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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10040.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10052.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9881.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10070.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9822.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9894.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9841.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9685.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9645.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9753.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9766.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9740.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9999.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9910.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9892.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9979.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9917.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9858.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10097.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9883.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9873.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9951.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10103.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10051.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9889.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9938.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10057.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9719.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10042.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10056.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9815.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9954.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9738.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9991.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9687.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9771.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9930.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10099.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9621.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9622.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9669.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9885.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10094.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9663.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9915.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9959.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9789.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9724.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9802.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9631.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9957.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9710.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9814.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10085.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9820.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9739.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9786.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9862.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9914.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10047.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9680.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10081.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9658.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10019.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9896.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9857.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9931.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9843.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9886.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9909.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9633.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9779.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9772.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9701.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9796.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9879.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9783.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9697.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9849.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9824.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10008.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9793.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9855.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10001.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9769.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9839.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10016.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9871.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9908.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9678.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10014.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9846.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9949.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10063.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10067.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9800.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9742.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9801.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9964.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9703.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9664.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9952.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9853.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9847.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9995.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9924.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9967.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10032.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9880.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9767.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9920.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9643.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9925.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10018.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9747.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9998.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9972.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10025.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10030.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9926.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9674.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9655.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9713.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10076.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10060.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10003.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9932.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9807.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9634.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10068.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10031.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9683.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9642.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9632.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9699.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9888.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9989.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9652.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10064.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9974.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9864.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9758.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9831.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9613.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9646.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9705.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9752.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9679.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10007.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9610.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9660.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9969.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9929.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9943.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9653.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9654.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9762.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9934.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9754.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9606.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9757.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9902.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9907.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9706.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10044.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10043.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9835.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9986.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9670.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9876.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9729.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9868.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10013.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9860.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10046.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9732.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9960.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9717.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9735.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9805.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9775.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9626.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9988.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9764.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9780.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9817.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9936.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9708.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9852.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9867.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9798.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9866.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9869.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9726.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9808.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9956.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10035.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9933.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9759.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9725.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9787.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9671.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9818.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10071.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9782.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10088.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9842.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9704.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9657.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9625.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9693.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9821.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9714.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10026.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9639.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9665.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9795.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9832.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9760.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10029.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9905.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9662.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9906.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9966.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9684.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9980.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9763.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9607.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9826.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9833.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9668.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9945.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9825.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9628.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10021.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9696.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10028.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10101.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9781.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10072.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9987.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9700.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10039.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9941.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9755.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9840.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9813.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10020.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9834.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9947.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9865.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9649.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9615.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9695.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9870.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10095.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9733.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9746.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9887.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9605.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10066.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10065.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9863.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9916.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9899.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10034.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9616.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10069.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9851.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9778.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9837.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9723.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10041.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9734.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9939.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10011.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9659.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9644.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9661.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9803.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9828.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9878.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9810.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10024.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9721.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9993.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9691.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9656.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9736.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9667.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9898.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9715.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9737.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10053.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9722.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9901.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9707.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10061.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9942.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10006.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10038.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9768.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9785.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9895.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10045.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9777.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9900.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9968.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9611.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10000.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9688.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9981.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9614.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10080.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9850.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9770.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9619.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10005.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9953.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10009.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10050.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10096.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10092.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9897.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10059.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9676.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9806.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9816.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9983.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9859.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9893.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10017.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10055.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9812.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9620.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9792.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9640.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10015.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9830.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9690.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10048.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10089.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9965.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9765.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9749.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9638.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9962.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9788.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9791.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10033.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9750.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9848.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9994.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9819.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10036.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9692.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10086.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9624.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9677.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9630.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9712.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9992.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9672.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9838.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9944.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9689.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9709.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10049.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10083.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9856.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9970.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9845.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9937.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9912.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9976.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9647.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9623.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10027.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9804.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9971.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9797.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10090.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9673.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9686.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9903.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10054.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9702.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9612.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9730.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9617.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9761.jpg\n",
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      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9773.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9716.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9756.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10037.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9985.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9743.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9627.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10102.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9921.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9784.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9990.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9794.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9799.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10062.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9955.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9904.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9666.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9982.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9946.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10100.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10093.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9681.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10022.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9711.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10104.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9727.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9745.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9751.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9923.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9919.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9637.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9635.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_10012.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9872.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9935.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9774.jpg\n",
      "Resized and saved: /home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1/melanoma_9927.jpg\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "def resize_images(input_folder, output_folder, size=(224, 224)):\n",
    "    \"\"\"\n",
    "    Resize all images in the input folder to the specified size and save them to the output folder.\n",
    "    \n",
    "    Args:\n",
    "        input_folder (str): Path to the folder containing input images.\n",
    "        output_folder (str): Path to the folder where resized images will be saved.\n",
    "        size (tuple): Target size for resizing (width, height).\n",
    "    \"\"\"\n",
    "    if not os.path.exists(output_folder):\n",
    "        os.makedirs(output_folder)\n",
    "    \n",
    "    for filename in os.listdir(input_folder):\n",
    "        input_path = os.path.join(input_folder, filename)\n",
    "        output_path = os.path.join(output_folder, filename)\n",
    "        \n",
    "        try:\n",
    "            with Image.open(input_path) as img:\n",
    "                img_resized = img.resize(size, Image.Resampling.LANCZOS)\n",
    "                img_resized.save(output_path)\n",
    "                print(f\"Resized and saved: {output_path}\")\n",
    "        except Exception as e:\n",
    "            print(f\"Error processing {input_path}: {e}\")\n",
    "\n",
    "# Example usage\n",
    "input_folder = \"/home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign\"  # Replace with the path to your input folder\n",
    "output_folder = \"/home/ubuntu/vnet/TaoST/Model3/melanoma-skin-cancer-dataset-of-10000-images/versions/1/melanoma_cancer_dataset/test/benign1\"  # Replace with the path to your output folder\n",
    "resize_images(input_folder, output_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eaf63bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch size: torch.Size([32, 3, 224, 224])\n",
      "Labels: tensor([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 1, 0])\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "def create_data_loader(data_dir, batch_size=32, shuffle=True):\n",
    "    \"\"\"\n",
    "    Create a DataLoader for the given directory.\n",
    "\n",
    "    Args:\n",
    "        data_dir (str): Path to the directory containing the dataset.\n",
    "        batch_size (int): Number of samples per batch.\n",
    "        shuffle (bool): Whether to shuffle the data.\n",
    "\n",
    "    Returns:\n",
    "        DataLoader: A PyTorch DataLoader for the dataset.\n",
    "    \"\"\"\n",
    "    # Define transformations: resize to 224x224, convert to tensor, and normalize\n",
    "    transform = transforms.Compose([\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Standard ImageNet normalization\n",
    "    ])\n",
    "\n",
    "    # Load dataset from directory\n",
    "    dataset = datasets.ImageFolder(root=data_dir, transform=transform)\n",
    "\n",
    "    # Create DataLoader\n",
    "    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)\n",
    "\n",
    "    return data_loader\n",
    "\n",
    "# Example usage\n",
    "if __name__ == \"__main__\":\n",
    "    # Define paths to datasets\n",
    "    train_dir = \"/home/ubuntu/vnet/TaoST/Data9kBulubulaz/Data9kBulubula/train\"\n",
    "    test_dir = \"/home/ubuntu/vnet/TaoST/Data9kBulubulaz/Data9kBulubula/test\"\n",
    "\n",
    "    # Create DataLoaders\n",
    "    train_loader = create_data_loader(train_dir, batch_size=32, shuffle=True)\n",
    "    test_loader = create_data_loader(test_dir, batch_size=32, shuffle=False)\n",
    "\n",
    "    # Iterate through the train DataLoader\n",
    "    for images, labels in train_loader:\n",
    "        print(f\"Batch size: {images.size()}\")  # Should print: [batch_size, 3, 224, 224]\n",
    "        print(f\"Labels: {labels}\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6655ee1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset sizes: {'train': 7211, 'test': 1803}\n",
      "Class names: ['benign', 'malignant']\n",
      "Using device: cuda:0\n",
      "Input shape: torch.Size([32, 3, 224, 224])\n",
      "Labels: tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=EfficientNet_B0_Weights.IMAGENET1K_V1`. You can also use `weights=EfficientNet_B0_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/19\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.3445 Acc: 0.8832\n",
      "test Loss: 0.3350 Acc: 0.8702\n",
      "\n",
      "Epoch 1/19\n",
      "----------\n",
      "train Loss: 0.2994 Acc: 0.8904\n",
      "test Loss: 0.3042 Acc: 0.8857\n",
      "\n",
      "Epoch 2/19\n",
      "----------\n",
      "train Loss: 0.2864 Acc: 0.8929\n",
      "test Loss: 0.3035 Acc: 0.8813\n",
      "\n",
      "Epoch 3/19\n",
      "----------\n",
      "train Loss: 0.2812 Acc: 0.8927\n",
      "test Loss: 0.2983 Acc: 0.8819\n",
      "\n",
      "Epoch 4/19\n",
      "----------\n",
      "train Loss: 0.2764 Acc: 0.8928\n",
      "test Loss: 0.2892 Acc: 0.8874\n",
      "\n",
      "Epoch 5/19\n",
      "----------\n",
      "train Loss: 0.2754 Acc: 0.8946\n",
      "test Loss: 0.2858 Acc: 0.8918\n",
      "\n",
      "Epoch 6/19\n",
      "----------\n",
      "train Loss: 0.2778 Acc: 0.8950\n",
      "test Loss: 0.2780 Acc: 0.8952\n",
      "\n",
      "Epoch 7/19\n",
      "----------\n",
      "train Loss: 0.2684 Acc: 0.8968\n",
      "test Loss: 0.2779 Acc: 0.8891\n",
      "\n",
      "Epoch 8/19\n",
      "----------\n",
      "train Loss: 0.2714 Acc: 0.8954\n",
      "test Loss: 0.2775 Acc: 0.8896\n",
      "\n",
      "Epoch 9/19\n",
      "----------\n",
      "train Loss: 0.2664 Acc: 0.8961\n",
      "test Loss: 0.2700 Acc: 0.8935\n",
      "\n",
      "Epoch 10/19\n",
      "----------\n",
      "train Loss: 0.2630 Acc: 0.8967\n",
      "test Loss: 0.2705 Acc: 0.8907\n",
      "\n",
      "Epoch 11/19\n",
      "----------\n",
      "train Loss: 0.2630 Acc: 0.8983\n",
      "test Loss: 0.2682 Acc: 0.8941\n",
      "\n",
      "Epoch 12/19\n",
      "----------\n",
      "train Loss: 0.2621 Acc: 0.8999\n",
      "test Loss: 0.2691 Acc: 0.8930\n",
      "\n",
      "Epoch 13/19\n",
      "----------\n",
      "train Loss: 0.2596 Acc: 0.8986\n",
      "test Loss: 0.2701 Acc: 0.8918\n",
      "\n",
      "Epoch 14/19\n",
      "----------\n",
      "train Loss: 0.2629 Acc: 0.8997\n",
      "test Loss: 0.2657 Acc: 0.9002\n",
      "\n",
      "Epoch 15/19\n",
      "----------\n",
      "train Loss: 0.2565 Acc: 0.8993\n",
      "test Loss: 0.2653 Acc: 0.8991\n",
      "\n",
      "Epoch 16/19\n",
      "----------\n",
      "train Loss: 0.2620 Acc: 0.8992\n",
      "test Loss: 0.2641 Acc: 0.9002\n",
      "\n",
      "Epoch 17/19\n",
      "----------\n",
      "train Loss: 0.2641 Acc: 0.8946\n",
      "test Loss: 0.2621 Acc: 0.9013\n",
      "\n",
      "Epoch 18/19\n",
      "----------\n",
      "train Loss: 0.2573 Acc: 0.9003\n",
      "test Loss: 0.2607 Acc: 0.9018\n",
      "\n",
      "Epoch 19/19\n",
      "----------\n",
      "train Loss: 0.2542 Acc: 0.8983\n",
      "test Loss: 0.2617 Acc: 0.8963\n",
      "\n",
      "Training complete in 11m 31s\n",
      "Best val Acc: 0.901830\n"
     ]
    },
    {
     "data": {
      "image/png": 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AQAAvvPACgwYNsjokERERERGpQDR9T0RERERERERESp2D1QGIiIiIiIiIiEjlo6SUiIiIiIiIiIiUOiWlRERERERERESk1KnR+SXKzc3lyJEjeHt7Y7PZrA5HRERESolhGJw8eZIaNWrg4KDf712IrpdEREQqp6JeLykpdYmOHDlCaGio1WGIiIiIRQ4dOkTNmjWtDqNM0/WSiIhI5Xax6yUlpS6Rt7c3YH6DfXx8LI5GRERESktycjKhoaEF1wJyfrpeEhERqZyKer2kpNQlyi9B9/Hx0UWWiIhIJaTpaBen6yUREZHK7WLXS2qEICIiIiIiIiIipU5JKRERERERERERKXVKSomIiIiIiIiISKlTT6kSlpOTQ1ZWltVhiB04Ozvj6OhodRgiIiIiIiLlku6PKw573R8rKVVCDMMgNjaWEydOWB2K2FGVKlUIDg5Wc1sREREREZEi0v1xxWSP+2MlpUpI/l+4atWq4eHhoSRGOWcYBqmpqcTFxQFQvXp1iyMSEREREREpH3R/XLHY8/5YSakSkJOTU/AXLiAgwOpwxE7c3d0BiIuLo1q1aprKJyIiIiIichG6P66Y7HV/rEbnJSB/jqyHh4fFkYi95f9MNQ9aRERERETk4nR/XHHZ4/5YSakSpJLEikc/UxERERERkeLTvVTFY4+fqZJSIiIiIiIiIiJS6pSUkhLXq1cvxo0bZ3UYIiIiIiIiIpbS/XFhanQuBS5Wejd8+HBmzJhR7PP++OOPODs7X2JUIiIiIiIiIqVL98elQ0kpKRATE1Ow/e233/L888+zc+fOgn353fXzZWVlFekvk7+/v/2CFBERERERESlhuj8uHZq+JwWCg4MLHr6+vthstoKv09PTqVKlCt999x29evXCzc2NL7/8koSEBIYOHUrNmjXx8PCgefPmfP3114XOe2Z5Yu3atXnllVe466678Pb2JiwsjKlTp5bypxURERERERE5N90flw4lpUqJYRikZmaX+sMwDLt+jqeeeoqHHnqI7du3069fP9LT02nbti2//fYbW7du5e6772bYsGGsWrXqgud56623aNeuHRs2bOC+++7j3nvvZceOHXaNVUREyqikw3Ay1uoopJyLP5XBqn0J7D12yupQRESkmKy6P7b3PbLujy+fpu+VkrSsHJo8/2epjxs5sR8eLvb7MY8bN47BgwcX2vf4448XbD/44IPMnz+f77//no4dO573PNdccw333XcfYP5Ffuedd1i8eDGNGjWyW6wiIlJGpCbC/iWw/1/YtxgS94GLNzywBnyqWx2dlFMf/LOHGcsPMLZnXZ7ur+sHEZHyxKr7Y7DvPbLujy+fklJSLO3atSv0dU5ODq+++irffvst0dHRZGRkkJGRgaen5wXP06JFi4Lt/DLIuLi4EolZRERKWVYaRK0wE1D7/oWYTcAZv5XMPAmbvobuj1oRoVQAYf4eABxKTLU4EhERqax0f3z5lJQqJe7OjkRO7GfJuPZ05l+mt956i3feeYfJkyfTvHlzPD09GTduHJmZmRc8z5kN4Gw2G7m5uXaNVURESkluDhzZCPsWmYmoQ6shJ6PwMYGNoU5PqNMLjh+E+U/BxtnQ7RG4yOo2IueSn5SKUlJKRKTcser+OH9se9H98eVTUqqU2Gw2u06jKysiIiK4/vrrueOOOwDIzc1l9+7dNG7c2OLIRESkxBgGxO/+bzre/gjISCp8jE+ImYAK72kmo7yD/3st4yT8/RIk7DETWGHnL2cXOZ+wACWlRETKK90fS76K96dASlW9evWYM2cOy5cvx8/Pj7fffpvY2Fj9pRMRqWiSY/KSUHmJqJNHCr/u6gvh3c1EVJ1eEFDv/BVQrt7QZBBs+go2fqmklFySUD8zKZWUlkVSaha+HhdfhltERKQk6f64+JSUksvy3HPPsX//fvr164eHhwd33303gwYNIikp6eJvFhGRsis9CQ4sy6uE+heOnbECjKOrmUzKT0JVbwUOxSiHb327mZTa+hNc/Sq4XLjXgsiZ3F0cCfR25djJDA4dT8XXw9fqkEREpJLT/XHx2Qx7rodYiSQnJ+Pr60tSUhI+Pj6FXktPT2f//v2Eh4fj5uZmUYRSEvSzFZEKLTcXIufCqo/h8Fowck570QY1WuVNx+sFYZ3A2f3SxzIMeK8VHD8AN3wCLW+9nMhL1YWuAaSwkv5e3ThlOesOHuej29twTXOt5CgiUhbpHqriutDPtqjXAKqUEhERqewMA/b8bfZ5it38337/unmVUD2hdnfw8LffmDYbtLodFv3PbHhejpJSUnaE+Xuw7uBx9ZUSEREpp5SUEhERqcwOrYaFL8HBpebXLl7Q+QFzel2VsJIdu+VQWPQK7F9irsjnV6tkx5MKJ1Qr8ImIiJRrSkqJiIhURke3wd//B7v+ML92dIX2o6H7o+BZtXRiqBJqVmHtWwybvoZeT5fOuFJhhOUlpQ4pKSUiIlIuKSklIiJSmSTuN6uTtnwPGGBzMKfR9XoafGuWfjyt7jCTUhtnQ48nwcGh9GOQcitMlVIiIiLlmpJSIiIilcHJWFjyBqybAbnZ5r4m10PvCRDYwLq4Gg8AV184EWVOIQzvYV0sUu7kJ6Wij6eRnZOLk6OSmiIiIuWJklIiIiIVWdpxWPYerJwC2WnmvrpXwJXPQ43W1sYG5gp+zQbDuumwYbaSUlIs1bxdcXFyIDM7l5ik9IIeUyIiIlI+6NdJIiIiFVFmKkS8De+2hKVvmwmpkHYw/FcY9lPZSEjla3W7+Rz5M6QnWxuLlCsODjZC/dwB9ZUSEREpj5SUEhERqUiyM2HNZ/BeK/j7JUhPgsDGcOtXMHph2axEqtkOqjYwE2fbfrI6Giln1FdKRESk/FJSSkREpCLIzYXN38GH7eH3x+DUUagSBjd8Avcug0bXgs1mdZTnZrP9Vy21cba1sZRjH330EeHh4bi5udG2bVsiIiLOe+zSpUvp2rUrAQEBuLu706hRI955553zHv/NN99gs9kYNGhQCUR+eZSUEhERKb+UlBK76tWrF+PGjSv4unbt2kyePPmC77HZbMydO/eyx7bXeUREyhXDgJ3z4eNu8OMYOH4APKtB/zfggbXQ8lZwcLQ6yotreSvYHOHQKojfbXU05c63337LuHHjePbZZ9mwYQPdu3enf//+REVFnfN4T09PHnjgAZYsWcL27duZMGECEyZMYOrUqWcde/DgQR5//HG6d+9e0h/jkoQqKSUiImWU7o8vTkkpKTBw4ED69OlzztdWrFiBzWZj/fr1xTrnmjVruPvuu+0RXoEXX3yRVq1anbU/JiaG/v3723UsEZEy7cAymNYPvh4CcdvA1QeumAAPbYCOd4OTq9URntPxlEyOnEgrvNM7GOrl/R+kaqlie/vttxk1ahSjR4+mcePGTJ48mdDQUKZMmXLO41u3bs3QoUNp2rQptWvX5o477qBfv35nVVfl5ORw++2389JLL1GnTp3S+CjFll8ppZ5SIiJiT7o/Lh2WJ6WKU2oO8OGHH9K4cWPc3d1p2LAhM2fOPOuYOXPm0KRJE1xdXWnSpAk//XR2f4rijlsZjBo1in/++YeDBw+e9dq0adNo1aoVbdq0KdY5AwMD8fAonZVwgoODcXUtmzdgIiJ2FbMJvrwRZlxjVhY5uUHXh+HhTdDjCXD1sjrC88rOyWXQR8vo+cYift10pPCLrfOm8G36BnJzSj+4ciozM5N169bRt2/fQvv79u3L8uXLi3SODRs2sHz5cnr27Flo/8SJEwkMDGTUqFFFOk9GRgbJycmFHiUtLECVUiIiYn+6Py4dlialiltqPmXKFMaPH8+LL77Itm3beOmll7j//vv59ddfC45ZsWIFQ4YMYdiwYWzatIlhw4Zxyy23sGrVqkset7IYMGAA1apVY8aMGYX2p6am8u233zJo0CCGDh1KzZo18fDwoHnz5nz99dcXPOeZ5Ym7d++mR48euLm50aRJExYsWHDWe5566ikaNGiAh4cHderU4bnnniMrKwuAGTNm8NJLL7Fp0yZsNhs2m60g3jPLE7ds2cIVV1yBu7s7AQEB3H333Zw6darg9REjRjBo0CDefPNNqlevTkBAAPfff3/BWCJSwRgGLHgefh0HifusjubSJOyF70fCJz1gz0JzulvbkWZl1FUTwcPf6ggvaumeeA4mpJKVY/DQNxv4cuVpF3oN+oO7P5yMgb2LrAuynImPjycnJ4egoKBC+4OCgoiNjb3ge2vWrImrqyvt2rXj/vvvZ/To0QWvLVu2jM8//5xPP/20yLFMmjQJX1/fgkdoaGjxPswlCPUzL+6Pp2aRnK7/w0VExD50f1w698eWJqWKW2o+a9Ys7rnnHoYMGUKdOnW49dZbGTVqFK+99lrBMZMnT+aqq65i/PjxNGrUiPHjx3PllVcW+sEXd1y7MAzITCn9h2EUOUQnJyfuvPNOZsyYgXHa+77//nsyMzMZPXo0bdu25bfffmPr1q3cfffdDBs2rFDC70Jyc3MZPHgwjo6OrFy5ko8//pinnnrqrOO8vb2ZMWMGkZGRvPvuu3z66acFzVeHDBnCY489RtOmTYmJiSEmJoYhQ4acdY7U1FSuvvpq/Pz8WLNmDd9//z0LFy7kgQceKHTcokWL2Lt3L4sWLeKLL75gxowZZ/2jIyIVROwWWPYurJsOH7SH3x6Fkxe+YS8zkqLhl4fMuLf9aO5rdhM8sAYGTgafGpaGVxw/ro8GIMjHFcOACXO38sE/u83/d5xcoMUt5oEbv7QwyvLJdkYje8Mwztp3poiICNauXcvHH3/M5MmTCy6mT548yR133MGnn35K1apVixzD+PHjSUpKKngcOnSo+B+kmDxdnajq5QJoCp+ISLlh1f1xMe6RdX9cOvfHTiV69gvILzV/+umnC+2/UKl5RkYGbm5uhfa5u7uzevVqsrKycHZ2ZsWKFTzyyCOFjunXr19BUupSxs0fOyMjo+DrYpejZ6XCKxbcNDxzBFw8i3z4XXfdxRtvvMHixYvp3bs3YJYmDh48mJCQEB5//PGCYx988EHmz5/P999/T8eOHS967oULF7J9+3YOHDhAzZo1AXjllVfOmuc6YcKEgu3atWvz2GOP8e233/Lkk0/i7u6Ol5cXTk5OBAcHn3es2bNnk5aWxsyZM/H0ND//Bx98wMCBA3nttdcKfpvs5+fHBx98gKOjI40aNeLaa6/l77//ZsyYMUX8jolIuXEgb5q2sydkpcDaz2HjV9DxHug2Dtz9LA3vnFITYenbsPpTyE4399XvC1c8B9VbWBvbJUhOz+LPbWYi8NM727Eg8ijv/7OHN//axfHULJ69pjEOrW6HVR/Djt/Nz18Oqr+sVrVqVRwdHc+qioqLizureupM4eHhADRv3pyjR4/y4osvMnToUPbu3cuBAwcYOHBgwbG5ubmAeZG+c+dO6tate9b5XF1dLZkqEOrvQfypTA4lptK0hm+pjy8iIsVk1f0xFOseWffHJX9/bFml1KWUmvfr14/PPvuMdevWYRgGa9euZdq0aWRlZREfHw9AbGzsBc95qSXuVpSjW6FRo0Z06dKFadOmAbB3714iIiK46667yMnJ4X//+x8tWrQgICAALy8v/vrrryJPe9y+fTthYWEFf+EAOnfufNZxP/zwA926dSM4OBgvLy+ee+65Yk+t3L59Oy1btiz4CwfQtWtXcnNz2blzZ8G+pk2b4uj436pU1atXJy4urlhjiVQWK/YmsDP2pNVhXLr9eUmpnk/CiN+hZgfIToNlk+HdlhDxlvnbs7Ig4xT8+7oZ1/L3zYRUWGcY+Qfc/n25TEgBzNscQ0Z2LvWredE8xJfH+jbkuQFNAPh86X6e+GEz2dWaQXBzyMmErXMsjrh8cHFxoW3btmeV/C9YsIAuXboU+TyGYRT8Aq5Ro0Zs2bKFjRs3Fjyuu+46evfuzcaNG8vcdVCYVuATEZESoPvjkr8/tqxSKl9xSs2fe+45YmNj6dSpE4ZhEBQUxIgRI3j99dcLfeOKcs7ilriPHz+eRx99tODr5OTk4l2QOXuYGdnS5lz8JmqjRo3igQce4MMPP2T69OnUqlWLK6+8kjfeeIN33nmHyZMn07x5czw9PRk3bhyZmZlFOq9xjjLJM7/nK1eu5NZbb+Wll16iX79++Pr68s033/DWW28V6zNc6Od5+n5nZ+ezXsv/TbCImE5lZPPc3K38tCEamw1ubR/GE/0a4u/pYnVoRZebAwfzqmHDu0NIWxj1F+yaD3//n7ly3d8TYdUnZqPwNsPNqWSlLTsD1k6HiDch5Zi5L6g5XPk81L8KLjIVq6zLn7o3uE3Ngn+LR3ULp4q7M0/O2cyc9YdJTs/io+ZDcY7dAhu+hA6qXC2KRx99lGHDhtGuXTs6d+7M1KlTiYqKYuzYsYB5HRMdHV2wQMyHH35IWFgYjRo1AmDp0qW8+eabPPjggwC4ubnRrFmzQmNUqVIF4Kz9ZYGSUiIi5YxV98f5YxeD7o9L9v7YsqTUpZSau7u7M23aND755BOOHj1K9erVmTp1Kt7e3gX9DoKDgy94zkstcb/scnSbrVjT6Kx0yy238PDDD/PVV1/xxRdfMGbMGGw2GxEREVx//fXccccdgFnGv3v3bho3blyk8zZp0oSoqCiOHDlCjRpmqeaKFSsKHbNs2TJq1arFs88+W7DvzNUOXFxcyMm58KpMTZo04YsvviAlJaUgG7xs2TIcHBxo0KBBkeIVEdh06AQPfbOBgwmpONgg14CvV0cxb0sMj/dryG0dwnB0KAeJktjNkJFEuoMHj/ydTeOQ3TQP8aVZyBUEju1rVuT88zKcOAjzHjerk3o/C81vAgfHi5//cuXmwOZvYdEkSMr7zZdfOFwxAZoOBgfLF8u9bFEJqaw+kIjNBje0Din02o1ta+Lj7sz9X61nQeRR7k2py6cOzthiNsLRbRDU1Jqgy5EhQ4aQkJDAxIkTiYmJoVmzZsybN49atWoB5rLQp/9WNTc3l/Hjx7N//36cnJyoW7cur776Kvfcc49VH+GyhBYkpdIsjkRERIpE98e6P85j2VXu5ZSaOzs7U7NmTRwdHfnmm28YMGAADnkX7J07dz7rnH/99VfBOe1V4l6ReXl5MWTIEJ555hmOHDnCiBEjAKhXrx4LFixg+fLlbN++nXvuueeiq/qcrk+fPjRs2JA777yTTZs2ERERUegvV/4YUVFRfPPNN+zdu5f33nuPn376qdAxtWvXZv/+/WzcuJH4+PhCvb7y3X777bi5uTF8+HC2bt3KokWLePDBBxk2bNhF+2uICOTmGnzy715unLKcgwmphFRx59t7OvPdPZ1pFOxNUloWz83dynUfLGXdwUSrw72g6BNp/PbLdwAsy2rIH5HxvL1gFyNnrKH9/xbS6dXFjN4QzntNvmZHmxfI8axmJqd+uhs+7gY7/yjWohHFYhiw/VeY0gXm3msmpLyCYcA7ZhPz5jdViIQUwI8bDgPQrV5Vgn3dznr9qiZBzLyrA96uTiw8mMNyp/bmCxtml2aY5dp9993HgQMHyMjIYN26dfTo0aPgtRkzZrB48eKCrx988EG2bt1KSkoKSUlJrF+/nnvvvbfgeupcZsyYUWgVn7Ikv1JKjc5FRMTedH9csiy90n300Uf57LPPmDZtGtu3b+eRRx45q9T8zjvvLDh+165dfPnll+zevZvVq1dz6623snXrVl555ZWCYx5++GH++usvXnvtNXbs2MFrr73GwoULGTduXJHHFbNE8fjx4/Tp04ewsDDAnD7Zpk0b+vXrR69evQgODmbQoEFFPqeDgwM//fQTGRkZdOjQgdGjR/O///2v0DHXX389jzzyCA888ACtWrVi+fLlPPfcc4WOufHGG7n66qvp3bs3gYGB51x208PDgz///JPExETat2/PTTfdxJVXXskHH3xQ/G+GSCUTdzKd4dNXM+mPHWTnGvRvFsy8h7rTvrY/HcL9+e3Bbky8vik+bk5sO5LMjVNW8Nh3m4g7mW516IUknMpg4q+R9H5jMe7R5m+dEgI78Ow1jRnUqgZ1Az2x2SA2OZ2F2+N4+58DXL28Ic0SXuUDh9tJcfCCuEj4+lYyp/bByO9JZS/7/oXProRv74BjO8CtCvR5CR7aAO3uAkfni56ivDAMo2Dq3o1tap73uE51Avj67k4EeLrw+SnzF0U5m76BnJJdiljKv/yk1OHjqeTkllASWUREKi3dH5ccm3GuiYyl6KOPPuL1118vKDV/5513Cn6zN2LECA4cOFDwm73t27dz2223sXPnTpydnenduzevvfYaDRs2LHTOH374gQkTJrBv3z7q1q3L//73PwYPHlzkcYsiOTkZX19fkpKS8PHxKfRaeno6+/fvJzw8/KzVAqV8089WKrrFO+N4/PtNxJ/KxM3ZgRcGNuXW9qHnnIOecCqD1+fv5Nu15pLv3q5OjLuqAXd2roWzo3W/8ziZnsVnEfv5LGIfKZk5OJLDFvd78DBS4e7FUKN1wbGnMrLZHpPMlsNJbI1OYkt0EnuPnSLXAB9OMdbpN0Y6zsfdZvYG2OLWjo0NHqJaw440C/Glhq/bBfsRnlP0OrN/1b7F5tfOHtDpPujyILhXscv3oKxZcyCRmz9egaeLI2sm9MHD5cLdA/YdO8WIz1bwQ/poqtlOEHP1Z1TvdHMpRXtxF7oGkMJK63uVk2vQ+Ln5ZObksuzpKwip4l5iY4mISPHoHqriutDPtqjXAJYnpcorJaUqJ/1spaLKyM7hjfk7+WzpfgAaBXvz/tDW1A/yvuh7Nx46wfM/b2Xz4SQAGgZ58+J1TelcN6BEYz5TelYOX648yIeL9nA81aysaRbiw8R2mbT5czC4+sJT+y/aIyo1879E1ZboZGIO7+ea47MY4rAIZ5s5X/+3nE68lX0zSR61aBbiS/MQH5rV8KVZiC81/dzPnag6thP++T9zuh6AgzO0GwndHwfvij2t+Ok5m/lmzSFubluTN25uWaT3xCSlseTDexmS+ROLaYffqDm0DK1SsoEWkZJSRVea36sr3lzMvvgUvh7TqdT//RERkfPTPVTFZY+klOWr74mIiLX2HTvFQ99sYGt0MgDDO9di/DWNcXMuWoPvVqFVmHtfV75be4jX5u9g59GTDP10JQNb1uCZaxpR3bdkKxayc3KZs/4w7y7czZEkcwphnaqePNa3If2bBeOw/F3zwFpditS03MPFiba1/Glbyz9vT0vSMgeyc9cWPJa9Ru2YPxjguJKrHVbzfUZP3t01mCW7/rsB9nFzoqq3K77uzvi6O1PLMZHrTsykdeIfOJCLgY0jta7naOtHcKtWB98cZ3zSs/BydSp+1VU5kJ6Vw++bYwBz1b2iqu7rTr87HodpP9HNWM8Vn85n0p196FqvakmFKuVcqL8H++JTOJSYqqSUiIhIOaGklIhIJZXf5+e5n7eSmplDFQ9nXr+xBX2bBhf7XA4ONm7tEMbVzYJ5669dzF51kF83HeHv7Ud58Ir63NWtNq5O9l3FzjAM/tgay5t/7WTfsRQAqvu68fCV9bmpbU2c8qcQHlhqPod3v+Sx3F0cadasFTT7GmK3wj//h9Ou+Qx1WsQtLstY5j+YKTnXsTbORnJ6Nsnp2QSQxP1OP3O740JcbdkA/JnTjjezb2H3zpqwMxqILhjD0cGGj5tTQTLLJ+/5zG1fd2ea1fAlLKB4yxlb5a/Io5zMyCakijsdw/0v/obTVAlrRk6NtjgdWcdV2UsYOd2X94a24upm1UsoWinPwgpW4FOzcxERkfJCSSkRkUroZHoWE+Zu5eeNRwDoVMefyUNan3NVtOKo4uHC/w1qxpD2obzwyzbWHTzOa/N38P3aQ7xwXVN6Ngi87NgNwyBidzxv/LmTLdHmlEE/D2fu712POzrVKlzhlZMFUXlL69a+9KRUIcHN4LZvIWolLHwJx6jl9Ij/hh6u88jucz8Hwm7EbdMXBEd+jlO2eXMc5dOW+cF3s82hISFpWXinZZGUlkVSWjbJaVlk5uSSk2twPDWrYOrhhXi5OrH4iV5U9XK1z2cqQT+uN1fdG9wmBAeH4leCOba5A46s4y7P5XyefA33zV7PpMHNGdI+zN6hSjmnpJSIiEj5o6SUiEgls/HQCR76egNRiak4Oth4pE997u1VD8dLSBicT7MQX34Y25mfNkTzyrwd7ItPYfi01fRtEsRzA5oQ6n9pVT7ro47z+vwdrNyXCICniyOjutdhTPdwvN3OsVpdzCbIPGWubBfU7DI+0TmEdYKR82DPQvj7JYjdgtO/k6jHpP+Oqd4K+rxAWJ3e3H2eqXmGYZCelZuXpPrvkXyerzdHJ3HsZAYzlx/g0b4Nz3nOsiIuOZ0lu44BxZu6V0jTwTB/PCGZ+3m0aSpvb/PkqTlbOJGaxT0969oxWinvQpWUEhERKXeUlCpBubm5VocgdqafqZRnubkGUyP28eafO8nONQip4s57Q1ud1jvJvmw2G4Pb1KRPkyDeW7ib6csP8FfkUf7ddYz7etXjnp51ity3atfRk7zx504WRB4FwMXRgds7hXF/73oXrhbav8R8rt0NHEpgRUCbDepfBXWvhMif4J//QeJeCKgPV0yAJtebx1zwFDbcXRxxd3EsUqXaH1tiuHf2emauPMjYXnUvupKdlX7eeIRcA9qEVSG8quelncS9CjQaAFt/4EG/VaT0HM0n/+5j0h87SEzN5OmrG1XIXlxSfPmVUoeUlBIRKZN0L1Xx2ONnWnavZMsxFxcXHBwcOHLkCIGBgbi4uOiCuZwzDIPMzEyOHTuGg4MDLi4uVockUixxyek8+t0mlu6JB+Da5tV5ZXBzfN3PUV1kZz5uzkwY0IRb2ofyws/bWLEvgXcW7uKH9Yd4fkBT+jSudt5/Iw8lpvLOwl38tCEawwAHG9zYpiYP96lPTb8iVFsdiDCfa3ez4yc6BwcHaHYjNL4Ojm4zq7IcS+a/2L5Ng6kd4MGBhFS+W3OIEV3DS2Scy2UYBnPypu7d2PYSq6Tytb4dtv6Abev3jH/sf/h5uPDqHzv45N99JKVm8b8bmtu10k/Kp1B/c1GFhJRMTmVk4+Wqy1wRkbJA98cVjz3vj/W/dQlwcHAgPDycmJgYjhw5YnU4YkceHh6EhYXhUBIVFyIlZNGOOB77fhOJKZm4OTvw0nVNuaVdaKlfDDQI8uarMR35fUsML/+2nUOJaYyZuZZeDQN5YWDTQpU0x05m8ME/u/lqdRRZOQYAVzcN5vF+DahXzbtoA+ZkmX2fwH79pC7G0RlqtCrZIRxsjO5ehwlzt/LZ0v3c0anWf03dy5BtR5LZEXsSFycHBjSvcXknC+8JPjUh+TDs/J2xPW/Ez8OZ8T9u4Zs1h0hKy2Lyra3s3kxfyhdvN2f8PV1ITMnkUGIqjauff/lpEREpPbo/rrjscX+spFQJcXFxISwsjOzsbHJycqwOR+zA0dERJ6eKuWS7VEwZ2Tm8Pn8nny/dD0Dj6j68P7RV0ZM6JcBmszGgRQ16N6zGh4v28GnEPhbvPMbyPUsY3T2c4V1qM2vFQaYt209qpvlvZ7d6VXmiX0NahlYp3mDR6yErFdz9oVoT+38YC93UtibvLNjF4eNpzNsay3UtLzPpUwJ+XG+uLHhV4yB8PS6zIs/BEVoNhSVvwIbZ0OxGhrQPw9fdmYe+3sgfW2M5OWMtnwxri6eqYyq1UH8PElMyiVJSSkSkTNH9ccVjr/tjXbmVIJvNhrOzM87OJT89RkTkdPuOneLBrzew7UgyACO61Obp/o2K3MOppHm6OvHk1Y24qW1NXvo1kn93HeOjxXv5aPHegmNahlbhyX4N6Vqv6qUNUjB1r2vJ9JOykJuzI8O71ObtBbv45N+9DGxRvUwlzLNycvl5o5mUurFtiH1O2uo2Mym19x9IigbfEK5uVp3pI50ZM3MtS/fEc9tnq5gxoj1+nppiXVmF+Xuw6dAJ9ZUSESmDdH8s51KxrtJFRCo5wzD4fu0hBry/lG1HkvHzcOazO9vx4nVNy0xC6nR1Ar2YMbI9U4e1paaf2Q+mXjUvPr6jLXPv63LpCSk4LSnVww6Rlj3DOtXC3dmRbUeSWb43wepwClmy6xgJKZlU9XKhe/1A+5zUvw7U6goYsOnrgt1d61XlqzGd8PNwZtOhE9z8yQpiktLsM6aUbSeiYOscc5XNPGF5faW0Ap+IiEj5oKSUiEgFkZyexcPfbOSJHzaTmplD5zoBzB/Xgz5NgqwO7YJsNht9mwaz8NGe/HRfF/4c14OrmwVfXuVPdgZErTK3S7rJuUX8PF0Y0j4UgE+W7LM4msLyG5xf3yoEZ3v2u2p1u/m8cTYYxn+7Q6vw/djOBPu4sSfuFDdNWcG+Y6fsN66UTcvehR/ugi3fF+zKX4FPSSkREZHyQUkpEZEKYNOhE1z7XgS/bDqCo4ONJ/o15MvRHQnycbM6tCJzc3akdZiffVZRi14P2WngURWqNb7885VRo7qF42AzK5Mi86ZqWi0pNYuFkXEADG5jp6l7+ZpcD86ekLjvvyb2eepV8+aHeztTp6on0SfSuPnjFWyNTrLv+FK2BLcwn2M2F+wKVVJKRESkXFFSSkSknPtmdRQ3f7yCQ4lp1PRz5/uxnbm/dz37JHfKq4Kpe92gDPVasrdQfw+uaV4dgE8jyka11K+bj5CZk0ujYG+a1vC178ldvaDpDeb2xi/Permmnwffje1MsxAfElIyeeibDeTkGmcdJxVE9Zbmc8ymgsq5/Eqpw4lp5OpnLyIiUuYpKSUiUk5lZOcw/sctPP3jFjJzcunbJIh5D3enTZif1aFZ7/SkVAV3T4+6APyy6QjRJ6zvpfRj3tS9G9vULJkBWt1mPm+bC5kpZ71c1cuVr8d0om+TID66vU3lTs5WdNUag4MzpJ8w+0sB1X3dcXKwkZmTy9GT6dbGJyIiIhelpJSISDkUm5TOrVNX8vXqKGw2eKJfQz6+oy0+blrNhOwMOLTa3A6vmE3OT9e8pi9d6gaQk2swbel+S2PZd+wU66NO4GCD61vXKJlBanUBv3DIPAWRv5zzEG83Z6be2Y5GwT4lE4OUDU6u/03PzWt27uhgK1g0ISpBU/hERETKOiWlRETKmdX7Exnw/lI2RJ3A192Z6SPac3/vejioIsR0eA1kp4NnNajawOpoSsU9Pc1qqa9XR5GUmmVZHD9tiAagR4NAqnmXUD8zm61ww3Op3E6fwpcnv6/UQfWVEhERKfOUlBIRKScMw2D6sv3c9ulK4k9l0CjYm18f6EavhtWsDq1sObDUfK7g/aRO16N+VRoFe5OamcOXqw5aEkNursGP682kVIlN3cvXaihgM6dpJlpbHSYWO0dSKr+v1CElpURERMo8JaVERMqBtMwcHvtuEy/9Gkl2rsF1LWvw431dCAvwsDq0smd/Xj+p8O7WxlGKbDYb9/SsA8D0ZQdIz8op9RhW7U8k+kQa3m5OXNUkqGQH860JdXqZ25u+LtmxpGzLT0rF/rcCX5hW4BMRESk3lJQSESnjDiWmcuOU5fy4IRpHBxvPDWjCu7e2wsPFyerQyp6sdHP6HkDtypOUAhjQogY1fN2IP5XB3LxpdKVpTl6D8wEtquPm7FjyA7a+w3ze+DXk5pb8eFI2BTUFmwOcOgonYwElpURERMoTJaVERMqwiN3HGPjBUiJjkgnwdOHLUR0Z1S0cWyWZllZsh1dDTgZ4BUNAPaujKVXOjg7c1S0cgKkR+8jNNUpt7NTMbP7YEgPA4JKeupev0bXg6gtJUXBgSemMKWWPi+d/vePypvCFavqeiIhIuaGklIhIGWQYBlMW72X4tNWcSM2iRU1ffn2wG53rBlgdWtmWP3WvEvWTOt2tHcLwdnNi37EUFm4/Wmrj/rktlpTMHML8PWhXy690BnV2h+Y3mtsb1PC8Ujujr1T+tOb4U5mkZGRbFZWIiIgUgZJSIiJlzKmMbO7/aj2vzd9BrgG3tKvJd/d0pkYVd6tDK/vym5xXon5Sp/NydeKOTrUAmLpkX6mNO2edOV1wcJuQ0q3ia5U3hW/7L5CeVHrjStlyRlLKx82ZKh7OABw6rmopERGRskxJKRGRMmR/fAo3fLiMeVticXa08fKgZrx2Y4vS6dFT3mWmVtp+Uqcb2aU2Lo4OrD14nHUHE0t8vJikNJbtjQdgcOtSmrqXL6QNBDaC7HTY+mPpji1lxwVW4ItKUFJKRESkLFNSSkSkjPh7+1Gue38pu+NOUc3blW/u7swdnWqpf1RRHV4NuVngXQP861gdjWWq+bhxQ+sQAD75t+SrpX7aEI1hQIfa/qW/GqTNBq1uM7c3agpfpRXc3HxOOgSpZiI2VM3ORUREygUlpURELJaba/DOgl2M+mItJzOyaVfLj98e7Ebb0urNU1Hk95MK714p+0mdbkwPMym3YPtR9h47VWLjGIbBj+vNqXs3tg0psXEuqMWtYHM0q+SO7bQmBrGWmy/4mU3+C/pKqdm5iIhIuaCklIiIhZLSshgzcy3v/r0bgOGda/HVmE5U83GzOLJy6MBpTc4ruXrVvOjTOAjDgM8iSq5aavPhJPbEncLVyYFrmlcvsXEuyDsI6l9lbm/8ypoYxHpnNjtXpZSIiEi5oKSUiIhFdh09yaAPl/H3jjhcnRx48+aWvHR9M1yc9E9zsWWmQPQ6c7sS95M63dieZrXUnPXRxJ1ML5Exflx/GIB+TYPxdnMukTGKpNXt5vOmbyBHq61VSkpKiYiIlEu68xERscDvm2MY9OEy9senEFLFnTn3duGmtqXcJLoiiVoJudngGwp+ta2OpkxoV9ufNmFVyMzO5YvlB+x+/szsXH7ZdASAG63+s9vgavAIgFOxsPcfa2MRa5wnKXXoeBq5uYZVUYmIiMhFKCklIlKKsnNymfTHdu7/aj2pmTl0rRfArw92o1mIr9WhlW8HlprPtbtV+n5Sp7unZ10AZq04SEqGfSuIFu2M43hqFtW8XelWr6pdz11sTi7Q/BZze+OX1sYi1shPSiXuhfRkqvu64ehgIzM7l7iTGdbGJiIiIuelpJSIXDLDMJi14gCPfbeJgwkpVodT5iWmZDJi+pqCFdHu6VGHL0Z2wN/TxeLIKoCCflKaune6qxoHUaeqJ8np2Xyz5pBdzz1nnTl174bWITg6lIFEYOu8KXw7/yhYgU0qEc+q4JPXbP/oVpwcHQip4g5oCp+IiEhZpqSUiFySrJxcxv+4hed+3sac9Ye5enIE05ft1zSJ89gancTA95eydE88Hi6OfHBba8Zf0xgnR/0zfNkyTkL0enNbTc4LcXCwMbq72Vtq2tL9ZOXk2uW8iSmZLNoZB8DgNmVk2mlwcwhuATmZsOV7q6MRK6ivlIiISLmjuyERKbbk9CxGTl/DN2sO4WCDJtV9SMvK4aVfIxkydQX741U1lZaZw7+7jvHKvO1c+14EA95fSvSJNGoHePDTfV0Z0KKG1SFWHFGrwMiBKmHgV8vqaMqcwW1CqOrlQvSJNH7fHGOXc/666QhZOQbNQnxoGOxtl3PaRes7zOcNmsJXKZ2RlApVUkpERKTMc7I6ABEpXw4lpnLXjDXsjjuFh4sj7w9tTe+G1Zi9OopX521nzYHjXD15CY/3bchd3cLLxrSeUpCdk8vm6CSW7Y5n6Z54NkSdIPOMqpR+TYN4/aaW+LpbuEpZRXRgiflcu4e1cZRRbs6OjOhSmzf/2sUnS/Zxfasa2C6z71b+qnuDW5eRKql8zW+GP5+F2M0Qu8WsnpLK43zNzpWUEhERKbOUlBKRItt46ASjv1hL/KkMgnxc+Xx4+4IG3cM61aJXg0DG/7iFpXvi+d+87czbGsMbN7WgXrUyVElhJ4ZhsCfuFEv3xLNsTwKr9iVw8oxG0iFV3OlaL4Cu9arSpW5VAr1dLYq2gju9ybmc0x2davHR4r1sj0kmYnc8PRoEXvK59sSdZNPhJJwcbFzXqoxV/Hn4Q8P+sP0X2DAb+r9qdURSmvKTUsd2QGaqpu+JiIiUA0pKiUiR/LElhke+20h6Vi6Nq/swbUQ7qvu6Fzom1N+DWaM68O2aQ/zv9+1siDrBNe8tZVyf+tzdvU65758Uk5TGsj0JLNsTz7I98Wet6FTFw5kudQPoUrcq3epVpVaAx2VXpMhFpCfDkY3mdrianJ9PFQ8XhrQPZfqyA0xdsu+yklJz1kcD0KthIFW9ymCitfUdZlJqy3dw1URzZT6pHLyrg2cgpByDuEjC/OsDSkqJiIiUZUpKicgFGYbB1CX7eHX+DgwDejcM5P3b2uDleu5/Pmw2G7d2CKNHg0Ce+WkLi3ce4/X5O5m/NZY3bmpZtvrPXERSahYr9iWwfK85JW/fscK9slydHOgQ7k/XelXpWrcqTWr4VJrpimVG1Eqzn5RfOPiWsalkZcyobuHMXHGQpXvi2RqdVFDlWBw5uQY/5SWlbiwrDc7PVPdK8AqGU7Gwaz40uc7qiKS02Gxms/u9f0PMJsKampVTx05mkJaZg7uLo8UBioiIyJmUlBKR88rKyeX5n7fx9eooAO7sXIvnBzQpUsVTjSruTB/Rnh/WHeb/fotk8+EkBrwfwUNX1Gdsr7o4l8GqqfSsHNYfPG5OydubwJbDJzh9MUEHG7SoWaVgSl6bMD/cnHWTY6mCflKauncxNf08GNCiOj9vPMLUJft4b2jrYp9j+d54YpPT8XV35orG1UogSjtwdIKWQ2DZu7BxtpJSlU31lgVJKd/2zvi4OZGcns2h46k0CCo/vxQRERGpLJSUEpFzSk7P4v7Z64nYHY/NBs9d24SRXWsXazqazWbj5nah9GgQyLM/bWHh9jjeWrCL+dvMqqkmNXxK8BMUTUxSGn9sieWfHXGsOZBIRnbh5uR1Az3pVq8qXepVpVOdADUpL2v2R5jP4WpyXhR396jDzxuP8PuWGJ7o17BgdbKi+jGvSmpgy+q4OpXhhGyrO8xeYw2vsToSKW1nNjsP8GBrdDJRCUpKiYiIlEVKSonIWaJPpHHX9DXsPHoSd2dH3r21FX2bBl/y+YJ83Pj0znb8vPEIL/66jW1Hkrnug6Xc17seD/Suh4tT6VZNRZ9I448tMczbEsP6qBNnxOpK17pVzSl59aoS7OtWqrFJMaSdMFdZA1VKFVHTGr50r1+ViN3xfL50Py9e17TI7z2Vkc38rbEADC6rU/fyBTaAMf9YHYVYIT8pFRcJ2ZmE+eclpdRXSkREpExSUkpECtl8+ASjvljLsZMZBHq7Mm14e5rXLH7vmTPZbDYGtQ6hS70Anp+7jfnbYnnv7938lVc1ZY8xLuRQYip/bI3h9y2xbDp04rS4oH0tf/o1C6Zng6rUDfRSc/LyImoFGLngXxd8ytgqcGXY3T3qELE7nm/XHOLhK+vj51m0RuB/bIkhLSuHOlU9aR1apWSDFLlUfrXB1RcykuDYjoJqQCWlREREyiYlpUSkwJ/bYnn4mw2kZ+XSKNibz0e0J6SK+8XfWAzVvN2Yckcbft8Sw/M/b2NH7EkGfbSMe3rU4eE+9e06JSgqIZV5W82KqM2Hkwr222zQobY/17aoTr+mwQT5qBqqXDqw1HzWqnvF0q1eVZpU9yEyJpkvVx7kwSvrF+l9c9YfBuDGtjWVuJWyy2aD6i3gQITZ7NzfnNp7SEkpERGRMklJKRHBMAw+X7qf/83bjmFAjwaBfHhba7zdSqZ/ks1mY0CLGnSuE8Dzv2zj980xfLR4L39FHuWNm1rQOszvks99ID6F3/Om5m07klyw38EGHcMDuKZFdfo1DaKatxJR5d7+/CbnSkoVh81m456edXj4m418seIAY3rUuWjD/sPHU1m5LxGbDQa1DimlSEUuUfWWZlIqdjNh9a8GVCklIiJSVikpJVLJZefk8uKv2/hypbnC3m0dw5h4XdMirbB3uQK8XPnwtjYMbBHDhLlb2RN3ihunLGdM9zo8clWDIq9st/fYKf7YYk7N2x7zXyLK0cFG5zoB9G8eTL+mwVT1ci2pjyKlLTURYreY2+onVWzXNK/O6/N3En0ijTnrD3N7x1oXPP6nvAbnnesE2L16UsTuTmt2Htbxv+l7hmGoyk9ERKSMUVJKpBI7mZ7FA19t4N9dx7DZ4Jn+jRndPbzUL9qvbladjuEBvPTrNuZuPMInS/axIPIob9zcgra1/M/5nj1xJ/l9cyx/bI1hR+zJgv2ODja61A3gmubV6dskiAAloiqmqBWAAQH1wfvSm/BXVs6ODozqFs7E3yL5LGI/t7YPw9Hh3H/vDcPgxw1mUqrMNzgXgf+SUrFbqOHjgoMNMrJzOXYyg2qari0iIlKmKCklUkkdOZHGXTPWsCP2JG7ODkwe0pqrm1l3c+/n6cLkW1tzbYsaPPvTFvbFp3DTxysY2SWcx/s1wN3ZkV1HTzEvb2re7rhTBe91crDRtV5Vrm1enauaBBW5cbOUY/sjzGf1k7pkQ9qH8u7fu9kfn8KCyKPn/fu/PuoE++NTcHd2pL+F/0aIFFlAPXD2gKxUnE/so0YVdw4fTyMqMVVJKRERkTJGSSmRSmhrdBJ3zVhD3MkMqnq58vnwdrQsI6tpXdUkiA61/fm/3yP5Yd1hpi3bz4Ltsbg4OrD3WErBcc6ONrrXD6R/s2D6NgnG16Nk+l9JGZXf5Fz9pC6Zp6sTwzrV4oNFe/hkyV76NQ06Z5Xkj3kNzvs3C8bTVZcNUg44OEJwczi0Kq/ZeVhBUqpd7XNX34qIiIg1dHUpUsksjDzKg19vIC0rhwZBXkwb0Z6afh5Wh1WIr4czb97ckmtbVOeZH7dwKDENABdHB3o0qEr/ZtXp0yQIX3cloiql1EQ4qn5S9jC8S22mRuxjQ9QJ1h48TvszbtjTs3L4ddMRwFx1T6TcqN6yIClVK6ARy/cmqNm5iIhIGVTynYwv4qOPPiI8PBw3Nzfatm1LRETEBY+fPXs2LVu2xMPDg+rVqzNy5EgSEhIKXs/KymLixInUrVsXNzc3WrZsyfz58wud48UXX8RmsxV6BAdrSoJUfNOX7WfMrLWkZeXQvX5Vfri3S5lLSJ2ud8Nq/PlIDyZe35R3hrRk7XN9+Gx4e25sW1MJqcosv0oqsBF4VbM2lnIu0NuVG/P6RH3y796zXv9nRxzJ6dlU93WjU52A0g5P5NIFtzCfYzYR6v9fs3MREREpWyxNSn377beMGzeOZ599lg0bNtC9e3f69+9PVFTUOY9funQpd955J6NGjWLbtm18//33rFmzhtGjRxccM2HCBD755BPef/99IiMjGTt2LDfccAMbNmwodK6mTZsSExNT8NiyZUuJflYRS+TmwJYfyD5+mBd+3spLv0ZiGDC0QyjTRrTHx63sJ3Z83Jy5s3Ntbmhds1zEK6WgYOqeqqTsYUz3cGw2WLg9jj1xJwu9NmedOXXvhtYh522ELlImFazAt5kwP3PFyENKSomIiJQ5lial3n77bUaNGsXo0aNp3LgxkydPJjQ0lClTppzz+JUrV1K7dm0eeughwsPD6datG/fccw9r164tOGbWrFk888wzXHPNNdSpU4d7772Xfv368dZbbxU6l5OTE8HBwQWPwMDAEv2sIpZY/CrMGcXOT4bxxYqDAIzv34hXbmiOs6PlhZIil+ZAXkWt+knZRZ1AL/o2CQJg6pJ9BfuPncxg8a5jgFbdk3IosBE4ukBGEvWczYp6VUqJiIiUPZbdlWZmZrJu3Tr69u1baH/fvn1Zvnz5Od/TpUsXDh8+zLx58zAMg6NHj/LDDz9w7bXXFhyTkZGBm1vhlVXc3d1ZunRpoX27d++mRo0ahIeHc+utt7Jv3z5EKoqk1CzWr11BTsTbADRO20BNpyQ+ur0N9/Sse85mxiLlQko8xEWa26qUspu7e9QFYO6GIxxNTgfgl01HyMk1aBlahXrVvKwMT6T4nFygWhMAQjN2AXA0OYP0rBwroxIREZEzWJaUio+PJycnh6CgoEL7g4KCiI2NPed7unTpwuzZsxkyZAguLi4EBwdTpUoV3n///YJj+vXrx9tvv83u3bvJzc1lwYIF/Pzzz8TExBQc07FjR2bOnMmff/7Jp59+SmxsLF26dCnUm+pMGRkZJCcnF3qIlAXHUzKJ2H2Mjxbv4b7Z6+jx+iJaTZxPzi8P4WhkA+BgM/imayzXNK9ucbQilyl/6l61JuBZ1dpYKpC2tfxoX9uPzJxcpi87APw3de/GNiEWRiZyGfKm8HkkbMPbzVzb5/BxVUuJiIiUJZbP3zmzYsMwjPNWcURGRvLQQw/x/PPPs27dOubPn8/+/fsZO3ZswTHvvvsu9evXp1GjRri4uPDAAw8wcuRIHB0dC47p378/N954I82bN6dPnz78/vvvAHzxxRfnjXPSpEn4+voWPEJDQy/nY4tckoRTGfy76xgfLtrD2Fnr6PrqP7T+vwUM+3w1r8/fybwtsUQlpjLUcRHtHXaRbnNnQ+idANSM/sPi6EXsQFP3Skx+tdTsVQdZeyCRyJhknB1tDGxRw+LIRC5RXlLKFrOJMDU7FxERKZOcrBq4atWqODo6nlUVFRcXd1b1VL5JkybRtWtXnnjiCQBatGiBp6cn3bt35+WXX6Z69eoEBgYyd+5c0tPTSUhIoEaNGjz99NOEh4efNxZPT0+aN2/O7t27z3vM+PHjefTRRwu+Tk5OVmJKStSxkxlsjU5iS3QSW/MeR5LSz3ls7QAPmoX40jzEl9Z+GbT//XvIALd+L9C66WB4+0s4vBqOHwS/WqX8SUTsSE3OS8yVjapRN9CTvcdSuP+r9QBc0agafp4uFkcmcokKmp1vIqy6O9uOJBOVoKSUiIhIWWJZUsrFxYW2bduyYMECbrjhhoL9CxYs4Prrrz/ne1JTU3FyKhxyfgWUYRiF9ru5uRESEkJWVhZz5szhlltuOW8sGRkZbN++ne7dz/+bd1dXV1xdXS/6uUQuRVxyOlsKJaCSiU0+dwKqTlXPggRUsxBfmtTwwdf9tFXpvhsOGclQozV0uBscHM0b+P1LYNtP0G1c6XwoEXs7FQfHdgA2JaVKgIODjbt71OGpOVs4mpwBwI1qcC7lWVBTsDlCajxNvFP4A4hKTLM6KhERETmNZUkpgEcffZRhw4bRrl07OnfuzNSpU4mKiiqYjjd+/Hiio6OZOXMmAAMHDmTMmDFMmTKFfv36ERMTw7hx4+jQoQM1apjTC1atWkV0dDStWrUiOjqaF198kdzcXJ588smCcR9//HEGDhxIWFgYcXFxvPzyyyQnJzN8+PDS/yZIpZWVk8ubf+7kxw3RHDuZcdbrNhvUDfSiWQ2fgiRUkxo+eLs5n+NseXbOh8i55kX4wPfMhBRAsxvNpNTWH5SUkvIrv0oqqBl4+FsbSwU1qHUIb/61i2MnM/DzcKZXw2pWhyRy6ZzdIbAhxEXSzOEAUFXT90RERMoYS5NSQ4YMISEhgYkTJxITE0OzZs2YN28etWqZ04tiYmKIiooqOH7EiBGcPHmSDz74gMcee4wqVapwxRVX8NprrxUck56ezoQJE9i3bx9eXl5cc801zJo1iypVqhQcc/jwYYYOHUp8fDyBgYF06tSJlStXFowrUtKS07O4f/Z6InbHA+Bgg3rVvApXQFX3wdO1GH9FM07BvMfN7c73Q/UW/73W+Dr4/TGI3QLHdkFgAzt+GpFSUtBPSlVSJcXVyZF7e9Zl4m+R3NohDBcny1tPilye6i0hLpLwrD1AVQ4pKSUiIlKm2Iwz571JkSQnJ+Pr60tSUhI+Pj5WhyPlSPSJNO6avoadR0/i7uzIqzc256omQXi4XGaOeP4zsPJDqBIG960EF8/Cr8++GXb/Bb3GQ6+nL28sESu83w4SdsOtX0Gja62OpsIyDIPtMSdpEOSFk6OSUueia4Cis/x7tXIKzH+a1PC+NNk+AndnRyIn9jvvojoiIiJiH0W9BtDVpkgp2nz4BIM+XMbOoycJ9Hblu3s6c32rkMtPSB3ZAKummNvXvnN2QgrMKXwAW+eActFS3pyMNRNS2KBWF6ujqdBsNhtNavgoISUVQ16zc/f4rTjYIC0rh/hTmRYHJSIiIvl0xSlSSv7cFsstn6zg2MkMGgV7M/f+rjSv6Xv5J87Jhl8eAiMXmt0E9fuc+7iG14CjK8TvgqNbL39ckdKU308quDm4+1kbi4iUH0HNALCdPEJjHzMZpb5SIiIiZYeSUiIlzDAMPovYx9gv15GelUuPBoF8P7YzIVXc7TPAqikQuxncqsDVk85/nJsPNOhrbm+dY5+xRUpLfj+p8B7WxiEi5YubD/jXBaCr5xEA9ZUSEREpQ5SUEilB2Tm5PPfzVl7+fTuGAbd3DGPa8HYXXkGvOI4fhEWvmNt9/w+8LrJSlqbwVVxJh2HN55ASb3UkJWO/mpyLyCXKm8LX2vkgoEopERGRskRJKZEScjI9i1FfrOXLlVHYbDDh2sa8PKiZ/fq0GIa5ol5WKtTqBq2HXfw99fuBixeciILodfaJQ6yXnQlf3gi/PwrvtoRFkyA92eqo7Cf5CCTuBZsDhHW2OhoRKW/yklL1c/cCSkqJiIiUJUpKiZSAIyfSuPnjFfy76xhuzg5Mub0to7vXse9qP9t+hD0LwNEFBk6GopzbxcPsLQWawleRLHsXju0AbJB5Cv59Fd5rBSs+hKx0q6O7fAX9pFqAexVLQxGRcigvKVU9dRegpJSIiEhZoqSUiJ1tjU5i0IfL2BF7kqpernx7d2eubhZs30HSjsMfT5nb3R+HqvWL/t6CKXw/Qm6OfeOS0he/B5a8YW4P/hRungEB9SA1Af58Bt5vC+tnmg3xy6v9S8zn8O7WxiEi5VNeUsozJQpvUtVTSkREpAxRUkrEjhZGHuXmj1cQdzKDBkFezL2/Cy1Dq9h/oAUvQMoxqNoAuo0r3nvrXgFuvnAqFg4ut39sUnoMA34bBzkZUK8PNL8Jmt4A962Cge+BTwgkH4ZfHoSPOsG2ueWzl1h+pVRtNTkXkUvg4Q++oQA0sR0kNjmd9Cz9UkZERKQsUFJKxE6mL9vPmFlrScvKoXv9qvxwbxdq+nnYf6CDy2H9F+b2wHfBybV473dygcbXmduawle+bZxtrkrn5A7XvvXfFE5HJ2g7HB5cD33/B+7+kLAbvh8OU3vBnr/LT3Iq6TAc3w82RwjrZHU0IlJe5VVLtXE5iGFA9Ik0iwMSERERUFJK5LJl5+Tyws9beenXSAwDhnYIZdqI9vjYa4W9QoNlwK8Pm9ttR0CtLpd2nvwpfJE/Q06WXUKTUnbqGPw1wdzu/Qz41T77GGc36PIAPLwJej5lNrmP2QhfDoYvBsLhtaUZ8aXJX3WvRitzaXcROa+PPvqI8PBw3NzcaNu2LREREec9dunSpXTt2pWAgADc3d1p1KgR77zzTqFjPv30U7p3746fnx9+fn706dOH1atXl/THKBl5Sal2rocA9ZUSEREpK5SUErkMpzKyuXvWOr5YYS4zPb5/I165oTnO9lph70xL34H4XeBZDfq8eOnnqd0dPAMhLRH2/Wu38MqtvybAmw3K14qEfz5j9hYLbg6d7rvwsW4+ZuLqoY3Q8V6zOf6BCPjsSvj6NojbXiohX5KCqXvdrI1DpIz79ttvGTduHM8++ywbNmyge/fu9O/fn6ioqHMe7+npyQMPPMCSJUvYvn07EyZMYMKECUydOrXgmMWLFzN06FAWLVrEihUrCAsLo2/fvkRHR5fWx7KfvKRUY2MfgPpKiYiIlBE2wygvczjKluTkZHx9fUlKSsLHR7+9r4xiktK4a8Zatsck4+rkwOQhrejfvHrJDXhsF3zcFXIy4abp0Gzw5Z3v98dhzafQ8ja4YYp9YiyPYrfCx90AA3zD4J5/zf4jZdmehfDljWBzgNELIaRt8d5/IgoWvwabvgIjF7BBy1uh13jwq1UiIV+yyc3NeG+fA/X7WB2NCFA2rwE6duxImzZtmDLlv3/PGzduzKBBg5g0aVKRzjF48GA8PT2ZNWvWOV/PycnBz8+PDz74gDvvvLNI5ywz36uTsfBWQ3JxoGn6Z9zerTETBjSxLh4REZEKrqjXAKqUErkE+SvsbY9JpqqXC9/c3alkE1K5uXkNrTOhfl+zmfXlyp/Ct+M3yEq//POVV3+/BOTl5pOiYO695ve7rMpMhd8eNbc73FP8hBRAlTAY9CHctxIaDwQM2PS1uVLfvCfgVJxdQ75kxw+aCSn1kxK5oMzMTNatW0ffvn0L7e/bty/LlxdtQYsNGzawfPlyevbsed5jUlNTycrKwt+/jCfuz8U7GLyCcCCXxrYoTd8TEREpI5SUEimmv7cf5ZZPVnA0OYP61bz46b6utA7zK9lBN34JB5eBs0fhhtaXI7SjuTpbRrJZeVMZ7Y+A3X+BgxPcPAMcXWHXfFjxvtWRnd+/r8KJg+BTE6549vLOFdgQhnwJY/6BOr0gNwtWT4V3W8Hf/wfpSfaI+NLlT90LaQOuXtbGIlKGxcfHk5OTQ1BQUKH9QUFBxMbGXvC9NWvWxNXVlXbt2nH//fczevTo8x779NNPExISQp8+569azMjIIDk5udCjzAhuAUATh4NKSomIiJQRSkqJFMMXyw8wZuZaUjNz6FovgB/u7UKofwmssHe6U3GnNbR+1qxysQcHh/8qrirjKnyGAQueN7fbjjC/F/1fM79e+BJErbQstPOK3QLLPzC3r30TXL3tc96QtnDnz+ajRhvISoGIN2FyC1g62azOskJBP6nu1owvUs7YzviFhWEYZ+07U0REBGvXruXjjz9m8uTJfP311+c87vXXX+frr7/mxx9/xM3N7bznmzRpEr6+vgWP0NDQ4n+QkpLXV6qZbT+HElNRBwsRERHrKSklUgQ5uQYv/bqNF37ZRq4BQ9qFMmNkB3zdS2CFvTPNH29WrFRvCR3H2vfczW8yn3f+ARmn7Hvusi5yLhxZD86e0ONJc1/bEdD8ZjBy4PuRkBJvZYSF5ebALw+ZsTW5Hhr2t/8YdXqZVVNDvoSqDSH9BCx8Ad5vA2unle5KjYZhNmMHNTkXuYiqVavi6Oh4VlVUXFzcWdVTZwoPD6d58+aMGTOGRx55hBdffPGsY958801eeeUV/vrrL1q0aHHB840fP56kpKSCx6FDh4r9eUpMflLK4QApmTkkpmRaHJCIiIgoKSUlIjM7lz+3xbJqXwIxSWnk5pbf30amZGRzz6y1TF92AIAnr27IqzeW4Ap7p9u9ELb+YDa0HvgeODrZ9/zVW4F/HchOM6etVRY5WfD3RHO7ywPgnXfTZrPBgMlQtQGcPAI/jik7/aXWfGYm0Vx94OrXSm4cm83sM3XfCrj+I/ANhZMx8Nsj8GEH2PJD6XxPjh+ApEPg4Kx+UiIX4eLiQtu2bVmwYEGh/QsWLKBLly5FPo9hGGRkZBTa98Ybb/B///d/zJ8/n3bt2l30HK6urvj4+BR6lBl5SamGDodxIUtT+ERERMoAO9/hikBursHD32zgj63//cbWxdGBmn7uhPp7EJb3CPX3INTfnTB/D7zdSqHi6BIcTU5n1Bdr2BqdjIuTA2/f0pIBLWqUzuCZKfD7I+Z2p/ugRiv7j2GzmQ3Pl7xhTuHLr5yq6NZ/AYn7wKMqdHmw8GuuXnDzF/DpFbD3H4h4C3o+YU2c+ZIO/5dE6/Mi+JRgU/18Do7Q+nbzz8Ta6eafkcR9MGeUOaXvyueh/lX26W92LgX9pNqCi2fJjCFSgTz66KMMGzaMdu3a0blzZ6ZOnUpUVBRjx5oVtuPHjyc6OpqZM2cC8OGHHxIWFkajRo0AWLp0KW+++SYPPvjfv4mvv/46zz33HF999RW1a9cuqMTy8vLCy6sc9nmrEgZuVXBOP0F922GiElNLviekiIiIXJCSUmJ3by3YyR9bY3F2tFGjijvRx9PIzMllX3wK++JTzvkePw/ngkTV6UmrMH8Pqvu64WTHqqTsnFyS07NJSss665Gc98j/et3B48SdzMDf04VP72xH21qlePG6eJK58phvKPQaX3Lj5Celdi+AtOPgXsEv0DNOweK8SqOeT527L1NQExjwtrkS3+JXILQD1Dn/ilQlyjDMFfEyT5nN6duOLN3xnVyh01hofQes/AiWvw9Ht8BXN0NYZ7jyBajV2f7jauqeSLEMGTKEhIQEJk6cSExMDM2aNWPevHnUqlULgJiYGKKiogqOz83NZfz48ezfvx8nJyfq1q3Lq6++yj333FNwzEcffURmZiY33VT4FxYvvPDCOaf5lXk2m1kttf9fmjkc4JAqpURERCxnM9Tl8ZIkJyfj6+tLUlJS2SpNt9icdYd57PtNALx9S0sGt6lJdk4uMUnpHDqeyqHEVKISU4lKTCMq0fz6Yj0dHB1shFRxP6u6KszfA09Xp/MmlAo/sgteO5WRXazPVDfQk+kjOhAWUMINzU8Xswmm9jb7B932PTToe/H3XI6POkNcJFz/oZl8qMgWv2YmmvzC4f7V4ORy/mN/vh82fAme1WDs0v+m+ZWmyF/gu2HmNLaxEVCtcenHcLrURFj6Nqz+FLLTzX31+8GVz0Fwc/uMYRjwTlNIjjabr9fpZZ/zitiJrgGKrsx9r/6aAMvfZ2b2VWxt9Ryv39TS6ohEREQqpKJeA6hSSuxmzYFExv+4BYD7e9dlcJuaADg5OuQlkzyg7tnvO5WRXZCs+i9pZW4fOp5GZnZuwT578nRxxNfdGR93Z3zzHqdv+7o7E+DlQq+G1fByLcW/Krk58OvDZkKq6Q0ln5ACaDYY/ok0p/BV5KTUqWOw/D1z+8rnLpyQAuj/BkSvNxN2c0aZCRIHx5KPM196EvyR14S968PWJ6QAPPyh78vQ8V749zUzabf7T9j9l1l11/sZCDjHX/TiSNxnJqQcXaBmB/vELSICZi9FoJnDfuapUkpERMRySkqJXUQlpHLPrHVk5uTSv1kwj13VsMjv9XJ1onF1HxpXPzt7mptrEHcyoyApFZWYyuG854OJqaRn5pyVSPJ1d8bX478kk4+b01mv+7g7l06j8kuxeioc2QCuviXb0Pp0TQfDPy/Dvn/NxI1XYOmMW9qWvG5Og6vRGprccPHjXTzglpkwtZc5nWzxJLhiQomHWeDviWaTcf+60MPivlZn8g2B696DLg/Bov/Bth/NpvyRc6HNneaKhpfa+yp/6l5IO/NnICJiL3nNzhvbojiSUMlWnRURESmDlJSSy5acnsWoL9aQmJJJsxAf3rqlJQ4O9ml+7OBgI9jXjWBfNzqE+9vlnGXaiUPw9/+Z21e9VHrTxQLqmomaIxtg+8/QfnTpjFuaEvbC2mnmdp+XwKGIScmq9WHgu2al1JI3ILQT1O9TcnHmO7Qa1nxubg94B5zdSn7MS1G1Htw8HbqNM5Noexaa3+eNX0PHu6HrOLO6qjjym5yHd7d3tCJS2fnXJdfZE/esFNxO7iMzOxcXpzL6SyoREZFKQP8Ly2XJzsnlwa82sDvuFEE+rnx2Z3s8XJTrvCSGAfMeh6wUs4F0m+GlO36zG83nrT+W7ril5Z+XITcb6l5Z/KblzW+CdqPM7R/HmKvhlaTsTHMKJwa0ut26JuvFUb0l3DEHRswzG7Jnp8Gyd+HdVrDkTXM1yaIwDNivJuciUkIcHLBVbwFAU/YTfSLN4oBEREQqNyWl5LK8/Pt2/t11DDdnBz67sz3BvmW0mqM8iPwZds03G1oPfLfolTz20jRvOtvB5ZAUXbpjl7To9eb0MmxmBdql6PeKmXhJS4Qf7oKcLLuGWMjy98w+Vh4BZv+m8qR2V7jrTxj6LVRrChlJ8M//mcmpVVPNhNuFJOyFU7Hg6Kp+UiJSImx5U/iaORywe79KERERKR4lpeSSzVp5kBnLDwAweUgrmtf0tTag8iztBPzxlLnd/VEILHpPLrvxrQlhXQADtv1U+uOXFMOAhS+Y2y1uufQV4pzd4OYZ4OoDh1aZU9VKQsJe+Pd1c7vfpOJPfSsLbDZoeLW5WuDgT8GvNqTEwR9PwAftYNM3ZkP/czmwxHwO7VB2pyyKSPkWnFcppaSUiIiI5ZSUkksSsfsYL/6yDYAn+jXk6maX2NBYTH+/ZFaHBNSDbo9aF0ezwebz1jnWxWBve/+G/UvMldx6P3t55/KvA9d/aG4vfw92zLv8+E5nGPDbOMjJgDq9zSRaeebgaH6G+9fANW+CVxCcOAg/3QMfd4Mdv5uf+XSauiciJS2vUqqJ7QCH1OxcRETEUkpKSbHtiTvFfbPXk5NrMLhNCPf1uszl3yu7qJX/NeAe+K611SFNBoHNAY6sh8R91sVhL7m5sOBFc7v9GPCrdfnnbHIddLrP3J47Fo4fvPxz5tv0jZlAc3KDAW+bFUcVgZMLdBgDD22AK18AN19zeuI3t8HnV/2XiDKM/5qc11aTcxEpIYENyXFwwceWRurRPVZHIyIiUqkpKSXFcjwlk1FfrOFkejbtavkxaXBzbBXlxtkKBQ2tgdZ3WF8d4hUI4XlNtStCw/Mt38PRLeaUux6P2++8fV6CkHaQngTfj4DsjMs/Z0oC/PmMud3zKbMqq6Jx8TSnpz68Cbo9Ak7ucHgNfDEAZt0AW34wp/k5uUHNdlZHKyIVlaMzKVUaAeCRsM3iYERERCo3JaWkyDKzc7nny3UcTEilpp87nwxri6uTo9VhlW/L34VjO8CjKlz1f1ZHY6ooq/BlpZsr7gF0G2ff3kxOLnDzdHCrYlaV/fXc5Z/zr2fNJurVmkKXBy//fGWZux/0eREe3gjtR4ODE+z9B34cbb4e2gGcXK2MUEQqOCOvr1S1UzswzpxGLCIiIqVGSSkpEsMwmDB3C6v3J+Ll6sS0Ee0J8NJN42VJSYCIt83tq18tOw2tGw8wVwCM2wZx262O5tKt/RySosC7OnS81/7nrxIGN3xibq/+BLbNvfRz7V0Em74GbHDde+DobI8Iyz7vYLj2LXhgDTS/Bciruqx7haVhiUjF51GrDQANcvdxPLUEV1MVERGRC1JSSork04h9fLf2MA42+OC21jQI8rY6pPJv9SeQlWquAtT8Jquj+Y+7H9TrY26X12qp9CRY8oa53Ws8uHiUzDgNr4au48ztnx8wV84rrqw0+O0Rc7vDmMo5bc2/Dtz4Kdy7DAZMhk73Wx2RiFRwzjVbAXkr8CWkWBuMiIhIJaaklFzUgsijTPpjBwDPDWhCr4bVLI6oAsg4Cavyqmy6P1b2GloXTOGbc/bqaOXB0smQdhyqNoBWt5fsWFc8B2FdIPMkfD/cTDIVx7+vw/H94F3DPFdlFtQU2o00p0eKiJSkak3JwYEA20nioivAwh4iIiLllJJSckHbjiTx8DcbMAy4o1MYI7rUtjqkimHtdEg/AQH1oPFAq6M5W8P+ZhPqxL0Qs8nqaIon+QisnGJu93kRHJ1KdjxHJ7jpc7MvWOwWmP900d97dBssf8/cvuYNcPMpmRhFRKQwZzeOuoYDkH14g8XBiIiIVF5KSsl5xSWnM+aLtaRm5tCtXlVeGNhUK+3ZQ3YGrPjQ3O46DhzKYLN4Vy9o0M/c3vqDtbEU1+JJkJ0GoR2h4TWlM6ZPDXP6GTZYNwM2f3fx9+Tmmisv5mZDowFmLy8RESk1J3ybAOASt8XiSERERCovJaXknNKzchgzax1HktKpE+jJh7e3wdlRf1zsYuNXcCoWfEKgxRCrozm//D5XW38yEyjlwbGdsOFLc/uqiaU7LbLuFdDzSXP713FmLBey9nM4vAZcvKH/6yUenoiIFJYV1BwAv+RyvKiHiIhIOacsg5zFMAwe/34Tmw6doIqHM9OGt8fXvZKsBlbScrJh2bvmducHynbvnHpXmQmT5MNweLXV0RTNwpfAyIWG10JYp9Ifv+dTEN4DslLgu+GQeZ7muclHzFgBrnwefENKL0YREQHANbQ1AKEZeyyOREREpPJSUkrOMnnhbn7bHIOzo42P72hL7aqeVodUcUTONZtau/tD2+FWR3Nhzm7/TSnbOsfaWIoiaiXs/B1sDmaixwoOjnDj5+AVBMe2w++PnbtR/LwnzMboIe2g/ajSj1NERAio25Zcw0Y1Esg8EWt1OCIiIpWSklJSyM8bo3n3790A/G9QczrVCbA4ogrEMMxV4QA6jgWXcpDsy1+Fb9tPZpVXWWUYsOAFc7v1HVCtkXWxeFWDm6aZybFNX/83nTDf9t9gx2/g4AQD3y2bPcVERCqBqv7+HKA6AMf3rrU4GhERkcpJSSkpsD7qOE/8sBmAe3rU4Zb2oRZHVMHsXgBHt4CLF3QYY3U0RVOnF7j7QcoxOLjU6mjOb+c8OLTSXDGw13iro4Ha3aD3s+b2vMchdqu5nZ5sVkkBdHkQgptZE5+IiGCz2TjgUg+A1Kh1FkcjIiJSOSkpJQBEn0jj7pnryMzOpU/jIJ682sJKk4oq4i3zud1I8PC3NpaicnSGJteb22V1Cl9O9n/9mTrda66EVxZ0exTq9YHsdPh+OGSchH9ehpNHwK+22X9KREQsFe9tXu84xG62OBIREZHKSUkp4VRGNqNmrCH+VAaNq/vw7q2tcHQoxVXLKoODy81KHkcX6HS/1dEUT/4UvshfIDvT2ljOZeNsiN9pVnR1fdjqaP7j4AA3TDVXWUzYA7NvgdVTzdcGvAPO7tbGJyIipAWYFas+JyItjkRERKRyUlKqksvJNXj46w3siD1JoLcrnw9vh6erk9VhVTwRb5vPrW4Dn+rWxlJctbqajbvTT8C+RVZHU1hmKiyeZG73eALcq1gazlk8A+Cm6Wb/qKjlgAEthkDdK6yOTEREAKeQVgD4ZRyBtOPWBiMiIlIJKSlVyb36x3b+3hGHq5MDn97ZjhpVVL1hdzGbYc8Cs/F1WarkKSoHR2h6g7m95QdrYznTqilwMgZ8w6D9aKujObewjtDnRXPb3Q/6vWJpOCIi8p/goGAO5QaaX8RusTYYERGRSkhJqUrsm9VRfBqxH4A3b25Jq9Aq1gZUUS19x3xuegP417E2lkuVP4Vv5zyzOqksSE38bzXDK54FJ1dLw7mgzg+YK/IN/w08q1odjYiI5Anz92CrURsA48hGS2MRERGpjJSUqqSW741nwlxzRbBH+jRgYMsy0hy6oknYC5Fzze1uj1gaymWp2d6sRso8Bbv/sjoaU8RbkJEMQc2h+S1WR3NhNpuZ2NNqeyIiZUpNPw+25oYDkHV4o7XBiIiIVEJKSlVC6Vk5PPjVBrJzDa5rWYOHrqxndUgV17J3wciF+n0huLnV0Vw6mw2aDTa3y8IqfMcP/tc0vM+LZlNxERGRYnJ3ceSwW30AclUpJSIiUup0J1cJrdyXQEJKJkE+rrx+UwtsNq20VyKSj8DGr8zt7o9ZG4s95E/h2/0XpCdbG8uiVyAnE8J7QL0rrY1FRETKtVN+TQFwTdoHGacsjkZERKRysTwp9dFHHxEeHo6bmxtt27YlIiLigsfPnj2bli1b4uHhQfXq1Rk5ciQJCQkFr2dlZTFx4kTq1q2Lm5sbLVu2ZP78+Zc9bkWyeOcxAK5oFISbs6PF0VRgKz6E3CwI6wJhnayO5vIFN4eA+pCdDjv/sC6O2C2w+Vtzu89LZhWXiIjIJfINDCHW8MOGAUe3WR2OiIhIpWJpUurbb79l3LhxPPvss2zYsIHu3bvTv39/oqKiznn80qVLufPOOxk1ahTbtm3j+++/Z82aNYwe/d+qWxMmTOCTTz7h/fffJzIykrFjx3LDDTewYcOGSx63IjEMg392xAHQu2GgxdFUYKmJsHa6ud39UWtjsZf8vkhg7RS+hS8CBjQdDCFtrItDREQqhFB/D7bl1ja/iNlkaSwiIiKVjaVJqbfffptRo0YxevRoGjduzOTJkwkNDWXKlCnnPH7lypXUrl2bhx56iPDwcLp168Y999zD2rVrC46ZNWsWzzzzDNdccw116tTh3nvvpV+/frz11luXPG5Fsi8+hajEVFwcHehaT6uAlZjVUyErxawuqtfH6mjsJ7+v1N6/zcRbadv3L+xZCA5OcMWE0h9fREQqnNNX4FNSSkREpHRZlpTKzMxk3bp19O3bt9D+vn37snz58nO+p0uXLhw+fJh58+ZhGAZHjx7lhx9+4Nprry04JiMjAzc3t0Lvc3d3Z+nSpZc8bv55k5OTCz3Ko0V5VVId6/jj6epkcTQVVMYpWPWxud3tkYo1vSywobnaXW42bP+1dMfOzYWFL5jb7e6CgLqlO76IiFRIYQGqlBIREbGKZUmp+Ph4cnJyCAoKKrQ/KCiI2NjYc76nS5cuzJ49myFDhuDi4kJwcDBVqlTh/fffLzimX79+vP322+zevZvc3FwWLFjAzz//TExMzCWPCzBp0iR8fX0LHqGhoZf60S21aGf+1L1qFkdSga3/AtKOg38daDLI6mjsr2AVvh9Kd9zIuXBkA7h4QY8nS3dsERGpsML8PdiaGw6AcWw7ZKVbHJGIiEjlYXmj8zNXfjMM47yrwUVGRvLQQw/x/PPPs27dOubPn8/+/fsZO3ZswTHvvvsu9evXp1GjRri4uPDAAw8wcuRIHB0LN/QuzrgA48ePJykpqeBx6NCh4n5Uy53KyGb1fnPKVe9GSkqViOwMWP6Bud31YXCogI3k85NS+yPg5PkTuXaVnQl/TzS3uzwIXuqHJiIi9hHo5UqCUyCJhhe23GyIi7Q6JBERkUrDsqRU1apVcXR0PKs6KS4u7qwqpnyTJk2ia9euPPHEE7Ro0YJ+/frx0UcfMW3atIJKqMDAQObOnUtKSgoHDx5kx44deHl5ER4efsnjAri6uuLj41PoUd4s3R1PVo5BeFVPwqt6Wh1OxbTpGzh5BLyrQ8uhVkdTMvxqQ0g7wIDIn0tnzPVfwPH94BkInR8onTFFRKRScHCwEervWVAtRexmawMSERGpRCxLSrm4uNC2bVsWLFhQaP+CBQvo0qXLOd+TmpqKg0PhkPMroAzDKLTfzc2NkJAQsrOzmTNnDtdff/0lj1tR5PeT6qVV90pGbg4se9fc7vwAOLlaG09JKq1V+E4ehc3fw+JXza97PgWuXiU7poiIVDph/h5Eqtm5iIhIqbO00/Wjjz7KsGHDaNeuHZ07d2bq1KlERUUVTMcbP3480dHRzJw5E4CBAwcyZswYpkyZQr9+/YiJiWHcuHF06NCBGjVqALBq1Sqio6Np1aoV0dHRvPjii+Tm5vLkk08WedyKyDCMgn5SV2jqXsmI/BkS94K7H7QdYXU0JavpDfDnM3BoFZyIgiph9jlvxkk4sAz2LYb9/xaeQuFft+J/X0VExBJmX6na5hdKSomIiJQaS5NSQ4YMISEhgYkTJxITE0OzZs2YN28etWrVAiAmJoaoqKiC40eMGMHJkyf54IMPeOyxx6hSpQpXXHEFr732WsEx6enpTJgwgX379uHl5cU111zDrFmzqFKlSpHHrYi2HUkm7mQGHi6OdAj3tzqciscwYOnb5naHeyp+NY9PdajdDQ5EwLafzP5ZlyI7E6LXmkmofYshep25sl8BGwQ3hzq9oONYcHS+/NhFRETOEOrvweL8SqnYrZCTpf9zRERESoHNOHPemxRJcnIyvr6+JCUllYv+Uu//vZu3FuziqiZBfHpnO6vDqXh2L4TZN4KzJzyyFTwqQeJv7TT47RGo3hLuWVK09+TmQty2vCTUv3BwOWSlFD7GL9xMQtXpCbV7gGeAvSMXEbks5e0awErl5Xu1IPIod89czTb3MXgYaXDvcghqanVYIiIi5VZRrwEsrZSS0pM/da93Q03dKxH5VVJtR1SOhBRA4+vh98fNaQ7xe6BqvXMfd/zgf5VQ+5dAanzh1z2qmgmoOr0gvCf4VdyKRRERKZvC/D0wcCDSqE07tpv/tykpJSIiUuKUlKoEElMy2XDoBAC9G6nJud1FrYSDy8DBGTrfb3U0pcczAOr2hj0LYduP0DOvb1tKgtkPav+/ZiLq+IHC73P2hNpdzQRUnV5QrQk4WLbmgoiICKH+7gBsyq5NO6ftELMZWt1mcVQiIiIVn5JSlcCSXccwDGgU7E11X3erw6l4IvKqpFreCr4h1sZS2prdaCalNn4FGcnmlLwzl9J2cIKQdnlT8npBSFtwcrEiWhERkXPycHGiqpcr21LzqnXV7FxERKRUKClVCfyzQ6vulZjYrbD7T7A5QLdHrI6m9DW6Fhxd4Ph+WP7+f/urNf1vSl6tLuDqbVmIIiIiRRHm787WlHDzi9jNZh9EVfKKiIiUKCWlKricXIN/dx0DoLeSUva39B3zucn1EFDX2lis4OYL3R+HrXMgtD2E94LwHuAdZHVkIiIixRLm78GvUTXIdnDFKfMUJO47f79EERERsQslpSq4DVHHSUrLwtfdmdahVawOp2JJ3Gf2UoLKWSWVr9dT5kNERKQcC/P3IAdHjrjVJSw1EmI2KiklIiJSwlSTXMHlr7rXo0EgTo76cdvVsvfAyIV6faB6S6ujERERkcsQ6u8BwC5bHXOH+kqJiIiUOGUpKrh/dphT967Qqnv2dTIWNs42t7s9am0sIiIictnC8pJS67Pymp2fuXCHiIiI2J2SUhVYbFI622OSsdmgZwP1k7KrFR9CTiaEdjQbeYuIiEi5VivAE4BlKTXMHTGbwDAsjEhERKTiU1KqAsufutcqtAr+ni4WR1OBpB2HtdPM7W6Pgs1mbTwiIiJy2ap5u+Li5MD2nJoYDk7m//dJh6wOS0REpEIrdlKqdu3aTJw4kaioqJKIR+zonx1mUuqKhqqSsqvVn0LmKajWFBr0szoaERERsQMHBxuhfu5k4kyKbwNzp/pKiYiIlKhiJ6Uee+wxfv75Z+rUqcNVV13FN998Q0ZGRknEJpchIzuHZXviAejdSEkpu8lMgZVTzO3uqpISERGpSPL7SsV5NjR3KCklIiJSooqdlHrwwQdZt24d69ato0mTJjz00ENUr16dBx54gPXr15dEjHIJVu9PJDUzh2rerjSt4WN1OBXH+pmQlgh+taHJIKujERERETvKT0rtdapr7lBSSkREpERdck+pli1b8u677xIdHc0LL7zAZ599Rvv27WnZsiXTpk3DUGNISy3KW3WvV8NAbKrmsY/sTFj+vrnd9WFwdLI2HhEREbGr0Lyk1Oac2uaOGK3AJyIiUpIu+a46KyuLn376ienTp7NgwQI6derEqFGjOHLkCM8++ywLFy7kq6++smesUgz5Tc6v0NQ9+9nyHSRHg1cQtLzN6mhERETEzvIrpValVgdscCoWTsaCd7C1gYmIiFRQxU5KrV+/nunTp/P111/j6OjIsGHDeOedd2jUqFHBMX379qVHjx52DVSKbn98CvvjU3B2tNG1XlWrw6kYcnNg6WRzu/P94OxmaTgiIiJif2EBZlJq9/FcqNoA4nea1VJKSomIiJSIYiel2rdvz1VXXcWUKVMYNGgQzs7OZx3TpEkTbr31VrsEKMW3KG/Vvfa1/fF2O/vnI5dgx2+QsBvcfKHdXVZHIyIiIiUg1M9MSh1PzSKzcXNc4neafaUa9LU4MhERkYqp2Empffv2UatWrQse4+npyfTp0y85KLk8+VP3ejfU1D27MAyIeMvc7nA3uHpbG4+IiIiUCE9XJ6p6uRB/KpNE78YEA8RstDgqERGRiqvYjc7j4uJYtWrVWftXrVrF2rVr7RKUXLqUjGxW7UsEoLf6SdnH3n/M35I6uUPHsVZHIyIiIiUov9l5lEs9c8eRDZCwFzJTLYxKRESkYip2pdT999/Pk08+SceOHQvtj46O5rXXXjtnwkpKz/K9CWTm5BLq707dQE+rw6kYlr5jPrcdAZ7q0SUiIlKRhfl7sCHqBJFGbTqAucjJ+23MF918wbsG+FQ/7bk6+NT479mjKjhc8gLXIiIilUqxk1KRkZG0adPmrP2tW7cmMjLSLkHJpfsnr5/UFQ2rYbPZLI6mAji0Bg5EgIMzdHnA6mhERESkhOWvwLfnpCP0eAK2/ggnYyArFdKTzMex7ec/gYOz2Rjdu/oZyaszkljO7qX0iURERMquYielXF1dOXr0KHXq1Cm0PyYmBienYp9O7MgwDBbn9ZPqpal79rH0bfO5xRDwrWltLCIiIlLiCqbvJabBoAlwxQSzv2R6kpmcSj6S9xwDJ48Ufk45BrlZkHTIfFyIWxUzOeUbav7iK1wrV4uISOVT7CzSVVddxfjx4/n555/x9fUF4MSJEzzzzDNcddVVdg9Qim5H7EliktJxc3agc50Aq8Mp/+K2w855gA26jbM6GhERESkF+ZVShxJP6yFls4F7FfNRrfH535yTBSdjz0he5T2fjP1vOysV0k+Yj7hI2Ps3DJgMbYaV3AcTEREpg4qdlHrrrbfo0aMHtWrVonXr1gBs3LiRoKAgZs2aZfcApejyp+51qVsVN2dHi6OpANZ8Zj43HgBV61sbi4iIiJSK/KTU4eOp5OQaODoUox2CozNUCTUf53Nm1dXG2bB1DvzyABw/YFZmqQWDiIhUEsVOSoWEhLB582Zmz57Npk2bcHd3Z+TIkQwdOhRnZ+eSiFGKKH/qnlbds4OsdNjyvbnd7i5rYxEREZFSE+TjhoujA5k5ucQmpxNSxc69n86suqp7BfjXgSVvQMSbcCIKrv8AnFztO66IiEgZdElNoDw9Pbn77rvtHYtchhOpmaw7eByA3g0DLY6mAtj5u/lbTJ+aEN7T6mhERESklDg62Kjp586++BSiElLtn5Q6k81mVkdVCYNfx8GW78wqqiGzwN2vZMcWERGx2CV3Jo+MjCQqKorMzMxC+6+77rrLDkqKb8nueHINaBDkRU0/D6vDKf82zDafW94KDpoKKSIiUpmE+nuwLz6FQ4mpdK5bSn0629wJPiHw3XBz5d/P+8Ht34NfrdIZX0RExALFTkrt27ePG264gS1btmCz2TAMAwBb3tz3nJwc+0YoRbIor59U74aaunfZkqJh7z/mdqvbrI1FRERESl1YwQp8qRc50s7qXQl3/QGzb4H4nfBZH7jtWwhpU7pxiIiIlBKH4r7h4YcfJjw8nKNHj+Lh4cG2bdtYsmQJ7dq1Y/HixSUQolxMTq7Bv7uOAeonZRebvgYMCOsCAXWtjkZERCqwQ4cOcfjw4YKvV69ezbhx45g6daqFUYllSSmA4OYweiEENYOUOJhxLez8o/TjEBERKQXFTkqtWLGCiRMnEhgYiIODAw4ODnTr1o1Jkybx0EMPlUSMchGbDp8gMSUTbzcn2tZS74HLYhjmKjgArW+3NhYREanwbrvtNhYtWgRAbGwsV111FatXr+aZZ55h4sSJFkdXeYVamZQC8A2BkX9A3SshKxW+uQ1Wf2pNLCIiIiWo2EmpnJwcvLy8AKhatSpHjhwBoFatWuzcudO+0UmRLM6butejfiDOjsX+kcrpolZC4j5w9oQmg6yORkREKritW7fSoUMHAL777juaNWvG8uXL+eqrr5gxY4a1wVVi+ZVSh6xKSgG4+ZhT99rcCUYuzHsc/nwWcnOti0lERMTOit1TqlmzZmzevJk6derQsWNHXn/9dVxcXJg6dSp16tQpiRjlIv7ZmddPSlP3Lt/GL83npoPA1cvSUEREpOLLysrC1dUVgIULFxYsGNOoUSNiYmKsDK1SC/U3V9xLSMnkVEY2Xq6XvDbQ5XF0hoHvQZVa8M//wYoP4EQUDJ4KziW8KqCIiEgpKHZZzYQJE8jN+w3Nyy+/zMGDB+nevTvz5s3jvffes3uAcmFxyelsjU4GoGeDQIujKecyU2DbXHO7labuiYhIyWvatCkff/wxERERLFiwgKuvvhqAI0eOEBBQvFXfPvroI8LDw3Fzc6Nt27ZERESc99ilS5fStWtXAgICcHd3p1GjRrzzzjtnHTdnzhyaNGmCq6srTZo04aeffireByynvN2c8fd0ASyulgKw2aDH4zD4M3B0ge2/wBfXQUq8tXGJiIjYQbGTUv369WPw4MEA1KlTh8jISOLj44mLi+OKK66we4ByYYt3mg3OW9b0JdDb1eJoyrnIXyDzFPiFQ60uVkcjIiKVwGuvvcYnn3xCr169GDp0KC1btgTgl19+KZjWVxTffvst48aN49lnn2XDhg10796d/v37ExUVdc7jPT09eeCBB1iyZAnbt29nwoQJTJgwoVCD9RUrVjBkyBCGDRvGpk2bGDZsGLfccgurVq26vA9dTljeV+pMLW6GYT+Bmy8cXm2uzJew1+qoRERELovNMAyjqAdnZ2fj5ubGxo0badasWUnGVeYlJyfj6+tLUlISPj4+lsVx75fr+GNrLA9fWZ9HrmpgWRwVwowBcCACek+Ank9YHY2IiJRR9r4GyMnJITk5GT+//xYrOXDgAB4eHlSrVrSp+R07dqRNmzZMmTKlYF/jxo0ZNGgQkyZNKtI5Bg8ejKenJ7NmzQJgyJAhJCcn88cf/638dvXVV+Pn58fXX39dpHOWleulS/Hg1xv4ddMRJlzbmNHdy1CLimM7YfZN5jQ+d38Y+jWEdbI6KhERkUKKeg1QrEopJycnatWqRU5OzmUHKJcvMzuXiN1m6fYV6id1eRL3mwkpbNBqqNXRiIhIJZGWlkZGRkZBQurgwYNMnjyZnTt3FjkhlZmZybp16+jbt2+h/X379mX58uVFOseGDRtYvnw5PXv2LNi3YsWKs87Zr1+/Ip+zvAvL6ytVZiql8gU2hNF/Q43WkJZoTuXbVjmmVYqISMVzST2lxo8fT2JiYknEI8Ww9kAipzKyqerlQvMQX6vDKd825f3Gt04v8K1paSgiIlJ5XH/99cycOROAEydO0LFjR9566y0GDRpUqOrpQuLj48nJySEoKKjQ/qCgIGJjYy/43po1a+Lq6kq7du24//77GT16dMFrsbGxxT5nRkYGycnJhR7lVVhZm753Oq9qMOJ3aHgt5GTA9yNg2btQ9AkQIiIiZUKxk1LvvfceERER1KhRg4YNG9KmTZtCDyk9i/JW3evZoBoODjaLoynHcnNhY15SqvUd1sYiIiKVyvr16+nevTsAP/zwA0FBQRw8eJCZM2cWewEZm63wtYBhGGftO1NERARr167l448/ZvLkyWdNyyvuOSdNmoSvr2/BIzQ0tFifoSwp6CmVUAaTUgAunjBkFnQca3694Hn4/THIybY2LhERkWIo9vq2gwYNKoEw5FL8s8NMSvVupFX3LsuBJZAUBa6+0Ohaq6MREZFKJDU1FW9vbwD++usvBg8ejIODA506deLgwYNFOkfVqlVxdHQ8q4IpLi7urEqnM4WHhwPQvHlzjh49yosvvsjQoeY09uDg4GKfc/z48Tz66KMFXycnJ5fbxFR+pdTh42nk5Bo4lsVfADo4Qv/XoEot+PMZWPs5JB2Gm6aBq5fV0YmIiFxUsZNSL7zwQknEIcUUlZDK3mMpODrY6F5fSanLsmG2+dxsMDi7WxuLiIhUKvXq1WPu3LnccMMN/PnnnzzyyCOAmfwpamNwFxcX2rZty4IFC7jhhhsK9i9YsIDrr7++yLEYhkFGRkbB1507d2bBggUFMYGZOOvS5fwr1Lq6uuLqWjFWA67u646rkwMZ2blsPHSctrX8rQ7p/DrfZ7Yf+HEM7P4TZlwDt30H3sFWRyYiInJBxZ6+J2VD/tS9trX88HV3tjiaciw9Cbb/Ym5r6p6IiJSy559/nscff5zatWvToUMHOnfuDJjJn9atWxf5PI8++iifffYZ06ZNY/v27TzyyCNERUUxdqw5tWv8+PHceeedBcd/+OGH/Prrr+zevZvdu3czffp03nzzTe6447//Cx9++GH++usvXnvtNXbs2MFrr73GwoULGTdunH0+fBnn6GDj+lY1AJi6ZJ/F0RTB/7d33/FRVOsfxz+bTSMhJISEhFCSgPRepAQUC1JEEEUFC0VBQVEEy71wFeXasMtPECw0CwKiKChY8ErvvfcWSkIIgRRC6s7vj00CgQCpOynf9+u1r0xmz8w8swN6ePac5zToCQN+A49KELENpnSCqD1mRyUiInJdeR4p5eTkdN1aAlqZzzEyk1L5XnUvIcpeDNPr+sP6S72d8yAtCfzqQtWWZkcjIiJlzAMPPECHDh2IiIigadOmWfvvvPPObKOebqRPnz6cPXuWN954g4iICBo1asSiRYsIDg4GICIigvDw8Kz2NpuN0aNHc+TIEZydnalVqxbvvvsuQ4YMyWoTFhbG7NmzefXVVxkzZgy1atVizpw5tGnTphDuvGR46taa/LDxBH/tPs2hMwnU8i/mU+Kq3wyD/4bvHoCYQzC1i73uVM2ONz5WRETEBBbDyNsyHfPnz8/2e2pqKlu2bOHrr7/mv//9L4MGDSrUAIuruLg4vL29iY2NzfXw+sJyMSWdZm/8RXKajT9H3ErdQK+8nSAtGcY3AVsaPLsBPIrxcPSiNqUTnNgAd70B7Z83OxoRESkBiqoPcOLECSwWC1WrVi20c5rNzP5SYRn89Ub+3nOah1tXZ9z9TcwOJ3cSY2D2IxC+BpxcoOcEaPaw2VGJiEgZkts+QJ6n7917773ZXg888ABvv/0277//PgsWLChQ0JI7aw5Hk5xmo6pPOeoE5OMbu3PHICESEqNh/VeFH2BJcWa/PSFlsUKTvmZHIyIiZZDNZuONN97A29ub4OBgatSogY+PD2+++SY2m83s8AQY2rEmAD9tOklUfJLJ0eSShy/0+wUa3g+2VPhlKBxZbnZUIiIiVym0mlJt2rTh77//LqzTyXVkrrp3W13/Gy71nKNzRy5tr5sMyQmFFFkJszWjwHntuzSNUURETPHKK68wceJE3n33XbZs2cLmzZt55513mDBhAmPGjDE7PAFahfjSMrgiKek2pq86anY4uefiDr2nQtNH7L8veA5SLpgbk4iIyBUKJSl18eJFJkyYQLVq1QrjdHIdhmGwZO8ZoAD1pM4dvbR98Rxs/rrggZU06WmwbbZ9u9mj5sYiIiJl1tdff82UKVN4+umnadKkCU2bNuWZZ57hq6++YsaMGWaHJxmG3GofLfXd2mPEJ6WaHE0eODlBt/egQjV7/+9/b5odkYiISDZ5TkpVrFgRX1/frFfFihXx8vJi2rRpfPDBB0URo1zmQFQCJ89fxNXZiXa1KuXvJDEZI6W87CvKsHqivc5UWXLoH/sURo9KUKer2dGIiEgZFRMTQ7169a7aX69ePWJiYkyISHLSqX4Atfw9iU9KY/b642aHkzfuFaDH/9m3130O4WvNjUdEROQyeU5KffLJJ9len376Kb/99hvHjh2jZ8+eeQ5g0qRJhIaG4u7uTsuWLVmxYsV128+cOZOmTZvi4eFBlSpVePzxxzl79my2NuPHj6du3bqUK1eO6tWrM3LkSJKSLtUAGDt2LBaLJdsrMDAwz7GbIXPqXrualfBwzfPiiXaZI6XaDwevKhB/CrbPKZwAS4qt39l/Nn4InF3NjUVERMqspk2bMnHixKv2T5w4kSZNSkhR7TLAycnCkFtrATB15RFS0kpYva/anTJGhhswfxikXjQ7IhEREQDynNUYOHBgoV18zpw5jBgxgkmTJtG+fXu++OILunXrxu7du6lRo8ZV7VeuXEn//v355JNP6NGjBydPnmTo0KEMHjyYn3/+GbAnrUaNGsW0adMICwtj//79WTF/8sknWedq2LBhthpYVqu10O6rKC3JSErle+oeXKop5VcH2j0Lf70CK8fbOytOJeNzKJDEGNj3u327uabuiYiIed5//326d+/O33//Tbt27bBYLKxevZrjx4+zaNEis8OTy9zbPIgP/9pHZFwSC7ad4oGWJaxsRZe34eD/4OxBWDrOvvKwiIiIyfI8Umr69OnMnTv3qv1z587l66/zVpvo448/ZtCgQQwePJj69eszfvx4qlevzuTJk3Nsv3btWkJCQhg+fDihoaF06NCBIUOGsHHjxqw2a9asoX379jzyyCOEhITQuXNnHn744WxtAJydnQkMDMx6+fv75yl2M8ReTGXjsXMA3F43n0kpm+3SSCnfUGg5EMpVhJhDsKeMrJ64Yy6kp0BgEwhsbHY0IiJShnXs2JH9+/dz3333cf78eWJiYrj//vvZtWsX06dPNzs8uYybs5UnOoQC8OXyQ9hshskR5VG5inBPxhe0qyfAiU3mxiMiIkI+klLvvvsufn5+V+2vXLky77zzTq7Pk5KSwqZNm+jcuXO2/Z07d2b16tU5HhMWFsaJEydYtGgRhmFw+vRpfvzxR7p3757VpkOHDmzatIn169cDcPjwYRYtWpStDcCBAwcICgoiNDSUvn37cvjw4evGm5ycTFxcXLaXo608EE26zaCWvyc1Knnk7yQJpyEtCSxW8K4ObuWh9RD7eys+BqOEdbDyY0vG1D0VOBcRkWIgKCiIt99+m59++ol58+bx1ltvce7cuTx/2SdF75E2NSjv5sz+0wks3R9ldjh5V+9uaPQAGDb7NL6yVlNURESKnTwnpY4dO0ZoaOhV+4ODgwkPD8/1eaKjo0lPTycgICDb/oCAACIjI3M8JiwsjJkzZ9KnTx9cXV0JDAzEx8eHCRMmZLXp27cvb775Jh06dMDFxYVatWpx++23M2rUqKw2bdq04ZtvvuHPP//kq6++IjIykrCwsKtqU11u3LhxeHt7Z72qV6+e63stLJn1pPI9SgouTd3zrgZWF/t2myHg4gmR2+HQ/woYZTEXucN+n04u0PhBs6MRERGREqSCuwuPtrGXmPh86fW/0Cy2ur0PHn5wZg8s/9DsaEREpIzLc1KqcuXKbN++/ar927Zto1KlvK8GZ7FYsv1uGMZV+zLt3r2b4cOH89prr7Fp0yb++OMPjhw5wtChQ7PaLF26lLfffptJkyaxefNm5s2bx2+//cabb15aArdbt2707t2bxo0b06lTJxYuXAhw3W8kR48eTWxsbNbr+HHHrrxisxks218Y9aSO2n/6XpZY9PC1T+MD+2ip0mzLTPvPut3AM5+rF4qIiEiZ9Xj7UFysFtYfjWFTRlmFEsWzEnTPSEat/Bgiru7Xi4iIOEqek1J9+/Zl+PDhLFmyhPT0dNLT0/nnn394/vnn6du3b67P4+fnh9VqvWpUVFRU1FWjpzKNGzeO9u3b8/LLL9OkSRO6dOnCpEmTmDZtGhEREQCMGTOGfv36MXjwYBo3bsx9993HO++8w7hx47DZcl4pxdPTk8aNG3PgwIFrxuvm5kaFChWyvRxpx8lYohNSKO/mTKsQ3/yfKCZjpFTFkOz72w2zjx46tgrC1+X//MVZWgrs+MG+3fwxc2MRERGREinQ251ezaoC9tpSJVLD+6B+T7ClwfxnID3V7IhERKSMyvPqe2+99RbHjh3jzjvvxNnZfrjNZqN///55qinl6upKy5YtWbx4Mffdd1/W/sWLF3PvvffmeExiYmLWNTNlrppnZNRCSkxMxMnJ6ao2hmFktblScnIye/bs4ZZbbsl1/I62ZJ99lFSHm/xwdc5zLvGSzOl7Fa+YguldFZr2hS3f2r81e2RO/q9RXB34ExLPQvlAqHWn2dGIiEgZdv/991/3/fPnzzsmEMmXIR1rMnfTCf7afZpDZxKo5V/e7JDy7u4P4egKe2mDVePh1pfNjkhERMqgPGc3XF1dmTNnDvv27WPmzJnMmzePQ4cOMW3aNFxdXfN0rhdeeIEpU6Ywbdo09uzZw8iRIwkPD8+ajjd69Gj69++f1b5Hjx7MmzePyZMnc/jwYVatWsXw4cNp3bo1QUFBWW0mT57M7NmzOXLkCIsXL2bMmDH07NkzK4H10ksvsWzZMo4cOcK6det44IEHiIuLY8CAAXn9OBxmSWY9qXoFXCUwc/relSOlANqPACyw/w84vatg1ymOMqfuNe0D1jznY0VERArN5XUqc3oFBwdn6wNJ8XJTZS861Q/AMGDKihJaW8orALq+Z99e9j5E7TE3HhERKZPy/S/z2rVrU7t27QJdvE+fPpw9e5Y33niDiIgIGjVqxKJFiwgODgYgIiIiW/H0gQMHEh8fz8SJE3nxxRfx8fHhjjvu4L333stq8+qrr2KxWHj11Vc5efIk/v7+9OjRg7fffjurzYkTJ3j44YeJjo7G39+ftm3bsnbt2qzrFjdn4pPZdiIWgNsKUuQcLk3f8726WD1+N0HDXrDrZ1j5CfSeUrBrFSfxp+HAX/btZpq6JyIi5po+fbrZIUgBDe1Yk7/3nOanTScZeVcdKnu5mx1S3jV5CHbNs38h+cszMGixvrgTERGHshjXmtN2DQ888ACtWrXKtpodwAcffMD69euZO3duoQZYXMXFxeHt7U1sbGyR15f6cdMJXpq7jYZBFVg4vABTDJPjYVw1+/aocHD3vrpNxDb44lawOMFzm3NOXpVEqz6FxWOg2s0w+G+zoxERkRLMkX2Akq60f1a9J69m07FzPHNbLf7VtZ7Z4eRP3Cn4rA0kx8Fdb0L74WZHJCIipUBu+wB5nr63bNkyunfvftX+rl27snz58ryeTnIhs55UgVbdg0tT98r55pyQAqjSFG7qBIYNVn9asOsVF4YBWzOm7jV71NxYREREpNQYcmtNAL5de4yE5DSTo8mnCkHQJWNGwZK3IfqgufGIiEiZkuekVEJCQo61o1xcXIiLiyuUoOSS1HQby/efAYp46t7lOrxg/7nlO4iPvH7bkuDkZjizF5zLQaPrF5YVERERya1O9QOo6e9JfFIas9aF3/iA4qp5P6h5O6QlwfxhcI0Vq0VERApbnpNSjRo1Ys6cq1dmmz17Ng0aNCiUoOSSTcfOEZ+URkUPF5pV9ynYya5X5PxywWFQvQ2kp8Cazwp2zeJg63f2n/V7XHuEmIiIiEgeOTlZskZLTV15hJS0EprMsVig56fgWh6Or4UNX5kdkYiIlBF5TkqNGTOGN998kwEDBvD111/z9ddf079/f9566y3GjBlTFDGWaZlT9zrW8cfqZCnYyc5ljJSqeIORUhbLpdFSG6fBxXMFu66ZUi/Cjp/s2801dU9EREQKV6/mVans5UZkXBILtp0yO5z886kBncbat/8ee2mEvYiISBHKc1KqZ8+e/PLLLxw8eJBnnnmGF198kZMnT/LPP/8QEhJSBCGWbUv22pNStxe0nhTkfvoeQJ0uULkhpCTA+hK8Ct/ehZAcC97VIeRWs6MRERGRUsbN2coTHex9qy+XH8Jmy9MaQsVLq0EQ3AFSE2HBc/a6nCIiIkUoz0kpgO7du7Nq1SouXLjAwYMHuf/++xkxYgQtW7Ys7PjKtBPnEtl/OgEni32kVIHldvoeZIyWGmnfXjcZUhILfn0zbMmYutf0YXDK1x93ERERket6pE0Nyrs5s/90Akv3R5kdTv45Odmn8TmXg6MrYNMMsyMSEZFSLt//Sv/nn3947LHHCAoKYuLEidx9991s3LixMGMr85bssxc4b1GjIj4eVxeXz5P0NIg9bt++0fS9TA3vsyewEs/C5m8Kdn0zxJ6Aw0vt280eMTUUERERKb0quLvwaJsaAHy+7LDJ0RRQpVpwZ0ZJjr/GwPnj5sYjIiKlWp6SUidOnOCtt96iZs2aPPzww1SsWJHU1FR++ukn3nrrLZo3b15UcZZJSwtz6l7cCbClgdUNvKrk7hirM7R/3r69egKkpRQ8DkfaNgsw7MPQczNlUURERCSfHm8fiovVwvojMWwOL8H1OAHaDIVqrSElHn4boWl8IiJSZHKdlLr77rtp0KABu3fvZsKECZw6dYoJEyYUZWxlWlJqOqsORQNwe91CrCdVMThv09iaPgLlA+xJrR1zCx6HoxgGbP3evq0C5yIiIlLEAr3d6dWsKgBfLDtkcjQF5GSFez+zf5l58O+ML/pEREQKX66zE3/99ReDBw/mv//9L927d8dqtRZlXGXe2sNnSUq1EVjBnfpVvAp+wtyuvHclF3doN8y+vfITsKUXPBZHCF8DMYftSxs3uNfsaERERKQMeOrWmgD8tfs0h84kmBxNAfnXgdtG2bf/GAVxEebGIyIipVKuk1IrVqwgPj6eVq1a0aZNGyZOnMiZM2eKMrYy7dKqe/5YLJaCnzAvRc6v1OoJcPeGswdg728Fj8URtsy0/2zYC1w9TQ1FREREyobaAV50ql8Zw4ApK0p4bSmAsOFQpRkkxcLCFzSNT0RECl2uk1Lt2rXjq6++IiIigiFDhjB79myqVq2KzWZj8eLFxMfHF2WcZYphGFlFzgtl6h5cmr6Xn9pKbl7Q+in79oqPi3+HJDkBdv1s3272mLmxiIiISJkypGMtAH7adJKo+CSToykgqzP0mgROLrBvEez8yeyIRESklMnz6nseHh488cQTrFy5kh07dvDiiy/y7rvvUrlyZXr27FkUMZY5h85cIDwmEVerE+1v8iuck2aNlMpnwe82Q+3LA0dshcNLCiemorJ7PqReAN9aUKOt2dGIiIhIGXJziC8tgyuSkm5jxqqjZodTcAEN4daX7NuLXoYEzZQQEZHCk+ek1OXq1q3L+++/z4kTJ5g1SwUQC8vSffape21q+uLp5lzwExpGwabvAXj6QcuB9u0VHxc8pqK0NWPqXrNHoDCmPoqIiIjkwZCM2lLfrj1GQnKaydEUgg4vQEAjuBgDv79sdjQiIlKKFCgplclqtdKrVy8WLFhQGKcr89YfiQHgtsKaupcYA8lx9u2Kwfk/T9iz9uHbR1fAiY2FE1thizkMx1aBxQmaPmx2NCIiIlIGdaofQE1/T+KT0pi9PtzscArO2RXunQgWq71Ewm71+UVEpHAUSlJKCtfkx1oy75kwejStUjgnzBwl5VUFXMrl/zze1aBJH/t2cR0ttfV7+8+at4N3VXNjERERkTLJycmSNVpqyoojpKTZTI6oEAQ1h/bP27cXvmj/0lNERKSAlJQqhqxOFlrUqEhlL/fCOeG5jCLn+a0ndbkOIwAL7FsIp3cX/HyFyZYOWzOmkTZ7xNxYREREpEzr1bwq/l5uRMYlsWDbKbPDKRwd/w1+deFCFPwx2uxoRESkFFBSqiw4V4CV967kVxvq97Bvrxpf8PMVpiPLIe4EuHtDvXvMjkZERETKMDdnK0+0t/e9vlx+CJutmK9enBsu7nDvZ4AFts+G/X+aHZGIiJRwSkqVBTFH7T/zW+T8Sre8YP+548dLUwOLg8wC540esHeaREREREz0SJsalHdzZv/pBJbujzI7nMJR/WZoN8y+/evzcPG8qeGIiEjJpqRUWVCY0/fAXlOg5u1gpMPqCYVzzoK6eB72/Grfbv6oqaGIiIiIAHiXc+HRNjUA+HzZYZOjKUS3vwK+NSE+AhaPMTsaEREpwZSUKgsyRzMV1kgpuDRaast3kFAMvvnbNQ/SksC/PgS1MDsaEREREQAebx+Ki9XC+iMxbA4/Z3Y4hcPVA3pOtG9v/gYO/WNuPCIiUmIpKVXapSZBXEZxzcKoKZUp5BaodrM9EbR2UuGdN7+2ZEzda/4oWCzmxiIiIiKSIdDbnV7N7CsCf1maRkuFtIebn7RvL3gekuPNjUdEREokJaVKu/PHAANcvcCjUuGd12KBDhmjpTZMNbeewJl9cHIjWKzQpI95cYiIiIjk4KlbawLw5+5IDp9JMDmaQtRpLHjXgNhw+L4vRO0xOyIRESlhlJQq7S6fulfYI4jqdLVPl0uOgw1TCvfcebHlu4x4ukD5yubFISIiIpKD2gFedKpfGcOAr1aUotFSbuWh12dgdYVjK2FyGPz2AlyINjsyEREpIZSUKu1iMoqc+4YU/rmdnKDDSPv22smQklj417iR9DTYPse+3UwFzkVERKR4GtKxFgA/bTpJVHySydEUotBb4Zm1UO8eMGywcSp82gJWfQppyWZHJyIixZySUqVdURQ5v1yj3uBTAxKjL41YcqSDf0PCafDws4+UEhERESmGWgVXpEUNH1LSbcxYddTscApXpVrQdyYM+A0Cm0ByrH1Vvs/a2FdHNgyzIxQRkWJKSanS7lzGSKmKhVjk/HJWZwgbbt9e/SmkpxbNda5la0YirEkfsLo49toiIiIiuWSxWBiaMVrq27XHSEhOMzmiIhB6Czy1FO79DMoH2Puhcx6Dr3tAxDazoxMRkWJISanSLmv6XhElpQCaPwae/hB7HHb8WHTXudKFs7DvD/t2s0ccd10RERGRfOhUP4Ca/p7EJ6Uxe3242eEUDServW/43Ca45SVwdoejK+CLjjB/GMRHmh2hiIgUI0pKlWY2W8bqexTd9D0Al3LQ9hn79spP7Nd1hB1zwZYKVZpCYCPHXFNEREQkn5ycLAzJWIlv6sojpKQ5qM9kBjcvuHMMPLvBXu4Bw17q4dMWsPxDSL1odoQiIlIMKClVmiVEQloSWKzgXb1or3XzIHDzhuh9sG9R0V0n9SIcXgp/j4WVH9v3NXus6K4nIiIiUoh6Na+Kv5cbEbFJ/LrtlNnhFD2fGvDANBi0GKq2gtQL8M+bMPFm+wh71ZsSESnTlJQqzTKLnPtUL/p6S+7e0HqwfXvlx4XXwbClw8lNsOIj+LonvBsM39xrH5GVcNp+3cYPFM61RERERIqYm7OVJ9rbyyp8sfwQNlsZScpUb21PTN3/FVSoai/78NMgmNYFTmwyOzoRETGJs9kBSBHKrCdVlFP3LtfmaVjzmT2JdGQZ1Lwt7+cwDDh70D4a6vBSew2CpNjsbbyCoGZH+/lv6gQevgWPXURERMRBHmlTg8+WHGT/6QSW7o/ijnoBZofkGE5O0OQhqHcPrJlo/5Lx+DqYcod90Zo7XwfvqmZHKSIiDqSkVGlW1CvvXam8P7ToD+u/hBUf5z4pFR8Jh5fZE1mHl0Lcyezvu3nbV3OpeRuEdgS/2mCxFHLwIiIiIo7hXc6FR9rU4Mvlh/l82eGyk5TK5OoBHf9lL4j+vzdh2/ewfQ7sXgDth0P758HV0+woRUTEAZSUKs0yp+85aqQUQNhzsHGaPcF0chNUbXl1m6Q4OLbq0mioM3uzv291hept7EmomrfbC5lb9UdVRERESo8n2ocyfdUR1h+JYXP4OVrUqGh2SI5XIQjumwytn4Q//wPha2DZe7D5G/uoqSZ97KOrRESk1NK/9EuzzOl7vg4aKQX2YpaNH4Rts+yjpfrOhLRkOLEhIwmVkawy0i87yGJPPGVOyave1v4NmoiIiEgpFejtTq9mVZm76QTDZ21h+sCbqR3gZXZY5qjaAh7/HXbPh8Vj4Hw4/DIU1n8BXcZBcDuzIxQRkSKipFRpljVSyoFJKYD2I+xJqb2/2YuSH18PqYnZ2/jWvDQdL/RW1YUSERGRMueFznXYcDSGo2cTuX/yaiY/2pIOtf3MDsscFgs07AV1usK6ybD8Izi1BaZ3hQa94K7/Onb0v4iIOITGw5ZWyfGQGG3fdvT/wCvXsxewBPvoqNRE8PSHRg9Az4kwYgcM3wL3fGLvfCghJSIiImVQFe9yzHumPTeHVCQ+KY2B09cze3242WGZy8UdOoyE4ZuhxQCwOMHuX2BSO63SJyJSCmmkVGmVOXXPoxK4V3D89bu9Z09E+dW2j4iq3EDFyUVERESu4OvpyneD2/DvH7fzy9ZTjJq3g6NnE/lXl7o4OZXhvlP5ytDzU2j9FCx8wb5K39wBMGS5vtAUESlFNFKqtDKjyPnlvKtBj/HQbhgENFRCSkREROQa3JytfNKnGc/fWRuAz5cd4tlZm0lKTb/BkWVAYCN4dK69HEXscZj3FNhsZkclIiKFREmp0upcxkgpR9eTEhEREZE8s1gsjLyrDh8/1BQXq4VFOyLp8+VazsQnmx2a+dy94aFvwNkdDi6GlR+ZHZGIiBQSJaVKKzNW3hMRERGRArm/RTW+G9QGHw8Xth0/T6/PVrH/dLzZYZmvShO4+0P79pJ37HVLRUSkxFNSqrQye/qeiIiIiORLm5qV+PmZ9oRU8uDk+Yv0nrSaFQfOmB2W+Vr0g+aPgWGDHwdB3CmzIxIRkQJSUqq00vQ9ERERkRIr1M+Tn59pT+sQX+KT0xg4fQOzyvrKfGAfLRXQyL7K9NzHIT3V7IhERKQAlJQqjdLT4Pxx+7ZGSomIiIiUSBU9Xfl2cGvua16VdJvB6Hk7GPf7Hmw2w+zQzONSzl5fyq0CHF8Lf481OyIRESkAJaVKo9jjYKSD1Q28qpgdjYiIiIjkk5uzlY8fasqITvaV+b5Ydphh32/mYkoZXpmvUi3oNcm+vWYi7F5gbjwiIpJvSkqVRllT90LASY9YREREpCSzWCyM6FSHT/o0xdXqxO87I+n71Vqi4pPMDs089XtAu2ft2/OHwdlD5sYjIiL5ooxFaaQi5yIiIiKlzn3Nq/Hd4Esr89332eqyvTJfp7FQox0kx8EP/SEl0eyIREQkj0xPSk2aNInQ0FDc3d1p2bIlK1asuG77mTNn0rRpUzw8PKhSpQqPP/44Z8+ezdZm/Pjx1K1bl3LlylG9enVGjhxJUlL2b5Lyet0SJSZjpJSvipyLiIiIlCatQ335+Zn2hPp5amU+qws8MB08/eH0Tlj0stkRiYhIHpmalJozZw4jRozglVdeYcuWLdxyyy1069aN8PCcVxZZuXIl/fv3Z9CgQezatYu5c+eyYcMGBg8enNVm5syZjBo1itdff509e/YwdepU5syZw+jRo/N93RJHI6VERERESq1QP0/mPR1G69BLK/N9v66U9GPzqkIV6D0FLE6w9TvY/K3ZEYmISB6YmpT6+OOPGTRoEIMHD6Z+/fqMHz+e6tWrM3ny5Bzbr127lpCQEIYPH05oaCgdOnRgyJAhbNy4MavNmjVraN++PY888gghISF07tyZhx9+OFubvF63xMmqKaWRUiIiIiKlUUVPV74ddGllvv/8vIN3FpXRlflq3ga3/8e+vegliNhuajgiIpJ7piWlUlJS2LRpE507d862v3PnzqxevTrHY8LCwjhx4gSLFi3CMAxOnz7Njz/+SPfu3bPadOjQgU2bNrF+/XoADh8+zKJFi7La5Oe6JYphQMxR+7am74mIiIiUWpkr843sVAeAL5cf5pmZZXRlvg4vQu3OkJZkry918bzZEYmISC6YlpSKjo4mPT2dgICAbPsDAgKIjIzM8ZiwsDBmzpxJnz59cHV1JTAwEB8fHyZMmJDVpm/fvrz55pt06NABFxcXatWqxe23386oUaPyfV2A5ORk4uLisr2KpcQYSMkoeOlTw9xYRERERKRIWSwWnu9Um//r2wxXqxN/7Iqk75dryt7KfE5OcN8X4F3DPmtg/jD7l7UiIlKsmV7o3GKxZPvdMIyr9mXavXs3w4cP57XXXmPTpk388ccfHDlyhKFDh2a1Wbp0KW+//TaTJk1i8+bNzJs3j99++40333wz39cFGDduHN7e3lmv6tWr5/VWHSNz6p5XELiUMzcWEREREXGIe5tVZeaTbajo4cK2E7Hc99lq9kWWsZX5PHzhoRlgdYW9v8HqCTc8REREzGVaUsrPzw+r1XrV6KSoqKirRjFlGjduHO3bt+fll1+mSZMmdOnShUmTJjFt2jQiIiIAGDNmDP369WPw4ME0btyY++67j3feeYdx48Zhs9nydV2A0aNHExsbm/U6fvx4AT+BIpJZ5FxT90RERETKlJtDsq/M98Dk1SzfX8ZW5qvaErqOs2//PRaOlYLyHCIipZhpSSlXV1datmzJ4sWLs+1fvHgxYWFhOR6TmJiIk1P2kK1WK2Af6XS9NoZhYBhGvq4L4ObmRoUKFbK9iqWYzCLnIaaGISIiIiKOF3LFynyPz9jAzHXHzA7LsVoNgsYPgpEOcx+HhCizIxIRkWswdfreCy+8wJQpU5g2bRp79uxh5MiRhIeHZ03HGz16NP37989q36NHD+bNm8fkyZM5fPgwq1atYvjw4bRu3ZqgoKCsNpMnT2b27NkcOXKExYsXM2bMGHr27JmVwLrRdUs0rbwnIiIiUqZlrsx3f8bKfK/8vJNXf9lBSprN7NAcw2KBe8aDX11IiIQfnwBbGSz+LiJSApialOrTpw/jx4/njTfeoFmzZixfvpxFixYRHBwMQEREBOHh4VntBw4cyMcff8zEiRNp1KgRDz74IHXr1mXevHlZbV599VVefPFFXn31VRo0aMCgQYPo0qULX3zxRa6vW6JlTt/TSCkREZEyZdKkSYSGhuLu7k7Lli1ZsWLFNdvOmzePu+66C39/fypUqEC7du34888/r2o3fvx46tatS7ly5ahevTojR44kKamMFdAuodycrXz0UFNe6lwHiwW+WxvOw1+tJSqujDw/t/LQ51tw8YSjK2DJ22ZHJCIiObAYhpalyI+4uDi8vb2JjY0tXlP5PqoP8adg8P+gWiuzoxERESl1imMfYM6cOfTr149JkybRvn17vvjiC6ZMmcLu3bupUePq1XhHjBhBUFAQt99+Oz4+PkyfPp0PP/yQdevW0bx5cwBmzpzJoEGDmDZtGmFhYezfv5+BAwfSp08fPvnkk1zFVRw/q7Lon72neX72VuKT0qjs5cbkx1rSMrii2WE5xo4f4adB9u1HfoA6XcyNR0SkjMhtH0BJqXwqlp2s1IvwdqB9++XD4FnJ3HhERERKoeLYB2jTpg0tWrRg8uTJWfvq169Pr169GDduXK7O0bBhQ/r06cNrr70GwLPPPsuePXv43//+l9XmxRdfZP369dcdhXW54vhZlVVHoi/w1DcbORCVgIvVwtieDXm0TSmYJZAbC1+CDV+Buw8MWQ4Vy8h9i4iYKLd9AFOn70khO58x1dHVy74kroiIiJR6KSkpbNq0ic6dO2fb37lzZ1avzt3KYzabjfj4eHx9L/UfOnTowKZNm1i/fj0Ahw8fZtGiRXTv3r3wgheHCfXz5Odh7enWKJDUdHudqdHztpOcVgZqLXV5274qX9J5mDsA0pLNjkhERDIoKVWaZK685xtiL/AoIiIipV50dDTp6ekEBARk2x8QEEBkZGSuzvHRRx9x4cIFHnrooax9ffv25c0336RDhw64uLhQq1Ytbr/9dkaNGnXN8yQnJxMXF5ftJcVHeTdnJj3agn91rYvFArPWH6fPF2uJjC3ldaac3eDBGVCuIpzaAn+MNjsiERHJoKRUaaIi5yIiImWW5YovpAzDuGpfTmbNmsXYsWOZM2cOlStXztq/dOlS3n77bSZNmsTmzZuZN28ev/32G2+++eY1zzVu3Di8vb2zXtWrV8//DUmRsFgsPHPbTcx4vDXe5VzYevw890xYyfojMWaHVrR8asD9XwEW2DgVtv9gdkQiIoKSUqXLuYyRUhVDzY1DREREHMbPzw+r1XrVqKioqKirRk9dac6cOQwaNIgffviBTp06ZXtvzJgx9OvXj8GDB9O4cWPuu+8+3nnnHcaNG4fNZsvxfKNHjyY2Njbrdfz48YLdnBSZjnX8WfBse+oFehGdkMwjX63lmzVHKdXlZmvfBbe+bN/+9XmI2mNuPCIioqRUqZI1fU9JKRERkbLC1dWVli1bsnjx4mz7Fy9eTFhY2DWPmzVrFgMHDuT777/PsU5UYmIiTk7Zu4pWqxXDMK6ZuHBzc6NChQrZXlJ8BVfyZN4zYfRoGkSazeC1+bt4+cftJKWW4jpTt42C0I6Qmghz+kFyvNkRiYiUaUpKlSaaviciIlImvfDCC0yZMoVp06axZ88eRo4cSXh4OEOHDgXsI5j69++f1X7WrFn079+fjz76iLZt2xIZGUlkZCSxsbFZbXr06MHkyZOZPXs2R44cYfHixYwZM4aePXtitVodfo9SNDxcnfm0bzNeubs+Thb4cdMJHvpiDSfPXzQ7tKLhZIXeU8ErCM4esI+YKs2jw0REijlnswOQQmKzXZaU0kgpERGRsqRPnz6cPXuWN954g4iICBo1asSiRYsIDg4GICIigvDw8Kz2X3zxBWlpaQwbNoxhw4Zl7R8wYAAzZswA4NVXX8VisfDqq69y8uRJ/P396dGjB2+//bZD702KnsVi4clba1K/SgWem7WZ7Sdi6TFhJZ890oJ2tSqZHV7hK+8PD06HGd1h509QvS20ecrsqEREyiSLUaonjheduLg4vL29iY2NLR5D0+NOwcf1wWKFV0+D1cXsiEREREqlYtcHKMb0WZU8x2MSGfLtJnZHxGF1svDK3fV5vH1IrormlzhrPoM//wNOLvDEH1CtldkRiYiUGrntA2j6XmmRWU/Kp7oSUiIiIiKSL9V9Pfjp6TB6NQsi3Wbwxm+7GTlnKxdTSmGdqbbPQP2eYEuFHwbA2UNwIRounoeUC5Ceqql9IiJFTNP3SgutvCciIiIihaCcq5VP+jSjSTUf3l60h1+2nmL/6QS+6NeS6r4eZodXeCwWuPczbKd34RRzCCa0yLmdk7N9NJXVFayZ2xmvnLadnDPaXrHt7Abu3uDuY/9ZLuOne8XLtr31BbOIlClKSpUWKnIuIiIiIoXEYrHwRIdQ6lepwLPfb2Z3RBw9J65kwsMt6FDbz+zwCsWeiDhmrQ9nZ8zTvG/7mBCn0ziTw4gwW5r9leag4u8unhlJKp/Lklc+VySyfLJvZ77n6umYGEVEComSUqVF5vQ9X42UEhEREZHC0a5WJX59rgNDv9vE9hOx9J+2jlHd6vHkLTVLZJ2pxJQ0ftsewffrwtl6/HzG3iA68SEAw++4iRfuCLVP6UvPeGVu29IgPSX7vhy3M9pduT81CZJiIem8/efF89l/T46zh5N6wf6KO5n3G2z8ENz3uX2VQRGREkBJqdJCI6VEREREpAgE+ZTjhyHtePWXnfy46QTvLNrL9hOxvP9AEzxcS8Y/J3afso+K+mXLSeKT0wBwdrLQuWEAj7QOJvZiKsO+38zkZYe4p2kQdQK8HB9kepo9MZV0PnvC6srk1cXzOSe2bGmw4wfwrgqdxjo+fhGRfCgZ/xeRG1NNKREREREpIu4uVj54oAlNq3nz319389v2CA5G2etMBVcqnlPGLiSn8dv2U3y//jjbskZFQQ1fDx5uXYMHWlbD38sNAMMw6FQ/gL/3nGb0vB3MHdIOJycHjwSzOoOHr/2VV4YBO36EeYNh5ScQ2Bga9S78GEVECpmSUqVBUhwknrVva6SUiIiIiBQBi8VCv3Yh1KtSgae/28zeyHh6TlzFpw83p2Mdf7PDy7LrVGzGqKhTJGSMinKxWujcMJBHWtegXc1KVyWcLBYLb/ZqyJpD0Ww6do6Z68Pp1zbYjPDzx2KBJg9C5HZY/Sn8Mgz86tiTUyIixZiSUqVB5tQ9j0rgXsHUUERERESkdLs5xJffMupMbT1+noHT19PhJj/qBXpRN7ACdQO8uKlyecq5Oq6u0YXkNH7ddopZ68PZdiI2a39wpUujovzKu133HFW8y/GvrvV4fcEu3vt9L3fVDyDQ272oQy9cncbC6Z1w6B+Y/Qg8uRQ8K5kdlYjINSkpVRpo6p6IiIiIOFCgtztzhrRl7IJdzFp/nBUHollxIDrrfYsFQip5UiegfFaiqm5geUIqeeJsdSq0OHaetI+Kmr/16lFRj7auQdscRkVdz2Ntg/ll60m2hJ/ntfk7+bJ/q0KL1SGcrNB7Knx1h/3fCD8OhMd+tk8NFBEphvRfp9JAK++JiIiIiIO5OVsZd38THm0TzPYTsew/Hc++yHj2nY4n5kIKR6IvcCT6An/uOp11jKvViVqVy1M3M1kVaP8Z5O2e69X8Ei4bFbX9slFRIRmjonrnYlTUtVidLLx7fxO6f7qCv3af5o+dEXRtVCVf5zKNhy/0/R6mdIIjy2HxGOg6zuyoRERypKRUaaCV90RERETEJI2qetOoqnfW74ZhEJ2QkpWg2h8Zz97T8Rw4HU9iSjp7IuLYExEHnMo6pryb82WjqspTJ9CLeoEV8PV0zWqz82Qs368PZ/6Wk1xISQfso6K6ZNSKyuuoqGupG+jF0I61mLjkIK/N30XYTX5UcHcp8HkdKqAB3Pc5/NAP1k6CwCbQ7GGzoxIRuYqSUqWBpu+JiIiISDFhsVjw93LD38uNDrX9svbbbAYnzl20J6pOx7M30p6wOnQmgYTkNDaHn2dz+Pls5/Ir70bdwPLEXkxl58m4rP2hfp483Lo6vVtUo1I+R0Vdz7N33MSiHREcjr7Ae7/v5e37SmDB8AY94dZ/wfL34dfnwb8uVG1hdlQiItkoKVUaaKSUiIiIiBRzTk4WalTyoEYlD+5qEJC1PyXNxpHoC+w7Hc++yDj2RSaw/3Q84TGJRCckE30wGbBP/evSKJCHW1enXc1KuZ7ulx/uLlbeub8xfb9cy8x14fRqXpWbQ3yL7HpF5rbR9hX59v8Bcx6Dp5ZC+cpmRyUikkVJqZIuPRXOH7dvq6aUiIiIiJQwrs5O1A30om6gFzQNytp/ITmNA1EJ7I+MJ9Vmo2vDwCIZFXUtbWtWou/N1Zm94TijftrOoudvwc3ZcSsKFgonJ7j/S/jqTjh7AH7oD/0XgLPrjY8VEXGAwlv6QswRexyMdHB2h/KBZkcjIiIiIlIoPN2caVbdh4durs6jbYIdmpDKNLpbffzKu3HozAUmLTnk8OsXCndveHgWuFWA8DXwxyizIxIRyaKkVEmXOXXPJ9j+TYiIiIiIiBQKbw8XxvZsAMCkpQc5GBVvckT55Fcbek8BLLBxKmyaYXZEIiKAklIlX0xGkXNN3RMRERERKXTdG1fhznqVSU03GPXTDmw2w+yQ8qdOF7jjFfv2wpcgfJ258YiIoKRUyaci5yIiIiIiRcZisfBmr0Z4ulrZeOwc368PNzuk/LvlJajfE2yp8EM/iDtldkQiUsYpKVXSncsYKVVRI6VERERERIpCkE85XupSF4D3ft9LZGySyRHlk8UCvSZD5QaQcNq+Il9qCb0XESkVlJQq6WKO2n9q+p6IiIiISJHp3y6EZtV9iE9OY+yCXWaHk39u5aHvTHD3gZObYNGLYJTQKYkiUuIpKVWSGYam74mIiIiIOIDVycK7vRvj7GThj12R/Lkr0uyQ8s+3JjwwDSxOsOU7WP+V2RGJSBmlpFRJlngWUjJWAPEJNjcWEREREZFSrl5gBYZ0rAnAa/N3EpeUanJEBXDTndDpv/btP0bBkRXmxiMiZZKSUiVZ5igpryBwcTc1FBERERGRsuC5O2oTUsmD03HJvP/HXrPDKZiw56DRA2Ckw9wBcP642RGJSBmjpFRJFpNR5Fz1pEREREREHMLdxco79zcG4Lu14Ww8GmNyRAVgsUDPCRDYxD4LY/YjkJJodlQiUoYoKVWSaeU9ERERERGHC6vlx0OtqgEwet4OktPSTY6oAFw97IXPPSpB5Hb4dbgKn4uIwygpVZKpyLmIiIiIiCn+c3d9/Mq7ciAqgc+XHjY7nILxqQEPfg0WK+yYC2smmh2RiJQRSkqVZJq+JyIiIiJiCh8PV17r0RCAz5Yc5GBUvMkRFVDoLdB1nH178Wtw6B9z4xGRMkFJqZIsa/peiKlhiIiIiIiURT2aVOH2uv6kpNv4z7yd2GzmTXtLTkvHKOi0u9ZPQbPHwLDB3MchpoSPABORYk9JqZIq9SLER9i3VVNKRERERMThLBYLb93XGA9XK+uPxjB7g+NXr4uKS+KNX3fTZOxf3D95NafjkvJ/MosFun8EVVtC0nmY/SgkJxRarCIiV1JSqqQ6d8z+060CePiaG4uIiIiISBlV1accL3WuC8C43/cQVZCkUB5kJqNueX8J01YdITnNxpbw89w7cRU7T8bm/8Qu7tDnO/CsDFG74ZenVfhcRIqMklIlVVaR82D7NxoiIiIiImKKAWEhNK3mTXxSGq8v2FWk14qKT+LN37Ino1rU8OH/+jbjpsrliYxL4oHPV/P7joj8X6RCkD0x5eQCexbAio8K7wZERC6jpFRJlVVPSlP3RERERETMZHWyMO7+JlidLPy+M5K/dkUW+jWi4pN467fd3Pr+EqauvJSM+nZQa356Oox7m1Vl3jNh3FrHn6RUG0/P3MzEfw7kv85UjTbQ/UP79j9vwf4/C+9mMqUkQuwJiNwBh5fB7vn2bREpM5zNDkDySSvviYiIiIgUGw2CKvDUrTWZvPQQr83fRbtalfBydynwec/EJ/PFskN8t+4YSak2AJrX8GFkpzrcUtsPy2WzJiq4uzBtQCveWriHGauP8uFf+zkQlcB7vZvg7mLN+8VbDoSIbbBxGvw0GJ78B/xqX90uLRkunrO/EmPgYsxl2+cu+/1c9t/TrjHVMaAxNHsEmjwEnn55j1tESgwlpUqqrOl7IWZGISIiIiIiGZ6/szaLdkRw7GwiH/y5jzfubZTvc52JT+bL5Yf4du2Nk1GXc7Y6MbZnQ2oHlOf1+buYv/UUx84m8mX/llT2cs97IF3fg9O74fha+K43BDW/LLF03p54Sr2Q7/vEyRnK+drr5LqWh8jtcHoH/DkaFo+BOl3tCarancFa8CRfiWMYcCEazuzNeO2zF6Fv9QQEh5kdnUiBWYwCrxtaNsXFxeHt7U1sbCwVKlRwfAATb4bo/dDvF6h1u+OvLyIiUkaZ3gcoQfRZSVm06mA0j05Zh8UCPw4No2VwxTwdn1Myqll1H0beVYdbr5OMysnqg9E8PXMzsRdTCfJ2Z8qAm2kQlI+/i/Gn4cvbIP7UtdtYnMDdx55cKlfRnmgqVzHjd18od433XMtnr5GbGAM7f4KtM+HUlkv7PfygSR97giow/8m+Yssw7KurZyaesn7usycAr2Rxgltego7/BqvGmkjxk9s+gJJS+WRqJ8tmg7cDIT0Zhm/VFD4REREHUqIl9/RZSVn10txt/LjpBHUCyvPbc7fg6nzjUr7RCcl8ufww36w5WuBk1OUOn0lg8NcbORx9AQ9XK+P7NKNzw8C8nyj6IOz6Gdy8sieXPCrat928wamQSxaf3g3bvodtc+BC1KX9gU2g+WPQ6AHwrFS41yxqNhvEHr8i8bTXPuAgOe4aB1nsM2T869pfcRGw4wf7W9XbwP1f2RfAEilGlJQqYqZ2smJPwicN7ENdXzmtzLiIiIgDKdGSe/qspKw6dyGFTh8v4+yFFF68qw7P3ZlDHaYMmcmob9cc42JqOmBPRo3oVJuOdfzznYy6XGxiKs98v4lVB89iscC/utRjaMeahXJuh0hPg4N/20dP7fsdbKn2/U4uULcrNHsUbupUvKb32dLtJVcun3Z3Zi9EH4DUxJyPsVihUi3wqwP+9TJede11vFzKZW+740f4baQ9keXmDT0+gUa9i/y2RHJLSakiZmon6+hKmNHdvvLe81sde20REZEyTomW3NNnJWXZ/K0neX72VlytTvw+4hZq+ZfP9n50QjJfLT/MN5clo5pW92FkISajLpeabuONX3fz7dpjAPRuUY137m+Em3M+CqCbKTEGdsy1J6gitl3a71nZXhi92aMQ0MCx8Zw9CGcP2X/GHLInnqIP2Ge25MTqCpVqg3+dS4kn/3rgWwucXXN/7XNH4acn4cR6++/NHoNu74Fb+eseJuIISkoVMVM7WVu+g/nDoObt0P8Xx15bRESkjFOiJff0WUlZZhgGA6dvYNn+M7QO9WX2k21xcrJcMxk1olNtbiuCZNSVvllzlP/+upt0m0Gr4Ip83q8lfuXdivSaRSZyJ2z9HrbPgcToS/uDmtuTU41626caFlRy/GVJp8PZk1BJ5699nHM5e+LJr+6lxJN/PftUvMKa7ZKeBsveheUfAgZUugl6T4WgZoVzfpF8KjFJqUmTJvHBBx8QERFBw4YNGT9+PLfccss128+cOZP333+fAwcO4O3tTdeuXfnwww+pVMk+l/i2225j2bJlVx139913s3DhQgDGjh3Lf//732zvBwQEEBkZmeu4Te1k/e9NWPGhfcWFez5x7LVFRETKOCVack+flZR1x2MS6fzJci6mpjOqWz3OJabwzerLklHVvBlxVx2HJKMut3z/GYZ9v5n4pDSq+pRj6sBW1AsswX9H01PhwGL76Kn9f4Atzb7f6gp177YnqGrdcf1EUOpFiDlyabTT2YNw9rB9O+H09a9foap92p1vLXtSqNJNULkeeNco/Dpb13J0Jcx7CuJO2qc1dnod2g5z3PVFrlAiklJz5syhX79+TJo0ifbt2/PFF18wZcoUdu/eTY0aNa5qv3LlSjp27Mgnn3xCjx49OHnyJEOHDqV27dr8/PPPAMTExJCSkpJ1zNmzZ2natClTpkxh4MCBgD0p9eOPP/L3339ntbNarfj7++c6dlM7WT8Ogp0/wl1vQPvnHXttERGRMk6JltzTZyUCU1Yc5q2Fe7Lta1rNmxGd6nBbXccmoy53MCqBQV9v4NjZRDxdrXz6cHPurB9gSiyF6kI0bP/BnqA6vfPS/vKB0LQP1OthX80u25S7wxB7ArjOP409/S9LOtW0//StBb41wdWjyG8rVxJj4NfhsOdX+++17oBen4NXKXiuUuKUiKRUmzZtaNGiBZMnT87aV79+fXr16sW4ceOuav/hhx8yefJkDh06lLVvwoQJvP/++xw/fjzHa4wfP57XXnuNiIgIPD09AXtS6pdffmHr1q35jt3UTtZXd8DJTfDQt9Cgp2OvLSIiUsYp0ZJ7+qxEIN1m0HvyarYeP0+Tat6MNDkZdblzF1J4euYm1h6OwWKBV+6uz6AOocUitkIRsd2enNr+gz0RdSNu3vYRT5VqXUo6Zf7u7l308RYGw4BN0+GP/0DaRXsyrddkqH2X2ZFJGZPbPoBpy7alpKSwadMmRo0alW1/586dWb16dY7HhIWF8corr7Bo0SK6detGVFQUP/74I927d7/mdaZOnUrfvn2zElKZDhw4QFBQEG5ubrRp04Z33nmHmjVrXvM8ycnJJCdfKlQXF3et5TodIOaI/advqHkxiIiIiIjIDVmdLMwc3IajZy/QoEqFYpXwqejpyjdPtOH1BTuZtf44by3cw4HTCbzZqxGuzqVg2leVJvbXXW/CgT/t9afC10KFoCum22X89KgExej55IvFYi/zUiMMfnwConbBzAeg7TPQaSw4l9D6YVJqmZaUio6OJj09nYCA7EMJr1fbKSwsjJkzZ9KnTx+SkpJIS0ujZ8+eTJgwIcf269evZ+fOnUydOjXb/jZt2vDNN99Qp04dTp8+zVtvvUVYWBi7du3Kqk11pXHjxl1Vh8oUSbGXsvwVQ0wNRUREREREbszTzZmGQcVzpI2rsxPv3NeY2pW9eGvhbuZsPM6Rsxf4/LGW+HrmYSW44szZFer3sL/Kisr14Ml/4O/XYd3nsHYSHF0BvafZi6+LFBOmp7+v/KbAMIxrfnuwe/duhg8fzmuvvcamTZv4448/OHLkCEOHDs2x/dSpU2nUqBGtW7fOtr9bt2707t2bxo0b06lTp6wC6F9//fU14xw9ejSxsbFZr2tNFyxy547af3r4gZuXOTGIiIiIiEipYbFYeKJDKFMH3kx5N2fWH4mh12erOHA63uzQpCBc3KHbe/DwHPsosMgd8GVH2PS1fZqfSDFgWlLKz88Pq9V61aioqKioq0ZPZRo3bhzt27fn5ZdfpkmTJnTp0oVJkyYxbdo0IiIisrVNTExk9uzZDB48+IaxeHp60rhxYw4cOHDNNm5ublSoUCHbyxSZU/c0SkpERERERArR7XUrM++ZMKr7liM8JpH7J61m6b4os8OSgqrbFZ5eDTVvg9REezH0uQPg4jmzIxMxLynl6upKy5YtWbx4cbb9ixcvJiwsLMdjEhMTcbpiSUur1QrYR1hd7ocffiA5OZnHHnvshrEkJyezZ88eqlSpkpdbMEfmSCnVkxIRERERkUJWJ8CL+cM60DrEl/jkNJ6YsYHpq45c9e8tKWG8AuGxn6HTf8HJGXbPh8kd4NgasyOTMs7U6XsvvPACU6ZMYdq0aezZs4eRI0cSHh6eNR1v9OjR9O/fP6t9jx49mDdvHpMnT+bw4cOsWrWK4cOH07p1a4KCgrKde+rUqfTq1SvHGlEvvfQSy5Yt48iRI6xbt44HHniAuLg4BgwYULQ3XBjOZY6UUlJKREREREQKn6+nK98NbsODLathM+C/v+7mlV92kppuMzs0KQgnJ+gwAgb9Bb41Ie4EzLgbloyD9DSzo5MyyrRC5wB9+vTh7NmzvPHGG0RERNCoUSMWLVpEcHAwABEREYSHh2e1HzhwIPHx8UycOJEXX3wRHx8f7rjjDt57771s592/fz8rV67kr7/+yvG6J06c4OGHHyY6Ohp/f3/atm3L2rVrs65brGWOlNL0PRERERERKSKuzk68/0AT6gR48c7ve/h+XThHzlzgw4eaUtWnnNnhSUFUbQlDlsOif8G272HZu3B4KfT+CnxqmB2dlDEWQ+Mw8yUuLg5vb29iY2MdW19qfBM4fwwe/x2Cc57mKCIiIkXHtD5ACaTPSqR0+N+e0wyftYULKem4OTsxqEMoT99WCy93F7NDk4LaPhd+Gwkp8eDmDT3GQ6P7zY5KSoHc9gFMX31P8iA9FWJP2Lc1UkpERERERBzgzvoB/DysPW1r+pKcZmPS0kPc9sFSvl17jDRN6SvZmjwIQ1dA1VaQHAs/Pg7zh0HKBbMjkzJCI6XyyZRv/mIOw6fNwdkd/hNhnxMsIiIiDqXRP7mnz0qkdDEMg7/3RDHu9z0cPmNPWtTy9+Q/d9fnjnqVsVgsJkco+ZaeCkvfhRUfAQZYXcHdB8r5gLt3Ll4+Ga+M351dTb0dMV9u+wCm1pSSPIrJLHIeooSUiIiIiIg4lMVi4a4GAdxW159Z68MZ//cBDp25wKCvN9KuZiVe6V6fRlW9zQ5T8sPqAneOgZq3wc9D7UXQL0TZX/nhXO76SaxyFaFyAwhqDp5XL04mZYeSUiWJipyLiIiIiIjJXKxO9G8XQq/mVZm05BDTVh1hzeGz9Ji4kvuaV+WlznUJUjH0kin0Fnh+K8RHQFIsXDxv/5nbV3Ks/TxpFyHhIiRE3via3jWganN7giqoOVRpZh+hJWWCklIlybnMkVKh5sYhIiIiIiJlXgV3F0Z1q8ejbWrw4V/7mL/1FPM2n2Th9gievKUmQ2+rRXk3/ZOzxLG65H8VPls6JMdflqg6n3PyKiEKInfA2QMQG25/7Z5/6Ty+NS8lqYJaQJUm4OZVKLcnxYv+C1GSZE7f81VSSkREREREiofqvh78X9/mPNE+lLcX7mH90RgmLjnI7A3hjLyrDn1aVcfZqvIjZYKT1T7KKbcjnZJiIWIbnNpy6XXuqL2ecsxh2PlTRkML+NW5LFHVHAIbg6tH0dyHOIwKneeTKYU7J3eA0zvgkR+gThfHXFNERESyUfHu3NNnJVL2GIbBX7tP8+7vezkSbS+GXrtyef5zd31uq+uvYuhyY4kxELH1UpLq5BZ7jasrWazgXy/71L+ARuDs5vCQ5Wq57QMoKZVPDu9kGQaMqwYpCTBsA/jXKfprioiIyFWUaMk9fVYiZVdKmo3v1x3j//53gHOJqQB0uMmP/9xdnwZB+u+B5FFCFJzaetmIqs2QcPrqdk4uEJBRQL16W6jb1V5UvRAZhsG2E7GsOhhN98ZVCPHzLNTzlxZKShUxh3eyLkTDB7UAC7wSCS7uRX9NERERuYoSLbmnz0pEYi+m8tmSg8xYdZSUdBsWCzzQohovdq5LoLf+TSMFEBdxKUGVmaxKPJu9jZOLfUXBhr2gXvcCJagOn0ngl62nWLD1JEfPJgIQUMGNn59pr8L+OVBSqog5vJN1fANM7QQVqsILu4v+eiIiIpIjJVpyT5+ViGQ6HpPIe3/s5bftEQCUc7Hy5K01GXJrTTxVDF0Kg2FA7PGMKX+b4cBiiNp16X0n54wE1X1Q927w8L3hKaPikvh1ewTzt55k+4nYrP3lXKx4uTsTFZ9M3QAvfhjaDu9yLkVwUyWXklJFzOGdrO0/wLwnIbgDPL6w6K8nIiIiOVKiJff0WYnIlTaHn+PthXvYdOwcAP5ebrx4Vx0ebFUdq5PqTUkhO7Mfdv8Cu37JOUHVoJd9BNVlCaq4pFT+3BnJ/K2nWH0oGltGxsTqZOGW2n70alaVuxoEcP5iKvdPWsXpuGTa1azE10+0xtVZBf0zKSlVxBzeyVr2Pix5G5o9Br0+K/rriYiISI6UaMk9fVYikhPDMPhjZyTv/rGXYxnToOoGePGf7vXpWMff5Oik1Io+YE9O7f4FTu+8tN/JGVvIrez2vZMZMQ1ZsD+JlDRb1tstavjQq3lV7m5cBb/y2Yuo7zoVy0Ofr+FCSjq9mgXxSZ9mKuafQUmpIubwTtbPT8O27+GOV+HWl4v+eiIiIpIjJVpyT5+ViFxPSpqNb9ce49P/HSD2or0Yuq+nK1YnC1aLBScLODlZsDpZcMr4/dJ2xn6njP0W+7b9J5fez2prP9bV6kS1ih7UqORBsK8HwZU8qezlhpNGaQGQmm7j5y0ncXN24t5mVc0Op+hEH8C282cubvsJz3N7s3anGlZW2xqyweNWvJv3osvNDalRyeO6p1q+/wxPzNhAms3gmdtq8a+u9Yo6+hIht30ATd4tKc4dsf+sGGpuHCIiIiIiIoXA1dmJQR1C6d2iKhP+Ocg3a44ScyHF4XG4OTsRXMmDGr6eBFfyyHh5EuzrQdWK5XCxlv4pWYZhsHj3ad79fS+Hoy8AEBmbxJCOtUyOrHAZhsHuiDjmb01jwdaWRMY1JNQSwd1O67jXdT11OEpH63Y6Jm+H9Z/DmY4ZRdLvuWYNqlvr+DPu/sa8/ON2Ji09RNWK5Xi0TbBjb6wE00ipfHL4N38f1YP4CBj8D1RrWfTXExERkRxp9E/u6bMSkbyIuZBCVHwSNhvYDIN0m4HNMDK27ftsNoP0jPcMA9Izfjcy2lzavvx4srYvpqRz/Fwix87aXyfPXyTddu1/EludLAT5uBN8WcLq8uSVh2vJH+ex40Qsby3czbojMQCUd3MmITkNgHH3N+bh1jXMDK9QHI9JZP7Wk/yy9RQHoxKy9nu5O3N3oyrc2zyINqGVsMYculSD6vSOSyewWKFmR3sNqvo9ckxQ/d/fB/jk7/04WeCr/q24s35Akd9Xcabpe0XMoZ2s1IvwdqB9+19HcrVKgIiIiBQNJVpyT5+ViBR3qek2Tp2/mJGkumD/GZNI+NlEjsVcICnVdt3j/b3cCPbNnA5oT1a1qFHxhlO+ioNT5y/y4Z/7mLflJGAfuTa4QyhP31aLz5Yc4vNlh7BY4NO+zenRNMjkaPPubEIyC3dEMH/rqazC+mC/zzvrVebeZlW5vZ4/bs7Wa5zgEOz62Z6kirwiQRV6q30EVa07wac6YB+FNeqnHczZeJxyLlZmP9WWptV9iuz+ijslpYqYQztZUXthUhtwqwCjwkGF00REREyjREvu6bMSkZLMMAyi4pM5djaRo2cvZCSqEgk/e4GjZxOz6mDl5La6/gwIC6Fjbf9iV68qITmNz5ce4qsVh0nOKOjdq1kQL3etR1WfcoD93l/5ZSffrwvH2cnCVwNacXvdymaGnWtJqemM+WUn87aczBoFZ7FAWK1K3NusKl0bBVLB3SVvJz2bOYLq5+wJKgDv6lCjHQS3I7VaWwYtjGf5gWj8yrsy7+n2JSJBWRSUlCpiDu1k7fsdZvWFwCYwdEXRXktERESuS4mW3NNnJSKlWWxiKsdiLmQbZXXoTAKbw89ntQn186Rf22AeaFUt74mQQpaWbuOHjSf4ePF+ohOSAWgd4ssr3evnOKIn3WYwYs5Wft12CncXJ755og2tQ4v3rJ34pFSe+mYTaw6fBaBxVW/ubRZEj6ZBBFRwL5yLZCao9i6EU1vBSM/2tlHOl7VpdfhfYi1OVWjG208/SsUKnoVz7cKQHA9n9kHUHjizFyqGQOsnC/0ySkoVMYd2stZMgj9HQ/2e0Ofbor2WiIiIXJcSLbmnz0pEyqKj0Rf4Zs0x5m48TnxGbSYPVyv3t6jKgHYh1A7wcnhMS/dF8c6iPew/ba+nFFLJg1Hd6tOlYQCW68zESU23MeTbTfyzNwovN2dmPdWWRlW9HRV2nkQnJDNw+np2nozD09XKF/1a0aG2X9FeNDkBTmyA8DVwbDWc2AhpF7M1ScINl5A2WIPDILgdVLsZXB2QpEpOsCefzuy5lICK2gtxJ7K3C+4Ajy8s9MsrKVXEHNrJWvQvWP8FtH8e7nqjaK8lIiIi16VES+7psxKRsuxCcho/bznJ16uPcuCy4trtb6pE/3YhdKofgLWIp/btjYzj7YV7WHEgGgDvci48f2dtHmsbjKtz7lYVTEpNp/+09aw/EoOvpys/DGnHTZXLF2XYeXbiXCL9p67ncPQFKnm6MuPx1jSuZkLyLC0FIrZB+GouHFhB2tHVeHMhexsnZ6jSNGPKX5j9Z0HqRicnQPQ+e8LpzJ6Mn/sgNvzax5QPhMr1wL8+VG0BTR7K//WvQUmpIubQTtbMB+HAX3DPeGj1eNFeS0RERK5LiZbc02clImKvz7Tm0FlmrD7K33tOk7nYX1WfcvRrF0yfVtWp6OlaqNeMik/i47/288PG49gMcLFaGNAuhOfuqI23R96nEcYnpfLIV+vYcTKWKt7uzB3ajmoVi0etpP2n4+k3dR2n45Kp6lOObwe1pqZ/8UiarTt0hjem/UxzdtMn4CSN03ZfPVIJwL8e1GgLNTJGU/nksOJhyoWMkU97L416OrMHzl8v+RQA/nXtyafMJJR/XYcsnqakVBFzaCdr4s0QvR/6/QK1bi/aa4mIiMh1FddEy6RJk/jggw+IiIigYcOGjB8/nltuuSXHtvPmzWPy5Mls3bqV5ORkGjZsyNixY+nSpUu2dufPn+eVV15h3rx5nDt3jtDQUD766CPuvvvuXMVUXD8rERGznDiXyHdrw5m9IZzzifZC6W7OTvRqVpX+YcE0DCrY6J6LKel8teIwny87RGKKvdbR3Y0D+XfXegRXKtiUsZgLKTz4+WoOnblAqJ8nPwxph7+XW4HOWVCbjp3jiRkbiL2YSp2A8nzzRBsCvQupdlQhWbDtFMNnbQHg1e71GdzYGY6tgfDV9p/R+64+qEI1e3LKq4o9FxCVmXy6RvrG09+e2KpcP/tPBySfrkVJqSLmsE6WzQZvB0J6Mjy/zV6ETERERExTHBMtc+bMoV+/fkyaNIn27dvzxRdfMGXKFHbv3k2NGld/2zpixAiCgoK4/fbb8fHxYfr06Xz44YesW7eO5s2bA5CSkkL79u2pXLky//nPf6hWrRrHjx/Hy8uLpk2b5iqu4vhZiYgUB0mp6SzYeooZq4+yOyIua//NIRUZEBZCl4aBuFhzN70OwGYzmLflJB/+uY/IuCQAmlX34dXu9WkVUniJiYjYizwweQ0nz1+kfpUKzH6qLd7lzCngvnRfFE9/t5mLqek0r+HD9IE34+NRuCPOCssXyw4x7ve9WCzw2SMtuLtxlUtvXoiG8LWX6lJFbLuqeHoWD7/LEk+ZI5/qgWclx9xIHigpVcQc1smKPQGfNLTPO33lNFidi+5aIiIickPFMdHSpk0bWrRoweTJk7P21a9fn169ejFu3LhcnaNhw4b06dOH1157DYDPP/+cDz74gL179+Likr9/cBTHz0pEpDgxDINNx84xY/VR/tgZSVrG3L7ACu482qYGfVvXuOFopNWHonl74R52nbInt6r6lOPf3erRo0mV6xYxz68j0Rd48PM1RCck0zK4It8Oao2Hq2P/nTp/60le/GEbaTaDjnX8mfxYC4fHkBeGYTB2wS6+XnMMV2cnZg5uw83XShZeXjz94jnwq3Np9JNnERduL0S57QMU36cmdueO2n96V1dCSkRERK6SkpLCpk2bGDVqVLb9nTt3ZvXq1bk6h81mIz4+Hl/fSx3kBQsW0K5dO4YNG8b8+fPx9/fnkUce4d///jdWqzXH8yQnJ5OcnJz1e1xcXI7tRETEzmKx0CrEl1YhvpyOS2Lm2mN8vz6cyLgkPlq8nwn/HKR7kyoMCAuhWXWfbMcejErg3d/38PeeKAC83JwZdsdNDAwLwd0l5/9OF4ZQP0++HdSaPl+sYdOxcwz5dhNTBrTCzbnornm5GauOMPbX3QD0bBrEhw82zXXRdrNYLBZe69GQiNgk/tp9mie/2ciPQ8NyLhjvVt5etqeMlO4p3k9OIOaI/advqLlxiIiISLEUHR1Neno6AQEB2fYHBAQQGRmZq3N89NFHXLhwgYceurT6zuHDh/nxxx9JT09n0aJFvPrqq3z00Ue8/fbb1zzPuHHj8Pb2znpVr149fzclIlIGBVRw54XOdVk16g7G92lGs+o+pKTb+HnLSXp9top7P1vFvM0niIxN4rX5O+kyfjl/74nC6mShf7tglr58G0M71irShFSm+lUqMP3x1pRzsbLiQDQj52wl3Va0k7AMw+Djv/ZlJaQGhoUwvk+zYp+QymR1svB/fZvTvIYP5xNTGTh9PVHxSWaHZToNvSnuMkdKqZaUiIiIXMeVUzQMw8jVtI1Zs2YxduxY5s+fT+XKlbP222w2KleuzJdffonVaqVly5acOnWKDz74IGuK35VGjx7NCy+8kPV7XFycElMiInnk5mylV/Oq9GpelW3Hz/P1mqP8ti2CbcfP88Lx89nadqpfmVHd6uc84qaItQyuyJf9WzJoxkYW7YikvNt23uvdpEimDKbbDF6bv5OZ6+wrzb1wVx2eu+OmIrlWUSrnamVK/1b0nryao2cTGTRjI7OfaounW9lNzZSMlGJZdi5jpFRFjZQSERGRq/n5+WG1Wq8aFRUVFXXV6KkrzZkzh0GDBvHDDz/QqVOnbO9VqVKFOnXqZJuqV79+fSIjI0lJScnxfG5ublSoUCHbS0RE8q9pdR8+fqgZq0ffwUud6xBYwb6yXMOgCnz/ZBumDLjZlIRUpltq+/Ppw81wssAPG0/w9sI9FHbZ6uS0dIbP2sLMdeFYLPBmr0YMv7N2iUtIZapU3o0Zj7fG19OVHSdjefb7zaSl28wOyzRKShV3mr4nIiIi1+Hq6krLli1ZvHhxtv2LFy8mLCzsmsfNmjWLgQMH8v3339O9e/er3m/fvj0HDx7EZrvUUd6/fz9VqlTB1bV4rm4kIlJa+ZV349k7arPi37ez4l+38+uzHQirVTyKXndtVIX3ejcBYMrKI0z852ChnTshOY1BMzaycEcELlYLEx5uTr+2wYV2frOE+HkyZUAr3F2cWLLvDGPm7yr0ZF5JoaRUcafpeyIiInIDL7zwAlOmTGHatGns2bOHkSNHEh4eztChQwH7tLr+/ftntZ81axb9+/fno48+om3btkRGRhIZGUlsbGxWm6effpqzZ8/y/PPPs3//fhYuXMg777zDsGHDHH5/IiJi52J1orqvB05OxWuU0IOtqjPmngYAfLR4PzNWHSnwOWMupPDoV2tZeTAaD1cr0we25p4mQQU+b3HRokZFPu3bHCcLzFofzqSlh8wOyRRKShVnSbFwMca+raSUiIiIXEOfPn0YP348b7zxBs2aNWP58uUsWrSI4GD7t8kRERGEh4dntf/iiy9IS0tj2LBhVKlSJev1/PPPZ7WpXr06f/31Fxs2bKBJkyYMHz6c559//qpV/kRERAAGdQjl+TtrAzD21938tOlEvs918vxFHvx8NdtOxFLRw4VZT7alQ+3iMTKsMHVuGMjYng0B+ODPfczbnP/PrKSyGGV1jFgBxcXF4e3tTWxsbNHVSzi1Fb7sCB5+8K+ymTUVEREpbhzSBygl9FmJiJQthmHw3193M2P1UaxOFiY/2oLODQPzdI6DUfH0m7qeiNgkqni78+2gNqbWzXKEcYv28MXywzg7Wfj6ida0v6nkJ+By2wfQSKniLHPqnupJiYiIiIiISDFnsVh47Z4G9G5RjXSbwbPfb2H1wehcH7/1+Hke/HwNEbFJ1PL35Kenw0p9Qgrg313r0aNpEGk2g6HfbmJvZJzZITmMklLFmVbeExERERERkRLEycnCe70b06VhACnpNgZ/s5Et4edueNyKA2d45Ku1nEtMpWl1H+YODSPIp5wDIjafk5OFDx9sQutQX+KT0xg4bQMRsRfNDsshlJQqzlTkXEREREREREoYZ6sTnz7cnA43+ZGYks7A6RvYFxl/zfa/bT/FEzM2kJiSzi21/fh+cBt8PcvWSq9uzla+6teKmyqXJzIuiYHTNhCXlGp2WEVOSaniLCZjpJSm74mIiIiIiEgJ4uZs5Yt+LWlew4fYi6n0m7qOY2cvXNXu2zVHeW7WFlLTDbo3qcKUAa3wdHM2IWLzeXu4MOPxm/H3cmPf6Xie+mYjp86X7hFTSkoVZ1nT90JMDUNEREREREQkrzzdnJk+8GbqBngRFZ/MY1PXcTouCbAXRR//937GzN+FYcBjbWvwad/muDlbTY7aXNUqejB94M14ulpZeziG2z5cypu/7eZsQrLZoRUJJaWKq/RUiM1YDlI1pURERERERKQE8vFw5dtBrQmu5MHxmIs8NmUdZxOSGbtgF+P/PgDA83fW5s17G2F1spgcbfHQqKo3c4a0o3WoLylpNqauPMKt7y/h47/2lbopfUpKFVfnw8GwgbM7eOVtCU0RERERERGR4qJyBXe+G9SGgApuHIhK4LYPl/L1mmNYLPDfng0ZeVcdLBYlpC7XqKo3c55qyzdPtKZxVW8upKTz6T8HufX9JXyx7BAXU9LNDrFQKClVXF1e5Fx/OUVERERERKQEq+7rwXeD2lDRw4X4pDScnSyM79OMAWEhZodWbFksFm6t48+CZ9vz+WMtuKlyec4npjLu9710/GAJ3649RkqazewwC0RJqeIqq56Upu6JiIiIiIhIyVc7wIvvBrfhniZVmPF4a+5tVtXskEoEi8VC10ZV+HPErXz4YFOq+pQjKj6ZMb/s5M6PlzJv8wnSbYbZYeaLklLFVYyKnIuIiIiIiEjp0jDIm4mPtKBDbT+zQylxrE4WHmhZjX9e6sgb9zbEr7wbx2Mu8sIP2+j2f8v5c1ckhlGyklNKShVXmdP3fDVSSkRERERERETs3Jyt9G8XwvJ/3ca/u9bDu5wL+08nMOTbTfT6bBUrD0SXmOSUklLFVVZNKSWlRERERERERCQ7D1dnnr6tFsv/dTvP3XETHq5Wtp2I5bGp63jkq3VsDj9ndog3pKRUcWQY2Qudi4iIiIiIiIjkwLucCy92rsuyl2/n8fYhuFqdWHP4LPdPWs3grzeyJyLO7BCvSUmp4uhCNKQkABbwqWF2NCIiIiIiIiJSzPl7ufF6j4Ysefk2+rSqjpMF/t5zmrs/XcHzs7dwNPqC2SFeRUmp4ihz5b0KQeDibm4sIiIiIiIiIlJiVPUpx3sPNGHxCx3p3qQKhgHzt57izo+XMXreDiJiL5odYhbTk1KTJk0iNDQUd3d3WrZsyYoVK67bfubMmTRt2hQPDw+qVKnC448/ztmzZ7Pev+2227BYLFe9unfvXqDrOpTqSYmIiIiIiIhIAdTyL89nj7Tgt+c6cHtdf9JtBrPWh9Pxg6W89dtuziYkmx2iuUmpOXPmMGLECF555RW2bNnCLbfcQrdu3QgPD8+x/cqVK+nfvz+DBg1i165dzJ07lw0bNjB48OCsNvPmzSMiIiLrtXPnTqxWKw8++GC+r+twMRkjpXxDTA1DREREREREREq2RlW9mf54a+YObUfrEF9S0mxMWXmEW99fwseL95NuM2+lPlOTUh9//DGDBg1i8ODB1K9fn/Hjx1O9enUmT56cY/u1a9cSEhLC8OHDCQ0NpUOHDgwZMoSNGzdmtfH19SUwMDDrtXjxYjw8PLIlpfJ6XYdTkXMRERERERERKUQ3h/gyZ0hbvn6iNY2qVuBCSjqbjsVgdbKYFpNpSamUlBQ2bdpE586ds+3v3Lkzq1evzvGYsLAwTpw4waJFizAMg9OnT/Pjjz9eNTXvclOnTqVv3754enrm+7oO1/Fl6DsL6t9rdiQiIiIiIiIiUkpYLBY61vHn12c7MPnRFozuVt/UeJzNunB0dDTp6ekEBARk2x8QEEBkZGSOx4SFhTFz5kz69OlDUlISaWlp9OzZkwkTJuTYfv369ezcuZOpU6cW6LoAycnJJCdfmm8ZF1eESyr61rS/REREREREREQKmcVioVvjKmaHYX6hc4sl+zAxwzCu2pdp9+7dDB8+nNdee41Nmzbxxx9/cOTIEYYOHZpj+6lTp9KoUSNat25doOsCjBs3Dm9v76xX9erVb3RrIiIiIiIiIiJyDaYlpfz8/LBarVeNToqKirpqFFOmcePG0b59e15++WWaNGlCly5dmDRpEtOmTSMiIiJb28TERGbPnp2tCHp+rwswevRoYmNjs17Hjx/Py+2KiIiIiIiIiMhlTEtKubq60rJlSxYvXpxt/+LFiwkLC8vxmMTERJycsodstVoB+0iny/3www8kJyfz2GOPFfi6AG5ublSoUCHbS0RERERERERE8se0mlIAL7zwAv369aNVq1a0a9eOL7/8kvDw8KzpeKNHj+bkyZN88803APTo0YMnn3ySyZMn06VLFyIiIhgxYgStW7cmKCgo27mnTp1Kr169qFSpUp6vKyIiIiIiIiIiRcvUpFSfPn04e/Ysb7zxBhERETRq1IhFixYRHBwMQEREBOHh4VntBw4cSHx8PBMnTuTFF1/Ex8eHO+64g/feey/beffv38/KlSv566+/8nVdEREREREREREpWhbjynlvkitxcXF4e3sTGxurqXwiIiJliPoAuafPSkREpGzKbR/A9NX3RERERERERESk7FFSSkREREREREREHE5JKRERERERERERcTglpURERERERERExOGUlBIREREREREREYdTUkpERERERERERBxOSSkREREREREREXE4JaVERERERERERMThlJQSERERERERERGHczY7gJLKMAwA4uLiTI5EREREHCnz//2ZfQG5NvWXREREyqbc9peUlMqn+Ph4AKpXr25yJCIiImKG+Ph4vL29zQ6jWFN/SUREpGy7UX/JYuhrvnyx2WycOnUKLy8vLBZLoZ47Li6O6tWrc/z4cSpUqFCo5y7OyuJ9657Lxj1D2bxv3XPZuGcoe/dtGAbx8fEEBQXh5KRKCNdTlP0lKHt/9kD3XFbuGcrmfeuey8Y9Q9m877J2z7ntL2mkVD45OTlRrVq1Ir1GhQoVysQf1iuVxfvWPZcdZfG+dc9lR1m6b42Qyh1H9JegbP3Zy6R7LjvK4n3rnsuOsnjfZemec9Nf0td7IiIiIiIiIiLicEpKiYiIiIiIiIiIwykpVQy5ubnx+uuv4+bmZnYoDlUW71v3XHaUxfvWPZcdZfW+xXxl8c+e7rnsKIv3rXsuO8rifZfFe84NFToXERERERERERGH00gpERERERERERFxOCWlRERERERERETE4ZSUEhERERERERERh1NSyiSTJk0iNDQUd3d3WrZsyYoVK67bftmyZbRs2RJ3d3dq1qzJ559/7qBIC8e4ceO4+eab8fLyonLlyvTq1Yt9+/Zd95ilS5disViueu3du9dBURfM2LFjr4o9MDDwuseU9OcMEBISkuNzGzZsWI7tS+JzXr58OT169CAoKAiLxcIvv/yS7X3DMBg7dixBQUGUK1eO2267jV27dt3wvD/99BMNGjTAzc2NBg0a8PPPPxfRHeTd9e45NTWVf//73zRu3BhPT0+CgoLo378/p06duu45Z8yYkeOzT0pKKuK7yb0bPeuBAwdeFX/btm1veN6S+qyBHJ+ZxWLhgw8+uOY5S8KzluJJ/SX1l3JS0p8zqL8E6i+pv6T+0pVKwrMuCkpKmWDOnDmMGDGCV155hS1btnDLLbfQrVs3wsPDc2x/5MgR7r77bm655Ra2bNnCf/7zH4YPH85PP/3k4Mjzb9myZQwbNoy1a9eyePFi0tLS6Ny5MxcuXLjhsfv27SMiIiLrVbt2bQdEXDgaNmyYLfYdO3Zcs21peM4AGzZsyHbPixcvBuDBBx+87nEl6TlfuHCBpk2bMnHixBzff//99/n444+ZOHEiGzZsIDAwkLvuuov4+PhrnnPNmjX06dOHfv36sW3bNvr168dDDz3EunXriuo28uR695yYmMjmzZsZM2YMmzdvZt68eezfv5+ePXve8LwVKlTI9twjIiJwd3cvilvIlxs9a4CuXbtmi3/RokXXPWdJftbAVc9r2rRpWCwWevfufd3zFvdnLcWP+kvqL+WkNDxnUH8J1F9Sf0n9pZwU92ddJAxxuNatWxtDhw7Ntq9evXrGqFGjcmz/r3/9y6hXr162fUOGDDHatm1bZDEWtaioKAMwli1bds02S5YsMQDj3LlzjgusEL3++utG06ZNc92+ND5nwzCM559/3qhVq5Zhs9lyfL+kP2fA+Pnnn7N+t9lsRmBgoPHuu+9m7UtKSjK8vb2Nzz///Jrneeihh4yuXbtm29elSxejb9++hR5zQV15zzlZv369ARjHjh27Zpvp06cb3t7ehRtcEcrpvgcMGGDce++9eTpPaXvW9957r3HHHXdct01Je9ZSPKi/pP5STkrjczYM9ZcMQ/2laylp/w9Vfyln6i9dm0ZKOVhKSgqbNm2ic+fO2fZ37tyZ1atX53jMmjVrrmrfpUsXNm7cSGpqapHFWpRiY2MB8PX1vWHb5s2bU6VKFe68806WLFlS1KEVqgMHDhAUFERoaCh9+/bl8OHD12xbGp9zSkoK3333HU888QQWi+W6bUvyc77ckSNHiIyMzPYs3dzc6Nix4zX/jsO1n//1jinOYmNjsVgs+Pj4XLddQkICwcHBVKtWjXvuuYctW7Y4JsBCtHTpUipXrkydOnV48skniYqKum770vSsT58+zcKFCxk0aNAN25aGZy2Oo/6SnfpLVyuNz1n9JTv1l66tNPw/VP0l9ZeuRUkpB4uOjiY9PZ2AgIBs+wMCAoiMjMzxmMjIyBzbp6WlER0dXWSxFhXDMHjhhRfo0KEDjRo1uma7KlWq8OWXX/LTTz8xb9486taty5133sny5csdGG3+tWnThm+++YY///yTr776isjISMLCwjh79myO7Uvbcwb45ZdfOH/+PAMHDrxmm5L+nK+U+fc4L3/HM4/L6zHFVVJSEqNGjeKRRx6hQoUK12xXr149ZsyYwYIFC5g1axbu7u60b9+eAwcOODDagunWrRszZ87kn3/+4aOPPmLDhg3ccccdJCcnX/OY0vSsv/76a7y8vLj//vuv2640PGtxLPWX1F9Sfym7kv6cr6T+kvpL6i9drTQ86/xwNjuAsurKb0EMw7juNyM5tc9pf0nw7LPPsn37dlauXHnddnXr1qVu3bpZv7dr147jx4/z4YcfcuuttxZ1mAXWrVu3rO3GjRvTrl07atWqxddff80LL7yQ4zGl6TkDTJ06lW7duhEUFHTNNiX9OV9LXv+O5/eY4iY1NZW+fftis9mYNGnSddu2bds2W5HL9u3b06JFCyZMmMCnn35a1KEWij59+mRtN2rUiFatWhEcHMzChQuv2/EoDc8aYNq0aTz66KM3rHVQGp61mEP9JfWXclKanjOov3Q59ZeuVhr+H6r+kvpL16ORUg7m5+eH1Wq9KsMbFRV1VSY4U2BgYI7tnZ2dqVSpUpHFWhSee+45FixYwJIlS6hWrVqej2/btm2JzRR7enrSuHHja8Zfmp4zwLFjx/j7778ZPHhwno8tyc85c8WgvPwdzzwur8cUN6mpqTz00EMcOXKExYsXX/dbv5w4OTlx8803l9hnD/ZvsoODg697D6XhWQOsWLGCffv25evveGl41lK01F9Sf0n9pRsryc9Z/SX1l9RfurHS8KxzQ0kpB3N1daVly5ZZK2xkWrx4MWFhYTke065du6va//XXX7Rq1QoXF5cii7UwGYbBs88+y7x58/jnn38IDQ3N13m2bNlClSpVCjk6x0hOTmbPnj3XjL80POfLTZ8+ncqVK9O9e/c8H1uSn3NoaCiBgYHZnmVKSgrLli275t9xuPbzv94xxUlmB+vAgQP8/fff+fqHgWEYbN26tcQ+e4CzZ89y/Pjx695DSX/WmaZOnUrLli1p2rRpno8tDc9aipb6S+ovqb90YyX5Oau/pP6S+ks3Vhqeda44tq66GIZhzJ4923BxcTGmTp1q7N692xgxYoTh6elpHD161DAMwxg1apTRr1+/rPaHDx82PDw8jJEjRxq7d+82pk6dari4uBg//vijWbeQZ08//bTh7e1tLF261IiIiMh6JSYmZrW58r4/+eQT4+effzb2799v7Ny50xg1apQBGD/99JMZt5BnL774orF06VLj8OHDxtq1a4177rnH8PLyKtXPOVN6erpRo0YN49///vdV75WG5xwfH29s2bLF2LJliwEYH3/8sbFly5aslVPeffddw9vb25g3b56xY8cO4+GHHzaqVKlixMXFZZ2jX79+2VaQWrVqlWG1Wo13333X2LNnj/Huu+8azs7Oxtq1ax1+fzm53j2npqYaPXv2NKpVq2Zs3bo129/x5OTkrHNcec9jx441/vjjD+PQoUPGli1bjMcff9xwdnY21q1bZ8Yt5uh69x0fH2+8+OKLxurVq40jR44YS5YsMdq1a2dUrVq11D7rTLGxsYaHh4cxefLkHM9REp+1FD/qL6m/ZBil8zlnUn9J/SX1l0rns86k/lLuKCllks8++8wIDg42XF1djRYtWmRb6nfAgAFGx44ds7VfunSp0bx5c8PV1dUICQm55h/s4grI8TV9+vSsNlfe93vvvWfUqlXLcHd3NypWrGh06NDBWLhwoeODz6c+ffoYVapUMVxcXIygoCDj/vvvN3bt2pX1fml8zpn+/PNPAzD27dt31Xul4TlnLst85WvAgAGGYdiXOX799deNwMBAw83Nzbj11luNHTt2ZDtHx44ds9pnmjt3rlG3bl3DxcXFqFevXrHqaF7vno8cOXLNv+NLlizJOseV9zxixAijRo0ahqurq+Hv72907tzZWL16teNv7jqud9+JiYlG586dDX9/f8PFxcWoUaOGMWDAACM8PDzbOUrTs870xRdfGOXKlTPOnz+f4zlK4rOW4kn9JfWXSuNzzqT+kvpL6i9dUpqedSb1l3LHYhgZlQFFREREREREREQcRDWlRERERERERETE4ZSUEhERERERERERh1NSSkREREREREREHE5JKRERERERERERcTglpURERERERERExOGUlBIREREREREREYdTUkpERERERERERBxOSSkREREREREREXE4JaVERBzIYrHwyy+/mB2GiIiISLGl/pJI2aGklIiUGQMHDsRisVz16tq1q9mhiYiIiBQL6i+JiCM5mx2AiIgjde3alenTp2fb5+bmZlI0IiIiIsWP+ksi4igaKSUiZYqbmxuBgYHZXhUrVgTsQ8UnT55Mt27dKFeuHKGhocydOzfb8Tt27OCOO+6gXLlyVKpUiaeeeoqEhIRsbaZNm0bDhg1xc3OjSpUqPPvss9nej46O5r777sPDw4PatWuzYMGCor1pERERkTxQf0lEHEVJKRGRy4wZM4bevXuzbds2HnvsMR5++GH27NkDQGJiIl27dqVixYps2LCBuXPn8vfff2frRE2ePJlhw4bx1FNPsWPHDhYsWMBNN92U7Rr//e9/eeihh9i+fTt33303jz76KDExMQ69TxEREZH8Un9JRAqNISJSRgwYMMCwWq2Gp6dnttcbb7xhGIZhAMbQoUOzHdOmTRvj6aefNgzDML788kujYsWKRkJCQtb7CxcuNJycnIzIyEjDMAwjKCjIeOWVV64ZA2C8+uqrWb8nJCQYFovF+P333wvtPkVERETyS/0lEXEk1ZQSkTLl9ttvZ/Lkydn2+fr6Zm23a9cu23vt2rVj69atAOzZs4emTZvi6emZ9X779u2x2Wzs27cPi8XCqVOnuPPOO68bQ5MmTbK2PT098fLyIioqKr+3JCIiIlKo1F8SEUdRUkpEyhRPT8+rhoffiMViAcAwjKztnNqUK1cuV+dzcXG56libzZanmERERESKivpLIuIoqiklInKZtWvXXvV7vXr1AGjQoAFbt27lwoULWe+vWrUKJycn6tSpg5eXFyEhIfzvf/9zaMwiIiIijqT+kogUFo2UEpEyJTk5mcjIyGz7nJ2d8fPzA2Du3Lm0atWKDh06MHPmTNavX8/UqVMBePTRR3n99dcZMGAAY8eO5cyZMzz33HP069ePgIAAAMaOHcvQoUOpXLky3bp1Iz4+nlWrVvHcc8859kZFRERE8kn9JRFxFCWlRKRM+eOPP6hSpUq2fXXr1mXv3r2AfaWX2bNn88wzzxAYGMjMmTNp0KABAB4eHvz55588//zz3HzzzXh4eNC7d28+/vjjrHMNGDCApKQkPvnkE1566SX8/Px44IEHHHeDIiIiIgWk/pKIOIrFMAzD7CBERIoDi8XCzz//TK9evcwORURERKRYUn9JRAqTakqJiIiIiIiIiIjDKSklIiIiIiIiIiIOp+l7IiIiIiIiIiLicBopJSIiIiIiIiIiDqeklIiIiIiIiIiIOJySUiIiIiIiIiIi4nBKSomIiIiIiIiIiMMpKSUiIiIiIiIiIg6npJSIiIiIiIiIiDicklIiIiIiIiIiIuJwSkqJiIiIiIiIiIjDKSklIiIiIiIiIiIO9/9pjUvAzmMBAwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[1560   46]\n",
      " [ 141   56]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      benign       0.92      0.97      0.94      1606\n",
      "   malignant       0.55      0.28      0.37       197\n",
      "\n",
      "    accuracy                           0.90      1803\n",
      "   macro avg       0.73      0.63      0.66      1803\n",
      "weighted avg       0.88      0.90      0.88      1803\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms, models\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "# Định nghĩa đường dẫn\n",
    "train_dir = \"/home/ubuntu/vnet/TaoST/Data9kBulubulaz/Data9kBulubula/train\"\n",
    "test_dir = \"/home/ubuntu/vnet/TaoST/Data9kBulubulaz/Data9kBulubula/test\"\n",
    "\n",
    "# Định nghĩa transforms\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.RandomVerticalFlip(),\n",
    "        transforms.RandomRotation(20),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "    ])\n",
    "}\n",
    "\n",
    "# Tạo datasets\n",
    "image_datasets = {\n",
    "    'train': datasets.ImageFolder(train_dir, data_transforms['train']),\n",
    "    'test': datasets.ImageFolder(test_dir, data_transforms['test'])\n",
    "}\n",
    "\n",
    "# Tạo dataloaders\n",
    "batch_size = 32\n",
    "dataloaders = {\n",
    "    'train': DataLoader(image_datasets['train'], batch_size=batch_size, shuffle=True, num_workers=4),\n",
    "    'test': DataLoader(image_datasets['test'], batch_size=batch_size, shuffle=False, num_workers=4)\n",
    "}\n",
    "\n",
    "# Kiểm tra kích thước dữ liệu\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}\n",
    "class_names = image_datasets['train'].classes\n",
    "print(f\"Dataset sizes: {dataset_sizes}\")\n",
    "print(f\"Class names: {class_names}\")\n",
    "\n",
    "# Kiểm tra device\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Kiểm tra hình dạng của dữ liệu\n",
    "for inputs, labels in dataloaders['train']:\n",
    "    print(f\"Input shape: {inputs.shape}\")  # Nên là [batch_size, 3, 224, 224]\n",
    "    print(f\"Labels: {labels}\")\n",
    "    break\n",
    "\n",
    "# Tạo mô hình EfficientNet-B0 (thay thế DenseNet-121)\n",
    "def create_model():\n",
    "    # Tải mô hình pretrained EfficientNet-B0\n",
    "    model = models.efficientnet_b0(pretrained=True)\n",
    "    \n",
    "    # Đóng băng tất cả các lớp để bắt đầu\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "    \n",
    "    # Thay thế lớp phân loại\n",
    "    # EfficientNet-B0 có 1280 đặc trưng đầu ra ở lớp cuối cùng\n",
    "    num_ftrs = model.classifier[1].in_features\n",
    "    \n",
    "    # Thay thế lớp phân loại của EfficientNet với một lớp mới có 1 đầu ra\n",
    "    # Sigmoid sẽ được áp dụng trong BCEWithLogitsLoss\n",
    "    model.classifier[1] = nn.Linear(num_ftrs, 1)\n",
    "    \n",
    "    return model\n",
    "\n",
    "# Tạo mô hình và chuyển sang device\n",
    "model = create_model()\n",
    "model = model.to(device)\n",
    "\n",
    "# Định nghĩa hàm loss và optimizer\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "# Chỉ tối ưu lớp phân loại\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
    "# Learning rate scheduler\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True)\n",
    "\n",
    "# Hàm huấn luyện mô hình\n",
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "    \n",
    "    best_model_wts = model.state_dict()\n",
    "    best_acc = 0.0\n",
    "    \n",
    "    history = {'train_loss': [], 'train_acc': [], 'test_loss': [], 'test_acc': []}\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        print(f'Epoch {epoch}/{num_epochs - 1}')\n",
    "        print('-' * 10)\n",
    "        \n",
    "        # Mỗi epoch có một phase training và testing\n",
    "        for phase in ['train', 'test']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # Đặt model ở chế độ training\n",
    "            else:\n",
    "                model.eval()   # Đặt model ở chế độ evaluation\n",
    "                \n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            \n",
    "            # Lặp qua data\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                # Chuyển đổi labels sang float và reshape để phù hợp với sigmoid\n",
    "                labels = labels.float().view(-1, 1).to(device)\n",
    "                \n",
    "                # Zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "                \n",
    "                # Forward\n",
    "                # Theo dõi history nếu đang training\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    preds = torch.sigmoid(outputs) > 0.5\n",
    "                    loss = criterion(outputs, labels)\n",
    "                    \n",
    "                    # Backward + optimize chỉ khi training\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "                        \n",
    "                # Thống kê\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.byte())\n",
    "            \n",
    "            if phase == 'test' and scheduler is not None:\n",
    "                scheduler.step(running_loss)\n",
    "                \n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "            \n",
    "            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
    "            \n",
    "            # Lưu lại lịch sử\n",
    "            if phase == 'train':\n",
    "                history['train_loss'].append(epoch_loss)\n",
    "                history['train_acc'].append(epoch_acc.item())\n",
    "            else:\n",
    "                history['test_loss'].append(epoch_loss)\n",
    "                history['test_acc'].append(epoch_acc.item())\n",
    "            \n",
    "            # Sao chép mô hình tốt nhất\n",
    "            if phase == 'test' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = model.state_dict()\n",
    "                \n",
    "        print()\n",
    "    \n",
    "    time_elapsed = time.time() - since\n",
    "    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
    "    print(f'Best val Acc: {best_acc:4f}')\n",
    "    \n",
    "    # Tải model tốt nhất\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, history\n",
    "\n",
    "# Huấn luyện mô hình\n",
    "num_epochs = 20\n",
    "model, history = train_model(model, criterion, optimizer, scheduler, num_epochs=num_epochs)\n",
    "\n",
    "# Lưu mô hình\n",
    "torch.save(model.state_dict(), 'melanoma_classifier_efficientnet_b0.pth')\n",
    "\n",
    "# Vẽ đồ thị training và validation accuracy/loss\n",
    "def plot_training_history(history):\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history['train_acc'], label='Train')\n",
    "    plt.plot(history['test_acc'], label='Validation')\n",
    "    plt.title('Model Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history['train_loss'], label='Train')\n",
    "    plt.plot(history['test_loss'], label='Validation')\n",
    "    plt.title('Model Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('efficientnet_training_history.png')\n",
    "    plt.show()\n",
    "\n",
    "plot_training_history(history)\n",
    "\n",
    "# Đánh giá mô hình trên tập test\n",
    "def evaluate_model(model, dataloader):\n",
    "    model.eval()\n",
    "    y_true = []\n",
    "    y_pred = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in dataloader:\n",
    "            inputs = inputs.to(device)\n",
    "            outputs = model(inputs)\n",
    "            preds = torch.sigmoid(outputs) > 0.5\n",
    "            \n",
    "            y_true.extend(labels.numpy())\n",
    "            y_pred.extend(preds.cpu().numpy())\n",
    "    \n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    report = classification_report(y_true, y_pred, target_names=class_names)\n",
    "    \n",
    "    return cm, report\n",
    "\n",
    "cm, report = evaluate_model(model, dataloaders['test'])\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(report)\n",
    "\n",
    "# Lưu confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "\n",
    "thresh = cm.max() / 2.\n",
    "for i in range(cm.shape[0]):\n",
    "    for j in range(cm.shape[1]):\n",
    "        plt.text(j, i, format(cm[i, j], 'd'),\n",
    "                horizontalalignment=\"center\",\n",
    "                color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.savefig('efficientnet_confusion_matrix.png')\n",
    "plt.show()\n",
    "\n",
    "# Hàm dự đoán cho một ảnh mới\n",
    "def predict_image(image_path, model, transform):\n",
    "    from PIL import Image\n",
    "    \n",
    "    model.eval()\n",
    "    img = Image.open(image_path)\n",
    "    img_t = transform(img)\n",
    "    batch_t = torch.unsqueeze(img_t, 0).to(device)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        output = model(batch_t)\n",
    "        prob = torch.sigmoid(output).item()\n",
    "    \n",
    "    return prob\n",
    "\n",
    "# Ví dụ sử dụng hàm dự đoán\n",
    "# image_path = '/path/to/image.jpg'  # Thay đổi đường dẫn này\n",
    "# probability = predict_image(image_path, model, data_transforms['test'])\n",
    "# print(f\"Probability of malignant: {probability:.4f}\")\n",
    "# if probability > 0.5:\n",
    "#     print(\"Prediction: Malignant\")\n",
    "# else:\n",
    "#     print(\"Prediction: Benign\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "551d7c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mô hình đã được chuyển đổi thành công sang ONNX và lưu tại: /home/ubuntu/vnet/TaoST/Model2-Efficient/Model5.onnx\n",
      "Mô hình ONNX đã được kiểm tra và xác nhận hợp lệ.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "\n",
    "# 1. Định nghĩa lại kiến trúc mô hình (giống hệt khi training)\n",
    "def create_model():\n",
    "    # Tải mô hình pretrained EfficientNet-B0 thay vì DenseNet-121\n",
    "    model = models.efficientnet_b0(pretrained=False)  # Không cần pretrained vì sẽ load weights\n",
    "    \n",
    "    # Thay thế lớp phân loại\n",
    "    # EfficientNet-B0 có 1280 đặc trưng đầu ra ở lớp cuối cùng\n",
    "    num_ftrs = model.classifier[1].in_features\n",
    "    \n",
    "    # Thay thế lớp phân loại của EfficientNet với một lớp mới có 1 đầu ra\n",
    "    model.classifier[1] = nn.Linear(num_ftrs, 1)\n",
    "    \n",
    "    return model\n",
    "\n",
    "# 2. Khởi tạo mô hình và tải trọng số đã lưu\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = create_model()\n",
    "\n",
    "# Tải trọng số từ file .pth\n",
    "model_path = \"/home/ubuntu/vnet/TaoST/Model2-Efficient/Model5.pth\"\n",
    "model.load_state_dict(torch.load(model_path, map_location=device))\n",
    "model.to(device)\n",
    "model.eval()  # Chuyển sang chế độ inference\n",
    "\n",
    "# 3. Tạo dummy input với kích thước phù hợp (batch_size, channels, height, width)\n",
    "dummy_input = torch.randn(1, 3, 224, 224).to(device)\n",
    "\n",
    "# 4. Xuất sang ONNX\n",
    "onnx_path = \"/home/ubuntu/vnet/TaoST/Model2-Efficient/Model5.onnx\"  # Đường dẫn tới file ONNX đầu ra\n",
    "torch.onnx.export(\n",
    "    model,                       # Mô hình cần xuất\n",
    "    dummy_input,                 # Input đầu vào mẫu\n",
    "    onnx_path,                   # Đường dẫn tới file ONNX đầu ra\n",
    "    export_params=True,          # Lưu các tham số của mô hình\n",
    "    opset_version=12,            # Phiên bản ONNX opset\n",
    "    do_constant_folding=True,    # Tối ưu hóa các hằng số\n",
    "    input_names=['input'],       # Tên của input\n",
    "    output_names=['output'],     # Tên của output\n",
    "    dynamic_axes={               # Kích thước động (batch size có thể thay đổi)\n",
    "        'input': {0: 'batch_size'},\n",
    "        'output': {0: 'batch_size'}\n",
    "    }\n",
    ")\n",
    "\n",
    "print(f\"Mô hình đã được chuyển đổi thành công sang ONNX và lưu tại: {onnx_path}\")\n",
    "\n",
    "# 5. Kiểm tra xem mô hình ONNX có thể được tải và sử dụng không (tùy chọn)\n",
    "try:\n",
    "    import onnx\n",
    "    \n",
    "    # Tải mô hình ONNX\n",
    "    onnx_model = onnx.load(onnx_path)\n",
    "    \n",
    "    # Kiểm tra mô hình ONNX\n",
    "    onnx.checker.check_model(onnx_model)\n",
    "    \n",
    "    print(\"Mô hình ONNX đã được kiểm tra và xác nhận hợp lệ.\")\n",
    "except ImportError:\n",
    "    print(\"Thư viện ONNX không được cài đặt. Bỏ qua bước kiểm tra.\")\n",
    "except Exception as e:\n",
    "    print(f\"Lỗi khi kiểm tra mô hình ONNX: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "779e716c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Đã nén file /home/ubuntu/vnet/TaoST/Model2-Efficient/Code.ipynb thành /home/ubuntu/vnet/TaoST/Model2-Efficient/Code.zip\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "\n",
    "# Đường dẫn đến file ipynb\n",
    "ipynb_file = \"/home/ubuntu/vnet/TaoST/Model2-Efficient/Code.ipynb\"\n",
    "\n",
    "# Đường dẫn file zip đầu ra\n",
    "zip_file = \"/home/ubuntu/vnet/TaoST/Model2-Efficient/Code.zip\"\n",
    "\n",
    "# Tạo file zip\n",
    "with zipfile.ZipFile(zip_file, 'w') as zipf:\n",
    "    zipf.write(ipynb_file, arcname=\"Code.ipynb\")  # arcname để đặt tên file trong zip\n",
    "\n",
    "print(f\"Đã nén file {ipynb_file} thành {zip_file}\")"
   ]
  }
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