjG+tUXHTWBGcy2cRWnWswCFwxXCBtz/W5puuK5dYU6bdxqYAVwzrOC4WReCJqxXUuLX9uSP1mYlahfN8k9bUOCtFmx+amiSwsFh7fgH5dmCwPf7OdyZ2RETUbVPa6flGZlcWa8Ozrx/X5q99e62KkYP01hyTBwN3T1FwziNR6BAojWLP4fZYFIFfzFXwmTfV4EKAJEVrWeOTWmJXWiAxI1+gqVXi/g16knnHRQKFaR0fw9Q8BYtGqHj3pES9VxuaBbRq4Ex/H8k8w+4hv1+g4POrtIbjh85ri28ClhiqgzeO1hI7APjzRxLn/XMULx2qza3sjM0iUJTe6c1MkixahTOc4RkCY7IQ7KMXb21OAjjphYiIqA99d5bAZf6ej6rUW6Fk2ID/d7mC2UUC15coPZLUBWSmaDtqXDpEa0308k0KvjBF70l4/wYV/zqs4sHNEmUN2n3GZcNUyevI12bo2wwGfGVa+BQj3yHw8BVKcD5bgEVo7XQCpufreygHkjoAWDwidqnLQsNOJBPjrDFxACt2REREfSjFKvDwAgVP7ZX4791ayxhAa5wdOpetJ43OEvjDlXr29dlJwPYqie3V2h68D2/VK2fJFuCXcxUkddJfMKDAIXDnRG0uH6Dt3dxRRWtCrsC/lio4UKttOXiiHpg/TGCoYdhUEQLXlwjTimt7krkRel9bNk5gc6VEq6rtmR2PmNgRERH1MUVoQ34TcyVeOaJi/jAR3CWlr1gUgZ/PVXDvu2qwShfw7ZkiuO9zpD49UaCsHjjnkfhumFXCoWwWgWl55r1aQ11XIkzJ7xXD+q6dTTjpNoEnro7vua9M7IiIiGKkvcbLfWWwXeCZ67XK2ZHzWuWsJBP4xJjok6cUq8AD83o26cp3CMwu0raNAyJf4TqQMbEjIiIawJIsAlPzgKlxugDm2zMV/GKjinE5WpJHHWNiR0RERHGreJDAP5bE9/BnPOGqWCIiIqJ+gokdERERUT/BxI6IiIion2BiR0RERNRPMLEjIiIi6ieiWhVbU9eI59/ajJOVNUhOsqJ0wggsXVAKRWmbH67Zuh/rtx9GvcuDIYOzcMuiWRhRpO2/0dzSin+v244dh8rQ1NyC4oIc3Lp4NobkZfXMsyIiIiIagKKq2D25Yh2y0h144J5l+NanrsaeIxVYvfVAm9vtOlyONz/YjbtuvBwPfXM5po4djsdeWo2m5hYAwKvrtuNYxVn84LNL8NA3lyMvOwNPrFjbM8+IiIiIaICKOLErq6zB6bMXsPTKUqSm2JCXnYGFsydi464jbW67cdcRXDJlNEYOGQxbkhWL5kyEEAr2Hq0AAKTYkrBs4UxkD0qDLcmKK2ZehHMXGlHX6O65Z0ZEREQ0wEQ8FFtWVYuczDQ4UpOD1w3Lz0Z1bQM83makJtv0256pxcwJI4M/CyEwND8LZWdqcfHEUfjEFTNMj32+wYUkqwWOVBs6IoSAEJF1xg4MD4cbJh5oGAszxsOM8TBjPHSMhRnjYcZ46Ho7FlJKSCk7vyGiSOxcHi/sKebEK5DkuTxeU2LX3m2d7qawj/vSO1uwaPZEJFk7PhyH3Q6LJbru0w67Parb92eMhRnjYcZ4mDEeOsbCjPEwYzx0vRULn88Hp8sV0W27taVYZLmj/7YSbQZ+6xvd+MsL72JYQQ6unzet08dwud1RVewcdjtcbjdUVY3iSPsfxsKM8TBjPMwYDx1jYcZ4mDEeut6ORaTVOiCKxC7NngKn22u6zu3xQvh/1+a2nra3LcrLDP587kID/vjs25g8ehiWL54VUfkymlJkgKqqA/6EC2AszBgPM8bDjPHQMRZmjIcZ46GLh1hEPBhcXJiD8/VO03DqycoaFA7ORIotqc1ty8/UBn9WVRXl1bUYUTQYAOB0N+HPz72Ly6aOwe3XzOH4PBEREVEPiDijGl6QgxFFuXh17XZ4vM2oqqnDmq37MW/GOADA/U+8gmMV1QCA+aXjsHnvMZw4fRbNLa1YtXEPrBYLJo8eCkBrdzJySC6uu3xazz8jIiIiogEqqjl2X1q2AM+t2oT7HlkBe4oNsyeXYH7peABAdW0DvP4+dRNLhuKmBaX45+sbUO/0oLggB/cuvwq2JO3Pfbj7GBQhsPPQ06bHv/O6yzBncklPPC8iIiKiAUf43BXRTVqLgWhWgwQoioL0tDQ0Op0xH++ONcbCjPEwYzzMGA8dY2HGeJgxHrq+ikWaw9Fpd5CEmNwW7YKJwH18Pl+X7tvfMBZmjIcZ42HGeOgYCzPGw4zx0PVVLCJ5/ISo2DU3N8PT1LYHHhEREdFAkZqSAput480cutXHrq9YrVakpqRAUZSI+9gRERER9QdSSqiqCmsnGzkACZLYKYrSaYZKRERENNAlxBw7IiIiIuocEzsiIiKifoKJHREREVE/kRBz7KJRU9eI59/ajJOVNUhOsqJ0wggsXVA6YLYtq6134qV3tuJYRTWE0JpFL188C+6mZvz00X/BajHH4XOfmIfSi0bE5mD7wE/++jLqnR4Y19zMnT4Wt109BwdOVOI/7+1AdW09sjIcuPqSSZgzZXTsDraXHS2vwp+fe6fN9a0+Fd+58xr84Zm3+v35sf/4aTz1+gcYV1yAu5deYfpdZ+fDmq37sX77YdS7PBgyOAu3LJqFEUW5ffsEelhH8ThSVoVX121H5bkLsKckY+60sbju8qkAgA93H8X/vbERlpDz5df33oKMtNQ+Ovqe1V4sauoaO33vHEjnxsoNu7Fyw27TbaUEMtPt+NW9n+yX5wbQ/merPSU57t47+l1i9+SKdSguzMXd98xHo7sJj764GumOVCyeMynWh9YnHn9pDYYV5OBX934S3uYWPPbSWvxr9UeYP3M8rBYFj/zoM7E+xD7l9jbjO5++BiVD80zX1zW68cSKtVi+eDZmTRqJ4xVn8ehLa1CQm5nwb8jtGTO8oM2///rth7Bl3wmkJCf1+/Pj7U178eGuo8jLymjzu87Oh12Hy/HmB7vx9dsWYUheFt776BAee2k1fnnPsjZ7ZSeKjuJxocGFR19ajWVXzsRl08fidPUF/PHZt5CbmYbZk0vgbmrG+JFF+OYdi2Nw5D2vo1i4m5o7fG0MtHNjydypWDJ3qum6v7/6HnIztdv2t3MjoL3P1hvmT4+7945+VcYqq6zB6bMXsPTKUqSm2JCXnYGFsydi464jsT60PuFu8mJ4QQ6WLihFii0Jg9LsmDO5BEcrquFuaoY9ZWCtLFZVFV5vCxxhnvfW/SeQn52By6aNQZLVivEjizB17PABc64AQIPTg9fW78Tt18wZEOdHktWCH33uegzOTm/zu87Oh427juCSKaMxcshg2JKsWDRnIoRQsPdoRV8/jR7TUTzqnR5cNnUM5pWOh0VRMLwwB+NHFuFoubYfuLupGY7U/nO+dBSLzl4bA+3cCLX/+Gl8fLoGS+ZOAdD/zg2g48/WeHzv6FcVu7KqWuRkpsGRmhy8blh+NqprG+DxNiM1uX+dbKHsKcn4zA1zTdedb3AhM90Od5MXUgKPvrgaJ06fQ1pqMi6fMQ4LZ03ot70B3U3NkABeX78Lx0+dhZTA1LHDcPPCmSg/U4vhBTmm2w8ryMb2Aydjcqyx8Pr6nZg+rhhD87Ox49DJfn9+XHnxhHZ/19n5UHamFjMnjAz+TgiBoflZKDtTi4snjuqV4+1tHcVjRFFum8r1+XoXhgzOAqB90J0934gH//cNVNfWIy87A5+4ohQTRhX16jH3lo5i0dl750A7N4xUKfHy6q24acGM4F7w/e3cADr+bI3H945+VbFzebxtvlkFkjyXxxuLQ4qpssoavPfRQVx72RRYFQsGZ6XjipkX4cFv3IpbF8/GGx/swoe7j8b6MHtNq0/F8IIclAzLwy+/ejO+ecdiHD91Fs+v2qSdKyHfKh0pyXC6B8YOJxcaXNiy7wSuvnQyAAzI88Oos/OhvfeWgXK+rNt2EDV1jZg3YxwA7bkPSkvFZ66fiwe/cSsunjgKj764GlU1dbE90F7Q2WtjIJ8bOw+VQVWlaR7uQDg3jJ+t8fje0a8qduHE/X5pveRYRTUee2kNll5ZiotGat+UpowdFvz9xJIhuHz6OGzacwyXTRsbq8PsVZnpdvzkCzcEfy4anImbFpTi8ZfWYPTw/Da3lwDQP4pTnXrvo4OYWDIEuZnaUMuUscMG3PnRmc7OBynRz74ah7du20G89v4OfG35VcHJ7zfMm266zVWzJ2Lb/o+xdd8J3HjFjFgcZq/pymtjoJwbqzfvw4KZF5kWJ/b3cyP0s/WdTfva3CbW7x396tRLs6fA6TZX5tweL4T/dwPFnqMVeOTF1bh10awOS+q5mWmoa/T04ZHFXm5mGlQpIYQIe66kD5DzZPvBk5g2bniHtxlI50d77x2B8yHNngKnZ+CdL/95bwfe+nAPvn3nNRg9rO2XIaPczDTUOQfG+WJ8bQzUc6O23omTlTWdvo8A/efcCPfZGo/vHf0qsSsuzMH5eqepxHmysgaFgzMTdnVStI6fOounXvsAX7r5CtNy6x2HTuKDHYdNt62qqUduVlpfH2KfOVV9Hq+s/ch0XVVNPawWBZNKhqC8qtb0u5NnajCiaHBfHmJMnKo+j5o6JyaVDA1eNxDPD6PiwpwOz4fiwhyUn9F/r6oqyqtr+/X5snrLfmzbfwI/uOu6NnOI3vpwDw6XnTFdV1Vbj9zM/ne+dPbaGIjnBqCtBi7Ky0JWhsN0fX89N9r7bI3H945+ldgNL8jBiKJcvLp2OzzeZlTV1GHN1v3BeSH9nU9V8X9vbsTSBaWYMGqI6XeKUPDy6m04+HElfKqKQx9X4sM9R3FF6fgYHW3vc6Qm4/3th/DOpn1oafWhurYer6/fiXkzxmH25BLU1jmxYecRtLS2Yu+xU9h37BQun97/hx3Lq2phT7GZFhkNxPPDaNakUR2eD/NLx2Hz3mM4cfosmltasWrjHlgtFkwePbSTR05MNXWNeH39Tnz1loXIGdT2A7nB1YTn39qM6tp6tLS2YvWW/Th3oRGX9MM+kJ29NgbauRFQUVUbnMph1B/PjY4+W+PxvUP43BX9ahrahQYXnlu1CcdPnYU9xYbZk0tw/eXT+s3Kvo4cLa/G7/9vVZtGmgDwi6/ejAPHT2P1lv2oa3QjJzMNV82eiEunjonBkfado+VVeGXNR6isqYMjNRmlF43EjfOnI8lqwdHyKvxrzUeoqqlHTmYarr98GqaPL471Ife6tzbuwZZ9J3D/l28yXf/BjsP9+vy498GnAQA+VXvLsyjae0KgP1ln58P72w9h7bYDqHd6UFyQg1sXz8aQvKw+fhY9p6N4vPnBbryxfmebJrM5g9Lwi6/ejJbWVry6dju2HyyDt6UFQwZn4ZNXXYyRQxKzStXZudHZa2MgnRsBf3n+HQzOysDt18wx3be/nRtA55+t5+udcfXe0e8SOyIiIqKBql8NxRIRERENZEzsiIiIiPoJJnZERERE/QQTOyIiIqJ+gokdERERUT/BxI6IiIion2BiR0RERNRPMLEjIiIi6ieY2BERERH1E0zsiIiIiPoJJnZERERE/QQTOyIiIqJ+4v8DEjqiZA5GvVQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "history_df = pd.DataFrame(history.history)\n",
    "# Start the plot at epoch 5. You can change this to get a different view.\n",
    "history_df.loc[5:, ['loss']].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681adcbd",
   "metadata": {
    "papermill": {
     "duration": 0.05081,
     "end_time": "2023-06-30T16:37:44.194429",
     "exception": false,
     "start_time": "2023-06-30T16:37:44.143619",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3) Evaluate Training\n",
    "\n",
    "If you trained the model longer, would you expect the loss to decrease further?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "555e536a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-30T16:37:44.301296Z",
     "iopub.status.busy": "2023-06-30T16:37:44.300912Z",
     "iopub.status.idle": "2023-06-30T16:37:44.310403Z",
     "shell.execute_reply": "2023-06-30T16:37:44.309478Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.067353,
     "end_time": "2023-06-30T16:37:44.312811",
     "exception": false,
     "start_time": "2023-06-30T16:37:44.245458",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n",
       "This depends on how the loss has evolved during training: if the learning curves have levelled off, there won't usually be any advantage to training for additional epochs.  Conversely, if the loss appears to still be decreasing, then training for longer could be advantageous."
      ],
      "text/plain": [
       "Correct: \n",
       "\n",
       "This depends on how the loss has evolved during training: if the learning curves have levelled off, there won't usually be any advantage to training for additional epochs.  Conversely, if the loss appears to still be decreasing, then training for longer could be advantageous."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View the solution (Run this cell to receive credit!)\n",
    "q_3.check()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248f807c",
   "metadata": {
    "papermill": {
     "duration": 0.054967,
     "end_time": "2023-06-30T16:37:44.428967",
     "exception": false,
     "start_time": "2023-06-30T16:37:44.374000",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "With the learning rate and the batch size, you have some control over:\n",
    "- How long it takes to train a model\n",
    "- How noisy the learning curves are\n",
    "- How small the loss becomes\n",
    "\n",
    "To get a better understanding of these two parameters, we'll look at the linear model, our ppsimplest neural network. Having only a single weight and a bias, it's easier to see what effect a change of parameter has.\n",
    "\n",
    "The next cell will generate an animation like the one in the tutorial. Change the values for `learning_rate`, `batch_size`, and `num_examples` (how many data points) and then run the cell. (It may take a moment or two.) Try the following combinations, or try some of your own:\n",
    "\n",
    "| `learning_rate` | `batch_size` | `num_examples` |\n",
    "|-----------------|--------------|----------------|\n",
    "| 0.05            | 32           | 256            |\n",
    "| 0.05            | 2            | 256            |\n",
    "| 0.05            | 128          | 256            |\n",
    "| 0.02            | 32           | 256            |\n",
    "| 0.2             | 32           | 256            |\n",
    "| 1.0             | 32           | 256            |\n",
    "| 0.9             | 4096         | 8192           |\n",
    "| 0.99            | 4096         | 8192           |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7062fcd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-30T16:37:44.534058Z",
     "iopub.status.busy": "2023-06-30T16:37:44.533691Z",
     "iopub.status.idle": "2023-06-30T16:38:21.137999Z",
     "shell.execute_reply": "2023-06-30T16:38:21.136961Z"
    },
    "papermill": {
     "duration": 36.717229,
     "end_time": "2023-06-30T16:38:21.197879",
     "exception": false,
     "start_time": "2023-06-30T16:37:44.480650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x7e9790004d30>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE: Experiment with different values for the learning rate, batch size, and number of examples\n",
    "learning_rate = 0.99\n",
    "batch_size = 4096\n",
    "num_examples = 8192\n",
    "\n",
    "animate_sgd(\n",
    "    learning_rate=learning_rate,\n",
    "    batch_size=batch_size,\n",
    "    num_examples=num_examples,\n",
    "    # You can also change these, if you like\n",
    "    steps=50, # total training steps (batches seen)\n",
    "    true_w=3.0, # the slope of the data\n",
    "    true_b=2.0, # the bias of the data\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a543e5",
   "metadata": {
    "papermill": {
     "duration": 0.054987,
     "end_time": "2023-06-30T16:38:21.309625",
     "exception": false,
     "start_time": "2023-06-30T16:38:21.254638",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4) Learning Rate and Batch Size\n",
    "\n",
    "What effect did changing these parameters have? After you've thought about it, run the cell below for some discussion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6a1433b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-30T16:38:21.422910Z",
     "iopub.status.busy": "2023-06-30T16:38:21.422547Z",
     "iopub.status.idle": "2023-06-30T16:38:21.430444Z",
     "shell.execute_reply": "2023-06-30T16:38:21.429560Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.066689,
     "end_time": "2023-06-30T16:38:21.432368",
     "exception": false,
     "start_time": "2023-06-30T16:38:21.365679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"4_Q4\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n",
       "\n",
       "You probably saw that smaller batch sizes gave noisier weight updates and loss curves. This is because each batch is a small *sample* of data and smaller samples tend to give noisier estimates. Smaller batches can have an \"averaging\" effect though which can be beneficial.\n",
       "\n",
       "Smaller learning rates make the updates smaller and the training takes longer to converge. Large learning rates can speed up training, but don't \"settle in\" to a minimum as well. When the learning rate is too large, the training can fail completely. (Try setting the learning rate to a large value like 0.99 to see this.)\n"
      ],
      "text/plain": [
       "Correct: \n",
       "\n",
       "\n",
       "You probably saw that smaller batch sizes gave noisier weight updates and loss curves. This is because each batch is a small *sample* of data and smaller samples tend to give noisier estimates. Smaller batches can have an \"averaging\" effect though which can be beneficial.\n",
       "\n",
       "Smaller learning rates make the updates smaller and the training takes longer to converge. Large learning rates can speed up training, but don't \"settle in\" to a minimum as well. When the learning rate is too large, the training can fail completely. (Try setting the learning rate to a large value like 0.99 to see this.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View the solution (Run this cell to receive credit!)\n",
    "q_4.check()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316df178",
   "metadata": {
    "papermill": {
     "duration": 0.058183,
     "end_time": "2023-06-30T16:38:21.546389",
     "exception": false,
     "start_time": "2023-06-30T16:38:21.488206",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Keep Going #\n",
    "\n",
    "Learn how to [**improve your model's performance**](https://www.kaggle.com/ryanholbrook/overfitting-and-underfitting) by tuning capacity or adding an early stopping callback."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e683c2",
   "metadata": {
    "papermill": {
     "duration": 0.056227,
     "end_time": "2023-06-30T16:38:21.659912",
     "exception": false,
     "start_time": "2023-06-30T16:38:21.603685",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/intro-to-deep-learning/discussion) to chat with other learners.*"
   ]
  }
 ],
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