diff --git a/MineVSRock/data/sonar.all-data.csv b/MineVSRock/data/sonar.all-data.csv
new file mode 100644
index 00000000..e8413ab4
--- /dev/null
+++ b/MineVSRock/data/sonar.all-data.csv
@@ -0,0 +1,208 @@
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diff --git a/MineVSRock/exercise/readme.md b/MineVSRock/exercise/readme.md
new file mode 100644
index 00000000..bfec4e8f
--- /dev/null
+++ b/MineVSRock/exercise/readme.md
@@ -0,0 +1,14 @@
+Learning Goals
+Learn the concept of neural networks and tensorflow framework
+
+Exercise Statement
+Mines vs Rocks, identifying whether the obstacle is rock or a mine
+
+Prerequisites
+Primary programming language like python, confusion matrix, epoch definition.
+
+Evaluation Metric
+The evaluation metric for this task is MSE(Mean Squared Error).
+
+Data source/summary:
+https://www.kaggle.com/mattcarter865/mines-vs-rocks/download
\ No newline at end of file
diff --git a/MineVSRock/solution/readme.md b/MineVSRock/solution/readme.md
new file mode 100644
index 00000000..00f8db2f
--- /dev/null
+++ b/MineVSRock/solution/readme.md
@@ -0,0 +1,3 @@
+Check out this google colaboratory:
+
+ https://colab.research.google.com/drive/1QiR2O0UpeRIvt6MlMtTCBr1VMq0XnN2D?usp=sharing
\ No newline at end of file
diff --git a/MineVSRock/solution/sonar.ipynb b/MineVSRock/solution/sonar.ipynb
new file mode 100644
index 00000000..2ca5f92c
--- /dev/null
+++ b/MineVSRock/solution/sonar.ipynb
@@ -0,0 +1,1470 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "sonar.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "faumw5Z6yTF6"
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.utils import shuffle\n",
+        "from sklearn.model_selection import train_test_split"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6hpdMgxL04ft"
+      },
+      "source": [
+        "def read_dataset():\n",
+        "  df = pd.read_csv('sonar.all-data.csv')\n",
+        "  X = df[df.columns[0:60]].values\n",
+        "  y = df[df.columns[60]]\n",
+        "  encoder = LabelEncoder()\n",
+        "  encoder.fit(y)\n",
+        "  y = encoder.transform(y)\n",
+        "  Y = one_hot_encode(y)\n",
+        "  print(X.shape)\n",
+        "  return(X,Y)\n",
+        "\n",
+        "\n",
+        "def one_hot_encode(labels):\n",
+        "  n_labels = len(labels)\n",
+        "  n_unique_labels = len(np.unique(labels))\n",
+        "  one_hot_encode = np.zeros((n_labels, n_unique_labels))\n",
+        "  one_hot_encode[np.arange(n_labels),labels] = 1\n",
+        "  return (one_hot_encode)\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5DpwzIt4CXbP",
+        "outputId": "b9fec3d0-aad4-42c5-9dd0-11f658a5cd81",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "X, Y = read_dataset()\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(207, 60)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "A0gJeo6rDRk6"
+      },
+      "source": [
+        "X, Y = shuffle(X, Y, random_state = 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yTzYsBxTEQBj"
+      },
+      "source": [
+        "train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size = 0.20, random_state = 415)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sAXCO-LlF_Kq",
+        "outputId": "2d0c84d1-819c-4ed9-f48a-086abd5f3398",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        }
+      },
+      "source": [
+        "print(train_x.shape)\n",
+        "print(train_y.shape)\n",
+        "print(test_x.shape)\n",
+        "learning_rate = 0.3\n",
+        "training_epochs = 1000\n",
+        "cost_history = np.empty(shape = [1] , dtype = float)\n",
+        "n_dim = X.shape[1]\n",
+        "print(\"n_dim\" , n_dim)\n",
+        "n_class = 2\n",
+        "model_path = \"E:\\datasets\"\n",
+        "n_hidden_1 = 60\n",
+        "n_hidden_2 =60\n",
+        "n_hidden_3 = 60\n",
+        "n_hidden_4 = 60\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(165, 60)\n",
+            "(165, 2)\n",
+            "(42, 60)\n",
+            "n_dim 60\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NaJ9Y7l_rPEF",
+        "outputId": "392e308d-bed1-4c5e-a311-2494e829d6da",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 88
+        }
+      },
+      "source": [
+        "import tensorflow.compat.v1 as tf\n",
+        "tf.disable_v2_behavior() \n",
+        "\n",
+        "x = tf.placeholder(tf.float32, [None, n_dim])\n",
+        "W = tf.Variable(tf.zeros([n_dim, n_class]))\n",
+        "b = tf.Variable(tf.zeros([n_class]))\n",
+        "y_ = tf.placeholder(tf.float32, [None, n_class])\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "non-resource variables are not supported in the long term\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "O7WQ1wHo76fu"
+      },
+      "source": [
+        "def multilayer_perceptron(x, weights, biases):\n",
+        "  layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
+        "  layer_1 = tf.nn.sigmoid(layer_1)\n",
+        "\n",
+        "  layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
+        "  layer_2 = tf.nn.sigmoid(layer_2)\n",
+        "\n",
+        "  layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])\n",
+        "  layer_3 = tf.nn.sigmoid(layer_3)\n",
+        "\n",
+        "  layer_4 = tf.add(tf.matmul(layer_3, weights['h4']), biases['b4'])\n",
+        "  layer_4 = tf.nn.relu(layer_4)\n",
+        "\n",
+        "  out_layer = tf.matmul(layer_4, weights['out']) + biases['out']\n",
+        "  return out_layer\n",
+        "\n",
+        "weights = {\n",
+        "    'h1' : tf.Variable(tf.truncated_normal([n_dim, n_hidden_1])),\n",
+        "    'h2' : tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2])),\n",
+        "    'h3' : tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3])),\n",
+        "    'h4' : tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4])),\n",
+        "    'out' : tf.Variable(tf.truncated_normal([n_hidden_4, n_class]))\n",
+        "}\n",
+        "\n",
+        "biases = {\n",
+        "    'b1' : tf.Variable(tf.truncated_normal([n_hidden_1])),\n",
+        "    'b2' : tf.Variable(tf.truncated_normal([n_hidden_2])),\n",
+        "    'b3' : tf.Variable(tf.truncated_normal([n_hidden_3])),\n",
+        "    'b4' : tf.Variable(tf.truncated_normal([n_hidden_4])),\n",
+        "    'out' : tf.Variable(tf.truncated_normal([n_class]))\n",
+        "}\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "swfdrRan_D6I",
+        "outputId": "b9cf9a4b-c265-4b10-95d8-4f9e5ef15b96",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "source": [
+        "init = tf.global_variables_initializer()\n",
+        "saver = tf.train.Saver()\n",
+        "y = multilayer_perceptron(x, weights, biases)\n",
+        "\n",
+        "cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y, labels = y_ ))\n",
+        "training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)\n",
+        "\n",
+        "sess = tf.Session()\n",
+        "sess.run(init)\n",
+        "\n",
+        "mse_history = []\n",
+        "accuracy_history = []\n",
+        "\n",
+        "for epoch in range(training_epochs):\n",
+        "  sess.run(training_step, feed_dict={x: train_x, y_: train_y })\n",
+        "  cost = sess.run(cost_function, feed_dict= {x: train_x, y_ : train_y})\n",
+        "  cost_history = np.append(cost_history, cost)\n",
+        "  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
+        "  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
+        "\n",
+        "  pred_y = sess.run(y, feed_dict = {x : test_x})\n",
+        "  mse = tf.reduce_mean(tf.square(pred_y - test_y))\n",
+        "  mse_ = sess.run(mse)\n",
+        "  mse_history.append(mse_)\n",
+        "  accuracy = (sess.run(accuracy, feed_dict = {x : train_x, y_: train_y}))\n",
+        "  accuracy_history.append(accuracy)\n",
+        "\n",
+        "  print('epoch:', epoch, '-', 'cost :', cost, \"-MSE :\" , mse_, \"train accuracy : \", accuracy)\n",
+        "\n",
+        "save_path = saver.save(sess, model_path)\n",
+        "print(\"model saved in file : %s\" % save_path)\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "\n",
+            "Future major versions of TensorFlow will allow gradients to flow\n",
+            "into the labels input on backprop by default.\n",
+            "\n",
+            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
+            "\n",
+            "epoch: 0 - cost : 163.5157 -MSE : 34932.097950023424 train accuracy :  0.54545456\n",
+            "epoch: 1 - cost : 7.804611 -MSE : 93.03726206382522 train accuracy :  0.45454547\n",
+            "epoch: 2 - cost : 35.421574 -MSE : 1549.2737785319678 train accuracy :  0.54545456\n",
+            "epoch: 3 - cost : 1.3659307 -MSE : 25.196513923440584 train accuracy :  0.45454547\n",
+            "epoch: 4 - cost : 3.2749307 -MSE : 19.465339069090767 train accuracy :  0.54545456\n",
+            "epoch: 5 - cost : 0.86963147 -MSE : 5.654707138958843 train accuracy :  0.45454547\n",
+            "epoch: 6 - cost : 0.69590336 -MSE : 2.9155635369700237 train accuracy :  0.45454547\n",
+            "epoch: 7 - cost : 0.68673617 -MSE : 2.934046928802648 train accuracy :  0.53939396\n",
+            "epoch: 8 - cost : 0.6864089 -MSE : 2.9552379222377914 train accuracy :  0.54545456\n",
+            "epoch: 9 - cost : 0.6861561 -MSE : 2.9644733187077885 train accuracy :  0.54545456\n",
+            "epoch: 10 - cost : 0.68587255 -MSE : 2.9855684845001464 train accuracy :  0.54545456\n",
+            "epoch: 11 - cost : 0.68562084 -MSE : 2.9965297561632194 train accuracy :  0.54545456\n",
+            "epoch: 12 - cost : 0.68536574 -MSE : 3.007418603136822 train accuracy :  0.54545456\n",
+            "epoch: 13 - cost : 0.6851059 -MSE : 3.016310858860754 train accuracy :  0.54545456\n",
+            "epoch: 14 - cost : 0.68480545 -MSE : 3.0386198773934887 train accuracy :  0.54545456\n",
+            "epoch: 15 - cost : 0.68453515 -MSE : 3.052430915622806 train accuracy :  0.54545456\n",
+            "epoch: 16 - cost : 0.684262 -MSE : 3.0648661533158665 train accuracy :  0.54545456\n",
+            "epoch: 17 - cost : 0.68398196 -MSE : 3.0744698404577213 train accuracy :  0.54545456\n",
+            "epoch: 18 - cost : 0.68369293 -MSE : 3.082226924105873 train accuracy :  0.54545456\n",
+            "epoch: 19 - cost : 0.6833996 -MSE : 3.0901646379312835 train accuracy :  0.54545456\n",
+            "epoch: 20 - cost : 0.683094 -MSE : 3.0986892275950684 train accuracy :  0.54545456\n",
+            "epoch: 21 - cost : 0.68278307 -MSE : 3.1064459095067942 train accuracy :  0.54545456\n",
+            "epoch: 22 - cost : 0.68245757 -MSE : 3.1116961284714555 train accuracy :  0.54545456\n",
+            "epoch: 23 - cost : 0.6821292 -MSE : 3.1169961983961825 train accuracy :  0.54545456\n",
+            "epoch: 24 - cost : 0.6818065 -MSE : 3.107060251757269 train accuracy :  0.54545456\n",
+            "epoch: 25 - cost : 0.68147224 -MSE : 3.095800425135862 train accuracy :  0.54545456\n",
+            "epoch: 26 - cost : 0.6811298 -MSE : 3.083416497887684 train accuracy :  0.54545456\n",
+            "epoch: 27 - cost : 0.6807769 -MSE : 3.0862352576794327 train accuracy :  0.54545456\n",
+            "epoch: 28 - cost : 0.6804107 -MSE : 3.0748013221487915 train accuracy :  0.54545456\n",
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+            "epoch: 983 - cost : 0.11672279 -MSE : 14.412088495866044 train accuracy :  0.95757574\n",
+            "epoch: 984 - cost : 0.11259224 -MSE : 16.5529588086672 train accuracy :  0.9757576\n",
+            "epoch: 985 - cost : 0.09123547 -MSE : 16.61378714618596 train accuracy :  0.9757576\n",
+            "epoch: 986 - cost : 0.08677223 -MSE : 19.54902439430896 train accuracy :  0.9757576\n",
+            "epoch: 987 - cost : 0.08374916 -MSE : 17.299305937768324 train accuracy :  0.9818182\n",
+            "epoch: 988 - cost : 0.085101396 -MSE : 21.540756529074958 train accuracy :  0.9757576\n",
+            "epoch: 989 - cost : 0.09239123 -MSE : 17.87047301144822 train accuracy :  0.969697\n",
+            "epoch: 990 - cost : 0.10087808 -MSE : 23.286365096180607 train accuracy :  0.9757576\n",
+            "epoch: 991 - cost : 0.11975463 -MSE : 17.34018566126236 train accuracy :  0.93939394\n",
+            "epoch: 992 - cost : 0.1388779 -MSE : 23.44321206337047 train accuracy :  0.95757574\n",
+            "epoch: 993 - cost : 0.17911753 -MSE : 15.630354353500893 train accuracy :  0.92121214\n",
+            "epoch: 994 - cost : 0.21840546 -MSE : 24.074265014543347 train accuracy :  0.8969697\n",
+            "epoch: 995 - cost : 0.28219384 -MSE : 13.338031292828903 train accuracy :  0.8424242\n",
+            "epoch: 996 - cost : 0.20530361 -MSE : 14.51994114232459 train accuracy :  0.91515154\n",
+            "epoch: 997 - cost : 0.09637799 -MSE : 16.111188012192436 train accuracy :  0.9757576\n",
+            "epoch: 998 - cost : 0.0697759 -MSE : 18.060339884394093 train accuracy :  0.9878788\n",
+            "epoch: 999 - cost : 0.06076094 -MSE : 19.996565109550207 train accuracy :  0.9939394\n",
+            "model saved in file : E:\\datasets\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZeDcg3iIDIOW",
+        "outputId": "79810721-7b00-4bb5-e3af-03d0abc88e04",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        }
+      },
+      "source": [
+        "plt.plot(mse_history, 'r')\n",
+        "plt.show()\n",
+        "plt.plot(accuracy_history)\n",
+        "plt.show"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiQMA0kS8P8DexcaHTRHsQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<function matplotlib.pyplot.show>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hg+hG2hr6pJ4AAowMJBZ6FEucvmoRKmF6vRIGCNsyxySx0Kl+XQsDTYDK8taE3oot+SIU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\nUoIJumEYRkr4P3NvFo2Mv1ocAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pitL-QiuIpEZ",
+        "outputId": "17902e96-ce7c-41d0-bfd5-98cf8f24eb80",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        }
+      },
+      "source": [
+        "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_, 1))\n",
+        "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
+        "print(\"test accuracy : \", (sess.run(accuracy, feed_dict = {x: test_x, y_: test_y})))\n",
+        "\n",
+        "pred_y = sess.run(y, feed_dict = {x: test_x})\n",
+        "mse = tf.reduce_mean(tf.square(pred_y - test_y))\n",
+        "print(\"MSE : %.4f\" % sess.run(mse))\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "test accuracy :  0.9285714\n",
+            "MSE : 21.9924\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "R4DyRHUuTpOG",
+        "outputId": "1f94a4f3-4058-40db-b75f-28e58fc89e8f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 66
+        }
+      },
+      "source": [
+        "%tensorflow_version 1.x\n",
+        "import sklearn.metrics as metrics\n",
+        "import seaborn as sns"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow is already loaded. Please restart the runtime to change versions.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+            "  import pandas.util.testing as tm\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4HmPxVFyVWqI",
+        "outputId": "083a7a81-58c3-4659-9513-899d9c0564ce",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 840
+        }
+      },
+      "source": [
+        "LABELS = [ 'Rock', 'Mine' ]\n",
+        "max_test = np.argmax(test_y, axis=1)\n",
+        "max_predictions = np.argmax(pred_y, axis = 1)\n",
+        "confusion_matrix = metrics.confusion_matrix(max_test, max_predictions)\n",
+        "\n",
+        "plt.figure(figsize=(16,14))\n",
+        "sns.heatmap(confusion_matrix, xticklabels = LABELS, yticklabels = LABELS, annot =True, fmt = \"d\")\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.xlabel('Predicted_label')\n",
+        "plt.ylabel('True Label')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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80fP+PBd/+cupVB565P3y6Ic9OF+99Gt5yn/9s5z7pfNz8E0Oyguf97Rc/3r7bfR2AbaU\nv9z2wjzg/vfKBRdelEN/9p4bvR3Yetq0xilUzmA32XuvvfJf/tNv5W2v35a/3vbf8zdv+bv82+e+\nkL86/o05/LBDc9IbXpXDDzs0r3rdGzd6qwBbznHHvTEPeOCjNnobAOsSzmA3ufEB++f2t/t3SZJ9\n9rlufuxWt8j5F16c937ggznyfvdKkhx5v3vlf73/gxu5TYAt6QP/9KFc8uWvbPQ2ANY1a1tjVf1o\nd39uh2P/T3d/eM51YaOdc975+eSZ/5af+cnb5eIvfyU3PmD/JMkBN7phLvaPAwBgqzGtcZK5K2dv\nrqqbbX9RVXdP8uqZ14QNdfnl38h/fvof5w+e9Ljsu88+3/deVaWqNmhnAACMbO5w9rgkb62qm1TV\n/ZO8OMn91zq5qo6pqlOq6pS/Ou6EmbcGu993rrgiv/f0P84D7nOP3PuIuyRJbnTDG+TCiy5Jklx4\n0SXZ/wbX38gtAgAwqFnbGrv7w1X1pCTvSfLNJPfq7gvXOX9bkm1J8p2LzlL7ZFPp7jzzz/4iP3ar\nW+ToRzzku8ePuOvh+dt3/n0e++iH5W/f+fe5x93uvIG7BAC4+vWSaY1TzBLOqurtSVaGq+sm+WqS\nV1VVuvtBc6wLG+n/fPTjefu7/iG3vc2t8ytHPyFJ8ruPOzqPffTD8pT/+qd5y9+9Owff5MC88Hl/\ntME7Bdh6Xnf8y3L3X7hzDjhg/3z+rFPynOf+eV7z2r/Z6G0BfJ/q3v0FqsV3y9bU3f+4s2uonAGM\n4zoH322jtwDAwhXfPmfTfYH9sj87esh/2+/ztGOH+lvOUjnbHr6q6keTnNfd31y8vk6Sg+ZYEwAA\nGJRpjZPMPRDkTUlWNpheuTgGAADACnOHs727+9vbXyyeX3PmNQEAAH5oVfXqqrqgqs5YcezZVXVO\nVZ2+eKw6jb6q7ltVn66qz1bVH05Zb+5wdmFVfXf4R1UdmeSimdcEAABG0ktjPnbutUnuu8rx/97d\nhy4eJ+34ZlXtleRlSe6X5PZJjqqq2+9ssVlH6Sd5fJLXV9XLFq+/mOTRM68JAADwQ+vu91fVrXfh\no3dM8tnuPitJqupvkhyZ5BPrfWju+5z9W5LDq2rfxeuvz7keAADA1eCJVfVrSU5J8pTu/vIO798s\ny4Wp7c5OcqedXXTWtsaqun5VvSjJ+5K8r6peWFXXn3NNAABgMEs95KOqjqmqU1Y8jpnw27wiyW2S\nHJrkvCQv3F1/prnbGl+d5IwkD1u8fnSS1yR5yMzrAgAArKu7tyXZdhU/c/7251X1l0n+bpXTzkly\nixWvb744tq65w9ltuvtXVrx+TlWdPvOaAAAAs6iqm3b3eYuXv5zlYtSOPpzktov7Pp+T5BFJHrmz\na88dzr5RVXft7n9Kkqq6S5JvzLwmAAAwkqVJkxGHU1UnJDkiyQFVdXaSZyU5oqoOTdJJPp/kcYtz\nD07yV919/+6+oqqemOTdSfZK8uru/vjO1ps7nP12kmMX3zOrJJckOXrmNQEAAH5o3X3UKodftca5\n5ya5/4rXJyX5gTH765l7WuPpSQ6pqustDl2W5ZLeR+dcFwAAGMhSb/QONoVZpjVW1fWq6mlV9dKq\nuneSryX5tSSfzfeGgwAAALAwV+Xs+CRfTvLBJL+V5OlZbmv85UU1DQAAgBXmCmc/1t0/nSRV9VdZ\nnv9/y+7+5kzrAQAAo+rNORDk6jbXTai/s/1Jd1+Z5GzBDAAAYG1zVc4OqapLF88ryXUWrytJd/f1\n1v4oAADA1jNLOOvuvea4LgAAsAmZ1jjJXG2NAAAAXAXCGQAAwABmvQk1AABAL5nWOIXKGQAAwACE\nMwAAgAFoawQAAOZlWuMkKmcAAAADEM4AAAAGoK0RAACYl7bGSVTOAAAABiCcAQAADEBbIwAAMK92\nE+opVM4AAAAGIJwBAAAMQFsjAAAwL9MaJ1E5AwAAGIBwBgAAMABtjQAAwKxaW+MkKmcAAAADEM4A\nAAAGoK0RAACYl7bGSVTOAAAABiCcAQAADEBbIwAAMK+lpY3ewaagcgYAADAA4QwAAGAA2hoBAIB5\nmdY4icoZAADAAIQzAACAAWhrBAAA5qWtcRKVMwAAgAEIZwAAAAPQ1ggAAMyqW1vjFCpnAAAAAxDO\nAAAABqCtEQAAmJdpjZOonAEAAAxAOAMAABiAtkYAAGBe2honUTkDAAAYgHAGAAAwAG2NAADArFpb\n4yQqZwAAAAMQzgAAAAagrREAAJiXtsZJVM4AAAAGIJwBAAAMQFsjAAAwr6WN3sDmoHIGAAAwAOEM\nAABgANoaAQCAWbkJ9TQqZwAAAAMQzgAAAAagrREAAJiXtsZJVM4AAAAGIJwBAAAMQFsjAAAwLzeh\nnkTlDAAAYADCGQAAwAC0NQIAALNyE+ppVM4AAAAGIJwBAAAMQFsjAAAwL9MaJ1E5AwAAGIBwBgAA\nMABtjQAAwKxMa5xG5QwAAGAAwhkAAMAAtDUCAADzMq1xEpUzAACAAQhnAAAAA9DWCAAAzKq1NU6i\ncgYAADAA4QwAAGAA2hoBAIB5aWucROUMAABgAMIZAADAALQ1AgAAszKtcRqVMwAAgAEIZwAAAAPQ\n1ggAAMxLW+MkKmcAAAADEM4AAAAGoK0RAACYlWmN06icAQAADEA4AwAAWEVVvbqqLqiqM1Yc+3+r\n6lNV9dGqOrGqbrDGZz9fVR+rqtOr6pQp6wlnAADArHppzMcEr01y3x2OnZzkp7r7Z5J8JsnT1vn8\nPbr70O4+bMpiwhkAAMAquvv9SS7Z4dh7uvuKxct/SXLz3bWecAYAAGxJVXVMVZ2y4nHMVbzEbyZ5\n5xrvdZL3VNWpU69rWiMAADCrUac1dve2JNt25bNV9fQkVyR5/Rqn3LW7z6mqA5OcXFWfWlTi1qRy\nBgAAcBVU1a8neWCSR3V3r3ZOd5+z+HlBkhOT3HFn1xXOAAAAJqqq+yZ5apIHdffla5yzT1Xtt/15\nkvskOWO1c1fS1ggAAMyra6N3sEuq6oQkRyQ5oKrOTvKsLE9nvFaWWxWT5F+6+/FVdXCSv+ru+yc5\nKMmJi/f3TvLX3f2una0nnAEAAKyiu49a5fCr1jj33CT3Xzw/K8khV3U9bY0AAAADUDkDAABmNeq0\nxtGonAEAAAxAOAMAABiAtkYAAGBWvbQ5pzVe3VTOAAAABqByBgAAzMpAkGlUzgAAAAYgnAEAAAxA\nWyMAADCrbgNBplA5AwAAGIBwBgAAMABtjQAAwKxMa5xG5QwAAGAAwhkAAMAAtDUCAACz6iXTGqdQ\nOQMAABiAcAYAADAAbY0AAMCsujd6B5uDyhkAAMAAhDMAAIABaGsEAABmZVrjNCpnAAAAAxDOAAAA\nBqCtEQAAmJW2xmlUzgAAAAYgnAEAAAxAWyMAADArN6GeRuUMAABgAMIZAADAALQ1AgAAszKtcRqV\nMwAAgAEIZwAAAAPQ1ggAAMyqW1vjFCpnAAAAAxDOAAAABqCtEQAAmFUvbfQONgeVMwAAgAEIZwAA\nAAPQ1ggAAMxqybTGSVTOAAAABiCcAQAADEBbIwAAMCs3oZ5G5QwAAGAAwhkAAMAAtDUCAACz6iVt\njVOonAEAAAxAOAMAABiAtkYAAGBW3Ru9g81B5QwAAGAAa1bOquoO632wu0/b/dsBAADYmtZra3zh\nOu91kl/czXsBAAD2QKY1TrNmOOvue1ydGwEAANjKdvqds6q6blU9o6q2LV7ftqoeOP/WAAAAto4p\n0xpfk+TUJD+/eH1Okjcl+bu5NgUAAOw5llpb4xRTpjXeprtfkOQ7SdLdlyfx1wUAANiNpoSzb1fV\ndbI8BCRVdZsk35p1VwAAAFvMlLbGZyV5V5JbVNXrk9wlya/PuSkAAGDP0doaJ9lpOOvuk6vqtCSH\nZ7md8Xe7+6LZdwYAALCFTKmcJcndk9w1y62N10hy4mw7AgAA2IJ2Gs6q6uVJ/l2SExaHHldV9+ru\nJ8y6MwAAYI/QvdE72BymVM5+MclPdPf2gSDHJvn4rLsCAADYYqZMa/xskluueH2LxTEAAAB2kzUr\nZ1X19ix/x2y/JJ+sqn9dvL5Tkn+9erYHAABsdm5CPc16bY1/frXtAgAAYItbM5x19z9enRsBAADY\nyqZMazw8yUuS/ESSaybZK8ll3X29mfcGAADsAdyEepopA0FemuSoJGcmuU6SxyZ52ZybAgAA2Gqm\nhLN092eT7NXdV3b3a5Lcd95tAQAAbC1T7nN2eVVdM8npVfWCJOdlYqgDAABwE+pppoSsRy/Oe2KS\ny7J8n7OHzLkpAACArWanlbPu/sLi6TeTPCdJquoNSR4+474AAAC2lCltjau5827dBQAAsMdyE+pp\nfHcMAABgAGtWzqrqDmu9leQa82znew6+zf3mXgKAiS776F9v9BYAYI+3XlvjC9d571O7eyMAAMCe\nyU2op1kznHX3Pa7OjQAAAGxlvnMGAAAwgF2d1ggAADCJaY3TqJwBAAAMYKfhrJb9alU9c/H6llV1\nx/m3BgAAsHVMqZy9PMs3nT5q8fprSV42244AAIA9Sg/6GM2U75zdqbvvUFX/J0m6+8tVdc2Z9wUA\nALClTKmcfaeq9soiXFbVjZMszborAACALWZK5ezFSU5McmBV/UmShyZ5xqy7AgAA9himNU6z03DW\n3a+vqlOT3DNJJXlwd39y9p0BAABsITsNZ1V1yySXJ3n7ymPd/f/PuTEAAICtZEpb4zuy/H2zSnLt\nJD+a5NNJfnLGfQEAAHuI1tY4yZS2xp9e+bqq7pDkd2bbEQAAwBY0ZVrj9+nu05LcaYa9AAAAbFlT\nvnP25BUvfyTJHZKcO9uOAACAPYr7cE0zpXK234rHtbL8HbQj59wUAADARquqV1fVBVV1xopj+1fV\nyVV15uLnDdf47NGLc86sqqOnrLdu5Wxx8+n9uvv3r9JvAQAAsPm9NslLkxy34tgfJvmH7n5+Vf3h\n4vUfrPxQVe2f5FlJDsvycMVTq+pt3f3l9RZbs3JWVXt395VJ7rIrvwUAAECSdGrIx0733f3+JJfs\ncPjIJMcunh+b5MGrfPSXkpzc3ZcsAtnJSe67s/XWq5z9a5a/X3Z6Vb0tyZuSXLZio2/Z2cUBAABG\nVVXHJDlmxaFt3b1tJx87qLvPWzz/UpKDVjnnZkm+uOL12Ytj65pyn7NrJ7k4yS/me/c76yTCGQAA\nsGktgtjOwth6n++q6t21n/XC2YGLSY1n5Huh7Lv72F0bAAAA9mxLe1Z6OL+qbtrd51XVTZNcsMo5\n5yQ5YsXrmyd5384uvN60xr2S7Lt47Lfi+fYHAADAVvO2JNunLx6d5G9XOefdSe5TVTdcTHO8z+LY\nutarnJ3X3c+9qjsFAADYE1TVCVmugB1QVWdneQLj85O8saoek+QLSR62OPewJI/v7sd29yVV9bwk\nH15c6rndveNgkR+wXjjb+fgSAACAnVjapNGiu49a4617rnLuKUkeu+L1q5O8+qqst15b4w8sCAAA\nwDzWDGdTym4AAADsHlNG6QMAAOyyKTd8Zv22RgAAAK4mwhkAAMAAtDUCAACzWtroDWwSKmcAAAAD\nEM4AAAAGoK0RAACYlWmN06icAQAADEA4AwAAGIC2RgAAYFamNU6jcgYAADAA4QwAAGAA2hoBAIBZ\naWucRuUMAABgACpnAADArNznbBqVMwAAgAEIZwAAAAPQ1ggAAMxqSVfjJCpnAAAAAxDOAAAABqCt\nEQAAmNWSaY2TqJwBAAAMQDgDAAAYgLZGAABgVr3RG9gkVM4AAAAGIJwBAAAMQFsjAAAwq6WN3sAm\noXIGAAAwAOEMAABgANoaAQCAWS2Vm1BPoXIGAAAwAOEMAABgANoaAQCAWbkJ9TQqZwAAAAMQzgAA\nAAagrREAAJiVm1BPo3IGAAAwAOEMAABgANoaAQCAWS25B/UkKmcAAAADEM4AAAAGoK0RAACY1VL0\nNU6hcgYAADAA4QwAAGAA2hoBAIBZ9UZvYJNQOQMAABiAcAYAADAAbY0AAMCs3IR6GpUzAACAAQhn\nAAAAA9DWCAAAzGppozewSaicAQAADEA4AwAAGIC2RgAAYFZuQj2NyhkAAMAAhDMAAIABaGsEAABm\n5SbU06icAQAADEA4AwAAGIC2RgAAYFZuQj2NyhkAAMAAhDMAAIABaGsEAABmpa1xGpUzAACAAQhn\nAAAAA9DWCAAAzKrdhHoSlRHSLowAABWrSURBVDMAAIABCGcAAAAD0NYIAADMyrTGaVTOAAAABiCc\nAQAADEBbIwAAMCttjdOonAEAAAxAOAMAABiAtkYAAGBWvdEb2CRUzgAAAAYgnAEAAAxAWyMAADCr\npdroHWwOKmcAAAADEM4AAAAGoK0RAACYlZtQT6NyBgAAMADhDAAAYADaGgEAgFlpa5xG5QwAAGAA\nwhkAAMAAtDUCAACz6o3ewCahcgYAADAA4QwAAGAAwhkAADCrpRrzsTNVdbuqOn3F49Kq+r0dzjmi\nqr664pxn7urfyXfOAAAAVtHdn05yaJJU1V5Jzkly4iqnfqC7H/jDrqdyBgAAsHP3TPJv3f2FuRZQ\nOQMAAGa1h9yE+hFJTljjvTtX1UeSnJvk97v747uygMoZAACwJVXVMVV1yorHMWucd80kD0ryplXe\nPi3Jrbr7kCQvSfLWXd2PyhkAALAldfe2JNsmnHq/JKd19/mrXOPSFc9PqqqXV9UB3X3RVd2PcAYA\nAMxqD7gJ9VFZo6Wxqm6S5Pzu7qq6Y5a7Ey/elUWEMwAAgDVU1T5J7p3kcSuOPT5JuvuVSR6a5Ler\n6ook30jyiO7epTwqnAEAAKyhuy9LcqMdjr1yxfOXJnnp7lhLOAMAAGa1tCc0Nl4NTGsEAAAYgHAG\nAAAwAG2NAADArPaQm1DPTuUMAABgAMIZAADAALQ1AgAAszKrcRqVMwAAgAEIZwAAAAPQ1ggAAMzK\ntMZpVM4AAAAGIJwBAAAMQFsjAAAwq6Xa6B1sDipnAAAAAxDOAAAABqCtEQAAmNWS21BPonIGAAAw\nAOEMAABgANoaAQCAWWlqnEblDAAAYADCGQAAwAC0NQIAALNa2ugNbBIqZwAAAAMQzgAAAAagrREA\nAJiVm1BPo3IGAAAwAJUzAABgVupm06icAQAADEA4AwAAGIC2RgAAYFbuczaNyhkAAMAAhDMAAIAB\naGsEAABm5T5n06icAQAADEA4AwAAGIC2RgAAYFaaGqdROQMAABiAcAYAADAAbY0AAMCs3IR6GpUz\nAACAAQhnAAAAA9DWCAAAzKrNa5xE5QwAAGAAwhkAAMAAtDUCAACzMq1xGpUzAACAAQhnAAAAA9DW\nCAAAzGrJtMZJVM4AAAAGIJwBAAAMQFsjAAAwK02N06icAQAADEA4AwAAGIC2RgAAYFamNU6jcgYA\nADAA4QwAAGAA2hoBAIBZLW30BjYJlTMAAIABCGcAAAAD0NYIAADMqk1rnEQ4gxkcfLOb5GWvfEFu\nfOCN0t05/rVvzLZXHrfR2wLYUp754tfmH0/5WPa//n458SXPTpJ8+nNfzPNe8fpc/s1v5uADD8jz\nn/yY7Hvd62zsRgEWtDXCDK684so86xnPz13v9IDc914Pz2/+1iPz47e7zUZvC2BLedA9fz6veNaT\nvu/Ys196XH7v1345b3nxs3PPww/Na098zwbtDuAHCWcwg/PPvzAf/cgnkiSXff2yfObTZ+WmBx+0\nwbsC2FoO+8kfz/X33ef7jn3h3PPzcz/540mSOx9y+/z9P5+2EVuDLWdp0MdoZg9nVXXXqvqNxfMb\nV9WPzr0mjOQWt7xZfvpnfiKnnvKRjd4KwJZ3m1scnPd+6PQkyXv++dR86aJLNnhHAN8zazirqmcl\n+YMkT1scukaS1825Joxkn32um9cc/+I842l/mq9/7bKN3g7AlvfcJx2dN7zzfXn4k/84l33jm7nG\nNXz9HhjH3P+L9MtJfjbJaUnS3edW1X5rnVxVxyQ5Jkn2vfaBufY1bzDz9mA+e++9d15z/IvzP9/4\n9rzj7Sdv9HYASPKjN79p/sdz/nOS5PPnnJ8PnPKxDd4RbA2mNU4zd1vjt7u7k+X/GlW1z3ond/e2\n7j6suw8TzNjs/uKlf5LPfPqsvPJlr93orQCwcPFXLk2SLC0tZdsb35H/eN9f2OAdAXzP3JWzN1bV\n/0hyg6r6rSS/meQvZ14TNtydDv+5PPyoB+fjZ3w67/3AW5Mkf/LcF+XvT37/Bu8MYOt46p//ZU45\n49P5yqVfz71+86n5naMelMu/+a284aT3Jknuefgd8uB73mWDdwnwPbVc2Jpxgap7J7lPkkry7u6e\n1N914+vfTu0TYBBnf2jbRm8BgIVr/fu710bv4ao6+ta/MuS/7Y/9/JuH+lvO/i3YRRjzhRsAAIB1\nzD2t8SFVdWZVfbWqLq2qr1XVpXOuCQAAsBnNXTl7QZL/0N2fnHkdAABgUEszf5VqTzH3tMbzBTMA\nAICdm7tydkpVvSHJW5N8a/vB7n7LzOsCAABsKnOHs+sluTzL0xq36yTCGQAAbBGaGqeZNZx192/M\neX0AAIA9xSzhrKqe2t0vqKqXZJWg3N1PmmNdAACAzWquytn2ISCnzHR9AABgk1jS2DjJLOGsu9++\n+HnsHNcHAADY08zV1vi29d7v7gfNsS4AAMBmNVdb452TfDHJCUk+lKRmWgcAABhca2ucZK5wdpMk\n905yVJJHJnlHkhO6++MzrQcAALCp/cgcF+3uK7v7Xd19dJLDk3w2yfuq6olzrAcAALDZzXafs6q6\nVpIHZLl6duskL05y4lzrAQAAY1ra6A1sEnMNBDkuyU8lOSnJc7r7jDnWAQAA2FPMVTn71SSXJfnd\nJE+q+u48kErS3X29mdYFAADYlOa6z9ks32UDAAA2HzehnkaIAgAAGIBwBgAAMIDZpjUCAAAkbkI9\nlcoZAADAAIQzAACANVTV56vqY1V1elWdssr7VVUvrqrPVtVHq+oOu7qWtkYAAGBWe8BNqO/R3Ret\n8d79ktx28bhTklcsfl5lKmcAAAC77sgkx/Wyf0lyg6q66a5cSDgDAABYWyd5T1WdWlXHrPL+zZJ8\nccXrsxfHrjJtjQAAwKy6x5zWuAhbKwPXtu7etsNpd+3uc6rqwCQnV9Wnuvv9c+xHOAMAALakRRDb\nMYzteM45i58XVNWJSe6YZGU4OyfJLVa8vvni2FWmrREAAGAVVbVPVe23/XmS+yQ5Y4fT3pbk1xZT\nGw9P8tXuPm9X1lM5AwAAZrW0eW9CfVCSE6sqWc5Of93d76qqxydJd78yyUlJ7p/ks0kuT/Ibu7qY\ncAYAALCK7j4rySGrHH/liued5Am7Yz1tjQAAAANQOQMAAGa1B9yE+mqhcgYAADAA4QwAAGAA2hoB\nAIBZ9ead1ni1UjkDAAAYgHAGAAAwAG2NAADArDbxTaivVipnAAAAAxDOAAAABqCtEQAAmFW3tsYp\nVM4AAAAGIJwBAAAMQFsjAAAwq6WN3sAmoXIGAAAwAOEMAABgANoaAQCAWbWbUE+icgYAADAA4QwA\nAGAA2hoBAIBZLWlrnETlDAAAYADCGQAAwAC0NQIAALPq1tY4hcoZAADAAIQzAACAAWhrBAAAZmVa\n4zQqZwAAAAMQzgAAAAagrREAAJhVa2ucROUMAABgAMIZAADAALQ1AgAAs1pyE+pJVM4AAAAGIJwB\nAAAMQFsjAAAwK02N06icAQAADEDlDAAAmNWS2tkkKmcAAAADEM4AAAAGoK0RAACYlbbGaVTOAAAA\nBiCcAQAADEBbIwAAMKtubY1TqJwBAAAMQDgDAAAYgLZGAABgVqY1TqNyBgAAMADhDAAAYADaGgEA\ngFm1tsZJVM4AAAAGIJwBAAAMQFsjAAAwKzehnkblDAAAYADCGQAAwAC0NQIAALNyE+ppVM4AAAAG\nIJwBAAAMQFsjAAAwK9Map1E5AwAAGIBwBgAAMABtjQAAwKxMa5xG5QwAAGAAwhkAAMAAtDUCAACz\nam2Nk6icAQAADEA4AwAAGIC2RgAAYFZLbkI9icoZAADAAIQzAACAAWhrBAAAZmVa4zQqZwAAAAMQ\nzgAAAAagrREAAJiVaY3TqJwBAAAMQDgDAAAYgLZGAABgVqY1TqNyBgAAMADhDAAAYADaGgEAgFmZ\n1jiNyhkAAMAAhDMAAIABaGsEAABmZVrjNCpnAAAAAxDOAAAABqCtEQAAmJVpjdOonAEAAAxAOAMA\nABiAtkYAAGBWpjVOo3IGAAAwAOEMAABgANoaAQCAWXUvbfQWNgWVMwAAgFVU1S2q6r1V9Ymq+nhV\n/e4q5xxRVV+tqtMXj2fu6noqZwAAAKu7IslTuvu0qtovyalVdXJ3f2KH8z7Q3Q/8YRcTzgAAgFkt\nbdJpjd19XpLzFs+/VlWfTHKzJDuGs91CWyMAAMBOVNWtk/xskg+t8vadq+ojVfXOqvrJXV1DOAMA\nALakqjqmqk5Z8ThmjfP2TfLmJL/X3Zfu8PZpSW7V3YckeUmSt+7qfrQ1AgAAs+oes62xu7cl2bbe\nOVV1jSwHs9d391tWucalK56fVFUvr6oDuvuiq7oflTMAAIBVVFUleVWST3b3i9Y45yaL81JVd8xy\nxrp4V9ZTOQMAAFjdXZI8OsnHqur0xbE/SnLLJOnuVyZ5aJLfrqorknwjySN6F0uFwhkAAP+3vXuN\n1ewsywB837YEqkVOFtI0gRpBbW3qYMFglaYEJSJoxdSgKB6KqQcCiTFG/5GSKBhJjFhjUSBIQDQe\naoqiLWDsyVYbS8/YGIEgWqURVIqtEObxx15Tdybt9OvufDNr2utKJrP2Wu9a6137x5fc+3nW+8FW\nHcOrNV6TpA8x5uIkFx+O+2lrBAAAWAHhDAAAYAW0NQIAAFu11tUa10blDAAAYAWEMwAAgBXQ1ggA\nAGzVfm2NG1E5AwAAWAHhDAAAYAW0NQIAAFs1x+iXUB9pKmcAAAArIJwBAACsgLZGAABgq3wJ9WZU\nzgAAAFZAOAMAAFgBbY0AAMBW7bda40ZUzgAAAFZAOAMAAFgBbY0AAMBWWa1xMypnAAAAKyCcAQAA\nrIC2RgAAYKv2a2vciMoZAADACghnAAAAK6CtEQAA2CqrNW5G5QwAAGAFhDMAAIAV0NYIAABs1f5o\na9yEyhkAAMAKCGcAAAAroK0RAADYKqs1bkblDAAAYAWEMwAAgBXQ1ggAAGzVfm2NG1E5AwAAWAHh\nDAAAYAW0NQIAAFs1voR6IypnAAAAKyCcAQAArIC2RgAAYKus1rgZlTMAAIAVEM4AAABWQFsjAACw\nVaOtcSMqZwAAACsgnAEAAKyAtkYAAGCrfAn1ZlTOAAAAVkDlDAAA2CoLgmxG5QwAAGAFhDMAAIAV\n0NYIAABslbbGzaicAQAArIBwBgAAsALaGgEAgK3S1LgZlTMAAIAVEM4AAABWoFZOge1qe+HM/PbR\nngcAPpOBdVM5g+278GhPAID7+UwGVks4AwAAWAHhDAAAYAWEM9g+7zYArIfPZGC1LAgCAACwAipn\nAAAAKyCcwR60/VLbm9re1vb9bZ+8h2uc2/bPtjE/gMeSttP2Pbt+Pr7t3Qc+Y9t+T9tfPHozBNiM\ncAZ7c+/M7JuZM5J8Jslrj/aEAB7DPp/kjLYnLD9/R5J/OXBwZi6bmTcflZkBPAzCGTxy1yU5JUna\n7mt7fdtb2l7a9inL/me3/VDbm9ve2PZrdl+g7fPbfuTg/QBs7ANJXrZs/2CS9x040PbH2l68bL+r\n7Vvb/k3bj7U9f9e4n297w/IZftERnT1AhDN4RNoel+TFSS5bdr07yS/MzJlJbk3yhmX/e5P85sx8\nY5Kzk9y16xpnJ7kkyXkz809Hau4AjzK/n+QH2j4hyZlJ/vYQY09O8m1JXp7kzUnS9iVJnpPkm5Ps\nS3JW23O2OmOAgwhnsDcntL0pyb8leUaSD7Z9UpInz8yVy5jfTXJO2ycmOWVmLk2SmblvZv5nGXNa\ndpZ1/u6Z+eSRfQSAR4+ZuSXJqdmpmn3gIYb/6czsn5k7svMZniQvWf59JMmNSb4+O2EN4IgRzmBv\n7p2ZfUmelaTZ+ztndyW5L8lzD9fEAB7DLkvyluxqaXwQ/7tru7v+f9PyPvG+mXn2zLxjG5MEeDDC\nGTwCSwXs9Ul+LjsvpH+27QuXw69OcuXMfC7Jp9p+b5K0fXzbL1/G/Gd23pF4U9tzj+jkAR593pnk\nopm5dQ/nXp7kgrYnJknbU9o+/bDODuAhHH+0JwDHupn5SNtbstNK86NJLlnC18eS/Pgy7NVJ3tb2\njUm+mOT7d53/721fnuQv2l4wM4d6TwKABzEzn0ry1j2ee0Xb05Jc1zZJ7knyw0k+ffhmCHBonZmj\nPQcAAIDHPG2NAAAAKyCcAQAArIBwBgAAsALCGQAAwAoIZwAAACsgnAEAAKyAcAawMm2/1Pamtre1\n/cNdX1q+l2u9q+35y/bb255+iLHntj17D/f4RNuvOsTxex7i/FPb3vYw73n/cwHAo4VwBrA+987M\nvpk5I8kXkvzU7oNtj9/LRWfmJ2bmjkMMOTfJww5nAMDhIZwBrNvVSZ69VLWubntZkjvaHtf2V9ve\n0PaWtj+ZJN1xcds7234oydMPXKjtX7d93rL9nW1vbHtz2w+3PTU7IfBnl6rdC9ue1PaPl3vc0PZb\nl3Of1vaKtre3fXuSbvIgbU9c7nVj21vbnrfr8PFt39v2o23/6EC1sO1Zba9s+/dtL2978iP+jQLA\nSu3pr68AbN9SIXtpkr9cdn1TkjNm5uNtL0zyXzPz/LaPT3Jt2yuSPDfJ1yU5PckzktyR5J0HXfek\nJL+T5JzlWk+dmc+0vSTJPTPzlmXc7yX5tZm5pu0zk1ye5LQkb0hyzcy8se3Lkrxmw0e6L8krZua/\nlzbI65ewmWXOr5mZa9u+M8nPtP31JL+R5LyZubvtK5P8UpILHsavEQCOGcIZwPqc0PamZfvqJO/I\nTrvh383Mx5f9L0ly5q73rp6U5DlJzknyvpn5UpJ/bftXD3D9FyS56sC1ZuYzDzKPb09yent/Yewr\n25643OP7lnP/vO1nN3yuJvnltuck2Z/klOwEyCT555m5dtl+T5LXZyeUnpHkg8scjkty14b3AoBj\njnAGsD73zsy+3TuWcPL53buSvG5mLj9o3Hcdxnl8WZIXzMx9DzCXvfihJCclOWtmvtj2E0mesByb\ng8ZOdp7x9pn5lr3eEACOJd45Azg2XZ7kp9s+Lknafm3br0hyVZJXLu+knZzkRQ9w7vVJzmn71cu5\nT132fy7JE3eNuyLJ6w780PZAYLwqyauWfS9N8pQN5/ykJJ9egtmLkjxr17Fntj0Qwl6V5JokdyY5\n6cD+to9r+w0b3gsAjjnCGcCx6e3ZeZ/sxmUZ+rdlpxvi0iT/uBx7d5LrDj5xZu5OcmGSP2l7c5I/\nWA69P8krDiwIkp3WwuctC47ckf9fNfKi7IS727PT3vjJDef83uV6tyb5kST/sOvYnUle2/aj2Ql7\nvzUzX0hyfpJfWeZ5U6wmCcCjWGcO7iQBAADgSFM5AwAAWAELggDwiLV9WpIPP8ChF8/Mfxzp+QDA\nsUhbIwAAwApoawQAAFgB4QwAAGAFhDMAAIAVEM4AAABWQDgDAABYgf8Do7K9IP05ehoAAAAASUVO\nRK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1152x1008 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aKo2b9DBThe1"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rGNqWY-hTCH3"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file