sklearn-KMeansClassifier.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":86,"metadata":{"id":"KR_UYRMQSxEb","executionInfo":{"status":"ok","timestamp":1664261423053,"user_tz":-330,"elapsed":668,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","from sklearn import preprocessing\n","from sklearn.cluster import KMeans"]},{"cell_type":"code","source":["df = pd.read_csv('College_K_Means.csv')\n","df.head()"],"metadata":{"id":"U_KOHt-6Te5w","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1664261423978,"user_tz":-330,"elapsed":9,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"82f70397-643c-49b9-883e-136351f722b3"},"execution_count":87,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  private  apps  accept  enroll  top10perc  top25perc  f_undergrad  \\\n","0     Yes  1660    1232     721         23         52         2885   \n","1     Yes  2186    1924     512         16         29         2683   \n","2     Yes  1428    1097     336         22         50         1036   \n","3     Yes   417     349     137         60         89          510   \n","4     Yes   193     146      55         16         44          249   \n","\n","   p_undergrad  outstate  room_board  books  personal  phd  terminal  \\\n","0          537      7440        3300    450      2200   70        78   \n","1         1227     12280        6450    750      1500   29        30   \n","2           99     11250        3750    400      1165   53        66   \n","3           63     12960        5450    450       875   92        97   \n","4          869      7560        4120    800      1500   76        72   \n","\n","   s_f_ratio  perc_alumni  expend  grad_rate  \n","0       18.1           12    7041         60  \n","1       12.2           16   10527         56  \n","2       12.9           30    8735         54  \n","3        7.7           37   19016         59  \n","4       11.9            2   10922         15  "],"text/html":["\n","  <div id=\"df-704729c4-6192-4a03-8ce1-ecdb10bd01c9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>private</th>\n","      <th>apps</th>\n","      <th>accept</th>\n","      <th>enroll</th>\n","      <th>top10perc</th>\n","      <th>top25perc</th>\n","      <th>f_undergrad</th>\n","      <th>p_undergrad</th>\n","      <th>outstate</th>\n","      <th>room_board</th>\n","      <th>books</th>\n","      <th>personal</th>\n","      <th>phd</th>\n","      <th>terminal</th>\n","      <th>s_f_ratio</th>\n","      <th>perc_alumni</th>\n","      <th>expend</th>\n","      <th>grad_rate</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Yes</td>\n","      <td>1660</td>\n","      <td>1232</td>\n","      <td>721</td>\n","      <td>23</td>\n","      <td>52</td>\n","      <td>2885</td>\n","      <td>537</td>\n","      <td>7440</td>\n","      <td>3300</td>\n","      <td>450</td>\n","      <td>2200</td>\n","      <td>70</td>\n","      <td>78</td>\n","      <td>18.1</td>\n","      <td>12</td>\n","      <td>7041</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      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<td>137</td>\n","      <td>60</td>\n","      <td>89</td>\n","      <td>510</td>\n","      <td>63</td>\n","      <td>12960</td>\n","      <td>5450</td>\n","      <td>450</td>\n","      <td>875</td>\n","      <td>92</td>\n","      <td>97</td>\n","      <td>7.7</td>\n","      <td>37</td>\n","      <td>19016</td>\n","      <td>59</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Yes</td>\n","      <td>193</td>\n","      <td>146</td>\n","      <td>55</td>\n","      <td>16</td>\n","      <td>44</td>\n","      <td>249</td>\n","      <td>869</td>\n","      <td>7560</td>\n","      <td>4120</td>\n","      <td>800</td>\n","      <td>1500</td>\n","      <td>76</td>\n","      <td>72</td>\n","      <td>11.9</td>\n","      <td>2</td>\n","      <td>10922</td>\n","      <td>15</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-704729c4-6192-4a03-8ce1-ecdb10bd01c9')\"\n","              title=\"Convert 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label_encoder.fit_transform(df['private'])\n","df['private'].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K8GtwPsOU1cQ","outputId":"de9ce447-4bf7-4288-9639-bd166a1c5e30","executionInfo":{"status":"ok","timestamp":1664261423978,"user_tz":-330,"elapsed":6,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}}},"execution_count":88,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0])"]},"metadata":{},"execution_count":88}]},{"cell_type":"code","source":["# Data Pre-processing\n","# Check for missing values\n","print(df.isnull().sum() != 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h29T8HCmVuPI","outputId":"be73048d-26a8-4f9c-be85-a4a24076c736","executionInfo":{"status":"ok","timestamp":1664261423978,"user_tz":-330,"elapsed":5,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}}},"execution_count":89,"outputs":[{"output_type":"stream","name":"stdout","text":["private        False\n","apps           False\n","accept         False\n","enroll         False\n","top10perc      False\n","top25perc      False\n","f_undergrad    False\n","p_undergrad    False\n","outstate       False\n","room_board     False\n","books          False\n","personal       False\n","phd            False\n","terminal       False\n","s_f_ratio      False\n","perc_alumni    False\n","expend         False\n","grad_rate      False\n","dtype: bool\n"]}]},{"cell_type":"code","source":["wcss = []\n","\n","for i in range(1, 11):\n","    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=0)\n","    kmeans.fit(df)\n","    wcss.append(kmeans.inertia_)\n","\n","import matplotlib.pyplot as plt\n","plt.plot(range(1, 11), wcss)\n","plt.title('The Elbow Method')\n","plt.xlabel('Number of clusters')\n","plt.ylabel('WCSS')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"ntkYmx5wTnDG","outputId":"aa57241b-a94b-4c8c-d33b-ba1058b71c8c","executionInfo":{"status":"ok","timestamp":1664261426718,"user_tz":-330,"elapsed":2744,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}}},"execution_count":90,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["from sklearn.preprocessing import StandardScaler\n","\n","sc = StandardScaler()\n","x = sc.fit_transform(df)"],"metadata":{"id":"Y-BkYAH9tJUb","executionInfo":{"status":"ok","timestamp":1664261438248,"user_tz":-330,"elapsed":695,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}}},"execution_count":93,"outputs":[]},{"cell_type":"code","source":["# 3. Training the K-means algorithm on the training dataset\n","kmeans = KMeans(n_clusters=3, init='k-means++', random_state=0)\n","y_kmeans = kmeans.fit_predict(x)\n","\n","y_kmeans"],"metadata":{"id":"dzGTVpSmWSOB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1664261441432,"user_tz":-330,"elapsed":418,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"9343eed9-b60a-4cfb-dec4-a2a97bde74db"},"execution_count":94,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 2, 0, 0, 2, 2, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0, 2, 1,\n","       0, 1, 2, 0, 2, 1, 0, 2, 0, 2, 0, 0, 0, 0, 2, 2, 0, 1, 2, 0, 0, 0,\n","       2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 2, 1, 0, 2, 2, 0,\n","       0, 0, 0, 1, 2, 2, 2, 0, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2,\n","       0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 0, 2, 0,\n","       2, 0, 0, 0, 2, 2, 0, 2, 1, 0, 2, 0, 2, 2, 0, 1, 0, 2, 0, 0, 2, 0,\n","       0, 0, 0, 0, 0, 2, 2, 2, 2, 1, 0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0,\n","       0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2,\n","       1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 2, 0,\n","       0, 0, 2, 1, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 2,\n","       0, 2, 1, 1, 0, 2, 0, 2, 0, 0, 2, 0, 0, 1, 0, 0, 0, 2, 0, 0, 2, 0,\n","       2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0,\n","       0, 0, 0, 0, 2, 1, 2, 0, 0, 1, 1, 0, 0, 1, 2, 1, 0, 0, 0, 2, 2, 0,\n","       0, 2, 1, 0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 2, 0, 2, 0, 0, 0, 0, 2, 2,\n","       2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 2, 2, 2, 2,\n","       2, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 0, 2, 1, 2, 0, 2, 0, 0, 0,\n","       0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 2, 0,\n","       0, 1, 0, 0, 0, 2, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2,\n","       0, 2, 2, 0, 2, 0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 0, 1, 0, 2, 0, 0, 1,\n","       1, 1, 1, 0, 0, 0, 2, 0, 0, 1, 2, 2, 2, 2, 1, 2, 0, 0, 1, 0, 0, 0,\n","       1, 2, 0, 2, 0, 1, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 2, 2, 1,\n","       0, 0, 2, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 2, 1, 1,\n","       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0,\n","       0, 2, 0, 0, 1, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2,\n","       2, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2,\n","       2, 0, 2, 0, 0, 0, 2, 2, 0, 2, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n","       0, 0, 2, 2, 2, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 2, 2, 0, 2,\n","       2, 0, 2, 2, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 2, 2, 1, 2,\n","       0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 2, 1,\n","       1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 0, 2, 0, 1, 1, 1, 0, 0, 0, 0,\n","       0, 1, 1, 1, 1, 0, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n","       2, 0, 0, 2, 2, 2, 2, 1, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 2, 2,\n","       2, 1, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 1, 0, 0, 0, 2, 1, 0, 0, 1, 0,\n","       0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 2,\n","       2, 2, 0, 0, 0, 2, 0], dtype=int32)"]},"metadata":{},"execution_count":94}]},{"cell_type":"code","source":["# 4. Visualizing the clusters\n","plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 50, c = '#DF2020', label = 'Cluster 1')\n","plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 50, c = '#81DF20', label = 'Cluster 2')\n","plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 50, c = '#2095DF', label = 'Cluster 3')\n","plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n","plt.title('Clusters of colleges')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"hjabqIOTWa4f","outputId":"7c94078d-85a8-4269-d79e-865ae7f95c2e","executionInfo":{"status":"ok","timestamp":1664261768628,"user_tz":-330,"elapsed":905,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}}},"execution_count":99,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxV5bX/8c/KABmYx4IMQVutMhQh8TqLWrUq1VrbOt5bqJVbx+vP2iq3g/21tmjr7zpcrZbrQFsHrFixUq3YKg69FhMoIgiKYhAQmUEykWn9/tiHEMiZcs7JyTnh+369fHGyn72fvbKJi51nP3s95u6IiEj2yensAEREJDFK4CIiWUoJXEQkSymBi4hkKSVwEZEspQQuIpKllMAlKWb2EzN7pLPjSBULPGxm283szRT2O8vMbgl9nmRm61LVtxy4lMAlJjO72MwqzKzKzDaY2fNmdnwK+y8xMzezvFT1mYTjgdOAYe5+VGcHIxKNErhEZWbXA3cCvwAGAyOAXwPndmZcraU48Y8EKt29OoV9inQIJXCJyMx6Az8FrnL3P7p7tbs3uPuz7v69MPu3GRows0oz+2Lo81GhO/lPzWyjmf1XaLdXQ3/uCN3lHxPa/1tmtiI0nPGCmY1s1a+b2VVmtgpYFRr6uMPMNoX6f9vMxkT4voaa2Z/MbJuZvW9ml4e2XwY8ABwTiuP/Rjj+8lBcu8zsHTObENp+uJktMLMdZrbczM6J8zoPNbOnzGyzmX1oZte2ais0s9+GrsEKM/t+62sc49hI11u6iEz4lVUy1zFAAfB0ivq7C7jL3X9vZj2APQn2ROBDoI+7NwKY2bnAfwJfBlYBNwGPA8e26u8rwL8AtcDpoX4OBXYCnwd2RIhjNrAMGBra70Uz+8DdHzSzJuDb7h52iMjMvg78JHTuCuAQoMHM8oFngYdCsRwPPGNmpe7+bqQLYmY5oeOeAS4ChgF/NbN33f0F4GagBDgYKAaea8exka63dBG6A5do+gNb9iTVFGgAPmtmA9y9yt3/EWXf7wAz3H1F6Py/AMa3vgsPtW9z99pQ3z0JErKFjtuwf6dmNhw4DrjR3evcfQnBXfe/xfk9fBv4pbuXe+B9d18DHA30AG5193p3fwmYR5BYoykDBrr7T0PHrQb+B7gw1P4N4Bfuvt3d1wF3t+PY9lxvyUJK4BLNVmBACseYLyO4Q15pZuVmNjnKviOBu0LDETuAbYABB7XaZ+2eD6GEeQ9wL7DJzGaaWa8w/Q4Ftrn7rlbb1uzXbzTDgQ8i9LvW3Zvb2e9IYOie7zP0vf4nwfOGln5b7b+2Hce253pLFlICl2jeAHYTDBfEoxoo2vOFmeUCA/d87e6r3P0iYBBwGzDHzIqBcCUx1wL/7u59Wv1X6O7/22qffY5z97vdfSJwBEHiajNOD3wM9DOznq22jQDWx/k9riUYNgnX7/DQsEZ7+l0LfLjf99nT3c8KtW8gGBrZY3i8x0a53tJFKIFLRO6+E/gxcK+ZfcXMisws38zONLNfhjnkPaDAzM4OjQn/EOi+p9HMLjWzgaG71D3j083A5tCfB7fq635gupmNDh3bOzT+HJaZlZnZv4TOWw3Uhfrc/3taC/wvMMPMCsxsHMGdarxz2R8AbjCziaEHp58NDessBGqA74eu0SSC8fvZMfp7E9hlZjeGHljmmtkYMysLtf8hdB36mtlBwNXxHhvleksXoQQuUbn7/wOuJ0jGmwnu+q4G5obZdydwJUGSW0+QSFvPSvkSsNzMqggesF3o7rXuXgP8HPh7aCjgaHd/muCucbaZfUrw0PHMKKH2Ihj/3U4wdLEV+FWEfS8ieDD4McED2pvd/a8xLsWe7/HJUKyPAbsIrkM/d68nSNhnAlsIplr+m7uvjNFfEzAZGE/wIHcLwfXrHdrlpwTX8EPgr8Acgt+K4jk27PWO5/uU7GBa0EEke5jZFQSJ+KTOjkU6n+7ARTKYmQ0xs+PMLMfMDgO+S+qmdUqW0zxwkczWDfgNMIpgHHs2wfCMiIZQRESylYZQRESyVFqHUAYMGOAlJSXpPKWISNZbtGjRFncfuP/2tCbwkpISKioq0nlKEZGsZ2Zrwm2POYRiZg9ZUOFtWattvzKzlWa21MyeNrM+qQxWRERii2cMfBbBCwGtvQiMcfdxBG/fTU9xXCIiEkPMBO7urxIUEmq9bX6rCnX/YN9aDSIikgapGAP/FvBEpEYzmwZMAxgxYkQKTicimaShoYF169ZRV1fX2aFkvYKCAoYNG0Z+fn5c+yeVwM3sB0Aj8Gikfdx9JjAToLS0tN2Tznc3V7G8eh7bGirpl1/C6OLJdM/pkXDMIpJa69ato2fPnpSUlGBmnR1O1nJ3tm7dyrp16xg1alRcxyScwM1sCkEhnVO9g94G+qiunMc2TsVxGryGfCti/rZbuHjww4woKIvdgYh0uLq6OiXvFDAz+vfvz+bNm+M+JqEXeczsS8D3gXNCleRSbndzFY9tnEq9V9MQOkWD11Dv1cH2Zq05K5IplLxTo73XMZ5phI8TFPY/zMzWWbDw6z0Ey1e9aGZLzOz+RIKNZnn1PDxsnX9wnOXV81J9ShGRrBLPLJSL3H2Iu+e7+zB3f9DdP+vuw919fOi/76Q6sG0NlS133vtr8Bq2NYSd1y4iB6BPPvmECy+8kEMOOYSJEydy1lln8d5771FZWcmYMYmt5Txr1iw+/vjjpOJauXIlxxxzDN27d+f2229Pqq9wMrYaYb/8EvKtKGwSz7ci+uWPDHOUiGS65qoqqufNo6GykvySEoonTyanR+ITE9yd8847j29+85vMnh0sgPTWW2+xceNGhg8fHuPoyGbNmsWYMWMYOnRo3Mc0NjaSl7c3rfbr14+7776buXPbrH+SEhlbzGp08WSM8ONBhjG6WOuzimSbuvJy1h59NNt+9jM+/c1v2Pazn7H26KOpKy9PuM+XX36Z/Px8vvOdvQMBX/jCFzjhhBP22W/WrFlcffXeFekmT57MggULaGpqYsqUKYwZM4axY8dyxx13MGfOHCoqKrjkkksYP348tbW1LFq0iJNOOomJEydyxhlnsGHDBgAmTZrEddddR2lpKXfdddc+5xw0aBBlZWVxTwtsr4y9A++e04OLBz/cZhaKYVw8+GG65WhtVpFs0lxVxcapU/HqvRMQvCb4DXvj1KkMX7iQnOL2/3+9bNkyJk6cmHBcS5YsYf369SxbFlQL2bFjB3369OGee+7h9ttvp7S0lIaGBq655hqeeeYZBg4cyBNPPMEPfvADHnroIQDq6+s7pc5TxiZwgBEFZVw/fGFoHvga+uWPZHTxZCVvkSxUPW8eRJpx7E71vHn0vOCC9AYFHHzwwaxevZprrrmGs88+m9NPP73NPu+++y7Lli3jtNNOA6CpqYkhQ4a0tF/QCXFDhidwgG45xRzZs3MujoikTkNlZcsd9/68poaGNYlNTBg9ejRz5syJuV9eXh7Nzc0tX+95c7Rv37689dZbvPDCC9x///384Q9/aLmzbonPndGjR/PGG2+E7bs4gd8cUiFjx8BFpGvJLynBiorCtllREfkjE5uYcMopp7B7925mzpzZsm3p0qW89tpr++xXUlLCkiVLaG5uZu3atbz55psAbNmyhebmZs4//3xuueUWFi9eDEDPnj3ZtWsXAIcddhibN29uSeANDQ0sX748oXhTKePvwEWkayiePJltt9wSvtGM4smJTUwwM55++mmuu+46brvtNgoKCigpKeHOO+/cZ7/jjjuOUaNGccQRR3D44YczYcIEANavX8/UqVNb7s5nzJgBwJQpU/jOd75DYWEhb7zxBnPmzOHaa69l586dNDY2ct111zF69OiosX3yySeUlpby6aefkpOTw5133sk777xDr169Evpe23zv6VwTs7S01LWgg0jXsmLFCg4//PC49q0rL2fj1KngjtfUBHfkZgx++GEKylQeA8JfTzNb5O6l+++rO3ARSZuCsjKGL1wYzANfs4b8kSODeeCdNIac7ZTARSStcoqLO2W2SVekh5giIllKCVxEJEspgYuIZCklcBGRLKUELiJZL1PLyT766KOMGzeOsWPHcuyxx/LWW28l1d/+NAtFRNIq1evcZnI52VGjRvHKK6/Qt29fnn/+eaZNm8bChQsTjml/SuAikjYdsc5tpHKyAJWVlS3bZs2aRUVFBffccw8QlJO94YYbOOGEE7jsssuoqKjAzPjWt77F8OHDW8rJ7nkT85133uH666+nqqqKAQMGMGvWLIYMGcKkSZMYP348r7/+OhdddBHf/e53W8557LHHtnw++uijWbduXULfYyRK4CKSFq3Xud1jz4Itj22cyvXDFyZUaTRbysk++OCDnHnmmQnHGY4SuIikRTzr3HZG5dF0lJN9+eWXefDBB3n99ddTGrsSuIikRUetc5vp5WSXLl3Kt7/9bZ5//nn69+8fz7cUN81CEZG02LPObTjJrHObyeVkP/roI7761a/y+9//nkMPPTSh7y8a3YGLSFqMLp7M/G3hy8kms85tJpeT/elPf8rWrVu58sorgeC3gFRWZI1ZTtbMHgImA5vcfUxoWz/gCaAEqAS+4e7bY51M5WRFup72lJMNNwtlzzq3ic5C6WpSXU52FnAP8LtW224C/ubut5rZTaGvb0w4YhE5IGid29SKmcDd/VUzK9lv87nApNDn3wILUAIXkThondvUSfQh5mB33xD6/AkwOEXxiIhInJKeheLBIHrEgXQzm2ZmFWZWsXnz5mRPJyIiIYkm8I1mNgQg9OemSDu6+0x3L3X30oEDByZ4OhER2V+iCfxPwDdDn78JPJOacEREJF4xE7iZPQ68ARxmZuvM7DLgVuA0M1sFfDH0tYhIp8jUcrLPPPMM48aNY/z48ZSWlqb/VXp3vyhC06kpjUREDghV9c3Me7+ayh0NlPTJZ/Jni+nRLfHHcZlcTvbUU0/lnHPOwcxYunQp3/jGN1i5cmXCMe1Pr9KLSNqUb6jj6N+u5Wevb+M3Sz7lZ69v4+jfrqV8Q13CfUYqJ3vCCSfss9+sWbO4+uqrW76ePHkyCxYsoKmpiSlTpjBmzBjGjh3LHXfcwZw5c1rKyY4fP57a2loWLVrESSedxMSJEznjjDPYsCGYiDdp0iSuu+46SktLueuuu/Y5Z48ePTAzAKqrq1s+p4pepReRtKiqb2bqvI1UN+ydtFbTGHyeOm8jC6cMpzi//feUmV5O9umnn2b69Ols2rSJP//5zwnHGY7uwEUkLea9X02kyh3uMG9VdfjGDta6nOxf/vIXevXq1Waf1uVkx48fzy233LLP4gzRysmed955rFy5krlz5/KjH/0opbHrDlxE0qJyR0PLHff+ahqdNTsbEuo308vJ7nHiiSeyevVqtmzZwoABA2LuHw/dgYtIWpT0yacoL/wYcFGeMbJ3fkL9ZnI52ffff589BQMXL17M7t27U1oTXHfgIpIWkz9bzC1/3xa2zQwmfy6xglaZXE72qaee4ne/+x35+fkUFhbyxBNPpPRBZsxysqmkcrIiXU97ysmWb6hj6ryNuAfDJkV5hhk8PHkwZUMKOjjS7JDqcrIiIilRNqSAhVOGM29VNWt2NjCydz6TP1ec0OwTUQIXkTQrzs/hgiN6dnYYXYISuIik0SaCNWKWAjuB3sA4YCqgYnftpQQuImlQDswAng993frNyz8CNwNnAtMBLa0WLw08iUgHu49gAa+5BIl7/9fma0Pb5ob2uy+NsWU33YGLSAe6D7gBqIljXw/td0Po6ys6KqguQ3fgItJByok/ebe2J4m3b8pxpJKy7ZVoGdmzzjqLHTt2tNn+k5/8hNtvv73d/cVDCVxEOsgMguGRRNSGjo/PnpKykyZN4oMPPmDRokXMmDGDjRs3tvvM0RJ4U1NTxOOee+45+vTp0+7zJUMJXEQ6wCaCB5aJvijowHNAfOvoRisp+6tf/YqysjLGjRvHzTffDEBlZSWHH344l19+OaNHj+b000+ntrY2bBnZkpISbrzxRiZMmMCTTz7J448/ztixYxkzZgw33nhjy/lKSkrYsmULAD//+c859NBDOf7443n33Xdb9rn77rs54ogjGDduHBdeeGGC12YvJXAR6QCzUtCHxd1PpJKy8+fPZ9WqVbz55pssWbKERYsW8eqrrwKwatUqrrrqKpYvX06fPn146qmn+NrXvkZpaSmPPvooS5YsobCwEID+/fuzePFiTjzxRG688UZeeukllixZQnl5OXPnzt3nnIsWLWL27NksWbKE5557jvLy8pa2W2+9lX/+858sXbqU+++/P7HL0ooSuIh0gKW0nW3SXrXA20n1MH/+fObPn8+RRx7JhAkTWLlyJatWrQJg1KhRjB8/HoCJEydSWVkZsZ895WLLy8uZNGkSAwcOJC8vj0suuaTlH4Q9XnvtNc477zyKioro1asX55xzTkvbuHHjuOSSS3jkkUf2WbknUUrgItIBdqaon+1x7TV69GgWLVrUZru7M336dJYsWcKSJUt4//33ueyyywDo3r17y365ubk0NjZG7D+ecrHx+POf/8xVV13F4sWLKSsri3rOeCiBi0gH6J2ifvrGtVekkrK9evXioYceoqqqCggqD27atClqX63LyO7vqKOO4pVXXmHLli00NTXx+OOPc9JJJ+2zz4knnsjcuXOpra1l165dPPvsswAtZWxPPvlkbrvtNnbu3NkSV6I0D1xEOsA44CmSG0YpBMbGtWe0krJ9+vThmGOOAYI1Kh955BFyc3Mj9rV/GdnWhgwZwq233srJJ5+Mu3P22Wdz7rnn7rPPhAkTuOCCC/jCF77AoEGDKCsL3ixtamri0ksvZefOnbg71157bdKzVlROVkSSEr6c7CZgJMkl8ALgIw60GintKSerIRQR6QCDCGqbJLp4gQFncaAl7/ZKKoGb2f8xs+VmtszMHjczVWQXkZDpBMMgiSgMHS/RJJzAzewg4Fqg1N3HALlA8jPTRSTrhB+KLQNuB4ra2VtR6Lg2IwZdXnuHtJMdQskDCs0sj+Cqt7+AgIhktYKCArZu3Roh+VzB3iQeazjF2Ju8D7xCVu7O1q1bKSiIfyAj4Vko7r7ezG4neMpQC8x39/mJ9ici2WnYsGGsW7eOzZsjvfY+iYKCWfTvP5MePV4FjJyc3S2tzc3dAaeq6kS2bp1GXd0YYEUaIs88BQUFDBs2LO79E07gZtYXOBcYBewAnjSzS939kf32mwZMAxgxYkSipxORDJWfn8+oUaNi7HU48HWC2iazCN6w3A70JSdnLDCFXr0G0qtXh4ba5SQzD/yLwIfuvhnAzP4IHAvsk8DdfSYwE4JphEmcT0Sy3kDge50dRJeRzBj4R8DRZlZkZgacyoH6e4+ISCdIOIG7+0JgDrCY4PehHEJ32iIi0vGSepXe3W8mWI1URETSTG9iiohkKSVwEZEspQQuIpKllMBFRLKUEriISJZSAhcRyVJK4CIiWUoJXEQkSymBi4hkKSVwEZEspQQuIpKllMBFRLKUEriISJZSAhcRyVJK4CIiWUoJXEQkSymBi4hkKSVwEZEspQQuIpKllMBFRLKUEriISJZSAhcRyVJK4CIiWSqpBG5mfcxsjpmtNLMVZnZMqgITEZHo8pI8/i7gL+7+NTPrBhSlICYREYlDwgnczHoDJwJTANy9HqhPTVgiIhJLMkMoo4DNwMNm9k8ze8DMivffycymmVmFmVVs3rw5idOJiEhrySTwPGACcJ+7HwlUAzftv5O7z3T3UncvHThwYBKnExGR1pJJ4OuAde6+MPT1HIKELiIiaZBwAnf3T4C1ZnZYaNOpwDspiUpERGJKdhbKNcCjoRkoq4GpyYckIiLxSCqBu/sSoDRFsYiISDvoTUwRkSylBC4ikqWUwEVEspQSuIhIllICFxHJUkrgIiJZSglcRCRLKYGLiGSpZN/E7HC7m6tYXj2PbQ2V9MsvYXTxZLrn9OjssEREOl1GJ/CP6sp5bONUHKfBa8i3IuZvu4WLBz/MiIKyzg5PRKRTZewQyu7mKh7bOJV6r6bBawBo8BrqvTrY3lzdyRGKiHSujE3gy6vn4XjYNsdZXj0vzRGJiGSWjE3g2xoqW+6899fgNWxrWJPmiEREMkvGJvB++SXkW/g1kvOtiH75I9MckYhIZsnYBD66eDKGhW0zjNHFk9MckYhIZsnYBN49pwcXD36Yblbccieeb0V0s+Jge06b9ZNFRDLO7uYqFu+azV+33criXbPZ3VyVsr7NPfyDwo5QWlrqFRUV7TpmV+Mm/rb9NrY2rKZ//sGc2vdGeuYN6qAIRURSJ9xUaMPaPRXazBa5e5vFczL2DhyCb/7e9aewouYvrK9fwoqav3Dv+lP4qK68s0MTEYkqHVOhMzaBax64iGSzdEyFztgErnngIpLN0jEVOmMTuOaBi0g2S8dU6IxN4EW5/WK0901TJCIi7ZeOqdAZm8A37F4Wvb0+eruISGdKx1TopKsRmlkuUAGsd/eUvV2zvfGj6O0Na1N1KhGRDjGioIzrhy8MlcReQ7/8kYwunpyy91hSUU72P4AVQK8U9NViQP4hfFz/VpT2g1N5OhGRDtEtp5gje17QIX0nNYRiZsOAs4EHUhPOXqf0/X7U9lP73pjqU4qIZJVkx8DvBL4PNEfawcymmVmFmVVs3rw57o675xSTS7ewbbl0o1tO+Ke7IiIHioQTuJlNBja5+6Jo+7n7THcvdffSgQMHxt3/8up55Fj4EZ4cy9M8cBE54CVzB34ccI6ZVQKzgVPM7JGURIXmgYuIxJJwAnf36e4+zN1LgAuBl9z90lQFpnrgIiLRZew8cNUDFxGJLiUJ3N0XpHIOOAST4CPNNDm1742qBy4iB7yMvQPf3VzF37bfFrbtb9tvUzVCETngZWwCVzVCEZHoMjaBaxaKiEh0GZvAe+UNidH+mTRFIiKSmTI2gdc1fxqjPXULg4qIZKOMTeDLq56N2r6s6pk0RSIikpkyNoHXN4cf/463XUSkq0tFOdkO0T2nFzSvj9hekNM7jdGIiCRmd3NVqB54Jf3ySxhdPJnuOT1S0nfGJvD6GGPcsdpFRDrbR3XlPLZxKo7T4DXkWxHzt93CxYMfZkRBWdL9Z2wC39UcvfTsruaNaYpERKT9djdX8djGqeyo7U7Fu5ezs2o4vXuspfSw/+GxjVO5fvjCpN8oz9gE3sTuqO2NMdpFRDrT8up5LKs8k78vvzK0xdiy8wg+WH8Gx43+Ncv7zUt6pZ6MfYhJhEJW8beLiHSe1Ts/CSVvY2++Cj7/ffmVfPjpJ0mfI2MTeC8bHLW9t+lFHhHJXHOXHhe1/em3jk36HBmbwL8y8M6o7V8d+N9pikREpP0qtw4i8kiBhdqTk7EJfFvTaiKHl8OWplXpDEdEpF0Kc6On14K85NNv5ibwhkoir5XcrGJWIpLRPteve9T2w2K0xyNjE3hRbr8Y7X3TFImISPsNKIyeXvsXdeE78I/qKmK0L0pTJCIi7bdjdzNEWNMAnJ11kUYY4pexCXxtjAS+tq48TZGIiLTfxup6oj3E3FhVn/Q5MjaBxxJptR4RkUzwcc2OqO3rY7THI2MT+PCC0qjtIwuOSlMkIiLt171b9Bd1unXfkPQ5MjaBj4iRwIcXTExTJCIi7ff5Ie8SbQz8iCHvJX2OjE3gOxrWxmiPXGpWRKSzTf5s9Jl0Xz4k+Zl0CSdwMxtuZi+b2TtmttzM/iPpaFpZt/ufUdvX7l6cytOJiKTUlq2nkWMNYdtyrIFNW09P+hzJVCNsBL7r7ovNrCewyMxedPd3ko4K2N74UVLtIiKdafU2o9m7hW1r9m5Ubk++IF/CCdzdNwAbQp93mdkK4CAgJQm8wetitNcm2PMmYBawFNgJ9AbGAVOBgQn2KSKyry21TVHbN9UkPw88JfXAzawEOBJYGKZtGjANYMSIEXH32c2KqPOdUdoL2xllOTADeD70det/IP4I3AycCUwHkl8pQ0QObNtqoyfobXXRE3w8kn6IaWY9gKeA69z90/3b3X2mu5e6e+nAgfHf4ZYURC+1OKrg+HZEeR8wCZhLkLj3v7uvDW2bG9rvvnb0LSLSVoxaVqSgllVyCdzM8gmS96Pu/sfkw9nr+N5XRm0/ofdVcfZ0H3ADUEPkKT17eGi/G1ASF5FknDKyKGr7ySOit8cjmVkoBjwIrHD3/0o6kv28vvPXUdtf+/SeOHopZ2/ybo89STz66/wiIpGc//keFOSGbyvIhfMPT35l+mTuwI8D/hU4xcyWhP47K+mIQj6sez1q++ra6O2BGQTDI4moDR0vItJ+Pbrl8LtzPkNh7r4LqhXmwu/O+QzF+cmPoSQzC+V1OnBhyt2+K0Z7m+H2/WwieGCZaM0UB54DNqPZKSKSiJVb6mk9GcWB2qZge9mQgqT7z9g3MZM3KwV9WIr6EZEDzcaqRn782rawbT9+bRubaxqTPkfGJvBcoq9WkUesf72W0na2SXvVAm8n2YeIHIh+9OpWotVC+dErW5M+R8Ym8Fhil5ONPIe8fbanqB8ROZC8vq6WaPXAX1ub6PO5vTI2gTexO6n24A3LVNDSbSLSfo3N0YdIYrXHI2MTePLGQcxhllgKgbEpiEVEDjQDeuwk2hBK/x7JjxJkbgIPvYXa0FjIe2vPomLl5by39iwaGgv3aY9sSgqC8BT1IyIHmryYz/GSX5U+JbVQOsrGbWN4sfxWnBwamwrJy63lzXeu5LSymxjcZ1mMowcR1DaZS2JTCQ04C00hFJFEbKyK9qKOxWiPT8begTc0FfJi+a00NBXT2BTcdTc2FdLQVBzaHs/wyHSCYZBEFIaOFxFpv4bm6MMEsdrjkbEJ/MP1J+MRwnNyqFx/Shy9lAG3A+2tOVAUOi76sm4iIpEY0asNxmqPR8Ym8Ortw1vuvPfX2FRIzfbhcfZ0BXuTeKwXR429yfuKOPsXEWkrJ8aDuljt8Z0jQ/Xq/TF5ueHnSebl1tKjz8ft6O0K4BXgPIKZKfv/w1AY2n5eaD8lbxFJTmGMZ29FCZf52CtjH2KOPOgl3lj172HbjGZKhr7Uzh5LCSrfbiZ4PQq7C14AAApSSURBVP5tgpd0+hJMFZyCHliKSKrUWUHU+RN1lnwtlIxN4Pndajmt7KY2s1CMZk4ru4m87om+xTQQ+F4qQxURaaPJm4AI9WSBJs+QJdU6gtPM4H7L+OpJ/0rFu9PYWTWc3j3WUnrYTIoKtuPJ//YhItJhCvIbqGnIIfyzN6cgvz7pc2RsAoe288B3VB3MR5+cEMwD7xtrHriISOcZ1rue97ZEGiYxhveJVQ4ktox9iNnQGGMeeGPy40ciIh2lNz1jtCdfryljE/iHH0efB/7hhnjmgYuIdI4dH20k4livO9vXbEj6HBk7hPJpzUFR54F/Wj00zRGJiISziWBm21KCMta9gXHUFv8LeIR3T8z4OKc46TNnbALvVbyevNzasEk8L7eWXsXtmQcuIpJq5QTr5j4f+rr1AjJ/5MXLm1mw5iTu++eVLN30hTZH1+QnWuZjr4wdQhk15GUswptKRjOjhrR3HriISKrcB0wiKJZXR9vVv2opyNvNaaNe5PFzL+TiIx5p00NzCpYUztgEnp8XzAPPz61ueSMzL7eW/NzqYHtessuliYgk4j7gBqCGWJVOc3Ocovw6fnDcL9om8ZxOXJU+HQb3W8YFp36NDzecwqfVQ+lV/DGjhrwUJG/NAxeRtCtnb/KOX1F+LT847he8vXkcb28eF9qa/B145iZwBwzy8+o4dPhz4dtFRNJqBsFi5+3XPbeOKyb8mitfuD9l0SR1D29mXzKzd83sfTO7KVVBAdCQZLuISEptInhgmdjdY26Oc/LIBfQrSH41+j0STuBmlgvcS7DszRHARWZ2RKoCixlZxo7eixxINgG/BC4Fvhz685cEReO6mllJ9+BunP/5OcmHEpLMEMpRwPvuvhrAzGYD5wLvpCIwEclk0afQwc0E93bTCRZW6QqW0na2SfsU5tfx+f7vpiYckruPPQhY2+rrdaFt+zCzaWZWYWYVmzfH/69yrEkmmoQi0lliT6ELts0N7XdfGmPrSMmvIg/Qq1tq+oE0DES4+0x3L3X30oED46+33feD4M9Iq9L3XdUR0YpIdPFPoQvaa0L7d4UknnztEoBP61PTDyQ3hLIeaL2u2bDQtpQY9QosGz6GFyvCrEpfehNHvbIMvpqqs4lIbIlNodubxMvI7nVmxxEsCpP4r/+1DQWs3HpYyiJK5g68HPicmY0ys27AhcCfUhMW9FlcyIsVEaoRVtxK38WqRiiSXolPoQuOm5HCWDrDlKR7MHOeWvm15EMJSTiBu3sjcDXwArAC+IO7L09VYLMvPJlmD/8LQrPnMftiVSMUSZ/kptAFxz1Hds9OGUTwYDaxF3Camo2X10xiW13/lEWU1Bi4uz/n7oe6+yHu/vNUBQWwtsdImpq7h21rau7OuuIRqTydiEQ1KwV9WIr66UzTabsoenx2NxVw3+IrUxpNxs6mbtjVl8j/2ju7q/qlMxyRA1zyU+iCYZS3UxBLZyoDbgeK2nVUTUMhP//7f7Z6jT41MjaB567rQ+RfVYy8tal7kisisaRq6tv2FPXTma5gbxKPPpzS1Gwtyfuxdy5NeSQZWwslvz76is2x2kUklVJ1w9Q3Rf10tisI7sZnEIztG/s+4C2krrGZl9dM4r7FV6b8znuPjE3gx3zwD8pHHQUW5l84d45Z/QbBxBcR6XjJT6ELxo7HpiacjFBKcE02E4ztv03wG0ZfYCzH/fZktu0e0KERZOwQyhnLX6B7Y/hVm7s37uaM5fPTHJHIgWxKCvrwFPWTaQYC3wN+Bzwb+vN7KZ1tEknGJvCihlp+9eT3KKyvIb+xHoD8xnoK62uC7Q16l14kfZKbQhccdxZBspNUydghlLxRoxj74TKe+vX5vPT5U/i4z1CG7viYU1a+RGFDHXmHHNLZIYocYKYTvPbR3jcxIRg+mZ7acDKdN4PlRm9PUsbegQ964AEAChvqOPvt57j8tQc4++3nWu68Bz34YGeGJ3IASmwKXbD/7WT3a/Ttd9Y/nwWPMBXanS8vmpv0OTI2gXc7+GB6XnZZ2Lael11Gt5Ej0xyRiLRnCl3Qvid5X9HBcWWeH18yAZqbwjc2N/HDqcckfY6MTeAA/X/4Q4YuWEC3CRPIGTyYbhMmMHTBAvr/8IedHZrIAewK4BXgPKCAtm8mFoa2nxfa78BL3gCDTzmRe9+8m9z6WmgODZc0N5NbX8u9b97NoBOPTfoc5pFu8TtAaWmpV1RUpO18ItLRwk+hC2ab6IElwKZX/5cn//tJPi4cwNDaLXz9mq+3O3mb2SJ3bzMGpQQuIpLhIiXwjB5CERGRyJTARUSylBK4iEiWUgIXEclSaX2IaWabgTVpO2F4A4AtnRxDJtH12Jeux166FvvqzOsx0t3bTOtJawLPBGZWEe5p7oFK12Nfuh576VrsKxOvh4ZQRESylBK4iEiWOhAT+MzODiDD6HrsS9djL12LfWXc9TjgxsBFRLqKA/EOXESkS1ACFxHJUl0+gZvZ181suZk1m1nEKUBmVmlmb5vZEjPrshW32nE9vmRm75rZ+2Z2UzpjTCcz62dmL5rZqtCfYZdNN7Om0M/GEjP7U7rj7Eix/q7NrLuZPRFqX2hmJemPMn3iuB5TzGxzq5+Hb3dGnHAAJHBgGfBV4NU49j3Z3cdn2lzPFIt5PcwsF7iXYBHEI4CLzOyI9ISXdjcBf3P3zwF/C30dTm3oZ2O8u5+TvvA6Vpx/15cB2939s8AdwG3pjTJ92vGz/0Srn4cH0hpkK10+gbv7Cnd/t7PjyBRxXo+jgPfdfbW71wOzgXM7PrpOcS7w29Dn3wJf6cRYOkM8f9etr9Ec4FQzS3R140yXVT/7XT6Bt4MD881skZlN6+xgOtlBwNpWX68LbeuKBrv7htDnT4DBEfYrMLMKM/uHmXWlJB/P33XLPu7eCOwE+qcluvSL92f/fDNbamZzzGx4ekJrK2NXpW8PM/sr8JkwTT9w92fi7OZ4d19vZoOAF81spbvHM+yScVJ0PbqMaNej9Rfu7mYWaV7tyNDPx8HAS2b2trt/kOpYJSs8Czzu7rvN7N8Jfjs5pTMC6RIJ3N2/mII+1of+3GRmTxP8KpWVCTwF12M90PquYlhoW1aKdj3MbKOZDXH3DWY2BNgUoY89Px+rzWwBcCTQFRJ4PH/Xe/ZZZ2Z5QG9ga3rCS7uY18PdW3/vDwC/TENcYWkIBTCzYjPrueczcDrBw74DVTnwOTMbZWbdgAuBLjXzopU/Ad8Mff4m0OY3FDPra2bdQ58HAMcB76Qtwo4Vz99162v0NeAl77pvAMa8HqF/6Pc4B1iRxvj25e5d+j+CpbHXAbuBjcALoe1DgedCnw8G3gr9t5xgqKHTY++s6xH6+izgPYK7zK58PfoTzD5ZBfwV6BfaXgo8EPp8LMGqvW+F/ryss+NO8TVo83cN/BQ4J/S5AHgSeB94Ezi4s2Pu5OsxI5Qn3gJeBj7fWbHqVXoRkSylIRQRkSylBC4ikqWUwEVEspQSuIhIllICFxHJUkrgIiJZSglcRCRL/X/0gO01Q84+ogAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]}]}