main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "\n",
    "from anomaly_flow.data.netflow import NetFlowV2\n",
    "from anomaly_flow.train.trainer_flow_nids import GANomaly\n",
    "from pathlib import Path\n",
    "import tensorflow as tf \n",
    "import json\n",
    "from anomaly_flow.data.synthetic_data import generate_synthetic_data\n",
    "from anomaly_flow.view.distribution_plot import generate_dist_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n",
    "\n",
    "hps = dict()\n",
    "\n",
    "with open('hps.json', 'r', encoding='utf-8') as file:\n",
    "    hps = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dataset NF-ToN-IoT-v2-DDoS-downsample...\n",
      "Loaded dataset NF-ToN-IoT-v2-DDoS-downsample [OK]\n",
      "Initialized Columns Split Preprocessing.\n",
      "Using cached file: 0c64a61de3189470f64df188bc55130f.\n",
      "Finished Columns Split Preprocessing. [OK]\n",
      "Experiments using the following columns: \n",
      "PROTOCOL\n",
      "IN_BYTES\n",
      "IN_PKTS\n",
      "OUT_BYTES\n",
      "OUT_PKTS\n",
      "FLOW_DURATION_MILLISECONDS\n",
      "DURATION_IN\n",
      "DURATION_OUT\n",
      "MIN_TTL\n",
      "MAX_TTL\n",
      "LONGEST_FLOW_PKT\n",
      "SHORTEST_FLOW_PKT\n",
      "MIN_IP_PKT_LEN\n",
      "MAX_IP_PKT_LEN\n",
      "SRC_TO_DST_SECOND_BYTES\n",
      "DST_TO_SRC_SECOND_BYTES\n",
      "RETRANSMITTED_IN_BYTES\n",
      "RETRANSMITTED_IN_PKTS\n",
      "RETRANSMITTED_OUT_BYTES\n",
      "RETRANSMITTED_OUT_PKTS\n",
      "SRC_TO_DST_AVG_THROUGHPUT\n",
      "DST_TO_SRC_AVG_THROUGHPUT\n",
      "NUM_PKTS_UP_TO_128_BYTES\n",
      "NUM_PKTS_128_TO_256_BYTES\n",
      "NUM_PKTS_256_TO_512_BYTES\n",
      "NUM_PKTS_512_TO_1024_BYTES\n",
      "NUM_PKTS_1024_TO_1514_BYTES\n",
      "TCP_WIN_MAX_IN\n",
      "TCP_WIN_MAX_OUT\n",
      "ICMP_TYPE\n",
      "ICMP_IPV4_TYPE\n",
      "DNS_QUERY_ID\n",
      "DNS_QUERY_TYPE\n",
      "DNS_TTL_ANSWER\n",
      "URGENT_POINTER\n",
      "ACKNOWLEDGEMENT\n",
      "PUSH\n",
      "RESET\n",
      "SYNCHRONISATION\n",
      "FIN\n",
      "CLIENT_URGENT_POINTER\n",
      "CLIENT_ACKNOWLEDGEMENT\n",
      "CLIENT_PUSH\n",
      "CLIENT_RESET\n",
      "CLIENT_SYNCHRONISATION\n",
      "CLIENT_FIN\n",
      "SERVER_URGENT_POINTER\n",
      "SERVER_ACKNOWLEDGEMENT\n",
      "SERVER_PUSH\n",
      "SERVER_RESET\n",
      "SERVER_SYNCHRONISATION\n",
      "SERVER_FIN\n"
     ]
    }
   ],
   "source": [
    "dataset_name = \"NF-ToN-IoT-v2-DDoS-downsample\"\n",
    "netflow_dataset = NetFlowV2(dataset_name, train_size=100000)\n",
    "\n",
    "netflow_dataset.configure(\n",
    "    hps[\"batch_size\"], 52, 1,\n",
    "    hps[\"shuffle_buffer_size\"], True, True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"discriminator\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " sequential (Sequential)     (None, 256)               710400    \n",
      "                                                                 \n",
      " dense_3 (Dense)             multiple                  256       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 710656 (2.71 MB)\n",
      "Trainable params: 710656 (2.71 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "Model: \"encoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_8 (Dense)             (None, 1024)              54272     \n",
      "                                                                 \n",
      " leaky_re_lu_6 (LeakyReLU)   (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 512)               524800    \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 512)               2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_7 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 256)               1024      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_8 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 746368 (2.85 MB)\n",
      "Trainable params: 744832 (2.84 MB)\n",
      "Non-trainable params: 1536 (6.00 KB)\n",
      "_________________________________________________________________\n",
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 256)               33024     \n",
      "                                                                 \n",
      " leaky_re_lu_3 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 512)               131584    \n",
      "                                                                 \n",
      " leaky_re_lu_4 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 512)               2048      \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1024)              525312    \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 1024)              4096      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_5 (LeakyReLU)   (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 52)                53300     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 749364 (2.86 MB)\n",
      "Trainable params: 746292 (2.85 MB)\n",
      "Non-trainable params: 3072 (12.00 KB)\n",
      "_________________________________________________________________\n",
      "Step  50 . d_loss:  1.27068424 , g_loss:  14.1734524 , e_loss:  0.0108600995 lr:  0.0002\n",
      "Step  100 . d_loss:  1.29790235 , g_loss:  8.51597691 , e_loss:  0.00645443145 lr:  0.0002\n",
      "Step  150 . d_loss:  1.31708932 , g_loss:  6.38890266 , e_loss:  0.00485349726 lr:  0.0002\n",
      "Step  200 . d_loss:  1.33177769 , g_loss:  5.25742483 , e_loss:  0.00400447 lr:  0.0002\n",
      "Step  250 . d_loss:  1.34150136 , g_loss:  4.55043411 , e_loss:  0.00342667615 lr:  0.0002\n",
      "Step  300 . d_loss:  1.34824169 , g_loss:  4.07507181 , e_loss:  0.00307443808 lr:  0.0002\n",
      "Step  350 . d_loss:  1.3530513 , g_loss:  3.72638464 , e_loss:  0.002799073 lr:  0.0002\n",
      "Step  400 . d_loss:  1.35720479 , g_loss:  3.45846438 , e_loss:  0.002577584 lr:  0.0002\n",
      "0 Epoch completed\n",
      "Threshold using only benign data: 0.091217540204525\n",
      "Optimized Threshold based on F1-Score value 0.030303030303030304\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.5650234741784037\n",
      "Validation f2-score: 0.5677422398339467\n",
      "Validation accuracy: 0.6288060897435898\n",
      "Validation Current AUC RC: 0.46157488226890564\n",
      "Best AUC-RC on validation set:  0.461574882\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\generator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\generator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Current AUC ROC: 0.6540108323097229\n",
      "Best AUC-ROC on validation set:  0.654010832\n",
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 256)               33024     \n",
      "                                                                 \n",
      " leaky_re_lu_3 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 512)               131584    \n",
      "                                                                 \n",
      " leaky_re_lu_4 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 512)               2048      \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1024)              525312    \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 1024)              4096      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_5 (LeakyReLU)   (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 52)                53300     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 749364 (2.86 MB)\n",
      "Trainable params: 746292 (2.85 MB)\n",
      "Non-trainable params: 3072 (12.00 KB)\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\generator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\generator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_8 (Dense)             (None, 1024)              54272     \n",
      "                                                                 \n",
      " leaky_re_lu_6 (LeakyReLU)   (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 512)               524800    \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 512)               2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_7 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 256)               1024      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_8 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 746368 (2.85 MB)\n",
      "Trainable params: 744832 (2.84 MB)\n",
      "Non-trainable params: 1536 (6.00 KB)\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"discriminator\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " sequential (Sequential)     (None, 256)               710400    \n",
      "                                                                 \n",
      " dense_3 (Dense)             multiple                  256       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 710656 (2.71 MB)\n",
      "Trainable params: 710656 (2.71 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step  450 . d_loss:  1.38384402 , g_loss:  1.54253387 , e_loss:  0.00081029447 lr:  0.0002\n",
      "Step  500 . d_loss:  1.38448656 , g_loss:  1.53091133 , e_loss:  0.000752501423 lr:  0.0002\n",
      "Step  550 . d_loss:  1.38498878 , g_loss:  1.5195297 , e_loss:  0.000705516315 lr:  0.0002\n",
      "Step  600 . d_loss:  1.38552439 , g_loss:  1.51187193 , e_loss:  0.000687349413 lr:  0.0002\n",
      "Step  650 . d_loss:  1.38570368 , g_loss:  1.50238681 , e_loss:  0.000655647716 lr:  0.0002\n",
      "Step  700 . d_loss:  1.38575208 , g_loss:  1.49750233 , e_loss:  0.000647754 lr:  0.0002\n",
      "Step  750 . d_loss:  1.38575828 , g_loss:  1.49308836 , e_loss:  0.000643027131 lr:  0.0002\n",
      "Step  800 . d_loss:  1.38587117 , g_loss:  1.48680425 , e_loss:  0.000626940629 lr:  0.0002\n",
      "1 Epoch completed\n",
      "Threshold using only benign data: 0.09841091185808182\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.4223288036237677\n",
      "Validation f2-score: 0.3926377328577091\n",
      "Validation accuracy: 0.5657051282051282\n",
      "Validation Current AUC RC: 0.4681011736392975\n",
      "Best AUC-RC on validation set:  0.468101174\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\generator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\generator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_rc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Current AUC ROC: 0.6642393469810486\n",
      "Best AUC-ROC on validation set:  0.664239347\n",
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 256)               33024     \n",
      "                                                                 \n",
      " leaky_re_lu_3 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 512)               131584    \n",
      "                                                                 \n",
      " leaky_re_lu_4 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 512)               2048      \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1024)              525312    \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 1024)              4096      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_5 (LeakyReLU)   (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 52)                53300     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 749364 (2.86 MB)\n",
      "Trainable params: 746292 (2.85 MB)\n",
      "Non-trainable params: 3072 (12.00 KB)\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\generator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\generator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_8 (Dense)             (None, 1024)              54272     \n",
      "                                                                 \n",
      " leaky_re_lu_6 (LeakyReLU)   (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 512)               524800    \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 512)               2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_7 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 256)               1024      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_8 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 746368 (2.85 MB)\n",
      "Trainable params: 744832 (2.84 MB)\n",
      "Non-trainable params: 1536 (6.00 KB)\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\encoder\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"discriminator\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " sequential (Sequential)     (None, 256)               710400    \n",
      "                                                                 \n",
      " dense_3 (Dense)             multiple                  256       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 710656 (2.71 MB)\n",
      "Trainable params: 710656 (2.71 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: log\\NF-ToN-IoT-v2-DDoS-downsample\\results\\auc_roc\\discriminator\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step  850 . d_loss:  1.38583791 , g_loss:  1.44402146 , e_loss:  0.000518960471 lr:  0.0002\n",
      "Step  900 . d_loss:  1.38621759 , g_loss:  1.43352437 , e_loss:  0.000478196773 lr:  0.0002\n",
      "Step  950 . d_loss:  1.38636148 , g_loss:  1.42807555 , e_loss:  0.000452177308 lr:  0.0002\n",
      "Step  1000 . d_loss:  1.3863678 , g_loss:  1.42696512 , e_loss:  0.000458490598 lr:  0.0002\n",
      "Step  1050 . d_loss:  1.38632894 , g_loss:  1.4225111 , e_loss:  0.000442908786 lr:  0.0002\n",
      "Step  1100 . d_loss:  1.38636315 , g_loss:  1.42078984 , e_loss:  0.000438205694 lr:  0.0002\n",
      "Step  1150 . d_loss:  1.38647032 , g_loss:  1.41875207 , e_loss:  0.000434798421 lr:  0.0002\n",
      "Step  1200 . d_loss:  1.3864634 , g_loss:  1.41720212 , e_loss:  0.000431631139 lr:  0.0002\n",
      "2 Epoch completed\n",
      "Threshold using only benign data: 0.091200090944767\n",
      "Optimized Threshold based on F1-Score value 0.06060606060606061\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.5658107795512376\n",
      "Validation f2-score: 0.5735321703245169\n",
      "Validation accuracy: 0.6239983974358975\n",
      "Validation Current AUC RC: 0.46040886640548706\n",
      "Validation Current AUC ROC: 0.6526576280593872\n",
      "Step  1250 . d_loss:  1.38604844 , g_loss:  1.39863455 , e_loss:  0.000364994979 lr:  0.0002\n",
      "Step  1300 . d_loss:  1.38594484 , g_loss:  1.39314008 , e_loss:  0.000367164641 lr:  0.0002\n",
      "Step  1350 . d_loss:  1.38613415 , g_loss:  1.39120853 , e_loss:  0.000353822223 lr:  0.0002\n",
      "Step  1400 . d_loss:  1.38610256 , g_loss:  1.3898685 , e_loss:  0.000363023282 lr:  0.0002\n",
      "Step  1450 . d_loss:  1.38618279 , g_loss:  1.38686025 , e_loss:  0.000350983551 lr:  0.0002\n",
      "Step  1500 . d_loss:  1.38616312 , g_loss:  1.38562286 , e_loss:  0.000345448876 lr:  0.0002\n",
      "Step  1550 . d_loss:  1.38618088 , g_loss:  1.38504815 , e_loss:  0.000346854271 lr:  0.0002\n",
      "Step  1600 . d_loss:  1.38618338 , g_loss:  1.38391018 , e_loss:  0.000344426982 lr:  0.0002\n",
      "3 Epoch completed\n",
      "Threshold using only benign data: 0.0826316773891449\n",
      "Optimized Threshold based on F1-Score value 0.05050505050505051\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.6793523969722455\n",
      "Validation f2-score: 0.7280306444344298\n",
      "Validation accuracy: 0.694511217948718\n",
      "Validation Current AUC RC: 0.46300122141838074\n",
      "Validation Current AUC ROC: 0.659290075302124\n",
      "Step  1650 . d_loss:  1.38640559 , g_loss:  1.36673748 , e_loss:  0.000297938444 lr:  0.0002\n",
      "Step  1700 . d_loss:  1.38632035 , g_loss:  1.37156951 , e_loss:  0.00031162487 lr:  0.0002\n",
      "Step  1750 . d_loss:  1.3861587 , g_loss:  1.36841011 , e_loss:  0.000294983067 lr:  0.0002\n",
      "Step  1800 . d_loss:  1.38609409 , g_loss:  1.36727595 , e_loss:  0.000308210874 lr:  0.0002\n",
      "Step  1850 . d_loss:  1.38597357 , g_loss:  1.36555707 , e_loss:  0.000302827393 lr:  0.0002\n",
      "Step  1900 . d_loss:  1.38607442 , g_loss:  1.36496913 , e_loss:  0.000299579173 lr:  0.0002\n",
      "Step  1950 . d_loss:  1.38610291 , g_loss:  1.36437392 , e_loss:  0.000297606981 lr:  0.0002\n",
      "Step  2000 . d_loss:  1.38610125 , g_loss:  1.36456549 , e_loss:  0.000299436942 lr:  0.0002\n",
      "4 Epoch completed\n",
      "Threshold using only benign data: 0.07833632081747055\n",
      "Optimized Threshold based on F1-Score value 0.07070707070707072\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.7272544889156386\n",
      "Validation f2-score: 0.8003267540954652\n",
      "Validation accuracy: 0.7276642628205128\n",
      "Validation Current AUC RC: 0.46408897638320923\n",
      "Validation Current AUC ROC: 0.6599718928337097\n",
      "Step  2050 . d_loss:  1.38656163 , g_loss:  1.35504711 , e_loss:  0.00027637271 lr:  0.0002\n",
      "Step  2100 . d_loss:  1.38618684 , g_loss:  1.35857058 , e_loss:  0.000286094815 lr:  0.0002\n",
      "Step  2150 . d_loss:  1.3863976 , g_loss:  1.35345221 , e_loss:  0.00026565147 lr:  0.0002\n",
      "Step  2200 . d_loss:  1.38629401 , g_loss:  1.35234725 , e_loss:  0.00027364117 lr:  0.0002\n",
      "Step  2250 . d_loss:  1.3863256 , g_loss:  1.35147583 , e_loss:  0.00026937091 lr:  0.0002\n",
      "Step  2300 . d_loss:  1.38631105 , g_loss:  1.35068977 , e_loss:  0.000266754505 lr:  0.0002\n",
      "Step  2350 . d_loss:  1.38627779 , g_loss:  1.3506397 , e_loss:  0.000265112 lr:  0.0002\n",
      "Step  2400 . d_loss:  1.38625085 , g_loss:  1.35021031 , e_loss:  0.000266808638 lr:  0.0002\n",
      "5 Epoch completed\n",
      "Threshold using only benign data: 0.10381712019443512\n",
      "Optimized Threshold based on F1-Score value 0.05050505050505051\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16219421793921426\n",
      "Validation f2-score: 0.140812438861144\n",
      "Validation accuracy: 0.433994391025641\n",
      "Validation Current AUC RC: 0.4362434446811676\n",
      "Validation Current AUC ROC: 0.6146873235702515\n",
      "Step  2450 . d_loss:  1.38572335 , g_loss:  1.34166932 , e_loss:  0.000259401801 lr:  0.0002\n",
      "Step  2500 . d_loss:  1.38623536 , g_loss:  1.34419715 , e_loss:  0.000262519141 lr:  0.0002\n",
      "Step  2550 . d_loss:  1.3862952 , g_loss:  1.33961952 , e_loss:  0.000242744063 lr:  0.0002\n",
      "Step  2600 . d_loss:  1.38622713 , g_loss:  1.33979535 , e_loss:  0.000250782556 lr:  0.0002\n",
      "Step  2650 . d_loss:  1.38623381 , g_loss:  1.33861506 , e_loss:  0.000245878909 lr:  0.0002\n",
      "Step  2700 . d_loss:  1.38628232 , g_loss:  1.33866704 , e_loss:  0.000240661611 lr:  0.0002\n",
      "Step  2750 . d_loss:  1.38628542 , g_loss:  1.33840823 , e_loss:  0.000240515088 lr:  0.0002\n",
      "Step  2800 . d_loss:  1.38625395 , g_loss:  1.33900237 , e_loss:  0.000242767186 lr:  0.0002\n",
      "6 Epoch completed\n",
      "Threshold using only benign data: 0.09088820219039917\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.2451630735212825\n",
      "Validation f2-score: 0.22270764286431655\n",
      "Validation accuracy: 0.45292467948717946\n",
      "Validation Current AUC RC: 0.4380408525466919\n",
      "Validation Current AUC ROC: 0.6177505850791931\n",
      "Step  2850 . d_loss:  1.38595629 , g_loss:  1.33529544 , e_loss:  0.000259443681 lr:  0.0002\n",
      "Step  2900 . d_loss:  1.38644195 , g_loss:  1.3359412 , e_loss:  0.00024049732 lr:  0.0002\n",
      "Step  2950 . d_loss:  1.38641 , g_loss:  1.33258021 , e_loss:  0.000224699848 lr:  0.0002\n",
      "Step  3000 . d_loss:  1.38641024 , g_loss:  1.33312595 , e_loss:  0.00023092945 lr:  0.0002\n",
      "Step  3050 . d_loss:  1.3864162 , g_loss:  1.33202171 , e_loss:  0.000226889591 lr:  0.0002\n",
      "Step  3100 . d_loss:  1.38638449 , g_loss:  1.33217227 , e_loss:  0.000220941554 lr:  0.0002\n",
      "Step  3150 . d_loss:  1.38638473 , g_loss:  1.33142483 , e_loss:  0.000221341485 lr:  0.0002\n",
      "Step  3200 . d_loss:  1.38636482 , g_loss:  1.3312031 , e_loss:  0.000222559771 lr:  0.0002\n",
      "7 Epoch completed\n",
      "Threshold using only benign data: 0.09675994515419006\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.06033182503770738\n",
      "Validation f2-score: 0.05179200331468821\n",
      "Validation accuracy: 0.3760016025641026\n",
      "Validation Current AUC RC: 0.42774635553359985\n",
      "Validation Current AUC ROC: 0.5987621545791626\n",
      "Step  3250 . d_loss:  1.38610065 , g_loss:  1.32802355 , e_loss:  0.00024934992 lr:  0.0002\n",
      "Step  3300 . d_loss:  1.38621426 , g_loss:  1.32882786 , e_loss:  0.00022453416 lr:  0.0002\n",
      "Step  3350 . d_loss:  1.38614261 , g_loss:  1.32720804 , e_loss:  0.000212356521 lr:  0.0002\n",
      "Step  3400 . d_loss:  1.38621533 , g_loss:  1.3262645 , e_loss:  0.000217418492 lr:  0.0002\n",
      "Step  3450 . d_loss:  1.38622594 , g_loss:  1.32558787 , e_loss:  0.000215689855 lr:  0.0002\n",
      "Step  3500 . d_loss:  1.38629258 , g_loss:  1.32432389 , e_loss:  0.000208589932 lr:  0.0002\n",
      "Step  3550 . d_loss:  1.38625991 , g_loss:  1.32430959 , e_loss:  0.000210884391 lr:  0.0002\n",
      "Step  3600 . d_loss:  1.38624835 , g_loss:  1.32430899 , e_loss:  0.000211394843 lr:  0.0002\n",
      "8 Epoch completed\n",
      "Threshold using only benign data: 0.10136125981807709\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16315711971727284\n",
      "Validation f2-score: 0.1422774667420001\n",
      "Validation accuracy: 0.43078926282051283\n",
      "Validation Current AUC RC: 0.432006299495697\n",
      "Validation Current AUC ROC: 0.6084393262863159\n",
      "Step  3650 . d_loss:  1.38590884 , g_loss:  1.30954671 , e_loss:  0.000162992292 lr:  0.0002\n",
      "Step  3700 . d_loss:  1.38638103 , g_loss:  1.31537426 , e_loss:  0.000210462895 lr:  0.0002\n",
      "Step  3750 . d_loss:  1.38631618 , g_loss:  1.31568182 , e_loss:  0.000199596965 lr:  0.0002\n",
      "Step  3800 . d_loss:  1.38635588 , g_loss:  1.31566358 , e_loss:  0.000201044648 lr:  0.0002\n",
      "Step  3850 . d_loss:  1.3863554 , g_loss:  1.31599009 , e_loss:  0.000201162664 lr:  0.0002\n",
      "Step  3900 . d_loss:  1.38635039 , g_loss:  1.3158356 , e_loss:  0.000195388115 lr:  0.0002\n",
      "Step  3950 . d_loss:  1.38627172 , g_loss:  1.31667066 , e_loss:  0.000198970316 lr:  0.0002\n",
      "Step  4000 . d_loss:  1.386271 , g_loss:  1.31690931 , e_loss:  0.000200326685 lr:  0.0002\n",
      "9 Epoch completed\n",
      "Threshold using only benign data: 0.11065731197595596\n",
      "Optimized Threshold based on F1-Score value 0.06060606060606061\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.13159956133479556\n",
      "Validation f2-score: 0.11017260374586853\n",
      "Validation accuracy: 0.44481169871794873\n",
      "Validation Current AUC RC: 0.4297213852405548\n",
      "Validation Current AUC ROC: 0.6055315732955933\n",
      "Step  4050 . d_loss:  1.38653445 , g_loss:  1.30769336 , e_loss:  0.000155514557 lr:  0.0002\n",
      "Step  4100 . d_loss:  1.38635278 , g_loss:  1.31798899 , e_loss:  0.000200643059 lr:  0.0002\n",
      "Step  4150 . d_loss:  1.38624609 , g_loss:  1.31689715 , e_loss:  0.000198765949 lr:  0.0002\n",
      "Step  4200 . d_loss:  1.38625169 , g_loss:  1.31574786 , e_loss:  0.000198828478 lr:  0.0002\n",
      "Step  4250 . d_loss:  1.38627708 , g_loss:  1.31479812 , e_loss:  0.000196913519 lr:  0.0002\n",
      "Step  4300 . d_loss:  1.38626838 , g_loss:  1.31408465 , e_loss:  0.000190302773 lr:  0.0002\n",
      "Step  4350 . d_loss:  1.38629293 , g_loss:  1.31431425 , e_loss:  0.000191958374 lr:  0.0002\n",
      "Step  4400 . d_loss:  1.38631 , g_loss:  1.31414497 , e_loss:  0.000192341453 lr:  0.0002\n",
      "10 Epoch completed\n",
      "Threshold using only benign data: 0.1115458533167839\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1515645371577575\n",
      "Validation f2-score: 0.12357818645689382\n",
      "Validation accuracy: 0.4785657051282051\n",
      "Validation Current AUC RC: 0.42316678166389465\n",
      "Validation Current AUC ROC: 0.5941251516342163\n",
      "Step  4450 . d_loss:  1.38612795 , g_loss:  1.30309427 , e_loss:  0.000120876975 lr:  0.0002\n",
      "Step  4500 . d_loss:  1.38612592 , g_loss:  1.31032908 , e_loss:  0.000187369733 lr:  0.0002\n",
      "Step  4550 . d_loss:  1.38612652 , g_loss:  1.3118906 , e_loss:  0.000192458057 lr:  0.0002\n",
      "Step  4600 . d_loss:  1.38624835 , g_loss:  1.31078172 , e_loss:  0.000193283544 lr:  0.0002\n",
      "Step  4650 . d_loss:  1.38629436 , g_loss:  1.30960405 , e_loss:  0.000188822829 lr:  0.0002\n",
      "Step  4700 . d_loss:  1.386289 , g_loss:  1.30841732 , e_loss:  0.000181413925 lr:  0.0002\n",
      "Step  4750 . d_loss:  1.3862505 , g_loss:  1.30951762 , e_loss:  0.000182780394 lr:  0.0002\n",
      "Step  4800 . d_loss:  1.38624585 , g_loss:  1.31070483 , e_loss:  0.000186622245 lr:  0.0002\n",
      "11 Epoch completed\n",
      "Threshold using only benign data: 0.10980136692523956\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.15590703679793413\n",
      "Validation f2-score: 0.1279537988767617\n",
      "Validation accuracy: 0.476161858974359\n",
      "Validation Current AUC RC: 0.42472517490386963\n",
      "Validation Current AUC ROC: 0.596433699131012\n",
      "Step  4850 . d_loss:  1.38696194 , g_loss:  1.29144025 , e_loss:  8.13982333e-05 lr:  0.0002\n",
      "Step  4900 . d_loss:  1.38622046 , g_loss:  1.30490446 , e_loss:  0.000181311945 lr:  0.0002\n",
      "Step  4950 . d_loss:  1.3862828 , g_loss:  1.30844605 , e_loss:  0.000191324158 lr:  0.0002\n",
      "Step  5000 . d_loss:  1.38624775 , g_loss:  1.30733204 , e_loss:  0.000177502181 lr:  0.0002\n",
      "Step  5050 . d_loss:  1.38624561 , g_loss:  1.30791628 , e_loss:  0.000186086414 lr:  0.0002\n",
      "Step  5100 . d_loss:  1.38618875 , g_loss:  1.30637276 , e_loss:  0.000176212518 lr:  0.0002\n",
      "Step  5150 . d_loss:  1.38616586 , g_loss:  1.30649519 , e_loss:  0.000173604989 lr:  0.0002\n",
      "Step  5200 . d_loss:  1.38616633 , g_loss:  1.30601037 , e_loss:  0.000178058312 lr:  0.0002\n",
      "Step  5250 . d_loss:  1.38617074 , g_loss:  1.30578268 , e_loss:  0.000175558729 lr:  0.0002\n",
      "12 Epoch completed\n",
      "Threshold using only benign data: 0.10334613919258118\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16646341463414635\n",
      "Validation f2-score: 0.14190664310219359\n",
      "Validation accuracy: 0.45232371794871795\n",
      "Validation Current AUC RC: 0.4261512756347656\n",
      "Validation Current AUC ROC: 0.5994435548782349\n",
      "Step  5300 . d_loss:  1.38580692 , g_loss:  1.30383742 , e_loss:  0.000175436711 lr:  0.0002\n",
      "Step  5350 . d_loss:  1.38588524 , g_loss:  1.30390561 , e_loss:  0.000181550553 lr:  0.0002\n",
      "Step  5400 . d_loss:  1.38583028 , g_loss:  1.30173731 , e_loss:  0.000163177174 lr:  0.0002\n",
      "Step  5450 . d_loss:  1.38577259 , g_loss:  1.30112374 , e_loss:  0.000170507876 lr:  0.0002\n",
      "Step  5500 . d_loss:  1.38574922 , g_loss:  1.30040979 , e_loss:  0.000167567661 lr:  0.0002\n",
      "Step  5550 . d_loss:  1.38574326 , g_loss:  1.29995716 , e_loss:  0.00016344458 lr:  0.0002\n",
      "Step  5600 . d_loss:  1.38572764 , g_loss:  1.29971 , e_loss:  0.00016799006 lr:  0.0002\n",
      "Step  5650 . d_loss:  1.38567591 , g_loss:  1.29888 , e_loss:  0.000164571684 lr:  0.0002\n",
      "13 Epoch completed\n",
      "Threshold using only benign data: 0.10651298612356186\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.15626000640409862\n",
      "Validation f2-score: 0.12893679983090253\n",
      "Validation accuracy: 0.47215544871794873\n",
      "Validation Current AUC RC: 0.4273533225059509\n",
      "Validation Current AUC ROC: 0.6016175150871277\n",
      "Step  5700 . d_loss:  1.38476562 , g_loss:  1.29942334 , e_loss:  0.000169888619 lr:  0.0002\n",
      "Step  5750 . d_loss:  1.38497806 , g_loss:  1.29832101 , e_loss:  0.000171946784 lr:  0.0002\n",
      "Step  5800 . d_loss:  1.38483834 , g_loss:  1.29751301 , e_loss:  0.000154742826 lr:  0.0002\n",
      "Step  5850 . d_loss:  1.38485801 , g_loss:  1.29706645 , e_loss:  0.000164031269 lr:  0.0002\n",
      "Step  5900 . d_loss:  1.38508582 , g_loss:  1.29673171 , e_loss:  0.000161462231 lr:  0.0002\n",
      "Step  5950 . d_loss:  1.38529253 , g_loss:  1.29677665 , e_loss:  0.000158900031 lr:  0.0002\n",
      "Step  6000 . d_loss:  1.38545477 , g_loss:  1.29758811 , e_loss:  0.000161409902 lr:  0.0002\n",
      "Step  6050 . d_loss:  1.38557446 , g_loss:  1.29747546 , e_loss:  0.000161699456 lr:  0.0002\n",
      "14 Epoch completed\n",
      "Threshold using only benign data: 0.10523020476102829\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.15339596488526105\n",
      "Validation f2-score: 0.12988367847269314\n",
      "Validation accuracy: 0.44941907051282054\n",
      "Validation Current AUC RC: 0.43190598487854004\n",
      "Validation Current AUC ROC: 0.6096987724304199\n",
      "Step  6100 . d_loss:  1.38616335 , g_loss:  1.29371285 , e_loss:  0.000152402645 lr:  0.0002\n",
      "Step  6150 . d_loss:  1.3862884 , g_loss:  1.29605567 , e_loss:  0.000169028426 lr:  0.0002\n",
      "Step  6200 . d_loss:  1.38627565 , g_loss:  1.29608393 , e_loss:  0.000155627393 lr:  0.0002\n",
      "Step  6250 . d_loss:  1.38629377 , g_loss:  1.29569697 , e_loss:  0.000163069955 lr:  0.0002\n",
      "Step  6300 . d_loss:  1.38629031 , g_loss:  1.29573894 , e_loss:  0.000160373602 lr:  0.0002\n",
      "Step  6350 . d_loss:  1.38628078 , g_loss:  1.2955153 , e_loss:  0.000156481008 lr:  0.0002\n",
      "Step  6400 . d_loss:  1.38623261 , g_loss:  1.29558253 , e_loss:  0.00015951239 lr:  0.0002\n",
      "Step  6450 . d_loss:  1.38603449 , g_loss:  1.29567814 , e_loss:  0.00016053603 lr:  0.0002\n",
      "15 Epoch completed\n",
      "Threshold using only benign data: 0.10375875979661942\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16308745720510426\n",
      "Validation f2-score: 0.13714405360134005\n",
      "Validation accuracy: 0.461338141025641\n",
      "Validation Current AUC RC: 0.43441879749298096\n",
      "Validation Current AUC ROC: 0.6138923168182373\n",
      "Step  6500 . d_loss:  1.38525927 , g_loss:  1.29291749 , e_loss:  0.000144847218 lr:  0.0002\n",
      "Step  6550 . d_loss:  1.38440716 , g_loss:  1.29414713 , e_loss:  0.000162993965 lr:  0.0002\n",
      "Step  6600 . d_loss:  1.38403904 , g_loss:  1.29346371 , e_loss:  0.000147479455 lr:  0.0002\n",
      "Step  6650 . d_loss:  1.38394701 , g_loss:  1.29441428 , e_loss:  0.000158370065 lr:  0.0002\n",
      "Step  6700 . d_loss:  1.38398767 , g_loss:  1.29378319 , e_loss:  0.000154461552 lr:  0.0002\n",
      "Step  6750 . d_loss:  1.38446271 , g_loss:  1.29328322 , e_loss:  0.000150874068 lr:  0.0002\n",
      "Step  6800 . d_loss:  1.38471544 , g_loss:  1.29276168 , e_loss:  0.00014964091 lr:  0.0002\n",
      "Step  6850 . d_loss:  1.38490546 , g_loss:  1.292871 , e_loss:  0.000153082379 lr:  0.0002\n",
      "16 Epoch completed\n",
      "Threshold using only benign data: 0.10628259181976318\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1623335447051363\n",
      "Validation f2-score: 0.13483619509111977\n",
      "Validation accuracy: 0.4707532051282051\n",
      "Validation Current AUC RC: 0.4327164590358734\n",
      "Validation Current AUC ROC: 0.6111947298049927\n",
      "Step  6900 . d_loss:  1.38637841 , g_loss:  1.2884196 , e_loss:  0.000137489507 lr:  0.0002\n",
      "Step  6950 . d_loss:  1.38627338 , g_loss:  1.29220653 , e_loss:  0.00015361706 lr:  0.0002\n",
      "Step  7000 . d_loss:  1.38626993 , g_loss:  1.29059362 , e_loss:  0.000143268466 lr:  0.0002\n",
      "Step  7050 . d_loss:  1.38619184 , g_loss:  1.29018927 , e_loss:  0.000151839646 lr:  0.0002\n",
      "Step  7100 . d_loss:  1.38618946 , g_loss:  1.29035223 , e_loss:  0.000150476844 lr:  0.0002\n",
      "Step  7150 . d_loss:  1.38620389 , g_loss:  1.29072058 , e_loss:  0.000147294413 lr:  0.0002\n",
      "Step  7200 . d_loss:  1.38620901 , g_loss:  1.29052031 , e_loss:  0.00014652306 lr:  0.0002\n",
      "Step  7250 . d_loss:  1.38619721 , g_loss:  1.29090202 , e_loss:  0.000149449319 lr:  0.0002\n",
      "17 Epoch completed\n",
      "Threshold using only benign data: 0.10544577986001968\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1676665614145879\n",
      "Validation f2-score: 0.13964864296233956\n",
      "Validation accuracy: 0.4719551282051282\n",
      "Validation Current AUC RC: 0.42850250005722046\n",
      "Validation Current AUC ROC: 0.6035286784172058\n",
      "Step  7300 . d_loss:  1.38642144 , g_loss:  1.28211975 , e_loss:  0.000146248232 lr:  0.0002\n",
      "Step  7350 . d_loss:  1.38630295 , g_loss:  1.28746009 , e_loss:  0.000154839203 lr:  0.0002\n",
      "Step  7400 . d_loss:  1.38626647 , g_loss:  1.28674686 , e_loss:  0.000141920464 lr:  0.0002\n",
      "Step  7450 . d_loss:  1.38621938 , g_loss:  1.28753459 , e_loss:  0.000151173721 lr:  0.0002\n",
      "Step  7500 . d_loss:  1.38617671 , g_loss:  1.28625751 , e_loss:  0.000147528946 lr:  0.0002\n",
      "Step  7550 . d_loss:  1.38614285 , g_loss:  1.28670788 , e_loss:  0.000142639372 lr:  0.0002\n",
      "Step  7600 . d_loss:  1.3859663 , g_loss:  1.28712142 , e_loss:  0.000142543402 lr:  0.0002\n",
      "Step  7650 . d_loss:  1.38594258 , g_loss:  1.28758073 , e_loss:  0.000146434701 lr:  0.0002\n",
      "18 Epoch completed\n",
      "Threshold using only benign data: 0.10597454756498337\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16430955029397742\n",
      "Validation f2-score: 0.1362606082968742\n",
      "Validation accuracy: 0.47325721153846156\n",
      "Validation Current AUC RC: 0.4262280762195587\n",
      "Validation Current AUC ROC: 0.5995983481407166\n",
      "Step  7700 . d_loss:  1.38597047 , g_loss:  1.28471625 , e_loss:  0.000143809317 lr:  0.0002\n",
      "Step  7750 . d_loss:  1.38513684 , g_loss:  1.28820062 , e_loss:  0.000153888497 lr:  0.0002\n",
      "Step  7800 . d_loss:  1.3844372 , g_loss:  1.28583479 , e_loss:  0.000140209595 lr:  0.0002\n",
      "Step  7850 . d_loss:  1.38482201 , g_loss:  1.28662395 , e_loss:  0.000147546583 lr:  0.0002\n",
      "Step  7900 . d_loss:  1.38475156 , g_loss:  1.28552401 , e_loss:  0.000144534511 lr:  0.0002\n",
      "Step  7950 . d_loss:  1.38465178 , g_loss:  1.28464174 , e_loss:  0.000137258656 lr:  0.0002\n",
      "Step  8000 . d_loss:  1.38463664 , g_loss:  1.28442538 , e_loss:  0.000137897354 lr:  0.0002\n",
      "Step  8050 . d_loss:  1.38437343 , g_loss:  1.28511429 , e_loss:  0.000141005366 lr:  0.0002\n",
      "19 Epoch completed\n",
      "Threshold using only benign data: 0.10200929641723633\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.182010582010582\n",
      "Validation f2-score: 0.15601513502306535\n",
      "Validation accuracy: 0.4580328525641026\n",
      "Validation Current AUC RC: 0.43134668469429016\n",
      "Validation Current AUC ROC: 0.6084361672401428\n",
      "Step  8100 . d_loss:  1.385216 , g_loss:  1.27990413 , e_loss:  0.000143782236 lr:  0.0002\n",
      "Step  8150 . d_loss:  1.38339067 , g_loss:  1.28432512 , e_loss:  0.000148175546 lr:  0.0002\n",
      "Step  8200 . d_loss:  1.38341188 , g_loss:  1.28381312 , e_loss:  0.000138443633 lr:  0.0002\n",
      "Step  8250 . d_loss:  1.3829248 , g_loss:  1.28378284 , e_loss:  0.000143852885 lr:  0.0002\n",
      "Step  8300 . d_loss:  1.38295472 , g_loss:  1.2831918 , e_loss:  0.000141479657 lr:  0.0002\n",
      "Step  8350 . d_loss:  1.38316178 , g_loss:  1.28254986 , e_loss:  0.000133829672 lr:  0.0002\n",
      "Step  8400 . d_loss:  1.38332093 , g_loss:  1.28289962 , e_loss:  0.000134755843 lr:  0.0002\n",
      "Step  8450 . d_loss:  1.38321352 , g_loss:  1.28376579 , e_loss:  0.000137108102 lr:  0.0002\n",
      "20 Epoch completed\n",
      "Threshold using only benign data: 0.10389818996191025\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1721771678103556\n",
      "Validation f2-score: 0.1445783132530121\n",
      "Validation accuracy: 0.468349358974359\n",
      "Validation Current AUC RC: 0.4312427043914795\n",
      "Validation Current AUC ROC: 0.6082369089126587\n",
      "Step  8500 . d_loss:  1.38316929 , g_loss:  1.28281879 , e_loss:  0.000157511589 lr:  0.0002\n",
      "Step  8550 . d_loss:  1.38231218 , g_loss:  1.28375947 , e_loss:  0.000146831007 lr:  0.0002\n",
      "Step  8600 . d_loss:  1.38266468 , g_loss:  1.28197563 , e_loss:  0.000134086105 lr:  0.0002\n",
      "Step  8650 . d_loss:  1.38232255 , g_loss:  1.28244841 , e_loss:  0.000135865412 lr:  0.0002\n",
      "Step  8700 . d_loss:  1.38218713 , g_loss:  1.28254068 , e_loss:  0.000136812931 lr:  0.0002\n",
      "Step  8750 . d_loss:  1.38245976 , g_loss:  1.28238082 , e_loss:  0.000131270659 lr:  0.0002\n",
      "Step  8800 . d_loss:  1.38257265 , g_loss:  1.28298867 , e_loss:  0.000132470857 lr:  0.0002\n",
      "Step  8850 . d_loss:  1.38266349 , g_loss:  1.28332174 , e_loss:  0.000135487207 lr:  0.0002\n",
      "21 Epoch completed\n",
      "Threshold using only benign data: 0.10463066399097443\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.17509481668773702\n",
      "Validation f2-score: 0.14574344943701986\n",
      "Validation accuracy: 0.47716346153846156\n",
      "Validation Current AUC RC: 0.4311286509037018\n",
      "Validation Current AUC ROC: 0.6080843210220337\n",
      "Step  8900 . d_loss:  1.38433886 , g_loss:  1.27585411 , e_loss:  8.8424451e-05 lr:  0.0002\n",
      "Step  8950 . d_loss:  1.38216829 , g_loss:  1.27898765 , e_loss:  0.000153000059 lr:  0.0002\n",
      "Step  9000 . d_loss:  1.38239956 , g_loss:  1.27839267 , e_loss:  0.000137695955 lr:  0.0002\n",
      "Step  9050 . d_loss:  1.38218439 , g_loss:  1.28050864 , e_loss:  0.000138116913 lr:  0.0002\n",
      "Step  9100 . d_loss:  1.38182926 , g_loss:  1.28026569 , e_loss:  0.00013926781 lr:  0.0002\n",
      "Step  9150 . d_loss:  1.38211584 , g_loss:  1.2798394 , e_loss:  0.000132668763 lr:  0.0002\n",
      "Step  9200 . d_loss:  1.382254 , g_loss:  1.28030956 , e_loss:  0.000133172332 lr:  0.0002\n",
      "Step  9250 . d_loss:  1.38233614 , g_loss:  1.28034055 , e_loss:  0.000134016111 lr:  0.0002\n",
      "22 Epoch completed\n",
      "Threshold using only benign data: 0.10561458021402359\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16916215355208666\n",
      "Validation f2-score: 0.14006119434479847\n",
      "Validation accuracy: 0.4775641025641026\n",
      "Validation Current AUC RC: 0.4307570457458496\n",
      "Validation Current AUC ROC: 0.6074766516685486\n",
      "Step  9300 . d_loss:  1.38488412 , g_loss:  1.26680553 , e_loss:  7.30366082e-05 lr:  0.0002\n",
      "Step  9350 . d_loss:  1.38184822 , g_loss:  1.27596402 , e_loss:  0.000130402928 lr:  0.0002\n",
      "Step  9400 . d_loss:  1.38231516 , g_loss:  1.27611458 , e_loss:  0.000133230336 lr:  0.0002\n",
      "Step  9450 . d_loss:  1.38184822 , g_loss:  1.27716041 , e_loss:  0.000135273352 lr:  0.0002\n",
      "Step  9500 . d_loss:  1.38167119 , g_loss:  1.27700567 , e_loss:  0.000135155846 lr:  0.0002\n",
      "Step  9550 . d_loss:  1.382285 , g_loss:  1.27660966 , e_loss:  0.000128441796 lr:  0.0002\n",
      "Step  9600 . d_loss:  1.382388 , g_loss:  1.27711725 , e_loss:  0.000128770465 lr:  0.0002\n",
      "Step  9650 . d_loss:  1.38248539 , g_loss:  1.27698064 , e_loss:  0.000129385662 lr:  0.0002\n",
      "23 Epoch completed\n",
      "Threshold using only benign data: 0.10639864951372147\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16129549462882797\n",
      "Validation f2-score: 0.1329632566745969\n",
      "Validation accuracy: 0.47606169871794873\n",
      "Validation Current AUC RC: 0.4406892657279968\n",
      "Validation Current AUC ROC: 0.6248465180397034\n",
      "Step  9700 . d_loss:  1.38645089 , g_loss:  1.26930141 , e_loss:  6.09577655e-05 lr:  0.0002\n",
      "Step  9750 . d_loss:  1.3812052 , g_loss:  1.27729118 , e_loss:  0.000129666194 lr:  0.0002\n",
      "Step  9800 . d_loss:  1.38182473 , g_loss:  1.27776253 , e_loss:  0.000134537244 lr:  0.0002\n",
      "Step  9850 . d_loss:  1.38139701 , g_loss:  1.27829337 , e_loss:  0.000134470421 lr:  0.0002\n",
      "Step  9900 . d_loss:  1.3813864 , g_loss:  1.27788544 , e_loss:  0.000133933514 lr:  0.0002\n",
      "Step  9950 . d_loss:  1.38169897 , g_loss:  1.27672982 , e_loss:  0.000124732484 lr:  0.0002\n",
      "Step  10000 . d_loss:  1.38191807 , g_loss:  1.27705169 , e_loss:  0.000121673351 lr:  0.0002\n",
      "Step  10050 . d_loss:  1.38189042 , g_loss:  1.27758467 , e_loss:  0.000130255867 lr:  0.0002\n",
      "Step  10100 . d_loss:  1.38197041 , g_loss:  1.27724826 , e_loss:  0.000125988401 lr:  0.0002\n",
      "24 Epoch completed\n",
      "Threshold using only benign data: 0.10600852221250534\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16912235746316462\n",
      "Validation f2-score: 0.1395201352922524\n",
      "Validation accuracy: 0.48036858974358976\n",
      "Validation Current AUC RC: 0.43737202882766724\n",
      "Validation Current AUC ROC: 0.619076669216156\n",
      "Step  10150 . d_loss:  1.38130617 , g_loss:  1.27687633 , e_loss:  0.00013475545 lr:  0.0002\n",
      "Step  10200 . d_loss:  1.38143337 , g_loss:  1.27853322 , e_loss:  0.000138169242 lr:  0.0002\n",
      "Step  10250 . d_loss:  1.38114476 , g_loss:  1.27625847 , e_loss:  0.000120930599 lr:  0.0002\n",
      "Step  10300 . d_loss:  1.3812623 , g_loss:  1.27694118 , e_loss:  0.000133877067 lr:  0.0002\n",
      "Step  10350 . d_loss:  1.38139904 , g_loss:  1.27583671 , e_loss:  0.000124514263 lr:  0.0002\n",
      "Step  10400 . d_loss:  1.3816756 , g_loss:  1.27612364 , e_loss:  0.000119489334 lr:  0.0002\n",
      "Step  10450 . d_loss:  1.38179421 , g_loss:  1.2761246 , e_loss:  0.000126254803 lr:  0.0002\n",
      "Step  10500 . d_loss:  1.38200223 , g_loss:  1.27572811 , e_loss:  0.000122707745 lr:  0.0002\n",
      "25 Epoch completed\n",
      "Threshold using only benign data: 0.10605867207050323\n",
      "Optimized Threshold based on F1-Score value 0.05050505050505051\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16764940239043827\n",
      "Validation f2-score: 0.13876431171846146\n",
      "Validation accuracy: 0.4768629807692308\n",
      "Validation Current AUC RC: 0.4364834725856781\n",
      "Validation Current AUC ROC: 0.6176915764808655\n",
      "Step  10550 . d_loss:  1.38149095 , g_loss:  1.27197778 , e_loss:  0.000123925318 lr:  0.0002\n",
      "Step  10600 . d_loss:  1.38140905 , g_loss:  1.27385378 , e_loss:  0.000128768603 lr:  0.0002\n",
      "Step  10650 . d_loss:  1.38121545 , g_loss:  1.27265894 , e_loss:  0.000114369883 lr:  0.0002\n",
      "Step  10700 . d_loss:  1.38143241 , g_loss:  1.27298427 , e_loss:  0.000121515186 lr:  0.0002\n",
      "Step  10750 . d_loss:  1.38158298 , g_loss:  1.27250731 , e_loss:  0.000119619159 lr:  0.0002\n",
      "Step  10800 . d_loss:  1.38187957 , g_loss:  1.27281809 , e_loss:  0.000114950046 lr:  0.0002\n",
      "Step  10850 . d_loss:  1.38199985 , g_loss:  1.27382743 , e_loss:  0.0001240446 lr:  0.0002\n",
      "Step  10900 . d_loss:  1.38215101 , g_loss:  1.27353883 , e_loss:  0.000120376106 lr:  0.0002\n",
      "26 Epoch completed\n",
      "Threshold using only benign data: 0.10665062069892883\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16412397623253572\n",
      "Validation f2-score: 0.13514943136736313\n",
      "Validation accuracy: 0.47866586538461536\n",
      "Validation Current AUC RC: 0.4360751807689667\n",
      "Validation Current AUC ROC: 0.617019534111023\n",
      "Step  10950 . d_loss:  1.38241971 , g_loss:  1.27188849 , e_loss:  0.000123609512 lr:  0.0002\n",
      "Step  11000 . d_loss:  1.38150632 , g_loss:  1.27500665 , e_loss:  0.000146114 lr:  0.0002\n",
      "Step  11050 . d_loss:  1.38148856 , g_loss:  1.2744441 , e_loss:  0.000131852343 lr:  0.0002\n",
      "Step  11100 . d_loss:  1.38270652 , g_loss:  1.27412534 , e_loss:  0.000133369918 lr:  0.0002\n",
      "Step  11150 . d_loss:  1.38392901 , g_loss:  1.27383173 , e_loss:  0.000128952917 lr:  0.0002\n",
      "Step  11200 . d_loss:  1.38431597 , g_loss:  1.27426422 , e_loss:  0.000121587975 lr:  0.0002\n",
      "Step  11250 . d_loss:  1.3845644 , g_loss:  1.2738198 , e_loss:  0.000123641512 lr:  0.0002\n",
      "Step  11300 . d_loss:  1.38477337 , g_loss:  1.27400327 , e_loss:  0.00012398223 lr:  0.0002\n",
      "27 Epoch completed\n",
      "Threshold using only benign data: 0.10737256705760956\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16194331983805668\n",
      "Validation f2-score: 0.1326048904683605\n",
      "Validation accuracy: 0.4816706730769231\n",
      "Validation Current AUC RC: 0.4355919361114502\n",
      "Validation Current AUC ROC: 0.6162487268447876\n",
      "Step  11350 . d_loss:  1.38623 , g_loss:  1.26831305 , e_loss:  0.00010117887 lr:  0.0002\n",
      "Step  11400 . d_loss:  1.38620257 , g_loss:  1.27119637 , e_loss:  0.000114521892 lr:  0.0002\n",
      "Step  11450 . d_loss:  1.38608444 , g_loss:  1.26980817 , e_loss:  0.00010059496 lr:  0.0002\n",
      "Step  11500 . d_loss:  1.38611603 , g_loss:  1.27005613 , e_loss:  0.000105309817 lr:  0.0002\n",
      "Step  11550 . d_loss:  1.38614225 , g_loss:  1.27000523 , e_loss:  0.000102993501 lr:  0.0002\n",
      "Step  11600 . d_loss:  1.38615012 , g_loss:  1.2696209 , e_loss:  9.78085445e-05 lr:  0.0002\n",
      "Step  11650 . d_loss:  1.38611937 , g_loss:  1.26993906 , e_loss:  0.000101167665 lr:  0.0002\n",
      "Step  11700 . d_loss:  1.38609767 , g_loss:  1.27020621 , e_loss:  0.000101666694 lr:  0.0002\n",
      "28 Epoch completed\n",
      "Threshold using only benign data: 0.10584302246570587\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16666666666666666\n",
      "Validation f2-score: 0.13884506153360682\n",
      "Validation accuracy: 0.47115384615384615\n",
      "Validation Current AUC RC: 0.43558868765830994\n",
      "Validation Current AUC ROC: 0.6162203550338745\n",
      "Step  11750 . d_loss:  1.38594365 , g_loss:  1.26446414 , e_loss:  8.83641e-05 lr:  0.0002\n",
      "Step  11800 . d_loss:  1.3858813 , g_loss:  1.26728439 , e_loss:  9.76370284e-05 lr:  0.0002\n",
      "Step  11850 . d_loss:  1.38602436 , g_loss:  1.26732934 , e_loss:  8.97167e-05 lr:  0.0002\n",
      "Step  11900 . d_loss:  1.38592649 , g_loss:  1.26787412 , e_loss:  9.80218829e-05 lr:  0.0002\n",
      "Step  11950 . d_loss:  1.38596928 , g_loss:  1.26834273 , e_loss:  9.57584416e-05 lr:  0.0002\n",
      "Step  12000 . d_loss:  1.38601792 , g_loss:  1.26820433 , e_loss:  9.08211878e-05 lr:  0.0002\n",
      "Step  12050 . d_loss:  1.38604 , g_loss:  1.26879609 , e_loss:  9.21781393e-05 lr:  0.0002\n",
      "Step  12100 . d_loss:  1.38601065 , g_loss:  1.26913989 , e_loss:  9.67778615e-05 lr:  0.0002\n",
      "29 Epoch completed\n",
      "Threshold using only benign data: 0.10653116554021835\n",
      "Optimized Threshold based on F1-Score value 0.05050505050505051\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16470588235294117\n",
      "Validation f2-score: 0.13530688979505376\n",
      "Validation accuracy: 0.480869391025641\n",
      "Validation Current AUC RC: 0.43652981519699097\n",
      "Validation Current AUC ROC: 0.6177768111228943\n",
      "Step  12150 . d_loss:  1.38591146 , g_loss:  1.26598978 , e_loss:  9.17395373e-05 lr:  0.0002\n",
      "Step  12200 . d_loss:  1.38564396 , g_loss:  1.26725745 , e_loss:  9.89110194e-05 lr:  0.0002\n",
      "Step  12250 . d_loss:  1.38588655 , g_loss:  1.26668203 , e_loss:  8.91270902e-05 lr:  0.0002\n",
      "Step  12300 . d_loss:  1.38576686 , g_loss:  1.26693606 , e_loss:  9.69351386e-05 lr:  0.0002\n",
      "Step  12350 . d_loss:  1.38586938 , g_loss:  1.26601911 , e_loss:  9.37543809e-05 lr:  0.0002\n",
      "Step  12400 . d_loss:  1.38594139 , g_loss:  1.26645875 , e_loss:  8.99414881e-05 lr:  0.0002\n",
      "Step  12450 . d_loss:  1.38594055 , g_loss:  1.26617587 , e_loss:  8.97156788e-05 lr:  0.0002\n",
      "Step  12500 . d_loss:  1.38592577 , g_loss:  1.26663911 , e_loss:  9.41915278e-05 lr:  0.0002\n",
      "30 Epoch completed\n",
      "Threshold using only benign data: 0.1074201837182045\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16416938110749185\n",
      "Validation f2-score: 0.1339143373365926\n",
      "Validation accuracy: 0.4859775641025641\n",
      "Validation Current AUC RC: 0.4446200132369995\n",
      "Validation Current AUC ROC: 0.6312297582626343\n",
      "Step  12550 . d_loss:  1.3859551 , g_loss:  1.26511109 , e_loss:  0.000111302717 lr:  0.0002\n",
      "Step  12600 . d_loss:  1.38553345 , g_loss:  1.2664572 , e_loss:  0.000108882356 lr:  0.0002\n",
      "Step  12650 . d_loss:  1.38582742 , g_loss:  1.26542842 , e_loss:  9.69356115e-05 lr:  0.0002\n",
      "Step  12700 . d_loss:  1.38573313 , g_loss:  1.26658618 , e_loss:  0.000102360966 lr:  0.0002\n",
      "Step  12750 . d_loss:  1.38584018 , g_loss:  1.26552629 , e_loss:  9.73996212e-05 lr:  0.0002\n",
      "Step  12800 . d_loss:  1.38590932 , g_loss:  1.26574767 , e_loss:  9.25262721e-05 lr:  0.0002\n",
      "Step  12850 . d_loss:  1.38594198 , g_loss:  1.26595938 , e_loss:  9.32889598e-05 lr:  0.0002\n",
      "Step  12900 . d_loss:  1.38592482 , g_loss:  1.26670718 , e_loss:  9.57179655e-05 lr:  0.0002\n",
      "31 Epoch completed\n",
      "Threshold using only benign data: 0.10660501569509506\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16566898560103543\n",
      "Validation f2-score: 0.13574420700991569\n",
      "Validation accuracy: 0.4834735576923077\n",
      "Validation Current AUC RC: 0.438381552696228\n",
      "Validation Current AUC ROC: 0.6208479404449463\n",
      "Step  12950 . d_loss:  1.38610816 , g_loss:  1.26722968 , e_loss:  0.000105554609 lr:  0.0002\n",
      "Step  13000 . d_loss:  1.38596535 , g_loss:  1.26540518 , e_loss:  0.000102875805 lr:  0.0002\n",
      "Step  13050 . d_loss:  1.38607717 , g_loss:  1.26519108 , e_loss:  9.15075871e-05 lr:  0.0002\n",
      "Step  13100 . d_loss:  1.3859266 , g_loss:  1.26608729 , e_loss:  9.70926849e-05 lr:  0.0002\n",
      "Step  13150 . d_loss:  1.38599944 , g_loss:  1.26484823 , e_loss:  9.50986287e-05 lr:  0.0002\n",
      "Step  13200 . d_loss:  1.38603449 , g_loss:  1.26423275 , e_loss:  8.91812597e-05 lr:  0.0002\n",
      "Step  13250 . d_loss:  1.38604069 , g_loss:  1.26432705 , e_loss:  8.95793783e-05 lr:  0.0002\n",
      "Step  13300 . d_loss:  1.38601458 , g_loss:  1.26432097 , e_loss:  9.15439741e-05 lr:  0.0002\n",
      "32 Epoch completed\n",
      "Threshold using only benign data: 0.10800245404243469\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.15967479674796747\n",
      "Validation f2-score: 0.1303909071595496\n",
      "Validation accuracy: 0.4823717948717949\n",
      "Validation Current AUC RC: 0.4357842803001404\n",
      "Validation Current AUC ROC: 0.6166487336158752\n",
      "Step  13350 . d_loss:  1.38584113 , g_loss:  1.2861656 , e_loss:  0.000200483119 lr:  0.0002\n",
      "Step  13400 . d_loss:  1.38549149 , g_loss:  1.27306473 , e_loss:  0.000134291404 lr:  0.0002\n",
      "Step  13450 . d_loss:  1.38571048 , g_loss:  1.27108109 , e_loss:  0.000117617885 lr:  0.0002\n",
      "Step  13500 . d_loss:  1.38571024 , g_loss:  1.27114356 , e_loss:  0.000120539473 lr:  0.0002\n",
      "Step  13550 . d_loss:  1.38574231 , g_loss:  1.26908493 , e_loss:  0.000113213784 lr:  0.0002\n",
      "Step  13600 . d_loss:  1.38583195 , g_loss:  1.26860189 , e_loss:  0.000105902451 lr:  0.0002\n",
      "Step  13650 . d_loss:  1.38588023 , g_loss:  1.26849198 , e_loss:  0.000104513005 lr:  0.0002\n",
      "Step  13700 . d_loss:  1.38588643 , g_loss:  1.26813352 , e_loss:  0.000104862491 lr:  0.0002\n",
      "33 Epoch completed\n",
      "Threshold using only benign data: 0.10752756148576736\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1630042636930141\n",
      "Validation f2-score: 0.1323498082658713\n",
      "Validation accuracy: 0.48878205128205127\n",
      "Validation Current AUC RC: 0.43850094079971313\n",
      "Validation Current AUC ROC: 0.6210026144981384\n",
      "Step  13750 . d_loss:  1.38612723 , g_loss:  1.26087081 , e_loss:  5.41772752e-05 lr:  0.0002\n",
      "Step  13800 . d_loss:  1.38637471 , g_loss:  1.26459336 , e_loss:  0.000102691825 lr:  0.0002\n",
      "Step  13850 . d_loss:  1.38626349 , g_loss:  1.2636162 , e_loss:  9.0817528e-05 lr:  0.0002\n",
      "Step  13900 . d_loss:  1.3861419 , g_loss:  1.26499736 , e_loss:  9.41191247e-05 lr:  0.0002\n",
      "Step  13950 . d_loss:  1.38611114 , g_loss:  1.26403153 , e_loss:  9.27949732e-05 lr:  0.0002\n",
      "Step  14000 . d_loss:  1.38613272 , g_loss:  1.2644707 , e_loss:  8.86966e-05 lr:  0.0002\n",
      "Step  14050 . d_loss:  1.38612926 , g_loss:  1.26474285 , e_loss:  8.90302108e-05 lr:  0.0002\n",
      "Step  14100 . d_loss:  1.38611495 , g_loss:  1.26504338 , e_loss:  9.17672078e-05 lr:  0.0002\n",
      "34 Epoch completed\n",
      "Threshold using only benign data: 0.10711736977100372\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16456318636289136\n",
      "Validation f2-score: 0.13365993929389214\n",
      "Validation accuracy: 0.4894831730769231\n",
      "Validation Current AUC RC: 0.44049644470214844\n",
      "Validation Current AUC ROC: 0.6243916749954224\n",
      "Step  14150 . d_loss:  1.3862927 , g_loss:  1.26061594 , e_loss:  5.33543571e-05 lr:  0.0002\n",
      "Step  14200 . d_loss:  1.38550627 , g_loss:  1.26608109 , e_loss:  0.000103357081 lr:  0.0002\n",
      "Step  14250 . d_loss:  1.38567185 , g_loss:  1.26709664 , e_loss:  0.000109468514 lr:  0.0002\n",
      "Step  14300 . d_loss:  1.38567853 , g_loss:  1.26535308 , e_loss:  0.000105284176 lr:  0.0002\n",
      "Step  14350 . d_loss:  1.38575327 , g_loss:  1.26463473 , e_loss:  0.000100359597 lr:  0.0002\n",
      "Step  14400 . d_loss:  1.38583457 , g_loss:  1.26440537 , e_loss:  9.37374643e-05 lr:  0.0002\n",
      "Step  14450 . d_loss:  1.38587391 , g_loss:  1.26403606 , e_loss:  9.25371496e-05 lr:  0.0002\n",
      "Step  14500 . d_loss:  1.38586676 , g_loss:  1.26419055 , e_loss:  9.44004933e-05 lr:  0.0002\n",
      "35 Epoch completed\n",
      "Threshold using only benign data: 0.10735160857439041\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16440150646798754\n",
      "Validation f2-score: 0.13361724780409903\n",
      "Validation accuracy: 0.48888221153846156\n",
      "Validation Current AUC RC: 0.441392183303833\n",
      "Validation Current AUC ROC: 0.6259174346923828\n",
      "Step  14550 . d_loss:  1.38628101 , g_loss:  1.25197518 , e_loss:  3.90135574e-05 lr:  0.0002\n",
      "Step  14600 . d_loss:  1.38574 , g_loss:  1.25941694 , e_loss:  7.80282862e-05 lr:  0.0002\n",
      "Step  14650 . d_loss:  1.38584971 , g_loss:  1.25999427 , e_loss:  8.75684927e-05 lr:  0.0002\n",
      "Step  14700 . d_loss:  1.38583302 , g_loss:  1.26103306 , e_loss:  9.29103408e-05 lr:  0.0002\n",
      "Step  14750 . d_loss:  1.38588989 , g_loss:  1.26117969 , e_loss:  9.02878091e-05 lr:  0.0002\n",
      "Step  14800 . d_loss:  1.38595843 , g_loss:  1.26054299 , e_loss:  8.46382172e-05 lr:  0.0002\n",
      "Step  14850 . d_loss:  1.38600111 , g_loss:  1.26124251 , e_loss:  8.36954059e-05 lr:  0.0002\n",
      "Step  14900 . d_loss:  1.38597059 , g_loss:  1.26134264 , e_loss:  8.81077794e-05 lr:  0.0002\n",
      "36 Epoch completed\n",
      "Threshold using only benign data: 0.10799305886030197\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1604837391730675\n",
      "Validation f2-score: 0.13060594775762088\n",
      "Validation accuracy: 0.48547676282051283\n",
      "Validation Current AUC RC: 0.4391581118106842\n",
      "Validation Current AUC ROC: 0.6223374605178833\n",
      "Step  14950 . d_loss:  1.38606191 , g_loss:  1.24757218 , e_loss:  3.28433453e-05 lr:  0.0002\n",
      "Step  15000 . d_loss:  1.38577032 , g_loss:  1.2590884 , e_loss:  8.41867441e-05 lr:  0.0002\n",
      "Step  15050 . d_loss:  1.385602 , g_loss:  1.26037288 , e_loss:  9.19415252e-05 lr:  0.0002\n",
      "Step  15100 . d_loss:  1.3858161 , g_loss:  1.25976217 , e_loss:  8.29191413e-05 lr:  0.0002\n",
      "Step  15150 . d_loss:  1.38573933 , g_loss:  1.26048803 , e_loss:  9.18738e-05 lr:  0.0002\n",
      "Step  15200 . d_loss:  1.3858453 , g_loss:  1.25971115 , e_loss:  8.37303742e-05 lr:  0.0002\n",
      "Step  15250 . d_loss:  1.38590503 , g_loss:  1.25966215 , e_loss:  8.01895658e-05 lr:  0.0002\n",
      "Step  15300 . d_loss:  1.38589251 , g_loss:  1.26009738 , e_loss:  8.75926e-05 lr:  0.0002\n",
      "Step  15350 . d_loss:  1.38593817 , g_loss:  1.25952196 , e_loss:  8.50940705e-05 lr:  0.0002\n",
      "37 Epoch completed\n",
      "Threshold using only benign data: 0.1075512170791626\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16488750205288225\n",
      "Validation f2-score: 0.13374540416688868\n",
      "Validation accuracy: 0.49068509615384615\n",
      "Validation Current AUC RC: 0.4421209990978241\n",
      "Validation Current AUC ROC: 0.6271446943283081\n",
      "Step  15400 . d_loss:  1.38559926 , g_loss:  1.26081669 , e_loss:  9.34173077e-05 lr:  0.0002\n",
      "Step  15450 . d_loss:  1.38550687 , g_loss:  1.25869191 , e_loss:  9.38900776e-05 lr:  0.0002\n",
      "Step  15500 . d_loss:  1.38573921 , g_loss:  1.2582624 , e_loss:  7.80780319e-05 lr:  0.0002\n",
      "Step  15550 . d_loss:  1.38567972 , g_loss:  1.25866425 , e_loss:  8.58096246e-05 lr:  0.0002\n",
      "Step  15600 . d_loss:  1.38580203 , g_loss:  1.25833964 , e_loss:  8.43544476e-05 lr:  0.0002\n",
      "Step  15650 . d_loss:  1.38588214 , g_loss:  1.25860369 , e_loss:  7.99884365e-05 lr:  0.0002\n",
      "Step  15700 . d_loss:  1.38583827 , g_loss:  1.25894809 , e_loss:  8.78411884e-05 lr:  0.0002\n",
      "Step  15750 . d_loss:  1.38587856 , g_loss:  1.25873613 , e_loss:  8.46477487e-05 lr:  0.0002\n",
      "38 Epoch completed\n",
      "Threshold using only benign data: 0.10767366737127304\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16184595385115372\n",
      "Validation f2-score: 0.13222175021240443\n",
      "Validation accuracy: 0.4833733974358974\n",
      "Validation Current AUC RC: 0.438514769077301\n",
      "Validation Current AUC ROC: 0.6212992668151855\n",
      "Step  15800 . d_loss:  1.3857199 , g_loss:  1.26369655 , e_loss:  9.60065227e-05 lr:  0.0002\n",
      "Step  15850 . d_loss:  1.38562047 , g_loss:  1.26176763 , e_loss:  9.63376442e-05 lr:  0.0002\n",
      "Step  15900 . d_loss:  1.38586211 , g_loss:  1.25959361 , e_loss:  7.86499077e-05 lr:  0.0002\n",
      "Step  15950 . d_loss:  1.38579297 , g_loss:  1.25994027 , e_loss:  9.12616742e-05 lr:  0.0002\n",
      "Step  16000 . d_loss:  1.38588321 , g_loss:  1.25998282 , e_loss:  9.13703916e-05 lr:  0.0002\n",
      "Step  16050 . d_loss:  1.38593149 , g_loss:  1.25932026 , e_loss:  8.55946491e-05 lr:  0.0002\n",
      "Step  16100 . d_loss:  1.38592255 , g_loss:  1.25977528 , e_loss:  8.94376717e-05 lr:  0.0002\n",
      "Step  16150 . d_loss:  1.38594353 , g_loss:  1.25928271 , e_loss:  8.91739546e-05 lr:  0.0002\n",
      "39 Epoch completed\n",
      "Threshold using only benign data: 0.10819917172193527\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.15947497949138636\n",
      "Validation f2-score: 0.12944121877164014\n",
      "Validation accuracy: 0.4868790064102564\n",
      "Validation Current AUC RC: 0.4376368522644043\n",
      "Validation Current AUC ROC: 0.6197606325149536\n",
      "Step  16200 . d_loss:  1.3859911 , g_loss:  1.25512052 , e_loss:  7.9501e-05 lr:  0.0002\n",
      "Step  16250 . d_loss:  1.38551116 , g_loss:  1.25817442 , e_loss:  9.11079551e-05 lr:  0.0002\n",
      "Step  16300 . d_loss:  1.38577878 , g_loss:  1.2571454 , e_loss:  7.8875215e-05 lr:  0.0002\n",
      "Step  16350 . d_loss:  1.38567615 , g_loss:  1.25781655 , e_loss:  8.52148369e-05 lr:  0.0002\n",
      "Step  16400 . d_loss:  1.38580179 , g_loss:  1.25746202 , e_loss:  8.34664752e-05 lr:  0.0002\n",
      "Step  16450 . d_loss:  1.38587916 , g_loss:  1.25741565 , e_loss:  7.86677847e-05 lr:  0.0002\n",
      "Step  16500 . d_loss:  1.38589942 , g_loss:  1.25799823 , e_loss:  8.31249927e-05 lr:  0.0002\n",
      "Step  16550 . d_loss:  1.38589811 , g_loss:  1.25790906 , e_loss:  8.42163645e-05 lr:  0.0002\n",
      "40 Epoch completed\n",
      "Threshold using only benign data: 0.1081145629286766\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16073478760045923\n",
      "Validation f2-score: 0.13049267643142476\n",
      "Validation accuracy: 0.48747996794871795\n",
      "Validation Current AUC RC: 0.4394247531890869\n",
      "Validation Current AUC ROC: 0.6227733492851257\n",
      "Step  16600 . d_loss:  1.38578725 , g_loss:  1.25911021 , e_loss:  8.42547088e-05 lr:  0.0002\n",
      "Step  16650 . d_loss:  1.38540137 , g_loss:  1.25779688 , e_loss:  9.08134316e-05 lr:  0.0002\n",
      "Step  16700 . d_loss:  1.38572252 , g_loss:  1.25602353 , e_loss:  7.64949e-05 lr:  0.0002\n",
      "Step  16750 . d_loss:  1.38562942 , g_loss:  1.25626934 , e_loss:  8.53052334e-05 lr:  0.0002\n",
      "Step  16800 . d_loss:  1.38583136 , g_loss:  1.25560796 , e_loss:  8.2929022e-05 lr:  0.0002\n",
      "Step  16850 . d_loss:  1.38589692 , g_loss:  1.25726664 , e_loss:  7.92559367e-05 lr:  0.0002\n",
      "Step  16900 . d_loss:  1.38592315 , g_loss:  1.25695074 , e_loss:  7.94148218e-05 lr:  0.0002\n",
      "Step  16950 . d_loss:  1.38591671 , g_loss:  1.25713408 , e_loss:  8.34518942e-05 lr:  0.0002\n",
      "41 Epoch completed\n",
      "Threshold using only benign data: 0.10822263360023499\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1591095105581928\n",
      "Validation f2-score: 0.12934475967424283\n",
      "Validation accuracy: 0.48547676282051283\n",
      "Validation Current AUC RC: 0.43770861625671387\n",
      "Validation Current AUC ROC: 0.6199409365653992\n",
      "Step  17000 . d_loss:  1.38616908 , g_loss:  1.25436938 , e_loss:  8.61594526e-05 lr:  0.0002\n",
      "Step  17050 . d_loss:  1.38600075 , g_loss:  1.25538886 , e_loss:  8.47351475e-05 lr:  0.0002\n",
      "Step  17100 . d_loss:  1.38609159 , g_loss:  1.25406122 , e_loss:  7.51383923e-05 lr:  0.0002\n",
      "Step  17150 . d_loss:  1.38592124 , g_loss:  1.25412846 , e_loss:  8.38998603e-05 lr:  0.0002\n",
      "Step  17200 . d_loss:  1.38599217 , g_loss:  1.25411475 , e_loss:  8.11372447e-05 lr:  0.0002\n",
      "Step  17250 . d_loss:  1.38603032 , g_loss:  1.25460839 , e_loss:  7.68209502e-05 lr:  0.0002\n",
      "Step  17300 . d_loss:  1.38604808 , g_loss:  1.25468302 , e_loss:  7.73430438e-05 lr:  0.0002\n",
      "Step  17350 . d_loss:  1.38601077 , g_loss:  1.25512028 , e_loss:  8.09007761e-05 lr:  0.0002\n",
      "42 Epoch completed\n",
      "Threshold using only benign data: 0.10796709358692169\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16073178699771318\n",
      "Validation f2-score: 0.13085106382978723\n",
      "Validation accuracy: 0.4853766025641026\n",
      "Validation Current AUC RC: 0.4377377927303314\n",
      "Validation Current AUC ROC: 0.6199694275856018\n",
      "Step  17400 . d_loss:  1.38568127 , g_loss:  1.25199831 , e_loss:  7.85710799e-05 lr:  0.0002\n",
      "Step  17450 . d_loss:  1.38552046 , g_loss:  1.2560215 , e_loss:  8.61910448e-05 lr:  0.0002\n",
      "Step  17500 . d_loss:  1.38581383 , g_loss:  1.255494 , e_loss:  7.66398589e-05 lr:  0.0002\n",
      "Step  17550 . d_loss:  1.38603806 , g_loss:  1.25608563 , e_loss:  8.48148557e-05 lr:  0.0002\n",
      "Step  17600 . d_loss:  1.38608086 , g_loss:  1.25544286 , e_loss:  8.3720668e-05 lr:  0.0002\n",
      "Step  17650 . d_loss:  1.38611484 , g_loss:  1.2561 , e_loss:  7.92317151e-05 lr:  0.0002\n",
      "Step  17700 . d_loss:  1.38610899 , g_loss:  1.25584352 , e_loss:  7.94194493e-05 lr:  0.0002\n",
      "Step  17750 . d_loss:  1.38607323 , g_loss:  1.25592351 , e_loss:  8.31307625e-05 lr:  0.0002\n",
      "43 Epoch completed\n",
      "Threshold using only benign data: 0.108254075050354\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16527299557304476\n",
      "Validation f2-score: 0.1342067422911008\n",
      "Validation accuracy: 0.49008413461538464\n",
      "Validation Current AUC RC: 0.44330894947052\n",
      "Validation Current AUC ROC: 0.6291680932044983\n",
      "Step  17800 . d_loss:  1.38608539 , g_loss:  1.25295413 , e_loss:  8.28795091e-05 lr:  0.0002\n",
      "Step  17850 . d_loss:  1.38564444 , g_loss:  1.25474155 , e_loss:  8.71103039e-05 lr:  0.0002\n",
      "Step  17900 . d_loss:  1.38585365 , g_loss:  1.25279272 , e_loss:  7.75566077e-05 lr:  0.0002\n",
      "Step  17950 . d_loss:  1.38574529 , g_loss:  1.25386763 , e_loss:  8.37445041e-05 lr:  0.0002\n",
      "Step  18000 . d_loss:  1.38586056 , g_loss:  1.25300193 , e_loss:  8.09624689e-05 lr:  0.0002\n",
      "Step  18050 . d_loss:  1.38591766 , g_loss:  1.25300717 , e_loss:  7.58386814e-05 lr:  0.0002\n",
      "Step  18100 . d_loss:  1.38595104 , g_loss:  1.25400817 , e_loss:  7.70198458e-05 lr:  0.0002\n",
      "Step  18150 . d_loss:  1.38594198 , g_loss:  1.25415945 , e_loss:  7.93296203e-05 lr:  0.0002\n",
      "44 Epoch completed\n",
      "Threshold using only benign data: 0.1082516610622406\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16151315789473683\n",
      "Validation f2-score: 0.1308774922699648\n",
      "Validation accuracy: 0.48938301282051283\n",
      "Validation Current AUC RC: 0.44731342792510986\n",
      "Validation Current AUC ROC: 0.6357033252716064\n",
      "Step  18200 . d_loss:  1.38601041 , g_loss:  1.25438082 , e_loss:  9.57409138e-05 lr:  0.0002\n",
      "Step  18250 . d_loss:  1.38550007 , g_loss:  1.25399244 , e_loss:  8.82840613e-05 lr:  0.0002\n",
      "Step  18300 . d_loss:  1.38567495 , g_loss:  1.25331604 , e_loss:  7.90512e-05 lr:  0.0002\n",
      "Step  18350 . d_loss:  1.38561773 , g_loss:  1.25380874 , e_loss:  8.50567958e-05 lr:  0.0002\n",
      "Step  18400 . d_loss:  1.38576221 , g_loss:  1.25325096 , e_loss:  8.20656642e-05 lr:  0.0002\n",
      "Step  18450 . d_loss:  1.38583422 , g_loss:  1.25369 , e_loss:  7.6767421e-05 lr:  0.0002\n",
      "Step  18500 . d_loss:  1.38587523 , g_loss:  1.25406241 , e_loss:  7.72745261e-05 lr:  0.0002\n",
      "Step  18550 . d_loss:  1.38588619 , g_loss:  1.25462258 , e_loss:  8.09117118e-05 lr:  0.0002\n",
      "45 Epoch completed\n",
      "Threshold using only benign data: 0.10821544378995895\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1592659347861707\n",
      "Validation f2-score: 0.12938608167829188\n",
      "Validation accuracy: 0.48607772435897434\n",
      "Validation Current AUC RC: 0.4392457902431488\n",
      "Validation Current AUC ROC: 0.6225760579109192\n",
      "Step  18600 . d_loss:  1.3860296 , g_loss:  1.25358021 , e_loss:  0.000119886354 lr:  0.0002\n",
      "Step  18650 . d_loss:  1.38546538 , g_loss:  1.25207245 , e_loss:  9.01843887e-05 lr:  0.0002\n",
      "Step  18700 . d_loss:  1.38572145 , g_loss:  1.25272632 , e_loss:  7.87975951e-05 lr:  0.0002\n",
      "Step  18750 . d_loss:  1.38568914 , g_loss:  1.2521317 , e_loss:  8.17077962e-05 lr:  0.0002\n",
      "Step  18800 . d_loss:  1.38575959 , g_loss:  1.2511971 , e_loss:  8.1065e-05 lr:  0.0002\n",
      "Step  18850 . d_loss:  1.38584125 , g_loss:  1.25184047 , e_loss:  7.7025994e-05 lr:  0.0002\n",
      "Step  18900 . d_loss:  1.38588107 , g_loss:  1.25267768 , e_loss:  7.99467e-05 lr:  0.0002\n",
      "Step  18950 . d_loss:  1.38587987 , g_loss:  1.25267589 , e_loss:  8.3341547e-05 lr:  0.0002\n",
      "46 Epoch completed\n",
      "Threshold using only benign data: 0.10823439061641693\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.15938471608574703\n",
      "Validation f2-score: 0.12959710468891372\n",
      "Validation accuracy: 0.48547676282051283\n",
      "Validation Current AUC RC: 0.4381788372993469\n",
      "Validation Current AUC ROC: 0.620772659778595\n",
      "Step  19000 . d_loss:  1.38615501 , g_loss:  1.25364077 , e_loss:  4.36664814e-05 lr:  0.0002\n",
      "Step  19050 . d_loss:  1.38536239 , g_loss:  1.25354564 , e_loss:  9.26303837e-05 lr:  0.0002\n",
      "Step  19100 . d_loss:  1.3856293 , g_loss:  1.25243676 , e_loss:  7.97059402e-05 lr:  0.0002\n",
      "Step  19150 . d_loss:  1.38562381 , g_loss:  1.25230432 , e_loss:  8.33520389e-05 lr:  0.0002\n",
      "Step  19200 . d_loss:  1.38569963 , g_loss:  1.25212586 , e_loss:  8.46559124e-05 lr:  0.0002\n",
      "Step  19250 . d_loss:  1.38579154 , g_loss:  1.25221038 , e_loss:  7.99521804e-05 lr:  0.0002\n",
      "Step  19300 . d_loss:  1.38582158 , g_loss:  1.25233817 , e_loss:  8.01524802e-05 lr:  0.0002\n",
      "Step  19350 . d_loss:  1.38583744 , g_loss:  1.25251091 , e_loss:  8.23345617e-05 lr:  0.0002\n",
      "47 Epoch completed\n",
      "Threshold using only benign data: 0.10852060467004776\n",
      "Optimized Threshold based on F1-Score value 0.030303030303030304\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16360052562417873\n",
      "Validation f2-score: 0.13268677395289355\n",
      "Validation accuracy: 0.48998397435897434\n",
      "Validation Current AUC RC: 0.4432187080383301\n",
      "Validation Current AUC ROC: 0.6291917562484741\n",
      "Step  19400 . d_loss:  1.38684464 , g_loss:  1.25171244 , e_loss:  4.35944967e-05 lr:  0.0002\n",
      "Step  19450 . d_loss:  1.38545823 , g_loss:  1.25291348 , e_loss:  8.21422072e-05 lr:  0.0002\n",
      "Step  19500 . d_loss:  1.38564086 , g_loss:  1.25151968 , e_loss:  8.78379869e-05 lr:  0.0002\n",
      "Step  19550 . d_loss:  1.3856504 , g_loss:  1.25087 , e_loss:  8.88219e-05 lr:  0.0002\n",
      "Step  19600 . d_loss:  1.38571727 , g_loss:  1.25174034 , e_loss:  8.63810128e-05 lr:  0.0002\n",
      "Step  19650 . d_loss:  1.38584697 , g_loss:  1.25182891 , e_loss:  8.10004931e-05 lr:  0.0002\n",
      "Step  19700 . d_loss:  1.38589633 , g_loss:  1.25224149 , e_loss:  8.14912273e-05 lr:  0.0002\n",
      "Step  19750 . d_loss:  1.38590407 , g_loss:  1.25263667 , e_loss:  8.30528e-05 lr:  0.0002\n",
      "48 Epoch completed\n",
      "Threshold using only benign data: 0.10878625512123108\n",
      "Optimized Threshold based on F1-Score value 0.020202020202020204\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.1653165812530997\n",
      "Validation f2-score: 0.13349708976344316\n",
      "Validation accuracy: 0.49429086538461536\n",
      "Validation Current AUC RC: 0.4462072253227234\n",
      "Validation Current AUC ROC: 0.6338719129562378\n",
      "Step  19800 . d_loss:  1.38624239 , g_loss:  1.24435544 , e_loss:  2.88264819e-05 lr:  0.0002\n",
      "Step  19850 . d_loss:  1.38570833 , g_loss:  1.24900973 , e_loss:  7.29341e-05 lr:  0.0002\n",
      "Step  19900 . d_loss:  1.38551354 , g_loss:  1.24991465 , e_loss:  8.04548254e-05 lr:  0.0002\n",
      "Step  19950 . d_loss:  1.38563478 , g_loss:  1.25097561 , e_loss:  8.15187523e-05 lr:  0.0002\n",
      "Step  20000 . d_loss:  1.38568103 , g_loss:  1.25055563 , e_loss:  8.09193953e-05 lr:  0.0002\n",
      "Step  20050 . d_loss:  1.38578486 , g_loss:  1.25061953 , e_loss:  7.37126902e-05 lr:  0.0002\n",
      "Step  20100 . d_loss:  1.38586533 , g_loss:  1.25084639 , e_loss:  7.03867045e-05 lr:  0.0002\n",
      "Step  20150 . d_loss:  1.38585925 , g_loss:  1.25126135 , e_loss:  7.89576297e-05 lr:  0.0002\n",
      "Step  20200 . d_loss:  1.38588786 , g_loss:  1.25035954 , e_loss:  7.48322636e-05 lr:  0.0002\n",
      "49 Epoch completed\n",
      "Threshold using only benign data: 0.10766119509935379\n",
      "Optimized Threshold based on F1-Score value 0.04040404040404041\n",
      "NF-ToN-IoT-v2-DDoS-downsample: \n",
      "Validation f1-score: 0.16758508914100487\n",
      "Validation f2-score: 0.1371498302207131\n",
      "Validation accuracy: 0.4855769230769231\n",
      "Validation Current AUC RC: 0.45482683181762695\n",
      "Validation Current AUC ROC: 0.6475649476051331\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.45482683,\n",
       " 0.64756495,\n",
       " 0.16758508914100487,\n",
       " 0.1371498302207131,\n",
       " 0.4855769230769231)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflow_trainer = GANomaly(netflow_dataset, hps, tf.summary.create_file_writer(\"logs\"), Path(\"log\"))\n",
    "\n",
    "netflow_trainer.train(\n",
    "    hps[\"epochs\"], hps[\"adversarial_loss_weight\"], hps[\"contextual_loss_weight\"],\n",
    "    hps[\"enc_loss_weight\"], hps[\"step_log_frequency\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Test dataset not provided, using models local data.\n",
      "WARNING:tensorflow:From l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\keras\\src\\saving\\legacy\\saved_model\\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\keras\\src\\saving\\legacy\\saved_model\\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\keras\\src\\saving\\legacy\\saved_model\\load.py:178: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\keras\\src\\saving\\legacy\\saved_model\\load.py:178: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the best model selected via  auc_rc\n",
      "Experiment 'NF-ToN-IoT-v2-DDoS-downsample' Results\n",
      "Test AUC-RC: 0.685\n",
      "Test AUC-ROC: 0.848\n",
      "Test F1-Score: 0.665\n",
      "Test F2-Score: 0.633\n",
      "Test Accuracy: 0.738\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the best model selected via  auc_roc\n",
      "Experiment 'NF-ToN-IoT-v2-DDoS-downsample' Results\n",
      "Test AUC-RC: 0.685\n",
      "Test AUC-ROC: 0.848\n",
      "Test F1-Score: 0.665\n",
      "Test F2-Score: 0.633\n",
      "Test Accuracy: 0.738\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ltm7dCrlcjl69euV5G0+ePNGYMvK25HI5+vbtiy+//BInT55Ep06d3ngdCwsLdO/eHevWrcP169dRvXp1CCHQtWtXHD16FB999BHq1KmDgwcPYvLkyXj06JHqdSnzOIotW7agefPmWk9bA5RTFnv27AkPDw/MnTsXL168wJAhQ7K9R+U3k6enJ3bt2oW4uDhYWVlBCIFTp07BwMAAf/31F7p27QoA+Ouvv2BgYIDmzZtr/JyxY8fC1tYWM2fOREREBJYsWYIxY8Zg69atAJTTo9q3b4/SpUtjypQpsLGxQUREBIKDgzVuZ+nSpejatSv69euH1NRU/Prrr+jVqxf27t2b7e/y119/Yffu3ar3rrlz56Jz58747LPPsGLFCnz88ceIjo7G/PnzMXToUPz5558a14+OjkbHjh3Ru3dv9O3bF0FBQRg9ejSMjY0xdOjQXH/32tY5udKq5C4EmZ+Ajhw5Ip4/fy4ePHggtm/fLkqXLi1MTEw0vnZs27atqFmzpsaohUKhEM2aNROVK1dW9c2YMSPXUZvMT22bNm0SBgYG2b5OXbVqlQAgTp06perLOhIthBBTp04VRkZG4uXLl6q+lJQUYWNjozE6/NFHHwlHR8dsX+v06dNHWFtbq0aJM0cPKlSokK+v7DO/gsptpFoIoRqB+eGHH4QQ6k9iZmZm4uHDh6r9zpw5IwCI8ePHq/ry+3vO/Nu1aNFC4ytuIUSO9yNzBD0gIEDVl9dXoLmNRFerVk2kpKSo+pcuXSoAqEbUU1JSRKlSpUTDhg1FWlqaar8NGzYIAG8ciR4/frwAIC5cuJDnfpkyR71e/2age/fuolSpUhp9Of1efHx8RIUKFTT6XFxcBABx4MCBbPvn5zZiYmKEpaWlaNy4cbav3LOOXOQ2dUKb5wcAYWBgIK5du5btdvDaSLS1tfUbR9Ryy5TTaIKXl5ewtLQU//77b673MSdHjhzJ9q1RppYtWwp3d3fx/Plz8fz5c3Hz5k0xefJkASDbiFadOnWEtbV1nj9r0aJFAoDYvXu3EEKIunXrvvE6OalSpYr44IMP8txH28eutiPROf2dDx48mOPvsmPHjhqPSW0eU+8q6xSr48ePCwBi0aJFqstzG4nO6V9+vwJ+/bXs2bNnwtjYWLRv315jat6yZcsEAPHzzz+r+jJHooUQYuPGjdnev/AOI9G+vr7C2NhY3L17V9X3+PFjYWlpKby8vHK8HW2mc6xevVrj9TeTh4eHaNOmTZ7XPXHihJDJZOLLL798488RIu+RaCGUo7oAxNKlS1V9eY1ECyHE4sWLNUZqf/vtNwFAfPPNNxr7ffjhh0Imk4k7d+4IIZSvMZnfnDk4OIi+ffuK5cuXZ3stykudOnWEo6Ojxsj5oUOHsn0rmN9Mmd+UZ44wX758WQAQvXr1Eo0bN1Zdr2vXrhoj1pnv5d7e3hqvnePHjxdyuVyVL/P3+/rMgde9/h6VOe3t9ccDAGFiYqLxOMt8PJUpU0bExcWp+qdOnZrtMZn5+8/6TVNKSoqoU6eOsLe3F6mpqUKInN878lvnvEmROVrO29sbpUuXhrOzMz788EOUKFECu3fvVn0ie/nyJf7880/07t0b8fHxiIqKQlRUFF68eAEfHx/cvn1btZrHjh07ULt27Rw/SWR+st+2bRuqVasGd3d31W1FRUWhTZs2AICjR4/mmtXPzw9paWkan74OHTqEmJgY+Pn5AVB+ctyxYwe6dOkCIYTGz/Dx8UFsbCxCQ0M1bnfQoEFvHCkBlAdGAMoRktxkXhYXF6fR7+vri3Llyqm2GzVqhMaNG2Pfvn0AtPs9Zxo+fHi2Axiz3o+0tDS8ePEClSpVgo2NTbb7ra0hQ4ZojHh5enoCUB6sBQDnzp3DixcvMHz4cI3RgX79+ml8s5GbzN9ZXr/fnIwaNUpj29PTEy9evND4G2T9vcTGxiIqKgotW7ZEeHg4YmNjNa7v5uam+lYjq/zcxuHDhxEfH48pU6ZkOzA3P6Nb2j4/WrZsCQ8Pjzfero2NDc6cOaOx+sTbev78OU6cOIGhQ4eifPnyGpe96T6+ePECAHJ9PNy8eROlS5dG6dKl4e7ujgULFqBr167ZVieIj49/4+Pk9ediXFyc1o+tzKxvWkbxbR+7+ZXT37lNmzaws7NTjVYBytGhw4cPq14PgXd7zX0XXl5eaN26NebPn4+kpKQ89+3WrRsOHz6s8S+n52B+HDlyBKmpqfj00081DkwfPnw4rKys8Pvvv+d4vX79+qFy5cr46quv3nmVpYyMDBw6dAi+vr6oUKGCqt/R0RH+/v44efJktvcIbfXo0QOGhoYaf/+rV6/i+vXrGn//1z179gz+/v5wc3PDZ5999k4ZMmV+a5X5Hvk219m3bx/kcjnGjRunsd/EiRMhhMD+/fsBKF9jDh48iG+++Qa2trb45Zdf8Mknn8DFxQV+fn6IiYnJ8+dGRkbi4sWLGDRoEKytrVX97dq1y/Ycy2+munXrwsLCAidOnACgHOV1cnLCwIEDERoailevXkEIgZMnT6reN7MaMWKExmunp6cnMjIyVEuAZn4Lt3fvXqSlpeV637K+R0VHRyM2Nhaenp45vve3bdtW45vLzG9GevbsqfE6ltmf+T6fydDQECNHjlRtGxsbY+TIkXj27BnOnz+fY763qXNyU2SmcyxfvhxVqlRBbGwsfv75Z5w4cULjK7Q7d+5ACIEvv/wSX375ZY638ezZM5QrVw53795949HNt2/fxo0bN1C6dOlcbys3tWvXhru7O7Zu3YqPPvoIgPKrKzs7O9UbwvPnzxETE4OffvoJP/30U75+hpubW56ZM2U+sOLj4zW+Ws4qt0K7cuXK2fatUqUKgoKCAGj3e84rd1JSEubOnYv169fj0aNHGm8GrxeL2nq9YMoshKKjowFA9YSvVKmSxn6Ghob5WnvTysoKgHYvxG/KlXmbp06dwsyZMxESEpJt6a3Y2FiNF9PcHg/5uY27d+8CAGrUqKHVfcik7fMjv4/d+fPnY9CgQXB2dkb9+vXRsWNHDBw4UOMNPr8yX0zf9j4CyLVIcXV1xZo1a6BQKHD37l3MmTMHz58/z/aBxNLS8o2F7evPRSsrq2xvBPnN+qYPB2/72M2vnP7OhoaG6NmzJwIDA5GSkgITExMEBwcjLS1No4h6l9fchIQEjdUj5HJ5rreTk1mzZqFly5ZYtWoVxo8fn+t+Tk5Oua4SkZSUlO21K7fl7wD161DmNKxMxsbGqFChQq5rk8vlcnzxxRcYNGgQfvvttxwHg548eaKxbW1tneMAzPPnz/Hq1atsGQDltDmFQoEHDx6gevXqud6PTLndfzs7O7Rt2xZBQUH4+uuvASjfDw0NDdGjR48cbysxMRGdO3dGfHw8Tp48qTFl613+1pnX0+ZD5OvX+ffff1G2bNlst5E5zTDr383ExATTp0/H9OnTERkZiePHj2Pp0qUICgqCkZERNm/ejNTUVLx8+VLjtkqXLq26nZzek6tWrapRcOY3k1wuR9OmTfHXX38BUBbRnp6eaNGiBTIyMnD69Gk4ODjg5cuXORbRb3pvbdmyJXr27InZs2dj8eLFaNWqFXx9feHv769Rr+3duxfffPMNLl68iJSUFFV/Tq9fr//MzPdAZ2fnHPszs2QqW7YsSpQoodFXpUoVAMopeE2aNMn2M9+mzslNkSmiGzVqhAYNGgBQjpa2aNEC/v7+CAsLg4WFhWp91kmTJuU6MvB60ZQXhUKBmjVr5rq0zet/wNf5+flhzpw5iIqKgqWlJXbv3o2+ffuqRj4z8/bv3z/b3OlMry99lJ9RaED5xPntt99w+fJleHl55bjP5cuXASBfo4NZvc3vOafcY8eOxfr16/Hpp5+iadOmsLa2hkwmQ58+fXJdaze/clu2711HbTJlLkt05coV1KlTJ9/Xe1Ouu3fvom3btnB3d8eiRYvg7OwMY2Nj7Nu3D4sXL872e8np96rtbbwtbZ8f+X3s9u7dG56enti5cycOHTqEBQsWYN68eQgODsYHH3zwzrnzq1SpUgCyvyBnKlGihEYx1bx5c9SrVw/Tpk3DDz/8oOqvVq0aLl68iPv372d7M8j0+nPR3d0dFy5cwIMHD974OpNVdHR0jm+4WWn72M2tKM/IyMixP7e/c58+fbB69Wrs378fvr6+CAoKgru7O2rXrq3a511ec7///nvMnj1bte3i4qLV2vJeXl5o1aoV5s+fn+0bo/zaunUrhgwZotFXUK85r+vXr59qbrSvr2+2yx0dHTW2169fX+gnBMvr/vfp0wdDhgzBxYsXUadOHQQFBaFt27Y5znVOTU1Fjx49cPnyZRw8eDDbh+B3+VtfvXoVgHa1wNtcJyeOjo7o06cPevbsierVqyMoKAgbNmzA33//jdatW2vse+/evXf6WXlp0aIF5syZg+TkZPz111+YPn06bGxsUKNGDfz111+qY59yKqLf9B6WuQb56dOnsWfPHhw8eBBDhw7FwoULcfr0aVhYWKjmXnt5eWHFihVwdHSEkZER1q9fj8DAwHz/zMJ8ny/IerLIFNFZyeVyzJ07F61bt8ayZcswZcoU1UiVkZHRG9eTrFixouqJkdc+ly5dQtu2bd/q4A0/Pz/Mnj0bO3bsgIODA+Li4lQH0ADKT5qWlpbIyMjI9/qX+dW5c2fMnTsXAQEBORbRGRkZCAwMhK2tbbYDB27fvp1t/1u3bqlGaLX5Pedl+/btGDRoEBYuXKjqS05OzvYVV0EcOPO6zAM+7ty5o/HilZ6ejoiIiDeu2/rBBx9ALpdj8+bNBXqA1p49e5CSkoLdu3drFFzafI2d39uoWLEiAOUbRF4vBrn9/t/1+ZEXR0dHfPzxx/j444/x7Nkz1KtXD3PmzFEV0fn9eZmP1Tc913OSWWzm982sVq1a6N+/P1avXo1JkyapfvedO3fGL7/8goCAAHzxxRfZrhcXF4ddu3bB3d1d9Xfo0qULfvnlF2zevBlTp07N189PT0/HgwcPVAcG5Ubbx66trW2OXztrewZHLy8vODo6YuvWrWjRogX+/PNPTJ8+XWOfd3lMDRw4EC1atFBt5/dDW1azZs1Cq1atsHr1aq2vCwA+Pj65ntAjJ5mvQ2FhYRrftKSmpuLevXt5vr5mjkYPHjwYu3btynb56zlyG0kuXbo0zM3NERYWlu2ymzdvwsDAIN8f5PK6/76+vhg5cqRqSsetW7dyfGwrFAoMHDgQf/zxB4KCgtCyZcts+7zt3zrzfc/c3Fzj+nlJSEjAzp074ezsrBrVdXFxwZEjR7JN1co8eUrWEzPlxMjICLVq1cLt27cRFRWF2rVrZ/u9lSlTRjVym9N78ut/L20yeXp6IjU1Fb/88gsePXqkKpa9vLxURXSVKlVUxfTbaNKkCZo0aYI5c+YgMDAQ/fr1w6+//ophw4Zhx44dMDU1xcGDBzVGp9evX//WPy8vjx8/RmJiosZodOYa+7l981xQdQ5QBM5YmJtWrVqhUaNGWLJkCZKTk2Fvb696AYyMjMy2//Pnz1Xtnj174tKlS9i5c2e2/TI/xfTu3RuPHj3CmjVrsu2TlJSkWmUiN9WqVUPNmjWxdetWbN26FY6OjhoFrVwuR8+ePbFjx44c3+Sz5tVWs2bN4O3tjfXr1+d4RrTp06fj1q1b+Oyzz7K9AP32228ac33Onj2LM2fOqAoYbX7PeZHL5dk+Mf7444/ZRrgyH/hvmj+mjQYNGqBUqVJYs2YN0tPTVf1btmzJdeQxK2dnZwwfPhyHDh3Cjz/+mO1yhUKBhQsX4uHDh1rlyvxk/frUFm1eXPJ7G+3bt4elpSXmzp2L5ORkjcuyXrdEiRI5Tq951+dHTjIyMrL9LHt7e5QtW1bjK7/cMr2udOnS8PLyws8//4z79+9rXPam0Ypy5crB2dlZq7OlffbZZ0hLS9MYSf3www/h4eGB7777LtttKRQKjB49GtHR0RorF3z44YeoWbMm5syZg5CQkGw/Jz4+PlsBev36dSQnJ7/xZBfaPnYrVqyI2NhY1Wg5oJyrmdNrZ14MDAzw4YcfYs+ePdi0aRPS09OzzYd9l8dUhQoV4O3trfr3+uBAfrRs2RKtWrXCvHnzsj0n8sPR0VEjw5vefL29vWFsbIwffvhB4/G4bt06xMbGvnH1iP79+6NSpUoao7JZbzvrv9dHpjPJ5XK0b98eu3bt0hjNffr0KQIDA9GiRQvVFKA3yev+29jYwMfHB0FBQfj1119hbGyc4wj62LFjsXXrVqxYsSLXqR5v87fOyMjAuHHjcOPGDYwbNy5f9ykpKQkDBgzAy5cvMX36dNUHu44dOyIjIwPLli3T2H/x4sWQyWSq98rbt29ne90BlO9lISEhsLW1RenSpWFra5vt92ZqagpHR0fUqVMHGzdu1Hi9O3z4MK5fv65xm/nNBCjnDhsZGWHevHkoWbKk6gOWp6cnTp8+jePHj+c4Cp0f0dHR2V5bM7/xynwNl8vlkMlkGu/1ERERhbL6DqAcYMj6wTg1NRWrV69G6dKlUb9+/RyvU1B1DlBER6IzTZ48Gb169cKGDRswatQoLF++HC1atEDNmjUxfPhwVKhQAU+fPkVISAgePnyoOi3u5MmTsX37dvTq1QtDhw5F/fr18fLlS+zevRurVq1C7dq1MWDAAAQFBWHUqFE4evQomjdvjoyMDNy8eRNBQUE4ePCganpJbvz8/DBjxgyYmprio48+ynZWw++++w5Hjx5F48aNMXz4cHh4eODly5cIDQ3FkSNHss2T0kZAQADatm2Lbt26wd/fH56enkhJSUFwcDCOHTsGPz8/TJ48Odv1KlWqhBYtWmD06NFISUnBkiVLUKpUKY0DO/L7e85L586dsWnTJlhbW8PDwwMhISE4cuSI6mv0THXq1IFcLse8efMQGxsLExMTtGnTBvb29m/9uzE2NsasWbMwduxYtGnTBr1790ZERAQ2bNiAihUr5msUbOHChbh79y7GjRuH4OBgdO7cGba2trh//z62bduGmzdvanzzkB/t27eHsbExunTpgpEjRyIhIQFr1qyBvb19jk/kd7kNKysrLF68GMOGDUPDhg3h7+8PW1tbXLp0Ca9evcLGjRsBAPXr18fWrVsxYcIENGzYEBYWFujSpUuBPD9eFx8fDycnJ3z44YeqU10fOXIE//zzj8Y3FrllyskPP/yAFi1aoF69ehgxYgTc3NwQERGB33//HRcvXswzT7du3bBz5858zTUGlNMxOnbsiLVr1+LLL79EqVKlYGxsjO3bt6Nt27Zo0aKFxhkLAwMDERoaiokTJ2o8VoyMjBAcHAxvb294eXmhd+/eaN68OYyMjHDt2jXVt0hZl+g7fPgwzM3N0a5duzfm1Oax26dPH3z++efo3r07xo0bh1evXmHlypWoUqWK1gcA+/n54ccff8TMmTNRs2bNbEtVFsZjSlszZ87M9tV6YSldujSmTp2K2bNno0OHDujatSvCwsKwYsUKNGzYEP3798/z+nK5HNOnT882hUJb33zzDQ4fPowWLVrg448/hqGhIVavXo2UlBTMnz//nW47Kz8/P/Tv3x8rVqyAj49PtuN1lixZghUrVqBp06YwNzfH5s2bNS7v3r17trmtOYmNjVVd99WrV6ozFt69exd9+vRRzcvO6tGjR6rrJCQk4Pr169i2bRuePHmCiRMnahyY1qVLF7Ru3RrTp09HREQEateujUOHDmHXrl349NNPVd/yXbp0Cf7+/vjggw/g6emJkiVL4tGjR9i4cSMeP36MJUuWvPGMwXPnzkWnTp3QokULDB06FC9fvsSPP/6I6tWra8wLz28mADA3N0f9+vVx+vRpdOnSRfXa5uXlhcTERCQmJr51Eb1x40asWLEC3bt3R8WKFREfH481a9bAysoKHTt2BAB06tQJixYtQocOHeDv749nz55h+fLlqFSpksaH9YJStmxZzJs3DxEREahSpQq2bt2Kixcv4qeffsrz7LYFUecAKDpL3OW0ZEpGRoaoWLGiqFixomoJtbt374qBAweKMmXKCCMjI1GuXDnRuXNnsX37do3rvnjxQowZM0aUK1dOtaj/oEGDNJabS01NFfPmzVMtdG5rayvq168vZs+eLWJjY1X7vb7EXabbt2+rlkE6efJkjvfv6dOn4pNPPhHOzs7CyMhIlClTRrRt21b89NNPqn3e9ixT8fHxYtasWaJ69erCzMxMWFpaiubNm4sNGzZkW+Ir68lWFi5cKJydnYWJiYnw9PQUly5dynbb+fk95/W3i46OFkOGDBF2dnbCwsJC+Pj4iJs3b+b4u1yzZo2oUKGCkMvlGktE5bbE3eu/p9wWUv/hhx+Ei4uLMDExEY0aNRKnTp0S9evXFx06dMjHb1e5eP/atWuFp6ensLa2FkZGRsLFxUUMGTJEYwmx3M5YmPn7ybokz+7du0WtWrVUi8LPmzdP/Pzzz9n2y2tZpvzeRua+zZo1E2ZmZsLKyko0atRI/PLLL6rLExIShL+/v7Cxscm2rFJ+nx/IY/ktZFniLiUlRUyePFnUrl1bddKH2rVrZztRTG6Zcvs7X716VXTv3l3Y2NgIU1NTUbVq1XwtmRUaGioAZFtyLa9ltI4dO5Zt2T4hlMuZTZgwQVSqVEmYmJgIGxsb4e3trVrWLifR0dFixowZombNmsLc3FyYmpqKGjVqiKlTp4rIyEiNfRs3biz69+//xvuUKb+PXSGUS2rVqFFDGBsbi6pVq4rNmzfnebKV3CgUCuHs7JzjUlyZ8vuYeld5nUU0c1ms93WylWXLlgl3d3dhZGQkHBwcxOjRo3M92crr0tLSRMWKFd9piTshlI91Hx8fYWFhIczNzUXr1q3F33//nevtvM0ZC+Pi4oSZmZkAIDZv3pzt8kGDBuW6jGB+f9brJ2OysLAQlStXFv379xeHDh3K8TqZy4UCEDKZTFhZWYnq1auL4cOHizNnzuR4nfj4eDF+/HhRtmxZYWRkJCpXrpztxCZPnz4V3333nWjZsqVwdHQUhoaGwtbWVrRp0yZbPZKXHTt2iGrVqgkTExPh4eGR68lW8pMpU+aSnPPmzdPor1SpkgCgsdyhELm/l2c+njIf06GhoaJv376ifPnywsTERNjb24vOnTuLc+fOaVxv3bp1onLlysLExES4u7uL9evX5/s1JWutklOWrI/tnE624uLiIpYtW5bjbb7+3pHfejIvsv/uCOm5iIgIuLm5YcGCBZg0aZLUcSShUChQunRp9OjRI8evlKn4adu2LcqWLYtNmzZJHSVXFy9eRL169RAaGqrVga5ERPqsVatWiIqKeqvjYgpKkZ0TTfQukpOTs83dCggIwMuXL9GqVStpQlGR8+2332Lr1q1aH0j3Pn333Xf48MMPWUATERUxRXpONNHbOn36NMaPH49evXqhVKlSCA0Nxbp161CjRo03noaWio/GjRsjNTVV6hh5+vXXX6WOQEREOWARTXrJ1dUVzs7O+OGHH/Dy5UuULFkSAwcOxHfffadxtkMiIiKit8E50UREREREWuKcaCIiIiIiLbGIJiIiIiLSUrGbE61QKPD48WNYWloWyimniYiIiOjdCCEQHx+PsmXLZjuZXVFR7Irox48fw9nZWeoYRERERPQGDx48gJOTk9QxclTsimhLS0sAyj+KlZWVxGmIiIiI6HVxcXFwdnZW1W1FUbErojOncFhZWbGIJiIiIirCivLU26I5yYSIiIiIqAhjEU1EREREpCUW0UREREREWmIRTURERESkJRbRRERERERaYhFNRERERKQlFtFERERERFpiEU1EREREpCUW0UREREREWmIRTURERESkJRbRRERERERaYhFNRERERKQlFtFERERERFpiEU1EREREpCUW0UREREREWpK0iD5x4gS6dOmCsmXLQiaT4bfffnvjdY4dO4Z69erBxMQElSpVwoYNGwo9JxERERFRVpIW0YmJiahduzaWL1+er/3v3buHTp06oXXr1rh48SI+/fRTDBs2DAcPHizkpEREREREaoZS/vAPPvgAH3zwQb73X7VqFdzc3LBw4UIAQLVq1XDy5EksXrwYPj4+hRWTiPSdUAApscr/RcZ//7/hX3oikJEGyGRSp9c/QkidQE/x91oo+HgtcAqFAtfWfSF1jDeStIjWVkhICLy9vTX6fHx88Omnn+Z6nZSUFKSkpKi24+LiCiseERVVaUlA1GUgJhxIjASSngNJUcCr58Crp8CLa0BqvNQpiYiKvcg4CwzZ6ovjdxsCOC51nDzpVBH95MkTODg4aPQ5ODggLi4OSUlJMDMzy3aduXPnYvbs2e8rIhFJ7dVz4PHfysL4xQ3g6XkgOkw5ekxEREXWrqtVMWxbV0QllgCQLHWcN9KpIvptTJ06FRMmTFBtx8XFwdnZWcJERFTg0hKBiyuAsK3Kollbls6AbWXA0AyAASAzAAzkyv8zt3P6lxoPmNr+dz0qWJwmUyg4/aiQ8Pf6LhKTgIkrTbF6j7Gqz95W4Fm0hKHyQaeK6DJlyuDp06cafU+fPoWVlVWOo9AAYGJiAhMTk/cRj4jeNyGAe/uAnZ1z38fAEChVA7CvA9jVAEqUBczsAHN75f9mdoAhXyOIiKRw/vxj9BsVjLCwF6o+X193LFrUEhUqzJMw2ZvpVBHdtGlT7Nu3T6Pv8OHDaNq0qUSJiEgS6SnAnZ1A6A9AZIjmZTaVgErdgTINANsqQEl3wNBUmpxERJSjjAwFvv/+b3zxxVGkpyun25mbG2HJEh8MG1YP8fFF/zgVSYvohIQE3LlzR7V97949XLx4ESVLlkT58uUxdepUPHr0CAEBAQCAUaNGYdmyZfjss88wdOhQ/PnnnwgKCsLvv/8u1V0govdFkaGcqnE9ALj5K5D8QvNy59ZAq8WAfW1p8hERUb4lJ6dj7doLqgK6fn1HBAb2RJUqpSROln+SFtHnzp1D69atVduZc5cHDRqEDRs2IDIyEvfv31dd7ubmht9//x3jx4/H0qVL4eTkhLVr13J5OyJ9kfYKiLsPvHoCJDwC4v4FYu4CL28Czy4C6a+yX6dUdaDJF0BVP873JCLSESVKGCMwsAdatFiPiRObYtasVjA2lksdSysyIYrXAodxcXGwtrZGbGwsrKyspI5DpL9S44GEx8rVMpKigOSXytHjJ/8ARiUARTqQEgMkRwOPTwFyEyAj5Y03C0C5b+UeQI2hQPm2LJ6JiIq4+PgUxMWloFw5zdrr0aO4bH2AbtRrOjUnmoiKoNQE5TSL55eUS8tFXQXi72u/7vKbCmgrV+U8Z6dWgHtfwKzk2yYmIqL3KCTkAfr334kyZSxw/PhgGBqqT5idUwGtK1hEE5H2kqOBq+uBe/uBh8cBRdq73Z6xFWBgBChSAbeOytt3+wCwKKc8UNCmAmBiXTDZiYjovUhPV2DOnBP4+usTyMgQCA+Pxrx5JzF9upfU0QoEi2giyr+UWODUDODa+txHmuUmQAlHwMoFsHQCzB0As1KAaSnAtKTyn9xEucScibVynWW5cc63RUREOik8PBr9+wcjJOShqq9ZM2f4+9eUMFXBYhFNRG8mFEDI10DoEuU85qwsnQGX9sqpFvZ1Aft6gNxIipRERCQxIQQ2bbqMMWP2IT4+FQAgl8swc2ZLTJ3qqTGVQ9exiCaivMU9AI7+T7kuc1YlqwGdAoHStXlgHxERITo6CaNG/Y6goGuqvgoVbLFlSw80aeIkYbLCwSKaiHL34BgQ/AGQnqzuc2wMtP4BcGwkVSoiIipi4uJSUKfOaty/H6vqGzy4Dn74oQMsLfXzrLD6M6ZORAXr1g5gh4+6gDYwArpsB/xPs4AmIiINVlYm6N7dHQBga2uKoKAPsX59N70toAGORBNRTi4sA/4cB+C/ZeRdfYAOG4ASZaRMRURERdh333kjOTkd06d7wtlZ/1dUYhFNRGpCABtrAC+uq/uqDwLareHBgkREBEB58OCaNaGQy2X46KN6qn5TU0OsWtVZwmTvF4toIlI7OV2zgK41EvBeyQMHiYgIAPD8eSKGD9+DXbvCYGZmiGbNnFGtWmmpY0mCc6KJSOnyWuDsXPV2m2VAu1UsoImICABw6NBd1Kq1Crt2hQEAkpLSsXfvLYlTSYcj0UQExN4DDg9Xbzf5Eqj7iXR5iIioyEhOTsfUqUewZMkZVZ+dnTl+/rkrunSpKmEyabGIJiruUhOAwKbq7epDgGazpctDRERFxpUrT9GvXzCuXHmm6uvQoRLWr++GMmUsJEwmPRbRRMXdkdHAq6fqba95nMJBRFTMCSHw449n8dlnh5GSkgEAMDGRY8GCdhgzphFkfJ9gEU1UrEWeAW5sVm/3Pw+YF88DRIiISC0hIRULF4aoCuhatRywZUsP1KhhL3GyooMHFhIVV0IAxyaqt1stBhzq5b4/EREVG5aWJti8uTvkchnGj2+CM2eGsYB+DUeiiYqrs3OBx6eUbcvyQN0x0uYhIiLJJCamIjExDfb2JVR9np4uuHVrLCpUsJUwWdHFkWii4iglFjjzrXq79RLAgJ+piYiKo/PnH6N+/Z/Qt+8OKBRC4zIW0LljEU1UHB0YDKQlKtuVfIHK3aVMQ0REEsjIUGDevJNo0mQdwsJe4M8/72Hx4hCpY+kMDj0RFTdPLwB3dinbBkaA13xp8xAR0Xv34EEsBg78DceORaj66td3LNbrPmuLRTRRcfPXFAD/fV1XawRgW1nSOERE9H4FBV3DyJF7EROTDEC5qumUKS0wa1YrGBvLJU6nO1hEExUnNwKBfw8p21YuQMvvpc1DRETvTVxcCsaN24+NGy+p+pydrbBpU3e0bOkqXTAdxSKaqLhITwEODlFvN50JGJpKl4eIiN6b2Nhk1Kv3E8LDo1V9fn7VsXJlJ9jamkmYTHfxwEKi4uLsd0BGqrLt2BioPkjaPERE9N5YW5uiTRtXAIClpTECAnzxyy89WUC/A45EExUHCZHAP/8dQCiTA21XAjJ+hiYiKk4WL+6ApKR0fPVVay5dVwBYRBMVB0f/B6S/UrZrDAUc6kqbh4iICo0QAps2XYaRkQH69q2p6rewMMbmzT0kTKZfWEQT6buYu8Ct7cq2aSnAc660eYiIqNBERydh1KjfERR0DRYWxmjUqBwqViwpdSy9xO9zifTdyS+gWtKu/njArJSkcYiIqHAcOxaBWrVWISjoGgAgISEV27dflziV/uJINJE+e3gCCPtV2TYtBdQaKW0eIiIqcKmpGZgx4yjmzz8F8d+YiY2NKX76qTN69aoubTg9xiKaSF+lpwCHhqu3G00BzO2ky0NERAUuLCwK/v7BCA2NVPW1auWKgABfODtbS5hM/7GIJtJX5xcD0beUbccmyqkcRESkF4QQ+Omn8xg//iCSktIBAEZGBpgzpw0mTmwGAwOZxAn1H4toIn2UmgCcmaPebr0EMOCpXImI9EVsbApmzTquKqCrVi2FwMCeqFfPUeJkxQcPLCTSR+cXAWkJyna1/sqTqxARkd6wsTHFhg3dAACjRtVHaOhIFtDvGUeiifRN0gvg9Nf/bciAJl9KGoeIiN5dcnI6Xr1KQ8mS6jMM+vhUwtWro1G9ur2EyYovjkQT6Zt/5gMK5dd7qNwDKFlF2jxERPROrlx5ioYN12DgwJ0Qmctv/IcFtHRYRBPpk5Q44OIKZVsmB5rOkDYPERG9NYVCYOnS02jYcA2uXn2G33+/jVWrzkkdi/7D6RxE+uT4RPVcaI/+QOla0uYhIqK3EhkZjyFDduHgwbuqvlq1HODp6SJhKsqKRTSRvnh4EriyVtk2tgQafyFtHiIieiu7dt3EsGF7EBX1StU3fnwTfPttW5iasnQrKviXINIHQgHs76/ebjYbsK0kXR4iItJaYmIqJk48hNWrz6v6HB0tsHGjL9q1qyhhMsoJi2gifXDlZyDuX2XbsjxQ+2Np8xARkVaio5PQtOk6hIW9UPX5+rpjzZousLMzlzAZ5YYHFhLpuqfngT9Gq7e9VwKGJtLlISIirdnamqF+/bIAAHNzI6xZ0wXBwb1ZQBdhHIkm0mUZacC+Aeol7aoPBtw+kDQSERG9neXLOyIpKQ3ffeeNKlVKSR2H3oBFNJEu+3sm8PKGsm3lCrRbDchkkkYiIqI3Cwq6BhMTObp1c1f12diYIjjYT8JUpA0W0US66v6fwNm5yrZMDnQKBOTG0mYiIqI8xcWlYNy4/di48RJsbU1x+XJZODlZSR2L3gLnRBPposizwLa26u1GnwNlm0qXh4iI3igk5AHq1FmFjRsvAQCio5OxefNliVPR2+JINJGuib4N7PBRb1forFzSjoiIiqT0dAW++eYEvvnmBDIylKfttrQ0xvLlHdG/P0+KpatYRBPpkrgHQFArICVGuW1ZHvggADDgU5mIqCgKD49G//7BCAl5qOpr1swZmzd3h5ubrYTJ6F3xnZdIVwgB7B8AJDxWblu7Af3PA6Z8ESYiKmqEEAgIuIQxY/YjISEVACCXyzBjRktMm+YJQ0POqNV1LKKJdMXZecDD48p2CUeg798soImIiqjo6GRMnHhIVUBXqGCLLVt6oEkTJ4mTUUHhxyAiXXB3D3BymrItkwMd1gMlykibiYiIclWypBnWru0KABg8uA4uXhzJAlrPcCSaqKhLjgH29QOgPBgF9ScArj55XYOIiN6z1NQMpKSkw9JSfcZYX193nDs3XHUmQtIvHIkmKsqEAA4MAlLjlduOjQHPudJmIiIiDWFhUWjadB2GDdsDIYTGZSyg9ReLaKKi7MIy4O5uZduoBNAxEDCQS5uJiIgAKA8eXL36HOrWXY3Q0EgEBV3Dpk1c97m44HQOoqLqcQhwfIJ6+4PNgE0F6fIQEZHK8+eJGDZsD3bvDlP1Va1aCjVq2EuYit4nFtFERVFyNLCnN6BIV27X+QSo7CtpJCIiUjp48A4GD96FJ08SVH2jRtXHwoU+MDc3kjAZvU8soomKogODgYT/FuYv1wJotVjSOEREBCQnp2Pq1CNYsuSMqs/Ozhw//9wVXbpUlTAZSYFFNFFRc2qmeh60aUngg02AnCMbRERSevkyCa1abcCVK89UfR06VML69d1QpoyFhMlIKjywkKgoeXkLOP21ervtcsDaVbI4RESkZGtrigoVlCe4MjGR44cfOmDfPn8W0MUYR6KJigpFhnI5u8z1oKsPAtz7SBqJiIiUZDIZ1q7tiqSkYCxc2J4HEBKLaKIiY/9AIPK0sm1TCWizTNo8RETF2O7dYTAxkcPHp5Kqz87OHAcP9pcwFRUlnM5BVBQ8DgFuBv63IQParwWM+RUhEdH7lpiYilGj9qJbt18xcOBvePYsUepIVESxiCaSmhDAn2PV2/X+Bzi3lC4PEVExdf78Y9Sr9xNWrz4PAHj2LBE//3xB4lRUVHE6B5HUrv4MPFW+YMO2CuA1X9o8RETFTEaGAt9//ze++OIo0tMVAABzcyMsWeKDYcPqSZyOiioW0URSSksETn2p3m69lMvZERG9Rw8exGLAgJ04fvxfVV/9+o4IDOyJKlVKSZiMijoW0URSurIWSIxUtit2A9w6SJuHiKgYCQq6hpEj9yImJhkAIJMBU6a0wKxZrWBsLJc4HRV1LKKJpBL3APh7lnq76QzJohARFTdRUa8wfPgexMWlAACcna2waVN3tGzpKm0w0hk8sJBIKienASkxyrbHQMCB8+6IiN4XOztzrFzZCQDg51cdly6NYgFNWuFINJEUHhwDbmxWto0tgVaLpExDRKT30tMVSE3NgLm5+rgTf/+acHKygqdnechkMgnTkS7iSDTR+yYE8Nc09XbzOYAZD14hIios4eHR8PJajzFj9mW7zMvLhQU0vRUW0UTv240tQGSIsm1bBagzWto8RER6SgiBgIBLqF17FUJCHmL9+ovYtu2a1LFIT3A6B9H7lJ4M/DVVve21ADDg05CIqKBFRydh1KjfERSkLporVLCFs7O1hKlIn/Ddm+h9Ovc9kPBQ2Xb1ASp1lTYPEZEeOnYsAgMG7MTDh3GqvsGD6+CHHzrA0tJEwmSkT1hEE70vzy6pl7STGQAt5koah4hI36SmZmDGjKOYP/8UhFD22dqaYvXqzujVq7q04UjvsIgmel+OfQqIDGW74WeAQ11J4xAR6ZMXL16hffvNCA2NVPW1bu2KgIDucHKykjAZ6SseWEj0Pjw+rVzWDgCsXICmM6VMQ0Skd2xtzWBnZw4AMDIywPz53jhyZCALaCo0LKKJCpsQwNH/qbcbTAIMTaXLQ0SkhwwMZNiwoRtatCiP06eHYfLk5jAw4NJ1VHg4nYOosN35DXhyVtm2rQLUGilpHCIifXDo0F2YmhrCy8tF1efoaIm//hoiYSoqTiQfiV6+fDlcXV1hamqKxo0b4+zZs3nuv2TJElStWhVmZmZwdnbG+PHjkZyc/J7SEmkpIxU4MVm97bUAkBvlvj8REeUpOTkd48cfgI/PZvTrF4zo6CSpI1ExJWkRvXXrVkyYMAEzZ85EaGgoateuDR8fHzx79izH/QMDAzFlyhTMnDkTN27cwLp167B161ZMmzYtx/2JJPf3TCDmrrJdzhOo2EXaPEREOuzKlado1GgNliw5AwB4+DAOP/10XuJUVFxJWkQvWrQIw4cPx5AhQ+Dh4YFVq1bB3NwcP//8c477//3332jevDn8/f3h6uqK9u3bo2/fvm8cvSaSxJNzwD/zlW0DQ6D1EoCnliUi0ppCIbB06Wk0bLgGV64oB9pMTOT44YcO+Oyz5hKno+JKsiI6NTUV58+fh7e3tzqMgQG8vb0REhKS43WaNWuG8+fPq4rm8PBw7Nu3Dx07dsz156SkpCAuLk7jH1Ghy0gDDo8AhEK53eRLwKGetJmIiHRQZGQ8Onbcgk8/PYiUFOUyoTVr2uPcuREYO7YxZBycIIlIdmBhVFQUMjIy4ODgoNHv4OCAmzdv5ngdf39/REVFoUWLFhBCID09HaNGjcpzOsfcuXMxe/bsAs1O9EZ/TQGeXVC2S7oDjabmvT8REWWza9dNDBu2B1FRr1R948c3wbfftoWpKddGIGlJfmChNo4dO4Zvv/0WK1asQGhoKIKDg/H777/j66+/zvU6U6dORWxsrOrfgwcP3mNiKpZi7wGXf/pvQwZ03MyDCYmItPT8eSL69QtWFdCOjhY4eLA/Fi3yYQFNRYJkj0I7OzvI5XI8ffpUo//p06coU6ZMjtf58ssvMWDAAAwbNgwAULNmTSQmJmLEiBGYPn06DAyyfyYwMTGBiYlJwd8BotwcGQ2kJSjbFToCDvWlzUNEpINKly6BJUs6YPjwPejWrSrWru2qOpkKUVEg2Ui0sbEx6tevjz/++EPVp1Ao8Mcff6Bp06Y5XufVq1fZCmW5XA4AEEIUXlii/Lq7B4g4qGxbOAEdt0ibh4hIR2RkKJCSkq7R99FHdbF/fz/s3OnHApqKHEmnc0yYMAFr1qzBxo0bcePGDYwePRqJiYkYMkS5UPrAgQMxdap6LmmXLl2wcuVK/Prrr7h37x4OHz6ML7/8El26dFEV00SSSXwKHBqu3vaaB5hYS5eHiEhHPHgQC2/vTZg06ZBGv0wmQ4cOlXjwIBVJkk4q8vPzw/PnzzFjxgw8efIEderUwYEDB1QHG96/f19j5PmLL76ATCbDF198gUePHqF06dLo0qUL5syZI9VdIFLKSAX2+QOv/pue5NIecO8rbSYiIh0QFHQNI0fuRUxMMo4di8AHH1RGx46VpY5F9EYyUczmQcTFxcHa2hqxsbGwsrKSOg7pAyGAA4OA65uU22algcFXAXN7aXMRERVhcXEpGDduPzZuvKTqc3a2wpYtPeDp6ZLHNak40IV6jYe3Er2rs/PUBbShOdB1BwtoIqI8hIQ8QP/+OxEeHq3q8/OrjpUrO8HW1kzCZET5xyKa6F1cWgWczLIGdLtVgJOndHmIiIqw9HQF5sw5ga+/PoGMDOUX4ZaWxli+vCP696/Fuc+kU1hEE70NIYBTXwJnsszHbzoL8BggWSQioqLsxYtX6NLlF4SEPFT1NWvmjM2bu8PNzVbCZERvh0U0kbYyUoGDQ4EbWZavazAJaDpDukxEREWcjY0pDA2ViwXI5TLMmNES06Z5qvqIdA2LaCJtKDKAfQOAW0HqPq8FQIOJAL+GJCLKlVxugE2buqNHjyAsX94RTZo4SR2J6J2wiCbKr4w04PBwdQEtNwY6BADuftLmIiIqgo4fj4CZmREaNSqn6nNxscG5c8M595n0Ar9DIcqPtEQg+APg2kbltoEh0GkrC2giotekpmZg6tQjaN16I/r23YH4+BSNy1lAk75gEU30Ji/DgMAmwP3/TlFvYAh0+hWo7CtpLCKioiYsLApNm67Dd9+dghBAeHg0Vq48J3UsokLB6RxEuVFkAJdWAscnARn/jaQYWwG+uwDnVpJGIyIqSoQQWLMmFJ9+egBJSekAACMjA8yZ0wYTJzaTOB1R4WARTZSTJ+eAI6OBp1lGUGyrAF2DAbvq0uUiIipinj9PxPDhe7BrV5iqr2rVUggM7Il69RwlTEZUuFhEE2UVE65c//lmoGZ/3bFAi28BYwtpchERFUEHD97B4MG78ORJgqpv1Kj6WLjQB+bmRhImIyp8LKKJACA9RXnmwQvLAEWaur+kO9D6B8C1nXTZiIiKoKdPE+DruxXJycrpG3Z25vj5567o0qWqxMmI3g8eWEjFmxDA9U3AukrA+cXqAtq0JNB6CTDgIgtoIqIcODhY4Lvv2gIAfHwq4sqV0SygqVjhSDQVX6+eAVtbAS9vqPsMTYH6E5UnTzHlaWiJiDIpFAIZGQoYGclVfWPHNoaTkxW6d68GAwMuXUfFC0eiqXh6cBzY3FCzgC7bHOgfCrT4hgU0EVEWkZHx+OCDLfjiiz81+g0MZOjZ04MFNBVLHImm4uffI8D2LFM0DM2B9muBan2ly0REVETt2nUTH320Gy9eJOHw4bvw8amENm3cpI5FJDkW0VS8PDoF7Oyk3ja1BfqGACU5j4+IKKvExFRMnHgIq1efV/U5OHCFIqJMLKKp+Ii6BmxrC2SkKredWipPnGJiLW0uIqIi5vz5x/D3D8atWy9Ufd26VcXatV1hZ2cuYTKiooNFNBUPaa+AX5qqzzxYzhP48DAg5zqmRESZMjIU+P77v/HFF0eRnq4AAJibG2HJEh8MG1YPMhnnPhNlYhFNxcOZb4HUeGXbygXovpcFNBFRFlFRr9Cr1zYcOxah6qtf3xGBgT1RpUop6YIRFVFcnYP0X9RV4PxCZVsmB7psA0yspM1ERFTEWFubICFBOd1NJgOmTm2Bv//+iAU0US5YRJN+EwI4NAJIT1Zu1x4FlGkobSYioiLIyEiOLVt6oFo1Oxw9OgjfftsWxsbyN1+RqJjidA7Sb49DgMgQZbtkNaDl99LmISIqIkJCHsDc3Ai1a5dR9VWpUgpXr37MdZ+J8oEj0aTfzn6nbjecrDwjIRFRMZaersDs2cfg6bkeffvuwKtXaRqXs4Amyh8W0aS/HocA4XuU7RJlAHd/afMQEUksPDwaXl7rMWvWcWRkCNy4EYUVK/6ROhaRTuJ0DtJfV9er281mA4Ym0mUhIpKQEAKbNl3GmDH7EB+vPHhQLpdh5syW+PTTJhKnI9JNLKJJPwkB3NuvbBsYAlX9pM1DRCSR6OgkjBr1O4KCrqn6Kla0xebNPdCkiZOEyYh0G4to0k//HgYSHirbLu14VkIiKpaOHYvAgAE78fBhnKpvyJA6WLq0Aywt+e0c0btgEU366Wagul19iHQ5iIgkEhkZDx+fzUhNzQAA2NqaYvXqzujVq7rEyYj0Aw8sJP0jFMC9A8q2oTlQoZO0eYiIJODoaImZM1sCAFq3dsXly6NZQBMVII5Ek/559Dfw6qmyXb4NYGQubR4iovdACAGFQkAuV4+Pff55czg7W6Ffv1pcuo6ogHEkmvTPv4fU7QqdpctBRPSePH+eiO7dt+Kbb05o9MvlBhgwoDYLaKJCwJFo0j93d6nbLKKJSM8dPHgHgwfvwpMnCdi79xbat6+Ipk2dpY5FpPdYRJN+iY0Anl9Wth3qA5blJI1DRFRYkpPTMXXqESxZckbVZ2trploHmogKF4to0i8PjqnbHIUmIj115cpT9OsXjCtXnqn6fHwqYsMGX5QpYyFhMqLig0U06ZeIg+q2c2vpchARFQKFQuDHH8/g88+PICVFuXSdiYkc8+e3w5gxjTj3meg9YhFN+kMI4MFRZdvYEijbTNo8REQF6MWLV+jXLxgHD95V9dWsaY/AwJ6oUcNewmRExRNX5yD9ERuuXtqubDNAbiRtHiKiAlSihDEePYpXbY8f3wRnzw5nAU0kERbRpD8enVS3yzaXLgcRUSEwNTVEYGAPuLnZ4ODB/li0yAempvxCmUgqfPaR/nh0St12bCRdDiKiAnD+/GOUKGEMd3c7VV/Nmg64dWssDA05BkYkNT4LSX9Env6vIeN8aCLSWRkZCsybdxJNmqxD3747kJKSrnE5C2iiooHPRNIP0beBqCvKtn0d5YGFREQ65sGDWLRtG4ApU/5AeroCFy8+wYoV/0gdi4hywOkcpB/+PaJuV+ktXQ4iorcUFHQNI0fuRUxMMgBAJgOmTGmBTz7h9DSioohFNOmH+3+o205e0uUgItJSXFwKxo3bj40bL6n6nJ2tsGlTd7Rs6SpdMCLKE4to0n1CqItoMzseVEhEOiMk5AH699+J8PBoVZ+fX3WsXNkJtrZmEiYjojdhEU2678V1ICVG2XZsChjwYU1ERd+jR3Fo1WojUlOVZx60tDTG8uUd0b9/LchkPPMgUVHHAwtJ9z3OsrQdp3IQkY4oV84KkyY1BQA0a+aMS5dGYcCA2iygiXQEh+xI9/17WN128pQuBxFRHoQQAKBRJM+a1Qrly1vjo4/qcek6Ih3DZyzpNiGAx/+tD21oDtjXkzYPEVEOoqOT0KfPDixcGKLRb2Qkx8iRDVhAE+kgjkSTbnsWCiQ8VLbLNQfkRtLmISJ6zbFjERgwYCcePozDzp030LatG+rWdZQ6FhG9I370Jd12O1jdrvKhdDmIiF6TmpqBKVOOoE2bjXj4MA4AYGFhjCdPEiRORkQFgSPRpLuEAri1/b8NGVCxq6RxiIgyhYVFwd8/GKGhkaq+1q1dERDQHU5OVhImI6KCwiKadNejU0D0LWW7XHOgRBlp8xBRsSeEwE8/ncf48QeRlJQOADAyMsCcOW0wcWIzGBhw5Q0iffFORXRycjJMTU0LKguRdm7+qm7X+Ei6HEREAF6+TMKQIbuwe3eYqq9q1VIIDOyJevU4B5pI32g9J1qhUODrr79GuXLlYGFhgfDwcADAl19+iXXr1hV4QKIcZT1LIQBU7iFdFiIiACYmcty8GaXaHj26AUJDR7KAJtJTWhfR33zzDTZs2ID58+fD2NhY1V+jRg2sXbu2QMMR5erfI0D0f6M9Tl6ACecYEpG0SpQwxpYtPVC2rCV27+6DFSs6wdycKwYR6Suti+iAgAD89NNP6NevH+Ryuaq/du3auHnzZoGGI8rVwSHqds3h0uUgomLrypWnCA+P1uhr0KAswsPHoUuXqhKlIqL3Resi+tGjR6hUqVK2foVCgbS0tAIJRZSnpxeAhEfKtrUbULW3tHmIqFhRKASWLj2Nhg3XoF+/YKSnKzQuNzHhMftExYHWRbSHhwf++uuvbP3bt29H3bp1CyQUUZ7+madu1x0HyI1z35eIqABFRsbjgw+24NNPDyIlJQOnTz/EypX/SB2LiCSg9cflGTNmYNCgQXj06BEUCgWCg4MRFhaGgIAA7N27tzAyEqklRAJhW5Vtc3ug+mBJ4xBR8bFr10189NFuvHiRpOobP74Jhg+vL2EqIpKK1iPR3bp1w549e3DkyBGUKFECM2bMwI0bN7Bnzx60a9euMDISqV0PULdrDAVMbSSLQkTFQ2JiKkaN2gtf362qAtrR0QIHD/bHokU+MDXl9A2i4uitnvmenp44fPhwQWchypsiHfhngXrbY6B0WYioWDh//jH8/YNx69YLVZ+vrzvWrOkCOztzCZMRkdS0HomuUKECXrx4ka0/JiYGFSpUKJBQRDkK/x1I/u+x59IeKFVN2jxEpNcePIhFs2Y/qwpoc3MjrFnTBcHBvVlAE5H2RXRERAQyMjKy9aekpODRo0cFEoooR1eyrENe5xPpchBRseDsbI2PP24AAKhf3xEXLozEsGH1IJPx1N1EpMV0jt27d6vaBw8ehLW1tWo7IyMDf/zxB1xdXQs0HJHKyzAg/L8DV83tAZe20uYhIr0khNAokufO9Ub58tb45JNGMDaW53FNIipuZEIIkZ8dDQyUg9YymQyvX8XIyAiurq5YuHAhOnfuXPApC1BcXBysra0RGxsLKyue5U5n/FwFiL6tbNf7FGi9WNI4RKRf4uJSMG7cfjRqVA4ff9xQ6jhExZ4u1Gv5HolWKJSLybu5ueGff/6BnZ1doYUi0nBnt7qAtigLtPhG2jxEpFdCQh6gX79g3LsXg61br6F1a1dUq1Za6lhEVMRpPSf63r17LKDp/REC+Otz9XajaYBRCenyEJHeSE9XYNasY/D0XI9792IAAEZGBrh7NzrvKxIR4S2XuEtMTMTx48dx//59pKamalw2bty4AglGBAB4eAJ4eVPZdmgA1B4lbR4i0gvh4dHo3z8YISEPVX3Nmjlj8+bucHOzlTAZEekKrYvoCxcuoGPHjnj16hUSExNRsmRJREVFwdzcHPb29iyiqeAoMoA/sqzCUW8cYMADe4jo7QkhEBBwCWPG7EdCgnIQSC6XYcaMlpg2zROGhlp/QUtExZTWrxbjx49Hly5dEB0dDTMzM5w+fRr//vsv6tevj++//74wMlJx9ddU4MU1ZbtkNaBqH2nzEJFOi4lJRp8+OzB48C5VAV2hgi1OnhyKGTNasoAmIq1o/Ypx8eJFTJw4EQYGBpDL5UhJSYGzszPmz5+PadOmFUZGKo6eXwbOZflQ1nY5IDeSLg8R6TyZDDhzRj19Y/DgOrh4cSSaNHGSMBUR6Sqti2gjIyPVcnf29va4f/8+AMDa2hoPHjwo2HRUPGWkAjt8APy3lGK9T4HyraVMRER6wNraFJs2dYednTmCgj7E+vXdYGlpInUsItJRWs+Jrlu3Lv755x9UrlwZLVu2xIwZMxAVFYVNmzahRo0ahZGRipsjo4HEJ8q2ZXkuaUdEbyUsLAolShjDyUm9xqynpwsiIv6HEiWMJUxGRPpA65Hob7/9Fo6OjgCAOXPmwNbWFqNHj8bz58+xevXqAg9IxcytHcDVn5VtmRzouIVL2hGRVoQQWL36HOrWXY2BA3dCodA8QRgLaCIqCPk+Y6G+0IUz4BRbj/4GtnoCQnliHzSYDLScL20mItIpz58nYtiwPdi9O0zVt3JlJ4wa1UDCVESkLV2o1wrsUOTQ0NAif8pvKsJS44HfuqgLaOsKgNd30mYiIp1y8OAd1Kq1SqOAHjWqPgYOrC1hKiLSV1oV0QcPHsSkSZMwbdo0hIeHAwBu3rwJX19fNGzYUHVqcG0sX74crq6uMDU1RePGjXH27Nk894+JicEnn3wCR0dHmJiYoEqVKti3b5/WP5eKkLREYHN9IPmlctuuBtD/HCDjclNE9GbJyekYP/4AOnTYgidPEgAAdnbm2L27D1au7Axzc67sQ0QFL98HFq5btw7Dhw9HyZIlER0djbVr12LRokUYO3Ys/Pz8cPXqVVSrVk2rH75161ZMmDABq1atQuPGjbFkyRL4+PggLCwM9vb22fZPTU1Fu3btYG9vj+3bt6NcuXL4999/YWNjo9XPpSIkIw3Y3BCIvq3u67IDMOUZw4joza5ceYp+/YJx5cozVZ+PT0Vs2OCLMmUsJExGRPou33Oia9WqhQEDBmDy5MnYsWMHevXqhSZNmiAoKAhOTm+3xmbjxo3RsGFDLFu2DACgUCjg7OyMsWPHYsqUKdn2X7VqFRYsWICbN2/CyOjtRhZ0YY5NsZGeAgQ2Bp5fUm6blgQ6/QK4tpc2FxHphH//jUHVqsuQkpIBADAxkWP+/HYYM6YRDAxkEqcjonehC/Vavr8vv3v3Lnr16gUA6NGjBwwNDbFgwYK3LqBTU1Nx/vx5eHt7q8MYGMDb2xshISE5Xmf37t1o2rQpPvnkEzg4OKBGjRr49ttvkZGRkevPSUlJQVxcnMY/KgKSXgLb2qoLaABou4IFNBHlm4uLjWq+c82a9jh3bgTGjWvMApqI3ot8T+dISkqCubk5AEAmk8HExES11N3biIqKQkZGBhwcHDT6HRwccPPmzRyvEx4ejj///BP9+vXDvn37cOfOHXz88cdIS0vDzJkzc7zO3LlzMXv27LfOSYXgcQjwSzPNvq47gMo9pMlDRDpr8WIfuLhYY+LEZjA11frUB0REb02rV5y1a9fCwkI5xyw9PR0bNmyAnZ2dxj7jxo0ruHSvUSgUsLe3x08//QS5XI769evj0aNHWLBgQa5F9NSpUzFhwgTVdlxcHJydnQstI+UhPQU48w1weo5mf5ftLKCJKE+JiamYOPEQmjRxwuDBdVT9JUoYY/p0L+mCEVGxle8iunz58lizZo1qu0yZMti0aZPGPjKZLN9FtJ2dHeRyOZ4+farR//TpU5QpUybH6zg6OsLIyAhyuVzVV61aNTx58gSpqakwNs6+gL6JiQlMTHhaV8k9uwTs6we8uKbuK9MI6BIEWLlIl4uIirzz5x+jX79ghIW9wJYtV+DpWR4VK5aUOhYRFXP5LqIjIiIK9AcbGxujfv36+OOPP+Dr6wtAOdL8xx9/YMyYMTlep3nz5ggMDIRCoYCBgXI6961bt+Do6JhjAU1FxM2twMEhQHqSctvAEKg3Hmj+NWDIDzhElLOMDAW+//5vfPHFUaSnK5dQVSgErl59xiKaiCQn6UK8EyZMwJo1a7Bx40bcuHEDo0ePRmJiIoYMGQIAGDhwIKZOnaraf/To0Xj58iX+97//4datW/j999/x7bff4pNPPpHqLlBeEp8Ce/sAv/dRF9AlHIHex5VnImQBTUS5ePAgFm3bBmDKlD9UBXT9+o64cGEkunVzlzgdEZGWc6ILmp+fH54/f44ZM2bgyZMnqFOnDg4cOKA62PD+/fuqEWcAcHZ2xsGDBzF+/HjUqlUL5cqVw//+9z98/vnnUt0FyolQAGfnAyenavab2QH+pwGr8tLkIiKdEBR0DSNH7kVMTDIAQCYDpkxpgVmzWsHYWP6GaxMRvR/5XidaX+jCuoM67ck5YEvD7P31xwMtv+dZCIkoV/HxKRg7dj82blQvfensbIVNm7qjZUtX6YIR0XunC/Ua1wOigpEcDfzuD0Qc0Oyv3ANoOgsoXVOSWESkO1JSMnDo0F3Vtp9fdaxc2Qm2tmYSpiIiyhmLaHo3iU+ACz8C/ywAFGnq/pLuypFnt47K72KJiN7Azs4cGzf64sMPt2HZsg/Qv38tyPj6QURF1FsV0Xfv3sX69etx9+5dLF26FPb29ti/fz/Kly+P6tWrF3RGKqpuBCqXrcvK0BSoNQrwmgfIuWIKEeUuPDwaJUoYwcHBQtXXrl1F/Pvvp7CxMZUwGRHRm2k9QfX48eOoWbMmzpw5g+DgYCQkJAAALl26lOsJT0jPJD4B9vTKXkB7DAQG3wBaL2YBTUS5EkJg48aLqF17FYYO3Y3XD81hAU1EukDrInrKlCn45ptvcPjwYY21mdu0aYPTp08XaDgqgh6eBFY5Are2q/vcPgCG3gI+2AhYu0oWjYiKvujoJPTpswODB+9CQkIq9u27jfXrL0odi4hIa1pP57hy5QoCAwOz9dvb2yMqKqpAQlERJARwYRnw12fqPrkx4PU9UHcM5z0T0RsdOxaBAQN24uHDOFXf4MF10KuXh4SpiIjejtZFtI2NDSIjI+Hm5qbRf+HCBZQrV67AglERkhwDHB4J3ApS95WuBXTZDthWliwWEemG1NQMzJhxFPPnn0LmzA1bW1OsXt0ZvXrxOBoi0k1aF9F9+vTB559/jm3btkEmk0GhUODUqVOYNGkSBg4cWBgZSUrPLwO7ewAx6mWn4NwK6LwNMLeTLBYR6YabN6PQr18wQkMjVX2tW7siIKA7nJyK5tqvRET5oXURnXmabWdnZ2RkZMDDwwMZGRnw9/fHF198URgZSSp39wC/dVVvG5oDrZcCNT/i9A0ieqPw8GjUq7caSUnpAAAjIwPMmdMGEyc2g4EBX0OISLe99RkL79+/j6tXryIhIQF169ZF5cq68bW+LpwBR3JCAezoAPx7WN1nagv0PQ2UrCJdLiLSOf37B2PLliuoWrUUAgN7ol49R6kjEZEO0IV6TeuR6JMnT6JFixYoX748ypcvXxiZSGpHP9UsoMs2A3z3AGYlJYtERLpp+fKOcHGxxvTpXjA3N5I6DhFRgdF6ibs2bdrAzc0N06ZNw/Xr1wsjE0np4krlGQgzNZ4G+B1nAU1EeUpOTsf48Qewbds1jX5ra1PMmdOWBTQR6R2ti+jHjx9j4sSJOH78OGrUqIE6depgwYIFePjwYWHko/fp4V/A0XHq7RZzlP8MeHZ4IsrdlStP0ajRGixZcgYjRuzFgwexUkciIip0WhfRdnZ2GDNmDE6dOoW7d++iV69e2LhxI1xdXdGmTZvCyEjvQ9orYF9/QKE8AAjVBytHoYmIcqFQCCxdehoNG67BlSvPAABJSWk4d+6xxMmIiArfOw0xurm5YcqUKahduza+/PJLHD9+vKBy0ft2aDgQf1/ZdmoJtPtJ2jxEVKRFRsZjyJBdOHhQvfxlzZr2CAzsiRo17CVMRkT0fmg9Ep3p1KlT+Pjjj+Ho6Ah/f3/UqFEDv//+e0Fmo/fl6QX1iVRkBspl7OScv0hEOdu16yZq1VqlUUCPH98EZ88OZwFNRMWG1iPRU6dOxa+//orHjx+jXbt2WLp0Kbp16wZzc/PCyEeFTQjgj080p3HY15Y0EhEVTYmJqZg48RBWrz6v6nN0tMCGDb5o376ihMmIiN4/rYvoEydOYPLkyejduzfs7HjGOp338DgQGaJs21QC2i6XNg8RFVlxcSnYseOGatvX1x1r1nSBnR0HUYio+NG6iD516lRh5CCpXFimbjf5AjA0lS4LERVpjo6WWLu2C/z9g7F0aQd89FFdyHj2UiIqpvJVRO/evRsffPABjIyMsHv37jz37dq1a56XUxHy/Apwe4eybVoKqNJb2jxEVKQ8eBCLEiWMUbKkmaqvWzd33Lv3P9jbl5AwGRGR9PJVRPv6+uLJkyewt7eHr69vrvvJZDJkZGQUVDYqbFfWqNuNpwFGZrnvS0TFSlDQNYwcuRfe3hUQFPShxogzC2gionyuzqFQKGBvb69q5/aPBbQOSU8Grq5Xtg3NgOqDpM1DREVCXFwKBg/+DX5+2xETk4zt268jMPCK1LGIiIocrZe4CwgIQEpKSrb+1NRUBAQEFEgoeg9u7wTSEpTtSt0Bs1LS5iEiyYWEPECdOquwceMlVZ+fX3V07FhZwlREREWT1kX0kCFDEBub/ZSu8fHxGDJkSIGEovfgwo/qdo2h0uUgIsmlpyswe/YxeHqux717MQAAS0tjBAT44pdfesLWllO9iIhep/XqHEKIHI/GfvjwIaytrQskFBWyqKvqZe3sagDlebp2ouIqPDwa/fsHIyTkoaqvWTNnbN7cHW5uthImIyIq2vJdRNetq1zKSCaToW3btjA0VF81IyMD9+7dQ4cOHQolJBWwm7+q2zWHA1yiiqhYunPnJerVW434+FQAgFwuw4wZLTFtmicMDd/6hLZERMVCvovozFU5Ll68CB8fH1hYWKguMzY2hqurK3r27FngAakQPDyublfm34youKpY0RZt21bAb7/dRIUKttiypQeaNHGSOhYRkU7IdxE9c+ZMAICrqyv8/PxgasqTcuik1ATgyVll28oFsCwnbR4ikoxMJsOaNV3g4mKNr79uDUtLE6kjERHpDK2/rxs0aBALaF325B8gQ/nVLVzaS5uFiN6b1NQMTJlyBL//fkuj387OHEuWdGABTUSkpXyNRJcsWRK3bt2CnZ0dbG1t8zzN68uXLwssHBWCyNPqdtmm0uUgovcmLCwK/v7BCA2NxPr1F3H58ig4OFi8+YpERJSrfBXRixcvhqWlpaqdVxFNRdyzUHW7TCPpchBRoRNC4KefzmP8+INISkoHAERHJ+HUqQfo0aOaxOmIiHSbTAghpA7xPsXFxcHa2hqxsbGwsrKSOs7797M7EB0GyI2BcYmAgdarHBKRDnj+PBHDhu3B7t1hqr6qVUshMLAn6tVzlDAZEdGb6UK9pvWc6NDQUFy5oj4F7K5du+Dr64tp06YhNTW1QMNRAUtNAKL/mw9pV5MFNJGeOnjwDmrVWqVRQI8e3QChoSNZQBMRFRCti+iRI0fi1i1lIRYeHg4/Pz+Ym5tj27Zt+Oyzzwo8IBWgyDMA/vviwaGBpFGIqOAlJ6dj/PgD6NBhC548SQCgPHBw9+4+WLGiE8zNjSROSESkP7Quom/duoU6deoAALZt24aWLVsiMDAQGzZswI4dOwo6HxWkrAcVlmshXQ4iKhTPniVi/fqLqu0OHSrhypXR6NKlqnShiIj0lNZFtBACCoUCAHDkyBF07NgRAODs7IyoqKiCTUcF6+l5dduhnnQ5iKhQlC9vjZUrO8HERI4ffuiAffv8UaYMV+EgIioMWk+KbdCgAb755ht4e3vj+PHjWLlyJQDg3r17cHBwKPCAVICiLiv/NyoB2HJkikjXRUbGo0QJY1hZqdd47tu3Jlq0KA9nZ2sJkxER6T+tR6KXLFmC0NBQjBkzBtOnT0elSpUAANu3b0ezZs0KPCAVkIxUIDZC2bapDBjIJY1DRO9m166bqFVrFcaN25/tMhbQRESFr8CWuEtOToZcLoeRUdE+cEUXlkwpFJFngcDGyra7P9Bpi7R5iOitJCamYuLEQ1i9Wj09a/v2XujZ00PCVEREBUsX6rW3XuPs/PnzuHHjBgDAw8MD9epxjm2R9vyiuu3YRLIYRPT2zp9/DH//YNy69ULV5+vrjpYtXaULRURUTGldRD979gx+fn44fvw4bGxsAAAxMTFo3bo1fv31V5QuXbqgM1JBeHJO3bavI1kMItJeRoYC33//N7744ijS05UHdpubG2Hp0g746KO6PIssEZEEtJ4TPXbsWCQkJODatWt4+fIlXr58iatXryIuLg7jxo0rjIxUEF5cU7dL15YuBxFp5cGDWLRtG4ApU/5QFdD16zviwoWRGDasHgtoIiKJaD0SfeDAARw5cgTVqlVT9Xl4eGD58uVo3759gYajAqLIAJ5fUrZLOAImRXNuERFpunXrBRo3XouYmGQAgEwGTJnSArNmtYKxMQ8OJiKSktYj0QqFIseDB42MjFTrR1MRE30bSEtUtstyBRUiXVGpUkk0blwOAODsbIWjRwfh22/bsoAmIioCtC6i27Rpg//97394/Pixqu/Ro0cYP3482rZtW6DhqIBEXVG37WpKl4OItGJgIMP69d0wYkQ9XLo0igcQEhEVIVoX0cuWLUNcXBxcXV1RsWJFVKxYEW5uboiLi8OPP/5YGBnpXT27oG6XZhFNVBSlpyswe/Yx/PnnPY1+R0dLrF7dBba2ZhIlIyKinGg9J9rZ2RmhoaH4448/VEvcVatWDd7e3gUejgpI1uXtyjSSLAYR5Sw8PBr9+wcjJOQhypWzxOXLo1GyJItmIqKiTKsieuvWrdi9ezdSU1PRtm1bjB07trByUUGKuaP838gCsCgnbRYiUhFCYNOmyxgzZh/i41MBAE+eJODo0Xs8eQoRURGX7yJ65cqV+OSTT1C5cmWYmZkhODgYd+/exYIFCwozH70rRToQd1/ZtnJRHt5PRJKLjk7CqFG/IyhIvfxkhQq22LKlB5o0cZIwGRER5Ue+50QvW7YMM2fORFhYGC5evIiNGzdixYoVhZmNCkL0bSAjRdkuVV3aLEQEADh2LAK1aq3SKKAHD66DixdHsoAmItIR+S6iw8PDMWjQINW2v78/0tPTERkZWSjBqIBkTuUAgJJVpctBREhNzcDUqUfQps1GPHwYBwCwsTFFUNCHWL++GywtTSROSERE+ZXv6RwpKSkoUaKEatvAwADGxsZISkoqlGBUQKJvq9u2laXLQUR4+DAOP/54FkIot1u1ckVAgC+cna2lDUZERFrT6sDCL7/8Eubm5qrt1NRUzJkzB9bW6jeARYsWFVw6encvb6rbtlWky0FEqFDBFkuXdsDo0b9jzpw2mDixGQwMeJwCEZEuyncR7eXlhbCwMI2+Zs2aITw8XLUt40FrRc8L9ZxLlHSXLgdRMRQV9Qrm5kYwN1ef5XXo0Lpo2dIVlSqVlDAZERG9q3wX0ceOHSvEGFQohACeX1K2rVwAE35lTPS+HDx4B4MH70KPHu5YvryTql8mk7GAJiLSA1qfsZB0yKunQFqisl2Ka84SvQ/JyekYP/4AOnTYgidPErBixTn8/vstqWMREVEB0/qMhaRDorOszGFdUbocRMXElStP0a9fMK5ceabq69ChEurXLythKiIiKgwsovVZ1uXtbCpIl4NIzykUAj/+eAaff34EKSkZAAATEzkWLGiHMWMa8XgRIiI9xCJan2VdmYMHFRIVisjIeAwZsgsHD95V9dWsaY/AwJ6oUcNewmRERFSYWETrs8TH6raVm3Q5iPRUWFgUWrRYj6ioV6q+8eOb4Ntv28LUlC+vRET67K0OLPzrr7/Qv39/NG3aFI8ePQIAbNq0CSdPnizQcPSO7u5Rt0s4SJeDSE9VqlQSHh6lAQCOjhY4eLA/Fi3yYQFNRFQMaF1E79ixAz4+PjAzM8OFCxeQkpICAIiNjcW3335b4AHpHZhnKZxNbCSLQaSv5HIDbNrUHQMG1MLly6PRvj0P4CUiKi60LqK/+eYbrFq1CmvWrIGRkfoEAs2bN0doaGiBhqN3IIRyibtMPLCJ6J1kZCgwb95J/P33A43+8uWtERDQHXZ25rlck4iI9JHW3zmGhYXBy8srW7+1tTViYmIKIhMVhPiHQEqMsu3SXtIoRLruwYNYDBiwE8eP/ws3NxtcvDgKVlYmUsciIiIJaT0SXaZMGdy5cydb/8mTJ1GhApdRKzLi76vbpapJl4NIxwUFXUOtWqtw/Pi/AICIiBgcOnT3DdciIiJ9p3URPXz4cPzvf//DmTNnIJPJ8PjxY2zZsgWTJk3C6NGjCyMjvY2kKHXbzE66HEQ6Ki4uBYMH/wY/v+2IiUkGADg7W+Ho0UH48EOeAZSIqLjTejrHlClToFAo0LZtW7x69QpeXl4wMTHBpEmTMHbs2MLISG8jLkLdtnCSLAaRLgoJeYD+/XciPDxa1efnVx0rV3aCra2ZhMmIiKio0LqIlslkmD59OiZPnow7d+4gISEBHh4esLCwKIx89Lbi/lW3ebZConxJT1dgzpwT+PrrE8jIEAAAS0tjLF/eEf371+KZB4mISOWtFzM1NjaGhwe/0iyyorPMW+eJVojy5e7dl5g796SqgG7WzBmbN3eHm5utxMmIiKio0bqIbt26dZ6jMX/++ec7BaICEv/fSLTcGLAsJ20WIh1Rtaod5s9vhwkTDmLGjJaYNs0ThoZvdU4qIiLSc1oX0XXq1NHYTktLw8WLF3H16lUMGjSooHLRu0p8ovzf3AGQsQggykl0dBLMzY1gYqJ+KRw7thHatHFDjRr2EiYjIqKiTusievHixTn2z5o1CwkJCe8ciAqAIkO9Ooc5T/dNlJNjxyIwYMBO9OlTHQsWqNdSl8lkLKCJiOiNCmyIsn///vj5558L6uboXSS/AIRC2S7BIpooq9TUDEydegRt2mzEw4dx+P77EPzxR7jUsYiISMe89YGFrwsJCYGpqWlB3Ry9i8ypHABgXka6HERFTFhYFPz9gxEaGqnqa93aFVWrci11IiLSjtZFdI8ePTS2hRCIjIzEuXPn8OWXXxZYMHoHiU/VbY5EE0EIgZ9+Oo/x4w8iKSkdAGBkZIA5c9pg4sRmMDDg0nVERKQdrYtoa2trjW0DAwNUrVoVX331Fdq3b5/Ltei9SnikbnMkmoq5588TMWzYHuzeHabqq1q1FAIDe6JePUcJkxERkS7TqojOyMjAkCFDULNmTdjact3UIivhobpt6SxdDiKJhYVFoVWrjXjyRH3Q8+jRDfD99+1hbm4kYTIiItJ1Wh1YKJfL0b59e8TExBRoiOXLl8PV1RWmpqZo3Lgxzp49m6/r/frrr5DJZPD19S3QPDov/oG6zTWiqRirUMEWzs5WAAA7O3Ps3t0HK1Z0YgFNRETvTOvVOWrUqIHw8II7kn3r1q2YMGECZs6cidDQUNSuXRs+Pj549uxZnteLiIjApEmT4OnpWWBZ9EZ81pHo8tLlIJKYkZEcW7b0QI8e1XDlymh06VJV6khERKQntC6iv/nmG0yaNAl79+5FZGQk4uLiNP5pa9GiRRg+fDiGDBkCDw8PrFq1Cubm5nkul5eRkYF+/fph9uzZqFChgtY/U+9lrs4hMwDMS0ubheg9USgEfvjhDC5ciNTor1y5FHbs6I0yZSwkSkZERPoo30X0V199hcTERHTs2BGXLl1C165d4eTkBFtbW9ja2sLGxkbredKpqak4f/48vL291YEMDODt7Y2QkJA8s9jb2+Ojjz56489ISUl550Jf5yQ+Vv5fogzPVkjFQmRkPDp23IL//e8A/P2D8epVmtSRiIhIz+X7wMLZs2dj1KhROHr0aIH98KioKGRkZMDBQXMZNgcHB9y8eTPH65w8eRLr1q3DxYsX8/Uz5s6di9mzZ79rVN2RkaoeibZwkjYL0Xuwa9dNDBu2B1FRrwAAN29GYf/+2+jZ00PiZEREpM/yXUQLIQAALVu2LLQwbxIfH48BAwZgzZo1sLPL38kRpk6digkTJqi24+Li4OysxytWJGb5KtuSRTTpr8TEVEyceAirV59X9Tk6WmDDBl+0b19RwmRERFQcaLXEnUxWsCcksLOzg1wux9OnTzX6nz59ijJlsq9vfPfuXURERKBLly6qPoVCeXprQ0NDhIWFoWJFzTdPExMTmJiYFGjuIi0+yxrRJbgGLumn8+cfw98/GLduvVD1+fq6Y82aLrCzM5cwGRERFRdaFdFVqlR5YyH98uXLfN+esbEx6tevjz/++EO1TJ1CocAff/yBMWPGZNvf3d0dV65c0ej74osvEB8fj6VLl+r3CHN+xd5Vt204Gkf6JSNDgQUL/saXXx5FerryA7S5uRGWLPHBsGH1CvyDPhERUW60KqJnz56d7YyF72rChAkYNGgQGjRogEaNGmHJkiVITEzEkCFDAAADBw5EuXLlMHfuXJiamqJGjRoa17exsQGAbP3F1qvn6jbPVkh65ubNKI0Cun59RwQG9kSVKqUkTkZERMWNVkV0nz59YG9vX6AB/Pz88Pz5c8yYMQNPnjxBnTp1cODAAdXBhvfv34eBAVeYyLcHf6rbJVhEk36pXt0eX3/dGtOm/YEpU1pg1qxWMDaWSx2LiIiKIZnIPGLwDeRyOSIjIwu8iH7f4uLiYG1tjdjYWFhZWUkdp+D95gvc3aVsD7wElK4laRyidxEfnwIzMyMYGqo/SGdkKHDhwhM0aFBWwmRERFSYdKFey/cQbz5rbZKaUKjbZjzRCumukJAHqFNnNb755oRGv1xuwAKaiIgkl+8iWqFQ6PwodLGQrF6tAGacJ0q6Jz1dgdmzj8HTcz3Cw6Px9dcn8PffD6SORUREpEGrOdGkA5KilP8blQDkxtJmIdJSeHg0+vcPRkjIQ1VfkyZOcHTkKbuJiKhoYRGtbxL+WyeaZyskHSKEwKZNlzFmzD7Ex6cCAORyGWbMaIlp0zw15kQTEREVBSyi9UlqApCWqGxb8EQrpBuio5MwevTv2Lr1mqqvQgVbbNnSA02a8MMgEREVTSyi9Ul8lnmjMi77RUVfWFgU2rXbhAcP4lR9gwfXwQ8/dIClZTE60ygREekcfkeqTxKfqNtcmYN0gIuLDWxsTAEAtramCAr6EOvXd2MBTURERR6LaH2SoD4YC46NpctBlE+mpoYIDOyJjh0r4/Ll0ejVq7rUkYiIiPKFRbQ+0Tjlt4N0OYhyIITATz+dx/XrzzX6a9Swx++/+8PJqWgupk9ERJQTFtH6JOtItGU56XIQveb580T4+m7FyJF74e+/Aykp6VJHIiIieicsovVJ1gMLucQdFREHD95BrVqrsHt3GADg0qWn2Lv3lsSpiIiI3g2LaH2S8FjdtuBINEkrOTkdn356AB06bMGTJwkAADs7c+ze3Qc9e3pInI6IiOjdcIk7ffL4b+X/praAIVc3IOlcufIU/v7BuHr1marPx6ciNmzwRZkyPPsgERHpPhbR+ojL25FEFAqBH388g88/P4KUlAwAgImJHPPnt8OYMY1gYCCTOCEREVHBYBGtL9JT1O1ozjclaVy58hQTJhyCQiEAADVr2iMwsCdq1LCXOBkREVHB4pxofZGqPuMbKnSSLgcVa7Vrl8G0aS0AAOPHN8HZs8NZQBMRkV7iSLS+SH6pbpuWlC4HFSuvXqXB1NRQY5rGjBkt0b59RXh6ukiYjIiIqHBxJFpfZD3Rimkp6XJQsXH+/GPUrbsaCxf+rdFvZCRnAU1ERHqPRbS+iA1Xty25RjQVnowMBebNO4kmTdbh1q0XmD79T4SGRkodi4iI6L3idA59kfVEK1auksUg/fbgQSwGDNiJ48f/VfXVquUACwtjCVMRERG9fyyi9UXyC3Xb3EG6HKS3goKuYeTIvYiJSQYAyGTAlCktMGtWKxgbyyVOR0RE9H6xiNYXiU/VbTMeWEgFJy4uBePG7cfGjZdUfc7OVti0qTtatnSVLhgREZGEWETrizu/qdtcnYMKSFhYFDp2DER4eLSqz8+vOlat6gwbG1MJkxEREUmLBxbqizIN1W0TG8likH5xcrKCoaHyZcLS0hgBAb745ZeeLKCJiKjYYxGtL7KebEVuIl0O0islShgjMLAHWrVyxaVLozBgQG3IZDx1NxEREYtofRH/UPl/CUflEV9EWhJCICDgEu7efanRX79+Wfz550C4udlKlIyIiKjoYRGtDzLSgKT/TrbC5e3oLURHJ6FPnx0YNOg39OsXjLS0DI3LOfpMRESkiUW0Psg6lcPURrIYpJuOHYtArVqrEBR0DQBw5swj7N17S+JURERERRuLaH2QlGWNaJ7ym/IpNTUDU6YcQZs2G/HwofKDmK2tKbZt64Xu3atJnI6IiKho4xJ3+iA1Vt02sZYuB+mMsLAo+PsHa5yuu3VrVwQEdIeTk5WEyYiIiHQDi2h9kKxew5dFNOVFCIGffjqP8eMPIikpHQBgZGSAOXPaYOLEZjAw4NxnIiKi/GARrQ84nYPy6cKFJxg16nfVdtWqpRAY2BP16jlKmIqIiEj3cE60PkjOUkSbsYim3NWr54gJE5oAAEaPboDQ0JEsoImIiN4CR6L1wavn6raZnXQ5qMhJSUmHsbFcY4m6b79tiw4dKqFdu4oSJiMiItJtHInWB8lZTo7BIpr+c+XKUzRosAYrV57T6DcxMWQBTURE9I5YROuDV0/VbXN76XJQkaBQCCxdehoNG67B1avPMHHiIVy//vzNVyQiIqJ843QOfRB/X90uwfmtxVlkZDyGDNmFgwfvqvoqVy4pYSIiIiL9xCJaH8Q/Uv5v7gAYmkqbhSSza9dNDBu2B1FRr1R948c3wbfftoWpKZ/qREREBYnvrPogc3UOrsxRLCUmpmLixENYvfq8qs/R0QIbNviifXvOfSYiIioMLKJ1XXoykJ6kbPOgwmLn1q0X6NLlF9y6pV7m0NfXHWvWdIGdnbmEyYiIiPQbi2hdl/VEK4YsmoobB4cSSE3NAACYmxth6dIO+OijuhpL2hEREVHB4+ocui41Tt1OfCxdDpKEtbUpNm/ujsaNy+HChZEYNqweC2giIqL3gEW0rkuMVLdd2kuXg96Lbduu4cGDWI2+5s3LIyTkI1SpwjnxRERE7wuLaF0X96+6beUiXQ4qVHFxKRg8+Df07r0dAwf+howMhcblHH0mIiJ6v1hE67r4h+q2ZXnpclChCQl5gLp1V2PjxksAgGPHIrB37y2JUxERERVvLKJ1XdbpHBY80Yo+SU9XYPbsY/D0XI/w8GgAgKWlMQICfNG1a1WJ0xERERVvXJ1D1yU+Ubd5tkK9ER4ejf79gxESov6moVkzZ2ze3B1ubrYSJiMiIiKARbTuS3qubnOdaJ0nhMCmTZcxZsw+xMenAgDkchlmzGiJadM8YWjIL4+IiIiKAhbRui5zJNrYkqf81gPnzj3GoEG/qbYrVLDFli090KSJk3ShiIiIKBsOa+m6ZOVcWY5C64eGDcth5Mj6AIDBg+vg4sWRLKCJiIiKII5E67L0ZCD5pbJtVlraLPRW0tIyYGhooLFE3cKF7dGxY2UePEhERFSEcSRal8XeAyCUbZtKkkYh7YWFRaFJk3WqpesylShhzAKaiIioiGMRrcuyrhFtxTWidYUQAqtXn0PduqsRGhqJsWP3486dl1LHIiIiIi1wOocuiwxRty04b1YXPH+eiGHD9mD37jBVX7lylkhKSpMwFREREWmLRbQuS09Wt0s4SJeD8uXgwTsYPHgXnjxJUPWNGlUfCxf6wNzcSMJkREREpC0W0bos69kKbTmHtqhKTk7H1KlHsGTJGVWfnZ05fv65K7p04d+NiKQhhEB6ejoyMjKkjkLFlJGREeRyudQx3hqLaF2W8FjdtigrXQ7K1Z07L9Gjx1ZcufJM1dehQyWsX98NZcpYSJiMiIqz1NRUREZG4tWrV1JHoWJMJpPByckJFha6+X7IIlqXZS5vJzMATEtKm4VyZGtrihcvkgAAJiZyLFjQDmPGNNJY0o6I6H1SKBS4d+8e5HI5ypYtC2NjY74m0XsnhMDz58/x8OFDVK5cWSdHpFlE67KkKOX/JtYAXwCLpFKlzLFhQzdMnnwYmzf3QI0a9lJHIqJiLjU1FQqFAs7OzjA3N5c6DhVjpUuXRkREBNLS0lhE03ukSAfiHyjbVq6SRiG1PXvC0LBhOY2pGu3aVcT5826Qy7miJBEVHQYGfE0iaen6NyB8BumquH8B8d/BINYVpM1CSExMxahRe9G1668YOnQXhBAal7OAJiIi0i98Z9dVcf+q2xyJltT5849Rr95PWL36PABg//472Lv3lsSpiIiIqDCxiNZVWYtoazfpchRjGRkKzJt3Ek2arMOtWy8AAObmRlizpgs6d64icToiIiIqTCyidRWXt5PUgwexaNs2AFOm/IH0dAUAoH59R1y4MBLDhtXT+XleRERFWUhICORyOTp16pTtsmPHjkEmkyEmJibbZa6urliyZIlG39GjR9GxY0eUKlUK5ubm8PDwwMSJE/Ho0aNcf/6GDRsgk8kgk8lgYGAAR0dH+Pn54f79+xr7xcXFYfr06XB3d4epqSnKlCkDb29vBAcHZ5v2lxchBGbMmAFHR0eYmZnB29sbt2/ffuP1Hj16hP79+6NUqVIwMzNDzZo1ce7cOdXlCQkJGDNmDJycnGBmZgYPDw+sWrVK4zZatWqluq+Z/0aNGpXv7PqMRbSuir2nbluVly5HMbR161XUqrUKx48rvw2QyYCpU1vg778/QpUqpSROR0Sk/9atW4exY8fixIkTePz48ZuvkIvVq1fD29sbZcqUwY4dO3D9+nWsWrUKsbGxWLhwYZ7XtbKyQmRkJB49eoQdO3YgLCwMvXr1Ul0eExODZs2aISAgAFOnTkVoaChOnDgBPz8/fPbZZ4iNjc13zvnz5+OHH37AqlWrcObMGZQoUQI+Pj5ITk7O9TrR0dFo3rw5jIyMsH//fly/fh0LFy6Era2tap8JEybgwIED2Lx5M27cuIFPP/0UY8aMwe7duzVua/jw4YiMjFT9mz9/fr6z6zOuzqGrXj1Vty2dpctRzJw+/RB9+uxQbTs7W2HTpu5o2dJVulBERMVIQkICtm7dinPnzuHJkyfYsGEDpk2bpvXtPHz4EOPGjcO4ceOwePFiVb+rqyu8vLxyHMnOSiaToUyZMgAAR0dHfPTRRxg3bhzi4uJgZWWFadOmISIiArdu3ULZsupvjKtUqYK+ffvC1NQ0XzmFEFiyZAm++OILdOvWDQAQEBAABwcH/Pbbb+jTp0+O15s3bx6cnZ2xfv16VZ+bm+b0z7///huDBg1Cq1atAAAjRozA6tWrcfbsWXTt2lW1n7m5ueq+khpHonVV5nQOnmjlvWrSxAkDBtQCAPj5VcelS6NYQBMRvUdBQUFwd3dH1apV0b9/f/z8889aTY3ItG3bNqSmpuKzzz7L8XIbG5t839azZ8+wc+dOyOVyyOVyKBQK/Prrr+jXr59GAZ3JwsIChobKccxZs2bB1dU119u+d+8enjx5Am9vb1WftbU1GjdujJCQkFyvt3v3bjRo0AC9evWCvb096tatizVr1mjs06xZM+zevRuPHj2CEAJHjx7FrVu30L59e439tmzZAjs7O9SoUQNTp07lmS7/w5FoXRUXofzfohxgwD9jYVEoBAwMNOc3L1vWEZ06VUbv3tU595mI6D1bt24d+vfvDwDo0KEDYmNjcfz4cdVoan7dvn0bVlZWcHR0fKscsbGxsLCwgBBCVVSOGzcOJUqUwLNnzxAdHQ13d/c33o6dnR0qVqyY6+VPnjwBADg4OGj0Ozg4qC7LSXh4OFauXIkJEyZg2rRp+OeffzBu3DgYGxtj0KBBAIAff/wRI0aMgJOTEwwNDWFgYIA1a9bAy8tLdTv+/v5wcXFB2bJlcfnyZXz++ecICwtDcHDwG++bvmP1pYvSk9Wn/OZUjkITHh6N/v2D8emnTdC7d3VVv5WVCfz8akiYjIioeAoLC8PZs2exc+dOAIChoSH8/Pywbt06rYtoIUS+BkIsLNQnz+rfv7/qwDtLS0uEhoYiLS0N+/fvx5YtWzBnzhzVbefXmDFjMGbMGK2y54dCoUCDBg3w7bffAgDq1q2Lq1evYtWqVRpF9OnTp7F79264uLjgxIkT+OSTT1C2bFnVyPeIESNUt1mzZk04Ojqibdu2uHv3bp7Ff3HAIloXZZ0Pbe6Q+370VoQQ2LTpMsaM2Yf4+FTcuLEXTZs6wdnZWupoRETF2rp165Cenq4xRUIIARMTEyxbtgzW1tawsrICoBwpfn1KRkxMDKytla/lVapUQWxsLCIjI/Mcjb548aKqnXnbgPKMj5UqVQIAVKtWDXfv3sXo0aOxadMmlC5dGjY2Nrh58+a73mXVXOSnT59q5Hz69Cnq1KmT6/UcHR3h4eGh0VetWjXs2KE8ricpKQnTpk3Dzp07Vauc1KpVCxcvXsT333+vMX0kq8aNGwMA7ty5U+yLaM6J1kVxD9RtSyfpcuih6Ogk9OmzA4MG/Yb4+FQAQMmSZnjxIkniZERExVt6ejoCAgKwcOFCXLx4UfXv0qVLKFu2LH755RcAQOXKlWFgYIDz589rXD88PByxsbGoUkW5jv+HH34IY2PjXFeayDywsFKlSqp/9vb2ueabMmUKtm7ditDQUBgYGKBPnz7YsmVLjquHJCQkID09PV/3283NDWXKlMEff/yh6ouLi8OZM2fQtGnTXK/XvHlzhIWFafTdunULLi4uAIC0tDSkpaVlO/175pzu3GR+qHjbaTB6RRQzsbGxAoCIjY2VOsrbu7lViO+h/Hd2vtRp9MbRo/eEk9MiAcxS/Rs8+DcRF5csdTQiogKTlJQkrl+/LpKSkqSOopWdO3cKY2NjERMTk+2yzz77TDRo0EC1PWLECOHq6ip27dolwsPDxfHjx0WTJk1EkyZNhEKhUO23fPlyIZPJxNChQ8WxY8dERESEOHnypBgxYoSYMGFCrlnWr18vrK2ts/X37t1bdOrUSQghxIsXL4S7u7twcnISGzduFNeuXRO3bt0S69atE5UqVRLR0dFCCCF+/PFH0aZNmzzv+3fffSdsbGzErl27xOXLl0W3bt2Em5ubxt+wTZs24scff1Rtnz17VhgaGoo5c+aI27dviy1btghzc3OxefNm1T4tW7YU1atXF0ePHhXh4eFi/fr1wtTUVKxYsUIIIcSdO3fEV199Jc6dOyfu3bsndu3aJSpUqCC8vLzyzJtfeT0WdaFeYxGti/75Xl1EX98idRqdl5KSLj7//LCQydTFs43NdyIo6KrU0YiICpyuFtGdO3cWHTt2zPGyM2fOCADi0qVLQgjlfZw5c6Zwd3cXZmZmws3NTYwYMUI8f/4823UPHz4sfHx8hK2trTA1NRXu7u5i0qRJ4vHjx7lmya2IDgkJEQDEmTNnhBBCxMTEiClTpojKlSsLY2Nj4eDgILy9vcXOnTtVxfzMmTOFi4tLnvddoVCIL7/8Ujg4OAgTExPRtm1bERYWprGPi4uLmDlzpkbfnj17RI0aNYSJiYlwd3cXP/30k8blkZGRYvDgwaJs2bLC1NRUVK1aVSxcuFCV7f79+8LLy0uULFlSmJiYiEqVKonJkycXWA2l60W0TIi3WBdGh8XFxcHa2hqxsbEac5t0ypGPgUsrle0+J4FyzaXNo8PCw6PRq9c2hIZGqvpatXJFQIAv50ATkV5KTk7GvXv34Obmlu+1iokKQ16PRV2o1zgnWhfF3FG3rd1y34/eyMzMEPfvK88aZWRkgPnzvfHHHwNZQBMREVGeWETrosT/Rk0NzYESPIPQu3B0tMS6dV3h7m6H06eHYfLk5tnWhSYiIiJ6HZe40zVCADF3lW3LcsozFlK+HTkSjrp1y6BUKXNVX9euVfHBB5VgZCSXMBkRERHpkiJRgS1fvhyurq4wNTVF48aNcfbs2Vz3XbNmDTw9PWFrawtbW1t4e3vnub/eSYwE0v9bbs2msrRZdEhycjrGjz+Adu02YeTIvdkWwmcBTURERNqQvIjeunUrJkyYgJkzZyI0NBS1a9eGj48Pnj17luP+x44dQ9++fXH06FGEhITA2dkZ7du3x6NHj95zconE/atuW1eQLocOuXLlKRo1WoMlS84AAHbsuIEDB+684VpEREREuZO8iF60aBGGDx+OIUOGwMPDA6tWrYK5uTl+/vnnHPffsmULPv74Y9SpUwfu7u5Yu3YtFAqFxiLkei0hy4cFi3LS5dABCoXA0qWn0bDhGly5ovxQZmIixw8/dECHDpUkTkdERG8rJCQEcrlcdaa9rI4dOwaZTKY6WUpWrq6uWLJkiUbf0aNH0bFjR5QqVQrm5ubw8PDAxIkT8xyc27BhA2QyGWQyGQwMDODo6Ag/Pz/cv38/277Xrl1D7969Ubp0aZiYmKBKlSqYMWMGXr16lW3fCxcuoFevXnBwcICpqSkqV66M4cOH49atW2/+pWRx//59dOrUCebm5rC3t8fkyZPfeHKXW7duoVu3brCzs4OVlRVatGiBo0eP5rjvixcv4OTklOPvecuWLahduzbMzc3h6OiIoUOH4sWLF1rl1xWSFtGpqak4f/68xqklDQwM4O3tjZCQkHzdxqtXr5CWloaSJUvmeHlKSgri4uI0/um0FzfUbRuOROcmMjIeHTtuwaefHkRKSgYAoGZNe5w7NwJjxzaGTMaDB4mIdNW6deswduxYnDhxIsczAubX6tWr4e3tjTJlymDHjh24fv06Vq1ahdjYWCxcuDDP61pZWSEyMhKPHj3Cjh3/b+++46K4tjiA/9iFXTqIgBQRFBRFxYYooDGWCLZgiWLF3omJLbFjRWOLJbZYUBMUK2rUYEGxIE9siwXFAsQG2EGQtux5fxBHNxRBkQU8389nP2/2zr0zZ3bWl8PdO/fuQXR0NLp3765U53//+x+aNGmCzMxMHDp0CLdv38a8efOwefNmfPPNN8jMzBTqHjx4EE2bNkVGRgYCAgJw8+ZN/PnnnzAwMMD06dMLfU3Z2dno0KEDMjMzce7cOWzZsgWbN2/GjBkzCmzXsWNHyOVynDhxApcuXUK9evXQsWNHJCQk5Ko7ePBgODo65ioPCwuDt7c3Bg8ejBs3bmDXrl2IiIjA0KFDCx1/maLKSaofPXpEAOjcuXNK5RMnTiRnZ+dCHWPkyJFUrVq1fCeN9/X1JQC5XqV58u4C/W79bqGVhMuqjqZU2r//FhkbL1RaeXDs2GBKS8tSdWiMMaZyZXWxlbdev35Nurq6dOvWLfLy8qJ58+Yp7T958iQBEFYEfJ+1tTX9+uuvRET04MEDkkgk9OOPP+Z5nrzav5XXYisrVqxQyi8UCgU5ODiQk5MTZWdnK9WVyWSkpqZGCxYsICKi1NRUMjY2ps6dOxc5lv86fPgwiUQiSkhIEMrWrFlD+vr6lJGRkWebp0+fEgA6ffq0UJacnEwA6NixY0p1V69eTS1atKCQkJBcn/OiRYuoWrVqSvVXrFhBlpaWeZ63rC+2ovLhHJ9iwYIFCAwMRFBQUL4Txk+ePBlJSUnC68GDByUcZTF7f0y0UU3VxVFKhYXdh6dnIJ49y/mZzMxMF0eO9MXSpe7Q1OTJaBhjrKzbuXMnatasCXt7e/Tt2xebNm3K9bB4YezatQuZmZn46aef8txvaGhY6GM9efIEQUFBEIvFEItzHlSXyWSIiorCuHHjIBIpp1v16tVDmzZtsH37dgDAkSNH8OzZs0LFYmNjg5kzZ+YbS3h4OOrWrYtKlSoJZe7u7khOTsaNGzfybFOxYkXY29tj69atSE1NhVwux7p162BqaopGjRoJ9aKiojB79mxs3bo11zUBgIuLCx48eIDDhw+DiJCYmIjdu3ejffv2+cZblqk0iTY2NoZYLEZiYqJSeWJiIszMCp7/ePHixViwYAGOHj2a508Kb0mlUujr6yu9yjSp4bttDS2VhVFaubpaoUuXnD8uPD3tce3aSLRta6viqBhjjBWXjRs3om/fvgAADw8PJCUl4dSpU0U+zp07d6Cvrw9zc/OPiiMpKQm6urrQ0dFBpUqVcPLkSYwePRo6OjoAIIxjrlWrVp7ta9WqJdS5c+cOAKBmzQ93jtna2sLY2Djf/QkJCUoJNADhfV5DMwBATU0Nx48fx5UrV6CnpwdNTU0sXboUwcHBqFChAoCc4bG9evXCokWLUKVKlTyP4+bmhoCAAHh5eUEikcDMzAwGBgZYtWrVB6+rLFJpEi2RSNCoUSOlhwLfPiTo4uKSb7uFCxdizpw5CA4OhpOTU0mEWjqkJgIZr3K2dSurNJTS4r+9D2pqali/vhP8/T0RFOQFY2PtfFoyxhgra6KjoxEREYFevXoBANTV1eHl5YWNGzcW+VhEVKjnY3R1dYXXiBEjhHI9PT3IZDJcvHgRS5YsQcOGDTFv3rw8z1OYWAorJCQEPj4+ha5fGESE0aNHw9TUFGfOnEFERAQ6d+6MTp06IT4+Z4G3yZMno1atWsIfMHmJiorCDz/8gBkzZuDSpUsIDg5GXFyc0udWnqj89+1x48ahf//+cHJygrOzM5YtW4bU1FQMHDgQAODt7Q1LS0vMnz8fAPDLL79gxowZ2LZtG2xsbIS/qt5+wcu15Lh327bfqiyM0uLBgyR4e+/D+PEu6NixhlBesaI2Bgyor7rAGGOMfRYbN26EXC6HhYWFUEZEkEql+O2332BgYCD84pyUlJRrSMarV69gYGAAAKhRowaSkpIQHx9fYG+0TCYTtt//NVskEsHOLmemp1q1auHevXsYOXIk/vjjD+H4AHDz5k00aNAg13Fv3rwp1Hn7v7du3SqwE7EwzMzMcq2f8fYX//x+5T9x4gQOHjyIly9fCte4evVqHDt2DFu2bMGkSZNw4sQJXLt2Dbt37wbwLvE3NjbG1KlTMWvWLMyfPx9ubm6YOHEiAMDR0RE6Ojpo3rw55s6d+9G9/qWVysdEe3l5YfHixZgxYwbq168PmUyG4OBg4aeH+/fvC38FAcCaNWuQmZmJ7777Dubm5sJr8eLFqrqEkvN2pUIA0M/7p5Qvxc6dN+DouBahoXEYNGg/EhJSVB0SY4yxz0gul2Pr1q1YsmQJZDKZ8IqMjISFhYUwvrh69eoQiUS4dOmSUvuYmBgkJSUJCet3330HiUSChQsX5nm+t1O32dnZCS9TU9N845s0aRJ27NiBy5cvA4AwFe+vv/4KhUKhVDcyMhLHjx8XetTbtm0LY2PjD8ZSGC4uLrh27ZrSehvHjh2Dvr4+HBwc8mzzdrq9/45zFolEQux79uxBZGSk8Llv2LABAHDmzBmMHj1aOM5/j/F2jPjHjFsv9VT1RKOqlIWnPfN1ZMi7mTmid6k6GpVISkqn/v2DlGbesLJaSpcuPVZ1aIwxViaU1dk5goKCSCKR0KtXr3Lt++mnn8jJyUl4P2zYMLKxsaH9+/dTTEwMnTp1ipo2bUpNmzYlhUIh1Fu1ahWpqanRoEGDKDQ0lOLi4ujs2bM0bNgwGjduXL6x5DU7BxFRjx49qEOHDsL7sLAw0tbWps6dO9P58+fpn3/+oZ07d5KVlRW5urpSenq6UHffvn2koaFBnTp1omPHjlFsbCxduHCBJk6cSF5eXkK9Vq1a0cqVK/ONTS6XU506daht27Ykk8koODiYTExMaPLkyUKd8+fPk729PT18+JCIcmbnqFixInXt2pVkMhlFR0fThAkTSENDg2QyWZ7nyWsWFH9/f1JXV6fVq1fTvXv36OzZs+Tk5JTvjGtlfXYOTqLLkr0d3yXR8RGqjqbEnTt3n6pWXaaUQHt57aIXL96oOjTGGCszymoS3bFjR2rfvn2e+86fP08AKDIykohyrtHX15dq1qxJWlpaVLVqVRo2bBg9ffo0V9tjx46Ru7s7VahQgTQ1NalmzZo0YcIEevw4/86Z/JLo8PBwAkDnz58Xyq5evUrdunUjIyMj0tDQIFtbW5o2bRqlpqbman/hwgXq2rUrmZiYkFQqJTs7Oxo2bBjduXNHqGNtbU2+vr75xkZEFBcXR+3atSMtLS0yNjam8ePHU1bWu2le3ybAsbGxSudu27YtGRkZkZ6eHjVt2pQOHz6c7znym0pwxYoV5ODgQFpaWmRubk59+vQRkvX/KutJtBpReexfz19ycjIMDAyQlJRU9mbqWPLeAxBjUgGNL+OhOblcgblzT2Pu3NPIzs75uurpSbBqVXv07evIC6cwxlgRpKenIzY2FlWrVs13eljGSkJB38WykK+p/MFCVgS6lYGUhznbX0gCHRf3Cr1770F4+EOhzNXVCn/+2QVVq1ZQYWSMMcYY+5Kp/MFCVkhZqe8SaNPcT/mWVyKRGqKingIAxGI1zJr1NU6dGsAJNGOMMcZUipPosuLZ9XfbX1ASXaWKAdau7Yhq1Srg7NlBmDGjBdTV+WvLGGOMMdXibKSseB71btu4juri+MzOnPkHyckZSmU9e9bBjRuj0LQpLzDDGGOMsdKBk+iy4uWdd9sV7FUXx2eSmZmNSZOOo0WLzfj++79z7dfU5OH7jDHGGCs9OIkuK17ff7dtYKOyMD6H6OhncHHZiF9+CQMRsHVrJI4evffhhowxxr4oAwYMgJqaWq7X3bt3AQCnT59Gp06dYGFhATU1Nezbt69Qx42MjMS3334LU1NTaGpqwsbGBl5eXkoLlpRmL168QJ8+faCvrw9DQ0MMHjwYKSkfXoQsPDwcrVq1go6ODvT19fHVV18hLS1Nqc6hQ4fQpEkTaGlpoUKFCujcubPS/rzuR2BgYHFeXqnF3Xtlxcvb/26oAfo2qoyk2BARfv/9EsaOPYK0NDkAQENDhHnzWqFNm2oqjo4xxlhp5OHhAX9/f6UyExMTAEBqairq1auHQYMGoWvXroU63tOnT9G6dWt07NgRR44cgaGhIeLi4nDgwAGkpqYWe/xvZWVlQUNDo1iO1adPH8THx+PYsWPIysrCwIEDMWzYMGzbti3fNuHh4fDw8MDkyZOxcuVKqKurIzIyUmnFwT179mDo0KHw8/NDq1atIJfLcf369VzH8vf3h4eHh/D+v8utl1sqnqe6xJWFybtzUWQTLdPOWWRlg62qoykWT56k0LffbldaOMXefiWvPMgYY59ZWV1shYiof//+5OnpWai6ACgoKOiD9YKCgkhdXV1pMZK8XL9+nTp06EB6enqkq6tLzZo1o7t37xIRUXZ2Ns2aNYssLS1JIpFQvXr16O+//xbaxsbGEgAKDAykr776iqRSKfn7+xMR0fr166lmzZoklUrJ3t6eVq1aVajreysqKooA0IULF4Syv//+m9TU1OjRo0f5tmvSpAlNmzYt3/1ZWVlkaWlJGzZsKPD8hf2c81LWF1vh4RxlQfI/gDxnXXtUrK3aWIrBkSN34ei4FgcORAtlI0c64fLl4WjY0FyFkTHGGPvSmJmZQS6XIygoCJTP+nOPHj3CV199BalUihMnTuDSpUsYNGgQ5PKcX1GXL1+OJUuWYPHixbh69Src3d3x7bff4s6dO0rHmTRpEn744QfcvHkT7u7uCAgIwIwZMzBv3jzcvHkTfn5+mD59OrZs2SK0+frrrzFgwIB84w8PD4ehoSGcnJyEsjZt2kAkEuH8+fN5tnny5AnOnz8PU1NTuLq6olKlSmjRogXOnj0r1Ll8+TIePXoEkUiEBg0awNzcHO3atcuzJ3r06NEwNjaGs7MzNm3alO/nWN7wcI6y4NV744Mr1FBdHMXgzJl/4OERILw3NtbGpk3folOn8vewJGOMseJ38OBB6OrqCu/btWuHXbt2ffTxmjZtiilTpqB3794YMWIEnJ2d0apVK3h7e6NSpUoAgFWrVsHAwACBgYHCEIwaNd7993jx4sX4+eef0bNnTwDAL7/8gpMnT2LZsmVYtWqVUO/HH39UGmbi6+uLJUuWCGVVq1ZFVFQU1q1bh/79+wMAqlSpAnPz/DuYEhISYGpqqlSmrq4OIyMjJCQk5NkmJiYGADBz5kwsXrwY9evXx9atW9G6dWtcv34d1atXV6qzdOlS2NjYYMmSJfj6669x+/ZtGBkZAQBmz56NVq1aQVtbG0ePHsWoUaOQkpKCMWPGfOijL/M4iS4LkmLebRvaqi6OYtCsWRV4eNghOPguPDzs4O/vCTMz3Q83ZIwxxgC0bNkSa9asEd7r6OgUuq2fnx/8/PyE91FRUahSpQrmzZuHcePG4cSJEzh//jzWrl0LPz8/nD59GnXr1oVMJkPz5s3zHMOcnJyMx48fw83NTanczc0NkZGRSmXv9xanpqbi3r17GDx4MIYOHSqUy+VyGBgYCO+3bt1a6OsrLIVCAQAYPnw4Bg4cCABo0KABQkJCsGnTJsyfP1+oM3XqVHTr1g1AztjnypUrY9euXRg+fDgAYPr06cJxGzRogNTUVCxatIiTaFZKvD+9XRl/qFBNTQ3+/p4ICrqJESOcoKampuqQGGOMlSE6Ojqws7P7qLYjRoxAjx49hPcWFhbCdsWKFdG9e3d0794dfn5+aNCgARYvXowtW7ZAS0vrk+MGlBP+t7NnrF+/Hk2aNFGqJxaLC31MMzOzXLOIyOVyvHjxAmZmZnm2eduz7eDgoFReq1Yt3L9/P986UqkU1apVE+rkpUmTJpgzZw4yMjIglUoLfR1lEY+JLgueXft3Q61MjYlOSEhBhw7bEBISo1RuZqaLkSMbcwLNGGOsRBkZGcHOzk54qavn3ZcokUhga2srzM7h6OiIM2fOICsrK1ddfX19WFhYICwsTKk8LCwsV5L6vkqVKsHCwgIxMTFKMdnZ2aFq1aqFviYXFxe8evUKly5dEspOnDgBhUKRKzl/y8bGBhYWFoiOjlYqv337NqytrQEAjRo1glQqVaqTlZWFuLg4oU5eZDIZKlSoUO4TaIB7osuGp1dz/leqD+hZqjaWQjpwIBqDBx/As2dvEBmZgMjIEahYUVvVYTHGGCvHUlJShDmjASA2NhYymQxGRkaoUqVKnm0OHjyIwMBA9OzZEzVq1AAR4a+//sLhw4eFqfR8fHywcuVK9OzZE5MnT4aBgQH+97//wdnZGfb29pg4cSJ8fX1ha2uL+vXrw9/fHzKZDAEBAXme861Zs2ZhzJgxMDAwgIeHBzIyMnDx4kW8fPkS48aNAwB4e3vD0tIS8+fPz/MYtWrVgoeHB4YOHYq1a9ciKysLPj4+6Nmzp9DT/ujRI7Ru3Rpbt26Fs7Mz1NTUhJjr1auH+vXrY8uWLbh16xZ2794NIOePgxEjRsDX1xdWVlawtrbGokWLAADdu3cHAPz1119ITExE06ZNoampiWPHjsHPzw8TJkwo7C0r0ziJLu3SngOp8Tnbpg0AtdL940FqaibGjz+Kdeve/UWsUBDi4l5xEs0YY+yzunjxIlq2bCm8f5uI9u/fH5s3b86zjYODA7S1tTF+/Hg8ePAAUqkU1atXx4YNG9CvXz8AOUM9Tpw4gYkTJ6JFixYQi8WoX7++MA56zJgxSEpKwvjx4/HkyRM4ODjgwIEDqF69eoHxDhkyBNra2li0aBEmTpwIHR0d1K1bFz/++KNQ5/79+0pzN+clICAAPj4+aN26NUQiEbp164YVK1YI+7OyshAdHY03b94IZT/++CPS09MxduxYvHjxAvXq1cOxY8dga/vu2atFixZBXV0d/fr1Q1paGpo0aYITJ06gQoUKAAANDQ2sWrUKY8eOBRHBzs4OS5cuVRrjXZ6p0ZcyD8m/kpOTYWBggKSkJOjr66s6nA+7fxLY1Spnu8H3QKsVBddXoUuXHqNPn72Ijn4ulHXuXBPr13eCsTEn0IwxVhqkp6cjNjYWVatWhaampqrDYV+wgr6LZSFf457o0u7xe2OsjOuoLo4CZGcrsHjxOUybdhJyec7TvNraGli+3AODBzfgsc+MMcYYK3c4iS7tUt+b49GoluriyMfDh8no1y8IoaFxQlmjRubYtq0batSoqLrAGGOMMcY+I06iS7snV95tG5W+BUnS0rJw4cIjAICaGjBpUjPMnPk1JJLCT8/DGGOsFPjTSbnjpqTomAF9L5b8eRn7RJxEl2akeDe9nb41oG1acH0VqF69IlasaIeZM0Pxxx9d0KKFjapDYowx9jFSE4CUR6qOgrEyg5Po0uz1IyDzdc52KRkPHRHxCHXqmEJb+92qTQMH1kePHrWhqytRYWSMMcY+iU7eC3OU2/My9qnoC5OUlEQAKCkpSdWhfNjdv4gWI+cVOlGloWRlZdPMmSdJLJ5FI0ceVGksjDHGPl5aWhpFRUVRWlqaqkMpsv79+xOAXK87d+4QEZGfnx85OTmRrq4umZiYkKenJ926deuDxw0NDaWWLVtShQoVSEtLi+zs7Mjb25syMjI+9yUVi3/++Yfat29PWlpaZGJiQhMmTKCsrKwPtjt48CA5OzuTpqYmGRoakqenp7Dv2bNn5O7uTubm5iSRSKhy5co0evRopfzpzJkz5OrqSkZGRqSpqUn29va0dOnSQsdd0HexLORr3BNdmsX/7922aQOVhRET8xJ9++5FePhDAMCaNRfRvbsDWrYs/IpKjDHGWHHw8PAQFkF5y8TEBABw6tQpjB49Go0bN4ZcLseUKVPQtm1bREVFKS25/b6oqCh4eHjg+++/x4oVK6ClpYU7d+5gz549yM7O/izXQETIzs7Od8XEosjOzkaHDh1gZmaGc+fOIT4+Ht7e3tDQ0ICfn1++7fbs2YOhQ4fCz88PrVq1glwux/Xr14X9IpEInp6emDt3LkxMTHD37l2MHj0aL168wLZt2wDkLGPu4+MDR0dH6Ojo4OzZsxg+fDh0dHQwbNiwT762Uk/VWXxJKwt/2Qh2u7/riU76p8RPr1AoaMsWGenp+REwk4CZJBbPolmzQikrK7vE42GMMfbpynpP9Pu9pR/y5MkTAkCnTp3Kt86vv/5KNjY2HzzW2bNnqUWLFqSlpUWGhobUtm1bevHiBRERpaen0/fff08mJiYklUrJzc2NIiIihLYnT54kAHT48GFq2LAhaWho0MmTJyk7O5v8/PzIxsaGNDU1ydHRkXbt2lXo6yMiOnz4MIlEIkpISBDK1qxZQ/r6+vn2pGdlZZGlpSVt2LChSOdavnw5Va5cucA6Xbp0ob59+xbqeGW9J7p0L3/3JSMCEv9d9U+zAqBnVaKnf/kyDb167UH//vvw+nUmAKBatQo4e3YQZsxoAXV1/uowxhgr3ZKSkgAARkZG+dYxMzNDfHw8Tp8+nW8dmUyG1q1bw8HBAeHh4Th79iw6deok9FT/9NNP2LNnD7Zs2YLLly/Dzs4O7u7uePHihdJxJk2ahAULFuDmzZtwdHTE/PnzsXXrVqxduxY3btzA2LFj0bdvX5w6dUpoY2Njg5kzZ+YbW3h4OOrWrYtKlSoJZe7u7khOTsaNGzfybHP58mU8evQIIpEIDRo0gLm5Odq1a6fUE/1fjx8/xt69e9GiRYt861y5cgXnzp0rsE65ouosvqSVhb9siIjoxZ13vdB7O5ToqUNDY8nKaqnQ+wzMpAED9lFycnqJxsEYY6z4lfWeaLFYTDo6OsLru+++y7NudnY2dejQgdzc3Ao8plwupwEDBhAAMjMzo86dO9PKlSuV8oRevXrle5yUlBTS0NCggIAAoSwzM5MsLCxo4cKFRPSuJ3rfvn1CnfT0dNLW1qZz584pHW/w4MHUq1cv4X2rVq1o5cqV+cY/dOhQatu2rVJZamqq0POdl+3btxMAqlKlCu3evZsuXrxIvXr1oooVK9Lz58+V6vbs2ZO0tLQIAHXq1CnP742lpSVJJBISiUQ0e/bsfGP9L+6JZp/HU9m7bdNGJXbaU6fi0LLlFjx4kAwAqFBBEzt3fgd/f0/o6UlLLA7GGGMsLy1btoRMJhNeK1asyLPe6NGjcf36dQQGBgplI0aMgK6urvACALFYDH9/fzx8+BALFy6EpaUl/Pz8ULt2bcTHxwN41xOdl3v37iErKwtubm5CmYaGBpydnXHz5k2luk5OTsL23bt38ebNG3zzzTdKMW3duhX37t0T6oWEhMDHx6eIn1LBFIqc1YWnTp2Kbt26oVGjRvD394eamhp27dqlVPfXX3/F5cuXsX//fty7dw/jxo3LdbwzZ87g4sWLWLt2LZYtW4bt27cXa7ylFT9YWFq9v8iKab0SO22zZlXw1VfWOHXqH7RsaYOtW7ugcuXSuWY9Y4yxL4+Ojg7s7OwKrOPj44ODBw/i9OnTqFy5slA+e/ZsTJgwIc82lpaW6NevH/r164c5c+agRo0aWLt2LWbNmgUtLa1ii/2tlJQUAMChQ4dgaWmpVE8qLXynlZmZGSIiIpTKEhMThX15MTc3BwA4ODgonbNatWq4f/9+ruObmZmhZs2aMDIyQvPmzTF9+nThGABQtWrORAN169ZFYmIiZs6ciV69ehX6Gsoq7okureLf+wdRqeR6osViEf74owuWLGmL48e9OYFmjDFWZhARfHx8EBQUhBMnTgjJ3Vumpqaws7MTXvmpUKECzM3NkZqaCgBwdHRESEhInnVtbW0hkUgQFhYmlGVlZeHChQtKSep/OTg4QCqV4v79+0ox2dnZwcqq8M9Bubi44Nq1a3jy5IlQduzYMejr6+d7/kaNGkEqlSI6Olop5ri4OFhbW+d7rrc92BkZGQXWKWh/ecI90aWRQg48PpezrVsZ0KvyWU7z9GkqRow4hHHjmsLN7d05rKwMMG6cy2c5J2OMMfa5jB49Gtu2bcP+/fuhp6eHhIScZcwNDAzy7U1et24dZDIZunTpAltbW6Snp2Pr1q24ceMGVq5cCQCYPHky6tati1GjRmHEiBGQSCQ4efIkunfvDmNjY4wcORITJ06EkZERqlSpgoULF+LNmzcYPHhwvrHq6elhwoQJGDt2LBQKBZo1a4akpCSEhYVBX18f/fv3BwC0bt0aXbp0yXdIR9u2beHg4IB+/fph4cKFSEhIwLRp0zB69GihRzsiIgLe3t4ICQmBpaUl9PX1MWLECPj6+sLKygrW1tZYtGgRAKB79+4AgMOHDyMxMRGNGzeGrq4ubty4gYkTJ8LNzQ02NjYAgFWrVqFKlSqoWbMmAOD06dNYvHgxxowZU5TbVmZxEl0avbgFyN/kbFu4AGpqxX6KI0fuYsCA/UhISMHly/GIjBwBfX0e88wYY6zsWrNmDQDg66+/Vir39/fHgAED8mzj7OyMs2fPYsSIEXj8+DF0dXVRu3Zt7Nu3T5hlokaNGjh69CimTJkCZ2dnaGlpoUmTJsKQhQULFkChUKBfv354/fo1nJyccOTIEVSoUKHAeOfMmQMTExPMnz8fMTExMDQ0RMOGDTFlyhShzr179/Ds2bN8jyEWi3Hw4EGMHDkSLi4u0NHRQf/+/TF79myhzps3bxAdHY2srCyhbNGiRVBXV0e/fv2QlpaGJk2a4MSJE0LMWlpaWL9+PcaOHYuMjAxYWVmha9eumDRpknAMhUKByZMnIzY2Furq6rC1tcUvv/yC4cOHF3jd5YUaEZGqgyhJycnJMDAwQFJSEvT1S+lQhavrgWP/TlLezA9oMrnYDp2eLsekScexfPl5oczYWBt//90HTk4WxXYexhhjpVN6ejpiY2NRtWpVaGpqqjoc9gUr6LtYFvI17okujRLeGw9t6ZZ/vSK6di0RvXvvxfXr78ZNeXjYwd/fE2ZmusV2HsYYY4yx8o6T6NLo0b8PJ6iJimW5b4WCsHLlefz883FkZORMDC+VirFo0Tfw8XGG2mcYLsIYY4wxVp5xEl3aJD8AXvw7r6RZE0Ci90mHi49/jYED9+PIkXdzTtata4pt27qhTh3TTzo2Y4wxxtiXiqe4K23enx/a6tOXzXzxIg2hoXHC+7FjmyIiYign0Iwxxhhjn4CT6NLm7t532+ZNP/lwtWubYtGib2BmposjR/pi6VJ3aGryDxCMMcYYY5+Ck+jShBRA7N852xq6gPU3RT5EZGQCMjLkSmU+Ps6IihqFtm1tiyNKxhhjjLEvHifRpckTGfDm35kzrFoCGtqFbpqdrcAvv5yFk9N6TJ16QmmfmpoaKlQoniVLGWOMMVULDw+HWCxGhw4dVB1KqXL//n106NAB2traMDU1xcSJEyGXy/OtHxoaCjU1tTxfFy5cAABER0ejZcuWqFSpEjQ1NVGtWjVMmzZNac5pAFi2bBns7e2hpaUFKysrjB07Funp6Z/1elWNf9cvTR6dfbdt3brQzR48SEK/fkE4deofAMCSJeHo3LkmmjX7PCsdMsYYY6q0ceNGfP/999i4cSMeP34MCwvVrXOQmZkJiUSisvO/lZ2djQ4dOsDMzAznzp1DfHw8vL29oaGhAT8/vzzbuLq6Ij4+Xqls+vTpCAkJgZOTEwBAQ0MD3t7eaNiwIQwNDREZGYmhQ4dCoVAIx922bRsmTZqETZs2wdXVFbdv38aAAQOgpqaGpUuXft4LVyHuiS5NHp5+t235VaGa7Nx5A46Oa4UEWk0NmDy5GZydLT9HhIwxxphKpaSkYMeOHRg5ciQ6dOiAzZs356rz119/oXHjxtDU1ISxsTG6dOki7MvIyMDPP/8MKysrSKVS2NnZYePGjQCAzZs3w9DQUOlY+/btU5oKdubMmahfvz42bNigtEhIcHAwmjVrBkNDQ1SsWBEdO3bEvXv3lI718OFD9OrVC0ZGRtDR0YGTkxPOnz+PuLg4iEQiXLx4Uan+smXLYG1tDYVC8cHP5ejRo4iKisKff/6J+vXro127dpgzZw5WrVqFzMzMPNtIJBKYmZkJr4oVK2L//v0YOHCgcM3VqlXDwIEDUa9ePVhbW+Pbb79Fnz59cObMGeE4586dg5ubG3r37g0bGxu0bdsWvXr1QkRERJ7nLS84iS4tMpKA2MM521JDwKRugdWTkzMwYMA+eHntxqtXOT+XWFnp4+TJ/vDzaw2JRPyZA2aMMcZK3s6dO1GzZk3Y29ujb9++2LRpE95ffPnQoUPo0qUL2rdvjytXriAkJATOzs7Cfm9vb2zfvh0rVqzAzZs3sW7dOujqFm3Bsbt372LPnj3Yu3cvZDIZACA1NRXjxo3DxYsXERISApFIhC5duggJcEpKClq0aIFHjx7hwIEDiIyMxE8//QSFQgEbGxu0adMG/v7+Sud5u1y5SCSCjY0NZs6cmW9M4eHhqFu3LipVqiSUubu7Izk5GTdu3CjUdR04cADPnz/HwIEDC7z24OBgYUl0IKdH+9KlS0LSHBMTg8OHD6N9+/aFOm9ZxcM5SouYQ4A8LWfb3gsQ5X9rwsMfoG/fIMTEvBTKvLxqY82aDjz2mTHGWLm2ceNG9O3bFwDg4eGBpKQknDp1Cl9//TUAYN68eejZsydmzZoltKlXrx4A4Pbt29i5cyeOHTuGNm3aAMjpaS2qzMxMbN26FSYmJkJZt27dlOps2rQJJiYmiIqKQp06dbBt2zY8ffoUFy5cgJGREQDAzs5OqD9kyBCMGDECS5cuhVQqxeXLl3Ht2jXs378fAGBrawtjY+N8Y0pISFBKoAEI7xMSEgp1XRs3boS7uzsqV66ca5+rqysuX76MjIwMDBs2DLNnzxb29e7dG8+ePUOzZs1ARJDL5RgxYgSmTJlSqPOWVdwTXVqcmfRuu0b3fKuFhsaheXN/IYHW05Ng69bO2L69GyfQjDHGyrXo6GhERESgV69eAAB1dXV4eXkJwzEAQCaToXXrvJ8rkslkEIvFSr2oH8Pa2lopgQaAO3fuoFevXqhWrRr09fVhY2MDIOdhv7fnbtCggZBA/1fnzp0hFosRFBQEIGdoScuWLYXjhISEwMfH55PiLsjDhw9x5MgRDB48OM/9O3bswOXLl7Ft2zYcOnQIixcvFvaFhobCz88Pq1evxuXLl7F3714cOnQIc+bM+WzxlgbcE10avIoBUh7nbOtWBqq0yreqm5sVGjWyQETEI7i6WuHPP7ugatUKJRQoY4wxpjobN26EXC5XepCQiCCVSvHbb7/BwMAAWlr5dygVtA8ARCKR0tAQALlmoQAAHR2dXGWdOnWCtbU11q9fDwsLCygUCtSpU0cYj/yhc0skEnh7e8Pf3x9du3bFtm3bsHz58gLbvM/MzCzXGOTExERh34f4+/ujYsWK+Pbbb/Pcb2VlBQBwcHBAdnY2hg0bhvHjx0MsFmP69Ono168fhgwZAgCoW7cuUlNTMWzYMEydOhUiUfnssy2fV1XWXP0doOycbaMaOU8H5kNDQ4yAgK6YO7clTp0awAk0Y4yxL4JcLsfWrVuxZMkSyGQy4RUZGQkLCwts374dAODo6IiQkJA8j1G3bl0oFAqcOnUqz/0mJiZ4/fo1UlNThbK3Y54L8vz5c0RHR2PatGlo3bo1atWqhZcvXyrVcXR0hEwmw4sXL/I9zpAhQ3D8+HGsXr0acrkcXbt2/eC533JxccG1a9fw5MkToezYsWPQ19eHg4NDgW2JCP7+/sJsHh+iUCiQlZUljPd+8+ZNrkRZLBYLxy636AuTlJREACgpKUnVoeTIziJajHev1ERh14sXb6h37z108eIjFQbIGGOsPElLS6OoqChKS0tTdShFEhQURBKJhF69epVr308//UROTk5ERHTy5EkSiUQ0Y8YMioqKoqtXr9KCBQuEugMGDCArKysKCgqimJgYOnnyJO3YsYOIiJ4/f046Ojo0ZswYunv3LgUEBJCFhQW9ny75+vpSvXr1lM6fnZ1NFStWpL59+9KdO3coJCSEGjduTAAoKCiIiIgyMjKoRo0a1Lx5czp79izdu3ePdu/eTefOnVM6lqurK0kkEhoxYoRSeatWrWjlypX5fj5yuZzq1KlDbdu2JZlMRsHBwWRiYkKTJ08W6pw/f57s7e3p4cOHSm2PHz9OAOjmzZu5jvvnn3/Sjh07KCoqiu7du0c7duwgCwsL6tOnj9JnoqenR9u3b6eYmBg6evQo2draUo8ePfKNl6jg72Kpy9fywEm0qt3a+S6BDvIUik+ejKXKlZcSMJPs7VdSamqm6mJkjDFWbpTVJLpjx47Uvn37PPedP3+eAFBkZCQREe3Zs4fq169PEomEjI2NqWvXrkLdtLQ0Gjt2LJmbm5NEIiE7OzvatGmTsD8oKIjs7OxIS0uLOnbsSL///vsHk2giomPHjlGtWrVIKpWSo6MjhYaGKiXRRERxcXHUrVs30tfXJ21tbXJycqLz588rHWfjxo0EgCIiIpTKra2tydfXt8DPKC4ujtq1a0daWlpkbGxM48ePp6ysLGH/yZMnCQDFxsYqtevVqxe5urrmeczAwEBq2LAh6erqko6ODjk4OJCfn5/S9ycrK4tmzpxJtra2pKmpSVZWVjRq1Ch6+fJlgfGW9SRajag897PnlpycDAMDAyQlJUFfX1/V4QB/NAKeXM7Z/jYImdadMGPGSSxcGIa3d6ZCBU0cOdIXjRvz3M+MMcY+TXp6OmJjY5XmOGalx5w5c7Br1y5cvXpV1aF8dgV9F0tdvpYHfrBQle4eeJdAaxkjOtsNvV024vLld6sHtWxpg61bu6By5dL5BWKMMcbYp0tJSUFcXBx+++03zJ07V9XhsELgBwtVab8nAIAIWBc/DQ0a/i4k0BoaIixc2AbHj3tzAs0YY4yVcz4+PmjUqBG+/vprDBo0SNXhsELgnmhVubEFAPA0RRtDdnfBgeuvhF329hWxbVs3NGxorqLgGGOMMVaSNm/enOcS5qz04iRaFUgBXPoVAPDglQEO36wu7Bo50gmLF7eFtvaHp5hhjDHGGGOqwcM5VOH2buBpJACgYW1NzJ3TEsbG2jhwoCdWr+7ACTRjjDHGWCnHPdEl7Na1h7D9uxc0xP8WtFiKCdWaYeDgRjA1zb0CEmOMMcYYK324J7qEKBSE5cv/h/pOGzH3+Fc5hZW/Auw8IRaLOIFmjDHGGCtDOIkuAfHxr9G+fQB+/PEIMjKBuce/QsR9S8BtboFLfDPGGGOMsdKJh3N8Zvv338KQIX/h2bM3QtmYZufh2PoboHJzFUbGGGOMMcY+FifRn0lqaibGjz+KdesuCWVm+inY4hWEtrXjgRY3VBgdY4wxxhj7FJxEfwaXLj1G7957cfv2c6HM0yUVGzxWw1jnDeAwHDCwUV2AjDHGGGPsk3ASXcxOnIiFu/ufkMsVAABtbQ0sm2GDIeI+OcOfpQZAs3mqDZIxxhhjjH0SfrCwmLm5WcHBwQQA0KiROa6c74OhJjPfPT/YdAagVVF1ATLGGGOMsU/GPdHFTCpVx7ZtXbF9+3XMmP4VJAfaAS/v5OysWBto8L1qA2SMMcYYY5+Me6I/QXJyBoYOPYAbN54oldeubYq5c1pCcnIIcP/4ux3t/wTEvBohY4wxxlhZxz3RHyk8/AH69g1CTMxLREQ8RkTEEEil/36cb54Ch3orJ9BtNwKm9VUSK2OMMcYYK17cE11EcrkCs2aFonlzf8TEvAQAxMa+xNWriTkVHoUBm6orJ9CtVwF1B6kgWsYYY4wx9jlwT3QRxMS8RN++exEe/lAoc3W1wp9/dkHViinAtqZA4iVAIc/ZqaELuM4C6o9SUcSMMcYYY+xz4CS6EIgIf/xxFT4+h/H6dSYAQCxWw4zpzTHFG1C/PRG4vlG5kWUzoNMuQMdMBREzxhhjjLHPiZPoD3j5Mg0jRx7Cjh3vVhisZqOLgEmv0BR9gb2PczeqPxposQRQl5ZgpIwxxhhjrKRwEv0BN28+w65dUcL7Aa53sKL9LuilZCpX1NABag8AXGcDWkYlGyRjjDFWRESk6hDYF66sfwc5ic5PUiwQ+zdcEw9japsMrDzTCL9/9xe613uXUEOkAdi4AzW6AzW65STSjDHGWCmmoZEz1eqbN2+gpaWl4mjYlywz8+0QWbGKI/k4nES/LyUesSF/okrSToifXhSKp7cWYXiT87A0eA2oiQGrFoB1W6BWH0CvsgoDZowxxopGLBbD0NAQT57krHGgra0NNWFZXcZKhkKhwNOnT6GtrQ119bKZjpbNqIvbi9ugA93w+0Epxh7wgO83Uvzc6t1uDQMzWNZvD1Rpk9PzrGmoslAZY4yxT2VmlvPQ+9tEmjFVEIlEqFKlSpn9I46T6Nu78XTXMAzZ7o4DN2oCAKYFt0Lbphpo0KYVUK09YNoAUOMptRljjJUPampqMDc3h6mpKbKyslQdDvtCSSQSiERlN7/6spPoxCs4stQXA7YNQsJrPaF4iLc97MdPB7R5iW7GGGPll1gsLrPjURlTtVKR/q9atQo2NjbQ1NREkyZNEBERUWD9Xbt2oWbNmtDU1ETdunVx+PDhIp8z/U0Gfuy7GB7reggJtLGxFg4c6Ik1/n2gzQk0Y4wxxhjLh8qT6B07dmDcuHHw9fXF5cuXUa9ePbi7u+c7TuvcuXPo1asXBg8ejCtXrqBz587o3Lkzrl+/XqTzfu22HMuP1xDee7hXxbVro9Cpk/0nXQ9jjDHGGCv/1EjFk/Q1adIEjRs3xm+//QYg52lNKysrfP/995g0aVKu+l5eXkhNTcXBgweFsqZNm6J+/fpYu3btB8+XnJwMAwMDAJMAaEKqLseiGQ7wmdazzA5sZ4wxxhgrT97ma0lJSdDX11d1OHlS6ZjozMxMXLp0CZMnTxbKRCIR2rRpg/Dw8DzbhIeHY9y4cUpl7u7u2LdvX571MzIykJGRIbxPSkp6uwcOlZ5i44RUOAybiNevX3/StTDGGGOMseKRnJwMoHQvyKLSJPrZs2fIzs5GpUqVlMorVaqEW7du5dkmISEhz/oJCQl51p8/fz5mzZqVx55fEZUIuEwEMDHwY8JnjDHGGGOf0fPnz/8dQVD6lPvZOSZPnqzUc/3q1StYW1vj/v37pfamsOKTnJwMKysrPHjwoNT+HMSKD9/vLwvf7y8L3+8vS1JSEqpUqQIjIyNVh5IvlSbRxsbGEIvFSExMVCpPTEwUJoL/LzMzsyLVl0qlkEqlucoNDAz4H+EXRF9fn+/3F4Tv95eF7/eXhe/3l6U0zyOt0sgkEgkaNWqEkJAQoUyhUCAkJAQuLi55tnFxcVGqDwDHjh3Ltz5jjDHGGGPFTeXDOcaNG4f+/fvDyckJzs7OWLZsGVJTUzFw4EAAgLe3NywtLTF//nwAwA8//IAWLVpgyZIl6NChAwIDA3Hx4kX8/vvvqrwMxhhjjDH2BVF5Eu3l5YWnT59ixowZSEhIQP369REcHCw8PHj//n2lrnxXV1ds27YN06ZNw5QpU1C9enXs27cPderUKdT5pFIpfH198xziwcofvt9fFr7fXxa+318Wvt9flrJwv1U+TzRjjDHGGGNlTekdrc0YY4wxxlgpxUk0Y4wxxhhjRcRJNGOMMcYYY0XESTRjjDHGGGNFVC6T6FWrVsHGxgaamppo0qQJIiIiCqy/a9cu1KxZE5qamqhbty4OHz5cQpGy4lCU+71+/Xo0b94cFSpUQIUKFdCmTZsPfj9Y6VLUf99vBQYGQk1NDZ07d/68AbJiVdT7/erVK4wePRrm5uaQSqWoUaMG/396GVLU+71s2TLY29tDS0sLVlZWGDt2LNLT00soWvYpTp8+jU6dOsHCwgJqamrYt2/fB9uEhoaiYcOGkEqlsLOzw+bNmz97nAWiciYwMJAkEglt2rSJbty4QUOHDiVDQ0NKTEzMs35YWBiJxWJauHAhRUVF0bRp00hDQ4OuXbtWwpGzj1HU+927d29atWoVXblyhW7evEkDBgwgAwMDevjwYQlHzj5GUe/3W7GxsWRpaUnNmzcnT0/PkgmWfbKi3u+MjAxycnKi9u3b09mzZyk2NpZCQ0NJJpOVcOTsYxT1fgcEBJBUKqWAgACKjY2lI0eOkLm5OY0dO7aEI2cf4/DhwzR16lTau3cvAaCgoKAC68fExJC2tjaNGzeOoqKiaOXKlSQWiyk4OLhkAs5DuUuinZ2dafTo0cL77OxssrCwoPnz5+dZv0ePHtShQwelsiZNmtDw4cM/a5yseBT1fv+XXC4nPT092rJly+cKkRWjj7nfcrmcXF1dacOGDdS/f39OosuQot7vNWvWULVq1SgzM7OkQmTFqKj3e/To0dSqVSulsnHjxpGbm9tnjZMVv8Ik0T/99BPVrl1bqczLy4vc3d0/Y2QFK1fDOTIzM3Hp0iW0adNGKBOJRGjTpg3Cw8PzbBMeHq5UHwDc3d3zrc9Kj4+53//15s0bZGVlwcjI6HOFyYrJx97v2bNnw9TUFIMHDy6JMFkx+Zj7feDAAbi4uGD06NGoVKkS6tSpAz8/P2RnZ5dU2Owjfcz9dnV1xaVLl4QhHzExMTh8+DDat29fIjGzklUa8zWVr1hYnJ49e4bs7GxhtcO3KlWqhFu3buXZJiEhIc/6CQkJny1OVjw+5n7/188//wwLC4tc/zBZ6fMx9/vs2bPYuHEjZDJZCUTIitPH3O+YmBicOHECffr0weHDh3H37l2MGjUKWVlZ8PX1LYmw2Uf6mPvdu3dvPHv2DM2aNQMRQS6XY8SIEZgyZUpJhMxKWH75WnJyMtLS0qClpVXiMZWrnmjGimLBggUIDAxEUFAQNDU1VR0OK2avX79Gv379sH79ehgbG6s6HFYCFAoFTE1N8fvvv6NRo0bw8vLC1KlTsXbtWlWHxj6D0NBQ+Pn5YfXq1bh8+TL27t2LQ4cOYc6cOaoOjX0hylVPtLGxMcRiMRITE5XKExMTYWZmlmcbMzOzItVnpcfH3O+3Fi9ejAULFuD48eNwdHT8nGGyYlLU+33v3j3ExcWhU6dOQplCoQAAqKurIzo6Gra2tp83aPbRPubft7m5OTQ0NCAWi4WyWrVqISEhAZmZmZBIJJ81ZvbxPuZ+T58+Hf369cOQIUMAAHXr1kVqaiqGDRuGqVOnQiTifsLyJL98TV9fXyW90EA564mWSCRo1KgRQkJChDKFQoGQkBC4uLjk2cbFxUWpPgAcO3Ys3/qs9PiY+w0ACxcuxJw5cxAcHAwnJ6eSCJUVg6Le75o1a+LatWuQyWTC69tvv0XLli0hk8lgZWVVkuGzIvqYf99ubm64e/eu8McSANy+fRvm5uacQJdyH3O/37x5kytRfvsHFBF9vmCZSpTKfE1ljzR+JoGBgSSVSmnz5s0UFRVFw4YNI0NDQ0pISCAion79+tGkSZOE+mFhYaSurk6LFy+mmzdvkq+vL09xV4YU9X4vWLCAJBIJ7d69m+Lj44XX69evVXUJrAiKer//i2fnKFuKer/v379Penp65OPjQ9HR0XTw4EEyNTWluXPnquoSWBEU9X77+vqSnp4ebd++nWJiYujo0aNka2tLPXr0UNUlsCJ4/fo1Xblyha5cuUIAaOnSpXTlyhX6559/iIho0qRJ1K9fP6H+2ynuJk6cSDdv3qRVq1bxFHefw8qVK6lKlSokkUjI2dmZ/ve//wn7WrRoQf3791eqv3PnTqpRowZJJBKqXbs2HTp0qIQjZp+iKPfb2tqaAOR6+fr6lnzg7KMU9d/3+ziJLnuKer/PnTtHTZo0IalUStWqVaN58+aRXC4v4ajZxyrK/c7KyqKZM2eSra0taWpqkpWVFY0aNYpevnxZ8oGzIjt58mSe/z1+e4/79+9PLVq0yNWmfv36JJFIqFq1auTv71/icb9PjYh/82CMMcYYY6woytWYaMYYY4wxxkoCJ9GMMcYYY4wVESfRjDHGGGOMFREn0YwxxhhjjBURJ9GMMcYYY4wVESfRjDHGGGOMFREn0YwxxhhjjBURJ9GMMcYYY4wVESfRjDEGYPPmzTA0NFR1GB9NTU0N+/btK7DOgAED0Llz5xKJhzHGyjtOohlj5caAAQOgpqaW63X37l1Vh4bNmzcL8YhEIlSuXBkDBw7EkydPiuX48fHxaNeuHQAgLi4OampqkMlkSnWWL1+OzZs3F8v58jNz5kzhOsViMaysrDBs2DC8ePGiSMfhhJ8xVtqpqzoAxhgrTh4eHvD391cqMzExUVE0yvT19REdHQ2FQoHIyEgMHDgQjx8/xpEjRz752GZmZh+sY2Bg8MnnKYzatWvj+PHjyM7Oxs2bNzFo0CAkJSVhx44dJXJ+xhgrCdwTzRgrV6RSKczMzJReYrEYS5cuRd26daGjowMrKyuMGjUKKSkp+R4nMjISLVu2hJ6eHvT19dGoUSNcvHhR2H/27Fk0b94cWlpasLKywpgxY5CamlpgbGpqajAzM4OFhQXatWuHMWPG4Pjx40hLS4NCocDs2bNRuXJlSKVS1K9fH8HBwULbzMxM+Pj4wNzcHJqamrC2tsb8+fOVjv12OEfVqlUBAA0aNICamhq+/vprAMq9u7///jssLCygUCiUYvT09MSgQYOE9/v370fDhg2hqamJatWqYdasWZDL5QVep7q6OszMzGBpaYk2bdqge/fuOHbsmLA/OzsbgwcPRtWqVaGlpQV7e3ssX75c2D9z5kxs2bIF+/fvF3q1Q0NDAQAPHjxAjx49YGhoCCMjI3h6eiIuLq7AeBhj7HPgJJox9kUQiURYsWIFbty4gS1btuDEiRP46aef8q3fp08fVK5cGRcuXMClS5cwadIkaGhoAADu3bsHDw8PdOvWDVevXsWOHTtw9uxZ+Pj4FCkmLS0tKBQKyOVyLF++HEuWLMHixYtx9epVuLu749tvv8WdO3cAACtWrMCBAwewc+dOREdHIyAgADY2NnkeNyIiAgBw/PhxxMfHY+/evbnqdO/eHc+fP8fJkyeFshcvXiA4OBh9+vQBAJw5cwbe3t744YcfEBUVhXXr1mHz5s2YN29eoa8xLi4OR44cgUQiEcoUCgUqV66MXbt2ISoqCjNmzMCUKVOwc+dOAMCECRPQo0cPeHh4ID4+HvHx8XB1dUVWVhbc3d2hp6eHM2fOICwsDLq6uvDw8EBmZmahY2KMsWJBjDFWTvTv35/EYjHp6OgIr++++y7Purt27aKKFSsK7/39/cnAwEB4r6enR5s3b86z7eDBg2nYsGFKZWfOnCGRSERpaWl5tvnv8W/fvk01atQgJycnIiKysLCgefPmKbVp3LgxjRo1ioiIvv/+e2rVqhUpFIo8jw+AgoKCiIgoNjaWANCVK1eU6vTv3588PT2F956enjRo0CDh/bp168jCwoKys7OJiKh169bk5+endIw//viDzM3N84yBiMjX15dEIhHp6OiQpqYmASAAtHTp0nzbEBGNHj2aunXrlm+sb89tb2+v9BlkZGSQlpYWHTlypMDjM8ZYceMx0YyxcqVly5ZYs2aN8F5HRwdATq/s/PnzcevWLSQnJ0MulyM9PR1v3ryBtrZ2ruOMGzcOQ4YMwR9//CEMSbC1tQWQM9Tj6tWrCAgIEOoTERQKBWJjY1GrVq08Y0tKSoKuri4UCgXS09PRrFkzbNiwAcnJyXj8+DHc3NyU6ru5uSEyMhJAzlCMb775Bvb29vDw8EDHjh3Rtm3bT/qs+vTpg6FDh2L16tWQSqUICAhAz549IRKJhOsMCwtT6nnOzs4u8HMDAHt7exw4cADp6en4888/IZPJ8P333yvVWbVqFTZt2oT79+8jLS0NmZmZqF+/foHxRkZG4u7du9DT01MqT09Px7179z7iE2CMsY/HSTRjrFzR0dGBnZ2dUllcXBw6duyIkSNHYt68eTAyMsLZs2cxePBgZGZm5pkMzpw5E71798ahQ4fw999/w9fXF4GBgejSpQtSUlIwfPhwjBkzJle7KlWq5Bubnp4eLl++DJFIBHNzc2hpaQEAkpOTP3hdDRs2RGxsLP7++28cP34cPXr0QJs2bbB79+4Pts1Pp06dQEQ4dOgQGjdujDNnzuDXX38V9qekpGDWrFno2rVrrraampr5HlcikQj3YMGCBejQoQNmzZqFOXPmAAACAwMxYcIELFmyBC4uLtDT08OiRYtw/vz5AuNNSUlBo0aNlP54eau0PDzKGPtycBLNGCv3Ll26BIVCgSVLlgi9rG/H3xakRo0aqFGjBsaOHYtevXrB398fXbp0QcOGDREVFZUrWf8QkUiUZxt9fX1YWFggLCwMLVq0EMrDwsLg7OysVM/LywteXl747rvv4OHhgRcvXsDIyEjpeG/HH2dnZxcYj6amJrp27YqAgADcvXsX9vb2aNiwobC/YcOGiI6OLvJ1/te0adPQqlUrjBw5UrhOV1dXjBo1Sqjz355kiUSSK/6GDRtix44dMDU1hb6+/ifFxBhjn4ofLGSMlXt2dnbIysrCypUrERMTgz/++ANr167Nt35aWhp8fHwQGhqKf/75B2FhYbhw4YIwTOPnn3/GuXPn4OPjA5lMhjt37mD//v1FfrDwfRMnTsQvv/yCHTt2IDo6GpMmTYJMJsMPP/wAAFi6dCm2b9+OW7du4fbt29i1axfMzMzyXCDG1NQUWlpaCA4ORmJiIpKSkvI9b58+fXDo0CFs2rRJeKDwrRkzZmDr1q2YNWsWbty4gZs3byIwMBDTpk0r0rW5uLjA0dERfn5+AIDq1avj4sWLOHLkCG7fvo3p06fjwoULSm1sbGxw9epVREdH49mzZ8jKykKfPn1gbGwMT09PnDlzBrGxsQgNDcWYMWPw8OHDIsXEGGOfipNoxli5V69ePSxduhS//PIL6tSpg4CAAKXp4f5LLBbj+fPn8Pb2Ro0aNdCjRw+0a9cOs2bNAgA4Ojri1KlTuH37Npo3b44GDRpgxowZsLCw+OgYx4wZg3HjxmH8+PGoW7cugoODceDAAVSvXh1AzlCQhQsXwsnJCY0bN0ZcXBwOHz4s9Ky/T11dHStWrMC6detgYWEBT0/PfM/bqlUrGBkZITo6Gr1791ba5+7ujoMHD+Lo0aNo3LgxmjZtil9//RXW1tZFvr6xY8diw4YNePDgAYYPH46uXbvCy8sLTZo0wfPnz5V6pQFg6NChsLe3h5OTE0xMTBAWFgZtbW2cPn0aVapUQdeuXVGrVi0MHjwY6enp3DPNGCtxakREqg6CMcYYY4yxsoR7ohljjDHGGCsiTqIZY4wxxhgrIk6iGWOMMcYYKyJOohljjDHGGCsiTqIZY4wxxhgrIk6iGWOMMcYYKyJOohljjDHGGCsiTqIZY4wxxhgrIk6iGWOMMcYYKyJOohljjDHGGCsiTqIZY4wxxhgrov8De4m375q1/zAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fPHmyACDOnj1baxurV68WAERSUlKlY4mJicLZ2Vm0b99eFBcXG8ur2x5ACCG+/PJL4eDgIPr372/m3dzQvXt30aNHD6HVaisdy8zMrFebQvzz87Br1656t0GmBg8eLAYPHix1GGRF+EqIrMrjjz+Ozp0744033qi0i21TOXr0KCIiIuDh4QE3Nzfce++9OHjwYJV1n332WXh5eVl0A0EAKCoqwgsvvAB/f38olUp06dIF7777rkWeyYgRI9ChQ4cqjw0YMAB9+/YFcGPn5B49elSq8/DDDwMAUlNTGxTHPffcg9mzZ+PChQv48ssv63TOuHHjMGXKFPz222/46aefTI6tX78eoaGhcHZ2ho+PD8aPH48rV66Y1Pnzzz8RFhYGhUJRqW1fX986xXD58mWMGjUKrq6u8PX1xfPPP1/ta6raYtq8eTNkMhmOHz9uLPv2228hk8lMesoAoFu3biabXspkMsTExGDTpk3o2bMnlEolevToUel1aEFBAZ577jkEBgZCqVTC19cX9913n8nCfHv37sVjjz2GgIAAKJVK+Pv74/nnn0dJSYlJWxWvOi9evIgRI0bAzc0Nbdu2RVxcHADgxIkTuOeee+Dq6or27dtXes1W8Ypwz549ePrpp9GiRQt4eHhg4sSJyM3NrfXZa7VazJ07Fx07djTG+fLLL3NnazvBhIWsilwux2uvvYZjx45h48aNdTonJyfH5JOfn1/v6//+++8YNGgQjh07hpdffhmzZ89GWloahgwZgt9++61SfQ8PDzz//PPYsmWLxVZmFUJg5MiReO+99/DAAw9g6dKl6NKlC1566SXExsY2uP2xY8ciLS0NSUlJJuUXLlzAwYMH8fjjj9d4fkZGBoAbCU1DTZgwAQDw448/Nuic+Ph4jBkzBnK5HAsWLMDUqVOxYcMG3HXXXcjLyzPWa9++PRITE3H58uV6xVtSUoJ7770XP/zwA2JiYvDqq69i7969ePnllyvVrUtMd911l/EXeIW9e/fCwcEBv/76q7EsOzsbp0+fxt13321yjV9//RXTp0/H448/jnfeeQelpaV49NFHce3aNWOdf//73/jwww/x6KOPYsWKFXjxxRfh7OxsknCuX78excXFeOaZZ7B8+XKEh4dj+fLlVW6PoNfrERERAX9/f7zzzjsIDAxETEwM4uPj8cADD6Bv375YtGgR3N3dMXHiRKSlpVVqIyYmBqmpqZg3bx4mTpyIhIQEjBo1qsaE3GAwYOTIkXj33XcRGRmJ5cuXY9SoUXjvvffM3r2cmilpO3iIbrh55+Hy8nLRqVMnERwcLAwGgxCi+ldCACp9zOlGvvWV0KhRo4RCoTDZ4fevv/4S7u7u4u677zaWVbwCWL9+vcjLyxNeXl5i5MiRJrHV95XQpk2bBADx5ptvmtQbPXq0kMlk4ty5c5XaMOeVUH5+vlAqleKFF14wKX/nnXeETCYTFy5cqPZcrVYrunfvLm677TZRVlZW67VqeiVUQa1Wiz59+hi/1/RKSAghcnNzBQDx8MMPCyGE0Ol0wtfXV/Ts2dNkd+etW7cKAGLOnDnGsk8//VQAEAqFQgwdOlTMnj1b7N27V+j1+lrvRQghli1bJgCIdevWGcuKiopEx44dTV4JmRNTjx49xJgxY4zfb7/9dvHYY48JACI1NVUIIcSGDRsEAHHs2DFjvYr7uPnn4dixYwKAWL58ubFMrVaL6OjoGu/r5ldyFRYsWFDp56Hiv7m3337bWJabmyucnZ2FTCYTa9asMZafPn260s9kxc9DaGio0Ol0xvJ33nlHABDfffedsezWV0JffPGFcHBwEHv37jWJc+XKlQKA2LdvX433SM0fe1jI6tzcy7Jp06Ya66pUKvz0008mnyVLltTrunq9Hj/++CNGjRpl8sqkdevWePLJJ/Hrr79Co9FUOk+tVuO5557D5s2bcfTo0Xpd+2bbt2+HXC7Hf/7zH5PyF154AUIIfP/99w1q38PDAxEREVi3bp3J/9GuXbsWd9xxBwICAqo9NyYmBqdOncIHH3wAR0fLjNl3c3OrcrZQTfUBGM85fPgwsrKyMH36dKhUKmO94cOHo2vXrti2bZux7KmnnsKOHTswZMgQ/Prrr5g/fz4GDRqETp06Yf/+/bVee/v27WjdujVGjx5tLHNxcTEZtG1uTIMGDcLevXuN93Ts2DFMmzYNPj4+xvK9e/fC09MTPXv2NLnOsGHDEBQUZPzeu3dveHh44Pz588YyT09P/Pbbb/jrr7+qvS9nZ2fjn4uKipCTk4M777wTQogqf6anTJli0n6XLl3g6uqKMWPGGMu7dOkCT09Pk1gqTJs2DU5OTsbvzzzzDBwdHbF9+/ZqY1y/fj26deuGrl27mvSo3nPPPQCAXbt2VXsu2QYmLGSVxo0bh44dO9Y6lkUul2PYsGEmn9DQUOPxjIwMk8+t7+Rvlp2djeLiYnTp0qXSsW7dusFgMODSpUtVnjtjxgx4enpWO5YlPz/fJI7r169XG8eFCxfQpk0buLu7V4qh4nhdVXf/Y8eOxaVLl3DgwAEAN8Z2JCcn19i1vnjxYqxatQrz58/Hgw8+aCzX6/WVrqPT6eocY2FhYaV7ra0+AOM5Fc+jqr+3rl27Vnpe4eHh+OGHH5CXl4c9e/YgOjoaFy5cwIgRI5CVlQXgxs/CzfdTcc0LFy6gY8eOlWaw3Xptc2IaNGgQrl69inPnzmH//v2QyWQYMGCASSKzd+9eDBw4EA4Opv9kV5Vcenl5mYwHeeedd3Dy5En4+/ujX79+mDdvXqUk4uLFi5g0aRK8vb3h5uaGli1bYvDgwQBQ6RWrSqVCy5YtTcrUajXatWtX6bmo1eoqx6Z06tTJ5Lubmxtat26N9PT0SnUrnD17Fr///jtatmxp8uncuTMAGP/uyHYxYSGrVNHLkpKSgu+++67e7bRu3drks3btWgtG+Y/aellmzJhhEsetAyobS3X3HxkZCRcXF6xbtw4AsG7dOjg4OOCxxx6rsp34+Hi88sor+Pe//43XXnvN5NilS5cqXacuvRXAjQGs+fn5JlO0a3Py5EkAMOucqri4uGDQoEH44IMP8NprryE3N9fYexUWFmZyP++++26DrlWTu+66CwCwZ88e7N27F7fffjtcXV2NCUthYSGOHj2KQYMGVTpXLpdX2ebNSf6YMWNw/vx5LF++HG3atMHixYvRo0cP473q9Xrcd9992LZtG1555RVs2rQJP/30E+Lj4wHcGDtSl2vWJZaGMBgM6NWrV6Ue1YrP9OnTLXIdsl5ch4Ws1vjx4/Hmm2/i9ddfx8iRI+vVxq0zSaqa9VKhZcuWcHFxwZkzZyodO336NBwcHGpc++O5557DsmXL8Prrr8PT09Pk2Msvv4zx48cbv3t5eVXbTvv27bFz504UFBSY9DycPn3aeLyuqrt/V1dXjBgxAuvXr8fSpUuxdu1aDBo0CG3atKnUxnfffYcpU6bgkUceMc4GuVmrVq0qXaeqdXCqUrEuTnh4eJ3qV3VOxfM4c+aM8fVAhTNnztTpeVXMjLp69SoAICEhwaQ3ruIVYfv27XHy5EkIIUx6E279mTEnpoCAAAQEBGDv3r04f/68MTG5++67ERsbi/Xr10Ov11cacGuO1q1bY/r06Zg+fTqysrJw++2346233kJERAROnDiBP/74A59//rnJINtb/04t6ezZsxg6dKjxe2FhIa5evWrSc3eroKAgHDt2DPfee2+d1mgiGyTh+Bkio5sH3d4sPj7eZL2Txl6HZdSoUUKpVIq0tDRjWUZGhvDw8Kh20O3N5s2bJwCI4ODgBg+6vXlgoxBCjB071iKDbit8++23AoD46KOPBACxYsWKSnV++eUXoVKpxNChQ0VpaWmd265Ql3VYbrvtNpOBqTUNuk1ISBAODg5iwIABxrKKAa69e/c2iXH79u2VBrju3LmzyjifeeYZAUBs2LChxvsxd9BtXWISQohx48aJgIAAoVKpxKZNm4QQQpSXlwt3d3fRuXNn4ezsXGntGABVDqZt37698We6vLxc5OXlVaoTFhYm+vbtK4QQ4vjx4wKAiI+PNx43GAxi+PDhAoBYvXq1sby6/+YGDx4sevToUWUsN/9s1zbotuLeK9q8edBtxb8FH330UaXrFBcXi8LCwkrlZFvYw0JWbdy4cZg/fz5SUlKa5HpvvvkmfvrpJ9x1112YPn06HB0d8dFHH0Gr1eKdd96p9fwZM2bgvffew7Fjx+Dq6lqvGCIjIzF06FC8+uqrSE9PR3BwMH788Ud89913eO6550wGWTbEgw8+CHd3d7z44ouQy+V49NFHTY5fuHABI0eOhEwmw+jRo7F+/XqT4717965yhdqqfP/99zh9+jTKy8uRmZmJn3/+GT/99BPat2+PzZs3mwxMrfDNN9/Azc0NOp3OuNLtvn37EBwcbBKLk5MTFi1ahMmTJ2Pw4MF44oknkJmZiffffx+BgYF4/vnnjXUfeugh3HbbbYiMjERQUBCKioqwc+dObNmyBWFhYYiMjKzxPqZOnYoPPvgAEydORHJyMlq3bo0vvvgCLi4uJvXMiQm4MY4lISEBMpnM+IpILpfjzjvvxA8//IAhQ4ZUuXZMbQoKCtCuXTuMHj0awcHBcHNzw86dO5GUlGQcnN61a1cEBQXhxRdfxJUrV+Dh4YFvv/22Tuui1JdOp8O9996LMWPG4MyZM1ixYgXuuuuuGntSJ0yYgHXr1uHf//43du3ahYEDB0Kv1+P06dNYt24dfvjhB2NPGdkoqTMmIiGq72ER4p//K0MTrXR75MgRER4eLtzc3ISLi4sYOnSo2L9/v0md6npYhPinh6AhK90WFBSI559/XrRp00Y4OTmJTp06icWLFxuned+qvivdjhs3TgAQw4YNq3Ss4h6r+9TlWjf/3eHvabitWrUS9913n3j//feFRqOpdE7F86v4qFQq0a5dOzFixAjx2WefVdvTs3btWtGnTx+hVCqFt7e3GDdunLh8+bJJna+//lo8/vjjIigoSDg7OwuVSiW6d+8uXn311SpjqcqFCxfEyJEjhYuLi/Dx8REzZswQO3bsqHKl27rEJIQQv//+uwAgunXrZlL+5ptvCgBi9uzZlc5BHXpYtFqteOmll0RwcLBwd3cXrq6uIjg4uFJv2qlTp8SwYcOEm5ub8PHxEVOnTjVOkW6MHpZffvlFTJs2TXh5eQk3Nzcxbtw4ce3atUpt3rpEgU6nE4sWLRI9evQQSqVSeHl5idDQUPH666+L/Pz8Stcn2yITQqLlRImIyK7Ex8dj8uTJSEpKYm8ImY2zhIiIiMjqMWEhIiIiq8eEhYiIiKwex7AQERGR1WMPCxEREVk9JixERERk9ew+YRFCQKPRWGy/CyIiIrI8u09YCgoKoFarzdre3hpdvXoV8+bNM+6FQkREZEvsPmGxFYWFhThw4AAKCwulDoWIiMji7H6WkEajgVqtRn5+Pjw8PKQOh4iIiKpgtz0scXFx6N69O8LCwqQOxSL0ej00Gg30er3UoRAREVkce1hspIflyJEjCA0NRXJyMm6//XapwyEiIrIou+1hISIiouaDCQsRERFZPbtNWGxtDAsREZEts9uEJTo6GqdOnUJSUpLUoRAREVEtHKUOgCyjV69eyMrKgqenp9ShEBERWRwTFhvh5OSEli1bSh0GERFRo7DbV0K2Noblzz//xMiRI/Hnn39KHQoREZHFcR0WrsNCRERk9ey2h4WIiIiaDyYsREREZPWYsBAREZHVs9uExdYG3bZt2xZLlixB27ZtpQ6FiIjI4jjotpZBt0IIaDQaAICHhwdkMllTh0hERGT37LaHpa40Gg2WbD2KJVuPGhMXa5Sbm4v169cjNzdX6lCIiIgsjglLHahc3aFydZc6jBqlpaVhzJgxSEtLkzoUIiIii2PCQkRERFaPCQsRERFZPbtNWGxtlhAREZEts9uEJTo6GqdOnUJSUpLUoViEs7Mz+vTpA2dnZ6lDISIisjju1mwjunXrhiNHjkgdBhERUaOw2x4WIiIiaj6YsNiIo0ePQqlU4ujRo1KHQkREZHFMWGyEEAI6nQ52vnAxERHZKCYsREREZPXsNmHhtGYiIqLmw24TFlub1kxERGTLOK3ZRnTr1g0nT55Ehw4dpA6FiIjI4piw2AhnZ2f06NFD6jCIiIgahd2+ErI1Fy5cwJQpU3DhwgWpQyEiIrI4Jiw1EEIgPz8fgPVPFb527Ro+/fRTXLt2TepQiIiILI4JSw00Gg2WbDwAnVYrdShERER2jQlLLZQurlKHQEREZPeYsBAREZHVs9uExdYWjvPz88PMmTPh5+cndShEREQWJxN2vvmMRqOBWq1Gfn4+PDw8TI7l5+fjrXV74eLhBYVSheihHaFWqyWKlIiIyH7ZbQ+LrSkoKMDu3btRUFAgdShEREQWx4TFRpw9exZDhw7F2bNnpQ6FiIjI4piwEBERkdVjwkJERERWjwkLERERWT0mLDbCyckJbdu2hZOTk9ShEBERWRx3a7YRvXr1wuXLl6UOg4iIqFGwh4WIiIisHhMWG3HixAm0a9cOJ06ckDoUIiIii2PCYiPKyspw5coVlJWVSR0KERGRxTFhISIiIqtntwmLrW1+SEREZMvsNmGJjo7GqVOnkJSUJHUoREREVAu7TVhsTadOnbBr1y506tRJ6lCIiIgsjuuw2Ah3d3cMGTJE6jCIiIgaBXtYbMSVK1cwa9YsXLlyRepQiIiILI4Ji43IzMzEwoULkZmZKXUoREREFseEhYiIiKwex7DYOCEENBoNAMDDwwMymUziiIiIiMzHhMXGaTQa5H//9o0vEf8HtVotbUBERET1wITFRrRo0QL/+te/0KJFi0rH1K4qCSIiIiKyHCYsNqJ9+/b45JNPpA6DiIioUXDQrY0oKSnB77//jpKSEqlDISIisjgmLDYiNTUVPXv2RGpqqtShEBERWRwTFhsmhEB+fj4EhNShEBERNQgTFhum0WhwZfPb0Oq0UodCRETUIExYbJy7y40ZQsbeFsHeFiIian5sJmEpLi5G+/bt8eKLL0odiiRkMhkUCkW1C8NpirXI3r7AuIgcERFRc2Iz05rfeust3HHHHVKHIZk+ffpAq6351Y+7i7KJoiEiIrIsm+hhOXv2LE6fPo2IiAipQyEiIqJGIHnCsmfPHkRGRqJNmzaQyWTYtGlTpTpxcXEIDAyESqVC//79cejQIZPjL774IhYsWNBEEVun1NRU3H777ZzWTERENknyhKWoqAjBwcGIi4ur8vjatWsRGxuLuXPn4siRIwgODkZ4eDiysrIAAN999x06d+6Mzp07N2XYVqekpARHjx7lwnFERGSTJB/DEhERUeOrnKVLl2Lq1KmYPHkyAGDlypXYtm0bPvvsM8ycORMHDx7EmjVrsH79ehQWFqKsrAweHh6YM2dOle1ptVqTsR4chEpERGT9JO9hqYlOp0NycjKGDRtmLHNwcMCwYcNw4MABAMCCBQtw6dIlpKen491338XUqVOrTVYq6qvVauPH39+/0e+DiIiIGsaqE5acnBzo9Xr4+fmZlPv5+SEjI6Nebc6aNQv5+fnGz6VLlywRKhERETUiyV8JWdKkSZNqraNUKqFU2t703ttuuw3r1q3DbbfdJnUoREREFmfVCYuPjw/kcjkyMzNNyjMzM9GqVasGtR0XF4e4uDjo9foGtWMtvLy88Nhjj0kdBhERUaOw6ldCCoUCoaGhSExMNJYZDAYkJiZiwIABDWo7Ojoap06dQlJSUkPDtAqZmZlYunQpMjMzjcvwc+NDIiKyFZL3sBQWFuLcuXPG72lpaUhJSYG3tzcCAgIQGxuLqKgo9O3bF/369cOyZctQVFRknDVEN1y5cgUvvPAChgwZApVKhfzv34amWAudTgdAJXV4REREDSJ5wnL48GEMHTrU+D02NhYAEBUVhfj4eIwdOxbZ2dmYM2cOMjIyEBISgh07dlQaiEum1K43kpRs7tRMREQ2QPKEZciQIbXuIBwTE4OYmBiLXtfWxrAQERHZMqsew9KYbG0MCxERkS2z24TF1qjVakRGRkKtVksdChERkcVJ/kqILCMoKAibN28GAOTn51dZp2L2EAB4eHhAJpM1WXxEREQNYbc9LHFxcejevTvCwsKkDsUiysrKkJ2djbKysmrrFJboULp76Y0ZRNxDiYiImhG7TVhsbQzLiRMn4OvrixMnTtRYT+2qNM4gIiIiai7sNmEhIiKi5oMJCxEREVk9JixERERk9ew2YbG1QbdERES2zG4TFlsbdBscHIz8/HwEBwdLHQoREZHFcR0WGyGXy+Hh4SF1GERERI3CbntYrEXFYm75+fm17qlUk7NnzyI8PBxnz561YHRERETWgQmLxDQaDfK/f7vBi7kVFBTgxx9/REFBgQWjIyIisg58JWQFGrKQmxACGo2mQb0zRERE1s5ue1hsZZaQRqPBpW/noLCwUOpQiIiIGo3dJiy2NEvIw4VL7RMRkW3jKyErU/GKB6h5R+WKevn5+RAQaNeuHT744AP4+/s3ZbhERERNggmLlakYhAsAiPg/qNXqGutpirVQOAJ+Pj6Ijo4GAOTn5zdVuERERE3Cbl8JWTO1q6pOA3HVrip4uCgBALm5ufjyyy9x/fr1xg6PiIioyTFhsREXLlzAhAkTkJ6eLnUoREREFseEhYiIiKye3SYs1jituWLVW4H6r6liiTaIiIisjd0mLNYwrfnW5EKj0eDK5reh1WnNbqdiHZaMjAxc/u6tGtswXpeLzRERUTNhtwmLNagqQXGvx5oqhSU6yE+uQ+8OrfDl3jOQyWr+a9UUa5G9fUGDtgIgIiJqSkxYJFaRoBiX2K/HqxwhBIJaeyHh1bFo1b5jHa+rNPs6REREUuE6LFZCU6zFtZ+Wws1ZCcC8XpaiUh3WZLaDvqwcWt8yQN44MRIREUmFPSwSqBhDcus4khvJSv3kXr+GZW+/juxL5y0RIhERkVVhD4sENBoNlmw9Cm1pKYZdL0BLtRJA1UvwV6Ui4fHgTCAiIrIT7GGRiMrVHTIHGb685IOCYp1Z59Z3NhEREVFzxYSliVX0juDv3hGFsn47LVc3m0gIgcISLacsExGRTbHbhEWqheM0Gg2WbDwAnbZxekfKdKX16rUhIiKyZnabsEi5cJzSxdX4ZyEECoq10BSb9oqYu7hbS18/TJvxAjx929S714aIiMha2W3CYi3KdFrEX/LDp+k+KCr9p1fE3MXdHJ2c4NXCB45OisYKlYiISDJMWKyAUuUMpcq5Urk5i7vlXb+GLd+sRcH1bEuGRkREZBWYsFgJ46uhohLkF5ZWekVUm5KSEpw6ngJtSVEjRklERCQNrsNiJXTaUvzvqhcAoJWvF+RyR4xvc8XsdvTlZVydhYiIbA57WOqpMXY8VigVUCgV1b4iqgtd1nkYDHqLxURERGQNmLDUk0ajwVtr91rdjseO7DMjIiIbxISlAVSubo3WtrkLwLm5e6D/3UPhfNOUaSIiIlvBhMVK6bSliE/3qbUH50Zio4ObuzsGDL4Hzo2YRBEREUmFCYsVU6pqXwCuoESHry62QEFBEdL/PIsy7i9EREQ2iAmLDVColMi7fg2bvvofCvLzpA6HiIjI4piw2KGKNV+4QSIRETUXdpuwSLX5oZSEENAUafHXtQJ8dE5tdTOciIiIqmO3k2Cjo6MRHR0NjUYDtVrdJNesWLsFDVjazbj+Sz3aKCrVYU1mOxQXl0LmIINGo4EQAjKZrN7xEBERNQW77WGRgkajwZKNB1CmLWtQG1c2vw2dTmdSLnd0hNrLGw4O8hrPVzk7Q6lSQafTYsWPJ9nLQkREzQITliamNGOdFCEENBpNpRV13V0qzx7y8fXD5Jjn4dnC5599iWoZp6J05potRETUPDBhsWI6nRaf7k3Dkq1HzeoJKdNpEX/JD5+m+6Co9EZPTMV6LRxoS0REzRETFiundHGDytW91nrZGVfx0ZIFyM3JvnHeLfsRVazXoivjPkNERNT8MGGxEQaDASXFxRDCUG0dhUrZhBERERFZjt3OEmoeBLQ6LXTa0kZ5lfPPrCXAw8ODs4WIiMhqMWGxYmXlehguH0GJQoHLPdQWn36tKy3Gip2pUCiVeGFEnyab3k1ERGQuvhKycgpHOWSG8kaYgiyg1enqPEaGiIhISkxYmonapiB7tfDBmMlT4a72qlN7ZeV6aC+nQKvV1V6ZiIhIYkxYbIRCqUSbdgFwUihMyiuW4y8o1uLWFXadHPlGkIiImgf+xrIRBfn5OPTrLwjs2h0ubjde8QghkJVXhC2aDigp1qKsvFjiKImIiOqHPSzNRMWMnltXva1QXFSIo7/tR2nJP0mJTluKb676wkGugFJVeXVcIiKi5oI9LM1ExYweAHioRAdFHf/mFEpF7ZWIiIisHBOWZkTl6nbjD8W3jk1xkjQuIiKixtbsXwnl5eWhb9++CAkJQc+ePbFq1SqpQ2oSRaU6fHTeB19cbIGyci63T0REtq3Z97C4u7tjz549cHFxQVFREXr27IlHHnkELVq0kDo0CxMordjI8O/ZPipnZ+gNAIqL4eziit59+5nsH0RERGQrmn3CIpfL4eLiAgDQarUQQtjkjsRaXRm06YcghECuohCuKtPXQB6enrgnIhLZ16+b3XbFgF4uz09ERNZK8ldCe/bsQWRkJNq0aQOZTIZNmzZVqhMXF4fAwECoVCr0798fhw4dMjmel5eH4OBgtGvXDi+99BJ8fHyaKPqmpXRyhMxQjq//8kNRaZnJsTKdDllX/0J5WVk1Z1dPW1yEpVuSLLySLhERkeVInrAUFRUhODgYcXFxVR5fu3YtYmNjMXfuXBw5cgTBwcEIDw9HVlaWsY6npyeOHTuGtLQ0fPXVV8jMzGyq8Ovk5inJty7eVh8KZeVdl6/nZOOrTz6EJs/8HhYAUDm7NTQsIiKiRiP5K6GIiAhERERUe3zp0qWYOnUqJk+eDABYuXIltm3bhs8++wwzZ840qevn54fg4GDs3bsXo0ePrrI9rVYLrVZr/N5YvQpCCGg0GmOy8umv51FaXIQyrfk9IERERPZO8h6Wmuh0OiQnJ2PYsGHGMgcHBwwbNgwHDhwAAGRmZqKgoAAAkJ+fjz179qBLly7VtrlgwQKo1Wrjx9/fv1Fi12g0WLL1KN5e/ysWrv8FDo4KqFwss8mgEAJFpTqbHKtDRERUFatOWHJycqDX6+Hn52dS7ufnh4yMDADAhQsXMGjQIAQHB2PQoEF49tln0atXr2rbnDVrlvH1TH5+Pi5dutRo8atc3aFycYdKZfq6RQAoLdVBW6ozzvgxR5lOi2+u+kJXxunMRERkHyR/JdRQ/fr1Q0pKSp3rK5VKKKsYA9KUdDodZOmHoC0thcFQv6Tj1hVsZTIZFAolAM7yISIi22PVCYuPjw/kcnmlQbSZmZlo1aqVRFFZhtLJEdDL61RXCIHyMh0gr743xrd1G0x/5bV6TWsmIiKydlb9SkihUCA0NBSJiYnGMoPBgMTERAwYMKBBbcfFxaF79+4ICwsz6zxLz/ipC125HqVXfodez1dARERknyTvYSksLMS5c+eM39PS0pCSkgJvb28EBAQgNjYWUVFR6Nu3L/r164dly5ahqKjIOGuovqKjoxEdHQ2NRgO1Wl3n8yoG05YWF8JBXvUePhVJjSUTGifHml/15GRlYvOaL3HnfRFQe9vaKr9ERGTvJE9YDh8+jKFDhxq/x8bGAgCioqIQHx+PsWPHIjs7G3PmzEFGRgZCQkKwY8eOSgNxm5LK1R2ADDptqbGsYhqzh4fHjaRm4wF4tWrXZDHpy8txPScben15k12TiIioqUiesAwZMqTW6bkxMTGIiYmx6HXj4uIQFxdnsdcsGo0Gb63di1fHDgIAKF1cLdIuERERWfkYlsYUHR2NU6dOISkpyWJtqly5WiwREVFjsNuEhYiIiJoPyV8JkWWovbwROeZJqDzqPoD4Zv8MFAZ3bSYiIqvDHhYboXJ2RlCXblAoVfU6X1dSjBU7U7Fk61Hu2kxERFbHbhOW+q7DUpWK3gkhDE2+RkuFooICHPr1F5QUFdW7DZWr298zoIiIiKyL3SYslhx0WzGNuTAvDyt2puKDH443+a7MhQUa7N+1EyXFhU16XSIioqZgtwmLpVVMY1a5ullsV2YiIiK6gQkLERERWT0mLERERGT17DZhseSgW2ugVKnQqVsPOCmUUodCRERkcXabsDTGSrdS8vRugeGjH4e72tOMswS0Oi10utImntNERESNYciQIXjuuefqVHf37t2QyWTIy8tr0DUDAwOxbNmyBrVRF1w4rtEJaHU61LJdUoPpy8tRoMmHXq+HXC6v0zll5XoYLh+BVhig8+zYuAESERE1gN32sDQVnU6LovRkaLW6Rr1OTlYmPn3/XeRfzzHrPIWjHEon5q1ERGTdmLA0Acc69ngQERFZyhdffIG+ffvC3d0drVq1wpNPPomsrKxK9fbt24fevXtDpVLhjjvuwMmTJ02O//rrrxg0aBCcnZ3h7++P//znPyiqZpFSIQTmzZuHgIAAKJVKtGnTBv/5z38scj92m7DY2qBbIiKim5WVlWH+/Pk4duwYNm3ahPT0dEyaNKlSvZdeeglLlixBUlISWrZsicjISJSV3Vj89M8//8QDDzyARx99FMePH8fatWvx66+/IiYmpsprfvvtt3jvvffw0Ucf4ezZs9i0aRN69eplkfux23cB0dHRiI6OhkajgVpdvw0DiYiIrNVTTz1l/HOHDh3w3//+F2FhYSgsLISbm5vx2Ny5c3HfffcBAD7//HO0a9cOGzduxJgxY7BgwQKMGzfOOJC3U6dO+O9//4vBgwfjww8/hEplun/dxYsX0apVKwwbNgxOTk4ICAhAv379LHI/dtvDQlX7Z18kzhsiImrOkpOTERkZiYCAALi7u2Pw4MEAbiQVNxswYIDxz97e3ujSpQtSU1MBAMeOHUN8fDzc3NyMn/DwcBgMBqSlpVW65mOPPYaSkhJ06NABU6dOxcaNG1FeXm6R+2HCYiN8W7VGzKy58PLxbVA72uIiLN2SxB2biYiasaKiIoSHh8PDwwMJCQlISkrCxo0bAQA6Xd0ngRQWFuLpp59GSkqK8XPs2DGcPXsWQUFBler7+/vjzJkzWLFiBZydnTF9+nTcfffdxldMDWG3r4RsjczBAY6OjpDJZA1uS+XsVnslIiKyWqdPn8a1a9ewcOFC+Pv7AwAOHz5cZd2DBw8iICAAAJCbm4s//vgD3bp1AwDcfvvtOHXqFDp2rPvSF87OzoiMjERkZCSio6PRtWtXnDhxArfffnuD7qlePSwdOnTAtWvXKpXn5eWhQ4cODQqI6ud6TjbW/+9TaPJypQ6FiIgkFhAQAIVCgeXLl+P8+fPYvHkz5s+fX2XdN954A4mJiTh58iQmTZoEHx8fjBo1CgDwyiuvYP/+/YiJiUFKSgrOnj2L7777rtpBt/Hx8fj0009x8uRJnD9/Hl9++SWcnZ3Rvn37Bt9TvRKW9PR06PX6SuVarRZXrlxpcFBkvjKdDlcupKO8rHHXeyEiIuvXsmVLxMfHY/369ejevTsWLlyId999t8q6CxcuxIwZMxAaGoqMjAxs2bIFCoUCANC7d2/88ssv+OOPPzBo0CD06dMHc+bMQZs2bapsy9PTE6tWrcLAgQPRu3dv7Ny5E1u2bEGLFi0afE9mvRLavHmz8c8//PCDyewavV6PxMREBAYGNjiophAXF4e4uLgqEy8iIqLmaPfu3cY/P/HEE3jiiSdMjt88oWLIkCHG7yNGjKi2zbCwMPz444/VHk9PTzf+edSoUcbeGUszK2GpCEImkyEqKsrkmJOTEwIDA7FkyRKLBdeYOK2ZiIio+TArYTEYDACA2267DUlJSfDx8WmUoIiIiIhuVq9ZQlXNvSZpuas9MWzEQ3Bx85A6FCIiIour97TmxMREJCYmIisry9jzUuGzzz5rcGC2RqvTAgYZGms5NhdXV/Ts0xfZ16+bfa4QTbOjNBERUX3VK2F5/fXX8cYbb6Bv375o3bq1Rdb+sGXlegMMl49AKwzQedZ9Lrs5iouK8Mfvx6H2bQ2Vs7NZ5+rK9dBmpUDb2h8KR64lSERE1qdeCcvKlSsRHx+PCRMmWDoemyEAlJbqoC3VQUBA4SiHHA7QNtL1CvLzsHPrd4gY86TZCQsAODn+86NQsTy/h4cHk1EiIrIK9frfaZ1OhzvvvNPSsdgUnU4HbfohaC8egcHQvKZO60qKuTw/ERFZlXolLFOmTMFXX31l6VhsjtLJEUonudRh1AuX5yciImtSr1dCpaWl+Pjjj7Fz50707t0bTk5OJseXLl1qkeCIiIiIgHomLMePH0dISAgA4OTJkybHmsuYB3NXur15XIc1clIo0LZ9IBydFFKHQkRETeTixYvIyclpkmv5+PgYN0mUQr0Sll27dlk6jiZn7kq32uIiLN2ShNjIsCaIznzePi3x2MR/1WtaMxERNT8XL15E127dUFJc3CTXc3ZxwenUVLOTlri4OCxevBgZGRkIDg7G8uXL0a9fP7OvX+91WOzRreM6Knpd/v7W9AHdHIvBgPLycgghmk0vFxER1V9OTg5Kiosx7pXF8AsIatRrZV78EwmLXkJOTo5ZCcvatWsRGxuLlStXon///li2bBnCw8Nx5swZ+Pr6mhVDvRKWoUOH1vhL8eeff65Ps82OrqQYK3amwqAvQ5m2TNJYsjKu4ouVyxEx5kl4t/STNBYiImo6fgFBaNeph9RhVGnp0qWYOnUqJk+eDODGsijbtm3DZ599hpkzZ5rVVr0SlorxKxXKysqQkpKCkydPVtoU0dapXN1gKC9HmfZarXUrVpRVKpxqrUtERNSc6XQ6JCcnY9asWcYyBwcHDBs2DAcOHDC7vXolLO+9916V5fPmzUNhYWF9mrQLFSvKol2I1KEQERE1qpycHOj1evj5mfb6+/n54fTp02a3Z9F12MePH899hGpx84qyREREVDcWTVgOHDgAlUplySZtkEB5mQ5SD9KtTcWA4vz8fAjuikhERGby8fGBXC5HZmamSXlmZiZatWpldnv1+t/9Rx55xOS7EAJXr17F4cOHMXv27Po0aTfKyvUou/I79G3N/8uqiY+vH/4140UUaXX1bEGY7ChdMaBYoVTihRF9oFarIYSARqPhHkNERFQrhUKB0NBQJCYmYtSoUQAAg8GAxMRExMTEmN1evRKWW9ctcXBwQJcuXfDGG2/g/vvvr0+TdsXJ0fK/7OWOjnD3UKO0nuuwlJXrTXaUVioUULm6QaH8p8dMo9Hg0rdz4P/oG3Vau4aIiOxbbGwsoqKi0LdvX/Tr1w/Lli1DUVGRcdaQOeqVsKxevbo+p1Ejyrt+Db/8sB3dQvvBXe1ZrzbqsqO0u7PSuPYMe1qIiKSXefFPq73G2LFjkZ2djTlz5iAjIwMhISHYsWNHpYG4ddGgEaDJyclITU0FAPTo0QN9+vRpSHPUANrSUpxN/R0dewVbvO2bx7NoSrRQ7l4KKJRAxP+xp4WISCI+Pj5wdnFBwqKXmuR6zi4u8PHxMfu8mJiYer0CulW9EpasrCw8/vjj2L17Nzw9PQEAeXl5GDp0KNasWYOWLVs2OLDGZu5eQvZL4PLly3A/+T8UFOug0+ngq/aASsHB1UREUgoICMDp1FTuJVSTZ599FgUFBfj999/RrVs3AMCpU6cQFRWF//znP/j6668tGmRjMHcvIXul02px9fs4tOzgC5mLEtm6ml4YERFRUwoICJA0iWhK9UpYduzYgZ07dxqTFQDo3r074uLiOOjWBrk5szeFiIikVa91WAwGA5ycKi8v7+TkBIPB0OCgyHxu7h64c+gwOLu41V6ZiIiomalXwnLPPfdgxowZ+Ouvv4xlV65cwfPPP497773XYsFR3bm6u6PfXYPh7OoqdShEREQWV6+E5YMPPoBGo0FgYCCCgoIQFBSE2267DRqNBsuXL7d0jFQHpSUl+PNMKnTa0ga1U7FBI9e2JSIia1KvMSz+/v44cuQIdu7cadzAqFu3bhg2bJhFg6O6y8+9ji3rvkLEmCfh3bL+Y04qNmhUduhnweiIiIgaxqwelp9//hndu3eHRqOBTCbDfffdh2effRbPPvsswsLC0KNHD+zdu7exYqUmcvMGjUIIFJZouZ8QERFJyqyEZdmyZZg6dSo8PDwqHVOr1Xj66aexdOlSiwXXfN14rWLtGxzWhbakCF9cbIG/cgqgKf4ncalYTI6JDBERNQWzXgkdO3YMixYtqvb4/fffj3fffbfBQTV3Op0WJRdToG9t/tLD1kOgtFQHbakOkAHxl/wglztilNd5AICmWIvs7QugfmIh17EhIpLIxYsXuXBcVTIzM6uczmxszNER2dnZDQ7KFtz8WqUpyB0d4e3TEnK5Za6r1ZVBm34I2tJSGAx6KFXOldp2d1Fa5FpERGS+ixcvolu3riguLmmS67m4OCM19bRZScuePXuwePFiJCcn4+rVq9i4caNx52ZzmfXbrW3btjh58iQ6duxY5fHjx4+jdevW9QqEGsbH1w8Tn/kPsuu5W7MpgfIyHZROckAvt0B7RERkaTk5OSguLsGX/zcG3QIad0uc1IvZGP/2OuTk5JiVsBQVFSE4OBhPPfUUHnnkkQbFYFbC8uCDD2L27Nl44IEHoFKZzkQpKSnB3LlzMWLEiAYFRNIrK9ej7Mrv0LdtZVJ+YwCu7u9xK9ylmYjIGnQLaInbO7eVOowqRUREICIiwiJtmZWwvPbaa9iwYQM6d+6MmJgYdOnSBQBw+vRp40aCr776qkUCa2rNffBo1tW/sObTj3DvqNHwbunb4PacHCsnJDptKb6+7ofO7XRQKvg6iIiImo5ZCYufnx/279+PZ555BrNmzTL+kpfJZAgPD0dcXBz8/JrnQNPmvgmiEAI6nRaNPTNJoVQCKG7UaxAREd3K7BGa7du3x/bt25Gbm4tz585BCIFOnTrBy8urMeJrMs29h4WIiMiW1WtpfgDw8vJCWFgY+vXrJ2mycunSJQwZMgTdu3dH7969sX79+nq1U1BQYOHIiIiIyFKadu5tI3B0dMSyZcsQEhKCjIwMhIaG4sEHH4SrBJsACsC4domwgUXjiIiIrEWzT1hat25tnErdqlUr+Pj44Pr165IkLDqdDrKb1i5pSt4+LfHklGegd2jcachCCBQUa6EtBxwdDMjPz4eHhwdkMs4aIiIiU4WFhTh37pzxe1paGlJSUuDt7W32InSSJyx1WVQmLi4OixcvRkZGBoKDg7F8+XL061d5c77k5GTo9Xr4+/s3UfSVKZ0cJVm7xEmhgG/rNhZah6V6ZTqtyaq3blztlohIUqkXG3/B1vpe4/Dhwxg6dKjxe2xsLAAgKioK8fHxZrUlecJS26Iya9euRWxsLFauXIn+/ftj2bJlCA8Px5kzZ+Dr+8/03evXr2PixIlYtWpVU4ZvNTR5eTi452d06N4Lru6V93qypJtXveVqt0RE0vDx8YGLizPGv72uSa7n4uIMHx8fs84ZMmSIxSa1SJ6w1LaozNKlSzF16lRMnjwZALBy5Ups27YNn332GWbOnAkA0Gq1GDVqFGbOnIk777yzSeK2NiXFRTh++BDadujY6AkLERFJLyAgAKmpp7mXkDXQ6XRITk7GrFmzjGUODg4YNmwYDhw4AODGmIpJkybhnnvuwYQJE2ptU6vVQqvVGr9rNBrLB05ERNQEAgICJE0imlK9pzU3hZycHOj1+kqL0fn5+SEjIwMAsG/fPqxduxabNm1CSEgIQkJCcOLEiWrbXLBgAdRqtfEj5XgXIiIiqhur7mGpi7vuugsGg6HO9WfNmmUc9APc6GFh0mI+032FiIiIGpdVJyw+Pj6Qy+XIzMw0Kc/MzESrVq2qOatmSqUSSqXtDRR1cXVDn/53QuXs0iTXq9hX6HlvLtNPRESNz6pfCSkUCoSGhiIxMdFYZjAYkJiYiAEDBjSo7bi4OHTv3h1hYWF1PkcI8feYF+vrVXBXqzH4/gi4uLk32TUVNpj4ERGRdZK8h6W2RWViY2MRFRWFvn37ol+/fli2bBmKioqMs4bqKzo6GtHR0WZteqgrKUbcjiPw8w9q0LUbg06rRcZflwFHBZwUCqnDISIisijJE5baFpUZO3YssrOzMWfOHGRkZCAkJAQ7duyQbFdolcqtilIBrU4HKXtecq/lYN3qVYgY8yS8WzbNs7mx6m0p8vLyAIAr3hIRUaORPGGpy6IyMTExiImJaaKIzKfTaVGUngzHNr2kDqVJlem0WPWHB3y3H4e7hzteGNGHK94SEVGjsOoxLI2pPmNYauIob/rl+K2BQqWE0sUNKtemGztDRET2x24TlujoaJw6dQpJSUlSh0JERES1sNuExdY4ODjA2cUFMhn/SomIyPZIPoaFLKNlq9Z4+oVZjb5bMxERkRTs9n/HLT2GBQDKy6SdKURERGSr7DZhsfQYlnK9AaVXfoder7dIe+bKycrE6g/eQ961ptm1k4iIqCnZbcLSGJwcpVuDRF9ejvzc6zAYpEmYiIiIGhMTFiIiIrJ6TFjIIoQQyM/P5+7NRETUKOw2YWmMQbf2TFtchKVbkv7eHJKIiMiy7DZhsbWF4zy9W2DUkxPhrvaULAaVc1X7LBERETWc3SYstkapUiEwqBOcFEqpQyEiIrI4Jiw2orBAgwO//IySokLJYqgYx8KxLEREZGlMWGxEUUEBftuzCyXFRZLFoCspxoqdqViy9SjHshARkUVxaX6yKJWrGxRKldRhEBGRjbHbHhbOEiIiImo+7DZhscQsIQGgtFQHbakOgnsIERERNRq+EmoAnU4HWfohaEtLJV8SX+nsjK49e/N1DBER2SS77WGxFKWTI5ROcqnDgKeXNx54+DG4eailDoWIiMjimLDYiPKyMuRdvwZ9ebnUoRAREVkcExYbcS07C/Fxy5Cfe62Jryyg1Wmh05UaR/FwXyEiIrI0JizUIGXlehguH4E2LQk6nQ4A9xUiIiLLs9uEhdOaLUfhKIfSyXT8NvcVIiIiS7LbhMXWNj8kIiKyZXabsFDj4jgWIiKyJCYst2iuv2j92rTFc7Pnw7uln9ShALixrxDHsRARkaUwYbmFRqPBW2v38hetBXAcCxERWQoTliqoXJvfL9rrOdlY89nH0ORelzoUIiIii2PCYiPKdDpkXLmE8vIyqUMhIiKyOCYsREREZPWYsJBFCCGg1enQzMYqExFRM2G3CQsXjrMsXbke2ssp0Gp1UodCREQ2yG4TFltbOM7D0wvhDz0KV3fpdmt2cnSsvRIREVE92G3CYmucXVzQrXcIlCqV1KEQERFZHBMWG1FcVIhjSb+htKRY6lCIiIgsjgmLjSjIz8euHVtRXFggdShEREQWx4SFiIiIrB4TFiIiIrJ6TFiIiIjI6jFhsREKpRIBHTrC0UkhdShEREQWx4TFRni18MEj46Lg4ekldShGQgjk5+cjPz8fgkvgEhFRA3ClLxthMBig1ZbCYDDAwcE68lBdSTFW7EyFk0KBqXcHwd/fHzKZTOqwiIioGbKO32zUYNkZV/HhO28h71q21KGYULm6QSZzwNItSdBoNFKHQ0REzRQTFmoSKmc3qUMgIqJmzG4TFm5+SERE1HzYbcJia5sfEhER2TK7TViIiIio+eAsIRvh49cK02JnoqCYmx8SEZHtYQ+LjZDL5XBxdYWDXC51KERERBbHhMVG5F2/hs1rvkRBfp7UoRAREVkcExYboS0txfmzZ1Cm00odSq24Ai4REZmLCQs1OY1GgyVbj2LJ1qNcTI6IiOqEg26pSVT0qlT8WeXqLnFERETUnDBhoSZRsa+QQqnElEEdpA6HiIiaGSYsNsLNwwN33/cAnF2tdwl8lasbFEqV1GEQEVEzxDEsNsLVzR233zEQzi6uUodCRERkcUxYbERpSTH+OHUS2tJSqUMhIiKyOCYsNiI/Nxfbv12LooJ8qUMhIiKyOCYsREREZPWYsJCkKqY7cwE5IiKqiU0kLA8//DC8vLwwevRoqUMhM2k0Gry1di8XkCMiohrZRMIyY8YM/O9//5M6DEk5OjmhZavWkMub30x1lRVPxSYiIutgEwnLkCFD4O5u3yuntmjpi3FTp0Pt3ULqUIiIiCxO8oRlz549iIyMRJs2bSCTybBp06ZKdeLi4hAYGAiVSoX+/fvj0KFDTR8oERERSUbyhKWoqAjBwcGIi4ur8vjatWsRGxuLuXPn4siRIwgODkZ4eDiysrKaOFLrlnn1Cpa/PQ/Xs/lciIjI9kg+4CEiIgIRERHVHl+6dCmmTp2KyZMnAwBWrlyJbdu24bPPPsPMmTPNvp5Wq4VWqzV+N3ewpwBQWqoDDLq/v1kJAej1elhVTFUQQvz9zAUAmdThEBFRMyF5D0tNdDodkpOTMWzYMGOZg4MDhg0bhgMHDtSrzQULFkCtVhs//v7+ZsekTT8EbVoStLqyesVgz7TFRYjbcQS6m5JGIiKi2lh1wpKTkwO9Xg8/Pz+Tcj8/P2RkZBi/Dxs2DI899hi2b9+Odu3a1ZjMzJo1C/n5+cbPpUuXzI5L6eQIpZPknVPNlkrFWUFERGQem/itu3PnzjrXVSqVUCqV9bqOAKDVWdmroGZBQKvToS5rw/3zygjw8PCATMbXRkREZOU9LD4+PpDL5cjMzDQpz8zMRKtWrRrUdlxcHLp3746wsLA6n6PT6VByMeXvsSLWxbulLyY8HQMPL+ub1qzTaVGUngytVldrXY1GgyVbj2LJ1qNcTI6IiIysOmFRKBQIDQ1FYmKiscxgMCAxMREDBgxoUNvR0dE4deoUkpKSzDrPydE6O6WcnJzQwtcPjlYan6NcXue6Kld3qFzte10dIiIyJflvt8LCQpw7d874PS0tDSkpKfD29kZAQABiY2MRFRWFvn37ol+/fli2bBmKioqMs4bohvy8XOz/eSc69gqBm4eH1OEQERFZlOQJy+HDhzF06FDj99jYWABAVFQU4uPjMXbsWGRnZ2POnDnIyMhASEgIduzYUWkgrr0rLS7G7ynJCOjcBYDUCcuNMSsKhaLmWiZTnP/ZCJFTnomI6FaSJyxDhgypdafemJgYxMTEWPS6cXFxiIuLs8rxKM1dxZgVBIbWWO/GFOdTUHvfSD41Gg2WbDwAr1btoFCqmiJUIiJqJqx6DEtjqu8YFqqbuo5ZuXWKs9LFtTHCISKiZs5uExYiIiJqPiR/JdRcCSFQXqYD5NaxJouLmxv63jkIKufm10Pxz9gVgGvcEBFRVew2YWnoGBZduR6lWb9D37Zh68FYiruHGnfdez+yr1+XOhSz6UqKsWJnKgz6MpRpud0BERFVZrevhCwxhsXJ0Xpmsui0WlxKT0OZrvbF2ayRytUNKheuvUJERFWz24TF1uRey8G3X3yGgvxcqUMhIiKyOCYsREREZPWYsJAFCWh1WmhLb7yWquuGh9W29vdg3NrW6SEiIttntwlLfTY/pJqVlethuHwE2otHoC3TQXs5pU4bHlZHo9HgrbV7uQkiERHZb8JiawvHOcjlcHP3gMyh7psMNgaFoxxKpxsxWGKjSJWrW+2ViIjI5tltwmJrWvq1wpTnXoJXCx+pQyEiIrI4JixERERk9Ziw2IjszAx8smwxcq/lSB0KERGRxTFhsREGvR6FBRoIA3efJiIi22O3CQtnCRERETUfdpuw2NosISIiIltmtwkLERERNR92u1uzrfFq4YNHJzwFucpZ6lCIiIgsjj0sNkKhVMI/8DY4KRRSh0JERGRxTFhsRIEmH78m/ojiwkKpQyEiIrI4Jiw2oriwEIf370VpSZHUoViEEOLvPYSE8XteXh7y8vKQn58Pg8GA/Pz8ZrM5IjdyJCJqGLsdwxIXF4e4uDjo9Vy3xBppi4sQt+MU1N5+AG5shDj38x/g7O4JhVKJKYM64JO95wEAL4zoA7VaLWW4tarYyPHVsYOsPlYiImtktz0snNZs/VQq040PlS6uULm6QeXqfuO4q7vxz80BN3IkIqo/u01YiIiIqPlgwmIjVC4u6BESCoWS05qJiMj2MGGxEWpPL9wXOQpuHh5Sh0JERGRxTFhsRFlZGa5lZaK8vFzqUIiIiCyOCYuNuJ6dhS8++gCa3GtSh0JERGRxTFiIiIjI6tntOizUfJkuKieTOpxq/RMnuGAcEVED2W0PS1xcHLp3746wsDCpQyEz3VhU7gh0Wq3UodRIo9FgydajWLL1qDFxISKi+rHbhMXmFo6TAXK5HNbc42BJty4qZ62a2+J2RETWiq+EbIRf67Z49v/mIfv6dalDISIisji77WEhIiKi5oMJi424lp2FhFUrkH+d05qJiMj2MGGxEeVlZcjOuAq9ngvHERGR7WHCQkRERFaPCQsRERFZPSYsREREZPU4rdlGqL288OCjY+HirpY6FCIiIotjD4uNUDm7oHP3nlCqVFKHQkREZHFMWGxEUWEBjhzch5LiIqlDISIisjgmLHUmoNXpIGCdm9gVajTY89MOlBQVSh3KTQS0Oi20pbrGu4IQyMvLQ25uLvLy8pCfn9/oGw0KIZCfn2+Ra1W0xc0Riag5uPnfv6Zmt2NY4uLiEBcXB71eX6f6Op0WJRdT4MAUr87KyvUwXD6CMl0ZDMLQKNfQFhdh4fpTAAC1tx8USiVeGNEHanXjjeWp2NQQQIOvpdFo8NbavXh17KBGjZmIyBJu/vfvjXFDmvTadvvrtz6bHzo52m1+V28KRzmUTvJGvYZK5Xbj4+rWZBsNWnJTQ5Vr89jIkYgIkG5TV7tNWIiIiKj5YMJiI5QqFTp06gInhVLqUIiIiCyOCYuN8PRugZGPj4e72lPqUIiIiCyOCYuN0Ov1KC4qgqGOg4iJiIiaEyYsNiInMwMfL12IvOs5UodCRERkcUxYiIiIyOoxYSEiIiKrx4SFiIiIrB4TFiIiIrJ6XLrVRrRs1RrPvPwq8gqsaS8hIiIiy2APi41wcHCAUqmCAzc7IiIiG8TfbjYi91oONiR8Dk1ertShEBERWRwTFhuh02px8fw5lJfppA6FiIjI4piwEBERkdWziYRl69at6NKlCzp16oRPPvlE6nCIiIjIwpr9LKHy8nLExsZi165dUKvVCA0NxcMPP4wWLVpIHRoRERFZSLPvYTl06BB69OiBtm3bws3NDREREfjxxx+lDqvJuavVGPrACLi4uUsdChERkcVJnrDs2bMHkZGRaNOmDWQyGTZt2lSpTlxcHAIDA6FSqdC/f38cOnTIeOyvv/5C27Ztjd/btm2LK1euNEXoVsXF1Q3BYf2hcnaROhQiIiKLkzxhKSoqQnBwMOLi4qo8vnbtWsTGxmLu3Lk4cuQIgoODER4ejqysrCaO1LqVFBcj9XgKtKWlUodCRERkcZKPYYmIiEBERES1x5cuXYqpU6di8uTJAICVK1di27Zt+OyzzzBz5ky0adPGpEflypUr6NevX7XtabVaaLVa43eNRmOBu5CeJi8XP3z3LSLGPAmlSiV1OFUQ0Oq0gEEGAYHSUh1g0EH8ffTW78azhEB+fr6xjX/a0kHcWrmac4UQkMlkxjKZTAaZTAZ3d3cUFBRA/N2QWq021ru5DY1GY6xbUXZz+x4eHgBQ6Vo34hYQouLnTJi0WXHerdfy8PCATCar9L2q+6v4+a2uDpE94X8Ttk3yhKUmOp0OycnJmDVrlrHMwcEBw4YNw4EDBwAA/fr1w8mTJ3HlyhWo1Wp8//33mD17drVtLliwAK+//nqjx06mysr1MFw+Aq0woMAlALJr5wB9GXSeHQEAsvRDJt8r6EqKsWJnKgz6MpRpy26U6bQoSk+GRws/KByr7yTUaDSY+/kP0JVpofb2g0FfhoL861B7+0GhVGLKoA74ZO95lBYXQqfT4vXxw6BWqyu18dbavYh5oDc+2XseADBlUAcAgLa4CEu3JOH18TfOqepafv5BMJSXI27HKai9/UzafHXsoCqv9erYQVCr1ZW+V3V/S7YeBQC8MKJPlXWI7An/m7BtVp2w5OTkQK/Xw8/Pz6Tcz88Pp0+fBgA4OjpiyZIlGDp0KAwGA15++eUaZwjNmjULsbGxxu8ajQb+/v6NcwNkQuEoh/zvt5BKJ0fAQaCir+vW7zdTubrBUF6OMu01Y5mjXF6naypdXOGgdbqpDR1Urm5QKFV/t+0OQAYHuVO1bahc3W6qe8sxZ7car2Wsp3IzPc/V9Ht15dXV++c4B1kT3Yz/Tdguq05Y6mrkyJEYOXJkneoqlUoolcpGjoiIiIgsSfJBtzXx8fGBXC5HZmamSXlmZiZatWolUVTWyUmhQKu2/nB0rL6ngIiIqLmy6oRFoVAgNDQUiYmJxjKDwYDExEQMGDCgQW3HxcWhe/fuCAsLa2iYVsHbpyUef2oaPLy8pQ6FiIjI4iR/JVRYWIhz584Zv6elpSElJQXe3t4ICAhAbGwsoqKi0LdvX/Tr1w/Lli1DUVGRcdZQfUVHRyM6OhoajYYDs4iIiKyc5AnL4cOHMXToUOP3igGxUVFRiI+Px9ixY5GdnY05c+YgIyMDISEh2LFjR6WBuPYu868r+GLlckSMeRLeLflsiIjItkiesAwZMsS4rkV1YmJiEBMTY9HrxsXFIS4uDnq93qLtEhERkeVZ9RiWxhQdHY1Tp04hKSlJ6lCIiIioFnabsBAREVHzwYSFiIiIrJ7kY1jIMlq09MWk6OdQWs4xOUREZHvstofF1tZhcXRygqd3C8gdmYMSEZHtsduExdYG3eblXseOjetRqMmvvTIREVEzY7cJi63RlpTg9Mnj0GlLpQ6FiIjI4piwEBERkdWz24TF1sawEBER2TK7HaFZsZdQfn4+PD09UVhYCI1GA41GA21xETQaDQBAW1IMuaMC2pIi6HRaGAzl0JaWory8DGU6rcmfAVR7rK716tuG7O8Ve8vKaj6vseOo7ZgcAjp9CbSGUkBfBp282Fiv4ru2uAiG8nJoS/4+dtN3bXHRjb8LrRbakiIIuYNpPX05NBoNZDLZjb/LkmKUaXWV2jDoy1FQoEJpcSG0xUXQ6UqN592s4uehoKAApcWFAFDleRU/K1Veq+JeZPin7s0/Y1X82Rj/Td9vpdFojDFVV4fInvC/icZ36zN2d3dvsucsE7Wti2/jzp8/j6CgIKnDICIianaysrLQsmXLJrmW3fawVPD29gYAXLx4kbs2NwGNRgN/f39cunQJHh4eUodjF/jMmx6fedPi8256Fc9coVA02TXtPmFxcLgxjEetVvMHvQl5eHjweTcxPvOmx2fetPi8m15Tvnaz20G3RERE1HwwYSEiIiKrZ/cJi1KpxNy5c6FUKqUOxS7weTc9PvOmx2fetPi8m54Uz9zuZwkRERGR9bP7HhYiIiKyfkxYiIiIyOoxYSEiIiKrZxcJS1xcHAIDA6FSqdC/f38cOnSoxvrr169H165doVKp0KtXL2zfvr2JIrUN5jzvVatWYdCgQfDy8oKXlxeGDRtW698PVWbuz3iFNWvWQCaTYdSoUY0boI0x93nn5eUhOjoarVu3hlKpROfOnfnvipnMfebLli1Dly5d4OzsDH9/fzz//PMoLeVu9nWxZ88eREZGok2bNpDJZNi0aVOt5+zevRu33347lEolOnbsiPj4eMsHJmzcmjVrhEKhEJ999pn4/fffxdSpU4Wnp6fIzMyssv6+ffuEXC4X77zzjjh16pR47bXXhJOTkzhx4kQTR948mfu8n3zySREXFyeOHj0qUlNTxaRJk4RarRaXL19u4sibL3OfeYW0tDTRtm1bMWjQIPHQQw81TbA2wNznrdVqRd++fcWDDz4ofv31V5GWliZ2794tUlJSmjjy5svcZ56QkCCUSqVISEgQaWlp4ocffhCtW7cWzz//fBNH3jxt375dvPrqq2LDhg0CgNi4cWON9c+fPy9cXFxEbGysOHXqlFi+fLmQy+Vix44dFo3L5hOWfv36iejoaON3vV4v2rRpIxYsWFBl/TFjxojhw4eblPXv3188/fTTjRqnrTD3ed+qvLxcuLu7i88//7yxQrQ59Xnm5eXl4s477xSffPKJiIqKYsJiBnOf94cffig6dOggdDpdU4Voc8x95tHR0eKee+4xKYuNjRUDBw5s1DhtUV0Slpdffln06NHDpGzs2LEiPDzcorHY9CshnU6H5ORkDBs2zFjm4OCAYcOG4cCBA1Wec+DAAZP6ABAeHl5tffpHfZ73rYqLi1FWVmbc44lqVt9n/sYbb8DX1xf/+te/miJMm1Gf571582YMGDAA0dHR8PPzQ8+ePfH2229D//cO61Sz+jzzO++8E8nJycbXRufPn8f27dvx4IMPNknM9qapfm/a9F5COTk50Ov18PPzMyn38/PD6dOnqzwnIyOjyvoZGRmNFqetqM/zvtUrr7yCNm3aVPrhp6rV55n/+uuv+PTTT5GSktIEEdqW+jzv8+fP4+eff8a4ceOwfft2nDt3DtOnT0dZWRnmzp3bFGE3a/V55k8++SRycnJw1113QQiB8vJy/Pvf/8b//d//NUXIdqe635sajQYlJSVwdna2yHVsuoeFmpeFCxdizZo12LhxI1QqldTh2KSCggJMmDABq1atgo+Pj9Th2AWDwQBfX198/PHHCA0NxdixY/Hqq69i5cqVUodms3bv3o23334bK1aswJEjR7BhwwZs27YN8+fPlzo0agCb7mHx8fGBXC5HZmamSXlmZiZatWpV5TmtWrUyqz79oz7Pu8K7776LhQsXYufOnejdu3djhmlTzH3mf/75J9LT0xEZGWksMxgMAABHR0ecOXMGQUFBjRt0M1afn/HWrVvDyckJcrncWNatWzdkZGRAp9NBoVA0aszNXX2e+ezZszFhwgRMmTIFANCrVy8UFRVh2rRpePXVV+HgwP9Xt6Tqfm96eHhYrHcFsPEeFoVCgdDQUCQmJhrLDAYDEhMTMWDAgCrPGTBggEl9APjpp5+qrU//qM/zBoB33nkH8+fPx44dO9C3b9+mCNVmmPvMu3btihMnTiAlJcX4GTlyJIYOHYqUlBT4+/s3ZfjNTn1+xgcOHIhz584ZE0MA+OOPP9C6dWsmK3VQn2deXFxcKSmpSBgFd6OxuCb7vWnRIbxWaM2aNUKpVIr4+Hhx6tQpMW3aNOHp6SkyMjKEEEJMmDBBzJw501h/3759wtHRUbz77rsiNTVVzJ07l9OazWDu8164cKFQKBTim2++EVevXjV+CgoKpLqFZsfcZ34rzhIyj7nP++LFi8Ld3V3ExMSIM2fOiK1btwpfX1/x5ptvSnULzY65z3zu3LnC3d1dfP311+L8+fPixx9/FEFBQWLMmDFS3UKzUlBQII4ePSqOHj0qAIilS5eKo0ePigsXLgghhJg5c6aYMGGCsX7FtOaXXnpJpKamiri4OE5rrq/ly5eLgIAAoVAoRL9+/cTBgweNxwYPHiyioqJM6q9bt0507txZKBQK0aNHD7Ft27Ymjrh5M+d5t2/fXgCo9Jk7d27TB96MmfszfjMmLOYz93nv379f9O/fXyiVStGhQwfx1ltvifLy8iaOunkz55mXlZWJefPmiaCgIKFSqYS/v7+YPn26yM3NbfrAm6Fdu3ZV+e9yxTOOiooSgwcPrnROSEiIUCgUokOHDmL16tUWj4u7NRMREZHVs+kxLERERGQbmLAQERGR1WPCQkRERFaPCQsRERFZPSYsREREZPWYsBAREZHVY8JCREREVo8JCxEREVk9JixEJKkhQ4bgueeekzoMIrJyTFiIiIjI6jFhISKqB71eb7IDMxE1LiYsRDZgx44duOuuu+Dp6YkWLVpgxIgR+PPPPwEA6enpkMlk2LBhA4YOHQoXFxcEBwfjwIEDJm18++236NGjB5RKJQIDA7FkyRKT44GBgXjzzTcxceJEuLm5oX379ti8eTOys7Px0EMPwc3NDb1798bhw4eN51y7dg1PPPEE2rZtCxcXF/Tq1Qtff/11tffxxhtvoGfPnpXKQ0JCMHv27Fqfw+7du9GvXz+4urrC09MTAwcOxIULF4zHt2zZgrCwMKhUKvj4+ODhhx82HsvNzcXEiRPh5eUFFxcXRERE4OzZs8bj8fHx8PT0xObNm9G9e3colUpcvHgRWq0WL774Itq2bQtXV1f0798fu3fvNp534cIFREZGwsvLC66urujRowe2b99e670QkSkmLEQ2oKioCLGxsTh8+DASExPh4OCAhx9+2KQH4NVXX8WLL76IlJQUdO7cGU888QTKy8sBAMnJyRgzZgwef/xxnDhxAvPmzcPs2bMRHx9vcp333nsPAwcOxNGjRzF8+HBMmDABEydOxPjx43HkyBEEBQVh4sSJqNhTtbS0FKGhodi2bRtOnjyJadOmYcKECTh06FCV9/HUU08hNTUVSUlJxrKjR4/i+PHjmDx5co3PoLy8HKNGjcLgwYNx/PhxHDhwANOmTYNMJgMAbNu2DQ8//DAefPBBHD16FImJiejXr5/x/EmTJuHw4cPYvHkzDhw4ACEEHnzwQZSVlRnrFBcXY9GiRfjkk0/w+++/w9fXFzExMThw4ADWrFmD48eP47HHHsMDDzxgTHaio6Oh1WqxZ88enDhxAosWLYKbm1ttf6VEdCuL7/9MRJLLzs4WAMSJEydEWlqaACA++eQT4/Hff/9dABCpqalCCCGefPJJcd9995m08dJLL4nu3bsbv7dv316MHz/e+P3q1asCgJg9e7ax7MCBAwKAuHr1arWxDR8+XLzwwgvG74MHDxYzZswwfo+IiBDPPPOM8fuzzz4rhgwZUus9X7t2TQAQu3fvrvL4gAEDxLhx46o89scffwgAYt++fcaynJwc4ezsLNatWyeEEGL16tUCgEhJSTHWuXDhgpDL5eLKlSsm7d17771i1qxZQgghevXqJebNm1dr/ERUM/awENmAs2fP4oknnkCHDh3g4eGBwMBAAMDFixeNdXr37m38c+vWrQEAWVlZAIDU1FQMHDjQpM2BAwfi7Nmz0Ov1Vbbh5+cHAOjVq1elsop29Xo95s+fj169esHb2xtubm744YcfTOK61dSpU/H111+jtLQUOp0OX331FZ566qlan4G3tzcmTZqE8PBwREZG4v3338fVq1eNx1NSUnDvvfdWeW5qaiocHR3Rv39/Y1mLFi3QpUsXpKamGssUCoXJMzhx4gT0ej06d+4MNzc34+eXX34xvpL7z3/+gzfffBMDBw7E3Llzcfz48VrvhYgqY8JCZAMiIyNx/fp1rFq1Cr/99ht+++03AIBOpzPWcXJyMv654jWJuYNGq2qjpnYXL16M999/H6+88gp27dqFlJQUhIeHm8RV1b0olUps3LgRW7ZsQVlZGUaPHl2n+FavXo0DBw7gzjvvxNq1a9G5c2ccPHgQAODs7GzWvVbF2dnZeI8AUFhYCLlcjuTkZKSkpBg/qampeP/99wEAU6ZMwfnz5zFhwgScOHECffv2xfLlyxscC5G9YcJC1Mxdu3YNZ86cwWuvvYZ7770X3bp1Q25urlltdOvWDfv27TMp27dvHzp37gy5XF7v2Pbt24eHHnoI48ePR3BwMDp06IA//vijxnMcHR0RFRWF1atXY/Xq1Xj88cfNSjb69OmDWbNmYf/+/ejZsye++uorADd6hxITE6s8p1u3bigvLzcmesA/z7V79+41Xkuv1yMrKwsdO3Y0+bRq1cpYz9/fH//+97+xYcMGvPDCC1i1alWd74eIbnCUOgAiahgvLy+0aNECH3/8MVq3bo2LFy9i5syZZrXxwgsvICwsDPPnz8fYsWNx4MABfPDBB1ixYkWDYuvUqRO++eYb7N+/H15eXli6dCkyMzNrTAKAG70S3bp1A4BKiVR10tLS8PHHH2PkyJFo06YNzpw5g7Nnz2LixIkAgLlz5+Lee+9FUFAQHn/8cZSXl2P79u145ZVX0KlTJzz00EOYOnUqPvroI7i7u2PmzJlo27YtHnrooWqv2blzZ4wbNw4TJ07EkiVL0KdPH2RnZyMxMRG9e/fG8OHD8dxzzyEiIgKdO3dGbm4udu3aZbw3Iqo79rAQNXMODg5Ys2YNkpOT0bNnTzz//PNYvHixWW3cfvvtWLduHdasWYOePXtizpw5eOONNzBp0qQGxfbaa6/h9ttvR3h4OIYMGYJWrVph1KhRtZ7XqVMn3HnnnejatavJuJKauLi44PTp03j00UfRuXNnTJs2DdHR0Xj66acB3FhRd/369di8eTNCQkJwzz33mMxWWr16NUJDQzFixAgMGDAAQghs377d5JVXVVavXo2JEyfihRdeQJcuXTBq1CgkJSUhICAAwI1xPNHR0ejWrRseeOABdO7cucGJIJE9kgnx9/xDIiIrIYRAp06dMH36dMTGxkodDhFZAb4SIiKrkp2djTVr1iAjI6PWtVeIyH4wYSEiq+Lr6wsfHx98/PHH8PLyMjlW04Jr33//PQYNGtTY4RGRRPhKiIiajXPnzlV7rG3bthaZukxE1okJCxEREVk9zhIiIiIiq8eEhYiIiKweExYiIiKyekxYiIiIyOoxYSEiIiKrx4SFiIiIrB4TFiIiIrJ6TFiIiIjI6v0/Aj1uJGiPuSMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.6853517,\n",
       " 0.84801996,\n",
       " 0.6653182650969579,\n",
       " 0.6325703809357909,\n",
       " 0.7382830231502496)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflow_trainer.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.15.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_samples = 100000\n",
    "\n",
    "synthetic_dataset = generate_synthetic_data(\n",
    "                        netflow_trainer.get_generator(), \n",
    "                        number_of_samples=number_of_samples, \n",
    "                        columns=netflow_dataset.get_columns(),\n",
    "                        latent_vector_size=hps['latent_vector_size'],\n",
    "                        scaler=netflow_dataset.get_scaler())\n",
    "\n",
    "# synthetic_dataset\n",
    "synthetic_dataset.to_parquet(\"./datasets/synthetic_ton_iot_ganomaly.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PROTOCOL</th>\n",
       "      <th>IN_BYTES</th>\n",
       "      <th>IN_PKTS</th>\n",
       "      <th>OUT_BYTES</th>\n",
       "      <th>OUT_PKTS</th>\n",
       "      <th>FLOW_DURATION_MILLISECONDS</th>\n",
       "      <th>DURATION_IN</th>\n",
       "      <th>DURATION_OUT</th>\n",
       "      <th>MIN_TTL</th>\n",
       "      <th>MAX_TTL</th>\n",
       "      <th>...</th>\n",
       "      <th>CLIENT_RESET</th>\n",
       "      <th>CLIENT_SYNCHRONISATION</th>\n",
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       "      <th>SERVER_URGENT_POINTER</th>\n",
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       "      <td>6.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>6.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>224.0</td>\n",
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       "      <td>4294655.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>64.0</td>\n",
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       "    <tr>\n",
       "      <th>282041</th>\n",
       "      <td>6.0</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>282042 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PROTOCOL  IN_BYTES  IN_PKTS  OUT_BYTES  OUT_PKTS  \\\n",
       "0            6.0     120.0      2.0      104.0       2.0   \n",
       "1            6.0      44.0      1.0       40.0       1.0   \n",
       "2            6.0      44.0      1.0       40.0       1.0   \n",
       "3           17.0     138.0      2.0      234.0       2.0   \n",
       "4            6.0      44.0      1.0       40.0       1.0   \n",
       "...          ...       ...      ...        ...       ...   \n",
       "282037       6.0     268.0      5.0      164.0       3.0   \n",
       "282038       6.0      44.0      1.0       40.0       1.0   \n",
       "282039       6.0     276.0      5.0      172.0       3.0   \n",
       "282040       6.0     224.0      4.0      224.0       4.0   \n",
       "282041       6.0     284.0      5.0      440.0       8.0   \n",
       "\n",
       "        FLOW_DURATION_MILLISECONDS  DURATION_IN  DURATION_OUT  MIN_TTL  \\\n",
       "0                        4294951.0         16.0          16.0     64.0   \n",
       "1                              0.0          0.0           0.0      0.0   \n",
       "2                              0.0          0.0           0.0      0.0   \n",
       "3                              0.0          0.0           0.0     64.0   \n",
       "4                              0.0          0.0           0.0      0.0   \n",
       "...                            ...          ...           ...      ...   \n",
       "282037                   4294592.0        375.0         328.0     64.0   \n",
       "282038                         0.0          0.0           0.0      0.0   \n",
       "282039                   4294452.0        515.0         515.0     64.0   \n",
       "282040                   4294655.0        281.0         312.0     64.0   \n",
       "282041                   4294326.0        641.0         641.0     64.0   \n",
       "\n",
       "        MAX_TTL  ...  CLIENT_RESET  CLIENT_SYNCHRONISATION  CLIENT_FIN  \\\n",
       "0          64.0  ...           0.0                     1.0         0.0   \n",
       "1           0.0  ...           0.0                     1.0         0.0   \n",
       "2           0.0  ...           0.0                     1.0         0.0   \n",
       "3          64.0  ...           0.0                     0.0         0.0   \n",
       "4           0.0  ...           0.0                     1.0         0.0   \n",
       "...         ...  ...           ...                     ...         ...   \n",
       "282037     64.0  ...           0.0                     1.0         0.0   \n",
       "282038      0.0  ...           0.0                     1.0         0.0   \n",
       "282039     64.0  ...           0.0                     1.0         0.0   \n",
       "282040     64.0  ...           0.0                     1.0         0.0   \n",
       "282041     64.0  ...           0.0                     1.0         1.0   \n",
       "\n",
       "        SERVER_URGENT_POINTER  SERVER_ACKNOWLEDGEMENT  SERVER_PUSH  \\\n",
       "0                         0.0                     1.0          0.0   \n",
       "1                         0.0                     1.0          0.0   \n",
       "2                         0.0                     1.0          0.0   \n",
       "3                         0.0                     0.0          0.0   \n",
       "4                         0.0                     1.0          0.0   \n",
       "...                       ...                     ...          ...   \n",
       "282037                    0.0                     1.0          0.0   \n",
       "282038                    0.0                     1.0          0.0   \n",
       "282039                    0.0                     1.0          0.0   \n",
       "282040                    0.0                     1.0          0.0   \n",
       "282041                    0.0                     1.0          0.0   \n",
       "\n",
       "        SERVER_RESET  SERVER_SYNCHRONISATION  SERVER_FIN  Label  \n",
       "0                0.0                     0.0         0.0    NaN  \n",
       "1                1.0                     0.0         0.0    NaN  \n",
       "2                1.0                     0.0         0.0    NaN  \n",
       "3                0.0                     0.0         0.0    NaN  \n",
       "4                1.0                     0.0         0.0    NaN  \n",
       "...              ...                     ...         ...    ...  \n",
       "282037           0.0                     1.0         1.0    NaN  \n",
       "282038           1.0                     0.0         0.0    NaN  \n",
       "282039           0.0                     1.0         1.0    NaN  \n",
       "282040           0.0                     1.0         1.0    NaN  \n",
       "282041           0.0                     1.0         0.0    NaN  \n",
       "\n",
       "[282042 rows x 53 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = netflow_dataset.get_test_distribution()\n",
    "\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\seaborn\\distributions.py:2125: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try kdeplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\seaborn\\distributions.py:2125: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try kdeplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "l:\\Experimentos\\anomaly-flow\\.env\\lib\\site-packages\\seaborn\\distributions.py:2125: UserWarning: `displot` is a figure-level function and does not accept the ax= parameter. You may wish to try kdeplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 613.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 613.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 613.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# features_to_plot = list(test_df.columns)\n",
    "features_to_plot = [\"FLOW_DURATION_MILLISECONDS\", \"OUT_PKTS\", \"BYTES_PER_PACKET\"]\n",
    "\n",
    "# Filter only the benign samples to evaluate\n",
    "test_df = netflow_dataset.get_test_distribution()\n",
    "test_df = test_df[test_df[\"Label\"] == 0]\n",
    "\n",
    "test_df[\"BYTES_PER_PACKET\"] = test_df[\"IN_BYTES\"] / test_df[\"IN_PKTS\"]\n",
    "synthetic_dataset[\"BYTES_PER_PACKET\"] = synthetic_dataset[\"IN_BYTES\"] / synthetic_dataset[\"IN_PKTS\"]\n",
    "\n",
    "for i, feature in enumerate(features_to_plot):\n",
    "    generate_dist_plot(\n",
    "        series=test_df[feature][:number_of_samples], \n",
    "        comparison_series=synthetic_dataset[feature], \n",
    "        title=f\"{dataset_name}-{feature}\", \n",
    "        save_to_file=True,\n",
    "        ax=None\n",
    "    )"
   ]
  }
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