E2E_framework_WGAN_Rayleigh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a060dfa1-4be6-4214-a85d-ca8ae31e6631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x78327c12bc90>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torch.nn import functional as F\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm\n",
    "\n",
    "import pickle\n",
    "import csv\n",
    "import time\n",
    "import sys\n",
    "\n",
    "sys.path.append('src/')\n",
    "\n",
    "# Own libraries\n",
    "import utils\n",
    "import trainer_WGAN as trainer\n",
    "import model_WGAN\n",
    "import models\n",
    "import WGAN\n",
    "\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c97ad605-5e1c-46f8-8f97-2f363762dcc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "\n",
    "# Encoding parameters\n",
    "k = 4\n",
    "n = 7\n",
    "M = 16\n",
    "TRAINING_SNR = 12\n",
    "rate = k/n\n",
    "noise_std = utils.EbNo_to_noise(TRAINING_SNR, rate)\n",
    "save_every = 1000\n",
    "\n",
    "\n",
    "#GAN parameters\n",
    "g_input_size = 2*n      # Random noise dimension coming into generator, per output vector\n",
    "g_hidden_size = 256 # Generator complexity\n",
    "g_output_size = n     # Size of generated output vector\n",
    "\n",
    "d_input_size = 2*n    # Minibatch size - cardinality of distributions\n",
    "d_hidden_size = 256    # Discriminator complexity\n",
    "d_output_size = 1     # Single dimension for 'real' vs. 'fake' classification\n",
    "\n",
    "batch_size = 5000\n",
    "num_epochs = 30000\n",
    "\n",
    "G = WGAN.Generator(input_size=g_input_size, hidden_size=g_hidden_size, output_size=g_output_size)\n",
    "D = WGAN.Discriminator(input_size=d_input_size, hidden_size=d_hidden_size, output_size=d_output_size)\n",
    "G = G.to(device)\n",
    "D = D.to(device)\n",
    "\n",
    "d_lr = 5e-5\n",
    "g_lr = 5e-5\n",
    "\n",
    "channel_ray = model_WGAN.Channel_ray_only(noise_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38a7cf46-1d60-4093-8846-ef1e5d62011b",
   "metadata": {},
   "outputs": [],
   "source": [
    "milestone=80\n",
    "G.load_state_dict(torch.load(f'state_dict_WGAN/G_Rayleigh-{milestone}.pt', map_location=\"cpu\"))\n",
    "G.eval()\n",
    "G = G.to(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5f8a74-0c00-4804-89c3-e4dec24cd93e",
   "metadata": {},
   "source": [
    "# Training GAN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b31dfeb-169f-43c3-9d61-bd88442ad557",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "epochs = tqdm(range(num_epochs), ascii=True)\n",
    "for epoch in epochs:\n",
    "    kl, g_loss = WGAN.train_WGAN(G, D, batch_size, n, channel_ray, d_lr, g_lr)\n",
    "    if (epoch+1) % 50 == 0:\n",
    "        epochs.set_description(f\"Epoch {epoch+1}: KL Div {kl:.3e} G loss {-g_loss:.3e}\")\n",
    "    if (epoch+1) % save_every == 0:\n",
    "        torch.save(G.state_dict(), f'state_dict_WGAN/G_Rayleigh-{(epoch+1)//save_every}.pt')\n",
    "        torch.save(D.state_dict(), f'state_dict_WGAN/D_Rayleigh-{(epoch+1)//save_every}.pt')\n",
    "\n",
    "    #    WGAN.plot_GAN(G, 10000,M, n, channel_awgn, epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b96b9c9-5cf7-4beb-b98c-6b936bc1d884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1:  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WGAN.plot_GAN_m(G, 10000, M, n, channel_ray, 1, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5179631c-21b8-4276-bad7-2567a9b96958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49999:  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 50000: KL Div 5.324e-03 G loss -1.247e-02: 100%|######################################| 50000/50000 [3:56:31<00:00,  3.52it/s]\n"
     ]
    }
   ],
   "source": [
    "num_epochs_acc = 32000#num_epochs\n",
    "num_epochs = 50000\n",
    "save_every = 1000\n",
    "d_lr = 1e-5\n",
    "g_lr = 1e-5\n",
    "epochs = tqdm(range(num_epochs), ascii=True)\n",
    "for epoch in epochs:\n",
    "    kl, g_loss = WGAN.train_WGAN(G, D, batch_size, n, channel_ray, d_lr, g_lr)\n",
    "    if (epoch+1) % 50 == 0:\n",
    "        epochs.set_description(f\"Epoch {epoch+1}: KL Div {kl:.3e} G loss {-g_loss:.3e}\")\n",
    "    if (epoch+1) % save_every == 0:\n",
    "        torch.save(G.state_dict(), f'state_dict_WGAN/G_Rayleigh-{(num_epochs_acc+epoch+1)//save_every}.pt')\n",
    "        torch.save(D.state_dict(), f'state_dict_WGAN/D_Rayleigh-{(num_epochs_acc+epoch+1)//save_every}.pt')\n",
    "\n",
    "        WGAN.plot_GAN(G, 10000,M, n, channel_ray, epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94715ff0-368c-40e4-bac9-cedcab08607b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Measuring the sampling time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cfce548b-0820-42eb-ba56-0c75ee1fd496",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0015850067138671875\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# Set the message samples to test out\n",
    "num_samples = 2 ** 13 # per message\n",
    "#m_sample = torch.eye(M)\n",
    "#m = m_sample.repeat_interleave(num_samples, dim=0)\n",
    "batch_size = 1024 \n",
    "\n",
    "#encoder = models.Encoder_1h( M, n, n).to(device)\n",
    "x = torch.ones(batch_size, n).to(device) \n",
    "t = time.time() # tic\n",
    "\n",
    "#m_batched = torch.split(m, batch_size)\n",
    "#for m_b in m_batched:\n",
    "for i in range(num_samples // batch_size):\n",
    "    #m_b = m_b.to(device)\n",
    "    #x = encoder(m_b)\n",
    "    x_seq = G(x)\n",
    "    #samples_DDPM = x_seq[-1]\n",
    "sampling_time_WGAN = time.time() - t # toc\n",
    "print(sampling_time_WGAN)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43096bd1-8797-44d0-aa02-bdebfb25fe63",
   "metadata": {},
   "source": [
    "# Training GAN Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23194890-8bc3-4913-bb24-de6235227d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.random.randint(0,M, size=10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d5ec0282-b081-4785-a897-7fdd225fbf61",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_GAN = models.Encoder_1h( M, n, n)\n",
    "decoder_GAN = models.Decoder_1h( M, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6543a073-d13f-4f3d-b1d5-be39f01acb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decoder_1h(\n",
       "  (_f): Sequential(\n",
       "    (0): Linear(in_features=7, out_features=16, bias=True)\n",
       "    (1): ELU(alpha=1.0)\n",
       "    (2): Linear(in_features=16, out_features=16, bias=True)\n",
       "    (3): ELU(alpha=1.0)\n",
       "    (4): Linear(in_features=16, out_features=16, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_GAN.load_state_dict(torch.load('state_dict_WGAN/enc_GAN_Rayleigh.pt', map_location=\"cpu\"))\n",
    "decoder_GAN.load_state_dict(torch.load('state_dict_WGAN/dec_GAN_Rayleigh.pt', map_location=\"cpu\"))\n",
    "encoder_GAN.eval()\n",
    "decoder_GAN.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d58c10c6-1688-48e0-9ef7-393d403f23ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 7.39e-02 SER 1.97e-02: 100%|####################################################| 10000/10000 [01:58<00:00, 84.05it/s]\n",
      "epoch 2: loss 1.45e-02 SER 4.46e-03: 100%|####################################################| 10000/10000 [01:58<00:00, 84.18it/s]\n",
      "epoch 3: loss 1.14e-02 SER 3.54e-03: 100%|####################################################| 10000/10000 [01:58<00:00, 84.63it/s]\n",
      "epoch 4: loss 1.05e-02 SER 3.26e-03: 100%|####################################################| 10000/10000 [01:58<00:00, 84.48it/s]\n",
      "epoch 5: loss 9.93e-03 SER 3.04e-03: 100%|####################################################| 10000/10000 [01:59<00:00, 83.85it/s]\n",
      "epoch 6: loss 9.46e-03 SER 2.93e-03: 100%|####################################################| 10000/10000 [01:59<00:00, 83.56it/s]\n",
      "epoch 7: loss 9.25e-03 SER 2.86e-03: 100%|####################################################| 10000/10000 [01:58<00:00, 84.24it/s]\n",
      "epoch 8: loss 9.08e-03 SER 2.80e-03: 100%|####################################################| 10000/10000 [01:58<00:00, 84.73it/s]\n",
      "epoch 9: loss 8.96e-03 SER 2.79e-03: 100%|####################################################| 10000/10000 [01:59<00:00, 83.85it/s]\n",
      "epoch 10: loss 8.85e-03 SER 2.75e-03: 100%|###################################################| 10000/10000 [01:58<00:00, 84.13it/s]\n"
     ]
    }
   ],
   "source": [
    "channel = model_WGAN.Channel_GAN(encoder_GAN, decoder_GAN, G).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=10, noise_std = noise_std, batch_size=1000, M=M,\n",
    "                              learning_rate=1e-3, rate=rate)\n",
    "weights = filter(lambda p: p.requires_grad, list(encoder_GAN.parameters())+list(decoder_GAN.parameters()))\n",
    "t_gan = trainer.Trainer(channel, dataset, tconf, one_hot=True)\n",
    "t_gan.train(weights=weights, GAN=True, global_epoch=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ee8449d-c873-4610-a24e-b7055a989353",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 7.14e-03 SER 2.34e-03: 100%|###################################################| 10000/10000 [01:36<00:00, 103.83it/s]\n",
      "epoch 2: loss 6.40e-03 SER 2.10e-03: 100%|####################################################| 10000/10000 [01:47<00:00, 93.03it/s]\n",
      "epoch 3: loss 6.18e-03 SER 2.03e-03: 100%|###################################################| 10000/10000 [01:29<00:00, 111.75it/s]\n",
      "epoch 4: loss 6.07e-03 SER 1.99e-03: 100%|###################################################| 10000/10000 [01:21<00:00, 122.40it/s]\n",
      "epoch 5: loss 5.95e-03 SER 1.95e-03: 100%|###################################################| 10000/10000 [01:22<00:00, 121.84it/s]\n",
      "epoch 6: loss 5.93e-03 SER 1.94e-03: 100%|###################################################| 10000/10000 [01:24<00:00, 119.02it/s]\n",
      "epoch 7: loss 5.87e-03 SER 1.94e-03: 100%|####################################################| 10000/10000 [01:40<00:00, 99.02it/s]\n",
      "epoch 8: loss 5.84e-03 SER 1.92e-03: 100%|###################################################| 10000/10000 [01:22<00:00, 121.86it/s]\n",
      "epoch 9: loss 5.79e-03 SER 1.89e-03: 100%|###################################################| 10000/10000 [01:36<00:00, 103.37it/s]\n",
      "epoch 10: loss 5.91e-03 SER 1.92e-03: 100%|##################################################| 10000/10000 [01:35<00:00, 105.14it/s]\n",
      "epoch 11: loss 5.75e-03 SER 1.90e-03: 100%|##################################################| 10000/10000 [01:36<00:00, 104.12it/s]\n",
      "epoch 12: loss 5.83e-03 SER 1.91e-03: 100%|##################################################| 10000/10000 [01:32<00:00, 108.12it/s]\n",
      "epoch 13: loss 5.78e-03 SER 1.88e-03: 100%|###################################################| 10000/10000 [01:40<00:00, 99.12it/s]\n",
      "epoch 14: loss 5.74e-03 SER 1.90e-03: 100%|###################################################| 10000/10000 [01:43<00:00, 96.64it/s]\n",
      "epoch 15: loss 5.83e-03 SER 1.91e-03: 100%|##################################################| 10000/10000 [01:39<00:00, 100.45it/s]\n",
      "epoch 16: loss 5.71e-03 SER 1.88e-03: 100%|###################################################| 10000/10000 [01:54<00:00, 87.62it/s]\n",
      "epoch 17: loss 5.73e-03 SER 1.88e-03: 100%|###################################################| 10000/10000 [04:13<00:00, 39.48it/s]\n",
      "epoch 18: loss 5.78e-03 SER 1.90e-03: 100%|###################################################| 10000/10000 [02:41<00:00, 61.93it/s]\n",
      "epoch 19: loss 5.79e-03 SER 1.91e-03: 100%|###################################################| 10000/10000 [02:25<00:00, 68.67it/s]\n",
      "epoch 20: loss 5.75e-03 SER 1.87e-03: 100%|###################################################| 10000/10000 [02:26<00:00, 68.30it/s]\n"
     ]
    }
   ],
   "source": [
    "channel = model_WGAN.Channel_GAN(encoder_GAN, decoder_GAN, G).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=20, noise_std = noise_std, batch_size=1000, M=M,\n",
    "                              learning_rate=1e-3, rate=rate)\n",
    "weights = filter(lambda p: p.requires_grad, list(encoder_GAN.parameters())+list(decoder_GAN.parameters()))\n",
    "t_gan = trainer.Trainer(channel, dataset, tconf, one_hot=True)\n",
    "t_gan.train(weights=weights, GAN=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d7998f08-5e84-4f5f-95f4-e8ef4087f522",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 5.58e-03 SER 1.83e-03: 100%|####################################################| 10000/10000 [02:25<00:00, 68.81it/s]\n",
      "epoch 2: loss 5.46e-03 SER 1.78e-03: 100%|####################################################| 10000/10000 [02:09<00:00, 77.03it/s]\n",
      "epoch 3: loss 5.45e-03 SER 1.80e-03: 100%|####################################################| 10000/10000 [02:14<00:00, 74.49it/s]\n",
      "epoch 4: loss 5.53e-03 SER 1.81e-03: 100%|####################################################| 10000/10000 [02:11<00:00, 76.29it/s]\n",
      "epoch 5: loss 5.45e-03 SER 1.79e-03: 100%|####################################################| 10000/10000 [02:08<00:00, 77.66it/s]\n",
      "epoch 6: loss 5.46e-03 SER 1.78e-03: 100%|####################################################| 10000/10000 [01:59<00:00, 83.68it/s]\n",
      "epoch 7: loss 5.43e-03 SER 1.78e-03: 100%|####################################################| 10000/10000 [02:00<00:00, 82.96it/s]\n",
      "epoch 8: loss 5.43e-03 SER 1.78e-03: 100%|####################################################| 10000/10000 [01:59<00:00, 83.38it/s]\n",
      "epoch 9: loss 5.41e-03 SER 1.79e-03: 100%|####################################################| 10000/10000 [02:08<00:00, 77.99it/s]\n",
      "epoch 10: loss 5.44e-03 SER 1.80e-03: 100%|###################################################| 10000/10000 [02:12<00:00, 75.42it/s]\n",
      "epoch 11: loss 5.41e-03 SER 1.79e-03: 100%|###################################################| 10000/10000 [04:36<00:00, 36.22it/s]\n",
      "epoch 12: loss 5.48e-03 SER 1.81e-03: 100%|###################################################| 10000/10000 [02:18<00:00, 72.35it/s]\n",
      "epoch 13: loss 5.41e-03 SER 1.77e-03: 100%|###################################################| 10000/10000 [02:17<00:00, 72.72it/s]\n",
      "epoch 14: loss 5.46e-03 SER 1.79e-03: 100%|###################################################| 10000/10000 [02:18<00:00, 72.34it/s]\n",
      "epoch 15: loss 5.39e-03 SER 1.77e-03: 100%|###################################################| 10000/10000 [02:11<00:00, 75.93it/s]\n",
      "epoch 16: loss 5.46e-03 SER 1.78e-03: 100%|###################################################| 10000/10000 [02:14<00:00, 74.61it/s]\n",
      "epoch 17: loss 5.45e-03 SER 1.80e-03: 100%|###################################################| 10000/10000 [02:05<00:00, 79.83it/s]\n",
      "epoch 18: loss 5.46e-03 SER 1.79e-03: 100%|###################################################| 10000/10000 [02:11<00:00, 76.24it/s]\n",
      "epoch 19: loss 5.41e-03 SER 1.77e-03: 100%|###################################################| 10000/10000 [02:41<00:00, 62.01it/s]\n",
      "epoch 20: loss 5.50e-03 SER 1.80e-03: 100%|###################################################| 10000/10000 [02:12<00:00, 75.25it/s]\n"
     ]
    }
   ],
   "source": [
    "channel = model_WGAN.Channel_GAN(encoder_GAN, decoder_GAN, G).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=20, noise_std = noise_std, batch_size=1000, M=M,\n",
    "                              learning_rate=1e-4, rate=rate)\n",
    "weights = filter(lambda p: p.requires_grad, list(encoder_GAN.parameters())+list(decoder_GAN.parameters()))\n",
    "t_gan = trainer.Trainer(channel, dataset, tconf, one_hot=True)\n",
    "t_gan.train(weights=weights, GAN=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "12e35c96-035c-4c24-b3ac-8141e7aeaa06",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(encoder_GAN.state_dict(), 'state_dict_WGAN/enc_GAN_Rayleigh.pt')\n",
    "torch.save(decoder_GAN.state_dict(), 'state_dict_WGAN/dec_GAN_Rayleigh.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c0b37e47-c436-410e-aa26-fdb7468eb636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel.load_state_dict(torch.load(path, map_location=device))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc83fc0-eb93-4eef-a2bf-a80e9f726fed",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Training AE Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7db9a9d-916e-4934-980b-70420f64ce7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 6.58e-02 SER 1.73e-02: 100%|#| 10000/10000 [00:54<00:00, 181.94it/\n",
      "epoch 2: loss 1.59e-02 SER 5.43e-03: 100%|#| 10000/10000 [00:43<00:00, 228.88it/\n",
      "epoch 3: loss 1.13e-02 SER 3.85e-03: 100%|#| 10000/10000 [00:40<00:00, 247.22it/\n",
      "epoch 4: loss 1.04e-02 SER 3.55e-03: 100%|#| 10000/10000 [00:44<00:00, 226.42it/\n",
      "epoch 5: loss 9.77e-03 SER 3.34e-03: 100%|#| 10000/10000 [00:38<00:00, 262.92it/\n",
      "epoch 6: loss 8.78e-03 SER 2.99e-03: 100%|#| 10000/10000 [00:46<00:00, 217.20it/\n",
      "epoch 7: loss 8.06e-03 SER 2.76e-03: 100%|#| 10000/10000 [00:47<00:00, 210.98it/\n",
      "epoch 8: loss 7.82e-03 SER 2.68e-03: 100%|#| 10000/10000 [00:48<00:00, 206.83it/\n",
      "epoch 9: loss 7.57e-03 SER 2.58e-03: 100%|#| 10000/10000 [00:53<00:00, 185.20it/\n",
      "epoch 10: loss 7.43e-03 SER 2.54e-03: 100%|#| 10000/10000 [00:54<00:00, 184.72it\n"
     ]
    }
   ],
   "source": [
    "encoder_AE = models.Encoder_1h( M, n, n)\n",
    "decoder_AE = models.Decoder_1h( M, n)\n",
    "model_2 = model_WGAN.Channel_GAN(encoder_AE, decoder_AE, channel_ray).to(device)\n",
    "#model_2 = model_WGAN.Channel(encoder_AE, decoder_AE).to(device)\n",
    "\n",
    "tconf = trainer.TrainerConfig(max_epochs=10, noise_std = noise_std, batch_size=1000, M=M, rate=rate)\n",
    "weights = filter(lambda p: p.requires_grad, model_2.parameters())\n",
    "\n",
    "t_ae = trainer.Trainer(model_2, dataset, tconf, one_hot=True)\n",
    "t_ae.train(weights=weights, GAN=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee2ef17c-9a56-44be-a02a-3aa6badba71f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 6.75e-03 SER 2.32e-03: 100%|###################################################| 10000/10000 [01:07<00:00, 147.60it/s]\n",
      "epoch 2: loss 6.79e-03 SER 2.32e-03: 100%|###################################################| 10000/10000 [01:09<00:00, 144.45it/s]\n",
      "epoch 3: loss 6.81e-03 SER 2.32e-03: 100%|###################################################| 10000/10000 [01:09<00:00, 144.21it/s]\n",
      "epoch 4: loss 6.78e-03 SER 2.30e-03: 100%|###################################################| 10000/10000 [01:06<00:00, 150.94it/s]\n",
      "epoch 5: loss 6.71e-03 SER 2.29e-03: 100%|###################################################| 10000/10000 [01:09<00:00, 143.83it/s]\n",
      "epoch 6: loss 6.75e-03 SER 2.30e-03: 100%|###################################################| 10000/10000 [01:08<00:00, 145.41it/s]\n",
      "epoch 7: loss 6.73e-03 SER 2.30e-03: 100%|###################################################| 10000/10000 [01:06<00:00, 150.65it/s]\n",
      "epoch 8: loss 6.76e-03 SER 2.31e-03: 100%|###################################################| 10000/10000 [01:08<00:00, 145.57it/s]\n",
      "epoch 9: loss 6.65e-03 SER 2.27e-03: 100%|###################################################| 10000/10000 [01:09<00:00, 143.85it/s]\n",
      "epoch 10: loss 6.73e-03 SER 2.30e-03: 100%|##################################################| 10000/10000 [01:06<00:00, 150.95it/s]\n",
      "epoch 11: loss 6.62e-03 SER 2.27e-03: 100%|##################################################| 10000/10000 [01:08<00:00, 146.15it/s]\n",
      "epoch 12: loss 6.65e-03 SER 2.28e-03: 100%|##################################################| 10000/10000 [01:08<00:00, 145.91it/s]\n",
      "epoch 13: loss 6.66e-03 SER 2.26e-03: 100%|##################################################| 10000/10000 [01:07<00:00, 148.87it/s]\n",
      "epoch 14: loss 6.63e-03 SER 2.27e-03: 100%|##################################################| 10000/10000 [01:08<00:00, 146.66it/s]\n",
      "epoch 15: loss 6.60e-03 SER 2.25e-03: 100%|##################################################| 10000/10000 [01:08<00:00, 146.43it/s]\n",
      "epoch 16: loss 6.66e-03 SER 2.28e-03: 100%|##################################################| 10000/10000 [01:06<00:00, 149.92it/s]\n",
      "epoch 17: loss 6.58e-03 SER 2.24e-03: 100%|##################################################| 10000/10000 [01:08<00:00, 146.30it/s]\n",
      "epoch 18: loss 6.74e-03 SER 2.30e-03: 100%|##################################################| 10000/10000 [01:08<00:00, 145.35it/s]\n",
      "epoch 19: loss 6.57e-03 SER 2.24e-03: 100%|##################################################| 10000/10000 [01:07<00:00, 148.38it/s]\n",
      "epoch 20: loss 6.61e-03 SER 2.26e-03: 100%|##################################################| 10000/10000 [01:07<00:00, 148.35it/s]\n"
     ]
    }
   ],
   "source": [
    "model_2 = model_WGAN.Channel_GAN(encoder_AE, decoder_AE, channel_ray).to(device)\n",
    "\n",
    "tconf = trainer.TrainerConfig(max_epochs=20, noise_std = noise_std, batch_size=1000, \n",
    "                              learning_rate=1e-4, M=M, rate=rate)\n",
    "weights = filter(lambda p: p.requires_grad, model_2.parameters())\n",
    "\n",
    "t_ae = trainer.Trainer(model_2, dataset, tconf, one_hot=True)\n",
    "t_ae.train(weights=weights, GAN=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2fb49e1a-eb87-40be-97ac-67f32d451737",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch 1: loss 6.60e-03 SER 2.25e-03: 100%|###################################################| 10000/10000 [01:19<00:00, 125.81it/s]\n",
      "epoch 2: loss 6.64e-03 SER 2.27e-03: 100%|###################################################| 10000/10000 [01:25<00:00, 116.79it/s]\n",
      "epoch 3: loss 6.69e-03 SER 2.28e-03: 100%|###################################################| 10000/10000 [01:17<00:00, 129.85it/s]\n",
      "epoch 4: loss 6.55e-03 SER 2.23e-03: 100%|###################################################| 10000/10000 [01:18<00:00, 127.51it/s]\n",
      "epoch 5: loss 6.71e-03 SER 2.28e-03: 100%|###################################################| 10000/10000 [01:20<00:00, 124.87it/s]\n",
      "epoch 6: loss 6.57e-03 SER 2.26e-03: 100%|###################################################| 10000/10000 [01:17<00:00, 128.55it/s]\n",
      "epoch 7: loss 6.65e-03 SER 2.27e-03: 100%|###################################################| 10000/10000 [01:19<00:00, 125.89it/s]\n",
      "epoch 8: loss 6.54e-03 SER 2.24e-03: 100%|###################################################| 10000/10000 [01:28<00:00, 113.30it/s]\n",
      "epoch 9: loss 6.59e-03 SER 2.25e-03: 100%|###################################################| 10000/10000 [01:24<00:00, 118.31it/s]\n",
      "epoch 10: loss 6.62e-03 SER 2.26e-03: 100%|##################################################| 10000/10000 [01:20<00:00, 124.66it/s]\n",
      "epoch 11: loss 6.50e-03 SER 2.22e-03: 100%|##################################################| 10000/10000 [01:13<00:00, 136.19it/s]\n",
      "epoch 12: loss 6.55e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:11<00:00, 140.51it/s]\n",
      "epoch 13: loss 6.63e-03 SER 2.25e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 141.07it/s]\n",
      "epoch 14: loss 6.54e-03 SER 2.24e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.83it/s]\n",
      "epoch 15: loss 6.58e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.08it/s]\n",
      "epoch 16: loss 6.62e-03 SER 2.26e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 141.17it/s]\n",
      "epoch 17: loss 6.53e-03 SER 2.22e-03: 100%|##################################################| 10000/10000 [01:09<00:00, 143.76it/s]\n",
      "epoch 18: loss 6.56e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 141.46it/s]\n",
      "epoch 19: loss 6.51e-03 SER 2.22e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.86it/s]\n",
      "epoch 20: loss 6.56e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.36it/s]\n",
      "epoch 21: loss 6.49e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:11<00:00, 139.52it/s]\n",
      "epoch 22: loss 6.55e-03 SER 2.24e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 141.43it/s]\n",
      "epoch 23: loss 6.55e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:09<00:00, 143.08it/s]\n",
      "epoch 24: loss 6.53e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:09<00:00, 143.19it/s]\n",
      "epoch 25: loss 6.58e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.80it/s]\n",
      "epoch 26: loss 6.53e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 141.46it/s]\n",
      "epoch 27: loss 6.54e-03 SER 2.22e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.76it/s]\n",
      "epoch 28: loss 6.58e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 140.85it/s]\n",
      "epoch 29: loss 6.49e-03 SER 2.20e-03: 100%|##################################################| 10000/10000 [01:09<00:00, 143.51it/s]\n",
      "epoch 30: loss 6.49e-03 SER 2.21e-03: 100%|##################################################| 10000/10000 [01:09<00:00, 143.29it/s]\n",
      "epoch 31: loss 6.55e-03 SER 2.24e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.76it/s]\n",
      "epoch 32: loss 6.45e-03 SER 2.22e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 142.68it/s]\n",
      "epoch 33: loss 6.57e-03 SER 2.26e-03: 100%|##################################################| 10000/10000 [01:09<00:00, 143.44it/s]\n",
      "epoch 34: loss 6.50e-03 SER 2.22e-03: 100%|##################################################| 10000/10000 [01:11<00:00, 140.70it/s]\n",
      "epoch 35: loss 6.53e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:12<00:00, 138.58it/s]\n",
      "epoch 36: loss 6.48e-03 SER 2.20e-03: 100%|##################################################| 10000/10000 [01:09<00:00, 142.90it/s]\n",
      "epoch 37: loss 6.53e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 141.78it/s]\n",
      "epoch 38: loss 6.43e-03 SER 2.20e-03: 100%|##################################################| 10000/10000 [01:12<00:00, 138.82it/s]\n",
      "epoch 39: loss 6.53e-03 SER 2.23e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 141.79it/s]\n",
      "epoch 40: loss 6.47e-03 SER 2.22e-03: 100%|##################################################| 10000/10000 [01:10<00:00, 140.88it/s]\n"
     ]
    }
   ],
   "source": [
    "model_2 = model_WGAN.Channel_GAN(encoder_AE, decoder_AE, channel_ray).to(device)\n",
    "\n",
    "tconf = trainer.TrainerConfig(max_epochs=40, noise_std = noise_std, batch_size=1000, \n",
    "                              learning_rate=1e-4, M=M, rate=rate)\n",
    "weights = filter(lambda p: p.requires_grad, model_2.parameters())\n",
    "\n",
    "t_ae = trainer.Trainer(model_2, dataset, tconf, one_hot=True)\n",
    "t_ae.train(weights=weights, GAN=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b447c298-6d74-42f5-9ce0-17e6fbf33088",
   "metadata": {},
   "source": [
    "# Testing AE & GAN Encoder under real channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18846fc9-872d-4803-857b-e0bd77ab0014",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.random.randint(0,M, size=10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e9ea3ec-2a53-443c-bd8d-4f0bbdd8da32",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 1  iter 9900: SER 3.285e-01: 100%|###| 10000/10000 [00:17<00:00, 564.72it/s]\n",
      "SNR 2  iter 9900: SER 2.684e-01: 100%|###| 10000/10000 [00:17<00:00, 564.73it/s]\n",
      "SNR 3  iter 9900: SER 2.124e-01: 100%|###| 10000/10000 [00:17<00:00, 569.11it/s]\n",
      "SNR 4  iter 9900: SER 1.631e-01: 100%|###| 10000/10000 [00:15<00:00, 643.08it/s]\n",
      "SNR 5  iter 9900: SER 1.211e-01: 100%|###| 10000/10000 [00:16<00:00, 622.13it/s]\n",
      "SNR 6  iter 9900: SER 8.732e-02: 100%|###| 10000/10000 [00:17<00:00, 582.55it/s]\n",
      "SNR 7  iter 9900: SER 6.075e-02: 100%|###| 10000/10000 [00:17<00:00, 580.50it/s]\n",
      "SNR 8  iter 9900: SER 4.121e-02: 100%|###| 10000/10000 [00:17<00:00, 579.42it/s]\n",
      "SNR 9  iter 9900: SER 2.723e-02: 100%|###| 10000/10000 [00:17<00:00, 579.62it/s]\n",
      "SNR 10  iter 9900: SER 1.767e-02: 100%|##| 10000/10000 [00:17<00:00, 579.73it/s]\n",
      "SNR 11  iter 9900: SER 1.131e-02: 100%|##| 10000/10000 [00:17<00:00, 580.64it/s]\n",
      "SNR 12  iter 9900: SER 7.159e-03: 100%|##| 10000/10000 [00:17<00:00, 582.57it/s]\n",
      "SNR 13  iter 9900: SER 4.537e-03: 100%|##| 10000/10000 [00:17<00:00, 563.65it/s]\n",
      "SNR 14  iter 9900: SER 2.877e-03: 100%|##| 10000/10000 [00:13<00:00, 744.05it/s]\n",
      "SNR 15  iter 9900: SER 1.859e-03: 100%|##| 10000/10000 [00:13<00:00, 752.08it/s]\n",
      "SNR 16  iter 9900: SER 1.238e-03: 100%|##| 10000/10000 [00:12<00:00, 770.67it/s]\n",
      "SNR 17  iter 9900: SER 8.608e-04: 100%|##| 10000/10000 [00:12<00:00, 790.37it/s]\n",
      "SNR 18  iter 9900: SER 5.965e-04: 100%|##| 10000/10000 [00:16<00:00, 611.58it/s]\n",
      "SNR 19  iter 9900: SER 4.570e-04: 100%|##| 10000/10000 [00:17<00:00, 583.30it/s]\n",
      "SNR 19  iter 9900: SER 4.509e-04: 100%|##| 10000/10000 [00:17<00:00, 579.98it/s]\n",
      "SNR 20  iter 9900: SER 3.328e-04: 100%|##| 10000/10000 [00:17<00:00, 575.11it/s]\n",
      "SNR 20  iter 9900: SER 3.266e-04: 100%|##| 10000/10000 [00:17<00:00, 575.79it/s]\n",
      "SNR 21  iter 9900: SER 2.739e-04: 100%|##| 10000/10000 [00:17<00:00, 585.36it/s]\n",
      "SNR 21  iter 9900: SER 2.603e-04: 100%|##| 10000/10000 [00:18<00:00, 531.61it/s]\n",
      "SNR 22  iter 9900: SER 2.158e-04: 100%|##| 10000/10000 [00:19<00:00, 519.06it/s]\n",
      "SNR 22  iter 9900: SER 2.260e-04: 100%|##| 10000/10000 [00:17<00:00, 581.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.227e-04: 100%|##| 10000/10000 [00:17<00:00, 580.18it/s]\n",
      "SNR 23  iter 9900: SER 1.878e-04: 100%|##| 10000/10000 [00:17<00:00, 563.19it/s]\n",
      "SNR 23  iter 9900: SER 1.856e-04: 100%|##| 10000/10000 [00:17<00:00, 572.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.903e-04: 100%|##| 10000/10000 [00:17<00:00, 573.07it/s]\n",
      "SNR 24  iter 9900: SER 1.645e-04: 100%|##| 10000/10000 [00:17<00:00, 583.72it/s]\n",
      "SNR 24  iter 9900: SER 1.776e-04: 100%|##| 10000/10000 [00:17<00:00, 574.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.560e-04: 100%|##| 10000/10000 [00:16<00:00, 621.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.610e-04: 100%|##| 10000/10000 [00:13<00:00, 727.58it/s]\n",
      "SNR 25  iter 9900: SER 1.516e-04: 100%|##| 10000/10000 [00:15<00:00, 666.36it/s]\n",
      "SNR 25  iter 9900: SER 1.444e-04: 100%|##| 10000/10000 [00:14<00:00, 673.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 2800: SER 1.432e-04:  29%|8  | 2872/10000 [00:04<00:10, 687.31it/s]"
     ]
    }
   ],
   "source": [
    "bler_testGAN =[]\n",
    "#ebno_range = [4,5,6,7,8]\n",
    "ebno_range = [*range(1,25+1)] # [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]\n",
    " \n",
    "model_testGAN = model_WGAN.Channel_ray(encoder_GAN, decoder_GAN).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=5, noise_std = noise_std, batch_size=1000, M=M, rate=rate)\n",
    "\n",
    "t_testGAN = trainer.Trainer(model_testGAN, dataset, tconf, one_hot=True)\n",
    "\n",
    "\n",
    "bler_testGAN = t_testGAN.test(snr_range=ebno_range, one_hot=True, erasure_bound=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "942adfb0-6280-4ae8-8072-41b5bbeec211",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 1  iter 9900: SER 3.285e-01: 100%|###| 10000/10000 [00:30<00:00, 328.21it/s]\n",
      "SNR 2  iter 9900: SER 2.682e-01: 100%|###| 10000/10000 [00:34<00:00, 290.33it/s]\n",
      "SNR 3  iter 9900: SER 2.126e-01: 100%|###| 10000/10000 [00:36<00:00, 274.49it/s]\n",
      "SNR 4  iter 9900: SER 1.630e-01: 100%|###| 10000/10000 [00:32<00:00, 305.57it/s]\n",
      "SNR 5  iter 9900: SER 1.211e-01: 100%|###| 10000/10000 [00:31<00:00, 314.87it/s]\n",
      "SNR 6  iter 9900: SER 8.717e-02: 100%|###| 10000/10000 [00:36<00:00, 276.15it/s]\n",
      "SNR 7  iter 9900: SER 6.079e-02: 100%|###| 10000/10000 [00:35<00:00, 280.68it/s]\n",
      "SNR 8  iter 9900: SER 4.121e-02: 100%|###| 10000/10000 [00:35<00:00, 282.70it/s]\n",
      "SNR 9  iter 9900: SER 2.722e-02: 100%|###| 10000/10000 [00:35<00:00, 278.86it/s]\n",
      "SNR 10  iter 9900: SER 1.773e-02: 100%|##| 10000/10000 [00:35<00:00, 283.51it/s]\n",
      "SNR 11  iter 9900: SER 1.129e-02: 100%|##| 10000/10000 [00:35<00:00, 281.19it/s]\n",
      "SNR 12  iter 9900: SER 7.195e-03: 100%|##| 10000/10000 [00:35<00:00, 281.90it/s]\n",
      "SNR 13  iter 9900: SER 4.578e-03: 100%|##| 10000/10000 [00:35<00:00, 280.23it/s]\n",
      "SNR 14  iter 9900: SER 2.911e-03: 100%|##| 10000/10000 [00:35<00:00, 282.06it/s]\n",
      "SNR 15  iter 9900: SER 1.872e-03: 100%|##| 10000/10000 [00:35<00:00, 279.83it/s]\n",
      "SNR 16  iter 9900: SER 1.243e-03: 100%|##| 10000/10000 [00:35<00:00, 281.84it/s]\n",
      "SNR 17  iter 9900: SER 8.464e-04: 100%|##| 10000/10000 [00:35<00:00, 281.42it/s]\n",
      "SNR 18  iter 9900: SER 5.958e-04: 100%|##| 10000/10000 [00:35<00:00, 283.63it/s]\n",
      "SNR 19  iter 9900: SER 4.339e-04: 100%|##| 10000/10000 [00:35<00:00, 281.91it/s]\n",
      "SNR 19  iter 9900: SER 4.481e-04: 100%|##| 10000/10000 [00:35<00:00, 281.83it/s]\n",
      "SNR 20  iter 9900: SER 3.296e-04: 100%|##| 10000/10000 [00:35<00:00, 279.37it/s]\n",
      "SNR 20  iter 9900: SER 3.407e-04: 100%|##| 10000/10000 [00:35<00:00, 284.76it/s]\n",
      "SNR 21  iter 9900: SER 2.715e-04: 100%|##| 10000/10000 [00:35<00:00, 282.30it/s]\n",
      "SNR 21  iter 9900: SER 2.735e-04: 100%|##| 10000/10000 [00:35<00:00, 279.78it/s]\n",
      "SNR 22  iter 9900: SER 2.249e-04: 100%|##| 10000/10000 [00:35<00:00, 280.67it/s]\n",
      "SNR 22  iter 9900: SER 2.249e-04: 100%|##| 10000/10000 [00:35<00:00, 281.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.193e-04: 100%|##| 10000/10000 [00:35<00:00, 282.69it/s]\n",
      "SNR 23  iter 9900: SER 1.861e-04: 100%|##| 10000/10000 [00:35<00:00, 280.42it/s]\n",
      "SNR 23  iter 9900: SER 1.870e-04: 100%|##| 10000/10000 [00:35<00:00, 284.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.791e-04: 100%|##| 10000/10000 [00:35<00:00, 282.11it/s]\n",
      "SNR 24  iter 9900: SER 1.670e-04: 100%|##| 10000/10000 [00:35<00:00, 282.32it/s]\n",
      "SNR 24  iter 9900: SER 1.646e-04: 100%|##| 10000/10000 [00:35<00:00, 280.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.654e-04: 100%|##| 10000/10000 [00:35<00:00, 281.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.640e-04: 100%|##| 10000/10000 [00:35<00:00, 282.47it/s]\n",
      "SNR 25  iter 9900: SER 1.519e-04: 100%|##| 10000/10000 [00:35<00:00, 282.13it/s]\n",
      "SNR 25  iter 9900: SER 1.436e-04: 100%|##| 10000/10000 [00:35<00:00, 279.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.540e-04: 100%|##| 10000/10000 [00:35<00:00, 282.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.492e-04: 100%|##| 10000/10000 [00:35<00:00, 281.90it/s]\n"
     ]
    }
   ],
   "source": [
    "bler_testGAN =[]\n",
    "#ebno_range = [4,5,6,7,8]\n",
    "ebno_range = [*range(1,25+1)] # [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]\n",
    " \n",
    "model_testGAN = model_WGAN.Channel_ray(encoder_GAN, decoder_GAN).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=5, noise_std = noise_std, batch_size=1000, M=M, rate=rate)\n",
    "\n",
    "t_testGAN = trainer.Trainer(model_testGAN, dataset, tconf, one_hot=True)\n",
    "\n",
    "\n",
    "bler_testGAN = t_testGAN.test(snr_range=ebno_range, one_hot=True, erasure_bound=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c9e54194-cf6f-4953-879f-eda61fc58639",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 1  iter 9900: SER 2.826e-01: 100%|###| 10000/10000 [00:17<00:00, 556.09it/s]\n",
      "SNR 2  iter 9900: SER 2.217e-01: 100%|###| 10000/10000 [00:17<00:00, 572.01it/s]\n",
      "SNR 3  iter 9900: SER 1.679e-01: 100%|###| 10000/10000 [00:17<00:00, 555.78it/s]\n",
      "SNR 4  iter 9900: SER 1.218e-01: 100%|###| 10000/10000 [00:17<00:00, 573.18it/s]\n",
      "SNR 5  iter 9900: SER 8.475e-02: 100%|###| 10000/10000 [00:17<00:00, 564.50it/s]\n",
      "SNR 6  iter 9900: SER 5.656e-02: 100%|###| 10000/10000 [00:17<00:00, 570.75it/s]\n",
      "SNR 7  iter 9900: SER 3.628e-02: 100%|###| 10000/10000 [00:21<00:00, 461.79it/s]\n",
      "SNR 8  iter 9900: SER 2.242e-02: 100%|###| 10000/10000 [00:21<00:00, 465.29it/s]\n",
      "SNR 9  iter 9900: SER 1.344e-02: 100%|###| 10000/10000 [00:21<00:00, 460.13it/s]\n",
      "SNR 10  iter 9900: SER 7.736e-03: 100%|##| 10000/10000 [00:21<00:00, 461.46it/s]\n",
      "SNR 11  iter 9900: SER 4.392e-03: 100%|##| 10000/10000 [00:21<00:00, 466.12it/s]\n",
      "SNR 12  iter 9900: SER 2.450e-03: 100%|##| 10000/10000 [00:21<00:00, 463.51it/s]\n",
      "SNR 13  iter 9900: SER 1.360e-03: 100%|##| 10000/10000 [00:22<00:00, 446.34it/s]\n",
      "SNR 14  iter 9900: SER 7.580e-04: 100%|##| 10000/10000 [00:25<00:00, 384.65it/s]\n",
      "SNR 15  iter 9900: SER 4.162e-04: 100%|##| 10000/10000 [00:24<00:00, 402.82it/s]\n",
      "SNR 15  iter 9900: SER 4.148e-04: 100%|##| 10000/10000 [00:23<00:00, 421.80it/s]\n",
      "SNR 16  iter 9900: SER 2.384e-04: 100%|##| 10000/10000 [00:22<00:00, 441.65it/s]\n",
      "SNR 16  iter 9900: SER 2.352e-04: 100%|##| 10000/10000 [00:22<00:00, 439.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 16  iter 9900: SER 2.408e-04: 100%|##| 10000/10000 [00:22<00:00, 439.60it/s]\n",
      "SNR 17  iter 9900: SER 1.416e-04: 100%|##| 10000/10000 [00:22<00:00, 436.75it/s]\n",
      "SNR 17  iter 9900: SER 1.354e-04: 100%|##| 10000/10000 [00:22<00:00, 437.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 17  iter 9900: SER 1.371e-04: 100%|##| 10000/10000 [00:19<00:00, 507.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 17  iter 9900: SER 1.432e-04: 100%|##| 10000/10000 [00:19<00:00, 512.64it/s]\n",
      "SNR 18  iter 9900: SER 8.343e-05: 100%|##| 10000/10000 [00:19<00:00, 506.54it/s]\n",
      "SNR 18  iter 9900: SER 8.444e-05: 100%|##| 10000/10000 [00:19<00:00, 517.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 18  iter 9900: SER 8.676e-05: 100%|##| 10000/10000 [00:19<00:00, 513.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 18  iter 9900: SER 8.040e-05: 100%|##| 10000/10000 [00:19<00:00, 516.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 18  iter 9900: SER 8.080e-05: 100%|##| 10000/10000 [00:19<00:00, 520.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 18  iter 9900: SER 8.181e-05: 100%|##| 10000/10000 [00:19<00:00, 506.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 18  iter 9900: SER 8.736e-05: 100%|##| 10000/10000 [00:19<00:00, 522.91it/s]\n",
      "SNR 19  iter 9900: SER 5.242e-05: 100%|##| 10000/10000 [00:19<00:00, 518.30it/s]\n",
      "SNR 19  iter 9900: SER 5.807e-05: 100%|##| 10000/10000 [00:19<00:00, 520.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 5.262e-05: 100%|##| 10000/10000 [00:19<00:00, 521.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 5.111e-05: 100%|##| 10000/10000 [00:19<00:00, 517.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 5.484e-05: 100%|##| 10000/10000 [00:19<00:00, 519.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 5.262e-05: 100%|##| 10000/10000 [00:19<00:00, 518.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 5.222e-05: 100%|##| 10000/10000 [00:19<00:00, 519.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 5.242e-05: 100%|##| 10000/10000 [00:19<00:00, 521.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 4.949e-05: 100%|##| 10000/10000 [00:19<00:00, 518.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 19  iter 9900: SER 5.100e-05: 100%|##| 10000/10000 [00:19<00:00, 520.98it/s]\n",
      "SNR 20  iter 9900: SER 3.757e-05: 100%|##| 10000/10000 [00:19<00:00, 521.62it/s]\n",
      "SNR 20  iter 9900: SER 3.717e-05: 100%|##| 10000/10000 [00:19<00:00, 518.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.373e-05: 100%|##| 10000/10000 [00:19<00:00, 522.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.777e-05: 100%|##| 10000/10000 [00:19<00:00, 519.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.293e-05: 100%|##| 10000/10000 [00:19<00:00, 521.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.555e-05: 100%|##| 10000/10000 [00:19<00:00, 520.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.636e-05: 100%|##| 10000/10000 [00:19<00:00, 518.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.697e-05: 100%|##| 10000/10000 [00:19<00:00, 523.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.252e-05: 100%|##| 10000/10000 [00:19<00:00, 518.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.454e-05: 100%|##| 10000/10000 [00:19<00:00, 520.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  100000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.545e-05: 100%|##| 10000/10000 [00:19<00:00, 519.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  110000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.798e-05: 100%|##| 10000/10000 [00:19<00:00, 515.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  120000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.333e-05: 100%|##| 10000/10000 [00:19<00:00, 522.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  130000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.484e-05: 100%|##| 10000/10000 [00:19<00:00, 516.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  140000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 20  iter 9900: SER 3.262e-05: 100%|##| 10000/10000 [00:19<00:00, 513.64it/s]\n",
      "SNR 21  iter 9900: SER 2.586e-05: 100%|##| 10000/10000 [00:19<00:00, 519.37it/s]\n",
      "SNR 21  iter 9900: SER 2.262e-05: 100%|##| 10000/10000 [00:19<00:00, 514.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.505e-05: 100%|##| 10000/10000 [00:19<00:00, 516.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.757e-05: 100%|##| 10000/10000 [00:19<00:00, 517.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.575e-05: 100%|##| 10000/10000 [00:19<00:00, 512.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.353e-05: 100%|##| 10000/10000 [00:19<00:00, 523.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.626e-05: 100%|##| 10000/10000 [00:19<00:00, 515.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.586e-05: 100%|##| 10000/10000 [00:19<00:00, 517.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.505e-05: 100%|##| 10000/10000 [00:19<00:00, 516.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.525e-05: 100%|##| 10000/10000 [00:19<00:00, 517.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  100000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.192e-05: 100%|##| 10000/10000 [00:19<00:00, 517.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  110000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 3.131e-05: 100%|##| 10000/10000 [00:19<00:00, 517.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  120000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.848e-05: 100%|##| 10000/10000 [00:19<00:00, 516.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  130000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.495e-05: 100%|##| 10000/10000 [00:19<00:00, 516.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  140000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.747e-05: 100%|##| 10000/10000 [00:19<00:00, 514.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  150000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.222e-05: 100%|##| 10000/10000 [00:19<00:00, 516.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  160000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.555e-05: 100%|##| 10000/10000 [00:19<00:00, 515.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  170000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.737e-05: 100%|##| 10000/10000 [00:19<00:00, 517.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  180000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.515e-05: 100%|##| 10000/10000 [00:19<00:00, 518.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  190000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 21  iter 9900: SER 2.414e-05: 100%|##| 10000/10000 [00:19<00:00, 514.84it/s]\n",
      "SNR 22  iter 9900: SER 2.070e-05: 100%|##| 10000/10000 [00:19<00:00, 515.99it/s]\n",
      "SNR 22  iter 9900: SER 1.757e-05: 100%|##| 10000/10000 [00:19<00:00, 514.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.727e-05: 100%|##| 10000/10000 [00:19<00:00, 516.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.040e-05: 100%|##| 10000/10000 [00:19<00:00, 515.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.757e-05: 100%|##| 10000/10000 [00:19<00:00, 514.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.101e-05: 100%|##| 10000/10000 [00:19<00:00, 519.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.060e-05: 100%|##| 10000/10000 [00:19<00:00, 514.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.697e-05: 100%|##| 10000/10000 [00:19<00:00, 516.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.010e-05: 100%|##| 10000/10000 [00:19<00:00, 511.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.980e-05: 100%|##| 10000/10000 [00:19<00:00, 513.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  100000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.757e-05: 100%|##| 10000/10000 [00:19<00:00, 512.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  110000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.767e-05: 100%|##| 10000/10000 [00:19<00:00, 516.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  120000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.980e-05: 100%|##| 10000/10000 [00:19<00:00, 511.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  130000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.040e-05: 100%|##| 10000/10000 [00:19<00:00, 514.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  140000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.808e-05: 100%|##| 10000/10000 [00:19<00:00, 509.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  150000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.666e-05: 100%|##| 10000/10000 [00:19<00:00, 517.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  160000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.980e-05: 100%|##| 10000/10000 [00:19<00:00, 513.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  170000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.646e-05: 100%|##| 10000/10000 [00:19<00:00, 512.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  180000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.889e-05: 100%|##| 10000/10000 [00:19<00:00, 525.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  190000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.848e-05: 100%|##| 10000/10000 [00:17<00:00, 584.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  200000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.767e-05: 100%|##| 10000/10000 [00:18<00:00, 553.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  210000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 2.081e-05: 100%|##| 10000/10000 [00:20<00:00, 485.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  220000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.959e-05: 100%|##| 10000/10000 [00:20<00:00, 482.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  230000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.990e-05: 100%|##| 10000/10000 [00:20<00:00, 490.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  240000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.707e-05: 100%|##| 10000/10000 [00:20<00:00, 487.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  250000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.798e-05: 100%|##| 10000/10000 [00:20<00:00, 496.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  260000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 22  iter 9900: SER 1.828e-05: 100%|##| 10000/10000 [00:23<00:00, 420.40it/s]\n",
      "SNR 23  iter 9900: SER 1.525e-05: 100%|##| 10000/10000 [00:24<00:00, 408.37it/s]\n",
      "SNR 23  iter 9900: SER 1.434e-05: 100%|##| 10000/10000 [00:23<00:00, 417.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.485e-05: 100%|##| 10000/10000 [00:23<00:00, 423.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.606e-05: 100%|##| 10000/10000 [00:23<00:00, 420.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.212e-05: 100%|##| 10000/10000 [00:24<00:00, 414.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.404e-05: 100%|##| 10000/10000 [00:23<00:00, 422.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.555e-05: 100%|##| 10000/10000 [00:23<00:00, 417.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.677e-05: 100%|##| 10000/10000 [00:23<00:00, 421.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.313e-05: 100%|##| 10000/10000 [00:23<00:00, 420.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.273e-05: 100%|##| 10000/10000 [00:23<00:00, 419.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  100000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.182e-05: 100%|##| 10000/10000 [00:21<00:00, 473.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  110000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.414e-05: 100%|##| 10000/10000 [00:19<00:00, 507.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  120000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.333e-05: 100%|##| 10000/10000 [00:19<00:00, 502.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  130000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.353e-05: 100%|##| 10000/10000 [00:19<00:00, 501.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  140000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.353e-05: 100%|##| 10000/10000 [00:19<00:00, 505.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  150000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.414e-05: 100%|##| 10000/10000 [00:19<00:00, 510.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  160000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.323e-05: 100%|##| 10000/10000 [00:19<00:00, 511.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  170000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.182e-05: 100%|##| 10000/10000 [00:19<00:00, 510.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  180000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.363e-05: 100%|##| 10000/10000 [00:19<00:00, 509.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  190000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.222e-05: 100%|##| 10000/10000 [00:19<00:00, 505.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  200000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.535e-05: 100%|##| 10000/10000 [00:19<00:00, 510.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  210000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.343e-05: 100%|##| 10000/10000 [00:19<00:00, 508.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  220000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.525e-05: 100%|##| 10000/10000 [00:19<00:00, 510.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  230000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.212e-05: 100%|##| 10000/10000 [00:20<00:00, 499.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  240000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.273e-05: 100%|##| 10000/10000 [00:20<00:00, 486.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  250000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.485e-05: 100%|##| 10000/10000 [00:20<00:00, 494.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  260000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.616e-05: 100%|##| 10000/10000 [00:22<00:00, 444.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  270000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.515e-05: 100%|##| 10000/10000 [00:24<00:00, 412.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  280000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.343e-05: 100%|##| 10000/10000 [00:24<00:00, 414.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  290000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.545e-05: 100%|##| 10000/10000 [00:24<00:00, 416.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  300000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.475e-05: 100%|##| 10000/10000 [00:23<00:00, 421.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  310000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.525e-05: 100%|##| 10000/10000 [00:23<00:00, 417.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  320000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.374e-05: 100%|##| 10000/10000 [00:23<00:00, 417.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  330000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.495e-05: 100%|##| 10000/10000 [00:23<00:00, 421.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  340000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.414e-05: 100%|##| 10000/10000 [00:23<00:00, 417.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  350000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 23  iter 9900: SER 1.353e-05: 100%|##| 10000/10000 [00:23<00:00, 419.44it/s]\n",
      "SNR 24  iter 9900: SER 1.293e-05: 100%|##| 10000/10000 [00:23<00:00, 416.92it/s]\n",
      "SNR 24  iter 9900: SER 1.161e-05: 100%|##| 10000/10000 [00:24<00:00, 414.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.081e-05: 100%|##| 10000/10000 [00:20<00:00, 478.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 9.090e-06: 100%|##| 10000/10000 [00:19<00:00, 507.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.172e-05: 100%|##| 10000/10000 [00:19<00:00, 500.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.313e-05: 100%|##| 10000/10000 [00:19<00:00, 510.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 9.898e-06: 100%|##| 10000/10000 [00:19<00:00, 513.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.182e-05: 100%|##| 10000/10000 [00:19<00:00, 513.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.212e-05: 100%|##| 10000/10000 [00:19<00:00, 512.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.091e-05: 100%|##| 10000/10000 [00:19<00:00, 509.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  100000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.232e-05: 100%|##| 10000/10000 [00:19<00:00, 512.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  110000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.323e-05: 100%|##| 10000/10000 [00:19<00:00, 509.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  120000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.303e-05: 100%|##| 10000/10000 [00:19<00:00, 512.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  130000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.202e-05: 100%|##| 10000/10000 [00:19<00:00, 511.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  140000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.384e-05: 100%|##| 10000/10000 [00:19<00:00, 513.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  150000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.192e-05: 100%|##| 10000/10000 [00:19<00:00, 508.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  160000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.030e-05: 100%|##| 10000/10000 [00:19<00:00, 514.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  170000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 9.393e-06: 100%|##| 10000/10000 [00:19<00:00, 506.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  180000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.212e-05: 100%|##| 10000/10000 [00:19<00:00, 506.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  190000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.141e-05: 100%|##| 10000/10000 [00:19<00:00, 513.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  200000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.111e-05: 100%|##| 10000/10000 [00:19<00:00, 514.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  210000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.050e-05: 100%|##| 10000/10000 [00:19<00:00, 509.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  220000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.192e-05: 100%|##| 10000/10000 [00:19<00:00, 510.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  230000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.343e-05: 100%|##| 10000/10000 [00:19<00:00, 514.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  240000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.141e-05: 100%|##| 10000/10000 [00:19<00:00, 510.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  250000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.151e-05: 100%|##| 10000/10000 [00:19<00:00, 506.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  260000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.273e-05: 100%|##| 10000/10000 [00:19<00:00, 511.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  270000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.323e-05: 100%|##| 10000/10000 [00:19<00:00, 510.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  280000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.212e-05: 100%|##| 10000/10000 [00:19<00:00, 514.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  290000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.161e-05: 100%|##| 10000/10000 [00:19<00:00, 510.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  300000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.212e-05: 100%|##| 10000/10000 [00:19<00:00, 508.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  310000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.020e-05: 100%|##| 10000/10000 [00:19<00:00, 507.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  320000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.212e-05: 100%|##| 10000/10000 [00:19<00:00, 512.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  330000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.192e-05: 100%|##| 10000/10000 [00:19<00:00, 511.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  340000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.141e-05: 100%|##| 10000/10000 [00:19<00:00, 514.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  350000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.283e-05: 100%|##| 10000/10000 [00:19<00:00, 508.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  360000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.182e-05: 100%|##| 10000/10000 [00:19<00:00, 512.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  370000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.192e-05: 100%|##| 10000/10000 [00:19<00:00, 506.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  380000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.071e-05: 100%|##| 10000/10000 [00:19<00:00, 508.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  390000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.141e-05: 100%|##| 10000/10000 [00:19<00:00, 509.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  400000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.141e-05: 100%|##| 10000/10000 [00:19<00:00, 508.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  410000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.101e-05: 100%|##| 10000/10000 [00:19<00:00, 509.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  420000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 24  iter 9900: SER 1.040e-05: 100%|##| 10000/10000 [00:19<00:00, 508.63it/s]\n",
      "SNR 25  iter 9900: SER 9.898e-06: 100%|##| 10000/10000 [00:19<00:00, 511.24it/s]\n",
      "SNR 25  iter 9900: SER 9.393e-06: 100%|##| 10000/10000 [00:19<00:00, 512.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  20000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.393e-06: 100%|##| 10000/10000 [00:19<00:00, 509.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  30000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.050e-05: 100%|##| 10000/10000 [00:19<00:00, 509.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  40000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.999e-06: 100%|##| 10000/10000 [00:19<00:00, 510.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  50000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.060e-05: 100%|##| 10000/10000 [00:19<00:00, 507.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  60000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.030e-05: 100%|##| 10000/10000 [00:19<00:00, 511.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  70000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.797e-06: 100%|##| 10000/10000 [00:19<00:00, 509.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  80000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.131e-05: 100%|##| 10000/10000 [00:19<00:00, 510.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  90000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.030e-05: 100%|##| 10000/10000 [00:19<00:00, 511.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  100000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.696e-06: 100%|##| 10000/10000 [00:19<00:00, 506.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  110000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.999e-06: 100%|##| 10000/10000 [00:19<00:00, 510.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  120000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.393e-06: 100%|##| 10000/10000 [00:19<00:00, 508.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  130000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.393e-06: 100%|##| 10000/10000 [00:19<00:00, 514.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  140000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.050e-05: 100%|##| 10000/10000 [00:19<00:00, 509.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  150000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 8.585e-06: 100%|##| 10000/10000 [00:19<00:00, 513.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  160000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.060e-05: 100%|##| 10000/10000 [00:19<00:00, 506.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  170000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 8.888e-06: 100%|##| 10000/10000 [00:19<00:00, 515.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  180000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.050e-05: 100%|##| 10000/10000 [00:19<00:00, 515.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  190000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 8.686e-06: 100%|##| 10000/10000 [00:19<00:00, 506.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  200000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 8.585e-06: 100%|##| 10000/10000 [00:19<00:00, 513.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  210000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.091e-05: 100%|##| 10000/10000 [00:19<00:00, 514.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  220000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.494e-06: 100%|##| 10000/10000 [00:19<00:00, 514.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  230000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.292e-06: 100%|##| 10000/10000 [00:19<00:00, 509.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  240000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.191e-06: 100%|##| 10000/10000 [00:19<00:00, 520.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  250000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.030e-05: 100%|##| 10000/10000 [00:17<00:00, 572.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  260000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.797e-06: 100%|##| 10000/10000 [00:17<00:00, 579.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  270000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 7.979e-06: 100%|##| 10000/10000 [00:17<00:00, 577.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  280000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.202e-05: 100%|##| 10000/10000 [00:17<00:00, 577.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  290000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.797e-06: 100%|##| 10000/10000 [00:17<00:00, 578.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  300000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.999e-06: 100%|##| 10000/10000 [00:17<00:00, 579.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  310000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.595e-06: 100%|##| 10000/10000 [00:17<00:00, 576.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  320000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.999e-06: 100%|##| 10000/10000 [00:17<00:00, 570.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  330000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 8.989e-06: 100%|##| 10000/10000 [00:17<00:00, 581.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  340000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.494e-06: 100%|##| 10000/10000 [00:17<00:00, 579.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  350000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.091e-05: 100%|##| 10000/10000 [00:17<00:00, 579.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  360000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.797e-06: 100%|##| 10000/10000 [00:17<00:00, 580.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  370000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.192e-05: 100%|##| 10000/10000 [00:17<00:00, 579.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  380000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 8.888e-06: 100%|##| 10000/10000 [00:17<00:00, 579.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  390000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.797e-06: 100%|##| 10000/10000 [00:17<00:00, 579.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  400000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.090e-06: 100%|##| 10000/10000 [00:17<00:00, 576.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  410000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.393e-06: 100%|##| 10000/10000 [00:17<00:00, 578.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  420000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.191e-06: 100%|##| 10000/10000 [00:17<00:00, 587.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  430000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 8.888e-06: 100%|##| 10000/10000 [00:16<00:00, 589.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  440000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.797e-06: 100%|##| 10000/10000 [00:16<00:00, 594.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  450000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.121e-05: 100%|##| 10000/10000 [00:16<00:00, 590.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  460000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.696e-06: 100%|##| 10000/10000 [00:16<00:00, 591.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  470000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 9.595e-06: 100%|##| 10000/10000 [00:16<00:00, 589.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  480000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.060e-05: 100%|##| 10000/10000 [00:16<00:00, 589.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  490000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.071e-05: 100%|##| 10000/10000 [00:17<00:00, 587.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of samples is not enough. One more epoch is running. Total # of samples used:  500000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SNR 25  iter 9900: SER 1.030e-05: 100%|##| 10000/10000 [00:17<00:00, 588.18it/s]\n"
     ]
    }
   ],
   "source": [
    "bler_testAE =[]\n",
    "\n",
    "model_testAE = model_WGAN.Channel_ray(encoder_AE, decoder_AE).to(device)\n",
    "tconf = trainer.TrainerConfig(max_epochs=5, noise_std = noise_std, batch_size=1000, M=M, rate=rate)\n",
    "t_testAE = trainer.Trainer(model_testAE, dataset, tconf, one_hot=True)\n",
    "bler_testAE = t_testAE.test(snr_range=ebno_range, one_hot=True, erasure_bound=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92420f2f-3f1d-45f8-bdb3-39b057b5ba8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABTQAAANTCAYAAACQNpLWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxU9f7H8deZYQBRGUUUUHFfccFcMHdNSU1ts7JosWzfu97q1u/eW9ltubfVFsoWTcs026y0NBX3Jfc1FxQ3FFHRZBBEhpnz+2NySxFEZGbg/Xw8zmPinO/MfA7OB/Lt95yvYZqmiYiIiIiIiIiIiIgfsHi7ABEREREREREREZGiUqApIiIiIiIiIiIifkOBpoiIiIiIiIiIiPgNBZoiIiIiIiIiIiLiNxRoioiIiIiIiIiIiN9QoCkiIiIiIiIiIiJ+Q4GmiIiIiIiIiIiI+A0FmiIiIiIiIiIiIuI3FGiKiIiIiIiIiIiI31CgKSIiIiIiIiIiIn5DgaaIiIiIiIiIiIj4DQWap5k6dSpNmzalcePGfPrpp94uR0RERERERERERP7CME3T9HYRviA/P5+YmBjmzJmD3W6nXbt2LF68mGrVqnm7NBEREREREREREfmTZmj+admyZbRo0YJatWpRqVIl+vfvz4wZM7xdloiIiIiIiIiIiJymzASa8+fPZ9CgQdSsWRPDMPjhhx/OGpOYmEi9evUIDg6mY8eOLFu27OSxtLQ0atWqdfLrWrVqsXfv3tIoXURERERERERERIqozASa2dnZxMbGkpiYeM7jkyZNYvjw4Tz//POsWrWK2NhY+vbty4EDB0q5UhERERERERERESmuAG8XUFL69+9P//79Czz+1ltvce+993LXXXcBMGrUKH7++WfGjBnDM888Q82aNc+Ykbl3717i4uIKfL3jx49z/Pjxk1+73W4OHz5MtWrVMAyjBM5IRERERERERESk/DBNk6ysLGrWrInFUvA8zDITaJ5PXl4eK1eu5Nlnnz25z2Kx0KdPH5YsWQJAXFwcGzZsYO/evdjtdqZNm8a///3vAl/z1VdfZcSIEZe8dhERERERERERkfIkNTWV2rVrF3i8XASaGRkZuFwuIiIiztgfERHB5s2bAQgICODNN9+kV69euN1unn766fOucP7ss88yfPjwk19nZmZSp04dduzYQeXKlc/5HKfTyZw5c+jVqxc2m60EzkxELgX1qoh/UK+K+Af1qoh/UK+K+L7y0KdZWVnUr1+/wGzthHIRaBbV1VdfzdVXX12ksUFBQQQFBZ21PywsjNDQ0HM+x+l0EhISQrVq1crsB0+kLFCvivgH9aqIf1CvivgH9aqI7ysPfXrivAq7nWOZWRTofMLDw7Farezfv/+M/fv37ycyMtJLVYmIiIiIiIiIiMiFKheBZmBgIO3atSMpKenkPrfbTVJSEp06dfJiZSIiIiIiIiIiInIhyswl50ePHmXbtm0nv96xYwdr1qwhLCyMOnXqMHz4cIYOHUr79u2Ji4tj5MiRZGdnn1z1XERERERERERERHxfmQk0V6xYQa9evU5+fWLBnqFDhzJ27FiGDBnCwYMHee6550hPT6dNmzZMnz79rIWCLpbT6cTpdBZ47PRHEfFN6lUR/6BeFfEP6lUR/6BeFfF95aFPi3puhmma5iWupUxLTEwkMTERl8tFcnIyEyZMICQkxNtliYiIiIiIiIiI+JWcnBwSEhLIzMwscNFtUKBZYhwOB3a7nYyMjPOucj5z5kzi4+PL7GpUImWBelXEP6hXRfyDelXEP6hXRXxfeehTh8NBeHh4oYFmmbnk3FfYbLZCP1RFGSMi3qdeFfEP6lUR/6BeFfEP6lUR31eW+7So56VAU0REREREREREzuB0OnG5XN4uQ07jdDoJCAggNzfX5/9sLBYLNpsNwzAuyesr0BQREREREREREcBzyW9GRgbHjx/3dinyF6ZpEhkZSWpq6iULCkuS1WolJCSEGjVqEBgYWKKvrUBTRERERERERERwOBzs3buXSpUqER4efkln2MmFc7vdHD16lEqVKmGxWLxdToFM08TlcnHs2DEyMzPZuXMntWvXLtFFtBVoioiIiIiIiIgIGRkZVKpUidq1ayvI9EFut5u8vDyCg4N9OtA8oVKlSoSFhbFr1y4yMjKoU6dOib22As0S5nQ6cTqdBR47/VFEfJN6VcQ/qFdF/IN6VcQ/qFclPz+f3NxcqlWrhmmamKbp7ZLkL078mZimidvt9nI1RWMYBlWrVmXfvn0cO3aMgIDzR5FF/RlkmPqEXpTExEQSExNxuVwkJyczYcKEEp1CKyIiIiIiIiJyqQUEBBAZGUl0dHSJ3+9Qyre8vDxSU1NJT08nPz//vGNzcnJISEggMzOT0NDQAscp0CwhDocDu91ORkZGgd9wp9PJzJkziY+PL/Iy9CJS+tSrIv5BvSriH9SrIv5BvSq5ubmkpqZSr149goODvV2OnINpmmRlZVG5cmW/uiVAbm4uO3fuJDo6utDPlsPhIDw8vNBAU5eclzCbzVboD/+ijBER71OvivgH9aqIf1CvivgH9Wr55XK5MAwDi8XiF/dnLI9OXGZ+4s/JX1gsFgzDKHJmVqTXLInCREREREREREREREqDAk0RERERERERERHxGwo0RUREREREREREiiE2NhbDMAgKCuLQoUMFjrvzzjsxDKPQ7c477yy94v2Y7qEpIiIiIiIiIiJygZYvX866desAz0re48eP5/HHHz/vcxo2bEjXrl0LPH6+Y3KKAk0REREREREREZELNHr0aABq1arF3r17GT16dKGBZteuXRk7dmwpVFe2KdAsYU6nE6fTWeCx0x9FxDepV0X8g3pVxD+oV0X8g3pVnE4npmnidrtPrqZdGlxuk+U7D3Mg6zg1KgfRoV4YVotRau9fXDk5OUycOBGAcePGce2117J+/XqWLl1Khw4dzhpvmubJx+J+f0viNbzB7XZjmiZOpxOr1XresUX9GaRA8yIlJiaSmJiIy+UCYMaMGYSEhJz3OTNnziyN0kTkIqlXRfyDelXEP6hXRfyDerX8CggIIDIykqNHj5KXl1cq75m05RCvzdrO/qxT7xdROZCn+zSgd9NqpVJDcU2cOBGHw0Hz5s1p164d1113HV988QWjRo2iadOmZ40//R8NHA7HRb13VlbWRT2/tOXl5XHs2DHmz59Pfn7+ecfm5OQU6TUN80S8KxfF4XBgt9vJyMggNDT0nGOcTiczZ84kPj4em81WyhWKSFGpV0X8g3pVxD+oV0X8g3pVcnNzSU1NpV69egQHB1/y95u+IZ2HJ6zmr6HUibmZiQmX0a9l5CWvo7h69uzJggULePPNN3niiSdYvHgx3bp1w263s3fvXipUqHDG+LvuuovPP/+cO+64g88++6xY72maJllZWVSuXBnD8P1ZrCfk5uayc+dOoqOjC/1sORwOwsPDyczMLDBfA83QLHE2m63QH/5FGSMi3qdeFfEP6lUR/6BeFfEP6tXyy+VyYRgGFosFi8Vy1nHTNDnmdJXMe7lNRkzdeFaYCWDiCTVfnLqJbk2ql8jl5xVs1hINAJOTk1mwYAE2m43bb78di8VC165dadasGZs3b+b777/n9ttvP+M5J97/xPe4OE5cZn4xr+ENFosFwzCKnJkVhQJNERERERERERE5r2NOFzHP/Voq72UC6Y5cWr0wo0Reb+OLfQkJLLkIbMyYMQBcffXVVK9e/eT+YcOG8fTTTzN69OizAs0Txo0bx7hx4wp87cmTJ3PttdeWWK1llQJNERERERERERGRIsjPzz8ZSA4bNuyMY3fccQf/93//x/z580lJSaFhw4ZnPb9hw4Z07dq1wNevU6dOyRZcRinQFBERERERERGR86pgs7Lxxb4l8lrLdhzmzs+WFzpu7F0diKsfdtHvV8F2/pW1L8TPP/9Meno6tWrVom/fM78fERERXHXVVfz000+MGTOGl19++aznd+3albFjx5ZYPeWVAk0RERERERERETkvwzBK7LLtbo2rE2UPJj0z95z30TSASHsw3RqXzD00S9Lo0aMBz0I3PXr0OOv43r17ARg7diwvvvgiVmvJhalyigJNEREREREREREpNVaLwfODYnhw/CoMOCPUPBFfPj8oxufCzH379vHLL78AcOjQIRYtWlTg2LS0NKZPn86AAQNKq7xyxX+WRBIRERERERERkTKhX8soPrytLZH24DP2R9qD+fC2tvRrGeWlygo2duxYXC4XHTt2xDTNArenn34aODWbU0qeZmiWMKfTidPpLPDY6Y8i4pvUqyL+Qb0q4h/UqyL+Qb0qTqcT0zRxu9243e5Sec8rYyLo3awGy3ce5kDWcWpUDqJDvTCsFqPUargQJ1Y3v/32289b32233cZrr73G1KlT2b9/P9WrV8c0PfNQT3yPi6MkXsMb3G43pmnidDoLvQS/qD+DDPPEd0OKJTExkcTERFwuF8nJyUyYMIGQkBBvlyUiIiIiIiIiUmQBAQFERkYSHR1NYGCgt8vxOYsWLWLgwIEEBQWxefNmqlSpct7xPXv2ZO3atfznP//hkUce4aGHHmLixInUr1+fyy+/vMDn1a5dm//7v/8r4eq9Ky8vj9TUVNLT08nPzz/v2JycHBISEsjMzCQ0NLTAcQo0S4jD4cBut5ORkVHgN9zpdDJz5kzi4+Ox2WylXKGIFJV6VcQ/qFdF/IN6VcQ/qFclNzeX1NRU6tWrR3BwcOFPKGeGDh3K+PHjGTx4MF9//XWh4999913+9re/0bx5czZs2MBdd93F559/XujzYmNjWbVq1TmPmaZJVlYWlStXxjB86/6i55Obm8vOnTuJjo4u9LPlcDgIDw8vNNDUJeclzGazFfrDvyhjRMT71Ksi/kG9KuIf1Ksi/kG9Wn65XC4Mw8BisWCxaMmVv/riiy/44osvijz+iSee4Iknnjj59bhx4xg3btxF1XDiMvMTf07+wmKxYBhGkTOzIr1mSRQmIiIiIiIiIiIiUhoUaIqIiIiIiIiIiIjfUKApIiIiIiIiIiIifkOBpoiIiIiIiIiIiPgNBZoiIiIiIiIiIiLiNxRoioiIiIiIiIiIiN9QoCkiIiIiIiIiIiJ+Q4GmiIiIiIiIiIiI+I0AbxdQ1jidTpxOZ4HHTn8UEd+kXhXxD+pVEf+gXhXxD+pVcTqdmKaJ2+3G7XZ7uxw5B9M0Tz7605+R2+3GNE2cTidWq/W8Y4v6M8gwT3w3pFgSExNJTEzE5XKRnJzMhAkTCAkJ8XZZIiIiIiIiIiJFFhAQQGRkJNHR0QQGBnq7HClD8vLySE1NJT09nfz8/POOzcnJISEhgczMTEJDQwscp0CzhDgcDux2OxkZGQV+w51OJzNnziQ+Ph6bzVbKFYpIUalXRfyDelXEP6hXRfyDelVyc3NJTU2lXr16BAcHe7scOQfTNMnKyqJy5coYhuHtcoosNzeXnTt3Eh0dXehny+FwEB4eXmigqUvOS5jNZiv0h39RxoiI96lXRfyDelXEP6hXRfyDerX8crlcGIaBxWLBYtGSK77oxGXmJ/6c/IXFYsEwjCJnZkV6zZIoTERERERERERERKQ0KNAUERERERERERERv6FAU0REREREREREpBApKSknL/c+ePDgOceMHz8ewzAwDIPx48efc8zBgwdPXoadkpJy1vG5c+dy7733EhMTQ9WqVbHZbFSrVo3LL7+cp556ilmzZlGUJXEee+yxk7VMmTLlvGPHjh17cmzr1q0LXEV94cKFGIZBvXr1Cn3/S0mBpoiIiIiIiIiISCEaNmxIdHQ0pmkyb968c46ZM2fOyf+eO3fuOcfMnTsX0zSJjo6mYcOGJ/dnZGTQt29fevXqxaefforD4aBLly7cdNNNdOrUiQMHDvDpp5/St29f2rVrd95ajx8/zpdffnny6zFjxhT5PNevX19gGOsrFGiKiIiIiIiIiIgUQa9evYAzg8vTzZ07l+rVq1O7du3zBpqnvxbAkSNH6Nq1KzNmzKBZs2bMnj2bPXv2MHXqVL788kumTp3K9u3bWbRoEXfddRdbtmw5b52TJ0/m8OHD1KxZE8MwmDp1Kvv37y/0/EJCQgB47rnnOH78eKHjvUWBpoiIiIiIiIiISBGcL9BMTU1l+/bt9OjRgx49epCSkkJqaupZ40489/RA89FHH2XLli00aNCAxYsXn3HsdDExMXz66acFBqonjB49GoDHH3+cHj16kJ+fz+eff17o+XXq1Im4uDh27drFBx98UOh4b1GgKSIiIiIiIiIi3pMyB96P8zz6uBNB46ZNm86a8Xhi5mXPnj3p0aPHGftO2L9/P5s2bTrjtVJSUpgwYQIAb7/9NlWrVi20jri4uAKP7dy5k6SkJAICArjjjju4++67gaJfdv6///0PgJdffhmHw1Gk55Q2BZoiIiIiIiIiIuIdpglJIyBji+exCIvdeFPdunWpX78+cHZYeeLrEzM04eyZnCfG1K9fn7p16wIwdepU3G43VatWZeDAgRdd45gxYzBNk6uuuorIyEgGDx6M3W5n8+bNLF68uNDn9+zZk/79+3Po0KGT4aavUaApIiIiIiIiIiLnZ5qQl13y25ZfIG215z3SVnu+Lun3KOGQtKDLzk/cP7NFixY0adKEyMjIAkPP0y8pX7lyJQBt27bFYrm4qM7tdjN27FgAhg0bBkCFChW4+eabgVOXohfm1VdfxWKxMHLkSPbt23dRNV0KAd4uQEREREREREREfJwzB16peenf56uEkn/N/0uDwIol9nK9evVizJgxZwSau3fvZvv27QwePBjDMADPTM1Jkyaxa9euk7Mxz3X/zIyMDACqV69+zvdbu3Ytb7/9NqZp4nQ6sdlsGIbBPffcQ9euXc8YO2PGDFJTU4mIiGDAgAEn999999189NFHfP3117zzzjtUqlTpvOcYGxtLQkIC48ePZ8SIEYwaNaqo355SoRmaIiIiIiIiIiIiRXQijExOTj45e/H0y81P+Ot9NNPT00+uTl7Qoj/nkpqayrhx4/j888+ZOHEin3/+OePGjWPbtm1njf30008BuOOOOwgIODWPsUOHDrRs2ZKjR48yadKkIr3vf/7zHwIDAxk9ejTJyclFrrc0aIZmCXM6nTidzgKPnf4oIr5JvSriH9SrIv5BvSriH9Sr4nQ6MU0Tt9uN2+0+e4A1GJ7ZU3JvaJoY4wbC/g0YpuvUbsMKES0xh06FP2c6XjRrMJzrnIopKiqKxo0bs3XrVpKSkkhISDg587J79+4nv3/dunUDPLMyb7/9dmbPng1A48aNiYqKOjmuWrVqABw8ePCc3/urrroKl8uFaZpkZWUxePBgZs+efdaf1cGDB/npp58AuPPOO896rbvuuou///3vjBkzhrvuuuuMY6ePPfHfderU4cEHH+Sdd97h2Wef5ZtvvjnnuMK43e6Ts0utVut5xxb1Z5ACzYuUmJhIYmIiLpen+WbMmEFISMh5nzNz5szSKE1ELpJ6VcQ/qFdF/IN6VcQ/qFfLr4CAACIjIzl69Ch5eXmX/v12zqNS+tqz9humC9LXkr1lDvn1epzjmcWQm1Uyr3Oazp07s3XrVmbOnMnAgQOZM2cOYWFh1KlT5+TK4LVr1yY8PJzZs2fjcDiYMWPGyeeevnp4TEwM4LmX5pEjRwq9j+aJDCo3N/eM1/n0009xOp0EBAScvH/m6bKzswFYvHgxK1asoEmTJieP5ebmApCfn3/Gaz766KOMGTOG77//njlz5pz8bLjd7iKvgJ6Xl8exY8eYP38++fn55x2bk5NTpNdUoHmRHn74YR5++GEcDgd2u50rr7yS0NDQc451Op3MnDmT+Ph4bDZbKVcqIkWlXhXxD+pVEf+gXhXxD+pVyc3NJTU1lUqVKhEcHHxp38w0MZa9jYmBwdkL9pgYVFz2NmargSU3S7OEXXnllYwbN45FixZx5MgRdu3axbXXXovdbj9jXPfu3fn+++85fPgwS5YsOfnc07OjwYMH869//YsjR46wcOHCAlc6PzFD88Qsx+Dg4DNeZ8KECYAnlFy6dOl56//666957bXXTn594s88ICDgjNcMDQ3lqaee4rnnnuPll1/mhRdeAMBisRSYf/1Vbm4uFSpUoHv37oV+tooakirQLGE2m63QH/5FGSMi3qdeFfEP6lUR/6BeFfEP6tXyy+VyYRgGFovlolfaLlT+ccjcC+cIMwFPyOnYi2HmgzXo0tZSTFdccQUAKSkpJ4PEXr16nfW969mzJ99//z0TJ048eR/KK6644oxxTZo0YciQIUycOJEnn3ySHj16nBWMwqlLvE8sOnT6n9WSJUvYuHEjQUFBpKenU6VKlXPWPW3aNK666irGjx/Pf//735P32Ty9nr+ew/Dhw/nggw+YM2cOnTp1KnBcQSwWC4ZhFDkzKwoFmiIiIiIiIiIiUnoCguC+OZCdUfCYitU943xUZGQkzZs3Z9OmTbz55puAJ7z8qxMLA7311lsANG/enMjIyLPGJSYmsnz5crZu3Urnzp354IMPzlhg6ITdu3ezZ8/Z9zIdPXo0ANdcc02BYSZ4ZodGRkaSnp7O1KlTufbaaws7VSpWrMhzzz3HQw89xMiRIwsdXxoUaIqIiIiIiIiISOmy1/ZsfqxXr15s2rSJw4cPExYWRqtWrc4a06pVK8LCwjh8+PDJ55xL1apVWbRoEQkJCSQlJdGzZ09q165NmzZtqFKlCseOHWPr1q2sX78e0zRp1aoV7du3Bzhj5fKhQ4eet2ar1UpCQgJvvfUWo0ePLlKgCXDvvffy9ttvs3Xr1iKNv9Qu8RxiERERERERERGRsuf0cLJ79+4nLwU/nWEYJ1c7/+tz/qpGjRrMmjWLWbNmMWzYMCpWrMj8+fP56quvmD17NgEBAQwdOpRff/2VNWvW0LJlS8BzP8yjR48SGRlJ3759C637jjvuADyXn6elpRXpXAMCAnj55ZeLNLY0aIamiIiIiIiIiIjIBbrhhhswzXPfB/R0P/zwwwW9bu/evendu/dZ+0+sLB4aGnrG/SuHDRt2zlXNCxIbG3tW3XfeeSd33nnneZ934403Ful8S4NmaIqIiIiIiIiIiIjfUKApIiIiIiIiIiIifkOBphRNyhx4P87zKCIiIiIiIiIi4iUKNKVwpglJIyBji+fRR+6XICIiIiIiIiIi5Y8CTSlcShKkrfb8d9pqz9ciIiIiIiIiIiJeoEBTzs80YfZLp+0wYPqz4Mr3WkkiIiIiIiIiIlJ+KdCU8zt9diYAJmQkw//qwnf3wLqvIfuQ18oTEREREREREZHyJcDbBYgPOzE707CC6TrzWN5RWP+NZ8OA2u2hUTw0joeoNmBRVi4iIiIiIiLib0ytmyEl7FJ8phRoSsHOmp35Fy2uh4ytsH897Fnu2ea+AhWrnwo3G14BFaqUWskiIiIiIiIicuEsf05McrlchYwUuTAnPlOWEpz8pkBTzu3kvTMtgPscAyzwxw54YAFk7YNts2DrDEiZC9kHYe0Ez2ZYIbqjJ9xsfCVEtADDKN1zEREREREREZHzstlsWK1Wjh07RqVKlbxdjpQhWVlZ2Gw2bDZbib2mAk05N1ceZO7l3GEmnv2OvZ5xoTWh7R2eLT8PUn/zhJtbZ8LBzbB7sWdLGgGVa54KNxv0gKDKpXlWIiIiIiIiInIOhmEQEhJCZmYmYWFhWK1Wb5ckZcCxY8dwOBxUqVIFowQnuCnQLGFOpxOn01ngsdMffZsFhs2E7AzcpsnGfVn8kZ1H1YqBxERVxmIYnkvLTQuccT4G1O7k2Xo9D0d2Y0mZhbFtFsbOBRhZabBqHKwah2mxYdbphNmoD+6G8VCtkWZvik/wr14VKb/UqyL+Qb0q4h/UqwJQtWpVUlNT2blzJ2FhYQQGBpZoCCUXxzRN8vLyOHbsmE//uZimicvl4ujRozgcDgIDA6lSpUqRfr4U9WeQYepurxclMTGRxMREXC4XycnJTJgwgZCQEG+XVWLWHjL4fqeFI3mnGqVKoMn19dzEVruwj47FnUe1o5uJcKwjInMNlfIOnHE8OzCcA6Gx7A+NJaNyc1yWoBI5BxEREREREREpGpvNRqVKlahQoUKJ3vNQyhfTNHE6neTk5JCdnV3khYFycnJISEggMzOT0NDQAscp0CwhDocDu91ORkZGgd9wp9PJzJkziY+PL9H7Blwqv/6+n0e/WstfPyAnos33bo6lb4uI4r/B4RQs22ZhpMzC2LUIw5V38pBpDcKs29Uze7NRH6hav/jvI3KB/K1XRcor9aqIf1CvivgH9ar8VX5+Pi6XS6ue+5D8/HwWL15M586dCQjw3YuuDcPAYrEQEBBwwTNJHQ4H4eHhhQaavnv2fqooNzkt6RuhXgout8nL07acFWYCmHhCzZenbaF/61pYLcWc5hzRzLN1eQTysmHH/JP33jQyUzG2J8H2JKwznvVcjt74Ss/9N+t2gQDN3pRLzx96VUTUqyL+Qr0q4h/Uq3KCPge+x+l0kp+fT6VKlcrsn09Rz0uBppzTsh2H2ZeZW+BxE9iXmcuyHYfp1LDaxb9hYEVo2t+zmaZnMaETCwvtXgKHtnm23z4AW0XPgkKN46FRPFSJvvj3FxERERERERERv6BAU87pQFbBYebpUv/IoRMlEGiezjCgRnPP1uVxyHXA9rmnAs6j6bDlF88GUCPmVLhZ53Kw/iXNT5kD0/4B/f8HDXuVbK0iIiIiIiIiIlKqFGjKOdWoHFykcf+cvJ55Ww4yKLYmPZtWJ9hmLfligkMh5mrPZpqQvv5UuLlnGRzY6NkWvQNBodCg56nL0ytFQNIIyNjieWzQUyupi4iIiIiIiIj4MQWack5x9cOIsgeTnpl7zvtoAlgtBk6Xyc/r9/Hz+n1UDgrgyhaRDIqNokujcGzWS7AammFAVGvP1v1JyDkMKbM94ea2mZBzCDb95NnAs5jQHzs8/522GlKSoFGfkq9LRERERERERERKhQJNOSerxeD5QTE8OH4VBpwRap6Y3/j+LZdRu2oIU9alMWVtGvsyc/lu1R6+W7WHsIqB9G8ZydWxNelQLwxLcRcOKkxIGLS6wbO53Z7QcusMz5a26lSYecLkB+CmLyC6I1guQeAqIiIiIiIiIiKXlAJNKVC/llF8eFtbRkzZeMYCQZH2YJ4fFEO/llEAtKpt55l+zVi5+w9+WpPGL+v3cSg7jy+X7ubLpbuJDA1mYOsoBsXWpHVtO8aluuTbYoHa7Txbr2dh/Xfw3bAzx2QfhM/6QeUoaH41tLgWoi9XuCkiIiIiIiIi4icUaMp59WsZRXxMJMt2HOZAVi41KgcTVz8M619mXFosBh3qhdGhXhjPD4phccohpqxNY/rv6aQ7cvl04Q4+XbiDutVCGNS6Jle3qUmTiMqXrnDThCXvgWEF03XaAcNz2XrWPlj2kWerFAnNB3nCzTqdwHIJ7gMqIiIiIiIiIiIlQoGmFMpqMejUsOgrmQdYLXRvUp3uTarz0nUtmbflIFPW7WPWxv3sOpTD+3O28f6cbTSNqMzVbWoysHUUdatVLNmiU5I8l5+fxfSEnT2fhT92wuZfPKumL//Es1WscSrcrNtF4aaIiIiIiIiIiI9RoCmXVFCAlStbRHJli0hy8vKZtekAP61JY17yAbbsz+L1X7fw+q9biI2uwqDWUQxsXZNIe9FWWC+QacLslwAL4D7HAAskT4d754ArD7bPg40/wOapkH0AVoz2bBWrQ7OBf4abXcGqdhERERERERER8TYlNFJqQgIDuDq2JlfH1iQzx8mvv6czZV0ai7ZlsDb1CGtTj/DyL5uIqxfG1W1q0r9lFGEVAy/8jVx5kLmXc4eZePY79nrGBQRBkys9W/5I2PFnuLlpqud+mys/82wh1U6Fm/W6K9wUEREREREREfESpTLiFfYQGzd1iOamDtEczDrOtA37+GlNGit2/cHSHYdZuuMwz//4O10bhzOodU2ubBFB5WBb0V48IAjumwPZGQWPqVjdM+6M5wVC43jPNnDkn+Hmj55wM+cQrBrn2SqEQfOBEHMN1O8B1iLWJSIiIiIiIiIiF02Bpnhd9cpB3NGpHnd0qsfeI8f4eV0aP61NY8NeB3O3HGTuloMETrZwRdMaXN2mJlc0q0GwrZB7W9pre7bistqgUR/PNuAt2Lngz3Bzyp/h5ueerUJVaDYAYq71hJsBxZhRKiIiIiIiIiIiRaZAU3xKrSoVuK97Q+7r3pDtB48yZe0+flq7l5SD2Uz/PZ3pv6dTMdBzX85BsVF0bVSdwABLga/ncpuFrtBeKKsNGl7h2a56E3YtPBVuZh+E1eM9W7Ddc1l6zLXQoKfCTRERERERERGRS0CBpvisBtUr8XifxjzWuxGb9mUxZV0aU9amseePY0xevZfJq/dSJcRG/5aRDGpdk44Nqp0RVk7fsI8RUzayLzP35L4oezDPD4qhX8uo4hVlDfCElQ16wlVvwK5F8PsPf4abB2DNl54tyA7NrvKEmw17nX15u4iIiIiIiIiIFIsCTfF5hmEQUzOUmJqhPN23KatTj/DTmjR+Xr+Pg1nHmbgslYnLUqleOYgBraK4uk1N9mfm8tCXqzD/8lrpmbk8OH4VH97Wtvih5gkWK9Tv7tmueh12L/kz3PwJju6HtRM9W1AoNO3/Z7h5BdguchV3EREREREREZFyTIGm+BXDMGhbpypt61Tl3wNjWLr9EFPWpfHL+nQOZh1n7OKdjF28E6vBWWEmePYZwIgpG4mPibzwy88LYrFCva6erf//IHXpqXAzax+sm+TZAitD036ecLNRb7BVKJn3FxEREREREREpJxRoit+yWgw6Nwqnc6NwRlzdkoXbDvLTmjSmbUjneL67wOeZwL7MXJbtOEynhtVKvjCLFep29mz9/gt7lnnCzY0/QlYarP/GswVWgiZ9PeFm4/hzh5spc2DaPzwhacNeJV+riIiIiIiIiIifUaApZUJggIUrmkVwRbMIuq5I5clv1xX6nANZuYWOuWgWC9S53LP1fQX2rjgVbjr2wIbvPJutIjS58s9w80oIDAHThKQRkLHF89igJxglNKNURERERERERMRPKdCUMqdW1ZAijZu35QDt6laldhHHXzSLBaLjPFvfl2HvSvh9Mmz8CTJ3e/7798lgC/HM2AxrCGmrPc9NWw0pSdCoT+nUKiIiIiIiIiLioxRoSpkTVz+MKHsw6Zm557yP5gnfr05j8po0ujWuTkJcNL2bR2CzWkqnSMOA2u0925UvQdqqP2du/gBHdntmcJ4x3gJJL0HD3pqlKSIiIiIiIiLlWimlNyKlx2oxeH5QDOBZAOh0xp/bPd3q07VROKYJ85MP8sD4VXR6dTb/nbaZnRnZpVuwYUCtdnDlf+DxdXDfXM+l56cz3bBvNXw9FA5uKd36RERERERERER8iGZoSpnUr2UUH97WlhFTNrIv89S9MiPtwTw/KIZ+LaMA2HUom0nLU/l6xR4yjh5n1LwURs1LoXPDatwcV4e+LSIICrCWXuGGAVFt4MguMKxgus48vulHz1arPVx2K7S4HipUKb36RERERERERES8TIGmlFn9WkYRHxPJsh2HOZCVS43KwcTVD8NqOTVvs261ijzdrxl/i29C0qYDfLV8N/OSD7I45RCLUw5RNcTG4La1uTmuDo1qVCqdwlOSTt0785wsnsWF9q6A6c9Cs4GecLN+D88K6yIiIiIiIiIiZZgCzRLmdDpxOp0FHjv9UUpH+zqhQCgAblc+bte5x/VuWo3eTaux98gxvl25l29W7WW/4zifLtzBpwt30L5uFW5uX5u+LSIItl2i4NA0sSa9hIGBcY47gJoYmBExmC1uwLL+K4yDm2HDt7DhW8zQWrhbDcHd+mYIa3Bp6isn1Ksi/kG9KuIf1Ksi/kG9KuL7ykOfFvXcDNM0z7duihQiMTGRxMREXC4XycnJTJgwgZCQUlo1Wy4plwmbjhgs2W/w+x8G5p935KxgNelQ3aRTDTc1K5bse1rcTuJ//xvB+Y4Cx+QG2JnZ4i3cRgBVju2gzqEF1PpjCYGunJNjDlVswu5q3UirEke+tULJFikiIiIiIiIicgnk5OSQkJBAZmYmoaGhBY5ToFlCHA4HdrudjIyMAr/hTqeTmTNnEh8fj81mK+UK5WKkO3L5blUa36zcw94jp+7J2SbazpD2tbmqZQQhgSU04dmxF7IzCj5esTqE1jxzX34uRvJ0LGsnYuyYg2G6ATBtIZjNBuGOvQWzTmfPaulSKPWqiH9Qr4r4B/WqiH9Qr4r4vvLQpw6Hg/Dw8EIDTV1yXsJsNluhH6qijBHfEl3NxhPxTXm0dxMWbstg4tLdzNq0nzWpmaxJzeSVX7ZwdZua3BJXh5a17Bf3ZtXqebYLYbNB7I2ezZEGayfCmgkYh7ZhrJ+EZf0kqFIX2iRA7C1Qte7F1VhOqFdF/IN6VcQ/qFdF/IN6VcT3leU+Lep5KdAUuQBWi0GPJtXp0aQ6B7Jy+W7lXr5avptdh3L4culuvly6m1a17NwcF83VsTWpHOyFHzChNaHb36HrcEhdBmvGw4bJnpXT577q2ep3hza3QfNBEKhbJIiIiIiIiIiI/1CgKVJMNSoH82DPhtzfvQG/bT/ExOWp/LohnfV7M1k/OZOXf97EoNY1uaVjHWJr2zEMo/AXLUmGAXU6erZ+/4NNUzzh5o75p7afK0PL6zzhZnSc5zkiIiIiIiIiIj5MgabIRbJYDDo3Cqdzo3AOZ+fx/ao9TFi2m+0Hs5m0IpVJK1JpFlmZhI51uKZNLewVvDBrMzAEYod4tiO7Yc1EWPOlZ9bmqs89W7VGpy5J/+s9OkVEREREREREfIRWCBEpQWEVA7mnWwOShvfg6/s7cd1ltQgMsLA5PYvnfvydjq/MYvjXa1ix8zBeW4+rSh3o+Q94bA0MnQqxCWALgUPbIOlFeLsFfHE9bPgOnLmFvpyIiIiIiIiISGnSDE2RS8AwDOLqhxFXP4wXBrVg8uo9TFyWypb9WXy/ai/fr9pLoxqVuLlDNIPb1qZqxcCzXsPlNlm24zAHsnKpUTmYuPphWC0leEm4xQL1u3m2q16D33+ANRNg92JISfJswXZoeQNcdivUbKtL0kVERERERETE6xRoilxi9hAbd3apz9DO9VideoSvlu1mytp9bDtwlJd+3sRr07fQr2UkN8dF06lBNQzDYPqGfYyYspF9madmSEbZg3l+UAz9WkaVfJFBlaHt7Z7tUIon2Fw7ERx7YcVoz1a9uSfYbD0EKtUo+RpERERERERERIpAgaZIKTEMg7Z1qtK2TlX+NTCGn9akMXHZbn5Pc/DT2jR+WptG/fCKtImuwuTVe896fnpmLg+OX8WHt7W9NKHmCdUaQu9/Q6//gx3zYPWXsHkqHNwEM/4FM5+Hxld6ws3GfSHg7NmlIiIiIiIiIiKXigJNES8IDbZx2+V1ue3yuqzfk8mEZbv5ac1edmRksyMj+5zPMQEDGDFlI/ExkSV7+fm5WKzQ8ArPduwI/P69J9zcuwKSp3m2kGrQ6iZPuBnZ6sznp8yBaf+A/v+Dhr0uba0iIiIiIiIiUm5oUSARL2tV286r17di2T/7cF/3+ucdawL7MnNZtuNw6RR3QoUq0H4Y3JsEDy2FLo9DpQjIOQRLP4RRXT3bb6Mg+xCYJiSNgIwtnkdvLYAkIiIiIiIiImWOAk0RH1ExKIAWNe1FGnsgy4urj9doBvEvwt82QsLXEHMNWGyQvh6m/wPebApj+kLaas/4tNWeBYZEREREREREREqAAk0RH1KjcnCRxk1Zm1bgpemlxhoATfrCTZ/Dk8nQ/zWIbA1uJ6QuPW2gATNf0CxNERERERERESkRCjRFfEhc/TCi7MEUdnfMWZsOcMWbc3ngi5Ws3PVHqdR2XiFh0PF+eGABDHjzLwdN2L8exg2C1GUKNkVERERERETkoijQFPEhVovB84NiAM4KNY0/t+HxTbiiWQ1ME6b/ns7gDxdzw4eLmfF7Om63l8NC04TV48Gwnn1s5wIYHQ8f9/AsLuQ8Vvr1iYiIiIiIiIjfU6Ap4mP6tYziw9vaEmk/8/LzSHswH97Wlsd6N2bMnR2Y8bfu3NiuNjarwYpdf3DfFyvp8/Y8Ji7bTa7T5Z3iU5I898w0C3h/iw32rYUfH4K3YmDm83Bkd+nWKCIiIiIiIiJ+LcDbBYjI2fq1jCI+JpJlOw5zICuXGpWDiasfhtVyat5mk4jKvH5jLE/2bcpni3by5dJdbD+YzbPfr+fNGcnc2bkut11elyohgaVTtGnC7Jfw/DuJ+xwDLFCjObS4DlaMgcxUWDQSFr8LTa+CuHuhfg8wCrvgXkRERERERETKMwWaIj7KajHo1LBaoeMiQoN5pn8zHrmiEV8t282YhTtIy8zljRnJfDA3hZvaR3N31/pEh4Vc2oJdeZC5l3OHmXj2H02HTg9D58cgeTos+wh2zIfNUz1beFNPsBl7CwRVurT1ioiIiIiIiIhfUqApUkZUCgrgnm4NGNq5Hj+v28dH87ezaZ+DsYt38sVvu7iqVRT3dWtAq9r2S1NAQBDcNweyMwoeU7G6ZxxA84Ge7cBmWP4JrJkIGVvglych6UVokwAd7oXwRpemXhERERERERHxSwo0RcoYm9XCtZfV4po2NVm4LYOP529nwdYMpqxNY8raNDo1qMZ9PRrQs0l1jJK+vNte27NdiBrNPCuj937OE2ou+xgOp8DSUZ6tYW+Iuw8ax4PlHIsNiYiIiIiIiEi5okBTpIwyDINujavTrXF1fk/L5JP525mybh9Lth9iyfZDNI2ozL3dG3B1bE0CA3xgfbBgO1z+gCe83D4bln0Cyb96FhpKSYKq9aDDPdDmVggJ83a1IiIiIiIiIuIlPpBiiMil1qKmnZE3X8b8p3txd9f6VAy0smV/Fk9+s5bur83ho3kpOHKd3i7Tw2KBRn0gYRI8tho6PeIJO//YCTP+5Vkd/adHIX29tysVERERERERES9QoClSjtSqUoF/D4xh8bO9ebpfU6pXDiLdkcur0zbT+dXZvPLLJvZlHvN2maeE1Ye+L8PwzTDoXYhoCfnHYNXnMKorjOkPG74Hl4+EsSIiIiIiIiJyySnQFCmH7BVsPNSzEQv/0YvXBremUY1KHD2ez8fzt9Ptf3MYPmkNm/Y5vF3mKYEh0G4oPLAQ7poGLa4Dwwq7F8O3d8HIVjDvNcja7+1KRUREREREROQS0z00RcqxoAArN3WI5oZ2tZmbfICP5m1n6Y7DfL96L9+v3kv3JtW5v3sDOjesVvILCBWHYUDdzp7NkQYrPoOVn0HWPpjzsifUbHGd5z6ctdt7xouIiIiIiIhImaJAU0SwWAyuaBbBFc0iWJt6hI/nb2fahn3MTz7I/OSDtKwVyr3dGjCgVRQBVh+Z2B1aE674J3R/Ejb+6Fkdfc9yWP+1Z4tqAx3vhxbXgy3Y29WKiIiIiIiISAnxkWRCRHxFbHQVEm9ty5wne3JHp7oE2yxs2Ovg8a/W0OP1uYxZuIPs4/neLvOUgCBofRPcMwvunQOxCWANgn1r4IcH4a3mMOsFOJLq7UpFREREREREpAQo0BSRc6pbrSIvXtOSxc/05m99mlCtYiB7jxzjxakb6fzf2bz+62YOZOV6u8wz1WoL130IwzdC7+cgtDYcOwwL34Z3WsNXt8KO+WCa3q5URERERERERIpJgaaInFdYxUAe79OYRc9cwcvXtaR+eEUyjzlJnJNC1//O4R/frmPbgaNnPMflNlmScogf1+xlScohXO5SDhArhkO3v8Pja2HIeKjXDUw3bJ4K4wbBB5fD8k/h+NHCX0tEREREREREfIruoSkiRRJss3Jrx7rc3KEOMzfu5+P5KazafYRJK1KZtCKVPs1rcF/3hhw6epwXp25kX+ap2ZtR9mCeHxRDv5ZRpVu0NQCaD/JsBzbBsk9g7VdwcDP8/HeYNQLa3Aod7oHwRiefZuyYR6+Nz2A0rwhN+pRuzSIiIiIiIiJyXpqhKSIXxGox6Ncyku8f6sK3D3QiPiYCw4BZmw5w00dLePDLVWeEmQDpmbk8OH4V0zfs81LVQI3mMPAtz+Xo/f4LYQ3huAOWfgjvt4PxgyH5V3C5sMx5idDjaVjmvKTL00VERERERER8jGZoikixta8XRvt6YaQcPMrH81OYtHzPOceZgAGMmLKR+JhIrBajVOs8Q4UqcPmDEHc/pMz2rI6+dQZsm+XZKkVgObofAMu+1ZCSBI00S1NERERERETEV2iGpohctIbVK3Ftm9rnHWMC+zJzWbbjcOkUVRiLBRr3gVu/hsdWQadHICgU/gwzAUwMmPm8ZmmKiIiIiIiI+BAFmiJSIoq64vl+h4+tjA4Q1gD6vgzXjTpjt4EJ+zfA+OvhYLKXihMRERERERGR0ynQFJESUaNycJHGvTljC7/+no7pa7MeTRPmvw6G9exjKbMhsQN8dSvsWVn6tYmIiIiIiIjISQo0RaRExNUPI8oezPnujmkAqX8c4/4vVnJN4iLmJR/0nWAzJQnSVoPpKnjM5qnw6RUwdiBsS9Kl6CIiIiIiIiJeoEBTREqE1WLw/KAYgLNCTePP7Y0bW/Nwr4aEBFpZtyeToWOWcdNHS/ht+6HSLvdMpgmzX6LgH4kWqN4cWt8ClgDYucBzGfpH3WHD9+A+TwgqIiIiIiIiIiVKgaaIlJh+LaP48La2RNrPvPw80h7Mh7e1ZXC7aJ7q24z5T/fi7q71CQywsHznH9z88W/c9ulSVu/+wzuFu/Igcy/gLmCAG44dgqvfgcfWQMcHwRYC6evg27vg/faw4jNw+uD9QUVERERERETKmABvFyAiZUu/llHEx0SybMdhDmTlUqNyMHH1w7BaTs3bDK8UxL8HxnBvtwa8P2crXy1LZeG2DBZuy6BP8xr8Lb4JLWraS6/ogCC4bw5kZwDgzM9n0aJFdOnSBVvAnz8mK1b3jKsSDf3/C92fgmUfw7KP4PB2mPoEzH0VLn8I2g+D4NDSq19ERERERESkHFGgKSIlzmox6NSwWqHjIu3BvHRtK+7v3pB3krby/ao9zNp0gFmbDjCgVRR/i29MoxqVS6FiwF7bswE4nWSG7IWoWLDZzj2+YjXo9Sx0fhRWfQ5L3gfHXpj1PCx4CzrcDZc/CJVqlE79IiIiIiIiIuWELjk/zXXXXUfVqlW54YYbvF2KSLkSHRbCGzfGMnN4DwbF1gTg5/X7uPLt+QyftIbdh3K8XOF5BFWCTg95LkW/5gMIbwLHM2HhW/B2S5g6HA7v8HaVIiIiIiIiImWGAs3TPP7443z++efeLkOk3GpYvRLv3XIZ0x7vRnxMBG4Tvl+9lyvenMuz368n7cgxb5dYsIBAuOxWeGgpDPkSarUH13FYMRreawvf3g3p671dpYiIiIiIiIjfU6B5mp49e1K5cild3ioiBWoeFcond7Tnx4e70L1JdfLdJhOX7abn63N54affOZDlw4vvWCzQfCDcMwvu/Bka9QHTDRu+hVFdYfwNsHORZ2V1EREREREREblgfhNozp8/n0GDBlGzZk0Mw+CHH344a0xiYiL16tUjODiYjh07smzZstIvVERKTGx0FT4fFsfX93cirn4YeS43YxfvpMdrc/nvtM38kZ3n7RILZhhQryvc9h3cPx9aDgbDAttmwtirYHQ8bP4Z3AWtrC4iIiIiIiIi5+I3gWZ2djaxsbEkJiae8/ikSZMYPnw4zz//PKtWrSI2Npa+ffty4MCBk2PatGlDy5Ytz9rS0tJK6zREpBji6ocx6b7LGX93R2Kjq3DM6WLUvBS6vTaHt2cm48h1ervE84uKhRvGwKMrPSugW4Ngz3L4KgE+uBzWTACXj5+DiIiIiIiIiI/wm1XO+/fvT//+/Qs8/tZbb3Hvvfdy1113ATBq1Ch+/vlnxowZwzPPPAPAmjVrSqye48ePc/z48ZNfOxwOAJxOJ07nuYOJE/sLOi4i59exnp1v7u3A7C0HGZmUwub0LN5J2sq4xTu5p2s9br88mpDAi/+xdsl6tXI09H0Nuvwdy/KPsawcg5GxBX54EHP2S7g7PoS7zW0QWLFk31ekjNLvVRH/oF4V8Q/qVRHfVx76tKjnZpim/93IzTAMJk+ezLXXXgtAXl4eISEhfPvttyf3AQwdOpQjR47w448/Fvm1586dy/vvv8+333573nEvvPACI0aMOGv/hAkTCAkJKfL7iUjxuE1Ye9hgWqqF/ccMACrZTOJruekSYWLzg/nnAa4c6mXMpuGBXwnOzwTguLUSO6rHs716H5wBuqeviIiIiIiIlB85OTkkJCSQmZlJaGhogePKRKCZlpZGrVq1WLx4MZ06dTo57umnn2bevHksXbq0SK/bp08f1q5dS3Z2NmFhYXzzzTdnvN7pzjVDMzo6moyMjAK/4U6nk5kzZxIfH4/NZivi2YrI+bjcJj+t3ce7c1LY84dnFfSI0CAe7tmAwZfVIjDgwpPNUu/V/Fws677CsuR9jCM7ATBtIbgvux13x4cgtNalr0HED+n3qoh/UK+K+Af1qojvKw996nA4CA8PLzTQ9JtLzkvDrFmzijw2KCiIoKCgs/bbbLZCP1RFGSMiRWMDboqry3XtovlmxR7em72VfZm5PPfTJj5ZuJMnejfh2stqYbUYF/7apdWrNht0vBfa3wWbfoSFIzHS12Fd9hHWFaOh9RDo8jhUb3rpaxHxQ/q9KuIf1Ksi/kG9KuL7ynKfFvW8/OCizMKFh4djtVrZv3//Gfv3799PZGSkl6oSkdJks1pI6FiHOU/25LmBMYRXCiT18DH+/s1arnx7HlPXpeF2+/iEdGuAZzX0++fDbd9DvW7gzoc1X0JiHHx1K6Qu93aVIiIiIiIiIl5VJgLNwMBA2rVrR1JS0sl9brebpKSkAi8ZF5GyKdhmZVjX+sx/uhf/6NcMewUbKQezeWTCaga8t5BZG/fj83faMAxo1BvunAr3JEGzgZ79m6fC6D4wdiBsmwW+fh4iIiIiIiIil4DfBJpHjx5lzZo1J1cq37FjB2vWrGH37t0ADB8+nE8++YRx48axadMmHnzwQbKzs0+uei4i5UtIYAAP9mzIgn/04ok+jakUFMCmfQ7u+XwF136wmAVbD/p+sAlQuz3c/CU8vAza3AaWANi5AMYPho+6wfpvwZV/anzKHHg/zvMoIiIiIiIiUgb5zT00V6xYQa9evU5+PXz4cMCzkvnYsWMZMmQIBw8e5LnnniM9PZ02bdowffp0IiIiSrVOp9NZ4BLzJ/YXdQl6Ebl4FazwcI/6JHSoxacLd/L5b7tZm3qE20cvo0O9qvytdyM61Kt6crzLbfJbykFWZhjYtx7g8obVi3X/zRJXpQEMGAndnsKy9EMsq7/ASF8P392NmfQf3Jc/jLv1zVhnjcCSsQX3rBG4ort4ZnuKlFH6vSriH9SrIv5BvSri+8pDnxb13PxylXNfkpiYSGJiIi6Xi+TkZCZMmEBISIi3yxKRAjjyYNZeCwv3G7hMT9jXzO7mqjpujhw3+H6nhSN5p0LAKoEm19dzE1vNt35U2vKzqH8wiQYHZxDkOgpAniWEQHfOyTGLGz7JwdDW3ipRRERERERE5ILk5OSQkJBQ6CrnCjRLiMPhwG63k5GRUeA33Ol0MnPmTOLj48vsalQi/mJfZi4fzNvOtyv3kn+exYJORJvv3RxL3xalO+O7SPKysaz9EsuS9zGy0k7uNjEwI1rhujtJszSlzNLvVRH/oF4V8Q/qVRHfVx761OFwEB4eXmig6TeXnPsLm81W6IeqKGNE5NKqE27jv4NjebBnI0bOSmby6rRzjjPxhJovT9tC/9a1fOPy89PZqkDnhyG8EUy46eRuAxNj/zos04ZD/9cgUDPHpezS71UR/6BeFfEP6lUR31eW+7So5+U3iwKJiFwKdatV5Kb2dc47xsQzo3PZjsOlU9SFMk2Y+yoY1rOPrf4CRraG3z4EZ27p1yYiIiIiIiJSwhRoiki5dyCraEFfUceVupQkSFsNpuvcx3MOwvRn4N3LYPloyM8r3fpERERERERESpACTREp92pUDi7SuAOO45e4kmIwTZj9EgX/ODfAHg2Va0JWGvw8HN5vB6vHgyu/NCsVERERERERKREKNEWk3IurH0aUPZjC7o758i+buO/zFew6lF0qdRWJKw8y9wLuAgaY4DoOD/3muZdmpQg4sht+fBgS42Dd1+AuYGaniIiIiIiIiA/SokAlzOl04nQ6Czx2+qOI+I5/9m/Ko1+txcBzz8wTTnzdo3E1FqYcZsbG/czZcoC7OtflwR4NqBTk7R+jFhg2E7IzCh5SsToEhEDbYdDqZiwrx2BZ8h7G4RT4/l7M+W/g6vEMZtMBYOjfucR/6PeqiH9Qr4r4B/WqiO8rD31a1HMzTNM0Cx8mBUlMTCQxMRGXy0VycjITJkwgJESrCYv4o7WHDL7faeFI3qm5mlUCTa6v5ya2msm+HJi808KWTE/oV9lmMrCOm7jqJr62+HlhAlzHqH9wJo0O/EKgKweAIxXqsDlqMPtD24DhZyckIiIiIiIifi8nJ4eEhAQyMzMJDQ0tcJwCzRLicDiw2+1kZGQU+A13Op3MnDmT+Pj4Ii9DLyKly+U2+S3lILOXrOSKTu24vGF1rKellaZpMnvLQV6dlsyuw54gsGXNUP51VVPa1a3qrbKLLzcTy9IPsSwbhZF3FAB3zba4ezyLWb+ngk3xafq9KuIf1Ksi/kG9KuL7ykOfOhwOwsPDCw00vX2tZJljs9kK/VAVZYyIeIcN6NK4BplbTbo0rnHOXu3Xqha9mkcybvFO3kvaxoY0Bzd/upxBsTV5pn8zalWpUPqFF5ctHPr8Gzo/DIvegWUfY0lbhWXijVCnE1zxL6jX1dtVipyXfq+K+Af1qoh/UK+K+L6y3KdFPS/dLE1EpBiCAqzc170hs5/syc0dojEMmLI2jd5vzuXtmckcy/OzhXZCwiB+BDy+Fi5/CKxBsHsJjB0A466G1GXerlBEREREREQEUKApInJRqlcO4r+DWzPlka7E1Qsj1+nmnaStXPHmXH5csxe/u6tHpRrQ71V4fA20vxssNtgxD0bHw5c3Qtpqb1coIiIiIiIi5ZwCTRGREtCylp1J919OYkJbalWpwL7MXB7/ag03jFrCuj1HvF3ehQutCQPfgkdXwmW3g2GFrTPg457w1a2w/3dvVygiIiIiIiLllAJNEZESYhgGA1pHkfT3Hvw9vgkVbFZW7vqDq99fxJPfrOWAI9fbJV64qnXhmvfhkeXQeghgwOap8GEX+OYuOJjs7QpFRERERESknFGgKSJSwoJtVh7t3Zg5T/bkustqAfDtyj30emMuH8zdRq7Tz+6vCVCtIVz/MTz0G8RcC5jw+/fwQUeY/AAc3u7tCkVERERERKSc0CrnJczpdOJ0Ogs8dvqjiPimkurVaiFWXru+Bbd0qMVLv2xm3R4Hr03fwoSlu3m2XxPim9fAMIySKLn0VG0I130KnZ/AOv9/WJKnwdqJmOu+xoy9BVfXJ8Fe29tVSjmh36si/kG9KuIf1Ksivq889GlRz80w/W7FCt+SmJhIYmIiLpeL5ORkJkyYQEhIiLfLEhEf4zZhZYbBlF0WMp2eELNxqJvr6rmpVdHLxV2EKtnbabbveyKy1gHgMgLYVa0nWyMHkWur6uXqRERERERExJ/k5OSQkJBAZmYmoaGhBY5ToFlCHA4HdrudjIyMAr/hTqeTmTNnEh8fj81mK+UKRaSoLmWvZh/P56MFOxi9aBd5+W4sBgxpX5vHezeiWsXAEn2v0mSkLsUy71UsuxYCYAYE4253F+5Oj0HF6l6uTsoq/V4V8Q/qVRH/oF4V8X3loU8dDgfh4eGFBpq65LyE2Wy2Qj9URRkjIt53KXq1is3GP/rHkNCxHq9O28Qv69OZuHwPU9en80SfJtzRqS42qx/e3rhBV2jwM2yfB3NexkhdinXph1hXfQ4d74fOj0JImLerlDJKv1dF/IN6VcQ/qFdFfF9Z7tOinpcf/q1ZRMT/RYeF8MGt7fjqvstpHhVKVm4+/5m6kX4j5zNnywFvl1d8DXrAsF/h1u+g5mXgzIaFb8E7sTDnVcjN9HaFIiIiIiIi4ucUaIqIeNHlDaox9dGuvHp9K6pVDCTlYDZ3fbacOz9bxrYDR71dXvEYBjTuA/fOgZsnQERLOO6Aef+Fka1hwZtw3E/PTURERERERLxOgaaIiJdZLQa3xNVhzlM9ubdbfWxWg7lbDtJv5HxenLKRzGN+uoKdYUCzAXD/ArjhMwhvArlHIOlFz4zNxe+D89ip8Slz4P04z6OIiIiIiIhIARRoioj4iNBgG/8cEMOvT3Snd7Ma5LtNxizaQa835jL+t1243H66hpvFAi2vh4d+g+s+hqr1IScDZvwT3mkDSz8GZy4kjYCMLZ5HrVcnIiIiIiIiBVCgKSLiYxpUr8ToOzswblgcjWpU4nB2Hv/6YQMD3l3A4pQMb5dXfBYrxA6BR5bD1e+BPRqOpsO0p+DtFpC22jMubTWkJHm3VhEREREREfFZCjRFRHxUjybVmfZ4N14YFIO9go3N6VkkfLKUB75Yye5DOd4ur/isNmh7Bzy6Eq56AypFemZsnmBYYPZLmqUpIiIiIiIi5xTg7QLKGqfTidN57vvdndhf0HER8Q2+1qu3xtXmqpY1eHd2ChOX72H67+kkbd7P3V3qcX/3+lQK8vwod7lNVuz6gwNZx6lROYj2datitRherv58LHDZnRgVIwj45vZTu003pK3Gteh93B0f8F554vN8rVdF5NzUqyL+Qb0q4vvKQ58W9dwM09QUmIuRmJhIYmIiLpeL5ORkJkyYQEhIiLfLEpEyKi0HJu+0kJzpmWAfajMZWMdNkNWz/0jeqQCzSqDJ9fXcxFbz4R/zpkn3LS9gP7YLC+6zDu+1d2BjrZvICYrwQnEiIiIiIiJSmnJyckhISCAzM5PQ0NACxynQLCEOhwO73U5GRkaB33Cn08nMmTOJj4/HZrOVcoUiUlS+3qumaTJ780FenZ7MrsMFX3p+Itp87+ZY+rbwzUDQSJlNwFc3nXeMabHhbj8Md5e/Q0hYKVUm/sDXe1VEPNSrIv5BvSri+8pDnzocDsLDwwsNNHXJeQmz2WyFfqiKMkZEvM+Xe7Vf61r0iolkzMIdvDZ9C+f6lykTT6j58rQt9G9dy/cuPzdNmP8qnts5nz07EwwIqoRxPAvrso+wrvsKuj0JcfeBLbiUixVf5su9KiKnqFdF/IN6VcT3leU+Lep5aVEgERE/FRRgpU101XOGmSeYwL7MXJbtOFxaZRWdKw8y93LuMBPABFsFuGUSRLSE3EyY+W9I7ADrvwV3Qc8TERERERGRskwzNEVE/NiBrNwSHVeqAoLgvjmQnVHwmIrVwV4LGsfD2ome1c+P7Ibv7oYliXDlS1CvS+nVLCIiIiIiIl6nQFNExI/VqFy0S6/tFXz0cgR7bc9WGIsVLrsNWlwHSz6ARSMhbRWMvQqaDYQ+L0B440tdrYiIiIiIiPgAXXIuIuLH4uqHEWUPprC7Y/5z8nrmbjlQKjVdUoEVocdT8NhqaD8MDAtsngqJHeHnJ88/21NERERERETKBAWaIiJ+zGoxeH5QDMBZoeaJr8NCAtl7JJc7P1vOYxNXczDreKnWeElUqgED34YHl0CTfmC6YPkn8E4bWPAmOI95u0IRERERERG5RBRoioj4uX4to/jwtrZE2s+8/DzSHsyo29qy4B+9uLtrfSwG/LQ2jT5vzePr5amY5vmWE/ITNZpBwiQYOgWiYiEvC5JehPfaw5qJWjhIRERERESkDNI9NEVEyoB+LaOIj4lk2Y7DHMjKpUblYOLqh2G1eOZp/ntgDNe2qcUz36/j9zQHT3+3ju9X7+GV61rRoHolL1dfAup3h3vnwvpvYPZ/IDMVfngAfvsArvwPNOjp7QpFRERERESkhGiGpohIGWG1GHRqWI1r2tSiU8NqJ8PME1rVtvPjw13451XNqWCz8tv2w/R7ZwHvJW0lL78MzGS0WCB2CDyy3LNIUFAopK+Dz6+BL2+EA5u8XaGIiIiIiIiUAM3QLGFOpxOn01ngsdMfRcQ3lfVevbNTNL2bVeOFKZuYv/UQb85M5sc1e3npmhja1a3q7fJKQAB0fARa3oxl4ZtYVn2GsXUG5rZZmLG34urxDFSK8HaRUgLKeq+KlBXqVRH/oF4V8X3loU+Lem6GWSZuouY9iYmJJCYm4nK5SE5OZsKECYSEhHi7LBGRQpkmrDpk8P0OC0fzPbM5u0S4GVjHTUgZ+ueuirn7iEn7hpqZKwDItwSxtcYAUmr0x2UN8nJ1IiIiIiIickJOTg4JCQlkZmYSGhpa4DgFmiXE4XBgt9vJyMgo8BvudDqZOXMm8fHx2Gy2Uq5QRIqqvPXqkRwnr81I5puVewGoUTmIfw9oRt+YGhjGX9dO919G6m9YZj2PJW0lAGalCFw9nsVsfQtYrF6uToqjvPWqiL9Sr4r4B/WqiO8rD33qcDgIDw8vNNAsQ3NwfIPNZiv0Q1WUMSLifeWlV6vbbbx+YxuubxvNPyevZ3tGNo9+tZY+zWvw4jUtqVmlgrdLLBkNusG9SfD7ZJj1AsaRXQT8/AQs/xji/wONekMZCnDLk/LSqyL+Tr0q4h/UqyK+ryz3aVHPS4sCiYgIAJ0aVuOXx7vxWO/G2KwGszYdIP6teXy2aAcudxmZzG8Y0PJ6z8JBV74MwVXgwEb4cjB8cR2kr/d2hSIiIiIiIlIIBZoiInJSsM3K8Pgm/PJYN9rXrUp2nosRUzZy/QeL2Jjm8HZ5JScgCDo/Ao+thk6PgDUQts+BUd3gh4cgc6+3KxQREREREZECKNAUEZGzNI6ozNf3d+Ll61pSOTiAtXsyGfT+Qv47bTPH8lzeLq/khIRB35fh4WXQ4nrAhDVfwnvtIOk/cDzL2xWKiIiIiIjIXyjQFBGRc7JYDG7tWJek4T0Y0CoKl9tk1LwUrhw5j/nJB71dXskKqw83fgb3JEGdTpB/DBa8Ae9eBstHgyvf2xWKiIiIiIjInxRoiojIedUIDSbx1rZ8ekd7atqDST18jDvGLOOJr1aTcfS4t8srWbXbw13TYMh4CGsI2Qfh5+HwYSfYMg3MMnIvURERERERET+mQFNERIqkT0wEM4b34K4u9TAM+GFNGn3emsc3K1Ixy1LQZxjQfBA8vBT6vw4VwiAjGSbeDOMGQdpqb1coIiIiIiJSrinQFBGRIqsUFMDzg1rww0NdaB4VypEcJ099u46ET5ayIyPb2+WVLKsNOt4Hj6+Brn8DaxDsXAAf94Tv7oUju0+NTZkD78d5HkVEREREROSSUqApIiIXLDa6Cj890oVn+zcj2GZhyfZD9B05n/dnbyUv3+3t8kpWsB36vACProTWN3v2rf8a3msPM5+DnD8gaQRkbPE8lqXZqiIiIiIiIj5IgaaIiBSLzWrh/h4NmfFED7o1Dicv380bM5IZ+N4CVu76w9vllbwq0XD9R3DfXKjXDVzHYdE7MLLlqcvQ01ZDSpJXyxQRERERESnrFGiKiMhFqVMthM+HxTFySBuqVQwkef9Rbhi1mH//sAFHrtPb5ZW8mpfB0CmQ8DVUawJ5R08dMyww+yXN0hQREREREbmEFGiKiMhFMwyDay+rxazhPbixXW1ME774bRfxb81j+oZ0b5dX8gwDmvSFvi+fud90e2ZprpngnbpERERERETKgQBvF1DWOJ1OnM5zz0g6sb+g4yLiG9SrxVcp0OCVa2O4unUk//5pIzsP5fDA+JX0aVad5wY2J8oe7O0SS45pYp3zCoZhxTBdZx768WHcB7fh7voE2EK8U185oF4V8Q/qVRH/oF4V8X3loU+Lem6Gaeq6uIuRmJhIYmIiLpeL5ORkJkyYQEiI/vIqIuJ0w4w9FmalGbhNgyCrycBoN10jTSyGt6u7eNUd6+ic8sZ5x+TYwthQK4F9VTp4ZnWKiIiIiIhIgXJyckhISCAzM5PQ0NACxynQLCEOhwO73U5GRkaB33Cn08nMmTOJj4/HZrOVcoUiUlTq1ZKVvD+Lf/24kdWpmQDE1rbz0jUxNIus7OXKLoJpYv3sSox9azA4+9eoiQGWAAy3518X3fW64bryVajerLQrLdPUqyL+Qb0q4h/UqyK+rzz0qcPhIDw8vNBAU5eclzCbzVboh6ooY0TE+9SrJaNF7TC+e7ALXy7bzWvTNrN2TybXffgb93ZvwOO9GxNss+JymyzbcZgDWbnUqBxMXP0wrL48jTP/ODj2wjnCTMATclaoAm3vgMXvY9m5AMsnPaDj/dDzGQi2l2q5ZZ16VcQ/qFdF/IN6VcT3leU+Lep5KdAUEZFLzmIxuP3yulwZE8ELP/3OtA3pfDg3hV/W7+PaNrX4ekUq+zJzT46Psgfz/KAY+rWM8mLV5xEQBPfNgeyMgsdUrA72Wp5Q89d/wuap8NsHsP4b6DMCYm8Bi9bmExERERERuVD6m5SIiJSaiNBgPrytHR/f3o7I0GB2HcrhnaStZ4SZAOmZuTw4fhXTN+zzUqVFYK8NNdsUvNlrecZVrQc3fwm3fQfVGkH2QfjxIRhzJexd5Z3aRURERERE/JgCTRERKXVXtohk+hPdCAm0nvP4iQu5R0zZiMtdRm713KgPPLgE4l+EwEqwZzl8cgX89Oj5Z3qKiIiIiIjIGRRoioiIV2zal0VOnqvA4yawLzOXZTsOl15Rl1pAIHR5HB5ZAa2HACas+hzeawtLPwZXvrcrFBERERER8XkKNEVExCsOZOUWPugCxvmV0Ci4/mO4azpEtoLcTJj2FHzcA3Yu8nZ1IiIiIiIiPk2BpoiIeEWNysFFGlcpqAyvX1e3E9w3Dwa8CcFVYP8GGHsVfHs3ONK8XZ2IiIiIiIhPUqApIiJeEVc/jCh7MEYh4/79wwYWp5The0xarNDhHnh0FbS7CzBgw7fwXntY8BbkH/d2hSIiIiIiIj5FgaaIiHiF1WLw/KAYgLNCzRNfV68USFpmLgmfLGXElN/JdRZ8z02/V7EaDBoJ982F6I7gzIakEfBBJ0ie4e3qREREREREfIYCTRER8Zp+LaP48La2RNrPvPw80h7MqNvaMvepXtzasQ4Any3ayYB3F7A29YgXKi1FNdvAsF/huo+gYg04nAITboQJN8Ph7d6uTkRERERExOvK8I3JRETEH/RrGUV8TCTLdhzmQFYuNSoHE1c/DKvFM0/z5etaER8TwdPfriPlYDbXf7iYh3s14tErGmGzltF/lzMMiL0Zml4F8/4HS0dB8jRImQ2dH4VuwyGworerFBERERER8Yoy+jdBERHxJ1aLQaeG1bimTS06Nax2Msw8oWfTGsz4W3eujq2Jy23ybtJWrvtgEVv3Z3mp4lISHAp9X4YHF0ODXuA6DgvegPfj4PfJYJrerlBERERERKTUKdAUERG/UCUkkHdvuYz3brmMKiE2Nux1MOC9hXy6YDtudxkP9qo3hdsnw5DxYK8Djj3wzZ0wbhAc2OTt6kREREREREqVAk0REfErg2Jr8usT3enZtDp5+W5e+nkTt3zyG6mHc7xd2qVlGNB8EDy8FHo8AwHBsHMBfNgFpj0Dx454u0IREREREZFSoUBTRET8TkRoMJ/d2YFXr29FSKCVpTsO02/kfCYt341Z1i/DDgyBXs/Cw8ug2UAwXbD0Q3i/PaweD263tysUERERERG5pBRoioiIXzIMg1vi6jD98e50qFeV7DwX//huPfeMW8GBrFxvl3fpVa0LN38Jt30P1RpD9kH48WEYHQ97V3q7OhERERERkUtGq5yXMKfTidPpLPDY6Y8i4pvUq/4lKtTGF3e157PFu3hr1laSNh+g79vzGTGoOf1bRnq7vEuvbne4dx6W5R9jWfA6xt4VmJ/0xoxNwNXr31Ax3NsVXjLqVRH/oF4V8Q/qVRHfVx76tKjnZphl/tq8SysxMZHExERcLhfJyclMmDCBkJAQb5clIlIupeXA+K1W9uZ4VklvF+7mhvpuQsrJP98FOY/QYu8kov9YBECeNYTNUdezM7w3pmH1cnUiIiIiIiLnl5OTQ0JCApmZmYSGhhY4ToFmCXE4HNjtdjIyMgr8hjudTmbOnEl8fDw2m62UKxSRolKv+re8fDeJc7czav523CZEhAbx6nUt6Nao7M5U/CsjdSnWX5/B2L8eALNGDK4rX8Ws28XLlZUs9aqIf1CvivgH9aqI7ysPfepwOAgPDy800Cwnc1ZKj81mK/RDVZQxIuJ96lX/ZLPB0/2b06dFJH//ei07MrIZNm4Vt19el2evakZIYDn41degK9w/D1aOhdn/wTiwkYDx10DLwRD/H7DX8naFJUq9KuIf1Ksi/kG9KuL7ynKfFvW8tCiQiIiUSW3rVOWXx7oxtFNdAL74bRdXvbOAlbv+8HJlpcRihQ53w6OroP0wwIAN33lWQ1/wJuQf94xLmQPvx3keRURERERE/IACTRERKbMqBFoZcU1Lxt/dkSh7MDsP5XDjqMW8Nn0zeflub5dXOkLCYODbnhmb0R3BmQNJL8IHl8OWXyFpBGRs8TzqLjQiIiIiIuIHFGiKiEiZ17VxONOf6M71l9XCbcIHc1O4JnERm/Y5vF1a6YmKhWG/wnUfQaUIOLwdJt4Eaas9x9NWQ0qSd2sUEREREREpAgWaIiJSLtgr2HhrSBtG3daWsIqBbNrn4Jr3F/Hh3BRc7nIyM9EwIPZmeGQFdHrkrwch6T+apSkiIiIiIj5PgaaIiJQr/VpG8esT3enTPII8l5v/Td/MkI+WsOtQtrdLKz3BodCw1192mrBvjef+miIiIiIiIj5MgaaIiJQ71SsH8ckd7XjthtZUCgpgxa4/6P/OAsb/tguzPMxQNE2Y/RIY1rOPzf4PTLoDHGmlX5eIiIiIiEgRKNAUEZFyyTAMbmofzbTHu9Gxfhg5eS7+9cMG7vxsOemZud4u79JKSfLcM9N0nfv4ph/h/Q6w+H1w5ZdubSIiIiIiIoVQoCkiIuVadFgIE++9nH8PjCEwwMK85IP0HTmfn9aW0RmKJ2ZnFvi/AAbYKkLeUZjxT/i4B+z+rTQrFBEREREROS8FmiIiUu5ZLAZ3d63Pz492pVUtO5nHnDw2cTUPT1jFH9l53i6vZLnyIHMv4C5ggAmBFWHAW1ChKuzfAGP6wo8PQ/ah0qxURERERETknAK8XYCIiIivaBxRme8f6kzinG28N3sbP6/bx7Idh3ltcGt6Navh7fJKRkAQ3DcHsjMKHlOxOthrQcy1MOt5WP0FrB4Pm3+GPiPgstvBon8TFRERERER79DfRkRERE5js1p4ok8TJj/UmYbVK3Iw6zh3jV3Os9+v4+jxMnI/SXttqNmm4M1eyzOuYjW45n0Y9ivUaAHH/oApj3lmbKav91LxIiIiIiJS3inQFBEROYfWtavw82PduLtrfQAmLkul/zvzWbbjsJcr84I6l8P986HvKxBYCfYsg4+6w/RnIdfh7epERERERKScUaApIiJSgGCblX8PjGHCvR2pVaUCqYePMeTjJbzyyyZynZ4Vwl1ukyUph/hxzV6WpBzC5Ta9XPUlYg2ATg/DI8s9l6KbbvjtA89q6Bu+8yw2JCIiIiIiUgp0D00REZFCdG4YzvQnuvHilI18s3IPH8/fztwtB7ihbW0+W7yTfZm5J8dG2YN5flAM/VpGebHiSyi0Jtw0DrbNgl+egsPb4dthsOoLuOoNCG/k7QpFRERERKSM0wxNERGRIqgcbOP1G2P55I72hFcKJHn/UV6ZtvmMMBMgPTOXB8evYvqGfV6qtJQ06gMPLoGe/wfWINg+Bz7sBLNfBucxb1cnIiIiIiJlmAJNERGRCxAfE8Evj3UjKODcv0JPXHg9YsrGsnv5+Qm2YOj5D3hoCTTsDa48mP8afHA5bJ3p7epERERERKSMUqApIiJygVIOZnM8313gcRPYl5lbfhYQqtYQbvsObvocKteEP3bClzfApNsgc4+3qxMRERERkTJGgaaIiMgFOpCVW/igCxhXJhgGxFwDjyyDTo+AYYVNU+D9OFj0Lric3q5QRERERETKCAWaIiIiF6hG5eASHVemBFWGvi/DAwsg+nJwZsPMf8OobrBrsberExERERGRMkCBpoiIyAWKqx9GlD0Yo5BxK3YdLvv30SxIRAu4axpckwgVwuDgJvisP0x+ELIzvF2diIiIiIj4MQWaIiIiF8hqMXh+UAzAWaHm6V+/OSOZ20cvZb+jHF16fjqLBS67DR5dCe3u9OxbOwHeawcrxoC74PuQioiIiIiIFESBpoiISDH0axnFh7e1JdJ+5mXlkfZgPry1La/d0JoKNiuLUw7Rb+R8kjbt91KlPiAkDAa9A3fPgshWkHsEpv4NRveBtDXerk5ERERERPxMgLcLEBER8Vf9WkYRHxPJsh2HOZCVS43KwcTVD8Nq8czTbFe3Ko9OWM3GfQ7uHreCOzvX49mrmhEUYPVy5V4S3QHunQvLP4XZL8HelfBJL+hwL1zxTwi2e7tCERERERHxAwo0S5jT6cTpPPdKrif2F3RcRHyDelUuVPs6oUAoAG5XPm6XZ3+dKkF8fV8cr89IZtyS3YxdvJOl2w8x8qbWNKhe0XsFe1u7u6HJVVhnPYdl42RY9hHm75Nxxf8HM+Z6z4rpRaBeFfEP6lUR/6BeFfF95aFPi3puhmma5XS1gpKRmJhIYmIiLpeL5ORkJkyYQEhIiLfLEhERH7PhD4MJ2yxk5xsEWkwG13fTsbpZ1OyuzKru2EDrPZ9T6Xg6AAcrxbAu+g6OBtf0cmUiIiIiIlLacnJySEhIIDMzk9DQ0ALHKdAsIQ6HA7vdTkZGRoHfcKfTycyZM4mPj8dms5VyhSJSVOpVuVT2O3J56rsNLNl+GIABrSL5z9XNqRxczj9n+cex/PY+lkVvY+TnYlpsuC9/BHfXv4Gt4H8kVK+K+Af1qoh/UK+K+L7y0KcOh4Pw8PBCA01dcl7CbDZboR+qoowREe9Tr0pJq13Nxvh7Luej+Sm8OSOZn9ens3ZPJu/echlt61T1dnneY7NBr2cgdghMexpj6wysi9/GuvE76P86NO1XyNPVqyL+QL0q4h/UqyK+ryz3aVHPS6uci4iIlCKrxeChno345oFO1K5agT1/HOPGUUtInLMNt7ucXzQRVh8SvoYhX0JobTiyGyYOga9u9fy3iIiIiIgICjRFRES8om2dqvzyeDcGto7C5TZ5/dct3D5mKfsdud4uzbsMA5oPhIeXQpfHwRIAm6dCYkdY+Dbk550aumMevTY+g7FjnhcLFhERERGR0qZAU0RExEtCg228d8tlvDa4NRVsVhZtO0T/dxYwe/N+b5fmfUGVIP5FeGAh1O0CzhyY9QJ81A12LgTTxDLnJUKPp2GZ8xLoluAiIiIiIuWGAk0REREvMgyDmzpEM+XRrjSPCuVwdh7Dxq5gxJTfOZ7v8nZ53lejOdz5M1w7CkLC4eBmGDsAPr8Gy77VAJ7HlCQvFyoiIiIiIqVFgaaIiIgPaFSjEpMf6sxdXeoB8NminVyXuJiUg0e9W5gvMAxocws8ugLaD/Ps2zGPE3MyTcMKszVLU0RERESkvFCgKSIi4iOCbVaeH9SC0UPbUzXExsZ9Dga+u5CvV6RiKqyDClVh4NvQ738AGH/uNkwXpGmWpoiIiIhIeaFAU0RExMf0bh7B9Ce606lBNY45XTz97Toe+2oNjlynt0vzPtOEdV+BYT372I+PgPN46dckIiIiIiKlSoGmiIiID4oIDWb8PR15qm9TrBaDKWvTGPDuAlbv/sPbpXlXSpJnNqZ5jvuLZu2D9zvA3pWlX5eIiIiIiJQaBZoiIiI+ymoxeLhXI755oBO1q1Yg9fAxbhy1hA/mbsPtLoeXoJum516Z5/vfl8xd8ElvmP5/kJddaqWJiIiIiEjpUaApIiLi49rWqcovj3djYOso8t0mr03fwu1jlnLAkevt0kqXKw8y9wLugscEBAMm/JYIH1wO23RfTRERERGRsibA2wWIiIhI4UKDbbx3y2V0b1yd53/6nUXbDtHvnQW8eWMsvZrV8HZ5pSMgCO6bA9kZADjz81m0aBFdunTBFvDn/9JUrA4HNsKUJ+DIbhh/PcQmQN+XISTMe7WLiIiIiEiJ0QxNERERP2EYBjd1iGbKo11pHhXK4ew87hq7nBenbOR4/jnuKVkW2WtDzTaeLSqWzJB6EBV7ap+9FjSOh4d/g44PAAasnQCJcbDhO89l6yIiIiIi4tcUaIqIiPiZRjUqMfmhztzZuR4AYxbt4LrExaQcPOrdwnxJUGXo/z+4ewZUbwbZB+HbYTDxlj8vWxcREREREX+lQFNERMQPBdusvHB1Cz69oz1VQ2xs3Odg4LsL+XpFKqZmIZ4SHQf3z4eez4LFBsnTILEjLPsE3Oe5F6eIiIiIiPgsBZoiIiJ+rE9MBNMe706nBtU45nTx9LfrePyrNThynd4uzXcEBEHPZ+CBBVC7A+RlwS9Pwtir4GCyt6sTEREREZELpEBTRETEz0Xagxl/T0ee6tsUq8Xgp7VpDHh3Aat3/+Ht0nxLjeYw7Ffo/xrYKsLuJTCqC8x7HfLzvF2diIiIiIgUkQJNERGRMsBqMXi4VyO+vr8TtapUIPXwMW4ctYQP5m7D7dYl6CdZrNDxfnh4KTSKB1cezHkJPu4Je1Z6uzoRERERESkCBZoiIiJlSLu6Vfnl8W4MaB1FvtvktelbuH3MUg44cr1dmm+pEg23fgPXfwIVwuDA7zC6D0z/P8jL9nZ1IiIiIiJyHgo0RUREyhh7BRvv33IZ/xvcigo2K4u2HaLfOwuYs/mAt0vzLYYBrW+CR5ZD6yFguuG3RPjgctiW5O3qRERERESkAAo0RUREyiDDMBjSoQ5THu1C86hQDmfncdfY5fxn6kaO57sAcLlNlqQc4sc1e1mScghXeb00vWI4XP8x3Pot2KPhyG4Yfz1MfhByDnu7OhERERER+YsAbxcgIiIil06jGpWZ/FBn/jttM2MX72T0wh38tv0QN7avzUfztrMv89Sl6FH2YJ4fFEO/llFerNiLGsfDQ0tg9kuw9CNYOwG2zoD+/4OWgz0zOkVERERExOs0Q1NERKSMC7ZZeeHqFnx6R3uqhtj4Pc3BCz9tPCPMBEjPzOXB8auYvmGflyr1AUGVPQHm3TOgejPIyYDv7oaJN0PmXm9XJyIiIiIilECgmZOTw7vvvsuAAQNo2bIlDRs2PON4ZmYmEyZMYOLEiRf7ViIiInIR+sREMPXRbgRaz/3r/8QF5yOmbCy/l5+fEB0H98+Hns+CxQbJ0yGxIyz7BNxub1cnIiIiIlKuXdQl52vWrOGaa65hz549mKbnLz7GXy7HCg0N5aWXXmLLli1ERERwxRVXXMxbioiIyEXYfTiHPFfBgZwJ7MvMZdmOw3RqWK30CvNFAUHQ8xmIuRZ+ehT2LINfnoT138LV70H1Jt6uUERERESkXCr2DM1Dhw4xYMAAUlNTadu2LW+88QahoaFnjTMMg7vvvhvTNPnpp58uqlgRERG5OAeycgsfdAHjyoUazWDYdOj/GtgqQupvMKoLzHsd8vO8XZ2IiIiISLlT7EDz7bffZt++ffTu3ZulS5cyfPhwKlSocM6xAwYMAGDJkiXFfTsREREpATUqB5fouHLDYoWO98PDS6FRPLjyYM5L8HFP2LPS29WJiIiIiJQrxQ40p0yZgmEYvPbaa1gs53+Zpk2bYrPZSElJKe7biYiISAmIqx9GlD2Y863XHWg1qBceUmo1+ZUq0XDrN3D9pxBSDQ78Dp/2hunPQl62t6sTERERESkXih1obt++ncDAQNq0aVPoWMMwCA0NxeFwFPftREREpARYLQbPD4oBKDDUzHOZXJu4iJW7/ii9wvyJYUDrG+Hh5dB6CGDCbx/AB5fDtiRvVyciIiIiUuYVO9B0u90EBASctQjQuZimydGjR6lYsWJx305ERERKSL+WUXx4W1si7WdeVh5lD+aFq2NoVKMS+x3HufnjJXzx266TC//JX1SsBtd/DLd+C/ZoOLIbxl8Pkx+AnMPerk5EREREpMwq9irntWrVIiUlhQMHDlCjRo3zjl2+fDnHjx+nefPmxX07ERERKUH9WkYRHxPJsh2HOZCVS43KwcTVD8NqMbihXTRPf7uWX9an8+8fNrBm9xFevq4lwTart8v2TY3j4aElMPslWPoRrJ0IW2dC//9By8GeGZ0iIiIiIlJiij1Ds2fPngB89tlnhY4dMWIEhmEQHx9f3LcTERGREma1GHRqWI1r2tSiU8NqWC2e4K1SUACJCW15tn8zLAZ8t2oPgz9cTOrhHC9X7MOCKnsCzLtnQPVmkJMB390NE2+GzD3erk5EREREpEwpdqD5+OOPYxgGr7zyCrNmzTrnmP3793Prrbcybdo0AgMDefjhh4tdqIiIiJQewzC4v0dDxt/dkbCKgfye5mDQ+wuZn3zQ26X5tug4uH8B9HwWLDZIng6Jl8OyT8Dt9nZ1IiIiIiJlQrEDzRYtWvDKK6+QlZVF3759ad++PZmZmQAkJCTQpUsX6taty1dffQXAO++8Q506dUqmahERESkVnRuFM+XRrsTWtnMkx8nQz5aROGcbbrfuq1mggEDo+Qw8sBBqx0FeFvzyJHzWHw5u8YxJmQPvx3keRURERETkghQ70AR4+umn+eSTTwgNDWXVqlXk5uZimiaTJk1iyZIl5OXlYbfbGTt2LPfdd19J1SwiIiKlqFaVCky6vxM3d4jGNOH1X7fwwPiVZOU6vV2ab6vRDIZNh/6vQ2AlSP0NRnWFuf+DWS9AxhZIGgFadElERERE5IIUe1GgE+6++26GDBnCd999x6JFi0hLS8PlchEZGUmXLl248cYbsdvtJVGriIiIeEmwzcp/B7cmNroKz//4OzM27uea9xfx0e3taBxR2dvl+S6LFTreB037w9S/wbaZMPeVU8fTVkNKEjTq470aRURERET8zEUHmgCVKlVi6NChDB06tCReTkRERHzULXF1aB4VyoPjV7I9I5trEhfxxo2xXNUqytul+bYq0XDrN7DuG/jhATBdfx4wIOlFaNhbq6GLiIiIiBRRsS85nz9/Pr/99luRxy9btoz58+cX9+1ERETER7SJrsKUR7vSqUE1cvJcPPTlKl79ZRP5Li16c16GARXDTgszAUzYtxYWvOG1skRERERE/E2xA82ePXsyePDgIo8fMmQIV1xxRXHfTkRERHxIeKUgvrg7jvu7NwDgo/nbuX30Mg4dPe7lynyYacLsl8Cwnn1s9kvw3X2Qfaj06xIRERER8TMXtSiQeYE3sb/Q8aUpNTWVnj17EhMTQ+vWrfnmm2+8XZKIiIhPC7BaePaq5iQmtCUk0MqS7YcY9N5C1qYe8XZpviklyXPPzDNmaJ5m/SRIjIP132qhIBERERGR87ioQPNCZGdnY7PZSuvtLlhAQAAjR45k48aNzJgxgyeeeILs7GxvlyUiIuLzBrSO4oeHu9AgvCJpmbncOGoJXy3b7e2yfMuJ2ZkF/q+XAQHBkJMB390NE26CI6mlWaGIiIiIiN8olUBzy5YtZGRkUKNGjdJ4u2KJioqiTZs2AERGRhIeHs7hw4e9W5SIiIifaBJRmR8e6UJ8TAR5LjfPfL+eZ75bR66zgNmI5Y0rDzL3AgXdZ9SEoFDo8Q+wBsLWGfDB5bD0Y3Dr3qQiIiIiIqcr8irnP/74Iz/++OMZ+zIzMxk2bFiBzzFNkyNHjrBgwQIMw6Bbt27FLnT+/Pm8/vrrrFy5kn379jF58mSuvfbaM8YkJiby+uuvk56eTmxsLO+99x5xcXEX/F4rV67E5XIRHR1d7HpFRETKm9BgGx/d1o4P56XwxowtfLU8lU37HHx4WztqVqng7fK8KyAI7psD2RkFj6lYHey1oOVg+OkxSP0Npj0F67+Bq9+DGs1Kr14RERERER9W5EBzzZo1jB07FsMwTt4L89ixY4wdO7ZIz69evTrPP/98sYoEzyXrsbGxDBs2jOuvv/6s45MmTWL48OGMGjWKjh07MnLkSPr27cuWLVtOzgxt06YN+fn5Zz13xowZ1KxZE4DDhw9zxx138MknnxS7VhERkfLKYjF4uFcjWtay8/hXq1m7J5OB7y3k/Vsuo3OjcG+X51322p6tMNWbwl3TYMVomPUC7FkGo7pC9yeh69884aiIiIiISDlW5ECzTZs2DB069OTX48aNo0KFCtx0000FPsdisRAaGkrLli0ZPHgwVapUKXah/fv3p3///gUef+utt7j33nu56667ABg1ahQ///wzY8aM4ZlnngE8oez5HD9+nGuvvZZnnnmGzp07Fzr2+PFTK7k6HA4AnE4nTqfznM85sb+g4yLiG9SrIhevc/0qfP9ARx6ZuJaN+7K4bfRSnrqyCXd3qYthGCXyHmW+Vy+7ExrGY532FJZtM2Duq5gbvsc1YCRm7Q7erk6kyMp8r4qUEepVEd9XHvq0qOdmmMVcetxisRAZGUlaWlpxnn5RDMM445LzvLw8QkJC+Pbbb8+4DH3o0KEcOXLkrEvlz8U0TRISEmjatCkvvPBCoeNfeOEFRowYcdb+CRMmEBISUtRTERERKdPyXPDNDgvLDnpu292mmptbGroJtnq5MH9imtQ8spTWe74gKD8LE4Md1fuwKeoG8q3l/FJ+ERERESlTcnJySEhIIDMzk9DQ0ALHFXmG5l/NmTOHwMDA4j69RGVkZOByuYiIiDhjf0REBJs3by7SayxatIhJkybRunVrfvjhBwC++OILWrVqdc7xzz77LMOHDz/5tcPhIDo6miuvvLLAb7jT6WTmzJnEx8f79IrvIuWdelWkZF1jmkxYvoeXf9nMmkMWsiyV+eCWNjSoXvGiXrd89eoAyHkCd9JzWNZ9RYODM6l/fBOu/m9gNurj7eJEzqt89aqI/1Kvivi+8tCnJ66ALkyxA80ePXoU96k+qWvXrrgvYBXRoKAggoLOvoeVzWYr9ENVlDEi4n3qVZGSc2eXBrSqXYUHx68i5WA2gz9ayps3xdK3ReRFv3a56VV7BFz/EcQOgSmPYxzZTcCkm6HVjdDvv1CxnN+jVHxeuelVET+nXhXxfWW5T4t6XpZLXEepCA8Px2q1sn///jP279+/n8jIi/+LkoiIiFy8dnXDmPpYV+LqhXH0eD73f7GS13/djMtdrLvflF8Nr4CHfoNOj4Bh8ayC/n4HWDsJincnIRERERERv3LRgWZ6ejqvvPIKV111Fa1bt6Zhw4Y0aNDgnFvDhg1LouazBAYG0q5dO5KSkk7uc7vdJCUl0alTp0vyniIiInLhalQO5st7OzKsS30AEuekcOdny/gjO8/LlfmZwIrQ92W4ZxZEtIRjh2HyffDlDXBkt7erExERERG5pIp9yTnA5MmTGTp0KNnZ2RS0tpBhGCePXcyqpkePHmXbtm0nv96xYwdr1qwhLCyMOnXqMHz4cIYOHUr79u2Ji4tj5MiRZGdnn1z1XERERHyDzWrhuUExxEbb+cd361iwNYNB7y9k1G3taFnL7u3y/EutdnDfXFj0Dsx7DbbNgsTLofe/Ie4+sGj1JREREREpe4odaG7cuJGEhASOHz/OgAEDGDBgAA899BB2u50333yT9PR0Zs2axdy5cwkPD+eFF16gYsXi3/x/xYoV9OrV6+TXJxbkGTp0KGPHjmXIkCEcPHiQ5557jvT0dNq0acP06dPPWijoUnM6nQUuMX9if1GXoBcR71CvipSOq1rUoEG1jjw8cQ27Dx9j8IeLefHq5lx/Wa0iPV+9eppOj0OTq7D+/Dcsqb/B9Gdwr/sG14CRUKO5t6uTck69KuIf1Ksivq889GlRz80wC5paWYh7772X0aNHc9ttt/H5558DYLFYiIyMJC0t7eS4adOmceONN9KiRQsWLlxY5m5ampiYSGJiIi6Xi+TkZCZMmEBISIi3yxIREfEbOfnwxVYLG4947oTTJcLN9fXcBJSJO32XMtNN3UNzabF3Ejb3MdyGla0RA0mOuBq3pWz9P5iIiIiIlD05OTkkJCSQmZlJaGhogeOKHWg2btyY7du3s2HDBpo39/zL/7kCTYCPPvqIBx98kP/973889dRTxXk7n+dwOLDb7WRkZBT4DXc6ncycOZP4+PgyF+yKlCXqVZHS53abJM7bzntzUjBNuCzazrs3xxIZGlzgc9Sr5+HYh/XXp7EkTwPArNYY14CRmNEdvVyYlEfqVRH/oF4V8X3loU8dDgfh4eGFBprFvuR87969BAQEnAwzwXOPzOPHj5819vbbb+eRRx7hq6++KrOB5gk2m63QD1VRxoiI96lXRUrX8Cub0aZOVZ74ag2rUzO57sOlJCZcRscG1c77PPXqOVSrA7dMhI0/wi9PYRzaSsDnA6HDPdD7OQgu+H8ORS4V9aqIf1Cvivi+stynRT2vYl/MFRgYeFZSWqlSJTIzM8nPzz9jf0hICJUrVyYlJaW4byciIiLlwBXNIpjyaFeaRVYm4+hxEj5dypiFOwpcfFDOwzCgxbXw8FK47DbAhOWfwAeXw5bp3q5ORERERKTYih1o1qxZE4fDgdvtPrmvXr16mKbJ2rVrzxj7xx9/cOTIEfLy8opfqYiIiJQLdatV5PuHOnNNm5q43CYvTt3IE5PWkJOXX/iT5WwhYXBNItzxI1StB469MHEIfDsMjh70dnUiIiIiIhes2IFmkyZNyM/PZ/PmzSf3denSBdM0eeONN84Y+69//QuApk2bFvftREREpBwJCQxg5JA2PD8ohgCLwY9r0rj+g8XszMgGwOU2WbrjMCszDJbuOIzLrRmchWrQEx5cAp0fA8MCG76DxA6wZgJoBqyIiIiI+JFiB5q9e/fGNE2mTz91ydIDDzyAxWLh66+/pmXLltx6663/z96dx0VV738cf50ZBhAEFBFxF5dcMvfcyBaV1NKyvWixMi1TW6xudW+35d663V/bbaMyyyyN9tTMMsml3Pd9QxF3FHBhFASHmfP7Y8o0JRCRM8O8n4/HeYzM+cK8D/EJ+HC+3y9t2rThvffewzAM7r777nIJLSIiIpWfYRjclRBPypCuxFQNYePewwx4ey4v/7SRi/5vJreNXconm+3cNnYpF/3fTKatzbQ6su8LDoPL/w1DZkHcBXD0IEwaBuOvgYPbrE4nIiIiIlIqZd4U6MYbb2T58uUUFBQcf65Nmza8/vrrPPzww6xfv57169cfP3fLLbcwcuTIs0vrB1wuFy6Xq9hzJz6KiG9SrYr4lvb1Ipg0rAsjP1/Fip25JM86dU3uvbkFDJuwnLdubkuf82tZkNLP1Dwf7pyObdG72Oa8hLF1FuY73fBc8gSeC+8Fm93qhFKJ6PuqiH9QrYr4vkCo09Jem2Geg1X2N27cyNdff83OnTuJioqib9++9OzZs7xfxickJyeTnJyM2+0mLS2NlJQUwsLCrI4lIiJS6Rxzwz+W2jnmMYoZYVItGJ7p4MZW3BA5RXjBXtru/IiaRzYAcDCsMSsb3I2zSgOLk4mIiIhIoMnPzycpKYnc3NxTNiM/0TlpaAYip9NJVFQUOTk5xX7CXS4XqampJCYmlnobehGpeKpVEd+0KOMAt41dWuK4CXd3okt8dAUkqkRME2PlBOwznsEodGLagvB0ewDPRaMgKNTqdOLn9H1VxD+oVkV8XyDUqdPpJCYmpsSGZpmnnJ8pl8vF6NGjGTFiREW9pCUcDkeJX1SlGSMi1lOtiviW/fml2+V8f36RarcsOt8NLa+AHx7F2DAF+7zXsG/8Dga8CY0SrE4nlYC+r4r4B9WqiO+rzHVa2usq86ZApeV2u3n//fdp2rQpDz300Ll+OREREamkYiNKd6dgacfJaUTEwU0T4MbxUDUO9m+BcVfA9w9DQa53TPoseLuz91FERERExAJlamjm5+ezatUqli9fzsGDB087xjRNxo0bx3nnncewYcPYuXMnmt0uIiIiZdU5PpraUaH81fKYIUE2WtaOqLBMlVarq2D4IugwyPv20rGQ3BU2TIUZz0HOJu+jfrYTEREREQucUUMzNzeXQYMGUaNGDTp06MCFF15IzZo1ufbaa8nMzDw+bvbs2bRp04bBgweTkZEBwNVXX82iRYvKN72IiIgEDLvN4JkBrQCKbWoWFnm47t35pGcfqbhglVWVanDVmzDoe4huAof3wBdJsGeF9/yeFZA+w9KIIiIiIhKYSt3QLCoqIjExkQkTJlBYWIhpmpimicfjYfLkySQmJnLs2DFeffVVevfuzbp167DZbCQlJbF69WomTpxIp06dzuW1iIiISCXXt3Vt3r2tA3FRJ08rrx0VyhP9WlA7KpT07DwGvj2PmRv3WZSykonvAcPmQcJDfzphwMzndZemiIiIiFS4Um8K9PHHH7N0qXdn0Z49e9K3b19M0+Snn35i5syZbNiwgXvvvZePP/4YwzC44447ePrpp2ncuPE5Cy8iIiKBp2/r2iS2imPBliymz1nE5T260K1pLHabwXUd6nH/p8tYsu0ggz9eyqOXN+f+S5tgGH81UV1K5KjibWzOe/2EJ03vXZorU6D9rVYlExEREZEAVOqG5ldffYVhGAwZMoT33nvv+POPPfYYQ4cO5YMPPuCTTz6hevXqfPvtt1xyySXnJLCvc7lcuFyuYs+d+Cgivkm1KuIfOtSLYH+MSYd6EXjcRXjcUC3UxrhBHXnhx42kLN7Fyz9tYs2uQ/z3mvMJDyn1jz3yZ6aJfcbzGIYdw3SffGrycDyHs/B0uQ9s+hzLqfR9VcQ/qFZFfF8g1Glpr80wS7lTT926ddm7dy/bt2+nXr16J53buXMnDRs2xDAM3nvvPYYMGXLmif1UcnIyycnJuN1u0tLSSElJISwszOpYIiIiAW/+PoOvM2y4TYPaYSb3NHcTow3Qy6SmczXd01/5yzEHw+JZ2WAwzioNKiiViIiIiFQ2+fn5JCUlkZubS2RkZLHjSt3QDA0NxeFwcPjw4dOer1q1KkePHmXPnj3UqlWrbKn9mNPpJCoqipycnGI/4S6Xi9TUVBITE3E4HBWcUERKS7Uq4h9KU6vLdxxixGcryT5yjKgqQbx+Y1sualqjgpP6OdPE/tHlGJkrMTj1x0YTAwwDw/Rg2oLwdHsAz0WjIEjdY/HS91UR/6BaFfF9gVCnTqeTmJiYEhuapZ4XdOzYMaKjo4s9HxERwdGjRwOymXkih8NR4hdVacaIiPVUqyL+4a9qtUuTmkwZ2YN7Jyxj1c5DDP5kGU/2a8k9PeK1rmZpFRWCczecppkJeJucVWpA/S4Ym6Zin/ca9o1TvDukN+xesVnFp+n7qoh/UK2K+L7KXKelvS4tdCQiIiKVWlxUKF8M7co/J63lq2W7eOGHDazbk8t/r2tDqMNudTzfFxQCQ2dBXk7xY8JrQlRd2DAFpj4C+zfDR/2g02Do/SyEFv/XdRERERGRM6WGpoiIiFR6oQ47L13fhgvqRfGvKeuZtHIPW7KPMPr2TtStVsXqeL4vqp73KEnLAdCoB6T+E5Z/Aks/hLRpcOVr0Lzvuc8pIiIiIgHBdiaD9+3bh91uP+2RlZUFUOx5u91OUJD6pyIiImINwzC4o1sjJtzThejwYNbudnLVW3NZuHW/1dEqlyrV4Kq3YNAUqB7vna7+2U3w9d1wJNvqdCIiIiJSCZxRQ9M0zbM+RERERKzUtXENvhuRwPl1Itmfd4zbPljEJwu26eeU8hZ/Mdy/ABIeBMMOa7+B5Ath5Wegz7WIiIiInIVS3zL5zDPPnMscIiIiIhWmXvUwvr6vO098u5rJK/fw9OR1rN2dy78HtiYkSOtqlhtHFUj8F5x/LXw3AvaugUn3wZovof/rUL2h1QlFRERExA+poSkiIiIBqUqwnddvakfrOlG8+OMGvly6i7R9Rxh9e0dqRYZaHa9yqdMOhsyCBW/D7P9C+kx4pyv0/Cd0uRdsaiKLiIiISOlpUcty5nK5cLlcxZ478VFEfJNqVcQ/lFet3tmtPk1qVuHhL1ezcuch+r85h+Rb2tG+QbVySCkn6TICmvXDPvVhbDvmw09P4lnzNe4rX4fYllank3NE31dF/INqVcT3BUKdlvbaDFMLRp2V5ORkkpOTcbvdpKWlkZKSQlhYmNWxRERE5AzlFMAHG+1kHjWwGyY3xHvoVks/Jp0TpoeG+3/h/N2f4/AcxWPYSas1gM21BuCxOaxOJyIiIiIWyc/PJykpidzcXCIjI4sdp4ZmOXE6nURFRZGTk1PsJ9zlcpGamkpiYiIOh35YF/FVqlUR/3AuajWvsIjHv13LT+uzALi1c33+3q85wUFntI+ilJYzE/tPf8OW9iMAZsx5uK98HbNeZ4uDSXnS91UR/6BaFfF9gVCnTqeTmJiYEhuamnJezhwOR4lfVKUZIyLWU62K+IfyrNVqDgfv3d6J5FlbeDU1jU8X72RzVh7Jt3agZkRIubyGnKBGA7jlM1g/GX54DCMnjaCPr4TOQ6DX0xASYXVCKUf6viriH1SrIr6vMtdpaa9LtxuIiIiInMAwDEb0bMYHd3QiIiSIxdsOcNXbc1mzK9fqaJWTYcD5A2H4Imh3G2DC4vchuSukTbc6nYiIiIj4IDU0RURERE6jV8taTBqRQOOa4WTmFnD9e/P5dvkuq2NVXmHRMDAZbp8E1RqCcxek3ADf3AN5OVanExEREREfooamiIiISDGa1KzKpOEJ9GoRS2GRh1FfruLf36+nyO2xOlrl1eQyuH8BdBsBhg3WfAVvXwirvgAt/S4iIiIiqKEpIiIi8pciQx2MuaMTD/RsCsCHczMY9NFiDuYdszhZJRYcDn1egHt+hlqt4egBmDgUPr0BDu2wOp2IiIiIWEwNTREREZES2GwGoy5vznu3dSAs2M68LfsZ8PZc1u9xWh2tcqvbEYbOhp5PgT0YtqR619ZcNBo8bqvTiYiIiIhFytzQjI+Pp0mTJmzZsqU884iIiIj4rL6tazPx/gQaRIex6+BRrnt3Pt+v3mN1rMrN7oCLH4P75kGDbuDKgx//BmP7QtZGq9OJiIiIiAXK3NDMzMwkOzubpk2blmceEREREZ/WPC6C70Yk0KNZDEddbkakrOClaRtxe7S+4zlV8zy48we48lUIjoBdi+G9i2D2f6FI0/9FREREAkmZG5p16tTB1MLsIiIiEoCqhQUz7q7O3HtxYwDemZ3O4I+XkHvUZXGySs5mgwvvgeEL4by+4HHB7Bdh9MWwc4nV6URERESkggSV9R179+7Nhx9+yIoVK2jfvn15ZvJrLpcLl+v0v8z8/nxx50XEN6hWRfyDL9Tqo4lNaV4rnL9PWsfsTdlc/fZc3klqR7PYqpZlCghhteD68RgbJmH/6UmM7A2YHybiuXAonkufhGB9/n2JL9SqiJRMtSri+wKhTkt7bYZZxtsst27dSrt27bjgggtITU0lLCysLB/G7yUnJ5OcnIzb7SYtLY2UlJSA/VyIiIgEql158MFGOwePGYTYTW5v6uGCaM1kqQiOosO03p1CgwPzAMgPjmFl/TvJjmxjcTIREREROVP5+fkkJSWRm5tLZGRksePK3NDcsWMH8+bN49577yUmJoaRI0fSvXt3YmNjsdvtxb5fgwYNyvJyPs/pdBIVFUVOTk6xn3CXy0VqaiqJiYk4HI4KTigipaVaFfEPvlar+/OO8eAXq1iUcRCABy5rwvBLG2OzGRYnCwxG+kzsPz6CkbsTAM8FN+Lu/W8Iq2FxMvG1WhWR01Otivi+QKhTp9NJTExMiQ3NMk85j4+PP/7vvLw8Hn300RLfxzAMioqKyvqSfsHhcJT4RVWaMSJiPdWqiH/wlVqNq+Zgwj1deWHqBsbN38abs9LZuO8Ir93UjqohZf6RS0qrRR+IT4BZL8DCd7Gt+RJb+kzo93/Q+jowDEifBT8+7n2uyWVWJw44vlKrIvLXVKsivq8y12lpr6vMmwKZpnnGh8fjKevLiYiIiPg8h93Gs1edz8vXtyE4yMb09fu4JnkeGTl5VkcLDCFVoe+LcM/PENsK8nPgm8Hw2c1waCfMeA5yNnkftbmliIiIiN8q8+0CGRkZ5ZlDREREpNK4oVN9mtWK4N7xS9mcdYSr3p7Lm7e057Lmsbg9JoszDpB1uIDYiFA6x0dj17T08lWvEwz9Beb+D359GdKmwdZfoOio9/yeFZA+A5r2tjaniIiIiJRJmRuaDRs2LM8cIiIiIpVKu/rVmDLyIoZNWM6y7Qe5e9wSrm5bh4UZB9ibW3B8XO2oUJ4Z0Iq+rWtbmLYSCgqGSx+HVlfD5BGwe8kf5wwbzHwemvTyTkUXEREREb9S5innIiIiIvLXYiNC+WxIV27p3ADThEkr95zUzATYm1vAsAnLmbY206KUlVxsC7jk8ZOfMz3euzTTfrImk4iIiIiclXJtaG7fvp0lS5awZMkStm/fXp4fWkRERMQvBQfZeH5gayJDTz8x5veVHJ+bsh63R+s6ljvThNkvgGE/9dxXg2DX0orPJCIiIiJn5awbmpmZmTzwwAPExsbSuHFjunbtSteuXWncuDGxsbE89NBDZGbqjgMREREJXIszDuAsKCr2vAlk5hawOONAxYUKFOkzvHdjmu5TzxUVwAe9Ydrf4Zg2bhIRERHxF2fV0Jw3bx5t2rQhOTmZnJycU3Y1z8nJ4a233qJt27bMnz+/vDKLiIiI+JWswwUlDzqDcVJKpuldK/Mvf+Q1YWEyvNMN0mdVVDIREREROQtlbmhmZWVx1VVXsX//fiIiIvjb3/5GamoqGzZsYMOGDaSmpvL4448TFRVFTk4OV111FVlZWeWZXURERMQvxEaElus4KSX3McjdDXiKHxNaDSLqwqHtMH4gTLof8nWnrIiIiIgvK/Mu56+++ioHDx6kRYsWpKamUrdu3ZPON2/enF69ejFy5Eh69+7Npk2beO211/jvf/971qFFRERE/Enn+GhqR4WyN7eA4lbJDAmy0TwuokJzVXpBITB0FuTlFD8mvCaERsKMf8Pi92Hlp7B5OlzxMrQaqF3QRURERHxQme/QnDp1KoZhMGbMmFOamSeqU6cOY8aMwTRNvv/++7K+nIiIiIjfstsMnhnQCoDi2mOFRR6uf28+W7OPVFywQBBVD+q0K/6IqgshEXDFS3D3TxDTHPKy4as74fNbwbnHuuwiIiIiclplbmhu27aN8PBwEhISShybkJBAeHi4dj4XERGRgNW3dW3eva0DcVEnTyuvHRXK3/u1oE5UKFuz8xiYPI+5m//ijkI5dxp0gfvmwCWPg80Bm6ZCchdYOhY8fzFtXUREREQqVJmnnJeFaRY3yarycLlcuFyuYs+d+Cgivkm1KuIf/LFWezWP4dJmPVi6/SBZhwuJjQihU8Pq2G0G/S+oxf2frWTlzlwGfbSYp65ozm1dGlgdOQDZ4KLH4Lz+2Kc+hG3PMvj+YTyrv8R9xf+gRlOrA/odf6xVkUCkWhXxfYFQp6W9NsMsY5exdevWbNiwgXnz5tG1a9e/HLtgwQISEhJo1aoVa9euLcvL+azk5GSSk5Nxu92kpaWRkpJCWFiY1bFERETED7k88MVWG0uyvZNoEmp5uK6RB3uZ59TIWTE9NM6eTsvMrwnyHMNtONgUN5AttfphGhV6X4CIiIhIQMjPzycpKYnc3FwiIyOLHVfmhuZjjz3Gq6++SuvWrZkxYwY1a9Y87bisrCx69erF+vXreeSRR3jppZfK8nI+z+l0Ht/RvbhPuMvlIjU1lcTERBwORwUnFJHSUq2K+IfKWqumafLBvG28PH0zpgld46vz5s1tqR4WbHW0wHVoB/YfH8G2dRYAZq0LKLryf1C7nbW5/ERlrVWRyka1KuL7AqFOnU4nMTExJTY0y/yn5UcffZQPP/yQdevW0bJlS4YNG0avXr2ObxC0a9cuZsyYwejRo9m/fz/VqlXjkUceKevL+Q2Hw1HiF1VpxoiI9VSrIv6hMtbq/Zedx3m1onjw8xUszDjIDe8v5sNBnWgaq13QLVGzCdw+EVZ9Dj89ibFvDY6P+kC34XDpkxCs2TmlURlrVaQyUq2K+L7KXKelva4yT2CqVasWEydOJCoqigMHDvCf//yHXr160aJFC1q0aEHv3r158cUXjzczJ02aRK1atcr6ciIiIiIBpXerWnx7fwL1qldh+/58rkmez6xNWVbHClyGAe1ugeFL4PxrwXTD/Dfh3e6w9Rer04mIiIgElLNakemSSy5h9erV3HvvvVSvXh3TNE86qlevzrBhw1izZg0XX3xxeWUWERERCQjN4yKYPDyBzvHRHC4sYvC4JXwwZ2tAbLTos6rWhBs+gls+h4g6cDADPrkKJo+Ao4esTiciIiISEM56ifl69erx7rvvkpOTQ3p6OgsWLGDBggWkp6eTk5NDcnLy8WnoIiIiInJmalQNYcLgLtx8YX08Jjw/dQN/+3o1hUVuq6MFtub9YPgi6DTY+/aK8ZDcGdZ/Z20uERERkQBQ5oZmz5496dWrF+np6cefi4+Pp0uXLnTp0oX4+PhyCSgiIiIS6IKDbLx47QU83b8VNgO+WraL2z5YRM6RQqujBbbQSOj/Gtz1I9RoCkf2wZe3wxe3weG9VqcTERERqbTK3NCcO3cuCxcupEmTJuWZR0REREROwzAM7r4ono/u6kxEaBBLth3k6rfnsSHTaXU0adgd7psHPR4FWxBsmAJvd4ZlH4OWBxAREREpd2e1KVBwcHB5ZhERERGRElxyXk0m3p9Aoxph7D50lOvenc/0dbob0HKOUOj1Txg6G+q0h8JcmPIAfDwA9qeX+O4iIiIiUnplbmhefPHFOJ1ONm/eXJ55RERERKQETWOrMml4AglNa5B/zM29E5aRPGuLNgvyBXEXwOCf4fLnIagKbJvj3Ql93hvgLrI6nYiIiEilUOaG5qOPPkpQUBCPPPKIfngWERERqWDVwoIZd1dn7ujWENOEl3/axMNfrKTApc2CLGcPgu4j4f75EH8xFBVA6tPwQU/IXG11OhERERG/V+aGZvv27fnss8+YPXs2CQkJTJw4kX379qm5KSIiIlJBHHYb/7q6Nf8e2Bq7zWDSyj3c9P5CspwFVkcTgOjGcMd3cNXbEBoFmavg/Uvh52fBddTqdCIiIiJ+q8wNTbvdzg033EBeXh6LFi3i+uuvp06dOgQFBWG32097BAUFlWd2EREREQFu79qQ8Xd3JqqKg1U7D3HV2/NYuzvX6lgCYBjQ4XYYvgRaXQ2mG+b+D95NgG1zrU4nIiIi4pfK3NA0TbNMh4iIiIiUv+5NY5g8PIEmNcPZ6yzg+vfmM3V1ptWx5HcRteDGT+CmT6FqHBxIh3FXwpSHoEDNZxEREZEzUeZbJmfNmlWeOURERETkLDWKCWfi8ARGpqzgl7RshqcsZ3NWMx7o2QybzbA6ngC07A+NLvKuqbn8Y1j2EaRNgytfhRZXWp1ORERExC+UuaF5ySWXlGcOERERESkHkaEOxt55IS/+sIEP5mbw+s+b2bzvCK/c0JYqwXar4wlAlWpw1ZtwwQ0w5QE4sBU+T4JWA+GKl6FqrNUJRURERHxamaecx8fH06RJE7Zs2VKeeURERETkLNltBk/1b8VL17XBYTeYuiaTG0bPJzNXG9H4lPgeMGw+JDwEhh3WT4K3L4QVn4KWahIREREpVpkbmpmZmWRnZ9O0adPyzCMiIiIi5eTGC+uTMqQr0eHBrN3t5Kq357Fix0GrY8mJHFUg8TkYMhPi2kDBIZh8P4wfCAcy/hiXPgve7ux9FBEREQlwZZ5yXqdOHbKzs8szS6XgcrlwuVzFnjvxUUR8k2pVxD+oVkunXd0Ivrm3C/dOWEFa1hFuen8h/xl4Ple3rW11NDlRzfPhrunYFr6Dbc5LGFtnY77bHc8lT+DpNBT7z89hy9mE5+fncNdP8O6e7idUqyL+QbUq4vsCoU5Le22GWcatx4cOHcqHH37I0qVLad++fVk+RKWQnJxMcnIybrebtLQ0UlJSCAsLszqWiIiIyEkK3DB+s421B70TdHrX9XBlfQ/aK8j3hBfspe3Oj6h5ZAMAh0PiiCjce/z8/CaPkh3Zxqp4IiIiIudMfn4+SUlJ5ObmEhkZWey4Mjc0t27dSrt27bjgggtITU0N+Cae0+kkKiqKnJycYj/hLpeL1NRUEhMTcTgcFZxQREpLtSriH1SrZ87jMXnt5y2MnuOdyty7RU1evv4CqoaUedKOnCumB2PlBOw/P4Nx7DAmYACmYceMa4P7rul+c5emalXEP6hWRXxfINSp0+kkJiamxIZmmX96DQoKYvTo0dx77720bt2akSNH0r17d2JjY7Hbi99Bs0GDBmV9Sb/gcDhK/KIqzRgRsZ5qVcQ/qFbPzJNXtqJFnUge/2YNP2/M5pYPljDmjk7Ujw7sP077pM6DITQCvh3C761Lw3RjZK7AtuNXaNrb0nhnSrUq4h9UqyK+rzLXaWmvq8wNzfj4+OP/zsvL49FHHy3xfQzDoKioqKwvKSIiIiLl4Jr29WhYI5yhnyxj497DXJ08j9G3d+TCRtFWR5MTmSYsfMe7A7rpPvncN0Ng+BKoGmNNNhERERELlXmXc9M0z/jweDzlmV1EREREyqhDg+p8NyKB8+tEciDvGEljFvLlkp1Wx5ITpc+APStObWYCHD0Ab7aFlZ95G58iIiIiAaTMd2hmZGSUZw4RERERqWB1qlXhq/u68ciXq/hx7V7+9s1q0vYd5skrWmLXbkHWMk2Y+Tze+w+KuSng2BGYdB+s+gz6/w9qNKnIhCIiIiKWKXNDs2HDhuWZQ0REREQsEBYcRHJSB96YsZk3Zmzmg7kZbM46wltJ7YkMrZxrM/kF9zHI3U2xzUyA4KrgdkHGL/BON7jkb9D9AQgKrrCYIiIiIlbQlpYiIiIiAc5mM3g48Tya1arKo1+t4pe0bK59Zz4f3NGJRjHhVscLTEEhMHQW5OUUPya8JrgL4ftRsHUWzPw3rPkaBrwBDbpUXFYRERGRCqaGpoiIiIgA0L9NHRpGhzPkk6VsyTrCwHfm8c6tHejeRBvPWCKqnvcoye0TYfWX8NOTkL0Bxl4One6GXs9AlWrnPKaIiIhIRSv1pkA9e/bkhhtuOO25r776ik8++eQv37927doEBal/KiIiIuLLLqgXxXcjEmhbL4pD+S7u+HAxExZuB8DtMVmQvp/JK3ezIH0/bo82o/EJhgFtb4IRS6Hdbd7nlo6F5M6wbqI2DRIREZFKp9QdxtmzZxMXF3facw888ADZ2dnccccdf/kxTP0wJSIiIuLzYiND+eLebjz+zWomr9zDU5PWkrp+H5v2Hmavs+D4uNpRoTwzoBV9W9e2MK0cFxYNA5O9zc0pD8GBdPjqTjivL1zxClSrb3VCERERkXJR6js0S6JmpYiIiEjlEeqw8/pN7XisT3MAfknLPqmZCbA3t4BhE5YzbW2mFRGlOPEXw7D5cPHfwOaAtGmQ3AUWJIO7yOp0IiIiImet3BqaIiIiIlK5GIbBfZc0oXrY6Xc7//3P2c9NWa/p577GEQo9/wH3zYUG3cCVBz/9HT7oCXtWWp1ORERE5KyooSkiIiIixVqccYCD+a5iz5tAZm4BizMOVFwoKb3YFnDnD96dz0OjIHMVjLkMfvoHFB6xOp2IiIhImaihKSIiIiLFyjpcUPKgMxgnFrDZoOOdMHwJnH8tmB5Y8Da80xXSfrI6nYiIiMgZU0NTRERERIoVGxFaruPEQhG14IaP4NavIaoB5O6ElBvhy0FweK/V6URERERKTQ1NERERESlW5/hoakeFYvzFmJAgGy1qR1RYJjlLzRJh+ELoPhIMO6yfBG93hiUfgsdjdToRERGREqmhKSIiIiLFstsMnhnQCqDYpmZhkYcb31vAjv35FRdMzk5wOFz+PAydBXXaQ2EuTB0FH/WFrA1WpxMRERH5S0FnMvjAgQP07NnztM8Dpz335zEiIiIi4l/6tq7Nu7d14Lkp68nM/WOtzNpRodzZvRFj52WwOesIA9+Zx3u3daRzfLSFaeWM1G4L98yAxe/DzOdh5yJ4rwckPAgXP+bdLV1ERETEx5xRQ9PlcjF79uxizxd3zjAMTNPEMP5qspKIiIiI+Kq+rWuT2CqOxRkHyDpcQGxEKJ3jo7HbDK5uV5chnyxlze5cbv1gIS9e24brO9azOrKUls0OXYdBywHww2Ow6QeY8wqs+xb6vw6NL7E6oYiIiMhJSt3QvPjii9WQFBEREQlgdptBtyY1Tnk+LiqUL+/txqgvV/Lj2r08+tUq0rOP8NjlzbHZ9POj34iqBzenwIYp8OPf4MBW+OQqaJvknZ4efup/exERERErlLqh+Vd3ZoqIiIhIYKsSbCc5qQOvpm4ieVY6785OZ2v2Ef53UzvCgs9oUpBYyTCg1VXeuzJn/Mu7UdCqFEibBn3+A21v9o4RERERsZA2BRIRERGRcmGzGTzWpwX/u6ktwXYbP63bx/XvLiAz96jV0eRMhUbBla/C4OkQ2wqOHoBJ98EnV8P+dKvTiYiISIBTQ1NEREREytU17euRMqQLNcKDWZ/p5Oq357Fq5yGrY0lZ1O8M9/4KvZ6BoFDI+AXe7Q6/vgJFx6xOJyIiIgFKDU0RERERKXedGkUzaXgC59WqStbhQm4cvYCpqzOtjiVlYXdAj1Fw/wJofCkUFcDMf8Poi2HHIqvTiYiISABSQ1NEREREzon60WF8M6w7lzWvSWGRh+Epy3lzxmZM07Q6mpRFdGO4fRJc8z6E1YDsDTC2D3z/MBw9ZHU6ERERCSBaob2cuVwuXC5XsedOfBQR36RaFfEPqlX/EGqHd5Pa8d9pmxi3YAevpaaxeZ+TFweeT4jDbnU8KYtW10KjS7HPeBbb6hRYOhZz41Tcl7+I2WLAKZsGqVZF/INqVcT3BUKdlvbaDFN/Ij8rycnJJCcn43a7SUtLIyUlhbCwMKtjiYiIiPic+fsMvsqw4TENGlU1GdzcTWSw1ankbMQcXk/bnR9RtXAfAHsj27G6/h0cDY6xOJmIiIj4o/z8fJKSksjNzSUyMrLYcWpolhOn00lUVBQ5OTnFfsJdLhepqakkJibicDgqOKGIlJZqVcQ/qFb904Kt+xnx2SqcBUXUiQpl9G3taREXYXUsORtFBdjm/Q/b/DcxPC5MRzieS57Ac+EQsAXh3jyDY5MfJvjq/2Fv1svqtCJSDH1fFfF9gVCnTqeTmJiYEhuamnJezhwOR4lfVKUZIyLWU62K+AfVqn+5uHkck4ZXZfDHS8nIyePmMYt54+b29G5Vy+poUlYOB/R+GtrcCN8/hLFjAfaf/4l93dfQ/w3sc/5LaOEePHP+i61ln1OmpIuIb9H3VRHfV5nrtLTXpU2BRERERKRCNa5ZlYn3d6db4xrkHXMzZPxSPpizVZsF+bvYFnDnDzDgDQiJgsxVMOYybJkrALyP6TMsDikiIiKVQZkbmvn5+Wf8PqtWrSrry4mIiIhIJVItLJhPBnfmls71MU14fuoGnvx2DceKPFZHk7Nhs0HHO2HEEmh1DfBHk9rEgJnPgxrXIiIicpbK3NC87rrrcLvdpR6/Zs0aEhMTy/pyIiIiIlLJOOw2/nPNBfyzfytsBny+ZCd3jF3EwbxjVkeTsxVRCzrcdtJTBibsWQFLPrAolIiIiFQWZW5o/vTTTwwePLhUY9evX0+vXr3Yv39/WV9ORERERCohwzAYfFE8HwzqRHiwnYVbD3DNO/NIzz5idTQ5G6bpvRvTsJ967odHYfJIyMup+FwiIiJSKZS5oRkbG8v48eN5/PHH/3Lcxo0b6dWrFzk5OfTo0aOsLyciIiIilVjPFrX45v7u1K1WhW3787kmeR5zN6vh5bfSZ3jvxjSLmdG14hN4swMsfBfcrorNJiIiIn6vzA3NH3/8kfDwcF555RXeeOON045JS0ujV69e7Nu3j4SEBH744YcyBxURERGRyq1FXCSTRyTQoUE1nAVFDPpoMRMWbrc6lpyp3+/OLPZXDQOCqkBhLkx7At67CNJnVWRCERER8XNlbmi2b9+eb7/9FofDwSOPPMIXX3xx0vnNmzfTs2dPMjMz6datGz/++CNhYWFnHVhEREREKq+YqiGkDOnKwHZ1cHtMnpq0lme/W0eRW5sF+Q33McjdDRT338yEkAjo9wpUiYbsjTB+IHx+KxzIqMCgIiIi4q+Czuade/fuzUcffcRtt93GoEGDqFmzJj179mTr1q306tWLPXv20LlzZ6ZNm0bVqlXLK7OIiIiIVGKhDjv/u6kdTWOr8sr0NMbN38a2/Xm8eUt7IkMdVseTkgSFwNBZx9fIdBUVMW/ePBISEnAE/fbrR3hNiKoLba6H2f+FxWNg4/ewORW6j4QeoyA43MKLEBEREV9W5js0f3fLLbfw8ssvc+zYMa699lq+/fZbevbsya5du+jYsSPTp08nIiKiPLKKiIiISIAwDIMRPZvxzq0dCHXYmL0pm+vemc/OA/lWR5PSiKoHddp5j9ptyQ1rBLXb/vFcVF3vuCrVod//wbB5EH8JuAthzivwVidY87V3+rqIiIjIn5x1QxNg1KhRPPLIIzidTm644QZ27NhBu3btmD59OpGRkeXxEiIiIiISgK64oDZf3tuN2IgQNmcd4erkeSzZdsDqWFLeYlvCHZPhpglQrQEc3gPfDIaP+kHmKqvTiYiIiI8pl4YmwMsvv8xtt92GaZq0bduWGTNmUL169fL68CIiIiISoNrUq8bkEQmcXyeSA3nHuHXMIr5dvsvqWFLeDANaDoDhS6DnU+AIgx0LYPQl8N0Dx6ewi4iIiJRqDc2ePXuW6oO5XC4Mw8A0Ta677rpTzhuGwYwZM84soYiIiIgEvNpRVfjqvm6M+mIV09btZdSXq9iSdYRHL2+OzWZYHU/KkyMULn4M2iZB6tOw9mtY/jGsmwSXPQkX3gN2raUqIiISyErV0Jw9e/YZfdDVq1ef9nnD0A+bIiIiIlI2YcFBvHNrB16Zvol3Zqfzzux0tmbn8dpNbQkLPqu9LsUXRdWF6z+ECwfDj3+DvWtg2hOwbBz0/S80uczqhCIiImKRUv3k98wzz5zrHCIiIiIiJbLZDP7WtwVNalblyW/XMG3dXnaNzueDOy4kLirU6nhyLjTsDkN/geWfwIx/QfZGGD8QWvSHy5+H6HirE4qIiEgFU0NTRERERPzOdR3r0aBGGPeOX8ba3U6uensuHwzqRJt61ayOJueCzQ6d7oLzB8Ls/8LiMbDxe9icCt1HQo9REBxudUoRERGpIOW2KZCIiIiISEW6sFE0k4cn0Cy2KlmHC7lx9AJ+WJNpdSw5l6pUh37/B8PmQfwl4C6EOa/AW51gzddgmlYnFBERkQqghqaIiIiI+K360WF8c393LjmvJgUuD/d/upy3Z27GVGOrcottCXdMhpsmQLUGcHgPfDMYPuoHmausTiciIiLnWJkbmuvWrePaa6/lqaeeKnHsE088wbXXXsvGjRvL+nIiIiIiIqcVGergw0GduCuhEQCvTE/j4S9WUuByWxtMzi3DgJYDYPgS6PkUOMJgxwIYfQl89wDk5VidUERERM6RMjc0x48fz+TJk2nUqFGJY2vVqsXkyZOZMGFCWV9ORERERKRYQXYbzww4n+cHtsZuM5i0cg9JYxaSc6QQALfHZEH6fiav3M2C9P24PbqDs9JwhMLFj8GIpdD6esCE5R/Dmx1g4bvgdlmdUERERMpZmRuaP//8MwD9+/cvcezNN9+MaZpMnz69rC8nIiIiIlKi27o25OO7OhMZGsTyHYe4+u15fDB3Kxf930xuGbOQBz9fyS1jFnLR/81k2lqtt1mpRNWF6z+Eu36EuAugMBemPQHvXQTps6xOJyIiIuWozA3NHTt2ULVqVeLi4kocW7t2bapWrcrOnTvL+nIiIiIiIqVyUbMYJg5PoFGNMHYfOsrz328gM7fgpDF7cwsYNmG5mpqVUcPuMPQX6P86VImG7I0wfiB8fiscyLA6nYiIiJSDMjc0nU4nQUFBpR4fFBTEwYMHy/pyIiIiIiKl1qRmVb6+rzvBduO053+fcP7clPWafl4Z2ezQ6S54YDl0uQ8MO2z8HpK7wIx/w7E8qxOKiIjIWShzQzMmJoZDhw6xf//+Esfu37+f3NxcqlevXtaXExERERE5I5uzjnDMXXyz0gQycwtYnHGg4kJJxapSHfr9HwybB/GXgLsQ5rwCb3WCNV+DqWa2iIiIPypzQ/PCCy8EYNy4cSWO/eijjzBNk44dO5b15UREREREzkjW4YKSB53BOPFjsS3hjslw0wSo1gAO74FvBsNH/SBzldXpRERE5AyVuaF5yy23YJom//znP/npp5+KHTdt2jSefvppDMPg1ltvLevLiYiIiIickdiI0HIdJ37OMKDlABi+GC57ChxhsGMBjL4EvnsA8nKsTigiIiKlVOaG5g033ECPHj0oKCjgyiuv5Oqrr2bMmDFMnz6d6dOnM2bMGK666ir69+9PQUEBPXr04JZbbinP7CIiIiIixeocH03tqFBOv4qmV3RYMJ3joyssk/gARxW45DEYsQRaXweYsPxjeLMDLHwX3K4/xqbPgrc7a5d0ERERH1PmhqZhGHz77bd0794dj8fD999/z3333Ue/fv3o168f9913H1OnTsXj8XDRRRfxzTfflGduEREREZG/ZLcZPDOgFUCxTc2D+cf4aF4GptZSDDxR9eD6sXDXjxB3ARTmwrQn4L2LvA1M04QZz0HOJu+jvkZERER8RpkbmgA1atTgl19+YcyYMXTr1o2goCBM08Q0TYKCgujevTtjx45l1qxZ1KhRo7wyi4iIiIiUSt/WtXn3tg7ERZ08rbx2VCgJTWpgAs9P3cDj36zmWJHHmpBirYbdYegv0P91qBIN2Rth/ED48HLYs8I7Zs8KSJ9hZUoRERE5QdDZfgC73c7gwYMZPHgwbreb/fv3YxgG0dHR2O328sgoIiIiIlJmfVvXJrFVHIszDpB1uIDYiFA6x0djM2DsvG28MHU9Xy7dxbacfN69rQM1qoZYHVkqms0One6C8wfC7P/Covdh1+I/zht2mPk8NOnlXYtTRERELHVWd2j+md1uJzY2lpo1a6qZKSIiIiI+w24z6NakBle3q0u3JjWw2wwMw2DwRfF8eOeFRIQEsXjbAa5OnsemvYetjitWqVId+v0f9H/t5OdNt/cuzbTiN0MVERGRilOuDU232012djbZ2dm43e7y/NAiIiIiIufEZc1jmTi8Ow1rhLHr4FGufWceMzbsszqWWMX8bZMg4zQ3aHx5O6z5RutpioiIWOysG5r5+fm89tprXHjhhYSFhREXF0dcXBxhYWF07tyZ119/nfz8/PLIKiIiIiJyTjSNjWDS/Ql0bRxN3jE393yylNG/pGuzoECUPsN7N6Z5mhs03Mfgm7thTE/YNrfis4mIiAhwlg3NTZs20a5dOx577DGWLVuGy+U6vimQy+Vi6dKlPPLII7Rv3560tLTyyiwiIiIiUu6qhwczfnAXkro0wDThxR838uhXqyks0syjgGGa3rUy//LXJBvsWQ7jroSUmyFrY0WlExERkd+UeVOgw4cPc/nll7Nz506CgoK49tprSUxMpF69egDs2rWLn3/+mW+++YbNmzfTp08f1qxZQ9WqVcstvIiIiIhIeXLYbbwwsDXnxVblX9+v55vlu9i2P4/Rt3ckRpsFVX7uY5C7G/iLHe/DoqFFf1gxHtJ+hM0/Qfvb4bK/Q0RchUUVEREJZGVuaL7++uvs3LmTOnXq8P3339OuXbtTxgwePJhVq1Zx5ZVXsmPHDt544w3+8Y9/nE1eEREREZFzyjAM7kyIp3HNqgxPWc6y7Qe5+u15fDCoEy1rR1odT86loBAYOgvycoofE14ToupC9xHw87Ow8XvvmptrvoJuIyDhAQiJqLDIIiIigajMU84nTZqEYRiMHj36tM3M37Vt25b3338f0zT59ttvy/pyIiIiIiIV6uLzajJpeALxMeHsPnSU696dz/R1e62OJedaVD2o0674I6qud1xMM7j5U7j7J6jXGVz58OtL8GZ7WDwG3C6LLkBERKTyK3NDc8uWLYSEhHDllVeWOLZfv36EhoayZcuWsr6ciIiIiEiFa1KzKhPv705C0xrkH3Nz74RlJM/aos2C5A8NusLg6XDjeIhuAnnZ8MOj8E5X2DBFO6KLiIicA2VuaLpcLoKDg0s11jAMgoODcbn0V0oRERER8S/VwoIZd1dnbu/aENOEl3/axKgvV1Hg0mZB8hvDgFZXwfBFcMUrEBYD+7fAF7fB2D6wY5HVCUVERCqVMjc069Wrx+HDh1m/fn2JY9euXYvT6Ty+YZAvOnToEJ06daJdu3a0bt2aMWPGWB1JRERERHyEw27j3wNb8++rz8duM5i4Yje3jFlI1uECq6OJL7E7oPMQeGAFXPwYBFWBnYtg7OXe5maOZqyJiIiUhzI3NHv16oVpmgwbNoyCguJ/kCsoKOD+++/HMAx69+5d1pc75yIiIvj1119ZuXIlixYt4j//+Q/79++3OpaIiIiI+JDbuzXik7s7E1XFwYodhxj49jzW7s61Opb4mtBI6PmUt7HZ4Q4wbN7p58mdYeojcCTL6oQiIiJ+rcwNzccee4yQkBDmzp1L27Zt+fDDD9m2bRsulwuXy0VGRgYffPABbdu2Ze7cuQQHB/Poo4+WZ/ZyZbfbCQsLA6CwsBDTNLU2koiIiIicIqFpDJOGJ9C4Zjh7cgu44b0FTFubaXUs8UWRteGqt2DYfDivL5huWPKBd+OgX16CY3lWJxQREfFLZW5oNm7cmI8//pigoCA2b97M0KFDadKkCaGhoYSGhtK0aVPuvfdeNm/ejMPh4OOPP6Zx48ZlDvrrr78yYMAA6tSpg2EYTJo06ZQxycnJNGrUiNDQULp06cLixYvP6DUOHTpE27ZtqVevHo899hgxMTFlzisiIiIilVd8TDgT70+gR7MYjrrc3DdhOW/N2Kw/iMvpxbaEpC9g0PdQpz0cOwKzXvA2NpeNA3eR1QlFRET8SpkbmgA33ngjCxYsoE+fPgDH72r8/TAMg379+rFw4UJuvPHGswqal5dH27ZtSU5OPu35L774glGjRvHMM8+wfPly2rZtS58+fcjK+mM6x+/rY/752LNnDwDVqlVj1apVZGRkkJKSwr59+84qs4iIiIhUXlFVHHx054Xc2b0RAK+mpvHg5yu1WZAUL74H3DMTrh8L1RrCkX0w5UF4tzts+lE7oouIiJRS0Nl+gA4dOvDjjz+Sm5vL8uXLjzcQY2Nj6dChA1FRUWcdEqBfv37069ev2POvvfYaQ4YM4a677gLgvffeY+rUqYwdO5YnnngCgJUrV5bqtWrVqkXbtm2ZM2cO119//WnHFBYWUlhYePxtp9MJcHzK/en8/rx2exfxbapVEf+gWhVf8Y9+59EkJoznvt/Ad6v2sC3nCO8ktaNWZKjV0XyCavU0ml8FTfpgWz4O29xXMHI2wWc342nQHU/PZzHrdrA6oQQg1aqI7wuEOi3ttRmmH86LMQyDiRMnMnDgQACOHTtGWFgYX3/99fHnAAYNGsShQ4eYPHlyiR9z3759hIWFERERQW5uLgkJCXz22WdccMEFpx3/7LPP8txzz53yfEpKyvG1OEVEREQkcGzONRibZiO/yCAq2GRIczf1q1qdSnxdUFEezbKm0iTrJ+ym95e4XdW6sKHO9eSH1LI4nYiISMXKz88nKSmJ3NxcIiMjix131ndo+oKcnBzcbje1ap38Db9WrVps3LixVB9j+/btDB069Ph0+ZEjRxbbzAR48sknGTVq1PG3nU4n9evX5/LLLy/2E+5yuUhNTSUxMRGHw1GqXCJS8VSrIv5BtSq+aOD+fO79dAXp2Xm8vTGYl65tTb/WcVbHspRqtTRuwOPcjfHLfzFWf069Q4uo61yOp+PdeC4aBWE1rA4oAUC1KuL7AqFOf58BXZJK0dAsD507dy71lHSAkJAQQkJCTnne4XCU+EVVmjEiYj3Vqoh/UK2KL2kaF8XE4Qk88NkKZm/K5oEvVvPQ/qM82KsZhmFYHc9SqtUS1GgE174H3UfAz89gbPkZ+5LR2Fd/Bhc9DF2HgaOK1SklAKhWRXxfZa7T0l5XqRqaZ7M7+YkMwyA9Pb1cPtaJYmJisNvtp2zis2/fPuLiAvsv4iIiIiJSsSJDHXw46EL+88MGPpybwes/b2Zz1hFeub4tVYLtVscTXxfXGm77BtJnQerTsHc1zHgOlnwAl/0D2t4MNn0diYhIYCtVQ3Pbtm3l8mLn6q/SwcHBdOzYkRkzZhxfQ9Pj8TBjxgxGjBhxTl5TRERERKQ4dpvBP/u34rxaVXlq0lqmrs5kx/58xtzRibgobRYkpdDkMoj/BdZ+DTP+Bbk7YfL9sCAZEv8FTXtBgN/1KyIigatUDc2PPvroXOco0ZEjR9iyZcvxtzMyMli5ciXR0dE0aNCAUaNGMWjQIDp16kTnzp15/fXXycvLO77ruYiIiIhIRbvpwgY0qhHOfROWsWZ3Lle9PZcxd3Sibf1qVkcTf2CzQZsboeVVsGQM/PoyZK2DT6+D+Eu8jc067axOKSIiUuFK1dAcNGjQuc5RoqVLl3LZZZcdf/v3DXkGDRrEuHHjuOmmm8jOzubpp59m7969tGvXjmnTpp2yUdC55nK5it1i/vfnS7sFvYhYQ7Uq4h9Uq+IvOtSP5Jv7unDvhBVszsrjxtEL+O8159O/TW2ro1UI1Wp5sMOF90Hrm7DN+x+2pR9gZPwC71+Cp/X1uC/5O1RrYHVI8XOqVRHfFwh1WtprM0zTNM9xlkotOTmZ5ORk3G43aWlppKSkEBYWZnUsEREREfExBUXw8WYb6w/ZALi8rod+9T3YNGtYzlCVwmxaZn5D/YPzAXAbQWytmcjmWlfhCgoHoKZzLa13TWBtvdvIjmxtZVwREZFSy8/PJykpidzcXCIjI4sdp4ZmOXE6nURFRZGTk1PsJ9zlcpGamkpiYmKl3Y1KpDJQrYr4B9Wq+CO3x+Tl6Wl8OG87AH1axfLSda0JCy7VxCm/pFo9hzJXYZ/5LLZtcwAwQ6vhSXgYT8e7sY+/ClvmCjy12+O+a7rW25QSqVZFfF8g1KnT6SQmJqbEhma5/+R05MgRfvjhBzIyMggPD6dTp0507dq1vF/GZzkcjhK/qEozRkSsp1oV8Q+qVfEnDuCfA1rTonYUf5+4hp/WZ7Hr0FLG3NGJOtWqWB3vnFKtngMNOsGgKbBlBqQ+jZG1DvuMZ7AveBvyswGwZa7AtuNXaNrb4rDiL1SrIr6vMtdpaa/rjBuaCxcuZPLkyRw5coTmzZtzxx13HO+Yfvfdd9x5553k5uae9D7dunXj66+/Ji4u7kxfTkRERESk0rmhU33iY8K5d/wy1u1xctXb8xhzR0faN6hudTTxN4YBzXp7d0Vf9TnM+DccyTzhvA1mPg9NtCu6iIhUHrYzGfyvf/2LhIQEXnrpJd555x0efPBBWrRowc6dO1myZAk33XQThw4dwjTNk44FCxbQr18/ioqKztV1iIiIiIj4lU6Nopk0PIEWcRHkHCnkpvcXMmnFbqtjib+y2aH9rdD/1ZOfNz2wZwXMegE8HmuyiYiIlLNSNzQXLFjAs88+e7xJGR0djWma7Nu3j3/84x88//zzFBYWcs011/DDDz+wbt06fvrpJ+644w5M02T16tWMHz/+XF6LiIiIiIhfqR8dxtfDutO7ZS2OFXl46IuVvDRtIx6PlrmXMjBN+PVlMOynnvv1ZXivB2yYosamiIj4vVI3NEePHg1Ap06d2LlzJ9nZ2ezcuZNOnToxZcoUpk2bxj333MM333xD3759admyJYmJiYwbN46///3vmKbJV199dc4uRERERETEH1UNCeL92zsy7NImALwzO537Jiwjr9A7u8ntMVmQvp/JK3ezIH0/bjU7pTjpM7x3Y5ru05/PWgtf3AbvXwwbvvc2QEVERPxQqdfQnD9/PoZh8MYbb1C3bl0A6taty+uvv05CQgKGYTBixIjTvu/IkSP5z3/+w6pVq8ontQ9zuVy4XK5iz534KCK+SbUq4h9Uq1LZjOrVhMY1qvD3SeuYvn4f170zj6Qu9Xln9lb2OguPj4uLDOGpK1rQ5/xaFqYtPdVqBTFN7DOex8DA4NRGpYkBVWOh8DDG3jXwxa2YtS7A3eMxzPP6aX1NUa2K+IFAqNPSXpthmqX7s1x4eDgul4vCwkKME77ZeTweQkJC8Hg8HD16lODg4NO+f/Xq1Tl69CgFBQWlCuYvkpOTSU5Oxu12k5aWRkpKCmFhYVbHEhERERE/lXEYPthk54jLgOONqRObTd7n7j7PQ9sausNOvGweF4nrHia0yFnsmIKgKGY3f47GOTNonJ1KkMf7u9mhKg3ZVPsa9ka2V2NTREQslZ+fT1JSErm5ucc3IT+dUjc0bTYbsbGx7N2795RztWrVIicnB7e7mKkNQO3atcnKyvrLMf7M6XQSFRVFTk5OsZ9wl8tFamoqiYmJpd6GXkQqnmpVxD+oVqUy23kgn8vfmEdRMdPLDSAuKoRZoy7GbvPtBpRqtQI5d0NeTvHnw2tCZB3vv/MPYFv0DralYzCO5QHgiWuL5+K/YTa9XI3NAKRaFfF9gVCnTqeTmJiYEhuapZ5yDt6m5pk8H4gcDkeJX1SlGSMi1lOtivgH1apURvuOFBXbzATvPZqZuYWs2HWYbk1qVFyws6BarQA1GnmP0oiqBZc/BwkPwIK3YNH72PauwvblrVCnPVz6JDRTYzMQqVZFfF9lrtPSXpc6kSIiIiIiPibrcOmWaSrtOJFihdeA3s/CQ6sh4SFwhHs3Fkq5Ecb0hLTp2jxIRER8jhqaIiIiIiI+JjYitFzHiZQoPAYSn/utsfkgOMJgz3JIuQE+6AWbU9XYFBERn3FGDc19+/Zht9tPObKysgBOe+73Y9++fefkAkREREREKpvO8dHUjgrlryb71qwaQuf46ArLJAEiPAYS/wUProbuD3gbm7uXwafXwwe9YfPPamyKiIjlzqihaZpmmQ8RERERESkdu83gmQGtAIptah4ucDE//S82gBE5G1VrwuX/9jY2u42AoCqweyl8eh18mAhb1NgUERHrlHpToGeeeeZc5hARERERkRP0bV2bd2/rwHNT1pOZ+8dambUiQ4gIcbAl+wh3frSE5we25pbODSxMKpVa1ZrQ5wXvNPR5b8CSD2HXEphwHdTrDJc+AU16avMgERGpUGpoljOXy4XL5Sr23ImPIuKbVKsi/kG1KoGgV/MYLm3Wg6XbD5J1uJDYiBA6NaxOkcfkH5PWMXlVJk9+u4b0rMM8ltgMm833mkqq1UoipDr0fBY6D8O24C1sy8dh7FoME67FU68znosfx2x0sRqbfky1KuL7AqFOS3tthqn54GclOTmZ5ORk3G43aWlppKSkEBYWZnUsEREREankTBOm7TKYtssOQJtoD7c39RBstziYBIQQ1yGa7ZtKo5yZ2E3vL5/7w89jY+1ryKnaSo1NEREpk/z8fJKSksjNzSUyMrLYcWpolhOn00lUVBQ5OTnFfsJdLhepqakkJibicDgqOKGIlJZqVcQ/qFZFvCavyuTJiWtxuU3a1I3kvVvbUzMixOpYx6lWK7nDe/+4Y9NdCICnflc8F/8Ns2EPNTb9iGpVxPcFQp06nU5iYmJKbGiWesq5lI7D4Sjxi6o0Y0TEeqpVEf+gWpVAd32nBjSoUZV7xy9l9W4nN7y/mLF3XkjzuAiro51EtVpJRdeHK1+CHg/DvNdh6UfYdi7E9um10DDBu8Zm/MVWp5QzoFoV8X2VuU5Le11ntMu5iIiIiIj4ns7x0Uy8P4H4mHB2HzrK9e/O59e0bKtjSSCJrA39/g8eXAmd7wV7CGyfBx8PgI+uhIw5VicUEZFKRA1NEREREZFKoFFMON8O607n+GgOFxZx17glfLpou9WxJNBE1oErXvI2Ni8cAvZg2D4XPu4P4/rDtnlWJxQRkUpADU0RERERkUqiengw4wd35tr2dXF7TP4xcS0vTF2Px6Nl86WCRdaBK1+BB1bChfd4G5vb5sC4K7yNze3zrU4oIiJ+TA1NEREREZFKJCTIzqs3tuXh3ucBMGZOBsM+XcbRY26Lk0lAiqoLV74KD6yAToPB5vA2Nj/q552Ovn2B1QlFRMQPqaEpIiIiIlLJGIbBg72b8cbN7Qi22/hp3T5uen8BWc4Cq6NJoIqqB/1f+62xebe3sZnxK3zUFz65GnYs/GNs+ix4u7P3UURE5DTU0BQRERERqaSubleXT4d0oXqYg9W7chmYPI+Ne51Wx5JAVq0+9P8fPLAcOt7lbWxunQ1j+8AnA2H7QpjxHORs8j6aWi5BREROFWR1gMrG5XLhcrmKPXfio4j4JtWqiH9QrYqUTru6EXw1tAtDxi8nY38+1707nzdvasvFzWIq5PVVq3Ja4bWh78vQ7QFs8/6HbVUKxtZZsPWEuzL3rKBo03TMJj2tyxlAVKsivi8Q6rS012aYpv7kdTaSk5NJTk7G7XaTlpZGSkoKYWFhVscSERERETlJngvGptnZ4jSwYXJdvIeL4vSrgPiGKoXZnLf3Oxoe+AXjt+dM4KijBjNb/Ad3UBUr44mISAXJz88nKSmJ3NxcIiMjix2nhmY5cTqdREVFkZOTU+wn3OVykZqaSmJiIg6Ho4ITikhpqVZF/INqVeTMHSvy8NTkdUxcmQnA3d0b8rc+52G3GSW8Z9mpVqW0jPSZBH1+4ynPm44wPO1vx9NxMEQ3tiBZYFCtivi+QKhTp9NJTExMiQ1NTTkvZw6Ho8QvqtKMERHrqVZF/INqVaT0HA547ab2NK4ZwaupaYydv51dhwp4/eZ2hAWf218NVKvyl0wTfn0RDDuY7pNOGa587ItHY188GpomQpd7oUkvsGlLiHNBtSri+ypznZb2uvQdQEREREQkgBiGwchev+2AHmRj+vp93DR6oXZAF2ulz4A9K05pZh5Xp4P3cUsqfHo9vN0JFr4LBbkVl1FERHyGGpoiIiIiIgHo6nZ1SbmnC9HhwazZ7d0BfUOmdkAXC5gmzHye4n89tQEmjFgGXYdDSBQcSIdpT8CrLeH7UZC1sQIDi4iI1dTQFBEREREJUJ0aRTPx/u40rhnOntwCbnhvAbM3ZVkdSwKN+xjk7gY8xQzwgHM3VKsPff8Do9bDla9BzZbgyoOlH8I7XeDjq2DD9+Ap5i5PERGpNLSGpoiIiIhIAGtYI5yJwxK4d8JSFm49wN3jlvDc1a25vWtDq6NJoAgKgaGzIC+n+DHhNb3jAEKqwoWDodPdsG0OLBoNm36AjF+8R1QD7/kOd0BYdMVcg4iIVCg1NEVEREREAlxUmINP7u7C3yeu4etlu/jnpLVsy8nj71e0PKc7oIscF1XPe5wJw4D4i73HoR2w5ENY/jHk7oCfn4HZL8IF10Pne6F2m3OTW0RELKEp5yIiIiIiQnCQjZevb8NjfZoD8OHcDO4dv4z8Y0UWJxMphWoNIPE5GLUBrk6GuDZQVAArJsDoHvBhH1j7DbhdVicVEZFyoIamiIiIiIgA3h3Qh1/WlLduaU9wkI2fN+zjxtEL2Kcd0MVfOKpA+9vg3l/h7unQ+jqwBcHOhfD13fC/1jD7/+DwPquTiojIWVBDU0RERERETjKgbR0+G+LdAX3tbicDk+exfo92QBc/YhjQoAtcPxYeWguXPAHhsXBkL8z+D/zvfPhmCOxc4t1lXURE/IrW0CxnLpcLl+v00xh+f7648yLiG1SrIv5BtSpybrWpE8FXQzszZPwKtubkccN783n9pjZcel7NM/o4qlWxXJUYuOhR6PYAxobvsC39ANvupbDmS1jzJZ7a7fB0GoLZ6moICrU6rWVUqyK+LxDqtLTXZpim/hx1NpKTk0lOTsbtdpOWlkZKSgphYWFWxxIRERERKRf5RTB2k43NThsGJtfFe+gRp18hxL9Vy99KfPbP1D24ELvpXSe2MCiC7TUuJSOmFwXB2h1dRMQK+fn5JCUlkZubS2RkZLHj1NAsJ06nk6ioKHJycor9hLtcLlJTU0lMTMThcFRwQhEpLdWqiH9QrYpUnGNFHp6ZsoGvl+8GYFC3BjzZt3mpdkBXrYpPy8vBtnI8tmUfYRzeA4Bp2DGbX4Gn0z2YDbp7p68HANWqiO8LhDp1Op3ExMSU2NDUlPNy5nA4SvyiKs0YEbGealXEP6hWRc49hwNevqEt8TWr8vJPm/h4wQ52HyrgjZvbEx5Sul8pVKvik6rVhkv/Bj1GwaapsOh9jO1zMTZOwbZxCsSeD12GwgU3QHC41WkrhGpVxPdV5jot7XVpUyARERERESnR7zugv530+w7oWdw4egF7c7UDulQC9iBodTXcNRWGzYeOd0JQFchaB1MehNdawk//gAMZVicVERHU0BQRERERkTPQv00dPhvSlRrhwazb490Bfd2eXKtjiZSfWufDgDfgkQ1w+QtQvREU5MKCt+HN9pByE2yZAR7Pqe+bPgve7ux9FBGRc0YNTREREREROSMdG1Zn4v0JNKkZzl5nATe8t4CZG/dZHUukfFWpDt1HwMjlcMsX0KQnYELaNJhwLSR3hkWjocDpHW+aMOM5yNnkfdR2FSIi54wamiIiIiIicsYa1Ajj2/sT6N6kBvnH3Nzz8VLGzdN0XKmEbHZo3hdunwgjlkLneyE4AvZvhh//5p2O/sNjsHw87FnhfZ89KyB9hrW5RUQqMTU0RURERESkTKKqOBh3V2du7FQPjwnPTlnPs9+tw+3x3pnm9pgsyjjAshyDRRkHjj8v4rdimsEVL8Go9dDvZajRDI4dgcXvw5SRf4wz7DDzed2lKSJyjmiXcxERERERKbPgIBv/d10bGsWE89K0TYybv42dB/IZ0LYO/zdtI5m5BYCdTzYvpXZUKM8MaEXf1rWtji1ydkIjvbufdx4CW2fBzBdg99I/zptu712aKz+F9rdZl1NEpJLSHZoiIiIiInJWDMPg/kubkpzUgZAgGzM2ZvHQFyt/a2b+YW9uAcMmLGfa2kyLkoqUM8OAxpd5G5jGaX69njwcxvWHNV9DUWHF5xMRqaTU0BQRERERkXJxZZvaTLinCzbj9Od/n3z73JT1mn4ulUf6DO/dmOZpdj0H2DYHvhkMr7aAaX+H7E0Vm09EpBJSQ1NERERERMpNkdvkr3qVJpCZW8DijAMVlknknDFN71qZxf5qbUDVOKhaG44egIXJ3t3RP+wDK1PgWH5FphURqTTU0BQRERERkXKTdbig5EFnME7Ep7mPQe5uoJi7MzG950Yug1u+gOZXeDcM2rkQJg3z3rU59RHIXF2BoUVE/J82BRIRERERkXITGxFaruNEfFpQCAydBXk5xY8Jrwkh4dC8r/dwZsLKCbB8PBzaDks+8B512kOHQXDB9RASUXHXICLih9TQLGculwuXy1XsuRMfRcQ3qVZF/INqVcQ3ta8XQVxkCPuchRQ38zymajDt60WofqVyCKvlPf7KiV/rVWKg20PQ9QGMjF+xrRyPsekHjD0rYM8KzJ/+gdlqIJ72d2DW6eDdeKgC6PuqiO8LhDot7bUZpmlqNe6zkJycTHJyMm63m7S0NFJSUggLC7M6loiIiIiIZVbtNxib9vvqVic2Y0zAIMgwub2Zh3Y19KuICECwy0n9A3NpuP8XIgozjz+fG1qf7TGXsKt6Aq6gcAsTiohUjPz8fJKSksjNzSUyMrLYcWpolhOn00lUVBQ5OTnFfsJdLhepqakkJibicDgqOKGIlJZqVcQ/qFZFfNtP6/bx/A8b2essPP5crYgQosIcpO07AsDQHo0Y1bsZ9uK2RRcJNKaJsXMBthXjMTZOwSjyrjVr2kMwWw7A0+52zAbdz8ldm/q+KuL7AqFOnU4nMTExJTY0NeW8nDkcjhK/qEozRkSsp1oV8Q+qVRHf1L9dPfq1qcuCLVlMn7OIy3t0oVvTWEzT5KWfNvH+r1t5f842Nuw9wps3t6d6eLDVkUV8Q5NLvMfRg7D6K1j+Mca+tRhrv8a29muo0RQ63AFtk6BqzXJ/eX1fFfF9lblOS3td2uVcRERERETOCbvNoEt8NB1jTLrER2O3GQTZbfz9ipa8dUt7qjjszNmcw4C357J2d67VcUV8S5Xq0GUo3DcX7pnpbWI6wmH/Fkh9Gl5rCV/eAVtmgKe4XdZFRConNTRFRERERKTCDWhbh4nDu9MgOoxdB49y3bvzmbhil9WxRHyPYUC9jnDVW/DoJhjwBtTpAB4XrJ8ME66FN9vCLy+Dc4/VaUVEKoQamiIiIiIiYokWcZFMGXERlzavSWGRh4e/WMWz363D5dbdZiKnFRIBHe+EobO8d25eOARCouDQDpj1PPzvfEi5GTb9CO4iq9OKiJwzamiKiIiIiIhlosIcfDjoQkb2bArAuPnbuPWDRWQfLizhPUUCXNwFcOUr8MhGGPgeNOgGpgfSfoTPbobXL4CZz8PB7VYnFREpd2poioiIiIiIpew2g0cub87o2ztSNSSIxRkHGPDWXFbsOGh1NBHfFxwG7W6Bu6fB8MXQbQRUiYbDe+DXl+GNtjD+Glg3CYqOWZ1WRKRcqKEpIiIiIiI+oc/5cUwankCTmuHsdRZw0+iFfL54h9WxRPxHzebQ5wXvXZvXj4X4SwAT0mfCV4Pgf61g+j8hZ8sp72pk/MJl65/AyPil4nOLiJwhNTRFRERERMRnNI2tyqThCfQ5vxbH3B6e+HYNT367hsIit9XRRPxHUAi0vg4GfQcPrICLRkHVWpCXDfPfhLc7wkdXwuovwVUApolt1vNEFu7BNut5ME2rr0BE5C+poSkiIiIiIj4lItTBu7d25LE+zTEM+GzxDm5+fyF7cwusjibif6IbQ+9n4OF1cNOn0OxyMGywfS58OwRebQ6f34otcwWA9zF9hsWhRUT+mhqaIiIiIiLic2w2g+GXNWXsnRcSGRrEih2H6P/WXBZnHLA6moh/sjugZX+49St4aA1c+iRE1oOCQ7Bp6vFhJgb8+Di4tDGXiPguNTRFRERERMRnXdY8likjL6JFXAQ5RwpJGrOQcfMyMDUlVqTsourBpU/AQ6vhsqdOOmVgwv4t8H8N4PNbYfkn4My0KKiIyOmpoSkiIiIiIj6tYY1wvr2/OwPa1qHIY/LslPU88uUqClxaV1PkrBg2792Zhv3Uc0UFsPF7+G4kvNYC3rsIZvwLdiwCj2pPRKwVZHUAERERERGRkoQFB/Hmze1oWy+KF3/cyLcrdrNp32Heu60j9aPDrI4n4p/SZ8CeFcWfb3Mz7N8Mu5fD3jXeY86rUKU6NOkF5/XxPobXqLjMIiKooSkiIiIiIn7CMAzu6dGYVnUiGZGygnV7nFz19lzeuqUDFzWLsTqeiH8xTZj5PN6Jm57TDLBBziYYMgvycrzNz83TYcvPcPQgrP3ae2BAvU7ezYaaXQ5xbcCmyaAicm7p/zIiIiIiIuJXujeJYcrIi7igbhQH813cMXYRo39J17qaImfCfQxyd3P6Zibe5527veOq1oS2N8P1Y+GxrXDXNLhoFNS6ADBh1xKY9QK8f4l3evqk4bBuEhTkVtz1iEhA0R2aIiIiIiLid+pWq8JX93XjqUlr+XrZLl78cSOrd+fy0nVtCA/RrzkiJQoKgaG/3X0JuIqKmDdvHgkJCTiCfquh8JrecSeyB0HDbt6j9zPepuiWVNicCumz4Mg+WDnBe9iCoEE3aJYIzfpAzeZgGBV8oSJSGek7fTlzuVy4XK5iz534KCK+SbUq4h9UqyL+4VzWqh34z9UtaV0nghd+2MjU1Zls3nuYd5La0bCG1tUUKVFYLe+Bt0Zzw3bjimkFDscfY0qq3bBYaHOr9ygqxNi5CGPLdGzpP2Ps3wLb5niP1Kcxo+rjadIbs2lvzEY9wKE6FTkTgfDzb2mvzTA1L+OsJCcnk5ycjNvtJi0tjZSUFMLC9D9lEREREZGKtNUJH6XZcboMqthNbm/m4fzq+lVHxEphhfuo5VxNrdxVxBzZgN38o1HhNhzkVG3Jvqg27ItsS35ILQuTioivyM/PJykpidzcXCIjI4sdp4ZmOXE6nURFRZGTk1PsJ9zlcpGamkpiYiKOE//iJSI+RbUq4h9UqyL+oSJrdZ+zgAe+WM3yHYcwDBh5WROGX9IYm01TXEVKcs5r1ZWPsW0uRvrP2LakYuTuPOm0WaPpb3dvJmLW73rqVHcRCYiff51OJzExMSU2NDXlvJw5HI4Sv6hKM0ZErKdaFfEPqlUR/1ARtVqvhoPPh3bjX9+vY8LCHbw5M531mUd47aa2RIbq/xMipXHOatURBa2u9B6mCdmbvLumb54OOxZg7N+Cff8WWPweBFeFxpd6195smghRdcs/j4gfq8w//5b2utTQFBERERGRSiM4yMbzAy+gTd1qPDV5LT9v2MfAt+cx+vaONKsVYXU8EQHvxkCxLbxHwgPe3dC3zv6twZnq3Vho4/feA7y7qTdLhGaXQ70LvRsTnSh9Fvz4OPT7P2hyWYVfjohUPDU0RURERESk0rnxwvo0j4tg2IRlbM3JY2DyPF69sS19W9e2OpqI/FloFLS62nt4PLB3tbexuXk67FoC+9Z4j7mvQWg1aNrL29xs2hvCasCM5yBnk/ex8aXaSV0kAKihKSIiIiIilVLb+tX4buRFjEhZzsKtB7hvwnLuv7QJj1zeHLvW1RTxTTYb1GnnPS55DPL2Q/oMb3Nzy89w9CCs/cZ7YECNJrB/i/d996zwjm3a28ILEJGKYLM6gIiIiIiIyLkSUzWECYO7MPiieADemZ3OXeOWcCj/mMXJRKRUwmtAmxvhug/gsXS4ezr0eBTi2gDmH83M331zD6z8HPJyLIkrIhVDDU0REREREanUguw2/tm/FW/c3I5Qh41f07IZ8PZc1u9xWh1NRM6EzQ4NukCvf8J9c+DaMaeOOXoQJt0LLzeF9y+DmS/AzsXgcVd8XhE5Z9TQFBERERGRgHB1u7p8OyyB+tFV2HngKNe+O4/JK3dbHUtEysI0YeE7YNj/dMKAoCqACXuWw68vwYeJ8FJj+OouWJkCh/dZkVhEypHW0BQRERERkYDRqk4kU0ZcxAOfr+TXtGwe/Hwlq3bm8uQVLXDYdb+HiN9In+FdM/MUJhQd9U5RLzoGW1IhfSYUHIJ133oP8E5Zb5boXW+zXudTd04XEZ+mihURERERkYBSLSyYj+68kNdSN5E8K52x8zJYtyeX5Fs7EFM1xOp4IlIS04SZz+OddOo5zQAbLEiGIbOg/a3gLoLdy7zNzS0/exuhe1d7jzmvQkgUNLnU29xs2hsi61Ts9YjIGVNDU0REREREAo7dZvBYnxZcUDeKR75cxaKMAwx4ay7v3daRtvWr4faYLM44QNbhAmIjQukcH62d0UV8hfsY5O7m9M1MvM87d3vHBYV4775s0MV79HwKjmR77/Dc8jNsmQFHD8D6yd4DIPZ8aNYbmiZC/S4QFFxRVyYipaSGpoiIiIiIBKy+rWvTpGZV7h2/jK05edwwegE3darPzxv2kZlbcHxc7ahQnhnQir6ta1uYVkQAb5Ny6Ky/3sk8vKZ33OlUrQltb/YeHrf3js0tP8PmVO+dnFnrvMe8NyA4AhpfAk17eRuc1eqfm2sSkTOihqaIiIiIiAS0ZrUimDQigVFfrOLnDfsYv3D7KWP25hYwbMJy3r2tg5qaIr4gqp73OFs2O9Tr5D0ufQLyD3jX3Nzys/fIy4aN33sPgJot/pia3rB78U1TETmn1NAUEREREZGAFxnq4N1bO9D+36kcKSw65bwJGMBzU9aT2CpO089FKquwaLjgeu/h8cDeVb/dvfkz7FoM2Ru9x4K3wREG8Rf/0eCMjrc6vUjAUENTREREREQEWLr94Gmbmb8zgczcAhZnHKBbkxoVF0xErGGzQZ323uPix+DoQdg629vc3PIzHNkLadO8B0CNpt5p6U17Q6MEcFSxNL5IZaaGpoiIiIiICJB1uKDkQWcwTkQqmSrV4fxrvIdpwr613nU3t8yAnQth/xbvsehdCAqFRhd5G5zNEiG6MRinubM7fRb8+Dj0+z9oclnFX5OIn1JDU0REREREBIiNCC3XcSJSiRkGxF3gPXqMgoJcyPj1twbnz95d1n9fh3Pa41C90R/NzUYXQXC4tyk64znI2eR9bHzp6ZueInIKNTRFRERERESAzvHR1I4KZW9uAWYxY+wGOOxqOIjIn4RGQcsB3sM0vetsbk6FLamwfQEc3AZLxngPe4h3Q6FqDb07rIP3MX2Gd7q6iJTIZnUAERERERERX2C3GTwzoBXg3QDodNwm3DJmIR/P34ZpFtf2FJGAZhgQ2xISHoBBU+DxbXDzZ9BpMFRrAO5C2DoLlo878Z1g6qPeXdZFpERqaIqIiIiIiPymb+vavHtbB+KiTp5WXjsqlNdubEu/1nG43CbPfLeOEZ+t+MtNhEREAAipCi2ugP6vwYOrYcRS6HjXnwaZcDADXmoMH/SGWS/CzsXgcVsSWcTXacq5iIiIiIjICfq2rk1iqzgWZxwg63ABsRGhdI6Pxm4zuKZ9XcbO28aLP2xg6upMNmQ6effWjjSPi7A6toj4A8Pw7oaeuRIMO5h/bliasGuJ9/jlv96p7I0vhSa9oGkviKpnQWgR36OGpoiIiIiIyJ/YbQbdmtQ45XnDMBh8UTzt6kcxImUFW7PzuDp5Li8MvIDrOqrRICKlkD7jj7UzT6fr/eDcA1tnQ8EhWD/ZewDENPc2Npv0hIYJEBxWEYlFfI4amiIiIiIiImeoY8Novh95EQ99sZI5m3N45KtVLN1+gGcGnE+ow251PBHxVaYJM5/HuwKg5zQDbLBjAQyZBaYHdi/3NkC3zIDdS707oudsgoXv/La5ULc/7t6MbaVd0iVgqKEpIiIiIiJSBjWqhjDurs68NXMzb8zYzGeLd7JqZy7v3taBhjXCrY4nIr7IfQxyd3P6Zibe5527veOCQqD+hd7j0ifg6EHY+stvDc6Z4NzlvYtz62xI/SdUjfPeudm0FzS+DMJPvctcpLJQQ1NERERERKSM7DaDh3qfR8eG1Xnw85Wsz3TS/825vHxDW/q2jrM6noj4mqAQGDoL8nKKHxNe0zvuz6pUh/MHeg/ThJzNf9y9uW0uHNkLq1K8BwbUaffH3Zv1LgS749xck4gF1NAsZy6XC5fLVey5Ex9FxDepVkX8g2pVxD8ESq12bVSNScO68tCXq1m+4xD3TVjG3d0b8ujlzXDYbVbHEylRoNSqTwir5T3+Smn+O1SLh473eI+iAoydizC2zsS2dRZG1nrvOp17VsCcVzBDIjAb9sBsfBmexpdB9UblcilSsQKhTkt7bYZpmuY5zlKpJScnk5ycjNvtJi0tjZSUFMLCtCiviIiIiEggcntgyg4bszK9Tcz4CJM7m7mpdpqbrUREzpVQ10FqOtcSe3gNNZ1rCXEfOen8kZBaZEVcQFbkBeRUbYnbHmpRUpGT5efnk5SURG5uLpGRkcWOU0OznDidTqKiosjJySn2E+5yuUhNTSUxMRGHQ7d6i/gq1aqIf1CtiviHQK3Vn9bt44mJ6zhSWER0uIPXbmhDwml2TRfxFYFaqwHB9GBkrsLYOgtj60yM3UsxPEV/nLY5MOt3xmzcE0/8pRB3ARi6s9wXBUKdOp1OYmJiSmxoasp5OXM4HCV+UZVmjIhYT7Uq4h9UqyL+IdBqtX+7erSuV537P13O+kwnd328jId6ncfInk2x2bQLsfiuQKvVgNGws/e47HEocELGr8fX3zQObcfYPg+2z8M+69/eNTwbX+Zde7NJT6gae+rHS58FPz4O/f4PmlxW8dcT4CpznZb2utTQFBEREREROQcaxYTz7f3defa7dXy+ZCf/+zmNZTsO8vpN7YgOD7Y6nogEqtBIaNnfe5gmHNgK6TO9mwtl/Ap52bDmS+8B3js2m/zW3GzQFezBMOM5yNnkfWx8KRj6Q41ULDU0RUREREREzpFQh53/XteGTo2ieWrSGn5Ny+bKN+fwdlIHOjasbnU8EQl0hgE1mniPzkOg6BjsXPTH7ul7V8PeNd5j3uvgCIeaLbybDYH3MX0GNO1t6WVI4FFDU0RERERE5By7vmM9WteN5P4Jy9mak8dNoxfw9ytacldCIwzd2SQiviIoGOJ7eI/ez8KRLO/08vQZ3rs487Jhz7KT3+fzW6FpIsQ08x41mnkbpGHRllyCBAY1NEVERERERCpAi7hIJo9I4Ilv1zB1dSb/+n49S7Yd4KXr2xARWjnXQhMRP1c1Ftre5D08Hlj6Ifzw6Mljigpg45RT3zesBtRo6m1wxjT949/R8RAUUjH5pdJSQ1NERERERKSCRIQ6ePuW9lzYsDov/LCBH9fuZePew7xzawda1i5+N1cREcsZBqz8FAw7mO4TnrdBRG1o1gcObIGcLXB4D+Tv9x47F/3p49igWoPTNzsj62g9TikVNTRFREREREQqkGEY3JkQT9v61Rj+6XIycvIYmDyPfw9szY2d6lsdT0Tk9NJn/LF25olMDzh3Q8sr/1hLs/AIHEiH/b81OPdv/uPfxw7DwW3eY8vPJ38sR9hva3o28zY5Y5r98Xao/ugjf1BDU0RERERExALtG1Rn6gM9eOiLlfySls3fvl7NkowD/Ovq1lQJtlsdT0TkD6YJM58HbIDnNANs3vNNennvsAypCrXbeo8/f5wjWd4GZ85vTc7fjwMZ4Mr/YxOiP6ta67c7OZue0OxsCtUbgf0Mlu1InwU/Pg79/g+aXFb69xOfooamiIiIiIiIRaqHB/PRnRfyzuwtvJaaxlfLdrFmdy7v3taR+Jhwq+OJiHi5j0Hubk7fzMT7vHO3d9xfrY9pGBBRy3s0uuhPr+Hy3rW5f8vJzc6czZCXBUf2eY/t805+P1uQt6l5SrOzmXcN0BOnsJsmzHgOcjZ5HxtfqinufkoNTREREREREQvZbAYjejajQ4PqPPD5CjbuPcyAt+by0vVtuOKC2lbHExHxNimHzoK8nOLHhNc8u81+7I4/dkpv3u/kcwW5vzU4039rdm7+421X/h/Nzz8Lifxtyvpva3S6jv4xbX7PCu80+t+nyYtfUUNTRERERETEB3RvGsPUB3owMmUFi7cd4P5Pl3NXQiOe7NeS4CCb1fFEJNBF1fMeVgiNgrodvceJPB44nHnyGp2///vQDih0ehuXp1v7E+DLQXD+Nb81PJtAdBPvLuyOKuf+muSsqKEpIiIiIiLiI2pFhpIypAsvT9/E6F+28tG8bazceYjkpA7UqaZfsEVETmKzQVRd79H40pPPFRV61+X8fb3OjDmwdebJY44dgRXj//RBDYisCzUaexucvzc6azTxTm0/m7tQpdyooSkiIiIiIuJDguw2nuzXkk4Noxn15UpW7DjElW/O4fWb23PJeTWtjici4h+CQiC2hfcwTdjwHRh2MN1/jDEM72ZDDRLg4FbYvxUKc8G5y3tk/HryxzRs3rtUT2p0/nZ3Z7UGZ7Y5kZwVNTRFRERERER8UGKrWkwd2YP7U5axdreTOz9azMiezXiwVzPsNm1iISJSaukzTj/t3DTh8F5on+RdS9M0IX+/d23OA+l/etzqvaPz0A7vsXXWyR/LsEP1hn9qdv52l2e1BmCzV8y1Bgg1NEVERERERHxUgxphfH1fd/71/XpSFu3gzRmbWb79IK/f3I6Yqpr2KCJSItOEmc8DNk6/S7vNe75JL+8dm+Ex3qNBl1M/zpGsPxqc+7f89u+t3mZn0VHv44GtsCX1Ty/h+G0n9j81Oms0gch63qnzpWBk/MJl65/AaBkO5wX2ZkZqaIqIiIiIiPiwUIed/1xzARc2qs7fv13L3C05XPnmHJKTOtCpUbTV8UREfJv7GOTu5vTNTLzPO3d7x/3V+piGARG1vEfD7n/6EL9tTnTSXZ1bvY8HMsBd+NtmRZtP/bj2EO9GRH9udEY3gcg63tcFME1ss54nsnAPnlnPQ7Nef5wLQGpoioiIiIiI+IFr2tfj/DpRDJuwjPTsPG56fyFP9G3BPT3iMQL4l1oRkb8UFAJDZ0FeTvFjwmue3WY/J25OFH/xyec8bm/D9M+Nzv3pcHCbt9mZvdF7nJK9CkQ39jY4bXZsmd5p87bMFd5p9E0D9y5NNTRFRERERET8xHm1IvhuxEU88e0apqzawws/bGDp9gO8dH1boqpoMwoRkdOKquc9rGCze9fQrNYAmlx28jl3EeTuPLXReSAdDm73TmPPWuc9TmBiYJw4TT4AqaEpIiIiIiLiR8JDgnjz5nZ0blSdf32/np/W7WND5lzeubUDretG4faYLM44QNbhAmIjQukcH61NhEREfJE96Lfp5vHQ9E/n3C7v5kP702HTD7Dso+OnDEzvJkcBfJemGpoiIiIiIiJ+xjAMbu/WiDb1qnH/p8vZcSCfa9+dzw0d6zFjYxZ7cwuOj60dFcozA1rRt3VtCxOLiMgZsTt+W0uzMcz+j3cXddP9x3nDfvJmRgGmdNsoiYiIiIiIiM9pW78aUx+4iJ4tYjlW5OHTRTtOamYC7M0tYNiE5Uxbm2lRShERKbP0Gd67MU9sZoL37d/v0gxAamiKiIiIiIj4sWphwYy+rSMRIaefgGf+9vjclPW4PeZpx4iIiA8yTe9dmMW272ze82bg/b9dDU0RERERERE/t3T7QQ4XFhV73gQycwtYnHGg4kKJiMjZcR+D3N2Ap5gBHu8O6u5jFZnKJ2gNTRERERERET+Xdbig5EFnME5ERHxAUAgMnQV5OQC4ioqYN28eCQkJOIJ+a+mF1/SOCzBqaIqIiIiIiPi52IjQch0nIiI+Iqqe9wBwucgN2w2124LDYW0ui2nKuYiIiIiIiJ/rHB9N7ahQStrndsXOg3i0jqaIiPg5NTRFRERERET8nN1m8MyAVgCnNDVPfPulaZsY9NFiTT0XERG/poamiIiIiIhIJdC3dW3eva0DcVEnTyuPiwrl3Vs78N9rLyDUYWPO5hyueGMOv6RlW5RURETk7GgNTRERERERkUqib+vaJLaKY3HGAbIOFxAbEUrn+GjsNu99mp0aVWdEygo27j3MoLGLGXpxYx69vDnBQbrXRURE/Ie+a4mIiIiIiFQidptBtyY1uLpdXbo1qXG8mQnQNDaCScMTuKNbQwDe/3Ur1783n205eVbFFREROWNqaIqIiIiIiASQUIedf13dmtG3dySqioPVu3K58s05TFqx2+poIiIipaKGpoiIiIiISADqc34cPz7Yg87x0eQdc/PQFysZ9eVK8gqLrI4mIiLyl9TQFBERERERCVB1qlXhsyFdeah3M2wGfLt8N/3fmsva3blWRxMRESmWGpoiIiIiIiIBzG4zeKj3eXw+tBu1o0LJyMnjmnfm8cGcrZimaXU8ERGRU6ihKSIiIiIiInSOj+bHB3tweatauNwmz0/dwN3jlpBzpNDqaCIiIidRQ1NEREREREQAqBYWzOjbO/Lvq88nOMjGrE3Z9HtjDvO25FgdTURE5Dg1NEVEREREROQ4wzC4vVsjvhuRQNPYqmQfLuS2Dxfx0rSNuNweq+OJiIiooSkiIiIiIiKnahEXyZQRF3FL5/qYJrwzO50bRy9g54F8q6OJiEiAU0NTRERERERETqtKsJ0Xr21DclIHIkKDWLHjEFe8MYfvV++xOpqIiAQwNTT/JD8/n4YNG/Loo49aHUVERERERMQnXNmmNj880IMODapxuLCIESkreOKb1eQfK7I6moiIBCA1NP/khRdeoGvXrlbHEBERERER8Sn1o8P44t5uDL+sCYYBny/ZyYC35rIh02l1NBERCTBqaJ5g8+bNbNy4kX79+lkdRURERERExOc47DYe69OCTwd3ITYihPTsPK5OnscnC7ZhmqbV8UREJED4TUPz119/ZcCAAdSpUwfDMJg0adIpY5KTk2nUqBGhoaF06dKFxYsXn9FrPProo7z44ovllFhERERERKRy6t40hh8f7EHPFrEcK/Lw9OR1DB2/jIN5x6yOJiIiAcBvGpp5eXm0bduW5OTk057/4osvGDVqFM888wzLly+nbdu29OnTh6ysrONj2rVrR+vWrU859uzZw+TJkznvvPM477zzKuqSRERERERE/FaNqiF8OKgTT/dvRbDdRur6fVzx5hwWbd1vdTQREankgqwOUFr9+vX7y6ngr732GkOGDOGuu+4C4L333mPq1KmMHTuWJ554AoCVK1cW+/4LFy7k888/56uvvuLIkSO4XC4iIyN5+umnTzu+sLCQwsLC4287nd51Y1wuFy6X67Tv8/vzxZ0XEd+gWhXxD6pVEf+gWq38bu9Sjw71I3n4y9Vk7M/nljELGX5pY+6/pDFBdr+5hybgqVZFfF8g1Glpr80w/XChE8MwmDhxIgMHDgTg2LFjhIWF8fXXXx9/DmDQoEEcOnSIyZMnn9HHHzduHGvXruWVV14pdsyzzz7Lc889d8rzKSkphIWFndHriYiIiIiI+LtCN3ydYWNxtreJ2STC5PZmbqqHWBxMRET8Rn5+PklJSeTm5hIZGVnsOL+5Q/Ov5OTk4Ha7qVWr1knP16pVi40bN56T13zyyScZNWrU8bedTif169fn8ssvL/YT7nK5SE1NJTExEYfDcU5yicjZU62K+AfVqoh/UK0GlmuA71Zl8vSU9aQfdvO/DaH8Z+D5XN6qVonvK9ZSrYr4vkCo099nQJekUjQ0y9udd95Z4piQkBBCQk79U6PD4Sjxi6o0Y0TEeqpVEf+gWhXxD6rVwHFdpwZ0iq/BA5+tYNWuXIZ/torbujbgqStbEeqwWx1PSqBaFfF9lblOS3tdlWJBk5iYGOx2O/v27Tvp+X379hEXF2dRKhERERERkcDUsEY4X93XnXsvbgzAhIU7GJg8j837DlucTEREKoNK0dAMDg6mY8eOzJgx4/hzHo+HGTNm0K1bNwuTiYiIiIiIBKbgIBtPXtGST+7uTEzVYDbuPcyAt+eSsmgHfriVg4iI+BC/aWgeOXKElStXHt+pPCMjg5UrV7Jjxw4ARo0axZgxY/j444/ZsGEDw4YNIy8v7/iu5yIiIiIiIlLxLj6vJj8+eDE9msVQ4PLw94lrGJGygtyjlXeXXhERObf8Zg3NpUuXctlllx1/+/cNeQYNGsS4ceO46aabyM7O5umnn2bv3r20a9eOadOmnbJR0LnmcrmK3WL+9+dLuwW9iFhDtSriH1SrIv5BtSoA1UJtfHBbez6cv43XUrcwdU0mK3ce5LUb2tChQTWr4wmqVRF/EAh1WtprM0zd639WkpOTSU5Oxu12k5aWRkpKCmFhYVbHEhERERER8Unbj8DHaXb2FxrYMOlX30PvuiY2AzwmpDsNnC6IdECTSO/zIiISGPLz80lKSiI3N5fIyMhix6mhWU6cTidRUVHk5OQU+wl3uVykpqaSmJhYaXejEqkMVKsi/kG1KuIfVKtyOocLinhmynqmrN4LQNf46gxoU5u3ZqWz11l4fFxcZAhPXdGCPudX7My7QKRaFfF9gVCnTqeTmJiYEhuafjPl3F84HI4Sv6hKM0ZErKdaFfEPqlUR/6BalRNFOxy8eUsHLmm+m6cnr2VhxkEWZhw8Zdw+ZyEjP1/Fu7d1oG/r2hYkDTyqVRHfV5nrtLTX5TebAomIiIiIiEjlYRgG13esx6ThCQQVM6/89+mEz01Zj9ujyYUiIuKlhqaIiIiIiIhYZv+RYxT9RbPSBDJzC1iccaDiQomIiE9TQ1NEREREREQsk3W4oFzHiYhI5aeGpoiIiIiIiFgmNiK0XMeJiEjlp02BypnL5cLlchV77sRHEfFNqlUR/6BaFfEPqlUpSft6EcRFhrDPWchfrZI5acVOmseGERGqX2PPBdWqiO8LhDot7bUZpmlqZeWzkJycTHJyMm63m7S0NFJSUggLC7M6loiIiIiIiN9Ytd9gbNrvEwhP3CDIPOm5asEmNzX20Kq6fo0VEamM8vPzSUpKIjc3l8jIyGLHqaFZTpxOJ1FRUeTk5BT7CXe5XKSmppKYmFjqbehFpOKpVkX8g2pVxD+oVqW0flq3j+d/2MheZ+Hx52pHhfCPfi2IquLg75PWsfPgUQAGtq3NP65oQbUwfU2VF9WqiO8LhDp1Op3ExMSU2NDUvfrlzOFwlPhFVZoxImI91aqIf1CtivgH1aqUpH+7evRrU5fFGQfIOlxAbEQoneOjsdu8d2f+9HANXp2exth5GUxalcnc9AP8++rz6XdBbYuTVy6qVRHfV5nrtLTXpYamiIiIiIiI+AS7zaBbkxqnPRcWHMQ/+7fiyja1+dvXq9mSdYRhny6nX+s4nrv6fG0aJCISQLTLuYiIiIiIiPiNDg2qM/WBixjZsylBNoMf1+4l8bVf+WbZLrSimohIYFBDU0RERERERPxKSJCdRy5vzuQRCZxfJ5Lcoy4e+WoVd360hN2HjlodT0REzjE1NEVERERERMQvnV8niknDE3isT3OCg2z8kpbN5a/9wviF2/F4dLemiEhlpYamiIiIiIiI+C2H3cbwy5rywwM96NiwOnnH3Pxz0lpuGbOQbTl5VscTEZFzQJsClTOXy4XL5Sr23ImPIuKbVKsi/kG1KuIfVKtSURpWD+HTuzsxYdEOXk3dzKKMA/R941ce6tWUO7s1PL5bupyealXE9wVCnZb22gxTqyafleTkZJKTk3G73aSlpZGSkkJYWJjVsURERERERAJWTgF8sdVGWq53UmLDqia3NHFTW7+qiYj4tPz8fJKSksjNzSUyMrLYcWpolhOn00lUVBQ5OTnFfsJdLhepqakkJibicDgqOKGIlJZqVcQ/qFZF/INqVaximiZfLdvNi9PSOFJYhMNuMOySxtzbI57gIK2+9meqVRHfFwh16nQ6iYmJKbGhqSnn5czhcJT4RVWaMSJiPdWqiH9QrYr4B9WqWOHWbvH0alWbpyat4ecNWbw5M53p67N4+fq2XFAvyup4Pkm1KuL7KnOdlva69GcpERERERERqbTiokIZc0cn3ri5HdHhwWzce5iB78zjvz9upMDltjqeiIiUgRqaIiIiIiIiUqkZhsHV7eqS+vDFDGhbB7fH5L1f0rnijTks2XbA6ngiInKG1NAUERERERGRgFCjaghv3dKeMXd0IjYihK05edw4egHPTF5LXmGR1fFERKSU1NAUERERERGRgJLYqhapoy7hxk71ME34eMF2+rz+K3M2Z1sdTURESkENTREREREREQk4UVUcvHR9W8YP7kzdalXYdfAot3+4mL99vYrcoy6r44mIyF9QQ1NEREREREQCVo9mNZn+8MXc2b0RhgFfLt1F4mu/MH3dXqujiYhIMYKsDlDZuFwuXK7T/zXv9+eLOy8ivkG1KuIfVKsi/kG1Kv4g2Ab/6HcefVrV5O8T15GxP5+h45dxZes4/tm/BTXCg62OeM6pVkV8XyDUaWmvzTBN0zzHWSq15ORkkpOTcbvdpKWlkZKSQlhYmNWxREREREREpAyOuWHaLhuz9hh4MAgPMrm2kYeOMSaGYXU6EZHKLT8/n6SkJHJzc4mMjCx2nBqa5cTpdBIVFUVOTk6xn3CXy0VqaiqJiYk4HI4KTigipaVaFfEPqlUR/6BaFX+1dreTJyeuZeO+IwBc1jyGf13VirjIUIuTnRuqVRHfFwh16nQ6iYmJKbGhqSnn5czhcJT4RVWaMSJiPdWqiH9QrYr4B9Wq+Jv2jWrw3cgevPdLOm/N3MysTTlc8eZ8/nFlS266sD5GJb1dU7Uq4vsqc52W9rq0KZCIiIiIiIjIaQQH2XigVzO+H9mDtvWrcbiwiCe+XcNtHy5i54H8k8a6PSYL0vczeeVuFqTvx+3RZEgRkXNFd2iKiIiIiIiI/IXmcRF8O6w7Y+dm8Mr0Tczbsp/L//crj/VpzqDujUhdv5fnpqwnM7fg+PvUjgrlmQGt6Nu6toXJRUQqJ92hKSIiIiIiIlICu81gyMWN+emhi+kSH81Rl5t/fb+exP/9wn0Tlp/UzAT+v707D6uyzv8//roPHjiIgB6QTQVxX0jNBdwyS00t/Y3pWGaWLeM0jTVjjlNZOWTLtFh9bSGb+fYt22xztDJbNHNfoNzKcEFFcWERkMMWeOSc3x8mIymLCedw4Pm4rnOp9/0593nfwLu768Xnvj/KsJXo7ne36atd6W6qGAAaLgJNAAAAAABqqG2wn96f1l9PjItRU7NJB08UXXDc2RvO5y5L5vZzAKhlBJoAAAAAAFwEk8nQlP5Reub3Pasc55SUbitRUmquawoDgEaCQBMAAAAAgN/A4azZzMusgpLqBwEAaoxAEwAAAACA3yDE31Kr4wAANUOgCQAAAADAbxAbbVV4oEVGFWO8DCmnsFTOGs7mBABUr4m7C2ho7Ha77HZ7pfvO/RNA/USvAp6BXgU8A72Khu7h0Z117wc7Zei/CwGdq8wp3fP+dr21+ZAeHt1Z3SMCXF1ijdCrQP3XGPq0pudmOPk10SVJSEhQQkKCysrKtG/fPi1atEhNmzZ1d1kAAAAAABfZmWNoySGT8k79d65mc2+nxkY6lFVi6NtjhuxOQ4ac6h/i1LVtHArwdmPBAFBPFRcXa/LkybLZbAoIqPwXQASatSQ/P1+BgYHKzs6u9Atut9u1cuVKjRgxQmaz2cUVAqgpehXwDPQq4BnoVTQWZQ6nvj98UlkFpQrx91HfqBbyMp0JOI/n/ax5K1L0+Y8ZkiQ/Hy/9+cp2mjogSj5N6seT4OhVoP5rDH2an5+v4ODgagNNbjmvZWazudofqpqMAeB+9CrgGehVwDPQq2jozJIGdwq94L6olma9cnMf3X44V3OXJeuHozbNW5Gij7Ye00PXdtU13UJlGFU9idN16FWg/mvIfVrT86ofvwoCAAAAAKCB6xNl1Sd/HqTnJvZUiL+PDucU6653turm1xO1Oz3f3eUBgMcg0AQAAAAAwEVMJkO/79Naq2cN1fSr2su7iUmbDuToupfW66GlPyqnsNTdJQJAvUegCQAAAACAi/n5NNHfR3bRqplX6rrLwuVwSosS0zT0uTV6ff1BnTrtcHeJAFBvEWgCAAAAAOAmbaxNlXBzb334x/7qHhGggpLTemL5bo2av06rdmeKdXwB4HwEmgAAAAAAuFlcuyB9ds9gPTPhMgU389bB7CLd+db3uvWNJO3LLHB3eQBQrxBoAgAAAABQD3iZDN3YL1KrZw3VXVe2k7eXSetTsjX6xfX6x6e7dLLolLtLBIB6gUATAAAAAIB6xN9i1uzRXbVy5hCN7B6qModTb28+rKHPrdGbG1NlL+P5mgAaNwJNAAAAAADqoaggP/3rlr5a9Ic4dQnzl+1nu+YuS9ao+eu0em+Wu8sDALch0AQAAAAAoB4b2CFYy/9yhZ68PkZWP28dOFGk29/8Tre9maT9WYXuLg8AXI5AEwAAAACAes7LZOjmuCitnjVUfxgcrSYmQ2v2ntCo+es0d9lPshXb3V0iALgMgSYAAAAAAB4i0NesR8Z004r7hmh41xCddjj15sZDGvrcar2z+ZBO83xNAI1AE3cX0NDY7XbZ7Rf+zdjZ7ZXtB1A/0KuAZ6BXAc9ArwJ1o01zHy2Y3Evr92frqS/3KiWrSHM+/Ulvbz6kh0Z30eAOQRd1PHoVqP8aQ5/W9NwMp9PprONaGrSEhAQlJCSorKxM+/bt06JFi9S0aVN3lwUAAAAAaCTKnNKmTENfHDGp+LQhSYpp4dDvohwK8XVzcQBwEYqLizV58mTZbDYFBARUOo5As5bk5+crMDBQ2dnZlX7B7Xa7Vq5cqREjRshsNru4QgA1Ra8CnoFeBTwDvQq4Tl6xXS+vPqD3ko6ozOGU2cvQrf0jNX1oO/lbqu4/ehWo/xpDn+bn5ys4OLjaQJNbzmuZ2Wyu9oeqJmMAuB+9CngGehXwDPQqUPdaBpr12LjLdOvAtnr8891au++E/m/jYX2yI10zr+mkSf0i5WUyqjwGvQrUfw25T2t6XiwKBAAAAABAA9IhxF9v3RGrN2/vp/Yt/ZRTdEoPL92l615ar00Hss8bX+ZwKjE1V1uzDSWm5qrMwY2cAOo3ZmgCAAAAANAAXdU5RIM7BOudzYc1/5t92pNRoMn/m6iR3UP18LXdFBnUVF/tStfcZclKt5VI8tLbKd8rPNCi+LHdNCom3N2nAAAXRKAJAAAAAEADZfYy6Y7B0Rp3eSv9z8p9ei/xsL7+KVOr95zQ0M4ttTI5U7+ej5lhK9Hd727Tgim9CTUB1Evccg4AAAAAQANn9fPW4+Ni9OVfh+iKjsE6VebQiguEmZLKt81dlszt5wDqJQJNAAAAAAAaic5h/nr7jljNuqZTleOcktJtJUpKzXVNYQBwEQg0AQAAAABoRAzDUBtr0xqNzSooqeNqAODiEWgCAAAAANDIhPhbajTO34elNwDUPwSaAAAAAAA0MrHRVoUHWmRUM+4v72/X01/uUWY+MzUB1B8EmgAAAAAANDJeJkPxY7tJ0nmh5tl/hwb4qPBUmV5be0CDn/lW9y/eqf1ZBS6tEwAuhEATAAAAAIBGaFRMuBZM6a2wwIq3n4cFWvTalN7a/OAw/e+tfdWvbQvZy5z66PujGv7COv3hre/03aFcOZ2sgA7APXgYBgAAAAAAjdSomHCN6BamzfuztGJ9oq65Ik4DOoTIy3RmnuaIbqEa0S1UWw/n6l9rD2rl7kx9sztL3+zO0uWRzXXXkPYa0S20fDwAuAKBJgAAAAAAjZiXyVBctFU5u52Ki7ZeMJzsE2XVv2+16sCJQr2+/qD+s+2Ytqfl6U/vblW7YD/94Yp2Gt+7lSxmLzecAYDGhlvOAQAAAABAjbRv2UxPje+hDQ9cpelXtVeApYkOZhfpoaU/avAzq5Wwer9sxXZ3lwmggSPQBAAAAAAAFyXE36K/j+yiTbOHac6YbooItCi7sFTzvt6rAU+v0mPLknUs72d3lwmggSLQBAAAAAAAv0kznya6c3C01t5/lf7nxp7qEuav4lNlemNjqoY8u1ozPtiu5OP57i4TQAPDMzRrmd1ul91+4en1Z7dXth9A/UCvAp6BXgU8A70KeIba6NUxMaG6rnuINuzP0f9uOKTNB3P1yY7j+mTHcQ3uEKRpg9tqQDurDIMFhIDfojFcU2t6bobT6XTWcS0NWkJCghISElRWVqZ9+/Zp0aJFatq0qbvLAgAAAADArY4USquOm7Qjx5BTZ0LM1n5ODYtwqGeQU17kmgB+pbi4WJMnT5bNZlNAQECl4wg0a0l+fr4CAwOVnZ1d6Rfcbrdr5cqVGjFihMxms4srBFBT9CrgGehVwDPQq4BnqMteTcst1sJNh/XxtmMqsTskSa2bW3T7oLb6fe8INfXm5lGgJhrDNTU/P1/BwcHVBpr8V6OWmc3man+oajIGgPvRq4BnoFcBz0CvAp6hLnq1fWigHr++h+67pove2XxYb20+pKN5JXp8+R69vPqAbh3QVlMHRCmomU+tfi7QUDXka2pNz4tFgQAAAAAAQJ2z+nnrr8M7auMDV+vxcTGKtDZVXrFdL61K0cCnv9Ujn/yoQ9lF7i4TgAcg0AQAAAAAAC7j6+2lW/pHafWsoUqY3Fs9Wgeq9LRD725J09XPr9Gf39uqnUfy3F0mgHqMW84BAAAAAIDLeZkMXdcjXNdeFqYtB3P1r3UHtGbvCX3xY4a++DFD/dtZddeQ9hrauSUrowOogEATAAAAAAC4jWEYGtA+SAPaB2lPRr7+ve6gPttxXFsO5mrLwVx1DvXXH4e009ieEfJuUvFG0zKHU0mpucoqKFGIv0Wx0VZ5mQg/gYaOQBMAAAAAANQLXcIC9MINvTTrms56c2OqFiWmaW9mgf728U7N+3qv7hwcrUmxbeRvMeurXemauyxZ6baS8veHB1oUP7abRsWEu/EsANQ1Ak0AAAAAAFCvRDT31cPXddM9V3fUosQ0vbExVRn5JXryi916aVWKBrQP0orkzPPel2Er0d3vbtOCKb0JNYEGjEWBAAAAAABAvRToa9bdQ9trwwNX6dkJPdS+pZ8KSk9fMMyUJOcvf85dlqwyh/OCYwB4PgJNAAAAAABQr/k08dIN/dpo5X1XatY1naoc65SUbitRUmqua4oD4HIEmgAAAAAAwCOYTIbaWJvWaGxWQUn1gwB4JAJNAAAAAADgMUL8LTUat2ZvlrLyCTWBhohAEwAAAAAAeIzYaKvCAy0yqhm3dPtxDXrmW838cId2HbO5pDYArkGgCQAAAAAAPIaXyVD82G6SdF6oafzymnZFtPpGtZC9zKkl249pzMsbdMO/NuurXRksFgQ0AE3cXQAAAAAAAMDFGBUTrgVTemvusmSl2/57W3lYoEXxY7tpVEy4JGnnkTy9sTFVy39IV1JqrpJSc9XG6qvbBkbrhr6t5W8xu+sUAFwCAk0AAAAAAOBxRsWEa0S3MCWl5iqroEQh/hbFRlvlZfrvvM2ebZrrxUmXa/bornpnyyG9l5imI7k/6/HPk/U/K/dpYt/Wun1gtCKDarbQEID6gUATAAAAAAB4JC+ToQHtg6odFxZo0d9HdtE9V3XU0u3H9MbGVO3PKtSbGw9p4aZDGtE1VHcMjlZctFWGUd3TOQG4G4EmAAAAAABoFHy9vTQ5LlI3xbbRupRsvbEhVWv3ndCK5EytSM5U94gA3TEoWmN6hsuniZe7ywVQCQJNAAAAAADQqBiGoSs7tdSVnVoqJbNAb246pCXbjuqn4/n628c79fRXe3RL/yhNjotUcDMfd5cL4FdY5RwAAAAAADRaHUP99c/rL9PmB4fp/lGdFRZg0YmCUr2wcp8GPv2t7l+8U3sy8t1dJoBzEGgCAAAAAIBGr4Wft/48tIPWP3CVXpzUSz1bB+rUaYc++v6oRs1fr5tf36JVuzPlcDjdXSrQ6HHLOQAAAAAAwC/MXib9rlcr/b+eEdqWdlJvbDikL3ela+P+HG3cn6PoYD/dPqitJvRuLT8fYhXAHeg8AAAAAACAXzEMQ32irOoTZdXRk8V6e/NhvZ+UptTsIv3j05807+u9uik2UlMHtlWr5r7uLhdoVLjlHAAAAAAAoAqtWzTVQ9d21ZbZw/TY77orOthPBSWn9e91BzXk2dWa/t42bT18Uk4nt6MDrsAMTQAAAAAAgBrw82miWwe01ZS4KK3em6U3NqZq4/4cLf8xXct/TFfPNs11x6C2uvaycJm9mEMG1BUCzVpmt9tlt9sr3XfunwDqJ3oV8Az0KuAZ6FXAM9CrF29IB6uGdLBqb0aBFm5O02c/pGvnkTz99YMd+ucXu3VLXKRu7NtazZua3V0qGojG0Kc1PTfDyXzoS5KQkKCEhASVlZVp3759WrRokZo2berusgAAAAAAgAsVg07uowAAIaxJREFU2KWNGYY2ZJpUYDckSWaTU7EtnRoS5lDYr6ICh1M6kG8o3y4FmKX2AU6ZDDcUDtQjxcXFmjx5smw2mwICAiodR6BZS/Lz8xUYGKjs7OxKv+B2u10rV67UiBEjZDbzGxqgvqJXAc9ArwKegV4FPAO9WntKTzv0xY8ZenPTYe3OKCjfPqRjkG4bEKXBHYK0IjlLT3yxRxn5peX7wwJ89Mi1XTSye6g7yoYHaAx9mp+fr+Dg4GoDTW45r2Vms7naH6qajAHgfvQq4BnoVcAz0KuAZ6BXL53ZLN0QG6WJ/SKVmJqrNzakauXuTK1LydG6lByFB1iUnl9y3vsy80t17wc7tWBKb42KCXdD5fAUDblPa3peBJoAAAAAAAC1zDAM9W8XpP7tgnQ4p0gLNx3Sh0lpFwwzJckpyZA0d1myRnQLkxf3nwOVYsktAAAAAACAOhQV5Kf4sd318uTeVY5zSkq3lSgpNdc1hQEeikATAAAAAADABQpLT9do3IEThXVcCeDZCDQBAAAAAABcIMTfUqNx8Z/t0r3vb1fiwRyxljNwPp6hCQAAAAAA4AKx0VaFB1qUYStRZTGl2cuQvcypZTuPa9nO4+oY0kw3x0VqfJ/WCrA0zIVggIvFDE0AAAAAAAAX8DIZih/bTdKZBYDOZfzyevmmy/X5vYN1U2wb+Zq9lJJVqEeXJSvuyVV6YPEP+vGozdVlA/UOgSYAAAAAAICLjIoJ14IpvRUWWPH287BAixZM6a1RMeGKaRWop8b3UOLDw/TY77qrU2gz/Wwv04ffH9HYVzbo/72yQR99d0Q/nypz01kA7sUt5wAAAAAAAC40KiZcI7qFKSk1V1kFJQrxtyg22iovU8V5mwEWs24d0Fa39I/S94dP6r0th/XFjxn64ahN9x/9QY8vT9aE3q11c1ykOob6u+lsANcj0AQAAAAAAHAxL5OhAe2DajTWMAz1a2tVv7ZWzRlTqsVbj+q9xDSl5RZr4aZDWrjpkOKirbq5f5RGdg+VTxOvOq4ecC8CTQAAAAAAAA8R1MxHd13ZXtOuaKcN+7P17pbD+mZ3phJTc5WYmqsgP2/d0K+NJsdGqo21qbvLBeoEgSYAAAAAAICHMZkMDenUUkM6tVS67Wd9kHREH3yXpsz8Ui1Yc0CvrT2gKzu11M1xUbq6S8h5t7MDnoxAEwAAAAAAwIOFB/rqvhGddO/VHfTN7iy9l3hY61OytWbvCa3Ze0IRgRbdFBupG/u1UUiApfoDAvUcgSYAAAAAAEAD0MTLpFExYRoVE6ZD2UValJSmj78/ouO2Ej2/cp9eXJWia7qH6ua4KA1sHyTDYNYmPBOBJgAAAAAAQAPTNthPD13bVTNHdNKXu9L13pY0fX/4pL74MUNf/JihdsF+mhwXqd/3aa3mTb3dXS5wUQg0AQAAAAAAGiiL2UvXX95a11/eWrvT87UoMU1Ltx/TwewiPbF8t579eq/G9AjXlP5RurxNc2ZtwiMQaAIAAAAAADQCXcMD9Pi4GD04uos+3XFc7245rOT0fC3ZdkxLth1T1/AATekfqd/1aqVmPkRGqL/46QQAAAAAAGhE/HyaaHJcpG6KbaMdR/L0XmKalu08rt3p+Xp46S499cUejbs8QjfHRalreECF95Y5nEpKzVVWQYlC/C2KjbaygjpcjkATAAAAAACgETIMQ5dHttDlkS30yHVd9Z9tx/Re4mEdPFGkd7ek6d0taeod2VxT+kfp2svCtWZvluYuS1a6raT8GOGBFsWP7aZRMeFuPBM0NgSaAAAAAAAAjVzzpt66c3C07hjUVpsP5ui9LWn6+qcMbUvL07a0PD3yyS4Vnyo7730ZthLd/e42LZjSm1ATLkOgCQAAAAAAAElnZm0ObB+sge2DlZVfoo++P6JFiWk6fs6szHM5JRmS5i5L1ohuYdx+DpcwubsAAAAAAAAA1D8hARbdc3VHzZvYs8pxTknpthJtOZjjmsLQ6DFDEwAAAAAAAJXKLiyt0bg/vPW9hnZuqcEdgzW4Q7CigvzquDI0VgSaAAAAAAAAqFSIv6VG4362l+nLXRn6cleGJKmN1VeDOwRrcIeWGtg+SC38vOuyTDQiBJoAAAAAAACoVGy0VeGBFmXYSuS8wH5DUligRS9OulxbDuZoQ0q2tqWd1JHcn/V+0hG9n3REhiHFRARqcMdgXdEhWL2jWshi9nL1qaCBINAEAAAAAABApbxMhuLHdtPd726TIVUINc8uARQ/tptio62KjbbqL8M6qqj0tBJTc7QhJUcb9p/QvsxC/XjMph+P2bRgzQFZzCb1a2s9M4OzY7C6hgXIxIJCqCECTQAAAAAAAFRpVEy4FkzprbnLkpV+zornYYEWxY/tplEx4RXG+/k00dVdQnV1l1BJUmZ+iTbuz9aGlGxt2J+trIJSrU/J1vqUbOlLKcjPWwM7nJm9OahjsFo193Xp+cGzEGgCAAAAAACgWqNiwjWiW5iSUnOVVVCiEH+LYqOt8qrBzMrQAIvG926t8b1by+l0KiWrUOtTsrVxf7a2HMxRTtEpLdt5XMt2HpcktQv206BfZm8OaB+kAIu5rk8PHoRAEwAAAAAAADXiZTI0oH3QJR3DMAx1CvVXp1B/3Tk4WqdOO7TjSJ42pJzQ+v3Z2nkkTwezi3Qwu0jvbDkskyH1bNP8zOzNDsG6PLKFvJuYaumM4IkINAEAAAAAAOA23k1M5c/fnHlNZ9l+tpcvLrRxf7YOZhdpe1qetqfl6aVv96upt5f6twvSoA7BuqJjsDqGNJNhVD9LtMzh/E2zS1H/EGgCAAAAAACg3gj0NWtk9zCN7B4mSTqW97M2pmRr/f4zAWdu0Sl9uydL3+7JkiSF+PuULy40qEOwQgMs5x3zq13p5z3/M7yS53+i/iPQBAAAAAAAQL3VqrmvbujXRjf0ayOHw6ndGfnliwudmXFZqiXbj2nJ9mOSpE6hzcpnb8ZFB2l9ygnd/e62CquzS1KGrUR3v7tNC6b0JtT0MASaAAAAAAAA8Agmk6HuEYHqHhGou65srxJ7mbYdPqn1v6ygvuu4TfsyC7Uvs1BvbjwkL+PMcz9/HWZKklOSIWnusmSN6BbG7ecehEATAAAAAAAAHsli9tLADsEa2CFYD4ySThad0qYDOdqwP1sb9p/QkdyfVVZ2oTjzDKekdFuJklJzL3mxI7gOgSYAAAAAAAAahBZ+3rquR7iu63HmFvL/23BQj3++u9r3vZ+UJh+zSZe1CpTZixXU6zsCTQAAAAAAADRI3cIDazTus53H9dnO4/I1e6lPVIvyVdd7tWkui9mrjqvExSLQBAAAAAAAQIMUG21VeKBFGbaSCz5HU5ICLE0UF23Vd4dPKq/Y/svt6tmSJG8vk3q1aa64dmcCzt6RLeTnQ5zmbnwHAAAAAAAA0CB5mQzFj+2mu9/dJkOqEGqeXQLo2d/30KiYcDkcTqVkFSopNUdbUnOVlJqrEwWlSjqUq6RDuZKkJiZDMa0CFffLDM6+ba0K9DW7+rQaPQJNAAAAAAAANFijYsK1YEpvzV2WrHRbSfn2sECL4sd206iYM8/bNJkMdQ7zV+cwf90yoK2cTqcO5RQr8WCOklJzlZiaq2N5P2vHkTztOJKnf607KMOQuoYFKDbaqv7trOrX1qqgZj7uOtVGg0DzHG3btlVAQIBMJpNatGih1atXu7skAAAAAAAAXKJRMeEa0S1MSam5yiooUYi/RbHRVnmZjErfYxiGooP9FB3sp0mxkZKkoyeLlfTL7M3E1FylZhcpOT1fyen5WrjpkCSpQ0iz8hmccdFBCgu0uOIUGxUCzV/ZtGmTmjVr5u4yAAAAAAAAUIu8TIYGtA+6pGO0btFUrVs01fjerSVJWfklSjqUq8SDZ0LOvZkF2p9VqP1ZhXovMU2SFBXUVLFtrYprF6S4aKtat/CVYVQepKJ6BJoAAAAAAADAbxASYNGYHhEa0yNCknSy6NSZZ26m5ioxNUfJx/N1OKdYh3OK9fHWo5Kk8EDLLzM4gxQbbVX7ln7VBpxlDqcSU3O1NdtQUGquBnQIqXJ2aUPnMYHmunXrNG/ePG3dulXp6elaunSpxo0bV2FMQkKC5s2bp4yMDPXs2VMvv/yyYmNja/wZhmHoyiuvlMlk0owZM3TzzTfX8lkAAAAAAACgoWrh562R3cM0snuYJCm/xK6th0/+MoMzRz8ctSndVqJPdhzXJzuOS5KCm3krNtpaPouzc6i/TOeElV/tSj/n+Z9eejvle4X/6vmfjY3HBJpFRUXq2bOn7rjjDo0fP/68/R9++KFmzpyp1157TXFxcZo/f75GjhypvXv3KiQkRJLUq1cvnT59+rz3rlixQhEREdqwYYNatWql9PR0DR8+XJdddpl69OhR5+cGAAAAAACAhifAYtZVnUN0Vecz2VTxqdPanpanxNQzAef2tDxlF57SFz9m6IsfMyRJgb5m9WvbQnHRQSpzOPXMV3sqrM4uSRm2Et397jYtmNK7UYaaHhNojh49WqNHj650/wsvvKBp06bp9ttvlyS99tprWr58ud544w09+OCDkqQdO3ZU+RmtWrWSJIWHh+vaa6/Vtm3bKg00S0tLVVpaWv5vm80mScrNzZXdbr/ge+x2u4qLi5WTkyOz2VxlLQDch14FPAO9CngGehXwDPQq4DpdWhjq0iJIU3sHqfS0Q8nH87UtLU9b0/K042ieTuYVa8UOm1bsOFTlcQxJcz5OUu/QQQ3m9vOCggJJktP56wi3Io8JNKty6tQpbd26VbNnzy7fZjKZNHz4cG3evLlGxygqKpLD4ZC/v78KCwv17bff6oYbbqh0/FNPPaW5c+eetz06OvriTwAAAAAAAAC4SGmSQh9zdxW1r6CgQIGBgZXubxCBZnZ2tsrKyhQaGlphe2hoqPbs2VOjY2RmZur666+XJJWVlWnatGnq169fpeNnz56tmTNnlv/b4XAoNzdXQUFBlT7INT8/X23atNGRI0cUEBBQo7oAuB69CngGehXwDPQq4BnoVaD+awx96nQ6VVBQoIiIiCrHNYhAsza0a9dOO3furPF4Hx8f+fj4VNjWvHnzGr03ICCgwf7gAQ0JvQp4BnoV8Az0KuAZ6FWg/mvofVrVzMyzTC6oo84FBwfLy8tLmZmZFbZnZmYqLCzMTVUBAAAAAAAAqG0NItD09vZWnz59tGrVqvJtDodDq1at0oABA9xYGQAAAAAAAIDa5DG3nBcWFmr//v3l/05NTdWOHTtktVoVGRmpmTNnaurUqerbt69iY2M1f/58FRUVla96Xh/4+PgoPj7+vFvVAdQv9CrgGehVwDPQq4BnoFeB+o8+/S/DWd066PXEmjVrdNVVV523ferUqVq4cKEk6ZVXXtG8efOUkZGhXr166aWXXlJcXJyLKwUAAAAAAABQVzwm0AQAAAAAAACABvEMTQAAAAAAAACNA4EmAAAAAAAAAI9BoAkAAAAAAADAYxBoulBCQoLatm0ri8WiuLg4JSUlubskAOd49NFHZRhGhVeXLl3cXRbQ6K1bt05jx45VRESEDMPQJ598UmG/0+nUP/7xD4WHh8vX11fDhw9XSkqKe4oFGqnq+vS222477xo7atQo9xQLNGJPPfWU+vXrJ39/f4WEhGjcuHHau3dvhTElJSWaPn26goKC1KxZM02YMEGZmZluqhhonGrSq0OHDj3v2vqnP/3JTRW7HoGmi3z44YeaOXOm4uPjtW3bNvXs2VMjR45UVlaWu0sDcI7u3bsrPT29/LVhwwZ3lwQ0ekVFRerZs6cSEhIuuP/ZZ5/VSy+9pNdee02JiYny8/PTyJEjVVJS4uJKgcaruj6VpFGjRlW4xr7//vsurBCAJK1du1bTp0/Xli1btHLlStntdl1zzTUqKioqH3Pfffdp2bJl+vjjj7V27VodP35c48ePd2PVQONTk16VpGnTplW4tj777LNuqtj1WOXcReLi4tSvXz+98sorkiSHw6E2bdro3nvv1YMPPujm6gBIZ2ZofvLJJ9qxY4e7SwFQCcMwtHTpUo0bN07SmdmZERER+tvf/qZZs2ZJkmw2m0JDQ7Vw4UJNmjTJjdUCjdOv+1Q6M0MzLy/vvJmbANzrxIkTCgkJ0dq1azVkyBDZbDa1bNlSixYt0u9//3tJ0p49e9S1a1dt3rxZ/fv3d3PFQOP0616VzszQ7NWrl+bPn+/e4tyEGZoucOrUKW3dulXDhw8v32YymTR8+HBt3rzZjZUB+LWUlBRFRESoXbt2uvnmm5WWlubukgBUITU1VRkZGRWusYGBgYqLi+MaC9Qza9asUUhIiDp37qy7775bOTk57i4JaPRsNpskyWq1SpK2bt0qu91e4brapUsXRUZGcl0F3OjXvXrWe++9p+DgYMXExGj27NkqLi52R3lu0cTdBTQG2dnZKisrU2hoaIXtoaGh2rNnj5uqAvBrcXFxWrhwoTp37qz09HTNnTtXV1xxhXbt2iV/f393lwfgAjIyMiTpgtfYs/sAuN+oUaM0fvx4RUdH68CBA3rooYc0evRobd68WV5eXu4uD2iUHA6HZsyYoUGDBikmJkbSmeuqt7e3mjdvXmEs11XAfS7Uq5I0efJkRUVFKSIiQj/88IMeeOAB7d27V0uWLHFjta5DoAkAvxg9enT533v06KG4uDhFRUXpo48+0p133unGygAA8GznPv7hsssuU48ePdS+fXutWbNGw4YNc2NlQOM1ffp07dq1i2fGA/VcZb36xz/+sfzvl112mcLDwzVs2DAdOHBA7du3d3WZLsct5y4QHBwsLy+v81aGy8zMVFhYmJuqAlCd5s2bq1OnTtq/f7+7SwFQibPXUa6xgGdp166dgoODucYCbnLPPffo888/1+rVq9W6devy7WFhYTp16pTy8vIqjOe6CrhHZb16IXFxcZLUaK6tBJou4O3trT59+mjVqlXl2xwOh1atWqUBAwa4sTIAVSksLNSBAwcUHh7u7lIAVCI6OlphYWEVrrH5+flKTEzkGgvUY0ePHlVOTg7XWMDFnE6n7rnnHi1dulTffvutoqOjK+zv06ePzGZzhevq3r17lZaWxnUVcKHqevVCzi5u21iurdxy7iIzZ87U1KlT1bdvX8XGxmr+/PkqKirS7bff7u7SAPxi1qxZGjt2rKKionT8+HHFx8fLy8tLN910k7tLAxq1wsLCCr9pTk1N1Y4dO2S1WhUZGakZM2boiSeeUMeOHRUdHa05c+YoIiKiwgrLAOpWVX1qtVo1d+5cTZgwQWFhYTpw4IDuv/9+dejQQSNHjnRj1UDjM336dC1atEiffvqp/P39y5+LGRgYKF9fXwUGBurOO+/UzJkzZbVaFRAQoHvvvVcDBgxghXPAharr1QMHDmjRokW69tprFRQUpB9++EH33XefhgwZoh49eri5etcwnE6n091FNBavvPKK5s2bp4yMDPXq1UsvvfRS+ZRgAO43adIkrVu3Tjk5OWrZsqUGDx6sJ598slE8fwSoz9asWaOrrrrqvO1Tp07VwoUL5XQ6FR8fr3//+9/Ky8vT4MGD9eqrr6pTp05uqBZonKrq0wULFmjcuHHavn278vLyFBERoWuuuUaPP/74eQt6AahbhmFccPubb76p2267TZJUUlKiv/3tb3r//fdVWlqqkSNH6tVXX+WWc8CFquvVI0eOaMqUKdq1a5eKiorUpk0bXX/99XrkkUcUEBDg4mrdg0ATAAAAAAAAgMfgGZoAAAAAAAAAPAaBJgAAAAAAAACPQaAJAAAAAAAAwGMQaAIAAAAAAADwGASaAAAAAAAAADwGgSYAAAAAAAAAj0GgCQAAAAAAAMBjEGgCAAAAAAAA8BgEmgAAAKhW27ZtZRiGFi5c6O5S3Gro0KEyDKPCa/78+Zd0rEcffbRWazxrxowZ59V622231clnAQAAuBKBJgAAQCPw6KOPnhduVfWqSzk5OWrSpIl8fX1VWFgoSVq4cGH5Z5tMJm3fvr3KY5wd666A1c/PT6GhoQoNDZWfn1+tH/9CwenZr01AQIB69eqlWbNm6fDhw5UeIyAgoLxGi8VS6zUCAAC4C4EmAABAI3M25KrqVZc+//xzlZWVadiwYWrWrNl5+51Opx588ME6reFSzZo1SxkZGcrIyNC0adPq7HPMZnOF70uLFi1UUFCgnTt36vnnn1fXrl315ZdfXvC9jz32WHmNN954Y53VCAAA4GpN3F0AAAAAXCsjI8Otn//pp59Kkn73u99VOmbFihX69ttvdfXVV7uqrHpp4MCBWrNmTYVtJSUl+vTTTzV9+nTl5ORoypQpSktLq5OZogAAAPURMzQBAADgMiUlJVqxYoUMw9DYsWMvOGbMmDGSpAcffFBOp9OV5XkEi8WiG2+8sfzZnbm5uVq3bp17iwIAAHAhAk0AAABclIKCAs2ePVudO3eWr6+vgoODNW7cOCUmJlb73pUrV6qoqEhxcXEKCwu74JinnnpKJpNJ3333nRYvXvyb61yyZInGjBmj0NBQeXt7KzQ0VGPGjNHSpUt/8zEvRllZmV5++WX17t1bfn5+slqtGjp06CWd07l69epV/vezzyIFAABoDAg0AQAAUGMnT55Uv3799PTTT+vQoUPy9vZWTk6OPv30Uw0cOFBvvPFGle8/e7v5uHHjKh0TExOjW265RZL0yCOP6PTp0xdV46lTpzRp0iRNmDBBy5cvV3Z2tpo1a6bs7GwtX75c48eP1+TJk2W32y/quBejtLRU1113nf7yl79o+/btKikpkdPp1Lp16zRx4sRaeUbozp07y//euXPnSz4eAACApyDQBAAAQI3NnTtXWVlZ+uijj1RUVCSbzabk5GRdeeWVcjgcuuuuu7Rt27YLvtfhcGjZsmWSqn5+pnRmQRsfHx/t27dPr7/++kXV+NBDD+nDDz+UYRiaM2eOcnJylJubq+zsbD300EOSpPfff19z5sy5qONejNmzZ+vrr7+WYRh64okndPLkSZ08eVIZGRm6++679cwzz2jHjh2/6dilpaVavHix7rvvPknSNddcox49etRi9QAAAPUbgSYAAEAjExYWVuXrr3/9a6Xvtdls+vjjjzVx4kQ1aXJmfcmzK2137NhRp0+frjQo3LJli7KystSpUyd16dKlyhojIyP15z//WdKZcLO4uLhG53bs2DG9+OKLks48g/Oxxx5T8+bNJUktWrTQk08+qZkzZ0qSXnjhBaWnp9fouBfj+PHjevnllyWdmWH68MMPKyAgQJIUEhKiV199VTfddJNsNlu1x9q0aVOF701QUJAsFosmTpwoi8Wi+++/X5988kmtnwMAAEB9RqAJAADQyGRmZlb5qipoGzRokIYNG3bedl9fX/3973+XJH311VcXPEZNVjc/19kgMD09vXwBnOr85z//0enTp2WxWCq9rfuRRx6Rj4+P7HZ7rT3P8lyLFy/W6dOn5evrq1mzZl1wzKOPPlqjY9nt9grfm9zc3PJ9NptNubm5NQpGAQAAGhICTQAAgEbG6XRW+Vq4cGGl77366qur3edwOC542/nFBppBQUG6//77JUnPPvtshTCvMt9//70kqV+/fuWzIn+tRYsW6tu3b4XxtensMfv27VtpDZ06dVKrVq2qPdaVV1553vfHZrPpm2++UY8ePfT6668rNjZWKSkptXoOAAAA9RmBJgAAAGqsqhDu3H1ZWVkV9u3Zs0d79+5VSEiIBgwYUOPPmzFjhsLCwmSz2fTkk09WO/7s51YXFrZu3fqCddaGi63hYgUEBGjYsGH64osvFBUVpSNHjuiuu+76TccCAADwRASaAAAAqHNnZ2eOHTtWJlPN/xfUz89P//jHPyRJCQkJSktLq5P6PJG/v79uvPFGSdLq1avr5HmgAAAA9RGBJgAAAGrs2LFjNdoXEhJSYd/F3m5+rmnTpqljx44qLS1VfHx8lWPPfu7Ro0erHHd2/6/rrA1nj1nV16om+2siKiqq/O+HDh265OMBAAB4AgJNAAAA1Njq1aur3WcymXT55ZeXb8/MzFRiYqKaNm2q4cOHX/RnNmnSRE888YQk6e2339ZPP/1U6dhzn41Z2WI5eXl5FZ61WdvOraGwsPCCY1JSUqoNXWvi3GP4+fld8vEAAAA8AYEmAAAAamzDhg1as2bNedtLSkr0/PPPS5JGjhyp5s2bl+/77LPP5HA4NHLkSPn6+v6mz504caL69u0rh8Oh2bNnVzpuwoQJatKkiUpKSvTMM89ccMw///lPlZaWymw2a8KECb+pnqpMmDBBXl5e+vnnn/Xcc89dcMxjjz12yZ9TUlKiJUuWSJKaNWumzp07X/IxAQAAPAGBJgAAAGosMDBQEyZM0OLFi3X69GlJZxb8ue6667Rnzx55eXmdF9Zdyu3mZxmGoaefflqStGzZskrHtWrVSn/9618lSU8//bTi4+OVl5cn6czMzDlz5mjevHmSpJkzZyo8PPw311RVDdOnT5ckPf7443rqqadUUFAgSTpx4oTuuecevfvuuwoMDPzNn5GcnKxJkyZp7969kqTp06fLx8fn0osHAADwAE3cXQAAAABcKywsrNoxS5Ys0cCBA8/bHh8fr3/961+aOHGifHx8ZLFYym/tNgxDCxYsKL/lWpKKioq0atUqeXl5acyYMZdU97BhwzRixAitXLmyynH//Oc/deTIEX300Ud67LHH9MQTTygwMFA2m00Oh0OSdNNNN+nxxx+/pHqq8swzzyg5OVnffPONHnroIc2ZM0cBAQHKy8uT0+nUAw88oC1btmjt2rVVHmfTpk3nfb8KCgpUXFxc/u8bb7yxTs8FAACgvmGGJgAAQCOTmZlZ7evUqVMXfG+LFi2UlJSkBx98UJGRkSotLZXVatXYsWO1ceNGTZs2rcL4r7/+WiUlJRo0aJCCgoIuufann35ahmFUOcbb21sffvihFi9erNGjRysoKEgFBQUKCgrS6NGjtWTJEi1atEhms/mS66mMxWLRl19+qRdffFG9evWSt7e3nE6nrrjiCn300Ufls02rY7fbz/veOBwORUdHa9KkSfryyy/1wQcf1Om5AAAA1DeG0+l0ursIAAAANEy33nqr3nnnHT3//POaOXOmu8u5ZEOHDtXatWsVHx+vRx991N3l1Nhtt92mt956S1OnTtXChQvdXQ4AAMAlYYYmAAAA6kRZWZmWL18uSRo3bpx7iwEAAECDQaAJAACAOpGTk6N7771Xzz33nNq1a+fucmrV3LlzZRiGDMPQ/Pnz3V3OBc2YMaO8xrfeesvd5QAAANQaFgUCAABAnQgJCfGo27Jrwmq1KjQ0tMI2Pz8/N1VTtYCAgPNqvZSV1QEAAOoLnqEJAAAAAAAAwGNwyzkAAAAAAAAAj0GgCQAAAAAAAMBjEGgCAAAAAAAA8BgEmgAAAAAAAAA8BoEmAAAAAAAAAI9BoAkAAAAAAADAYxBoAgAAAAAAAPAYBJoAAAAAAAAAPAaBJgAAAAAAAACP8f8B6fhaWPs1sXEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(16, 10))\n",
    "# Plot Autoencoder\n",
    "plt.semilogy(ebno_range, bler_testAE, 'o-')\n",
    "#plt.semilogy(bber_data[0], bber_data[2], 'o-')\n",
    "#plt.semilogy(bber_data[0], vec, 'o-')\n",
    "\n",
    "plt.semilogy(ebno_range, bler_testGAN, '^-')\n",
    "\n",
    "# Plot approx 16 QAM\n",
    "#ebnodbs = np.linspace(5,14,10)\n",
    "#a, _ = utils.MQAM_rayleigh_approx(16,ebnodbs)\n",
    "#plt.plot(ebnodbs, a, '^-');\n",
    "\n",
    "#b = [utils.rayleigh_error(t) for t in ebnodbs]\n",
    "#plt.plot(ebnodbs, b, '^-');\n",
    "\n",
    "#ebnodbs = np.linspace(0,11,22)\n",
    "#uncoded_16QAM_Rayleigh_SER = [0.5501, 0.5040, 0.4564, 0.4082, 0.3606, 0.3147, 0.2713, 0.2313, 0.1951, 0.1631, 0.1352, 0.1112, 0.091, 0.07396, 0.05989, 0.04832, 0.03886, 0.03119, 0.02497, 0.01996, 0.01594, 0.01271]\n",
    "#c = [(1-(1-t)**4) for t in uncoded_16QAM_Rayleigh_SER]\n",
    "#plt.plot(ebnodbs, c, '^-');\n",
    "\n",
    "plt.gca().set_ylim(1e-5, 1)\n",
    "#plt.gca().set_xlim(5, 18)\n",
    "plt.ylabel(\"Block Error Rate\", fontsize=18, rotation=90)\n",
    "plt.xlabel(\"Eb/No [dB]\", fontsize=18)\n",
    "plt.legend(['AE', 'WGAN'], prop={'size': 16}, loc='upper right');\n",
    "plt.grid(True, which=\"both\")\n",
    "#plt.savefig('ae-ray-testing.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa74c701-f06c-4c3c-a93a-13fb062a7aea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "data = (ebno_range, bler_testGAN)\n",
    "with open('Rayleigh_WGAN.pkl', 'wb') as f:\n",
    "    pickle.dump(data, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dffd2ba4-68a5-4487-843f-bccce1eb2851",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}