{ "cells": [ { "cell_type": "code", "execution_count": 18, "id": "6b92dece", "metadata": {}, "outputs": [], "source": [ "import os\n", "import time\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torchvision import transforms, models\n", "from torchvision.utils import save_image\n", "from torch.utils.data import DataLoader\n", "from torchvision.datasets import ImageFolder\n", "from PIL import Image\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "3e2168fa", "metadata": {}, "outputs": [], "source": [ "class ResidualBlock(nn.Module):\n", " def __init__(self, channels):\n", " super().__init__()\n", " self.block = nn.Sequential(\n", " nn.Conv2d(channels, channels, 3, 1, 1),\n", " nn.InstanceNorm2d(channels, affine=True),\n", " nn.ReLU(inplace=True),\n", " nn.Conv2d(channels, channels, 3, 1, 1),\n", " nn.InstanceNorm2d(channels, affine=True),\n", " )\n", "\n", " def forward(self, x):\n", " return x + self.block(x)\n", "\n", "class TransformerNet(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.model = nn.Sequential(\n", " nn.Conv2d(3, 32, 9, 1, 4),\n", " nn.InstanceNorm2d(32),\n", " nn.ReLU(),\n", "\n", " nn.Conv2d(32, 64, 3, 2, 1),\n", " nn.InstanceNorm2d(64),\n", " nn.ReLU(),\n", "\n", " nn.Conv2d(64, 128, 3, 2, 1),\n", " nn.InstanceNorm2d(128),\n", " nn.ReLU(),\n", "\n", " ResidualBlock(128),\n", " ResidualBlock(128),\n", " ResidualBlock(128),\n", " ResidualBlock(128),\n", " ResidualBlock(128),\n", "\n", " nn.ConvTranspose2d(128, 64, 3, 2, 1, 1),\n", " nn.InstanceNorm2d(64),\n", " nn.ReLU(),\n", "\n", " nn.ConvTranspose2d(64, 32, 3, 2, 1, 1),\n", " nn.InstanceNorm2d(32),\n", " nn.ReLU(),\n", "\n", " nn.Conv2d(32, 3, 9, 1, 4),\n", " nn.Tanh()\n", " )\n", "\n", " def forward(self, x):\n", " return self.model(x)\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "cbe2325b", "metadata": {}, "outputs": [], "source": [ "class VGGFeatures(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " vgg = models.vgg16(weights=models.VGG16_Weights.DEFAULT).features\n", " self.slice = nn.Sequential(*list(vgg.children())[:23])\n", " for param in self.slice.parameters():\n", " param.requires_grad = False\n", "\n", " def forward(self, x):\n", " features = []\n", " for layer in self.slice:\n", " x = layer(x)\n", " if isinstance(layer, nn.ReLU):\n", " features.append(x)\n", " return features\n", "\n", "def gram_matrix(x):\n", " b, c, h, w = x.size()\n", " features = x.view(b, c, h * w)\n", " G = torch.bmm(features, features.transpose(1, 2))\n", " return G / (c * h * w)\n", "\n", "def denormalize(tensor):\n", " mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(tensor.device)\n", " std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(tensor.device)\n", " return tensor * std + mean\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "c6a8e8b8", "metadata": {}, "outputs": [], "source": [ "image_transform = transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(256),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", "])\n", "\n", "dataset = ImageFolder(\"resized_output\", transform=image_transform)\n", "dataloader = DataLoader(dataset, batch_size=4, shuffle=True)\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "bd13fadd", "metadata": {}, "outputs": [], "source": [ "def load_image(path):\n", " image = Image.open(path).convert(\"RGB\")\n", " transform = transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(256),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", " ])\n", " return transform(image).unsqueeze(0)\n", "\n", "style_image = load_image(\"style Image.jpg\").cuda()\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "65ad58a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training 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epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", "Your model has trained this much epoch 4\n", 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"Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Your model has trained this much epoch 5\n", "Epoch [5/600], Loss: 0.9200\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Your model has trained this much epoch 6\n", "Epoch [6/600], Loss: 0.8858\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Your model has trained this much epoch 7\n", "Epoch [7/600], Loss: 0.7741\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has 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"Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has trained this much epoch 8\n", "Your model has 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"Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Your model has trained this much epoch 9\n", "Epoch [9/600], Loss: 0.4370\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has trained this much epoch 10\n", "Your model has 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11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has trained this much epoch 11\n", "Your model has 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much epoch 11\n", "Your model has trained this much epoch 11\n", "Epoch [11/600], Loss: 1.3299\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Your model has trained this much epoch 12\n", "Epoch [12/600], Loss: 0.7622\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Your model has trained this much epoch 13\n", "Epoch [13/600], Loss: 0.6175\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your model has trained this much epoch 14\n", "Your 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much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Your model has trained this much epoch 15\n", "Epoch [15/600], Loss: 0.7421\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Your model has trained this much epoch 16\n", "Epoch [16/600], Loss: 0.8547\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Your model has trained this much epoch 17\n", "Epoch [17/600], Loss: 0.5614\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Your model has trained this much epoch 18\n", "Epoch [18/600], Loss: 0.4775\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Your model has trained this much epoch 19\n", "Epoch [19/600], Loss: 0.5150\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Your model has trained this much epoch 20\n", "Epoch [20/600], Loss: 0.4460\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Your model has trained this much epoch 21\n", "Epoch [21/600], Loss: 0.6668\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Your model has trained this much epoch 22\n", "Epoch [22/600], Loss: 0.4705\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has 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much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Your model has trained this much epoch 23\n", "Epoch [23/600], Loss: 0.5704\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your 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much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Your model has trained this much epoch 24\n", "Epoch [24/600], Loss: 0.7467\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has 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much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Your model has trained this much epoch 25\n", "Epoch [25/600], Loss: 0.4077\n", "Your model has 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much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your model has trained this much epoch 26\n", "Your 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much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has trained this much epoch 27\n", "Your model has 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28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has trained this much epoch 28\n", "Your model has 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much epoch 28\n", "Your model has trained this much epoch 28\n", "Epoch [28/600], Loss: 0.3894\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your 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much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Your model has trained this much epoch 29\n", "Epoch [29/600], Loss: 0.4734\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your 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much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Your model has trained this much epoch 30\n", "Epoch [30/600], Loss: 0.4652\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has 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much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your model has trained this much epoch 31\n", "Your 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much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Your model has trained this much epoch 32\n", "Epoch [32/600], Loss: 0.5431\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Your model has trained this much epoch 33\n", "Epoch [33/600], Loss: 0.4509\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Your model has trained this much epoch 34\n", "Epoch [34/600], Loss: 0.8830\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your 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much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Your model has trained this much epoch 35\n", "Epoch [35/600], Loss: 0.4467\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Your model has trained this much epoch 36\n", "Epoch [36/600], Loss: 0.5814\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Your model has trained this much epoch 37\n", "Epoch [37/600], Loss: 0.4747\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Your model has trained this much epoch 38\n", "Epoch [38/600], Loss: 0.3677\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Your model has trained this much epoch 39\n", "Epoch [39/600], Loss: 0.4051\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Your model has trained this much epoch 40\n", "Epoch [40/600], Loss: 0.7330\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Your model has trained this much epoch 41\n", "Epoch [41/600], Loss: 0.5067\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Your model has trained this much epoch 42\n", "Epoch [42/600], Loss: 0.4966\n", "Your model has 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much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Your model has trained this much epoch 43\n", "Epoch [43/600], Loss: 0.4568\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has trained this much epoch 44\n", "Your model has 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45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has trained this much epoch 45\n", "Your model has 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much epoch 45\n", "Your model has trained this much epoch 45\n", "Epoch [45/600], Loss: 0.5098\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has 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much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Your model has trained this much epoch 46\n", "Epoch [46/600], Loss: 0.6527\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your 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much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Your model has trained this much epoch 47\n", "Epoch [47/600], Loss: 0.5535\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has 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much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Your model has trained this much epoch 48\n", "Epoch [48/600], Loss: 0.4315\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Your model has trained this much epoch 49\n", "Epoch [49/600], Loss: 0.5718\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your model has trained this much epoch 50\n", "Your 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51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your 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much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Your model has trained this much epoch 51\n", "Epoch [51/600], Loss: 0.4498\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your 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much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Your model has trained this much epoch 52\n", "Epoch [52/600], Loss: 0.6881\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has 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much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Your model has trained this much epoch 53\n", "Epoch [53/600], Loss: 0.4587\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has 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much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your model has trained this much epoch 54\n", "Your 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much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your model has trained this much epoch 55\n", "Your 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56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your model has trained this much epoch 56\n", "Your 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57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has 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much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Your model has trained this much epoch 57\n", "Epoch [57/600], Loss: 0.4703\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your 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much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Your model has trained this much epoch 58\n", "Epoch [58/600], Loss: 0.3457\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has 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much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Your model has trained this much epoch 59\n", "Epoch [59/600], Loss: 0.3985\n", "Your model has 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much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Your model has trained this much epoch 60\n", "Epoch [60/600], Loss: 0.4541\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has trained this much epoch 61\n", "Your model has 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62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your model has trained this much epoch 62\n", "Your 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much epoch 62\n", "Your model has trained this much epoch 62\n", "Epoch [62/600], Loss: 0.4571\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your 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much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Your model has trained this much epoch 63\n", "Epoch [63/600], Loss: 0.4461\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your 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much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Your model has trained this much epoch 64\n", "Epoch [64/600], Loss: 0.4204\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has 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much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your model has trained this much epoch 65\n", "Your 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much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your model has trained this much epoch 66\n", "Your 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much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has trained this much epoch 67\n", "Your model has 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68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your 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much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Your model has trained this much epoch 68\n", "Epoch [68/600], Loss: 0.3754\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your 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much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Your model has trained this much epoch 69\n", "Epoch [69/600], Loss: 0.6813\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has 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much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Your model has trained this much epoch 70\n", "Epoch [70/600], Loss: 0.5351\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has 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much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your model has trained this much epoch 71\n", "Your 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much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your model has trained this much epoch 72\n", "Your 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73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your model has trained this much epoch 73\n", "Your 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74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your 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much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Your model has trained this much epoch 74\n", "Epoch [74/600], Loss: 0.4992\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your 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much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Your model has trained this much epoch 75\n", "Epoch [75/600], Loss: 0.4847\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has 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much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Your model has trained this much epoch 76\n", "Epoch [76/600], Loss: 0.3643\n", "Your model has 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much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Your model has trained this much epoch 77\n", "Epoch [77/600], Loss: 0.4522\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your model has trained this much epoch 78\n", "Your 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much epoch 79\n", "Your model has trained this much epoch 79\n", "Epoch [79/600], Loss: 0.4637\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your 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much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Your model has trained this much epoch 80\n", "Epoch [80/600], Loss: 0.3773\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your 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much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 81\n", "Your model has trained this much epoch 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much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 82\n", "Your model has trained this much epoch 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much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your model has trained this much epoch 83\n", "Your 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much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your model has trained this much epoch 84\n", "Your 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85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your 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much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Your model has trained this much epoch 85\n", "Epoch [85/600], Loss: 0.5439\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your 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much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Your model has trained this much epoch 86\n", "Epoch [86/600], Loss: 0.3183\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has 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much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Your model has trained this much epoch 87\n", "Epoch [87/600], Loss: 0.3773\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has 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much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your model has trained this much epoch 88\n", "Your 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much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your model has trained this much epoch 89\n", "Your 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90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your model has trained this much epoch 90\n", "Your 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91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your 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much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Your model has trained this much epoch 91\n", "Epoch [91/600], Loss: 0.4326\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your 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much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Your model has trained this much epoch 92\n", "Epoch [92/600], Loss: 0.3679\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has 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much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Your model has trained this much epoch 93\n", "Epoch [93/600], Loss: 0.5078\n", "Your model has 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much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your model has trained this much epoch 94\n", "Your 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much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your model has trained this much epoch 95\n", "Your 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96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your model has trained this much epoch 96\n", "Your 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much epoch 96\n", "Your model has trained this much epoch 96\n", "Epoch [96/600], Loss: 0.3206\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your 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much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Your model has trained this much epoch 97\n", "Epoch [97/600], Loss: 0.4047\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your 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much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Your model has trained this much epoch 98\n", "Epoch [98/600], Loss: 0.4050\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has 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much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your model has trained this much epoch 99\n", "Your 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100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Your model has trained this much epoch 100\n", "Epoch [100/600], Loss: 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much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has trained this much epoch 101\n", "Your model has 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102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Your model has trained this much epoch 102\n", "Epoch [102/600], Loss: 0.4587\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has trained this much epoch 103\n", "Your model has 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"Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Your model has trained this much epoch 104\n", "Epoch [104/600], Loss: 0.5341\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has trained this much epoch 105\n", "Your model has 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model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Your model has trained this much epoch 106\n", "Epoch [106/600], Loss: 0.4329\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has 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model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Your model has trained this much epoch 107\n", "Epoch [107/600], Loss: 0.3468\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Your model has trained this much epoch 108\n", "Epoch [108/600], Loss: 0.4275\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Your model has trained this much epoch 109\n", "Epoch [109/600], Loss: 0.3648\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Your model has trained this much epoch 110\n", "Epoch [110/600], Loss: 0.5452\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Your model has trained this much epoch 111\n", "Epoch [111/600], Loss: 0.3860\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Your model has trained this much epoch 112\n", "Epoch [112/600], Loss: 0.3172\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Your model has trained this much epoch 113\n", "Epoch [113/600], Loss: 0.5039\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Your model has trained this much epoch 114\n", "Epoch [114/600], Loss: 0.3987\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Your model has trained this much epoch 115\n", "Epoch [115/600], Loss: 0.2711\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Your model has trained this much epoch 116\n", "Epoch [116/600], Loss: 0.2635\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Your model has trained this much epoch 117\n", "Epoch [117/600], Loss: 0.4742\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Your model has trained this much epoch 118\n", "Epoch [118/600], Loss: 0.3819\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Your model has trained this much epoch 119\n", "Epoch [119/600], Loss: 0.3682\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Your model has trained this much epoch 120\n", "Epoch [120/600], Loss: 0.5938\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Your model has trained this much epoch 121\n", "Epoch [121/600], Loss: 0.4843\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Your model has trained this much epoch 122\n", "Epoch [122/600], Loss: 0.3923\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Your model has trained this much epoch 123\n", "Epoch [123/600], Loss: 0.3920\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Your model has trained this much epoch 124\n", "Epoch [124/600], Loss: 0.4892\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Your model has trained this much epoch 125\n", "Epoch [125/600], Loss: 0.4865\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Your model has trained this much epoch 126\n", "Epoch [126/600], Loss: 0.4191\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Your model has trained this much epoch 127\n", "Epoch [127/600], Loss: 0.3311\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Your model has trained this much epoch 128\n", "Epoch [128/600], Loss: 0.3569\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Your model has trained this much epoch 129\n", "Epoch [129/600], Loss: 0.4388\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Your model has trained this much epoch 130\n", "Epoch [130/600], Loss: 0.4425\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Your model has trained this much epoch 131\n", "Epoch [131/600], Loss: 0.3601\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Your model has trained this much epoch 132\n", "Epoch [132/600], Loss: 0.6530\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Your model has trained this much epoch 133\n", "Epoch [133/600], Loss: 0.3970\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Your model has trained this much epoch 134\n", "Epoch [134/600], Loss: 0.3195\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Your model has trained this much epoch 135\n", "Epoch [135/600], Loss: 0.5109\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Your model has trained this much epoch 136\n", "Epoch [136/600], Loss: 0.2858\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Your model has trained this much epoch 137\n", "Epoch [137/600], Loss: 0.3379\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Your model has trained this much epoch 138\n", "Epoch [138/600], Loss: 0.4339\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Your model has trained this much epoch 139\n", "Epoch [139/600], Loss: 0.3988\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Your model has trained this much epoch 140\n", "Epoch [140/600], Loss: 0.3663\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your model has trained this much epoch 141\n", "Your 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has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Your model has trained this much epoch 142\n", "Epoch [142/600], Loss: 0.5029\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your model has trained this much epoch 143\n", "Your 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"Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Your model has trained this much epoch 144\n", "Epoch [144/600], Loss: 0.2963\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has trained this much epoch 145\n", "Your model has 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"Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Your model has trained this much epoch 146\n", "Epoch [146/600], Loss: 0.3860\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has trained this much epoch 147\n", "Your model has 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model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Your model has trained this much epoch 148\n", "Epoch [148/600], Loss: 0.4234\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Your model has trained this much epoch 149\n", "Epoch [149/600], Loss: 0.3564\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Your model has trained this much epoch 150\n", "Epoch [150/600], Loss: 0.3886\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your model has trained this much epoch 151\n", "Your 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has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Your model has trained this much epoch 152\n", "Epoch [152/600], Loss: 0.5741\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Your model has trained this much epoch 153\n", "Epoch [153/600], Loss: 0.3070\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Your model has trained this much epoch 154\n", "Epoch [154/600], Loss: 0.2402\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Your model has trained this much epoch 155\n", "Epoch [155/600], Loss: 0.3473\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Your model has trained this much epoch 156\n", "Epoch [156/600], Loss: 0.3741\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Your model has trained this much epoch 157\n", "Epoch [157/600], Loss: 0.3330\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Your model has trained this much epoch 158\n", "Epoch [158/600], Loss: 0.2801\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Your model has trained this much epoch 159\n", "Epoch [159/600], Loss: 0.4621\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Your model has trained this much epoch 160\n", "Epoch [160/600], Loss: 0.3981\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Your model has trained this much epoch 161\n", "Epoch [161/600], Loss: 0.3925\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Your model has trained this much epoch 162\n", "Epoch [162/600], Loss: 0.2878\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Your model has trained this much epoch 163\n", "Epoch [163/600], Loss: 0.2767\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Your model has trained this much epoch 164\n", "Epoch [164/600], Loss: 0.5462\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your model has trained this much epoch 165\n", "Your 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has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Your model has trained this much epoch 166\n", "Epoch [166/600], Loss: 0.4619\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Your model has trained this much epoch 167\n", "Epoch [167/600], Loss: 0.3012\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Your model has trained this much epoch 168\n", "Epoch [168/600], Loss: 0.4629\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Your model has trained this much epoch 169\n", "Epoch [169/600], Loss: 0.3281\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Your model has trained this much epoch 170\n", "Epoch [170/600], Loss: 0.4914\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Your model has trained this much epoch 171\n", "Epoch [171/600], Loss: 0.2436\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Your model has trained this much epoch 172\n", "Epoch [172/600], Loss: 0.2896\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Your model has trained this much epoch 173\n", "Epoch [173/600], Loss: 0.5532\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Your model has trained this much epoch 174\n", "Epoch [174/600], Loss: 0.3789\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Your model has trained this much epoch 175\n", "Epoch [175/600], Loss: 0.3610\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Your model has trained this much epoch 176\n", "Epoch [176/600], Loss: 0.4476\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Your model has trained this much epoch 177\n", "Epoch [177/600], Loss: 0.3028\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Your model has trained this much epoch 178\n", "Epoch [178/600], Loss: 0.3444\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Your model has trained this much epoch 179\n", "Epoch [179/600], Loss: 0.2688\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Your model has trained this much epoch 180\n", "Epoch [180/600], Loss: 0.2679\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Your model has trained this much epoch 181\n", "Epoch [181/600], Loss: 0.3803\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Your model has trained this much epoch 182\n", "Epoch [182/600], Loss: 0.3010\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your model has trained this much epoch 183\n", "Your 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has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Your model has trained this much epoch 184\n", "Epoch [184/600], Loss: 0.4776\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has 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model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Your model has trained this much epoch 185\n", "Epoch [185/600], Loss: 0.3501\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Your model has trained this much epoch 186\n", "Epoch [186/600], Loss: 0.3507\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Your model has trained this much epoch 187\n", "Epoch [187/600], Loss: 0.3385\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Your model has trained this much epoch 188\n", "Epoch [188/600], Loss: 0.4365\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Your model has trained this much epoch 189\n", "Epoch [189/600], Loss: 0.4093\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Your model has trained this much epoch 190\n", "Epoch [190/600], Loss: 0.2812\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Your model has trained this much epoch 191\n", "Epoch [191/600], Loss: 0.3755\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Your model has trained this much epoch 192\n", "Epoch [192/600], Loss: 0.4237\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Your model has trained this much epoch 193\n", "Epoch [193/600], Loss: 0.3481\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Your model has trained this much epoch 194\n", "Epoch [194/600], Loss: 0.3973\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Your model has trained this much epoch 195\n", "Epoch [195/600], Loss: 0.4300\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Your model has trained this much epoch 196\n", "Epoch [196/600], Loss: 0.3262\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Your model has trained this much epoch 197\n", "Epoch [197/600], Loss: 0.2746\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Your model has trained this much epoch 198\n", "Epoch [198/600], Loss: 0.3487\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Your model has trained this much epoch 199\n", "Epoch [199/600], Loss: 0.2921\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Your model has trained this much epoch 200\n", "Epoch [200/600], Loss: 0.4205\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has 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model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Your model has trained this much epoch 201\n", "Epoch [201/600], Loss: 0.3068\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Your model has trained this much epoch 202\n", "Epoch [202/600], Loss: 0.3476\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Your model has trained this much epoch 203\n", "Epoch [203/600], Loss: 0.5830\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Your model has trained this much epoch 204\n", "Epoch [204/600], Loss: 0.4027\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has 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model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Your model has trained this much epoch 205\n", "Epoch [205/600], Loss: 0.4098\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Your model has trained this much epoch 206\n", "Epoch [206/600], Loss: 0.2842\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your model has trained this much epoch 207\n", "Your 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has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Your model has trained this much epoch 208\n", "Epoch [208/600], Loss: 0.4314\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Your model has trained this much epoch 209\n", "Epoch [209/600], Loss: 0.3182\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Your model has trained this much epoch 210\n", "Epoch [210/600], Loss: 0.3944\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Your model has trained this much epoch 211\n", "Epoch [211/600], Loss: 0.3181\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Your model has trained this much epoch 212\n", "Epoch [212/600], Loss: 0.3511\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has 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model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Your model has trained this much epoch 213\n", "Epoch [213/600], Loss: 0.3300\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Your model has trained this much epoch 214\n", "Epoch [214/600], Loss: 0.3686\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Your model has trained this much epoch 215\n", "Epoch [215/600], Loss: 0.3840\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Your model has trained this much epoch 216\n", "Epoch [216/600], Loss: 0.3551\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has trained this much epoch 217\n", "Your model has 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trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Your model has trained this much epoch 218\n", "Epoch [218/600], Loss: 0.4219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has 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model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Your model has trained this much epoch 219\n", "Epoch [219/600], Loss: 0.2942\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Your model has trained this much epoch 220\n", "Epoch [220/600], Loss: 0.4466\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your model has trained this much epoch 221\n", "Your 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has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Your model has trained this much epoch 222\n", "Epoch [222/600], Loss: 0.3131\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Your model has trained this much epoch 223\n", "Epoch [223/600], Loss: 0.3802\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Your model has trained this much epoch 224\n", "Epoch [224/600], Loss: 0.3035\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Your model has trained this much epoch 225\n", "Epoch [225/600], Loss: 0.3508\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Your model has trained this much epoch 226\n", "Epoch [226/600], Loss: 0.3907\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Your model has trained this much epoch 227\n", "Epoch [227/600], Loss: 0.2838\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Your model has trained this much epoch 228\n", "Epoch [228/600], Loss: 0.3624\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Your model has trained this much epoch 229\n", "Epoch [229/600], Loss: 0.2689\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Your model has trained this much epoch 230\n", "Epoch [230/600], Loss: 0.2781\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Your model has trained this much epoch 231\n", "Epoch [231/600], Loss: 0.4088\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Your model has trained this much epoch 232\n", "Epoch [232/600], Loss: 0.3573\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Your model has trained this much epoch 233\n", "Epoch [233/600], Loss: 0.3606\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Your model has trained this much epoch 234\n", "Epoch [234/600], Loss: 0.4110\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Your model has trained this much epoch 235\n", "Epoch [235/600], Loss: 0.4828\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Your model has trained this much epoch 236\n", "Epoch [236/600], Loss: 0.3152\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Your model has trained this much epoch 237\n", "Epoch [237/600], Loss: 0.5950\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Your model has trained this much epoch 238\n", "Epoch [238/600], Loss: 0.4197\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Your model has trained this much epoch 239\n", "Epoch [239/600], Loss: 0.3621\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Your model has trained this much epoch 240\n", "Epoch [240/600], Loss: 0.2289\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Your model has trained this much epoch 241\n", "Epoch [241/600], Loss: 0.3400\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Your model has trained this much epoch 242\n", "Epoch [242/600], Loss: 0.3102\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Your model has trained this much epoch 243\n", "Epoch [243/600], Loss: 0.4231\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Your model has trained this much epoch 244\n", "Epoch [244/600], Loss: 0.4522\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Your model has trained this much epoch 245\n", "Epoch [245/600], Loss: 0.2964\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Your model has trained this much epoch 246\n", "Epoch [246/600], Loss: 0.3692\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Your model has trained this much epoch 247\n", "Epoch [247/600], Loss: 0.2848\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Your model has trained this much epoch 248\n", "Epoch [248/600], Loss: 0.2805\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Your model has trained this much epoch 249\n", "Epoch [249/600], Loss: 0.2853\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Your model has trained this much epoch 250\n", "Epoch [250/600], Loss: 0.3721\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Your model has trained this much epoch 251\n", "Epoch [251/600], Loss: 0.2680\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Your model has trained this much epoch 252\n", "Epoch [252/600], Loss: 0.2973\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your model has trained this much epoch 253\n", "Your 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has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Your model has trained this much epoch 254\n", "Epoch [254/600], Loss: 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much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has trained this much epoch 255\n", "Your model has 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256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Your model has trained this much epoch 256\n", "Epoch [256/600], Loss: 0.3403\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has 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model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Your model has trained this much epoch 257\n", "Epoch [257/600], Loss: 0.3029\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Your model has trained this much epoch 258\n", "Epoch [258/600], Loss: 0.3396\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Your model has trained this much epoch 259\n", "Epoch [259/600], Loss: 0.3633\n", "Your model has trained this much epoch 260\n", "Your model has 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260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Your model has trained this much epoch 260\n", "Epoch [260/600], Loss: 0.3641\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has trained this much epoch 261\n", "Your model has 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has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Your model has trained this much epoch 262\n", "Epoch [262/600], Loss: 0.3316\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has trained this much epoch 263\n", "Your model has 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"Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Your model has trained this much epoch 264\n", "Epoch [264/600], Loss: 0.2772\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has trained this much epoch 265\n", "Your model has 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has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Your model has trained this much epoch 266\n", "Epoch [266/600], Loss: 0.3562\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Your model has trained this much epoch 267\n", "Epoch [267/600], Loss: 0.2680\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Your model has trained this much epoch 268\n", "Epoch [268/600], Loss: 0.3439\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Your model has trained this much epoch 269\n", "Epoch [269/600], Loss: 0.3700\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Your model has trained this much epoch 270\n", "Epoch [270/600], Loss: 0.3261\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has trained this much epoch 271\n", "Your model has 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"Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Your model has trained this much epoch 272\n", "Epoch [272/600], Loss: 0.2986\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has trained this much epoch 273\n", "Your model has 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model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Your model has trained this much epoch 274\n", "Epoch [274/600], Loss: 0.2927\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has trained this much epoch 275\n", "Your model has 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"Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Your model has trained this much epoch 276\n", "Epoch [276/600], Loss: 0.4305\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your model has trained this much epoch 277\n", "Your 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has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Your model has trained this much epoch 278\n", "Epoch [278/600], Loss: 0.3047\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Your model has trained this much epoch 279\n", "Epoch [279/600], Loss: 0.2455\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Your model has trained this much epoch 280\n", "Epoch [280/600], Loss: 0.3683\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has 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model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Your model has trained this much epoch 281\n", "Epoch [281/600], Loss: 0.2421\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model 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"Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Your model has trained this much epoch 282\n", "Epoch [282/600], Loss: 0.2047\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has trained this much epoch 283\n", "Your model has 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"Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Your model has trained this much epoch 284\n", "Epoch [284/600], Loss: 0.2738\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has 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model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Your model has trained this much epoch 285\n", "Epoch [285/600], Loss: 0.2527\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Your model has trained this much epoch 286\n", "Epoch [286/600], Loss: 0.3988\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Your model has trained this much epoch 287\n", "Epoch [287/600], Loss: 0.4042\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Your model has trained this much epoch 288\n", "Epoch [288/600], Loss: 0.4174\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Your model has trained this much epoch 289\n", "Epoch [289/600], Loss: 0.2818\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Your model has trained this much epoch 290\n", "Epoch [290/600], Loss: 0.2957\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Your model has trained this much epoch 291\n", "Epoch [291/600], Loss: 0.3854\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Your model has trained this much epoch 292\n", "Epoch [292/600], Loss: 0.2570\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Your model has trained this much epoch 293\n", "Epoch [293/600], Loss: 0.5374\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Your model has trained this much epoch 294\n", "Epoch [294/600], Loss: 0.2506\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Your model has trained this much epoch 295\n", "Epoch [295/600], Loss: 0.2359\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Your model has trained this much epoch 296\n", "Epoch [296/600], Loss: 0.3341\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Your model has trained this much epoch 297\n", "Epoch [297/600], Loss: 0.2701\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Your model has trained this much epoch 298\n", "Epoch [298/600], Loss: 0.2712\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your model has trained this much epoch 299\n", "Your 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has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Your model has trained this much epoch 300\n", "Epoch [300/600], Loss: 0.3051\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has trained this much epoch 301\n", "Your model has 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model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Your model has trained this much epoch 302\n", "Epoch [302/600], Loss: 0.3337\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has trained this much epoch 303\n", "Your model has 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has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Your model has trained this much epoch 304\n", "Epoch [304/600], Loss: 0.4052\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has trained this much epoch 305\n", "Your model has 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"Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Your model has trained this much epoch 306\n", "Epoch [306/600], Loss: 0.2704\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Your model has trained this much epoch 307\n", "Epoch [307/600], Loss: 0.2536\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Your model has trained this much epoch 308\n", "Epoch [308/600], Loss: 0.2724\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Your model has trained this much epoch 309\n", "Epoch [309/600], Loss: 0.3162\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Your model has trained this much epoch 310\n", "Epoch [310/600], Loss: 0.3227\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Your model has trained this much epoch 311\n", "Epoch [311/600], Loss: 0.2548\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Your model has trained this much epoch 312\n", "Epoch [312/600], Loss: 0.3940\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Your model has trained this much epoch 313\n", "Epoch [313/600], Loss: 0.2988\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Your model has trained this much epoch 314\n", "Epoch [314/600], Loss: 0.5519\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Your model has trained this much epoch 315\n", "Epoch [315/600], Loss: 0.2738\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Your model has trained this much epoch 316\n", "Epoch [316/600], Loss: 0.2461\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Your model has trained this much epoch 317\n", "Epoch [317/600], Loss: 0.3935\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Your model has trained this much epoch 318\n", "Epoch [318/600], Loss: 0.5391\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Your model has trained this much epoch 319\n", "Epoch [319/600], Loss: 0.2287\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Your model has trained this much epoch 320\n", "Epoch [320/600], Loss: 0.4403\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Your model has trained this much epoch 321\n", "Epoch [321/600], Loss: 0.2628\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Your model has trained this much epoch 322\n", "Epoch [322/600], Loss: 0.3685\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Your model has trained this much epoch 323\n", "Epoch [323/600], Loss: 0.3148\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Your model has trained this much epoch 324\n", "Epoch [324/600], Loss: 0.2654\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Your model has trained this much epoch 325\n", "Epoch [325/600], Loss: 0.3485\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Your model has trained this much epoch 326\n", "Epoch [326/600], Loss: 0.2816\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Your model has trained this much epoch 327\n", "Epoch [327/600], Loss: 0.2622\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Your model has trained this much epoch 328\n", "Epoch [328/600], Loss: 0.2560\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Your model has trained this much epoch 329\n", "Epoch [329/600], Loss: 0.3026\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Your model has trained this much epoch 330\n", "Epoch [330/600], Loss: 0.3757\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your model has trained this much epoch 331\n", "Your 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has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Your model has trained this much epoch 332\n", "Epoch [332/600], Loss: 0.2754\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Your model has trained this much epoch 333\n", "Epoch [333/600], Loss: 0.4276\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Your model has trained this much epoch 334\n", "Epoch [334/600], Loss: 0.5440\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Your model has trained this much epoch 335\n", "Epoch [335/600], Loss: 0.3286\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Your model has trained this much epoch 336\n", "Epoch [336/600], Loss: 0.3148\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Your model has trained this much epoch 337\n", "Epoch [337/600], Loss: 0.3429\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Your model has trained this much epoch 338\n", "Epoch [338/600], Loss: 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much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has trained this much epoch 339\n", "Your model has 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340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Your model has trained this much epoch 340\n", "Epoch [340/600], Loss: 0.3480\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has trained this much epoch 341\n", "Your model has 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342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Your model has trained this much epoch 342\n", "Epoch [342/600], Loss: 0.2676\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained this much epoch 343\n", "Your model has trained 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344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Your model has trained this much epoch 344\n", "Epoch [344/600], Loss: 0.2869\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained this much epoch 345\n", "Your model has trained 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346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Your model has trained this much epoch 346\n", "Epoch [346/600], Loss: 0.4026\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your model has trained this much epoch 347\n", "Your 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has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Your model has trained this much epoch 348\n", "Epoch [348/600], Loss: 0.3759\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Your model has trained this much epoch 349\n", "Epoch [349/600], Loss: 0.3400\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Your model has trained this much epoch 350\n", "Epoch [350/600], Loss: 0.2922\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has trained this much epoch 351\n", "Your model has 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has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Your model has trained this much epoch 352\n", "Epoch [352/600], Loss: 0.2152\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has trained this much epoch 353\n", "Your model has 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has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Your model has trained this much epoch 354\n", "Epoch [354/600], Loss: 0.2313\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Your model has trained this much epoch 355\n", "Epoch [355/600], Loss: 0.2746\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Your model has trained this much epoch 356\n", "Epoch [356/600], Loss: 0.3226\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Your model has trained this much epoch 357\n", "Epoch [357/600], Loss: 0.3547\n", "Your model has trained this much epoch 358\n", "Your model has 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model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Your model has trained this much epoch 358\n", "Epoch [358/600], Loss: 0.2877\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your model has trained this much epoch 359\n", "Your 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has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Your model has trained this much epoch 360\n", "Epoch [360/600], Loss: 0.3181\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has trained this much epoch 361\n", "Your model has 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has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Your model has trained this much epoch 362\n", "Epoch [362/600], Loss: 0.3770\n", "Your model has trained this much epoch 363\n", "Your model has trained this much epoch 363\n", "Your model has trained this much epoch 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364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this much epoch 364\n", "Your model has trained this 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366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Your model has trained this much epoch 366\n", "Epoch [366/600], Loss: 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368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Your model has trained this much epoch 368\n", "Epoch [368/600], Loss: 0.2737\n", "Your model has trained this much epoch 369\n", "Your model has trained this much epoch 369\n", "Your model has trained this much epoch 369\n", "Your model has trained this much epoch 369\n", "Your model has trained this much epoch 369\n", "Your model has trained this much epoch 369\n", "Your model has trained this much epoch 369\n", "Your model has trained this much epoch 369\n", "Your model has trained this much 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"Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Your model has trained this much epoch 370\n", "Epoch [370/600], Loss: 0.3111\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has trained this much epoch 371\n", "Your model has 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model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Your model has trained this much epoch 372\n", "Epoch [372/600], Loss: 0.4632\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Your model has trained this much epoch 373\n", "Epoch [373/600], Loss: 0.4313\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Your model has trained this much epoch 374\n", "Epoch [374/600], Loss: 0.2598\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your model has trained this much epoch 375\n", "Your 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has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Your model has trained this much epoch 376\n", "Epoch [376/600], Loss: 0.3299\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has 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model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Your model has trained this much epoch 377\n", "Epoch [377/600], Loss: 0.4461\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Your model has trained this much epoch 378\n", "Epoch [378/600], Loss: 0.2952\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your model has trained this much epoch 379\n", "Your 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has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Your model has trained this much epoch 380\n", "Epoch [380/600], Loss: 0.2986\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your model has trained this much epoch 381\n", "Your 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"Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Your model has trained this much epoch 382\n", "Epoch [382/600], Loss: 0.3555\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has 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model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Your model has trained this much epoch 383\n", "Epoch [383/600], Loss: 0.2955\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Your model has trained this much epoch 384\n", "Epoch [384/600], Loss: 0.2119\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has trained this much epoch 385\n", "Your model has 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model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Your model has trained this much epoch 386\n", "Epoch [386/600], Loss: 0.4245\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Your model has trained this much epoch 387\n", "Epoch [387/600], Loss: 0.2958\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Your model has trained this much epoch 388\n", "Epoch [388/600], Loss: 0.4775\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Your model has trained this much epoch 389\n", "Epoch [389/600], Loss: 0.2873\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Your model has trained this much epoch 390\n", "Epoch [390/600], Loss: 0.3126\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Your model has trained this much epoch 391\n", "Epoch [391/600], Loss: 0.4073\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Your model has trained this much epoch 392\n", "Epoch [392/600], Loss: 0.4388\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has 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model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Your model has trained this much epoch 393\n", "Epoch [393/600], Loss: 0.3561\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", 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394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Your model has trained this much epoch 394\n", "Epoch [394/600], Loss: 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much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has trained this much epoch 395\n", "Your model has 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"Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Your model has trained this much epoch 396\n", "Epoch [396/600], Loss: 0.2675\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has trained this much epoch 397\n", "Your model has 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"Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Your model has trained this much epoch 398\n", "Epoch [398/600], Loss: 0.4786\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has trained this much epoch 399\n", "Your model has 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400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Your model has trained this much epoch 400\n", "Epoch [400/600], Loss: 0.3129\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has trained this much epoch 401\n", "Your model has 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402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Your model has trained this much epoch 402\n", "Epoch [402/600], Loss: 0.3593\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has trained this much epoch 403\n", "Your model has 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"Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Your model has trained this much epoch 404\n", "Epoch [404/600], Loss: 0.3192\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Your model has trained this much epoch 405\n", "Epoch [405/600], Loss: 0.2368\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Your model has trained this much epoch 406\n", "Epoch [406/600], Loss: 0.4927\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Your model has trained this much epoch 407\n", "Epoch [407/600], Loss: 0.3538\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Your model has trained this much epoch 408\n", "Epoch [408/600], Loss: 0.2692\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Your model has trained this much epoch 409\n", "Epoch [409/600], Loss: 0.2631\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", 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410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Your model has trained this much epoch 410\n", "Epoch [410/600], Loss: 0.4245\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has trained this much epoch 411\n", "Your model has 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has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Your model has trained this much epoch 412\n", "Epoch [412/600], Loss: 0.2904\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Your model has trained this much epoch 413\n", "Epoch [413/600], Loss: 0.3822\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Your model has trained this much epoch 414\n", "Epoch [414/600], Loss: 0.2875\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Your model has trained this much epoch 415\n", "Epoch [415/600], Loss: 0.3105\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Your model has trained this much epoch 416\n", "Epoch [416/600], Loss: 0.3559\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Your model has trained this much epoch 417\n", "Epoch [417/600], Loss: 0.4823\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Your model has trained this much epoch 418\n", "Epoch [418/600], Loss: 0.2484\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your model has trained this much epoch 419\n", "Your 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has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Your model has trained this much epoch 420\n", "Epoch [420/600], Loss: 0.2497\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has trained this much epoch 421\n", "Your model has 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has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Your model has trained this much epoch 422\n", "Epoch [422/600], Loss: 0.4062\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has trained this much epoch 423\n", "Your model has 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has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Your model has trained this much epoch 424\n", "Epoch [424/600], Loss: 0.5179\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Your model has trained this much epoch 425\n", "Epoch [425/600], Loss: 0.2550\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Your model has trained this much epoch 426\n", "Epoch [426/600], Loss: 0.3352\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Your model has trained this much epoch 427\n", "Epoch [427/600], Loss: 0.5945\n", "Your model has trained this much epoch 428\n", "Your model has 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model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Your model has trained this much epoch 428\n", "Epoch [428/600], Loss: 0.3421\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Your model has trained this much epoch 429\n", "Epoch [429/600], Loss: 0.4192\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Your model has trained this much epoch 430\n", "Epoch [430/600], Loss: 0.3221\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Your model has trained this much epoch 431\n", "Epoch [431/600], Loss: 0.3579\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Your model has trained this much epoch 432\n", "Epoch [432/600], Loss: 0.2829\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Your model has trained this much epoch 433\n", "Epoch [433/600], Loss: 0.4655\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Your model has trained this much epoch 434\n", "Epoch [434/600], Loss: 0.3896\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your model has trained this much epoch 435\n", "Your 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has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Your model has trained this much epoch 436\n", "Epoch [436/600], Loss: 0.3618\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has 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model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Your model has trained this much epoch 437\n", "Epoch [437/600], Loss: 0.2550\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Your model has trained this much epoch 438\n", "Epoch [438/600], Loss: 0.2934\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Your model has trained this much epoch 439\n", "Epoch [439/600], Loss: 0.2840\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Your model has trained this much epoch 440\n", "Epoch [440/600], Loss: 0.3172\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Your model has trained this much epoch 441\n", "Epoch [441/600], Loss: 0.4522\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Your model has trained this much epoch 442\n", "Epoch [442/600], Loss: 0.2676\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Your model has trained this much epoch 443\n", "Epoch [443/600], Loss: 0.3448\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Your model has trained this much epoch 444\n", "Epoch [444/600], Loss: 0.2743\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Your model has trained this much epoch 445\n", "Epoch [445/600], Loss: 0.2419\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Your model has trained this much epoch 446\n", "Epoch [446/600], Loss: 0.4323\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Your model has trained this much epoch 447\n", "Epoch [447/600], Loss: 0.4199\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Your model has trained this much epoch 448\n", "Epoch [448/600], Loss: 0.2518\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Your model has trained this much epoch 449\n", "Epoch [449/600], Loss: 0.3580\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Your model has trained this much epoch 450\n", "Epoch [450/600], Loss: 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much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Your model has trained this much epoch 451\n", "Epoch [451/600], Loss: 0.2247\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Your model has trained this much epoch 452\n", "Epoch [452/600], Loss: 0.4009\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Your model has trained this much epoch 453\n", "Epoch [453/600], Loss: 0.4038\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Your model has trained this much epoch 454\n", "Epoch [454/600], Loss: 0.3019\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Your model has trained this much epoch 455\n", "Epoch [455/600], Loss: 0.2904\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Your model has trained this much epoch 456\n", "Epoch [456/600], Loss: 0.2718\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Your model has trained this much epoch 457\n", "Epoch [457/600], Loss: 0.3079\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model 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458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Your model has trained this much epoch 458\n", "Epoch [458/600], Loss: 0.2648\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has trained this much epoch 459\n", "Your model has 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"Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Your model has trained this much epoch 460\n", "Epoch [460/600], Loss: 0.3084\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has trained this much epoch 461\n", "Your model has 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462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Your model has trained this much epoch 462\n", "Epoch [462/600], Loss: 0.4227\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has trained this much epoch 463\n", "Your model has 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"Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Your model has trained this much epoch 464\n", "Epoch [464/600], Loss: 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much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has trained this much epoch 465\n", "Your model has 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466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Your model has trained this much epoch 466\n", "Epoch [466/600], Loss: 0.2482\n", "Your model has trained this much epoch 467\n", "Your model has trained this much epoch 467\n", "Your model has trained this much epoch 467\n", "Your model has trained this much epoch 467\n", "Your model has trained this much epoch 467\n", "Your model has trained this much epoch 467\n", "Your model has trained this much epoch 467\n", "Your model has trained this much epoch 467\n", "Your model has trained this much 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468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Your model has trained this much epoch 468\n", "Epoch [468/600], Loss: 0.3873\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has trained this much epoch 469\n", "Your model has 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470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Your model has trained this much epoch 470\n", "Epoch [470/600], Loss: 0.2844\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained this much epoch 471\n", "Your model has trained 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472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this much epoch 472\n", "Your model has trained this 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"Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Your model has trained this much epoch 474\n", "Epoch [474/600], Loss: 0.4300\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has trained this much epoch 475\n", "Your model has 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"Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Your model has trained this much epoch 476\n", "Epoch [476/600], Loss: 0.4197\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has trained this much epoch 477\n", "Your model has 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"Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Your model has trained this much epoch 478\n", "Epoch [478/600], Loss: 0.2327\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has trained this much epoch 479\n", "Your model has 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"Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Your model has trained this much epoch 480\n", "Epoch [480/600], Loss: 0.6648\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has trained this much epoch 481\n", "Your model has 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"Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Your model has trained this much epoch 482\n", "Epoch [482/600], Loss: 0.2290\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Your model has trained this much epoch 483\n", "Epoch [483/600], Loss: 0.2023\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Your model has trained this much epoch 484\n", "Epoch [484/600], Loss: 0.3066\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has 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model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Your model has trained this much epoch 485\n", "Epoch [485/600], Loss: 0.3398\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Your model has trained this much epoch 486\n", "Epoch [486/600], Loss: 0.2571\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your model has trained this much epoch 487\n", "Your 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has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Your model has trained this much epoch 488\n", "Epoch [488/600], Loss: 0.3003\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Your model has trained this much epoch 489\n", "Epoch [489/600], Loss: 0.2766\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Your model has trained this much epoch 490\n", "Epoch [490/600], Loss: 0.4642\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Your model has trained this much epoch 491\n", "Epoch [491/600], Loss: 0.3426\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Your model has trained this much epoch 492\n", "Epoch [492/600], Loss: 0.2550\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Your model has trained this much epoch 493\n", "Epoch [493/600], Loss: 0.3384\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Your model has trained this much epoch 494\n", "Epoch [494/600], Loss: 0.2811\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Your model has trained this much epoch 495\n", "Epoch [495/600], Loss: 0.2645\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Your model has trained this much epoch 496\n", "Epoch [496/600], Loss: 0.2465\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Your model has trained this much epoch 497\n", "Epoch [497/600], Loss: 0.3426\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Your model has trained this much epoch 498\n", "Epoch [498/600], Loss: 0.4374\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Your model has trained this much epoch 499\n", "Epoch [499/600], Loss: 0.3155\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Your model has trained this much epoch 500\n", "Epoch [500/600], Loss: 0.3977\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Your model has trained this much epoch 501\n", "Epoch [501/600], Loss: 0.2490\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Your model has trained this much epoch 502\n", "Epoch [502/600], Loss: 0.4041\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Your model has trained this much epoch 503\n", "Epoch [503/600], Loss: 0.4285\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Your model has trained this much epoch 504\n", "Epoch [504/600], Loss: 0.2794\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your model has trained this much epoch 505\n", "Your 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"Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Your model has trained this much epoch 506\n", "Epoch [506/600], Loss: 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much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Your model has trained this much epoch 507\n", "Epoch [507/600], Loss: 0.4218\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model 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"Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Your model has trained this much epoch 508\n", "Epoch [508/600], Loss: 0.2614\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has trained this much epoch 509\n", "Your model has 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has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Your model has trained this much epoch 510\n", "Epoch [510/600], Loss: 0.4121\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has trained this much epoch 511\n", "Your model has 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model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Your model has trained this much epoch 512\n", "Epoch [512/600], Loss: 0.2982\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has trained this much epoch 513\n", "Your model has 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514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Your model has trained this much epoch 514\n", "Epoch [514/600], Loss: 0.2643\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has trained this much epoch 515\n", "Your model has 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has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Your model has trained this much epoch 516\n", "Epoch [516/600], Loss: 0.3097\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has trained this much epoch 517\n", "Your model has 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"Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Your model has trained this much epoch 518\n", "Epoch [518/600], Loss: 0.3339\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has trained this much epoch 519\n", "Your model has 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"Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Your model has trained this much epoch 520\n", "Epoch [520/600], Loss: 0.4191\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has trained this much epoch 521\n", "Your model has 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522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Your model has trained this much epoch 522\n", "Epoch [522/600], Loss: 0.3887\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has trained this much epoch 523\n", "Your model has 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"Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Your model has trained this much epoch 524\n", "Epoch [524/600], Loss: 0.3666\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has trained this much epoch 525\n", "Your model has 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526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Your model has trained this much epoch 526\n", "Epoch [526/600], Loss: 0.2867\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has trained this much epoch 527\n", "Your model has 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"Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Your model has trained this much epoch 528\n", "Epoch [528/600], Loss: 0.2453\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has trained this much epoch 529\n", "Your model has 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"Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Your model has trained this much epoch 530\n", "Epoch [530/600], Loss: 0.2877\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has trained this much epoch 531\n", "Your model has 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"Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Your model has trained this much epoch 532\n", "Epoch [532/600], Loss: 0.3134\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has trained this much epoch 533\n", "Your model has 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"Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Your model has trained this much epoch 534\n", "Epoch [534/600], Loss: 0.3708\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has trained this much epoch 535\n", "Your model has 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"Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Your model has trained this much epoch 536\n", "Epoch [536/600], Loss: 0.3174\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has trained this much epoch 537\n", "Your model has 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"Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Your model has trained this much epoch 538\n", "Epoch [538/600], Loss: 0.4367\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Your model has trained this much epoch 539\n", "Epoch [539/600], Loss: 0.3049\n", "Your model has trained this much epoch 540\n", "Your model has 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model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Your model has trained this much epoch 540\n", "Epoch [540/600], Loss: 0.3071\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has trained this much epoch 541\n", "Your model has 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"Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Your model has trained this much epoch 542\n", "Epoch [542/600], Loss: 0.4748\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has trained this much epoch 543\n", "Your model has 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"Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Your model has trained this much epoch 544\n", "Epoch [544/600], Loss: 0.2411\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has trained this much epoch 545\n", "Your model has 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546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Your model has trained this much epoch 546\n", "Epoch [546/600], Loss: 0.3977\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has trained this much epoch 547\n", "Your model has 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has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Your model has trained this much epoch 548\n", "Epoch [548/600], Loss: 0.2927\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has trained this much epoch 549\n", "Your model has 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"Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Your model has trained this much epoch 550\n", "Epoch [550/600], Loss: 0.3835\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has trained this much epoch 551\n", "Your model has 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"Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Your model has trained this much epoch 552\n", "Epoch [552/600], Loss: 0.2574\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has trained this much epoch 553\n", "Your model has 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554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Your model has trained this much epoch 554\n", "Epoch [554/600], Loss: 0.3087\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has trained this much epoch 555\n", "Your model has 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556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Your model has trained this much epoch 556\n", "Epoch [556/600], Loss: 0.2332\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has trained this much epoch 557\n", "Your model has 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"Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Your model has trained this much epoch 558\n", "Epoch [558/600], Loss: 0.2788\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has trained this much epoch 559\n", "Your model has 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"Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Your model has trained this much epoch 560\n", "Epoch [560/600], Loss: 0.2998\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much epoch 561\n", "Your model has trained this much 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562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Your model has trained this much epoch 562\n", "Epoch [562/600], Loss: 0.2583\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has trained this much epoch 563\n", "Your model has 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564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Your model has trained this much epoch 564\n", "Epoch [564/600], Loss: 0.4478\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has trained this much epoch 565\n", "Your model has 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"Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Your model has trained this much epoch 566\n", "Epoch [566/600], Loss: 0.4781\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has trained this much epoch 567\n", "Your model has 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568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Your model has trained this much epoch 568\n", "Epoch [568/600], Loss: 0.3072\n", "Your model has trained this much epoch 569\n", "Your model has trained this much epoch 569\n", "Your model has trained this much epoch 569\n", "Your model has trained this much epoch 569\n", "Your model has trained this much epoch 569\n", "Your model has trained this much 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570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Your model has trained this much epoch 570\n", "Epoch [570/600], Loss: 0.2124\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained this much epoch 571\n", "Your model has trained 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"Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Your model has trained this much epoch 572\n", "Epoch [572/600], Loss: 0.4299\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has trained this much epoch 573\n", "Your model has 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"Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Your model has trained this much epoch 574\n", "Epoch [574/600], Loss: 0.4380\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has 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model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Your model has trained this much epoch 575\n", "Epoch [575/600], Loss: 0.3078\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model 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"Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Your model has trained this much epoch 576\n", "Epoch [576/600], Loss: 0.2865\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Your model has trained this much epoch 577\n", "Epoch [577/600], Loss: 0.3968\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Your model has trained this much epoch 578\n", "Epoch [578/600], Loss: 0.2561\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Your model has trained this much epoch 579\n", "Epoch [579/600], Loss: 0.2987\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Your model has trained this much epoch 580\n", "Epoch [580/600], Loss: 0.5199\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Your model has trained this much epoch 581\n", "Epoch [581/600], Loss: 0.2511\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Your model has trained this much epoch 582\n", "Epoch [582/600], Loss: 0.2780\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Your model has trained this much epoch 583\n", "Epoch [583/600], Loss: 0.2928\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Your model has trained this much epoch 584\n", "Epoch [584/600], Loss: 0.3482\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Your model has trained this much epoch 585\n", "Epoch [585/600], Loss: 0.3662\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Your model has trained this much epoch 586\n", "Epoch [586/600], Loss: 0.3499\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Your model has trained this much epoch 587\n", "Epoch [587/600], Loss: 0.2176\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Your model has trained this much epoch 588\n", "Epoch [588/600], Loss: 0.4580\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Your model has trained this much epoch 589\n", "Epoch [589/600], Loss: 0.3585\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Your model has trained this much epoch 590\n", "Epoch [590/600], Loss: 0.2333\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has 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model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Your model has trained this much epoch 591\n", "Epoch [591/600], Loss: 0.3845\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model 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"Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Your model has trained this much epoch 592\n", "Epoch [592/600], Loss: 0.2376\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Your model has trained this much epoch 593\n", "Epoch [593/600], Loss: 0.3491\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Your model has trained this much epoch 594\n", "Epoch [594/600], Loss: 0.3821\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Your model has trained this much epoch 595\n", "Epoch [595/600], Loss: 0.3069\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Your model has trained this much epoch 596\n", "Epoch [596/600], Loss: 0.2450\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Your model has trained this much epoch 597\n", "Epoch [597/600], Loss: 0.5298\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Your model has trained this much epoch 598\n", "Epoch [598/600], Loss: 0.2424\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Your model has trained this much epoch 599\n", "Epoch [599/600], Loss: 0.2817\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Your model has trained this much epoch 600\n", "Epoch [600/600], Loss: 0.2401\n", "Model saved.\n" ] } ], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "transformer = TransformerNet().to(device)\n", "vgg = VGGFeatures().to(device)\n", "optimizer = optim.Adam(transformer.parameters(), lr=1e-3)\n", "mse_loss = nn.MSELoss()\n", "\n", "style_features = vgg(style_image)\n", "style_grams = [gram_matrix(f) for f in style_features]\n", "\n", "os.makedirs(\"stylized_training_output\", exist_ok=True)\n", "\n", "epochs = 600\n", "save_interval = 25 \n", "\n", "print(\"Training started...\")\n", "step = 0\n", "for epoch in range(epochs):\n", " for i, (x, _) in enumerate(dataloader):\n", " print(\"Your model has trained this much epoch \", epoch+1)\n", " x = x.to(device)\n", " y_hat = transformer(x)\n", " y_features = vgg(y_hat)\n", " x_features = vgg(x)\n", "\n", " content_loss = mse_loss(y_features[2], x_features[2])\n", " style_loss = sum(mse_loss(gram_matrix(f1), f2.expand_as(gram_matrix(f1)))\n", " for f1, f2 in zip(y_features, style_grams))\n", " total_loss = content_loss + 70 * style_loss\n", "\n", " optimizer.zero_grad()\n", " total_loss.backward()\n", " optimizer.step()\n", "\n", " if step % save_interval == 0:\n", " output = denormalize(y_hat.clone())\n", " save_image(output, f\"stylized_training_output/epoch{epoch+1}_batch{i+1}.jpg\")\n", "\n", " step += 1\n", "\n", " print(f\"Epoch [{epoch+1}/{epochs}], Loss: {total_loss.item():.4f}\")\n", "\n", "torch.save(transformer.state_dict(), \"final_styled_model.pth\")\n", "print(\"Model saved.\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "fc5ecb2d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "id": "08c433a5", "metadata": {}, "outputs": [], "source": [ "# Load model\n", "transformer = TransformerNet().to(device)\n", "transformer.load_state_dict(torch.load(\"final_styled_model.pth\"))\n", "transformer.eval()\n", "\n", "# Load new image\n", "def stylize_image(input_path, output_path):\n", " image = Image.open(input_path).convert(\"RGB\")\n", " transform = transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(256),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", " ])\n", " x = transform(image).unsqueeze(0).to(device)\n", " with torch.no_grad():\n", " y = transformer(x)\n", " y = denormalize(y)\n", " save_image(y, output_path)\n", "\n", "stylize_image(\"Testing6.jpg\", \"stylized_output4.jpg\")\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "75d120a6", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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", 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70ZxRdjsjlf8AhW/hP/oEf+TMv/xVL/wrfwl/0Cf/ACZl/wDiq6nn1oHWi7CyOWHw28Jf9An/AMmZf/iqP+FbeEv+gT/5My//ABVdSKUfSldisjlD8OPCIIB0oZ9PtMvP/j9L/wAK38JYz/ZH/kzL/wDF1zfizUHvtfniV28mA+Uq54yOv65qnZX2oae260u5Yuc7c5U/UHg1VmLQ7H/hWvhL/oE/+TMv/wAVR/wrbwj/ANAn/wAmZf8A4qm6T4zimdYNUVbeUnAlX/Vt9f7v8vpXVZOMjH+NJ3Hocqfhv4SH/MJ/8mZf/iqUfDbwkR/yCf8AyZl/+KrqNrHqePanKOOKV2Fkcr/wrbwl/wBAn/yZl/8AiqT/AIVv4R76SP8AwJl/+KrqyoPejAFF2OyOU/4Vv4S/6BP/AJMy/wDxVH/Ct/Cf/QI/8mZf/iq6zNGaLsLI5MfDbwn/ANAn/wAmZf8A4ql/4Vt4S/6BP/kxL/8AFV1VLii7CyOU/wCFb+Ev+gT/AOTMv/xVH/CtvCX/AECf/JmX/wCKrqulLRdhZHKf8K28Jf8AQJ/8mZf/AIqj/hW3hL/oE/8AkzL/APFV1dRXE8VrbvPcSLHEgyzN0FF2FkcyPht4S/6BP/kzL/8AFVWu/A/gWwAN3ZxQk9A13Lk/Qb81X1bxjdXbtBpYMEPQzEfOw9v7o/X6VzEsbsxkkZnkbqzHJP1qkmS2jsbXwJ4Ivo/MttPSVfVbqX/4rirH/CtvCX/QJ/8AJmX/AOKrB8FTtHrwi34jeNsjPBwM16R70ndArHK/8K18Jf8AQJ/8mZf/AIqvn6vqwV8p1UGKR6r4QZT4TskbkfvOCOPvt0rUksmK5gO//ZB+aqfgyC3l8H2IF0I5z5mVbgf6xq2HtJoOXTC/30ORXj1JctSVu7PdouEqUV1sjFcMhwRz6EVFnawKcGtuaLzl/eDd6MBzWZcWjwtn5WU9xWkKilozKcGtUaGn3pYBZGO717H610luHCqzpgMM4rk7QKQT+H1q9LdXMN1DMu54wuGx2rmrUlJ2QOk5K5talEJI0kGSoOGOO1Q2iQQ6k4EmUCkMT6ipry5b+zpIo2AlZNw559a5y3vXcvwFEnBY/wAJxToU3yJMKMJSg02XbjX2a8lCrujU4jU+lZp1J7i6beNp9Kzmka3lYAgnd1Faccdtb6abu4zLeTn90AfuDPX3Jru9mjZOFK3KipePmcGlS6MEZZDgkYyKRIjJfRrdgIh+Z+e1P1e8jvJ1S1t1SKMYUIvb3qlAqda2i2CxuHa7Dlq05p4w/mzYbHRTWJYyxwS+ZMAcdEPenXt8t3MDHGqdtq9KThrcXtVZGjqGu3FxEIzINp/hHSqrzlolUcmqnkqCDJMgPXAqzBepFwqKR0yeabjfcdOajolYv2n7pc5G49a0DqJij2xvt9a5g3DmYsDgZ6VK9wxXjNS4Fvlqas6iw1t3uPLlII9aTWZWuDtjII+tc3ZSOJt5rWN3bA75fvD0rJ0L6nPKhG/OjTgjWysSzcEisNla6uSatDUP7ScRKMRr1qN7m2gmEcXBzyayhSktWRCEldsq3ZkspEIPBqF4k1BRInyP3xV7UYhcqm0g/StHTdJDQ9MAV3zqRpwSW5wxouVSTqfCO0bR1RQxUfWtuWWKzj4xurMXUBaM1v1PY0zLTsWcnHpXDOuzV09bdB0ryXbbiSF9KbL5dtA0jMAB61IAxUhFJ4rKvtI1PVWEYYRQ9yauhDmd5Gkp8kdDndU1Nr6UqhxEv61QA4611ieC44/9bdN+Ao/4RO3zhZZGNd3PGOhwyp1Kj5mcoB708ECup/4Q0HkSuoqW38KRQvmRmkx61Mq0YkLDyb3Oc8Iyqmsa+CfvfZyM/wC61dOLkTwOnoK5C8Lab4w15LcYUG2GP+2ea2tPaeWaMvC+xhk+1ZVHLm5u5zVFyyIZHHmbVPzDqBSzXAiTk/Me1dK2kQAGYDkjmsG5sFnuSoUtitaUp7I9HBVMROMo09PUoQytv3MwIPpWkiW5TMuMHvVOTSHifjcAPSmzwTLDuByBnA6miUZ21OiGGqtL28rxRu6M1rBMfL/DNat3fwspQ45GMCuTisdQCxzJAQp9aleZXUDzCrZ5b0rncWtEcuLwrc39XV/IeWMU5xgKSQKtLcyCAZ5OKlGkJNaxzCYMrjer59OtR+aG0MzGMI0blQQckqf/ANVZOkedGjPllJrYYH3D5zx6g81Xm0/Urlt1vEdvZ2O0fkav6VbAQC8nTLN/qkIzgetE87tLmSRiR69K6aVBbszT0KBsL8oI7qIRDIy7N8tXrW3uLMO9jdRySqpzGBzjvj3qO8uXPkKzq6knGfwpLeV4rmN1IJUhgRVypQTGmU5Zp8+ZcSPKjHiXccfQ4qWIr0GRzzg81o3dqLZ5VKA27OcqfT/GsaW3kspEO8NC/wDqpM/ofeuS19Bmvvk27GZpYwM7Sece1S21tgLPbSRsp6KxwfoazoJ/lzg7hxjPSri7MjCgLJw3HQ9jTjq9RkV/eNFclZYyjjnB4z9KyX1YBWDg7c8cgk+1dFbNDdXH2a8iEjIuYmY9Paop9E0u3uFvY4JPODZVN2Vz61vGgnqF02YLWF9FNau8BVLonyxnkYwTkVrWtisDK1yfMYdEB+XPqa0Hm22dtKxBlDuQ2ehPFUNx2scHnBz61nUSTsg2LTTYQE8ke9OmlTMUz4MOcYzjefaqagO205GDliOw9Kk1LQ3vVhuS7RCIDyY1PCL7+5oiikrl65t1MeYz8p7Y6Vh36FUIYHI54FX57n+zrWMSMSvTrUa31teqdjZ7UW0JlHWxQ06WSNNwYtW1FcybN2CoFYMgNtKwhYYJ6ZqaW/kFvtf5RjrWThrcSuh1xeSSzFI8sxPah9MnkG5i2fSq2j3US367+R612yIkibgO1awh0GkzgLqF7OVXY/MpyPet/RdUF2ANrKV45q1eaWl9J8y8KahFoLI5RcH1qpqyHaxgeOJjH4j8Pt1x9ox+KqKy/tZ8tE2jC7h+daHiGNL/AMT+HYZ5xCjfacyEZxhAf6Vo/wDCJQFV2axD1zyn/wBetqLTppv+tTsoT9yxzkE5VkwMnGP0IpZrhmIYofugZrfTwiYplxq1mcEHBODTZPCV5IqrHeWTEDH+sPP6Vo5LoVfTcwxM/wBlkj8vhkAJ+hzUaSlAu0E4wfxBrePhPVhGFU27fLjiX/61Q/8ACI6yoGLeNsekop3ViHuiqgnkQzLATHb7N7ZHdsD9c/lVu5hkt5pY5LZlbe2AJUJB3Dg4+v6+1TnQtbww+zY46Aj5sNkd6h1PQ9Ta5Lx2UjkncXRSMn357VCSYpFVlK6cMWzcD7+Qejf/AKhVdgVkXKY257++ambT9WSIRGyudg7eWTinzJeymBGtGQR8bvJYdfU0xIdZXc+mXwliQK7LgB+ePWmiVoHKsjH5cYLdOlVpFnjlywOVPXaRSzTeaS5ZQfTHNGrKiorVl/U2lVbVjAYcJlTuGT+VY7uTyd2Sc9e9TPO8xXc2ccdajjj3tnsPWmkktQnK8rxJWlOwnbjKgc1c0nSpL/8AeNlIR/F6+wqXTtL+2z+dLuFuvt94+ldINqKEjAVV6AdhWE6n2UaRhfcSGCKyjCxj7vfOT+dMecZ6Y7UO/HTJqnIxLFQc1yy13NEiz5vXnj0oE4V+BnP6VVbcudtUGu5IJGbIP1ojFy2KdjpNhkXriq0jtG+30rPt9SLck544FaEriaASADOORVWezJEcDIYcg8Zp6OAOTyDUKShlGOnpSOdsm7+E9qNhEt5Gt1A6KxjlI4cDpXJ3ejXNmcsokUn769DXUBx5v3uKuW5jYMsmD9a0hVcWS43PPmSUKEMZB/nSpBMV27cD3rrNctECRyRLkDPQVgCTJNdkat0JU0RxIYoQrHnNRkkBsZz2xUkj8496gOSTnpjNO+7Fa75SVbC/ZkdrZz3Bx1HWtS4E9zELlXjSNIy3y8sOenHNaECvBb2cbyMBL57E4B4EQ5FZVnBcppV5Pbo/ytHnAyAmGyT6VKqPqaSw6irnL64WbRrouzBtqYB7/Ote6HpXiPiqUS2Fy2MHZGDx1+Yc17bnjPpVt3SOZbgePrTGJXDbsKODx1p46knPtTcj+I/makscCMUZAGSaiMgH3QzD2pQ0jnhQB70CuS5J5FISB1NN8t2GC35UCEdyTQCYhlUep+lKrsein8aeEUdBS5FAxoDnuBTguOppd1GaAPJvEEDW3iK/jbBJmZwR6N8w/Q021IYAMwre8d2Xl6lb3igbZY9jEf3lPf6gj8q56MMV3LkYxz/9atIsknktwVIOCK0tF8R3Oj4gnDT2P93PzRf7vt7GoLOeOSMxTW7Oe0inH50s9mpGY35xwrdavkTRNz0W3uoby2S4tpRJC4yrL39vrUwGK8ss9QvtGuDJaPtDH54nGUf/AD61tjx1dMrD7JCr8YyxwP8AGsnFlcx3BpMVxUnjW7ZNqWsCvxzvLfpUMfjDUgDuFuc+qn5aXKxc53fNLjiuDHi/Ugygm34J42dfTPNTL4zvtpDWtsW7MGOPyo5WHOdryKWvO7jxhqbTKyNFGAMYVcg8981ah8d3CIBNaxyN/eU7aLMfMd0KMVwa+PLsyHNpDt/ugnP51ZufHoERW2siJT0Mj8D8BSsPmOl1XV7XR7bzrl+Twka/ec+w/rXm2q61d63cbrhtsSn93Cp+Vf8AE1Vubm51G5a4upWllbu3Yeg9BVm3tS2ANvTgE1cYibKyYUZAqR0ywTHzDG7nue1TvM1s5U71cfxMOPqAaiIxGS5BYnPHFNxsybmx4KtFl12SZh/qIiR9Txz+Ga9DOfXFc14Isfs+itcsvz3Tlsn+6OB/U/jXTVm9y0IBXypX1ZXynVQJket+Dbexl8IWJluxFL+8+VlyP9Y1dHFZXkYzaXME/wDsbwcj6GuP8JgHwvaZP9/v/ttWkzbCduQfUV5dRXnJebPYhTl7KLT6I3RcKj7Lu3e3f1wdp/wqU2qSqSm1xjoMEVhJrN7brsEnmRsOUk5H61ZtNXtQ+djWj9zGcofqtZSoO10JOSHzWDQMXj6dx6U8uTCy5PIwQK1IrmO7Tny3GPvxf1XqKrz2oU7lG5R1xzWXNJP3jSFToznpJZopUYMSyEADOeKk0+NJNZt4JjtheQ7wxwDweK1tWtUKRXVrEqBANwHTjoaz9Wu49QCXkaCKVceYo7MO9d1OSkrmzfPHTS/4GVqEYgvJUByoYgH2pguPMmjMm4RoO3XHtV7U7SVDZyXERhW5TdnPbAP4GtSX+zLSyb7RZM6bP3by5BI9FroTWxxVJtfC9DDjvbT7S8ssZaMDCoWPP1NVlu5JJWWL5AxycU7Sba2v72W1kBDTRMtsd+0LL1XJ9DjH40luq25LbCZQcEHt68Vo7ROV15MZDbzTsdpGQe5p620wBJUhQeo5qdmjkuQrs0MQ5ZgOKvoyzOnk8W4755NZuQKUjJmt5IV3NnnoPWmIZMcqQv0rUlkiS8jWYltxwo64+tXLy3eObMflSxkdOhFLm7mkajTMVCpIUnn2qxM4RAqjH1q6uiyG2M7vHHzkAHJxVJbC7mY4UFR3pXR1U691ZiWkx+bOKjmdnfA7mpoo1STySjFz6VIwitX6Zf0Pak2bRldWuXLdGs7IvsILd6zZizfMBliasPeyyxlWYbfQVGsqgcClqaxTsX9GiILPO3HvXQpqMJXyo/0rjUlkaThsCr8EvlncTWU6XMzJ0VNamldw7G80HcQc1agkDgF+FIrNN8k4KYO4jHSpILa6nXbkqo6VzOLW5Hs0l7zN5tUs7WLAwWx0FUm1eed8RJhabb6Nk5OSfetaDT4oRkgZq1ObVkc0vY0/NlS3t5pjulc49K0UijjHA5o3KvTgVTudQSIEA5NJPzMXKU2W3kGME4qjNfJF0rIudVyT82KyZ795SQhJNV70jaFJLcXRYI9R8e+IpJBuUfZuP+2f/wBauy2wwgDYMD0FcT4K8weJ/ERPUfZs/wDfDV0mqaitpjbyTxW1Ru6S7L8jyq1lOXqRavfSGRILYHJ64o0TaySM+DKGwc1X06U3lz52M4FMnke11bKjEbjP41pSq8qszrhjaaw7ppWZtTQrn7vU1ky2DwTiZMmMNkrWw7Zt93esvM7OxYkKOTnpVVqvRHDHG1aa5Frc1XuozYS3IwEihZsepxXDIcYVj2B5rZutYjWzntIIRKZVKszcKB7e9cyZZFfJAz0NCvI9fLlLDwcqi1Zs2mozwWd3YIMrKAwOfu+uPrU8lx5Ol20bY8vzCWPcnPFULECSCaXPLfKv4VrwLutA4jSR4j91+hA61cYcx04mjGrQkorfU0ZGWOMbmCRxoBkngVlXF9HK4EEW9e7Px+VV9R1Jbm4YohEHVIz3Pcms9ppmG7btWtpTtpHY4cHlkIR5q+r7GjPMhKfuy7r91E71ZZQsihCWDYKZHUE1kWz8HLEH+96VtWv7yVDFjeI28vf0yBwT+NZ+8GZ4ahCiqkFbU27iSKfzFBU4qnBpkV1ZSwn7h5A64PqKo2VvdQQK93LGiDO5wcktnoBViDVIImIt4p5ATy3T9K4oU5uT5tDyFK5iOktlcGGVcNnKt6rV6GUPHweo4xV64t7bWbV0jLxXEYyqv2x3rn1kltrmSKVdk0Q+ZPf1HsaqcJLUUm1c3Fli+1W8quVcYcbvu47029u2vJktrXzUdZN3mlcIAOcZ6VjwIJl86cB8ggI3IAp8soW3Mau3l4xtzwK6IJ8juenhsrqVEpylZFiS/vQ6u7DkfcdOM81LDNHMcMuyQ8Jjo7elMiVby1+yMSDwUc9FPp+NUV3oxidCjg8nHAI9DWVSk46o5sXhJ0J23R0yW4trbEg/fN8z89D6VfDC4tCB361iRXT3VkuTlguGz1qa2vhBBhmANTFt9CaEZTleK2Jb2w+2QdBtX1qhb+HVSJhGxGeeKuSamghCxfMWqsdVa2GZPl/GplNrSxE1JSfMmYOq6VeabiVHZ0z/ABcmtG10We+tUNwSoODipJtTk1ExokZKbhlq6mCSF4UUMAcdK1i+YE0zjdQ0E6bF5tuzZBzz0qTT9euYYwsycDuK6DVPmiZeox0ri5rxYy6bSCpx0pJ3dkJ3udDa65vmIIwDVyZvPhZxwcVx9k81xdxMiHyQ3JNd9GLbyVGRnHrUTTE09jzvUd9x4n0GME71Nx/6AD/SurtJlZNsgIYcHIxXP6jbiHx7oWD8rtcY/wC+K6RUnWcp50m3JwCcitYL3I2/rU3pfCVtRtIpo/tEKqZYxzxyR3pLGWG4iVwF5HOazr7XLy3u3hEqfK4UoUHT8q0tPuBc6abuWJF2SbWCoPUf41crrc05NmVbqCOwnWRUHkytjK9jWiIElgaPc6hhjKkgj9azZ76ZLKKSSzhkiZ2ByuAMHr9at6ldJpdrFIIEbzOQAxH61KbexpKm4PUit3khneCSWbehyMuef1q5I1y0BEN1Mkg+YfMeapW94moXEDSWbq0h2K6yZ5xn+VPvNVttOuRBJHKXUAnawIoSbehDVlqPtdSu5gQbuYMpwRnvVuW81COMPHdMdv3gQD+NZ9jLZX91LLb+ckn8Stjn3qSTV7GG4aF5nLKdrArxTTd7E2Vi4mqXxXKzBhjIJjFNuNUvE2OyW8kR4JaLoaqWawyxu8E4kTcc8dDS/aLVkeJ7qEqRjGcYNCbbBpFxbrzVy2n2T9yfKqNpIftSJ/Y1mY2H31XGKisoJYk/18Ui9AVbtV+KPEe0nJJz9KU5WKhAiCohIjRUUdFXoKYxI4Hep2TBPYVWkk+UhfzNYM32IXY8jvTY02gsxAApdyIrEnnH61mX+qpGpSM5bvikoOTsHMkS3d+qdCMetZE9+s/yrzn1FV5hcXEh3KwH930p8Ng9zcJbwxuZX4UHjJrtp0lE55VLsIbkwyAHpmugjvV8ng9a5VjjKnkg4zVmKUpGMk0VKSepUJ9DoUnDMSPyqZmIRjg5asyLICtnqKttMSSQfb8K5HGzNR8MgIJzyOg96vC6QoGPBxg1jCY282P4c5Bq4Ihcq3zhSx4xQo66iY4azAkpil5XOOlUL2zh3CezDyRP1HUqfwrOu4JLS4YEEk/dPqafYy3en6jE6l/nPTpuH0rqjTSjdGKm72GGCVpkj2lS7BcspGPzq1Npe3mGaEDy+czA5Of0rr1u3YBmijJPIGSapy6gj3DW/wBkV9mBKdwwp7Clz6aFxmovVFa+ljlubCJJGYx2xj+UJgErtP6CoUzZx3VjC1wscyqHPkhsjnpg9K0DBYSYZ7BSfYLUElrpEaNJJaGNeBlV5znpwaTdy5Vk9DgfFSKlreqGchdgG9Nh+8O3OK9tO8jsK8w8ZafpieFL64t43WYeXgkEfxr716iWrZSTWhzrcb5ZPVjQsSDrzSk7iQCaOT14oKHAADgUAg9BTT0704e1ADvxppGR1p2aM+9ADPXGTRjinUnegBMU6ijFAzL8QaYdW0ia3THnDDxZ/vDt+IyPxrzexkEcv7whRnB3DkGvXRXlutQfZfE19ExGzzfMLegYbh/OnFktF/EE0eZXimbGf3ZKfngD+VU3kihY4KjPuXxVWK7kiVljfcjcEE1DJIGHIyT0FbqSIsabrbXEBKsQygFfkwv4msu5C7l24yPaoxNtzgEZ75qN5GZsk0pTTKSBpGJwScelOWQsCtR4wwNAH51HMwsTBS/zAkAU1yRwelPhYr1pZSGJq29BIr9aOlPGD0pD6CsrlCA89KVc46cCnBeB60/btAzxxTSbAfCuWHzKMf3ua1IJZx8sawyYOMbV3fqP61ig4PSpo5Dt24wD3qk7EtGszRXEbou4SDlRnGD7A8j8zVaGyuby/gsAXDTMBkj7q/xH8qYZw0YWYb8dGA+YVreEi8viEKZt0ccLsBzyDgd/r+lEmJHdwwx28EcMS7Y41CIPQAYFSUGgViai18p19WV8p1cCJHr/AIKs7abwdYtJuDHzMlVY/wDLRq0Z9IjJBgulAPXzFYf0rJ8IXDp4OsQoxgSck9f3jVoG6umPyc/hXkzUvayafVnuUIN0otvohkmj3RQ7fKk/3XGf1qkbS5WRYjA/mMdqgKeTV9pr9FBeBseuykj1a4hdXGVdTkEdquLmtzRwbWjRTe0vbR9wjliYenBH5VetPEFxG2LrEg6E9CavR6xpdxlp7byJScloxlGPckVNJp2k6im6O6jicfxA9fwNauEZq0jB2Wskx1vewSIwG142H3SeRWZ/ZElxcOYB/o2CZDn7o7cd6WbRbqwy8TLPGOrRc4+oqK0urlLpFjcAscZzgfjWEYODaRtDWLcGdGb2K+sAsqIzoOVcAjI68Guc8RXX2vTFB/5ZP8mOw9KkuZZ7Ofe6hSxzwcg/jWna2Fld6cbdrmXfNzg7Sqn6HmrhJqSbMatGEId7nDWhkSRZIc+cjB0IHQgjBrqNRso5LuO8QRQpeJ5xJbOH/jA/H+dVL5I4dKezaBIpIXwGVfmPPIz3q8ul3cnhhbaTyTOsqyQJu5QE8hj2z1recubU4XQlB2ZlW6WdvbOtyZZmkbBVGxzmtO4aC0QOY1hUINqKvTirVp4UhtB5k960tynzbUAEanHQ9/x4rm9X1EXJ2bmdjyWycD296uNpOyM5O2qQ2HVFhu/P2I5zxuHWrsOsxiZ53RSD/BjiudorbliZqo0dI9+bxtq7IozxjPSr1lDa2seHumdyecHiuOBPHJqWOaRTkNkdcGodNW0NI1mn7x2lxf2qN9nhijMjD7/cfjWV/Zkkk7P56FRySxqra6nGu0NEgbH3q0Ip01AABMgHkjjNYuEo7m8asU7xM14mVyHKgA9acERxhX5Fa9zZpccEBNo9ayJHSBtqkFs9qSdztpVYy3JY7SV1O0dKIkaKQiY9KRJZWGS21RQXVn+Y5FO50ruK94ySApgDNa1rqk00YVHUOO3rVKFbeRSGi4/vGq9uTb3bGONigPBFZzipIzq+h2th5+zfK45qa5u1jU7mrnP7YmSPAwD71W/tNpciU5Brn9jJHHOhJvmZfvNY4IU1jS30khIGTnvUaRGd2OflzxmpC0NsD0LCqUUhRixi2skp3OcCpS0FsvYtVOa/kfITgVUbe5y1aqDe5pFdiz4cvmXxF4hdMASG2z+CNXQ3uycLkjNcdoKO2ta4FBODBnH+6a6GAtOhIB+Tg+1Kq2np5fkeJXjKVSTXRlnSZU0yVlZsgknmr00ltcyAlhmsS4QPHlDkiqtsxadU35fPrStK1zndzsmmit4UDtkGqGuTj7BbR25/1shZyDggAdP1/SovJmcKGBIHSqt3GSm0k5Vtw9quLmbYJxWJjzbGdI685OD1qpIqtz6j86tXCruD/Lg8Coyf4QM54+lbq6Pq6qUviLtrHutIkXjgk1Z+02kcM0BuADIhGeeorOd5BGsMX3VHL1TeNl/h98GtI2WplKUlGyWhPbqW2s3Xpx0qeQZV/Sqmny5lMTY55FW5f9U/Y1m0rmtGd6ehUTcBjPFatne+TLnyg3yFTk44NZCruKgfWp1QjJ7VfNymHsIV1yzV0X2uDNIGnyMn5QOgq7DHIWIijZmxk4OB+dZcb/wv0x09fwqdbzybeSIZIC5jQnABJGSaTd9TzsblsKUHUo9DWhW3tLgSSTmWYrgww8jB9WNVNUjgubYTBCHj4JJ+bb6E96r27s0W5mBCjnaP5UTXEhRogqqrrz9K1SXLqeZRwlfEawjoQoAluiq3AHB9arO2cgDipG3quxWVx6YxTEG5CO/f2rOWuh9bTuoqL3RLHcEY27c+hqyt5IzruKYB6FQaoiPgEDkVKp3DHT1xTXZlOKl8Rdt94uTHbAvkgsB6VqPpG4bmGM9qyLS5+xswimKGX5XKAHj8a0F1O5V9y3BKrxiVQQffPWqguV3R49LDV6Tk4dWQy6fNHdoB8q59+lR31lC5BuZY40B/ibHFWtS125MG5R5IBwXiGWx681hTSNNL5/2vzieVaZFP8+Kubh9panUozqK1RK50lj/ZywDyLqJ0HGT8vP41T1iKW3ZJ4JChyDx3FZNtazatOI42+TOHnYYVPUADjNbmqW6Q2cNtAztHEoUMzZJ+tcc2tbHi4uhSo/C7srretNGpkYnjmoI4YJpeEzn2pkflwxYZjn3qFZpVnzGQFzisKdKTldGWDw08TNqPQvXk1rYIEQDceMY71nMs7TrOk7gDt2NaUS29xg3GNw71jatLJFLuhJ29ABXRKnIurga9O/kVr+8M3i3w6QPmR58+/wAorq9ZnktLKa4hIWRACCRnuK5i5gWPxD4XZhzILnP/AHwK6jWE3aZdg84Un64xV0lZJP8ArUmHwHOalEdRtYNVt48vIfKnRf4ZB/jWoLuHTII9JnVXjZcXUu7lWbk4+nH5UzwczCe6wSYNo5bpuzx+OKydQZYb2+iuoWecyEh2kIxken5Vb10NV5mpNZTyQW2kfaA0iTNnvtj67zxgcc9TTr27g16zuLe1RkltfnjBP+sUdcfhVkSPN4Re7WMCfySgc8ts3Y6/Suf0+7t7IRzok32uOXPGNhTGMeuamMbJsupVcrI6Cxsr63srO9nvU+xpiRYlA3cpgc49OKytd8u7EOp22Wik+Q7v4WHatPxGIrLS44YS8YkkyI8kggdcc8Y4FZegSrNcPp067oLlQQB/Cw6EUR0VzJtvQm8NiK2M1/cSCK3UCIFv4iTn+n61n6vbm11OQHkSN5it2KmrWuyLbSJpsK7YbZejclmbkn8sVNZLHq2mCGZtktqcK5GdyH+Gqfu6i3di14dj2afcyyEIkrbUDHG4gc8ViixTcVkyMsTnite6ljRY1AG2Jdqe1ZTyNJJkDgnk1lzX2NOWxo2sqW6CCMkqDXQ2XzxE4xXJwN5RYgnHtXTaTP5tkzAfLuwM1nM0iSXJ2ggde1ZNzL5fJboOg7mtKclgSBk561mm33v0zzWbZdjGvLqaYbUGAeDVAQyKdzZ3ggg49K6aW1UKfl/SqhjXGMc+hrSFVLQlwuYc1zLJO0rYDMeQKZ58gbcrsreoY5rQuLTLHjHuKoNbkvgHvXXGomYOm0RxoWIxzzzUpXLgdhVmKHy0981HjqR1zRzo0jDQl87kKueKtxAgZOR7VTijAyx61P5hA61zS1ZoloTPKDzIoGOlWLW5SL3XNY88jt93kCoI5pFbAY1oqd0Q5WZ1UsVvqEe0Nh/4cjvUh3XetNJcwMZPKG1zjHHB56VgwagI8F93Hoa17HxPbwttlgaSNhjHdfpSUZK6Ik0aUjXCW0jW0IeYD5AWHX8+3Ws+ws7m2tz58MnmsSXPXJPc0RXCXd5PJHLugEaBD05wc8euaiWWReRI33U7kckmp5HYq+hqITwhRwSe68VnS3P2nVGiKslvBwhKn943dq0rlnijXa8hZjjO88cVTtrqaW7uYi5KoV27sHGRzRGF0Q+5i+NJFbwnfAH/AJ59/wDbWvTu3vXlHia7e+8H6qzqgMUiJkKASPMWvWO1aUlaNg6iDqaAOfagDGeeKUADgZ4rQYmCfpRzTgKQ57UAKBgUhpenU0mQe+aAFpMUgLZPHHalwc9aAFoz70m2jgUgFBrzfxf5TeIJmRs7lQP7MOP5V2+p6vDY28hV1aYD5U9/evMrsGWdnfO9iWPeriupDld2RW5GTupQfen7SF4FRjnijYYpSkxTl54p4GBnrTAixz0+lKEyw9KkOMjtSqAWx2FABt6+tNI7VISAPfNRsaAG8etJ3zQW7Up6Y/KkBNGBgE845p5jZsHqcUkOAMnjJ71aeUBdiA9OWrWKEyk8YQ+/pTAxDVKynqaiYAngVMlYQrOcjHFbvgtDLr28gny4mOR054rBIxXWeA8ie+OONq/Nn69qhgdsaUUnelFQaC18p19WV8p1cCJHtXgCxhk8G2Msi7y3mcD/AK6N+VdIWs7fiOEO/YKM/rXL+BvPl8GWESNiP95kDjP7xutdnbWccUQL4WvNnBucn5s9BO1Ncz6bFfdfTrhEjhU9gMmlXT3OPtEzt6DOP0qa41C3tuFweKyZtZuZ2It0K9shaLxiJc7+FWLN/ZaWsf72NSR371ztwunq58qRkI6Z5q7Lp91dMTJnJHVjTF8NPIMElmP4YqFJNm8Kvs93crxag0LZWYN7E9aknFvc4nQ7JQckjvUo8JFeXn2j86X/AIR+0jJD37p+VPlj0ZpGum72Mya43qYnII7elLYvPPcLBCwDdOWwBVq40uxjG0akoPX94ox+hrJJNnNlZEZScB0bitEk1ZG6rXVlodSdHN1G5mmxepyGDAoxxwOmar2Ej/YZZLhZDIxKM2Mhcds9uayY9UYBGV8lTzzVy41V1QGN8JJkyLngnoah81uVmaoyve9zUS7CW07Tvt2qQzZ9eMj1rAis4pIPsrW0sueElCbVJ7fMRVqxuS6iQIW8o8jbnKn/AOvWuNRuNTt7m1wTK0ZVU+6Q/b6c1VJ8i13McRRb22OG1CwbTbgQSSB5APn2jhT6Z71TzXZ22iy26sdYeBhKu1SZMhTjufWufv8ARrm3uXENrO1unAm2EoffdjFdsZpq9zypRszMxR0ozzijNWSKDyM9KuW96YVKKGCH0NUjSgGh67hex0mm3glVo0XLdSXNX5YtPWzZliLTnstchDM8EgZT07Vt2OqWtpC0gDSXL/w4yBWM6fVHRTqfeQuWGDICkZ6CrISEbdrbie1TBPt5VpSFDHIUnFTPbrBOQVVcDgjmsGz0qNa1kyNSIxmQcf3agu71nULGgT6VLJsQFnYE9qihTzZdwjLHtVps7Wk1cv2Dt5f76FZM+1Ubq1l+1MViYL1AFasMN5jAjCL6mp47hbScRyqJWPPsKU49jlqRu/dMi3glKkbCv1qVtO3ZzjPvWnPc2rSMwlVR3C1DIkLwGRbhuBnmsGpJmK5rbGa1iq91qJrVQPvCg3kRJ5JqM3Ck/KtXaXU1hcm8EpF/wlHiOJ8EEWxH/fDf41prbt9uvreIbVJ4x+NYfhOUJ4s16RjgYtxgf7hrrWi2yPOjctTnJXt6fkeWqkY1KkZdbnPW2gXsdyoE5MWctW5b6PaWsqyso3j170+0eaKUhhuLe9LcibBxGxPsKyqTk3aJwySuTTXtupCDbu9KyLuNtzOASD1FZkthq73DSiylJydvPGK1tOtL8x5vgin+7npWyUramM77rc5y7zFlWGB1X2qzaxARq7HLMOma1ddsoYLNJT8zl9ijHX1NZVvKo4fGATWkb2PpMvryrR56nQtBQQARj8KZLApHSoJr/wAviNSx7ZNRx6pJu+dFK46+hrZRVtTulWinYgMJilVl+Uq2av3kZ8rcFO1xkHHWrUFkNSV3LLHbpgyyEZ49AK0bmaya3+xyAxWyYMe3qB6/U5qWrbmLqRhLlj13OWtI9zkjGRwB3q6jQq4TdnscdM+lQT27WFzPFuJJYMjHj5T6+9LaERyyXLQGWG3UM4H95jhT+dHmSqqp0+Y2IdGe82unl26f89ZDk59MU7UNDtbPTlia6aW7mcGSQDGVB9OwpsetwSyGN4pElzyCvQZqvcXjz3ks+P3ZzGqnoVFVCXkckJSxcuW+gkuyOFUiwFXpg9ajVGblzyRxzStLDyfXsKhlmDEAEgdDVtnqQgorliDiIcbwG9RUcTqJwARhu59akVFK9uafBYG7uFhiUlicnHGF78+tZ67ocrRXMyR0Ece8hlVhlCVwCPaqXmMoIjAJPQ10V/eXOn30UdzHEbbGYHYZAwOhHrWbqdrFatC8BASZC2Cfunv+FV7r+FmFOu52T6mci/MckVMr7erEj0J4qNQW6Lk9KspbkZ34xjpUpPdHSmkW4Z4WwGGQ3BGM0tro+noZGWPzZBlwrNlGHcY9RVIW6ZBjYqat210YnjVgpcSDYcdSe1bN3VpI4sZR56TcXrY1YYcwgKipGPuqi7QKnit45kKk7jTkl8yAfIEzycdqp6ZOiX7Rbs5PFcjjGL1PlFruRXumqW4GD2rIBCylOMg11l5jkk1zz2S/aTO2dpPQVcZezlc9PLMVDDyl7QzWE7XO4t+7qXMZlVnGVz3rpVgs2tQPJyCK5rULZ7Yu1uuR2BrSVVM9F5nQ5Wu43XmQ+KPDBXAH+lf+i1rpbmFZ4mifGyRSrZ9K4K9unutZ8OheHjNwTj/cBP8AI13TXLNbxzRgHMe7BOOw4rLsvI8ePwle5tLVbKSyjNnF+7YlCcENt+U9ev506TTLXV7W3uGjHnSRRsziQ5VSv86xtWZdUsV1CIEGMmKVN3T+6aseHpINLsjcXcjKt2wjjHXAHBP0yae3Uq2hoQhvtUrRyZtIUjRY9/DRlT2x16ms+30BbW/ku5lBsoSZUJIw4xkD86xr22uLC8ltzIxCkbSGIDKeh+nb8a6S9jJ8MLYearXUEaNJGDyFH/1qfkBRFxLrtlLa3Azc7PPtiMDkZ+X8qq6YBpdtNq0seXU+VBG4I3N3P4VT0sTnUbLyc7946Hpzz+ma2PGIkM1qwJ8jYduP72ef0p9bCuVNXI1Ozi1WNQrt+6nUdAw6H6GmRt9htliG4SMNzGk0OeVFuY8AwsgZgfUHiqd7OWkYg5zUvV8pcVbUGnMkmCSRn1q9Bas8f7vBzWPAw8w8E10tgzPEMDAxUVfdWhotdyGO2MYKuOTWvpamGyZeeWzWfcNscKOvqa0bJz5eCckVi5XLiid1+QZ696jWPGSRVp1LfNUG7cxCnOKhmiK8n3qoXKYYlcAn1q/McE4qnON+RilewNGfJll+YZ+lQCEZz3q75eEJz+GKYw2oGbirjIVivKpVMEAGqqrgGrLyeY/I+lKsY5OOQfzrRSsFiIJhctxnpSbCWAHTvU5Ujr96m+USSScDvRcLGfeYRsqTg9qhhG6QZ7mrt5EhAIFUNrocg11w1Ry1L3JJPvkDGAadAqTPszyahM7FHUqMv1OKs6VEz3O5RyvzfgKp6IytoWrcRW8ib5AQz7CMZyPWtyLwy8V3EHukkHyHYqkFhya5+8B+xwXMfIjlZSfc8iuvuby6fTbTU9L2PvURyLIu4ggf/WrOpoVC9h11Y3rwXEcphXYBseJySCe5yKy49A1mNp5I5Y3PU7ZcgkLkdR707/hJNSjnmSaG2J4VgcjjFaMuozW+iPKpyCgYDryR/wDWqXZMrc5DxJCll4H1C1LeZcny5J2GMKxkXivV+1eI6xeO3hfUonBZpWRy5Pfete24461payEndiHtk4FAYDjk0EL3/Wq8l/bRA7pkGDg5OOaCm0ixljjA496CDjk/lXNXfjC1gYhELMOo9D/hWc/jhw4McA245UnmkTznbfKKp3eq2tlGGkcAHpivP73xPe3Eshjcxo38Oayp7ue4OZZC340xXkzvJvGVoqHylLMD0PAI9ves2XxxMX/dW4245DHmuQB9uKQk5zmnyoLNnTy+MbuRSg2hT0PQ1FHrl/OpD3BK545rnQTnk4HpV+0Y71xz+NVFK4nE2HYsm5yWbHWsiZRv6HP1rVnkXGAf0qi4B54rawoqxSdSB1warknPWrsydz0qmQB05qJFksS5xk1M0I24ByPXpVQEjGK3tBhtZ5WS7jeXAP7tD83TtzUqz0YN2MKTcpOaaHwMV1UPhea8nuVWYLBu/cEgElevI9gcfWsnWPD9zo8yLK8ciPnayHr9R2pWV7JiUrmaGYmlOTSeW45Iq5HbMyBscdaai3oMpHIHTkU0mpJFwzVEF3YA70mnsCHq5yADVgSYHXr1NbeneE2ubX7RLdoGIyIoxk4x1z/SrujaLbW9tPPrMYzuOxWOMqO9NO3Ujm1OX3qepyfY0bQT1NLMkf2qRogBHuO0H0pvB6U2UKI/euj8HN5epyKWZdycDsT71zgOfc1ueH/+QpCPNeMMf4O/tUS2E3qehUUlLUGotfKdfVlfKdXAiR7b4Du4bbwLp56yfveP+2jVqNc3N5IVGQuO1YHgeFT4QsHc8fvOP+2jV0QuUV9qjg968ys3zv1PUpQXIn5D0skCjziWb+6vetCOKJAFRdv6msx730A49Kgl1CTBAYr6YrDmH7KUtDbku7e3yWyzVmXeukEhBgY4xWRPcO7YBOW4wDzV2K0jsQJLpPMumGUh6qnu3qatXZXsI01eWr7ERkvrmPzriX7Lb/33B3N9F6n+VSw6dBLF9ouEYQ9fNumJZv8AdRcVegtQqm/v2LheUQnqapzNNqd1ulfCDop6Ae1WkSrydlsuoNJZNiOx0u1HpJLCCfrjFX4dNubmPEvkKh7GBCP5U+3NvaIMrliOvemyXl1Nu8n92o7sf6VdrfES1bSC+bF/4RyywftCwPn0hVP5US6HowABEaf7p7/Sqos9+XnnmmZv4F4B/GrUOlW4+ZrdFPX523Gm5RZm+Za8xQu4JbO1YW06zwg5ZlYBh9R6VkDV3F950ZCs8YRz64PWuxGmWvZIwT/dGM1y+s+GJ7eYz2JE0f3jDn51+nqP1pRpbmtKur2kWzqscdqN8ccvzAhZBkZ9frV5bx77TriSQF4yhXyzxk+lc7p32SUH7ZvZQOittwf61ctNTuNPkdLeJ2tc5MgXgfWs1G2iNasYvSK1MbVNOit4HuppMXMpAW2CkFffP4frVGLSL2SLzjCUhALb24HFdtePa6iVLy7JlG6OXcRsJ61HczXWnaW8kcltdw5Hmp5BQ89+vNdMK+mpwzw0lqef0AkVvSafb6pcK8Aa2MnXMY2gdM4HTpVL+yZEuAsj/wCjlyglHGffB5roU01c53Bp2M4Vd0tv9MUbQSfWtG58N+VaySpeRbo03FWON30ptnaxWsMU0hCTn+Fu9DnGw1CSZvbLGWEpNETOg3K8fb602GC3u2x9oRWAztaiONQsBMmzILyADg1HJaWscTXFq0kkzn/gC/8A165JNNnUr21Kpa2F3iZCcHHyVbJwC8eEjHT1NY88F1HKWDKM8FjUlu7n92rbmJ5Oav0O7Dz5olx7qc8tKVQe9IsN/qBItoiqHgu9W0FnGoNwC0g+6opZ9XaFDGpVc9FFUkt2bycnpBWK48MXMY3yXMfHUU6TS7l4vLjkUp35qGXVCV5fn61DHqEu7KMcn3pTs9iFSm1ytlqLw3K7Ab1X61NL4ba3A8ydRnpin2l1uYB5CT6UmoLdXMxIVljUfKKwqe71OecZRla5k+GtPQeMPEEbSZWM23I75Q12N1bsgyh4H61wOhXa2PinXo5X2ySfZ9ue52H/ABr0W0VmgUOwYt39K0cU5a9l+R4Vf+LJdTNtpGW7G9SURcsQehqSW7urmQ7AVU9Ao6iqOo/2srtFYW+yEnLOSN7n2z0rJkGowMPtSXwJ9W4x+FOEowWhEIt9Tda3nKkP5uD3LdKrNayqckyKAMbjk/jWJ56d55xz0LmpDfNGMi7mC5A/1vWn7W/Qp0FLRsW4me4lXc7OkJO0O2PxqmxVyCBnIxn2qea7FzG0UgWSM8hh94VRhdvMZSeF4BPpWlN3Paw9WnyqlBWJxDvZUUZ96srY8EcU61UCPdk1OpJYAHFWtT0o01a7JtJW4tdQihEg8i4OyRPUY61HLp+oiT7V9lkktYZNzKTh2QHnj6Cqk97KJdkACFTzITU/2mVwXmuHlYgkktx07U2o6IwlTc5NxdinPLLqNzJc7VBkJPTgfjWjpkUdxp+oaUE/0i4i8xHzgsU5C/gf61UScnK4CxEE4FW9KglW++0x8xxB8SNxlipAGPqcVLvbcjE04ui0WX8i6tYLszRxyNGrsNw49RWetqVkkG8Om7cuw5ABrHt0Uqp2puXhlYd89DV57Bo7OS8tJSix4MkZYjBPAxTbfY8LBV/qk297l4QFpSqheO9Rzw+Su7aM9f8A61VbbU5IZsyoHTOD2I96syz/AG27WNGAhU7mIzzWicWvM+go4uFb4RrwOsCszYLHoO1WNN1KSzumjAVkkAV2PVR2I96dg+WNzZOM81XgA3EnqTQ0k7G8oc8Wmad9dLqNncWog82+tJVG9Rkcn5ufwNUNYWUXUQkDKnl/KG5IANLdXlxLHstwkQ+84i4Z2/vGqk7tcBTIz7wMbnOSay5FHRHNRoygaUNkzWqvGobJ52HNTCzYR/vXjRs5wzDpXOBJEc7XZP8AdOKNm5gX+b/aY1qqkUti71Gbb31haZUyedISPlQcD61DcXkcj77aIW7o4IkZs4PrVaG1SRCv94da6SxkS5hiE1taxxhNpc4B445qZznJaaI4MylXpRXs3uV7C5a5s5njOXjyJI88qfb2qjpm4XjXAB3g4reh+xGdo4JbYSONrbWwWFJPbfZ3ZlTaepAFc3LKaseJ7OcWuZDWjnvZQEOO5qxJZCKHldzelWrBVWESHAJGasMEbBJGMZrVU7rU19knqzFjvYwFR49uKZeGFAflBDcirtxbIYXCAFm6VQOkTtEN8nQVjOMoESjqcZd2q2fi/Q5VIImNxkHoPkx/Wuv2hYBD1+TAP4Vyesr5HifQFZs4Nxz/AMAFdYuCsR5xVU22o37fqbQ+EwtCkzqRtXHmQTqUdOoIGe1M8QMw1EwBfLhgVURRwAtTNFJo0E9wzAXE7GOIr/Cuck/jTX8zV9NWdUMl5A2yRR1YdjVrR3Lvc0NNEF/YR3dwrPPY/LkH7wHIBrDt9Tlj1Vr1wXaRwZAe4PGK1476PRGh08orhubk98n/AAqi+mFdVeyQ/LIQUb1Q8k/gM0KyCxeubWHRbae8hfe82Vt+PuA8kn8OKpac7ajp0umM26QDzbcse/cVfv54NU0yeC0DBrPDAf31HGay9Ej8y+85OI4Muz/hgD8zVPRX6iSuywIW0+x8qRdspJZ8Hp6CsKTLyYxzWvqc5kkb3NUbaEysT2Hepg9Ls0aGxoqABscnmulslSKBdvfpWG1rJMwZVwoPX1rdRfKtUB6gVjWd7WNIIguB8+496uWTbgMfdFUJpTJ93tVyxI2gDPXk1kWaxGU71nQs32iRDxWgrrg5PFUCqtM0i8AmhlIWYfNtwMe1R+X8pNSu2SAB1p23KnjA6VLAy2DNLgetVr35QRnpWiwWPPHNUJozK5NEdxFKIZOTV0RIwHXjnNRhAmfTHWp1YLAuATjv61o3cCrNIysUAycZyO1VmuGdCFU8dTUz3B3bRGS7AsarNJKIMGMgk9BWsUDlYcQZlB7Ac1WZV4x3NTgS85HAx0p6xSFWYgAnoT2FbRdjnqO5nOBnHvxW54dVI7tHI3q5CFR1GfasthiQ9CR2rTtrfdfQvDlZInDjjsOefyra5z3LkOlpcaJPK8+yB98g/wB8HAB+vPSrvhndLps2nOxQyA7SD9w9jWd5jXNhbksdkZJCk4ALMSTVvSnRLlHSQ7ieRn3rKq9C4bmZpd1qcmpSxS3AjjiObl2RTsC8HkjrWhb+IY9VupLBoRAk6lIZDyQT0yOn5UnjASWZRYmAhvCZHKrjcR2Pr1zXKISmHUkMpyCKqKU1cGuVlLxAJLbRry1kjVXVlDZ+8CGHFeuah4hsrMELKjuOwNcJ4tt7fUPA9xrDRlbopHuIPDHzFUmssyux5Yn61cWpLUVn0Oo1DxdLPCY4x5Z9a5ue9uJyTJKzZ689agyeKO9IpRDcc5JOfWjB9aDkUDknrQOwpznPX1pv40vNAGaAFHSkwO/FOwKUmgBmOehq5aYDD5aq5x3qSGYq1VHRgzXdiOxx9arM2CTgfnTxIHTnmq8noRitWyUiRwHQc1VkjOTgA/Sp1lBO0kflSsAw6DHrmoeoygQfSnJJKjBkbBHQg4NSuhB4NREsBwOPWoaA24PFeqwxhGaKQ9mZfmH4ioRdTahd/aru5LNGMrk9PpWUNzDGePpU0cZbHPSnFWYrItExzTMoHyMdy4q80Kw22ByMVUtYGVHlbIxVeR5ZXOWO30rpukhWuV5+Tkd6bbqjSbXbaPU9KklQgZqOJSz47VzPVlGxZanf2CbbW5xFnhWGRUF/qN5fP/pNwzgdFHA/IVAI2jGAx2+lRsMHjIqlFE2Ebnik5FJn1o3ZOKBki4bHHNdB4cQnUkcJuCjJJ7e/FYkCZ5ruvDViba0MjIQ8h7jtUSJtdm4GyP8A61OB9qKKg2Fr5Tr6sr5Tq4ESPX/BYLeDbAFjj95gf9tGrcbagwcLxn1rjPDGtva+GbS3S3Viu/52b1dj0/GtvTXu9Yum8yTy7WEB5jGuCeeFB9T9emTXm1MNNylKWi1PRp4mmoxhuzTJ6lRnj0xVdiSwyR9DV65nM7cDAxgAdAKZZ2a3F0sbHA6ufRa5Y6noKXJHmkWNPtFgg+1yKDKx/cg+v96rtlY+bKZJSWJ5JPelZhLKCBhANqj0FaFuwBA7etbx8zz6k5O8urMfVpw9z5JOIoxjA9arLJtTP3VAqS8iVb2WV+meAe9ZV3cySkhOBVQmtzppW5EkWZL9QCAce7HrUX9sBOB82aynQk5Z6YQAccH61d7jl6Gx/a8v8WSPTdxR/brIc9D6CsNyqniomYH8apIxkbM/iK9DYSRQp7A5P51ROoXU7EvOze5NVI4Xc4VeKtwwoswjBEkp6KPWqskJR6lfzXikI5G70qza3lxaq0mZVR+MAkA10lv4YWaEyTOVkI4I7Vgz21/FHJGbeSVMYDDkflUqSZSqRfukukXVoLh2uo2IJyAr4X8a1pZoLuTyIJZViYgyKgBdv9np0rjw7R4BwprV0vT59QYOSUgz1Lbd39fypSgo+8zW8XuzqoUj3tDFClnJKDsbaTJnHWqF/ov7x7m6vUlhTDSuFO7j2zitO2tIbUHDyS8Y5OFH0706cxywNbyW8bRSDYwUEEj2NYOvFHHNJv3Tk57q1nWdlt2ZUIKyZxn8MmnX93C9itulqizZG5y25gPYe9bKDSLaT7GliXCkguXJOfXNVtaFhY6cXtzHHLkMCcvIT/vdq3jNN2Q3GW7RStpZ7W1Z7zKPJwFZcuR7DtU11qUdvDEICMYztk+UZ+nektLPUNXVLp7h7VAMb5OpHt3q7c+FLaeITR3kqOON8oyGP86dlfUl3OenuptSxbvtjYHcMnAH1rQ06BYomdl2qh+Z/wDCnWfhV4Ell1CeGMc7ADkn34qaMRSQpE7hY/4nP8X0FNu2iOmgla6KqxSXMjfZVY5P+sep5NEtLeDddXhaY87VqaG3nlZ2ST7HZofvyfexVF5tMhmLCSS5bPGTjNaJJI6XLmdrkotrW3jEscLMuceZIOM+lS+bHM6E2e4r/cXA+lQnVmutsUkQWBTxGvate3vLMRjcWX0WnFKQpKUVdozvss8l6J/JS2iA4XOTXQW8tqke64ZhgZy3ArGvby4RlNu0G1jgFV3EfXNVJdNvbohpbvzD/tcD8q5qjgmYThGVuZ2M+GC21Lxt4kmQAov2XZx0/d//AFq6Aa6tmEt5FAK/xHpXMaFI+m+L9etGXeXEG5lGQoCE5P51rzGJj5vliQ543dCPWtZQcrPyX5HkfU6las1T2vuar6vEELs2FA64pz6lEyJumPHYg1zLyMQwZsqRnp056VcmPmKOSCQD6ZpxpJHowymknq2aUl3b3k4MXlSHuuwZrKukhku2VIY+OwWoRHKCJo1xIp4xWtpMMM0LzkkzFjvVuoNTOEt0cOOwc8NaVN3RAmj2jW+943jYjOUbpWTqdqmn3MZhYPDMmVYn+IdQf0rq5rcyoUUEAjtWWPCyyBzNcmINzGoGcP2Jz2rSmpWszHCTq8/MtjMiuE2qg+p9KlF0kYJB9eKzpoZbC6ktbldkqdCOjD1FSKAQu4gDjmtElE+ihieaN2SRr5oLgbhk7gO31qRbSS4Uqh2jGC3bFSaTpQ1GeQlpo4Ih85Tq5zwK2rq3ljj8uOFti4AAHSlKpZHn1sao3UVdmKiWllcRmXzZIwCOncDv7Vbhin1TzZoZwtqpCu5wp6Z2rWbfQXUjEMuCpzkenpR+9Kvp5yYblg21OqMBwR/hWMve1OJ4idTRsNXlN3djUtoiWaTYyhfu44GfepbeUz6TqEEkeSsCvvHs2aivnl8jyZEMTSIJ1Eh5bnBP1OOlR6TK5hvIi+ENs64I9s1rGXunO1qUvPOMspYdAe9aOlSKLvbnckqHAPHzDtVRLRp7CScNiVDvCDunfH0pbGTZdWsyoXVJduWGBg8VaXU0oPlqRfmbq/NuG3HtnpVF2McmzGOavz7IJjvYly3CqMZ96hjRZJndgCBWkknoj6lPS5XZXkZTF8svY+v1qWErd/u3QR3CnBX+97ipgyhwowd3GR1qvdJFMdizpFOo3RZ6/T60nEyqNLVD/siFycHjjmkeOOI4K/J605NTdVIu7Tcw4LodpP1ps2oWckB/0S447s4xRyroL2yHiHaMxMCCfXnpVK8Ja4zjAZQQKf8A2vGqssVgu8rjeWPB9cZqvEJCFaQ7sDAPoKmfKlYFU9o7IlSNDhSM+tdfpzSTaLbtNJvfDDJ9AcD+Vc1ZWcl5OsMbKpbqxOMDufrXUyIkKxW0Q2pGoUds+9Y35FdHn5xWiqapre5YhEjRgLt+7S3FyYwqDaxxg0sLLbwklucVU8zLF8g805Voxjdnkc2g1LjddqmfrU97Bcywt9mkG7FQosZYljgmrMY8s/IxwR61z+0U27kXuea6rY3g8V6NFc8O3nlT9EzXYQuHs1bqRwQP1rE8W3O/xV4ddT8yG5z/AN8LWvC4WIFeFLFhW0Gmo27fqbQ+Ao6qyX1qzwcyWrYkVh29RUfhotHeTzO6pCi4ck4GT0FRJN9m1qUkEqX2MvqDTtbWOwjjsbdSIz+9Yn+LsPyqt9CyrrETxapKXYYZiwY9wa3IXl/4RUz7SZVQorleduf8Ko2dr/bdlFGZAstsQpY909ajbWAusHbn7EB5XlnoU6Z/rQ9XfsHkZ2m3L2eowyqwILAFP7wPBro763t9JtTBaoVWQ7mJPU+n86ox6WthfTXU6ZtbfMkZ7SH+HH5/pUF7fT3iwtOylwnOwcDPP8sUSfNsOK1KVwd3OfrWjaQBraMIpC7dztWa/INWlvEktxbeaYABtY9jUyu1ZFlmWVCwCHgD1p8t6PLAzjFZc0kMbqkD7wBjc3eo3uJWGNgVe7VHJcu9i/DPvkKYO7HFatk4KgHrnBqGzsDNoCzyricEuhHB254/rRCWRw4xtcZ47GonGw4SuzUZtysM8dBURYg4IFPb5oQRnNVHyQex96hs0LCNulAFTup5UdKoWh2vjIOa0zgYG4ZNSBnywZYjOMd6psypkEjPvWtsIn2Njmsm8jH2gnPy5wPeiwmRmPePfv6CnOriI7AAB/E39Ku28CZXcc+1Syx53cAZ4HHSlewGO80Ua4EbbiQORzUJmR5H2gnYMZ9atzuFVgyfIB9/PWs9ZUwojU4Yen9a6IK5DYJ5ssZZY1RieCaWWPau6WQjcNoHrSq00izAfIegb29akMACxrLmTGPmPpWsdzGbKaQtIH8sdflzjsK1o5YtPjZ3k3TEHj0rPeVYyI42IIyCT6GrcGlQ3sYMkxUtnBJ5JxmtjCxBpN9GIPssnIPIJ7elasMX2edAx3qSD6jr2rA1DSpdOXeGDxA/e7irWkXLM4jLE91yaVRXiUt7nSeJrUXemW8QUb/maPHqOtcXplhLqN4tsnyDOZHJ4Re5rp/FM1xDNo8kTdN2AO/K0niWU2Ol7II1iNy+JGRQCRjn8zUU20rFyd2c34s1m3k8LX2n2sRW2Xy0icnlsOvOPwqgPeqOuKToc7HttH/jwq7nPetlGyJiKetB4pcYGSeT2oHvQUJ2pw4AxSYBJxwO2aCQMY5zSAGpR+NJnI5H5UoNAC0EClPTgUAE8mmA3ApMc0pzTc46igC1DPj5SKdJk9qpbjVlJNyYNNMBhdgealSTH8S4+lMKnsKj2t2BpgWiyvwCoPuKj8pgc84piEo25iKvQ5nQ45wKqKuDKyR8nirESdAOhpGUDrIQafHkdGHt7VVrC3NAMsdiUHzE9qpRQO43EfpUuWXDqNxB5FakWo211AVW3Me3g5q9ydjDmtXKnAP0qrEjLu9a3JL+GOIhE3E5wB1NUGy+XkAQnoB2qHGw0ytvJGMio23HqDU4THQZpGQ9TxUgVCr9hTkVs9OKc0bZ/wDr0Bcdc/nSAu2aDeoOcZr0WxKC3QKpHHYYrzy0IVgBx9a9AsWcQJtkQ5AwN1RIlPUv/gaUU0M/8QFOBqTYBXypX1YK+U6uBEjtdBydGtgAWJLAADJJ3HivSUsxpWmxWKnLj55ip4Zz1/AdPwrkfhraLdPaO+ClsjzEEdTuIH6nP4V3dx5YlLn5vauLHTu1TR04OK5+ZlAQ7VLN949K0bWAQ2MkxGHf5Qfakig8352+6Og9an1JxDYRoBjIrkStqdlSo5NQXcqxSq8+1elaBYADnGKzbNCF3t36e9V7zUhGSqZJrV/COUOaVkW9R8iRMuxDY6isOa2J4V8k017l3YsWqJrg5O0isVGS1OunSUVa5DNEVOABmoBazucKFNX0QsfmwT6VNjYmBwO/vV+0aKcIMzG01wNrTJk9gOlSRWKKw6ux4xVvfzgU2Wf7JbtPtBc5WMH19fwqlOb0D2cILmsVNUuFgVbWIAMOZSO59B9Ku+F7Ay3fmuOnSsW2t3upx3yc5rt4pIdG0vccByvFaTlypRRwVJ/a77FjV9VWziEEfLt8oFXrCIG2Rz97FcRZSTatq6yycop4rullW3gAOMKMmhtfCzjlHlOY8U6HGXF9BHgqczKv8Q9cVlWV6Vx83HQYrootWS9vzDkfMCCR29Kz9Z0dbeJLu3XY/O9QMBvXjtW1akuRPodNGXu2luWE1VYoyZnCrWZd+J8sy2iAdvNc8/gKxprGR23FySeztiovs0keC8TAeuP61zww1Hfc0tJPYspeyq25XCc9TzWta31qsBe4vQ7AZEe0jP4kEVirKsS/PbuR6jmnx3diucxTMfQECtuXsjf2qtZs6uHxFYlU+QAHr5nzmorjWJLktJZxzSxRjLuU2oo+vrXMz6lb7CkNiig9S3JP+H4Uk+r3Nxai14jtx/yyj6H6+v40vZX3MHKmn7u50H2yHVYgDNDARyAVI3fU0lqLhZ0Kac0rdFJPH51zcV0EQq0KN6cYP6VLb6pe2wIguJIgeoU4pxptM1jVXLY7C4s7q9ZBqEcMKr92KNsZ+pzzVCRHWYQW+h9P4l+fd+PQVz/myTSGWVp5G7u/z1MLvU3/AHUFxJ5fovy5/AVasmCukrfgWpZ5GuHt/svlzA7cL1B98VNHpN1bzKLmeP5xu2q3P41nq95aYO6RD/0zjAJ/GmpqFzFKXUyZPeTk0m7g6sbnQpZ3MZ3WuHyOVHOBU8VxeFceXGx6dOawjrd7KNonCHGMoADVYzXC5ImfP1rCdOLZnHlk7srC9nt/GfiCMnYZRb7kHQ4j6frWlHIFfKkbOu09vasrQtPk1PxBr0ud00QgxuPXKH/CrbR4OD8jDgqeoNdF0moryNMHNe9FdGyzJLG7IoGDmriKXnCnLdO3ArHMZMy/vcZ4HtWnDK8luiICkg+8+etaQ3OrmZrKkACh3VCOSc1YzBGpljYEAcv0X8ayorVGdWdyR33Gqd7Ob11tov8Aj1jOWx/FXQ2rGE6fNozq7ZzOvyBWUDO7Pymo7mSKPmSRmz/yzTofasa0tntsW1sHmncbvL3YSL3NW8CP5VdZpAcM4+6D7VlKSSOOs4UdbhdLFeIn2uNBGB8ox8wrMj02Ge8WE71RgWLY6cVZcAXG6Q7mHPJqG11i2e42/clVsDJ4PtXI5OUtDzKmIlVdo6I1IbQ29qYrG6uY1zuZyoyT+VQS22uRZki1KO5BOPLnj2/qKedSjBCx4MrDA8w4x9KcLwqxaWZf90eldPLFmSumZEr6pErNc6Ssij7zxvxj261n2sqpqkb2VpPFdMjbI879zEHB/r+FdH9q89S4JEY7jtRb3Hk281/nhAfLBUZ9Bg/jROKUWzWjBznZC3Cvp9m0nkrfiIbpFBBZeuSDjJX1rmLa4+1XkkgSKB5kICr90qRiuihupba/lVvldcOOezf05rF8QaYljPFdW6lbO4JITH+rfuv0PUf/AFqzhD3W0dWIo2SnEvxWFtAxdpw54C7VJCjHIouEgaHyxIFToAI9pH0rMaUvp+yJpYyVGFPH41BLHM6CV7ksoG3G4jFNSODfQ1bq6jYrLG29iuwjuvvVc3KxYJYL9OeKzrLDRXKEgMMfMDktzVh4VWEHGAeua05+U+iw9SdSkmSmYsmYl257k81B5BD56tncT3qdSAFAxnHpVhEGB6nvU8zbOtU1LcY16yQeXLCJMtu84nk+xqhNHNPKC2Ch6KPSrlxtAOcEenrTLJ8xPGRkZyG9KetjCdJOfJcgjiaPcQCOc9K0bLT5NQnVPMSJQcszHnHsO9M8xl4bke9MN00Myuo5ByD6GpvezNJUnCDUWa0+jQM5RZZQFOAynBPv7Vet4rmKCNLiZZ2ThZh1Ydsj1FURrUUsAR4tlwB97sf/AK9XoGlnhAbhsda5pc17HyeIhUpVWq2rYy4vAJ1iTn1NDSluFzmozpohYSFjuJJzmoriUwME+6pGc1nKDloZc2upOXcD5m5NXxFIbX5H+YjiuTl1EyTMi52g9TXRaVO0lpnOfrV0qcE7SKTRxfiESJ4k0SOXG5ftBz6/IK3LZz9l9ay/FK7vFOhbvlDC4Gf+ACtDTFzFMjnAhyWP8v1rWK5Ypf1udUPhIZECanPduMxJhxn+JscD86ZPcHVbFpHAFzAd3AzlD/hWrJZF4rnTH2i9uIFuLcZ64JO36kAiud0t3GpwlM88MPatHG2pSaNTSLqHSIBNcBibk7VC9lHesy9tTa6g0QBZScx/7YPSr/iiExXdqycQyWyshHTPcfy/OrejtHd2AuJIzJPZBijE9QBkfyod0vUL3JtWiL+H1gSYSTWmwyopyRgY/r+lYtwR55QfdUBR+AxUWnag1vqXnSnek2Vmz3DHk1o6rYR2ESPJOCWOECjqo7n9Kmz2Li0tzONNSBppAqrktxgc1atrdLy1uJbfdJNFg+UR1X1FXft8mgwQRiKJryX95KrD/Vr2X696NehV0ZDGC2JUgySD5cdlrZtrC31K0guhKIIYx/pCdSccnB+mKzdYtkFwl1BkwXaiRPqeo/Ote0ktdMthpVxu8+5GZSv3Y2YfKp98VTS0J5royDqMt5rHmLmNGwiIp4C9APyrXtFLQ7SOVPNYcNnJba2ts4wySDJPpmtu0lU3lwvpIR7VjXWuhrTLcTMMqTTJvmyelSscE7VHWo2CMCTz/Kuc1KokQP8ALk47Ac1pSy7beOcKQB1zVHKxtmrcLieFlf7h6CgRKHSeA3EZyxGAa529uRBdbmOR0xWtbo9tM0YP7pz+Vc5rFnKl02emeK1opSdmRK6Nyy1K3nIG8KfetJZopnKhuQO1efojq3BI+lSi6uI5MrKwwfWtZYXsyVV7nT6ig3YwNvXkVlyvhWEZA4wPSoP7WkkUxzjOO4NQm5i65PXpVU6UluS5o0oYVkRC0mCGDEd/xqG91JcmNFyRleOwrPnvXm4T5FPp1qOKJmOMfjWyh1Mm7kiuzyKCctnJNa6zMksSYAA54OKoW8QWTDYz1Bqwif6ZycD1pSaCxcmm+120kTngHHNUNDt5n1JYQDnPXtxWraWLPdfIcBmyMf8A16ueRIviZlt2xAke6Ydgcdj9SP1rPmvdDSsS+IriCzkguHw5t4ykCDvIcFj9AMVjW2oSa5DNYXbK8jKXgcADDDnb/OofFSy/2qu4HyxGBHxgY7/qaybd2jljdCQ6sGXHrWkY2VxSdylr6FNBvEYEMu3I7j5xnNThgowB+NdH492p4NvG8lVeYx7jtGfvqa5viqjK6uCVhQaO1J9eKcBQMT9aMU4EY96b24oAUdxnFIDQOfrTk4zQA4YHU0uSox1pB0HrQCc0AISabg4PFSbSRwPx7U3CgcnPsKAE2k9Ov0oVccscUpd8cfKPamqpZgAMn2pgTCTavT8TTgrSDjn6UbEj/wBY25v7opA8sh2xj6KvWmAvkxx8yNn/AGRUsLyL/q/3Mfdj3H9aRYMZy+6TuOyfX1pTN8wSPLyH+LHA+npVJ2Bq5ZGJSNuMn+A9cetSJEY15jOT3qnCSJCAcnJJb+97fSplvLgqdrKRnAJ71qmnuTsXApUA7Sc9hSSjK5AwcYqv5l/swsihfXio1inkH7yZsZq9Ogaktk6Tu6sVV04U02VCM87scGlXTuv7wIQPzquYZUxiU47mk0SPHvn8KGBI46VWKMMZuAM+1RDJY/vGP0rJ6Fk7Y/vY+tML4PXNN+Y8HP50BTUiLNvKdwzxXf6HcxT2qxr/AKxB8yE/qPWvOlO3p1rasLoqEZX2un3SOopNCt1PRAMdOKXB9qydN1hLrEU+Em7H+Fv8DWrUNWNUOAr5Tr6sFfKdVAiR7J8MIAvhOe47vJ5efoSf/Zq6Nds83GcDmub+Gcwk8IzWnG9ZDKB6gkj/ANlroVBhQEhsv/KvKxMrV3c78Ir03bcvyuYIAqgB26Z9Krkx3FtHBJJ86tgE9xUeo3iqNxxnAxWC9zO7fIpHueMVGktUdFOldXe5s6ndMoMNsh44LelYEm9cs5GfXNIyTyE4lck+9WbfSnc5kJq3JRWrNFHl0RVAaU4HIq5DZhPvkGtCOy8peFP1NHkhWJNYSrX0RakQ7VVcKuTiq8iMeSausQvTr6VC8UjEnAAxUxkaRfchjtw7hSQB6+lZuqSi4uAseBGg2gf5961rpRZ2G7dmab5R7L61m2kAeQuwGBXRS/mZlVnzOy2LumwpbQ+dIvJFUNTvpb+cQr90dBU19eYj8lKhsLfY29uWrSKt7z3OaUbs2tHt0sotxwWPJ/wqPXNaMUDRRnDt1PpVe71DyojtxuxiudldpZMuck+tOFPmfNIzaTLulTTJdrIjfOSOT3rodf1IxzQ2RfLpEWlx6t2/L+dZelbLCMXs0edn+rU/xN2/Csm4mlnuXlkJeWRssfc1tzc3u9ClHZnX6TFb3dkFkAP1FF1okUTiSJWAJwwViPyqpoEhQtGw6dveurZQ9sT3xXnWaqNJjxEpRd0cwbCSCRMOTExwwdQSPfmlOgW95kMgD9NyKFIP4cV0CxxzQbWGMjHtVBBLHMybsvHgMPVex/p+FWnK90TTquaszEfw7LaMBKPMhJ4kQcj6ip18NQTDBDKx6Ff511kLCSPLd6cYlA+QZA7f4Vo/avVMzlWa0Zw9z4Pvovmt3SdPQnDVmNBe2EgWSN4mzxuQc/QmvSkdScenFSSwRXEZjljWRD1VhkVvB1GtSY4iUWeate30sXlSSzvF3QPgH8KsRanYW4xJYXI9Sso/wrqbnw3DkvbFh6ITx+dZ0ujYBDKcjsaXPKMtYnRGvFrR2M2fXNEa1Kx2t2ZT03FQB+INVZL+2ljT7Nbyq38XmMGH4U270wRksqjjqDVKAYkGzaBnoBVy5XrYhSlfcsGISNuMePcVOLSRkLJhvY1fszGxCsApPY9DWmtkBymPp61zTm+hspJO00YPgTDeIvERPGDbHH0Vqsa3ZmDVZ2OQsjb1PYg03w2Ba+MPE0Q4ybXt6oT/AFro9YhE9gYiMheQ+ORXS2t/JfkefSxCoYlye19TjHibYcYyBkVoMDBaqwPUY+lVUtbuWF7mGEvDHlmJ46UyXVBMpVogqdcjmtYNHtRr05+9BkzzPLtiUkAjGasqI7CEEANIMAL2x71VtSBEZn+QDnp0HrUN95qXH2eVPKZQCY8/MCfX3xg/jV81hyktF3LmnwTXwllMjfMcybT2z0rXdoLcBFkGcdB2rO06QWrgZGGQow9eKzzJ/EzHnoSa5anNc+ezSEo1/Jo1bmBZl3RygyDtmsjUHtoE8qeIGUjhl7GpY5lVQ4LY9RTbvSrjUpUa2xJ6k9qqlG2rOenHqUk1VhCiypHLGDtGfvD3BqzcwSQ29rdLJIqTdN/OB7mtqz8HhHj+1EMg5wPWuhe0jMSxNFHJGCMI4yBVt/ylto88e+uWG2Obc33QiLwe1di9q0FlaWZG5xtU+7bcn8M1pvDb21s0scUEOMAsFAAGaz9WuZLcTzW4zNASQuM5BA5rSzlHU6sKrKUkYzbWlhnO77QwMTk9MDp/Sta+MV3okdvIVUzgrgnkY6H865+0kIEcznKxgkE9WzS2gd5XuJP9Y3AJPT6U4rlTsetSo89KKkGsQSWo06VQ3lSWyRMTyBIBgjNY+2e4D4cEjjZXX3UX2rwlOrE5VQR7EGuOO9Jt+fnHUjpUw1R4U48snHsPsTi6GeA67Dn1q/IQ1uARyvy1W8yC4G6dGicciWL+oqy0KwwyK7ea5QGNo+OPUihs7MJi401yT6kSM8ajj2571MJz8vPG3rUaSb1VcZCjv1p0jxjkDCjjJ4FOKe568ai5bqWhBKXd0IHfpUgl8soijry1aul6KuoiQS3JiOMRlRn5vU+3tWRdwXGm3D294m2ZOVx0cf3gfSndHLHEQlN2epZ81JFBz1HOaryMfMWPGOQQasosMkaj5enWkEHlssu4YXrx0FJRtqd0uaSLWmgrrliAVwJTk47YNaOt3IhbydPDwKSd7EYz/u+nes2y2m6klNwkRiThX6lj2GPanyTeZHsuH8wDAWQVTWhzSoQqVOaSvYis53hD3PmMxzsw5Jznv+lFzIbuMwysRzwVqspEMrAco1XIng8o/aPMKL0MXUVEYrY2dChL3pQRSjsJIuFfzOeBjmnI+oRn/RYJDg8jNTNeYG2KMxpkjJOWIpufMwfmb60nGN9DjllFGcuZNoyNevXu9a0FZEaOeP7RvU9soMfyrZs3d0mSNdxljAPttO4n9KxNVDN4g0RWweLjHr9ytrRpUjuSZDhdkik+mVIH86GtEebWoqjN007pFnxZNJbatp13CSHS3VlP0J/xpLt4oLC81mOMLLeMIUA5Csy5dh+GfxNS6xEdbTSPsw3SFPKlwfuHjr+v4VFbSw6qt9oasvXfZydi68fqP0qnZysZL4SpZSrquhPp8p/0i1PmWzseoPVDVlr4eHLqxtFwxjAe6GP73b645/Ks7RY2tJpr6THlWwAkHvkDBp3im0MerNdxndb3iiaJs9cgZH+fUUcvNe/QehLc6JjX0tLc7rWfEsUnYRnt+HIq1qtxBrOnPNa7lazY5Vucoe4/KpdOFw3hm7283K2p2DvtJ+bH/Af51g6NdNa6pHgBo5T5TjHBB4/+vSs9wfYv+F1KX0lyzqkEUZV2Y4A3fdFReIYmTWJXY5WYiRGB6rjg/hzVnxDHHp1vBYWq7YZCZm5zkj5QPp1osYDrmmLah1W4ts+W7d4z1z+NPbUqLL/hjM2mzrNEsiQyboyy7trEZwP0rl7iaSa4lklJMhcswPrWze6i2nzxWllKyx2vDFTgSOTlvwwKL3TXvpYLuwhLx3IyQvRH75pJAatjDbahaw6jOrfabcGMkHh8YwT+dZiRtFeXinO4THB9easJq8OmXKaZGiPax/JJIc7mbuR7Zqa4iH2642jgPkn1GOP51hV0RpT3HmRjEN4wfWoxOOQ1SSDMeO2OtZ7nYuD0x1rmRuWJXDvj+EdTUsFyoYDsKyWlZM9xTQzHnke1Xyk3OjfDqCp9xVKcx3TGGXAfscUtq5aD3qtcIZG+XIZfSpWjK6FCexMDsNvQ9azWhPmEkcV00bGeLy51OR0YVTuNNKNuT5lPcVtCs1uZtGM8O09OMVA0LLzitZ4RyrED600W4PoQBW8axDijKAAIrRt4WcDaORyaU2RVgVUH2FSxRSqdoU4z+dOVVE8pKQjbUGMjnNX7exllO7HbrTrW1w+Xj6now21audUstMYpK53qPuL1zWfM3sK3c0LC1WzDSv8AOzDAU1karMZd9qpwpO6Uqcbj6fQVbs7t3QXN8wiecgxRD+FO2azLpPKunB7tkN9apIhiC1/tDTDZli9xAd8JbkkHquahsbMaajahex7RFxFE38b+v0FXdNUteAp/CNxPYD61LrYMjRgjMeODnIpqRNjk/E2sT3vhm/juGR95RlAGNp3rn8Kqjr607xRa7NCuZFPACcf8CFNzg1tC3LoUhR19qdk03NKOR9KBi/T86OnFAIxijgNmgBOnelB+U570ZJJ9KO3WgAUY6nFO3jsD+NMHOaO/tQBLnI5pmOhpUyTgdKkUImWJzjv6n0FAAkO4FmO1B3NI8u0ERjav6mmu7Pj07D0p0cfPIGcd+i+5pgJFCX+YnavXdVpCEQ7f3cXd+7U0AFfMkJES9B3c1DI/nHcxwg6CgCQyGYbEGyMfy9TTT8iiOPgt1Jpq9NxB8sfrSg8F26mgY4yCKIogySCOewqEtwADyKDksSe9AHNAhwnlHG6rEOoMoOVBHYVXWPIOKTyjmqUmgsWJNRldun6VEbmdicYNJsI60q5J7596bnILIjIZ2y36VIigDHb1q5Da7lBlG1SOWA6VO+nNEBucKSMjcMKw9QacU3uJtGeFx3ob3GKlkQxkgqR9f881Gcnim1YER55xjn61YspWEmOlViCoxiiBsSgj1qGxo6JHyMHGa6DTda2gQ3Tkjosh6j2b/GuXRuBj0q0kmQBQwaO9U5xjnvxXyrX0LpepSWzCJjviJ+6TynuPb2r56oirEtne+F7ma00u0mgkMcilsEem48EV2X/CTNKAz2a78dUc4/LFcR4e/wCQLbc/3v8A0I1rgis6tGlUfvIlVJQfus3V11HYiayCof4lbLD86v2sNjf7jbTmR1AJUjBGa5dT0rofDIVBqUmeQsQB/wC+q5sThacKTnDSx0YfFVXNRb3NNLXy87I81Jsm8olUAPv2qxakBAAeSM0k0myLA/irzHDueg5NysVSSAFaQZxzUZCHOWJ+lMJLHPenou5SSOQaytY2tYcNqA4UfWoP9ZIE7fxHPapJ8jikMTx2Uk+3lxsX6VUFdhdJXMa/nN3dnAAUDAHoKY8gt49i4JPX2qQx7BuI5NU7lgtd8UnoFrIYcFtzdaf9p2KSPwqo0xIyRUDyM5rZQvuYyZJNMZTzT7O3Ekm5/uLyfU0kFs0nzNkKO9Xm2xpgEAe9EpJaIIQuF1cGbGcBFGFX0FVY1dGS68lnijcElR6c1chtt0ct1MCIIV3t/tdgB9TVJbmZGbY+zzGAK9hVUYKVzHGVnSioR3OmsYlGr3Kqfk81sH2zXSqAsLZPauc0YBi0mRhmOM10JkUQscH7tef9u5WIu7IitJC8Z4wQetU9Y3200F9EMlflZc/fU9Qf5j3qTT5PvJz1qfUUWSwOR0rSWmpzQ0nqJbXCyLG0bbonXcjHuPT61eV+c9PauYtr3+zyI5ctaucnHWM+o/wroYzuVXDhgeQw5DD1ropWmrrcqtCzJZYC/wA8LBHzknHX601JmyEcqr5wR6/SpRJ6etJLEk6YIwex9K2trdGKfRigk85yPSmyJHMCrdexHUVRa6exYLdt+7JwJccL/vf41dyCM59wR3/xpKV72JcWmcN4ktb+wkJdS1tIcCZen0PoawEbB9xXrRKSI0UihgwwysMhh6YriPEHhdrANeWAZ7UfM8XUxD1Hqv8AKnZNXRak+pTtHZwMnpW3a3BX5Xb6NXN2U/GR3rVSbg+lcdWLvc7qdS/usNBG/wAb+JmJBI+ydP8Arma372Rpn8jBAPeub8Iqs3inxNk4/wCPXHP+w1aur3Yt54I0cM/Q45q5Nt28l+R4+JVpSJGlWC3lgzjKMMevFcrBGoCkgdOQfWtto5JD5hQkdTVeCzWTcTIqbGZguOvTFVS0vc6sprQpqSqaC2lm95cRwcLCoEs0jdAoOcfpVAyNqGryTycl3Ln156fpitOS78vQntsbZTcGIn1Ugn+lZkAMKGcY5+XJ+nWuhO70PWpqc6spvZPQkuiIt21h8rdfWsuaQvMBuwmPWty6sZrq2T7NFvJI59R3q9aeHAsS+YgYkd+1ZymlqeXmNVTxFl0VjJspIfs2w5xS2F7LpmpBC5aCX/x2tG7sBYKZDDmPuR2rmbyb/SA247D0xShL2l0cq1R6JBfltqtyDwDU6uRKcniuO0y/lTZHOGXPKMe9dRDcqoE0mdgGWHvUU+ZPkZhJ2Ev2hu4v7NuX8s3IIU/3ccj+VczNqEiSFLiWYAja5QA7scdfSm6jqTXt1LtBJc/M3ZV9qYV3xbc4XGBXXdpcp7eWUm6Lk+rAss6osJLBmCALyQK01h29l9eRgD2rmlaS2nyjFGzjK8cetWDf3gQulwxI7etVHRanc6/c1NSllXSVs0dlgaUmXZ1x/D+Ga5/7PliySEsD19fWuinF7FBbtJ5N2LiJXZcbWXIzg9j3qklpHdMwtQ0br8zW83DD6etJO2p5tShTrycqctexmukkB/eKYyw+V88GrG1njSVQUkUYOfStuGyju1NhMD8/p1BHcfpVFNI1BbctAv2tEYxzKnDowPHB68EdKm1zz6lCUXqrEEMa3IJbLsBklOCPfFVrm1lRFdn86HOQ4Bwv1FWogILgSOsiMByNh59q09KiknLJKgFq7bmeT+IegFLnSW5nzyWlynF9oi0+K6jLxqWCpnuPX6VekmbVrOMEobqA+ZCzDI91PqD0rQ1SFLm3KwsNoGFHoPSsG1ElvKElBXnqK43O75kzF3jK8TYjtrDWLVbpYjbSN8jhMDYw4KkVA3hiV+JL+OOEja+V+Yj2qWSE6VdRXzHFtdYS6H9x/wCFv1rWljaWMEnOOtd3PzRvbU61jK0I2TMTVLC0GkyG2hVPs6546uBjOT696x2lLThgRhuW9Aa3NUgYaPfbRz5ecfiK5wYZEKAehrOEpNXZ6WVVZzg+d3dyeVADlSMUtuzHKYyH44p+wMAMZp6KUcKAVIrRI9i3mNEJVmG35kOGLdjV37NGkZ+bLeo7VjapNctdurupg3hiV/i6Vt2EkdzGhZh0qZabHgZhjqk37Oi9OrOY1RNvinRQWOCJ+v8AuVsaLaifVVt3b5Xy/wBcDOP0qjr9usfinQdvR/tH/oAq3pMrQ30bbSzJvjCjuGBA/UilfRXOKF3DU2raCOHUkcuqRXnmwRSZ53Yxkfyrj/JuLDUxEQVuIpQuAe4Paul12JofCulZBV45SvXkHDf4UkU8N2Dr88JMtpAC+Okr52oT6c9fpVyVtC4uzIvE/mDR7Db9yeWSSYgfekGAM/hmodGUa5YppNwM+VMGhkHVFOSw+nFR6TeLqNldaTey48399BIeiSDn8j/jTrd28NW9pdSLm7nk3mLPSMcZ/HPFGysPcibxAYPEq3luoW2iPkpGOhjHH/16uLpNvaahdaipB06BPOiyecsPlT654qh4h05YtUWa0+e2vv3sBXpk/eH4E9PQitma2S50O70qJg17ZFJiv97AwR+A/pR5IOlyl57+ItFdHXN9ZEyoF/jQ/eUDPXv+HFN0OeHSbdtSus4lPlxqo5IHJP0rN0F5U1mExM2eckf3cVoeKUaK5ssAiFoMgAfLu3HcB+lJpv3RlXXbbyb8yId0NwBJE3qDW/4cRotGkVpRH9oLeQC2CeMZA+tUNDjh1iz+wXQc+QQ0bIcEKeozWVqN+1zqJlj/AHSRYSBB/Ao4H8qe+gFQxSec0Tg+YG2YPUEV1ivvuLkkHaW2r9BxVCVY74RaqhCyONsiejjgn6VJFJsjGTnLYzXPWd9DemrF2POxlPUDvVOQB5OmMDpVziWMyJ94cGonjYkfKQccisEinIzJkGSec+1JEDnOc/StBrXcpwKWCyIc8celXewrk1rnbjGKs+Uud3epoLB8jHQ1rw6coUbh9c1KV9h8yRjGHcP3YGfSm/Z5GJB4/CrV3bXcd5+6UCNuRVu1huiAWReeuRUWd7JFXRiy6PJKpYKM+9Uxpciyneyg/wB2usl0+SUjMu0egp6afbQEMw3t2zWqhJaszlJGPZ6YJBkgKi9WIwDUuq6la6HZGSC2WeQMFzjge9Q61qZ84wRY8lBhh/ebr+lczBqCy6k1ncSF7aYeUSf4WPQ/gcVtCld3MpSaMy71C71C6aaaZmdj0BwB6YrqLeCy1HTo9TvYzJNApEmGx5m3pn1rBi0a6Oq/Y9m1lblyOAo/irZi1q1inGlwQbrFR5fmEnJPTd9M1vKy0RldspG7ku5nll4LnO30HatCFPt0AUMoljO3nuKoSWzWk7QNkgE49we9adkkdjF5k52tJ90DsKzYyO4dLeP7LC3zA5kf1PpUduxuIJLUnB+9GT69cUy9hZblmz8jncpzTLWCSW7XYcBTvJ9BQBjeKbSRPCt9LICoHlgZ7nzFqjn1ra8fBz4anZWzHlc/XetY3BHNa0/hKF+9Tc46U/GF46009PemAAdKMc8mlDY6Um7dQAUc0p+lN70AA4xTwucc4HrSBRke3agnPXigBykFsHhB1NPUlyMDp0FNI2p/vdaWMgMMDn3oGTiHcvy8v3PtSqilTj/Vr1P98jqfpVuKDFu+7PIy59OOlV7gFYAgGCQNw9B2FMCpLKZHB52/wioySaU0qcnPYc0hEjn5UQnkdR9aYzcjHQU3PJPc0vbP6UASDBIpOlNB+bmnE570wJYivNTcbTjriqwJX7oqVHAfD9MU0MJNpUYPPf2p0ePKLhQXUjI9RTAgVgrH8acPkU889M+lUJl+K5iEGzLYP8BPIPpnuPyqBp2hyIZT5TDO3t9MVT5HUgqevFOZ+OcCqUhWHvLu4YtjsPT6Uzep9cVGSfSmZ3HANJsB0p7AZzS26ZYcV0Ni9vdWggv7YSoowk0QCyJ/j+NVrnSJbbMtrItzCvXaMOv1XtR7OW5aixqAgCpUf3qml6mBlT9RVpGV13IciheZTSLsUmMZrw+vaFPPWvF6drGczttA/wCQLbf8C/8AQjWstZOgf8ga3/4F/wChGtVazb1MmTKeRXQeHVV7a+wCSJIyeeowa51T0rc8LzBdQmtWPFzHx/vLyP03VniU5UJJDw8uSqmbsMmIz2IGKfOQ8Q5+tVshXkT1OR9KHk2tjqPSvHfw3PdcdbixjkYFSsu3GPrTImyx96kkYYPPaud7g73Fgt/NdppeIV/U+lS6k6zW8fk4KgcgdB9ai1NmjSOFOIwoOfWqAmwpBcY7gVaXVGcYOdp3Mq+uFQkKQfesmSXcckity5gtpTnBHsKrx2dvknYuO2c13QqRijSSkzJSKW4YBEJ+laMGnJGd0hyR2rVjt5GXEEPHqBirCaOCpa5ckf3E/rUTxKJ5YrcyVDyuI4Ii7ei9KvQ6WqZkuSHkHRB90fWtdI0trZhHGqZ6AVRZnOSRkD2rndZy0joVF32KWrQ3MliEt4nkVnHmbF4AFYkMEj3aJLE6hcs2VxwBXYw3wihEa2wdsctv6/gKg1Ka5m0uXfGiIzBQFXBJ69a6KOIlCPJbc5K2HdSp7SWw3RFKwJjoOoNalzL5Vsx3HJ6D1rOsWZIc4GR6U7VJNsIBfBI4pJXkaVtWT6cW2eYT96r16xNiwzjIrOs322o4GQOlS38+LYJnnFKepgl7yMy+iH2Vmz2GPrVWx1t9JIjkBktmOSg6r7r/AIVo3AzYsT0CZJrl5yZpSRwBwBV0G0zqnFONmekRTxSwJLG4aNxlWHepVZQN2Tj0rhtE1R9Nk8iclrVzz32H1H9a6wXCooUNkEZDDoR612XUtTi9k9jQkjSWMh1DKwwQeeK56Z7jw5JvIe40pjyvVrc+o9V9v8nVW6OAOtDTRzxvBIAVYY5oUeb3luCg1o9gW5iuIEmhkV43GUdD1pVvBHg84965uyA0q7nsPmUM29ATxirkl1tLJIwB7e9KFRc1tn1N/YKxQ8Q6LHbudRsQBC3M0YHCE/xD2z19KzYn+Xmrg1pkleCTlORg9xVaRYw2Y/uMNy+3tSxEEveiFPR2I/CySSeJfEgRgv8Ax7ZP/AGrdSCNbgFYzJL0JNYHhPcvivxAGPy/6PuH/ADXdR+WFJRQD9Kyklf5L8jy68b1JepWEMhGSgCgYIFcteTtb3juVKKWOC3SuzkDHG0HNU72w+2WcsUsSsSMqcd6TV3Y55wuc3cziXTnX5cnDcjnrVeb91Yxpjl2wOOlLDY3ENjcPdAL9nAyO55roBp0MkQcN82Mxr/dzWlP3YtHrYPEqhhpOTu76EdjcmzaGNVLRD5ScetdKgVkyOlco0N7byAuQYq6DT7gTwgA9BSo6XUjy6dRyk3LqJdQJIjRuAUYYIxXNz6RbRv5QjyByDXR3M20GqEoDEysflCkk0dbIrmbfLEyL6BJNPDZw0LZXHtUbal9r0UNCCH3Yam2iteTTPn930AzVu909INMWS2j4Q/vR/WnBpSSZdej7KXJ1MOGIrcmNmBOMnHSrhibaOM+gqtPJ5IxaleRkkjNQusjxbzPISf7pwK2trdnt4LER9ioQ1aH3FuzEkL8w61TaJ16LkEcjnpTvMnhwUlfjnk5p66teqGHmghhyCo6flVDqTjJ+8rG7psj/wBkRSyOj7ZDEoYckjp+AoneJphFMR5scYyxbac57H0qjp10BpUhj2CRZidvcZXGRWzplmYtPW8uwksx58yTkbR0A96W+h5E0oVHyEaT7WLRrIu9uOOfTr6d/wAKklllsZ5Z4HRY71AjkN80cqjqP94U6C/VJRNOCzPwEHRB6VK2lxX1rIdjLM2ZYsnow6fhSa0shybktWY95dNFZ7BIclvmJPNRaZduGIO9488GoLSE6lqMdsxOFyZBjoR2rtLWxhjjASNcDpxXOodGcaj3OdupXhAdCSp7+lVIrjfL85Ldwa664tYnQp5Yx3xXPXekLE/nWj4IP3T3odFRBo34401LTGjbBSSMpg+vY1bt4Ba2iwli4RQpY98Cue0jV1t7gWdxhN33Sf5V0+QwKjBBHFb0mrFLUpTxJNHJF/DMjRn8RXB2aEXPkSfKyna31HFduwZSV54Nc5q1qbfW0uAAYbkgj0D4wQfyp8x2ZZV5avI+pVeWRYiqkBjxkCpY4b24slwyEZI3Y+bHpUl9bxweSI33LKNyDvW5pNmkAUvyxGSPekrs7s0r2pR9k7XZgroN3eR+U22JB37mrcOhRWyLuuXLAdjXRTKWJI6YrMkRiDWVSTjseCm0jjdXjlj8V6EJJfMXM+32+QVZsZRFrhycBXVuewyKg1sFfFOg5GObj/0AVatwJdbS2ZEAeQKGCDcMnHX8aqLbgm/61Oqm7wNfWZF1bQzbWhEksd4XC5wShLYIz/vAVFYyWtvfS6BcMrRSwiGVweFl68fQ/rV24ihsr99QBQWtpLGjnrzwD+R61y+t2MllrtwA5w5M0b9d6scj6+n4VtZvVjQ6y0y4ttbkhlBQ2m55W7AAdfoR/OrfilBdR6fqcJ3W0sCxAj+FlJ+X26/oav6o1x/wiktyvM8jxxXD/wAQjx0z1xuwKydEuhc2dxo1wu+Kb54sdY39RTXcrzNLwuZpbTBXzTAJJLeNuQZAOAPTqa57TtTuLDVUvRITIX3OTzuz1zWrqGoNomr21tZH5bAAOf8Anq55bPtzipbzSIb/AFhLiyKizuR5zEnAi7sG9PX8aWy1BW3H3kVrpWn3V9bAq965ihT/AJ5L/GR/IfWoNPYaxosunysPtNuTLbuT9Mrn0I4/KrUM0XiDSbvTYlAngcz2mTgyAdR9cfz9qx9EBS6e7LbIYUJdj78AUdB+RfhvT4et40SGN7qb55g/O1f4V+vU1BqNgst1HNZqTFdfOnoD/EPzqXX7V7i+try1+eC7jXYw7MowwP5ZqbcNOsvs6SEsDkknuaic+XbccVchdfscK24cEg7mz6+lCyBwMYHtWfJP5kpw2c96sRA4xndWLj1ZvtsbenTL52JWwG+X7prXexKHJ5B+7jvWBY273JLLIwA44/nXW6aZBF5cpJCcBj1/OoiuhNyCCyQgEr9RVxLCIHOBnvVliB8oPNA4XArWFHmeuxLkCRpH90CnZpincx5oKjOM10xjGKsiPUcfcUhfFIVHrTSAMk1VorUGPfJjJHWqPz7iCcip550WPbkdO9VI3DKWbO0cDFediJc09NjWCstTk9dWSENNARlGO4ezd/zrmACdoU5YnjFdprLpBdpO6Zgk+SQkdQeKz002DQS+oSypNt4tou5Y9CfoK6qMvdsZTi0zX1Z7yDwsxJIn8tFlbHOO9claIQhYjPQDntVvTNXmOof6XM8sM+UlViSCD3x0GPatBNImS88iNW8sH5XI4C+v5VV+XRkb7GhZLHdWaSzruaMYye+OlUJZXuJmdiOensKttqUFowjTZ9nUbc5yW9TVDUJ47GbaVba+GjbsQaVriZcgj+1wmBuCpyjH07026xawG2iO525kbp+FLa3KWVsLu7UxmXiKMcsF9T7Vl64GeeO6idjbXAyrZ6EdqSQXM3xRcBfCl/Azgs3lkDOTw61S+hzVXX0YaJdcZPyse5UbgOTVrHPOa3jGyGhQcGl4JzSD0NA/Kkyg6UAD1pc5pOfSkAhpenShfxpDmmAZo70YpeBQBK/ylOf4afAoMm49B0HrUTkbUI7DFSg+Xbj+89MDVgYG3bJ4TLt7nt/T86oSklfmPIOW96lgJW2K55c/oOn8/wBKimTAJxTAqOBnilxhdvdjmlOA3NMZyWJHFSAjZHanAdyaQOKRm96AFJ596TPNNyc5pQelAEvmA/0pxZSDUHalB+U/SgCQyfjSiQkYzkU1duOtJvA6gU7jFBYnGTUnlOR049ajDei8VL5iomQW3elUtdxDX3BcZpYVG7pTDJuPIxUsLAMM8jPWqik2OO50NnFshUkBiexq9HuVwxbDAY4PSoLR0nUSL90DAFW9gXr0r0ForHUrWMfU9JLObi2Gc/MyD+YrLimeB8DIYHlSMH8q6pplUYP4EVSvootQhAdFWeM4E4HLD0PqPfrXPUp31REolVJFkQOp4NeM16nNb3Nk3zBinZl5B/wryysdephUO10A/wDEmt/+Bf8AoRrWU1kaCf8AiT2//Av/AEI1qg1jLcxZKp5qWKWS3uIp4jiSNwyntmq6tg1IHyMU0910ZDTTudnK8V7BHf2wOyXgjuj91NVx98bvTNc/p2qT6ZIxjw8MnEkT9G9/Y+9dHpZXWXkeKKSMKMfvG6H8K8ythakZNRV0evQxsOS02SxOqEk85FMZ/MZsAAYxmtBdHCndNOuB/Cnf8aoalIip5cIwg/P86yjhZL3p6GeIx9OGkNWUL+8dWVdyuAMAHqKzpL5EhLtCxPs9RTESSAAnJ9aWKMT3lradPNmRCfTJrup0IPW2hwQxNZfaOmbR4ImCvI5baCR71Nb21vFnEQ4HU80+5+ed5AQSSSPpRGQkYJGe9eNOTezPau+XVkzMCyxjgEdBxTJyrOEzwB2pkTnLzsOByKROjO2OeaztYlIS4fOFA6Vj65OLexEWP3lxwD6KPvH+QrVeSCPdJK2I1GWY1x+o6g9/dPcHCggKidlUdP6k+9duBoupO72Rjiq3s4ci3Yy3tHvbqO0gz5krbQSegHU/THNdTqSQ29hBZwEmKE4DE5LHufx5qLQLI2Gnm9kH+kXS/uwf4Y/8W/wp92d8APJ/e1via3PV5Y7IzwdFpc8h8B8uLBx0qrfOZpUXPFTbgSPpzVR+ZS3pSR1zV2aAkwqqD6UlzKHZVOfaqSSlpD6Cno26UFjwKzkiFHUn1J2WweNcYcBcVirb4Xca17ndNtUY2r8xNUXG5wininF2VjZK5TEeXPXGK0rHVgsgspG6fcc9Af7p9qhuALW3LN988KP61iMDv9yc1vT11IkrO6O5SUupyCCKr3d15ADA85rP0y5edNrMfNVec/xCi+YmM5JzjNbRqJMbSauV9Vu/Mmin6SA5Bq9qSGewiuoOqAHA9DWDPukiTGevFa1vrUEH2ezxvJARz2Gaxr8zkpwIc0otM568ZxcE55xVzT3d4ST/AAnir17okktyzR7dvY57Vdh0xYbQRofnHOf71KpiIOCRNKnJTuzJ8Kgt4p18AY5t/wD0A125l8sYU81yHg+Mp4p8Sbhgr9n4/wCANXTTFmfIXJNZ1b82nZfkeXX/AIkvUnN5ITjPOOlSGcRrvkcjAqp8kMZYsNwHzZ7Vj3F2b/CxviMHnnrTgpLc53KxO93Fdx3dqzqslwQ29xwMHNbMbQFVCsAe4znFYCQRSklQNwGKc8htz98c9cU5OUdQ57x5Wbd0Fa1ZeDnOKqaTIYyqnIwarQ3JdcDcRU8chDZxj0qPatvUSWpZ1JispHYjIqm0mdOdO78A1f1ApPDGQQHIxUEdhvhjLMAF5P1q5TSnodGGSjV52ULKwaGEI42jrWvbnylABBbpz0P1qOYHzOOlQtLt5bg+mawbk5GVabqTc2Z+r6dEr/bIMrg/vIB90+4rFUDysVtapHK1iZQW2bhkA9qylXJzjjNdtO/2j28ohelKS7lZYh3AAqeGwiz50jbFUhs9QfwqwqRpbs0rquAW571ANXiaZI47ceW/yKM/MWPAP0Fbxi5GmNqQpQaXxG3cWcMBkdYhCsjIx2/xcdvSq0syyXMImld/KOEt4z8qnsWNTXtukV2j3k8jFdqEYwshxksB6VnJseJJohjzQ7sg6jB70W5djxdXqzVha3km3rG0hDYYngBv8K1YjK0xAONjbePYfyrAstQiJlDRsSi4J6Bgeg/Ct2wmnczM0RWM4KuO+OtXGzKiZ1mkdh4nvYTGP3uJo3H8Skc/rmugAAxiuX8UTvYxWN9bELIXdG3Dkjt+XP51n23jK4QATRggDBIpKl1uRKOuh2soDZway7xSEyOMVVtPE1lcHBJQn1q5dyLLbb0wynuDUThozNo5W7jFxM29tuz5g3oa6zRb1p7CB2OSDg+4rlLtdyzcfwniuy0ZYZdEtjDt2hR061hDfQiF2M1B/JucdmGaz7pVlgkjZcqRuX2I71qanHut4ZAOhwTWJeS4ifLDlcU6jdxRXLWi721RnQqbnULcPwsa966W1fLs7dK5y0YC4RyylguK6S2j3Qjpg96mU3zWR3ZjVhVrJU9kK1+VbOz5c1IER18zse1ItlHI2Wk6dhVgxxwx4YnA6UUlOTvLY41F21OB8UJt8T6AQMZ+0Af98CkMvk66soOAnzk56AHP9KseMm/4qPw2cY/4+QP++FqvcAC5OOWkwT7ADp+dbyskrHRTXu2OgvUY+E9ZZ12tJOJio5xuKMP0rP0QJrltaQXgJ+wuzCQHnywMlT+n0qxd3hu7G8slAUzwxKpZsAMmM5PviqFvqS+Hp7OKPa7Kd1yAePm7D8KrmukkMNM1pJdVuUvV/wBCvso8QP3Afu4+nrTE086Hb3V68qO4kENqQeJOclvYYqtcaXnXWhtj/o8h82J8cLH979BxV7XpYdQ0SzmtXytrLJHIMdN2CD9OMU+vKNbEXiWOO8S31y2H7q7ULKAf9XKB0/Ifp71o6VaMdKuNLLeXf3FpujUnBIznGffgfQVT8Jg3HnW0wLWrMhdW5XIPf8qzLjVLttck1DcUnSQlQONoHAX6Y4ovdhboV9PuJbbUreSIEOJR0/lXVavapPYxQWzRp58jTXGwfeOcKP5n6iq7Q2txfPq6Db56giPHAkPVh/P8TV+0ijLoJVLEDHy+tZVKln7pcY9Sp9mnsrEQReYIAdwUnv61jXQlfOWyR29a7NVWzbEm8wd9/wA20H+lVdU0DejXFmvH8cfp7isop3uXe2xxMalWPmAj2FWlkdl2IoC57jNNuYHU5VST35qKOWTcF2n6d61equB0NhKqtmXsAAI1AH410EN8TtBPzEYGOSPxNcnav8+3coY+3St+F44gqhvMP+yODWLuNHRwJ8me56809vlG7IxVO1mfYcqqRr3JqZpVlXODtBrojUSjYhrUEk2scnPfOOntRDK0szkFSo4HFQnMspUZ2BfzJpeLZiAcKQT9aj2tmOyLeevsaguGJHyHkVWnvhFIe43D8iKrJe/vANxdSQP8/lU1arashxh1HyQu/LOOe2KGTZCqorMwPY1fTDALz60Paqw6VyK5ba6mTf6fFqmmtC24OvJA4OB/Oua1GaHWNOLWxcSWh3MjLg7Twa71Itgw2SMdDXneoSDRvF07KpMRf51JzlWHIrqw71sZ1NTIhLhwEBZidoGM5J6Cu2vFuYvDbxhj9pWJQ4Dcj1rOa0s9DLX6OZXPy26OOATznPfGax7HUJYr/wC0Suz7iRKCchs9a6m+fVGGwyHiINx1xgjg10mioLzTsXUKyeS5VCw9s4rPfQLiW4AgObaQ7/NJ4VambWmtZIobJV+yRfL838fqfrSdnsHmZl3cPe3zSucOflC/3R0Aq9prRXySaZKTsf548dVI64pmrWCLcLdxnEM/7wc/dz1GantfJ0aFLy7+aaYbIohwQp7mk9VpuLzMXxmsGmeHLnTrcF5H2NNKw5++pArPDHPfFaPjqJbzQJdUt93luFSRW/hIYVmgmtI/CWiTimkj8KTPalyMdKQxcego49aB170HNACZ5oz2xSg0maAEPWilPtzSc9aQDlOQAegOakcl9h9jUWRnHrTycxDHVT+lNAXYnwkaY6CrLx+ZHjnOKzYnGVyfrWjHIdmO3rVoCjNbuhz1U96gkjK5Fa5UOpFQvb5AypJHTFFgMrbigCrEsDIOlQc5qbAJigY7nFGaOlACjmkBOaemMj0p4CNwRg9j60ARYLdOlKE9acVaPryPak3880AP3BVqIvuPFIcsaeiDGcGmhgASRThwamiA78/SnvHj5tvBqkn0DY0LG8aKIKOgq8L5mHPSudEjQnAYj1qxb3n7wBuK6I1ejNoTRsm4HXB+lJHfxsQD16D2qN9piyuORWdw9yiD+9k1dzRm44LAg4IbqvY14bXtkkgjTA614nWdToc9XodloR/4k9v/AMC/9CNaqmsnQz/xJ7f/AIF/6Ea0wa55bmDJQaUGmA0uakkUnjmvQ/CtqINISUf8tfmrhbC0a9u44UUkscZr1OCFLW0jhQALGoUU72VgtcqX0uARXMajKApUHmtzUJDzhsCuUvZV81s5Y+grjqS5pWMFq7lPfiXzMZxVvQWE3iS0ZgMAu3Pshqr8qwZJ5Pap9CcR65aM2NrOyfiylR+pFdSTjRdjWFudXOlMgLbSeMdaWViSqjgHiopFKTurcKg57UbipLnBJ+6PSvn0tD6Jx7EzfMREPuL940hzM6xKMnoMGmAkqxU4UDLueB+JrB1LV1dGt7RmEbDDy92HoPQfzrWjQlVlZGVWrGjG73K+rX5upmijb/R0bGR/y0I7/Sk0TTv7S1ECUf6ND8859R2X8T/Ws6R8DjGK7K1tf7J0iK1bieQebOfcjhfwr1a0o4ejyQ3PMpRliat5E93OZ2JGM9gOw7U1oCbRxyCCGFNX513DrVqAeYJFPJZcc14yfKz1pKy06GMzMrkcYxUEjFUck96nEbCdwwztppi86bphBya7OZDauiKBGVDg/eq0uI1OcE0qrs+ZsAdvpTGYTuSBhV7+tQ3cmKdwZ9kTA9+adbQrlpXOFUbmz2FRqpnl4GFBwPc0zUZsj7NEeBzI3rRa7sjW1kUL+4a6n39FHCj0FRQ2+W3nvwBT44TLKM4wKs3E0dlGHIBkIxGvr7n2ro/uxM5uMFeRDcXH9nBVhIFy3JPXYKf/AGrDPFm4XZJjnaOG+lYryPJIZHYlmPJpNxrqVCPLZ7nlSxM3PmWxo3F9C0YS3R1I7vWeGw6HPRs8U0E4phz61rGCUbIzlOUpXZ3oYFI3HRkB+vFOBy+c9KqWUvm6PbyeilT9QasCQDPSvn5xabR7ad4qRmeHjs8X+KSP+nX/ANFmt15Ar7VJMuRx6VzGlXJXxX4mK9W+ykfhGa2kl8iNr6Q84wB6mvR2SfkvyPBrv95L1KmrPPI0ltbkF5ML9Ks2+mDT7SOOUhnAqHSnDXTzSYzjdzWjzMrTN35xSu3oc25QiMY3bRg5qqsEkl6safMrHJPoKncKrE7WH0NWNKhJvy+75SuADVOd1YRfhtlKmNBjHemzWJ+zYDEmr1vDhWx1pl0fKjUYqVSXLdjRmw7jA6MclOc/SoTeNJtjjJOeT7CrCkCXb/e4zWTPMNNkmYgt/CuB61go3egK/Q1zO7SKqkUXMKSjzxwy9QehqnpsitCs9w2ARzgc1R1bUtqMCSFJxGF6tXTQoOcrvYqMeYnv9YSC3aJNrHGGyOPpWMmrRCM/6O7MOmDgVnNvmnHmsQzEfKATgd6uu1mLg2tvkxZyrv1bj07V2yUex24erOjpBkdzLdXdu0rxMbeIgM8aZVSegJrU8OpFHJcQ31k4+1RhreZo8hSATwffj8qsacTJE9pbSxLbuvm3sbDgR8AsPQ49K14fEFhqEcmnWKShET5CRgbF4oT00CTcpc03qZr3yeYguHEnJQsOdoHYURpCcpHIFJZX6YzwD+pqtPAhlKhguBn7v5j8qdCvlklkyN24g+3Ss0Yt3ZdNhvZHdFyZCSVI2kCtmyR0gMO4gA/dBrLtuUkXaORnntmtaHLDJXBY5BHHFaxSBFLxRCg0Myzr5myVScfwg8Zrjta0+006eOKzu1u42jDM4IwDk8cV6BfWxv8ATLy2VQzSRHaD/eHI/WuSe00y/sBLp6LbEOAUnl+VT+PP/wCulNqJtFXVjAntJraGGaWCSJLgboWPO5f8/wBPWrFlqk9m5Ry20HBU9QfpW3c6ldx3EckMkSy26MI0KbliBAB2evA71lX13BfXdzPdMJZZ0UpKn8JAwMgd+OaadyHHoa9q1pqbq4l8ljw4HQ1oxWp8PGIR3JkgkbufWuMsjcoHltgSIxlwK6FLttS0hoYiN/VAexFZVKSkny7mMoW1R00k63Nk0ecNnisiSFGlSFhlmqvp18bi3DEFZF4dT2NX0IWU3RHCr0rhc5cyizJ6vUSTT7eKRRg5FWsllCIMDtirQ2XNqkwX5mHIqC5uYtKthJJgzEcLVcjvcpRS1EuG+wovzDf6VRmvJp4XkRlLL0AqhunvklnmJBfoPQVgC4urG4Ks52E4zWqjzXsC97YbrVzLceIdBMvVfP49PlFWrptl1F79PyqtP5V74p8PoWwD9o3Ef7grpNR8NzTCKW1lWQRkZU9TTUkopPt+rOimny3K8Ns9xJKUOPJhM575AHAH51kavB5oi1KJcwXBwWH8LgfdPpXXaRZyiWYzqU8y1eNQewGBmua8ObrnztKmGbecqWB7MGHT0NbwSSTQtbmlZpcTeFWjQZnMTFP7xQHnH5Vh6NOgu2tphm3uR5bAdvQ/UVfutRnsvFTyxJ5aWrGFEzwUXj9eT+NWJtPtLa9u9YiYCySPzETHIkbgJj68+wotqxplHVJG0r7PZWjSJs/fO54Lsen4YxS3NodVeK8tkwLjAlz0R+5qe7b+39AS+QD7bYjy51XJ3RdmH0/xqbwyiQCFLt/LS9Z1hz34xn8/1oelrFBZ3UU0TxQpiGBtiMT97HGa0rbcOQuf94E/pWfa2TWFt9mkUeajEPjn5sn/AOtW5pyAMNzhvXHP4Vzya5jVbGhBCNiuIyrDnlgtCFbKUshMivywTtVtHVX24JqR445RygPuRmrgr6omRga5pMU8DXdugB6uo4z71xNxC0TEqRt9e5r0dYTBKYBIGV+Crdga4bUIVguZY2IBVjx1PWk5ajRXsmKupx1PSujsJUeYAnA9Frmkb5sqela+nSNDwoBkY8E1EtyrHTFy8/lquIwcFT69fz71cf8Ad2m3GWP61T02IBNzybivLNnv/jVm4uFRDI3ZcgY6ZoXcRJGBDCodstkZPviuf1TV0S5CKQ+EwSD9Qaz77WZN8i7iOvesRP3rB9x680WBI0J9TlnA+bHC8554ra04CSHcpPmADOegrDtNPllCsUyuQOPeuq0y1EagkdM7/TAPFRPXYpGjbx7SrEnpjBq5mqu88Y6FuPpU4PFb0I2RnN3HkAg8Vx/ivRYpLmLU3YLCgAnA6nHT/CuwByKz9XjE2m3MJAO5DgN0p1fdaaEtTjLi8h16zkiiQxzw/vFVudwxzisOIMcIgy7HCjvmlsrh7K6WZeDGckDuO9dHex2mkxf2lBExuJCBED91CRnOK2Xu6dzNq5edDBojWKSq91HF8yK3zAdSK5MXUW/fJuwMZAHp2qC1vJra8S6DEyK25j/e55zW5PoK6nci9tJo47OceY+48x+oxTSUHZieuho+Gp3u7OZZIl8lJR5YK5AOOev4fnXJ6ldy3l/LNMSWLEBT/CAeAKv3mtyJcJDpshhtIDtiA/j9Wb1zS6pbG/ii1S2jJE3EqKPuSd/w6fnQkou4X6GDq97IPCmoW5clZPLyPcOvNPAqXxFp8Vh4RuJLost5Ps8qL+6odclvrUfJppK2hSDAo47HNIAeppRk0hgDjvTic03HPNL9KAEGRRjHrS8mk5PegBTgU00uDjFJQAdOvNKG49fam80q8GgBy8Y5q/azYwpFZ/FSxPgjJqkBtIoPI5oKsCefpVNJnU88j61OH3Lk9atAJIMjpz6VnTIM5HAq3I+ORUEjq49DSYFMjbSdO1SkdqYAR9agBOcHFHQc8mlxikxz70APEhxjH50rEsOgpuAOacp9KAI0GPrUikj3pMZcVbt4FeYDnJ7dapLoBPa27FBKQQFOMVNclRH82OPSpL24SIKi5wBj5ayJpy44JGTWztFWQEbtkkkjNRjrmgn2pQOmKxvqM17ac/Zss3I6VUjdvtW4U1WKR7c0kJIJfp6VupXsjXmujW8w4y2a8dr1NrjAOTXllOozOq72Ox0P/kD2/wDwL/0I1ois7Q/+QPb/APAv/QjWlXO9zIcDRmm0oBJx3qRHV+CbR5b+S6J+SNcY9Sa7WdtqYJ471neGtPXT9HjHV5f3jH61YvZMZzUTdkZzdomPfyrhsk47Vy87gyYXNbmoyIufmJrB37pMhSMVywXNK5nBCSlXUKo5HWoQdoGCVOeCDyD60M2WoHB4r0Y7WKsbsOvLPIg1CJdnSWWI8+x2/wA6dPrVkJD5UE8oxj5jsB/rWDmmFveuZ4Oi5c1jpjiqyjZMsXeoXF3hZHxGOkajCj/H8aps3fPNKTxUTH3rdKMVaKsjJuU3eTuySCRYrmGV1LojqzKO4Bya6yTVbHUJ2kiuUGecSfKR+dcXnmlyD15+tYVqEa2rZ0Ua8qOx2n2q0i+/dwAY5y/+FSf8JBpNqu77Q0jf7CE//Wrhxj0H5Uuaw+oU3uzWeNnLodUPEenz3DlreWIN0c4OfwHSoJNYtt6w2kMs8rHai42gk1zyn3xW9oVn5MLanIPnbKW4Pb+8/wDQUVMPSpR5hUq1WpLkTL1ypASJiGKj5yO7d/y6UxVMmEQdeuKkKknAGWPanTyJY2+FwZm6+1cd76I9hLlViG7mW1i8qMjew59hWcitJwDkZ5P9aljtprmQsil2PJOM4HvTZb63skKx7Z5/VT8in3Pf6VvCD2juZ1asKau2OuJorCLJwZD91fX3NYcssk8pkdiWNLJK88rSSMWdjkmmDOa7aVJQWu55FfESqvyCjJoOc9aO1aHOIaSlNN7VSA6jw3IZdPnhJz5bbsexrR2hXZcdRxWD4YmK3ssH/PWM/pzXROozu9q8TFR5arR7OGlzU0c5pMZbxrr8YHX7Nx/2zrU1CRXkMC8xx9/U1Q0aQJ4r8Tycb8Wyr+MZq9cxi0tzI5yzdvU10SvpbsvyR4mJ/iy9SCO3YukjOQMfdHetuwlAxEU+Wsy35VCeuK0bUEMPQ0k2mYIfcQWzylc7G7EVmZeC+KjIKtgY71o3EUksmFHPSq0y778Meqrg/hQ1qEkdBExUBmGMgVX1UhrUOP4TWJpWrTSX0lpODjkox9K2pQZ9PmXuprVyfLYtaqxjhgTnnjmqV3Mbi7+xJCXZgGLDtVkkDALfOBnFUY75/txSJApx8z96xgmmxKMo7lqUGGHyi+1uhz2FYChLnUrZ7qQxwzyBAS33EyBnnp/+utqf9665+bzEK5z1J/8A1VikK2pwF5IzGoxyMqAO3vXqL3YJG8I20CCwW5vLlbO6URo5xJM2391uADf41qXEiSQQ2N1BbQJFNumuLYDLgAhSKz7yCKWETo6lcnAC4wtVmBiYwwuHi4BfHBB9T2FTqzde6aMd7Fpl3cXNlKtzCoMBWZOHjPY+tXPDFl5enXN4wbbN+5jB7gcsfpmufa2urrd5NtNJtO0pGhIjOehxXdyQmzsbW3jj8pYo1GzrtJ5bPvQ3ZCeplzjLtu3NnPPp2qW0eHYI5IGdvMB+91qGZ4yDtY8Yyc8cnH8qfEUD5Ck5OcikrGRowzhbkPbQiI4IKk5z+dWoRlTkkY6c9/as6LkBTxznI61cjDS5jjznrn0rVArs0Y5MMpUnJwDiuR1RdO07ULvT73TFWCT97FPFIQwB74J5wc8e3Su0htYoBv3Fv8ay/EVnbX2ms88TSyxMGjEY+c8/dH1pS1NY6HMZvNKW2vIESeNFzb3Bj4YDgg9xwaLfVIbq8a7trVYbqRcTwoAVk5+8ueh9f/10kOpXd9c3HnsYbGcGJY/4YsDAUehoe4j020/s+3jkaNJRcfalwXzgDAHbnv8ApWV9Gi09S1ZaLK+qXNzeStEgXzCE6knoCPSsizuljvXgLf62Q/vF447Vs2U893O13vlmPlNDcQovMJycZPfOM1izRRxa2iQqGHDFcEbG7inBu4p2cdDpY4FmAlQASdJAnTNXU05pQDI+yMdc9xVHTpfs6S7MAbuBTru+uTtQghWPWuWvyxqbHHKKRYu9TWCeNIcbE/WsG9uvPdridicjhagv79Lf5DkyH9KxJrySY/NwPQU6dOUt9gjBy3O50me31HTgIxhl4YGq95YQN8rqcetcppuozadOJY87SfmHrXYpqNrf24kV1BI5B9aJx5NipR5djjNXhW38SaL5JIBM2D/wEV2+jXbsoV2I9T61yetRqfFmhIp4/f8A/oArsNNtCDjoO1Z1X7sf66nTh23E3oVGRIFyQCBzzg1wOo2k3hmyVfO3XVxMWWRRgBVx+uSPyrvIkKqB2qLUtOttWs3tblR6q+OUPqKdGqloypxRxGsx/wBuQ22q2qjzpNsNzEvUP0DfQ9PwFXlhivbC88PIwa7twskRzwzqMMv17fn6UzQrX+xddMF26qpm8tCejHHH8xWBLFe6PqzSFmW4t5PmcnqfX3yD+tdcXfUxH6A8yagfJYqDGwYg449/xrX1qylm8VLbhStpBGhhAGAEwDx/wLPNGsyC20uS6SBUm1KXErp0UKASB6En+tXbO4mn0m0FyoEkSFA38RXtn8qmc7K5cNWTLH5sg39Sc5JrXt4Whjyq+3asm0LPKcDv94966C2DFcOeAOtci1NmCEttyPqatB8gLwMjr/8AWqs7KnCKSR3zTVkw+S2WxnHb65rSEuVky1Ql2hiVjHGrM2ASTtAPr7/SuI1hy907MxZjjk12t7OWtXaMqONrN3PsPauK1FFD56kjODTk05aCijNiPzcetblijF9w5xiufQ7Jcn1ro9Mm8pVYfeznNTNFNnT2sC+THHkD+8PXjP8A9aqGt3LQwlCB5jAkqOw7VftiI33MRgjdgfgAP0qPWLNZLWWXBzgk465o6E3PPbgs8nuSM1o6VYvdymNV3Lj5gKpGJjcmMjGDW5YEwvtjbaB1xkZ/Gm2UbFrbp5yW4JwvJOfzJ9/atFU2W5XcOTgY68nj8+tV7WOOzts53SykDceeamM5llX5flDgn64pNCLAPzkYwNuAKlGSB9KqQyBUBJ5Pzf8Aj1Ww4yAe/NaU5qO5LVx4GBVHVG8u1d8ZGMdauF8qaxtcuB9lMQzvY9B6VNWanogSscVZWAjmkvL1WS0iJba3WQ9lH41dGo/8JFHLp80SRSn54Cp/iHY/UVX1wStb22CTGpIIHY9qyYTJFLHKrEOrBh9a6Yq6uZPRiQ28stwsEaFpmbYEHXNdfB9jh05tDW8Vrsqye2884z+lJfyR6fp326K3VLybCebjlcjk+1cgryJKrhvnVshh65zmm/f1FsNkjaKRo3GGU7SD6jius8IQzCG4lbPkMwC56E98Uz+zbbXI01NpfIzzOoGQSOpBrH1LVGuJ1jtGaK1hG2FEOMD1+pob51YPMyvGzTuuotcMS+9Rz2G4Y/SnLz3qfxUy6l4VlvsgXEGyOYd2G4YaqoNNfCNEpGBmkV8daVWprLjp0pFAeVzmm809MY560hGOe1ACg0lGaMjHzUAAOKb2paOKYCA0DNFHekAcdqXdg/ypAKXPemBYinIABxViO4I4IBHtWcCM9acHIOAcU7gaQKM2eaqTMd+Mce9Q+awGMmgzFhhqLgKqs3XpTmGPrTreG4uW2wxs59u1akHh6dsGaVI89h8xq1Tk9ilCT2MX+VGGPCgn6CujXQbVOWn3N9KsHStq/umH5Yq/YSNVRbOYEMrDhG/lTxaTEfcx+NbskEkP+sQketRZTGMY+tHsrbjdG25kLZzMwUgCtgNFZWqBMGQrgsBzU6wIkJlkccj0rMllGSAcinyKKuYysnZFKd5HclySagPoallfLYzUfesW7kjRUkalnHFIq5xgVciiKr70RVwIZPmfrxTC+OBya2BYRvFynNU7jTniG5Dke9bcrWxt7NpFNULED1rzevRhKytgrgivOaTZjM7LQ/8AkD2//Av/AEI1o1m6Gf8AiT2//Av/AEI1o54rN7kEkahm5PA61YsIGvdRgtox/rJAp9cdzVRSQcg811ngax829nvWX5Y12Kf9o/8A1v51DEdyFWGFUUYVQABWPfS8tWrO2ARXP6g5555rlrztoYVndpGNezAN8sRLeu7isrzCAc4BNWLqSTeSMiqE7HK859eadCN3ccVoIOTmnio1PFOzXXcdhSaYTTj0qNqVxpCOelQk5p7UygtCUlLRQMAaUUgoBpgXdPtkvb+C2kmSFXb5nc4AH+PHFdXc3dhuCrcwJDGuyNQ4OFHHQVxQII55pcj/ACK562H9q02zajXdL4UdHc+ILS3QpZRmaQj/AFr5Cj8OprFk1O6lk8xpFDD/AGRz+FVdxzwabmqhh6cdkEsRUm7tlqfUr25i8mW5cxf881+VT9QKrA9OaSnCtFFLYxbb3FFLilFOxQyRmKQ0/HakxQFxhpMe9PI4ptNDLejy+Rq9s5PG8A12cvDMD24xXBRuUlRx1BrvHYMoc/xqG/OvMx8ffUj0sE7xaOX0u8is/FXiV5VySLfavqfLNOeaXULtC/HfZnpWPPOIvFmuD+95H/oFdJoNgWhN1K23d3PpWrXLFS8l+R52Jj+8k/Nl+3iO3AUk1oQWxji82Ztiis2NnuJwsDkBT/DUt/dBwsJZmVfvc1gmc+iRYm1NV3LAuSRjdWVJLKsTLDzNIcDNRtKSfk/Cqst35NxE7E/IwYmqV2yb8zN6PRLpPKlZ18xR0H61pWTlp5LdgMsppYdXsby33JOq5Hc1UijZNTS5STdH0IBq9FJGqt0M+axmF5JcD7iKQRWLpS+bPJI5GXOOvQV1GqXyW1reuAecgYrN0W3ht7JA6As67s9/pSlJRia4iWiRRtnaa3TPE1s5Vs9OP/rVCljItlJd3U9tbRyxvc20LjJkx/CPTOR7mrWqINNvlvYm3W1x8sw/uv8A5/rVO6gDCO4jBmiiGVQknA9B7Zr0YNVIXQJ3Kts8Mru1/LIqlMosI6k9FxUUcpKm2t8skzbRGOWJ6KPxPpVy3nsCtpI7yjUGut0rMNqRp6emTx+v494dJ09p47gWcUU0BPlyIAMZHU+pHXnNQn0NVG5l6PcppdqNMuCIJY+ZXUgh2btu9RwKt3QEynqG4Ix9Oc1yE0cwSeCBxcxmXhd3MoznzD75xW/BJFcoyWl6twq/MY2+Rh+fUUKa2G0QG1fy2YxZweQOhHTNaNvCyMztH8rEnOPXpihBJZsq3EZ3E9B0fuMVahlaAsrmQqzDDMMbTjpVRUTK2pHHao8z5yDnA44Aq0jRxEpGpY4xuA7VXub9fMZTIqqevsfpViymWVSibwAT8xU/N9KrmSKRYt87S8g2jPyg1x+v63JaapamB/nj3GReuM8fniuicTQlpbmdIIw2RJK4AHtisVNQHmXEegwRXc/+turqYfePbaPb8qTkVa5h2shh0qdUkL/vA/lnjPqSKnu7SbTPIngvI3+3RFWx1QEZx9MGnzyjWtYsmuUNtPgx3W1cKWUnB/HgVYmVNStUECqhhRnck8oo+9n8azW5p0Oej8yEysrkh0yGjkxj61owXEM8cIiWYCAZcuc+Y/1HaorOK4tbi3mktopIZ0D4boVHBB+tXZbhbZmuI4Ui3vmK3jX+I9OK1iryMHorEzTfZ5UtgfmVcMw7searyXsk94cq+xOATWsdGmh0qGExhpvMMsz98ntVSOGW0T99ATg1yVH77ZEqco9CrJaRzr57qD7Go/sUUjZEIx9K0V1KwX5Z1KZ4xWjBa211GJLeUEY6ZpXlYyvIzYLWHZgwqfpVj+zLTZkJsPoOKs+Q8RP7vp3FMclugqLNEts5vU41HjDw+q8/8fH/AKAK7+wh4Vu1cPfwZ8aeG1/vfaf/AEWK9Et49kCjvSlBzlFeX6s7KMrUxXGGA7VEx2ydKlkZQ4z2qJyGauapGzdjaN2cV40gd9Rtpd2I9mMD13cn9R+VTAR6y8Ul0hD20ZNxKOd6LyDj+92/Gr/isQvaJC3Fxw8TdiQRkH2x/Ks+yuI9O1Ex3IzFLGY517qG9fcdT9a9Ci7xRhU3I9PuotQ026sr4fJIfPhKjOxx2/pn61ZllAj6Y+n5YqtBYPp00sLHIViI2zwydQw+oI/Wm3LscnIFZ1X71jWmrIfFPtlGMnn1roIb8LFyTj0zXJDduyHwf7tXIpyy7edw6hqyWhbZvvqqK5BXPFQSaqhU4jIHqDWNJIzDah79KYqEk+3Uk0ybl251GR49i/KD0Pc1mTyGaVmIz2H0oZyznnkDIphGcdh0/KmBUkhySQeQav2bumAw5A6Uzy92SB3pUHOVJ3D1pSd0M7LToUMSmVyXk546VreWuzaeR71yVheus0ZkYYAAAFdBJeBcEcirpyWzJkjN1PRo3kN0gUN1I7ZqgLYxyhGBwvMh7A+la8upQrgF1wORWdPqkKjEabsc89M+9LQC1GsqDMuAApGM9M9f8Pxqc3CpEYwfmRAx/wB5qwZtWaQ42jHAOTVRr+RpnOfvdcfSi4HSm53FgMDDD8iMj+tTm8VVyWA289a40Xsw43Nxgdew6fzpTOzMcsc5z1qXuPY6i51lEB8r5iR+VZDymZ/Mdst1/CqSSdeasRdPlqkhDbmNSCsh2pKAhPof4W/OqVtpy6buvL1ldI/9XGG5d/8ACtZY1kjMcnRiKztWcXNtvgfP2d8SIRyO2a1py6Gc0LDqMt7K1nfshjn+7hceWe3SstdMuWv/ALJHGfN34Hp9fpUKFiQqsQeigHkmuu1CW7t9CyB/pBRVkcdR6mtb20M9yCC+0+0i/sdPMaPBjeYfdZj1/DPFcxfWb2V01u4BkU8Y9OxpufL3HdwThcV0dhBaa3py3F4j+Zb/ALtmU43KBmqXuaivc5XWLORPB2oXkj7UkEaRg/x4kXJqIUeMNQOoafcMo2QoFWKMdAu4UdapbFxHe1PB4pmfWlBP4VJQE4NGSe9KADRt4oAaOlLkUoWkZcfShAJzmk6U7/lnx60npTASik5pc8c0BYKMcZpVVmOFBJ+matRadcSDJXaPeqUJPYaTexT6GlVWcgKpJrZh0dOrkmr6aeFG1cJ9BitY4eT3NFSfUwI7CQ8yEIPzNa1nosCp59wGKY4B6t/9atGCygiIYjcw6E1HeSMwxmt40Ix1ZrGmuo6C9SHKJGqRjoFGKDeIXIJ4HrWeq8Emq8kuDjg1anboaXsahuwuMMKvWs6TrjfyO1c5yVzjtUcV1Jbyh0b5hTdXuLmZ15AKNGVyG5JJ6VieQFdi3zKOgzWlY6lDqDbQAswGNp7j1H+FVNRIgdwFAGccUOzVzOtP3TMuZmY4O4Y96oSbu4P1q1LMATgiqruSK5JO5ykRA6d6QA4FKMsfSpEK9M1mkMfAmDk+lW7VGcmRug9KpvIWYIg56H3rYt4/LtwhHOK2hE0pRu7lyNQYxxQMMpBGRTIWPl4z0PanwrvcqDyewrex2GRfWQdTJH1HUV5NXtMgKSMCO1eLVlVjY466s0dhon/IHt/+Bf8AoRrRrO0Q/wDEot/+Bf8AoRrS3nAGBXO9zETrgdz0r1fQdPGmaNBDj5yN8h/2j/nFcL4W0pNU1QGQHyoMO3PU9hXpUjbRWcnYTKV5KVHGc9q5zUZhgg5zW1dy4JOeMVy9/NuBIbv0xXnTlzTOO95FG6kLA/MTiswMSTmrFzNsUEDGaqrk967qUbROiK0JVNOzTF96dWoC5pppSaaaAQ00w08mmE0hoaaSlJptBQ4UtNBpc0wF/Gjn1pM0ZoAXJpM0tGKLgANKKQClFAh4NOBpgpc0hD80hNN3UZoCwGm0ZNJmgYdx+ddZDrGnnS4fMuAkyLsZCDn+VcjuwaQsayrUFVSUjalVlSd4lvTLKDVvFOt3IkPkxGA4I5bKH/CuvljDaeGGVj6KBXnelvOms6m8UrImYQ5B6/Kf/r16BpcrHT1M5+UDIJrCvHlduiMKsnKTb6kiKLC3wq/vZB2qjIxDcjJPUGpJ7lTIXaUEkcKO1U13SE89elc5zSetiVIWlmKkKhHIGea34tJt3j3zRAvjpiuP1m4YaivkuUKKFOD3rc0XxOkgS2v22SDjzegNbKm7XNYxsijrWlfZy7xJiNuw4xWdpWsy2kyQzEuhbGRya7a9ltTAS8qFT6HNZljpULsbiOFYYs8zS8flRGWjTRdON3ZDtSkg+yMkoyZSCFHJzSWmmXcke+YrbQ46twcUNqlvG5i0q3+0TglTcSjIH0pY9Pu7qTzr64eQn+HOFH4UlTi9GdUcLzazZOkmlfPZLG98svEhPT8K4q1vJrB5IsF1UkFT1BBr0K2torfaIkAIOa4bxPbmz8Q3QH3ZT5q49G5P65rso+6tCqtOKiuUso63GlXEMAgSK4cb8r86sOeParth4guLWO2stRhkaFGwbiN+T3BrlVkKvuRtjeo6Veh1B4x5cyLKn95eorRpMwTcS6+oWB1yXUA7LCz7hEgIJXPPI6E0txbxy2813Zxu2mr8wjvPlZTnorA5NU1kspXUqfLYNu5HerV7eXl/bJF1BJYY6E96mUHbQuM11LEOoatZQrPHL5FugysLtvGD9ea2F1J7m0S6udRhjQ8F2TGD7D1rH1XUtMvNGW1FrdxXylfnZRgsPvZOeR17elUFup8M5MConzmKRNyyMOmBU8krDbizobO93mcaXZi6mRDI9xcjAcDptFVY7rW52Ek149ujEFAqgFiPvKoHpQdeDeZI8yiZ4dpO043YB2isqK5u3mSaOJkn3Mzljhcnuo7HFLllYLxQs8Ecgkubq7UTpljbzMWZ26Ee1SPJbaVIVtr3zZTFuV4lxtc4+U+oqeRrSXTzA1s4vS+8TgDdu9WPcU6CX7DZ+THDEinlpJcFt3dh6CrUHYl1FcTVL+9uDas1sAoQbwFwd3U0kEt5ci9ENsI0vAokcjaQB1A9iSc1E2oxrLmR3um9BUFxqM8xIlYQwkglF+9VqHclzfQuT3UNqoAl+0XA42oPlFWPDAF7rUlxcASSQwlkB+6rcVz7SFsrFH5aZzgn5m966jwdABZXlxjLtJ5XtgDP9acnZWQ6S5panTE8HPOevNRSxRyLgg4PfFKcMWI4pC2043HHpWXKmjtuZ1zoqyrlUik5+hrJfSJLCfzLeSSBvRhlTXSl+eB+NPDlhhjuHoahw7EyhGW6Ofj1S5tkP2mIuo6snSrcOo2V1B5oOPrxir8lrbSggrsJ7iqVxosU0BjJBU9xxUOLW5hLCxfwmNciOXx54XMbBlb7V/6KrspNRSORogPmU4rhprQaP4x8NDJKKt4wz/1yrUjuGd2kOdzckVM3ZK39akqLh7rNya+BB+XPtUKaptb54SR6KazmlJXmod/J71lyJ7lc7RJrUxvbmGSGIkR4IVj1Oc4qnqcSSag91bNvjuMSYHVGPUH3qzuyOaECr/CK2g+XQyeruQZKxKrbjgYGew9PpVOaTJPOPrWvgMOlBton+9GpNS4Nu5r7RLQ59n45z9aZ5jK3L81uS6fATkIw+lV5NHRjmOYj2YZpcthqaZTS+jAxIhz6io3uizCKEHBPzE+lTPolwWOx0I/KoH0y7iJPlEn1Bp8oXTJMqJCAQPf1qTcmW5Bz1qo1tJHzIjg+uKXG0gE/jioaKSRZDBRnOKRpwCTwPoKhy2MYJH0puGz6ChIVyUXRRhjuetXTeyP96Qggdax3OZAMmpt3I70+UbZdaUn7zD2qFpckjIqMthe5qNiwA4GKdibkhck5piltzAnmmeaW4IxTtwTmgdhSQR3zUgJH4VWdwDxnrTvNJ6D8aLBYtr+VWY3PIOPwqijnvVmNsVNxWNKLkcmsq7lFlrG9hmGZQsi+orThJ20/UbGKS0jvpgfKgO58Dkj0pwnrYUo6FA2tpog+2M5mccQqw4HvVG01si6/fs8kcrYkRvQ1YmuU162aONDFcRHeqseGBqXTdIS1KyzfPJ1xjgVrKpGC97czUL6IgHh9jdv5suIAcoVPLDtWl9oSwtPs8EaiIAghu+etWmDY3EZB/SsrUmxERv5z0rnVeU2b+ySMHxJaWieFL6aND5g8sgk9P3i8VUBxVjxBIv8AwiF8pILHZ/6GtVwec12Ub8uvczmknoHanAflSdKVfersSPAz3pTjjH403oRzigHk00gHjpSZ9aM8Uwk0WAXAFMJ/OnE7qbtJJ9utNIACsx+WrcMMS4MtRx5PCLz7DJ/KtSDQNRuo/M+yyY/2ht/nWigWrFy0+zKqlUUZ6H1rQ2oMFQOa52XT9RhlVWgcexYACrYudQtAFkg3D25rrpzjszaMkjYKYGe9MLENkjms+O7vZ5l5FvHn5meMt+QAq3czxptZZJCAPveQ3X8q0VSJfOh7Fscrz6VWmViD021YiubK4U7L8RsB925iKZ98jPFW4dHublC8E1pNH03Ry5BocodWJzRz88mwbR1qoi5JLck9K6C68Makx3BIvweiz8LXxd/MeKPA45zWDcb3uHOjFj2s20kc8YqG8hUNgDkV0w8IyLMr+Y3DdlHP5mrE/hWB2Db5wCOSCvNTKUWhc6OHUsjhkbBByMVvGOS801JC/mPjJJ61auvCDB90EzdOjrWdLaX+jybgxeMHMgXsP8KVNvboY1FdaGZMhDEYqMKRwQa12EF86m3kAdv4D1/CmG0mjQ7YiZAe4q/YPoZK+1jM8uR8BQQOM8UCExNgn5z2rUjsruRcSELntV2GwhtlMsp3SepHFONB9TSNNyMq3tjEFlkX6CtVgfIWToGGBWdfXBZuTjn8qvQP5ujA91NaRgk7HRFKLsSQLm3kYdjSQy7Z0INS2g/0SRieGOKzjJtlx6HAquWyLZav/lk3AEg+9eLV7TdSB4RkDOO1eLVliFaxy1uhcg1S8toViim2ovQbFPfPpUn9t6j/AM/H/ji/4UUVzWMC7p/jLX9K8z7Ff+V5mN37mM5x06rVxviN4rbrqxP/AGwi/wDiaKKTinugK8njnxHKMPqRI/64x/8AxNVm8U6y4w16T/2zT/Ciip9nBdEJRS2RE/iHVJBhrrP/AGzX/CkGvamP+Xn/AMhr/hRRVWQxf+Eg1Qf8vX/kNf8ACj/hIdU/5+v/ACGv+FFFMLB/wkOqf8/X/kNf8KP+Eg1T/n6/8hr/AIUUUBYQ6/qZ/wCXn/yGv+FJ/b2pf8/P/kNf8KKKVkAf27qX/Pz/AOQ1/wAKP7c1H/n4/wDHF/wooosgD+3NS/5+P/HF/wAKP7d1L/n4/wDHF/wooosgD+3dS/5+f/HF/wAKP7d1L/n5/wDIa/4UUUWQB/b2pf8APz/5DX/Cj+3tS/5+f/Ia/wCFFFFkAf29qX/Pz/5DX/Cl/t7U/wDn5/8AIa/4UUUWQB/b+p/8/P8A5DX/AAo/t7U/+fn/AMhr/hRRTsAf29qf/Pz/AOQ1/wAKP7e1P/n5/wDIa/4UUUWAT+3tS/5+f/Ia/wCFH9u6l/z8/wDji/4UUUrIA/tzUf8An4/8cX/Cj+3dSxj7T/5DX/CiinYBses38LzOk4DTFTIfLU52jA7cde1XH8Xa7JCsLXx8tegESD+lFFS4Re6CxEvibV1JIvOT1/dp/hUg8W64vS+/8hJ/hRRS9nDsLlXYik8SatLIZHu8sep8tBn9KjOvamwANzkDoNi8fpRRVcq7BZDo/EOqRSK6XXzKcjMan9CKuXfjbxFfAC51EuB0HlIB+QWiilyx7FJtbCw+NvENuoWK/CgdMQR//E1L/wAJ/wCJ/wDoJ/8AkCP/AOJooo5I9h88u4f8J/4nyD/afI/6YR//ABNVL3xZreoSLJdXvmOq7QfJQcenC0UU7ITk3uyt/bupf8/H/kNf8KVde1NTkXOP+2a/4UUUWQgbXtSY5NyM/wDXNf8ACkGu6kBxcn/vhf8ACiimBIviPVkGFvCB6bF/wpV8TauowLvj/rkn+FFFArIB4m1cf8vf5xJ/hQfEursMG8OP+ua/4UUUDsN/4SPVv+fs/wDfC/4UjeINUc5a6yfdF/wooouFhR4i1UDAugPpEg/pTf7e1Pdu+08+vlr/AIUUUAL/AMJBqm7d9q59fLX/AAq7aeN/EVhb+Rbah5cW7dtEEZ59eVoopWQ02tiX/hYHijGP7UP/AH4j/wDiaQ+PvE566mf+/Ef/AMTRRRZD5pdw/wCE+8T4x/aZx/1wj/8AiaUeP/E69NUP/fiP/wCJooosg55dwPj/AMTnrqf/AJAj/wDiaQePvE46an/5Aj/+Jooo5V2Dnl3Kt14s1y9u7a6uL4vNbb/Jby0G3cMNwBzketPXxjry9L//AMhJ/wDE0UUnCL6CuwPjLX266h/5BT/4mj/hMde/5/8A/wAhJ/8AE0UUckewXYf8Jlr/APz/AP8A5BT/AOJpf+Ez1/8A6CH/AJBj/wDiaKKOWPYLsUeNvEI6ah/5Bj/+Jpf+E38Rf9BH/wAgR/8AxNFFPlXYQHxt4iIx/aH/AJBj/wDiaX/hOPEf/QR/8gx//E0UUuWPYLh/wnPiPn/iY9f+mEf/AMTR/wAJx4jzn+0f/IMf/wATRRRyx7AB8c+Iz11EH6wR/wDxNRt4x11xhrxT/wBu8f8A8TRRRyR7DuyJvFOsscm7H4QoP/ZaQ+J9YP8Ay+f+Q0/wooo5I9guxp8SasTn7Xz/ANc0/wAKX/hJtXxj7X/5DT/Ciijkj2C7F/4SfWP+fz/yGn+FNPiXVz1u/wDyGv8AhRRRyR7Bdh/wkerf8/f/AJDX/Cj/AISTVv8An7/8hr/hRRRyR7Bdh/wkerf8/f8A5DT/AAo/4STVv+fv/wAhp/hRRRyx7BdijxNrAGBef+Q0/wAKePFWtDpe/wDkJP8ACiijkj2C7JF8Za+vS/8A/IMf/wATVj/hYHic27251IGJwQyG3iIIP/AaKKXs4dguyhb+JtXtZPMhuwjYxnykP9Ktf8Jt4hzn+0P/ACBH/wDE0UUOnCW6QKTWwHxt4iYYOof+QI//AImq8ninWZfv3mf+2Sf4UUUKnBbJBzPuVbjWb+6gaCafdG2MrsUd89h7U7+3NR/5+P8AyGv+FFFVZILsX+3dS/5+f/HF/wAKP7d1L/n5/wDHF/woop2FcP7c1H/n4/8AHF/wo/tzUv8An4/8cX/CiiiwXD+3dS/5+f8Axxf8KP7d1L/n4/8AHF/woooAP7c1L/n5/wDHF/wpV1/U0OVuef8Armv+FFFAXL8HjjxDa4MF7HGR3W1iB/PbUzfEPxU5y2q5/wC3eL/4miigLgPiF4pH/MTH/gNF/wDE07/hY3isf8xX/wAlov8A4miigd2H/Cx/Fn/QV/8AJeL/AOJpP+Fi+K8Y/tY4/wCuEX/xNFFAXZDL468STACTUQwHrbxf/E1AfF2uFtwvQreqwxr/ACWiigLstx/ELxVEMLq74/2oo2/mtSf8LI8W/wDQW/8AJeL/AOJooosF2VpvHPiS4JM2ps+exiTH5baW08deJLFSttqRjUnJUQxkfkVoop3drBdlj/hY/iw/8xX/AMl4v/iail8f+J5s+ZqQbIwc28XT/vmiikF2ZyeItVjnEyXW2QHcGEacH8qut458RscnUQT/ANcI/wD4miinzPuF2I3jbxCwwb8Ef9cI/wD4mmP4y1+RdrX+R/1xj/8AiaKKfPLuPnl3KzeIdVf711n/ALZr/hUkXinWYYzHHeYU9R5SH+lFFLmfcXM+5IvjDXVTYt9hfTyk/wDiah/4SbVyc/a/f/VJ/hRRT55dx80u48+KtaIwbzj/AK5J/hWNRRScm92Jtvc//9k=", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "from IPython.display import Image, display\n", "\n", "image_folder = \"stylized_training_output\"\n", "images = sorted(os.listdir(image_folder)) # You can use sorted for sequential viewing\n", "\n", "# View first 5 images\n", "for img_file in images[:5]:\n", " display(Image(filename=os.path.join(image_folder, img_file)))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "803408e3", "metadata": {}, "outputs": [], "source": [ "def save_image(tensor, output_path):\n", " tensor = tensor.clone().detach().cpu().squeeze(0)\n", " tensor = (tensor + 1) / 2 \n", " tensor = tensor.clamp(0, 1)\n", " image = transforms.ToPILImage()(tensor)\n", " image.save(output_path)\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "e2487e7e", "metadata": {}, "outputs": [], "source": [ "import torch\n", "from torchvision import transforms\n", "from PIL import Image\n", "from model import TransformerNet # Make sure model.py is in the same folder\n", "\n", "# Use GPU if available\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "# Load model\n", "transformer = TransformerNet().to(device)\n", "transformer.load_state_dict(torch.load(\"final_styled_model.pth\", map_location=device))\n", "transformer.eval()\n", "\n", "# Function to reverse normalization for saving\n", "def denormalize(tensor):\n", " mean = torch.tensor([0.485, 0.456, 0.406]).to(device).view(1, 3, 1, 1)\n", " std = torch.tensor([0.229, 0.224, 0.225]).to(device).view(1, 3, 1, 1)\n", " return tensor * std + mean\n", "\n", "# Save image to file\n", "def save_image(tensor, path):\n", " image = tensor.clone().detach().cpu().squeeze(0)\n", " image = image.clamp(0, 1)\n", " image = transforms.ToPILImage()(image)\n", " image.save(path)\n", "\n", "# Load new image and apply style\n", "def stylize_image(input_path, output_path):\n", " image = Image.open(input_path).convert(\"RGB\")\n", " transform = transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(256),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", " ])\n", " x = transform(image).unsqueeze(0).to(device)\n", "\n", " with torch.no_grad():\n", " y = transformer(x)\n", "\n", " y = denormalize(y)\n", " save_image(y, output_path)\n", "\n", "# Example use\n", "stylize_image(\"Testing6.jpg\", \"stylized_output4.jpg\")\n" ] }, { "cell_type": "code", "execution_count": 26, "id": "bc7b485a", "metadata": {}, "outputs": [ { "data": { "image/png": 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KAdPV0jMkUD0SjGSGJEOzIWY0QWiD0AVHdEbXKHdbZb8V4qyMQ0F9IJLJmphOI9MkFPMEtpgqWSCOhZwNdUq3dRiJMmZygWkqbDaO3gmvnCfvOkqKHJ63lFOEyeFNaZxwswu8ftnwR/vAnIypFR6K4zR6mhzYGsTG8CIEqTEK7wuNNzbBiGaUFLFyTzwZkZZ5Y7SuwYkRgvDy7Z74MDMdZo4fJsZxIseEpoK+9rShZdM5hvtnSg9WHIhHCkgUphP4TmmdZ+sdnSqxQDBBJBB0wy4ofgcJYxszj588cnzPfJqYfveRp24HFrh749l2ntx7ZAqUOCNzfcianGgiuEmZHzNyE1AHORt5LpQpk6bMPAmxCCkb8wwxO6YinA5Q5ojmgkfRbDAbJRYoGeaCTPUfCqIVQg1a39ZSBJIhViBnxNnyHivm6rONFaQIUpYn1oHFgiiIA7IhqSAp06jSqtEr9Cr0KmSBOWWSREq2Gi4rpb4XAk4Vv8CM26D4BLkUpmMmJSipkGMi+4lZEmOJiBl+syG8bHj9Zou/N5pkxMeZw8Gj6mg7x4uNw0TIBnE0slYlJbEQY2YahedPGUvC/bFw+DDDeKDpHduvXvLLn93x7kXHrnGMB2MaEtNpJj4eOdyPHCfheQ6MyYhzIadCehY4CqlVul4IvsqRdCocx8zxFJmHCZEJQygpME+e6VCIzxktE4FMUSWp0TXgMZIVfFvwJeIPI7D9W2X9j1YKIRecrkL7bDyvYpbVfrXl88WROAvUglUBei38qEZztXCXUc9jynnsZeCrXXLeX8exZd/6uV1+R6rLej2h5Xvr3OrndSu2HHsZABY4aZ2frPOxy7SuDzcTxKxaVVdrxS5fOCu0dS6LJpOr83Ce22Wt5/lzUaxyvi7rtbXzpaJUZca6LjOsQMmGuvqCqpxPDwVUq/vsvdIEpXgjBGXTK9uNMDojG9hYX6QkShozRiFZwegovi51NmOORleEoq5CfzGRUiYl43nIlKy82Tj61rPtO252O8Z4og+RTTfRmBAaoQnCLgjJwSDKqXX41tNg9E5pXanxp2LkXDDJoJkm1LnEaGRJ5DgxxcwUO3pVrCJj7HYt96eMCIzHkZQjlg3JDieBxjdsWg92wopiKD54nNV7EWfQUK3LoIqTqhidFFQcITT0G6U1xZwQc+HDi8j4/olyisTnZ+ZjJN0UvIPt3lFigNQyWaIko0jEk/EImjPzqZw98Ir/KzkLKcGUClPJRIMhRcaUmZIwTtXbQwznqtdakpGKka1QYsFygVygVEGurhpVgtbnuRjV5SwoRjFd3u3LsylWsKzLq3ExTESqnCAXJGecq0Kw8dD6+rtXcGJkyuIp29X7ACrVc6lgcYWszQRLNbYSU2YaZ6bpSPGFrIXQBXTf09z0bPoOO0wQjfEY4bmg3rPxEHFEA1eMaTbUGSoGyRhSYZwz40kZVHh8jBx/GCh5ou0bti+3vL3t2XYeVRhnYzoV5iEzDonDkDjOwiF7xlyYl8ucY4FZKAoVYCikBGMuDKfENGVSyohf3m2t98qWe+CdERqjaEUxVuPURcMZBMuEUvgx249WCs8Z0npxcr3JBauuolm1ht3ynCwyzS8S1BaFkJcb6wyyLILfjLwIMcUwuQjQbIJbxiha95lVBeNXPaBVkJXlkfTFKLKet57HlgepXCmFbIs7TI3PVhilWnBu2W/LOVdPw7MIWAGzQi5CMaFGEFalVWMLsoyzrsesvnRu8RTy8mKYSYUYlrWX9XpSYy5e7KwHZbkmQN1/fa2owl3FqrW2CKpUDKf1RS1YtaCKMCXDtVUgFgW0XjcTaINHW8V3SrcRplnQXmi2wv5GkCRMneBzQ5yMOUK0ES0TlgvHtpB6xTIcgxBnoVclekH7sNyfQpkzf/ZNxJyQf9WzV8+rux03tz33j4/c3Lbc3nU8f3ogW+R5ODGVPV4gOCO0Qrhp6XNgYyABYkxMU+T54cRUImqCtFu89zg6pmbLNJ1IcaKLI84rEUdyyjYE2rZFfSbGRzQo6h0+t3Rhw6bt2G89rp0x70GV21cNZazW+TQmtIVQajwsU6+ta5RQhM45ZNewCY6uEawUDiOMn55IU2E8PZNzxChIL7z9pWf7YsPjVrl/yJzygWgjYZPxQRExhqNhRWr8xwei2zA5xyjwlE5MpRIEPsbIODmmKDxPNQ4o3uG6BlNhjoXTkElWn/ZSwHyBVJWONkoI9R1NCvNoNS6wPK8rTlySoq6+cE6MnBcjpBgaVm/bMFvYcmR819BsoN0Ju1v49CDQKbJtUCJW0qLEFPOKeUHyEuErME7Vuy6iWKPQGfmUOI4Dh++e8XtHeBm4+eNXuNuOcBNoTHmeKsGAYcAflalvaFCeKKQsTJMwFMNrjX1MpTCMhfs5M2jh978rfPrmyIc//0Bxkc2rnjc/63nV1fjLc4YpGqdoDLPxPDneJzgVGAo8lsJQqHFIhSKFJMYxZdqUmXO9F1PMxJxI3nBtj6pQVClFafbCxjls8EhM+CKErOhkpFgogB3BeYdrm79bpfBDV61Ch9CpnYVsUuqLtwgut8A3harFTVZm0QVKktU8X/9e5KnUoMAZorFl34qyyGdeSN1vQg3crlY3NZAFqwFVT2hQFcjqSaznZ53LAne5FVqqDxqLcJVlPYuZjxSr5zmf9HNPZQWG7Go951DKcrzY6h0sq1/myoKHnq/TxQ07e0H1ei/zvnKoROo1X60rcddeUIW6ViWTi1HKCt8Z6gEVfKiKBCBmY3Mr9J3gGgid8FLgZis8tZ58EtLgkDYjXng6Zp4fJz7eN0gR5qdMmTLHNvPYBfqcIRdMCo+HkR7DivLDDxOfgtI2wq6rcMtNt2XvA98nI46Jj99/4t+2jl3r8a66OS+2AVGhCQEv8HiaeW8n7ucHRIzSOrqmofPK1DU8x8KoI41knCjeh4p3izASuN0KJSt+mtjtG9q+odvtebnr2XWO/dZx+w58I2jj+eldU1lEQyJ+OOHUcFItuMeYmbIxGoQQ6NvqQSSphkTOystXDS9e7YjHiBajayG0hmuEn70UjvuGbivkv9hShpk0j+Q0EnwgqKtPuRjeCVsN3G9BI8ghQz4yDiNPU+Z4dByHzBRhYkuRGtAOrzpGEfIhM06ZMkQsCOYEfAGpwl6dIL4+X06tKotYzVxz6zNa2Yjk+j5mv7xwWg0SyVbJdw5EcsWBzcgpEnMhFiMruJ0QJkeza8hjwhJonki6PKtZyGiNaw2FaVxA6AzWKWG3WOCWSZ8+4ZuXNN1rXv/xFroaIxkfCk8/GNP9BMcnggSk9OxHZRg9YkpJEFPGeUMVigWe48RzShwt8fGvDzz+5j3jb/+a/k9esdl7Xty2pAGmUyYbeA9jNoYsHJNymgOHMXOYBp5zIM5KmqqXp71hPXx8FFxMJDN86xAnhDbgfKCRaoBnICal6ZX9S+XhqXAcMuNUeD4KY4I8GulgnB6rUef3f3s8Af4jlEJZcO4qe85SvFqrssI/590XSGU55iyDqwRa0J4LzFF3rzgGfP7B9YB/8LecDXTO0eZFUcg5PnE90GrpX2IX12Nez3OVx+W8Jrl8/3qR69+sUM8Chi3rsev1rOeU633r95fjhc+OOf9+NVHjcs7Pr0td71lPyKpHrrwZNdCKmUpeoKUlmC1q54B/SZkZo+2rhpijMmSjb4RNAEyZFUZnNFOD7wMaIabC8yGjJpQ5o5aZonA6VetWrRoU7x8nQhfw3hhOieQdO3NsO+XxydgEZde27DY9Dw8D4zFx/3gg94GuCfjQLWicp4jgnKLOI1pzEuYEbirglE3wdM5xe7MhnPa4kuhCg/eeXLQaNqJs+wAFjk3HftOy3Xds77Z4qwFB8crLly1zNoYodI0HKcQCyZRQIMfMMEw8nSJTMmIxXN/RNQEJnqEoYkLJ0KjQdg3tpuF0qJCDLF5j21amyW72tJsGHwNMSpqM1FZLPfhqdQ4FWhVcUMQ7snOkIszRmKbMNBtTTExRmFNGW0UbR9h6ooFMldyQZjsbUOYBK/WVdLpAFwuLwxlkwxLV0JBq0CFgRc7e8fWzbKWiBrZ4iuJsUQqFnIwcDcuVGegbxXUOTY5cKj0aWVhOVuHPHIycjJIr6cNSjauUWEgpU1KFJsUH/K5lf9cQpdJhpwRxFKajUZ5mcgtjFzjNiTllvAKlGlFBwImSMGZThlI4xsQwTsxzAhG6l1tuXva82AesCPOUicloOiEVSCizecbiGZNxGgtTMvJslMmwJkMwaGE4TkxmNE7w6gmtQ7yAr4yqQkUGxAnmlSDKEAtzFmIpiBpOZYGHBQtgHbD7G+TF37D9aKXQLxDRAjtffkrNR6hcnnIOLtcArJwt+dVKN6vQycVQr0J0DSLLevwiLIsswvgM23AWiFZl4FmwXwtC1mOsPuxVB62WOasWufIGrjFLucQDzjpGztRQZAkSL0K7nEF9lgeXZU18JvwX0lEd82pfXdMSb5EKOazrPI+xCnQu3zuv8/xrPaddLl+9hlcHVcitPjB5PTjXl11XJW1QYiJGIQFOPVNx2GykTngt0DXC6154FCWJQw8BYkNpjJnE/JRxVVzTeGGMhj4nSiMEqTDEw3cD/+CPHX2rDA+RJy+IV7wKv/ntxNsXntufttzud5yejNM08PThAFtP7Bu6vZCKkDWTo/Gy7xiLkCRgYcNpiIypEEXZOiV4eHu75eH0Gk2RbWuEJmBJwGrOyX6n9I3nw7cN282G/a7jxa7h/tkYixAQfvGu5ZtPmb/+PtWEI6+LRajolCoOP098uD8wzQlLxqHfsul7bjc9T3PAiiJF8FYIwRO2LfS6CDxIuRIrvINtqzQ3DToFOAVOB4fzRtMXtp3jPsFmNrrFE1GnRO8ZTRmzMs3KYJFZhRnhdEx0neI7T7it0JabKjU1xlX4GyUsRkMpmFuYRIt7rg6yGnnxsGv2qqKl0qarB5qqzBBBvdRgsdSn2tTOxI2UC3EuxLFQJquy0Quud8jkIDkMjyxsxpxBp0xyhdxVSDllo8xGPBTmOTIcI3GaKWGH7jd0b1r2t57jJKQBkhopKXEU4lOBXWHoCoepMKaEYmR1lUrrFQfMWpicZzDj4RQZrVC6Fv/6HS/+/te8/cWOd68beDLSmJmGRC41lyijRFpOFjlk4zBBnIQ81Xk7zUhfkNaYHgpzH8ibQOg9/TagrspSW0ginhoPKgvN3gVBXDVfEzXnyTeCvxOaznC3Bf8m/wdl/Lr9aKXw82HgvoEnr4wqZ8xaF0jDVoG4aIzK+lkgEhMoFWNHZAnmVuFr2QiL8igLLLRCMIHKrDEqjc1yjQN4hIycBbaVgrK45LKwf6hcaL8IynyGcVhooivfZ5lHqetpkXPsQUSxXDMI6jkv89NieJNq8ekyxjKXIHoWxip1bCvQLBbyCn+t52yE5eWqyoflnCtzatUFZblW1CWgq0IpyzVcLEylQkPrOtcYTymgqd50VWHO1do0q4HZJXGaVAp5zlgulJSZDpUbrqLcDjvyu4Cp510jPMbC+yHz8ePM8TgxT4nN1jNPhgvKZtexbQRXQBIkkWolJuXxhyN/XZ7YdJXX/sd/+po/uvP8ae/5NzthG4S2KPvdLeOdI6ZA+viIM0FK4RgHjoeZkh0iHfFVxjWejXO86Xf8/sMnjqeBD88zXzslNI6dczyGLdlStSihWtGzsds7ghdmCnkeOd4rRNi4hm9/PZKccPOTwH/2sxY/JqZPJ05jU2nNuZDGmSkWoiTu85FP3/zAPIykknkaAv1uy8PLG3TYULSF0HD3tkVKQkmkYeD5/kDoG9p5x/1DvXElQd94vAbyJBz+8hvSV69xzrNrQMbMKPAxF1hIDq4UnIGzjDLjDLyrCaTx+wObFzf0rfHqRgiDkJzwJDBP9Vm1BBLn6lgq+Fnwzlc6aRQ0CRYzTBloFrbC4gWXxUv3QnVDDGtAYjUOSxFkAvNSlcM8UIaWdPSMz76GMWKFqKwIFoVygBKkwlZzIU9CtkIKhWkuNQ/lmHn6YWI6PpLGI/PwjAuF3SbzaivcSo2vWDG8gu4DcmyQH5Q8RvI0knPDnDyuBIoaftNgTrCGmhuQCqXU4LLmzOZFR/eLO375J3f8o5+3/OM3nr+aZp5y5HQYsJMSXu7AeaLAkFvGSZifM+nDhE3VKKMVXDY0FuJ9xpqAbxy3LxtuuprvFZPx+LzESB3sW+Eww3EwTsfMeMpMYyGNIEsCsW/gZ79QNl2hbz7DRf6/VwqrPK2Zy5wt69UsPbMrBUTkytpeGUd1vy2DyZVwPKMaXDyINQ69wuyyHLuOo1frk8VtMGzJYK7fcWcFJItnu9rT17/XiehnWM56FJ+fZ90rsgSR64uwrkvMrkZevmtnJ6cqtyXqbddj22W91/ER+Lw4lTvPpSpHFkiveuFXE6208tUxusybGrjOVl15t3ooUpXE6h2xnl+suvguU0qhRDg9TTRVnrF/WUsm3DTCvRfSYu3VzOx6t3IRhlOFj70Ju32gcSAlc3joOA2RkhJdA/cPY828jIUueLw6xgjSKq9ve3be8c1ck+TiZBXSMEeOwjxWrdvtwPfVe9nsNvjQcPiY+bcxse/hZqds2sCUlcMx0RuMc2EcModhptsqJhnVwjxFVD1TKqgaOWbefxf5bguPz5E4JH7z3YjzQi5l8cIUUU8jLU3TkeZMmiO5ZE7DQPxYcCnR9jtap5gFcimkkslkcp7IcabMmXzFytluHW1bvYAiRsyJcYo8PIw8GLjJ4TcO8Y5hzqRcKqUzFixB4z2haWkax/TK1XyVY+Lp9zNDdJRjoQnK5mVDpiz/FNWMSsHnjIsZEUVjla5qgraC66p3k/PiCcjl+V/fd7KtCNDy7NYsc4rhO0XUKDlzekqMpsxTJUWQclUEucoaUUF7T+irdWy5cPqUalB7SqQ8Mz+fyPGIlCP7d3s2W8ENiYffCUdTpiLMQyZPNR/DvCBtQVyGOHK8dxTf0TbC9iuBphqbj8+Z+6fE8zEjTtACTeu4fdnzy68aXmwdKcM8FawkhMzhOSEhEb0yFCE7TwkLXNTWiyRm+FDzloJBTpBypRNPueaCAcxJmGJFbKRUqPd4NI4nw0qhlESKiflgmHO4Rui3jsYLzhTmH1fq7kcrhZMqabHM3cJsMqnMIV1wHFkUwmrCr6wcM8FpFdDnfIErRbDCG2sceFUAxa6EonwuIM/pcovHslrDbsWUVouZ6hHoMsZq+dSxzy7BWRnVpBc5n2f1AMqV4F6ViluVApe4hWpV/KswXtcp1Od6Pc/5/La44GWBkhYFCAuNd1UnUo2xssRC9Grtdd6rcpara1jnIut5ZUkcMqmspCVDunLRdWFw1QFlwSPVwDVgMZNTZjo4nj2YE263yq2HrodveyHFQpxKtXpUMVPiZIzHmjTVBqXdBDadIhjPj4X5+IhNma6B734YeHjKvN9nfrZpcCocx1rO4Od3G3YvNxxHz/HjgTQm2r4mjUWMYQB/qrRVaeoLuLnbcWNweJ/49lnZ7OF/smnZNAGblPdDhQ/nOTOOxoePI9sXSuirMpyniIljLIm2g+Ep8cPvT/y7rTAdM3HK/Ou/OuIb8EHYBIGgeO/pxDHfRAzHOCdolXFOHO5HVAsvG0/vWgo1wBqzVaacTZQ8VSaTKG4jbHthvlHajcO1DlpPkcI4z3z3/shuLtjG4/cN0juOQ6oQypyY56rM232gu91QJBBjJiclPUU+PI8UCWRRukbx7zrilIhTZPYO1YRawh8nXGoQHCUpMs2oc7i+JWyFOICdbClZU59JV+yMAFgxpBK2aoUA0/pC5ILfecQbOWeeHxKzKSnVWIPNacU5IRZk53E7R9d5ZKrexPP3kWZT3ZvMSByOkI+E9sSrn79mt3VwiHz/3hi2nqlRTmMkPc3YOEMDbgOuyUgaef4o5FYo24D2AqG+Lz98SLx/mnkcZrRT1IzGKXc3Lf/gjacJytNYGE8JLKMucTwkJpeYg2c0R/Ke0oJtDJlrrodIjdu1rmb/lyJL3EN4HjkLnjnVDOZiVutwZePxqfB0Kpgzcp5Ic2R6LLBp6IOy3QuShTk64o9Dj368UvirviWIJyBspN4kW2ofrdROuLCN5Ep4IpX5sgrqSwCYBXq5CNH1eBM7C+3z2LoK9SpMVyy+iJ29CZWFFSQLDLTEEwQWvOUCU51Fv8hakohzEtkihcsqJFdBu3g/BcNUzl6RiVQYpqxCfQmocgWNXQlolQpHnde9ZlQjFK3CSmRlBi3XkM+VDAvsZstDI0ueAqLn9dT9LMqsjg2Lx8KqiBRbclDWfPNzJrkIJgVzGQsJcTOlwHQ0fvex8L0HSuF5KkjXEIpnjopztY6UBk/OizWgtZZS3zn6XnjX3PDwe0iHI+P8SJNq+ZMYEmkL86kwHQv5KOjXjtc3Hv9yiz4L6TBy/HjA7T3dTeAnNy04RyzGD59mDoeZsnK4Z0GTI/jAmOH7o/F8X/j4bWH7D1puXgdef2XMBlMceP5w4uPvP5G8o91veZlf8DifeDyeeP74zF9+E+n7QPfK8ftffyAlQ4Py9//hnqbbUFxgGATCLfvXN7x8945/9ee/I4/PpDkRGRjmI01q2NoW7xaFGQKlGTmmZ7799on+bscNgbwNRC+U4JGmwWuipMg8jHy8f+TleEJ7D8cG+o5pnjk+n5hOzxTLuEZofEPjGmgaXr3TykxJwpQcU6plTnIRdDRkMmQGO0XSeETLTLcN9Ld7XNMwJyG/P5KnBMOEWYtNpUIhG7vAR7qw3QTKmuFsgjmt76XzS0wwMsXCbJkyzszUekvEUt+xRnEWQAKuEdxSkcCsVGr4lImxUldzmsDN+CbT7x2v7jy7m0DTNqQkHMbC8VPi8PsT06cDJZ3QLhJaI7SFpjE23Z79XWD/OnD3RhkPxvNz5v6HgVN8ZowTwzHiXGTbt7zceYITOq1U6ds2k2dj9AYyVgWdA0/JOByEeRAsOQgR1WrI3OwDrSt03tFuSi2/c8oMz6XGrUKFdrU30mRMU+H5NPN0SjwPmafTyOFxZDqVGuMLmVkLj0NhVoeZQP5x4v5HK4X2ShidE7bWIO3l17MFL4swutiey7bCGleZWCtycvnr8pv+TftXC/nsm9p60rr464wyuVIq18Hay8eLULU/OH5RBnI5zYWZtAjh5YKsnso6nizKiLPqW78nZ0jJrsa+8rgvAn4ZSPg8WC+rkjOWPIrlrGrnz85B6POxq0d0NRVbkgkFTGtc4exJnZMJK/BWskGp1DhTSCnDaaK855wDEaeMOsU3gsu6FPgDH8CZkabKyjk9F/qt0nTCZqtMu7bWUHqembMt1qVxioU0ZKbnRB4TH/uAa+DnN45f7ytL5PhgOE20vbLZV/x/GjNPp5k4TTw9joxDYte19NrSJ4cvDZKNNBWOz4n7g6c4ZbdRNjtPetT6Qp9mSuiQBoaxMMzGMBmnU6R8GJlvjbgPPBwGpiEiAtsXgplj3wu9OZptoG+U2174ze/uiEkp2ZFzpiyF5cQ5mral7Xt831G8kFLi4f0DOdVMYt/WuJUlwYtDmq4aPTkTx8ikxkhhcoAK8zQzTwPFIqKCU0doFK+KiGO/8yRfiAkkGmXIlKS1LIYZYhksIVqVqgM2L3e8/eMNYdswxmqwTE+RdIxYozWJTGpGs7G8h1LOz6L69RlcrSNBgqLeL/lBYCmTMmR11eCbK+REUFTrtVrfuZIrwGVSufjpmKAkxBXCTc+mdbzYz3z1esf2riP0nmMy/IcKRcVDpswRXMHfdYRmou0c3bbj5lXL/kXDzStP1wqn58JpzpxOA8c8MkwT6XGiaxrarbDfGJZqmRimmiTmluS/FAuzS4wpMsyBeKBmnSdDvCO0QtcLu43SBqF1Dh/q8zHNxuNjYirgW8G3gs2FeUxMY2I4TgylEOfE88eB4TiRkmBti7aCOCFmI5WCaw2//UwS/3u3H60UdqUmViwldBYBfLHyF5l3ue9c4JZ1KutPMf5GOqVcHfM3fXjRI+skzj+u9q+/r0KNzxTEtQJaYSqTq4zsFa05W+CXdcjVl6+nX5/TC/H1Mr5cgFW58noWZSlXA1yyvdd9clFQssQvPrsYnJlaZleCH5Z6TuuRch7uem1reQurRlsNaJ89qCs/ymqNJVsom0YhpUKJxjTJEl+0itup4rzVYnu5Moy8GiUs1LtkDM+J4UZpN8KmF0LryW3LfOiIZcKbkSicopCnxDwkyinxfChsbo1/cuf44cZzP3iO34KTSBGl3RtkmMfE6XkmlZnD/Ymnp4ny0rB+YDc58txjsZDnwniMPDw5cB7x0PWO4ahIljpfau2ZcSrEpMxJaqLew0wWIXvhME5Mx+qVfP+DJ/gGbh1hE9h1gc3Gc7t19LsbxgnyDNP0TDYlLTQ83wSarsW3DVEyKSbGp0fUOpwHt3UVwoilsolDV2MYRaqAaY1SjJJAciHnRE4JJKPeI1oz050qoopvHVG1BjU146LViqII0QpqGbGEag08BefYvtzy1R/19PuGY4TpUThKZMwzqYEsVTEUubi0qxFmKrW0xZonI8uT7hRtFIs1JlFyZo4sXq5ANFxXPU5xggZXi5mlQiZTLFc4NUMeM1IKYSO0dz27TcPLm8TbN1v2L1pc7/lwKrRHcBTyUIW2tOBuW5qSabvAZr9l/6bj5kVgd+vwbimfMWfGaWKIE+M0kY8T/mVLvxV2vVHmUuHeqZbw11wgGyVbLY1BZjw50nGh3poRto7Q1Xdh29e6R2GpMJCLMEVIT5Ex1/ygplc0ZeKcGIfE8SmSXCGWzPgcmcdEEYduPS4o6mrsULQQnNFt/44zmn81DrxvhAfviL5mEq5M00pDrRJ3Db6uzBgQ1gT4c+6VLJWrreJjekXvXPPBsDrGlRj8THB9Lpz1SpguuDk1PqBLme1aXVTOWcRupctyCUhDZSut9f1WDH+lqa7K4xLvqH+tLKM1ae+svLSurVAzOt0aaBdDRRcqL2d3YY3HWFm9hQpL1WuyZHjLKujP7toCm11OfAnwr/Wj6knc6tmsF8td3L+cOcNVslS3BcNyzSovUl/ytBTNK6lgqXLNVQVt3JLcBH0LxylCVCxWr0dzgjFzGBLONqShZbzzaBIkOHzfEcdamuHTU6TEgpsiapFeO7TUwmuvAvyDn3qCD/z2Lzz2/YF8SIjWZK5pTKQxEZkpFkEiwjOH7xPlOPLfbgJdEU4PJ+anD9z/5Ybpsef0dsMvXnq2rSPtW272LzjFjEQjD9A0Hf1e6W+Fp4d7Up44ToXkjOwzaYx88+sfyI+Z05vI7h95gm8pInx/gM3NlpwzlJmHx2cmiagbmeb6XARfufhxGoljIh4M1UzWO2Yp0AUeH08cTyNBG3ABFxq8bXnx6oa3Nw2vN47ZCf7oKXNm10aKc+ADXevJ9SEhNB5yJifDFdCUkTljY0ZjwpVE0Awu0971bPeed79q+U//qOF273mIwvG7ghbBx6V6b6wYt0UuRqKrQlykxqBkpR2aLgmMWiHeqTKscoJySmS3PNfFUA1VKSAVjpoyDBHTglnCciaPBRki6hxhc8PdV453r4xffQX/5Jdb7vaBtnGoKHESjo+FT6mQgkd7odt5dvGWV3cdP/ujPe9+1tK3nsYrcc7M40wcRqacmB5OxGlGi+P2ref1G8e7nWFDQkqhjZFdGTmY8WyO4F0tajcV0qeZ/CRYUfAOvW3wi6ew3VADwmLMozJMsb5jVsgfY4UAN0rnEyXV2lbjISKtUrzStQ1OPVkcpg2Nerw4ggidN3oS23HixyQr/GilMDhHdlIxsCUJhcX6XO3KtZBbLdOwBp4vgmqNc6xMn8pEkIu1v+JFy7NTypUA43OY5TMre/nu6hucZR5LldTln6xjL8roM9t7oYfmcoHDkCXpEj4DwUxq4HRNgDvPaRG4a/2k1cpfWUYX6/1yrLEcf9ZztVZJoVIDzxAVNXC3XiZhYXNYnXsul3OaClrWmAvnIDtYLRuylO5Y2VLrxb9mK6G6QIOLV2KVvlpyWZTWUiZ5eQaSKS4VlIKzjCsJqAwN55SkGXyC5IgxMTwr0zHTbgTvDN85xiiVinifKAk6UfquZRM8WSrzYjZ4FQS7DfzmT/f8/l+OQEHKRLPtaU1osxKPnhdf33DnChZn7r8dGT5E3F803Nx2pGFichNWCjZG7D7yezytKL737N/dYZ8iEc9zUnY3DdvGc4PjmIwpzaRjokhPbGpGqpxmnueMP4788HFAm5auE5yrgeTJKvvlKWXcYWLKgfePMwrELBgtJU6VatlmxpxIx4GBB8w3nE4j42mC0NM2W9pNz93LPS9fbXh95/l672oF2EYgZcY4YlJhlxAgLuVpvNRSFaogUq9Pno00CmXMqGS8FASlaYy2gdaM57EwifHhaMxPNe4QXE2Zj4CkSg1fi2nVZ7EmWtViQtXAcVAznwuUmcoXX2NxWo0SE8At8bC8lrcplQabM1ISaAZX0Aacb2j6wO1XHV//3PP1XeHr28xdL2wXPL7fCZudsNk5wq6FeMIJdNrQbR0vv+r41T/o+eWd53EovH9OzMdEmiecn9EuA4pKQ/9yw+3tlt22IbiCjTMqmZZI1wsRT/KOH0z5/mNDTrXvCqaVNdfXfg1BhCYZXTRaqow6zVahvGzkkpjnjB0LPBmdr7Wi1ClIQJ3HNcqrr3tSLqQonE7hXEhyt1GCGM48ZeJHbT9aKRx8dTnR+qUVcijCJQC7egdXguxain8m0BerfY1FLLsuP/8QYrr6PtfWMjXAuia5VYErnwnTszV+NR3j6u/VgPmDfRXTr3vPHsCVh/LZ8dfr/MyLuYofnA1zOa+zLHSjtSLs+r3z6v7gnGUZ8frS1HVevsvV79d5DutsRFaFfFF15wog5/nJOSNbdLHyzDi7UQCuBrSRJf9iMRZ0gSAwQYvDecE5apDQ1TFzLoyHmgQYWui9VA59LJRjJHhBWkfTemiVZDBOhQ/JeOOFF73jFz/pefy2J6aId9D31fIcs+c4KJu9I2yU4WDYdwfmOXJ8OrG7DWgr+BtPkcwcR+yQMAncblq6pqG/2TAMkRSFOQvSOELj6LKi/UQZYI5L/auiFHH4JpAdTCXz9DTSbxIpezZbJZmQqHWW5mxISRQmDmOib2qCiGs6xI8VenFGKpk0jgwJTAJzTMSUEN/QbDb02y2b3ZZ209BvHTd7RyqV2nk4doTnWsVLnBJ0oS1S6r1wSwsODErCFkjQUgGtnyNCEyA0giuF51NBYubTE8RTgWT4molYiQVXsauz16kLN89YPOG1UGS1nmqejFwQACdni0n0AhFbAVsL9VmhUj1qGQ7xims87a5h97Lh9qXjdp/Z9gVPhqKk4pCl0GNoBNfU2lxOoEHp9i03L1q+eh142ytTMqacGI8jOU4IM+oKTgTvPe2+Z7NtaIODkohDoviC+kwfYFOUrQnbvcM91Z4utlQWFKdII/W9AFwGmUv19lVqKfqlrloyI8YKB6aSyL7QtJ6ma2i8Q33AN55uq+RszBOkqcYCvQpNU7PA02zM099xoPm3fUcQR2NCq+UMlYzLTZcV7jC7sI+Wm1+t4evs4FVQVhxdr6z9K/zoXJjuLN0Xqaq6jrGkvMP5gdTl80oztUsim3Du5LZ6C9d0V7vad/EU1nArS0XG9fjKbBJdWVbyWeG7Wm9ILus8B4TX8et6cqFCTCzKxxbIx63XcKGeLlpmTUwDPiu2J+WiQGrsoEJdLBDXmv9hUtlHkkEpy9jLepa0fqEGmm1NwFse1HotK3UWA3PgWndRfAt7pWRD1/GXhijOO5qNhwBeHb5xgJKOkeOgtcTO1pjnwjwk4uFE2zoa5yiLezxPxj2Z/+oA/8Xe865V/kc/6znZKx4PEzoOvLpr6CO4tnB8PBIcBC/4fcNupzjJNHeFn/9qhzrP+09bHn7zkeHwzOHTJ8anhvLqhv3tjs32lsNNZh4hJkdCsCD4veC2Dq8BP2WefvMeWxg++59uapKZwP3HI2o77LXn5V1bu5OpQ53H8DUvIY3E2dhsldB7tvmWEgwdT5T5yHQYSGMkzSPJjCIBcw3dtqO727G93dHvW6JTkihd42g8pNzw6djQ+EozFjVaNbJGsqu9AmpzJlA1KBOkhERDSkakAQIE6LaOzdZh48z770dS8TzdG9NzTeV0W4c5jztlpAWSnD3O2gyrvlimxmqi1HpgtvThqBLCnMdUa+zBLW99KbX6qlHjBiUjOSElLiU4rLrZQSsE9NLz4uvA/k5oQ8YscTpkjtEYvfL4rJWK7WwpNKm4Yvhi3P6059XXLe+2nq0H9casmWk4Mk8DaY74Utg0UMSxud1ws2kJUjg+DYynE6537PYet6k14hTHdnnmRQRVJXcOloZGrhPUCjYVppRqZzov5Knydus76Cl2IuWZNM9MU0JKj3cdfhvwXUPoA/vb2jFyGozTg5GWBllJ4FOGaVTmpx8h6PmPUAqbXBC9NLypQsrOsM3qLi425lkolrM1umIocmEf2dV+LslTdrWPZbx1DFkk+MoyMtYaRwuoYnJppvMH5vQlFLwCMKuwXKa2RmGvZnVxCf7A/L+a3aqvPrPg7fpHVWbXweD/4fFy/nuFpFYF8odeydkjWq+W1JyQszcllQp7mecK79n5VOdieouylLKccFFRayZ5zVBfiLoCuAssZaYXr0gL6gQtAjN43+BbR7MLmPhajM/XngquqQ/w8b4SfnOuCThxNoo5XLPFtz2oZ4qOT0+Rrim0KE9/rfzm5w35xvNV8PzsZUMXCu9/mGrwzsNuB83GcTiN5FPhFz+/oX/RUjYZ/2rDz1407PqGr14E/pzCp/uGfN+i00RMwulUaDeO7auAi8L9CT7+EBlL5qlEnk5HppSYLDGYIMURkpBp0TbgXO2zQNeQvWPMQhs8fdcxbzN+15CHiOGxTmi3nqZx7FQofgcHx/AAxSs5R6ImUl7gWXVYs6WEQHGV1pwGY26MKVevLi8K2oylhHgBf2JOoSaLuUzACJLwmvA+4puCbwVxAW2a+m/juXkB+x1sGqHkiE2RdMjEMSPeQ9fWzGRvtfmOY0k0A6Occ280yMI/rziRrUbV0sjmbDE6rccCJDkbidVerHWdRD1IrJTVUvBe6G49mztP34M3oynGVgr+FBlHGE3Jjx73aDRjxlOQUOi2wpuvNvzxTwJvX1RK8zeT8ZRrwqJ4Y5wyz88zORn97ZZu1/P2T/a8vnV0ccQeIzZNlMYRxZgG5fgcORwTwxjQsgT6u8rKs0Zwt1KZedmQXJjjDL4WHa3p1lUeuiAE6dHoCSkQrNC2De3G44OjDUrwUuMW0Wr59k4gCfMM9/fGw1iIYyIPCej427YfrRTuUmJySlyap5wTza7+rbi0rAJ+lbGr5bBs9tl/a5XQhZGwRmlXzH8Nzp6/bJeAMVeQyhlrYWHK2NmUrgLx4mys+1bv5Axh2ZXHcjaB64+zl7B8tjLr1nl/rgxWLWN/QDu9DCzYWS+eHYmrsVk9kLOaWOcnZ0F8Pt/qel9dozWj+RxjWYW3yZIweKUYr449b1eaaC1dgoC4SjKoPMDV06j7nUo1HMzhG6VpHW3nmSZXG65kwfsKJ5Ulb0GlKvU4lVrTRh2hV5quRVDmKOS5Zod6LeSnzMeHTJCG/Z3SBaF1lbM+TXOl+bkKSclUsJzpGyW0iqSFwpkLO4UXW8/XrzdsG+XQe57eP+KKqyUeUPqNx2XhOWaePkSO48QhT4zHI1MuzLbmk9RnLg1G6RUXArubju0+EDpHfd+VEBwhBILzJElLHkVNr6w9LBTvPE4djjXYv3jDycAL4GqLWpGlUmZlvlispAANWp/VUovVlZwpUmCu7CVR0JTQUnAl1eYrSwzBq6uslcbhuoDfN3Qbo2kN55cS8jmTpol4ykhToHU1YLxQMk3d1YvGWutleUaNtdfHGQFYrX25khVrnZnzu3R5ecXVrnRSUo3/raVvXO0U2KghaYGFGnA1PbjSRYcCM7WZFKUmHG6UmxvP252yCzDFwikW5mwEJ2ijtTfDKcIIzX7L/rbjFz/puAnAMRKPNUAcnNB4Bwni0kdhGl3tZGhUT3upEqALywgzLC9NrvKaxCoV7lAQNbw1NYaQHIFCExzBa/U+pEKAcSjEZMRELUWfIEVIB2M+FkpKWBn5MduPVgo/HUfet8pD8CQHklfBeFWzh0tmsazBTOrNzldwzZkYZUv9n8VYKKzCbIE+hLPVf4amFjhk6QB4UT5cYJxVoJlVaOY8jzU4XBb20eo7rEqrLJDPKq2tCsQFQamZx8t5KJxhmbWXgVEhIbf8vsYh1oxOvT7ned7rHO1q3otBpeu1Wj4o5xje2bsRqwJW7TL2Odptl7msx66emlotl1CfS1nmx+IlXOkE48wQUOVc/rsUKNMSy3GVStf4SlKPNPhOaDeOzcYzD4bNYBM4U4IIxVVmWFii4NO0MJ28Z7cL7PYd6QTTsVTXnUJHQfOR77/xjKeOfhNQMzRnLM48z4V209L2LX1vTHONbW9aITiwOfH44RO//uNXpOD55b7hH7/r4W1HSnv+7/99YbjP5Enxpmx7RxLh4VSYHwaODweOw4FTfGI2iFYpuJqrhzR/msm+xTWen/x8x91tg5hnGsDtlKb1dI2nwxNLIaUIxxmbIsXXZ0rmgkbDp4K3RMoZicCclxvgMSoFNUdjPGWKS8hUC+y1Iniq9VlmyDmTZSbHTIyK8wk7TWjMuJgIaaLJkWxLZz8PvvP4bYu/aatSaAzRWJk+2ZiGmfFphpBIWvHxeKw0T2taLsYeS/XL1fut7T9FpT6vuQpESRmkZsCzUG0ROfcFP3vjC7dfvcFcy21YBGmtFl6k0OeCnEA3Rr9VXMpoKjAZFittt2TDk+k3gdsbx6uXyk83Qi9wOGY+TRnM2AXh2AdiSoxPA/LkaF8pr14F/vkftfhcOD0E3g8BNzn2fcPdZsN0TOQ5MR6N01jPqRl0LmeSjbNKViFDioXkpPbFSEIOWtmMCuY8jQhiHimFhoRzgq9p4bXPdRIOT7kmiKOYOCZgHmH+wYiPGe8mmub0o2T9j2+y4x1RBRXDLRJ5WdMFDpJa5sEw4iL5zyweuWTkVglT7WCvwpw5l8BYOyqJVKbJaiUIdhbMxailvBc6kno5C2FbvltjACsts457ttOvEudWD6euxyqNdc2BwJBiC1RSDzvTWJfEMBWtr+kifL3ZGTJbFYwu2dcplfNJy1LrwwwScvae16Bz4cKEujKtaqE75Mz6OXsccgGfzptV9pWuVhgLRVeEtFy7urar1p5LkGal39aGSPW1XGM/slzPEuSsSKwIOUPJSu5aDgkOz4KdBKLUOIYK6bm+ID7AT18FHu9rSYcmJ+Zs5ATPU6HbKrd3jjfvlE/3gU8/DHz3fuR1Z9zdKc+TcOpnXm4FiYWudYynicMx8jAP3L4KdNtqHR/yzM3bHdp1fPx3J54eB069Z7cP/MX9wE3reNM7Xu1amp2ncQHZB55NOM7GXSf89CcN3TZgnzLHH47EOWOxFjF789NXvHr3kje3Gz7+/olP3z3zbxP8ya8amsZxmoQ/+Zmj4NjsAt/8eS3AEsuJ8fG35N1bWrfnj3cdf/H7I9PhSDMNvNp5pm1giPD4MTJlJcVcu5mVqq2FwsOnJ/oIL/cbTmnHKRntvuMXf/oTjuPIcTzx8fEDToxGCo2d2HslWqbtIxYSIVcMPI/1oXEF3rzwxFw5+tNc2V/TCBIa9IUnzsL8kJFmIgdHeOsY30+wPM9+rUBgYBFcs3QIEyhZaj2tUvABzAqlCNk8JMVUwSu+cxAqoSG+Bxz17TglNM2IJVox2iejJdEAm1+0vNg7vn7Z88MPI/fJ8z4JT8BghVgKLsGNRn7aFf6z10oZZ56GwhATYxOYMU6p8G9+mLh/LAwz3OwiX7Uz72RmO03cbQT3JvDL25d8et4RJ+PbUfh4KHwnxkNnPB4LY6wwXmi0EjY0I7MRo5BTYU6Cu+3Yvg40O880CkwGs5GfCy5C42vcZL8PYJkSM08/nBgeQRsInUdawdRxSi1PB2E8CHGGYk1tstP3f7ug5z9CKXwIgewcRaQ2s18EXz7LoSsT/W/YznvPwq/+Z3DmbApwSXCQJaELFlfkPFZZsJQVilxLOlyfYxV2n1Far/9bDj4zIRbMxukq+RdYaBm7sFZ5vYylywo+g10uhyDYWVGx8PzPR9pyBeWCVp1DDlJhCfsfDriqr/N1OV/6czGoS2mBqiguJTPQ9bp8fpdq+fBSPYb1mqwe/KLsr6oiURlJi6K2xTGR2p+hqGBtLVNgVj0nlcpmUVfv1WoAhEbOt1Yp+MXBKdlIRTAnuE4JO48eGiwa9AUaT1HPnITjVCtL5piIcUK6QN+1fH2347vHE5/KhDNlu9vgNPO0T9VIKEbAFmOh1CYnTvBae1PjjcZq6fCbrTC/8CQJPM0t3ZMjl9oe0ZVCGxzbXcvNbcfh/VDLJh8n4pxqYLkIoYGuUW6awK/f7hCODM8jzM8Mz4HGZTbb1/QNzK2SoqeEUhPpcvXEcqlzlRSxnGpLUIF5PjIMxvORivGrp+scvumY1ZAUKXPEZijeaJ2w8ULS2lVv19Wi6QVhmgvZKrMoLT2EUzQ0LUQCtMYSmsocynHJ0A1CdlDkEs0y1fVBrxn3lMV2XIHRsmRPG1glH7A+t+vztzz0ZvV5QAXRGjsUNVQL3idaSbTiaz/vG+Fur9xtlIe2QaIjT7V8+/GQOT5Fcpppe2G/d7zcKLMlSk5InpmycMqF5znzNCbGnDFndG3NVt83BZ9mOjwhKBICTyaMVj2DZyscxZickTTVctfRICZYelZLUbI4SqkU79G0GmAFTqPhplJhLgNEkSCE3hE6oNSujOpGTDMFYy4zcRDm4jgNxviszKMjmVA2Sm6l1s36EduP77wWAq04WgS30F3ODemvhMhiN15984xzsJZdOLuDi9CRpX7Ssv71aeA656C6IOUPdM5i518pjIuUXMaWy/4Vglq/ep72lWB2i1tmZxyzJp6dSz+sA54xqStlc74mi7pbhzjrucu5bXENPqtuuga5rwIYxoVeu5SOucBHq2LVVUhzYWWpXeCfhR4tSyJfKXY5/3KmctEi60Vc7g/nchrr36sSkvOhV8frYiIubRixWnd/hQjF1/EyVdCtk1a1RSFXBZyLkUzIKriN0twGzNWicL6RWpAMxzgmbK5lq8s8sNt67m47fvnihqcx8c1xpsGz325pG+O7u4nglLAo+S5Ur2fKmYrZ1cCsWG1U3wfgBmL2RFp2w5btrq2VTecZkYrxdm3Dpg/0XUM6JWyaiXPEeV/zQXzgtlfutoE/+8Ut6Al0ZHp+4PAAZonbN7eV6WMtxYzZBsjGnCFoJrva71bihKWGsiiFlIYaDD0IstnT9Y6udczSoFOsfZtPkRRzXXPj2LZC8sY4K7utX7x+xaZSS4enzHCqBQ5zqvGcGuj2SCNIY7W0tWbmaBRyzbKupXcrDClSg9xrxvvKPjq/7RmocZ/6POqSHb96wQtDLy3vhZMLZVUFCeC0EJpMFwpdMNqN8uKl48Wdsu+g6wJ+ciDCMGae7mceP0zMcaTZtmzvAtsOyhjJNuNtZIzK81x4HBKHMZHIuFDoti03O2XXFVyaCAaNBsQHShCiNyY1TlIY1Zi0Xg/LGeZMGdJCAFGsOLKvV8OsMCaFyXBkTg81UB6A1jm0VVxfjSPfA7nGm0KvFJfIGhnyxHCCcXAcngtDbMj1pSfuFW3BflzjtR+vFF5kq9pegLxCJ7UPa5Vhi7hfhbJdWb+LNFltamHB9qUKI70Skmcc46wUVkF5JXHtShAvJ1mF5WdWrq1nk7O1u9yGi+qS8+kuq1gtHKUmai38W1kWaLAklsmyFlmql1KPkxXKOV+QzyiuqyBcLe91lquVtAZddL12a9aerNTfz+eyDH1F6KrcofWcK8S0rrK+U3WQtTLqZ8HrlZm0XIO1xDZWlfbamEhVLg6c1Br1NXel1JaLaiiGWMFbLZ2tV5nbsxnmChrqvqyFHGvA8niodXsyIIsVah0kq0lAVhRmYZiMeEpMjxNTjMxdImDsnKfkwGEKtD7wwnvaRrj9+gVyyBwPhW8OIzuBmBIPQ2bfOihCzpnjYahxA4TolMbD7a7hJ+92JPcW+f094/MnVF3tnuaFXd+w2QTGJ+X04YlvvlWaTU/jOn79PYS3LV+/DPzTX97y77rMb7eej//mRJ6MeEw4Ed6823M7Gd1m5vHTM8pIYqSghFg7hqWUCSnSJmHvPH3rcWJMjxNDNyAmeNfTtA4RTxaPSiA0G9rtlu2bHbuglJhI4jjNhRQgd46YrQYsmeDZMw8Ry4W2FaRTbPEUwsbIUogY+WkmjoUUQTYtFRa1mnG8cLzVLda+gEktdMeS22A5YE4orlow6ldGny2N6Y1SqPEbVym24iNaCt7Vcilt42mbQOiFP94qO+CvPiXef5d5HOE4Cb//MPP9Xz5z/9sRDUCnJA/ffD8w5SMxjUzzyHfMfBoTD4fI44cRmSN942leBDZ7pWuNeDxi2wXmkkKrFfSdM0yTMU61t3IRA62GRo6FXGJNKEwNrq1yMJXaZKh8ijXfI9eSHj4o7Wapy7V13N4q+wAWITlHvG04TRPTOPF0f+TwODANwnR8RQk76Dx24+s1a4yyMy49gf/9249WCl/PkWPQmtmsCwNlCeTqRd6cBbastBsWsGMV5Isw1tVqXwPUZ/kn5+CDXkEo1/0Bropa1PHt85yD6+2sN1h9GLuoGrnMyM6zW7+4zMsWdtTZqr94L7WFo5wTc9bg+lkhwrmi61oW46IUVvdhuVbL/nXs88zWY5cr8dk6rxTBmjW9KstzNvPVgGvDH12u18XZWZXAcj1XDwepwSyRi+e0uAe2rH39ksrqUa0xkcIZdFpaOtYAvSwBRanZ5lK7elUIUCmlkCTXInMKCUM6JY6Vz62x4HeKF8XU0YSAhpYsG1I8kgcYDplTNsQFWt8yPkce+rk24Gk9dsikOfHxfuDVRmvvgTkTVDHLtX3iMXIaZ8ZcmFC0aTBRNq3nqxc35EPiuBkRDmy8slHHtvXsbzfEMZE+wvDwwHR8wrsW+e9m+r93w+5PbhCDmy7wZt+Tb7fkI/Rtw03fsdm2xF7wEmCcsZKZ40xpVoKyMaeIz4lQPH1QXrzcsrUJTyQ9z8wEnG/ZtrUnResCm11P1264vdnxctey9cI8Kccu4VVp1GiD0rSFoWQsRqZxJI0JsuG9ormhiFGyVIbWmfhgMNf7Q9dcqiZfnIOzL1ntQ1ko5ZVBY1KqwVeuPPklF+lMjVvbfpaMuYQufaCdCq0GQjFCyXQYnUEZCh+/n7n/7sS9BR6sqy0wh1omovuq48VNy1aV+29nXBiZOXFIJ+7HxOOYeD5G5kOmEXDB0TmtrTlLQcdEmRrMKeKa2uRnrnDbPOVa1mIqlMTiGVXKnuVcK8oepAbNAdWCmCKpCrPQekIwQjCasFzjDDYtSa4L4taJJ2bHHBVvmeBqnC931TMzX/NOHIrOGX2KQMPftv1opfAyRcwFooO0Ro2pdKgL2nEhXX5meK5W5ipoWWmYV9TSa0hkeZjOsAlXyoJFuF0J0PWc52PX/bZYxMue63OuCmcV87IEfa8VkbC08lxiE8kuCuHMulrGXEtGXDfIqclmdWGr4jojNBc5X5XFZxftehXrXD4vh1H+4PBLItrZOamO1zo/uSTgnaMgy2kWw/0PXKaLkvssV0JWptNyZ69yH+qQhpZasKwqhRpMvKZXYg4zrRUxpbrYRa0qhQxZMnOMVcBjkBzMVvsInwq9g64TTKuQTrklNVvS4RGblfFkHDOoD/RNy/OniY9dpNkYG+9IYqSY+fQQ2bmGvHSaa5umYuJmlCkyPx8ZpsgxKe3NFu1a2q7j1X7DeDPxsD9iMrFtHBun7FvH3V1HniOnxvN8fCCTcOqYP850Ftm+8LzYBAJw2waO+55khb5t2Xct+21ba1yp5/TpyDzPDIOSgyyZvQYkgqXKWXfK7a6nL4odE+kUmTXh+sKW2lu4cYHttufu9oaXt1tebBpahQEIweGlxlKCV3xjuNlgTsRpIs8ZKUYpHrKnLPRTi/WBdFSDRHJBYjm/3LK+02tOE6tKWDzNs1UDIpURaEUq/u4u7x+2lHvJgOVK4XWpNm1CCGhVCkCwQoOhxUhD5unjzMPHgUdXeAyOca69oBFh83bDy9uGfTAOH0/0m5lJZ442cniC45AZh0SeFNcH2saz8YFGa8KbTZE8z+QQsGDMsxGnSq2OcyZNhTRV4gQsVG2tbKtSDCFhfYGlBIdbZIUzIQSh8ZVi6xUkG2UqJFnqrBm4IrVnhwWiBbqg5N4jrjL7qgDNNUeigJsTOs78mO3Hs4+cX9hH0JaLZV/OAp8L3m1XFNRyEU7lKiSw0lKVWpOlrILFVsjhc/bNWi5bqNLtkqxlS+DpM3lWx9ZacmWlsl4K2kH+bLw/2M9FSa2lIHJZ3NnFC1hCD3XtC6wC9eeZecXqDFRvau1NLatSW3D/wkUwr/NIuSZ1wcXa16WMRD1+mbfxmSJar/W6rXWY1AQ1qyVKWMsOXHkmsiC8wplltCrjdPaQFgW5vOxJuGbT4ylgGUuRoJGywAtLeJSIIDhKcRRRsgmzZLIaqp7cQXE14ek4F3KZKUOC6JAMLkE3CH0SehTrhNc3jjJ2CMp4yphXknV8M0PYNrzF8evfjIy/P9D18Kd/7xbfFfIx8fHDyG2fq4XshGmKyBKcaVqhcYWhzAyHkZhm2u2WTeO46wL2ZouUN0z3Hbcvd+w3ys9eeG575cNWGO7fcfj+mfn4THw+YKdHxB2Bmf2btr7sJRMNwnZLt9uzvW24u/GYgVfhYdswRc+QqBLcCsWMMGY6zWyl0BajbQKuGIeDY5qNPhptWhpGOUfTtWxvX/Pm7YaXtx2vOlcD7ElIqSzVSwRzStM7giW8FOI4Y5YpKpTiKamQU2J+tqXCK3hVuqDQ+oVOeoE4vdSMWis1z8K8LuSQJa7glqDxLLVwXm3oXBWAgHpqnEmgaMFiQl1CNdM1Rte0bDplvw3cqLAJtdDfcaoNkIaY+PR05CFkDq0QZ4due7btjp/8sxf8vV85XrnMX56UKR0ZijJYDbg7Glpa/N2e1y82fHXb8NOXcNODs5mH45HwbDRWgMx0NMZD5nTIDMeJaTDiYMSDoptA2Hp0aJEhQym1tW5ONJ2j2zj2G4eW+paFBloFh2FTYjwUoinJO8KdY9PXInrSN/jmhpu7DS9lz/34xHGaeXw0np5nShRczPhTQL0hzd9xmYvfdQ3iPE6ETi8OUTq7eUsSz2LCroJJFglbymJdrtb2gjnZinUAK3Z+bbDW/fW/1RJe66ycDQ67Em5XRnatw3ONh1/OdQ15rfvP1vwiZEVYkyXOazlj/1cKa2VUVP641BaW57kvE1S55AxIfQnPnoJwGUvWdVxZ83oJcq/L08uEqoDXzzlQxgVuE6kv31qaVtb4yOrny5Ih7s634DJR9wfNjha4DGFpuclCDZbzvZacUJlZ2VdluSlmtexCNmqpZKsQUrElg9O5SicWjwSWpj+L8lniWa5VSqkW2TzAoQNLwixKox4pipuVU649nl/1oNvA9MOJMs4MX3tuQ8C1QhlKLe/hagmCOBX61tOGQN/3aBB0E5i1Gj/eF0KpHPPdxvP1V1uOzHgvyJQ5zYW+EV7dBN683XN43qFx4Dg/4+8C7QYoA4f3I6IJSAzDTOuVru/rXNZn3UHfKJugbJwSNdX+4SaUZmbjIhtmQpqJRyglwVxwS3LixS6v9z+jzCZMBifAG8y2HpVQ8XjnCRslFCWkxDhPQFUayTLEXFuuDpki1YyVoPiuwefacjRh55iVLQ/h6h2bVVAxr5Q+pfboWDzywvKc+vpPwzpOQYtRmoKTTJBMo579beD2JvD6bcttKmxcfR4fhgrhnEohauJ4Gnl4KJx+b+RyQ7jp2L7ybG7rte3f9sxPO2w29jmziQHxSts5wouX/ORVy7vbwKud0eRY80a05vmkUrBYGKfENGfmOTOXSEqQlwKPVkL1zheaLbEW9BNxeF8LDvbdUhpDwDdCI9XjqXGI2l+hhlccEoR25+haSCipeI6DoxwElZHx6USImfk0EYeZQTY1WH1dt+w/sP34dpze1SQKVgHFBVS4NnFXJbFuVWqwlsFYJZfoKqyvJbosAuoiHD8b56x85PL5Fd6+Cuz6kZ0F7zkAu8zxrINW6XcdAIazG3OmiC4aUK/Gv87MP6+rar3P5m5Xv8vVdz+T4CKfXYLrzy9rrSe4fG1RwlzWUPcuAn7dj1xiFusJ1u5rcpmfXQexrzwPzsrmKg5zjoWsekWW2jargqwZo9c03hWyWpVVoSoFwy3Bx8qUqKySykaqlnGFnkRrrSbnq0LOc7XExs0S31LBaw0U6yzMk7FxindKtw2QjDlHTqeZ2z7Upi9hOafImQLsvaPvAi4EJmpS0WmeazawUxqqBdd5xe0CPHtKNmyOHI+J9jawaZSXdx2fNhvKtCHHlu1+x+62o+0842GmMFNsqn2grSWlWHsiLIpQBbpG6IPQeeUkpfL7VaAxel/otBBSIg1gliAt/pxVAZxzbZdbzGpjmWhMsTCkQiNGXIrLQVkaInka5whR8LMtyr7CN3nJPM65kFLCfK17JUvZdJ0XwgBX7+b68q3vkJ2R4atns77vZTGUxAnSVGXj3JoAR/U1fanFFjG8c/Sbhu1Ny+3Llu1Qm9tYgdOUmcbMcU5MYgxT5PBUGO8N2+xo2trHgFCVT7sPlLEl20xjDY04ijp8CGzuNrx82fDq1rFrjHIyLClS279RitVGOjEzx0pTzqVUJWcVYci5esom1HdPF0XsasXT4AttA37Nr/JGI1Y9pmToSvNdqOAuCKGrFV+L1npORZQpZdIMrU+0S9e7ghFTRqTgrnsR/Ae2H60U9tmqJSm2wDGcPYKLgLaz17B8fObKXwvB868rM2fFIFcTeOFwni1zuGAkciWwPlvj9TlXOuYqINdgqV00wtWX7QrxlFWIrhb04tKpXis1u8BiyzUoVx7I2dNZBOR5xovVjtXjVwFwuWZ29npkncuV4lyhqPVarSsxk6vy3NWyOnsJXJZsV02vV4/oXDfq6rxnPV0TCM5ZmGKyYO4rtHatR9f1rzizr1UuF9aY4SjnT2u5MKOWvMBq6en1thSp7Qfr/St4XxvBOAGdMi4DIwwfjaGDtoFNq6TQVJz7BMOnyqKhhZ++6nn86x1PT/DD+8jdO6NVR2l7vNbOX3MyfOPZ9A03u5bGOWbZVN5sycQhV8jDV2jUqDJlbITpUC2y97/bEtiw3Tp+8qZh+PCCTpUuBN7+6h372y3bvuPjb58YTg9MUyanR+bimYaG+TSSWyU4ZYMRN0qalWEShhNYKDg1GtfStZ4mQJML6VBf/oaG4pRCbR5/PGWGKTOOCTlETksJjE+N48YbaY5IiqgJzju6rcdJw2jCYcFAC0umsykmucZjpFTo0oM2NavbzYrOWisFlOWZWmisRhX2VnR5TnLNdzCQIpWg4EAb0Nbjtnou15FOqXaqSxnflJr8VSqBo9k07O563r1tcM+ZMmROh4n5kHk+Tnz/8ch9Nh7mzMPTzPGhqfkiItgMn44grdF0MAeHxoDRghZEG5z0dNuGFy8Cb+6Uds48P9cM5d57LEOcCwOJccjMcyFmAzymheJqSfFxLszFiKlgLgIzEAltoA2Ozittt8RDzMhWCOqqbCkZa+sb2AajbYW2qQ15Gl/fkyIwjI5t20AvTDcOV5RhB8cE5X2qfTqeT8Ar/rbtRyuFPx5OPLQ9R98QnaD5ohz0imJgizSsTBu7CLAz1nhxa+vxhl8CKJ81qkcuAVhWYXrB0NdNYOm7UL9bsLOwsnI51pb918ISLsLNFqPGuwtMJbLwpIVFqVzOaXBprWm25NzZUrZDzi5z1QlyxeKpm5aLsD6X8xBqNnGu+OpFDy4aoVx5DWZrywNYS2isc9H6otWyGIuyXCZdcl2vt6VUyOq9LGMst+Ws1GytrnC+hgun6KqcB8u1qHN2BGmoXdpYCAIC4hBxJHSpfClI1lpbXgRJkOO6YFkMjlJjGmWBEMR4tXGEI2jKbDfKV1tH39RrYHex1uc3Tz7BASN1wldN4K9vep5j4unjyHEv6MbR9oZ3AUuVOfLuVcem9XVdywPizLFzHdZOIIr4hjk7IoUI9I2AJKacmB8e4FWFgl7fOeLPd2x3wu6D8uKrl7y62/D6puU3m57Dxy3H+x0Sn0jpCFHIhxNsAr5t6IIgvZLnwDA0nIJHcyIadBtP03iC83SiiNW+FsFnhuTIsXA8jMytEOdEHCv/vkRqH+XRkXwhTRFKwblaYdW3ntkph0lpmtqYniljCWgTRi03baX2NFCU4AtlkuX+A3Mta70S0Es2RJTitD58SI1fFChzDU7KnJC2Qb3iGsX3NZFQUlnKeWRsmMkUsBlXIrrZ4sTRBMfdXkkJplQNl/GQOdxP3H93YmqNOSuxVKWmndQqprlQTkCGhgIlkfLMNA8c7wNTrDkrd1EIVktgdGSeDgM2jHT7gKiSizIPECchx1pqRCSfUY9ihel+ZkpGKTOW8mJMKn72+OxxeGR2YAlyJj4PaOdRvyT0paXyQXGMQ6J1MCAMo3H3AjY9bFqwpKgLtJ3n+NYxzzCdjI9uYHw6MB2e/33i/bPtRyuFYIY3W252FRFnEWIVtlgFyucUx88/X9+2FbqpCcQXKb8Krz+0hs/JbVKx5sVpOI+zFtW7ZBmvws1Yz34lTxcLftU4nHFyWX3c5dNzbIQrDwXOOQR/6HesEJOxBm0XIboK8KtxrpXb+TotOXp1bXKe8x/CR2dPZFVMS1Od+qEtbCguZv9y7OVSy+WeyBpjsbOXdFbN17+v05Bl/XJZ9+rVKIqIP3t4aoIzwcRRpGYlyRKetqIUJ+d6WUtxSMRRE6KyYFkIorQOegchGiVFJAhv9oE8Jh6eRh4fnlAiFA/W4sVotQY7n46Gb1r6PnJ6mPn4PON9w7u3HTd9YE5Qljr16qrb7VXYBgeNkVRxwaGq+MbxPFVlp0VovFBaWeI0M3GYGE7K1ge8F7yvD8Mw14TB3TbwU3G8l8Ve/i5Q0pF5PPL0+Mi7l1tc62lVmZzQNY79puW06xEbGcaI5IKnFmBrnaLqa86QCscCcy6k04w0Qk6JEiMhR5roaGclTYmYCylWAeWDknUpO2FlMTJqc6Qz9Gg107ykQskZI+GSYLnSg0OA0tV2rRULqi+AuXq/60uxxsmq51BhIc4B5zONPdcTSqkZ5zW3xRamSv13Tt0RqrfZCnmqD2ZMte7VOMHoHLFUeJBWajE9B5aMNMKUC5YSx8PI4TTyfJgZT55Y6rglGvNUGEchjjUzHCuEpnaEUzyaa68G1M6QTaHS0DNGtlwzlyVhuXZoE4Wub3nzquOnX7fsOmU4CePJsHEhz+dcK97G+n5nEwZRGhHa4Gi72q0upRrIxypBwW8dQZUUhdgZPHhO1jDa314hFf4jlMLRae3WRG0KcUk+K1fSutYEMlZe/vI0yQrL29nCrnGmKpSSLSwW4TOq52r1A+fEtDUFbU2GNasJdNdVU9et4vMXhWArvKV/IMwXmASDlK9we6lN543PKaAiVXiXcoGXYAmEunMNsIU6u5zd6jpXhVfbkNYbWewPBPriplQL/Ur7LPM+w3cr/CR2ZipdhHT9u3ClOJbbARe4pw5QQZ2yjn0OGtdzZy65HXKlJbOdv372pmTF6CsvjkvL0kqINdOKQ8tS5TPXny7DfguhAQ3w9FCYRyUPxkaMN3eeXQPP70fmacD3wj96u+X/+a+O/MWvv+df/Hd/zn/xn7+j73aobnnz9S3vbgXxwv/hv0/sQ8/LGzg9T/y7754Ifsd/+Z+84Y13PFul0RILsxhZjLug9I1yCMKne6Fzji44+o3jZBGi4Uuh94q/9ZSdIWPi+XDgNEdS7jkOI8fTwIdPj0waCBvHr36+55+89vx5Aw8+E/9lx3h6ZDpN/Pbbb/nlz14RpKVZPM8ueN7c7Kqy1AdifGJ6HOlCg+9qspxr/NmTjmngdEqMp8iYx/pcWiEwwVI5YNj4ymMv9aFq24ZTEQ7DXKGOKaNmNE2oPZLVMPPkGIlTJk+ZrLEyjLzQbgN6ozRbhx4TNrvayH4qlVWGIEXI04IsqGCdq15ENlSbxZgAm414qGXYvat4ug9G6aBIPlO/sRo/KKVCNKETfFIIypSFsSizNhwnZc61q5DuOiQ0YIU8G8Ox8GCJ58eBx/tHnk8H7k8nnqcWDUoXPPGUub+HJhkunkgp0ngjbDvQ2hujL4FmzGjM5KHUxjjFmEsmAaXJWKgxNp6mGuFXz+3bHf/0n235n/4nHRuMP//dyF9953huheGYmIbMeKrlK3JM5JiwE7WgZ+t4+U6xAoeTcTpmYqzWZH8jbHrFspAbo7xo6dwtp/bvuPbRv9l0tOppETYL9pGBzwPacinzcLbIq4QsZ9jousTChSZ6hpqWkc4ZylzGwxa4Rz7ff7HyryzhK/P2wtu/fPEs+O3y1x/O4eyprMJvOXSNT6xfrTGTZU1n6c6qGRYFKguEtMj3lZ2xSOtaH2+NbvBZeYnr/I7z0FZnW7AFNrqsAlkaqF/PZZlPkXNu4Gq8VSHOqpiubuB5/RdVa+uarkpuiCzU5LX2vVygpdW3LGjNXvdCXBSkeuHty3rcD98Y998bTQvbO+FuJ5Rg5K4KlPGYYM78o3+m/KOXt7h55v/0f/lL/o//u/8tH757j4bX/NM//Z9xs39J52/ZNW8x5xioVuhsdc5bRpqtcUoj/+d/9TvwgZ+93PHLtzd8nxMpRsSMIQTe+Frb3r/sGL67rwX35ki3aUmWGccZF6eKH3uHbliEXIYSsTRT4pGSPrBp9hzjzL/8OJLf9mjv+cnrHZ/+4c/Jf/aJ6f6Z6eMDp+nEZD0772p/AhzBee68kovDu/8Pa3/2NF12nXdiv7WHM+TwTt9UI1CYQQAkRYqt1tytDkXYjlCEw7Kvfet/yb63246wHWErZCssS+5uSS2xJYoiQQDEUIVCzd/wjjmcYY++2Odk5gdSYimCiUDVW5kn9xnynDU861nPathVG6raYBWE5JFsmaGaNESyG8i5R6cJH5eEDoEYRnzMKBbl5xXQSpPzQPSB4AK7AXYD7LtMvx/IYtG1IU2BRwyREHpS8rhoII74bUUsyAfq0eIwc0MqWzp+sxCToFyabvfS3ZQllec/RKIqWWOBlwu2TozkWKbFoWNpcssBciBSpEk6n7hzmdwnfJ95CIBP7AZhGDWD9/hBEX2FWI1eGNTK4KzQ2QLjRpMYU6IfHN39Dp8uqB+BuRCaNaQcGfrEoDzLC8OiUVStRSnLmErNJyKHgUA+Q9/D7j6xe77FNapQakcHCHpR0a7XXH51wcWTinVdxnIuV4b1Wc1+49Hao1UqEHYeCLmMvN31EXYJaTXtXmEDiMplZGeIJVjbKXQqNRsCDCtFJxWd/ktmHx0UMmUyPIe/J0MiJ7ZjNsKn751uewyeJ/rnaXz/axjLr30if85Hv54hvLbSnzmAk4xiMoJHd/Vr38+ndlVee//0LXl9waM/OjHKp85idi4H6uu8dzk5n1Pv9OuHkA+HfvKFkzOYU6g/7/tzz4GcfG36YV+7UrOzm47l1EHPZ6KYaKn5WLs4es7pOPMJwjyxH0SOmd74UGoWV2uhWpTr5F0mjwmTE7VKtG1Cp4gKIy9/fo2oW0K35U/ff4UbR4xVqMbx8ovnZAfVRYW4QPSWbDTvPFJstordHoZNwlwl/OB5/jwjTc3jpmEhwm3KxFhS9a2PnCtFo4Sr1vISGHwg58A75w1DyPTJo0UQZch6purKNGkvEWPAR0cfBsJ2y27Rsj1bs7ts0EpYGMVqseC+bnGmIvoJxlgONNoQp9nEKEXd1CxXiRClFIiNwESVVVoVyRHyQRE3k9Gaw2AsCaUAnEJhEM1NnCklvE84l+j7wDiCG6XMsUjFu8/1oTQ/z2aCTYxGtC7r+kQKGZ0nnfWJ5VZIJGXWBqZEtlmVusIcpWQo7DWZsAQpeWyaiAYpT1G2hpyLQwlEXIz0LvKwTzBkXJfYdAkJic4VUUUfDDFpyBqxBmk0ulWoCpKCkBIuhKkxUAhOEVEkdBnSoIrERgiRZAKVFSorhFSySx9gHEvBOYaybUpCiBnnE74LZSASQnaxNBMua87eXPHGE8vFSqGVMIRyzqawVsi5ZNBRIi5HQsyEpIhRY3ykd4HNVtOEMqwn+CltAjwJQ5l8qBJkSegqUa//HKP657y+tFNYxVyGQ8jcEHWUjDg1FPP9UN6cLMPBSs7bFiszG8XjF39tnZN/HiLzPC/zehXjzziM1wzuyTHmk9TiZE+n4NOJKX99u4MzmI3/0YweVjhxiDC3OJwUuV87z+MFkCkVyuQTJ3Pc7ZxDnHYjHMQ5phRp9iNz9nVIYOb1Dnb7VOqjfHf+eQ7XS45Zy8lbZa+Ha3tc45jFHEjLlJLjlEXI5BRymQ2syeQAd88zxsI3vys8amC/h08+T4S7QNVk1uvM5VmkHkfC7Zb/6Z//hD948UPG/iXB9Fw8eszZkyv6tOOzX32O9oZH7QVhOyKmIi8N331T+FMnbJywvc2crzIuBe7vIs2FJj9JNHPQU2qO7IaAM5qF0bzRGl5pYYgBNzrOqivEJW6TwyozTXhR+HS8hiplQgoMwbP3jvTiFqVrLs4uGVxiWSlqEVampmpW9HVHJrG576mrDqNtkecWBRrqyrJcCVkMfT/io5uwa42xuhAkFBgrKF+ut7GqGAYg+lAwfJ9xIVKlUuTyPuLGSN8Hdn1kGBQuaEIsMiIoTVZS6jsqk42g6gptaow1WGNIY0BcqUUwzeY+ZJ0noyiTKw6EnIsTLdS2MjRI5cJWmoDG8uwkcoqFqktxcIghI4QYGH1k30duNwkZM34f2WwDEhPdkPFJCKkiZkMWTa4rVKPRTZnTnCURUmR0U6dv1qRYtk+51MCKOGMi5gjisaZCa2FwEQmZwSv2g8J14MeSLWXKTPGQEn5IsCrnl3zG1pZm3XD51RVffaaL9HuG7VCorRIjOSRChJAElwNjiLhYVGtNTBgfqUbPw70hRKFuhBxTIfzkaYaGVxgFxhQGU10F6mXgy7y+tFP4bt9x17RsrMJZhYSCHxzmH8BrxuLQmyUlJTzq7EwFWAp0lGddockAKSUT1gl6YiXBDLcUY6dFjpIPUpg2hzrEhGJAcZzqZO3ZaeQsR+bOfEz5GFwfbPe8NhNDSOaHfoKEpiwkn0TZMyafJxhFnax9wO2ZDfoxck4UiGmepVAMemnumg17kdyY9jMd37HLej6WKVI8UGSnusb0Pmk223Jolirnn0vUK/OQnbLePFv76BUmg5+n32o6by3FmJJL5B/jdP5KkCiHIrc5eCvIOnN1Ua7v5i6RUoELlirxlW8LF21iWQU++MUtv/rpn/Ly/ff57I//PRdvKR5/1fDk7XN2N9e8/PSa5z/8FePFb/LMnFN/PfLBv/0FzTtfYfnWU77zrYpXF4Z93/J88RiyY9kq3ni7ZrGySFPzp3eBqrKcLRQ6Rl7uer6QzL6xvFcblucGlzXxLhC9R2tYryv0UKSTMZrBCYFASgU+kuiRVIzx5z/+GU30LL/9mG9Wj7lOmed9EQC8Wras8hkg+H7k7vkD7tpBnTBtQ71coyqDqS0LUWi9YBwjOSXai5pnjxqqSuFd5KF3ExUsYpoWY2Tqvo/k6FEpQO/xujB7vMt0m67UIfYZhyJJgxZLvawIgxBHIEc0GmUNlTUszlY0raVpFcFF+q2j2wx090OZwJYhiyVXkLUtf0/ssiwKvBQ9o1AcaNEOk9IVnaUMgIqhQGIpk0JpfkSVQT0+K3Z7T1QDam3QQyB0ge1tj84R12f2rsL5VByD1Chd9CSygpATnfPEbiTuPDZXLOszLs9atsOaOFbsbjIPTWLRJkwbOFsGahTKJXa7nq6PdM6wGWt2e4tPFaIqmlbR1kJtFUoPRC9kn1Bdxl6csbxa8/StmndXCgN8sU3cPh/Y3nf0mxE3REIf8L1neAh0dz1uFEKoqMyIMgbdVahtKLO5k6JtSmFcBHLIZB9Jqeh4rfEYM2BVD1z9hbb+SzsFp6TMTgD0RBfJp5HtZDRkKjTPRcjJUk5O49jB/Jo89KSlk0+MGeQDK2WGWubXQZH0EGWXdYvBPzHqlE7JeR9zuC6UFO/wmqiu5Hx0DgcDWhZMHDt5mRxDcWZz38Bk4PXJe8CsbFoi59Neh9lxnuYARwrvoeibj2sdnBq87ogzk8bM0TnK7H1Omgbl8FvlQ11hvgrqZK3DG/Oh5jlTOVyuck3musJ0red+hpxnOCmTpKS3TI7PqOOiWcHZCiSXObgqepZV5uIqc24i4X7H7e09n/7wJ2xefUron1O9e89XH1Usibz4g/fZ9j1uP3BuI+PuY158VvH+zyp+47t/FwvkIfLJ84RXsFwb3v72GVFGlkvF2ZOKy0bTD8IvPgp852saVSmMzvht4vquY281qydLlsu6/IIp0nmPUsJ6VZHrQgNEaZIp92L04CWXngGjydbgY8TFgE8RRabVwnkFOyMMxhCqimQVq4sGFRW3tzvqpcIkYRCLamqUVgSELhtcsmgF9UXFamVoK4ULwmJpGYPBhwKVWKMxAt4bcIXWGyjTyGJM9D7RD4Guj+z7Ymy9FYItncfeF12f8txqRBRGV1RtRbMwLBYaX8VCmYyJ7XVX2Ek5F+gFXWZJq9KJPgeBHJ6KAn/pidwQyVMdIZca3HRPT9O7yjwOo4ghM3pIQ0LvPDIGUufpOwciRC+MWTMkRQianBQxFZhnHAL7Haz9HvY9YTtw8WhF0yjqNrPbLeiiInSKcZtRC2GxNFytGoxKhBDJORJiIsRIzJGQLXGCyGpUKfYGoX5jhQueGEDaiuWq4uLM8vhM0SiQkPF9ID5soR/QMaB0RdamzCZRgZQtKWaiF4IyhKTwkxqrHkFX5X7SlToEr4QiF6MVUwedILOo1F/w+s/QPlK4qbBg8tSRemKpRKZi6okRVQe8ZwJoJodx2s8lTDfCbHAmAzPVZQ/bzO+fsonmDGOKkUuEffJeWWMGWfIhyp5MEqc7PYBR89snBrX4i5PtOTqF4gtnts7U4HWy1Ryh51wc1EytnUd7InJofIOyzpzdTKNrDw2CWWbGz7HpbT6+OK0xv384z9ec88wCm8/nkN8csqWDSzvNojhe00N2JZOC6fSQl+tdPky5ZA4zO0DMlOVQblI1scCyyqyawkITH1kQONeRN5pAeNix+fwVzz/8nJsPfkzKW4zesv5qx2Xq0a86XvzhL9gLSG1Zr5fc7V9xf1Pxqw9bvvu9v4VRCZ0jn75IcCGYSvHsqy0PG12agFYVl61ivw98/srz1bcVKytYLbiU2TwMbBScrQ3P2gqrBR8TnS9Syou2ItQKoyyIYdCZnNJB/jxbU4axT8pmMWfG0dPHhLXCRSXcVIWNlUQRK2F1XpN64fNux5mxeOVAD5hhQVUVbLrPipAtlQJ7ZqkXRX/IJGhbTT9qBqdItcIajUXItQEiOieiFKpjSAkXI25MuDExOkrNIhdpkSClC9r78lvqCQbSYtCmwFZVVYYNG5cwden5SGlyCimTJJCSKgO64vTsqpn+V1hoyhRcXZiIEClPFOuENtNdODW1ZqXI1hCTJ0bwQyR3k1PoPa4PoA0pCjFrXMiEoEhRIGS8C4y9sNtGBr9HdT2xcyzebajqmnVU3NwbuE9sNxE/ZLTRLFaKy3VL3/cEQsmAp/pRluLEkgKsYJWiXglNEqp+Sbzdk4eEaisW64qzleFqpTAC0Sd8H0i7HSp4TAZlm9LVHTRiA0ggk8pQJSwxa3wUxiFjmoyd4EJtJ8ZizsRJakfJ5BOSIqQvN1DhSzuFX6xbFqJpERYhoSfqnpdCwywG7QTimYyQTNjHgXsPB2PIZCyPcgty7B/IJ3TUafOD6VSn63D4QE1bH0zd3DCXOWYKTMcyO7PphstTpiDp2JswU5vm9Q7Gn8lgT3DLwSoixyY0OOBHZWkphecTQ36AXpggs8mBHBxNns9dpn1mZL7Wk9PiYJTl4EyP3dvzdZmOT8lB8G9mu5b9qUNGc7iCpynEtL/yXMohM5njDkFQ+ng4c+PevCOXC1RkNMSQMSpjpKS5uzGxbDK/8xvQusjDF6/48b/5BT/5Fz+mGz8i6c/5re/8JvG8JraO1bjjn/+fP+DDn96zTxFBuFwtea9t+M7XnhCqFaNz/P5P/g1/51nFO28+5Rc/GdltFKqBZ49hcIExKuQ+881LzfkTxbnSfHw30AdY16WvYHuzIwZHrD3vfPVNHi0WLKzhRx98xpgydtGgVVOom8Yw1BCVQblIkzyrGFkPjtXa8PY3ntBUiusPvuBfv/EG33+y4u1VxedPasb39zy8fIWvE+tvfIVc1eTlHt1oYvQMtw+4bFhdLKmaiiEGxpyJSmEbxVZKI2KthGppqJ2iGkp/hTEGqxUqtWSj0DnQGo3RCS0JN1B6QJRmiWaXZ6XPwD5HRm/wSZX7ThV2TY4eu4/IpGoaUsL1ieQSptXgPSlGQhRiCsSs8NmQR8jKQDV1qkxDc5QC0Ufyco65dM7HovmDCGKm4q8WUBknmjwG8J7hQZDRwRCIQ0JXJaXISROHUqSNRKTPDPtINAETO9ZpDzHR6JaLxyvOVg0GxUub8c4zbDK50qwuLU+eKh6tK65FwATapaZpPQ99xm/grg8IBf4co5RRtUtNfWUZd0V6xbaKs8cVZ48sy6Xgc2Zwnn3XM4QtaIWpK5aLJaw1uoNRWvqtpWBtYLRCkiIMsO8cKINUlnVQpZFRUxxCVXqVhExnDDEqUvhLzhTaeEz9Zzx/sl7MwsjHJjQ5/LMYickxnH4yG+hpm0MRWnjtdVzj5MPJCM77yydby+l/y6xpVKzUQednNnST8Trd52v7P33/cI7Tf08ncyjknqYR87GoXz+fY2Q+O4qZhjq7ivz6CoC8TnM9rPBndjfnHn/mWuVf+14+rD2f2Z/t8Tj5+HVnfrJzOTk5SXLIsrQp8JxMTkGRD5yDWieMZDQlmjmrMgsT2N3u+flPfsHLX37Axz/69+x3Nzx5onj7jUvOxs94/rNrogz8b/7BX+fftdf06YYMvLusuVgZlgvH7cNLPD1JRhbtG7j9HlU73ngz8/6vEpubiNx7dmMAawk7xd07lnYhfMtoPvpIGDXUZIw2KF3hXGJ/O3D7dCxRbMjstqWJa1nHIm9sivR0Uwm+MaVLG6HtLK2tWeoF9m1NJQ21ahn2CXdRgEOtNGNS7MZMHiPP7z2rdcvbX31GHD3bTcdmvyeFjhFNcwY77xlHh8+K+86jlZCsoqoEmcbdZRFEK7RRWGuocgPWoHJEp4CeDEazsEVDxwi51UifyzD5KMStL+tYg9Sl2lVGaCaSG4gUhxFTJvSeNHraS4Mbarw3JC+UWW/lQUhqhpsTSalCOFCCEjX1v2Rm6WEhTwylBFKG2ejKoGuFriBHV+oNk8RLzgooRWjJBqU1pjL4ESSUekVuPBDJIRA8pLpGKoWpV2AMLgudL5InWYFpNKuVZlErKq3IOZGVIakMKMYk9D6xc4ntg2ebMh2ZIVVsQ2bvM0PnSz+GUuhlQ7VUGCtkl9k8JNwuMGwc3geQhkShzlox1MDyUhj2K3SbsMMx+hcgjBEXhH4QukFj+4y1gvdClYqzVRUYYpnHEE9J7f/x15d2CmchknT5cdL0gJ8akjky/HUmZP61z0/fPI2WD6/TtX/NQZwWZ7/M67W1v8TXDsd40iRxao9nOPQgg8H8/3yyxmxyf93Mzk1pR6N8MPZHL3ZYgV8705x5XQH2z71wrx8FzF3TB8/1+rZzJnfyY77ObjpdY/rW6Q968qPKrK1GKeQxF6GlFBHV5LJqNasgJSqTWEhEDR3Xt5/x4U9/zu2nH3J/+xmrS8eyrVhKzd3za15++gUpRdLdb1IHuKyEizPF49bQLhT1IhW5in5g6G95+OIl9ze37Pc7nl4u+OTTwP3guLvvCBVInelVYB8yF43iwsJHooqYWaRM2moqgo+EXcem92itsSKMXeHRD4tMqjNNU8670YKb4BRBqCpDbSyNalifN+hcYWODiUAqdTerivHOoohD5m7rMAt49uSMm9ueh62j23tc3yOLilQbxhDo3EjIwrZztFpRZU3UmqPiQKGBWqOorUaLQrJBYkT1A1oKrFc1sFiW6J2oibtEGAPjEJHsEV0wf7EGhUflQvmU4DiAoCmDD0iKNKvSMSxOE4dEKIObmYgx5d7M6dikqkq54KARJscg5XULoVBGo61CWUF0KM4iUWCppA4UuJwLa0pXGlUXWmzBNDOQpiE1ukB7taGxC7LWDBH2fWYcMjGBrihT3YyU+eFpdldlMNM4JvohstsHthvH1kd2KeGI7BLsY8bvIsnlIkVSVZhKFSKGz+xcIuw8487hXOlJyFoVPSldnPxiKXTrGqUztimyGSmUmeRpTHgfUb0wjIl6hJSLXla5r0rxuZZM1gGJ8deNxp/7+tJO4Zvdnpt6yX2l8UYVsfYDjj01ZU3KgDCxUTgaznm+gjB1806QREy5RDq51AS0cJBs0OpYV9CqYGNCYcnk+aaSwnSZ/55ZNDAzZ4pln4Q0DzDR/Hee1k7pcE8eoJcj/faAJB2SnJxLJ/V8A6vJyOd0qImV89TTzZSniHm6nlbNsx6OcNL8HKTjpeVAO52u4Wzb56L11ONzqFUgRVoipfkc5NC9LJmTUapTo+EEpeVJJ0mYWFYT3Jfi682Cedo2xSOsRS5r6STELOxzkddHimKBZKhUplUZnQMKj1WRx2vPwydbrj/9jD/5d/+Eh88+Y7m2fP9vfpNvv+l58cc/5sf/6F/wrz68x6UCJ/7RH77Pt2v4h+8Y/pf/8zX/zy88d06oVcvf+Mr3+cX7t/z7P/6MD9//d/z733+H1DzmH/6D3+X5e55OBT76lw88+dqKqo7oxUiXEioqKg3LRwqTiqTC21dLSJm7Sth/vOXVzUBImierBeM2IyN0VuFX0Lbl4q9EkAq81aQk3NYVi6riTFdcXF1SV6ZIPq8MjRL6kFhWwqOrhmHTcveF4+au43zt+NY3F8QQuXkBu5uRXjxmZdELyzh2bPb3iMCr2zUXWhHFMurSmW4ErGQqpVgbzboy2EaVZ9JHtkNCq1iYSZVG6VKQXUaF6TyyGQkPI9tN+W29EmytMCgkJiBSxQGbMlVK5KQRSSgL9myJrWAchBTNdC8LPoKkqQ8hgPiSIWgFOoVDDVFCLnhjTOA9VGaSwACdTZFx0ELMFeIyOEhaITFDEHIwoCw6a4wyVE0GlUg+kaMg0SApY7DY1YL2vOKN84qkFPtd4uXLwN1twsWEsomr1tBIBg+jSmU/PrHdjtze9FzfBV4+jzz/bMu203SDZWxr9kkzeIXfKHIQdGOQqi49LUkx7sENnrgdiA973BBRbUKF8hvW1STfsdCEM2G0Ge8g5YDzkX7IDJuOYR/xIVGtFMpY6iCkUTCNmmRQhLUUuFZn/6Vs/Zd2Cmlp0VZoVVEJjLroe2SdDwZk7mSdo2I10xml8NLnl57hJ8nTHO6jlIQSmJXg5iYnpve1KlZZq3wwpiIUid3ZaFEKmQBJFY5xkXoo9YrZuM9HMx3G4TunKm9yOJ/T19EZlVnIxxrETAed4TQ9vXkoFHNCKc0ZK5xozU+GW3JRlJyppifOIc/1iWn7+dpmgaw5MpGYnF46sqlOrxdSOp5FzXUIwZwUqvVMYZ1/rHy8TkdGWKlPHJljip0IDtiL0JoZ/s3oVCZKtTFx1nhs3EO/4+WPPuaH/+5fsr3/jEdXiW98d6B79ZzP/vG/5aPQcTl0PNuPvKuEQQRbVfzub3yV5smWsXX8t58l+vEx66vHvPftrxA29zi1IykH3vHh7/8r/PNbfvura/72N7/C76wv+L99uuNuGMkdPHqz5a4P7BXUjeKtM8311vG5C1zqFnvWIJ3n5x8H/FODOav41qLmdvEEyYIfLd9+VrFPit0m4WyZltVo4bEV3j5v0H7F9ZNLnl/vaeuGqzML20BMI/susGwjy2XF+vGSh31H7DxffLHn/1vdIGLY5oa8vsLfj+wHTeoTvRg6DClGvrgfeeNqyVIgaWGMpQFPZYUJAR0COhjaVnHWKIgwbhX9diSERMwKpWuU0tQmY72jTiNLNXLRxEJ1tYasNDY5tCSMSlRppFKRxiSCKHwAF+D+ZkDpmkZZOLfkXpEHwW9l5iOXe84X2Mto0N6RQy4R/AiiSjRNXdSJSRmSYBXYLJggqFrhziucC4x7j0gRgqwubZkLoYRKRZ5+3bB3iYc9PLy/R4LBiOXir6wxF4Ytwh+8gOH5CCmhTGI/9OADVopY4NpWnFnNg4889In7h8BHH/X0g6XzNaoVqkc1UgfcNrDdjrhoCNGQUoW+aGkeNzz63hIqzfVt5vojz9l5QGeBUBGGiiYb6iws6shCl/pAKwUm74GRUszekxhTxPWeJAp8JH1etJyWa+H8ytKeKxaNYBswQdPkhsb8JdcUHhqDnxpRTEgH2mSQ2aDKIYouUMSRXjlblVNg5DDGcbY4nDiL6TU7ixLZHtPPGcefjeKxIP3rTVgQZ6bLbNUOEMy83xN3Isf9z4b/CNOcQCwyQUYpcWgqOF1yzo/y6fpyNLSHE/wzGNDJuf5ZnL90lJ7oJM3XUOQguJczxFP46zVoS44ndqpoN3u2XLZSp9+YnXyer4ccmFAyecFMafOPqsgsZwN+almuRFgoqGPRkVpXEbYdw8M1X/zih2xf/ZIw3tBc1dR+w+bhmhcff1qopkZoKsVbZxWp0thFzcVVy+KNitQk7obEcmdpxOA2A2HosC08fXtF8zIy7u+5++iXfPrTD/jBe0+4WF/w7FLRvYJMIoeRzXZEp0gVDevkud12bMfA3dMVDmEUzXZQ3O4jV0OiQ9HpinETGDYjZytF1kUFVF1VXLQKrTIjkNXErDGasQ9oIj4WEbO+j1NXrCOnhDEKU9XkGBm6kReves7OFvikQBchs5g0PigCiogm5szeRYaciUrKPPCpjpZTmVSGD2ADTBIMkEk5EkLA+0hMmqq15bnWglGRSiK1irRVJreCNoqQBBsFI7kMuRFHLWUCXBB96Id46EBqDdoUxVOvCn/eSNGXykX0hDzNCo8gwUMEFeVI5pjgj4MScQQ9sZQ0gFJok4qcQypZuBZB61Lf0QKaxGqpUI2QjaKTqdYVFRjLKIrRwd1dYnjIaJ1YrCLejSg3IhJIYUlKhe3jXGI7JLZ9ZtdpklSINdSVoo4awwAewoMjhjJdjZDRK4u9qllcGCSUWR/9PpOqjM5Fcyt208xynVnEMsjJkNFGWFhB6jz1LQmDowj6dY6oVBlXC2VeuTYsL0qAkE3J4N0k3Ge+JPT+pZ3CR8uaNmnaWCavSSo0LD8blROWzmt2cEYYcn7dKB7+OEaoxcDLcR4CRwN/Wlie30fmYvFkHPNs846Gfh7yRM6HWkCecaDpIZn1iQBQs60t3mZupJsxnkN0TiKnSfB7ar6BWYuoGMpC1S5oeiIjh5mdcoB+DgXrw6nJNIZUOGUElWMrG83NefM+SqQ+ZxAc2FRpOq/iH2Sikc4Ocpr3KnLYfhbok3mbk2s6XwKlhBg5DA2Z/YnPoGyZL5taCGOB0VZt5o1WiD4TxshF7dh8fsfm41/x4z/5f/Ho4pzlRU0VP+fljz7nk8+3/HK3I+ZcZLa18Ne+ecby2QK9rnkxRK4Wb3P56JzVmwtevP8+Lz++5mf/n/e5/PYVl4/PeePrT9n8KPHLDzfcbW756b/4fX7vr7zHG19vee8is3c1WzL9dstDBlY1eml41W3Z3u0IPrJ68xIVoXOQdc31/UC9tjx+a8U+w82Ljt2H99xuVtiqom4r3vvdMy5qgxLh2mWcCzw4T/IOFRKESIxlkLrz0HnPuN/Q73qyi9R6iaRA8j3huicYS3agk8GYGpGanCzzdGIAlyIjEESorMbUGnTGR4fqBK+FYBW9U0WfJ0YG5wjRFYl2poYwXZxCVWfqMdPoTFOnogBrNeKhFkFHUNlRMWIlYCViKD0W1kgZ7qIiSRVMVymF1oKuJ2OfcmEcxkncLiYkOUQZUKULN+uS5SuVwE/SGCFh2rKWpOkZnmoVohQyaVZJpEBPCpSGGtCNYIziYWFwyYAY+vvMqxHSALsXpcu1sqB1IrgBGfbkPDB2F9zvDYtayGNiu4t0vaDVkmrVkGyRMX+wA1tRZTiR7sFRmvVcxF5Y6icVda3QlZBDIrfCJkZS70mbATaReBUQG7icnKdSGWtL9K9rQQehG0H6wj4K9zu8MeTKkF1krGtMnekG6DwYW2DqwQq1UvR/2U6hHRK2VIQmnF8mRkk+WO7ZKJ34hGNd8mDITqN+Tkky0/avKxEdOmvlaEFLNiAnWx3/kMMqvMaGmpkwhe1Two9ZmvqUxXRwKrNjUsf8Zg6uy+lPrXv5VP57gmtmBzIZ+EPxWeUpkssl6p+3RV4759N849R/zbDNPBt6dp4lMjx+PuFhSM6oJCcRIof1ZbqGJRGYznA65nIdZobU7CSkDJfJxSFEBKMVKZbsYWFhTBC6TH8Pbz4SiJnbF5kH7/n6eeLr68gf/LM/4Wf/4Z/y4pMfcn5pWd98jr974F9/8gm/fWX4eqV4+u1LBmdpzyzLC8srqXjoFcvU8oPf+F2G8MDzFw88/+kHfPjTz7i/79juHf+z3/kGV5dPqZYX/P5H/5r7+0QYKz74g/f5H/7xH/HN3/V87bvf4C56XnaJ+yGw9ZEWz6qGV5+8JLqI1pqHlzvOq5oqCGfrBfEmc+16/kd/w+Z2gNzRrgcebga8K0Yl68f4N5esF5bkAtp69vsd97sNG9cRlKHuBfWGZWkEkmb/GRiaMq3tsaK77fEBvN8T+gUaw2rdglpRryrsQuMS0AaCd6zE4vvEbue5M3C/69nsR3ZdJGVHrQWbE103TMq+if12IPR9yfqqaZiLBtFFJsM2Qh0VdawZTImSlFbEULSfDBllFZWytMZiV8vSoKc0WxVxucJjGJEina5AT7LcOQZy8oW6rSiF5mYa7akUKik0RcwtRznildlgl6rALS5DzKRYpDW0zijRk/5TOhiglBMuaJSFqoLV0woXDdkYZMgMW0/YJPpPBs6/Y2nWQlVZ2qsVJtY0KnJ1vmBZWSoU3gval/6Q1TOLKItXCi8Z7xMhpAlSV0hjMFWFXC5pLwxNLYifMjgS0kRi7xkeRsbrER0ii3MF2lC3oFUiJ8HFWNarVXEMorELQ702mIsFaFWm4FmFqjJaxdILNFHGq1oOkJ/WiS9j8r+0U3jqfOl01ELUgkwj5g5Qzev2bHpvMjEn3uFU2gEonbfy+vtzJH+Eko5U0zkyPX5yhHjksJ8JnkozZFL+LSdG9BiBywSFzRIax/0cil/w2v7nf8hEnzjUKmCCqtSUkUy9G9Mx6oPxZ5KzyKeXhtOUQaZtTi/r6813c/SfpyFDU+ZAkQFhkhApdN7yd2kgPBaOy6U/Fqzna3uE3+bfZD45mdYRjBw/K7LOYClrtwJxn6gtvHUB0iX87S0f/PwLfvmn/4Fhf8962fDmleHFR59y8+KGlw8Dz3XFk/OGN58swC5YXjUsnzTsXYu78+RBsb/esekfGEKHVpo333zC5VWgc5GLR2sWbYPRhseXl4gb2MXI7eZzPnv/E84vnvCd3/4udaNoJ/G7nYk0rWZVae6bBs1YJFWGgNY11mjOn67YXvf4LnL7qw39MKJyoDpXqFQ6XGNI9A97dhXQWSQFdOXoui277ZbtXUdcatrmjLZRLBqNTgnfGGrVkqSixhC7TOoiKRQyhrGaemmI2lAvNLZWLENFXtQELyysQoeE6xwvw8j93YZ+PyJZsJUFMt6NuC6AKpPP3OCJfgTRqBjL/ZOnRjJVZqAbI1hboJkyZ7iwXop+SSlit1ZY14p61UJl8cpwO3h0UKiosKHU/xQgKYIfycEX/CwLSmmM0lgz1f+mqX6EAhcRIzJJsM8OpAz4ziXa9onsUqlpTjUyNRWlycVpDH0RLcymnJfVCowUSnTM5BjJMUIyGKVoW8XatthU0arM4/OK84VmaYVBa0ZTHoWqrei8oneJfh/YP/S47UjqAhqN1JZsK8zClv33iS5BUKl0bMcAMaEyaKVoFg1NW1M3FmumwDFGQlRFUwkQozEGqkbRrg3toxafJql+BabKKJMKdTZlcizSM1YyVkWq5Cm503/69aWdwjt+4FYrHrRm0DLplOSDIZYJc5hpo0omo3MC8RybsKaGqsm46dmg5RNoJJ+whZj1W4qZ0hQFQZmMUDoYsvK+mg3mdOwis1aSHNZ+zVgrKWP+cjmWWaLjdKTpwUHIad3kpFg7pT9JMpw4hVl/L1E8d5gM+2yYZ/jnVMsp56Oe1Mzgmo6iZAgci/KHcaOTQzjVZyoxyRFe4rC/KXs5nE+e5jvMhXU5+Kf5Wh28HjIN0xR8loMqZxSwJrNUwhMLP/oksTqH731TyPeRn/z0U/7on/8BL1/8K9558zFvfuU9nq0e+A8POz54eUvImU+2AbsQvnW+YHG55vLNNZdvnxHHC168v+H2sz1ffPAp+3GHaoQ3v/UG3/veCtGKYcxU64hRFknCd7/xHp/kG75wd/zy1Sc8/+Bjnl2+TaVLwXohmlpbNnVkuRBWrbC6usTvtuAdMiTMAurGcvnuOeOYGK87ds839MqxPDdUj2pqX34N1yVCNzDcKXTvqWzEjz373ZaHu3vuXu5x54p2ecGy1VwtLVYym/OaRmu0Bmcqtnce70ZSX4yFroo2k9SKaqXQrbDOCYkNwStqEzExMW4Dn4wd3d09KitqU7Fatejg8ONAt9mSrSaJTEwchyiLlkiMCTWnuNM9YLRQVQp2EF0qhsdFJCWUybTasK4VlytDc7ZA6oqgDK8Ghx4jykEXpWQVJAge+g5xgRxT6Z+omjIsRkc8cxqaSKOeCs8BOcyCLs1YZUhaIrlIHiLZF5ql2ELB1arMZWAyvN0+omKZ+Sy58PilKjBVVJkgE0wVMhph2WqerRfUJBrg2aXlaqlYaVCVYagEZYX20tLfl999c+fYvtrSbzypS6XDfVmjli320qJSxt8HNhGqOqFVQklAp4RVgm4qzi5a1pc1y3WFsYrkMjFk/FgoskmBtBqjoGmFVTScPVvSjxHvI5ICts5oO4sImjKNrodKEpUKNGoE1n+hrf/yhebKMOhidE3IMyHgYNCAkyJoJkxWqEASxeBEjkb+NLpPB53vYokEmTp3j8Ywcxw/6SYrdxolJw405RPIg9IWPzkdNVWcRZ0SPctulShyzvh8Ii8xs6qYYKcsqGknpUtYDjzbObMQNeHtgEwFr9n5uHg8xlOHFg8pyPFipnyM4ucPlRydipysI9NJi0gp1k8ONakC+SShRA6p8OtzkgP9d84MFEwUbnktc4Oi4z5nBFoJ8zwFH8CaAg8UpBvUmHA3ib/1RqJtI9Vt4F/933/M+3/6L/js43/Bd95LbD/8kF/8gcN/RVh2A99eWN7Vmr/yN9+ll8yPn9+z/cWO3/3NmrefPCK0V2zzNa/iDe+++3UunyyxZxX9uaW9NGV2uIMv/ugTSIGqFX7rv/kBtL9k4zP2YceL7oEPb1/w6pWjrtZcXlSYRxqjYecyd2NmcX7FqCrG3cD9dcCqxPoczh/X7LjALyq2u0S6vsH5QJ+FZ199ytlKSB7Wtebs3LBYKlZnwqubLX4buXuV2Wx3RFXTbvd8sR/IBhZG2KWIajRtrTlrGs6erhiScHuzY+XBVoVeaCgT1VJfZCQqKxhVeNqOQDcMvPr0muF2x9njc9762jnf/NoT7h4GXl7v2L3c0mgQC5scUV6hRDAqoVzGqwQS6b0wTOMrrSqiKDF6xgDGjxjxWFEs2prVWcX5RUNzuSArxZCEyii8LwSUSkOjE0EcNu7JsUMJVLVmcX7O4rxmsTbEztH7geQ9ecykAWJUiNKF9q3KAznuY6GEjonsXInwc0KUYKyhqjRNXeYj5xBIQ6DfedhTGuFGi16XCXpWgSegJSA2krtI3gviNCnk8uxo2HvYjnmaza1oz0vdJBnoxsBm2/Nwc0/Y90hWVIuK9uIKvapQrSZWsPl8YHwIxO1Au4ZmqVicW5pGaBZLWr3i8sJyttS0dck+osskD26E4KbCsY0YW4r9tYXzM4veZgYmx5gccYiMorkj42qNrxSbFFjYyMKe2Lz/xOtLO4UvFjXzmEXjMyqViVxxxhyYDMnh75N/z1abE9jogMmUzt/5g1OcO8uRwnl8nxLVM8MnnEBTHLMWjmuX5qkTOGo2wrPjOlj+yaAeIKhyHLNhnhk4cmKqFZPzmCJpmQ5gPsK5Z2HWTjp8U/HaoJr5NQsGHs5pit4ynMBE83WdNlMnRV/hgIEdpCnice0Zc1UqTQ5lzuTkUIgnc9S7n73d7Mi1QCrbqimVTgKNgTCUndbLwBvnke7unl/85GN+8gf/mPbM8bt/83vw0Yd8unnOi+trfD7jW++8Rf3OI7749CMGozh/fMXf/uZv8Or9l+TR8/v/5ifYxWNWi4bf+f73ePb196hbGIPjo89vuL69RRlNs1yxtBVuAL/rcQ8dxExVV6yXFYrA0He8+GJPemtF1QhNI7QK7nu4vc9c1JqcFSkJcUzsd8Vg1FfCu09qLoyAB4kJbTNNo3n72Zqcyozg5KBeKtqV4tmlZtEYROCjFzvsZofWAsrzcN+RXMBqeLjbEsdE3xias3MgYige1/UOtEUrixsSQ444Asr40rmcIymOhKRxcaQf9ozOs5ZMs664WlaECJsxoquWqgalE9I5ci4SEikkQnBEl8kkBudwPhJjaZRSOqNNRkKEHMgSUVqKIF5bIA+tNWMqncCSJuaMlEzHqkwlmUrF0oBVVSwXay7ePqNeWWyjGcTi9xkJiex35GgooXEiU5W8ViJjH5BUdLLS9L8siaz1UfVAl+a2pMsc8NT7cr/m0lQ30fNwQ8T3njB6JI8oUyGSiS6we8g4lfEatiuhpWD6tRFqW4ZEXfcFmgquPFx2WbEwNU21QK1acqWJWvA+EGIghIE0bEhNBblCW0NVVyxrzVmtuLwqtQohM/ae7EsTZZQyEhadSJKKw3UFOjNZqKyBrIhREDw5RrzvGQeQpFGpBHT7lKnCX7JTuKkti6xo08Qdnl/lt5uMzgnePxmXI1vn16LbGbguXWAHA3UKH5XEYfpspkzmU40eDrTRw+nmU+NfWuUPBvtI25nQn8nQH9YuDmfuPC5siWNv8uwQTtk48zHPx/TrDKgZVir7P163GXLJ0zU8UdE79DsIHFi05f2y+Oz8Tov3h/el4KqJY5Z2et3LuaSDk5XDiU5ryAx7ycEpyyROJRNkNtNfdSXkaRCMseDHjOjE2TqyYMvd9ed88Ic/5Itf/o9877/4Dt/73u/w4fufset3fLF7gXfwd3/wXR6fZZ5vP6XXhqdnF3z/O9/heRB+/stP+dkvPmd15vkb/8Vv8b3vfoPm649IaWRzv0Hef8H21QOmqqiftayXDV3y7LaO4XZLchFrNKuFIauAHwZevthy/tZjKgtWl5m23mXu7zPrRxCjFKZxSPRdoXMuErx7YbloNYOy9K8SEGiWwhuPFyQUoxMe7mJhutTC+dJyflaTlOLRq5GbT7+gqg3aJHabnq73QCLdb/CDp2o0NityCEiOSIz40ZXxkTrg+kDnHH0cMYtAZRRaMiE6fND44PBxIKaEaKhaTVspFgvLYmyxzYK6joh4dCoCipkiV+6DAxJxmi3gJ9g/SUZUKkVoPDkHICJaU7eWZlHRtjVBK2JIjC6VvhQpdS2jS0Re6UytM8YITV1xcXHGm++uUU2Bs7LTGO+RwZHS1GAlRUwrSZ4QiEgafZnbHNP0WZnclqfu+UwJZpSliO5FIXeJHBUkQWoFujwbro/4LhBHj+DRNiEqERxsB08lmaChuzDsjWB1xtaCtuBDZrMPjEMghjLMplo3VIsFslwj1jJmGGMm7DIxBVJ25LgHUShjsHXpeG9bw9nKcHZuCC7hhyJQmGOcGISKbHOZx0AsLD6fyS6hs9BUFmPBDaUnKxMJacQFhYguTaraIElQX26cwpd3Cus+lkaTg8JhMSia0jTChFUfLRjT0PoSJR9a10UmjbWjgVTkUuQ6mrgD5DJj78WgzXMZ8knkP7OAykanzWbzGrMTKpPQjjz/Q2Q9NYrN/z84tnzC7slTAfdwjnneBDh2DUM+sINKMTkd1tUzVZQicXD0fKfHPKUqWQ67KnWJ2fNNx5p4veJ+AmcdxnjCVBgu17sUmtN0/cpuZKJlFWm5SaPm9HimIS1KlVZ/Vyb+YRQ4l1mYYlzTmFgsE0+azF+/0vy3/4d/zGeff0CfnvP3fuf3GO72/PD/8t/x7M0POG+3rEQxxFs+uP6IZJf87d/7OvbiTV6+eOB//0/+Tyzait/83rf43/39/5qz3/wWzo3s7zf8/v/jX3Bx9oTF2Yp3v/9N3pX3yNoQbcPQ3bLsey67kbRJpFl6YanoQ0fvr/n0w19w/u2n5GXNqySMK4XvM26fuYuhFOkysM70u4FuyNxWC978DcXlUvFbX634+FdrUgxUi4TUChdgrzKdVrheSEl4fpb5zoXi22+uSGfvsf3knqwiF1c1zo/kcSSniEk7Hm4HfEpsX2y4bBoYVJmNnAqF1KeeYd+x3e/ZuRHrFauzmsoq0uDZiScGj9SW5gJEa3Z3gU+7RM6KVVtxcX6J0Z4QR8R4CK70GeHJDoJXOBRu7wm5NDdmZfGxTHtzQwGolQWRhnbRsFrXrNcVm7FMHXNjpK3UBP8KZ40BiSgCqdMkt2R5tuLR20v+xl+teciKl/sCT3bRYZxHtzXaUO5FU5NUmWAXfUaNhXohKWNMoWmkVNCAWBi/hBxRlsnhAVWLaS2mqqguFohVpAS75yNh70jBoxaCXUYw0I+C32xolSI3lpQXVFpojJRBN30ug31uHOMwBUQLzapZoRY1amEZBsWwC4w7z/5mTwy+GPazmuZJw/Ky5fy8oQmWtjYs1oamFR7GxH7I9PspK1OZXMei8pqEmBRpmPFgRd1WXFxYTKvYbFv6vsMFx5CLkw+S6ZIqzjxNVWce8xe9vrRT+PrQ01UNvakISorA0tyAwpHBUoz2VPac1UjncH62fnN0TlnDJjkYvIP8RD5KVBySikShWc4Zw2QH08xCm7prT4fszEHwaeG6SDocz20e7JNyyUzmQ1VS5t4ejHaeI/VJzmKKxOfsYF776Kw4ZAk558PQoBnzDyf+5egKp+NjkqWeis4ZjtVoXq+fMElrQC4RQToiUJYDYaPURKZ968lTHcb6BFUygOmaGi2kLLiQsVVxqN7Doi4ElBDhsp7kNFKmahKtBPztA//9v/oZH//yT1DVnq997ZI/+r/+Ka9uX7Jz1/zV7YLvXyz53m9b/vBXW37wva/x3rvnDNefcvPqhnjdsxbhnbff5ulbj6kvDJ/8h1+w2ewIPvHdr32P3fYa19+zvYusLmrGYc/19QskDxil0KLo+h3O7UlxwIiQ88Aw3vL85Qd8d/ubxHrB9j5h362pVWbVZrbXDl0FjC1RbT90uP1IuG741fkZ41PDk7b8cLsXe7YPG36sLrGNLdGfMqytprGKvkt0a8Ea4a3Gsnx8iZHEG+cVNLr0esSEbR33vmPY79i/TKiLFVZatF0XOmEFGCFkRwwD0fWYZDGqwZqKoGDc7CFFWlOVuk5IdK/2vHw1smw1VkrmkEIieY3Ohqg8pDINzGVPyoqYheDHMvYRTbQwjoLvM+NuQA0DVaPJUWhqw7K1rFpD50NBCyK0tSlBfhR81gRXNIa8aEIcWOnI06Xw3kq4FQGbuXslBStXgiahlUWkTCuLIZJdIo+BlASlBTXNrmbaZ/SC5CIHnhZCCkJ0idAnjDLYRU1z0bK8akgRfJfoR4fyCpJC1wlCKqNJXSBtOlJbkRvL+tyyWinaSnh4iGx3nn2fiEN5/kQrlK3QWpOTEMfM2BWH4LYjYdsTvSeniK4MzVlNu6qorUE7mWzIFEAnppGemZhLT4sPwugTKUAOgtUaYytsY2jXltWVwbaKbBSialSvwFHkQlIxEMY5TO7R0n8pW/+lncLFFEWFnAkH+GaKODlCEzOQIvl1OiWHyF5eg5mm9hnIs9pqPhjSkybbSYcoH2YxnPSaHYTiBI5a/ievPL2vZuz8BMzKFOOepn9PNvawz8NGs0FnpooeGUJMmcv8XcW8n9PrcuJbJtiobFeygnmmwlwzmG+WeY3DOnl+75hNHNaer8Oho40Dy2r2rDL3L+SSNZRyjp6IHyVVF5l7UIASQJVIzGWqWkoDXMo0gJuw3cZmKu/pHu752b//Y9zDCy4eKa7qMz7//GNe3D1nyBvu67d5+13L40cLbgbh6ZMr1mdLdl9k9nc70hC4WLe8+fYbnF1dgjE8PH/FZrtHlObx9x4BW3ZDoI8RNzr6/cDu5oHo+6lZSpODIyWHUKaflZ6Rjof7zxh3D6jmDB+EfGnJoRRFN10qzU5mmm+QC/c8j57dLrJd6iInETJu6+mf7/n0U81iZWkay9mqZrmqyVEzdJr7QVi2wlIr2rMllZTZDNKU5jUfi9Jq9BtiGLm/2TL2I1QVy0WNqRSihZgLtJOJKJXKnODKUNcVyiuccyAZYwUIEDJuN/KwGZFkWVRC1oVggFIorYpO0BTQpFierZwomRKFredcIARFCIkwOoyPZKsQUdiqzFKwVh0py7PQFaWWZnKhgFa6DKYXSdQkFiRMgLrKLGyRXS+Za9FBKzpiQk4RQgl0BFVUPkUgqRL8HJ4JKWKJMRODIngh+kTwCaVLl2+1qFhfGMYuk52g0VS6KJ6KLjUQiZHUjTAETFtRt4b1SrNoFdXUyDrPnijdcWoapyhFQjwnggtFBbdz+MGRgycPvvw+C4OtDUbr0smdj4DJcYZEQRF8zPgQ6YfIOIYp41dIY9FGI1KkWZq2sNLWUSBZjAgS50FApfZb+UJA0acY9X/i9aWdwtDYoseuoJ0ymCSC0vkgoZyZpHImA3OKZRsth0YtmIetlM9DnImsmXkeQ8k8yg1WjP1BGLpse4K6GHNi4JFJT6lQV4MUwd9ZgE+mMD2d1iUodLSsis7fqU9R0xVKHGm0UKL+aXY66qSOUSigx0LzEWYqujTqaK+xujx84TXcPx+cWs5FM2iGsTST3lTmNWdQIP9jraVkVOXzeEo1nXDo6DPDUJQq0eVojQZfapcg4PwkUKhL72wKmaFLaKswkqkAt03oJtI2iUd1Zrfr2Fx/xgc//X/zN54+Zj0m/B/9jJfdh6QUeKwqhvGGn+8aXi5X/J2//5t048iPf/LAhz+8xoXE2eWCr37vMW/91neodctuo1g+eUrzKKAs9Isbnnz7Da5QvNiPfPbzDxg6j9Itv/zxJ7y4vuZ+98B/9Td/gJHMshFevrjDrC/IVWBz/Su+ePkJV6qhrjSf/sqyy4oYQKHRukZrg0uOar1geaa4etLybKHIu8RPr8G7jDaGuq15+ekNGQcqsTxf8/bbl1ysl6x1w61Yrs4N3700rK8W5VpeVLx3WXHjMtuQ+fr6Ar7+mPuHe/7lv/kR+3uHriyX714irqEbEw8PDj9mTGNYnbc8feOK1dkaW1fEc8G4Nck7xm7P7ctXRTbDOx52HcEbaiM451k2hmqp0YMmOl1gIltRiS3cfxK2NmAKy7B73uHEECnyCsYaTFvTrFrMwpIrhVMwRMFHIYTM56/2iDaIMeQKdIjoHFHasagSxge65wP/9uctzSMDraLbJtzOk4ZA1VrCCHEIxE1ElEY1FfrMkqJMmbBgopBMKSqXh0jIJPxuJFC6hqOHcZOpl4JWmidXipuQccDyrEZdKXLy9Ls963NN7gLdK0clNVdXS776tTXvPNIsDaiYqRp48siyGDPdLhP3ijBGQgrsbj2Ddwwehi7hkiNmj9QJ2TqUEpr1Apsgd5FuA+uVMGNf2x14X0jkPaUrud9F7r64J/ceuzAsni2pkuBcJJMw24wsM6slvP2mMK4r9hvN8880u/sAFqoa7HJB6NeFuPAlXl/aKby/bqmypkoK6yM6lSLQcMBn4EDjySf9C+rIwEGdlIhPWDl56jQmc9w2n6r2zFH+XDuYnMUE8bzWkTwZd5hoqlOEn6bQvPRDnKw94U1lMtpM35zM+cwQgmOXskzRdypdyQdYa5aK5vU6hCg5wGGcwDpKyyHzOTRrT9dmJsxqZCrizzmFHBZQGfLEMjo0fTJlCRNlNJ+c4nzNU8HFsLb8+FlK9DhNOkHlonNjZCpRR3h1n1g18O2vKAafWFphbeDFPvDWWabVI//hJx/x4oufw+0L/t5v/R7/9J/9I5qx4/uXZ/w33/g6Y9iRwo6rGt77wXs8/cpTdO/Z3DmqJPytv/5d+pwPQ4ruf3VDXVVUVhOVRkzpqh33CkVHiJ7tF1vGzR4fE6nKrC4qyCsuWsXTJ0se7JYQPDkNqDRCrhhiR7fdsLrYcbb0RelToDUKeauhqiJKR/w20/c9ez/iwoalVZytKp6eGey3G/I3NCq2dA87rl/dcnf/wHZ8xaaDuo48fZR5eBj5bJP57NPEZ59tWa4bqvoM96RGG8WCYhhaNE3OrB9fksKWpm5YLhukrpEqE5Rht90QYiaNETc44irRGM35WVU4+WPFaDRuNxK9I0lgHDrwwkgmjImw0GhTnpnKlIhVL9bUTTOpXyd8cLiUMCGyNwNVEoKC1dKwNJbVsgIj7GOiT5mYhWqhabyhTZHWG1yIuBDY3juGfYfbD4wPO4IbaCuwakXjPbvrzF2C4c7hgyNojx96ktNkD0RPShV5jIgKZF3opNpqdFVNDVqZ6MMk5Z0IViO+qLHGFowvdipEuN9nBl+o2vWZxlSloO6DZX87oJzH6MzF4zXL8xZRis9uQtEnConYlyJ4H4QhFKnt3gtdn9l1I6OLOJ9KdzPlXjaSYF1jrGa1tjS1waKRXGob3pdgSylwfSKMmRQN2gr1QrO+1MT1gKmFemVRYarBkmna8tiGmHFZULVQLzX1SvDoElSazG7IDFEzDl/O3H/5TEHUQZJ6ZhTNHbUzfHN4zQUGmaYqqaPRy5Mhl5NC8/zdA031NXcwvWaGzgn0MmE5R0z+dJ3D6+hAXsdyCvxy3Oe0dT7KX5weW1k3H9f+NYjqcNq//sbkHQ/B/Zzh/Nr3T6GyPDmeQwPe/I9csjI1ZVB5cjgzRffwXY7I0ms/zQSfoUq/gRYmMT0pHnCaA5CLymFxkglCKFnIqhFinHSvQmbdRirtyW7HzRcfcf3Fxyy6LVdNw/O7O/LugbWPvPPdtwhZMYyJhkzbLqjNAu8fMLqmWtQ8feeSrSuMjn7n0aqk/coIMeTDzNkUNd12w2634eMPXtBtO+yi4ezyjPNHK7J3xK7j1as7JEdqW0Tecg7E6MiMuGGHH/tJIqEoCRlA62kWgBJMGSdIiplhGOl6R12Vjte6BaMNlVVoHdkOFapTuM2I846QAsYm6AL9GLjd9zzcb0l5xWJd0YUFziiSUhgt1KJprEVXVdHxmQyf0ZoINBGsNYSsSo+JC2UOcopolRGlSBmitZjKAgUfTylNCveZOCaCLZGpQtAT1dUuF6zWDSkJzifcaBDnCbhyLEmhlKJZalqjqBoDWeiGyL6LdKYI8yWZufTgfWTwga5LjF1PGEZyDOX5URGUI7uRoQtseoi9JyVHwhNjIMcCP6sJxssCyaciiaEVui6zEqIrjVp5kgLOKpNU6brOqhj/ZMuEyBAT3S7jBsgxoytQVbntRcO4GTFEFrVh+aimXZZO5O1DJPtA9gEdHFkrXNb4pPAh43xmdInBR5wPk/JsJotCtMLq4nxtZWhqi9Hl+ks8siJTKtlO9JC8IKr89soYyBrvNcpm6kYhfZie44RIeQ7DkPGVHII8XRVJjBQLHDwQGRCGP8es/nmvL+0UlmMqwyaUHOAbJtBHnRSN51eWY7drsY3TDOVp+1PbeuhIluN7c2R8ZB+VkLpsLyf2NjOP8DwkK1OkPcM987ZzvJ2lTGwSyYeaxGy2587eY80jH5A4dXAWpbtyrilomeoUefZd+dDsNTuCQ3veTPucsp75mGXKVmI6cbKTA50dZcqCSnPGIKTZ2Z7ASeXaHz2wqJl6ypG+OyUHWpcMIUmJPSaeUbnGUhyCVxT9HFWykFWK7HeBhyHwg29qdt0D19fPuf/0h4w3G+zo8JIZguOm7+nCC/63f+craLOiH4ShS9xuYQg9q2y4eOMtVlfn1G/U7G97JAd0l3n63jvYqgBm20+ucXuPksjlM8v1Z3d8+vEn/A//8o9Zrx/xlW9+hW/93jPWC8vYDXz880/4+T/7jO9/513eenZOW8E+jQRfqmDd/o5+tyUOYNaCQVBR8D3kVqFrqBtNU1skJnL27PY9SpeZAV2AtirZlKo1ptGYWhGGgmsDiDVYvSe7ju2LOzZ39zg/gNG8enNFagzaaiSbQ6ADgvdFrTMZMGgaKUq/7WJBIiE+k8dAHAZ8pRitUFlbnj0DyiokFTVO0CVQiCVaUKoqv3kEYwyqrmkullw9aohpGjCzN6RuYEiZkA0Jg2hDs25ZqEitIAfh4dphHWifiVqxGQMbH+iJbN3Ivht52DrSEJCQMJViUbXYRhP1nn5XsdsptptimFPsiLknulgyb62x5zVgCF7jR8Gg0I2hOivDatwmk33JijFC0oKyCT/ROUULsVUEHRlHz+aVkF0hydR1Bl2cShRPf7OjaTXmK2dcvFmzag2VwO6lJ/WFLSa5I1Y1wdb41jKOmWFMdGOg96UwHHMiY9GVKUOBtMJaja0Ui9pgdNGDElNqX0pNz1yOxCAEr1C2FI+VhsVZZHA15IRVCReHUpDODh8CcSvE3lBFIdipEXYKbFIqkHA/5NJ/Egfg8j9q4+fXl3YKPxj27OqGva3pjCChGCI9GdaZ+znj6bOj0LPBiwVDLzIOhfHDZBj1DLEwYf65rKPUsfNZya91QecTQzexiUQxDfCZDN8U9Rf4KKMn7aOUKF3NUy1EqbnIwyEbKvss2847OtjqybpWMjmEdFJoTmVWbs5T3W0yxCkJ1Yz+ZCYK7izcJ4dahJ4choaD1O3sUHws9YachZDyxKo5DjCafYNmZhu97nwPcha5TPxSs7y2QI5SZK+F6ThLpmAk8/YzRQyR688977QJlXp02hF3NV/8/I/59Fc/Jd1c03/+itzveGU6viWe7z8+51vvvElFZGlr3mhWdLJFmZbaLvnq195g+fSsjE3cO8Jdxqqaq6+d8YO/919itDD2HT7+Cd3DA37seXh4gakdV48rvvveG6izM67eOEOkYXcPVbZ85ekFz67WvP3WGeszxbIScgqoPLKNiv1nr+irO/QbhfdOguxgvMmoK6gqwawrRteQDMR+ZBx2bNNAGgdSrNgl4TrD43WkTpnLVUX11iOevHHB+aMVUmskaKozzfmblleDJitPGO/pXrWMFD2jD5olX6kNISdijlTOUxFQfSLZoqzbNBWPH5/RDIZurIn+AX+/4eF+w40oluszrJ60lEaPylBVpR+gojDNklZcrSu0gs++2FKZjLYVdWWoK11m2uTMOBpSLuJvw+AZkyUaS7us0TqTQ2S/Hbn75IGw1AxXFl03bHvPQ+e42fV0u45hdIyDsKgq2pXlvFlyYTM2R0z03L+4Z8ylniE1dC5iU0TciGWBbi3tsyV9MGVWch/RsRS463NNVRtcn3ExkjYevUpl5jOmaCf5RAwRtfaE0THsNOE+InFCPK4gjx4/jIx3e/Leo2tF05YhSAurqRBio8oQ+taiZEEXFC4relfqbt5nvBPyCCSNEotdlOzXNJbKaqyVYidjJOzTFFBmmnNNu1K0bemI1yMwQvQKqWSyOXNwnRAVCa7AhyFEoigq1WB1xdhb6qUCo+hGYT+AH3LZ342jDXuW6QF46y+09V/aKcxDaAoN9QiJHLGbI14+wdbFGM1NUur1bGBu+MpTVX8e8alntgyzRZNDlM80XOeo9nkKjcwVhclwv5YqTeY8nyybf+3j+TxnSGY6xpMQ/xDVK/KxPnIwvHlqtJswnNPjmNebIJk585kpskfpi4lmq2Yoai7i58Mp5un9DAea1Gnv2+GYcvlBDn0K0+80+YFD78i8YinTTVIe075SBklgp4zFTDfn+VK4aDTPP/qQuy++YHd9w+bmM+6+eI7pOj6uMm0lXF61PP7GBTFVPDxE7qPn6195Rn32mGpxhmkti8tLqqZmrHa0fZlYhlbcvbonOM/29p4Pf/UFjCNaRVZ1i51E9rcPI43y+H0gjgmTa1brC5p3I6aOtKtMZqDSpRODVLo+97s9fdfD5BzN9NtYKwVz11OuqRWYUtNIU/9HlkRSCTEapQWsYJKhocK2ikdPF1ysa6QSfFfkEuqmplo2iNJkUWy7PYNXRKW5c4avVIpWK87WLelijTUtttLHeziDrTW1qshVYuhqfN8RnMe5VAIEY7EU5221KdTFqkwq0xmiBtuYMuxKTR3AaOapaHHSuvMh40MmhDLvwYVMAFo0Ic+0zcQujpAVyhRJ6/0QGDpP33n6MTC6REgGdEXVtFw8WvGoAu0Dcd/T7Tt0BdWiXFutVVE6jQpVaWxlWSxrklPEfmLJqTKm0hiFMYWeikz3tCq/h6nKnOYQA2lIyKUpNbYY8V1hMiUtuFFI0RGHkTwGdGWLBLo2mFxmNigBuxSsGDQFYk1dZhwU/S4zenC+NDzmKeNTSmFtRV1XVLXFWoXRQErEMeH7iOQiOIgqkxmtgdGXwDVE6PYJSzm/HCZ5Gp3JtjCSnPP40aG2PdIqVC34YfpdtcZFxTAKbsiELhGcoDCI+YvF8OA/wynsjWGcjJqOk0Q0E5aXZ5NWoBMlcuIUZtt+xJJmyKWolk4idpMtnfWJZlMlU/FATcY25SIUNRtOYOoUnpyJgtOUIk1U10m/rzxjcihPlP/OJTJmympei65PUvuSfUxGXVPke/OUJUxGWJjofXM94IRqKmoeMn7MbpJkTlVhFVMROnFYMx/Wma+7TDb+6M3KuU1CFifsozRlMacFj1JUn9vQpys9QXMyOX2Vy7mShDBmtILVQpP1yNXScGEr/s1/90fs71/SP2y5e/5LXn3yOXHroDnjnUvN+u0F7XfX3P1Y+Oz5LdfbB37vv/49Lt96B7tYcffhx1TrxywuVtSXDwx2T7cZGO87fv5HP+Xhesv1Fzf8/KOPOastl2cti6c1Avgx8KuPb3m01zR1T+o8C7tk9cRg3jzj4mpgs7nm7tWO2oDkRMoBwbPb79j3PUkKFdUqwWRolwrdZMRAjOkAOAdtoSrDWuxCSLEU9JZnFhkdpjM0tqa+aHn6eMm6rXE+4lIiZqGuauqzJTkpsrbc7TuGvtAM74cGvai5qCzPHl+SB4sSS7OyhK7UU2LIiC6SBpJrsixwYSgSCuNInyKiDZWqMUaTlaJWqpzclLLHnApbEMAokocijwD7scwkHscyd9j5eHAKQ0j4nGiCYFVG+TIXOHlHQqEMmGQYxsg4RAYXGXxmDGU0K7rGtguuHq952ipy7xhEc/vKYWuLnFW4ISPaIsqjskUbQ9UY2rbGC3gbUVLkurUpdZiCBuRSA9KCNhpTaepGo2qLBEd0EdNUaFMYI6Gfxn4aYewgh5E8evCZ5nzBYt2wMBXiJ4NkYXGmWDWK2li8C/j7wC5ldl2kG4TRCSEoMnWBjI0Uob/KUlclS1AT48+NGffgimVoSrYPGaOKDIUPpT6xfUhUTqMUpLEI9ukqYpcRFyLee9w4ItueujaIVUQfiZ0hac2QDf2gGfuM6xJEha5aUmP/YkPPf4ZT+NH5glVWZchOjIchO8OhQlridEUxdkrBaSFXzTBFLhxkMcVSHjp15yh2wtZP0PsDZDMzeA4RlDpuX7adisQzbp8O0PyUwUx/ZQ7rH7R/ZoN5wODn9w95y9QBPGU/k+WVuYltyiTmTGB+qamtPpbLUbKJ2TFM22qYGv0mZzqpz+ZUIrjZWZ0adpk8SIGs8mvZz3ya5BLsHqQuJkeqUkbH4sILADnxwid2UgpTtDzBZ3uXWTZwtYZzrbn54nN+9MEHfPjHP2UIr8h5w5sXC7a15WHX83x/zX/1V77Gs6sL7BfCj/7kh6RouDi/ZP3uGba2ZdqPabl9uKfHc/FozeqRJcUHHp7fc/MyYM8q3vntd/krf+8b9B8/4Ld7Vt/UvPjDjs8+vOOnN9c8GRNnz57x7uO3UY8Dw7Cj6x64e36N2znyBjAJmzJ1Bqcy4/09w8OGkAJii9Q3Svj8HswObEhE46lNpjkznD1dsGiEKmcqF9jh+OpFxW9/peXHLwIvtwPbu3uyrhkul9RiUDpTaQgkXAyc2YYQM6TIZuPY31/j/R77amSztDw5u+Jrbzzh7PwxGuHKWt7/zLG/dWxvBno1YhYG3WjWqwpdXTC6ln69YewKrq5VJIwRT6I3if12QRcz0SWG3chdq9C23N/O5cLL31VEkeIUhkTfO0YfcSkTxeODZfCBzWYkmYxNAa0idatRjUYtDbauySaRK0NbaYIkQhrx+4gfAylmVG05uzKoUdMpxeVdT9caequQEIpeUsxo5dDSIhlijBDN9MwndF0yHDcmBp9xLhVph0aKVI5IGQVqBRUr4lDTXpgiqucSWXcknQprcuNRrlDR2/U573z/gvOl5UIXOLjvEl0OjDGxqTJaEsP9yPWrgdsd7HcL9l4TkhDQZCtTI5smo3FBSA48hdaeUmYYPEM3klJicAPNrSEGSz8ouvvI7s6z2yb8oAkpluDag7IRozNKC7a1iGoxtaFetJiFIWvB7WMhE2RFnxLb3jJ0wniXcQtN3Rrai7/kTMGGSeTqaFeZrZQ6vHeCw+TXI+6cj8qkhyxiwsKn2txhnRk6OTgETizeIYfgkDnMXcWZI8w1rzMzePSJTRVm/v5x/RPfVtgBTBAPR4hHTRnLAYKZzm7uzp53PNcqjuWJ6eBOK+nHwyzQz6Einyecv/y30rNjzYePZ5eWJ+svcEx9pjrKvLskB2TtkGofXaWe6hkTE0LmfU37ViAmo31G54jJkd31jk/e/5j3f/RjKrPn4cVLuttrkmjeebLinSc1yUUev/2EZrliyBVXTy5RdcPF0ycs1m+SgiakRHNxzurqKbq1bLZb7m+23Lx64OMXD1w/31DtFMvR8PkvLB//6hN2+w1/d/VdVhePeO87lr/6/g1vfPVtvvnt99Bry82HL0vtQCtW63Pu3MCYKR5OlzbJKmpebu7Z7u7xQ6IKEZc1bhSyKFxI+F4hFmStqBrh/JHl0UKRfWR3HwibkX2nuRlqeu8ZXGIcM/mm4+68I2vFeVVjKkMVM4hCb1PxvxWkFIkuM2xH9l/0vLjcs6gaxlymeNWiWWpFSJHODTx0W4KK1LqitrbMYrANi9aybC3bh0iY9HKCH4k544dYBuv4RBwC+/s9ewvKSmHu9BllaryYIrGepUA+zhNCIMRICPkAnw57j7aZpCKtVpi6wjQaZaqicjwFNDpFxFqyCYTsSUkISXBZqKwgWSFGEaSovo4ukEN5Fk2lsMt60iiCMAaSL5ClWRTpj5wzvo/4mHGjJ6bSXZmn+1YUmEaTUdRBYRcKQiT7TNIyzYkusiZVUtjGsni65NmbNetG0wTIY8HugwuMQ4/KgRwD/abnfhPYeYPDEpOQUKAFXSuUUahpFoIPBdaTHmwDOcVyb+VIpnRn97sBFQNxrwku4UImqozUGlWpKQBNZDMxq3ImZY02FZWxLC6X1HWFVpq+j6XXJJcMrVDvpQiWalBtxjziS72+vPbReJTrnY3hPOpRTWHqLKUgs5WV0wh2itBlml+QmYrKk076HLsfSPv5YMEPgnaz5c+zfc2H9w9O6sQvzXDIkWKaD8ur6X6fC9uHNeaitcyHcoTHTplQx32WVz4o180HPb1/yExOMhxO/EI+KWDPX5+V7kQmpzBvMfsMOZzqcU7EEYKar3vOf871OOxnOkmmYs/kGObxnDJdGxHQIWElUeXAi89v+eKjz/j04w95+sjzxeaW+4++IMqa7/+Xj7l6UqNiYnlxBrrBh4q333sHaSztowvq5hHDfiAx0pytac+vSCRefP4xN6/uub7Z8Wq35/rhGhMiTRT6Xyj+8P2fcNvd89333uHZ3/g666vL4hR+8HUevfkG0ihuXtxQWcX5Zcvq/IzN9h4voLSZinsKowz74YFtt2XsA7ULxGgYOkXVCkNQOJ/RWRUEwQqrM8PjVWHA9IOA8+z3A883lmH0k2SFon/oedj06MqwPq+wlQEURlusuAJxNJocRoiC30WG5yMv39rTtA3KLLG1neQeBB8C/Tiw7XaIyUidsAEq3RbmkoaUW1QODH3A956uD6RQ4J+xc+ACsXd0mx1ZF8ekGkPqM9o0BKqp10eV+kFwBO+LsudMAxXwvWcMGWUzzUIVxlVlUNpMUjwCWqNMKcxmpYmMRVwwlfnDRgMakoIhZXof6SWTg0LrUjepVjVJaURlwhAmmRnBLMoEtZwyfh8ZXMAPZRZE0YufHgEFuiqGOgnYFlIPnlhQAFWMpR+LBIYyluWTBY8fW5aVQA99KENqvIuMu544jgTn6Pd96VGgItSBiCksSyVINUlwqCISGUMZXpT7TFwJUHoYYpkXSs7Q7wfyaAiq9I+EXArFUgmqmYJNNQkXqgLxpaiwqsy6WJ0tJkhR4faRFEKxS3lCS3TJ/o2BepFZnJ9arP/460s7hd/sNmyaBVvbsFOT4l4uXbZqAutVLmmoUlNncypBWmHxTBeP8sPqqTqaUlGGnTt11dwwlspNlOZiqoIcQSWoUpkToCa+fY4zywlyzAdmU4wZqzSZUiybxe9SzFhVPHpiUoOezsFMTWVMWVGIJRMywpGuKhBjni5e2Y+e05+YMTI1wlHS0zRBY5US4nyeUvSDZIJ45sY0ofCL9XSzFWNe/h1ymbmakSm7KNcpUeoGMRUnpJnGgE4P4uwEDUAUJJvSBISATA9wVIesyggY8sTMySgbOG8jb648v//v/j0+XvO17z/iGRW/0MInuw19vuM3+6/zXv02X/lOxT/9R/+GwJI3vvFb/P3/9f+CX/3sl/z8hz/j7sXHXDx5h7OrR2zur7l5/pz7m1t+9Pu/D3bJ+umKv/b3v8L2R98gLjry+Rb9ZKAbX/KrTxOrr62hqYgxUb/VEtMOv90QXw7EzZbUKlhEVrVlJWUYybJeElRAi3A/tsTU0/V7XlzfIJfn7MZM1zf8nf9Vy6f38Pm14u5Dg9cRJxCelOE/qxoWTwx1r+j3HZ/9bMfTRxc0FyvGtubVx9BsMlY7VBu4qi2qLn0f3ZVCK8WitXSLK9L1Nf5GiC8eePjknhtvaBph+cQSLGxjpHuxxd08EPb3uEXC+jMkaFqxWGMxVmNUBetMpwN9Gtn7PW4MuDwS+xYVHTmO+OxIKRBDpH/lkRyxVUMMCRFXjDsKnwbGvmj3xP2IXS3RJjH0HYyZ1CjUoqY2trAJcyKNiZgyIUWSC+BHVHClG3/qA2hsCX5CSAyj52HzwB6NsxW0GmsUi0UDovH7VLSLhh1KWiyW3Bh8VIWT30eGwRN9IKWEtKo4Wh3RyWJ0RluFGKExmZEyKjaN0zyGHKDrkUZTGeHRU8NZWxzxPmWUEcSUACs5TwwDEFg/ajlbL9hHw8NnCZ0DSoOpNNgSNKqpXueHTBoybpMIW4WoIvOttJ2CT8dw1xGAUQtnT5bopkYpzcZFRIOqpnnnu1LHGe4SatsTjUUWFTpXrFeGplX4GBm8Ks9sVGRbghpU5vIy83QReZa/nEzql29e0xo3FW51LMZwVt4saIP8Wij6GoR/VCOdotk5ai6aRse+g4P+0FSTmHsNij5Rma4kJbs8GOgkU8fABIGoKWpWqhhMhEOtI5PJeoqkT+CsNOFEOaWJYjtlNSfNBJojFKM5LQBPxeV81C5Bjs0phYlcipeH3gmZOoo5BvNzgpWnhr8D/DTtQ6up45pSDD7UO3T5bulbmHomZlXUqfug/BaCMqpIGytN14GIKvMIFsIQZkeVqVNGhYQbIhetww49d/s7fvXRvyTue5qs+OL+V5xv9vz1pxfcjfC137xk9dUlHzw88PXf+Q3q1Zqzd56RjRCNRS3PePz2OyzOrhCxbHZlboHrA7vbgV7t0HVmHd7l//j/+0csjOarb1zyO3/1u3z7u+9y8WjBoj5j86qHnHjzjbcIbkTliJieq2cNtobFRRnisno8suoj6tWGrISkQQKs1mtyEH71p3/Eb/zGm9hc0+0919sGWwlvPVFcUmNXiaqNnKuA6+5BJRoFT88gVga8ZrWoWDUWLYr3xeD3PabPXNnChhlD5G4YqHLpVt31A5t9jx8TytRQW+7uPOL2VHXDD87OqJXw0CVULhPCjAkgxSldreBxC4HSwFYpxePW4JShk4zf1vRdIriihSRiSmOU6cmxsI2whqapqJsKu6zY70ZCHPExITrS+8gwBBgCyjqUGNa1xliorGBzxrtEQJNElUJoDhgCKu+RcYMaB1am4p03K959u+ZbzxSdi9zd9Xz26R2b/Q5XV+QWrs7PMbo05m3uFX10eIGUFbsE/Rhxe0esywjS4CkNlqhJYTsWnd+cUa6HnSKLJkVNriCHOGVMHr0E08D6GytaVVPXis2nnlfnmvUC6qbYIx8Uodf0yoJZIESk0SgpDWWVhtbqqcA9P8Plac4KAkJQgkqKxSODqSFHQ78Ft4dx4wiS0Y1BtZaQNP21xztPrht674kx4oYRv+1Kd7IVVquaujHYlaXvBTOCM5CMIjpFcJmxK2quKsByJVRRYMi4/V9yR/O9NQRVJJb1BJMckI65jVbkSKHMR1OX4Tiz4PQ1O4/TBqu5lpCZagpHfF/00aDq+X0pHZ0zFPPakB593Oehea0E5K8ZYpFZII7SPHbymZL8+rbT3zNcNMtMn4hbHJrH1OQVZ9gpzLMa5joHpS5wGMXJ8RhnGOgACWUQnQvclOf9cZT4UByc2VyjKZPU0tRBXvY5p7hKl2EhQhErM3U5kSKKVxxQGYGaWEqAYcv1zWcE94ruesvmLvLZy1/yzSvDu8/O2WZhfalxxvPpTc/vfe+bnF0tUecVo3OINaweX7K8OCuzCvodY9+XBzdHcgjsxw2hO6OVip/94mc8qde8qVpW5+e8+9VnnJ9XLNs142YLKXJ2fs7u9gYtCWVGlmtVhsLoNDnACl0tiDQkKTBHVI62XZFT5ubzX1HngaQdjVI8bGBxDsuF0Dy2LNaJulJUyTO4jqgipjYsbICsUcpgElxUFctFxV1X5I9tjqy0LmSKFEhpxJJJITOOZbgLCbQp/Ne+C2yCo6kT2lOMjIfKmDKu0ha67LJRrFvFqhI6HwgJTLYYY2hQmGxZtxadAo6ENeUpUSmjlCbFcv+Z2tIsF2W+Q21wD64wj0JE24yPkehjec6nml1VK2ydMbo4hRxL9i/MTqFMaCP2SOgwYcQYy9WF4cmV5fFa+Pw2sR8D227EJ0dUgpiKqoK20ugsaC/ULuO1KoqhTkr9z8eS2c8K0HOkeShoFvJLdorcWxKmqI06IYdQvhSLMKK2ivayokkaE4X+PrDZlRGhVSNkDVEJXoRRdAlCRaOUJqcyiKmuVLl+VmGtTKOr80FM0yhFNIKIYv3YYFshDIroHHEISCrKplkbsBVZTKnljAkqCEPEjZ5+0xP2HUhGt4Z2VZG1QqzGBRiClC53DQlVuthdIrnyW1dtgZd91HTDbL3+068v7RT+ZL3kIglnUWhDLJ21UnjYM8iR5VhnyHM2oJlGVM4mnoMRy8VuUZtpaPgBGDzp9j39DqXYEnOBqmQK52d8v2RLc7e1kPLU+5wn5yJzVD85qhnrn3oTilLk1IcxGeaDAZ4MqJo8Q8rHpjHh2HxHmp3CBMWYki3kVGC2uVxy0EqaM41Z/lod91kg0nxozIuzjHfmOJFtdgJGmGf4lIJfcXAuTVCUTD0YoopW/nTMiJCN4LXgbSaIUBuoDTQi6DM4i4HnN5/wwx//c/7OD/4a//3Dv+Sf/Nt/zpgTX/nGN3jrN97g7e9kfvGHH3B9A/ec8w/+4TdZry0vP/2cl1+8ZHG24Le+9hvktOVP/6ef8enPP2d5UfH4W+/RnMHVo8zd+wM6eM4eGd5qF3znq8/423/rm7zx9hWrRWboHnH27Bk3yTEMHboqk7Bqk7HaQ9jg9pHhXuPud9xej9xdd+z2HucUISj2HppKEZPD7Z+z2d9SN5anC/jZ88SYNe0lqEa4WGpWNehgSJsBnRxEy7Z7IHUW6ZaMd4n89YqrqkWbBeuzTKsiy8qiTdG+GReW7u4BUdBUGlMtiH1NHBTd2BNtDQLnixYXFRIFazXn5wsyaxI9ahF4fNFysW6prMKHgBCxSeOyBTSNUqxtja0zXgm2bkkmEBBWWJwksoHz1RpbaZRWiBiSjShA60zS7nBfcGFRyxqp60muZoJSNTSmoq4sttaIiuTkkeiJ3Q6bBoxJLNuKVV1omU4oXb0ry/nTBdsgjEaIDXR9h4SG1tQ8u2wJywVuiOzvBvxDIDUQsjAMaaJLl/sUrQpkTCanQPSJsSvqoElVeCpcpwjek8RjFgFTG7SG7B3JZ0KCmEuhVlvwHrxLdPvAviv0WnIszWejnSJSzfmjmqrWBRJXihBi6fGICZ0y1UIj2rBAUV2U6DTsQSeNVZa0asnWorUiJ41pF1xcFqz95tqXInYUdBMQXQQXsQafAqOPDK7Mis4asGU8aLJC8kJWCqmLncJCV5VrvY2vMWL+o68v7RTOhsTCKKop7J0nc2UmlspkZBLFG8jsyScjeqjDTtsf0KYJjjk1wvPrz9BRTz98zemdxPLqNJU72XZyFMWPTAwhNX+YD8c4NUtMm08HneWg2XQ4MjnJZmb+6PTfhwxH5DB57QATzXTZKQqbD+5Qb8mHLQ/nXqKPfHAU+ZTFNGcSx9OY6hnTqUwaR/NPMX9NAXVVbioFE8WvwEe6Am0KREZOXD888PLlHfcf3VPJC76yzPzD3/wWf/r8lq9/4xkX7z3hpz+/4bYzmMuWv/q736Ky5fdars4ZzyuihsEFNg8Bszqnfer5+GcfsM2ZJIEXm4HbfWa9zVzfO7773rs8e3yOl8zL5z3BKXJq2I6RAV3UO4eI2Bbd1EhVkafRhCopdlvPGAWpKy6ulqStIY5wpoQxSRk56Uc+//xTnr1ZsbpY4V56rAGMprooI0YXlWCUJVYtMWRG5+i2CZOEZaXJdYvSFm2ENx5r7mKDhEhCk5MvM7VV0cHXdVFhfXKxIsVIN2rcq8/R1JiqSDuTBYnC0gisKkgLJF6QV4mz1YJF05BI+AwuJMgBsQmjDdSK80VDYwwuLqkv6iLl3I00Dz3KF+bP1VtnjH0ixkxMwmK9JKaSIQzDiEgoEt1NTbIlywoRUvJTLUtjFgV6sktLjp6QPdlbJCua5Rqz1lyurmiqBkmGfgQjQmVLhK1Mi7KaqGFz3xFMEbC7uFxxdmmILoNorNtjiFQ6Y3IiqNJ8ppOQrJC1oKgh5KKdlANj8Egl6FZwLpeeiuQwrdAsNHVjqLWgYsLYwGKdCaOjv1MEBcPg6buRoRsY3IDIJOmdC+dfRLALPc11B58jow84H/E+omKhfWMDoiyxSxAz49aDROxKMBc1MWuyU+AVstQ8etOwWGlyoxk2Fj/WrJzFS32oB4kDUYqYA8qWWquaH/zJpqRJuVpUBivoOlG1mXqZgb+YlvqlncKT4DDKonSRQ5gtjJIjtfNghg9wUjFqs5zDASIRmWCiyS5P8uRpsp7F/p9IQMyNWyeR9pRPlGJQOrqBolw6ZwRT0fl1G1pw/tmgTu+d1jiOVNJynKU0cKx7zCG6SNExOZrw47fktXUmmuthP6fb/jn/niC4A6no1EnMl/hkgcOaUuoBcrLtTMUtf0+/w/QbGF3qEUjpzEye0iNRz/IlmZQjt7evuL++Zrzd8f72U5bB8+hsyXs4akls7/d8/lnHar3k0RuXvP3uI9y+I48VCoOtNL4f6fcD98t7shWqVc3156/wyaFqEAt1u6CqWsDyztvPuDhfULct49YRgweV0L6IvKUkqCCYSS8fKU2QGIUSQ9x7EEFZw2LVsg+Cy5kmQw6aHMHj6TYb0pOeZZuRlwmGTB4yyoPOBVprjWIwljGWoe7j4DCmpllYnC5kCqNgWSlcq/Fu/j2Kvk1dlbGVMWhEKp5etGwfn7HpE+7mFiuCloSZxtKqchrUVhFqS2hraGBZ1zTWkoInx0zyER89jc2lEzsr2oUtQ+wTNOsKN3qUCE1TI7o0Vy0XNck7cioig3Vbk0UIOZOTIOLROiJaMSaLiwp8KvCTQNKgjMbWhnqhCT4TvEErixZN1Vqauub8bEVjLSorRpeRmCfiiJShPiETiAydKxz8rEg5U9caLLTrjLlV6LHAP0oCSopgoFKqqARrQYktMzNS0X7PMaEkoqtE9A68Q7zH2oqmqWhaW+6DPKIN1MtMcKFMoMuZfnAMg2McHT6WcaQgxOnXUSiU1kRf6oQuRIbe43zAuwg+IoMGoxFt0XsDEcIuokxCWUEazf+ftT/7lS1N0/uw3zetKSL2dPYZM09m5VBzVbPYbDaHlkkblmiCoAELsgBdyNOF/Y/4zn+AYehK8IVhGLBlARIFSbApiqTJHthDVXXXmHOek2faU0xr+CZfvN+Kvattq8tA7cburBM7YsWKiBXv8LzP+zzZy04QCBJgnKZqNKsThdOaMGp8BK80IQSmweOvJkFGQiRlYUmlKMKNOSWBzFIS7wwUqqgAdzbRVb9hP4VvTRvWeslGGbZGF75+xpWh7xyoIkKDkoo1l4GwLIRYLdXzzOKh3EfFRAzypRYcWwa2VgnunVEyZI3pwKTxBYfXWhFCOshm+ExhAmVCSrSVLgNnfmWpyyoKP/tW0G5ubtKdDeM7jUzZiZDbY5a5RgZ8LMkkQ0jq4IegkC9ZaYwQ6Xd1eJ6ZenpwZJNnLe+PPM6n2+3tnCnaR8JEmu09w53b8/yh3oHx5k1rq8QpTSnx2bbztF6JaJbyYHPG5AJbJTGYefbRT7n45JeEV9f8kz/6I945P+b7Tx/y3e8suXj2ks//7HOebTP/8N/9u3zw7Xc5Oz3j4tMXWNtwdP8RmpH+4oqLL2+IbDg5P6ddwIuff8T68xXL8wVv/e4prVry5PETzlbnfPOb77FYNJycHfPii54QdiQTcScnMjiMCaNq6laMXFKITDFgnaPuGuxmwiZFpQxLc0TvAykHucZMhcmROA746xts6DlZKRoVMT6Re03cZbIXhtaJM2ytI04O7zXjds/RccfxsuHmekLrSG3gvFIMK03vpYJzzmExaGeJ0THsxQP46UlHNg7amuHzN7R6xKWeFCOy+iRXXaUh1Ya0rHC14qSuWTjHxnvwkTh6phRZLDKt0zit4aQS2QUUixNLP1i2leHmosN5+bI2lWGvJfErbVgsW3RlyUaTvaKaJkIImMZyvSnV9uQJky9FnkUbTd0YlivDMECeikaSdhyvGlarjnunR7iqwihFvxM/6P2QGDyMY2YfIkOeCNsRrTwuRPr9fdRJpqoNR8eaptbodSCsN1LYJI3Jmli1KKPJWjizJgRMypiE2HWqRF0F/H5H7CfykOjOlyyXHd2qpg6RMGS0STRdZv1mIvuIToFxDIyTZ/ITaHBWk6wmieIgSlsyhqEPTH1k3Hn2U4+fZL/BD1tylC2nZGt0kK6iag3dvQZntEjehCj+JSlhAoy7jHFw70wzOYWfNEPUZCzTFNhuJ9aXI8oHsjKEKTKOBoyoDoSQRJRvDKSgJcZExXEHpzlwmj1w/JtLClPbEK3whxtfoAydUSqVpCAYxSxZYbIEM12icDQSJMXYuwROJUHL+3SwiZyDaSJDLIY5zKJz+RD53DxQTvKE4ksgx56hFhHwkXCr59lDqbxT2SA2INai5dg55ltl0rlKL0+r5ywxJwnSQfn1Ft4SpVOtyodSnKyY46+5ffx8uKRE/E7Nr71sacecD92VtLBiApSR1znvbWi5rg6ziBTlHFIWzZuD3HZOh04oF+bGTBnuNPRKNFYeHSlcyKTeM+63XHz8JduXr6jaib/51hO8nng13fA3Pvw9NukjWnvF/+Abv8U733lKe7Jge7PjaHWOcopJ7xhVwixrugcrNtcDhC/o1zf86Nkrel7xtfyEf/u9v0f/jqXtTgmTRt1/TK4NU1uxsdeEDNrVrI6PZQA6TSgLqQrss+fm8oZPP77k5GzFw7c6Rh/ph0Q/yJd0v/Hs9zJAzQg9tKlq+s2O2A+0KnF/CclmdMy4dWK/gRunWC1gUg2jSgw5kDhhGxzP+56L9YReN8RFzRQd614cCtPKUDtNYwy1cnRG89wOfLUeqaziw7OKBzW8fu+E6folYdpzsX/Ng1hTJUWatPgzR6FdptINgQSK3ajY7TIhRo7uJyojelNVkO35pBVNrUrgNLRHBuctSWU8Ez6MxCTUzfbI0HROOrqpZdxpwhioa8N+iuynKEPMcg2lUWCS4CPKa8IUmKbI6BNT0mjXUndL6mVNTFoWzbYBP01sdwPbfc/QR7ZjYBcyzlQ0lSLbSH+1Zb00LBYVqyWcnGb2+8BFFmaaCRYXHBlLyooUtUAwk2w+C1yasSlhxp46e0zlsFXN0ZMTzt9tWJ5Ywi5x9TIxDp79NjDESIoJFRI+JaJWUFVgQTmLcQ5TtzgjMhzaWMKU8WPGTwZUg65FjiJZS5o8yUfyVGjvjaY5b2mPLSkmdlcju8seawxNXaFyZFxr4qhYnBnSJMN8p7TsYgRFGkAjUgMxJNIQGG+UeGVMAoFJQoiEQXbKtLaYaHDZ0szB5zeVFF41VYlo8qYrVfTt1Vw9S+lukABtc4FxDpjHLDWtbkkDqsT04hKjUGRVdpkzoPLt8LlU9sjNBz2fw550yQGzVtEtTJJ/Fcopx59PbcZf1C1udQvulAPfhb5u7z/ffR5gl+OW/YtfYQ6V8zkshXGbc8rbdnv73eQ1/+1wQvlAT5q1iuRZ7nCfDgfl8Nf5BcnWpwzCYzGTlrmHaMhUdcZZWNZyzFEFhmHN9cuXvPnqOdP2JW89OiLqkWQC65sd1hlOzpa0TU0cPHGMrI7uw35PyJFpEu366D27qy19O7BQFt17LqeRIWfuh8CiOaIyFq0dw25L9JloEjEUxoiRilsZi3EyRVMGqAxhjPTbgfXNlnbRYGyFqx11SOSoUFEsISsNUWd8mFBZOOpxv2Ha7Zj6kW6RCU7acPpI7jNhUOxrBVljdIWtlui2JxnD5BNjHBmmif0QuCCz347onNgvK7IxjGg6a8gGskpMybNXmZXRHDWW5aMl6+kNftMzbK7w4ZyQLAlDjJ4QPSFO6KQJyL5NVJqYNSErpugZQ6DygUprYooElAT/rAkkYtENksVTkYaAKHWczhgr7KaqEvgiRVn2M0aJ+JxLIo8RlBApCCTvCaOh72HYTwzDyDiNsgmdMj5DRBNyYbSFxDh6plGW46DM4LLC1A7XKJE/KRCI0pmu0ayOLYtdUUbNiawiGYVOgRy0dMNRwSSXe3YahZiI5/1EbaBuHE3Tce9hzaNzx3KluVSKm9eKMCWGoWfKIhWiUiIVTwutJEIqY0FblHWY2mFc2Ti2ViA5B1VlyCaRTSb0gehl8B09IknRWk4f1NhGM+wC+5sg0JEVUoNpJD6kDOOQRek1CbzrA4QgfzO1FAfGGJRTYtvZJ3JMBB8JMZKiJ+dixZPBa8OgNNsD5vzf/fNrJ4VP20bYRwmsCbgShkbDQaIanXGoW6+BGRPR8/YwB1hDFXvOnLMYcd/GuyIjXaSsswTKw5KvhIPDiR9kp0tUNCVU5tIFzJ3BPHiRun3uLEpwnofTMx7/K7ffva+688d8yD4C+dyBakoLkMjCv84Ferortlci+q98TnezhbwJRalV7pXSHdXUmSE1HyTdUmdn57k566j5CcvtSUlwMQqyFiwtFbvBrpL5ZowZz8h695qrF8/47NPP+PLyS37wP/07LEwiTQPPPv2C89OK0wdHhHFi8/qG2tR88I2nvPzkhwzjwH6sOH1YMW73vPr4Bf6dxFv2jG50rHMk5kxEUZuWuq7wPrFdX+J3Ay4ZonK47MguoiuLzpIcZiMinCENA+N2ZLPecO/BGU3dkRcjVmeaKrC3PdtdwV51pB9HSIraWeLuimF9zXa9ZXF8zpQjIYLfT6idJTWaTaMgaipdY7oKdzShY5T5AgNTHBnHidejYrzeYFSkWy64Rtg5HZY6BqboCWHiOidapamdZvX0iOGqwu8i0/UbvH+CT5aYHXGaGKeeaRpQ0TCmWvyydeHho5nw9OOEcU6uwykSFMSoMdGIlERKYERhNOeED1GYRIby34wy8u+q0aQgngykJAmj0rgagpeqNapAmjzTXrM1me0wsN/t6fsdPogoXj8mRrFYFln6EBnGkXGaiD6gdcRohdOWpqvpGkNbWRnoailOVq3h+Kxi7RuWu5bhWtCJrAJ+H0hBQUzkpFFDlO+9UUJxnjx5nKgfV6yOG45OFzx8u+Ktc0PXKPaTMBnH3rO52MCqPdBbrXEoozEWYTglDcqiXY1qLLrSmEphaoWJWvyelwnVgKog9pEYRLcsZ2iODd1Cc+9ItsbXyrO2AWUsutbYhcIu5MuZc2Yc8mHGmRHr3GkSlzuzdGhjMM6iG804yua3IklCjpEUxeA3K01Umd7UKF0ECn+Nn187KZyNkZU1NEbJRZcliqlfCXYyQ5gHqtncvrC5ohfRqlJzqxKYKMTTEu8OKw1Z3TJ+Cs4+B+KZ+ZQP/0dh6tzOB/JcaqOYfRzuBuFcaFC3dXZJPnp+NfIaUXNHML+pmUN+KEsFM3x1EPxTCqOEMjffL4Tb5Hint5DkcZfZRD50H6hfXYyb05qaM9Wd7mhmMKUkkJSez+1udza/lwgVWAkZXRbachElC4qXlzc8/+ILfvLH/5Jvvv0WyzCS4wa/tEQlAmF513Nyds7jx/fwquH+u09pjla8Hr/i8zcvePnlNc+/3PO3/953+OKLV/zkk4/5+lsfMtIxKGmrKwyrpuHJOw+5ngJ9HwjJcVRXNM7Q1o6jqiWSSVqzixPJJxm0aql+laugOiboivr4hHvvvs3roFk9WaKN5fXnv2TnNrDpqdd7bq6uMVlx2hmu1xuuL1/w7MuP6Y7vU9lM1g5PwO4yGC0SEEZRNYb7x47BPqK/GegvdlTNklQbejuh+8Bu2GBSYHuTqSZHrzMvUuC8CvjgWVYRnSMhGZxSfPtoiXl0n5cpc/PFC/phj6kcxlaMk6cfRvp+INYVTRep28TCaTbOMFiDC5ZpHBgUNF6WnWKWbsKUbXYVIg2iCZVw+ByoakMMCVRg2g7kmBjqiEkBQyAg8tc5I+ZCRpGdXCs6ZOI44bViTJExbhn3O4b9nikG9n3AbD1u73HOoRIkn8SprY/sQiDS4DrHwtQ0VUtVQdUYFvdWLE4qFkvDstWc36tRR6dUTzv+7A+vsfuIGTP+OpD3mTwaCJqkE0klsolYq6iSoo6Zxw+W3HvUcnxeUVewHzPrIXN9GdmvB4b9wBg9wVuUtigcXWPLlrIEsBw0IRt6pTBOqKBkhXIaW6nyHkmnkSaIe4WtFE0Hi3uGulE4K4XslBKmMnTnLaZOGJNxjcZnj7Fl1uqhqiQK9j6x33rGKRBypDm2uNpgncEPsvsUksRgTCHxBEsVRZ3BVhpdRVydqLvfcFJ42+/IqibmikkrdNnSUKVaFbTiFtAARN1QzcE636qMlv/OG7mHPYM72TGXwJVK8gGpOOaqPxQ0Xx43e7FJ9p8TYsqZynII5qoMb2d570OGUIdXUKrs2zevgEC/Ah0VoAuN6KtE5kRWXmd5LqVumVaH490uYBxmAmZOAPOrLw45utx/zhcp3c4YmP3nCtQ0y3bMyrTzJ3FIGApxzUsc9I0Mt3schITNCYfI924vX7J+9iXrn37FJx/9HMI13/76CfWQGYaBsO/5xjc+4OysxtUaXS+5ubzh8vIanz3HR8fsFxO7i8/Yb644v3/E3/g7v8XT7z+m2o1crt/wyDlaa3jQWCY/EaNsi1qr6Grh4zujYJfIOZDJ1FUtdocxkpOiriqYIlppusWSpl3gbEtd11RVizYWg8YphUPhs0Ilj1aaxiqcmVB5T/Q7muwxdgIduRk9Rjl0NMSxIlcy4Oyc5tGi4nWfWOdAii1a17jKMQ4BTEJrWXrrasHwdcjs+x6jMsfO4nJiFkG5V1vuHy8I/ZLxpYIchPOPJsSRJBQXcogyQEU6Y2Mt1jkqb7E5YZKXSiBMcv0oTU7xoO/lrEEbuYtJUDkvPsIxEcaJTJGmCZ4cPTkFUsjkoMgeVCkWVNTonMheEauMz2nGJUshJJkopyiYui29u8rEFGRBayrCbqbC1g0myeavwbDoHEeNoXNaJLtrw8Nac7awfHZ/hOsJvQkMfST7BEHsPw0yl7TJYLzGKkWjM/dPWu4tK1YVrPeB/Rr6MbN5NTDtRmLyZAfRJ7IWe9ZUfAmwhhRLHEsyh8zSnDAOEEbxWA4J4iQS59HDeB2oV4b2WGM0jFsJ7rkX5lwE6lpjTEX0njgFttcZW1UYm2kqDVrA8hAiw2ZiHAKRTHVkpCAPmf46HYgrRieR5kfjrJJFQBQ2ahY+cxQT92a5/L/i59dOCg9jzyYottaQlbkNaiUIUoLmAa4pA985OM4JQpfoP3cXSZYbDnDRPCcQSW1VquNyvDS3CXOy4FBJ69JvzE5pIvucxBKvBMg5D+isDmY+c86Zn/vwNPPtJSHcOr+pAyRjCtQ1y15nCitpfs3Mc4I5SOcD1KXVvOhX/jJj/HB4nfNjZlQp5tsZiyBDJZkmWZI7DJqVdCZJIW1jKjTHcr6q6NGoVBK1zhAjLiccgv1u3nzF+svP2fziBT/+xU9465Hht7/3NqqPbDcj0+T54IOnxLwl5oDrlrx69pzdekM/DLz9d/86u+WWNGwI054Hjx7ywQcfcv7hMc9/8nNehg1Pj1qWjePhsmE/jARqgQw1uKbCWplf5aSIMZENVMahG1EEzVEM6JP1GOs4Oj2lbRcohAZqpf8XimJEgk9WOANGZ/FvthMp7hi2lwzXr2lWC0xdk21G6xaVKvzOkJRBZbGjPaksG+sIVGTVUrmGtnbsGA+b5U2tqZwsN9VO83K3RSuorSZ7j1ei93NiDfePO8K05Kq1GJVQBJRyErBSKFBsQuckhAGlcM5S1RVxsjjA5oim8IrjLG+ScGXBa3C6dApgg9BficjA0ntZHtKanD0pBvlNmRwMBCkCXTKyfW9m61ZVUEyhiBprUWl2TEykIFh3UpBUIqRAiIngISURJ3SuQo0Rk+Xfy9awrA2NFeZdVWuOrWFpNUdnNdOUSPuA1Um00Uwm5HBImC7V1Lpi4RynteH+ccNJa6hJvNlO7Law3SV2r/eM+0F8u534HKNm+rnAc1kLbTanhIoCqcYoRdt+jRjYhISPiTwm4pjxfWa6iaIKUCmiz2yvIuM24bcJazWuU9Qn4sPdB8/Ye7yfsFXCNZnqnpN9rwzBJ/zg8fuAz9AEB0rmS8M2ijKr01JEK1BaNqwtGpsVNbBMipOUuJfirxXrf33tI1vji7ici0nqFZUhpzILkA1ir6RNslD4vUJdnYOQQq6/WTsoaQ5+AKboD0VkGKpzOsBHiiSysvNJqxniEUxaz7i/neGjjJc0IY9XqugnFYexVDoApVAJokqSgApzSA6eD8Nt+S7qA4YvA3HRMmqsFr5wFoaQSYVxlFXx7M3FxSwLa0pLR5Fnxx1kHX7uFrQR9pAkGIqIXi7CebdD6PncTElkmuKXXWwStBJMd345seyDzG3EZkyctJqTSuFtZKUznU7EXc/Ln/2YT//sD/nol3/Cze6ab9iH/LX7D/npF5/x7Q/u89Zbj3m5eyGb2K7h5KzjrLpHfVHx1Y8v+NGPfsg4BB598DXef+/7jHHiZn/D4/FdttMj1tXID/7RSxb1Offvv0OvVyyaI/wwcL3ZMYU1dV3RtA3dgwXhGlEmff4FJ6tjFl3HatUR9nvQNcfvPObs0QlOJ16+/IqzkzOG9Zrd1Q27652IDDpLvXJ86/vvk0MgDVvS9cinv/gLfvjHP+Lz//j/zP37f43Hb32Hv/YPvsN733obZZb8/Bc7Hn39Pt453myhrmGrHLo1rD5s+dp9w8Nl5qNXPX6T6FKgn+DzYcu9RcvvPjmnso4vn13wJ3/+Cvdy4q0n5zy9f8L3u5anx0s6o7j42hOOlkuUtvQx4BPoyrFoK2rXsJ8yw3rgwVHD6VFH6yzPvEc7T10rzo9bvpxGpjHiY+KdVcWythig9wGtjbDXhsywD/jJg4az1uBag24UV9eBfhoZR4/WFXVrwFiSd7hljTGKttMszmuZNRi43iRy7jDKUj2sxJQ+G/p9JEwTKPApsPPQJ5hUhmRR2aCVYrmqOTu2nKyMdPZZ3MlqoxhKgRdVplIGvw5cfr5h/fwKtViCbdDBk9cZS8Px4oxv/N0FH7yl+d4DxXocuLrpefGqZ3udSNZgVCaknt1+w27yhMoRsSjtyNqRlCQkPwmen/uI8pDyRIqQvWL9pZBtUohivjNFdKuxC8PxNy3GQiLw+S9GxusRnTPHZw3dkXzPx5vE5cs9/W7PsN8T/YSmom5bVquaMZQKNkaae5bcKqZt4OarSaCg1lLfs4ct6ugztVU4o6g7kTtZ1JrjVrGo4KTJnDW/4U7hZ3VLixGNFRWRmiMR1CxNnclaTNANhUo6I0kKycKFfTNTPmc8Pc/6DIct41uGUIHVi31gCdB/CZI5QC9lejuDP8J+yneGrfN9bx97uL1AOMLqSXf+OJNOS4JR86nebhZQ4DB9OD63BCDmvQc5j3njGTUL180PSb+S6LJksLKMB+i/tNWtbv/n/H6lGa4y82lJklNlrnB4mzQYqzAJshYpjJwirtE0TpO8wu96xv0GVe/57vGCt1aZi4svyD7jKkVzpNAbcXwax4GP/vznnJ129PuBjz/+lA/MAxZHC84+POL4eMnNxZb1tWbq91TWc3KkMP4eIbZyznHA2ROihhhGQso4HVGVRydLpRW5dRzfe8TR8ZKqqtAo1v3IOAX63Q6T1/QeTHAcLRPNySnN0TFuaXn5/DVXFzfsX63ZXNzItupux7CXDmJZax6oY6b+OV98fkP+V2vq5e9w7623cVVH8pF+F9lXhvfvK7xTpFYxOkN2Gmcyuqkwiw4bLU4r4jpwEwZ+udrSWsvJ0YLpwRmLswWNs/Sj5+c5EqNn3w9kKmKKZB/ZbycmH6mqGtN0KCvVYJxkkGuURleOumtRSQzjx+SJKRGDZ5oCF7sNe1+Swn7AOovSmmVjCAvHZEVXCAMhBuI+0e964hTRZKpK/IJTNiyWpRfXYG1imgKTz5DEd0AlsJWj7RphzmTxF0hkYowM48S+Hxj3Ht8nWWhVjkxgmgL7jUcHgzMZkzRDI7o9kQafDUNUjPuB5HuMmjCNJdsExmNrh1tVLLqGs280fO8blsdniq6GzWtNGCL9Tc/6Zk2wNSOG/dQzeo8PWXSDykA5TNBvpHRMwBgTOpZZqTfEHRAUYwKVRK56moLscAhQibYRlSCHxHDVk8vcoMqGyjisEYinPWnIDqg0PnlUkm6r34v/slKInIfVqCajkxEIapL9Bu8TIUgHY0JEdxZVG6zT2EpT1ZqmUaiVIlSZvfsNy1xcGSdVcZLdA0sS7SOVD52CRiSXb2UV1G3cnKmr3AbLW5robQKZl6HnEbD8WW48CEiofND+KQ8vv796u2Y+jxl2KcG7/Hue8B9Oc04geZ4acDsDOQBJcKCwHh6e7ySk/CvnRDnuvDeR52QxJ4ACt83PmOdkIfjQ4eRUSTgzzDUf/XakXhJm+eLOx8jl85jPJZeWPxsxqFdkfABSwhkjwzifGPc7xv2eZDzvPFhx3MKr16+pu8fUdYVtFG6oiGHC9yPPnj1DpUeEwdOPPZWqWLUrVvdPqepKLugBgh9QasQYT54M4xQZqpE09iglrJhMQmuLs46mqgipIueATY5uuWK5WuKcJfhYpAYy0Xu835Eng0kiJZy1le6uqQsWHBj7ke16y7Dr6fs9OVWCz9eGs3bBy6sLri+/Ivxc8d5vvU19coyrWtke3ie2JqNOSpNsRNMnKIhKoSqHa1tcNGhlyENi5yc+v9rz4cmSpqk5PTvi3nHLpBSDjzz3HpM9cfSAsHuCDwx9T8KgG3C1w2gjBIKQmHyiqjTKaKq6Jg17cor4GGWjNUb8NHGz3dJbi1GKsfekKHRKZyyV0+SsIcrFEZNs5vpBqmFK16mUEBiaxhZiRiYlzzhOxBBIIWCVQhmFtYaqcqLWmyBrRQzCiBlGzzB4pjHgp4yrCrWVQAiJcdDYZNi3msZoUpQhakSBFTOoYbsn+RGlPKrWZBOFrOIM7cqxPKk5fdfxtYea0058DXLWBJ8Z+4n9bsNkA6Oq6P2ELzIfKWgxHdAidjcNmRgKNKSTWHVbhYmR6DMERQBIkZQiIXlinCA49KQYe0R2Y4wMNwN6qcnOMsZIVDIANkZTLSqilutIJQcpYxClU6VEjSFYSEoJC6qCNAiLLgbZqUizQGZIxCqTnHzHtRYfaGc1qgHcTBT4q39+7aTwYBL2UasVXpB54TKTyiBX1FNtKfbFNlkzzwesug2jt7IYggN65tmDltVtbgOmVrcMIVuI/ioLs2cOhvZQvsv95wBpShoS9s18fw543SzCl8p9cxaVQVN0qzNZNOFLgW70/HrK7IF5iKsO4w4F5GIMMs8NcpalPVlGk2PLQPo2kN/ubchG8UyF1TPUlJPw52dxP30LHykKfKQog/LZpUkS9h3ESHjLCcYAD1pDHALjNsAA7YmiNYnnmysuXr7k6vUbhinx+K2WtJv40x+u+Q/+/b/D2fkCqx1nTx9y+foVN/2ej35+wdVN5Pio5bd+99t875vvYauaXciy2JQCkwmgB7bjFc9fv+Df/NOPwWoeP3nIt9/7Ot29E1JlsKdHHC1a7p2dcO/sDJ8y203Pdtfz1fVNCVI1ymmSzujKUB03XHyWcbWmOq3YjJ4v//xnfPX5l2xeX9APnmmM9LsJlTLGWo6OO4ZkBcI0ifNF4Gq7Y+gv2Hwx8fnH38Ytz3nrnXvkTWTaBbY3lucLhY+KtIXcwHqC1zUoY2iXKxYpEo1m6nvW+4kXr9esvu9oG0tzvOTxScfFICb319eRVWMw2VCRGGNiKu9Vt1iyOFpwctoSIoRdwA+yRau1xVhN21X0gyNHEVCLyFZrGAOvX17jtMaiiT7hbIVxFtPWjN4TchaGTYE5/RhJU8bnKAuVk2UaAyEZlosGHIQY2a8n1jdb/DiRvWd50tJ2NW3j5HtWqN8+e3wMDFNgu5/Y74McL0Ripchqku92SoRoSdFByOQhEH1kuPK8sTtGYEqZ9RdX7DcTExOhjqIZFCyNNpzcr7j/pOFrX9d8ayEzvc96EJWLxDRG1uvE1AQmq9lPhilm8VfODfVJi65qtLayaDdOjLtAconUaVKlMGkiX0SICreqiSmRdCA7WVg0NmMdaJFfkCBYQ0oyXB/2FdOU0EYkOlxrSUaTrUN5L34xWYlsS8FiQohMk8aXDeloCmIQE3FI2M5gGi1CoBUoKxB6peTXWfnuL13mvPsNw0ffjNd41eKpGLSYOWhkqHqI4KU+1qjDYFZms0XmogTonG4HzSW9FCpnLgNg4VPfDnflGLPF592h70Esbw58Bz2LEmznMrvAKDDzdtQBSTJ6fn7peGbU6S4rSR6Y55ZCLO/UrdroXP3nA95DmaGoQ+g/yGmU5KVKl3CgqJZERXlPkipzCsWvCBDKYmk+JK6codbyd5XzQdbjthG6w3tOMCWYAlQL8En06VXOMgsZB9affsz2xTOm6wsqlRnfjDQx8airePxOR9NZfB+4ePac6l7Ng6cPuHe2Zb8eWLUL/ua/9bc5qzP7m5HrV1vGZse064mTR1WauIkMX/U8v3zD73zr23zn/Xd56/1zVsdHTD4z7CestaIkq8RKMex6/HpHEyyh37LpN+y3gWdffMRuv6Efe7745S+pdMWyW3F+VlE5x/2HDzg+aZnWO8I0yXXlWpRKKAY2L3dc3vS8uenp/QU6DRzVcBMmNq9fcPn8OWf3vkbjehLgR8OXX2msEz592CXeNCKvEvcRrQyuMjijMLYW90fvWT8f6ZuIbQyPly0Zy6IyHD9q6Uwi+IntZiCMe3IQL4XgZdlr30/ExCFoU2dqraicocJhK4tJiS5nTlyDqRN5nBiGQboApdA0ZBWIOTGMmaEfZHHUOcJCo5QRf+Vak8fI6CO7qWcapKPwLkBSTD6w3fUMNxtS8mgLxizFMa62AmmUwD+ljA9FDyglxNBcfn3I+MtAf7HndFVjT1e0RzVn95esXEbHQL/e4N9M9CGwC560z6hYZHWGRJ4CmsTi9JyHbzkePzY8ajJXITOO8PpNJG4CcYCQLFOG0cMUhSXEJElFUVE1HdWqpu4s/iSyfW1J3jBNPRrxcdEJwhTBQ64rVEhi2hMzOims0dSN4+iskqItJ6qtY/9GPlN8YuojOWmMke9yCgoVRMI7einsTs4rfJ/wYybvEmELISqCMqQoVbeqQKtEzuIFrXUULSmd8ENiMokhwi5pzkxmaRJnVQSqvzLW/9pJoSPSEwm5DGILFGJKtMn5TsAqj5m9COT2fNifOiA85b6/ygRShVaqSsV8+zM/7mCTUP6dSjC8TRCqVN23OkTMz02ZD6jDP4SmesgRt1AVpQMoeeAOhs8hQMvLmGcF3HohlNt1nhfvBIc6BGt151zgYKs533cW4ruFkUobeeh28mFxT6Hu7FbMdFkO1FwOr0GSqI/gQ6YWvjBD1qIflBJDP3L57DW79ZYwTTRWsNauctx/6KhcRqdIGjyvPr/hvH1Idbbk9OEJ+dWGum5ZrI4wcQ/Jy7p+mgdyXqiEUVPbmocPTnj6/mPeeu8Jq7NjjK3I40SYIsomJh8Zx1CkBAI5ZpaLjmwmxmlku9uw3qwZp56sMiFmKqup6grjHO3xiqatSaHFL3bEYST6gWQ6Uo6kYBibgN2OkCNWexobaW3kehzYXd2weXMj3Z7OpJTwU2SzTVSNoqphCpH1ToTU/BCJVhGNJqExrsK5DFEx7qQS133m5n4kG4PShvPO0OpMrxW6blEhkFUkxcw0TOy1QVlDipppSMQAaZEPFGO0prJGpAzIdHUtVX+I5P1wuKZNkclIZXM3RUXMiUjE+Ih1stzmKouNAlGFKZKDXDwpylUefJRq33tQCavFltM4g7Fyjt4H/BQYYiYmscxMKssyqCnkugRpiASfoa2xztB0FYtlRaMhTZqsKpHt9QmCQSlXBDjlGiSJLtHy2HH/geXhuebEZtb7RN9n+q04wcWYCUnhA4wpM5EJoyJHC9Ro06CNbMG3K0PVGeIE0y4TLieR7Umzvpg6FJtZa0glMGdxcNdaU9cGVRWYVmumdSCkAFlJoI+xKEWXrdksg3md5TlMER1VWaCq7JVARkqRtbnFprWWzzJlsi5wPomYIxOaKcqit1qAqTPW/4bho147Jm1IGlzOJJVAywdECZgxZUYlg+Y6y/LMXA0nlQ9BVN5cCZCpSFnoQuPMZc8gI4WFOWznZoK6fT9syQzz/VWWzWpZjJPn8jOUpX71OaHcVwlsk3MWvRglzzPPHzJZsnIulfTtZ3hIInBoRMTrQc9xXDqDlG9pqORcEpoq85jboD3jtyAfPrnIf5T3VpJqLp3QPJ8pwV+rOyvjhZY7by8nST3za3VKsQ+w7zNH1rC2ihujaBvNFAe213s+/slLLm9Gpqw572ra1ZIn5x3fOm8ZbnZopZmGwE9//AXvdS0P2iVvf/cJq1NPa2suX18RVWbYBVKGqnJkErt9z1fP9mRX8+SDx/x7Tx/w9Adf5/zJY7qThwz7QL8duX6zpT5CIkgUNcpxTChT8ei9J/T9luv1Nf3rF3iVccuWe49PwHvOH93j7fffYtUeMRHwyWPGkbCdmLY925evWO8V4zgw7nrW0TIpReUSy/sCX/jdxDiMXH15yWqxxjYG1TmSNwz7iB1ESqLPmX6amGKk2iQqH+lbh82Gba1xbUOTHUp7go/EXSR4zxfdQHvWsDoyPKgMtYYNCtetiuZXYpyu8P3Ibj9w1e8wqSYHhVaWo5NjuR7KtWiNwSKMl6OqwVYdqllgX18Tcpaiqa3wY2GgNRWjN0zeM/oJxkStE7qyuKbFJo2JirD1IvFgLSoZQgr4MRJ6ef3OOuqmpVo0VLXDaoOPAhFNo2fvozDtFHibSU72NqJO6L4ohPqMpaZtWhZHLcuVw0XwaLJJmFrT2oTN8GrqsYwYP+KUSG+0bcW9dys++MDx1onhSCWevw5MfSLtIyEIhDWmRL/X7MmMOTENFZEO5TpsdwSmQldFI0plVK7xSePXAZsibsrUBoytyUZjalfothpmpdPsIBp0LuKEFlKjsHUjicCIv3SY95K0LVIi0mVoqzAGpj3EEbIH7TMqKnJUxMLUmQu8lDIqiKAoRsv5DKJE0A/g2sQiGuJSEx346jc8aP6T7ohTNEc5o82EKzVv0vNwVzaVRT+wBNtShktlnG/VR5kxew66/2Ws+6uwx10ef6m05yB8oIbOt8+Yfr5TIavb587lHwcW0wyn5FLll+CrVD5AYYfKPedDcrv9VG7fm9uuR+5vlCS0Q0tw+8hStc9Z4JZeetiZUwIbzVCWLgknl8dpLdVKShQZjpneeqfrMLcnJ4PyGUbTqKRwlaJdShURjcK0htVCEdaRq8stz37xc8IwsOhqnr614u2TU56cH3H/3WM+/XTL4ryjfbDk7F6HiRPT9QbbnXB2qumqBqsqchyBjKuhbg3GZIgTbuxZuSVH56c8eecIRYPeGaZ2Ik6aSlfcv3+MXi2xThHo6X0khImpH/nkv/h/0I8XDP2e68sAzcTZk3M+ePqU9OaS+w/v8daTt/G7DZc/e8HViwtOH9XcP32IO2/ZmcDy5cDWRhG76+7jHy/w+oizt5d88WNDpSN/+nLLZv+KfnjDvbOOrbf4nWJcZ6rjhE/QT5HB99hpwhLoTITUkUKFiwAaXWsabTlrYLzxrF+PrF9FdtPEZoT2yPC2lcFgu2o4aw1+0bDoHDdXa3zKRK3xPqCR75fOwkCaEmz6kTZGagU5ao6OOpZaU6VMUK0oyqaEVXATB0KG80envGlGNtuR9XpHDsWhrDi95VqhoybX0By1uGYByrLepFJMTTgHTWdZnlR0lfhYDFNC64y2MgTVIyQVhAaeMsoEqqypUotbNHSPOxbHHWfOcu9exaJx9H1ms4tMW8/mzUgMsj+hTSaPFqaADppWddgFdMeWoyNJ6jFNXA2BN1/tIWm6umbInn4K9NvIeLNn8gKBZ9eiuhWqbVHLGh80fa9Y76HpFJNSKKtRlYHJk6dIbhpso7GdoX1cE3YRP0T03jKug2hzWU0Iijwp2W0YNKaqRHojBYbSKZALhbUWBVbVarSRPacXn3lMFgP3pBWpFlg6ZYj7DLHEgzGzeGJpzzXVkGRXJSWRf1ciltj3mVe9IzYa73/DSYGsyn7BrSzE7GR2iHZwh3kkYfAWUvlLrcsMIc2Bnnnb+Ffi7SH+phlOKVjI7Y7xTCMFDve5+zf5X1rlMku4c5wC2QDlGLdJZ35FB1rr4SzzHUqsOtw6n+chpyBzgbmiv0Wr7uxH33lL7jQNct87WNWvQGj8Sq6B+diHfHXrXzG/sxzOoGjCG0VdKwaRfkflTOvg2gd2657NxSU5JZqq4ni5olssqNoOZVtGf4MNEavh7K1T2tUCbSpysOQYAI2tW/IwEUhMMTCGMrisLItVS8iOrCxWVSjdoHUtn77O6EpRLRq8SQyTJ+4GNusEOZCCZxwHFJrKNay6RHWy5P75Ax6cP+aZ+ylOGVSG7XbLOEUyjq47ZnF6TOUcWkem6ZpUJUK/og8Z40fwirSN2Aq6c8vRcoEmkuIgBjleYLRc+OM5irxASvLly2kiuMQwTuiYufEZZyv5NKJCO4NpwCzk+p32mSl7Pj91LI4NK6tZOE1tHJXO5HyEBqaY8DmzHz1xzOik0UqTQibGRL/1aD2RdCIYy8qIplOrFNdtSwoBUkTFCEnmedrI4lRVO5qyIW6NE3tRnTE6obWRz7IyVK0R602TQSVSlg5AGyVDUyWLoiHlA/SrtEbbTCKSskB7ZCULa01D0y15+HDBg/stNkBrJfD1u0jYevzOMw2S+JKRuV2cMippnHF0C41roe00NiURRBwhDwPjfiRnSyKzmxK9j/iYSTToaoFzC1iuSFULdYWuFDFA2Gf6q0TyMO3F1hKiwJ+FMGIbjWk0xiqyU6SkMSFhGi0QXFUo4WSB6CawlSwQxjGTfSCW60cXrF2lLKrSGXLI7DceqxPaArUWEUQgZUX0ZSU6qzJcVphOVABcBB1F8sP4XPahFOOUGX1ijHcj6//vn187KZyGwNIZKg0Dt1CQmqk4SGQyag6Gt9AHeQ7chQZ6SFh32DElmZiUC/Zelt5KoJtpofPzaCUXn87zRvXt5u8cDvWd2+ZgOe8jzO337J9wkPgmCWJYzqHwjf5SCrgrwcGhmp9lKWYvXF26oUIIIubb5CHPP7+eubMqry2XTmFuaOb/l2+XulVxfTpQT8uBlbqbLwQ/ntlhOcOEIpct212QL5r2mc7ART+yud6wvrxC5URTNRx3R7SrFaZe4GPFkKOYlowjD7/+CGUXJNWw7R3T3mM1mKYlTRumGNn2I5t+ImRouobTh2cMvbBBxptAeypwh9Ya3CT2rcbRjzv6zZr95ZqLG0WzVNS1plm1LLp7GK3xu5HqxHH/0SMePXiHhhrlM2M/cPnmggmNO77H8f1HdPePsVaTMthhwrlEa+DSX+KvJtLVnt2znqEZqe4bHt4/Z71W5OjRUWGzwWJQCVRSZJ9IKcmyZEzEFEkqMsSRQMTnSNsh2vvJ8vhEQ+twpwaXpBLebzyXzcg90+COFUdWrthsaiqlcc4wxcCUItd9oN8H4phFSiJASpFpG9BmYDIJZR0PgMYqVtbwuq2IwUCK+GEkhYwvEtcooXJ2XUOMRjb/rRG/ax1BKWI0QjWtIIUstpsqElMEK7BlQhEREcHgy+JpLo9rZIchetnMVdFgXc2yW7F8dMLXnjZ88KjiapMY1x6/j0Kd3XtCL7sWfZb5R06KOGUMlraxVFWibjJ1A2oIXL3esNceM+0Zewi5YhugD4btFBkzKHdCvTpGrZaYe0t81MTC6AnbhPeKPCr8Vt4nv/fkHMTWN4u2km7B1IoUBApQOqNdxrZCPrA16KoUhzETx4xbaGyjGACmKAVFzlhkSVXFjA5JbKSnxLCbhMVUgbaGKYo8S4zIklwWJqSY+AgTUdUam0CnRLIa04taARrSlEUvLPx6sf7XTgrfiy8Y6Bh0zU5XUFbulQooAbrRKOId9g05HbwTUmENKBAcTM+5Qe4zV7vCvpHlFjPjS1AoqEXuF1mjnzeSzbxgljPM2j5KIRvHc1cRmd3Piq7GoVI3ZVNZWDoiJwCqnNdhp7noPenD/EOqB0k2Bl18m+U2hcwEZiOelClb1OVod5hEEtUPQlHCILozizCl24l/KcDLeybHsSVBZITfnIt0iMl3+E8KMkkGigYx5dh72ATatyrS5ob9y6+4vviU887Q5Q7tJx6/9ZDjThGmS8hbLj5+zYufZs7//m+zPFuh6yPCV4ohQNp7fvYHP+LeSjMMPfv9mv31MVMfAMP5209IU2baT6xfbQmxZ7vZsn61ph8r+tSz4ZKwrfH7AT/2VI8e8eE3v83jRw/ZXr1A+UjYJi43C+L1FaHeMlxfs1/v6E7POD55gv5Q89HPvuD5Fy+5+Wc/g+cb9q82fPTRC/qgSCaR2omrYcN+t2O73vHCe/7Wtx7xjXfu8fQd+OlPezY3Wz776cS9dzWL44pTWz4jn1GTdFwpOfE8sAk/Fr2aaWI/gFKOHB0vW8NRZ1geO1YLh/88c7MZCF9s2J8odl1D7TR9gp3PXFwHqimjrcZUmqV1tHUgmURlRDwuoWl9ZLq5Yup7dPLsn97nqLE0yrKolPhxB8UuZ+KwY7fumT5OqMphnKVtWuq2QVuDspr9biRtR8KQ8NNI2I94I7PE6EUjKvkdcdBEICwcYaqJJeEE+ZhBK5RxJC/6/mkdMNpSN1W4fIgAAQAASURBVBWn91Z84/2O8yODUvD6OhIuAmk3oZlwOqHqhNOJKiimoBiDwumaptVUS8Py3JJzgOjRuw3XVzdYNdFWGXBs+8zrl4Gga/YDjN5QnXacvn/C0SOx/PzqMrPeZjZrmPpIGhNjiPQhElUkEojRo8OE1hllW3SlSDFx9YsRdCyRATEoMkpkHLTM/sTvQYksiAVXw7Czois1RMacMC4TncLvOUhoZAfKiTwIg2bcyXU1jRliQC0MZmWpjxzD60D/eaBqYHWiqGqRYvFGH5wVzxeJMxs5CgFY/pWx/tdOCpLXpJLR+Rb6uQuvZ2QAOzcC84bvLZ5eqnPyAfs3gJqFu5DgJ6rv+TAMLlD7LbxDKrsJM/RTjq3ExnA+IRkiJ1Se3eEKlbV0C9KlzMMEOb41sxeEvJ6o5mUyJccuL1RUe3MJvGWpR5XOjlxobBz8kiVhMKtg38ptlCeaxyIzQHSQCp+H21lYLXIXdduZlA4j5ny4H4Upkst7PCeeeZ8ipMwYEo87jW8z0xRQOZO2rwlXz+inLd1ywXGVWVYaq6Hf91y8eMXFzcgXX214cbnnu09e8J495vj8mDAO3H9yjtGKzdWa7bQV4a+zY5rTFaPS2CxqkyiIRDbTluOzhygFN69e8OryFWPyeOP57GefcO9sydOn5zx4co7/9JLP/uwN+3DFBz94h+WqYpNeEsbA/nrP66++5OhkSdrtefbHP2b91VdcPn/F7tUFm1fP2V6sub7Z88nFDT5rrFUsGkW7UDRRZCHC5NlsR662E/XJA7B7svEszmthFSFLTFOQq9gqjc4iUac15ChDxGwgO0N0+gDeXflECkJ9jlExtQZ9YqlIvLgZ2eRIc1zx7tKxtJqtM4yDwkSwSbGfPMP1KDajp5pFV9N2hso1PNsbdvvAsLlhGyam1KKAEMWPIvuAnwa0jtgqgQnsNz1g6ZvE47bGGkXWcHHj2dyM7DcjVa1QTMUyMmHVQFMHVkeWQEXV1jR1RaYQKpJiShFjZCCaVCLFBFFIF6um5qSrOG0NHdCvE+sA/UXATAGjIxBZ7weCF88GqhqUpbKar73foKwCq4haMY2ZMCVSBEVFxNCnRIiaTXJsbcVu0Ixe/B3sskI5+a6qkFkYUDXYLqPbSNCC9WMzU0qMPtFfeIk9VqNrRYhScfvJ45aiDKAMpbNI+G0gbWpcYzGVoT0RAbswQJyUzPeKpahIisv+UtaSFFLMQl81CmMVttaYVJaEy5feWFEp1gdZ0CJvU0rbiGEfISaJdzejwjqNq37DJjt73RC0IykjEI+eK+t8ZyArQRGKD4Iu8ba86Lk0/tUAn0XrpJxMLoFOq1wYPPoAD6Ek4Kp8KzU9S0ZQKn13V/ZaiQqkUmIzOQtYzMHXwC0bCKnerZalj7lz8QfXnowrr1GgH8VshuO0LJcl1CzJLtvdigOzKaII6dYZLiKkhXl2oEviSShmjQylFFYpYhaWVkTe0KykazCowoLKYutXgn8qXQJqZkXJa1QFtwwR9jlxUmumJrOfxL4lbV8Tr58R4sDCNhzX0NUabRT73cizZ294fRP55esdP3t2zeKjV5w/foej04wfes4fvkuzaMEaLp5tScZSHS2pT4+os8EGxRgEF49ohjxwUgnuvu89l+sLQkoY1/DpR5+xaN/jweNTnp4d8ef/4qd8+vuf4VvP+994yNFZR6Vv2I2W7WVgiGvq2rB++YYvPvspF798zrDfsd9tef75M14NOy7DxLMUSAo6Y3joa06PjnCVQTWWU1UxeM+r9cDq0Sm4V+gmc/yoZjMpfJgZZXLlWm0O0GhGQQoELQw0DITKIDRFxXVMEDU6ZvpJEWqNPnWYKfDyZiDuFFWOvNMaWq2pK8OufMkNmjFN7LaB8XIUOer7C7pasVxUvH6huU6R9XrLPgY8cs2HGIkxQJgIYcC6RN0oYhUZX2/xk0Y1ikePTlDlurm6HtjejEz7kXtnDm08OUVMijgbaNuMpia4FuNq6qphUkbUVJWY2OdScMUUyUHE5CpjOV20nC5EoM6FzM0+CXR05VnUAWMDKUc2/cCwH8k+sDjVOKexFt76Wg1ZMfnExbUnTokwRCIKpxuyzowkdh622bFzLZcbcYgjwVFnZSFsjAQl8traZFybiV0guAgqYVewnzJqnxneBLQ16MqgG9mOjpN0EVVj0JUkhhAj0y4z3WSmStGdKdoTzeqhYnMpQnlpoiQFhSmyJTnNQ3hJNinIXEZli9IG3RoMmaQTNmVyAGOEbEAoELUVSEsVO8uYFX00hDJHdaMCZ1D1b3jQ/Pv1KQ+B05ywekATUSoVmmhhy2vZD5ahoWwI6+L/GBBp10PXoBSJRCCK7GuWcK2VISrhNWstUJJEyLl/kCAqcwBZukkqHjj98n2VjiaqeJj8prK3nFQWJ6qcDxRUpeyBIpqUDHYEmjLEIvynyMVDYt5Ijoft4ZgSShlmJhAlCJMUSts7XUHBEAGtZxFkyLG8N6VDScyS1gU9U6rMOcSQW2VJOHMCjhTorEybjVaHc5kTGJRORZdkbRRfjQkVPeiJGBXj+AI/fs6jRnG20iw7yMpz7723sFct6ZNfcPXims3o8WTcyYLu/JjF6RF6fMW0u6Bpz3jy9F1ql9nuerb7hN9n9KRokmW7uWR5dMby/Jjv3/ttvvjiC7abK9qjib/+5BsoNNFPPDIt7//g23znd3+LX/wf/wn/t//mP+c/+4M/ZWVbvv/v/w5vf+MpD753zut//RGf/ORz/uJHP6FpM2/ebHj27IYvtzuO0TQovvIDA7nM52RKFLVicvDFqEjWou+3/G++813++c+/4M8/veBvnN5neeJZPHmIWSZuvkzsAuhayQ6WNjJsTPMMrcCHKsuGtIbgbOnSEpOKDAl2s3tZMZlXNuCnnjxE0puJn3SGrnKQoF01tLVhtTA8fbjkzfGSizc9/YsNX77Z8cYHzk5rcl1hFzU0iqPO0VXSR+s2U1lZcsvRElSFHuFmyLQ1tFZTtw4VJ4Y+svdg+h01Edc5js4rgVNJtFbTuAUhK3xQDLrFGIczFXtlGMeI7T371xvCNJCiZ9gHTAp0ztItj3n66ITjVcPxwkEEGzNtDmSzx+fIFCIpiVx4JhKrSOUU1glzyaeISgoVEyYEXPJAJGqR/BDo1ZFSYBgNN71mt/GEBElnsh+IG5iGwIjFuuJvrDNVHbA2QvAErzAh4VKkXjhMY7CtQ9UVfgh4D3EBgVAUfC1JZ6gV+kg64WBgSMAu00+ZEBE2EkaYgo5S2N4W19kkMBl7UmGMMNKiVxAjRomfB2NAJ1B7COsR5QzaaegTUVuwmpgjqtbCQrRwsdDsKngT8282KaxCojVSRfsskhTMePUsw5nkhYofsixzzLFKqVvogzIkVWrmHN2ebC5/V7cR/vA7K4lKoCtj5Cyr4flwFMWMvWjhj5RbJWiDEj14KM2HlueEUi3NdY7ARrNuUcrl9tsHMnsjzAF8HvKWVQIUf2nbOs8B5LZDyIcZhJzbrHZ69/UcpgJ3Ziy6rMELkMEhIcxziVywIzVPw0sDkpQwRtrKsJkSi6RZGEuYet5c9by+Hjm931KddpjjJd3xETdXN1zf3DCQSXWFrhzWaZpTQ7tcsOiOWRyvIHXsbyIXFz/j7aePOTnVrPc9V6+uubm6Zr/d8WHzPlll1vsbXn78issvX2NV5oNvvcPR/TPCNHFzcUkMz0nTgIqe+7/zAe/94Vt85+fPePz+Iz7++M9Yrz/m85+/4PnnL7i+uOHi9SVtlzERHp/VohszRiYfUUrRKYPVjq5a4DpH11WcnracPnxCspB1RD9dsHh9Tfdqyx//xX/LvQ9/i0dvvY/Fo1WNtRm3VPSDQIf6sLQkn8Q8S5qLHihdscqEyeNJTGSUYE2QA8YIiyjGQNwkXr2sOFo03Fs2oBRThrXPdJ3hbFXTKsXHNxPjVnYGjEmkEGSOVi3KIlUSuYvgIQRyjPK8ylEZTW01ainsIlfX5CQQYt9HQGFri7IGVzfonLA6s2gti6YiKsOUNPvkQBm0tsRYcFujsNeWMWj8lJlGT6M11jlWRy0nJzWL1mKsYt8nhtEzec8wjkSdyDqiUmDKkViENoOJKK0xUGBOGfLH5Ik5EnIiJYUzRedKSYLQTmGyQS8deE+OAR8i05ixMeBUhdIOYw1VrYk+l23sxDSJSmoKoGuLaVwR4JPCKgGqKtL0c8llRGXWWol7WPlc95tA9HN3KUNp5RQGi3a6KJbLTDHkTFSQXdl9KNvSxijZoF+CVU3p+DNhspjaiFVqFOhbAlAZPmuxnx3KhnmvI9D8lbH+13dei56FNlilhcGS9aGCPlBfCvto5sXfkiMl0B2GvllJsM1zAimV8HyoOdAe+gp5jFBW55bg7j30DJzf3r/MNlIB3Q8k0pKg8iGkqvmsDp3GoZsRrOiAz1Nuy+W1coi3c9DOBxbRfOS5+p+PPz9bzgeU6HCM+ex1+buwq6Tql2piHhCow6xF30k689FF7kKecHZfyyXBBhRGaxZWMW6hy5raGKIPrNd7rtc9R0cN1aLGLRraRcfVqwtuNmtCVjRHHe1yR7vZYyqNrSqca2naBk1FnDLDds2i/QBTVyhruHl1TUiRIPKZDPs9u92ei9cXhJ2n6VqOj85Zro4Yh4FhPWArCzkQph324RH3zo949+yYt9+9z4vnn/P55xPPf7kmxBE/ebLN7P3EwjhWi4p7XtPbRD9FlNM0rqKuGhbdMW7hqBcVy9OGh289BaNJBKpHmSfvvc1+0PzXf/YL7n3zt+kWp8QU0TpjtQSDMkuUhcnD9XmnKIHbYQ8ZTSJHiF7057TWoJLQtK0W0kGGOHi2NyM2a8468ZYYfWQfIisDC6s5XtZUnWO6HvBjZL9TkBJGG2zVlBmZzJhSiCQfiEECIlnmIK2zVEsxBdbOkpOSTeVBWEfWakytMdZhVcIZaLsK01ZEbeiTJntDRFzprChgkHLGWiPxIWViiGRnMMbQdRXdwlE5Axn6PjKMgWnyDONE1NKRW13onwUpSBQKbAY/hULbTIQchPGVM0LnMGhMKXoEWnVGo0NGE4rQoCgyhJiJrigeaLCVEEoIkThGAmJ9mdJMrTXo4secmXXGBC5LFG8JU1wijUZl4TLGmEgTh+KVlGUeYJToExVqq7YASSDlnPG+kFgK00QbcXxzrcK5UjKmRJzKzMEp8pRRKcljUoGSdClth0hgIjIAp39lrP+1k8J3uWBIHT0Na+MgJkwWl6k5kupsmK0ob1VIdXk/ZA6gkTdH62IRmXVZyCkGIrkwe8gopW99EgpnVThFQkWdNwByLLpGZbVYUboQfeDdFOrq7cJclI9SZh/IBZKQ5DxDMarI1x50g3JEzfvOah6oS+ehMHKR3EkCOt9unc4JzRyG7eqAT0fm4D4L9MncQI4u78nM4pq7mrmLml+PXKDzTGZmWQmcMSeypGSjtQVWTvHFkNCVom0Mep3Yv3nN9uWXvHNacVpVHNcVp8uan/zLnzCFQHVS8+3feoSpLNkHxiuF38twTKtEbWGxWvHoyV/H6kmkqTee7/72B/TDE9Y31zz79E+5+srjB803vvsej373LRbLGuO2RO+xSnNy75yv/+C7dEtYX3zBz37xnG11Q/tNR263fPVlIEfNw/ce8YPvvUvOgS+ffcVPfvwl66sdV5uexbLmydkx59WCp8szzu4dY5cN6yPHbhzwQcxkHp6eiY6QDrRfP+Lv/4N/RAg1f/Q//l+xvZ746vmOl7uAbSGguB4h1fKBaq/IIZOyzEiCMmIGlDK4LIbyUFRHM6Qohj+IJIRxhnolOjnTYIjbLTkFYvCMkyemzG49cXU18cW9ig/OW95aVtw/a0jjxG7nCTcDzcrRti1ni8BKGyyKKUH0kbSfSMPIfjtijcVax/nREQotHPasWG8mGYIHqCrAydJW9NC2jq7WtLXB1g6fNftRPvNYJLvJujDtkKp7gGxh0mCMyEHXdVUowZlhn7l63ePjRIgju91GjqENZiG+xyrLQFVHiDEwJY/bVcjMTGQ4RBUWamMx2krwVqID5SpFYw1uCIRdFnglTcQKQq3IJwqMBaOxOuE3I8PNyLgbCaZCzAE0OtWoRhKOyVa+6zGRBk3ukiQN5VBKE7X0DWkUc6EcJTGmstuiVUIt9O2eyEJTVeAc6MoQYiZMsH0TpdNXmawV2olhT1UrXF3iSzaoWgbIumiESTJS+CTJWmXQMaGvRgwbrLoB3v4rY/2vb7JDhVeWpDQuI7SrOeQXQbmQMmMJtI6izTIHxhLCVMnOFEhGtnpTUVy9lbiQYl8q7wIalVg4B+zb4BvV7f0PWkxz8pCdKKG3IvCJ6ColTM4HNpAspCl0vsuEEprozG46DLzL+enDcwIFkkpKCSMP8Twma2IJ2CalkuSk8xCF4nx4X1Sp8tN87HKsWSoDOAg4zbsdqXQMBnktmUwQ7muBjDLzVEEDlZHXM3hul4y0Zh0DU65Jask2enpXMzrLGDyX+wldGR6fHXP09nvULz1RvWFzlYXKF0VlddhsqRrH+f2nDPtLxu0lr796wzDuWe/2XF+vMf2AoWF1vOL49AFZRQY/4nJLszwhDSPD+gU/+9Ef8OKLl7x+dsnaK6Y0EbLloxc37K8STd1w1jVshgmnHMerd/nb/70PqJzDWcfN61coXaGpyLvA9fERqYa9fsPVV1sIkU5bzL2a04f3OH9wyk3a8/MvPuGi3/KP/2f/a/7lf/OHfPKzf8Hri3+Ppw8s06D48rPI0w8tg1dcrzPJ33abKSAWlyqhkiJauV5UBm/AZ/lFgbIKVxm6hcOYlrE27Ih0tSWHyJfPr3lw0uCjJ8ee4YuBl1MknLcsFWRryNbQ+4QNmaw1etlSGYdB6KK1dvRqEsOYHKnrlrrrWCwqYlaEooyaiUQ8Xo0EFYkpk0bPMASsa6k7mbMNY2QMgd3Oc7UOxCBlTHKOnCGFhIojSkW0g3phaY0wh2KG/ZDQKTPtA3GaGOMoex1eYZ3DOkfbVBjtpTswCZywdIiJKRWcIIp5VJZSmKAVTgujJypQWnj5wy4Qrj1ql3De4NySyonfAMUEaOgju9eK/XrCj0k2qCuDzqInRdS4YKiiobWaqbYkD8Mm4mphGLnKEqMmjZB8Jg63SIOyilSsQ3MFeSrIg8qECnQqM6qQCSETPJicqCoR6rRaoK0YE/E6MiYvO19KYZQlF/8El8G64iFdaZSRAtxljbK1dA/5rxbDg/8/ksIbXWO1QytxLrrL5ZmtKGcdf6lN1YziHH4OVTsHG5uDjMRME73dML7zwEO1nu/83v1j+dvdx0DxcOAg9TB3ISgOHYA8rToYAKk0bz3fuf98jDvKs4f7M2PI8m1X83/vwD0H603NwX1OzrcADofbSkd0YB8VzDIfDj/nKXn4fA7l3FTB31KW136XjlpANlFQzTCE0iIrod2OgO06uqNjYr4iGUtQhv0ggzzjLK61VK5BKSsVsRfMRGuFNVZmFj4wDT2z+F6OgVdffsXNzZ7NZuDdt07pmiO6bkVOiZi1GNj0ey7e7Lm5vuH5l5/x6Uef8dUnz3nx6Wti1dGcdrijhsXxijjuqWvHyb0zlkcntLbD5gWmyVhnMcqQ8g5tFmjTkvsBe7xC1Zml2qO2a+I+UpEwy4Q5AnOsuPjRNZ9cfsbrYcNb7/4dusWf4weFz8LAMkU+JAnLkqBlD0BngUDFAjcfrrssED05I9vdwFhwb22F+izyMAZjLcY6nFHEyXNz1XO2NFib6DrF1HuG/cjNjcK2jqQ1qnKkJNo+5EQuw9YA9CkflHWFEp3RzhwCWYoym0spk1MgZ0/MnimJgFxC5BhCqslKiayHFxns3bZnv/bECEoZVClfxYLToxAPgqoyIt1thGkTiqpoCImMJyaBgLLSGOdwdU3T1mhjZEGOiHKIpk9KsgNQrnt0BJ0KwK9mBI8san+kMeJvIgwBkzTWVTTLirpVuEqKuTBEok+k3stG00zKMBqdDSgDyWB1eR1Wtpa10eRQKGZa9jvIMoNMURhEM6EDqwqtUjbCxQlAomSMiRAlIcSIBH8v1aJ1msop6qqIDI6QhkQaxckOo2X2EKXqNVbhrCoLdJIUjAKXIEYrI6z8G2Yf/bA+5omCezmj1IATQqVk5iyeCloXKIgSoJKwalSJYrJbNjOQ1OHNkb+V3QSlUFqSjXzIkmCE9lqSUC4Xeanu0bdEU6GFFmxdR+bRn2D7usygbweDMvw1JapmtNYC+2QFyog8belOhAssXzyt4qELkkBd3BFUOuD+kqfKayehUrpNUAdhuyK7MSecUunMF/k8P5HWJEtiKmp8Wd9+GdAFUlNZ1uH17eNn/SgJ1BmfM9uQ2efMHug1BGu49/YD3v7wKRcf3aB1RYyW11eebrWiWWiMi+h1T17v8LsttYam0jSdpTtp6NoF+MSnf/FD3vnwHbrWcnLS8fv/9A9ZvxrJU80//Ld/m7Y7JWfLy+cfcereJ6vEsy/+jD/9L3/KLz96xo8/+ZR3Hxxj04C1E42R97aqHb/793/A5z/7DJ0t3/nWN3j/7fdYNUtqW/P8yx9z+eqC16+v+HL9CY+P3+FB23LyaMH9+wtWS8ui7fhKTdxc3HDV94TllonIxetX/PP/6+/z0as3rHXmH/yHH/L2u7/FPXVOtcokJ4YlT94xXFzCpCA6iFaJoX3SZZajigfHTIaQLflx8My0sVxJYskZdmMGLw5n4nyQmEbP5tUF+u2O1VHD+VlN0lfEYWT/OmKOO5Kx6M5ivGKceiKBbAwjEuh9zkxqhmEFcrFO4WpDNpopRKaQ8T6SwkAOIzlO7IYeoryeaDQ5LchAHyKbnWe7H7i8uaRflzmbVWi1OAS7aZAd58pmKu1wSiQYVMqy1Zylk1UmoGMUKezKUHcNi2XH6qRmGH0x8PGYSok6rU0YV0u5lTIuRbATSSVRU84C2wUUYZrw64nx+SiJtm6pupajRxV1DYbEeLljuhnx24FhvWHx8BTtDOREcpIQdDYY5ahrS1UbjNPoxqDHTLKGbEQdIGojQ+JUPJxDIjktUiBOYU8kmZmcyp4FZJsJOgoldZJzj2OSzfFkoHXYStEsDMaUnafJE6eA0gqswrZZoCWrqBtN3Wg5RyszBZ0l4fSNJiRLSr/hPYUHU+DIaRoFQ4oHGWud56oaVIpUxSdh3jGYI7udq9a54lUCd9i5GmY2uimQjioqgHPgL+Mkmb0UOmoJdibfxtTDLENlbDnJ24GgwCxKyZdSz0Jx5dhl/wOr5NgZgVcywie2c7IBlC74cZlVkG9lxNM8N8gKmV6II13MMvhVQFZJ+MfldoM5QFY533Zcilw2rCX3xVQ2nmV8eWiQDsmTu5IZRRWz/AjV1Rw8Fdpa0WlFVyqTv/03v8s3ziz/5D/+mJOzFVVlefnpBV97921spZnGgesxkYzj5PSE83cfYZuOsU+Mo+f8nSXNosVtjrn6asMw9MQp8T/6d/8nbLcjm3VPbho2DEw+sNlM/MWf/+dsr69Q/cAw7mm6zP2TJatzy+46c3PjeXi84MNvvMO73/+Qb37338Fsf59x2HFyekq0kavhhv31yI/+6R+hlWd10vIf/OP/OfuXn7P7/BOm/8sf88++uuTjTc8fhMB1P+KjzLD+nfe/xnvv3ePJ1844PzG89d73YHXMn3z+Kd/5vb/P6cOvMzlPQKwRXQU3zzKqUbTnomqZsyInXdhx8wyNQ0uXE4x9JOlMcpoYxuL6BW4TZe8ngp8cY6MINZjVKW/WiVEFFseKtKrxUXR5umywjQOr2HvoX2VyUuhVy1j0jUKS72AkM6TEzkdioc8SM8pn8Ak1eXKOTES2eWK7n6iMoa4qjpdLnHUQFXkK+H7A73vCZhTNLK0x2ZKVJ3lZVLPKlO+sQWlNpQ2101QWnEa6Qx3IKgml2yisq3GNw3UWV2umoIlJgzJYp8iI0Fzd1fKdj5mkPD1KrDCzUHxjzPQxsN1M+BGqusYdL2mOarrjiqMTKwPaMTAmg6sUammpmxO6047gM/vgmWKQIksbTKtoWoWrlVid7uQzqM8tyon+VdgHwoiYa9kMi0NwJAFuIYFaZ40iikioyZjiiQAJ5UOZnWbUJqGCQaeMc+LU5iolboNdhTIJ7cAuDFbLblVnFdZJ3IspC0wVBZKKk6dqE81xBhZ/Zaz/tZPCo7Sljg5rhNoplfedjeayGDVvO8+JoYQsgcILnn7Lp5HbdC5VbpbvUVGZOKBKes4kBWOR3blSpR+gJW7P6Q5sdcv+mSGnXz2XeVicZ8XSeUhe7j2fZ1KiqX44h/n2fMsAEh5ELq+pwGtZxPikYs9FwmJeSpL3IsOBMFvi/GForBHLPY0qmkgl8HPLiCrXn8BDh4RQluvKMSnHVEk+dKfEmKdWMv9pG4d7/JgjPA/vtxx3tazqjxOr4wXWGbZ7sK7h+PiEx4/vs1otxU/4esPm8hp4gnUGpWG4WeOngFGO7nhJUJp+Enep/X7HZr3mq88/5eOf/ILryyvylNDWMPiAqzQP7x+xBew+8sF7b/Ht7/013v3+91guVyy7BpsDrVqQ9ntZILSWhc/cXN/wxbOX7O2/YXPxjO2LLxl/+jmfvbrmqp/ICY4b4ag3teOogooMY+L43orzr51jT0/4wz+84auPnrO5bli+/wjbQTQwJMXyBPHWdaCCXDPKKBFSyXfMk+ZvSC47PBphhVjNPHgyqiRxJUwgV4sEydn5kv0w4nNkF4Jo2ADaalylaRoDTjPmJIKEOlFXjloZnBLtrqD1bfGU5Sktci0Jw180dHTZuEwxEidPrjRWGY6WLZV1qKRIQSrgHCKzLLxSCluk6lNxBqxqxzwfU2n2bC8FU+n0cy4U75QF655p7CCVc5SKOaUMWZe5l6VtHGRIMeNCZvIRHTPJCxQVYiIMAb+LpGBxdUOzqFksHF1nMFoRxkzoE7EPaDR1U1PVVphyKaJyhDGBiWCS6A/pjNGZOEHcFv2pexqIcp4xEcbbGKWdOnyPy/CjwNT58JszZJclOSIubcqIlI5I/SeMylg971NBqiBPt3tThIi2GqtL7Czf9xgTyoOKGRUSCxXpbGRZzZtN/90/v3ZSeCdfMqaWnoakGoFCUCiVBCZCMmESVtuBOz8PTNPM6EFaSX1n/qDnZJKT2Gkqyn4AB8E7VdrgOYjqOyyjfCdZmDKEpmDy8U5HM39oM31Oz8FbiVlFLgC9OTynuuUlI8NpeSrZKJ7NpAQWkr9ELZ2LyrNW0h30pwyaZeitDyyrnMFlWfqbKbcJkdBWKYmuEvNmhnRfmkTOsqugC2xZGjAJUllESWYfiVyCQE5QaVhoaDQ0Soqbs65hcbqiry3vfW1FXVcMO4+NgdXxCtc4odEtjnn8aKLKiW65IvUjm5cXXD5/Qezfh1XHNG4Zry4gOeqjx/g0set33FyvOV003Fxe8OrlM37xoz/ilz97zsXFjs0YODs7oukE+33/6SOGasF1aPjbf+v7fPPv/g958M3v8+yLn7NowQZLFxbsb77CVY6j08d8bXHEn/70M/7kj3/EP/tP/rlAKfJ9otaKJ0bzjxYVj84XrFYNq9MF5vsPMc0SYzruHS948vUz3OmS5r81/Ov/7F/hzSf83v/yt1k9zEQDaw9PPlT0Ea4myBtJCAoNyZSNdvHuPdCss8JVDmcztgJbmbIlq6kqLRaUOkNlaRtDrQ3HneIXP/2S620k3hhOVi2NNrROUTeG5UJ2CYYpE2pxklstK5bGYJVh0pmdtRilxU5eyQ6RK79i2KLwzmIn2QNQMaK8x9iaSjvOzxZYYyELZz+HLIqeGlw2aCMOc0SNz5IsmtpRcGKyn3XB5Jdi6hNyIiZh55TNS3lMzExTJoxCH005kJJBozHKsOhkGdB7MYwxY0D7SE4QoydMibhJxG1GWUu36OhWFd3C0FYaPyamdWBYe6b1SL2oaLqKo/OWaVDkYcLEgJlGUWZ1AWVKsUUmDwl/5Yk+4+5XksBCJu5F+E5Zhao01OXLFiGNkeQkQeoUyUV6IOtMMkqqfhMPsHHRucOZjDMJRyZkiROoJPOflGGE2HvsQpMbMchKZR4ZJzATEBI6Jk66wLELnLhfTxHv104Ku1QRtMNng46S4SIJmxJz7Z8zYrKTFZXKBdvPcztw6BLuwjBSXScsEYNY1eUyhzA5Yg/VvHCQlQJHxh6mvqKEZJRceHJ7OR9thDZbnkMYr1osE+HwGJUzycjxRcRP9JJyimAE1jE5Y1SUgVJWYITbrIlYojhagVQ2hdlkyiuMBdIy+FI1KdmsNSKuZxGTPFUShDeakBIxyrnMMhW67FdI8NfMtqJayVCqrAvik2zUyrJded15ZjbJfyfEvzVNif0Q6OhRwxXD+g3KvcOPf/k5m/WWiobJWrqjEx6ePOSytzSLIx68ozl+8ITHTx5RO8PzLz/lkx/+jJzg+nLD17/3iHtPHnD29GvURytM/IzNly/5P/1H/wdeXVyz3fZUY6RFca+tebPr+b3f/g4Pzo8xteWDv/43SZs1w9dfUd1/yC9+9gf8/JM/YnnvXd5/5x2cV8TrNTFqPv2zn/Av/u//e/6LF1/RjxMmBL6vNd9adjxddFTtMSc/OKN+XJNWmdX5GU3UtNee5dNvY+sVSjdc794wXV/y6sUz+i8TN29+ydhcsblRPGoUO6P44WVi0woXPyiwbcZM8jnEpPEpFqkSTYrzNowmak3wkV0fMHWia2BCU4fIkRNjqmFQ/Pz5njgM5GHD07fPwMB+9PyL//RPOVqccP/8HvGbFR92jlVt0LVmpzWnjeO3n97jVTaMUexBj6Jl2ytuNom2dWQUY0gom+lag9XC/U9ROpmusxw1FfWiojuuefpwwWbM7MeICrJsZaOl8x1GGYyWBBZtpjbiDmYqfRi8e4OQKFSkDz1ptMQYGSeP94F+TOxHWHY1wWfGPhCGwBTGklQTY28xTmPEsqEMz5GFLQMxJ9abnjyKhWnTtDx5r6U6bVg8anl9FRl2ic1Lz7CeSHlC5cDRvYp22VI3jkVd0Z4Z+mNL12heP0/4CVJKOB1RI4Sg6G+8WIAq0DFiyt4KFsagCFMmTJHobxmMSiviXlztjUqYMv9MKTP2EVtDbqE9MdTGoJNi3I00dUVbZ3RMXD+f2N0M9NdbjJvKjEHDkNArhVpq3JlmtVRUjcLHzMWzkf4mENYe+8jSWEtuf8Pso4/siqU2NEr2hFWRuYB5gU2TDzQZCb5zCy3VuVT6t1aaZVqPtEm3LZYwgbJOgsnn0ocpdTuAzZKU5nW4XIbBqvxtpgxlROZixuhnuk4+JJTbcxVyUklT+fb2NE8rSndx6DaKeAWIpG7ORVdehVLrz32QHDffvkCBFO5sQMlCHxxanKLHTkpyrqpMRArb4gC+5SK5oTJK3Q6RtFYlKZQFmLltUiJCkgpxonHS3cWZZ58kzR3dW1J9brFK45YGW7coZQhjz7hRaJtYHXfUrSb4NWEf2F1taM4s7fKI1bee8PDr53THC2gSH/38z/nJH/+UH//Rj7m+uKROClNXPN9c8dbxkvtHLffOT/je979B11RsLi7YPb/GTzumGDhfdSzbgLUZ3e/54Z/8hOfPLvjyamJhNdV24GF7zt89lyW2qjY8tIGTtmbZ1bhVy4O3T3HLiis/cBTPaVA4u8VuE+PFG3bbDVfrLUM7slGB1hqOuzPU2dt8+N2WqtPsJqniYhTZBKVVoUAKq0s4CHfYYkWHCyVqmVI4iXb/OAWUSqw3GtUYGiedw6lyDNlzcSU6PE3taBvDyf0Vx92Co5OGced5dT1w4wPrzcSbix3muEYZTYeiRj7ntrWMrWNoK3T2ImGuBh52newMpYx2mqaxLFWFVy1xEXBtRds4amfYhnJ9l2LKaBkcowxGaSo9l0bynmilcNaglcZWmhSkKIkqkbOQU7TOVLXGB9HhsuV6D1G6CJ88FAYTRglEUsuCWMxlwav4WMQYDp7vWhtMXWMXFdlo1vvEsEv4XpbSlApS8CkpTJ3VOGswprB1nKaqDc7JtC/lLHtYzEVVRtdJPtYgzKgZnJ6lKg5yyBSEQITcQCtMBa6yQpmV6IQ2ScgsPhFCFjhsjMQQidGQcsLaTN0o8pFlcWyoa0PlLGay6KrC1o7TM8tiJbHTbyJGa6rKUK0yLByD01z9pv0UPjML3lJwn4RWE1oFFOIMdIuhlgShkGq/BFc1B2JVsPJyf/EUlUBJeQNnVhKFizvjoYJJzUFbQvJMHZ2TArkkH8q2oYqFRTQzm3Shas7dBAe5jbtBPB2S2Xzs0uHOrw1FUrG8JsFTUVpIQXM3UVIGJdmk+b7lOXThkQp9Uc+wf4nzM/5aEk/Bb5kXBbm9GOXtkWQ1dw7zYGcmKh0UYhEKatKKZBWVA5tlOz1pQ/CGjGVx1tE1FWPtqJYGV7eAot9t6NeK5VnL8mgBLrHfXjLebNhe3PDw/ISjkwXH77/H8rwFE9nvNvzkh/+GP/r9H/Nnf/AzHh5pzo+PcZXj880N9XHHw+Mj3js95f0P3yH6wHRxze75a0Y1EWsvVpmNwenE1Ys3/NH/81/xx3/+Eb+YIu8cP+K754/4B+98yPs7i+kUdlWR7Y6+toyVRS0iddtSpQqzhq47otEKUiRe9GwvXvL6xee8ufT4E824MDRuwf2Th7SP3+Pb32+xThOnTOVkWRKkSIkUkoMunx0U+YtctMBKYMiyTKmMLHB5H0kpYG4UeEdsLcuTmnudY5ccr73cZ9FZll3Fg3fOOW47TtqO/jrw6mqAnWJc97x5vaUhM2VYult83mjDsHD0Y0XcDWz7iT5qHjyS+YbOClMZmsaJNpBWxKVHNzVt5XBW6I6pFCszjGOsxiKBXzTYtGg+xYhSmtoarLU44/CTF4w7yhROK7BWUzeGEIu/hZPvS4iJlDwhB4GKjUhWGGewtRYl0SDGUJMXL+gQgsA7TpbkZkmKPiluLgLjJkJIoh/kErMhl4XDkNaaeeOfIilhStDOaHNn58cmTBtL3Nfy5Zqp67M9ImWzeE4IRRdNG4WrNfXCokrX46OCFNBJ9ipigaOST8QQCUGWA10lELfpas7uG9qFkxlgtASE5XR+asTu02fidcYYi+2kqEtHlsGA/01rHz0aJk4qTWuQJZK5E8gJrayE6JQO7COThE0kQxWRCMiF6jkHYbLgk6rIqc5ja6VAKy2snMJmkNCpZH0/i9lGLlhbaTTKyrs9xE1ddGBAkbMl5Cy4XYaUI2TDbKMjW9gUQw0JozJELgkjZVR5TnkWhc9lBR8r5iIU8x25nFDKEHPpYkjEHNBYRMSvoN05E1LA6qoMsYW2l5MYt7hs5n4EokBJIiNS9F0LEynmfHifFLed18y8kiq18KRTJoaMbhS1gcYmbPKAx+eJ6zQRFhkTNG0tEskpBta7LQMNR8XJ7OpiZPNyzebVa4Y+0T46Y2t6/pP/6H9H82KgdSuW997izfIFxMS7b73Lp88+5tXuFcerBf/hP/zHLO+fgQpcvfglP//l5+Rg2A+O7/xgwdnymJXOfPFf/af88x8+508+ueS/XHt+rzviv//oKf/bv/e7LP/O1xmy5sVnl3z+5z+hVi2dWfH8heHo4QM61TL8V3/AtgocnZzwzve+SXzUsPYDu08j2+0aNU3YxnG1f4UzNTk0XF5o3v7afd799pLf6hw/eZMYbmDhNVWSvY4USoLIZQFTSXWfKRRtU2ZgCbQyImmBJeWJOHqmyXP1xjNWjt2iItWWzhlGNMm3vH6eWK8H6tVEXi1ZnC45WS1IbmK7HekvBjbXN/Q3A1vneLaPPF1qKgNOy+ygaSrcskX5gWkbwKdDwSWJLGPbimVtaZctNjuC0ihnRd9JKZwxKOvQxmJtROtO9jYQ2mM2mRy1LO8lRdtUNE2FbR37wTCMkf0ukGI+SEIba4SNpbNAobnoP/kJn7zQLqmk0CorAzkKj98PgX47MPae6MVbunY1tnLYpcHWmf06snnhcVac5KwT1zQNqKRRPmN1xKhApTRTr4hDJHuPrgLGREiZpEdSKkoNVcAupPNJSgmpQAmUbMrwWFEc4gAQenhVa1xjaFpDvdCSFBSYIZE95FSWaY1IdKhVkAU7MpOfGEIgCl2SmC0hFdMdregWmkWneXCs2A+KTcj4AHWlaFZwcg+2Kook/HoAVr+5pPAhrzG5QiXHqF0Z9OaDxDVZJCtSUrKcUwa28tbMRa50FWJIr8ilWplZOSrP41TuSEYckHOyLswe7hx7LvfL85iZulR+BM9X2BlWyogloSpDsJwAcxjGzuFW5bn3yYd5gSnJR9hL+SB8Z3IsgXfeVFZloznd3o5U6C4XimlWBQaTYbgp8uGgiAVmS3eG3jJTOKBzIlRWBtDpTlIzJQU5ZMgckXmInK3I8MYMVUpMY6aKHqLHWGEa+XHH/tU1+8sNYZrozo9IQRzfjVZUC4erhRzs15dov6e2meVpx/K8pVkYfvvrT/nXP/kjfnn9khv7JV//2hlTCOQ08Xvffo8nX/8Gp48fobkm7kaMsrz3/nd5/MH7KD+x/epLnv3+D/l/Xa356GrHmy+/oluPdMrxv/id7/D+N8+4f9ox6YnLTz4lTBX22vHhW1+nbpZU1THEv6BZr/FfXvJff/kFvTIs6pofvPiK34l/i+5+jboPgzdMfWQadtRxoMvyJbW9oUkZl2C46RmvM2lyNEtdSAby2R1YZiqJUqYqkiZK+OOgUEFkt7US6YZokCWrnMHLUDqEgB8Tw2hRVcX9905IYSRME8PzCe8DvsvkE82De40QNVJkfxVoW40xcH25h7GhayyLRlPHxHbvCX2griuOjjRKW1RM9CExTSIrbRQC+WiF7yqmWK5LL5vZJitiLnsUSnaPRFpHZnWpSHsYpWgqy6Iy1M6gjCZqQzaIAZFKZaZliMxQaiFBJBEZDD7Ke2MUOYssSPaRaBNjHxmGyH7nGfZTuS9UVU1dVxgnBWQKsnjnshcqbK1xtZXrNoqcd4welRPEQPAaP0AYy26ElmW5rGSzOylftv/leymQrfhRK12gYJULjCiQ07wflbLGaY1TWnxaJsCX62csBBatqTtHVRusUgQVsLVUdENJgCIlo9hp8PvMaIsV7FmGY8trNMUUEaUV1ULjnKAOuk9U+4Hlfg08/s0lhXO1x+fEmBWKBhBJXcO8RcDhjdClgpglJEARSQeqp/6VgC7hXGVpu/TMVIKSJO6E+MKomX8V6jBymH9utUNLAkCAkxlxn5+y8EXmFDQDX3J+3Mn0hyOrgyPbvHtWDKYOiSgXfB+4M2gungdKqjebRCDtADeV5zSkQyJKiNta0gqTxBRUoCh1mHvoXN57dTuK0HCQukAd3oUDjCdQgPhC1GRiUZvMyWOMIuTAOPasX12xvd6iNbRL6RKIEa01rrFoAykEYr/H6YReOLpTi60EB26t5Xq749mbK17GzONFjW0NbWP59vtPefrtb7B8/JBPP/sj1KCpdM3Z6SltVTHFkSns+eWPPuaHX1zwo9cbeqP4rbOWp49O+Lf+2gccffcU0xp2X2zwNz1qiFST4/j+fdxyha4X3FtYtl/dcPXFNc+vLvmkXC/Ti9c8evSUJ+GMxTcTrjYMRIahx8WJJgpTRI0GGxM6JLY3e+Joscqy6BTX20IT1PO1km/naXq+duVDVFmhtQz/Z3hJadDGkk0khShMFJVJIUmV5yxnjxb0a8X2KjHseoL3jENgCJnTpWPXG/pKaJamNWgLN1d7+gFWy4pTKpoY2e49087THRkWS4XRMhSZQhSOfwiyAKUNlZWdArLAFtEnSKVTzwqVDRorVNrD95+D37BSiq42dE7jbCE0KFm6DfZWxSAgsHsqJjApS6GSSmLIWUggOYuoXwyJ6CPDAEPvGXqPH6XzUErjXIWtLMZIkRS8UGetilgLrhaf6doZ8SxAYGZyYS6FQJoy2Qsl1doSJ7zoO82wsrZa9qrKrFHPH6aaI9QMDc/famF4WRQOJaxEX2acIcGUUUaG81Vd0bQGaxST1ygVyDEwjfHwWo02TEMkB0VUmhAE+tLINn1t5L/OiRmU1pkQIO0iug9Uo///Etn/P39+7aQw6hXZNqArnBdz6Mxt5hShNk2cvYMTtwELERNTBV6x+oCWC6UyR3QuLJ7kD2+0UUV0L0NGE8q3yiqR8wXIWeOVPmgkWfGhlOpDWXm84rA8l5BKxah0eEyMiYxBKRHgMOXDTUlC/9wNaRV/hclTVAWES5zFPi9rjcsJozi0lFErkhZGls0zfKUIRpOUwv2/WfvPX1uzPL8P+6z0hB1PuPecG6qqq6ordZrcM5oZDjmcoUSTliyLMCjThrMhwPArv/FfYPsPEGBYNiSAEATJNEyJALMocoZmDyeQM9PTuSuHe+uGk3d6wkp+8Vv73Bb8wi2gLlB9G+ees88Oz7PWb31jFjUDWSaUqC1KZZzOWCd9DRlF0kY2sbwHveSiTNpI0TcSuRCKvDYVA6Axgm0bJVroqrw+70di9kSEXNysI1fbNT/80cc8P7tmeTxjfrJkJMixzTa42hC8Z3u1JWXLwf0Z1SSxmz7h6sMf8+jDK/6j//gfsx49M9vwtdkhl/05X374Jl9962vcefmE737yAy6++8956+ghb//Mz9PUlkc//h3+6//bH/KDT8/5o4sNhszX25a/ceeQxUnD0V94yPwbJ+TJnP6ior6acnjyBgdfPSL5ke35OY/e/ZBxDORFx511zX/5+BF/8NEH/HuyfvMhmb+Te/7sd/4hv/rpy/yvza9gHwykqw2b6w2hG6jrgGNkHAfMsCVvtzx6dE59OuHuBBYNnD2Tad9OIekk2vYccCYx6lKjmCUWQ7gpQyra9ZQyhExKlqwTsYro1mJbh7YKT6at4OTQYe7OePI8cR23hGcjZ6uOvqo4nS4BIVbd9AAzDSQSn35ygdIdJyczKjVnjIHLiy2b6w3NdMrBrKWunEzYoycNI0O3o8oWoxzZivDDxxE/BoZuDk5+zyjuFjRapKiqxHKHSA4erTWVsSznFlcZtNFSKqVL57MtK0AUgrjrIsOoGKIY/9L+9IUhKDlpuazpQyQPIzklbnYwDIGxD4zBY7TGGItqK7Q1oEr+WhcIPqBsxFmLs+As1DrjC68XU0TpiE5SfKSywuqMqTQ0jjBq/CBGsxhLYqqT5NWYoQ/xxUCsFCGDiqmksiIyZaVQUWOUwiZwUbiHtHcxR4mnMEZTGYMzGqsVozIiyfUw9F42MKck52heY63CmozuI9020W88XVMxO7K0M83RHc0wZMYObq4T/kLUYyb9/6/i/O+0KfxpfYcTrTkA0ANOCXGUUCV3Z78D7KdSVVQ8qqA2Zeou3gUwcjzT8QWGniNoI+Tu7c/cMgRkLUc6coFMCm6v9S1dLZO3Kh+XTiXKoriJlZHnpUUmuv8JZSzoF1P+Ho5BSSFGLpiRzpT8kDKxay3PvfAT5Rgkr63kGIizE1FOyYhdilmsmFWQ15OT/BKtRJ1wq4BKSBwvCqVfkN4FUAK9V2X9hJFwf2xQcrzdQ04C2eXyM3KCSLlEleiKQGbMnhS36BxwRlHNHE3jCLGn667kvfSRsQ4km6iOJrQz6P7ojB98912ef7blQTUlzjyzZcu9hwveev1rmHaKN5GnZz/itfsn/Nybr6AfPeO/+M//Fh8+vUTvVrzSBe5XFX/xsOFrv/0LPHzjhPuvHLJ79lQKQ84tpk+4eYd2iX5sCWWCqtuGg5Mpo9L4aPj8yRUX255r4AOlaLLiAZkRRa0cPhmed4nuYqQbwS4aDmxNZeQ6nBEZbi44//Qz4h/8kId/8Yh60mKMNEQmA7mi6OyVOIoR5/4+DSUrJT29SnH3FUeK4IdMtwWfArmkZ46jRxPpKotJGd8pLi/hlVPhwZqpJU8VeRjpn+14Om8ZPZiq4vjBki71DNsd65sLrOk5WGaMbiAkQvD048A21EyywmJIRpN0IKuMSZLGKhk8Gh97UlH0DNGLEsloti2QHAbDtHLCgflAHEZykigG5wyTaYMxFtCECHl8cQLPBDkNjIhJLhh0src+I4UimYzVBVs3AvWGIHR+zJCDtAQaXeoqrTSz7UuqBJlNKJ2kqyJ4Qi/w9egUOUVUjNQ2YY2caConnQ8UD1CtBMYax4QdhH+LWU47WUn0NjuPNjIYxxiJMZakHOFajFJoY8FajJH3R7MXkoCpkZpTMXQxriJ+k9FGU7dS2Zoy0nmtlSTqzmrq1kqMhUbc2VGUS33QtFrykFIFfZ/p+sjqcsSHjFJWTHU/xZ+f3qegKrzaL0YFvlEUTbbarznFU1D+lKshs98v9klH/y1sQ76uKJHPApxntXeT7X/4BUz1Qm+jbh97j+f/5B/FHrXcPx31//N1KfUpz2f/taIo+EnsKpfTCoXMla8rKGVC7GO2Vbol0/fP/4Uqq7yO25tA3o19veP+yRWvtLzaF9gcFAXX7XO8fWtehGLAT5xoy8++gPbKU1b5tm/YFrd0iB4fAiFFdCVKCVcZrDUYbQkZwhiIymOtISnNmHuGYQ05cP3oms8+esbl2YhzLQ8eTpkdzVicHDNbzDFtRa6h2inSuud6E7j45Akf//gDPj+/YTGvsUcLmmCo14m33njA0ZdPae/P8bsVm2cbdpcdV1cdD15pmB/OsLM5PtX74xrtwQTlE6FLXMaetqk5WR7wdLUikqlcyy8fvczRSwteeXCH+viIi90ZO68YteJ06rDjiO8jlcrEoWN3fc3wySfc2fw8dozEOuOcIhpFMAUCEWWELAgFa0cJTLL/HKqJltY8s88bMsRgUEGRYiSEzDCM2FKrOsTIclIzeumgcK0l9JochUhMaGxlmM0cV50STiIqUIGcA1rn20EslQHHZ43PmtpZYghEI8awfWx8yuXEe3ufln6Q4hvSRokMtYKqFNVHVQxtVjYFaw3WGFAaq0SBhZKkUykTKuFv+1O4LubTxC0kp8rQ8hO48YuK2bLu6DJh69JQ9uI+Kdh+6SBgD0Eh5uL9zqFVxhrZVKw16Frir1VWWKRwx7pMdmVTSBmvIalECCIfVTpKv3LcKy8plQBCpBsrBj+J9JbrIqgXLYwYbhWYYRACWxuYHRiSF3QhFTe8tkbSAoyW0wtZYk8KIpNtJhuJyu9DousSuz7Sj9Ibro14O36aPz89pzB6FpWmKYaRgpyWEhdzG/JmdZHq5oy+JXDl4tq7S1982vl26kUptLZyg+1lpez19+pFnhB7yagoAjJ7R3WZ6G/VR7ns3PtqSy0EFgUTzOm/NfXvrfYxxduThiiRZBXOGVQ2t49dypIgK3Su5HnkLJNYFimszlYe4zYUQ2R9GnEg6uJ2yCEhsbaqqJkKlLRXWRW3hCiYMhTSb3+Nq1xKVYq8tUjmyVly9fdcz162GyJ0OXO3NUyipQ6e9facbrch+Eh7ekBQGybzFpMqcrLE4BgHS09AH2TqBdycnZF/9AF5fcO7f3LG87ORrc/oWeIv/MrrLBbHbIcl3//sE97+yim/8LVXOH30Ev/Jf/n3+fu//694Pwb+4ukDfutLL3Hn56ZUi4bVxY7LH8I4n3F95bl8/Jzr7z/l4qMLPn9yzd99+oy/+o1X+epbL/HSbxzSpxOicmQzMLmzwO9Gurzh6fHIN6Yv8872Zf7TP/wWlznxzvKU/8tv/e84/V88RC0V6/MV7/7j3+Osu6DvRt5ZOtJ6JF9HKquIITCsbzjbfY83Ln4Lt/DsYhDXroIedWsSzMmUXo1YrpDiZSky6xilQ8TVUE/E/ZyTBW9QRFJIdLuOiJdMRe95akdiCozDwOJgRmgdKjnaaUU/Sirmay9Z7LVMuTdTj1VdSYpVDChyVaOmMK2mDNGBN5zOHX0ReQxWAtbketdQrtmYs9R4ImaoMSSRXZpMNuJBwBSyOUWsNTgnoRbOCnyUDWx6hxqTmNOGxBglrC+iUc7gakP05T6WLPmyUCrAiEtYa/Qex7eSTZatKLq0lkVyn3dmrGSeJW2oLKQ+klNi6EeIRRijxFdinMLWBtNWTOYVWsuJ3MfMsHdNe243sj7LJuBjImtDzoEQhd31nsLxCcfhKoutDLaxGCvwbRgTwxDwMRGSmAjVfigMiuwNxmbauWUMSjYGozFKo4y81pQUMSZ8iPQ3CWcNdWVwCwNVZoyJ63PYPAv4XnSQ2SVMm3CTFwzpF7IpfF19isoVMTqymd+qhVCx2Ac0OeuyGBbTVJKaQKX2wWy5SCTTi129LMgiDi1dx0omUU8oCodyFNdFupakiUnw2hclPpAxObAfM9JeDpqzqKW00M05iQnMqCyptsX3kMmQyoVTFtIoY3mJ1JY31SAbI0mmpL1Tu0iRIOXS1ZBRRaKoVSJiMFlJhno25T0UGtxEGREVCMSFWOIN8noUmoB4LRQSixH3201+IQO2pBLnLJunU1KAAomgNDEKlZ1yYIajIWLyyPrmCf3VJWGz42A6wS41k6Zhc7Fhef8UT2YYd6gHFbNTy+FRxZ/9ox/wwSeXXD/b8v7nG95+6YgH9+/yyje+wvSeJWgFQ+DPP7hP+6zj/G/9Mf+Hf/k9nl3d0E5a/q//w79Ed7Iltp4aw73Tr7C72nKU3+PVBy9x/cEjPv3D7/Avvv0uz3Yjl0NkiJGPP3mGDhGOD5n98hJtp6SbzMcf/St8zITkSGuNXw64ReTfvnvI3fyA13/mF/ny//nfxx05/O6SzI+Zb7c4NWAOHB/+8WOe7jzXUXFwNKE6rgjTCTq2LNqE1Z7dEMjaQaUkQCpR9PSgAiUEUSAoS7nuQubZDzIYjWk1s4WiqQy1qYjVhBwsKkUpiNGK5dTypdOK3Xbg4rrjuh/5ytEhs6ZmWjnutxWPNwL7nVaG6lAxVwm/nmN6w6yuySHT+4zTmuPGcW/WsEEm68NKM2jLJme6viUFVRR9UOURLCSTabThYu257j3nFx4XPJWDOFrSxKBjIA0DTnkqU6GcoT4QQ1jlDI2C1ZDZjhkfLGMvp5mQtZTb2ApjHYMOMuBFCZgDURhqrdG6kmnbaCoNMcWyEMuAk5MQ3bHEUkuMjNTNtqZmdzPi+0AcAylHmeR1KfApbWkaxbwWT8SYxQthEdjZaKR3ImZyNIwxgVZM57JA+6AkNTWIRDQbqfesmhJTPtG0E8Ggb84T8Towbkf63YBtLK7VVK0Wr4QFW5fuHyVxOSYZUTgpi3YVVmXwAjGGnUE5i1OG6dSgdWbYjTz9aGB4lnBWsbhrOZhZpm1gWn/BMReVelFsf5v5X9ANXcb0VDLckSW5GEXULWSxN6/slUN77we3lHMhVClHSV4cgSkkcZJdRRZ29ia1n8Qt8x50F2MdhdxBMoT22EpMYzGQKZSy/KTxRNs96awIseTLR4U15bWXDUOVxbjQHHt48BYu2sNA+wQerfZ5UQUDQojz/cb5wtQnZyKt5ZibKSa3IgVUSR4rlzcway2xHblIWSlSXCVfk5wpoMgJK6DNCp8COgXJMWqWXNsVGUUYNXXd0NQNeYzs1hu63YYx9bi45ua8Z/c889EH56yerPErz/3lktN7B9x5sGR5NKMPO1Ly1D7w+LNrNo/WXH56zaefP+PBnTnvvHrC2790n0/HGzZ+wHaANzhdcXB0iDGK5AfG7Rq6DpsirYUH1nHRe+LFlubRhjd/WfTzMaxRaaDWUJvMzfUV2yg5Svfe+jJvPPxlvvQzv4h7+Q45Z7aXOz79IHLx7IomdhzWiu+vPatB+iP2yZRUiuxhN/TM0sjxXLPdSgppTJBLx0LOYq7KMZdIEzFs6SQn1rpAlKKTpwwEgFIYa9HKUFUWawyVVWy3Cp0My7amsYpFZTFRkzrIFg5LLahNkqlVG7i7tGz7QOxH1queoIwojLYDZ6sdum2pteFyEAnpmKVONZRrL0RQuSoEqkRd+wHisI+PCcSQ2G4CBI1TCZcTzsr7QYJ5ZaisxlpFyArlDKqyUFXkQfgsozSV0iRlSFlLmYyVn5d5TQuJrMUEZ63BFiOdEjSNpGMBGBSuEvhKqZJHxj56SIyZOWtpx4t7uLYItI2cGKJPbLYJW4Gyco+FnMghE0opjkEUe6mY1IxDcpqSKJ10lARijKKpNK5SGCuDc+hFyTVuR6xOtBNFVVtCIaFNluhr18gJbNh5xvVI2EWMVswPK6qJpZ4a/CoQR4VKhuO7GpQ4sn2AYfAMXcD34vOwTtRkk0YzbxXL5guGjwbdonRL1tUL4wtgkyQI7nf6ZDLEhM7xhe+gSDXF7V2m87IgpkKY5BzFTp8SUZUGpZKvtDeMeS2nAq0UUcVbl3HQln0HtMArRftsLE5piFGKSPbEq7aEfidyUi1OSPEjpCKLFYVFRi4YHyJxTFRNhdZWJHqJAgTJRaP2wJjKSAyInIL2fbO5qINu1WspF0lqwVxNuTNTImrDvnVNq1AgKMBYkbTqJPLYKBdFUgpXvA0mZ0Ix1WQlWJ6+9WhrbF1cH1mzCj1NHJnlxP07r3F1tQHzmM06c7BombQTTFBcnp2x6zcMeQfrDZ++f8Pzjy/50++d43zisK74C6+/yvT1KfXxlEFlbi7WWL9jHnr+yd/+Nh+ue57GxF1j+e13HvLrv/w6k19cYL/t0KuAGSLrsy1KReYHR4xhYEwdyYycNoqZMgxaoXLLt246Pll5zKOB385LnLWMPOFw2VA7ufGfP/+McLFkcnSPV/7qN7n3l/+nnLz1NbwyxDHy7PPIH/3uhvc+eMqriw2v3DM8DplphgOtUK2GCpKLdDHw7GZN5TvevOf49AMJZfNaQQ05SUBiNBqi9HHUSpNihBSpcqI5LLEmOjGUhSSFBDHjKktTa+aLisYauk3kw493fOnUcXJQce9gQfSJy4vI1Toxe2h5eGSYNYphzOx2HhUCLx1qPnjU061HLlRmejBjs+m4Pt+wVQMvPzhhqQ3vrzMVMhzUtiIqCD4zDglyg84WlQPdLpLGjE2wbKHXgTAGNqvEsINJbVhMrCiNlMKjmU4cjZN7u4+QKk1uHXo+QaVK4vKtYuE0uyGx66LEXDiJv1AoApKK6kxFVYkvxlUC/6oBKQhSSSZ+o6hniroqG9mY2KVM76H3mVBiuFPWELOIWRRov5/mEjYnnl0E6tZwsDQ4Izuk7yPbm0RdIQU2ViAgpYBG40Mkj4GwG1G9dC9rB5NGYRygM2EIbDaBvgt0NwOzu456ZmkWDRfPPHEAnRSThWG5MDgHT552bJ51xDHTLBvuv9RQTw0JxaOn4qvQRvPa2zX9AF2X2W4C/WZg7EdUyjTLmraxNK2hnVjmE8fB5Kdb63/qTeGfuft8SStOSCTTYZCGpdGAzSVjyBgpZ9dF1hlTIYHUrbFHAb4YybKCqJNgZbm4h42TiUBncUrnIJhbriQUCykMIYlbASza7uEe6XOVTGMDRjJZpDnKiwEny8VTzZaYLEaZiEIqniCkntDvEELYEYyimkxpD1qs1ngf6AfP9XrNtG5prCvqp4KFqlBwfYGVsjYknYgqiewvlcJ205BVIBJJet9tbchak4syBDI+Dyhdl3/zAq2lYqvXtVygOpBKZLOcOmrQxQUNJWlWNuU+RzqVuSkQRzIGaxpGBUOUYC8TMs4r1AC7IXLwoMF5Q1KZ/+a/+g6fX2xZ9ZFff+dlpibgNDzXil987U3uvXaf+cEhf/RP/h5PP3rMd374nA+3HW8ulvxP7p/y9b/2DpO7c3AV3/5/f8bi/gn3Hxwyaw02tYzba9ZXHxNvVuRhwFQaMzM4FGPWfHRtOJ7f56uvvca//7/5n6Fqz8X1JeF6Qx0C280lm805/+arE350lblMienpv8Hk3n3q4xalIDiNPVlw75fe5uGP5jTtwG6Z+NXTmiMXaVrDB8ctcemg0hyagdBf0fcrssk4DdZk/AR2SZENhTRUOAwmAkHULVmJpHgY/a3RMjlF3sfs7nm1LKSiNoajY8vD04Zu7fEpcnYV0ANU1rG8V/HGA0vjFMOY+OFHI8b0zCeZh3cd6iunZDS1c6jaoXJA6UQMnuvViiGMTOyE552HDK2rODme0E4M86nlWiliMJAsVWsYtpF+iPTDKKbKWrNshKx2RmFdxivFDsk3wio6JX0dz8fE821gvY10fZIqUq3R1hKcxuRImxQhCY+igsCvlbZYW9G2Nc20EtGDVexGj00akzWpqqVjoNJUC4v04yS6bSKFRAyQe/kcVMzSBV9lkYpqGeIqZ4UHqQx370jwXs6w2ox025HdZmTsInEoTWrZCHGrFTk4+n7Aj0GSjGs5PVSNFs7IyFqldGRcbxm3I1VdFuu5ZTKxiP1UYZVC1Yabay8u7X5E5xFtoXIt2z6xCzCWGlBlDLY15IkmWSGXw1oxehlWQh6pqwZbG6rasrFiIBzVF3xScFHkdkbDmGPJEioXM4WMTalgSQIBaWVe/BsUlY1M2KosorKWF8dygWVQMnFnOUjektR7tY4oEPQt0fzCTSzyxJwLXFUMMDlLXEb0EUzGaEtlpf4qh4j3EVvXOOuo1IwwDrdkTtbivvSIGkra8hTO1SJxzXtLXjkpJM3+ZQlJnVCqhF6VI69IUEfRSVO4GErkMOLZSIj2XQx6sbweMdvI45oXkFfey3/lsX7i6RT/SDnHlIBBnzLbGGnROKWoSagUMAHqaJk2E5xrQGm63ZZZMKxXGz7/6Jzzq54UYNHWuGXD0cGUxWwK01PmB3PGTcf3v/8p77/7OZdPrll1A8ooZvem3PnaHWZzy83nKy7Oer73w3PeemvL6Ut3mHz1HqZqINYMYyZ2Pb0PAuXMHGow5NERzYzf+nd+m9e//iaHRy3rq2vyaoP2A2fvPialFUltyClyMwSe+x0TM6U2loDi/Q7aCF455qdLjk6W4Dds+h1mbkUG6RReZ1L0RD+Q0kgKAzmMpBRoJ5ZOIWTtrbCiXLqKciLcw6ZyfUv9pXwmMSRyDGVTgGAyOiiGQaoprTXcmVse94GhB99najSTWmFbxbQRj8wYYEiJ8WZL6CIH8wblwBlDW1dYa+kXLSFnGAcwRhbMPDKst1L/aBuWU0ulnfQKlLC2lEvtQzHopSD3kTJSs9nWVk7JOTIMHq8yFJ7Mh0zvE9uVJ9yMpC7AIJEtWktlZO0UIanblrC9l8fuq1+dpamlZMdoOWGHMZN94fKMQlnpI7aVwqgsG4FK5J9IBtZw60a2tkhdtQyGtpC3WM1iLrDyepfou8DQj/jQE1IkFEWYyuK9iEYTBwRByBJaqa0E3mknCMceAg8ETJOolMK1BlOJdHbYJZLXuEbRTgyqUoxdOQXFiLHyPG0FYYjkITMMIt3XTrKrijBN1kclQY0xKhJOso+cFAmlvcrpVpv+BW0Kh2Fgog1OQVea1xKqZAPpsinkW/ejbACiPqJsHpl9uNYL4eptU1rZGn4iAYlYyDdddDrqJ3B/VZ56zqVNLCck9roSBD+LQ9inQkArQ4iSsK8sOK1Fn508fhxxlRMDiWsYsmbII0PqxXyWEn0YicpKQiaKpmpLbwJlYZeog32GfmEFSHhUMeflFMQVmiHmoTi75WgraiJRHRkiKnlSFqI+5khWIjOUchID2cpNqlIpaldlkzS3GSya/edQuBVKzkxMDD4zrww1iSYj3ELUTLJjMV1gnQEV6XZrdhvN1dk1H793xnZMzNqa44MJatEyf+mU+yd3mdy5T51GLj4/4/f/0b/k0fVzQvToWjGfOuYvzZl85QjvB5788IwPfnDOv/7sKXq7I3cbDt9cUi0MYWvp+oympw+RaAzNvGIXrUQ4LE/5t/5H/33e/tk3+N6/+lOur9fk1ZrWD3zwg8+oXc/8ADZd4rILXJmeuWtotSEleH8L9xNYLLPjGYd3lqzPL9hutkwnhpFIUjDmBONAiIaQWrLvSL4nBM9kYtkmuYrNLZ8mN55GblJV+K691HqfaoFSxBjLSUGUSt6XG8BKkFvjYOIcScMYIfWgKkVdijO0KVycXAbcXKwZKs/iKBCswzpZECbOMp+1Qn76ik0nTuYUR8J2K1i3DvR3alqH6NhTIMdIikmGt8IFphDl3tEi42xqQ4qRvk/4fiAoUcrtO5j7PtDd9MSbDoaIClnuGZOxDhoHISpGK65fo0VIogsWXlWWuhKMHiXCFd/J+6aRRThrmY2ULmsLqQhRpJhKlMJa/EpKUlGV2cNUYhQzRjaFeavxKXOzSQydxw8jMY6EUqQjNQhCRuesCV6V35NBZ7SV3m1tZFNKKRXZaMROwLSaqrEYq4hBaktDgqaWjcG2iu2qjG85SQS50RiXiUOUIbUr618p5BlKsY+YYYXviEmRVY2xon5ytYQVWpUxRKTs4QvaFH7JfETG4pMjcYDJ+8U8CXdQ9Poq7yf4LDWDhfjViLN37zre88vyQYJOGZtFDrqfVHIGQyhYeaGiy9ddKq3o5SbbRznoHMp0IEWYe4mmU8jFAaioSLEnDhtCtyGOmtzKou1jL9JaZWnqluw0PiX63tP1PdZWVFXNctbIDu4TNgq1JZRAgWyUqKy08iRGUg5ENCYFgXOUOF8jlpwkFwUg61iIYjktuBiIZRLI2UgYYVJUOchiU1gNjUZnLReIosTlSW6TNMZllNG4nJliODaKEw0zMlOVqTHcaSv0csp495ibrqcbdoxdx7t/8DnnVzc8u9ry9sNjDk6mLE/nLOZvcPfBlIO7DZN+xx/8nT/huz/4lH/y6BP+4leOef21O7z+lQOuHnXcOX2Zk+V9nv7en/Fn3/+Y73xyxmOf2P3gfR5vr3jpnQXt7B4pReIQICpcVixmNUf5gN3FQO8a/oP/4/+At77yFdAV763f4+bxFdVqjU1b/vhix8tu4BtB894zz3075WsPjnj714+5e6fCa5gnUB5MVNRasxwz/rqnv7hhmXqcikQFlb/Cz0aUi9TMSetHDDeH7G7epK0dU63YZHG3KqESyOknRRKKnITvSTnRTGVT8jGjsoRE5BxQMTKMcloNYyIqy7i10rvQJxqrmBxYbMj4jediF/lXOvFwaWktfOkunH/3jJvLNZ/ZBtcsWRwcYU9bDo9rjmrHVBkmtmHTBba95+L6BusSxIAOA6uLS8ZdzXVdMa4GuWeM4cZEYsqoHAjDTpj12tLMGlSCOATG9Q76DqMTLmm2O89N71nvPOF6Q+y3EKHWFU1dY2uwThJKB4PESGuERzOiv6+tK16YTOpFBhp8orsZAI/WCTNavLcM3hT1YCCFSNiM+F1E54r5oqGzkHyS6dtE4fyUpJ82lRHjWyXmUd8ndjce5QOWBFZE5D57Yk5YVVHPHckaVqOk3eqIwG05kUdPGDNb091O7SEkpocVrja3aimJG8sQIPSJvos0WmNMpmkV42AwtRjeLJroIQfQUePaiK0zlsjVWYnpT5mwHgsBrlDW0rSyqVoUc5+Y4pnnkS+0jjNkR8aRVIVRonHJZHSOUs1H2a2Kk0AhFneKMifsQ7TKrpXzC9+CzlFm3RwF0kGmB51lw5EH0SQlEJPJGXIs0BVkZQtck8l55EUySyaX/FKlsiQPKtE9E4NIVY2hnTY4V0PWrFaXqODQSlQT1lXFpp7pjSdlzUimD1FqRLVsYjqLRllpi8ruluMAR9aQSuD+PuY7o18YWXTZ4Io7OpTvg1San+T8FJU4JSXpVWSlGSNZ9+wJ6P2UKhumyQJxFBRP8ou0YpIUVgMx4GME43DNhGa2oJrOMQTCGLm4WnFxtSXGzIPTJZgJqpqg9ITDlyfo7cDlk0v++Z9+xkcfPaePI3/5F17jrZ9ZcufkkIOTE8bLT7F2TlQTUqWxTjpl7yXp+175gPcerzwDga2PNAayNdh2SkyWetFxaA/5ys//CrPlIZ1PTOu3uLTfZ6w7Bqt560tH1GHNpRkwk4TXis4mrlcjzZCEagJqK723QRl8EiGEZN0rmjJ16pSp2gguonMm+JFx19Gt1ujDA6zS2KypjLhpyVLXGZMiF6kjUUu/cTnB7WsVSbFwQUUMgCaSGIki1thGnvaBeWNIU+kvCCkyrj1hEzhfGZ6fVrQTTRUSvq2JeWS9Cuhuh2tnmEpT69KwplSZdGVEmhiHOZgw+kDXR7wfGb0HJZGJVe2oXGZImo2PbL2n8yNGaRyKXE670jls0bbBNg3NpCX4TPYZHRIqeEwq2jsttacmJQhy7xGSnEAypVZSIl+kNyHBCD5FYsz4MQmkCrend+eFy9yZsk4k8TooxOhVTYyEF2rkOZF+Ip0Y9hWYlUmstoGhT+SQqCtNVo6sNJsukgOomDCVpW4syWgJCoymwOIJSYIROMnEEqKZZQANnRD2eYD2Tk0zNxwfa7Zr4UfH60TsFWEXUGOknRjhMLQUaBmtiU6jq7J2kQk5MlyUXKacSaNHafGWVLXFWl3gffncVTLo5H6qtf6n3hSuaamo0FhZ9lWWRSuXZjTJuiCV+AudBUdH7RduLTt0maD3OYIFQKHUvwjclE05YMjvkMVScPN9MiW8wA3JRQaaUzGovfAuSNeBKH2sFael0gbCXrZVYV2DUooYA6MfsFFubKsUaIMxRl6zMYwhEYOUodf7WG9dlEOKEoK2h7YgUxfyuJygdCaV01RS8j5kXSo3smCGqRyjbl3QxQCV1f44j+BWkqMtpzKVi5tasM+0R+jUXvYrfystuuyqXDEhIzdnrdC2wjUtpmrIHfg4cnOz42Yz0Daa4+WU0cxoZi1V01I7WH92w/X7z/jTP3iXOHEcn8z45tcesHhjwmR+SOvu4/QZKjtCsCRnMc7QWIWKil3M9DkxeM8QBrpxYNuPZFNyq4wl15pmqpm0c+699Bp109JHT9u+TDP9lKw2JNfw8sMj/KAZwoZ609FHzSYnLs566gcJU4mr1RqRIw5KcO2ktEAnjbhjFRntM7rKUEleVUyeMPT0uy3NQZCwtWxwWpNUJipVYhmEc0paFe5Mjm45l84M9ULGnTW3XQWZKERtloKVvo9Y47ARqiywzqbr6K57zGXi3Le0c8uBSsTaQarpt1uyHzgIAesUrgwDEcoiJc+psY6mnjBGD7uR3dXA6D0+RiaTRnrYXcbnSB88u3HEx4hyVkxbypTXYLDGoa2lndS001r8SIlb/84eYstKEol1iuRQ6m9DMZ4qMLY8Zs74AodkH/EhE0MijAIZFVFvOTmL8TOMxeOUi7bfaFRlsI3GDZlYXvu+W2svwNiHGFqV2HXgR1mDXC3qJ6UNQ/TkIaNCwjiDs9KiJ4OZyEmz1kCUGI6SvbZPd5DhAFLIhE1mcpSpK1guDSkFunWi3wTiaOQ0kzNVq2/FB2QlnRKlV0LphPcJ3weGjS82AGl0s7WRoqBGo8XzKxlrWbwhIf10y/1PvSn8bv0yb6jMwxwI6gaLFGEkrYnFwSu5Rb4sPpTSjRJiYgTXS4BPCa0rIeG0WNApUdxK1bLB6BIcBXIhqBqlw+3sb8jyuMpy22wGaIr5DV1iqaWDQcXIrJ1itEzxvX9M5abU7gCouFp/whh6ju68hVaKEDPdGFiNo7D9zjBzNZWT0K2L3cBUQVuOeKmQ3lK+45AElKokYA5ENdxmJMlmZySrXYnrNaQo0JuqyMaTUaisi4SuBWVBBbIRw1xOEaXbcvVFUPvpLaJKDr1sLvo2lXZPfGYtHMTl2ON8ovEwi6OEGeoKUqS/WrF5fsXqfMM4jlSqQmXN17/+DpMjjW1Gnv6TT/jWn/2I9z97yqmb8pu/8rO88vYx8y+LLtuOFa432FWANBDp6JMjOQetZRsjY5ZIh/PNmvzkOdvLHZfPL1lPauo00saBeXvI4URRz1uopO0sW0d1csLPfPUhYW1Zdw1dt2Zm7jBxkT9JH7F62nN+E/n+H19zfjDSkAlWcdooslN0KeN9WTSdYrfLZJPRJmNDwmnRjS8sbM2WHG/otjfccR5vKm6CeBqi2sNGUl6UNIX3UeJH0Jngy7QexbegtWjiszUIaCI5win2KKDV4tPY9h6fDfie7fqGfruGtMF+PMcoy+O85c0v36WZT1htNyidcBaWdZGFpswmZua1xUSFycL/LScT0Jml93wUL0idR/nMbOmomgrrLNthZLfbsd2OaJNpplOayQTT1CirqHXCmQrbGiatpa0l618r8fS42uEZSxdJIgVRYOkkvGRCSm50a/FKEVDkGFG9wD3eR9KArAM5szxoyYjDOJUHUkoUjqaczky22FqVomMwJZ8qRUqbY6lFypmU4y0cPXQyXLYTsFUlA2vMzKOhD4ag99W88jmaKDW/VmtsUlDpWyGBtYDW7Bsdq8oRfeZ6syN2kbHSdJVi3I2Mm5FxNdIcTmgnSuK9DcROAvFyzNQzjW4NemoZfCJuPHGXCH5HCPJeuFqh20aC85wGK/fJEDLbWrFBc/1FN68dDSMzB04nhhjJ+1apBHlfPpP3x5u9FU1gnZxlnRcIQwkMJXIYbE6kFAUPV1I/R07oJBETGonQSNmLEiKLxE9TmirKhpTL79EY9pUKSitq0xLDyG67YVo1UhOoa3ZdB22FqhIpBT783qdcXJ7zpZ813Du+R1O1tLWjHwI+BsIQaGZGKv+UpnUWV7LmVTnVKCT7hFiocqUgedFTKyNObEDlRMoDmqF4GYxIbMuLMAUaA4NOhoSEsO+d4CppSJaco/gZFGXDsYV0zyWHSaYplSkCADDsZbkKFQ0maqqoqLVlN46MmzWXFyvOLm84u7zh8mLFYjJnomuqquLh2/exeWR3fsF/8533uLpYczCf8W//pW/y5q++DSbxvd/5Ew6OJhycWuavKuq7B8RUsRsGdl2kmjUc3V/gzjesrkbGqLi6UUyHyM6PXA8dEwWbfgfdlrt3G0zdUh3M2IcVGqOo24owPWAce+JqwzhWsJwyPZ5w/HCDvtsQJw/4xp97g3uvzagOFasMjYIhJ3YhcrZb0W23ZO+xXULbRG0yfcyoKeA0xk5IfSb1IyYMTA2MGtqCgRrhf3Ep4ymDiJLTsUAUoAYK7PlCqQRyuhDVWOHRMDKFVoowRtIYCdGjVCDXCrusgCWTaorJjnFdkdWUoCK9PcTqkYs+8aOnK6xtRdOO5vWZos9aTjAGolMlA8swfXgXNyb8mHAaQgp0MXAzKFZesUuOejJFH8zQk5pUO7pQAt2NIihFjgo/Znovz9mPiVWM9FE8HEkl6jLMaQvKlUbxrErDm9g4Ql/sq0neW+1KB7lWWCtwjSkZRDnvobkSjinnLUARPAwexl0iRzkBaB0LRlF8Iinjg6HvIyiNsZq6sZhKk8p037QVlbOkKEF4IUkrmoAORtr1TJnoy31lDWUoE9d521g5KQyy+Q+bSOxgd+PJZOxEMTkQF7MuiskwBJIXEtsah7UyA487ccjrnHETc9tTkxHKZxwTmMREZZwDnHitxhgLTP0FEs2nacUsKex+lc/Fl1wKY4AScUG54tVtbpAYuvYwz14Ls4ef9iknsmCpfc0lAhWZoiIgp9sWNPn+8jtVLlCReIil/kUO5EkpKm3xBFLoIM1Q2Qkp5HuoWyARQsezDx/x4fsfcb7t+flv/Dwnp/c4OD6mCXLKCCmSYpBjnQanpOd1z2XszWpinJORYQ8MyLFYjsSiVsrkPKAZi2TWItU+kZzHUuZjX3w9y8VjkAhfsng6Ugqi+85GTHtQbrQXOVGSQVV+RskGZhS4cmyo0NRa47ST5xVHdusV3c2a3WrHphuZN9BOWk4e3qWtLcPVlvWzFdv1yOF8xv07M15785TFouHqcsXHP3rOg3tztKo4fmUEY4hjYvA9YQhYq5nMGvLgmfeZuqlwzQRX1bhqwLZy4XofGXcjk8EzaSeo2pWTo2xyVVVx0Udxf248tWuprcXpiuVshmYOyzscnMyZTi3aQuxhRELlcspk7dE6ykLMPloAVEQ292DQ0YnwfogwemySTooqIz0AcrVC3kt/5ZNIcgHI9SpUm8ClSsDB8o23uG9OiliwfzLkINCAQjJ9TOVQJcRt3k7l2m5abN2IUa5KZDbsesWzsx4foDKWWeNI92sxmGlonMEXEYI3msXcSWGLF9VQ3/fshsh2TAxBE7PBthOaaU3VONAGH9Ltc1clRXQMkpGUvGwwQ8z0oUTcKImv3+OZtTPEspC6PdeXS73v7XsoPKA2GlumdAW3WWWpKB/THnYtbF2KibHEfIShQNxKToBKyYKalGwKMSZGlakrizVgXcYY+VyzUtS1xrYGpaAbErsuEJI0L2qrMdZgKyN5abmoKSlnP5WFY9Cy+btWIrFzlPcnh4htNNXUUFeirhrHiKkNKYjqSRuB3W+Lv3wGL075qrZyOkU25JykI8XrRHSJbGTQ1CGhVUCb8ada6/87ZB99RMqKMVpQ90UvXxY9sw+OS7Jzo4prNqbbiVXMVPKG2RSliEKJA2DfduaIRT6WC/aekKivvflN1ENJa/bLQyYTytcyGpslJiJrimFMqi9VukTnJTlakZKFa3JqyGmk7y/4+E+/ze//49/n3ZuO/9X/9n/OL//Gr/DLf+nPcTxx+OjpfY/3o9jrtcHEjMFh9ouwkgknpwApovevO2dUioCXeAqk5lwzkrInIq/TZgO5J3MDuSVSk3NNhRP1EhGTK8YSPFjlTMiBXC7DlIqrO8vVo8omkn9io06yS4iqx2iGDFNnmNcVzjQ4Z9E60l0+ITy9xJ+t2YyebQ4cPDzi1//KN7n+6IrPvv8+733vfb529JCf+5n7vPrGIfquYfPBUx5/8JRv/+gjbj5v8P3A6f27DNc7+nWm20DaDdTAzFk4nPB6rlgcHfL6O1/i6P4D5u2c9Oqa9dU5m25HCIrdbqCpEkYJNh+ywJZNO+Xxu4/pHn3MbLjia2/coUkbuDjnTjMjhYYuG8Zx5HJI7DJ87ym8MYW2z1Q5c3qAmBpHx0xrJuUdnfUalR0xOsyQMX2GbWC87sm98BN1lngBV7iBgCB5WmYm9qGEMUk9YkYSN7NVJRSu6CWSEI4xaMZeYbQ4juMg015TKxpnqOqKyliO5y3LowmT1tFGzWcXnvU60k7n5P6cYR14frNldTYwOag5Pm1ZvdZy42FI0uO86zMjMNSGt6eGWksc/XevRobOs95lVpuAHyxGOxaLCXdmDZXTjF4xpEwIijiC6hBOUEGlE4MPjN7T9YndkEgp45QhugIrVZZpY/EJfJKTglEiHw9ooSeT3N11IzlKtTVse25loLc8WRaYNYRyKk4ZHzzDkBm2srhLaoFG2wJXoRhCJPtICBJCOW0MlYPKZllVsog0JhPDYqKpnOaqS5ydK0KAKkeck5rNuqlwWjwYSkl/dIiSrDqOkWEX8SFKt4Sgs8Scmcwd06VldmAJI9yc71hfDtx9uIQxomLGRS391jFhxkzcRlKfUEF+Hicbw+piJA6jENdhJDaWaBQ5gOsCrR6YVjvg7he3KfSxBV0RVYXLtkwwCZdymQCQBMUCH5mccObFwh2VLmoi0cvu6ywTBqf39JEYN7LWZKOLE7dM4xHBvGV9w9azYmBLdOOaGAbJnbEapSwpGvqk2IyXOK04ufN1rFIM/Y7dbsXR3Z9FO804dvzZv/xH/N4P/4Q/ePQRIRn+H3/zP+af//7v8Nfe/6v8lb/xN1gsD5jpCX30jHEkpIFJNSGNgZQVdVOhixY9xkweRzHvZUPKQSJwdYXGIwS5IlERtbs9DWnEiZ3TnGArNBU6y8SskinkcYVVIj0lR3RUZG3JpsKiUCmWTmojeU5KodQLUk0rqIxhRNHFiK6l9aw1NWPaoWtxkl7+8BHfefSElR949XjBr/27r/PwdMHF9z/lP/rPvsW9yYx3Hr7Bl//qO7RaHCNjVLz36Q/55NPHzJzGWHATw/TehPNPAtGCnVse3D/h47Nrtpdrnl4Gfv23fo53fuZNvvFrv84HHz7Bqx3mzpJWK6bzBQ++dJ/VdWC1jvSPVzx9nji8l9EOZlazOHlA7Nasnj7ls8uONvVUcYeyczZZ0efIdDljOjG0FTw8gVMLymWut4Hry5HxWjwgsZqQbMIZULMFwU4JTFB2inMtKtf0K2mhmxm4X2nOekWPxGTrGnYJdgGudsKJKaVoK8UwivHRC8ZJIuNTYth52tZQTxy2Vpidxik4ag2XUVPpSFtlRu9Z3wTizuOPm33zPFiNmlVMGji407C9MaxuAldXkTd/tSWMsgi9exkKyALPc+RqC7EyNKeKDzPUKVMlGI1msJrBGQ5ODqiwWONoZg3rqBgH2Gwl+qI2mnaqUBqGXupdBwVeSxJAbyC5BjIkazBtTbaaLihWz700otVGjFqFBminmpiED8hdYrXzYqzMClMXw2bKoGWDMc5gJxa0E+XeaLCDLu1nkllmKo2tNZNKMUaP95kUNabRNI3haFbz7HyH6QzzWUvVQBol86kfIjk7ZhPLpFIcLQyGyOo6yAkbgQ1THwVG04qmFuQjkEkpMIZAGgPDumNMEWM1Te1QrjTJbQdyUFS64vS04cHLE64vHGOXUEkzqwy2UmSXUTGRh0zsJeJDTxTGaSYzR6ohlTjtYRdgSChnWE4MddMwa75g9dHH+oCFMkxKO9k+8G5/1nuR8ZZu47D3VThAwfLyrVNZYiEEdxNlTvmqLkU4MmbfPkJWVtQaWajmkEMxjYkf4TaL3Rj2zt6IKChENidbdC4flFIK7zesV8/49HsfcHZ+yWrsqLXj8uwpzw4dz9YfF9lnT0wbUjKiQkkJlBWxa9ZkbenDKMSVkjjbvThInltxEyoNWYqBMpXgoiXKNyHGtqxbkraoLL2vcvKS81JSRc+iEMxC79Ug8n5TNkzJPSrQw4uRqnxNjrZjhtaIMxINKSqJZ9SGcb1mGAZUBacvz2ldpru+4ePzK8ZVT3tyyOkbS+48nDKe7wibkWo6pes6dn1HU2lGJOMnjonoBdaylaWagDWW7DW7XeTo5IT7r36JxdEJ6v1n6GRomwl6knBNjSOgzIh1Gju7g64q0BJfMqaMckmkepXFM2ABkw3j0JPUBOMM621HnRLZwGDl7TS1wrWaXR/IIdNOKsYx4rNUwtb1FOemxKrFtI5tn8jBk8aexshJdkiJOiliFjHD6CVNM0bQo8CLWiucNSQryqQUFbbWqJQgKsIoi2FdKeYLy6yWk11jNfouWJWpdCKEwLyKMGSauhKFUFRsR7hZS/NXU2t8rvE4kokc3W3ZbSLrVeLqSnq4bZE6xqxxWoL1Wq0IOXOTBP6JSRJH57MKkvBUYhCTa09pIX/HWHK/tGbMmqAzUytGNLTBZYWV/soSU+0ocVxEEk5JUY7WBRLKJf5S7VGBJCqlApnGUQhikZJbTBlABcbZ4xZy46Usn4VyoiyTbgV5z0kKay3GKbTRZfN4wfeJ2lG4y5QUw2gwRhbz0Ucp/SkDrs5JUqEpJ0OK4khT0gdg3wWvc7gNSFQqQlIkn4hjxChHU1smE0fbVowTjdXyfKvaoK0m6NKB4jQ2ShAeqsDmxZmNlgklp72ZLTEYQ28tnd2XEn9Bm8K/tvd5WwVexgNrbKmzzMbcykDlECnkp2B++5RUTVK+pIPu2wGcNKCpRFKygKuc0KYma1nQotKQxIymdE3WgZQiHmBci7IJS1QaZR1GiXx0Lw3LKRCzJSjwecApBQaMs8Thgk3/hLNnH/HuH33IzfmanBPWRJoMdeUZj64lrTOt2A7vkuNdxizEWsqGgJR3huS46VaEnHBVxUHTQkoE70nZEUvGkdJGonBzBtUS1XjLn0hipgFVkZQRUCzHogVvSNqRlS9BgBKhnXVNVtJ/LbJUIRWVFsgsIm1zmsK9lO8dUeyUYmoMqEQgFXWTJSvNOGypFUwWNQ9/5oDt02uen205//SKVw8f8OV3jnj4i1NqFdhubujPOk4XU2IQF2jbGjYxctONbC92Qh5qi3U12AETwQ0w9p75wV0O7zzAuBnKK6pkWFYLtlONzZ5KReomcPf0Ds3yFWZ35lS1YwiZa+/xaYuyI/WkRbUJGzIuw9n1GfqowbUVjz4/o7l3BNMJz4DXLEwrTTNxbPuRBsVsOmHX9YwI1DGZTrGLBTRThtpyeb7Fj2vwa+Z1YtCJuA3UAfogDVnnXRAHcs4om2m1JHwao8m1LJZGKZpWM0bN4BMpaGoHbSUpp9PsCAnWAV6634ipakxYlVnUmonTXK4SFYoU4fI68flTRZ8V9QG0vShdTKW4szRcZM1mF7h5tsO0UdI7m5a6VRy2mtcbka4+D/AsZroukMaIBQ6nlptOSx5WUliHJJpOMt0m0Y+Rm11CmRplpFjmsFWQEilmrKsE0iyFNxpZrGJKuDpSNyW6wyDxESkxek+InhilmtcpC0YGp+0u3CYkNJUm5iRRM0ZSSUE2Zy12J5yTbCRJ0s/43UD0AjdN24pcgdKZbe8xKGqjqJzIlaMW3oGoGcdYGNDMej3S7UZyEIFKyhqVpC1ur781JZdnnxBtlUC3VcloUxoUoi4qGBpV0zKbV8yWFc45plNLXcnByLVyAsk541qJJFdOoyvwWhNLlE3JJCEjPM/eGLiximgVwXzB6qOXu447DcxsYpcDKYlpSmVFVlIQA6CVBfaZoUYm+yyhd0JQK6AqC6Hgujp6ceRiyWkQUllnyEYWxaxIaSvxuIhJLPWy6CdG6ulEmoWUJCjF6MlJCJ5aKXL0XN1ccTRZ4LSjapfo/gLVj8T1wLs/+iHb1QqroDGJaYbm5or0+38M/84WN7XMTMK1d8iqEVVV+d6cwRlF3q0J0cNijmFKJpHSgDEVWlmsMvhxQBdJaqbDKZGwyimhMJH0susXjoaQyHTAIDkxIN+bNTrtN2DBKFU2KCwpBlAi0RPSWUA4Q8JkgeVUlKTVHAdiHJlMK26uL7n8/Ckf7WB2NOf+KzN+9vUD/qu/+T0uzrdQZ377r89oxoFPf/cJb/7GA1bbgavzC7qYuL7ckJPiwd0jtn3HvLLsuh3XZysuVwPXXcC5ETdpmB+1/JuvfJOv/Owvcvr6VzGuRk0q8HWJQ9bksWP0PaQJzb0l04cLjuuKZDTeR9SgyWmCNhPcvOJoZtD9mpg6rK3kPfIJf5GYeZhb+PoU7mpFDopN0vzx2ZZ7MXNYzxkHw9r3dDFwfbFjfmRxU03XDDx7es7Ihumu5r0/+oj6dEdeHHLUzrl7YEm14nwXGGKk94mrG8/qcmQ3OMa44Oj1KToo/CZz8yQRdCKZxGQSaLKhCtDfdOy8pVeam8rw55eai03iO5cwu2N5bap4tdZsWs1uzOyGTIvCtyKFPV5q1uvM5RYudhKAdhM0F4PhZVNxcmSYzmW6vrwQFeF4ZKGC2mdOu8z5OuJ2mejhAMPcGZJVVCVDJ5IZTeZmhC5puqBhKbh76xQfnWXiToPPWKup55qmkeRQP2RyYeZrJ49pKwVRpu9x9AzbHj9IRLcmQw1GG7RSXF4NIpzQCmNKS13QpDFIVDxCqsaoqCpNPbPMWiOlOWNktYs4p7DWcXhoCQRGH1hdd0xmDU1lqaqK7KT3xKJQ0eKswSrNmEQMYxB4KidZ25IuBt7SBOesxqQsHJivSdYSKkulNUMahA5X4LMiaoNxhvlRxfFRxXxaEbJmMVV7ixKjzwwhE7x0TedGgc3EvR+k6GpL4BA4adTTWgxvPkS8z4wu84Wqj17TZyxRVBk6KiR4RRdlEcVsUVqRiiFHF+dx4T7ZZ5nuYxfI6VadY26PYUHOXVm+xeRcpKmictLKYIwjKk8svIZRIlVVBGnICLJFWW2x2pGyHIs1YvLSGpQTSeNk0pB2HU0KLB0cTxVqlxlXIx//2TmX3/8jmne+RHNyB2MmxJxIcUc3bEErtHFU5oC6ksgCTRS1UOzJfo2xx7K+3/IjMjG9gMaUwFuMSGvXgKYil+hNSxYCWmXZTLMr75ciE5A4QKko0qgiUeOW7BeiWU5u2cr7VAFznbG+x6RBEhl1w+7JOVfvf8rN6PnSwYyjaU14vOPsuieZzFuvTLnbWD7+bMVHP3zGS1//CtYkmha2lyumzpAmDeOuQydDrSvm7YyjwwXQodTAdtsRTWQgYtVICh6VhU9Jyoiz2iiC7Ulek6MhJEUcEnkXy5AhooUxK2rdyKSqDVaLZj0Z4VJy9ORxIMfITUh0Hp55WJBphkSdM9988zXu1j1vv3bI7vkl+WZNWHf4lUyAMQSsiSymlj4adPSsV2csHiy5/7ClHzc8PrvmydkNHz9+Tt95xjHSDzA7vsfy6C6vv7wkL4VrsFaxqozcI1rjWoVRCaMzuhcDWd04Xl5YTq0iOcWsUZzWCpsUm21mvRMoEA+76yi4upGB6M4MDlr4UlK80oBZgBphfC8ShkQ1s7xyVHOeAlpB66M0e0XQOlPVcDNEdiEwSZFhFFy+0pkxiH9nHBJmF6kjgMY5xdIpprViN0M4lgKLta2irmHiFKtedPcqSrS0QH0QinqIlMR/k0QkIblEFcaIZbaugvx7WXNSTPgQ0L0W1zGZFDN+BFtZWiclWSlJdpGzQjjbCmylST5RaiJwtcMaK+hDkntGZ40zYlhzVkvPRLSolBmcpTKSDmBK0oBWckpwRsyvyQBJ7vgQpM9dh1zKv2StTFbEBm1lqK08XvSKulaSCq1gGKTXIezE2JhKfFDwiZReBIPuK0xVFjhJl4rYxkcmJjLzX3DJzmvmnKwUIVuUvg9ZJGJapbIgi+TiJ9NOTbGlywIIe32elPzJSYEsJJPe6/NLhpCcEuWxdZGb5vLNWldovZNTQVHZiHM0kkdpIdHKopTGmrpgvmLuMiqhVERVmsY0zOZT7DgyIWEs3J1puj7g1553v3/Nkz/8/7BofoPpw99CoRn7FZvdJecXz7DzlmYyZ+pmtG3GRpkAyAM57shhg1bH5dSUbyW7kksk/5OVFo109rebAllOYTmXclE1ILoMTZTbBacg5lA2BEvClAtZqgx1eb8TukRfyB9DplGimFG7HYYRa2RrWT16xtmPPmYbRg4Oapat4+bdG252I3fuWr7y5pyDoHn06Jp/+p0n/LWLG+oqw2HFoyfXLKcVzkx4sulQWVOZikU7I949oql6pm3H55+v2SbD0GfGbk2/XhOGLdY4kRgqKwoRwdPIyRJiJG4C4cYzhFj8LBDQ1KbBmJqkjWxuIPWNOhHCSBx6apu5SZB85tmoOIiJwzGxCInf/MZbHBwF7n15RvfpU/pn12zP1nQfbdjmRCBT14k7hw2dr9ilxHZ3hjP3eOmO5tPn5zz79AO+/e1P+OEP3qO7GUQGqRTv/Oo3uTNLvPPySzxzmSZqmilU0RZDl0U7S+gH8uhR/YhWmqmGl2Y1S53ZikiLVxtF7DLXm8zqJjOtJXl1fRlhLjxf2GnuzGE+VSzqzJdqRXsAjYbvfmckdDJJf3kx4SB5qdgcMihHrWBmM/MZNCFznRMzldAx4T3UIROjIo+ZcRMwY0ZrTeUEdplpaIGHU8U6Z+FuFEwaqJzCKdimLLWkIVNVIovWP7EhiMMsFSewyD2zq7Da4JRm0iY5BRMhR1HaeI/qIJtUBCkZHzRKi6KxH6QKNPksjXBtgZpUJidPjqCTwmmL0RbQxWSo0BHB743GWk1VNgadFV1V0ThRNllKg5sStEKgpLLWaY3RmRgMKWYYpDkyk6mdk1TUlGmdptrH0oRMNVE4JyrKFDOhT/htwCNZaBlFGJJsekpSEfalZhrxdmjExd2kzNREFuMXLEkdWYKqROmiKqIRnNreanOLzlrvS1yQmsIssjx0vt0MjEqQxHmcVUZncUdLqmpVmtRK9tGezE7yNamxBO3EwCW5F1Vhr6JIwbQF02DqObWuiaYhHCgSPSF7dIpUlUO7BVV7LCoe8a9wiOJLleEsJr61i/zO//2fkLo73Pv1fw+//V2+8/f+mH/1D7/L73/7Y176K9/gjV//Bv/jv/LXse0BJsOAwvstMfcE4YdJKGLSMr0iG1SmISqZGDWJrDNki8o1QVksVmKE7U157eIAtwgZplSPVg1ZOSK2dM8Ks2WUFtJYWdlKddFwK4VylkpJOfnzcYNpLNNJTcgDP/7wI/7kez9k0VYcHtZEk/hn3/6cn1s6XnprzoNfWnL9nWsuzzbceM/1pz/kS++8xOHJMR/98DF3XrvL3fqY+f05Fx+ssLnm6afXtPWUk9eOedBWnD464OlOcb6JPP3wCavHV9x8/IzZq5Zx29NvOgYXCEGDaVETRVTXBOXoQsXH54HDIwdas2xr1s0R0V2hqOi3HSYJlnp4vODRuWazzXzzmyfcO62ZThU/P4OrG9isBh4/OuPu106p4jXbdY97+YT5y0e0w8jqO8+5fHpD7xP6zpT60NCFzPWwwT/+Md++/IA//Rd/l5vzyKbLqDHy0mJDmmi2qy0//N73+c4/eJezd99hu9sw+/k/z/0Hc95+yTK1cJ0VlxHOOsXlZcJHz3JccTCrSGHg0092/Nn9Q7SzPJgZfnWiuUiZZz6zOM7UBnoPn93R+JtAf5G4uoqcLSPtUrM8dlSvNZw4+OYks3hlhVsapnWF72u6Z4+IPtFOj7C6oW4cbeuw85aX7zjB/W3N6AMpJaw17PrAahf5lEBaKUytqRaGmwGuLjOPusSsyXRdZPSZZBQuWkwlOHq1TThEOXY80fQ5swuZcYBQ4l0qV+ONoqQwYVrpTjYoFgea4CMheMbQE0Ik+sR2WOOMIABosNQMI1xcGy7PIypKAN+DlyvqRpze5xcjcTdCjFSuAmMJWeN7CGMklgynUIsHo86KyggxXdWW5WKCJcp0vl9IlZxWdMq4SkmP9Uz8JmMPG61FskwhjxsrJjkvY/TYRRiFm3AZKiUihuwjfvD0m55oEsEovNb4PpF0JtlioFNyOtBKzLAUaBl510n5C465+LY95Z5WHKCIysuupDKhQEIKURykQsFn4XRR5UyTlZS6yOemyMbe+hEEehKBasYJqaOQT5iiZlLiTMg5472X04OyZG0ZY7jNWjFuSkITSAzDmmklzy0pual19OjYY6sp0Z8xDM8xNcwG0V5fbyKnEzhx8PqoYDkQJiM5ec7+4O/y/d//Mb/3rx/zvSfXfPjPej48e8bdVxp+8bV/g8PpCU5N8HEgGYPWM4LS+FTUUkZirXU25GwJRRGkEU28UhalJyK9zSOJEaMaifzIEHG3skJVwgmTMiXOIwsXoTQSCFigOFVODap0XBf1SM6KwY60VuR8q5tPeHT5mEercw6OIO8865D5bDfyzd94g4MTy9mjnmEYOXSRn1lorocVy9WaiU8QIzfPb6hnLXfuHJE3mca1tLnC+oQZFdpa6rrh/uGcI11zOllw56VT6qNDbL2krmv6XhNuW8nkeG+1geRIqcJNHZXTaKVYzDT1LJE2mbTVGDJVslRpgnKGdggEV/HgeI6pLEGATB5dbbn6/Cmr935A3D0nr57iry+Y2Zrlw4bmwGFaha4ChBG/ucaHHYOPjB3ktifsDP2ZpleBrg+MIVEtKpSyxNRBvOLmZoN5Yvj0x3/Gl179Kuu54Ww644lWbJSi13Ay1dwxjnHachYX+GGA0eMYeTJE3LyiPm5Qi0N2KbFOiS8tTYmLUfxqY+kGuNlGPjvP6Mrz9NkNf/jtC/75j/8OTdpRJc/NxTXz2SGTumFSR/TQMaumPDz5ErgOjQx4J0cJpVuUnXH4+lfQpYB+eVwxUDNkx9TWNHcb2tYynTn6qLjYZi43mdVmJKmIttJb0mZFFaW4aZEzuSzcDvBKEgCM1fhoIYOpkvBuSmOVQ1WGfQWuqyxaa6yWKTgQCCqgU7o1IKI1IsrJRB+JQ5JpvdK0E4NxihDL6SFojFFMFpZm6sgZ/JgJXtrZsmFfQUKUHD8xICol+UR+L/dWhQyX/2LKqJgl0sSWaJ+cS4OjPM9UyclCmiDFPd2FxKgD06mSetMgvewxl2w0UzpZinjEVZpQXNWUfgmlNEqJ4W9PdHcukytFcl+w+uixmjNRiTmRzEBxDBCVHNNuTwvIZJ8RlkTkkvu/ZamSLVV2bEkN1bfsPkVeWjxaAi+hkNpJSCkSwkhlJNwuYRhTRCdx/VnXEoOnD56LXU9YOIniVVKQQUyoEMlU9Ns1m4tHZAOTCkyA613ELjUHTvFyBfogEZuesb/g0z/6Qz76/iM+erTh8y7CD7acr6947dfmvHr8OvPpEbWq6ZFwG6MafMr4nAg5Y5VEgwi7IeqGXKS9KRtxHitXajpFaSGb5AtPRy5GjYwhSTX8bW4/KJG7ZlMYi6IKK73USZX3OAugF10mW01WluvLzzlfXXA1bvjyiSHsRradNOvdf/uUykU+/PgzxlEczm8eWYbk2Wx3oukOkd35irrzzOZLJjPHpGqYTSbgA8pYgRyVYbGcY2cLDmhYnt6hPjjEVjNcJbhuTIaURUKoUpCY5qwJShqr6mKMsQ6Ui2SXxAlbjusuN5jsqCaD9GSkketNR0+EmPnk8TOuP/uM/tH7TPMF6fKc7tkTLlaZB/6Aw1cXZDMDKzehH3f4cYsfI6HL6Doxjoa+U6SlJ0VRo1h9QFU3pNZTq55Vv+Hmqubsk3c5XV/Sbeesty032bBVgFO8cwiz2jDWFavNjHiR0D4zU5Fh3ROI0Ga2fsFmTHQhclQbzq89KSReOarpFVx2mc5oprVmczFy9egZ/+w/+3/iuysUAa08x+0pE9uQ4g13pxV3D45446U3Gbozxn7AD56XTyPGzNHVMXd/7oJ60tBOKk4e1FAt0c2S+vCU+d1DJpXioLIEBJa0KhKHKH9ncDrT2ML5RWhuJdFiUBP8HelpzoaowZqEUka4Q23AChydclEwKX0rcNl/j42ptBLKpqB0+ZlYyFcrHgdbicybqKQjQRWN/7zCVZYYMzHJhoAsTyLFVTKMhpALzCXVmwXtIlPuv1tvhCSrRiVpvDHmQvZGQkhoI70HJlPu8X2ekwhs2mzwQUj9AAVuSkLAUFzZCmwt93bShqwFmZFoVNlUC4LFWCtUlUXZ/kVuCg/6jsMaWpvIaZQFSukidyy7Us6igy6Iz179khGsjfJhUhYwkKTFHEckhtsUD4F8X0wFHc9KZK9KS15JjNRuKsx/8KSYMEY+gKpyXHVrzq6u+bP3nvD61xJ3Dw+4V0/RKYFyaGdRquXp9z/ie7/7u1ylyOEMZgk+eZ4JNrOw8PKxQh8YNvFDPv/ob/G3/4tHnF1vuTfNfJ5hEwMXj6/4e/+n3+Wbr3+Tw6MFD9oTkt9hdU1TTThfX6NMhbV12TT3ZTcBm3O5qoIQ6kjKq6ZomVPFmHpUFnVXYsDkOSq5fWlXke/KYVuUX9LgttdM7yM3QHibMUX6FBjjwKw9pDENMWY++NFH3Gy32JnjlQc1jz68IY6Z3/76CdXCMo6RNCiedYrmqOL1u4b5csmw8XS7K242W84uZZr+8Q+f8LU373PnnRNe/pVXUaOh6yLr9cDTi8foUaOjo6t3NIenzI9fISOTpalaicvOZ8R+Swqe8WJHPE7MloaXa7nwV2Pkyc3A2fWOftujiDRF0x4zGDOiG4fOin/5j/8hf7J5yJPthN1Zz3zzI9p4xoLnrO5sOTrIHNkp//S732NXH/FyPiaePCTUBpUboEaphDGRykFzYMl9YFQjdZ2wIdGFzPCs4+5LLXcmjsu543o7sFtd8Oy9P+Xts4+ojqcsD+bkrmIYNMEoXnlJc2QgtYbx5QWfzSZUKfFqDToOdBvP5tLzx3qD70CP0M8U//Q//ZDHn3f82n/wNuNU0yXFeoS/9MqSrx5o3nhl4Pf/pid3EaOg1oolK+ZssTbwYAqH8zXt4jMuPnrCo8sVH65W8INER6bPoP7B36FC0Sg4BV6anXD68Mt8+df+LV799a9x98EppycnhOkhofB+X35o0NoJQqAU3ShKpjwkicL2in7IXGcJejueGm6GhAkwZk2fU0kDFvVMRFxRMZdF0GWImqo2jKEixEQeK3yOgg4oT06anCTiuz2oqVo5JYQMfhTiNilFNbdMpobj44r1kElDsVKrJCmjWmGb0nGeMmMfwRcOBOn9yCqDVdQ4WdtCIvaZsAsko7CNSPb9GOj7jiEOEtEzgrJROji8QFTWysml6zxpLKZAYOw8KUWUTSKi0Yjvw1iSNiLpV6oM2cIjuqxwWmTEySbqJjOZJH6aPz/1pvAV8x4NAZdgzHelCBuAgE2F1Miyo5dTizhvVUk5Kjkl+8KdVNjklIoCqTSllSQGMYUVdvSFvyGjjKYudZE5DZB6ctdB7dCuRgHDuOP68owP//V3+dpbr3NUT2mNQ1VzyEW9oGse//iCP/2v3+PRVcQtMzMLpxryTeaKzIcb+OrVjk9/78fcfO+S732+Y2Iji0nmpbtws1IMfSJst/Q3Z/TbK2jBMIpLOYyY1GOMxWkr0E0KkDyKjuIgkA8iRxQeUsDotgTceWpdk+MKGEXqmnu0ShjTEpMvAjlXNl4h6yMlIJASt52AwmfYkIixg3HFcr6k0Zk0dlx99iHzquOlkxq7C5yvd1SV4Z03jjn/4FPQiqNlxbYPrG/g+aj4pT//Fqw7uosVz2bPOTUtfR+5Xo0S9eA9/WpF3GSGQTF0Je5cXIbYSrohUvLE0LHdemJQLJcTdLVgfRm42W7ogGYemR4HnBIjpB97Hj//jPNHP8Rffo7zl9TVDq8iTU6Mvef5JnK+Szz79idsJl8nNEdMG8/ieMvMKJa0XD3+GD9s2A4drY7kvmd3tSGwIgVJxpwfLgh1pvYeEzzL5ZSN7lH9yEuv32d71bG7GSBZXnnrPvXEcW8xoH/3PZ5ceXZEPvzW71GHzOnJEceLQ9Kl4nqt+Bc/dBwsFU2jiBa+sbQ4lRnJHCrFynvGcYvbjtxbTDic1jR5xfs//s/5/vc+5dWf/d/zC7/5Fnra8nHXyy3tPdVuYGoydw4rjqeWsNvSe0+fR05rw7z2LGrPsk1cHVjuU7OwDa8Ez9Mh8miIPMqZL2vFDHg3Rj7YXXFx/hHDj/8FT64eg874uKUfpzTtksn0gJOTY47nC+aLBdMH99FHU9ykZjZrOZzXVJXFWEOwhl4pOgCfcUkqPK1PZCtwc45KIBwtQX6BshjWsvjFkIlBsRtr8pAIIRGikxygACopJnPLpNW0jSi3fBcIYyCHEWcszmZSSOzWA/0uMHae0ReiWxt0KC1qCeL4wkCnNYQonIJBQVVO5VkxjAEfSsDhoHBWEqNz9i/SU7MhjSUFIYmhcVJrrIb1dWAsCqWAYohRDLNJeENZazNWBKCSyksqpl8pcqq1kPs1MM+ZaY7MowcmX9ymcKB2QpQqXfwAlGUn3WYf7ZvX2PMMee/IlSC3pIQ/8Gp/bMq3LMJeXyRQU3HfUgKvCvyU0EV9JGQMSNG50hT2X/49hJ7d9ponn3yC8YmJcThtyboBlW5J7eurgcefrVgPiSHDpIgGtp0QPzFDGjyrzy+5fn9Nt/NMJ5kJcNwq9JDpIhA96yefcv3kE8LBBqskyC6lhDFOOgG0IZeqR60TpFCIYF1OVfvTZ96P+IKxqnKD5NKyJHMTWgXYx5JT3lv5f2VK2+vTSohYLiRYLvLdHGi0wSgIaWR9eYXJA9M6M/YjsypRNwrlR9aXPaaxkrdTa3xdMyrN/OCIkNekLjI7XNBMPXUfiMpgrDyvGGDoRoZBQrvQtjRtZWzVsF3fUF2eoY1mHBUhaHFjohgj7HwmTWp8GthuL3m6OiMlzdV2YNyssAwkPMEHgrMEY4kkzq/W3PSRbR9ZXe6wnDE1ick8Mm01jZFcmS4qtkGkktWigtowINhyylm6gJ0jtZArg/YaXTlMHXATy8HxAqcVtck0ybCsFHVlMQ/u8bWvD0w+X/Peo47rTz5k9cpLjKsLjhYtB1aC5W7OE32vqSewWIBdSALsSGbiFNddz/NPPsfPFO50SnVQ0V1ecP74e1w/f4S/uKTxnjrXLLMkgvZrz+ppR6NrDieGuweOXkXWQyDnxHyiqXXCJE8cBkwFs6lloVoWN5HBR3qtuM5wx2imCsYoQ0tMHWq44ubzx/g40A2XfP55T1XNmUwPmB8ccjifM18sOHjpIYenR8wP5xzePULfbZlOp0xmS6ZHR2hnwUjzmTERqwNVwfBDUvRjAmNFhFH6FqwrkJNTjKok0iYl3cRKSyZ56cNOSWoylZaE4G7IpDGRfJR7UEdCgm3n2a0G/BjIMVG5StQ7Skje6EtHdbk2UYBRtzBwztKXvL/lghITXYyZWNRPOUujonZWNhvnpKBLCf+hTIG8EoS0H4zlBLEPAdV71aAGpYS0T1lOMZAL6S29603ZFColNPMsZxYvQIMvZlMIao7VE7RpqUzBsEiYlMk5lI2g6K8LwxCzSKZCVgxqn12kXkRiqIwymQMoKqMgckxdyqYxhLLo5axI2qGVwpHp0xajNLaaUc1KtK6WTcGHLZvtcz745F2S76mNxirDqIqSwRhS2rEKmmejw2dJj+wMXMTMJx0cOfjyXGovxzHRbT0PgXmAWQ/3NpkqZLyCo6nis9/7Hdq447WXf4FF+5CUDUPMuMk9vNaMCkgJYzQaU5RCUnpSpVxSFjWkmpwl3lpugx6laqCW1NSiKIIVyizRSokcM0UpGSIXElqTlRHJn34BIhlXY3XCpla6JdJICj1nz7eM244cOs7WN/zaGxNcynz3W59QH05olw6dRiZ3D5nfPaRyS3LVEm1CtYr7b0/ZdGeMQ8/yYab2BlW3qGbOQKJPni5FqFtGm8kmUE/v8fGPfsCTzz/l5JVvkGPDOHqenV1xuVtzdb3jZpN4+fV7PP3kCR9/9Dn9nSV6nGJjzYKa4zdeYX1R8fj9HrdcSjiZgvf/6DPUpMZOZ7y+WGCXUE12TCaaTjnimNhtPPrwZcbxBj9c0EwMaWLpmoZBL/H9KOUkocI1jST4poGQIrGx6HpOfTAn24htPaepYv34EV1vqGYn/PZf/00+/PQ51//Jt3h68RnbR+/Rffwu9XLGS+2CB3XLo8/g4jyz1aBfUvxgBN1KjtKbleLzDz7nH/6/fofTtmNxNzOdRaoPn3L53iMObMtxG9mte1KuOegdqYOnn2759u+dMbcnLBaZ6ZEMH0da0ehEW+0wN1u21z3r8YJkNM28olm2/OHzG5oEjdMcZkXtJP4+jZ4HteXVxZRfeHCPR1tDNVmwODjg7599i836MecXkc/f3zFkgSnmyvBLswfcX97lzsuvcHJnwuLwIYenX+H13/xN2peXNPcm3JsZLsPIRvf4vpbJfUg8ufaQW7RxKKvAKhEATMSdu46KdcisfOmlqMU1bAP4PjNuMz5nwgjrIkvVOaJtoHISM9l3nqtVYjjvqJxicVhz524DKMKYufisZ9wE0iAptVXrsLVBzwxjF/Ex0aVM3yMKIGvQtSFbEUrE3rPaDaSUqaqaZlbjnKGxBm30bV5aH5I0v8UMlcUUfiKnhEuIEjNBbWTQQyVM8IQIMYmy09YK4yRLqrVSEmZLWGeL9K9/oZvCt8wbvJwDJ8EThpXY97VkfhpbCcWcIbLfIJSQPgg+aLXFGYfWkrqo91oulYnjM0gBmzJKiyQ1Ar3aL6CGrBy+SKwqEn2/kx3RWJ6tn7GoGxZVS6U9NxfPePrZYz776AP8MIoDGsU6iIW90ZpaNeSmIc1ruis4U5lcwTfuQdrKMHClgWxoJ47DqePIefoxsY2Z1Y2QS7WGxTTTPd1w9sEjPvzo93jnzf8e2h0y6hplJ+X1CO3rGSXOQ9ckldHZE1GQRnS2QMuIA0Z0HmkzpFgRsSQTIXmMylS2IqYFCXtLIOsSUR6zJSH1hZHS66A0yTgGBYM2dG5CwGBVKSVSl1SpQ/vIekzMjy02ZcYxc3+mqFpF9pk6VMQY8X5D3CqsqjETTeWmVNYQ5x7X1PRPbmhnM6p2yuxIMbWOVFVsV1esusjuZstquKByRyzuWtzbhumJxvSGlCpibhlu1mz7HWHb0RiR2V78/qdUU2gmluZ4wbSGg0lGn9xj3ioWiwOm0wVff/uCNDGomcGaDmXmMjCka+I6MgZPrAbmh0sWixOWh19lGi6Y2kRFZn1Wc3Z+zfU4cu7EEd9tO9bXA/5mhUojRnkufvwuu/XIdjVweeGJXWb0cKkMf/lPv4GrK+4dHhCGgatPf8jv/O3P+fDb3+WVd36Z+1/+OidvPWCyVfig0A20BT8dU+az9chnzz/l+SffwrzsWO/AhYh6/IR+c0ndLtl89GPWrz6kn2u2N4rZfMp4dYO6eoxSK8KQGFYWOk+qIl5H0rbHjIHWWu4ai6o1k7ZmMWnZHE2ZKE1rLJMe7rcth7Xh5yaeT9aZz3aB//Cf/x67mDlql7y6PCFpxWxWs0yJk5vIa630fVfHS0wzIRC57j5m/VHH+kd/wtnwDzj/O/8htjmkbg9pW8Ubr/8iX3r9HX7+N7/C8nDG6bLmrTeWkC2Pr0e+++iKcZwzay2tM0xMplIwKw1jN1Hw95wVo+g8cLUsuCZlSUhQSVKYyWQVaZqKujIsnOG5EbRhOq0wrSNH8VBMpokaTa4yum2oJ4ZsNVutyJUYHE1JbdVWAvqMUcQkjXHOaRqtiTGJ56Gu0EYTCxwm7ZDiuY0lIsXV0v1NLk2AJUPJJKiUpEPIcKtueQiVIsrqsuaCThKYaVJm60panVZf7KYQqW8NVRGLylYmWq2kuk5RDCUAucRJawLlPyWuWznemEJOg6ZI0JIpDmgxgqgkhT0aiXTISuRW4vNVcmTSAokMu53kJFU1pJ40doRuy26zI0Y5MieKE7CompQyKNug62lxHMuRzZbY/JBBIH5FbizMWuZqh956wi6jtlA7cA5qA+tN4Pp8zflnH+Nf7qjNAU5ZxiQfhuQOiUxIeHaJ7KUcEWMqii1kQyQbcrIlWl9OCSknFKYQNhU5V2J+Q24MQZ5EAZZJBaJT0qldNlSFcDRWlwTX0oudfCJF+ZnKqNITVD6DWmMqg3YVJEPwEFJk6EYx3FiL1QYz9mjtmBwuiKuAqSvQkgmv2gY9nTCGLTqM5DERoqLfbYnKsr444/rinG63I0VPQlMpx7SZMmkm1CeOECK7XaTOUCuFNYG6mhAbhatGcvRFQSL1jliR/VUq4yxFpteQfGQYE8FHVBjxu8wujVyePccEDyHy/FJzdbNhGyLbScP65ordtme1GgjbHbWOzGxmrBJxSIx94mY1MssWFeB81fPxh09Z3jmgvduyTIrtMHJ19hSjPqaZP2R6eJ/7X7lPVplhhK6Te0dXGdckrscN6/WaELdEUzEM4LcB1W/IWdz8q9VTmiYzP6iwFkkK9QN+3BFTkPcjSABc22ictdITUGlmtWNxNBMPQVVjmgmLwykOITGXO4VyDal13D2FMzUyw3N/dGwGhQ2O9c0OrzKTRjNxBuUdhwvHnXmNO5qyjZkxSDKpU14w8Qj+YsSbkcENPArXDBvF6maFm2yZzCyzScXJcsbxnTfJzYSX7lpy1NhWYRq46cS5HCK4pFhYCFoUPxuZR6mceITESyaKHenN1qRYFG5KfAdNK0IQ5yT/KxXY1jmDbUBZ0K2RTgStiB6BvvbJDAqJobGyUFPWpxwNtpaNyRSfg9z7qvRAFMd1ftHDwv7eKykI+7wLpTVW7d3KWsqHnMZZQRykVE+gNJ1UMQEq4VBNaYT7IjeFk5yZK4PTNYOe4JUtLWZgbVXYgCBmjBxI2aOyxWchS3y2dEE6DipJi0KTqVXiIFhsztiSlWTQoEVaqZUIXsecqK24XRulGLORixdHuO7ANLhZRuUBm3psGPBDIkZPzF4+uFgkm2V6ttWMqjnGqPepMlgP2w2ETj6XlEANkOcOP5/hlitmNwl1lWh62YOaChoDT7cJnm149qOPGH9my6RJVJWjj1Ga1YzghXtXR8pFV1EcnSmXvCOiLPRJkbK8QqXExZyDxZoKowxaN4WgF9KqpEIVy3svF9ytTG6/IUQMhhqFU4qcelISSWXqLWNQJBKzRrMdgAiu1oRKbprJZMomWfptpN9l1tst86ahsg5lKjKy4LjZAjftMJUjJy99EUZj6krSYiuNSg49Wq7PnrPe3nDwruGjHz9isx2ISfPglVMq03LnTsPx8QnTwxrlMh++/5SZczRNRdO0tO0hfeyIZsP62hPSli5EbvobcgLjFSoNVMeWZjJhWR+Q1BXdLuGvExfnZ6w3A+P1lu/90bfZrno2feTjkAlao61jOZuzGnb03tONAaXgqLXcm1aMdaJ1mtpqjo9r3qgnJA9nnefj6wuOJprXj+5wdGRR5xu6xwNn51ccPH3C0ckjbHibxslkenElMSxVnagXkYurM7brLWZSUS0M6SYSt1K36aoGZRznu8fcOa158OUDuiFyc7UhMDLmgSS6GbK2tDPH4YGhrRTjGFnonumk4vD0DuxGAhW9amnvLRmT5BrNa8U2VgxNxcm9CtZbXrYVv/GlU56dOx6vLnnv+hNWY2QytzSHFh9rxjuO7aKibWs2lxt2PhGM5Xg25UBV3M8ND68ywS4ZzIQ/+fyCx0+/w9nlj3j+yQ8Ifo3NmYVu+cVf+1/y9V//Cr/y736Z1lnWES6HxGdPEzdbxeBFr3/nWGOdnBbOPWQjBG9XJPLSpa4JgyEkCCEzjAptxcNUt43EQ1hNKGbWnDXOiexVZ1CtwVi5T+O4l1gisvniYtZaAv4ooaC50jgsJCSFoPAb+xTTFAUySjkiclPISlIatFJonWVAzjJEV1o2FqVlSLZOXOXGFh4jKcKoJLakPL3KJFqbmX3RktSv1fvqSFDtkpR0aZ3yRC8FKFY5BhKVrVnYipSTPNEM26RwhQB1+yMOGZM1FQ6b1W3+kcagsxUssdRXuSwdrnJwyLSmpTIaCzz63d9h8s1f5P7pXapqxqtHR1wcL7CjpxuesBseMzGHNG6JNkLEaAWVbpjaKXec4riG2sL31pkmwULDSwpSp0k3mqwyu6mmCYqDGl45BF/LBHGgoO0y6bLj2Q8+Zf35J0xsw/RQUY8tVA2+qqiVgqIqytljETOfKUS4ToGc1jg9Q2IWPU7tEKrJUOuluMSNwaj9mcCQskVM/h6IRb+tiqlNSo1E869IBKxSOGtR8QoVB1za8FIdONOZnVLMJpZpF0h9QveRV+7MmR3OyLalHmbE7UAXPPEm4cnoBsiZ3bojRi868dGjF5ZqvqDbPmcYN3Q3getnz9l0Gp8r7t4/5tU3fo7JfMbxy8dcX3ZccY0fR77yjTep2wpFImhNXVeEGPm4e0y32dCRUM9rVvVjhm7k8vmGi0cfYTE44/j8o09oFhpXw+qzpzw967nZem6CNG3FJLk5uyDdHiZnfrayHFvH6dwSfOLg3j1mdw6YvbwkeCkx8duR+sDwyusL3vrKHY6rObvVlu16x2a1Y2Kn+G3iz33nM977bE3UiU0MpOj/v6z9WbClWXqehz1r+Oc9njkzT06VNWRVV1d1N7rRAJpAo8EJgkiTdARDEYqQbMtBy2HfWI6wbuiwaYUcniTLNkOUJYdCtmTCBkmBk0CKA9CYu9FAd/VUY1blnCfPuOf9j2vwxdonC77DRWVERp5hn5P77PP/a63v+973eUlix86BphgWpMWaxewRH/zBh1x7eZ/BqEdxRVEvOqBF+4qt+BmvvSKJ/ztv8LnPF3C8wDxfYst9RJKjo4JMX2X28ZTls8dMy5jhfsTOcJtf+MYXef1uj17P0Mst3dlTdFvh25r5ckbc1cQ6Iutvs67OaWsHbc22TKnbjrZ1xMKTuQa/auh+3KGmhsMv/QT/2v/p38PLAU9+87v84D//u/wff+/vE1WaraKglQI5aXFzQzFK2M4VPosovWY1W1DZhjqYFFi3p6xR/NRXXmU8HFNEA9RyyM4bOZPpBf/s736T3/rb/zPk3+mz9ddv86c+/6+x+9oNdu7u8/pP3eTtg4g8Vcw6WJU2xG/awCHqNgciLT1WelCeJJH4NKIzmvl805ZpBIulRFoJPrjEQx8qnKeySKH0pschoTMBya1cCAVyl5htv0H8e0FpxQuEj209kRRBsCDCbe18iPbEblA/+IBCl36z8KuNZ+tSQBPw4lpJkk0etZAhb1t4j2t5wV8SPlRPsZIoFSqkfixIlSH1Buh9dptC1VncRhfbyHhjLAtlkfEOhQ8B5irCIFnbT18kBzixmS8QpsxBQBTyhRFReNwmqS00iHQIl2FzuvahRHI+pBkJmQQuvOn40Y+fkR1c5drdC9Kdl0hJSLzGWMesnLBqFgz7B3g0SMmlWteKjFYMMV6ihSfZtMASIEWgvcRUAqMCnVAlEU50WCWRyrFp4SEjUAkYYVnNK9bLkqbpyIXaYMLDa+E2xFIvNNbHsInZU2iQ4a9wCmQUymwhQJaARHgNXuM3pxDnN1et8J/6ONjorDeSzxc2941SCzbtIOGRwuE2LTmUQvYSfBL6pTqTdDZI8EAQZylpL4TNuHRAVpakZb3pj0YgVAiMMY626bCLNb00wicRPpF0WgbZnHe0LXRNuJmkvaxvgtUfoZFSBWChclhT05Ulz5+eI6XEOMf0+AxbrzBtS105slTTdS2L2YqLoyfBFIXk+ckpg0FCliiWp0vOpjWr2tA6T5JEJFogE8UoixAqQscx125vM5CbqFIyBttj0kGB2urTLGa0dUVTrxj2PVvDAUU64PDaK5TLFevliq4xLBeSMrPc+ukRj7qPaDpHV1tOj5ZI58mSiPyOp2tmnDzsOHo45cq7O+RFRlM6ZosJnpYsdaT+hNn8lNOLE8RJSj0taec1W1pQjIYIlXF2ds633jujkzlVl7A/HhE5A12JVyV137FOOiaPHuGqFdK0DGJH6wxxmuJlsWnrBkl5lm0c+W1LnEm6SAWhnNdU9YrWQD7QyGzEaH+f0d5hMFF6UNIz2OnRazpSB1aFVVAYD7WjmlesjGApDCJK0DolT1IORgl2vWK2nPHgtOINc0CRS772s9eov1lyMi95NHuPxP8aB+d7XHtywKj8KeKXrsHBNr2DPkUisVrQtGFmWBpY1x5jHFYFJ3UkLmGJkMb6RTYEToZEvA2CSUq/gdsFyScb3V84SIRWr9h0deRGJijxYcEPCwsbKU0gLWyMlby4ZUMOA5ePEgIUGx3ipRIxfI0UbBZ3EVpGKmwwSgaKq+1C+4lg43pRHQjCz6BVeN920PzJeHh/8k3huPWoWCNEQkcIvBHeEVuAwO53wqFVRGcdtbEhw3jT53Y67Mz4wPi+fAHCqxWFUBxrkVriN8a4F/33zdIhhQwQrNaQ5glIj20M77x7wu7Lz3h9esz23usoq1GdoHOWs+UZ0/WMW6PsBd+k3Qx3jE9pGFKHlG0igjpj4KFH2InbUqCANIJ4O8FqQ6sMaLcBYAER6FRghacqaxbLknFtGMqNO3kjI/POB94TcZCticBa104EXhMeIS1ebCoBafCbjAVPjPUJCPtikxTCbzJa5QaXfWmN85u+aEDvXVJrHSBUxEbpj91QSb2OccMUm2nsWqIyTVsLOhMCVGWSoLMMHeUYMSBbSfK1IOll6CgCIel8S2MdtXG0VU02yHFJhNGOyluE1xt/ig6hKU4iOkFXtdRKUS9rnFd4GSTGTbPCNjXL8wkffOfDjasTVsLSLhc065rZxZq8yHHeUtcrZmcneBOCWU5XS+qyoJ8mtJXFSU2SaYZaMOplpKkiyyVpmqKSAl30OfjcAQnBXTtKdsiyCBlFtKpgpY6oqzmlidhOGwqd4quEcf86uV7TS0uiKOHhoxqRW/Zux3z3aUl3PKMrLY8/npOriIOdAcnQsXw6Y/LojI/u/S6jXo9IapbTNafrZyBaBknMblrRdA3LpuEsjphVhspY3tztsX2wjZcx3//xlAf1mrV1QMQVvccoztnOc3Zujhj0IdEdTz/8gLpcEgvL5w6GSCVIBwU2SsnQCKVRWlL0ItbdClN54iKicRprJKp21G1DXVtcO0emV5BxTjzcx4kIhEFpGOwM6Jc1SWcxUtK6Dt86mpmjXlTUFmrZkhc9sjglz1OGMZw9Oef0yRm/df8j6ke3eOvuIX/233yT+QeeH5sn/Hj1MQ9Pfp31JKP8ZMTu6YLq7bc4ePU2L3/1BtvbBVGsaaWiF0fMKzgtPbWxGBEOp9qHg6sXISP6MkNbeUltPSFJN+j/NwkAeLWRyG8wFZ31AUEh5ItFWxAOx56QIxF8QWF5k86/4LWF0B2/mZ1ueANi0/ePwjojCCf9F5uO3OSkbP4zITetIx0ebzuPs5sZungxytiMJTxKhgG2MVB1n/Gm8CvRmLe04o4MprKhgAhFpwSRd0ETu6mxvJQ45djEneLxNN7RGIuxDuWCceWy77aNIBIKqUJgzqWL8RInKgghFZ0IQeNdohnGCRJDI0pOZ0e8d3/A/rsH3H71G6zXpywXzxDAux9/l93DIW9f+xqChBZYOtDC4W2N6tasjef5XNLEioNMkHWhz3dqPNoY9rXjWicZ7IxR/RgfRazPpswvHHXpKR0MUuikxdiSR2cfkF47YE9+gS5VuE3eoFMyDIyRSN2n9YYOi8VhMWgEWiaBn4NBOoGW27SOYEjTEd5GSCSp0gGWJcKVZTxsxvIbqKDHiQAgeOFbEBIjHGaTXCcE5DJDJYr4mkY+kciJIB+lqNMQ3ZP1NPXSU8aOtPDoWFHEEX6c0suHONNi2xZVS+JIo5Kc0eEuUnvqruHkwTHvf+sdBrtX2bt1h2u3tunKGG9jRvtD1ssG34YY1vFOgRRrFqclz+99zHo+ZXp6ypOn52S9GKnh4aMjXN3S1oblsuXq/g6DXs7OeIu91AbevYxZrZf0Bz36w4KDqxnjnasUgz7FlqaWLW1XUpUT8sEArQuwOd/5h/cxMiaJY8aFI8kaVOzwKiepPdZGSDeiF08YRzF7xIjJfeIGImIO3niba69K0AXF6FXk/hY/+qN3+fYvf58n8yPqquHeecbnvvJFemPDaFQixLUwf6GFU8nn1BV06kmHnsSfsV4oZucSrEMvJE0N4ysxW4cFic7orRWfn3+RdZuycLC2p0SxpejB/v6YJOqQbs2VXkhHq+uWd56cUHewMxzy1WjM9q0t4iJDkdHfifDuFLdsGSYx0ko6Abq13MxGXItGuCaQClh7eAqVbeglMTd6OdPpCq08mQTrOppFQ1eHCvHl7QKVJri8oC4SlkIyNwv+8Lfu842f+DxfunOdx49P+fHiKccfLRj/3Yyf/8URX754iYfvjOldu8Zkcc7zi+f8ne/9Cs0f/m0ylfGzOz/Fn//Tb3H7i7fZ/vk32D+8ySDXxKkhKz1rKygtrFqCSMKGnoVWm0rVSmI8VoGToeUklAdF+FeGTQEb/EJ4ApdJflonCOfCPNVdlgvhMOs24NAXup9N0qJQ/kVrSImNlFQAeJQJlUc4y8kXb19mQEsV/goh0KlERQF5fjkH7Ywjvlw+nWeybikrS1l9xujsN6XgEM+QYG6RKkhScynQPvTKvTF00qGFJhEafam19aEkT30Y9ijvMU68UMtEoeW3aTdtMgbEJvrvcjeWoWQSPsQcCuFwtqXr1kRCIOoau1jgvWF2tuL8+RIJTO6dcnHjhPpnKiKZYK0Kua1akCc5g2KIFBJjFa3VRGmC1gY6S1sbausou5ZVWaLbrZAeNjQorzG1QbRBC+eSTcvDGmwzw5klkg7tOyrraLxDSxdUVGH5R3BZsbhQfXmPwxBJvzEDKqyRCLEhH9rQjpJBArbhnAStc7gEwoUYsFKXjZnLnXnDTMEhvUV4G9LpNhIBNvkNUmuKXka1bBE2IBg6A2UZAoGSSCF1GpypcYw3Fu8ChypOezgNShfoONw8q8WaJ4/m7PiCfG9FkQ4hCS1E61tW9Yy2aSnXpxydnnJ+dsHpk2dUHz2gXK9YLRZM5w3jQUGextB4tgc5yVaEqxMODg8ZbQ3Z2RtizBoZCUQkaZdrVJKi04R+KonyFARMJwtW5ZpqvWY9XRHHHh3VKL1meGWXYdGnnxck/RyhBDJSiH7GVSKkX4Ob0RuPKPoD+sUINTvCSIuIMuLUISpHt1wwm/6Aw8EO/s4r2K+s+P0/+gPWZUnnOuKkQUcOrEGJFREl0jcs24okcURE9KMcjKKNNFE/3PG5sujSsTKSzCTUPuFpPeO4fETdKTwRxteM0oKkN6S/18PXa+r5iqNFS1V1eGPpCUVjDdZY2qZDxzkyTjFOo1WO7RTV2uF3Na6TtLWlWtQMeiO2trfRvf2QpJdI0i2FEoIsjSmKgvePT5DKQyQZ9nPyUYpzitokaLuidYJ1bShpkemQvi6I62OuvfU53vzpL/HvfuWr/Mrf/M84ffSEDx99wvjG62Racvhyymp9wTCriK8kxON9Lo7XLGcd7569Q/W7j7n1+Ao/efRFbv65n0XubNPmBYN0SOoFuZO4ucCY0EqxG6qo84KyhSwLLc7O+U2SYQiu6mzYEKTwQeCtQiUQcDSfRpSG+87/sQ5I+EcRTu/BPbq5I+XGoPpCDRg2i82HEPJShwjIMHcId7JHi02LyIeOwMaaFRSD7rL6CK0vIcPPUpma2WrJdLoEDj67TeGudAyFJ/cO3xmEjxFKkUeh1WGtpTE1netAaiIVg3Av2j+RisJiJ4LRwpkAwrIetP+03LEeLptv4nKB2/T+1EZWFhy5DmMqmmZBpkHbFl+u8d6ymK2YTZYoAcsnM+ZPLmi6FTLqB4e1E2ihKbKM0WBIpnQ4Y4sInRbE1iCVAVMzczWlMcyqCrOyREagI006kDSZgKXAlh4ng0msNQbfLvDdCkFN5B2ld7TOozbuSl60xjaDIi83fBcD3hAJv1EbiM21tDGyOfECciXcpYMxlMZCyBdV1afi1lDahm/iwrB5o0nZ+Ku5zNuORZC7aSEp0oQ2DaqHIg+Kpq7z6M4TS4XWiiTx6DjGNR1CWGSkSOiF18FIojzGWUdjWhbLjqzsaGyHRdL6DmMMbt4ymZxRrpb4uubpfMbFxYzjpycsy5qua+lMi/MC22pEpNkaDLhxfcSw6BGZAVtXrjPcHrJ9ZYhpSqzyOO2pplO8SkBGKNPgFHSmpVpZ6pWlWTqaqaBuW4ToUHHJ3t277A1HjIqCLlW0xmKFRxYtIylRPnyPNE2JYgUKZscnpLki3/KoWGJmDc35knufPGDnq1/m2vY1qrsXQeq4kQonGKS1NE1DW81IZYOkpW3WKAFOJ8g6GOykFcQ6Ii5iUu3pUodxltZE4CKmxnFhl3QOMpVhncernDhLyQcZrauprGfVekon0EJxNdasmtDqaFoT5M0yorMenKRrPGVl8E7hjMC0luW6YWeYkvd6EGWAQCaCZKxJZHiOOomxDlrb0TmB6mcM+jFSpdS2RzlvcHWQA7vEkSpNpDPizjG+usXtr9zlpZ/+Ah/9g3/IDx4+YjE9ZT29SbadMd6RTM9OUMoxTAXDrQHnVnLq1nz/5DkfPTpifvGY/mJNOc7o37lJ7+Z1dvYzsjghE5K6FHQiSJaNCm2k1kNtAidoA2PAWbEhMgeV0mW14ITbLPzgzCXNdXMPsjmIbaB/YnOjSkDYSxn85oBG2ARAvPj68A02cwjxx+5dcfnhcB9L2GQ6bOT7hBROLTbO580aIFV43lY6rC2p6xmr+fmfaK3/k7OPogbnSzpTM50LiFNcEqOjHFxwwtpqzux4ResqnKoZKYdQMaiUuLdPrDWxFOQKIEESI8g2L4gLaFgHTqgwPLmUe22egyJo64Wv8c5SlyfMLz7gaq+lcCVmucA7y3n1jNP1QzIJ80cTzh8esVo+IhvtEssIohDpt7/b53Mv7/P+HxYQOZI4Yr8YMIwhs4583vHdsxPmXcNFNWfvvYYbPuIgkmRboAuoekHGmsRQCo+tLaI8RbbHaDkh1466K6hMD6lz5ObUUBOyIBSKSApS3yJ8h3A1qUgRlAjfEusoDCX8pgdKgI2pTQqV82yYSgKJRWLwPt6k2Hm8EghvAkhwk9ughCIWMQhBkAXU3M47aumYe8F2nJCkITs7jUO+g9IhqjCNcxIt8D0YbW2xFqCkI4k1Q9enWrc8eviE/heu0BsOGWxn3Lr5iMGVXfLhkKWZc/TgmLOnFyzPl5TLBev1kvPpGVamm9yHjoNBn+FwxGgQ048kWTEkH4w5fPUON157jf5wjKtAmGRTVTmOP/gRswvDfOZ4/+MfMYrH9OIeK/ucQW+X/mjA3VduUWxpkJ6mbjn78JjF2TnryZS7hScXK+RyydEPL3gyechsNaVa1aTrE6plyel5zQWeXqLYziJmc8Mv/Xff4Ot/4SfQyXWayXs8+va7/C/+xt/i3/lb/yFXbt8iHd9Bqs2t5mDwMGHKhJP1Mfd+fI/XtjK2UklRVhQmgVnHw48mZMpSZAlX+gW3bl4lTWLwgocPz+lM8MBcH25xPb+GkjFxlvDk+IS0iOnFhkEnaBxIJXhjf5cOi5SWHWW5eHgcsqBXhnJliZKKztZUFzWLxRmzcoqv91CmQ9oGay21klSmpjr6iGT/NiLxxAcDriYx0jpW1vKLf+pV6sfP6GZz2q6iyIcUWYa3MU9OO5SBUd7jypdvU3WK6bRB1xX20bdp31/DcMygu+BOpNhNE3K1pOgM/Vpx/vFj6rYB4Ul3+vQGGXevx3xpdJNqbZnUDb937w/5p//Bj3jpjc/x87/4r7D1SxGD3X2y3pih8Cw01GXwFFRCUHlBFsN80/NPRcBmdJ4XnGErQyaJ0mFw7hx0pUHHIX87jiRdUNkjEUGRx8YfQbg/nQcjNmBQ73FeXO4BQJg9XMJDY80G80PYOPxleA4hpMv5DTBVhCE0YBqLCup9auVJUoHWHi8NhZ3Qq4+oyqef7abgbBCu26rm/u98l707N9g62MXnu9TrB9i2wzUJ//w/+X8ym8+wytPTa3SaoJKUJN4mkhHDfsYX3tzj4Is/RTq+gk6KAB4DvFRgzcZ9CG3bYY1B4olEHGIHCVkELTUPl2d859E9TDbm0aJj+cljvn72Lj6GfLyFUEesK8vJ2Yz37v2Qr779BkWW0pMaaw1n8wmPJsdcubbLvFlgnaWLHS0eKzyzTFP0cpJW4m1D7SyLlSedSNJdgc4EUSZoWs/4iqCIBM1jWC6WlGWLkBFS1GghiLUikUFhY1HBnCYUEo/GEkmJUiEsowM0GZpkMwzz+A1uQUmHEC6QU6XBI7FojGyRXqF8wAO7jR/De7sxwIBEgYg2RjpPJId4O8W4CeM7n2f7qUPwDKtzolGCrQ3tuiIaFiR5n7QouDg7IR9uM9jaYz1ZMD+Z0qzWjA+v8uz+M6qmIx/vESV9vIhoraMbKI4mxxx/+4wtrZlfVEwu1tw7ekSsRcBzeMnWbp/t/SE3buxg6479a1sc3jqg6G9TL+bY1rB782UiU1M/fcr62BBngihPSfpD4sEAaae4bsqbN6+ze+Mmw91d2uUFZXlBVS559u4zzs4uGOz2uPuTd7j7jS+zvPeQoz/4Lv/R//sfsTSWznsoO3qmQdqOmemIbMMQx3XheWNXsnM1Yf8wQy8kr7ySEo1r/Po3SA4PefXP/gz/h6uH+FHL9PT7PHh8zGv5HeIs5rw54+RZzc5rY96+00clgq28pogMeM9WlAGalZXkztF5RUlElw6pnKYzoIYpxd4ORDFRPqX0AUPdjyV1vEWsFYNBysLWIZ1W5uS3b6B00NHHQvPWcBfTdlgZ894nj/DW4G3H+TCibgw7WwNO1zM667He0h9KpmbFBx/cY/1//nu4cQ9TOcrTkjjSWBUxtYI9pVk7aC1sDXPmy5quUwzGI268esjz8wUfPplRPn6GFAVdFZSH2Aj8AD1+m6c+5d2qIm5rfkl6ksjSiIarw4xq7Wjalra1qNKAjWjSmHwIiYhJjESrPSZNza/+yq/wX/+zX+MrP/un+dov/CKvvnmH7YHEZmCc56NPLMYLXn5Zc3/tWbcEdZXeYOalREgVYHgeOhfSD6US9AehsR/AeQIlQ5s8lBd8epLdKIPwIDcKocth8yXyR1y2gQhVgo78p+UCArUxooWMiY1qc9MTiFX43OmFZXtLMigkW5GgrBsm0xWPHh3zzj/6lzS1g6j4bDeFD977kIQKs5xx/3vv4O0KX+0hlkcsZ/eCOUNt8ck732M6XYAWxGpBkifEWUoSDcBFDIuMYXdAvDNmiCHbDUYs4zq6tqYyLUk6IIp6SEswduAwvkUR4cPMDYSkNjVn5SmzxmC6hvbU8+H3vsPZ8Tl16yn6CqEljW05n59inX0hI1uv16yrJXW3JM0iaiFojcP6ms45rBe0UhHHgoSNUgZHYx1NF07gOhdEm4DtNBeoRJAm0HYtTbumsTOUihAqJD9JeMGG8gTXpLiEXknwKIQImdJuoxZyuE3mhMBJjRBmg7IIF6zbyE+929BouWwhXQ7BPr24QqZ2UEQZ74hFDqxwUpLuHFLsPKOeLBBKE6UxCkNTGaI8JeolxFlCd7bE9QxCQ3mxYHU+p60adq5rnA2be7E9omkMpmpYVS2dsCwWS9azFWfG0ZRQ14au6+gNCpJYI9KY3YMdrt3Y5dW715kfT9m/usXhzSukvT3mJ1AtFsQywq2WuLIFkaJShYwFXnRY3eJUDbJiPl9hphMm2uOWS2bzY5azC04fHnP29Izrt3d47a0Dtg6vkrY13ZMh1cWSRVnS2I5ES0ZpTBpLuliTphHbieCwrxgNHFtXEvZuZPQXgq1xhhICN3uMSAcMtvf46a/d4Xs/+jEnkxNWk2P29Q5TveC0PuWT8xPElR12tjJ6eYKMGpyEWCg6D0opekWfzFnWncB3irqJqNtg1sLpgGpGB628Aqlc6FNgcM7SNZ7FYoVdN7jaEA0zRBzmU6VRpEUfGzXMW8/ifIJt20DwNZIkSonihKprEVIhlSBOJRfrFYvTjtXFnLYfIaIYqWJUJFEbe39dGZrWYoFsmEPlgxsnjij6faK6pcXRmhbpI0znSePgSfIyQcRDRKQx0lOaDqkcSnla6xgPUlJhqEpLKSA2HiUsMg4eBKUFqdEoF2ObBrOY8N6H76NVj35/j0FfMxxtkWU9Mq2JcTgTTt653lANNgPakMi5kX8qufEWKKy1G0e4wl2qIjcD4EsSfuj0bIbT3oeZIHCZLf3HerthKrHxIWw0Iy/mBGxmquqFWlO8yK65zK+JNAgvaFtPte6Q3pP1BE3bsFyuOD2+4OjhM7zqkW4PP9tN4W/89f+AQ/ecUTvhk8eKsyfX2N5J0esJJ+cTsiLj+s1DJrMnGGvJIsm6XiB0ShSluGTFeiFYTGB5/j6lu+Dwrbtc++mfpj/6IsvVGWfn9zivK25cfZure3fpZTHOSYzrWHdLQqC9oBEtW3LEOJFsFae88+BjrvRjCp/xy3/j/0rpHY3wvPxKyrLVZCOFlQ1ShYDrynY8efwIU08Y5y2Tdk4cN+ioxc7X1M4RI8lFRBs5wCC9oVMOqzU+kphUkimHFp5e33KZCpQVYJSl6p5xtvg9hqOv4mQfKdwGaS2wSKwI6VlGeDrngS64ur0gkRHWS6wTSGnCYcMLZKRxbehNO9WhXLqRqYF0EZ4WKzqUK0AGndqnqOoAyLNC0CFppCZDgt5DyBQ1XpJt7dEbT4ncmiiOAE3Ss8RbKXGRkiQJuWqQtqIqT5jeh8nZGRaHigTX7tzARRp1uMP73/k2Z8/POD1ZEKVQTVecPpvynXsfkUjNVtHjF778Nlc/d0AxSBDzmuH2Vbb2trl6c4/zxSMKH8Pa0tSnVJNT1vMF9rQgjgzpeMC1b3yJdDikq1Ysjh5z8fxD6rqFteE//uX/mkfligvbsXFuoIQgJZxmf0Za/uzJFKUyBrevEkWf4391r6M7fY4tZ7TjlPhKD92PkLpjfDNCjyLsOOH04+co70kjxWhQk+ox/mJAtyjxyfdxscD0LM2DCHdSkS0adv2Yp6Kg8R3/4Mlv8fLiGoc/2MbGz1CiRTtL3MLjZcUw7/OVG3eQmaPtJE2tyHse03laY1mJNavzFmfgwyf3GRQD0kixjD1Pjo4QQpClGdvjksY0dLZlZ7WF0kHSPZmWRHGNlp7IK4zdzLeAs3VNkQqKRNCPU4qsQGtJVXfMygtWlcO7iHZSkBUx/VEMGQy3cva3B5x98Ay3bMhize6tLeRcIkgQRUwnLHEaczDocXBth9W6Y3ZRcridkQ4SulQg3YzXhjGMc06Xa3TsQq/farYPBjQFNEtoO0nbeqzpyLqapHQoa8hmJafLjmvXD3j7z3+N/+Qf/jrP3vs+v/LkEUcf/jl+8s9+g9e/+Cav3Mi4ecNzNoePHjt2r0ryHtQCZjWYVtB0QdQyzBRpJOms4GTZYJ0jlRtsjfhUVhok55uW0SaAxwq/URURgHlsZgQbgMHlKy/1pmrY5AeIy/mCFC8y1sVmPBg2i3AIJBLhiKk9jz6e0HU1yR5kqWc9m2NWF8SjGPI+ye74s90UdtoZQ1nRjyxXX0uJ+kFDPX++YD5d0xQN/ULy9hf6oWVhLYvzCuPBK0MxiBj0Y4SXpMZzcbygaT/m4mhF/+o7TC8WPH9yhsmGrN86Y3X3Y1678QsouckUjlKU0NgNqVKguTbe5+de/Vn+y/3fZKsxRLOO33q6QIqAAn777hBjPJFKSbK9wCJq11zMT/jh7/8mxw/fY1Hf56Nnp7x63TPMPU/nhtwHm7kRXTgBCo9Rnr7yaG8QncXMJXIE8a5g75Zgicc7wWAs6LShWp7z8JN3efXNLXTsSVVC4yyGAisUeqNTtt7SuQ7jLMGzbZBEYZiER3Y1QqqAtqjBRWGYFDcKn9pQBHQCK9tg07cJUgc+VbiCuhdlrRSWbnOBJehLzwxSCEZpziSRVJFFNCq0UJRA9gusyCjXjuXZhNOjNUUrGEY9xJYidjnOOkq3olzPWSxXPP7BH7CYT5kvG05nNc+Pz9F4iljzP/iLf5bR3h7FaEhvlDHaGhMJQR2fYRV0fk3VXXC2mmLTIT0zINmL2N97A9vB4x89ZLEukYuS5vxHVO9ecO/Hj/gX//gPcb4k15oYyaNmzZ18xJ/OBgwKhY8tcaG4cm1AuVyxtddjYkrO73+P8uSCi48eYl8d090yGJNg5ZC5CbGI1nVcq3qkXsEclo/GbO8MOLi1x/BGEwyHTmG7iK7L6YDV9JxoG4ZSYZtt/vbsmzxZn6BQHKhtrm8NuH4l48KkxI0lFbAziImfAEKxECWyButTnMwokhSZBuVe7gS6SOk6Q3QsQHeI2JClljgNraSiADkwqMZDJ7FFRBplxFFC/0qMbS5o65L1osaYFhVFRFFMT1riPCHJMw5Gexwfz1lO1khhkXFKnghUHNEmI6JUoxNPfRoxqyXnSxhsX4E0IpYd6zajq0psV9EtNXG34ny24nHZoFYl57OGk0mFiBz3Pz6G+D3i/RMq1RIf9Ch8Q9M60kySDQriwQ7peElbL5letNTnK5p1S7NqkNOKeVXx3sWKHxrPK13FXyTmz/zMiLNSczyL+b1f/zXe+973uX3zNf4n/9P/OcnhkP3dmGwAH31iEVow3FFs96HNofGCupOY1rOoAxE51nqDrN5I8NnkvLBB7Tv/okZ3ONRm8HxZv1+a2PzGABf4RJu20kZ66i/thGKjxvxj8wd5KTaRIZZg2YRUR7xneTJlvZqTCcW9s3MmJ0ccPfyQP/2v/xwHB1fYHu9+tptCr5sjWWNFjY5j2tUaYzzVfI2tGqw3rE/n6DT0iEVniJoQP2dxuFgSZSJwRDrH7HjKclIxOZ6RPsqoyprlrMQNt1BJievmFNWY8bhPnuWotI9PNEKGIanAkscjru68zS/9xb/M9IffZ/rxx7S1YRhLcuuYzzpkFFPEKTuDfsj6FcFFPTl7zPLiiHp5wbpukFKQZYK8cKSNR7nAEgm2xvBLkRIwDlcKmgsPPUFcwPAAuo0TMskEK28R3Yp6cgyuRFAjfE1wElj8RhIq+bTmVDRI3yF9Bz5CsCmNQ8QMcAnf2gydNgpUvxnSSza9SoJb0hGes7j01nsAFYI65Mbk5kI2g8IgVYKWoLzB1R3ObHJytcS1FtM66nmFNRYhFVGaYlOJWkvMsmZydMT87IKLyZz7zy/ofEdrwRnJuD9kNOyxuzvirTdeI+31kCpivVgSdxqNDHLIXJAVGdlwSLpzio8rlstnPP1hTdlFrErP03uPyJxgMCiIBgnzkzMmz45ZTxbsXhuzvd2j3894e7bmMIq5ksT0c8h6MdkgYffqiNnpjCjReCNoz+d0F2v8vKN3rcD4DusijNwiNo6uM7RliVg4RAtxT7O1s8dgmJLEEdE4x81r3KLBWoMTCoug8y06SUlTSeZg1k2pbU0uMq4mBYM8RuUCv4LGhpGmURKvFc4J1m1HojKEiBAyRkY5QqrQf8aSFCnKdmRFjHVtwCbIIHiQCnQCQjuE9QgniaOENMvI0oxie8x61rGcWRYXK6qmJY4lSZYgdXDOCh8iNntpEmIjEVS+BufxSqIiTZREpIUkSzPiOCFJM269fpPu7DG+nCNEjHUVnfFYEbz9KBWYWELjpcFriUgV04s5Tz56ijiesC5LjAoU0bb1GANZJDGNwwuNTFOiVKB0hZISnSaIGAopubr2/NF8zqqpqMoVfa1oU0WdQz+uqM+e82hp+Se/9o946099if3b19i7doWT3FB1gmbp6aWBEZVEQcCxbgMnyUhBliqE9LTObRZ/8cKWEGIy/QvagBCb+Ns/JlHaaGcuNUgvHMifAkw3HoXwHQN9wF2+F8yyklA9eB9YSLHwkAuSQtIYgbMtbV3Sdi1eKZJhQjGOGQz/ZMv9n3xTaE7wbc3ahRCV5fkK1zjaRUWCAwPLqkWmIK1HdI6maqkMtA70ukOODSJWNLVhvpjTWjAonHCkmaDoS2xzTrM8YXLvKdX9U+6+fo29q/vk+6+hB6DjIanq4+2MSI/Z2z7kb/z7X+ZX/+a/x689eZ+RFLzSE2Sx54cPFlx/6YDtouDO3ohEa4SIGGZ9losjVufPqE9O8c4hE4j7gm3viRfgG0/TeFotECrArIT0+NZjZo5SAbuSZCwY3hR0Z57aCWQP5pVBdUv8xRERHkWH8yWSGEmH9IrO6g3Mw6FwJG6F8h3ShgtfiA4lGlTcILwCIlwMug1OSpFahFGBoqossZUgOpAG4S710hs9hO82Pc6ABsEF17CzFi8ahKiQIkN5Eaih8wp8ilIaUYBfVNi6o5tXRDonH2T094co41icn1JXM5784Ijnz844na55cFEjcklWpIyHI7781ttcu3OLKy/dYLzXo5quWJ3NmDycYPZSZBxB2dLbHTHa2Wb78Dar6oL12UOeP3vAt//Jfd59OOHR+ZraKN4+OOCVG1cYJxnzeoXqBK/duskXf/4uV186YOfqNi9fe5lp/ZzSTYlVy7XdfQb9AUm/R/Fojqs6osZgzlr0EkaiYDfp44XGiSFNuoPLWmzXUD66wD1+RpzDYDhi8KVXkLaGZo4YXINZjV+sMV2FjTusUnhREfe2SFVEXDs6X6MQjMSAV3p94jSi0Y6yNazXHcJZKGCKAA9d7dguekQyR+scnYyDq1U4Ou3oDVOc7xhu55wdH1M7g+1lZFkIBlLJZTsiuGB7cZ9+kdMbFOxeP+BMrTFViVlVzJdril5Kv5/jtcMbh+taWrXg2tY2SbpD2c6oH54xWbdUHcjIE0vNoJ+zNxiyM+izuzvmq3/lTzP/8AcsnzxicbSklmuEdMRR2DwGWrKDJE37FC5ijMBVHZPzKfOLBS0SVy+p2o5l56grT5d5vLKszhaoVBLlmjhyREriIsV4Z4jIFYed42t5xfsffEgmPa0u4UIhI8cghS+/NuLJJ45nR8/43/1f/jr/1vn/mJ/7ha/z2utXeOWO5fjM8+ipIMliir4ijQU2gtVC0KxDnnmSCCLlUN6zkcewOXaFikBB44J0/pKMvFGOY/nUTWTdp7MDsVmIN7ikzTk0bCxyI0a5ZDsoGRRIlxiMUSzIEsVMKuY3e8ipoe4mpJGj389w+ibL9ZLpNCZyl8/kM9oUtI2IbYf2FiM9resAT7GVsbMF1jsmK0uKZL40XJSWz93IsFpQO8Gz8475IuyRygkaFZQ7W4Winrb0pGMsgma4mba0Z3M+eDBl/fFDdg7GjK89piu28VkEhWH1o/tcvfsSr379J/ElvPfgGd++1zIWkpsHBUmm+P0fLpFNQl9e5cb+N4h0jkeQxgNGIzgRNbNqyVsHcHAIxb7HnEFiPWajX9bCE0WSrK/Yji2RdhjtMK2nuvDUOYxflkTWB65P7tHWYXTLTJZhWCUjrAgnOkGCFy5EakqN8hLlHLGO0ShUZKmqEqksSnowQ4SOw1HCZghdhUuxyfCxDQNrl9OKEulSlBcYucT6DOcVjiqY+zYjKiWTDTojBCU5XwRaI4bGDWj8DsQLEh2hY0WSx7RNkKcqJIPbOXrkWNcXfPzOE+6995CTozOEliF8Rih2ruxw++Yhu1eucfX2axTjHIFjdTFl+ewRUTQijvu8+ed+kr3X7iKk5ckPfpNf/9XfZjGboVLD+08nSBypBmNadnf63L19hS++/RJ3fuZNtq5fIxke8vjhY1aLJTfKitdf/xxJPMK7jMHditX9HHe8QOKpU0UscnrpdXr9C1S8Jq9KxtsKNSiQo4RsJ8IrQect87Vh/vgpkycnvP+bD/jhsyNiAXe3R3zt5xVbbwzof35AMXoTtzrClE+pnz7HJRqrQFkJtkQ7xUB1hNrWsvArvrsSDI9SipVCFo6trQwVSZrxkFgbYjVka/QyhzvX6PWG9Poj9g6GtOuWZlmynD7EFh21WRDnoTUbKU0xTuiKBCkVURRhk4ysSYh8zpVXbnH91hX6gz5VpWkkNDqjmLUUzyEpItgusF1LWRka67Fas/v2S+xc3WLy8EOOHFQXa5plhckiXC9B9XOWEjIvKDtYTmc0VtJFfVaxwI01TjiaoSaRPVIbsW80w50RB6nEKcPxkx9z8nzCdLJiNluylSq8iVk6aL1nVRvqquL8dMl4J2eryFkrzaSTlDUbnlFGvl2QvTnmi/WKXh5x5/WrHH/4kPm84qi2vP7qkDvXUna2cmbfH/H+O99lXZ8zTx/w9a//Fe6+tMXhIXz0yDI595hjSTqSDDJB3lN0KLrO0VYCrTyRdpvFOyzUznus3SzqG/GIEqEF6Zx/MXO4rMKs+zR10YqgQFLSb3hp4Y/zDhnCAsK2sBGUIILh93RiMM4xLODlO9ucTST/9Nefcv9bH0Dk2b57len9U8pHJU/EAn7iK5/dppBf2WXAgkJWMMzpqg6cIM1zBmmDMZZoZciyhP7a0V9a9m9GNJ1jXRlK2+CjIoTY42kURKlkNFK44Yyka8h9R6MktRQ02lN1Fevzjq5cMpsZ1KiHiGO8VJw/fIzQhr2DlPe++QnrH93jOopIO67e3mO42+MriwtaoG66sAgCxhtqV+NlgxcdSMd4H6Ie+ChQBS+t5FFECOXWgjwOOapRDHEkSJUDCbYLphddCKIYiAWq9bjIAi2lm5D6MrCMxItiMVjkhcRuzhsWg/cG6xxSSxA6DJfV5VQqCu0rF2YFXroXUCUpBNJrvHAYXPhBLodWXuG5BA2Go4kTEgsbJHeEFCneRnidIpKMuGhIVBawFZki0paOAKer5yvqqgU55eTZCeVqgfcdvaxHUvRJBwMG124xSHPSZIhsBPW6Ieun9IY5me5TzR3lsuT49CFd4lFK8vC9J9x/+BzbrNjfidgpUpJCUPQFTml2x332t4e88oVXOXj1ZdLRNp2BejrHVA39IqM32iGKhjgTs7O/w/LJgrIypFSkZU6aObK+IUuGRG1KWkbkcY50NUK1RAdXICaQYz98xtnHz/nkg8d8+9kx92dLBHBUdzz5gx/yJX+bP7V/F/sq+N4WbGtY1JBKhHIo36CMQLQB366AWEoyFXP98Cbj7ZjeWKB2SnoJ6EjTyCHKKZJoRH9wh4OtK/T7IwajMePtgmq+Yj2Zo59O6cSSqNTkiSLeK0gSwe5OilpcIpU1stgmchmZ7vHSG7e4cuMaeX/AuoSR2WKwv01b1Synp7hNPobpWmIHSawZ7A1I+wk6jYjzgqIo6TcBDFdnMTqSdFha07JaV0wuljx/7whhl5jWYHVKndaYTcTU0q+QokBGPdLhkGyQIRPBenXKupJ0LqKTgl7kqYXCq3PSwRCVZDQri1dx4NXnGapWVBbmrSFfG6pyRd0JruzshbwB66BsGWeapg4my9nZCt0HqSPuHOaU7YyjB5bf/uclWXGD6y+9zN61a+xvR8xXgsUKqtqTxoI0EwgNJwsZMBJSvhgKO0Fo6/pgKlNyk5HuN/OBF1Zl/6nS9NKpfGlUu4RXbtRHn6qTRKA9BA7+ZtYQiA8GT2s81dqyOK24cqiRIsKTspycYLo1TpUMX9tF9lNU9hlXCtnhPttRxFgvkVGMdyZEzw1HqHKGqVuiwtHbLbBG0TWCwb5iPqlxFzXjShENxugkReGpXYDIDXYU2dgiFxKmnk5rujii1YplbZgtlixn4M4q+nsxWqa4qs9JPaGfSyY9zz/7m/8NblFzV2suRMfu9V2uvbTHz51lfPPeMauqojMtUjpq0zBv5hjfIqRFJ57+vkBmwdYuN3gQocL1RxLCdNJYIDNJlArS1FOkAhn7kLDkBCoP2d1SCLQBJx3etyzNKcItyNUmIIcQNiSlwoowcPJA51uENWA8edpDuBhnJchFGDt5HSz2JoDzvDYInyKEDG5vq+nERn1kggxWSIewCgh4jctupQUaIYgAK6IwqCICHaPShNQmpLJAKY2MPTqx0NVUTcn8pKN1jtY4zk/WWNtQ9DUHu0NGu2OG+3scvv0l1idL6qWjmpd0KPJBQX88pjdIKFfPuTg/5/vf/V1qsyLWKR/+wcc8ez5h0BNsDcdc294m34Js21L7jJ2dPju7Y6688RL58BAhEpr1M5bPTrEWBnmfJB8RpwOcVWyNhhw5h5/PwE8QcgeVCtJkRda/Qux6xFUw5NGYgDLfuwqZwDcr1Pc/4uiDZ7z7w0f84XTN2jmMd3zUtPzed99hEQvevPkS+z9ZIrMRYmeMnJ3hMQjRImWFXEhcZahtR6okvSimH/f4/N03GF/R5DsO9isGPYlWmtWsoD/oE6cjkuSQrcEevcGI/nBEr5dQzmYszhOMf0K3NkR+TZHEZDsxeU+ztZ2iTsB7iYoj8tEBqc7o5X1e/txtxleukhR9mkBiZPv5DtXsgo+++13Wq5q1dwjfMUhidJ4w3B+i04BxFnGYR/QygzENxBqpBK1r6WzLuiyZXCx49u5ziq0OnXlcnFEZS9e16FKw9CWxsvTSMXFRkA37RGlEmm/TH4AnRmSaIRZpJUJrejt7RCqmbhbIyCCyDPIcPQvGvEVr2F47LqqSrgZxNci9jTE0kzXjXoR2ktRpvn9+Ruw18UDy0rWE9x+sOHsy5/7JU9TokLfXJV8bD7i6t0OWSdCe6SlkAvJEEKdw2oTgHiECwnqzVodIgU0ozoaGvfGvi5AHvdkY1Obx3m/aQJtNQcqAyRBBYf5CRS42HpRQIWyUSiIoDttNRWIry4OPlmR5HyJFlg5w3YL1xXOacsq1qxlZf0zU/4xnChfr54jzC5bnc549s1x7NaN/kKL2W06+c8L0vOG8stz9/IjR1RG9gxGPv/uc+48rjs4N/X7G7YM+2VZBdXHG0w9mOGsY7SjeuJbSj1KykSNK+3SZpokEp/OKRAnKyrPsPKIWeOmIRMXLLw0YGMHz3z7j75zM2Ws7rgnBBM/W75xy8lFLLVoWS8d8Yjk7X3N1f5cnR6f83g/+iMVMkQ0yrtzJiPMKN7O4BsQ0mExkAvmYjX7MU3tHX3p0KpBDSboj0SagLZqFQyYCHYOJIO8kwgqaxjKZPCKRrzIYWEpX4kQOQhDjab3F+hbjV+i6I8IRC4Wuw6KOdIhK4JIOVAN1gY3DiErVMT7b4Hc7Sac7RCeIuxiVNDincVbiVAtWbciNDdaHTcH7wGaPvEADbbfCVy2ihjge4JzA2o5uXrJsl5wdTXj07hGPLgxOKqI4YufqiJuHV9jZG7C9fRO7bulqz9PvfYItHcNre7zx519h/85dzj96zie//y7//n/6HzEpG5RK+fqdr3D61JAWS5q+4Auv3uH6nR2++q+8Trx/SGsWlOszHjx8DtEQa0esJppueYaUEZ1TbN+4hReS3vY2UZYjowhTO/7wX9zjV/7xb/Db3/k2yneMpeDN7X3+lx/+EsVfSYiv7pBu30GkHn+U4GfA6ROsPaOaHPP+b/6Ybz444v1VyX7R407cp+kM95cLHtiS93/8kL83+Rf8tTfGDA9fQidDqgtofIdThijNOX44Z1W2tEPD1155nbXxqGHOW//9N5DWY+uOOm8ZJCm2Mvzo/Sd85fZNiuEWjUlxWrOqapb1GaOywKxXNHVF3I8Q6ZBWgcq3GO32SDMJuiXup0iZkqYF4+uvkImcVOc4UVDXLqjU4oxYCbCexWTFu8+OWK/W9LKcnZ6m6wydh6PTFWpYMbARdWuYdA3HZcWzszVLX1OMC3bFENEbU4x7bO2PEbf7uF5MFxnmzQPKe+csnyw4fj5nrWP6tw1Xf3LAzZ6iMVBetDx9ukTGKel2jJAScy6oZIQVfW58+fMUScr5/RN+7zd+gKktvmppfEWLJtZ98oNdts8t44Mt9r/xOl+0cxbHp5zNJ5yfz8h7KVsv5/z86pCyGDOzku9+/x3eu1ghVMrP3b7L9/7bf87x40e0dcVf+Yt/iX5eIKPQEmssnM49zgp0An0tkJXcKItC7/8Sc+FFmOkE08Nl5yEs+OrFNPmSbfapnFVeIlIJ4TuXA2U2zuWw82yQMwTJa1wItq9E+JHiycOK43NHMZL8/DdusDP8t3h8/z4fv/8jVs0FeiZfSGM/s03hQJWwrJkeN3xn6vjGSrNjY8bbERfOoVtDYRypcPi6Zn06pz6v0FXHSMHBtT7717aIs4zVx0dMpyukt+xmKXZq0QPBeDcl2c2olh3reYO8WDKSjl6qyLNwyratpFl52oGn7krkvKXY5DkYAr1VLla0CtapZzuL2O1ppFgicLSrCbMnP6aerehlinTch6qlnjjs2pN0kCVhU7BFcGa61kHtNxF8oBNJgqQYStIYlHdE2iNjgU4VTSvxa3Clp1tUuF6D6IfnqAikReeXBLuaIZKGVHVoL9FOB4yFc2EoHc2DE9knCG2Rmz6kiA3CRhtJgiOyQQSHdkgXc3k9haFUKH9CJkNQujjhkD6ooYTvoFtD10IXQIOm9bRNw3pxzsnphMnFivm8Ypz3ifKMtMgY7e5wsDdma3tA5FOcgqSnyHb3EK7BGMOj7/yAf/GPf4fT51Mujia8ke2THBbkW31u3tzl5mtXiBJFHiW05wv2ro0Z7Owg4xGia2ibFn02IzYRcTxApSnlYo23oIoxxXiI6TqacokxS5xzVEvHxw/fpycVX7hyi+rsCcdtxYPplN/87vf4818akmYiBPw2PejEpgHcxy6e0pydczapaZ1AxilJ1uesjlg2hmPXYBA8rUp+7+yYv/qwoxhrkn5OXGhck+JERFR0ZFmDkBEu1bz1E45nxxccX0xYPTyjrWrapmJ4a5fB9QN0EXNwrWJrZ5ckHzBfGaqywq4d3dpjBjMSLJqOYb+HJ6WXatzL14mURCeOeNAirSJNewy3dhhe2UeLGE1E7CTttKKa1nRuRjo2zE6esD46IYVwQLqyzd5uTrWsadYN7338nMXKszUeMOgHnU2/n3BV77B2EWkvpRimSOEDntNDtnNAHHe4eob98YR3fvyUp8dzzlZrnFS8RMxBX1K9WiHjGOl0uO6TGFUoEmBxvMDVjsZBb++QrUGBMJp1+weIZUeaedrJmtPVikVteLmMWK5XqFVFtxKslg11VSNti/QdiUrJIs3C10jZ0o8Tvnx4lQ+XD5h1LWtzzt6VQ7bTHPF4wkeffMJo54Cit812X7PoAll53nn6CURS4Ey4t7i8xwjD5gC295tqYDMJkKG6eEG12CiH1KYNJCRoyYtgnU8ftGkVeQH+U/jepVqpaqCxHmE8b32+QKceERk61bCantOsl/SKnOt7AyKdbhKEPsNNYSxa5nXHemE5ahxN59DOkScCjSfC0deQRR5pDd26wZWG2DmGsWJ/L2M0LhAixpeBzhgJR+w9ruqQg5i0n5DvJ2AM7XmLXFREuURmgrSvwMQ0TjFtBY3RmLZkXc7oeR+yggVsRRLZNDQrQekk42zI9mV4u/DQrfCrZ5STOb0Dy3CY0BEhEr/hh9gg60vBZQJMsMQ77+lKj4sFovBEWlJsxWS5xE8Ml1Z1FQviWGFqj+kE7arDtgacQ20Idh5HR7ORl1q06Ii1RTmJRL9g+SjfIVSLwIaEJu2Rl7/XyIegtXC9hEyGzclFOL3pX9qNg9KFi+pFLvZlI8kjvEV4gzcNYtPL6pqWujJUqzWTkwnHjy5YVx3OCw62B+TDAfmgRzoaMR6N6RcF7cyjVXCuMk5xpmPybMGD7z3gW+98wKxucULxl15/i53be/T2e9D3XL2+Q6Rj4tqz0prx3ph8tEO3Vph5RXl0weLhGX4pcF3M+E5DXZV4K0kTQRTHeGtp1hs9vHUspg0n5yfkScydq9co7YLphWNat/zO/Xv8xMPPM94ekB32EG0eXkcH3kXYVUt9seBi2SBlgCZGusdZa5i0ngtvUKRMOku3nrN8VtPdMYgrEBcx1sqAJVeWJI0RGkQv5farks6VnJysWD49p25KjO0Y7G+TJn2KwYCD62uKwQCpUnRV0TUrutJil45OQxKH6EUdxwgZkSqFunmNcrFCxY7+lQjKhqwYsH1lm3zcRwqN8AolNdW6oa07yq7F+opyekw1OaVQkOQJ21sZ+1dGXMg5XW2YlR35pEJYRaRSBIIsS4h7PdJWIWOFjoLMXBCw8DIbIuQau1iyen/N+/cv+GQ6ZUHDAM2WXtL2GrrK4LVHKkWWpLhoEwrlYhQBUNk5gc6HZKMRg70WGWus9zStpVkbZnXL3FiypI/xF9Rdi60l80VFW9b0RPD9eGfpjKNzFkdHHEW8vr9L75NnTNsl1i7ZG/foC8Hi/lMefvQxN7yiyIf0M0ULlC0YHxZwBSBDR4ENY0zBp16CS5MZGy6ZEDi5Sa0MRoTweSle9I8uQZd+c1+6S5cal/LVzdublpLwnqYBbx0xnju3EhaLOau6pKoqZicnVPMZaZIw7u1gWsdi0Xy2m0JZa0ovIRZ8vVBsZY51V/PgnTOMsYEUqTyptvSGCdlozNOzNbUB4xWZ9vjzJc7HFP2IK/sDpLZEV2OIM5pEMqsF0hfUuqVJBZGE+qxGJp6rNzX94Q5VE3PUt2S3tzh+csQH95+DgIGWXE0Ub+zn3Dea541DeDi8lrC9G5NmFiVgZxTzxp2C7/xXj2gmHlnHXLk75ubnHJiWD789o13b4BKOJVnfY2JPbWB94REp6E7gdyXFm9fIexknv/8e65XBth6pHUonJJnEGJisNOsaGu+AAiMSnNBIHEJKnK+p3IpEqw0gMKZrWhAtShisuYGKg7xB2DgESXsBTYGPm02ec4zQDu1ivJPYqMT5KGRDiwatYgQKKzSxSIiRFAg654KnxDusTlCDHmqUM3t8zmw+ZXq+4NGHZ8zWhp2tgrfv7DN69RWKwYi86LEuS9JY4Nqa1i0pioLWzHnww+/z6MfHTM9rppOOv/QTn2e4PSDf7nH1Zw6QKsXUntPvPmLYRkQuoVtW5MOY8ZWr7Nz6Kp/8y7/PvV//Pf7on/8u//TBnIl1FKOC/+2/fcStr3yV3u4hrpGsnl5gjUMmOb7VnJwcce+Dj0nTaxwNnzPr1bz5+iu8/M0THj+e8Pfsc/7Ut2oSKXj9zT66FfhK4NYCTh7Q3D9m+dGc+7MZh4Oc3WzEg+NtztrnzKzFIUm5jhIrtJsx+/gx8+spyWBF3BvRNAvMsmHyySk2LUAr2sZQDLboDSakWcpZd8Rw+4Dt4XX8eI92MKLY3eWVnRtML44xizlyrUjWNTujmK3Xxmwf7ELX0a1Lnr37HksjcSpi6+obnKY/oj/o8/rbX+dR+iEiEqTjgnK9JKUgiwb0t7dJFzNMW2JkwMIszIzOPGa3MKxNw5MnLbl0aJ1z9eZtfvJnfpr9w2vESczRhx9z8vARVVMSx3B2ek7TtXhhKfr7pIMRPhvz+KwmcinlyZjfe2/Ao7VlTotC8Qt6n8w0fDj5Fl9bfo3+1oB8K0KJl3jv3Yc8/uSYi/U5b75yl5FKcO98wPHRGUprdH/Ml77+NdbNgsqUrMoLfLaglwt+7t/489hf/W+xwhMf9Hi0nkG74Na2opxYHp/OqU4q3ryywxIQruVOnqAUFELwpk5ol2d8fP8TfvXHD/kfVUtu/+WYvdfvMG9bnA7hTzeHiomFynhyT/AHeI81jkgIjAvpbQGXHcypWkK7kQu96B5tqoPL6sEDxrkwA5QbRRObysMJLq0OkYbEC6raUZYO2Rpeuhaxty05q5b8g1/7+zz65DlX99/iyQdPcLJjdH2f4+eWxbzh/GL12W4KXTSm2O4ohGXPe7a3YvIsQsYZ0V6L6wwIyJKUNIrJ4oi9vR5Zz9I4xTCPydLA3bd7KS4e4SMYbCuGOiURHicdjpzOrwIDJlfoVqITSZ6n6NgTOUdWCFaL56ymJzSzRdhZpaSTklpr8ihGCUmsPfOzc57c+4AnP/hn5D9xk7Y9oio/IKahvTBMXB0Q2uM+/d2c176SMflgRrPo6CqIc08ycOSJpfYQpeATmFeOfQSJEsxbgzUOpSHWHhE7pA19xrlxaJ8QyQGNyPGbTcEJSYcDESHJsNQ0WFrXkUU9hIuwrkNHNTAAn4U5g9PhKooMIaVic4F5iRMWpzqcDT4EKcG6OLCTRGiveTwWT4PfJMdpECky6WFjR82K6fkJ7dpAbckzxf7VPXb2x1y7s0e2dx2dJ6gEls0UEUmEt0zuP+XdsymdcGxdHXP9sGCQCJRx7L2VMD7YIh/tojqPLx3Kam5+8Q16ox2oDYNUs4wcWhismTN7+pjJ+TnzpmU79kRWoF3HD9/5mPhwi2s9GPa3yfMEYzpM5PG0OGPxtSAvKooa6ipjtUp51D3miVviEXz07AFXToe8yqv4ugJRQb9DJmN8P0WOBAcJeC2Z4plVMyq3pqPB47C0DKXkMMpZyDnT2ZzkWY/hyNGuS5rZguXxc9RwD5WnJL2Ipl2Sxg1722CqDpWvkaLgys1Ddrb36RdDqtbie32savHCk6qcIo3oJRHKLnGtQ3QdvUEPKQa4JCHa9aj3Gp7+/if8P/7Wv+SdyYTPfeEt/tW/8OcYv3qIjmJwisnJOWZ5QbeesZoeobIlzekzsroll5Y40eisIBca7zVaxAy3hwy2d0mzHljJbF6zmE9Atty6eoBOFEkv5qMnS7p2zXJu+embfwZjJFwVfPn//iWe/69/hfkP/xDjv8Uf2gWH9gqv+lu0xmC8R8qYOI1xpcXOWvpFzmyxZjZfYGPPyZMS7Ut6vZgoG5LFCu1i5IHiTlmh4pit1+9w461b1PMZ3eSMplxSr0uOYkWqFQJF6iOyLY2PYxwxZdOnRdNYx7Qs2V1U5GUDzrCqn3N08gHF/TF7N75Ap8L9MowUzm+6jSpkd8qNlNSzMazJ4EJ+4UgmzBWCGilwkmDjVP50xPBCceQleOECD8mH85/XoWWEhEXtcV1oGZ2cNDz45AHWTtk/TEBfIdsqeLq+YPDFKzgzp50/Y7myWJeSF/3PdlNofUKWJMT9CCQhYIUI4SLiNMZHIZFNuBDMYRqI0pgMj3ISjMC1BqEEOlIUvRSvIU4lUZwE05azQVu/yReQqSYqHHESk+Q5KtJBlZBY1tMLVrMZVVkTBWUmnQyqGh1JlBLEwnA+b1lenLN4+Anrl+9TLp/SzM5IMNh1x7KD5ZGkuZ0zGCu2r2zhzixrX1OWDq0NUltE4lG7btMigrIKtMbEKMq1Q1oXOCV4pHI4KYPWyIByGiWSIDMVIc1AQnB6E4IzWjqUF0GbJDU4GcpN2tCX9H9ckgCXmX9i8/6l/M2/QOJdQlLk5uNs9M8Og6fxkAn14jFOOlrbUrUVq2UJrUIiGY1yDm7tM9gekA4KXtTP2rOaLqmVBdNwdnLB2fE5MtUc3N5hMCiIpaKew+Bqn97+gLToM7/3lHrhQMTc/tKrxP0Mp1uiJMJ3LV1VUy/OmU8XrKqGRkKWS/TGif3xozNunU7ZOlgxHgXkQsBtC5q6pqk7ms4joxAIlKqE1UQyazrmPpTPT5anPF+cYxuL62qgBmXwraW1llY49GZTEEawMh2dN5vXTyBkTKIlvdiysCsW5YreYk2eebq6oqlKyskcaVNi5+n3I7xrSRLY3smZtRphPRhLPiiIowg6x/x0TgCUKLKBIsv6xFKiZYStazAghaTY2sJT0HhBuZhw/MkpD965x4+/8yN+3LW0neLu1Tu80u/T3x+R9CJqU2Gblq5qaJZLElGjcAyHPXrTFK8TstGIRMf4KCMuehSDIVGao9Oc/s4e/a1trHe0ZomMJHGmSXsRSi1pOotzhtFoQFUrdJTwpS+/zM/8y2M0KScfLXleP6CHINJbSClxxtBWDc4K2tbQtZbeThEErApGu2OkjhEyRkUpUZaF2FnnSIeO4WiEjhNUL6O3M8A2K04eH1FWFdZbXJTQG45oKkFbCUhCHjc+Zu0VsZCkSuJiCZ1BdIHIbNyS8/Mj9L1POLj5eYQIa9sm0yqE5FxKRi+dy5chVn4TvCY2u8JmviAub0dxuQ9calPZpKrxIh708mv/WGHBZmIYBOwehAuHvMYYynXF/MEF5ycTFpMZZ/OnjG5vh6jc1KNVh4pSdJx+tpvCeuVwnaATmtbHdJUk7kLgirRxcN166DpJWTvUokVJTWMVrZOsj1r0bIaIQsLYvPFILUBBOgxsn0gobGfxViBEguxbiiImzXOS4RaePl4YmuKcyTtHnD2bcbZu2IpCuHWnobIembnA/6lKcA6/7GjurXl+6x9y9uwp60/W5NKyahyzhSd7r2TcF6StZ/SlQ/YPFXW/ZjZtaVnhZAhp6d1wCAvewPLMMb+Y4J1i9cwSSQ9jgbsCUgWMrvOCqJVENhi/8A5L4KpngKem8ytqP8fYJT36FHKEMAqLx0pJ3PTwMaAbZFfgovD7UJ3ARzZcpVbRKY8wAmk1RDW4CO8UXnmsD/83hHaTIbSz+iLIg/GGujtjvphwcb5kNg/kyqKIOXx5j93PvYY3nvJ0wuTkPoPdAf1Rzr3ff8zRxRHLes4wz9jLe+wcDNm7M6KXRPQGHZEpGe+8RNwf47zm4994h+dPFlgVs//ykGR/H5UUkBfM7i9ZM0FvfcDx1DP3EW4QowaGHeeRjee3Pprw+j3D1W2FvQFeZhinKEvD2fMJZ5MlF01HLQTxKCNXlmd/9IxyUWE2npD3miNuLA5ojpaIbIazFbapsA+fMDk+53hSctYPfKh1p5kIj0FvbpeILL5OlC9xg2c8W80o1hOyqqC/7lE1LWVVMz1fUc0tRb1NujPGYOhtDYiHN+ieSDoT0y4irDBUqynzmeG3/psfce3OFa7d3ObGF64StTvYtaFb1DSnK5J+StLPyW/coFvOmD8744NffZ+//6u/ywdPH/DMlQB85zvfY/WDx3zjF/8cX/wrX+XVb3weNYowZR+0oNitSQeWeDDkZS0pvYAipbh1wPr+Ej3Yobd/SH/7Jk4qamNIhyMGdw4xo4T5+QmracmqbHDLOYu6JIozels7yNzTrRTORsSp5D/8v/1lnv3+z/Nrf+1f5X/zyb+BTwTxqMc47eOWJaeLM4yUzOqGSjoODnYggmSUM+gPuPvVO+zuHdDL+5xPn2HKltY4mq0ad56Di1lOL+ic53xe8sNvv8/z2YqDvYxbd67x2uev8ezpikf3Z6xMQimS8Lv0DVe0ZDzI2L6zw+oCSh/uYxk1PP34GY+OFF//hb9EIhxafOpUjv6YKMgLESrxjdrIE2I2/SY50hGy6V+kIYoXx7VNPsqn+0f4upCKwmao7AMNM2Q7COjFgrYV1F4yvpny1Z96ncQf8u/87/9fPP6Xv8Ly0YfgG9Ktv8zeG9e5+xduczgy2GVCNfmMN4WXdzxRKVFtQiUThkVGEgd0rl0KvGnxzhLrBF30iAZ9XFdSd57OCiKdonpDkIp6NQPdIpQgjxSpTEikI1GWdFQgbEXsK5qeRpmOOEnJ04TGW8RqjXt6zvc/mHE8K1l0jht7ArnhE3Wuoec8sQ/O1Jd2Iw6uC3aur6k/fsD84SnTRwuyoWe7J0gtTOaeJx9WLC8sev4BV+/0GFxJ2b3RY/7MUa09ZdlSjIIw2FhYzD3rRY13AmE8OvYoB75yyNiQKNBZhBklxLHD2zVSJeRYnBd0TIiYoulQROQyI/EpkU8QKCLriZ1BxEdIxgjbR0QdygWfAlEXNgDhQVoiExobaIu0cYj9lC4wljZTLykVta8RhDCRZBMx2bZnnN77bdZPHsGsZrsfs721QzEo6O1FuC7A9opBjsgVi/MpT378Ib/73gd86fYuP/XmG+z/xAGybdBRTDG8Qr/XoxElanFGNhhQLyZcPHzCN7/9LvfnFSaK2f3//iY//T+8xmh/i4ZT1usVWnasT2ZEHWz1MvyNLfqVpT3umC8M58ayOD2nOjrFrB3dqqSrWkwD5XyJWTXEXcvgap90KolWFd+cfoAwLSMSZtRciQeMkCyXTynSPrZz1MsVs0/OOZk/4/lqQrMwmL6iUo4XaMoNsFhGM5xY0XQ1mRIUcUQ/SUmyFLkliaSlmRaczQWRa3H1Bd3UYGWLj1oyN2Q+WzE9njH75d9l9nDK6dEZ3/roHYpsyJe+8AX+7b/23+PwFUnkEoRLcUbjncRLgUwU3ZOaZ+8/4T/+//wXHJ0/w/uW14QiRzL3Ld9qnvPDb/59/mpWooXitW98hflgCr4mZYjOVkgRkYkcV3Z4I7CnNeeTOXmbIkXJfDljuL+PjCOOnz/jvW//kOV8RjGOyNIYEcfYztOLHNm4z3B/zEePLNVZSy9RlzEC6L0F/b/0R7z8nwuKwjM4uMBkoHWCNo5yPSNJY4pBgVmtKVtHVXcsJguePbrAtZpm0HB+3tLRgTLIMsKUAmsd9bKhGPTY2t9ma2/ML33hZ0kKT5pZ9l57i44jlqf3OT0/JU56RDqhbCv2c4GxEc4JlDOMpefVIqLvJc8vppw9MyzWS6K4x0DFlNaHIF0RgnAiwwsz2ab1jyYIU0RgjGA8xGKD4nbBYwD8/9XzXngiySabHpJIY9wmIc4EtI4EkkSQJTCMJWogeNh6FrZDNB13Dw649W/+VVZ1w8NHfU6++cucP7rPu996C7/fYlYxy0n22W4KiQJvPbaymAhkXxJFCiEUbecxTZiYx4lERBKUxJSWtvF0ThAnAhVpvJA4a+lai1BgY4GUChVJdCzQaYxNImwSY31EJBRxlIaoPxxCGLp5yemq5aJ2GOHJM4W3AmtACEeUQByDaz3joWRrBFnWsZjMcMslojX0h4IegtR6yhZEZ2mmLaf352yNIU0gHSXYUYJSBkFDog3Wh/58koJtDC0CHYFOQcYB3hU7h9QCNYhJByk60cAGgXvpVvECSbRRFniUSFEiQfoIt7HBS+HxPto4GC81CBtvgpcvPBSXZJRgnFGbj11+5tNIVDaPU0BMUDgZU1HVU+bHE5p1hcTT3+rT30rJeooo81TNmqbsqKclFRHeGtIs5urVAVdu9Nm7XjDaLnCrgPRIshSt4hC52O+BkFSLNedPjzlfrlg3Hd5ZHj98zuemM/JhjhEKYoVQAmcNSiviJCE3eTjdCY/tHI33rJeGcm7wxoMVSDQ6Unjbhtc5VuRJD7nyaNOyaJf0vEbLhKVrGOYFeZpgfIMQQ/BhY49TTVSpoKLKFD6KMErSujV/LEEXwxpDixGCrBeTD1KyYRZgj1kC5IwPRizdCpmGKjZNA3bESsne/g5Oa8Rkyem9Mx58+JxnR885On9GJOdEMuHX/8Xv82ei19nb3maYZAG0ZzxtbcAYprM5x6enPDl+Tuc6ciQ9IWm9pcVTO89idsG9Tx7z3vc/5vbXPofzHQiH8hJag28NWElnwXQO2Vo6L3EiBplQNYbMOrSHVV1TVwbTeqRIwgYIOJuTbyl62yP64xFNU2G6BBfFXGLdjFCsdIEXGiKgMNjYEacpyiukrRjsjfFZhJQGgcXWHetVhXWb+NGuoTYdnW/BdjSdoe0c0gkq06HShCjP8TpmtLuDUB1NOyPpbZOPGvKtCxbzBVm/IMkyXJuSDYc09Trks7sOoSXDIsZ6aJqWcr3ibDYh2Y7ppynT1hFF8lPRkBAv3vZys9Lz6cn/8u0Xw4XN18CnYLwXD96sCSFQa7PobvwJwosNtSAs2HLzqTQKwMB6YejWc/qvRCRJwVQecvpba7rKsZqmmGFLtSqZny//RGv9n3hTMEawnnZUZxV1FpPnFhUFaPhk0lKXDcZ7RAGJBd1YFmcrVpWn8xKVJuAsHlivGiaLCqnCYjfakahEoXOPTGNEFONljEciidAyQeUpWrWIGKplxUXnWDiP1oIkV7RNiNCLFKQDRZJJbANb24KtsSeLWhZ1je5KCuUZ7iu0B9l6tqyHFlzneHx/ze7YomnR25psN0X3PFHeYcuO2licsgzHHlqLaSHtQ9ITyBQaB7FxJLkm3u6RDPvookDIBIWmI2StCQoEKY6axs8oZAIiRxCycoUMQyzMHj4OJrkAsgtnDEyC11VYrJwKbmer8VZiouVGfSSQwiI2GC6HR4kwnI68x3tL3S5ZLs+ZPO1oK4vOJHvXdogzUNogZEW9hNPHUx68+5g6znjl8y9z9+1X2B3n5NsdcR/ausPNOnQi0bcibG0CL2k4wlnL7HzJ0wdntNIzysLC+3Q6Y3p2ymBUYNQWySjE/+pYItMYXWZEssBmBU6GXFwLLOaC+TQ4RxUJcSSQaRaG57FC9ST9LMc9X+OaOY1v2RUFWghmfs14PGAw6uMjhRQR2gcPTH73KjxdYaM1cdHhTQ9fORb2GOcv2ZWC2i6pvcfqmP5ej+HVIf2DAWpRI7IYnfVR0QET8xAnJUmRU/S3cXWLrUuuvvYKV2dzzp+e86t/7yEnJwtO5iWgaT28/8lDHvzN/4yd7X+XL3+5z8EbEn8haWtLZRuiQcXD42d88vwxnbfEIiYV4bf8wJeAZChDVfTxx0/5dfddfvpf/xmoLaIT+M5gu3WosmpP5RWNF7TOIrI+ajhGj7epKyhrg0Kw7CAuRiiV0usXJP0EpTVSaEa9C4aDEcPBFk2zRMSeOM42gVJQ1T0ePnmTcxPhtKXOBCa16HFCkqSUynJ1VDDuak5OznFtRyM8dVWR9hLiXoRVhlY1VHWNMTVV21CaLmxYriZLU2RasDSCWmZgPMtJh1ZD0q2W4saM0bJjeH1MvtXHVhFnyzX1+RlN51k6gdWaYlCwtAQntmn4+OljPtcfsj0Y89HMUKiIWAoaCHBJCNJyuZndXYZ3vOBXbKgFf3weeLlPhECFjRKJzdBC0FgLBKMaSLSSL4jKygqq1rFuHEXhmC8MZ0clTz55n1uvKJLRkMH2Hkp3dG2Ob28RF08pZ6es5xef7abg8wFpsSCq16SpYryVMdjKQcQ0k5hIgxOS3ZvbeKGpK4d1YWeMlCLPNckgxQtNOo0YtCG9Kysk7XzFegUucqRFRHOxppvV+G6ATED1C/Tt28hejyR5QPz9d7mzI1nUCh8LylGMnDtS4ykKwSDRpLFCF5KdKznj7YSobhnhSA40ewcF63XJ5KlndmqJYsHgqkBruHjseXCv4uzMcLhsOPzZPbIrEb3DEbN7Nb7qEFYSb1vKpcVUjmgEegQo8DUYY4hchxQWLTTet3R+gVNbGBHAWkYUNDg8CVJqjOiovKT1Db2owFuFsYooqYAe3qVI5fB2c1XpDlwUylHl8AYsDVaF9KzgoPR0NkKILpSyxC8USCENSlO3DefLCW15RppI0mLI8FDDsqacLHny0WM+vLcgjyWv38rJb91B9VNmqyOmkzOG126wtTXk9AeP+I1vvofXEX+hGFKM+zih8ZGiWa6ZT5dcLCoObhfkkUZ6yeOjmsnJmu3tmqSv2b65jUol6SiC9hlN2bAsa6RRuApMF45QRjqclmTpNnIcqlKXZZweH+OWa8x5yTxqaM87/LJCAg0rYhHzVnyAbJb4xLHzxuvEZyVkKeLqFsnn3yQ+iEifKN58VHK2yFmtVli/ApKAsVZjBvsj+qoiU2vynZg0S4lMjDn5GFXkiFQSD4IKrrMea+ekZkQzW1Kfn7B/96dRSjLza7733o95sj5mYSq8iPG+xfiKdTflt3/nfXq9Pm9+7gqSc7AF3vdwomT54Jzp+2csXU0ikgBrQ1J6R67G5NEhtfuEo+UZ1RPDJ5+UHG5pRj1B6jRdKTHEZDsjDl/bY9V6OtXnxusDdvZeYTh6CduVLMsLIply962bvHbnGr7psHXJo3uPML5DJi0vDa+AaJBMuP3KT/D9T1acueoFWtr4JRP3Qy7sjKHfZ1e9Riod68kZ56ua6dmSYmeXKOsh1BmFkphIEPc74qEiG6UkKqI1jvlsxXoxxZy1rBcLsjzHdTEdltq2LKZn/M5vPKUXxxz0h5RmhZAdmZScv7ui2BqQ3Y7JD/dZHF2hh6Rtl4iyYt1YluuGaFYijSKLFB9970fc2r3G3u41PC2xVyRIbOCJh0VdydD73ziTfQCLhY3Ag9Abo9vmxO8JhudLh7PfbAyXQV1Na4mUQknIM0WcBHy/t4Lp2tFVhqbseP+9Od/6J/8FH/zRr5Pu3ubw5CVE6XjwW/8povhJIrmmO/5lzqq75Ffu8NreL362m8Kya5FYkB7jPXVt0OsO5yS1DWleQkXYTtK0lvm8o7ahdYSH9dLQ6govFXVjaTuHcNBUBm0bUCCsp61a2srR1gQJpo5AR1hvcN2SZrVgelyRJwKXS7q+RKQxvnE449F9jcpTokRSpDH9IqJXKNLMwxBiL0mJEEZTCUPlBDoVKB/Ca8a7UB17zMIwf1qzPVmiooRoGJONBCKWyBZUAqoJlnS5sb8LAaL1IYTbWGzX4ssGV6xxdgXK8qkVJXQUvQ9tt9pZks2Z/vIRXogwMJbBPi+dfKFhFk68CPcWTtJJuxlKbdpKHrwLAUGSSwVSh9+ku4ZoiJKynrJcnNN1NmT4JqEls5gsWJwuWC0a0lgx3M3YfW2L7OoOIs6xSMonK5pVxZSW6XyCMC0Iz2RxTjwswHta09KdG8q1wYgYlSdYrzCdwHlL03Y0dYeKFdZ0uNZR1Z5101FbixOeSAha61l2YVDsOglOk/ZSJDlIgcuykHXRWUxrqNqWrrN03oaSWwT11co1GKeC7jsNaXVSgkoyZJoR5QVpv0dvJ6MSEdk0MKtCnq4k0jmD1JEJG8x+mxOhx9KtapwgBCJl0K1bWuvI6g7Vi9FRilYZHhVaKcTEXRSwKt6hZIqh2fTiHecnp6yWa2Sc4pTECIXVEXmScbYqeTKZYDYtQu/DaVKIjM475vacxrcYZ0nakubiFLZ2UWmKLTsMGivDyT3OE1xZMjubsX/3CrqXIgrN8plFRpCkEuMFOtagBCICkaQkKqUY5iwtzBeOpm65eiNG9wIATwA/mE/5o6N7/ODdf85BfMC1nasUVxVGKpIkI8rGeFJ6u2NkFmOijtlkQdQ6tq5cI8pTiCK8+P+x9mexlmZpeh72rOmf9nzGmCMjcqzKmrqqq6rnmU2RTdlq0hZtwoZg6EI3FiSI1pVhG4Z1YcCCYBsCxBuBsC2Lpk2aljg2yW52N5vVrKFZVZlVlVk5RcZ85j3/05p8sXZkNaALE3AeIHBiOHkicu9/rW+t73vf582QMkOYHFGU5LMC62LKchjmaB+pZlNuv3mP8+eXSO+xQjDfrtlerDh7uuKPn33E9n2Bz3Ne/+oNbBD0USDLgsn+FB8D54sVQ0DoiBCO+ck5Xd2gBRwYifDpsKt4cbAXn5BSIY2fxIuC8MnFIP7kFz/59En76MWveTFs3g2m5YvB9E69FHfmVB8C1vZEvUGUOWI4w5may8sVctFTn50RHBA7pFvz9S9/gfH0PiJc+3SLwrbbomOPVGkKXjeOSI+1gtoJotBkKqNvYLsNLC4tQiuCStegzdIiwpYoJX3r6F1EhEhbJ+u+MAkWlaRpEWslWS6JRhOlwjZb7HbF6ukznry/RqtIPpVwpFDS4HsgCvJphhlXGCMpmppRqRlWgmoc0BJckPRe080NlbZ0RqBGkm3r8TYyOwZ5Ba4JbE57Ns+XaFNQlCXVDFQu0I2gVxJdSKKMyM5j8p39vAMZI8J5fNvhY00Ybwlhk3ASIllWUhPJ43C4YOmCQwmJlOkumfqKMrWJDCA8wSuCThu+8pFgQKRwZ6z0CZ7lJWQuTbYCRLXLcYoB6BHCIJHoCL2/pN6esro6o3eRzFhQYLeOy+dXLM/WdI1n77Di6N6EgzePMKN9lBwRbcZycEmzWrBZrLlYXVBmEUzkbHHK3o3byBjp2pZ2bWm2FkyJDAVNDX0XkSLDBk9nLVkoaFY90TjaAKvGJmGBgRxJEwMLl3Ad0QqiVxTDDBGLJLvNy5Tp7SLepyxtGzxWBDKRwkgigVO74o1QpPdAelxo0r6uC0SUKGUweUVxUDAIUF04IAMiUkqMKRibBhNTbgUeYkzoErt1SfllNLLX1MsUMDOaedR+hikrTDHGRbBBEqJhLIcUGGoURuQAeFIm8PLyjKbeIvMKr3OczHB5RpaNeb6tebC8JAA2JtxySvIq6WLHxl7tjhbgXU938Rjx6hhZDOk2LTZobJQ45dFa0W1bTh/Mef1Xf4qYZdgMlhuPycAbWK46MrkDt2UZUWfkeclsesh2eUW97ZhfRjqnKAaBPAt47/mXZyf8wYN3+cE7v8tv3v1NXr1zTHUnYpViPN1nPN4nH5xRTVPethkPqf1jtBUc3A7osiQqjfeavBxQ+gmxUOR7GikzcpmRjXJEC4O9Ga997XNkbz+imS9x7ZqrzZr16ZLnDxb8/vP3cD/KyOKIN74qqHtP7SPDccHesKBzju7hs13QECAsi/OLT4rCjVwx7yI2eLT+SRhC4EU7KP6puMy0b37SYeIn84QX78t/t17spn9RsCvxxB1wkxduab3bC4KlmGyZ3b3O/ubzXCy+w8nJM6Jz2I1FhI/RErQ0/PrP/wzj/UO23U/+/k+lKNwdbhhIRz5WNJOMwaBEmYLe5uxNdVoww4zhdMy4DYyvWSZHFR6PC45ubgkmJwqBtAW98CAietepzU1gUASK6wWy6NFDhxYDgmipu5r63RMu//gt/vgHT/kvf7zkjRz8kX4TAAEAAElEQVSOrkuOBqD3G/K9iuH+gNfu7TEeVBREjKnZ0z2VjJixQomAqz26DYydpLyVc/O2RIwEP/xuw3zhyG7CtfvgO2jqyPkPOtpzS1jUvPT1kuEUnIWrJ55irEBKdOPRw+RhkCOB9RBDAGcRVYtRglKWWLYUscQLSRNrsujIdtOpHJmuprvwHekFIkZido6MA2Qo0zXUCaJwRFMj3AAhFChH5h2BjqAd2o9BCFKmhkfiEAS0EPRxu2sdSdz2R9jz93FPn6CaBTIKfBs5v1izfbTCbSxSCMqhZHw44eDWy3g7Znt2yfrZCW6zYf/OmHw4ZPnsMRebNXUjsd/Oef0NgMD2Ysn8sia2PYejEhNGPG1WdKuWYVEwGk8pBgO2i3NOHp3SB4seg9uuKLPAaFbSnVlq4DQkp0DfWtzWoltQZY4wBvIBe9MBvQ84kzPOe67sHHs6R6rIsckhCt7vV4igUb0jLE+QW4eMGmmA8yewXKM6w3H+WdTRhtNlwIhEsQ2uo9tuWT7POCwEx6OMA6kZR00RDWpYoYcZohQgGvpVi3MgbUCsW2K/wYsFiycbrlZLzk4u6PolPlg8ls4/BUakbaZjtXxEvZ1DyMlG19h20HnJstmw2pxQby/J5D6Rihg7NvGUn8S4SCBHiYB08PyH3+H2/ZcYjm/iwwrfnNM1q8QIWlyxePaMH77/hN+c/wbDO5psUOIqyZO3P6ZZN+zfrbj72l2OjybcPhzw6PcbNtSo+w3q85LxnsJUYwbTirvtdaxt+fZ736f9xnNevZjwH/z7/3u+/Ms1oXNcPYbyVoHWgkBHJjyijyAkVWbIZRJClAyIa4ETHUF2/NwvfY3edbS2od6uePC9d9hcrLh6cMn66grnAirfZzKeU6kMYa9RHg8wpwEb90irAYKSlPtj8qGmWAsG04x7Nw4xOvDw/Y8YFQLpZPJPLM4RfU0mI7dKg+97VjEQYspRhBcFQKB3ZGrn4092/hAxpAOvjxH9gmwaEp5/hzVKc4mdZGSca6QQhAhdF4k+SWG1jEwGgUkOfqaY2oKzec7JxvP+P/4uV0Eyun7EF/7SX8CfPURGTVld4+L8XU6ffcjVBfxbr7756RWF9cOGZ5cN23VDHEauzzpGwxI1mFCvWpxLEZD3XpGgNM7D04/O6FzijsTOIUyRkLOuxRuJTIIT6ssG4XuMdMxOVgwnhmKYEzKFiEW6XUjPatGyWvasfGTZQ9lEhsuAVJZeW0JmqRtPOdBUlWFy/5BiMEebhniyIV4FYpdOmuWgIJcKLw2tCOxf95gBSRUlEvsoHyYwXlYl5Utz1mNGElkqxrdG9L3DW4sMEp0DMhIsKC2JMZnUYmWIWQGqIEaD3+mQBZooFCGmLr+UCkmOQOMcKBFTBkOsiFIShEP4DCkTcgs/JIgmSU+DBtEiY3q9vNiAKIjo3fd4wW0XnwyaFSQ1S2dp245sMkbYFte0tBct+zdGKC3o+o7B7X2qXLB470MWl1lKgxIalTn6boX1ltY6ihy0lgz2c2Kw+CjAaMb7e8h6i9tAzpCiDhgrqNc9V+eXjMcjBqZI5ri+prNbnIR61dGeL4i+I3dwrDNObLNTaHhc36ZlGSyhX6NFSZZ5ikqiTEa/7xhcHzMtB6BzrA+IWtDISBPBtyL5bJxIZjIiMip0zMgzQX7hyBaO5BpJeq0swkEcM9WRvAiUGLQPSLczH4rdVmEtBwND16c2o6tbYhSYakib1ygtyETBE3/JOrY7B8Vg13aJGFXym7/2K7xy/yWen5yS2WSQcr7mRx+9TbOxZExx8SMkkZtHt/iln/5t+nslHz18zve/8y796TuE4Oij4sKPuVhcMrh8wiQrePLBKavlJRsZOZ4MKAYFi7bnw987AXmNo2rCbH9EUYxwNlBkPbP9IXHree+bj/l/fuu/Yd0uqd7K+Y1Hv8bhm7c4fH0f5wS+1XS1Yn624ea9Gfde3WemNXs3tmxXNbneEF0AF9BC4cshVrYQE7fp9dfucTmb8aP+HfoYCUIzKCtsb+k6z3rTYfSU8d4d8qymmI7pVj1GRqbHB4i6pmm39HTsT+/y9vYt/ujDB/gQEaVCDDXOpRO4iwrnJT0FohgxOdhDT8e4Rce2bVhtV9R1S/COKFKf38iUqMZug+eF8zi8GBzv+j288CfEHaoifqLIEiqZ0Xbxyrs/S0E8TR/RSqCkoMwlBkHw0LaB5xcb6npNUy/Z2jUff+99Tr7/Q8xwD7vd4Jqa5YMPmU0NVZExKCPf/cZ7bFeS+VzCv/P/e6//13c0rwSXF56LS4seNOQtMAmURwV1bWlbR1t7jg5bzLAiGMPyakPTevo+oERAZQElFZrEY1daIDU0iw7ftOBafF3jX5oStUJLi0Imt6oK1NZT9z4FowdYt5HhKslUs6LHVYr1tmc4Aa8NxeEYY2qUb4nLjjCPRCchKvKxImiFl4rORmaHjmwQuLj0CXaVQzZOudJZBgholxa0ISsN5WGJWLXYOiB9KgpBpixaFXcNIimJhUr6WFlCNITEJSVi8C9Eo9ERlQI0MaYAnBdXUEVB3N2qUjNzJ28IOVEl0qoIESHt7u3MCGK9G8jsNiixc1DurqQCkpHOR5z1WBsYjgfodQomcZ1ndnPCYKypV0vMwZDY9yw+eMjJc8Xo8JDJtX2k9vRdjW8aXAiUhQClqaYZIfj0tJuM4WCIk7CxHSoUZFWP2TiWF1s2qw3NpmZ8dMRgMiLWgWa9JgpBve25Ot+gqoAJgj2lERaUSo714D1ETfQpPlJKgzEFeQ7GBNpxT7VXMixLhMwI1mGExCuwQuCdwsjdXGD3oksUCk2uIKsDZu1QxOTeR2AIjBkylJ4icxiRpRhT79NsYPdDCNgblbRtKjTRR4Q26GKAlTVEjQyKs7CkxxJQCAoiDZnSzIoZv/RLP8fNu7e5OJuzVxV4EXCu5sHjd9iuO4TLCbHh6Pp13vzsZ/jtf+Mv03x5yHfeeo9NP+XkByvqxRk2eJZMWG63bFbnzPZusTpbcnU5pxvmvHp7SlYVrKzj3T95Qn58k+r6dSYv7zM+mqQ85dWC8WRAt204+f6SP/jgG5wuTyhkxkvbO8yODzj42RFNEESnCFbTbuHOZ0ZMJhkTqdHFBJM1SNa0JxepMPi0Vj0NQlikCty8eY3BoOLJyUOilgQhkdLs2ibgHBRmQFFOUbKgnIzoBx0qCPb39vGbhq1dUqs1o+qAy9rxvaePgYgZGMw4w/UhtemkAqmwZIisYjiboMqSsPZ01rPZhKSsCx6/s5VpmQJxgvhT7mVeKEhF4pTxYhDw4g/+9EdajTG+IA3sBEupvtC7XUERoLWglALbR5oQWC56Vqst680K6zpWTy+oT88Z3jyiuxKI0LN6+DF7k/tkhaDIO97/4RmLS1jMP+U8hTu/9Bk+c/6MfD2nnc2S+NoY/HDEG0eHRCfYnG5QY4kajlHDPY7fOKTdNPTbjtC1yLwiK3MmBzkxS4NW2TtkNMS6IayWPH33AWcnSz786IwqjpkcZgyORkxGb9CO0mC5ELAREJvI5jSQLyODomYysJzkkA1zVB6YDRxmoJBZgToaIfcM1B7WDn/pESFtk4PhADWDbCrx3qFMAt/FqUINBCoDmYEvBS4zSKVRAvQgQ+USWVl0mYZrpgFvI9F7ZLBI1aGkRIkhyAowsAt3b/AEQClDxOKiw0VPmWUQPT4IjPbgDRFJ0A3CJ1pl1A3C5SA96AC2wOMIosfEcVKySEUfXvQyBYgMR7vzLji6sMVHjxKGwUwyGk7IJyVdaDh45YisUvDEcfnsnPZyS3uywqkD/HS7WxA90XeE2FPmEvY1Xkp8CPggUcKgdUSosLsJlVg7RziHdj22b5lNJxzfPGbv2gRhBCw86/aKPDgyEdFGIoIE2sSEAcZ7QybHU/RgjBpIonO45RaVeQonGccKOba0VwJjI1mVUcoc3SlmSnOwM5uFfEDcdOAt0QdweaJ4ZI5RX7KNObnIkFGjkUQsfdywbK4xKSoOXYbMZyAnSb7aWsJSI7xBHe8zPQz0fcQPx5S37mHdlnpzQv30gvWp5fnTOQGLYIhEEdggiNwY3ePPvfpv8/P/5q/Rd54H3z9BHdTEvMGxZnNR8vT0CY8v3gPgP/0v/nN++Zd+kRuTCkvgt3/mi/yv/md/if/s9z7mH/+X/wfe/vt/g40b0ckBThtst+DaYcZsfETcv0HhBH084Sy0/I3FP+HZPzmk+fhL/Mr/2XK5vWS1ijQnDYe3r7F/5xDxqxOyf1AyWmUcygF/772/R3Za8ZJ7naM9wzoXDA8UdyYDzp4+5Yf/8l2+8Qd/ws/9xVd46fpN7h2+xLPNhr6xXGyueJ7VvHrrFrOy5PlH72FeqShnOV//tS9RzzOa5YZHD55x/f4b3H/tkLLIuHj6jCfvPeHy7IIvf/lzjF+WyCgoizHVK3ewNKn4tlva5orGXzJUGce3xlx7ZUbXg84Nw2nJ8fGQrDTklWE0LgltIHeRUkkmZUUmNSEkgY0mkgtoxQugTLoN+LhTEAl2aNSk8IthdwOISYL6ImgzJuTBJ+6XdJVPbaJBpXbHOcG6izRE8ImrdPtWRRoyTsnkktHoqxy+OUbcesrmg+ssP1rw4I9/TPNKQzZWaN0Qhgv6tWDrzadbFLzJmW8j8dIyOC6o9isikssHW55895zoI8YYbn7tLuvFlsXDOc3zU6yNxCgYzyqyYURnLcuLmmAyZJRkFo72RijriA1c/9znGds1rWtQw2Pa0+fYruPsyZK3Hi55eFazL2AkEsJ900bqLtI34FvLarRlPphjpGWMIOZbBmXPZOYwBwVynGGOJeZYJAOPDahgkLIkWoEoO3TukIWAMiG1hYlEEwkqpBNDb+nrFql0gsqpmHJZQzL4xT7ifUwDyKYn2JZITYwjnDDYKHYGsoDD4UOLQxCDh9BRaEmIjhAd2a4gJG9CgRU9AMpmBJX09ThBLxtEkIk9pVRqQgR2SqPkfHZ0pFNKTMAGJdBKoqSCXiOkR+aCYpLjNh1uA/V5R9cHnJQwzFPWQ25QKseGK6SvCbahbXusDTghcDW0TUTJQGcdMRa0faTpLb211G3PpulZtRa0pywlg9GMdu3Y1DUxFqyippWJyksuiPpFfLlAmRJVDpBDgdCR4B3OJ5+MEx4nPFmQJEiXRAtJIBCEI4MUIRksrgv0zhI7D21Ae0u0DlpLp5fYxuEawYvznAQ0GuFauj4wby31fEMzW5BnCWWc6YDWkLcljRc0nafvWkK1RmYOpXNCrmljx7Zrdjqw9DkXGfvlPRQZf/j47/Afhd9G6kg0l2wuLXYGm9zSrgyudwzHY77y8/8ur9x9mdmwwAv40MNQwLHR/I++fIOT35nwIyPoykeEckxUY7bzCPkAk/UIIxiNDAdDw000z3D8cPlN2sc1X/rw3yPLDVMhYa9gfzhB1o4PLp+QhcCR1ryaKb7dzXnyeMXT728xn7vCZgqnoPYt//Vf+3v88Ptv8/zsPT768T2+/vUv8Ft/PuCJ6DLHmIqXJzlP/uhP+Offf5//13d+n4P929x/4x5//n/y8wg3oG8DXaPwWcRJhw2QjzVHd29TTg4YTob0QhJtWjPttsfHQMgUXQgcXjvmKz/3ZbZ+xY1X7sB4TOsVHdCGyKb3jAcZbDJsBHRG0B0YCWG3xnwaGCS83gsT6M7JHCMyRkLcrbnIrgOQNn7vU9FIN/b4Sc/I+Z+0db2In6gR6zZQaolWAv1CjSSSf6eoDJ31bLvAxaomDgqm1/bZthcUmcVNc8Y3JoiBZ1Vfcvr4ku6soelKXDH+dIuC6C2bVU9z1ZM7UGVBCILN+SXvvXNC8IH96YDjn7pL1/Usz654/u7zxP/Jc4aTAUiB957N6SU9GZnUTHWGOj7CmABKkR1MkUuP2gbUeIS9uqTvPVHnBJUhlKYUKSKvJ2FpnYeeQCeh3ViabUO9FdS5IatbqHvy4KCymIHAVBo5yMDn4EBsBbH3+FpR5Iosj8hcQK6IJiQFj9pJ/3wgdB6nLaZIXRrJzmgmUjB3CDuxT4RoPdE1hLBCyClphCaROEyMKcsgunTqiEneirRE3E7VYnjhXCZqgkzfXASRKFoitcO8TEoHFdh97QvJm9hRGtPtAFxqJIlIpjPyLKMwGmlBmPRgSgVu3eH7QDPviEYitEKXBqJC6aSScn2HtC2+a7B1j3MSJwJ9G7DWg/J4FwkSvE+qoOADznl662hcepGUkmR5jsmSE5qocJ40k1ACpVMvLcRPnsZU7FTarGPwBGfxweG8xXmPsmpHokwLOP2ZI8aI9Q7nwk7ZkWCGykGMPhksQ8A7T28jvY14/G4rSNd+FV7k7gr6ztJ3HV3f0HsSpzBEtBf0IUlpe9vTbzdkUqMHWZLCZsktbaTGhW4nLlFUZkjA8njzIT56tBbIvKdfbmkLRSNANunfPB6P+JVf/02OD/aJQvJ4AycCphqqDF4/qvjsK8e88tmbxO4SFXqUEPjeI5RERQUuQBY5rAq+MjzgbHPGZf8MsZY088hoIslLhRlLyiJjfbXi7Xf/FcH2lAIKAn3oWV2uufh4zrXbE9ReCQjOnyz5/rd+wNvf/y5dOKW/bBgrw5uvXOfO/fsEFbHCsZ/n/PG7H/BHv/uH/P5b/5JJ8RFfODvnq7/+KnuzGyAVUad28+LsiuZyzXiWkZcGoTSu97g+taLyUqKUIgafEDRoZnszXnrlDnV3yXg8Tq1EQChFFMlMK7RCakkg7pLQErHBBJUUgTEpgF40YF9ISF9kKAh+8vNPHMm7pZhAaC++KP06hoT2j58Ui5141UecDZ/wL7UAt3vipQJtBK2LtNZxsVjTdj0iRMLGI/qA0ZK9O2Oi6KiXay4/PsEvHV4M8Crwr/Pxr10UBifPePR4ztmTLTde2qBv7eOiYvF4wR8/XeJt4PV5x2c3HhE80nY8eDhHIpjuDfnVl4/JZhXtpub97y6ZzwOTQcH91w44/vqXMVWE5pz23Y94/r0HPPvBCaNXe5qsRR0Oufv1L/GrdcM0z3j/wYIIFApKA00EpUSC4sWI6xy+sVgH23VNDB3qxGJPW8ojg7xdoF65g5geIMoh8vIR4rxBzT1hLRFaIHIBJXgt8TLgZUC59C56Fwg+okIg5qB8QFUBdGQnP0dYCBaEi3g7x9qHiGxIicaLSBsX6GgScFQECGtUyCjjBOU2qBSZhlQ6uWFiBO0QURLxRDVH+wxEBionQyHokbJFxgyJIGBwUiBodgokiY9z0qyhYDLY42AyppnlyOUc00dUsPh1Q2cDfe3Ynq0o7kzJCgXK0PUKYwRKeMJ8Rb9d45oWe9lhpcIbSYwd+AakRkaQ0aFCQPuYbi0Bgg+poPeaaBUyB2NyjMzQAcra0tpAIySD4LgKARsS9tvXW8J6BbUl6kDoLL51uH6LbXrabSAER+wblA/4KIh1R9u2LIKjaRy2S8Ux2gQhowJkIMqQVFt2QGMtG++xNC+WLA5HTsW0GHJtVuJRdF2kqwNRCoISBB1xWY0TKfoU2eGaOdlkQr43Zi/mLPY98qhhqCtssLShw8eemgukUChZIqRBl4pyr2T5eIldRUIXOGpWjBGMru/zV//9X0apnA+X8Lc/jNybCq5Gkedjz68Xit/47a9z+EbD3/2rf5vqiz2DmxEtO1Rs8Y3FNTmLYc+b4xlvvv4L/OH3/i4rH/Axvf75C9HD4QphPB8+ep//5K/9bymBqRA8jjUlhu7iktP33+GzX73NqFK0zvOD/+YZF2cf04VThFC0bsGD99/ld/9O5H/+H3+GRddyvr3kQN/iD999i7/z9jeAyKqdc3bxlI+/+wHX/8xd1EFJHPdMTM4//Xu/xz/7v/8TfuN//OtMX8+o9gse/ugxoe4YjAruffElBuOMrtmyWc3JhgOOjw546dZ1nj5dkrUStYoU1y2zcUHcaC6envLGm6+QiZSiqKNnYASuytBak5sXc7gd+2pHNE1HqJ98vNB92cAnfiIi6N2ZzvqAViJ5k0LAxMRGcgSkUsnf5EHZhMxRpHS2GNKNQgnwIVDXlvlFzcmDRywvn7PdnOOY0887tIm88bPHvP3t91h8dMnixwvsqgcWCHny6RYFhpKiEFQaauu5fLZIZ86h4Rde2UdKyXRccbbYUI7H7N99jes3T5FCMN4bMbi9hy5GBLWB0iBbhxlqqlFOeO+bxLu3ULfuUnz+gOrdLZW6IB9nZGON2K/obcfTB3MePl7zJMAUQRkFhUz8ESXFTuOfI8wAORhRHAxQVcR3Gzb1mvkC8q1jdNKwf3lGfmdLdqNAVD3iIKCHkqoSSKMRRhLzDIfEu4DrHd67F/J/lBZ4oVEhnWgVMqEmBhGtNaLPCH1J8DlBV0Q5hKixAhxxN1AWQILWZXKEEoagFG6XIicBi0OKJCn1LqBkjogS4QqEdiB6fAgolYE0ybeAw4ueKAJKqN0hRSIwaHGAoMXHDVKPqKYTZjcmbPqOEFp8cMjRCNs6ghRUsaBuG4JLbBynFO1GolTPZt3jGoftPJ0gzWJ0wIp+x47XqEJT5EOKTpCVPcJV6GpIPrAMxIboEzBRhAItWjKdUZiKWA3AbZBW4YJn5R1X3iIAIxVGKpCe6DyEgFAKGdLCUsFiVwHnHcFYpBUYpXFGo0QyK8miJMociyN4R3ABrTI6UbCl5Xy8xB1Y9LlCMsJREEnS0XdCC4OSo+uaR/2S+gqmTUvROkrh0V4DFY8d9FKio6K0LaOlYigij9YDntVXXIkLxjdKuKyoa8vanVKNJUVWMvI3ma8fstoGnj1/xOmDS8xhhtkXqL0tb3zmJsXgGKMyGpcORK9cE/zsLA3RL3zgwjqGg/u8cuQ53/5NOpfaHL1fUpoKHRP1t7vM2daadZXhd+mARmnuv3QLuecRI89L1w5RAjrbgYCfOrxP2295sjzhWB1g6sjqas3evQnTgymryw2Ls49wfYNEksuK2zPFJLes1+dE6dmfVVRlznv/9BkjZ/jNX/kqf/V/95/wH/2H/0uefnzOd39/zZtf7pkKTdY1fPDfPuJb33iLf/je7/Ot//wH/A/+wr/Jz3ztqwzvGzoZ6SNcvH+GqCGfBKa3c6JXaJERQ0kME6xQdCpiqjHl8ZiBaHDS0QtFEwW9zsn2BggtCS4Q8zGyKJCZItpk4ksn95jEDiF+ksyY2sfQOz4xtAUh6FzEh5S8Fna3foGg2/kaUphO+ASvXQwNRiesfdsF2k1qXYY8EnMHrseIlhAty+0Vi9UZB9dHxCoSabg6v2B5eUHrtpgbBru1KYMh+E+3KMQ8oxpoZiNFPjXoQYaUmsmtGYP9AqU11XiEuXFEMZyQ5xW3X72BEJJqPEBXFVLl6MwxPpxgBoHJsCQ/mOxk2Ra6BoIkG5cMbs7Ir03ZdlfYusadXeFCBKVQQhKEIOxA5C/yAmIQOCfxVhGcgWiQgwGqjKgqYPvE/+lFpNkG4rwhZD3mWiRIRygcal+gM4nQGkyBihpvPap1uN7uKnty/gqVTu0hSmLcXc0UyFwRhUJGhYz6J60PodMXoEDkSX0UQYldQKZII2Av5Sdtj/jJ2+QTEJCQhlBCgjC7W+0LdYMkivR9Xny/9JxlpAaS3n2OBBoyOcSUU6rplFpfEa1KQD2lgKTC0qWEpk8O7d4RjMM7h/fyJ1I8BfHFP0kErLOE2BPxIHKkSu2mdA1P1+xUDhMaQCRuMDozmDwjLwuyzKB0ut673tP7sEuvA5VLVCGJcvf4So80EmUERkkyoA8eek9oA23XpfkJAi0kpcnJTAEyJ0aHdwK6nv6ioVl1bOqOjdjQRIsVO2t9zHeWw5pVjCxDYOk9i1YwGEpGOiefZmS5RGqFpaB1DXVviX2HGo6xWmKFIMqAMY4iS7e3BDxUSFmgswFCKbp2zo++8xbj0ZBubYm2RgbQGKK07E1GVOM9pJA83cKlhVGeZpA6whhB4yJaVFTZPkVZ7g4WHSJElFIpLJ4UQyujQRWTnSjBEaMl1gqx59EGKi05f/6czXLBy3fucm0443wR6Bcxyaq9QzmPkRL6DltvaNeXqWgjiNFhTEGW52R5hlUNw8GM3OR86wff4vDmMS/dvc9Xv/Z1xuMhH7VPefr4im5piUNQQfHhDx7x9MkJl82c5ZMNjx+e88rthrtfGaCVZH0254//5feo646b92Z8pXoJM5QYExmORxxxhCgNRaXwfY+QkmJQUtw9Sk7imIbPxaCk7j1IlboCu2ly2K3P8KI/tJsNiE8GyX/q825JhhB3v//iz/7UnrpbAy86TC8+hErt0viiZSXFJ2E+3jls39HVW+p6jlc9ooCmW4GpCb5mdXVFkB2y8klqrUlijfgpD5pDUTGdZYytYXhvyPDGISKrMNMpfb1CSkM22ufwM58nioy+dXxefA4RBUrnaDXAe4lWmluvXkfogqIsKGcjZD5JkK6nH+O3knxPMfvaXfRLtzn99nPmzy7JuwTGmh6MOFSKqAVGS6RUuOgRO+df00C/EdhVyqIvjodkg4xRlZF7CcEiXM+mz+i3lvx5x6AKhMJBFmAqUaVM4R5qiKYgeo/vOtzappOCgKg8RE8MqZeuI6i4G0jmCRSmvMRYBTFgY48SGUIYlEiwPxs7PB4jQIVudx2VaRPaoXdVVLhI2spV3KUxJZ+9oErvjUzbvBAKhCaSp4UoUinRVIDAInD0xJgBIzKhyMs1g9mCK/UIekP0uzxoB8Inv4bqU16b9z1ROogeISI6SzGF0Uhkn67VnoDtO5zf4MMAqFKsT4zIANE7Yt8T2o462DQIzhTCRHRVkI0qymnJONNsNATp6NaW3nr87oSlRgI90Uk3rGTyYeSKzEvyTlEKg9ANqvaES8d8s2JYluRAhmSiS4Z6gBQDQhQEVxO2ju2Pz1m1F8z9iuUsZ7muWfbzXfGqENES2NCjWNWe5+ctY4bcuD5hdHiDGzMwMiGu141APu9oVg2rzZKDO3eQ4wFiVDAWghAlbiPo15Gub7F4lBoi1R693/L88gP+wV//u7zx+pu8+sbn2Kt6TDlAmQxvA5OiohiOAMH3LiKbEHn5UPDAwkzDNSO42iZ5KMJw+8YBRnn6Zk3lFNpYlExJhjG2CJVRDiapXUFP77acv98yLhxV6XDNire+833OHj7hz/78L8LiOdu4Qj4RLP2KKHpGSiI2lo1dMT85oVvNCT5lCjRhg1X7ZNWM/YPrbM2S4+E9Bu4a3/v2H/AL/4v/Pj/13/tFcj1CCEnbNZycPKE974hjhaoGvP393+fk+QmpbDkePj3n/Y9P+c2bM3ylOPngff7af/pf8FH/nF/+ma9ws/yfcvSVSDHIuXb3mFvDIXXd0nc9zfKc2HqG5YBbX7jJ+uEaFQPVsGA0HrHdOmJY0zQNzqWZn5eS4NgJOCJ+t8nLKFJLd1cohIyf1IywuwGIXZazCPIF4ugTuXiIu8z0nXzVR3AxtYu0kuhRIiYrEajrns16zdX5BecXD2AkGEwqzj74EYNKEGzH8w+foq8JigLapkMUgMghDD/dovC933ufxfM57arhjv+Q26/XZNWA508c7318Qu8Cg8GQP/fv7NH1lrPnF9C3SJHUH/MPznHe4n2H7S7Jyirhiy9X6PsFauXwyzXbC4+4NaZ44wZMNPXVgidvPeCj7zzi2vVDui5weDSg7QVOCnolKFVER5HsRVrStYL1leP8gw3SF8i9jDwOUHslKnMo1eKsI/SO3lnax1u63hFCQJaR8Z4gHxvy/QYzvoU0JcqMMMWa4ALBpY3Yu45oPaHxhCYiHOmBEOkEKJVBuy34FS4siXGNFxUhZugo0JiEw/UdWQAjAkZ0QIaPgZZIRYH3HT44tFZsRQ9IFBWEGiE0SpUEahQKBdjYEoWCqMg+gfSmUzIYfGzxYY2QGUYGCmUIVhJ8IEZPISTN2mJbi6oifRQEKRFKY4MGUWGyMbraoDswMbKwjvUWOiJdFem9QQdNT6QLgobAVjo2oWPRN1y1DUvnqaWjxWPrgO1bbHB4rWnHin7tcbalL1rKLLCvJc96j8gGUA4QJcSmx/UN7XaDcwovJKGCoPbp/ZLt4oxFtyW3PQpBHTyyLin6IcNiQDwKUPRQCOT4kNUPTqmfLngSAk9Oex6cz5OMV2lizBBhShA1q15wuin45Z+d8tlf+RKf+/LXyLIJwtfE0LJPh7z9Q55/9JT339L0omTTRkKsCYMZy7MhT08HPJk/xXq3OykK3nvwB8wGB3zx9q/w8ut3uPHKEbOXBty+9vP0QtB1Pc3lmh+99a9oteU/BH75pqAL6SL6sAYVoSqhqlLgldORz752j9hJzp/X3BgfEL2HCN44nJOYmwPK+wdc+ycG30sIgY+ePYFmg/7BhqOs5R/8zu+xbpbcfnVKt7DYLVRC0yG5dvc2b37tSwgk7VZQN5q4v080aRIjUBilGEwHHN6/hrF7dJeBbnXBx4sNv9BmjNyAZb1Cxxn3rn+Wv/xbf5GDexlmYhGhYPyq5+XlCDW/xUnY4Po1J4tHnD29JJvXzM/mvNc/ZRO2/PijD/hbf/Pv81de+m3UbI/J7ZLM7CPnc7armm4lcbpGZh5t9rEyILIho/GEbDzEXa5Z2o6L08BybWlsTPYgJwgOfFQgEl7Ehkiy5aQbgQ8RL1K7KAXFyZTdHOUL8D0iQNg5lSRJqSe8IAZJEyRCSISSkMOwlIgd8+j0vOXDs1Pee/AuD588QY1Koo6cvP2EPAtI4em7DhkVjoBXkfFnM+xC0J11n25RmEw8hRX4UjPY0wTrsbUjyyXHs1FiGZmMbrOlsw7f2lQUMoPKcoaTCts12M4Su4CILrFLyhz6LcIo9F6FOFnQXizp2gW1bLh8esXVVcvJNnLVKvogEDbgPRAVWiiqKmOgFZVSzDSUOkHG2tpRX3aIPiC2LWrtMaUnrxxkMalWfKDbOOqlp6sDvYvYY8HwEIaxQ5gGXWhkViBNgRAOKQJBgTKKoCWhu0pO4xcNQilA7HrcWUQqhxQ9RrzYoJOeRQeJiAoVFRkeHXtUbCEO06MSBdAjQ59eryiQwgESQ0eIdqd6CAgZEaSvUwRCTAA3uRO0AoSY4jjFrqwIsoSOloosWqJ0KB0xKIyKBOHB74b2jSVuLV5aYuuRfaSKmigzrMxQrsb3ES8FWmtUYdCFxgeNNMmkmLKiHTJYVLBpEflAsBbbrOlqh+t6QnAI79INzTvAo3ZBJADCO6RLhTf6gO96uqYmuqSYil4hK0mJZuA0LgRWMQ2WuxioO0svJNnBFIYlHEygP0KMCkZxyyRr4MyyWnrmK0uaAnW729sup9mV2N4w2ztgND2kGB8i1YDoBESDkjNmt2tcyFjOLaOjI4yJaNmjqjHXb5VIPeDPfPUXwChMqZnsFSw2PZkxXD/c46ASmL6hP1+S3zpAmbR5qK6g3a5YmSRPLgwQoA1QiaSAWtnIREGuNcOq4vj+DaTdUkjIK4WKu/8do1EqRd+KQnJNK2qpcFJyYz9iRzmUgmlVMhns4V0Eq7C9wzoHIjKUOeNBoibnVU4IkJcVfhWILpBJwyzbg7Zne7Xi4uEVNBkx1yid8fWf/zkqm/P4Wx/wjx/9Dn7lORxMyZgzfzYjtJKykDx/fMLZ4opl6Lg9POTO4T439ke0iw2hb1EicHe0z1krqHKJK7Y4FfG2oW1qtueW3jhEJjm8uYeUDdHXhG6DIpJnmsG4Is8zlJREF1mslrS9TesJQfTg/U5AALtmbsSLF9LlVCh+MoF+YRp9wTJi10Ld3SZ2kDsV2E2vBYNMJbAlAuEEziaUfgiett+mQ0oeQVVsL5bYZoWQGcguud47cE8dlJAd59yYKjbA+Zn9dIvCjbuabGhQDbQvD+lXiuBhvF+yN6ywQbCNgr5tcS6ipML3HcpIikJydO+AfruiXUVELQk6UBSS4d4I0a+Q4wn6eB/9YE17fsrFhxecXp3y7MEVlwvPqg48u7hCCMm+ybCk1neJYVQU7JUF08wwVgk7a2TCODTzjrjt8RdbZCnJhlBOIvm+RhuHFJF6E1idBbbzyPIywmXE3hBQWPRgC2RInTIRpJQp9UwbkIKYaYJTSRcbXzwEySWJ0YgCpPFILJnY9fRioI0SExUqGnTMyXDIaJFxA0GDSMgLYo+KDhEcMfiUR/1iSiBSHGcSTYKkR+BIcF+9s9bvrqm8kLV6iAIlSoQYoFSJ1hm56InKo3TAGE2eR+gd1jtC3eI2ln7l0EUHTY9sHaNgcOS0wqJ9enidFmTakFUZpsoITqMzgdLpJRHRoqNNaHEiwgVi39PVc/qtoW86vG2RNoWp+BiQ0aOJGLmbkfQtou8gKEKfRAB930Hb4TpBsBplBCWGoc1xRFYhjQcd0ESPzTXm+gHik3u6RBRjJsMGN7MU/5/HuM7SO001yEEk4BwCYlAUeUGuxuwdXicvp3ifJSmhtwg8ysyoju4wixnXlzXD/UPA4vyWTI6Y7Wfcfukm5WWBnpQM9kruvjrm/UcLGrdEDJ/T/skzwmrBcinp799A72nyLKI7BW5Lr1OKXmoTQh9grBJjZ95HRmUkNwY1HHL02g3CySmm6yiHKkmQhUAYiZYKmSUC7528YlW3bJTk9duabjrCV4q9mebld55SPH5C21zQW4sNjiBgrAuGZU41Nml+qAuG6wZ/2YH15DLjenkDv/2Y5fMrnnNGbAyMM0xZ8mf/wr/B4tkpb/+j7/DX//HfYCzucjAZsll+yLP3RnTHisMjzcN3HvHg9BmXdsmXBq/x6vExd45n9IsNUfdkmeDz+7f5YBk5mJZU1wMuD/h2yepsyfk355QvT5ncO+DW/UPCxtIsL+g3T8hknnxJcYgqMjJl0EEyXy3o+n7HCxMpDc3vXvMoECIph4Tcrf8Q8c6nhYdESJH6+UKRQrDEJ9GdQqSiEAjIkNpHUkiGpaLpknNb9GBFWushWLp2BbKnGOdk5YT1k6dszp8xuDnB5KtER114mscNel8zeKXgpetwvnKso/t0i4IIIx48rVmdOb725h6rzuK8ohrt88Hj9/EhMjrYB61Q2mOc4/F3T5iMSsT1GfnX7qGMINhAt9yQ7ZdI06OGHeJyjhwcYe69yeQ3pvgPHtD8WPG9P/qA9097ui7y01PDP7p0LF1EC8+eyXaV25GvLcJn2BwcLcWsYjSueOm1faJcQ6iRnaBVnjY41hc9+SagjEBkmj7PsANLyD2D/cjWQd8IVo8kvQoMJg2DyZJylCEzhcwyhN4jxjVC9ajZPqHf4HuLbRzxPOCFwxoH+YheB3rm6FgTxIwoSnzUSKl2t4s8tZXCBBHqnWFGI6JKCWvREfEI4TGx3uU0WKJKagYvIkaUhKgJWIgVQpZpfhEjHpuGy1ERRI4QQxT7SSFhcsRgwvr6d/FXzxIYTzl67bHSE/qevT2wBlrr0bNIlSd57mB6jcV8Tt0r1oOG+bpl20nEAlR+DaNn1E2HDR0xJFVWfT5neblls7RMEOztOcaTLXHZ4psC367x7QnOrClGjqOoca3EngeWOwd65wLbtqdeLWkXHbrc59bX3uTRD/4V25MLVusV145v8K0H7/JH333EJkY0kqHIeU0f8Cv/wz/PT/3azyKG+8R2iV2eYudnFLdfQR1+huHwPnfbb/JqnhExLFYZ6/6ScpRz4/Z1np2eoHLFYFxwI694evWcH//DH3L59hy5WVJmgdufP2By92VQBdnkkMV6Qb9Z0a0WLHvobXpX/MsFA3nK1jb88R9sOH28obWemAlmvWA7n3N19j7vfP87fPm3vsRnfv515BcF9//VbQ77Ahc9MgiGQrBfCHqXipeSEUGHwCN0R3EkuJpHtj2URUGnZjjRsPErZlmFHGniKPIbX/yLXL3zR3x7+5BBWXHrjevkN8ZkVc4Xt1fEb/f8w7/1TfrOsRGRoDN8WSGPh+T3JgyvTUBG+tWWm6M5hfac1xveWX6fSiq+fOdVfvZXf42bNw/IBorO1jw5f87f/wf/iA8+/Iif+vrPsxie4/qG955WlLcjE1piuGJmOkoVsCHwvfkJ3TuCpttw494hNq/Y2J6L/Y6j45LR2LC1Ld51uDPL4q0L/uO/9n/kt37hZ/mNX/4Fjn71N/mbf/tv8vTjR3zhz36BV78yxPgB7nREdnTIqC6ZrQ322w8ojGavzLBB8CwkyenRAK7aiCXh6W+NMzpvWfaej7Zr8JJMaG6PZtQofJBkQqJ1KiytjbRNpA8BF6AUjtwolE58pUKlGd2oFNRRcHZV8+6HT3j+/ITVZkW96dHdBXc//xpm8CW8f8hi/YAQDXe/eJ23/m8f0q96wgc1k4MZXmfsjz7lm4LQQ4bZAlN1DKYlIUisVWQ5DLTABsi0QCuL1AojCiodqSpFNcoRRmJGFXnXkuEwmUIXBlFpVHEbOSwIzQmh9VCUqL0JuTRcesG8j9xoI0dGUSpBE6EJHh0jOgToLVJ3iYLqG3rhaELLauqpjiUmC0gZUVKkyESZo41GmYDMPLEQSbcdLK73yDqAlLho2GwgRI/rO9ymQ5cCXSrkyCc9vuhQGQgxhCxJY6PeMXlkidICK3psqHcZrIIgJEhFH2WSp0qNiEk+KoVJbR6RTnK84Cgh0sRAOHaeXALJpyBEDmikSCqjXmgUBoHECr+TqZIULiI5FrpIItSKjKhKfKFwRhJ6ScgNojBIq1GlQhmPDBEnPdJb8DYZwbzA9oG2drRzz+XS0SLZH3lGsxF7e3tUhScfSMbbDYOLgvMPKh6KOU+tZxkC26sOu+gZHg8xxiCdoK9brp7N2bQNrRP0C8lJA09cIBCZ3bzL0b3XKaaHNKuOumnZPD2ljwJVjhnMBowm+3zxF7/I3uEBr37wMtmNIdkwp1CKo+tT6uaEj/7gn9F3ns3Vms3FCnO9oTweQWWolzmjG/sMxwOODjLmF8k4NFOKJ1dL6tayCoK56NlebannW7rFhqxdksueRX/CjY2lGAx2MlCN62usbfCNguDREspWYtDEzmCWOVl9Cd4hYkE5g7wqqIYZb7/1Ho/efchwkHHzs3vc+eIt1uuM99/6Pk/XCpRmb5KzvDzDlDCYZmyuLoiiJsSaxbMFwoExBts0+KbDtpZmBeW0x1Q9UkT27l2nPBkRNxHftPTbFbL2mHzG8sElm4dLCnJG1ypQlvllwo1v1ltOTk74TPQoVBJsFZ5MKXKRcsWVlGgtyQqNjyBNRq4k9ZXjbHnBo9UTBs8OGLwUGA5KDu8fcu3GEQeHM6bTnBt3bvKsmXOynbPuFyy7GavGUs+3RDlnOz+hkJHR2LB3MOTG9SPyYUAVPYVuafuW88tznj17xPr0IT98+i7vvv8RqwEcv/p5tA9cPbpk//oeo8MRt165wd7eMV3QnK56iqpAZ8lQtnLQ+5TH7FvPE9chVMqPfnVWEIMkBEXrXDKZRsHKQuxSh9khyLWkFEldVGE+EZe0beRqkfw1s5libyw4mBW8fn8fM1qzaQfU9QHTPUXXbOmbFetnW3QvsF6y2KwJPqQD59bjo0JoRVbkn25R8KKkyDPKSmMyQ56H5OyLnsIodEjXexksRgpMpslkIM80WWFwvcUUGpVlRGeTRFFJggCfjcA6wtlT7HJEEAoxHjDKDZ0QXPjISRcYKo2SIvX9iYgYkj84OHSwmADRps279YrlqSWbDJMRbHd1klpgCoUyyZmrtMQX6WSlkEjnEdIljaXM6DsBMQHX2PaYSqAHEu1t0hzLQD6MKaRFZUhjiKZHBIWIGRoBwuNjn/qOQnziG+iFSBkIO6mbiDvn7i5mRZASm3zcheukJnBSMUSHlzoVgx1xNbluJS8gvBDxMewiYl50N5PVficYTfZ8IkF5vAxECUGL5GLO03ukJDidgoNCY/GVTZ4NH7G9p28t3cqy3nqsitwUgdFkyOzalNEoko+GtPWaaqx5dDgi5oq5D9RE2jrgtunvJZdELVLbb23pXMTKjL73tFLSFYpxiBzeus3enbuIvKKzkc2mplmsKaoMM8xROg3sbr9+m5u3r/P5my+hX5ki9gx91vP8/Q+Zz5+x+fh9nBiyvOy4PG1Qh0umr86ojkZsrjTH1yrKYspsllG4BoKjQCKanuZ8zXre4sOcduHptx5ZWAq7Rbmas2VDzCrG0yFVqfBqnxAtITiClztfjaC0GoLBdykGo4gCGSMEST4U5CqDieEb39rw6IMnGKW49pUDrr9+jeFc8NE77/BoXRJNzuFBwcWTjyhGgvG1nLOPHuHigihqsnafa6MB5TBJD5yP+D7Sb6DJe3zVY5yjvJaKp4xg25q+yRB1wGSS5+894fzDM7RXHO4PcV3NYyAzObZ3LBfLNA/atflCLtFSkokkLTZqFy0pBTYIhMySN2QNq27LZXvJx0+fcf/wgL1JzrXjGcfHBxzuz5iMC+599lWeLM94dPKYNrRYDdZorA30/Zx2M8fESFFqhsOS2WSMzgNRW9QOD7Ntaq4WV2wvTnk4f8I7Jx/TfU/zZ5d30X3P0/c/ZPBTr5EPMg5vzjg+vIaLOZdry35ekJnUOqq73YIKEd8HrvqGvLCMSs9LkwoXFY2TfDS35MKASO0926fRAQoqnQpDpiNl1NgQsYm4T92mn2fjyIEWjIuMopwSxke0fYKMzg6GnDx8n4snWzbbDhUVthcsLtcQA1olLJrKMnRuyEvBv87Hv3ZROFtLlMzIioyzhy3Og3WRTX2F04nIadtAe9liVcIIPL2okYMRo23Pkz/5MbPrM/pNy4cPNtyZ7iOzlsv+lNX3vk8mNcPRGPPq67hphlaGV28Yrj+XnM7h+1vPREdKpRjmhqFSGCHQUjIbCAYjRVkoylVK3gpNpDnt2GZrrAqwrnFjjR4riqlCjnOU98gQaMuUvqaLnHKUoYcdRAgqQxlARKzrafqWrpfIjURuBEFrpJHkraKcCEwpycrkx4gqKX5yD1ZposqQNCkNS/hPqIiQHqzkpLSI2CCF2pnkJFEaZAhEEelJoetaZAhZ4KMl4AmiwYcdzhqFwaSWZgzIsCHGTVI5yIqcIZqIjhYVI8HNse1z4uqSUC8I7YouWJp2he9ajOloti3NScfyocepDfs2Jy8qelXi4gbvtvS2RwdPOZTcf1mwd7zP5HifYLeo4jqVO6bcO+b617/J/kcnTD6+pAiC6vqEMCl5+qPnbKo9WiLq2i3u/pTmaj7narVkfF1wxxmMNfRtwRe+/hLlHXj4/g/54JuPcdIzeGXAa5/9KqurOU8/eMA//a+/QXVtj+poQn7ccPnhB/gPJPu3j3j24JLLR885+fGHvPpLr7FqVjw/PwU34VAcMrnaQzZTuv4QkR0xOTxmdnSIFAIhFb90/SYfP3jM++9+RN98TL1e0Xct1WSAXUO3kGw/aplvW4KpUNk+FgkopKhgWCI1GC0wRQZeIq1Cqh6jZthNx7r1TNoxYRAJVYto4A//5B3+2Xsf8Rt/5be4ebdmPql59w8+5OCNV1GlYDtfQVA0G0v78RUPv/uY1fyMrllxPDnijX/rZ3j9c/cpshHd5QVXz654b/GY9UWDcjVarMnKK6q8ZU9qFvaSTO8hg+Tqe2/xO//s7/Gjdx4wLYb83Bd+Gp1veCd47sxucffmTW7eOST0PTGkDBUx2seqpIIpZcZYaQqhcVbhbYHzBiEVfT8ieI21lifPz9i7eZ3j0Zj920Pu3Jixf+0aw/19/sr/5j+g+D8VLH/8IZdG8NLnXuKlL3+Ww8+/zvkjj5tfslhvyeyYZR15fnLF5mzN9qzl5NziYmQwmjCbHRM3Q3CKpqv5+OwDwvJXePjwOX/jb/0u/+7nXmZ66wBdlvz0T3+BWTmjvfKwFxllgpEBr9MJq7WRZROo64T+tr3loulYOMnKSda+YipKKgl3xoIQEuy4DyTDa4xIF9MBKAq8gKqQfP4VTZ5BladD8dxFGhf44rXbVEqgBZy+fMQfl/BW7OjWD/GbU7qrnu7KMRkr8kIyONa89MoR80GgW9WfblE4LiVqVqEGEbMLS/cqEqxHz4ZIk6HUACUDWZWhqoyXXzlidn3G4HBA7DukkhSjnHtvXGP//nXyYUHmHXtv3EHnkmyocZmk2S64vLzgvYdLYm05VFBJWO8k/KWRDAtFqRWlkgwHGcOBYVBoSm8gi0gVqcaaWDvq3tEvHGLrMRuJrxVm46HzxN4jxwoxFMihSqC4GFBaIkpBXkqkSaTP3It0dFeBiEv/GASxl7jGp2SeLiCkRyiD0AVZPqWSI5SsMLtbg4iWOibUtYqgCcjYp4IQ1kQyopAIkSN9SvWCQKZkCvIhIKMjYtONJiR9jMRghMEIR4iREHt6f4KrH+HtGmsDMdOgUquJrCDaFredk7kNnjZ9z7ZFNi00XXqC24BrI3UPWiUDnc4CstQUQ0W+FlSFoMoE5dhw87N7ZFVFDA63eY5vWoKowGkOj2/wldeuM7OW6Z193vz8SwxHUz7+wfuc+R49GrF3c8bsizfoe0vb9Uid+ExGgK03vPTafSYHN3BWc/3Olqbb4rzH9ivq7YLVconPBOvtnPr5kmEeePLuU7qup18c4LqOoqg5vJexd63A+i2h8OhMIDPIKsXs+JDDW3vMDkcYo4k4QvBYZ1ktLmm3NcJrTKzQNuLbDF1k+DrdKF0jYSsROYiBRcQUASolGKFRzqNUAOtR0iK8J2szdBCUSlGWBc3ZnEYGggxMzQQpN1xua773O9+iuLePz3NEkW51MjNUSiJVhlIObSzh/j361YzYbtk/nnD93ueYXnsZKZJR0GKYPXc0ZycEKxHrnpBHZoXklhEsVycMt9fACM7feszVas3c1mxD4J9/+xGX2zlntuWbT95i+O6QW8fX4JdkCrGPgu26o3ceG9MAvvCK4AuGHKBNipkNAvbHFUWmk0sn9Mhth2o6MuPQQSIDBByD4YSgDevQs18ccOvggJduX+PmS9cxfs7lsxMuzy85uqYRgwLfZ/SbFf1yi1suKRAMBgXVZEAYJMPtpCp5/c5NtAzUtuOi6/noB485XPUMDqbceGXG0bFmWlmUC/RBpkKwtLjYY/LAYBD43HCEjZrGb3i+eIbOh+xnI14fVHw891y1HQ2GsVZIIIuC2MWdKS1SlQKhkqrpeZ1O+TEIahe5aj19dDgsiyqwcskztGxOabslRliKAQSfo0YjuDvjqHAMh4bxwYj7R3s8XW84N5tPtygMqwIVB6he4WKOlqBEIFORvDSoPEeZHF/XSBUxuWC2X1BUCkQgtBa77VBaMD2aUIyHKKVwjaM8mKDyiMwstu3YrpYsTq84XXYIH5goGGmBT/FDaC2pCs0g01RGUxU6bUgKKiORSqF0pByadLWMyYglbEwJT72HGmLrCX1AmYDKFMqCaB1KglaJp5QZicgi0kC2y0h+4WyMOvVyERBcxCMQIc0vhAEpFYgCJSsyWSFFSlOQeAIeEdMDIl8ogwg7otrOoxxfoLd+4lgWux/EgNxlJkQkiiRF1fhdzrMnxJa+v6TbPqNvrqjbGm8CShsynfKIhQv4tkPRoWUg6nRyUTIm2F+mET5DlxFVpVmQGWr0UBMLiS5TLKkpJHkmGQw0s1tjdCbBd/j2iuha0BNQIybjPe7eOqJ0jttfeomjG4cIl9NZWG4aSpmzH3P2jm5isuSaR+UoHYmhZ/nsEdVwQlEMCZlhejhGLT3LZoXvGlzb4vuebJixbTfU6x7lNX3X07ctm/kCZQTawORgRGbyNPCXoI1KEDQjGUwLshyUSuTN3nZYZ2ltz2a1ot3WhN6igsGIREbMREYULV45inxAmQ8psoosyyBkO9eyTC7vHb5ExpTgkACFgrwoyPKM3Ay4erJNfWuvOL59jf3YsF5c8MEPHnCoNeW1A4YH4yR8kIZMZqADRnky7YnHDkZDRFcjM4uShuCg3TaJ42NKytmEWG/wCqIyUGnGo5Lj0YDO1bTNlqgEl+dzRmXBwf4UoXJCblAuZzYcUvdbur4lpmDgdFBC4O2LwwlYIp6UKyJNtYu8TA3NKtcotSMTxB7pAjoIMqOQQaTM7WbDcDyhI7LwlsNsxKgomBQZg1HBaDQizwsWmy2lNoyqimoyQqEJDvrOYkQKrkEKepvaybnS7FUDIuBiWsPzqyVoQ91FXnrzOsOJoioTeNGTzGZEjwstKnq0joxzw6rtmDdrnjx6QlEOGVcTJlNDxoSBKcgjlCalpMeYAnUgOZWrQqQhtICFA6OS58lFgSYJ5ISS+OBY9VuW2zlX5x9xfvKE9fwc53sIkGeGw9t7HA8840HOZH9KJktwDa7706SmT6Eo6OkxmSmQdUPfjwnREqUF5RFOITXI3NO3Pc5bmn4LpqdeXbKdXyI2nvp8jSlzBkcT+i24pmP7dMW1L86QXU24uOSyW3DyZMHTR1ecBU9mYCYFqpAcK42XGi8Vs0HOoMwpqoJRbMlkwIRAQdrgTSGZzEqUygldR19EuiwiBwIz2uHNVIQ8wkRQHUp0KZLlvxQopdBagFFIAyqLKBMIQSRCqcmQagyqAJ1s7X7nVDWO5CzWOT4IohohxDEudilBTQTyHW4i0RYjTmliqFCQBtExI3EmMsCkkqHYKZI8UXog+2R4XESHFg4lUjJbiB7rWtbLM9qLc+rNFVebDa5bYwwMhgqmkkxVqFCA8hSFQumcUkLsKoLNyMYZQgbyvQ25jjComNyaUt6c0TQg5gIqRRhnVE1kOs44uj1F6ZboLbG7pLu6ROdL9GDKbHrI9VdfY7B/zP2vfhFljtksLPbaDZrnV/QxkF927N8fM7l5i71r1wlRsFjMuTw5453vvcvwek81C5TDjOm1Q7xznH/8DL9uUX2gIuPw1j7Nk471vCbfn3Djpz9DdIH15ZreRjKlGVUF9amhvzIom5EXFZXKGSAxbNlcPUKxxh3d4HJZ07Ydje1Yny3YXFzSLc4ZFkPG1QA1AFVKXJ7hJjP2xkOOXrrFYDSkKko6leZHIqTnRDgHziJlj46DdBAYd4j9JMAYH82ofjhite3ZBM+rv1FR/YtD3vn2R3zvw0fcn17n7uA2X/21L3B6qrBWI41hKO3OBOkpr1UY0eObNd/+3T9kf/hN2qc/4tlbz9m/9ypmMsQcD9gb3CRIgTWabiE5vnsTu3FQZSw2l9AtmKs1f+5nfgrnJbEcc+trU9YPL3n8+x/wo4/P+dJXXub1P3OPQJ9mWyJSKLebYUkEGXl2HUYzNocCKT2FFmiVHP/EBGCxcYtRhiobMSj3idGwXm1Yr58xnR1yYXs+6jtezo+RjUfOL+nbNZnMECrnedfw8kuvcu/N2wxf2+Pg4B6rp49Z6zm5lnRNw+LiivMPntNvO7z1NPMt26AQWcHhqMJmLU9Pz7GP1rz0058jn2qKiWTV98RMMywE98aSud/ShpbeW05ay4dPn/OjDx/y9u++Tec8GMXspRv8e3/h3+bz914hE5LaJbJzTwpj8jFJiBO/DaQQXJsYVkTamLwnL2vNQGgGlLzfzXl+esL333ubd7/9LZ48fMDi6oK9awV0juHA8Jkv3cU4RZXlzMYDHjyd8+7HgXcefcpF4eK5ZuQMhXPgFCHrCNLjW8/ickVWZYyvHWA3NX1v6ayjb9bYOnHrjRe4fosyGQd3xmS5xVrParlEtA/ZrJZcXZxjpsfUbEE03BzAmYO6T+yg6wc5mTa4JuPmXkVZ5ORZRtb7NB/wnlxBYTRFbpiWJdmsBDx+r6ALAYqIGoCPHuF6iD1hpslnClUpIooyJ6EXygyyHeiOiMYgco0wBi9zgqlAZWlorlPWADFlKyfObYYXOTY6XNykik0GaJRQJFuWQMhUBILSBJnjogdhEGickKmXnWbQiaiIQApF2CmMhJA7OxzI6HEsqP0ltb0Cd06hztDlMilCZEOMDuF9oruqKTqfMJ1EojMIr8l9Rj/3BOEpBxWutfgcioMWOTrAjKZEVeFtRGhFMTLcvDZimElmt4fMjq4jxQZpcorr98gPS6QcIdWYqCYc14pheUY1vIXrJNia0K7QUlEOKmbHE3Se07WO+ekKqgypMsbjGdNrBwgZsX1HXo5YPz8jNC03X73L3o27DCZbqiznh3/4gHHnEVEz3K4Y54cIrcm6njIfMt4bcHx3j24luVnlvHo8oRkO0BlkmUJ3kuXZnPV8y9P3GjZqnQbzIkP5OdXUkI/uc+v2IToIpAdMpG07rO0RXU1ZDYk+UK9rRJ6ltqJwSJfjdnKCjBwn0+1QqhITkpP8+XtnaAVFpoi1IJb73PrSS5ip5Hf/q+8QVEEx2eP2zfsEtaLvArnOic4h+g7ZtWSjHOkb6mbLe9/5EU/efg8bPW8/fsKN6QEH4zE3DvfQqk0iCV1iuy1PHj/j/OyKuQ/kqsD6wIdnT3jzlZeZTkaYOOfsmSB2LeObgu37c+r1luaqY3l2RT4e0rVbnNvxfnbErkakDTRuBV1jabsOrSXBDEkh2QIpDN5Zmq5h0dZYExnNRgzLIT/469/n2TefoLXks6/cZf/gEF0MCDEgOgGdwBIYHYwYjEpEHyDW5MPI3o0BX71zyJv3Drl9aw/X2gRPBKL3XJxvaRvHwd6AIh/SblqWiwsenz7n/ssLRNaxV45ACmxY87D9ESGe03Y9661nvblgsehQoeX4TY+1Ah8CQbzHu5sfsLho6UTFYrlHbjKOZpI9PN6l1+H5u5e8/d3I47OMwW/e5y9/XvHaQTpI/IvVlqtmja0viZxy9vQxZ48/5sff+Q6rdoOVFtlJzMqjXYHvR4SFRxlDL2raxYJ6uaJef8qS1OWFQynQ2mCGY1AJ4pUbxWaxAhFQpsCUJShNVA7brskKTZFLMmlwnUFEBbVEGoMxGcMZ5OMpvfVotURkgAvE4OhsIOzyhZURzCYZmc6phaEqc8o8wxiDCnL32Am0kWij0FqjhUHGHCEDqkjcD2EiQnm8CAjdIFCEQqFNUtpIo8jLdNsQhU5BGyKA8BgjEZlCZBqpMoLRRKWRMqCkTJyBKBMETKRWk4ie1PFPb8gLHF06QYmdvzkRU1POgtrdJlJb6JPQtKROhd1/8+IHJEWRiz2CHkVHDAuiXyHCGhPX6MymW0aW0M7OOlxboyVoVaBUQWbiLmwQggt4pwheEGNO10asNURTIvIBQWT0Fure4qRAVgUjPUAPS6Y39zDlNYJzBGVQ5Qwp9kk5EhlCzagGI0S9QcshPvbpet8GJDl5MWB0sEcxHIJQNHWHiII8V5gsY3o8JSvzhBpWGpFJVPni/RPkhWYwqRjPBsg8Y2oDQq6pxjkxCPy0ZDgeMdkbs3e8z9b0VGPFNFS05TAFDSlBUe4RTRr22zpifIezHevGo/2SQEZUgega+hZCm9qQ26aj61rCds5WruianouzS4rBgCwL6MzhRYW3HdH1GKEQwsLOhd5vVmy3LVdXNZNC4rtA28Bl5hG0hCC5qNdYm/wJwaf3K8QIO0Wf0AoVNGVVooLCNzVNY/HO0gfLtum48ueEekNsa2Ts0DojywZILM2mxfvAdrGktku63rGtlzRNQ6YVce1oe4dvNtjLKy6aNWdnc04fXHF4o0MVBucsTqSnPendIjE6YnAIn1Axzr0wYKaZmRSKMhsT84hVPV1jCaJHmUhZFrz3ox+wPD9jmBmGNwzZtEQUA4wp6P0FwXUIwIaQiLAd2M6mAJtKc3R9n+nxAYO9KaqQTMuKvcGQYjag6zxtnQQySgDCY33H048esvn8FxAKCi3osTT9nI+v3kb0J7R1y3JlWazWrNvIuoXaX9H3kb7xbJ7N+Wb7B1TjH9OSs9nMqIo9jo+u88qtALGjabb8+I+e8/afrHly6in8LY7PP8+zm3sM9jz//J3vcbW5wocNs6OO7eU5lxePWS7O6ZSFCpwCYqS3gc1iRbZRRNnh7Ja4WdOGDopP2bx28XSNHkExyxnfvo33l0Qa8jJy8vEZMhiK/QOykU463Ajh/Z7xZMhoOqYoCoQvcRvP5TtnDPePyKY5+VRTHt+iPXvOwccFlwNLeFKz3DgeLj3apxDrqhTs7xUoXdILQ17kmFwjtUJ0JPGwUiiZNm0yA8HQLyRCKbQZovLEAxDWEmRAKY3UbaJtugA2IjON1h6TS+QwT8iKmChYWZk2VmEkPtMEZYhSIlSHCOmkA5IYLJFIiBIRtsiQI6VDxh4rOjwdisFusaRBcfzkoh131NQdH1Wk3yNGnEgqJUgIihT4nairvd8gRIOmAXeBcQ0ytES5xAwUUFJ4T4+h62q6sCU3Gq0blMzpiYQg8U5gt4FmC9FKirFicRXxjUaKEVEOiVbRrXsuN3VCLY+GDKeHjKVjdHCANC/Tt1sQHl1MkOYe+BbCFqELSpOjTAo5ii7ge0W7VQhdUgxmzG7dYnRwQLe1bBYN9KAnGSaH4/uHDGcjsqJEG0N1c4pYQbtcYetLhBCYSnHtc7dRWQlSsbp4iA0Caz26PGK4P2M4nlLNjrD5CYUqMWWGGR2lWZIU6MEAJXNCiGzrJVdnS05OL3n2/BFu3tN5iROGYnVKfelpl5a8CCy2HdvNms3JB+hYslm3PHl8wmw0ZTyUjCYRN67ARYQPpFy8BvD0KJrzDfPllo8v51zXiuAjrYP4w5vsHxyhs5wPls/4Yr0hblsuz8+5Om9xURMmOYVxaC0QuqAaDdFqQBQCUU3Yu5lTlJJyNAa7xQfHOnTYVU2uHaNKMqhgUGZoMaLSkm3fEmLPYZnRNy3njWN5uWbTn7Larjifn/Ks3XD43lOuDz7kM1/7IsFavOvpZMCLQNjh4ZVP0nFFj/AK72Q6nPklIlqMNMxGNxFThS0s/drh/RohSpTK+M6D32O+esTRsELcb2E/Qw6mlIM9lvE9rF2SIbic15TzhtwU1EtP0wfaTDK8d4vyxi2yo2uUZcFLB0eItmXy2gHeC9bLjsurLTL0KO0JyvLON77HL3795xBCkMvIym04rZ/wJx/9Plw9pt2sWSxqlusSqxVWC66uarpVQ3vR8ewPt/z+8lvYPnzCtzLV6wwP/yy//JckWVHTN2u+/XefMD/9iHp1Ct+0fPja/5rhva9w7as1D/7Wf4bfnFMdTrj3y7cQaku3vaBza2IRUAMQlSBsofWe06dn7MkZK+tYr64YTyyNlhTH6r+zr///VRROfvwW5mBE2R1y8IUhetClDbO3OGvR3pDrHDXZo69bmvkae9khqn3yfI9qMkSLKVu/4fSjb6PLCZkZUlwfs/7Bc5zaIm/MeOXzd6i+8T14Oud6KdgvIM8EtYxcPluiVUclhujMIrsUZqhsg1YRo6BSHoVDO4nYtojGIZUgG4IakVpB0uNVRBYCtRvehC4Qeo+fN8SjgJhq1DAtLrRCmhxVFew6RAjlUVlFVCVET4g10Qeik4SuSwNipVHZBC08Aksm0iDY4tjEDolCxQSq0yF5Btj5BoQMKOlRWBQdAUvvI0YqtIiAhRjxURPIKOnIiEkTrXKMaAjSEyYgYk+wC+ziKf16hXcWFQK+tgjjwETivCf2BcFq3MrhbSAG8M6TCQV5jq4y9Kwk+IjrLcE6TK7JqpLx7ZsUxYB8MMU2JfX8lGzQE6kohwti85RQPyE//gtIM0YVPa5PXpe8Krhx7zo1A8rJkLZuU+tqmlGODet5z/LqBNtsGeQFT9/5LgjL8OAWq9NLXO8QUXD17C2UztDZEF1F6u0Jnd1i2nNimyPJ2duvaFjx7NETfviHDzl/uEEXimxoeO/DZyyuNqxXNaebntrFT4J9Cm3wPrLpO3JVoIRGS8U/cpJ5vWLTb6lUleyJwSN8T6kUzgfWXU/77ENyKRhqxeGwJFMKLQXRW3KV3CWdDzQ2bR4GzcfbhrntuLQ9E2u5ufaMB2NEhNn+AYfHN4i2YjoZEIDckLg93iGtxbU9PnbUyznNZoOTQ4q9KZ85Puby0cfYVY3YQFfk5IOc4WGFVBa9bMA5Hj07R0tFUWheurmXImkD5LMZx9pjW0G7F9HF57lx/y433hgzPjpCF4ZsbimaIdIrdokBlEj2VMWtcp/xwYhyUhFDQLcLhO8QwjPMam7O7nHrYJ/JWNKvelZyRXvV8HB+hh5E7k0nfPRPH8DLGv1axH/pdbaLC/x6zm2tEO4Sk1/j6OZtDq5lbOoWvbF0Fxv8+RpGG8TxiGt7Y4y6xtGbt7hRHLF9vqTuLcM8w3aeQsCDJ894uvoRZ/4O++aXee/0v+Xdj/8FF3/8DU7fW2Gtgwxmr1eMxymHZfFBQ/PAsrl0FG8IBmaId4rVeaR50uC6x6xO/h/80f+7YjTWjMaK/VcEZv811ldf5PKjt2ge/l/pnvxfWHyzRe8dcvDmbV79xUP6zSXrswtWJ+dI47BLhz0P+Aewd70g2wOGyeDaX/acP6lZftxgA8j4KReFZdsy22Zstj2rrsEoQEr6ticbTzCDgto5CmHwKuBVgdAZ0ccEx3MG7y04z/T4mGw2QJYmQdRcMlmF3hFO1zx4tOZ7DzdkDqxNt45aCUpiSiQagHMOEZLpTONShJ4UicpqNEYnRYMZmvQ586hKIXOJyDNiERE6IrRDFeB1ymcQlhQj6R2xblM/H0UUAmkFQoZPuCeeOVFuQGwIzqdnPyhkTO2jBKwPn/T9Q1wTxQBBQQJZp9bYiyGTiuKT0O8gIhFBsfM/Rjp6egwFCpGKS2jo+5a6bRiqiFaWoHokK5AuhdBrDaEl2A2EBhF7pAgIDWG9A855h1Il1itCJ4miIN/LkhrVpwF3VggGY00oquRb2LZYpRBS44XG94YgsjRctwXz04jQLRs75/rdFh0qBNfp5pf0bZ+UKFqlMJ5CUR0MsI0nii3bxTnPn0uMTtTX9drRrrb0m5rH9Zru6jkEx/CgZn5yhncWqQU6nhIDhCixomex2rDdbhn0a5a1xEZNMSzIlcHWDevzM5q5w+SKrFJcXCzp2g7XW0ovUSE9ez2STEmiUZispKpK8jynMAW+BbNWDLqKw4MJKgQUUCgD0e5QWIp2W5NJySDXjMZlel5jRHhHkSdRgxWRza6FEbwnWM3WOla2xyqJlDlNsFyb3aQY5sQ8MTeleKH3iWRZhnRJHaOVRKmMclgyHhTkIjGPgo8oq2iawPn5ik3dMe4rrulIOVC0raO2Ke0vxIAPga535FmGEJrW9QyGQ4bDgtnhiF4OOHjpGtfv36KoSqLwGCm4Nj4i0waQSJEzGx1SDof0mcV2HaZPgginul0bCUT0hJgc830H3nsQEZVJpO+xTUvf9Bi/IdzpMJlHZxJLylvuETR1ZHnVIE/O+Mzdz6LMnBih61LjVmiFlJFsUJL1A3CJpZaXBTePr2GqirBN849iWDLN99hTh0gC6+aUTfsUWdaorKP3gT4ImrbF5wqFRFuLbxzORib3MzabiF17ogqoYY4XnrC+Yju3FHKGHk4Y7UWs7Wh6S/HGGJqSsO3oT8+ZfO1VqjsDotsiux7R9buDp0eRIJz9RWDjLKY3jO8MGY8HZLmm7ioWP+5pukgdPuVB88IFll1gtLVcrpcUEaIMrDYb9N4+ujQs+w5CjiOj1Q5Zlngf6Tct8f/L2p/F6ral53nYM7rZ/v1qd7/36eucOtWwWCSLLFG0KVKiZClSZAWKEsFGrMjIRSwkNiIESWBfBUkQIBeJY+ciCAIDsVrSlmxRZk9VsXpW1em73XerX383+zmaXMxN3gUQgrrc2MDCwlrrn98c33jf5xlJuqLANR17L91BH4wh0XRVR9AabwN2XVJuLe++f8rvf7zmz+SStQ30ClwiiYRAGIGKB3ZMUAEjIJZhaMRKgTAKFRt0FKEiTTLKUEogXIscKWRuUBMD4wC+J/gGnzhkpPBKIf6kFe0toXBDL8AZlBty1GLwS9JHgd6e4KRHKA92EOgIEREzPOmFEvyJ5iagsKwQjAikSHLEC5AuQqAkg7NVgJDihc3JDw354PChoxUFIwIKjRGCzm+oq1MuLp5gkina1ChTI3WAKBu4fAh8KPBhiws9vDDUSRmwbRjsUj4QJRParSP0Yci8z1N661mfrJBGo6cRo+sZTacpW0tjPdYYpNZIYWhLhewGtnxiYy6OBZ2t0etz8nFBli7Q4grd0T36NhCCJpKCHodTDjOLCP6Mti+xpxUnmzOkGE5PrZOIVtGXPZ89+AB/XqMcZIsly7PnON8iI88k7WnLkmJdcLlZcn5WUawbxr3lQdFTWE+sFTfG2fCQZ6CImkhgIkmSJIwTSTzNyEaT4TI/KKogcInES40TCfk0Icty8myM6wKri4a6dtx5fRfROXSQTLMJVbdFKcEoz3CnJZEWpCODn2U46/DWotzwINaxJMSwvmwo65ZNUzILM7CCrnM8qFc8PD/j6HzF9Z07RJmhNw1BvYDxvDB/aR0NPQE/3K0lWYSQU/YXY3KlUa2naT26M9ha8Oh0yXm9Yb8ZkyCRYUTdOOrek8bxi1OxpGx7slkOXlJ3W7L0kGyWMp8ZjsqCyY09Du/cJEpjunaLFp7rsyvEOmLwEmbMp4fE4zEbXdMUW2TUE4yjjy1eDZA4Zz1NX1N3NXUt8MIjY0EyiUmF53hbsSoLzO4CqT3ZRBElmg5J6QQbD1WhuDwt2PAA8QtfQ5ocLwWthRAZVB4jNeg8Q1QtbSkpM0c0SnntpdtEkwl+taEXnun+lCvjW1w1LxHoabsNXdgQ7znyi4BbB6o6sC56ImExmSTygWA9FsH8pYj1Zx3VymJbjxrPkcLhqhZhA4aUVO8xy1u20SlRXrH/1Vv0l6/QPG/pzo6YfX6PdAbV8SMSK5GtQ1qHkX5IfWpBX8F21ZM0np1fnLO4Oqa3LSJtufi0omw86+5f71n/rz0UPrIKaodc14SHp9SqpO4K1k+fIzPPzsGUV3Yi4uQq56dPefL+B4xljNE5qRzRNFtWT1YUFwXr7SWjkwOSUUKyYzBmymbVcXm+5eoVxetaE6YJT+uGkRGMM8ViL6HtAigHwVIoTyKHN+xeBcILiX2nBhaLlBFOKfwoGnoShcUfNwRlIeroXUdwFVAjZgI/kzCSmHmE7AeVpe8VghJp9GCWywUy1ohE4wQDM114VKhAx0gRI4V4IcAZVkBCCAIt3q/R8hCBRdKgRIfEY4FKQMpwSKjlC/q2FJgXJwyLpRcdhIaWJSI4DJ66eUxXnSM3p1TbBC9qetkg8xwfe4JuEfYJ2BNs11BtFVXbE3qH7B3heU08GqPmGj3KSPLhTccJiRUaZztCJZi/fI3pwZzFtR2a9WoAEVYlysaM5lPyfEasriO8QEQZ3kKblGw3Bd2RJHz/WxiZIW3E8vwEk+WYJMW4Dzk+PmJbbako8C7GOUdnG5anDVVdUjcVkZoz3YsZzxNmowOedUfUm5J4O+blV/eI8xFOWZ49/IyT8wsujle89sac125NyLRmFhVcPLWA4dob+4xuHhCEotn2iDQesAwSOieQukGqHscEnMM76NsYh8dEGdn4ACNjmt5Rth1ZMox2HwKitdhOE5uEW7f2yLIpwQW6qqAcPUfaGiN65GiHvm1wXU0IAm9rXNNS1pK2WzFKxrx882cpz0pEJBG5YXnvIdc2MzQFf/jsX/DW0VfoloboVc16uaTtGoRsEf2ESIiBEpwrAgkmNlx/8yqHu3Mm4xHolFh5muWan/rgHhfPCuJFwuzlKUrIASHfetLUk4wSdGzwQhOPZ7jGs3l0SbPIkDKgXYe4W3LMU1SjuHbnFapqy8XJis8+vaCuejwdfTjn3uUTZifXePsooX/FYaKYbJJx+NZN0j/I6YPjrNzwcq/I45S9WzHZZIZORgSlGS1muJMjzpcN0fqUZbOlcA1dX7G8f8H5/UtWvieNDTOToEWCUhoTx6RpzngUkWeSNBVEqWQyiXA+Y7w/Id/dQxtN2y7Zu7rLNF1we3Sb//bZb+DNYI9b+d/jlSsZihv8q4fvsW09TRmQF7AuHPlMMZ5BvKOIc4/ZBMoPOsRT0GeC9rkjmm44uHLAa7/0FdyzmqPjcz567/vsfDpjuvMyP/vG5/lP/k//If/ok/t865vf4Xe//Q9JT2vyRmBWHU4HZOgZpZLXfmrM4w8aLp513PnzKdX9wGyccP21nNnuFU6erHn24VMe3Ctp+x1kfOMnOxRqW3BZl+RhQ3RP0cqeqm84ORtIiOJyzabuWX9cIhLPeDIjmSaIeJdez6i7CjmaksoUOY3IxruYLCYeS4QZkYqOvNvBH045ePsAP2q4+68eMTYSHUtaI2jqAaMs2wYThhNE7cArj9Ye7T1GeLyw9GHg9XeXJSI4QlkM+z9lkcbS+XZ4OMsOk0roJcINrgbj+6G5/MKQ9Cf8c+cgeI/ww2qHYMH3SNmixAt/gRpibgKHEC3SbYf7BaGALQQ1OAVIUcwQRETIP+Wzx0DPEFyNCSgKAgWBiokI6LBGhg3Wr7DdCmyJUjV9uUL4btBLbtf4yBN0B1ygQjWkuTpJ8I4QLNZasAMb3juFZIKSEicZ8L5O4DtPW0HbVnSNoqsErm8I3oEyCB2j0xwzGhOnY7qyp/Oe9eoIkyUoaynrkh/98DGuGe5b8kig9WCeW16shp+bDIS4A1KUUsSR5sa18YArNwHrdgfrnBZMJhOCu0boHTuzfa5cnxGnBgwcXF9wcX7OxcU506mGF29U4+k+o1diTJKyc2VBbQ3lqmFbnbO3uyAZZ0Rpwnq7RvgWgsWGiLZs8dYNlNa+o5MlTXmMJqbuPEXdoXxHbGKM1DSrFmESTKyhvMDIiLbuOT9dEXlJLC2ZdpiZR7wQ+lqtUK4lBEsjoC0riouG5581XB3NuPL5V7j2c19g+vqrbC57njw641986x9TNSVtUxOER2iJ8hohPNY6hFAIIWk3LbaqqaoNz55u8b2mKB3Or4hVoC0rzqqWZbPFrBuKZ8NlcNt6egvTaYysWoSS2LYnnTYoYRA4WmsRwqFsi0hjul5QrjqCCMRpynR/yst/dsad8hrmTBKPRly5tcvOocKJFe12Rl1UYAKbpqJ3g4OgtjV1b2laj+8CWgS0ABEkr771Fh2WNJXk432mB3tE+YTgJEluyMcRqdSDYGKcohcTnBfYIHHa0IuIzks6Fwhoeq+wXoNOkTLBu4i6NiTxLmJUY2Y189WCNtznqPo9jvrvsyqeszlfcvyBw1Xga6gL6EowMtBnAWED+RVFnwXOP+1pGOpMu1cV2Txn7zBi71rP7NZ1vpp+hTzd4/T0OR+//5Rt+ZhpMqJ+dsbq4TMGLWeDiCP0YUpUlhSnltXjlp2XY3Zv5By8NOeLb885vweGEYvRTRorKFpP0Qach2A8YfQTjqR6X7Fue7T1xE86nITaOs42FVmwcLlhsm44t5ccvrzPy1++QrybIOUU66e0a4sZZ0STQE6OySboSGMS8DrHBoe1LeJgxiKvUeOa8K0nSD0YiBoRaL0fEhttYOSHxm1Qw85bmmEwxCFg6ej8IMmoWzeAyZsKKyxSe1TksLJFS4vRDol/IaQZZN1IO/QC1AsugRz2mn/qWPUQvATVI+lRokeLYb8u5As2OgO9X/otUiiEjxBhiO4KLDLMUaJFkWDQOMSf2JvxAiICaXCocEEIl0DNSESEsCaEU6x7QugcIlgi0+HtJb639NbTtT3uRXoiyDVGtoAfIILaEZzF2R7hAt4GvBXgU0R40YdAItyAJnAWbN/StdBWjtAPshwZGYJXeDHoA3scle2p6o6L+ozgHUXdc77Z8OizZ3RVA9ZzuBghUbjec3y+ZH93hzyPiXBIGRGpiEk64uD2iNEsJh0nbJsZ61VFU3cko5Qs0cTacP3qTUaTCGMUQksObsxYrk65uDimbWrassV1lmgek8zGxHlGNh5RPrU0XaDYBPZ8ghI5WmeIUA1tIj/4tUNncU2gb6BvHda3uFAiRUzbB+rG0a23TLIxeZxRLdvBBW4C3ekFRkuqquf4uGA62SEzgUZZTNcM9yVK0mlN5DskFpsEQuep14GjZ2sOXo4YTUZcf/N1DnxDW3n27z8DGXC2o+9brBtihkIIlNRDPFUOR2hbO5p+y+rilNOjLUpo6tpiu5ZEe7qu46IchoJ2mjI4ZFPT9JbeB5pmgo8NDnBFQT63JFnGOE/o6wHZolyLzCK8ULSVo+s6okiTzVJufXXGyx/cII9yxrtzdl5S7O0kSFW/EDd1yGwgBWdJwnyc46UGJfE+YGuLCgEVQAbJrVdewdqSLHIw2mO22MGYBNcF8lnGfHfMbpoSjMQlGjnKsDZgPTipcJghhWYDILFOYZ0iyAglIwQxzkakZoFPLxEjuLo4oKwecv/5mm12TL1Zsl1eUhwH5nOJiANb6aEF30BXB0LlUbkkNpLld1rUoSTOBZOxZDRLmO5oJrOe2y9f5fOvfo03X/oZvvfBNzi5/HXuf/KY46Mzjj64y8XHD4f7TNeCEZj5cKN40QeKU0u7Nuy+mnJwe8qXv3iNZ9NAW6YoO6Fu15S1pRUCpQXeWEjrn+xQuEXH82B53Dsen54jhRzauN7ThEDb9vyLdcklT/nl8y3X7hv2/uf79ImjB5J4ipkYkpEiWyi682Yo2mg7WKuSCDP1uMUV+lhgu47X5+9StZZy49DIoTJvPc1ph1NicBuLAc+tpB2w1ZEiTsCYnkZpXN0jvMNIS74zJIrMVDEaR2gRUNIRpgE5BhEHgnRo6ZFGEPKAyga0BlKinUO+yFm71qJHIONAJAXiRQ8BP5CyRBgih1JF6DCwYIQ4RVIgqFBiRGBOEApDRByGMeJe3AUJOiRLQvgdgjsDOoy6Dmzw4QTX/YjIRURSIzMDuw2ibKBo6RpPsa5p2g4ruuFNQ1q8cYhoCLsKH+grQaABXZGnJbZOoY/Qo3j43UrP4sCQ74zReaC3BTqNiXcFuTE8++CS5dMjbOnZridsekfZB9aV4INPPuVyW1L2jj//Z77ArWsLskTywQ/fR8uYyWjCX/y1n0dnAikH/HlkdsjyCdOdOde/+BLpZE6UjinWF5w/OmZzuqLxgXi6Q5pl7F69AeWSrizYrtdYecL5/ec8/PAJTbXk9uuvcv3aFdr1Q5585ynNtmeyq0mzW8ROcp2I8PEJT7Yfc7I6w4uEeOSIMkEWz4lHA3NJpSlqkuLCcPGuhSM2CqM1Tz+oWRyO2TnYYzTJaAtHs9qw+vAhn/uFG0wP94hHV1m7De3Fku7onN654VTnPVUTMGWB7Dps4RHjKfmrdzj84i+htWd0cB3va8r1xyAWpIniywcvszuZIFRgfbFhc1Qgfc9kCr43mFHGdJYTm5jivIRti+5LIpuShQgpchYzRXAd095xVI1BSZIopmy3tG1BXzesQwVxClITOouOSrQQ+ERTntZ4LEI6pntX0cGA6PnsO+8y20uRRqKiMV/75bfp+4LpLEaHiEjMSfQVpuMJk3zGaDrm67/6c5j6lF989SpH1pNNczKdUi5LfN0je4cJMEnnvPLmG1x/cw9cTigczcM1yyvnXP3cK3xVBU7e/wi/3bI5XRFnV2jLAt+0yM6TKIXoHK7pkUYOJGMrUbZjPk2JxIy+OODq6IDjULLNe/7Gr/4U/+pffZNv/vNn/L3/+Ff5+OITQjjmy38u5c7nIrqt5dmHJZ8+h+pUUD/wfPpxwCWKXkialSffVRAkzcaR2oIQT5D2OjtXXiOZ7tIxaHAzYygvN/zaX/53KJ49pS+2oDXuvEGNLfNZyd6NBfasZvXoEoQik4K5kIy3Y0xVcfp0xR//+De5/vorON0xuSW4cttQbTva9vQnOxQKoeiFxwXPBsjDcIG6esHniZHsIbig54ftOduLD/h7373O/EsjRncmyKrFSj+UXo422LIBBDpOaPoL+r6lbQSXzx9w8uwxZ8+fEXJN1TiaLmC6wMQILIJ1NxB/jBZoo8BKZDR0FJSQWBVjdIQXEI8EGo9BIicGNVHoqYRsSIYIPE45/IsVkRIDpCsAwQ5v0oLBw4BiMHTp4Q1BmXgA50kBdjByeS8Q/Qt/gVQErwlKDcdAX+Ll0GMOrIE1AUMXBDnJEFEVmkZIXCho/QnePsR25QvFYYIMp2BPCN0Fsh+UmipKiOMEkQuYSqKRQJYJUa2olp6+gLb1lPVwgeh8wFvoV4J0p2FESZKWeOcIJMQqhlSh0ohY5lTunGpboYuKnetTzDxhOtWMHrccP9twedSxtj2lk9Q9FGXHG28uUPEVZJQxnkqyScR8POHP3/pzQ1RXaXYmOa6VyCCIlUBEOUoatA9sP3pCER7hXc/zZxtW64K66dCTMaJvyPMY4ztmewuiaU6qPafHD+lVi9kXHO7fIJlCk18y212gxgtsC0J66kKigmBkZsyv3Wa+WTM5ecbl5hRtGoy2ICzSO5yDqq2w1Ya66VnXjmrb0HUe28FukvDylz7H537+p8mnh5x//AEP3r3kd7/7Ad+79wAVRzidsqsDsutwVc2F9QNXMQRaFwaLmRterrIo5rXXtvz35rdxo5TurOTincd8/MFdXLagCIZoPCbfHzG7OuHazUPydEmwHWky9F5GozHz2WLwc8SWXtS8+tarXH/5CtPFBNcy6Cq7FjGZIHdygpLIJEY9D1THgf7S4o3ARxp0TBxlpDeuMZrOGOc53fklbd9ivaOKFeM4JkpyWifpOoH24MtA6GJs59lUhqkxWAONLlEpWN9SbgUi1kxee4Xro4h5VyJVAo3AnjjkKEJkBhFF9L4d0kMqh0RRXpZDUEU2TA8OQcHrv/x5Hh11BJXROCgah0WiswQvFUEpVKQYzVOSXGDKAMHQS0UwGVG6z/lmjYpSrl55iUv7PiQt8ajn6PIdqvYClQSufn6BM4LLTcsjH+hzTzEWXBRQtA3OSZwcMmFdrxG9xESS+RcOWFydkxz0fPv9B/zeb31A+WTJVixZH5fYZEyxPqcPbjg1ETg7OkVEMeN5xuu3FvAVT3LY8/Wv3+LysmR9VvGt+x9y8MYdrrw84+z4ko/f+5Q+F+hXEhhPsEVPuXH/P57u/38OhRWSWgwC9yiJhuipg6JpXuzQJRMMClg7z2dtTbFMGLURsRxkL9729K7DuS2uHxqMQSq8q7C9pWstZ3efcffBc56dnDGKJY0XVNYTW0gluBfboFYIghqwqb1QSKnxWtFrhTAR6AgtPJEJCOGQwUOkCUYRtMSrQBAKjxpSoMKDEIjohWtVgHfDpYJyw1u8iDXSaGSkIQKpzCDCEf3AJvIS5yTSyuFrIQhygNYhIIQSgn1xx1C9GAoKiwMxBhIkCYIIT4ulwIX1gAZwCuUKFENTGV8jgwTRD0pArQZPtAkIYQiJQdZDOUx0La4dkCR1GWjbQFeBrQK9sYhpS91WOOcJ0hP6BJ3F+MgRcqg2a1y3AV8wvQFxZoiiMaMpKG0JrkMaTxxFyBiQnpffWJCPR+hozGZ7SZ5ljCYjrs7neBTBCyLvsb2CIepNEMNQV0HgS0uwDX1X0ywrbNPhvcP1HfQNfR/wwRK0GyCF2lE2BbWr6E2HGY+o3YZybRHZjD7EOKkQ3mNxRGnC5GDG4s4VOJFstmcUF83gCxd2iEJ2Ad9D0whcsaJpOza1Y7ttKQpLXTmS67so4ZlMU/L5iMIIpG1pNhva1ZreByobaGKN8YHgHCuhibUi0pIgBGXb0lhPKQStaVnPLilPnzNNb+JtTbOu2T7fsg4tSxvwCuqmYrtd05YlfVtBsMMar3MYLei6GOEkTVPT2Z4ojsjyjPFkjLeSSAW6VpPUHcG3EKnhbqDKibZb5FbhFXilQBvAMN7bZ76zwyRJsWL42k3f0gWBiCRmYojyBJMkQ5eUEo1EeElbe6zvCA76EHC+pSkFddOhRgleJpjJgonTQzy68KzP11gxNJRV3xPk4DMe3kfD8CyxAi974jxlFObs3DnkdLukw9B3PW1rCQxyoT/hKyEHeVGSSkz0JxgOMegAGk9VN2SxIs+mPFxuMKPAVClOj5/ThR6VKEbziPOjnsszODsTSKnoUHRG4kyPjyKCVIjED884rdBT2Lm9y2ie0/iS+3cf8+y9Jxy/fw99uCAOMcIrrE4Jxg2Xi7ajqWq6RqDFmCSJmB2kkI154/U9Pn3Ps35acHK85uaXXsXkOWjL6dE5bqTZubNP5yRtJ+j+9bZH//pD4UMPsZTMYsUX7hwSMWdde+4+uMs29GyRlETkZOypGbfiQ7pXF1woz+bZEr09xoSeKLKM9xukORjY4uECY3LwPd6uePT9T/j+yXM+KZf8/CJh0wb6AJELnJUBXEAj6I0cRD6JGdg4kUZHGpsYdDoimIigHY4GFwaLkXVioFFVDu09ToHWChKJ0gGVgtnTiLoZmrZBDiU4MWSbTazReYbMU0KcDy4BX+GtwltFcApvNd7qod8QIBg93FcET8slmvyFwrsi8AwfLrEY2rCPEjNgD8UUQYsXDUiJl2a4MHPbgazJIHAP0XDx7Zylrs5fpE4UarRLOomIvSCd5bTHiupiQxAFPgZ34VieeFwHsnFEbce229DWNdZpYrsmY4E3nk19zmZ1SVus6ZsN178UY5Ib5Nltdu88p1/F5EYSRlfJR1OEiSl9xP7nc4QUNGtHc2+EMAnSRDy9/xFhG6G6hJ3xDDB4a9mUl0TpnHw6Jb+6y+LVl9F5iog0e42lbwraesvJ8pgoH1q5+zffoll/SLE55/TikuP1U44vzjk9vWR7cZ/qvKFZt4OCdaUI3jCfjXjzlTe4/bk77L18kyzPefD+BT/+5vf5zT+6S9VaeuexDiKhSKViz8SMM0cUCaLYDKRYoPWBzXbL5b3POPvjmPZazuXHZ5izDX/zSwdkwtG0jpPLnkYP1NDQWQ5ne+wdjJnv5KSJpnt+TFuUrFRMN5oSxiOOTu9x8xe/SjI5JIQRszdf4gff+YSnP/iYdXnOd377j/j4Bx9z/lP36XtHNlYcvpRy9sn5YC27skB1CeVmy+rikve//x6qaeDaVWbjQ/qkxrYN4rTCBIeRmljF+DSlMBG11DRSIIJB+AgpBTeu3eLKteuMIs3htSvU9YZie8GnP3xKnPWMx4Hbbx2S5yNC79mu1swuPKKrWW8r6qLE9+D6mM21A/pOU5cen3lOnp+x3W4w04bZ/gLfe6rtCZdn1wd2WnyByVPwl7TFJaLKCVhEqrCiw/kW7wOIKeOJpaoc/eWKrtoinRv8K11F6Cus74jzCeNxRLUGr2pk56jPLnjy/ju8Of86shfYqubo8ZaDWxnZaIdP/+geh1+OGE0kclPz6NtLnnzYc/muR99KIUqYkVDccfjZHK9T1vIUNfeke4qDz+W8cesWTVHz7jsfcPzuY4qzDdKVROorLJ9+SrndoL76txH+fRCP8JcnqOSA+c6I119JCH2NaCyJVVRth2t7ouA5eGnM3v6Cthc8fPKM7brDtYb+syUnD1b0K4lvzU92KOSTXUajltkC6q8fIJYZ+kxyeztmtTkmOIjFGCdart95mS+9+UWenq5pnhzRNgWqesh0BPPdhFfjG0TTEtE5eL5mcdUxXYzZu5nxlb/8jP0nhifPxvzwnefYzg8ZfiEp2uFSLU81kVG4WFPEEakUtIBwnnHZ4iw0WlHTk0c9MT25bVEiIlKSNJJEyWA8krFEjQx6JAarmolfrH8s2gt0ojGZIRoZ4jEvIrEdrnN0VYPtO9QLSTdBI0M0THgZENpDVA99BRmw7RpvKpBbWuHoxVU8OTJoOnGCYQ8jajJuIsUKyXLIb4uKnh4cKLnFiIIoDmjbDxdpHXTrhkHYJVH5GSKegExRMiGdLzBJjh51zLqS5VlNkpRUlx16lmCihO225vyioqwC+TRm4S06g861XL29ixIRoYXZaIqipC4+5eTekm3h8XHKbH+ObQV9cIhpw61bv0qWT+jbkh9ufgNrt2iT8/qXvkRTVPRlg6wDOk5oy46LDzYcP1qidMLi3pwrj5cIobFd4MG955yXW7Z9hZUNt18/YPf6lOPNYx7+8C7r8xVFsWXnlkFYiDv47OMtq1VL01oOdyR5npCmGfPZhGK15fzBMefzMftvZqRpyq2XX+LvvHEd50usa+mrGKlThNIENHmuUHoIHpjkxWB3nv5kw+1XbzHZO2B++y364mPiacLkLUnoO6J1hX16icxuoRQo1aF3Jsz2DhhNd+g7Q/PwE6qLEzZFCbkhzqZMdm5jJgcEZWiqDSGR3PnSbcY3d9h5M+Pp8zVl0XF5eU5xsWU0ihgnhyxPVnRpj/YpyVSic9jNUr6afIVbr77MfLEYmuwiRnQtsdKk+sUl/HRKMpKku3scvlGipgnWLHAkhLrnra98jp3dHegdPR1dW7JdjvjN/+KbnJ9kNHXH1esPWIzn0EvOjzZsC0tRW1ZFQasbfC/xLXQWFtd2uD4bs1mu2NY1665mVZeIqqYtKh7ce8De/iHd7oLxJKWsN1ycX3B2dkRvElQbyE3C8d0Twrqm2JZ89sERvZd4FCIypGmOlRHVqGNZxySrltFqC4CQGS7kFGuIRhPMpMOlC2YHC2RS04QN1165xv7BGqMiHsYPqJqArCGeZzSnS+pVoI0E2+cWq1ushiAi2tOGtmnwFw3EOXYtWX2y4ncvfwReUG47iqzH7lpkJlnLZ7R+g6+3hB//LugOXI3wgr2rY67cWXBwa8HxDx5Qrjr6LvD04Jzl2lO5jPLkku9+4x2aNhA2kr7yVOuGTdWz+Av71E8c63ean+xQeOn2CJVFxNOAyjJ8GyNyyWwSYZRFBkWuJ2x9z3R3h3QvYfXkiHq7pi03yLYA59FRR12VyFGKaD39RYs67IjzOaNrc278TMf0zpq9x6e8/8N/inWBICSZilkBiEBsNFKJF9A46F3Au+GYKJxH1hYpBSJYxMQjlCNyHfgAaKRUSGNQRqCMwhhNpASRlET+ha9ASoQacMgq15hRgkxqgm0JXYdtBH3rcXYos0kFQrxoKIcwiHDEIOMJwRJCj/ANwvWAQ8gCyQZBB0EgxRpJh8KgGSE5AZ4hWKN8ibM9wQYwJUJVKP1iQNlA8B5Fh3QQrMD6AmoxpKjssNLTKiLLY8zIABG2lGxFSYgjglC4sqdedZSFQ2NhL8Ng0FHKZKzQyhPaAhMMrqjpipLiqMY3GmM0+SSlXPUDCK1rwa/wtqNttrTdGtt5bNezsoJiXdGWLXGv0LGlKTuOTi+5OHckUU5OjJv2KAGygxTDJJ+gQkrbXwysedHjVYPXPTIJJEqz2NnDpikjpaCBOC+p2pbFTDGajsjSjEk0QaxjIiERvUVJTTYas3e4h8ksyBJCR19PB36WNjiVkiYa+UIzqSODFhLjBZvwjEkSYYAonwzhBdchgsBkE1Q8QqcTrF0gghtQ2Ysx2c6CZLZDaBRyvUDYHoVCpYIoTdDKEPoW1/b0qxqLQCvIRxHTieH8JNC4HusDUlgiE5OnKVkeEceDT0THgjjWGJmCSBjPR6STDCs7pDToXtOLwRWdZAlZNiRXBIY46Uj2Umy0oPcx5cWayXRElsd0m5I4z7Gxgq7j6OyIpp8wnQw+asFQiLSlpS1a6qJmuyogHVYivrXgAnEaM15M6JoKbTQISd9Y+ranb4a1idaSJI3IxjmsT/HO4no3rA19wFtLVzZ0I41tW6QXJKlGmAiVpOSjjD6yVFULgGstfW2H2LXQBBSu9SijScYpk505WimkUsTCMMsS8qhCOIXooVp5hPEs5pKuETgkZiwozwRNNSx95WKB6wOutoRe42tBiAQiUfR9QGpJlCW0q+1QkgX6tsV5wAvC5ilycgWhxggjiCNFrCXae9ptQWgckdBID5PpCKMFzSWcX1asVw1CKvZv5axXPccnNSb29JlDZT9hIN4vf33CeXBsA2SXEV1r8BLGOy27+1fJTcZuOuZZlzCeQylOqfuCECpi04JOQVdY31JsTklHU9hCddpi3m6IZ1PiW1/m1qu/QMBx7ekT/uX/7dd5HAQOza6ZcK43CBFYaE0dAtKD6gNt8NjO4npL2Vq8HxI2XgbSq4o4BScdCWBUIIkhziNio4ijodUaBYGxEHX9YDnWQOJhOoDx1GiMCAWhrnCbluYEepHhpUYpB7FEaolUDmUCUnmksgSpEVjwEk2H9sP9hZYBFyocHR09cbAYtmh6pEgIfAz8GMJDRNchKjd0CvIGoh6hA0oMpxZsQKeS0AZ8De35FmsbvDcoF5OOU1QUY1ROlGXokGP6mFIpKptQW0W17ggXJX5rEXrwYc/HY6LRlCzrEd7jbIaoJM1lwfrZmu39jmyaMdrVjOYZti/o1g3VyZKzZ/+SIHpOj1Ysn1YEq8FL3v/kG6zPArbSXN3ZR+mUurE8fPyMRGYczhUzFbEf7ZNGGfHU8NZX38bszwmp4tnDH1DpJWEM+UuHJKnD9x3aGBZmH7oNfX1G++Utp9tzVuWaznnSfEQkEpJNhj6NmUxGzOY52XhGsI5Qbbi8vBxWhBH49CohtYhYE6UHaCGRAcATwgbpNLKP8PKYqKsR5QqhLfbkmObje2zOTth75VUmV3YZfXGPo4+e0G46XNEyVQmjvCPJW0SAPkg8KV45IumQHmS9pl8+xtcR7ZGnzTylFayrlvXRA7ZPl9Rry/jKjNnBjP0re1x/9QqRGUxeSaZIshitEwSe3q4xpifWljzPUMmwk0d7dDvg5lMliNIM1aTEPSS5RqZ7tNbw+KRAB4mwlm61Yja9ToSkVykPlg9o6h2uTecspjuMpzl9ZVFdwK4KqvMly8tLzEQNhNQ2YJwjkgP5VuiBDqy8xDc1vk4I7bAmPry24PpLV5nszjk6e0wca9IkIcrHWFdhgh9i2XGEUoobB3P8KCDTlGg8Y7YzxXYO7yxp5Ih6hy6Gz5ITg89cdT1GBabzjNuvXSFULUYHskmOiASxdXTbFnXpWDaCrvDcHgVapyCD6VSzPIdmEygqTza/PkAARYuQAl+0BBHId+fcuHEVM1I0fcWjzy5oVz2hFnihoE9BeXBLhPoyKkoxo3so7wlFTXvSY7uGNIH5RHIwn5DPdkjiHHl2yO/+5nusL9eYieGnv77D8eOKk3/6GP/0AlGCyX7CmIt79Yjjp0vOj9dsL5+xP5sxmc2YvnYTIyEoxUUsGJdbRvMp8f4u1yMHLiO4Grsu8LYB4SiPDd0asjhl5/VX2f2L/y6hOOL4t36db/yDP2Z92XO+avheGbBeYFzgw+cNZR+wwnO5acgJxEaQGEksBuWfD56pDOQjRZwKohzme4I08iRO4DOBNUPYU9mUONJorTDWoNsGnXii22PkREAiEBF42WLXBfWHR/Sf1bg6DC3XXYPZl0RZhOh7QunxyiFHfhgiyeBesNUldVvTWkhmCcJ0DDC7S7ROCHiM26BVjZQ1AksQcwiXiNCj5XU6swJd0TUO3SdDMc5X+EYjdYo+mOPWF7RnNW3RUbURMo2RxiB9RHXZIG2LTjrimzuDIHZZsf/KLj6eYeWUZhm48brHOUlyMENlE5CSvq2xqym4DNdGFF2CDxI996gbj6mcoGojzu9f0lw2rM+33Pv4mB/97n3KquVi3ZAJjdIDEiNXEXvzOfP9CTvzGePFgr7tSQXceeMWe3t7XNm7wt7Ln0OJBOEFYeQRkxRSxdXZG9hJi4uhixI+ObtLHqW8vf951OUjjBljxntU0y3Lx+csN2d8/OGHfPVnrrBY7GAs5EnDREkmbQOPPqF7fkFz9xnJYsZochMznXDcl5iQYZvA8ZMjvv37f4zWEW9+6S3e+trLyFRjgyMNX8JtjyjKgrkr0JkgXYyQkaaf5FRphDaGY5Py2cNnfPiN93m4qQgIppMJ//7/6O+QXruJeO0lnLTYixWRa0kiSzqOkXFLbC/pgx7SZiLwxSs7PP7hfYrzLW++eodbP/Mm43GOXXZMdq8wSgyLccLo2g3K42cUJ89wiWcSp+ROsv3gxyy++DqT+ZT57oKLb3yHdH+HnZdeQWjH9sEziicnKBmh0xivE/LbM2aT0VD0nDbo7gLXN0Tqgv/w3/6L6Dxhfn0Xk1mifIxJNFfemnDtKz9H3fQ8f7qmPD5ie37B5mLJ7tU9xrMRcRwxmU7Ym6bYdYQK4z/Fgl87uILvSrp6CT5ifzEnj+Dq4YLC1fTLgkga3vzaF8myhLPjI45X9+jXDqUzomROf3FJPpsR5WO+/Iu/wPs//IBvf+N9Dq/dwFnDKN9jff8xj+4/QESCPjQ8fHbCvhhzZWfMKL3OR+8+4+6nJ/zoqeKV12J8lPHdKuHw5z9P2gQerzuW33hItWyxnaX46IjwgikWnCH0hlrmPBUv8+yHp9Be4rcrKjXGq4DvPW71BIRGxIbQvYzb3iXbT3jjr/wbXBmdE+KGb9+r+B/+tTc5Oz/n/sPnaFeyk7xBmhzy3Y9/nUmacevwBtvzgtH8JqN6zWRxyvJHDicinIx/skOh7HJaX2NFhXMtrS1onSSwwkZqKGh1BdpbEgIjlaKygLQS4YZurm0jfN9TdD1x3yFiQSUcq6eP2B494vF3P+LhJ0+4XDVcVs0gskYSAhRdR+s9UgQSIVHSDz5lIFKSSA4JgGkCo2lEkkuikSebOmLtiFwgxAohFa6X+I1EuKEJGo3GmDRHjcSAy+acUNX0q47Od7TrmuakRTSOIATECpFqkILgGUB5DEdOGRmQCu8doutx/dDUlEIOWG8UgTCstkL/gorkkKFFBAmihhcN5kCDRiCE/tOkk7NiSHvYwR6uVIqSGWiLTCLUqEMLQ9DgRXgBDuwJSKQe+PU+2CHttS6ReYzKckazGenYgIyJdg/waoRzgbZcE+oO3/f4LqWrFU3bUxQ15ycd1oIXDrFa01UNfd0xG+UUVY1xipHKuHplgY5TpEkY55LdxQ7T8YQsjhiNp3RVw0VmmC2mTHbmZDtzzGREqANuU2M7B21PyAJ23BClM0SaoIVhVZZY1SBVIJ8dYtQEraaIzV2a6pSTp0uePFzx+ssVe9mU8WTEpEsZ4dBFC3KLXG/RbUU8uYZOJV619OuSB58cc3K85uHplk/ev49UmmfrYThfeXXO7qsj8kWMaxVdAyF4iAUilwgX0LkZHjT9hgcf3OPTTx/z2dkl59uOxjmiVcV3vvUuL33+NXbuHDL7/AECgWm3pL5Am+E0KFKL8p6kkzStp25qUm/JtSCaGEb5mCxOoF4xne6QJoIkcYi6ITKC0WJEJHKySTKwiG5dIZ5lqFQRAiTGE0cKneRIUxNnCW48Qs9nqNGIoGOCS5GiBxeQqkWEnmBrQrvi2tWcYBRRCl3ZUqs1SiXMrhwQjRM66yBdsY5huphg6+vEM4NKJFIr0nHO4soClCPbQNlYmqIZOiFaIaVC6Yg4UQgSkiSQuTGV0wjvwXa024ZmtcRuC0Ii8EHgiMjHV8hGCSrS4DeIYAfC7SilPOo5PV/xzt33EC+PyWcxhBqloe8bNmuHaHsiM2L38JC3M01kOhywueyZTjWiA7/qsbUboJgBQuhBx0ONuegIXuI7RVNoXJ8R6o6wFYSdeOD0pIJwsXlh3ZUM0vUNynXMjOFgMUdGW6pwxrUr1xnlCsKKy+MzFvkFcZ5wfG+JXaf0lURsYP2gpbjoEAj6ArzICXrvJzsU6n6MjxrMuCavJEQNPR5XS7okQThBtLUIJ4g7x9jFdKpF+4DxCj3K6WVEK2ouqxXBttD00DqefvcbHD1+zkff/4yjVc1lXbDqSoxU+AECSGt7OhFIhWQhDcH0JBpyI0gSRWQGn/J4IsnnGfFIo/OOKG4xosN4D7FBCU3oFNQC2Us0ijifoK8kyJlG5OAunmFX59QnBYV1tI2grQRZKhGpRKYaMzeDh7YLyE6gEo2KDSqLh4hk3xDqLb3TCB2/ON4OSaYQhr4Hvn3RcPbI0A1ydzpCKAmhIoRmGCACpNQoGbAvGshBaKSMMD5BOwNMUVlKLDuCFjRtheta+sqig0VrhU/AiR73gkK5fVIQTSXxTkp+/RoiHiPiMXpynSBHOOtRKmN7/gTXDD2Msggsl1vOzi55fq/Ei4BQGqVTet+RJJI7N/Y4p6FrPSGkvP6llzDpBExOnFryfE4c54jOkuiUerkiiSRxkhKlOSqdEJShq7bUzy9oWweJJYwd4XOWRFwh1rtoNMvlhk57eluQ7byOiQ4Qck7wp1SbkuP7z3l2/5zTV8/Yz1Ku3JowkztkVYNaPSWEFl23xKJnsjuhTVsa1nTLLT/4ve/y7nuP+HBd4/zwd/jdj+/RPmj46V+5w1evvcR0cUB7KegqRfAeEgm5JLQd8UiDcdTrSz78xo/4+Mklj4sa7TVFcPR1ze/8/jf4uS281cW8/Auv4UODqjxRUaPNAESUqUQ6R7Ry6G1Hu1ySS5iPEqJFRKJTYhHjlWRvuouOe7xY0l5cokeByeEM5EB2NcaQ73wOoS3BOXzdE48iTJYgVEzwFcokxKM5en8PGU0H14dI8X1FcB2CdmjFtxV9cUE2frGK6kqaZUvozonSlPHe9UFI5XrGAaQt0WGPLJ6gRwp0ADncoSyu7ZLOInaLmNWyo1pv2UpPZCKUitAmJUokxsSIoEGMWVtJWxV06zW2LSjPThBVS5QlQ0ybjmu3r2CijKa2bC8fIH3LeDxhdn2fo/uPePLsmG9/8j323/gce92YKN+yN9/B+ZaLixVGtMxmC3auJXx+NOW9d55zfFbSFi1dbulLsI87fNsRggOlkGMHZkTwGWFzBELg+0C/avEuJ7QWyhVMIqSOkCMFMhpMOZ4X5daKBJjansPFDipVlP0Ji+kBO2PHJDrhD37jIXujp8QzOPlsTdh2dL1BeM3ZO2su6/IFrkbg/Bjk9Z/sUCh1jY0C5AZ7VcGqwxY99dJx0Glio1gaRXLc0GkF8wXxWY1WGUqlxM6zDSltkLh+yxkdsVDMRcRvff8ett/g5p6L5ZJ1aKi8JY4jFk6AD1z0LZmQGAlrOkZ+qPPbRJHMDGacEI0idscRajLCJJosLmn7Lb6TNLVF14JYK+ZpzP71MflhTrKfoa7ugSnx9Ra7OmL74SXbpzXHd3uaNGB2JPlVyWg2KDpFFA2JjEojrSRWApNr1DhBZmOa1SW2aujLGpkbzDTG5DFaBoKr8L5DWIlP7EBTdT1CeUToCGGDMhYRPME5rD1B2pQoDN2IzXmF63qscMRjhXSOvu+RdT2swtIE0Vm6NXQXjuVZRaIdUSSJQqDuCvrG0uiO+tOW7LBBjFum4wXOx/Q1lGcfE+KcEAxuA8sfPUZ0DdlY09cd1UlFddZxe2eHZDcjWYwYHVxBosEpbKc4fl4QjzTXb93k9lfepm8F1bJDP9kQXzXEmSLePaA5K6hWjnLdU61boqhAcUq5VJx+/Iwn79zj6XmFGWvGN0Z8+e2vMJdjECP60HO5NGy7NU/S+1z5yi2E7kDU9G7F1ZsHvPXFt3nvh5/y2//8x3zy3Wf8nb+Uc/XlBZGKMGKMfPst5Ooc/Sjl6PFz3AQ61XH6znucnpywrCpE8GRC0IbAZd/z6x99k7V4xkG34fP/67/K2kBRFTTPnuILgRJz4l2FHBec3j/jh//lj/jdu8cUdU8UYIvFBwEh8N36Ce9/+1/w9vIxP/vXfpnR4jpyvI/Ttwfchu4J833k9hLreqqy5OGZIlx9iUWec2X/kNwcktYTzKOr9CONnC2IJy/R3TmmrVdst2uefXaXuVowScfsHA5vyzKKkdMd7NWvESYjpM+x71dUm4TGQjYb41OH0B2mH/P8e0/wtmeyE8H2I4qLE86ePOWTe09o2oBKJ9z40l8gzcb41vPub36HP/rjD1iVJePdnLRdk2jDOJvyha//WSb7kni85uTuE4q2H7zGoUOkCQJNsthn1faIbUlWVuzfeR2tDUppnIPZ1et0bU1kItriFB9F3PryCjNJ8cpgZcx4Nufi9Iz7n97n9//gPk2wzPbmrI8usaJgsTfiV3/61/j6X3mLslrz3rfuMb66SxSnCDTbbcHu4Yxo0vHp6T1+958es7psePuv7LN++oyLTeBUAmNQ4wgxjZj8bE27uaA729A92wBzCB5XPxwuuNsOmnrAp6sWJT29Tgm2Bl8CPb/8ldf43JcOePVvTFCuY1U3nG4D33v6T1ikCTEJq2A4sxHCRog4o3IjvB5x7fCAj87OqaUlvbbLelXg6wpv7/1kh0J07zE2EQjjSFxEX/fY1qJtSbMTExKFMYJt7UmXG7ZHT6htgqZDhZJQK3rb4XyP8C0mikFLqtCTpglMUpyesFsU9F7Q9DV5EMO9C4NxzCiNBoKzRApSBSPDcFksHUmwRL1Atd2wknE9iQsoJ0mkIZ+k5NMxk50po+t7mLFCZoHu/Ji+WGPbkqZesn3aUK4cDSDHCjNWmJFGKQFBE3pFqIeLbi0FJlXDHYKC0G2wTY1rO0Tv0cqjpEUKh1Tx8DbpO6TTYAuCNIjQI2mHCo2IkOESEarhRNFUSG9AGoyWCOsIdY8LDmKH8x194zEv+BhCglIQ6UCsArF9scZqBZ2DvhoKOu2yp9z26L1AMIq2aag3HfW6Z/n8gng8R8sYWXpm165gDESpR60qhIwwMiK6uYPV4LWk7z2JkUipEBgip4l0Qj6aEilD8eSE4x884N6jJ9y6s8+1OwccfEnSd0PGXEcSjMfR0NRLpBPU1RmdW9LT0VpBu6n47Hs/pvqplnx/l15npCYwEimpzPDFc7zQwyXj5C3UaAtpiescZeXYZhWx2hDfOMCkEWodIRZjVHGJ6SricUZIPbZt+c4PHrG1HaPdmGtWcLFq0H1gP1Jc9o5Pn5/yz773IX+++0uMD8bEUUB6jzGGMElJF4G+Kym2Ne8cLZkoiTCKunfUIWBUQiQi8r6ntQ0Xxxc8+i+OeP1vT0kXBq8aCDEhDH4NtEKZgNIGLyT5LEelOf5Rw2/8q19nuazxyx71A0WejNnJ9virf/lXSPYFUdKTND3p3JDEGrk9Rc13kXmKmuSIpqA5XbJ6Z8lv/8t/zGW5pcPz0u3XePOX3uDaa1dIFnNMeoHvA8k4p28USkEceQ5vTtgsLW0DFx9/QnznZZLRlNnhLl/42TdpXUecabIo0JcN5UU9FOMCA2ZiyJzjvKOvVggxRmlJpDvWRxXb84KL0wtuvbxHlhgirRFuQt2WuOCI9lJMPmEcHFdffhkpNV07JI4iGdjd2yWKRvz1v6W5WF+ChDQP9FVLU1TIuGSaJLimpKkrthdrklhhVCBO58jE4FRJ8QCWp46L85bn7y7xIyhagb0UgEemCjWVmEmMEiNMm9CbjuBfQCar0eCNsAJCC934xdPVvcDo9wR6lPZ87Ve+zM987ZAw+jFHqw7JmsRs+ei7NZMoYpKmzA9j0pEcUo9xxO58F5XOCdMp46qm7gKFhSwJNLaj7Vc/2aHAoyPYGxOmMVGraGtwpSOqLJ2WiN4hI+haSbkt2J6CHu2jQ4OyUF9q6EqkcJhZQqQNQTiqzrI7GSHHCS53TI/GLNc9SnWkDhwOFwaqpBESJQbPQCwVqRLkCiIViIIn6nuUGDSbymuUdRgEkVSMk5jZ4ZRsb052uItajIEO25fUz46pz1a0RUXZthTbnt4HQqaIF4p4btCZGT6cVhOCgkYgE4nWCpUOLecgAq7e0jcNobOoAEoPaRDBC/EOMJhgLMI1w8UyHuEtEokQDhGK4YgewLU9Qr1QdKoXbL7O463D9w4seNuhVExQgApIqYgiiU8ErRn0mr71dNYTdKCvPfWlp24cqQenJM22orwIbM87Th9eMl4YEm0xVcfuV98kniiELJFxgXACEyTJzSus1hvW24pNvcVlnlgnJCIicopYRMRJjrSB7eNTHn7rfb757FPqyzvoomL6ypiulTjfESUGGYMXHXXdQuNouxVe14g00EtHU1Xc+94l3axlzAFhdMg4ksxNTixz7OocS45WO+j4Nr34PmW/xHZDiazre4wu0AcT9GQMsx6RxQjhEU1Fcn0GcUNRN7z78Qmj6zk7V0cQDMvqFGE9E6XYWs+zyw2bvqO6WLHYHZEvRoS1HbArwhDtJ2wfrqjLmidFxTSKCQFK53FeYlSEkTm5l5y4SzarLe//159y6y/9FPlu9AJcOKwJhQgvItISZTRSG+I8RYqY6l7J7/ze7/DZ2RHtsNBkxIjD+JCvHH6R618JjG7B2GiyJCaODaquBllVptF5jG0rLu4vefjNM379v/rHnDUFVil+6uoXiBeB8U7M4sohOtWgPVEWY6UcHtyJZDEdIWTD9txRn55h9w9Qownza7tMb+zQ9Q2bYsnOwR7lquDUHA0gSxfwvUAnMcp7hG+xfYVvFSpoVOwoLwraruP4yJFlHdM8Io802jta1+JVoJ9MhhWrihnPFggraX1D8AHpPNPxjPnONWbjGY8fPWazWZPmikhroliT7yhSo4mlQhpBW7X4angRTV4+REQG7z0Xn0q268CmsJwfF+iFoXMSaonUHpEEZC6QxqDjBBWnCBMRrIfgCU0yoPu9Q6CHf3vxYkUsCFwglWe0q/ni11/hKz+3x2ef/A5PmhbrCuK44OG3G1JdM1/UvPzFfaJEYFuLl5rJ3oJkskuFIlUpxll82WNUTC87CNVPdih8fFGRFlPiOKNNTygvG/q2o1CSW3j81FPtexZW4BrD0wJupx0+8fSmZ3VW0mFQccT+3LM0NaLwyAc9UZ+Qt5BYz1IIugDKBpYvkkfeeRoEMcMxXmrDbhSIomEVF7cB21i2SFLjyYMnQjIlZTLJyOeS6S1F/IW3kEkCLlB9epf6yQnl8TmrdUdtOzrv6UQgmSji3Yj5zYj0IMFLg/WGYlsT3IBmTVJNMs7Q4wgmAovH1R31uqMqQEcaNZeocQYmwaERfYt3ChdSfMgIIR72iC7gtCaoCBEylB4jnB88uvUITAY6Q0QRkW7xStDalrphwHc3Q+JJhACdQ2WG+GCE2UmIR5rN3SXlumXVDRiLsglcXjpcB6aRxBvB9mKJJ6f3ET6ZoSZThDK0TYnauYYwNd3ZMX1tkUCWRUzTEcc//Iwn793nk7MWM47Zv77PL/y5r2FMIE4U6TTFPrvkgwcP+Y3HH/KJbeDhPUSz5PALY6yZ0FvIZ3PiNMZ2Hcunp7R+Rl85QpYiZwFzYfEXNQ/eecgo2cVspqSvB26NrhM7xbbOefb+htHkmHyvROxd453vfsjv//YnbGwgCMB71uuSbvUU22aIs1PUS3vYLtBamLx0g/LoPs3Zmksb+PIXbnHztUNak3Pv+A+53Fyy7DomStH5QNf2/Pgf/Fd88S//D7j+1Z/HqXfwj0rcekWYwtH9T6hPn/PmVPFhJ7ECtIvYM3OcBeU0b01+ka74HkfdU/7+2f+GNy/+Sxbta+jdhm499GhkFtNVMVUQ1IljsnPAk82W5UlF9QeKcpNguIHhBiXvs2bNsv2Qv/F//rv8B7/y7/Lv/epf5eavLGgfaNwqxtz6WUglQRi61vLp//2Y/+aPv8X/85P/jsqdAh4VFNuTu5z+vy1f+N5d/qf/q79Ft1wREXDHguL5BUXdshEjLpaPaboWOc75qb/2b9OtKvq2J785ovj+knf/6Mf8J//Z/57/8a/9PV754hWu/7TBVYGLsuZMdmR7KWYCWe+pyikrWyGCRIgJ0WKMaT3KKabT28z350xmE6II2mZJtVnz+EcfsHzyhKrYULmWdLJDmk/JJwtWD2pk2mHlCe9+/y7v/ugD6q7l7/7v/i5/4d95GyEV68slRXNOloz4ub/yC2w3hofv3OPJhw95/dqYUXsI25h/+Bua5aUmuR2T/0/GzPtAsw1wBHWQ1Hga1VOuWtz2CLcJkLaIQhH6guCXoHYh9ARfwpHDRiN8NELSE0RgtEj4t/7jz3HljQ1Vv+IHTx/ywcMSJp7dtwSF92ybwdD3cqY4WVeUxytOziG9leNEQvnZGZu7GefPtjx+dA8hZwQMhNFPdiiUNqINW3RfErcVTWexziPxLDuYNIJZI2iMINEdiaxxiylGQdTBJguItsMFyzJAftJTLRuePV+Rr54zG2kWE03jWrqyohcdGQqURwnBDaWYRSDxNLaj6sHXQxy1ixVxFJPEhsU0Zr63w2gxZrZnSBYxJvPovMZdbmj7C/qi5PyjE6rlmrZs6Y1ATRVprMhTTTLWRBNNujM4mftOYAuPLSw6lZhcke7k6IlBpBJLT1/X9HVLXbRYYZCRQqYaEU8IKsIjadryBZpZ4kWMYCCqeu9wrX2BANdIVw9EPlKEGKx0BItQiig34AzCVUOj2jls79BS4UPAhUA8l8g8RugIPVVE446+BdVYamdx2qMnEqEhGEFTgS8qJlf2SHcndMEy2p2gVYyVMcsPP6JdnrN8dI8omSOlxUQerSQieITtiIVDCougw7kKLVqUt8hKsFltiLue20lM33REWFZtyZP7jzl46RWyNEGPOxIZqLuOarWi2FrqtqduW8qq4/hsw9lyy5PigjfqJZNwQGzMENZoOqpqhdhuWJ8F+OiITfoOF88fEiXD2qy2gc/OKv6zf/4xef89Xn/5Kovbc2Q0RhzsoN/YR0eB8+fHHH9yn1+6s+Arb7/C7ueus2osv3jnCh+0ge+dXPLX/+YXGVuPOKvoLkqK5/eoj8doUaLjIc4cNueI0w3ptuEwg29eNGycRKSGt24c8Phky/F5yyfVKZXbR2Fo+JBeSrxSaAkh7vFagtR0fYGziiAM2Z0Y942G0wdbvrV9lyN7SoNHsMXRATmKCY1b8t/++A+4qEr+j1//93DdKb7bQjHFjwy2aGger/gn7/8Lvvf0LrXrCEyQOsGkObPXr1I3NUfrJzy995ArV3ZJIw19jXcxIXhEOEPcfUa9quhJaO49wfcNaE3WXecf/bN/xje+9S2e1yv+m+/8On/W/QyvX/vzzN4es14LVktHU5Q8/azg6NEln979iPwazA9ypq+mXHvtFiZIRNPx7NEZTejwac/h6CaxHj5HcrRFJQmjSHF4fcFkb3/A1xDojzeENkOomNlYoGVNX1+yPH6Xen1GU3sef/op119/k8lih8MrhywvWzKnmGY5d27d4vh+yb2Pn7M8/5Cud+zpQ7525Stko6fY1rO5kfCu/5ijk5bTS49/vkX0CaqNMPMMMZ4RWjUko6Lx8AyoDaHfQLchuO0Azg81qRL8Gwe3qZ6+w117QXHRMc6H5+rJsWOtAuNck+8YLntQZUdbVDTSIsqGbrvlox+ccHKWsd1WeN8gGIHYBRY/2aFgiXC2p3c9uA7v/J/+X2stbScQrcJrSdAOoXq86BgA/R4ZeWRrsRbqjUeuGzariuPNErmBWWwo05hkrAhdj5IOYQcwnVYwM4JUgfUeGxydBd0H4i4gtCRSijyNmcwmTOZzxospo4MYPdEI1eGcozne0JUlzWbD6nRL17Y475FjgxpLolxgpjFJKtGpRscxzvdDjrhx0Dl0GlCxRE8MKpNgAn1dUW1L+rqn6xwiNaAECEkQQ7JAoOnrJTK4P3U1iDDgWIPzeDusiaAn+H546w9DWgk7OBCkMZhYI1JNKDx10dH3js45amkGobADdIKR5sU9R4JJYqJkKOgU1uG9QEaDk9oZSWfBVpZZFBNPRqjjYyAglCKZ5VT3n7J6/IzjT58xO4Qkl2RTTfCB4AMQSBKJV4Nj1/Xd4JwWEHpPsakQPjDPU26JmkgE6r7l7Pk5+3fukGQK1Qq0kUgB3vZ0TUPbtNRNSX/ScHF+wZPNmse+oi0adNmjPSgX8M7hugYnepqioz6pedo9YLu9QAKdCzQOqrLn25+c8fz2U66lKfNXrxKEwEcKN4mItKIuK8rVijtXUg4OZox3Zmwut9ycj1mOM9TZip//xZ/mQCrEkyXrJ3fpiiXN5XPy6RidxGATggvEkWaUG3YXCv/E4SREiWS2l3FZO/Q6cFydY0PG4N6DwBAZdt6BtCCHwdc1DXWtqTtNfjWnrZecnW652z1CkGNMRpaneDFilKRM04yzJ494cnJMbb9Ld/zvI3qGoqUboHK2sxTHG945/YiH2ws8CQiJVBHSpEQ7OVzWtL7m9MkpV28eEmUp4aIBIkQAZUvOH52zWlVgUlyxQSUGFRl0G/HORx/xo08+og3wydMPOZyPefLx21z72g2C0FjncXQ8u3vGu398n0+ffsrNdgrs0N9sme3OSZXGVxWP3lsjVxviFcRths40fRAQGVSeIZVncnUH7RKUAGU62uAIXiBkRJIZ8txQ15K+3tI1EeWm4+LpCbtX7jDbmbO32EP5Dfb6IUbHXDs84MF793nwyYamPhseru0I8Txn9LkxaqoYRzOeXX/Opt2ilzXdqkWIIUJu5hFKjgiNBFcSYg1WIEgIskarDhN1qGhEWyjSWHI9HbF88j6b6mhwziuB7eDi1FN7SF4E2StnkF144U8J9FVPW9Y8enTOqplR9QOJmj85JYidn+xQMGqCd8ULK1VAy6GboExA9z3UgaaE3RnIyNOkFvH0nDLTOKWQVINQfGvpH5zxzPdU3oN3rEKgqizbpua6iMkiQZ4KzlpH6gOxEownGtdb+t5TCcisJ9MDsnueGbKdCaPdHRY3rpLu7KPHI4gUzXqFrXvaZcn5szPquqbpG8gsyRjyzGAWOToKmFSS7uUI3xIIdE1H7zva3tJiSccOM4N4IWA2ZNJ917J++ojNmcchUYuYLI5BDDX3vjojym+hol2qsw+JY0kUa3RSg3uB2m5qRFshtEYGgcx6BBbhaoyUhLrD9UPcLU4iDD1i2XN+UVK1jlYEbO/ovKL3iraOSDYQpZYo0sRZhFikdLVn+dxiu0AfJC4StNFwM21rBTpB6piTT4/Y7ngmu3NuvHKVS6cotp7jswo5KfAyRWpNtW2o+4A1htlOxGbd4ttAVwa0ydDG4ELD8qJmGzRhf8FPhZpnZw3rsuf4k0ve/iXJ+GBEG6aYaYoJjmyRUckYu2zxdYPdFqybgmeu5BzH6p5gM4Hs5QJXgvKSOIXd3SlbUVJtGo7unrOqNtRFy6bx+Bfio9I5tpmj3kkR12/TFyeU509Zn52RvPQFVJKix4IwCTSiorlc8b2HF0yFIEki5ibiF3/lf8G1m/t0zQXf/8//lwgpKI4b8sUd9CxDZRNCn3D1lzzp8VP8H1uuXLTo2mNjON/rmNgdXnF7fOOzHxOCQgCKBGnX9M0F627DOMSYrEfJms2y48nTDacrwdc+f4uj8j0+PLuLp2Gmfo0re2/xpZ95k638lC+9JvnlL+T8k/9oxTcvfoej+mMe/dZDDr+aMLqxIMQJQSpa23Nc15y7kpIEJV8hiAtCOMfZ59TLhsPxPvNkh6fvPefLv/AzROmcprH4pEV3MdHG81//8RPSLvDS/h75YcLolbdR2R7NhzV3+7s8Dk9YiKvUPOM7H3yL+w8+4p//93+TK3eucuNzKe3zFf/p0/8X/59v/lM8ll59FeMM2ysXZF8wZFlKS8BNR6w3Nd0PH/Pe936H2dsvkd86GIqRu3OEtqx9xY//0/fJR4pXfn6HNM8hNrg4pk9HXH/5DodXD5mM32LrSoyz3Lj6s1TllnJzgXA7uDYwWyyY7e4zu7GPDBcUlwMpmTDn6aeO/8v/7Nf5a//bVzh84xrJ7j4+Pxy2AyEQ1g0yBzkWZAeaKIdQO0pf4WWDChD7AlntM114FoeOfO8GR589QLVLVvWGD7+zpXMdb/7NHZ5874zjC8dRCf0WlhvoloKrbyzY9BGb1hMlioulY3VR8OHyIYE9PALB/MUT/JQQzn+yQ+E1f8JJaFmGHuEFMjIYBTENlQv4JpBcgkhTEiMxsud+4RlQcQJRKsJFQ1V23G9qYiA1ivlkzKuip+ktm9aSW8ikQkhJ2VaUHkolGBeCrYBICd6YJUylJ000eR5xcHOf0cEu+c6MNDf4cku52lJWBVVR4EOLjGtEZkgnklQZrC3QBExwRAGMAI1H2wpXVXhn6XyAkSdKBPEUUiXQWYtOAkiLd4K+7tg8blidBGSm2dnTxInCOA+rFpE0EG8ROh0MSlWA3qCSZIjfeYaIalMjtR52yG6LkA5ki5QNovVQDesEuXMFmaToxMG6h9IhCOgctJEoNL5vaZYeu25QeyPinTHxLGMSCaaXLcvnHZ982nP9lkFh8UlHZT1VUdE7xbe/8wlXd7bcfv0lXvvpn8bEzwnSUvc11D2d9azLhtm9E9qzLa52jGcZm7rB0iGKwGg6IhlNSNMUX9WEskQ0DamK6G3HurdM64rqeENhNOtnT5m6HOcccWbYJWPrOtZ1TIgrXtIJQjoqv0WPA2EEXWnI1Zh8ZJjv7TEyNbZ4ipAFRdtyedlzsbQQ4OZIEQvBReUpQ822K+i3JSF0eKcR6QGb8/s0dotNItZrx6Y4RjYl3e+t+d0f3+d0ucYGiw8Vdf0Zm8sf4YUHB6K1hHZL9+gIV6xQ0xXjV38BPV/T+z/g5y+XvH9vycePLjn55Iy92TUmL10lfPYtXmiPMCguTk44eqzZihNuzHJyMSPWE5ZHRxw9WnO0ht5eZ9sv2fRnQOCLN6/ztT/zNn/r73+dP/ijHbTecpE0/Ft/fY/zb/6QZx84PvvRfbI33iKPpqAsrWspljWr9yxd22PDlhDugnCI0BJsTJ7f4PphzhWtOP/0AX5bocdjtK2YLRL6eBfZ3eYSydQFrI3wNsXZDe68Z/2PHPZk4O0EAX/xypdo+orn9Ybt0ZZ8VhAJwfq7G9rTAk8HZGBjusJx+uAJrmyRcYxWsLubcvcPP+Tj3/8ev/fgx8x/e487t17i7/4Hf4fdmxl22/P0X17yD779z8lszJ/58Kv8rf/DX0KnKeWy4wf/8H1OTu+iRpZf+HNfIFET+kTSuIb0Vs50b58028dkPcXplmK5RmUJOrdMrioGWe5m4Dr1Fe7u6+wfvMZXf+o2sx5m7glR/5jPHneoNkXLBLHW5KsIak9TSmZI0igmG2XcvL5gciVncn3MlWs7HN+aUW1OOL93BI0n0xnjsznH371k2cD0miJkiu0pnD3ukf2I4rLg9ElBvXZcHD1hfdFjQ0vg7MX3OwL/cNhGo36yQ0GEDhEcIoAkIlYJJhJEKuB60N5j+0BXDjFNIRXCDqujQEA0kqr1FJ2j8y/SECZinKXM0GxpqFvHuhtwFZGA2IMLAekF3ku0kSRGMx7HzKNAkkUk05R4MkGaCNcHirMNfQO2dYSypG16grKYrIe5QQaBeuEgHhbSASkl2oDSHls1hG5w5goRwAakESgpiccCnYJMB37RxZGjOOloC4/zIAiEEKAfPuRCKZQZ4XpH350jgiC4QAhuaGLqoaziO4toXwg5gyT07UBYZegrDE1HcF2PbwcUhsoikkxSbRzVhccsLH0i0WpQNUobUCKgyw6vFEIHiCKmt64yKTdEHx/z9LljkfbsTizxbIZXgrrtqLtA3fQ0dYftLE3V0XcOrfVQptIKHUWD46D3SC+J4xitNDhBsa2Gn20QdFWHUoIsS5hOx9i6Q+mWxDiUlkglUcpgdILvLT70Ayu/a+m7DttbPAEjBGOhuCoMuzs588MpZraA8zWi9bhVQ9kvqS4KuqYfxEhaoSKNlgoth+RaCFBWjqp2BOeG30FQuDZQHJ9RFzW9CxA8Jw/PaJoVDz8teXqxpmoaYiP50R/+PvGo5mL1CZPLmtx0CNkhBATr8G0HfY1O94mjnHxnymTPkZ82GKVJF2PS3YRIKbRSjM2ISGqavqKoLlmXMT5taKuWOIFoHCNFSxRZ0kQQQoUQAikGFPKtt6/wxlev89rrO3z73YTlcs3m6ZafubZDPk2IhWbVXND0DdYHdCTpbU/XdXS9GzoTCCAQwnDPFYIiqBFqNMFEksrX2KrGls0AZmzd8LdsDK/sHLI+X/O863j20ROu7+YkOqZ6sME37kUmKjBNJ0gpsNWG7UnJ4mZPNBcsjy1N7Rku0jReaFzQ+MawOm0wJieZJqQmolwWPPrsCffOj5jImmANwfWk6Yhi7Xjw48c8OntC1EbsdXvIIIiCoG5aPvzoEzabZ8wOE0ajlLOjguVZxbOHJ3zltS+hBFw+e4bD03eeurAIIYgiQRIPytMQ/iRFmHPysGB5p0OLGXujQ1Y7cHlF8UA+QZMQkZJGgnDe4ysYJRPiuicVinmWcfX2ASoThN5SrVY06y3NuuS8bvE+RpiYbTsh1QtSVWD7julBiu+gbySaCB0URkmSWcLps4qubcllTuUtDgt0DKof+cIT/xMcCideUQeFBCISRmZMnGrCOGHSbvFdR03H9sLR2ohYJlyLwWYOJz3iFE5tYONhVxheynLyLEGMUvAWaQU6tHxaWRbCsyMk+0IThEdJiVQx+3FEOorROxMmeSBdjIj2pkgHZeFYnqzxT4+xQuGRg3cgiZAaTOOgk0hpkaJHJpDGCpVKMBo5EgjVUxwVmNgiY4/JArZ1iCCQQWKuQTSJ0Nnw4P3k2wXPPu549bZETwRmJLG9pFsG1Egj5yPM/C2254/Ynt1Dk+Ntg3WDVlTmY4L3dNtzZA1BGjQprmrBBKT2BCsg0uDV8L2cH6PigJ7PmN+u2Kw8J3ctMvf4caBPPY1sMEpglKQ/q9GPKlSsMNczDn7pp5F7R9RP1vw//rDgpm/5Yt7x5r95G5FmNNue6f4B8XhMiATr4yNOn6+pC8dkNEHmhmw+YzKdYSOHEAItDNE0I8tyui7w/OSMnXGOdy11c0YySTg0B+TzCQ/v9WQjyzUF43FOfmXG+OYBWZxR+hJbtzjXcXKyorwsqZcFrfVUYchyf1mmvHnnCq9/4Q7Z51/nsz98RPX8jHJ5TH38kKrzbLtApgzznYxypHhyVFD4QAiejQ8sTxnWAVJhpiPYrOhOVxTNks1RQVM6Ymn5+A8f8vBZxx+dtEQIRkowk/B//Y/+Phd94KwX/P1fe5PpOEekc2QUIbMc0dXYcE5QKVJ7dK7oRjHkEeNRxK0/9yaZSOjOL0mjMW/vfZ5pnPPh5Q9ZdcesrWR/NKNbPaPPPMKMmOwGbvmMWRshxBljM2EWXeWsPuO1v/ISr/7SHbSQPDm/5LOPH3H55BG7v/Rz+CRiz6RU0zNKd07dTIgODrAXa3rfYHcsQsWDEIr9YZiLgBeKkoxqtEs7iskmjnqzoYgTRG9oLi9oxYpGVPztN7/KP/rwPX7v+UO++Y9+i1++eciNO9fZLB+8GOqCJgjWWrLtBRvvuHiw4srrFh0lPFlB2f7JYBL0JsJHE1JzhfufbLAi57Vr1zFuTVl1nGw2qCDpgqPyDTYsSdQh59slf/DN36MoC4IP/Hj1DvX5ljgDe1HwL+9/i9Q6Pje+zfzgJu/+d7/Nj7/1Hr/17R/x5r/5BUS35O73fsjVr/0SbQNdJ8knY0ZGkw1idhAGITO0PuRHP/oeai/w5urPkMkddncm7NtraPMDjBiRRWNuHnR8+mhNu1XceOk666ePMXHN7iJi9wuvcfbkPo/e+zHLYsPlkxP8pkJ97jX2b0xRo5hP/S6vfCEmP1/x46NjXrl1jWxPkl2FVBhmeYY6FFyZKL5/9pBq3XPNXOOJXVH7Gh9WCK6AyEGkP9mhcBkmBLYIarR3NKXBNYbxpue0LrGhR4pALRz7JSx0oJlqUq0YaY0fSd4MEX0aqGnZT4YH93Lr2I0kUx2zN8652VliYYjkkB5QyhNHmr39fUaThCgxmNTQuwK/qWm3Lau2JdjhdyaVR8UZ0kQEFWg68NZCH4htQBkwMUQxeOWx2lFtKnpnEcbS4TEzUJlERAFZghwJon017GNFi+0ant+rUNIz2xfIOVgnAYXvI0ySoo3h/8vafwTrtub3edjzhpW/uPM++ebUt9GN0ADYCARAECRVMkmLCrbKol1llewqV3nAgQaU7anL1sTlgcuWSlUu21Swy6bEImUwgCBAEGigGx1vvvece9LO+8srvdGDdT3XoAdncs7g7Drfd9b7rv//93selSq867Ctx9QatZcRmp4QwvBDqJIoh79TOQfO4V0g+oiQCULLIXOrNEEKbNsSg0YhwICYVJR3IkdvWbom4n0k6QVhaokmDLfVW0WWKtKiIGdMGz4iG4/4uf/F7/CvNb9PmEzJ92cUoxKVVDhjSMZyyO9Xc/Lpq+Tzl9i2gdiQV4ckRYXIcvy6o9t6tpsW/eWO64sNQSgm8wxUhgsCs+qIpqZzkd5LVDllbO1AxgyKvdMHHL7+Cs3oGVwkiAA2axglnpA5fGGwrufGWb4IPZfR8pe+qHnnoaP4xX2O33iIPdlHtZrVh4rbFzfY57ds1g1tBGcExjt0HAx4CsFIK6osRY9G6Ol9sl3H6OA5k/EpvrIw6tC2Y3MJiy1waQhS4DNJmCp6MXCO0gjdkSUcjNDzY+S4RBYpcStotw3l4k9o6iUvv/cj3KeatG6ZHgnen6U0jeAGz4P9ip/7tzIe3h/x9d9/n1/6tW+yfzJBdBtGes5oOqdMJuwdVkiXMNsVVJOK3/r61xhvJ/wf/uVP2Hx+zeXJS56OPXMZef/OCcXpIcVMc//hPm7xiNo0XD2/oagmjPYO6aTACyi2FvEVvjmSAS+J0eGdZnF2wrpcsxsJ2HacPVnhV4K0scjtOW26Zl1twHXE6NBJwje+9ZepCsXTy+/zv//hf8qz3QsEDikaJuMxB+WIA1Wh764IkwYfAy+6HbXvGUAigsPxAffvnPLom4fs3Tlkfv+Ayd6M7stzXl4950PzEoPDx4b1dsWTf3LDxLzg+vIFN/YJ99QELwNK9JzfniN2hnDb00WHi4INEscwfz+7WvDp5pzLLx/z4sU5/+f/09/j7/6v36NzDcv6mm+YN8ErCClgSap99uczfv5tx6J5jfdffYufOXydf/JffpdbtwXR8/reHZ59ec715S337v8au9s/ZrfYUsWchw/2Obg74ejNE9zNiv7qHLt5waMiJ0bP1cbwne8/5rXmmMM6Zy4c+nyPvCt4Hc1UzynHnjEdyU9uOf/uc569WFL9xUMmTrOVGZ/6z/nt+3+FjW35/Zd/TOACwV9B8O/+dA8FRDlgmoePA+8MPkS0t8QYSaSkTCVFqZiOCopphc4DOstQKkFWHhFTlAFvt/QxYHykMYaanEJpinHBLNHIICBI6h7KxFHmmnxakVc5WiuilLB1dMbQG4czBiGHNm2ZC2TikSGioqRuAtEPRrKgNAmRIMSQ9unsoDFUFh88MguI4qsRUBAoFMlEoieSdKaR0mIbS7swXHw++E6rKYQ2IhKBRIATKAFaa5KkxK6W2G2DbcPQLg5xSBy5SDBD89m1BpxDeYf3bii2+AGkJxRfobcjzjiih4hE90OqKR2nTE4VLz519G0k7UGkjmADoRWYG8l4nFDEhFBG1l9smd7JOHlvzNGbM+LokPz0BLPzBNFT7xz5dMxoPCevKkzb0XaOpvO0fWTylWEuOomzYEygN56+93Qm4IWg9wHjwXvHZtVQr5cEJEFqhAAbIs546s5hQ0TpoZQ1+DEUEYWqKlIXyZylKwyjxjIm8Dk9tlPEbih1Sa2/+o6l6FGGKpJBuSo1wXqc+woX/JUJDyDTCVmSI3UJUiF0hsim6PGMbDInqyf4XuLyBpdKhBAopRCpwlUJgTCkwVxEpSm6GKGKGSLdB31LUAprBbvVJbc3t3z+2ZovX0gWzuOzyKqt2a0Etzctje3o146+Cuy2A6MqyROMS9BpjlIaESNpOkEnIHUCMefwFc39ZcPoOyXb2x2f/PhLvvjsUz7//Cmxb8mwPHtuWL14QesM5+6ah35JF9as2zV9WNO6LSu5wothZCdEhyAlCo8QFhm3JDIhUQlWtFhn6UyL67cIU9PHDqsclIosU4yF5GqxoriaYgpPcS/n+OwOpZ0zHlWEkcRaCLZg23tu1lvM4oZFs6X3AcgQ4oBOemo6djaSG4Nxlhg9Sgg8ARMdlUw4mJ3w6N4rTO5NB1aWFTghyfVQNpNKYPpIHRxd3WGiHxwhXwmxfLRDkCQMe6euM5zfLGjbNZvthovLcx6ff8lysfjKKR8Zjwpm84Jyuubk1X3un5a0lxue/vhLbOFQ+wlxXePqBmc8iZEIF7F9x2J5weHBMRHNeD7DuZ4kdUy1YLPq2TSOjXUEU/OqTUj6EneR8tmnZ+zaHb1qOH6rophLqkJx9tE5xuwYTwN3H824O3/AqzcC/ZMPCNct9e0aIb+CdnFLjD8G/sZP71DIZIELLSF29HSIYJDRE0JPhmCkEw5HGXsnKeV0RDkZo1yHUiVKZOBqoqygFYjasukMnTFsTUcmcvZHKeOp5vBgAjYMrtSlosh6qkKipgVSZwQExkdYG+rdjpu6wYeAyvQQ1xQ5UTtEdGQ+YbUGgmCaa+xIk+pIkIa+74fdQeNIAO1Ae8gngtgND26lJPmBJp1rkplC2B3mxrB9Zjn/KDB/KBnPBeZ5IJuDjgJpQYtIohOSrKJ5+Sn9osE2Dt9ZZBieTLEPBLnC1T1200F0KOuGfQDJsLwMYjC4xeHQsJ3BS0cMmiwrIKakVcb0bsKn33PgIMkjunTEXuIawc21wCNwyoNx3D5p6fqe6kFk9PaMYnqH0d4Dlo8bGrOjNYHR/j7z+RGj0Yh6tWC9bFgvO9q1YT4NuDwSEoFxgf6rXyZA78BLQRcDNkLXO25WW86eXaGTjLKqKEtPbx2b2vBi3VN3DdEZQOA9+DD4M+R0RDYM6akngcOdwDaC7/s1MaREnxKjHZrdIQ7600wSc0UsNCorCL7FRTMwZuTQJhdAnqRkSYGUFYRhJEAyRxQTVDlFVVNaGamzNa1SKOQgXkkTXFkQlCHaCK0nUyVpOiYp9pDJETF5QZQa5zSrxS0vX9zwk49aPnjhCCmUewlfLhbsziOLFy3X9Zbzj1rEWcvnXyzp2g4IOKWQ+quxjrMoNSOqHicDweXM3tAch4Z5PmZ1veH68iO+9yc/ZHo3oFWDtGvW12sq7Rmn0MqGr8kH2PSQ2/oaF5fUZsOFusZhBxy72CHleNgByposWVJUU4pxRtAdUTms6DFuSYwNzvX4zuEnmrJM2IuCH33yMeFRyeTunLd+/nW672fsupbxkaIfQd85GiO5XTjE+ZJUKm53C4wLQIVQ91mGlovulrPbQ2S6YX4wojc9WZGjkwQB7KmUd05f5Z23f4bTb91BxxIvC5AlSu3QSpIlKd4l7KxnvW0xMTBBkKMRIWUQsjiEgDTN0TolEDFxxWpxy/PPXvL9z3/M8nyBWa+ByHycMJ9pbNLxjW/OmE8Ezz74gs/+/GMmd0tOsiPqi2t83aJ1ShUduZAI71muXrBajThxgslkxlW/JC0c8yTlk7M1lxvD0gUIkVwXjJnQnOV85ycfc7tZkmrNOw+nzN4dMX0k+YPn5+Rl5NGrFW+/f8qdR9/GuYp7fzDi//affZcXNzekucDbHO8f4+MF8L/66R0K78ZnnNNzg8FFgWdHjIEuBqZopumIXzi6h3x/RKYFuQepHuGswxuHbud0bGliRx9yJllgkgjmaco89hymBXdGMw4P9/G9xTWWg8HCSsSjb3Z0Zjv82a7hZrem9pYmOmSWUESJCpJlH/FdRwyWYAXRDuMTX6VUhUKqHuc6zEWNCZZWBw6UJCsFaSrRURHPIqSC4r6kuvMINVaAYfcnt9x+brk5i+xNJFUpUXoYUagmkjhH1hqKO5Aqg3BL7Kc3YBxaQ2zk8OSUIExLCJaw7fHbBq0iZAnCdBA8JHLAMCc1MvYoY4jLFp8mSKMJskWSUeaC5EHFKOnYrQP1AnQSEBkEJ7CdoG0DuvCU0dM7y8tPX3Lx/IL5/RF7hxMO7t1HbM6INw5vAqfzO0wmc8q8QImCzfWK1dUtpl6xqw7QUSOcZrezbPtIS0KWjhiVFl2Nef39n+Xk0T71YksSv2R5tSMtcqZ7EybzEem2xd/seGKHpnt3saE3O2wfICjyPGM/plhV0ScR+9KiSknoFNPdBXrmiVOL7wPmajOEAxJwtUGaQOolMlPQJYjoydKEmUggBLaI4YHgIfRbZCeIvQXvCfUWs97SLWtko4l1jrctgUiaZlRZxSyfkJqWrdtQ2x1Jm6PqFLGDUF8jokBkI+T+lC46WtuwFZKjeUKfSlyV4B8bwtZD3SM6y7/8zo8RCJZuw//02W+TT0vSYklKgex7wuoGREfoLHbp8dsN04PXuPsg5dHpPrdfvGBZ11ws/iXvv/lvce/+lMN7gZunn7Nd3dBuV6h1Txh5upGl8g3rYFhsPZvPwVsAhSSD0IFQiDglM3eRm0CMDYhTvB/sebHW+G5FF3fsYouwgjyJ7Jcpv/+T30PeS3lDvMPx14/4tW9OaDZbbj8658PPz1gtt7Tbhi+/eEyDpLx1tOoLvNwhZYrOTllfvuQibnlRZuSLhEnMuTydc3jvNSbzA5SAaSIJcUNrFqhNhZ7kpDol94Fz4xhnJSfTU0SxhaVFXPcoBKlMScmwrUeFfQpxzFRWpLrkaDrn5x8+oBoVtKbn7PyS0R98yub8kqvdOVJE/PYLWpGxaC3uzVt+8r3n/MN/8J/gCBycPuBYjLnZrvj66UNee/SA4784Y97f42qs+fT6kr/5V/8C9999hdndQ77zH/9DPvnkKY9fXtO7gNYlZbbPSFfE02OWkykvf9gPb+Ax0DvDP/vdD/jV7oRfPzhh/6HkF3/hkDdfGfPZH/0hrrcUe6c8eKDYK3qSN4+494t/hfwDwweff8F3Pv3gv9Oz/r/zobBF4MlJRQ7R0mPwDCMUicC7yKYxjM8ibSpoZGQ/r0HIr25nnqBTvJbM0kCaj0nkMMXMO8f+KOGwLEh1Dr3Af1VSS1wgeIu3PV1tB5Z/39NZixegdEaqM6LK6WSCMGBjgg8SbESnKSTQB4veRERsCLalbQPZELigjZAEiXKKWKckAVSSkMynSHUf3/bY9TlnP5Gsl5raQDr+CoNtIHQ9IR2Kakqp4RXcCvy2Z3vjMIknjCW2iwgrkErgvMQTsTZibByW4iGSIpBR4V1EGYtMh0KTjw5n3JAqCwHTB4JWiEyjyjH5wZL1OrBZRuQWcgVKCWIi6Y1ENgJRB+rocM7DJtB83pPuXzJ7eMXeqw/IDwK7ZcfZx88J1kEpKU/2UdMKuS5R0SLHBSHPsTqljYo2KBovqWOGlTlJXjLemzM9OECSk49r0uQFWVlQTGfkI8daXtP7lCZGbOfwJqKyKUm1w4uAbRPSpMQH8CvDysEmSmo5KBKjTnCJBgRSTVA6ResULUuENARtwUlilHgUSuT0XuG9x0VwaYlXBcEDvQEfESR4E+ltQusKam3oRYaP+eD7VVOCruhlgTIK4wwutmzshNaPMD7DbQLBJSBLdDbDsMInklGVkNQSoRNkltM5QeZK9kJGCClNEIQYsXhkLBGUOHGLswEZLUINCayAI0SPsJosGTMaFZweP6BvUySa9x9+i3e++Qp3H444vqfwP3OHZx99yLOPPuFFc0HII2IM7E3Ru56s6Bkd5iiVfZWcS4hUxLgD39NuGtpuD1PlZHqD0hIhJVYEIhkiWjI8dfSkVcb+8QTiPbrgObu6YmECVnvq3Zazl7esTcAqhSwTmtDQpWZI9KkKKSQx9nj3AtM7ZJxwcnBCXmakk5QsK+m8x4YUhGI+foVxcUqR7ZGVI4pJSl4VCAp24RqNRmhB8IEeSa0H1D1CEdCYGpo19DtBGlNuX27ZLR1lsYepLVmmOLozRhc1MV0T0jXzIiEVLTo6CiEI5y/pL1q22zXv3pvw6oHk0YngZ9+c8PbXTnj41inpYeTdv/UbCFOwbHtefe8dVBao20uKPHByVFLkRyyjY1JMKIsxNp9CnlCbFj9acedgxlGjSG3PVdew2Wywm4yf/5mMytRc/qDh+Y9q7n1twyTRXDUL+m7DYt3Q/eQjjvUcNx8xu/v1n+6hsELjREaCIhMdMSpstAgMAjDOc7tr4XlByDUuE4xGX2Fw04RERRKdErSGpCMrNUUimWlJ0sI0h0khcEGDs0gLygakcQRr8K2h3xhq61h5iyQgkoRE5SQqx8oCIzKicRiR4qNERkeeprjE09kWVi24jmA7rIiIBLQQdEgyJ5G9QoUMmSqSUYXeOyK6fexuye655+rLhA6NLwWTQiLCMAaKnSHkkqgVSgwO5GADvu/YrgJMI0qB60E6gUwUPiisiFgf6V1EyYgOAy9RRI30Q2FNMMhyQvRY7xEMyOXeRCIOLVN0NaI4SuA80DhPrIHREFqKiaS3gtCA3QXq4AdftPHcXNZMHlxxur1k/833Ge1rRtOW609fDAhLKUnnE5JpRTKpAI8aj5BlSUxznGwwUWGCoiPDqQx0TpqXFNUI2wqSfIrWBWlWkY3GJJnHi5zGKwwR5wIxSHRRoSuPigbhFUoWsPVYp1jbyC5CIxS5TIgqwasEkMhkjPCaJJUkukIlFpladNTD2yICSULnBdZFPBBUQZAZwQuwHoJAyhQXLD6mWAp6HbEixZEOew5VEmRJHxNELzAuJaLZhTFtKLE+x+0CwWmEzEnSMd7XCK0Y5wnBCVQoKPWIpVNUriB3YohexoTAEN0klsSQYwI4G5E4hB8izpEAeKTTJKqkKiT3jh5xeRnJVMLx3l3uP9zn5FHJ8d2M8eEEb9bcvnxGqwUhA1kqxLRC2YQ011TzYec3aEY1kWwQPHlL3/T0vcb5lFIZtBZILQeHhsxRBNLo2UlHPiqZS4HkLr33nF1f8eLSE4Rg1+14fr2kzDJ0mpAkYLDYJBAqSaJHCKEHn7m/xbsJSpYcHZxQ5Bn5JCfLSzbO4rxGyoTj4zeYzw4Yjw4pqpJinJBWOVLkNMGQk4IeRpJGCrpEDaIrlaBk+tWh4Om2ARkUy7OapnUoOaJd96RacXxnQjI2qLJFFx1Hs4xqFJmMAsf7OXm3IveW/XnKu/crXr+bc/8041s/c8CDnz3k6PU9TL3m7W++zf7BI3RZsFp7rs6ecfXkjKgcR8clD+6Oedn0VHlFXoxoZieoRuDaHj2pOdkfI1NFVq85b7Z0fYdvt/zMmwU3H265/KKmuXQk3ZbMeurbJzjfsNtGLj+qce+8ST86ZXT6+k/3ULiOczKZUsmE15Tkwhtq36Piio4lS9vweBn4cLHgIC24V464nlZM9jTjSUaejNDC0AvDImjKJiVPNLrQ3D8ao5Uh0sB1h9vVuHpHXLd0rhnm7L2jdxIbFCkRJwWJysl0xS6kBKOJRrARgjwdkScZZJZJ3oFpuLpdUm82SAKpjtw7FaQTRTIRWK/Y3Aj65bAkH337hNGDE/TJG7Sf/YDFF0vOPzCEfE5aasRIo11P2LWEpieJEmnUINqpEmICZtdRX9xwU3tGe4LpWBB6CEHgg8SKjN63tNbT9QFNQNlA5wLaG4JxROGRMuJ1xGXgShDp8PgIjce1OVpJ8mnJ5M0J082W1brhwgKNIPMC64fxkWwdqW0xypInnkkZuLqxLFc7Ll9eMZt+Qjl5lWI84f479+maBCET6qsryiLFzSqMdMxmRxRVhU4Tml0gzyrSrGNUjakbiwiKmy9v2T8e4xqDbCLCOzSaVE6Q0rPxkWvTDs34dERazpATTVzVBCmJ0Q+GOR/oQz/8GyMZZZKjpkJriZORGJIBFZ5pymqE2BsTWwFrzdlsysutIW4NXd/iHHjviUDaBZIuQIwIUaEzRzGpIZkwnU+Z9yXTrUCINfYrKm/nLXSGuIi0uwZrPanOsIXGkRO6Ah8MmIiMkrTQzHd7dKZmvM04//ya8lAzG4847jXtlWR5FejDDsXJVxysW1Y7WK4ivo502hGiHvg+ygzIcp0inUZ4xTgb82vv/gX+m2d/QFtvmc4Uz//pj6gfjtm+v8fdU81nf/4xP/rBYxJSSEeEpERuNzgpCDGhuBHg2qGXgCTyBGgRQjJJ75I3FrVaIfQYnQuSDDKRkUwreg+1cRxWGUVekjcNy4slZ+cdO9Nzft5QlSVRQuot2TQnFxmlLZAxQWxAXAYoU0SSodSYfPR10uQMmVj8SFIkY7K0AilJFEOgJS/57/+bv0MXIrrMmewX6Eqis5Qiz5G7iFaKPB8ThEHrHXnRcqJHvDY+5pX5McYY6k3NZrtlHWpYFvhtx+ZmxfWna5KJ42ieIwpJP8tQoeTN44zZccrpnTHf+oVXKXbnaJ/yP44HxDQiin1EccCDb5a4xRhzA8vvX3L+2X/KzYmg+NYe/+z/csH3/uyC3/vTJ/hU8d63j/nmL90h/ceeP/vwOWc7x2//0l2qVzP0YU68kjzut2xDw265YJJE5pOUydGYSbXP/Jsr3nhrzS/8/C3XTz7nB9+LfPDccXp/j+wo5XIjWK1KOn1IP33jp3sopCon1ZpES8xUUvSS1CckYg5RkTB4dtddP8zPVcT4GtODbQeet+8Upg+4umEpG4o0QYWSOg1k9CjX0HtLtJYQLb2vMW2NM8MrvoopGjGMe/wQ4ew6T68FvVJYqdFaY9A4KZkojV022HZLvdnRODdgtoXAKwECVBA4q1BWEmSKTEfQ7RF2Fd15zfmfNlw/67m4CIzvjkiKAkWCub7CdhB9ZDxS+C6CEegqReaeftGx+6Qm1B4ZNEqDryMSgZYCfCAYi6sdZhWwRUTnFtPXeB/A9EjTUfYROgOtRXTDAUEEsY20vUFbB2mkqI6Y7kvmh4bbi0C3hl4IdEiQSEKQNJ1HpREbAlvrqXJJnghwketPNkyPbykmgsnhfcoWXGfoFit8LYk2Q8mcJE9JyxydZiTVGJHkBJHSkeJjgvWSdevojcJERZcKoiwQRY6aJQRvSJOEUVoy72uykUZVEkygua25vdxwc7lCzRWurnF9Q+cMCRmZUvRC4ikJscDaHrfz6BgQZUDLDF1FkgNBcbsHlxusH7wXIThc9EMRTpckqkQ4CcEipERnJUErtC7QusSmDTYovNXEoTNOEIqgwIYB566Q6JiRJBm6yBBeoHI74OGTA26X57S9prUlXpYQcugT2qwkL6bslQwaVzwS0EjSIMFIlgbS2A6UWF0gXTnsxJVA2ZxoAjIJ7N89oHUd19cXtPUt29kIm87I5j1SatbrJaZv6YSh6Tc07ZrzZoKRN9SiZXXQEpQDWuAcMXjWKJTj/fktmfest4GYJqy3O7zpafsLfC2wrsc6i84iNgh6n/LZ+TPyQoIUGCBjhhCKmKxINXjnuO2g2BjEsqevOnQq0MmUJD+ivDvCLSy9M/S+oNla2nlP7y2z+zn5bHBjX7YbVpsWqVLefLIln8HqdofXglQXjKZj9l6dgcjovGTrAkEq1HRCur+PylMoC3Q5ZlofIGYDpi1RGcVU4nxHe70iG6e8/vY9svKU5WXDeJSwP9eUVWAkIhqHSDoClt5Hmm3N2R98xO//qOHHjzua5ZYkdbz5tWP+zq9/i1fei6w2LV98mvLqr9+naS3f+/uPufrSsmg0lhFfXi+4P5swO0ipXi04TPYpx2Aqz7/2jZR798ccH8wp4phympLuVUTdcfnDHeEy8tbbc+7eeZ/LRvLxFzf85M+2iLElfVj+dA+FXDi0CEipMIlGxkASIFcFmYZUOHIRUFqRS43SAhMsvelpW0HuDLHPsH3EmZ4gIzJ6jJLUmw4bOqRtiMoNZewQIViMNfS2xwdBEJqIxMWAwBGcxWHpQ6DXEacEiRC4MLDavY1stw222dG3PTIBrQU6FUQ5tJqjAbQEo5EqRVMSuhy7gq7ecvtFx+LSstkEqsME4TKkTehXgt5GhIik+wKzi0NEMRcI7QaM9nkPJiLi8B9NBJACVIxIP6SB6By+DoQAvnGErgcvCLEjJh30cWg79wHZRlw2tHJtA2YTCMGTVIGsmjMa98z2tmRXHbtW4JygyhO0VIAY4p9hCNz0MaKiQA3EDkzvsCNLljmyfEwiwCKwm8GmJr1ECI1OE3SWoNNBJRiFwgfo+kBnHDrCrukxxmNswISARRKkAi2J1gERJQSFUCRlgSpygutotx3r25qriw2lSBC7Ftt21KahipIQEprocV4SvRycuA4EAikFKs1QWUTkHpEVIBQxRHQEYiQytOulSJFCD78XIkJIZJKDACESIMFHhbUSa4Zx3SCrSJBqqJcPiXpI0GidoNMEYSNC5wPsUEOIS5xLsD5FiozgFF0daISgkDlFmgzAwzikrwSggwQn6FzABkua5EQSgv/qrSgZRiD4Yfw5OZ5SZikYy83uGq1PaXeKeq3wV5HdrsZ5T0i+ktg4S29aYtbQxYZa9wOqA8OwOUwQWDSRPd1CsDgbsbrCeo/xAuNaTK+wzuKsG/hccohI986ThQFs2NgOuh6hNL00BCPxDloTqDtPaYbospDDrF9IiUwdATcM03ROcBbvPF4E8olG5wIfPat1x7quUdrSNh7SgOs9UmpSlVKUBaPDMVKlGOdpeo9HEdMMURaoLCGpCtJqzGgzx6uAFwGlUqZ7JZubFf2ipniUce/ePQ7ujHlaviDrO6aZpSodlYpI6SE1RLEPfULvIssfv+T7f3LD731WY4CjaU5+ekJVvMP9NzVNU3B7EXjvN97jB3/0GR/+85d8uXMIuUeappzd3pBWFiHH7N8/4MFrDwh3DgmbE/7irybkaQACRX5EOY7keYP014xnGcbnzN55DfvwZyiutzw7W2KbBb5cosTqp3sozPxjbNTEkNIspyAUqdKMk4zp0ZxpoTlSCd4Yurqj3tUsWk+3M2xbi0sytOmJNlJ7w0QXqChpm5br5SW962it4dFoRJEPxMoxgssQufGBPgxz94CnCz3XDNEt6SO9kmgfSTQYIjpoRO256QwXzTWELXsC3p4lqInETiWCQNdC7GDqJVmSkecjqtE+btmxWTRsV5aL85Z644Yuws4jlCP2gn7hMcKhMzcsrE0gDZZ0ZBA4fNvR3YQBYe4ioo/kQpD6gLaWNLRY25G0BrWJw8Mtd6hFA65FSo/KPKKJ6CaS7iBZgM+H4nq7A3MhibuIk47x60fszSQ8iDx/fMZu7Wk7xUiXFKMcqQUq9AjpCS7gu0i9dhyvPUUf2b87YzTfI8tHuO0ZUpUUo5Lp0Ts8/eOPiWL4IqbpEP1VqUT0EdtbmqZm/fycm80NOk3Ym+2xWd1im0h907AxDVkzwi4dsu/Z7bas2g0xRtLRMensHl3zjM2y4fp8xfMXV5yagOgs3armantF4TtCzLhya0yzQuxqlNUURTIoGkdjfKyhDwRXs1sHXB/RMVICLcPPL4BgJcFBVA4RNUIqlJRf9RokphNwM6G+uWazHmxY9IokKxjLgpgYbN9hXUOpEookIcs0qoiINEOkIApL8nyNijd4nzBCUG87Pv/yGq16sukhZT5CEDDcDqIlAKeQnUB2DaQSKTJ0MkMoSVYNqAW1r4lSIJVk9s4ev/LKu2Q3Lf/oyWe8kzwkl4Kt2XLx4Q03NxucSrk3n5Pne3gxQniLQ2F6Sf+iG1rzAHgCN0TA+IybS83xqWVSCIrikHI2okwk8nxKMWpousjWRQqRQpaRVIqff/V12uC53q15cvEDQvwUgULJksS8Q5EqEmGw1jIe59y9u8dHn61p7BVd/Rn+8wItLUmas3dcUuiOfK7RRSSY4bsbXKD5QpLPM8b7JdP7I4oMJpczpumMKs0pizHF6JgiH9N2G+qrHhOgc9A7QVpmTI/3mZ8eM+7XLF521FsDouTtd17n8z+v+fKy5V5e8urpr/H6629wHP8/7P7wn5HrNfd+9VWE1APGRwRE9T9hJhR76zM+fPn3iNvhGwfw69++y2/91s+yt/+3mP4Fwzu/tOav/fvnlPpd/h/tf8Kf/NefYOKC6CN969h88SOePsl5dPcOXzv5a/yV/9FvcHL/lKrcQ8UF2/M/Ynv2B+w/+k10rpByQ0xv+aW/8xbo9xD5v00fevwf/BNe/tn/i8vPb1AvtoxfnAH/4U/vUEhFyghNjuY4CdzYht5D7x2xVmzqwFXX82A2p+1g1Sjm45I6tGxDz1Sk1ESkUMzlPp3fsTU7mm7Lm5LhgSk1F8aijEMgaIJCCMVIp+ReYGJCiIpEZNRxxwbJNsI0dOgwQoWSqhPYGDCxpXMrZPCoKEgIOKkpEs2s0LR9JLQBbyEkM6rJPvODPbLjh2zPXrBbbLm42mD6SJ6NmB1UjKoCFyNNbVl3kem+pkgSbn/ScPB6zuTNGeWrr3H2L37Iyx9seHIJb/6iIj8Y6KWiz3DOooFkmpPYGi0jqh9KcJmOqOARO4HwgyUtdqBKhRgJ1p859MEIvV8iyxK5d0KaHJBNTmjjHnHyJmXSc/j+R5R1hw9Q7e+jncWZjs12wYvHL8B7RkXB2BpSpXA2EOs1LrwEcYW/ecHo9VdIxvuIJGdv3OPZsDg/Y3t8gOAO+XyfdX3D3ihH37vH6P5d8scpEUE5yunXFoRmfm+Pr/t3CKmn77fsHt/SrT06ZoyEQMQEaxW7pWTy4C53hECFwO1uhxKaYjri3aP73K4Mt7VhLArG8wn5vKDv1xg0UWq8yrBqws3qmk8+fcqPfvQxq9Ua7QK177FxSPAoNLfScRUCpztFVF9FMEWCJbJpRmy7PVw8Z6Ir9pMx52gKvyJpDeY2ZeoGrtM2RpwTtD6wtZa4aHChgSRSHk/Y+pw6jjDdFJf0CKHIspxkf4ISFswt4qvDQCDJyNjefMnNtOVmpMhsQXD5YF8zkdWqY3drGNmE0emWbCYRhSI7PEYdHHDzxY4vdz33mxEP/Ii9b+zTZWN8d8Ztv+Xs7AI9ydm79z6+X1Kvtjx/eY11ASULUrlHGuf0YYWNO767fczPHNzhFT3maHSL83epRUabe9qVYFd7VruGtVVUIiNLK9Q4Ie/WTHvHo+J9trbD4YjS4e0GWewx3XuFk6MjytkEX2jSqURn9xGyw4v7pGWNKPZwyhNHCVZJtgvPp3/ylJefL7He8cef/YhXXjnk0Sjl6tk547Tg6vktH998ynH1gFeOH3H/tQOO7xwhWsXibMV+MWVvf8LooGC3amj7lqTUvPMzb/H2L7/G5dOnfPGZJSsSqnnJ3vGEE3GEe/ohi/4Lpkqw//MHJHmL4E9AtxAyhJ0Tzv83UN5H6SO+8VdHnOwcyUWHByrfo7cf8cUnf4fav401EltvMZPvcXH5kv34TQS/T2BLpAMsNkDrLNdtyfMvn3K9umDlYRpS6BXBfpPFZy84Pr7PZHLC5uYdytnbiHSPevd7/Ksnn/PRJx+SK41G0NdrFvan7Gju0IQYkKFHOVDekCjJOIVSBax3tF1H3zq2neem65mOC8qqpEwKEptiLDQusLM1hXTgBbiMM9GQSocWkVQkpFKghMSoDJUMfJ0QAtv4//c1C3JRgMpIVMFUJYhYEKNi6zpCHJj0IkASIROSqZJkeToY0YQcRj0hkghJme1Rzg7IJhNcF9ndtmyuazY3NUkuSTNBmqTEoPDOY13A+4CMQ2HNNcNDPt3LIZVsL3s2t5Y2REQpiHqIoCqvcNbjNESpcE5g+4htIPTgO7A1iN3wBuQ0hF4gRjlypIlsCTZDuBKRTNHjE1RyhCpPcGGETAvyXDE7FRT1Fu8Mqsqx2w3OxSFh4zUyRoQWoALGQL0ybP0tRvfoAObqKSJVpLuamG0ocsgLSTAd65tbnEjIGsfNzQ3GmgGUF6AxPc5FxlvL8nqL0AltJ3DG0fc9/bZnebOlbgw2CIRI2F7UXHx2zbMvnnC7WLG53rFYG25WHXkiGSWKVOYEHF0ctKRt07JaruhelDQ3G9IsxQpFs1txebni8mLJatPgnSdTmlwnEAb2fkSww3JrO242Hcb1IIa3BR8CVwvHYh3od4LoBCpKtJCkeJJgEfarrLscVsObbsfNasv4ek24WNM1W4JwFJsVly/WXF00nK8bGifwUhACpAiCsHjpqERCFwMRyIVmZxpWfc02C1zvejrV0uY1se5ZnO/YXneUjWLuM4raocaK1nmMEBg8jYNt61isW4q1Zrs1bOqWVgY2y57VdUNSbBHR0eyGUhdRkumccTolE4bWRlqrWdmOJgyofJUM2liEQOUF5IpgHV5lGK8pRIrQBVmaIJwjkY6pzpjmY4Z0eE9VedIsQ5AiyYhe4pzHwrBViRB9QwgWFyx1b0hJcAyR3eDD4PCI0NiWIARSaax1iFwgtUBET6okKoLrevIyI68ysjIl0RKdSVQmsS4glSQrUspZwux0St9OmE5LdJpSjgumB2MSl2Fut7Riy/i1O+RHb6HzQ0gOQNfQrvHLSz7742dMH1kO3iqZv/Uqe/tfMJU9NkS2tx0vv7jih3/4HRoavE3xbUdT7vPsyS3NV4VWQcpgS7AomRJEwm3rOT/bkqxqFjZSuaFIqZKKjblks1tSVZZdnTNvJVK13KzO+OCDn/D88+c444a3Yx8GUONP81C4RhPCmjQ0THYpRiiKsuT1aUU2gaY3tLWj30mu6w1PdpccTl/hjYcnnJzO2FzssLue637Fv1h/zK/nd7kjphyJ+/yu+S4NPYkQ/MXRhCzNKLKBxLrqFW3f0JuWL6KlC5GCyNf0CbPigKo6hkRyXXdcNDUf2kumKCoSxmIC4pqJ8jwsEqr9MSEVGOORXaCQgnGZc3ryiNm9KTpX3HxyweL5NfVmQ1d3JElK8Ammc0Tvcd5jvUEmDjoPLpAVcgDzTTLM7orVjaXtIqqC2oDvBYmXZF5jzEA9ta2mXsNmEVnfQJ5A5iLSRqSBIgpiBmUtSff3kPsF1b2ebZ8TFgVJMkJVJyT6CJkdQFORVlPS8ZjT0zmb5TnNbkG3XbK66GnrFm9BhAypNFEqOiyrpUd/vqNRj8lJkTZQX3/J7vmCfFThipTJ/TepDmfoTHD+9Brxcgsy4bOzBZQanWeYJwt+/OILWhtwdkxj3LB/auH6/DkxDrfh5XLBummoo8fKgqd/+pz64zW//51/QtMEGtOzNhsykTCflOxNC0I92P02AbyIPPniS9KYkr3Y0WxuyLKc/aMT1vVzzh9f8OLFEisSknTAjdx1c4IZuistng0Nz5tb8vNr1stIiHHAifQ5L69vuF4u8TXsdp7WOxIhKXRKpjOizFBpIHWC3EleLJ6TPT5g1UrC1TmbixpjWthbEy4rXry84s+efUnvq0GS5D2FyjDTDJFI7qgJS7aYaCmFYCEi0nnWa8Pi/Ir8qqa6MNjdFcuzHZubhnQSOX7ZMZ4dovcFl+cN6912cHzHhMW65fbza0aPE55cPeHl8px7Bw9ZX0qKxOAuPia7M2K1Emx2BiUKRvmEk+khSr+krw9pdnd53j4b2F1+SxAjpIAsSanGx8hRR9wU2DSFrSZJpqT5iEli2CiFkoJK/5g3771DnkzYNZHmTkfdO7bLmnXmqZY9+W3Dso705pbgv4TmBis09VZytdyQqAnzvUBaCB6+e8D8j0YoqZhNh6RYNZ+iipzpgwnH/ZyH0wPWrLldvOTphyN+6dtfI80T0jzFS4MXw77CC0VZVkzGLWEUyKYl06M97j+4h85zxvtTTu8f0beB5hJKn5P+0kP09BdR6RgYg7ggLH8X8/n/lv/qP9rwjX/d8Zf+52OSt/4qJ4f/La9kS3a946MPN1x/2fHBn3WEzA/jSuHozfs8X5zz2H5GBJQ4RYpDpPiSPC3Ras7L7ZaPP9aUaU4g5aW9QFcZ6aRE1DXGfIQNHjGHO/MLVIBnTz7nsw8+Z7O8ptluhj2aTIHJT/dQ+Hd/89fYnj9md/2c6/WKhbOIvmGyWPFgtk8yGrEfcuZlwYE95H37dR59TeN9zmYnePzyOZtNA97wb88e8rjd8cd2zYX/jN+s7rFfCEYjz+z4gOmkoChzrn3GfPWSyXpJLxT1xnPtHGeh570kJYwTzIFAbD3N4O/Bxi0rEowoGOs5ShbIDOIMGumwbcBsHT5WFLMxxf6U9igj6Q3htubJn/+I7XKJ9RafQskEi8WFHn1bY4TDKkehPcszRy08D18piaMx9S6y/r1nPP/QYHeBXMHuRSDMPXnl8NZQRIha0q0C9c1A69w2UG7AZoqMhMTDrgl0q0D2uabIRoRqihs3bDdjaCbsHR5Q7R0ixBSzzLg+X3DwcMxkPuf04ZhqNGJ1c8snVx/y8tKwXu+wscebYTFq2gSwKJ9CH+msZzwHXQrOm8izT8+RJCRJyfG5wFrDJitJT+5ASLCd55YlwnjS6MhSTeM9a2d42eyIa42PgdWqZrG4Rn+Fx25My9Z7LIo9NWfVeFq/5fnVio3riAQ0oDNN07UE1/JktQAvEVJzoOdc3W7wPIabBYWEvKpYmJ7b8y+5ubnhplsw3htxvd6y2vWoMifbK0mkRnWOVRJ51m7pnz5nc1kjgkTJlFSPWSwu2W5vEEmCkwaVQKlGlEWKzBRtJggCoh5Az6ubJTd7V+ikpOhrdn5Hb3r0jcXHLaoMPDi6y/k2HUZYwrCShqAasiRBTo6ZNwJrd6xwXDYbaEr8SCOlYdcu2Ox6etewWW9o64Y5mkV8yXa5wz8bJCsmN9wbHULuqL2kWUh2+YrWOZRK6JRj2zZwseKDi8Cdb8wQBPLDjOlugtRz3HiPtluzabZs3CV9fMFOfp212uc6DayvHKlqKG1k2TuauqFe77g9X3DH3edEZbRVRkg8WmaI0QlfXGfs+lse19/lLfdtArBuF5COsasJt6uS290VvVsysF0q5HhCHBdseEKl9jgpFaO9Bzw/W9PEHqEi+28K3N6a6yh5+vyYYqZYtjtWieFstUGEirCXoSYFca8mnEjefPXXePdX3uD1n3uEHkcuuiueLS/QtqSPAqsUrRR8/7sfEF2LjSViBAdHe4RS8of/+X/ObLyl1FsKd8md3/k2yfg++mf/Y/72//W7jI7fIX34CyDG/Dt/91t8+3/wJf/o7/9jnp7dsqt7ogvUoRrYXlHSjhVZep/X8rvsFxPyvROy+YTioEX2Nc54ljvF4x9JIjuc2rLbLmlMT2cNRSFxbo2IPYf7h3x/e4EQkdExpNNTlFdcPL/BOkEkBf1TdjS/eujZqJRtNWVyLZn3HnTGnfvHHJxMBo9w35BJRZKlpHnJXGieLja8vN4S2yGBMz2e85t/7ZfZ/+Mf8cHL5zw7/5Lp/oRZKSkyQ5KVRKUxURCdx9qAdYE+eIQUFFJzDFgcdd/QrSW2EWyNw4eOEZJEKDIh8TIwzhRVKumjw2z7gUDqoKoyinFOMk4xbc3ytsUsNiyXS3Z1SxSBTEu8GrzDsW8wzQ6rAiENlGJYUiMF+SRFJwrTGjaPWzaLgIpQzSB0Ed8MN1wZwwDj8+ANuA5cLwiSAWmRDa1XlQuU9Cgf8E7iWzXEIWNGuxMEFan2Bf6mZrPuuXz2AuVzsskhBw882WhE4QJd74mktF1kW3tcGDDgyoP1DhVAtg5BhzOQTTNkpuiCx3QBvCPRkvbpGQDBO8YPxyiVY/tI+uyaznU01lP7nsHQqkAmeAQ+CkKUuKghahSKiAahiUITVcpm16M7w84ZlMzQQpOoBKUiyEggkIiMROdokdJLizWGpmnRuiQrh7GmdZa2MzSdoTE9JRXWDI3xlIRMpsNCOsnI0pQsS8nLgjjXaJmQqQzfCrq8xNoKZCTRemioC4lEICMIb/FRYl3ExohAkemUUVkwKlKkLLB9j5IdvROQVuzXgZuuwzk7FOHCsOT1XrCxBhGGNXMkQoBEp0zm4+H7ZyXGJnRtQ55N8IeOo3lBkewBOetNQFZgk46jq31WpqOxltZ2jNGEmJPqMUVekuY5WVmyt5dSllNCNKTjLVIk2N6wiTcoEZAio0gVSEGW54jEY1zKuEgokpyikVQnBX03Y7uYsj854OjOHfZOjjlb1bTrDqTDBcGqWbDpN6zNjs12OKAIBfkoQyYeH2qKUUQncsiE0hD8FGsETdsRxz0qD2Rpisg8qIGiIMgIzmC6LavNBscxMlXkVU5cSchSssMJSZHQ1IbzZ0teLp5xcr3PneUJ6i1NsV9R7Sak6Rg5aCSQOkUXJVIkqELRxZ7Vbkezavjiw5fs5R3Cr1lcf87Xmor5gy2TB4r9175BNn4IyQzb1yRZRTE6ou0k1o0wNhmSYDIHNXCW1rXHtIOPZqwFtI6QeqrZIdgU2zSszne0dUOUHpkH6p2l7Xpa01NNR6RZQZokrG5gdbskLTJO3/smJ2/kTK7OubpoWN88xgbxVVjkp3goHFfXlHlgejDmwWREXQvICvbffUReKdpVi71xyC6gE0+RWdJVwtXTCz598YK3J/fwKuHo4R3+yt/997nzv/t/Mv5Xf8YH15dM7szIc4GILYKMrvOErsf3ge26pd0a+t5gyahkwqnIMd7QbVeE7ZZdSIbseHAckJGJDClTvHLsFYpMKVrXsV4YEqUYZSn705LRLEOPJf3Ngu35FfXNiuVmy84FdAJpVHjtiLEndhHT7AgJw2s6iiQIMqmo5jmJjvRbx/axYbuKVCPIKghu2BV4AVpHgox4LwlGYjuJdYKQCtREoaoEkWQkuSCVkUSGAe/dyaH05lOaTcBpyyg66qc3PPl8yw++95K3Hn2d2Z073Lc96fyQ1EPWOYRM6a2g7eKwV1CCrxB5aB3wrsc1sHOB6fGYvEzog8P6AE7iouHp0zVaSqZVxYNqRDGaEKJi/Nkl7eaWpu1YuR3eg5YpSVISVYIgkKgCoUZfyXQ0SpVIUSOEx6qU5aoH6dmFwEk2I9MlQmZYdggdkDKyl0ZSxkDKTbjAGYfrHWlQA6JcKxAC5wXWRvrOEztP33hCH1AhJytStEwBTZWXTCcTjg4P8XspRZZTpRnLZysQBpGA63dkSY6WKTF+5bUQAektlhxjI733JLpiMp5yeLDPJM2ZzjucMyhh2PaR9HZL00nU2YshRityhHPYOqOLkqtmgwwGBWRSkQrNOK84PLnD5OQO3gs6C/VqN3Sztebu6ZQkFpgWnj1e4SpFfplwe3GHJx++ZN1uMWGHVA+BiixJmRRTxpMp88N9xif7yMOKptuxm6wQQdNuanqzYbY3JmVEmZdU8VXKfI1OWnw3Yv9gxKycktzk3P+Fe1gTWF00ZEEyOZ6TzSvsDz/mol3gXU9vDJfdFVtbE0XCZmfIk4QsmzDZm5AWIMWGyTQnzdIBG+MX+HYPs/Ns1w5x15NkgkymJGVEpcNOwdQpJunQbFgnK9CQjwvmsxn64gqd5xQnM5IqYbfoePLjC37w+I8ZH5bsjw+Rv5Ywv3eIkQnKFahkIBKrrGR65wSBpW83tMtrLi6eUV+e8+mnG/Z0wm6n+JOPPL/24Ufcf/eGB795y7f/xn84ABGtYLP+kuVlysVzwydf7OjajK4VLBdbylGJLsfoasRycUvbtNi+I1qPqC3JokfEPUKvaXaCi2cNy/4SkWpG0z3aZqAZGAciK8mnU7KRYPd8w/W6Zpqm3Hn9V3jvV0dcvnjOiy96rp5cInqIwv90D4V/+gdrarOjD4YqOyTTlrwUrF/UTKKhXtc8/fKa2Ahe9iue9Td8M5vT4UiVoirHPFs8p7+94PF3vuD3Hp/zdBd4//hdpndeJRUR3zb0vUD5GuFqtp1l13raPtAHRRE1KSm5yOn8hk1sWcdAjEN2Q5MxFyWZHCFUSisDJi1xKtKaDpcXjEc5R3sV1esPEdHR9Q3XT59xfr5kt2nJXRiWWgyLQW8sPgS874nGEIRGREkpC6ZHOZNZwvzVY3qzpr3dsryO5OVXHucjQVukeJ3iRUbHGKMFqZK0u5yXtmAhPbd7gaRMUaIk7qbMRpJcC7JEkDSOaX+ETsf0p4L2tsbJFDupsFHQlpY1Nf24Z0fHYtti8x1d59g6iSuPEaO7yDbBe4OMLd73Q7GwXWKMxRmDco5dMmd/mvBskdAYjxKa/arEZxM6a7ld94hPew5PYDwvkXsPCL1mVy/4crdj7QU6zVjqDOEKjPUsu0gfSzQJhowa2EiD1RlZfsBzqXDRskITncIbw9assBjGWc4sK6n7SOeusMGhpEa2ApsprBAor2l7gVkbNk7QhIQ25rxcrtmYhtZ1PGt3ZHWOlAmRW8o332fqj0mLO9QigzRBZ5r8jRFJmiJEwWZ7xlbWbELLyvf43pHGdMB0U9KFFhsUTfEQMzrEjys6l1HnGc57MmfYmZZb0/Hs1nPWtvROIpMSFySyzfBW0PktkKPFFJ3cJzk5xu+XnDuBHB0RA7Si45NnS4pJzvSo5MGdeyQqR7QB3QjmD+dwMmd8aak//s+o/S0Rz+Xuihh6pPKI7T0eVWNGhyPWdcbuZofMUo6/+WtUTxytuKVfNuTzr7G8/pDl8nuo7GvcfnzD/Bn84ltvcP/tb3B4/5Dr646777/B8rbmsntBUR6h9lL0SHFy8nV+8sNzPniy4UV7S+1XCH3C3vhvc7X5+4R2hZSC7OldJrEmCz0XP0xYnt8Q/QIYSAHrJ9f86L8uKBvFSN9w+spLtiLFiJTeWn73j36PozuPOLl/ys+/uiXfS6mqE47f/hb1Jx9wcXXFFz96TvJXD9g7vcudt+/g/7nl+XLF/Oyas3XHciF4+njDD77/h3zzN/+XxP0T1idHvGglq8WW68szqqnkdhPY1BL78BHFaUWRBL79i/c5ne/z8rrj7/8XF/zuZ3+PV195nYd3X+HFxztu1y3LbQuHv8DixQ1rs2LTNzx89E1Gx3Nmp5pmtEVvdpimZXT6BsEIvIHPtp5UVAShMUWPVAqZScKoxDZLXHRf7QmOsCGlNpFd4zGuwrmKuK2wzZxt3XK2PmbnDnFigtQPf7qHwv7RKxxQI4Uhz8aI2KETRTWZM84j/diQqzF5WvBqa7mtW04yi5wVqHHBSIxRL0vSLOXq8QsUijt3Dnnva0fcfXSCcB63q/Ftgo4t+BZ1syEGi4yCoqnxMSMGQR0NNmqyqDgg0pPgkXgEN9Ezjp40DEybbTsshaVSzGcTZpOCal4So2Bd97TrLfW2YddbWh8IcbjVIwW9hLUJSAciBKQMKCkGTouV5PsV1VGBKAq6lzc0lz1NjEPr1UO9g3RWkuiBySLUmKAFTipEGBNkg8ihnCtEloEqELLCBEHoBb2BxFmSkJGLnFgW+LTFekdnN/QiIEvH7CRl+mqO3hf0osfvarqup64bglCkZUExmaCsQOseqRxSGmLcG5IMUqO6wMmdI4osY9M0ZF0cmEQiY7PdYlwgkKPyA4r5Heb39jhxFUYkBFkSk5ZJPIEkIZkUuHQogMkkIpMelRaovEB6QypnqGgpDkfM74/xoeRq+QoqpjjT401PFCk6n5JP9/BR07crvKmRQmHRWBQharo+IJzF9uCEQKSD5MgKgxMOi8OGFhe3CGERoqNuNXWr2XSO29qTiMBCO9IqZdVtWfYbmtZSW0frPR6BFQlCpAgpiCIhqJQoExohaS3UdaTrOprO4qOnyB298RjrMd6ik4KQaERWkCSe4CoGAWUCaDySzhuu25x0l1AkDW3hB8XsxnB1cUGyTqnbCa+8ccg4lcRe4KKj7z1RScaPxowmR8RkBGWGbQS2ucKbDW3T0TqBlSnTUYJbGBQp89Eex3dfQaUTioMdk6OHbPtr3PU5+Izej9g5yY2YkIz20eWYRb/h2eWO9W3LzTKwfzwmn2dMJ5q7b5R8w/4S8zfv8TO33+K/+X//A87PtrTdhzh/S4gWQcnN8pLq7gn7s7vcv5Px3qv/Pbz5HeYnJRJJqium5SmHdzMOT8eY2rLcdHS9RQhNZ2o2zRa9qnj5bMmnX74gCSmL1hGFxhnB5rpnsVlDpTl95wF/69/5HzI5fcDe6T2enNV88fiWZ8+WrGrLqgPjJW3QPH26Y3W7ZrXccndygitmMBUQA1sjiV1PvZKoUc5WJeiR5+rS06zPefJhzXYLnhIvMvTskKmcke5bRgc19957h/nRhNmhxucruqYlWMvR6T2UHcq0m0YiQ8RbS704wrh2wHwngtW4xNQdrndMjk5ICtA6MH9nn8nVXbI8Z1X3fPTnV5ydX7FdbvFiBNkd5Oitn+6hcHB8j1HSUSaWLM9wZvDwZtM9ionC946xnjI/nCFMTmgSuvic4t4eyf6Y26sNk3KKdLC+vKXIC/b3Kt56d4zKJ/iux+4SQlegsRB6DDlNs8V3PUH0bIKiD54+tEiRUqCphGJLThMdTbQscMRgKImkLsH2lgTPKNfM5hOmk4JsnGJ7x2rbsFptCU1H6zx9jAOPRwqEACPAmTC4juPgoxZaIJXCG4GuRmTzMVEodueW7aXBDH4gegObheDuaxVaV8hYIPUUNESliGKK1C1ZqREqJ88LhMxA5ggd8X4AogWpMGQokRKzHCskfbDU7ZptF7Gip5wpRvcL9FxipGG322G6lq7tiVKSViWF9yQ2RWUWrT2JtuhkzHhUMhmNkG2gnE+IAsrnN8gu4nuInWRrtzgvSdOCdHxIdXjC7O4RR4xpbCSIjLQyCFXglGAnW6ISQ9u7UAQcSVaSFRVdbEmEQAVPOhsz2h8TPKTlCSo4tGxIXI8WknK8x2jvGElGSHJktxngiDLDMHh8W+OJRKyIOC0JOkFlJSGxhAAxCvA9XoahEChSnMjpvGbbWy5uDME06NBwcDzjarHkar3A1z2NtfQxgEyJSU5Mc2ISCSIFmyF0QQfDTa32hJ2hbgwBRz/zQznMBpyIZNUEKTUxT8lkxPuSkERUNoagiVHThR3nK0mylBwnLf3BsFPb1YbF8gax1rR9x/VyhcsVymuMd7htR4elupMxmhxBFVEHMzaXK0KsB6lV37LrofWag2nGbjmU1kQnqSZzLBI5KkgmFaqaI5NDYkzxFPRRcx00GytRO8+zizXt9iX9LrK5dbxT5WTjkmKSsJfB+/vf5JXuPYRzfOdPP+bs7M9puh8ALYjhTWxdLwjhDpPylHe/NqIc32M8O+aVd0+QwqNCIHGB1XJF37XsrmtWqy02QlZN8aQ4Gahtz+XZjo8/e0mqcpa9QRcTpC5pa8HFzZI2eMrjA37nX/8bhElOrxKePml4cbblZtkh8wk3dcT5QOc1F1cd25Vht4vMY4XP9yCkxM7TWE/fN9xuMnSXglaMjnP6VrA662hXW7K9iizPSIuMrCqZjCpGLsFu4ei1e0z3CibTlE4WOGsR0XN4vEfuNdIptjbDG4cznm7nCMHhvcX6nul0hqktvg2UBwVgkcIxnhXUFxBcZOcslx8tuLm+oe+arzwhpyTzV366h8KmWw41/EwzObnD2Ojh1jaW+GbLemt4flVz1u842j/l+PQeP/yDF8zaSLUQfPHpU155/TWOTg6YnxZsxsfgYek19XVLaCzUkiRLSdDgE1Z0tI1ns9nxaXPJKjgcoFG8HSEXKSUlbRR4HD2Gig3Eli5othYOq5LZpODuA0ju7RGFoDWOlzcrzHaNb7Zge7bOsXWeABzLSMpAJ/WdH9SFUnJnrFCjBPKUtpYYkWFFgn1xy6ffWXB9s0VOh33ZbgHXz+Hrv3FIouYEO4FsD12CLCTJfAJujrEdMe2Zn+wRvKRrHGnpwfVEa7A7Q1pNiOR0RC5X56w3PZtE88EHtywXhrr2vPnrAhMExjmWq2vwkuAE2X7BqD6GckIfBd60rG5vOXt2Tt/dcHLniLv3JUpY4irQ1p4Pv3hBtBAs2MaxbLdMihmvTB4yejSnOJmSTMdUUnOgQJ8cMZceBDSt4cnzW/JCgQ+UrUO6E2SeQqYRTxvaF47dWpDf3mG7HNN0S56cfcL9B28xmZ1w+vAUm+xRjgtG05zD9zV7aY7qA3/wz79HpKZBDiMnHMYHtsYTELiQYrKIn+Zk5AOGQhmy2SFZMWKc59z7xUeMD0d0Vc7jzRk3z3eszzpEvqRdXWG7BZO7jlp3+HFBHr9BdZqjUzkgL3xCWlTI7hATFNedJ6kNqY2s+0BvHbrr8Ymi0Qo7P2FvfzSADKMnV4K0ECADbvJbhNphd0uun/8RP/mza7bbgtFvl0OXJZfUU40ZTzBS0GQpP/jylvHIkCc5CRkvX5wTpWU+BrE/JtUJ+Z19+qnEPzdEWeCD5OULjxwpjv/yQ1Yvf8KLT57xyf/9T+nT66FRbzRt+Eck5TeZ3P9tmqt/QXQNvXF88uE1/9H/cYOUFYuPl4TVivsPX+Ubv/xtfjtTuCC53cBZF5lNc+4dFpzmkv2DV0mKM9zm++jyPXQyJkty8gy2K8eTT7ak9i5f/9Uxx6cFrxQSHxW9h1sCn15pnv+k49mfnPHWb0w4eu2b/Py/kXO7yEj2ElQm6a4t3/t+TZ475L05bx/+dZQc3sr+8e+vuD5bcnO55Jf/+rvQlLSd4PEna/TpXV597TXuv/ka33m6wTQ9Qd3h8N0xqXlAURtedB3dJsE1BdJHTl87YlymJNuW3i8hC2Rvak7GR2xuW7aLjm/94vtcnC+5Oltw9sEl1awkSQQUDc8vz7lY5uRZQde3eNsTg2HXeFJVURUVb/3sPutrS986yrsBLQdAiw3wqjghL6EoBX0DJgSCCIyTyGRfYYzhgx9esfp0SwgJs/EJJafI/RP08fineyhsX3xBKyM3SvH08S1N3xFjpCpLkmAxvWW9McxGI24fX/Hd8F0en33JnatbDqo9Vts167JgPs259/bXePb0J7x4+oLvnj9hluyjo0X5mjKZURUJeSJhs8Z3HcEPXKVSaEyMODyfE5DRI6OlZ2gJpwTGw6MBFwMr4Ovzh9y7UzB/2GOKEabpaZsGakMKyCIjk4rHOK4izESkQFAFECbSRohaoBXklSLPFImUeCCqITFirjYsbg1NE7h7BzADZ6h1gv39Y4rxATDGbFOScYIeadQsZyQiUZYkhyXl3jeIIuLdCqkHZ0UMDWb7McF5vLXkWeC99wr6Lmf/zl2q9CWLG8tmJSnlFppLrLfEZoSSw2J1OqvYLQuEi9B4GmMZ5xlvvPIq213B9GDKaDrFdQbbCbSMPDi9z/G9U8qiRPSCP//+p1jjcXngsw8/5+piyWRvDz2fI5QnLzW2j+zWW7rakEfFpNQDOqKUiMgAINOa609zts01t5sXbLszBArnO4w5o+AXmY32OLgTubjwbG9v2VzVjMcz8nsnlHkOCrquw4SIWpfEdEim7RpD066JIRKCZLvdEUU68KxmI8aTgtF8RjUrKQ/3UeOcVkZevojcPHnB7uVHIDXetkBElO+BiKRjSZbOmM9HKCXxNtL3HVkyQk8UmSjBZJgmBQ29tTS1p282BFngbMTbhKKqiCEQrRnQI0WKHgn0N4b03OZ6ys3Lx2xWZ1y/jDz//BWmdouXim0XsL2gcw6/2/DRek2uSlJdUlYzbCqQSUJ3m3P77BPqrkY9KbChInQd0VmErNitLYvLmuW6pttKTO1ozQvufuNXmM4PGJdjLtY/phrfJc8O+fSP73LvvUdkkzGf/OklW9/jNmvqW0noLmB8wsH7czqgXztCDJzeydj6wPVuuFj9zX/vd/il3/lZhFgxH83Z9oGLdUdJhlIVUlVkckyZTLC7nJ98GWisQCaC2Vzy+t0pea+w2xwxkYwPIvfqEtm9GKjEeQLTFGE8MaSMpvtIaQnW4jqHTnO81ayvPX/y//0CqUZIVZCVo+Ftfttz+eIHCL038JAKxfoHa4RSoDSGhHJ0SjKNrC9XbOoEFRj2hGpGs6lZf77gS7NAZiOy0ZxdL9lsPG0jePSN90jHBSBolzUiyZFKobUiQxBDBiFQViV5WlCWJdlYEW4tvYuEPiKFx1uPaT1+IoaGu5JsdwYpB1/N2e0Gr0foRDGeV1STChN7bLePl1tk5pBi99M9FMx6eA0LAYLo2NmaSGBeTkjFMIs3bhDc1KFnYXekkwLnDLvFkuAcSSIpRhnVdIKpW9ZXCy7OLmAkSaVH0yHSBBUyRKaGEUOWUY7GHJg5mRG0zlO7jjY6DBErBlBAFQcaZs8wTvBIIgkHe3OODiuK+ZqVVVgf8Y1FtD3IgIwSEb+KTyLIAY2AMNyUhRrcy1oJlBp8vQIBahCfW+dw1zva2tP3EWcF7itIW55K8tGI0dExOj2ku+pIxgpdacSspExzhJakJxOS0UOQEOMWISeAgbjD1C2ubrBtT1a0vP5Wi3OB6d4UL7asVo7dRrM3ClRJR6ZqnM5QWqESUOOCZpITO0e7bREEtFboJAU5Iy8qlErpvcf5QIyS8WjO0Z07TGczSjJenNWs1hs8juvrBettT369ZXLPMpmlZIUa3NHWI0IgTxNSBVJJokjQGkSi8ELgPBjb0vYL6m751bcrIPBkumBUTZgfCnZ1w87VNLuGNqS0+z1JnnFwd5+ulcTgyYsRPgkQPF6bQZPpJcQEEg0iQeqUpCoGjaMcRnudldALovCst57tZke9voBYQKiRWtK1FQengnIUSZkxGuVIBi91Z3ZIqUiSlCrNKMoJOq0GtLSBBIGxLVqUKC1JspJyPCfagYmVUZDqBKmGaHSHp8k0iIq+W2BNhVIjglNImZCime3vk3YdvbW4fo2Jlqh7VO6G8ZqX7GpAavDgdgaVTNBZjiozquKQcjomyTP8oCZHSEk2zknLKdloTDnJqfQrVGVKpjrSasTk3n2K+T7qRw1t22G2HaYd7IDVNOXhmzNUKuj7QG8jZRpZ15HaBpY68s7PvcFbvEFaKI7GisWu48n1ljKU9K2ga2C39uRFSrCKmy7SOkgzqEaCcZVxcKI4fiMl3wvYALZPaC4aSAVRa2JV0vuaEBXeF4go8U7irKR3Gi9yUCW3lw4pLWmecjTOETrBt4LF5TWj2QGpSBG5ol55dKZJqpSgU5JqRF5qdjuFxeN8RGSBKDNCLejqmma9Jp8KZJWzaaFuJcam5LM99FgTibioQGrEAJFFJ8OFSUTQWYLSyRAJd2BCxEaIfnCLewO2C6hCoQyITtD2kVRHpIBt7alayIUkr3KKSUXr7dDCdy3CgWjtT/dQkCrBOIN3jsmk4s7+hCxPEFVJDD3BS4xJKVLNK8en3HnlVd771Vf5wXc/4MMffk677Pnlv/6XeOW9V1lcLLg4uyI6xc+/9yuYZI2OkEfNpBwjXUSEQDye8eh0H2l67GbD9mXDZtVwtViw7/tB1CmgY+DWr82af+Q/IRAZkfLz4oA7rx+xdzejCw3bXUtY7sgXO+qVoeksrTGYLjLxkpmEe0qwiZ7agW8F96vBkxBKRdeAsZCkgoMyozeW5a1h9fGK2DvaVvCvfhKJheSg0rxymmPyiDh5m9Hdv0C+/hAhVwgdkeUrCH2EEClEQxQLBBXwBlABNVCTTx7CRAKOyCUHb36GtS9ouk/Qd8dEBCpRiKsjsnRMko5ZXZd4kSJUwXh6SO4tWQxcPVuRVgnL1YbzZ9eM9ks66WlNy2pxQ6SAqBFW88XZllkruH96hJ+UiBhRxhN0RuM6tqsdF9sl1aigGpXs3zthfjglKsFt19MuF5jaY71if5xjTMOu77Bii5AlSh4R/BUwLHC1HJHNBdPDhAfHc47v5KwWJywuF1xd7YaYcPD8G/+zf3PAt1vL2fWSdrMlSJBVSicEOIFwCpuN0GmJ9/DJxx/x7MMvOP/4BTIKLi4j+WxMOskIaY8cVYjqNTCnRPsDojgnDecUlQABAABJREFUiISf+7Vv8+Z7p5TjKbfnG4KNaJkh04iXHicCuUjJdUaqU2SqOMTjg8fZIX6plERoiVaCrm7YLDZcPmlYL7ZsXqx5+sU5y8Vzmu0tkS1ExfH9t/ibf/svU9qeEAQ2at4Pr9P1LX0/6GQTqVCJRlc5VxcblpsNl6sLvvZX/wM6r7FSoGWDkhsS3XJ6fMJ0f59xMWYuKhajLxif7vG1o3+PP/+n/xX18nO8fcH0/n+Aax/juy9Ipu/xw3/6FNSX3H76D9DlXyBaQzD/EF38dV6/+z5/65cfYZxn3QQ2LSzbnmhBObjcelQKKgGnHHUUFKOct8sCpUANjEIuNh7XBKKFuRbcyyVIqPvIyw5EqXjr50Y8HAm+vM75ZD6ludVs+g2t7+hGEi9GtI3l4scLwm5NklYUsyM6axiV+7zyS/fIpxVBKjySvvccKslhkNx/413ikcCHiNk5qlONTgckRp8o1FdR8slrExRD9H3jO8oJlEeC47ffYbuJ9MZhrOP5jcCEfWwy47NPFqi9EWmZUSUlITq0i6RR0MoBWaGVpLMOuTNI7znbavIoSXVKkkO0CSKPyElAxkAfoGtAqYJOD32eYnpIIxQ2QFoI8tMjUpnjV4L15Yaoc0SZ/3QPhXff/w2mwjFSAT3O8SoSpMSqEmG2YCGanHVt6E3kyZdPmd/pybTk4SuPWMgLXvzhx1z88ec8PXvBF0+vkVpQTcB0ARECXehobiy+H+xoSkNULUo6Si+pTUf3VaOxHScUWjNSGegRkzrlcJfwreUFPY5cZrw7PiL0lutlz6Jdc/W8ZXGz5urylpvOYkOEELkbPXWAEAVNiOyiQAjBJCiWJiGREhkldXCUiaXKOjZjT3xikcHx4mnPpotYKZgVkriXUIwT3H7Oy2ctPv0QZ7bUtzeU04J8MqGsPIoKGA3IZuZEFJACCRFLYM0wqEq/+qjmSP0eiXzIKHmDojwECqTIceNLpKhQckK2pwnBE1Ek6QGvj0ecvNHx4GdeIR1ldMawXG/w3tM3Ld2upl1t2Ow89c6weHlN31pMqLGjLa/du0u4GxDCsmvBOYt3BuMUOpUkuaYqIc87PBFdGxJqlJJUaUVRWhLnUTFydEdRpUc8vDPDql8m05JES7JCcXI6Z3+uh06DMBSZYzoNZOmcJEnIipzzs0uCNUQT8V3ObL5PtZcxuVeSJgrvBd5JRJnw8nrH5e2Gk0PJ6a+8C1Ihc4GJGcYFurYjfW8O71cI9SbBZjTLt7Ct4+T+Ccf3JygB/dZRCIVMxHDDzyOkOSLV5MmQoddfAfU6Ij4GRNSUSYpWEpUoRoVGxAp3Z8zH9gyzuuZ2fcs4qzj+2ffJJxlFOuVq1XHv0REWwzaKIRmmMgoph+x9DGTaMC5ysiRBpJqHp8e0xrJqX0P5BCEHlLi1lhgcksjeZESV5+AlL190pL/4ywhgOpozCyuM+S2q6Yh26tleTmlvHpLOR7RqRN1ausWbHP/CHXSaUT8/YXw0ojlc8V/+7n9LG6bDLVcozKYBHYfPkIrWN3TO0zQRYzYEJwmhgCgpqzFFOUK4QGwZ9lhakugCoTRRSlxIhga5jDx4ULLd9SyWDV0qcUHjY0LvIMQeUsHkeI/y4YwQBKYTmM5Rx4ALkY2piTIhBolruuFChsQJiDYbwOrWkyRqsN71kTAdVMNKDOIeHyJRaYpyRKLscLhpSVE6kiIlIElsYJxL+P+x9ie/tu3Zfif0Gb9qzrmKvfc5+xS3jvpFxCtsP9vPdto4E1tOmwakQAI62aFLl78BIZo06ECHDsoEyUgIY5EikZN02s5MF6+OF/Hei/LGrU65q7XWnPNXjEHjt24Y0bLEvdLSPdXeq9hz/sYY3/EtGix3A7JPSIBQC8HFbp8vhgyRGBwpCEEG3ASiQvWOyTuid4TgMIVmRlYlacUJOC84Z6xAMcWFSpCeU++TZ9pULq4qz9/dMg4fsLTEatNXWxS+ODxwcpWtbzh7OGP7QpURlw9IA2ri9lg41cJCwU+v8P4SrQPHuzt++OqGdcm8fvuGE4lhity9VVQTYoq0giyOvJyoZSY6kLQSg3UXzFpp0giDJ+wFnxI+DgTAm8NKZBJHMxBRihTu5wMuNg6nE8u8sOZCVqM5AMELSBOqGEUc+5QYPcTguBgS02XER8E8SF1JQ+yPTeB0WpkPCy/vGqsaLsEuOCR5NDhO1fjs41tq+xmnm1vW0jqGvxTa8AUhecRv+vemv+5i1ncI9hK1V7haUYuYBZwMtLLQ8oKd7jGZcXFLTBtOp88Y0o5xuEJlgxUwDazB0GJQCzHMRKe4UZEUON0s6JJZqez2iTAZ0y4geeL0ACkm9hcDw2akQzyF4dQoJVOLZ8kgTnHBiD53o0GMTWyE6ez9GWAYGyUrVGO/c2zcFn20g/EJgfO+ZjTGSfA+s54OLNooywHaiegc3hQtjfv7G6Q1HIExjMTRIVHIpXL/4gXrUsilM6keDgvL4YTON3i/I8SR6dFEqAV3yrTTie0IcTMwbD3r2tjurtDsudoL6909b+6PlDoQasUjBJ9wA0gKkCJjhBQSwUWqCTNn00Yyc0p43225H5zhpSFWyfnYdzHbgN9N7N8dGS8nhvGSOs6ID/zkh7/AtYB3sT+nKBVDxZjSyn47MaTU9yBAaY3jnJmiJ0ZPjIGqhhbFqnK3LpTY0AYvXx7ZX+378lNXtlcbJolcPHvMW/0Z9eRg3RC2Sm0rPhfC6EjjzLDzpI8eEfyBN29+yX/9zz+hhWds9xObTWJ9WAmjEJInsuOwPnCaMzc3lYeH17TqsLZBZGTa7NlstgyuQfZQHQXByYjzARcdqiPNHM2MV5+PlJxZ5oV5Wfu9XCvHjinhnDCOW0gJq0YlU7NSFdaqmBhGwEywZQUZMOdpYug8ggjOjGHbbcnxDg3a80/ESIPDVMEZhkMXwTnwqRcgFYdJgDNF2Ing4og4z5fm5OdkFXB023YcguBd99+CDu01M9zZKM+0Z8QU7YXTieGcgYPie4Qvi9FWwwfDJsFKwFtkiCPTdoOo77DqV1kU/pP/4v/KqkeqrQyEsyxfcOIJNISeI4tEvAS8eP7V71aeb655NF6x5sqfHb/gUFfejTu+8eQjJEc+fvmSafu4B5S3E3Np5HKktAUv0l1Yo0PHkcEcwSfGqz37Rw43OnTwxNPMXJXDmnnjZt5owWxF8y/It7BvDqn3WPJc7iNXcsGxCr41XKushxP3a8bj+fDqGRc7x7iNjFc79heOjHKshX0+ME6JcTvy+FHixz/8gs/vTrycG1UgOSGeHV7rYhwPM5/f/4yPf/A5++3I+I13eHr9lqvHL7h8eEHYVWRQ2tQIumVW464VlsMtpdxS6h3xWLrJnMIYhOPNLfn1kfzjB9YojJcjF8/2vP408eTJFc/fuSbsvo4se6gb1E+0xTEvlRc3B6os+G0kXe14+9MveDgKc468+9FjLp5e8OhqYBMuqS4yjFvef/Iur2+PlDnDDGmbWFchL8phrYgqIg3hAV+7LcTTq4hcjJgDdQbVmJ0iTbkcPG4fiYPn4iIzHxUtRvRKro4yz7y9ecnDWtC2dvtYeUBrw8QRQ+Fiv2PYeqbHYNPMTT7w6o9n/vU//sfcvHrFfDrx/je+z8VHTxkfTTx8+jPu7hUZRj78ix8xXAq6NOy+onFCFsPfVZbjPfuryLiL3H8Brz+5Z73PzDhGp4hAM0eUgIqjes8mGH5MuCERmViCQ0VJtqDjgEjAV+Hu9Q1ilSH0YJwUR55/5x3cky1tPlHLwt38CyQ3Xr1Y+eE/vWfwG8T1w8kVoQo0UQZ3JO03hDQgOnCSRi0VO6y4MHb672aHv3DoaaadZpo5NnFDCIHiKn/nP/wtnK/8wR/+gJcPrwlbZW7Cq1+8ZL2v1LnhWmatE0tu+PgFyy9W/PXE46+PvPzdV7x8ec/vvb3j4vodHr1zwaNnW8IwETc9dMj5xHx44Pgw8+bFA8ebN7QmGAPjcH0+qIRhLIiMmEXWubvTioBPgOxoJdLWQM0ruIq5BjFB7LbitWacU0IKTJd7iva8l7wILnfigZpDcg+YwnrAl6YJE4/VrkPxLpB8ID1ZcT4gzrEuniCOIIIfPc4EE0GD0E79mnDBYSqY0PeURRDXcN4YN4EoHh+6v1apShBhwKFZQAWa0IaGVMOKMZ8KS/V4MXyCVoSmSqkNW0tX2FujqTDsPH5wrHcNZ4r3jmnvkSromqDuiNwjoxKmr1jR/PemRDWPAsPwuBtXucQQAhfDhhQCPnoO0jNzU3L89Bc/p9AraIqBD4bnuATfuriGEHiTj/zs/nO+63YMwZPChsfbQPDXRC/EYAy+Er2SnFBXZVkzD6c31Bfa8bgYgMCAZ5qEv/nkA0pbMA/h0Y6WH1huG1Ero4/4MeKnLdfDpjue5kz59A0390dOzVhEGENkSCPjtCF5evFQIbqBKAOJAedGnjy9ZPTCO1tYpGKDwz8eKKOjlUI7ZRY7cswrJz3hP23M95E3X0TixxPz/cKaK0tV9lsBCRQL3Ly6Z6kLa12odwvDFBimwP4ycLpfObzJvPpx464aw6Bc7SsbF8nvrOj7M9tvjAS9xTMR4ruoLdR5RV8c+bOffca8FhTl809fc7tWjk1478kTvva1D3jy5AofMmY70rBhffIF4/AE78GxstXE6DJ1qIwoSENECTExxMIwOjaPLzB8FwKax5XCcqycUuY0bHFhxMfENAXatrdHaYS1RlqulOVEXgBnOG/c3zTePNxwKAsbXXl0PXH5zPHs+ZboAnfHA8Vecv3cMYQN+ZRI2xOMnzJb4cf/5g+5u3tD1cLPf2/Pr/3699lfbhiGxum+B8Kbruy2HxFTZLvxqD/i9xkdYbh8xOUYoPUbVqbAOlfmQ2YcXbdjj4E6rIza37daoeSCmmFiRBasZWpdeXh5TxwmQhgpnyROtzdYVXb7Zzx655rLp8YU9zjpB40B69rx6lIqoiNiYKXQnCOGEY+nxtrFdVpYTg+wNqgFaRUXJ2qd0WZkfeDmiyuQyv2Lz9C7zPHmxMPnB54/eY92saK7zOPn77GmTPWVKH+F+a6gFMKw8vivPKatF5T1XT76zfdwXjGtZBNUO7e+asGtBXdSdC+MyxbzAT9sGPdbytwoh9KzQ1wFJ2zCBMVBE5waLo2ENDGMG5IOlFLIOTMvipsG3JAYppFp63He0Rqorpg4zAU8C9o8WiOqBacJEU8cDZEBxHViig74FIjbgE8jdqYPz8UTUiNEiG4E70hD4MmTDetaqM3QBqP3mDjUhDr3iFfvHJeD47gqpTWqFoREcHLOmxaMSrNCrYJEJSR4dpEYgxG8Ii4gJpg6WjNqNpoK1RINmDaOYRB0CSgB5xxT7P9+Ld1sM+3fxw8eN6Svtigk7xDtHWttincVs24FUWvGmUG39EKBYoFxmJAGRY3aMgGPCJxKprTCqRQSkVIz6/mlJBtwznf2jDe8czga2gzOwrJNikib0doopeAkEscNaYw8ur6g2gQBwkXkxfGGvK60pmTnEPF48YQ29OzdFilhS4hGlMaDwtgcQ/NUC5RasHZORmvgk8MToApzdtxnz8kiNUTEOxKJqt2Hp7WuqxBRAo3BKU5qxwePxuHuyDpXclZc8fgUu4CtZsgVK92vaHCOMAS2j7Zsph0XU2XiwGrGMEYudiPHtwsheepaeXj7QKKQXGW62DPte66yReXZ3UTTgXGTeP/phvtT5mGppDDx7NHA1T4wJE/THj05Upl8wXvBiyKuR4eqOqYEIgEnShgcKfQGLo1fGm8J5hzBImV0rBvHsmwxl8BFYgygAec94+goFnr04jKgrXeLIsbNfmb/ULm5h198/DOcjswvB+7+7KeU2lha5iE/YPMX1PnYC2d+xbeevMfzJ5dsfvMpNzeRZc1IcHxwbez3ynY7kB8P1BaoJfD29Q1hbriHDdvadxphm7h+vuEiBqjK6ejxFwlToVWIQSAEzHuyq1R1qBqteUoTmimVyrwNeDGGIN2BtQklw/xgbNMFYo5hvMR7j4pw9egx3hkmHY4qubCWLmZDC+G8N8AFbJhoatRtOseGOkQclYZoxVljGDakIKCNw/2J11+8wDljCok3h4/J64qKcQpvGHeBzT6Q0koLJ3xqPHu25Y0/dF+nvRK89TyFdWV/3RX087zgtxPkjNVGKxEZIIgyNIVFUGdYKPjpiFJomil3nT4sfmC4vMLpgFTBZiVule3e8+jxBsyxnKzHbz4oISk+NYaLSooVM2VZGnU+oMHBNKJSMA1oCzgxTJYe+xk5w7LSI1lTxA0RP0VqmGnVqEU5RSENjpgCFlrfQ8TAcSzMNVPVupOYBBCHAlUrQkdLonpaVCxq91zwhnO+7xFco7WM1kzJhjOhmVHqHcWE2AI+bojiQaw/PyuCw4nHiaNKpZaKt4z67h+lFWo9sdbMSWeKZpJsGMJXbJ394CA3JbfGsT6Q5IiXgJSJESVKIPiBqoL6fjpc7mN/Q9mYy9x1P9b49PiWYn0x9cRfUVpBqRQVfNwg50BjNTrDyAytSpJKdMJmu8Ny5bSs3J26yYWNE34aiBc7chDwxnaofHGvrKdCzpX7Zqh1rFumQAwJ7zw17PGDMLq+3Ns3z9Q8q0V8zb2TaYarQjBPlIgU4fZB+PTe8bAmQhgYCFw0oRZPqzNNCzVACkIaPU/2A2krSBQOWamuYK4QneEGT5ocafS4MbIswrp45r2wvR65uN7w4TefsnUTLjfyzR1jFMbdxHix5/f+xcfUY0Gzcv/qnsllplSJ21v2Vx8xXExc+A1BK9M08u7Xn7IfI8eHlbvbmVdvZsJmQxgnNiGSq6FN0JZwoXXsFJBR6LhQd2715nFihK0jOoc4h4YVsdaXjYORYndJbW2gLMJqQjWHWMBLIoTAdow0H7vOIDdCiB1TbY036z3zIfDqc+G//X/+M25/nkELD69XXh0PxMnz5PkFMin59sTdyyNLyfzONy/5q4+/xeZ/eMXb14XjnFntxGasTGlgP14xPd5iONal8U/+X39AWmbizZadC4xbz37v+doHe7baXXuXnSddDwybLcO062wQUbIZuTSKGq11iwJNgWLKUleOVDbTxOV2RyPy9s2J2zcnHh5Wttu+7ygl8cWnb2nq2V08JrmGnhXyWjJrFYqCaDnDEYITKOOAAm0ptKI4EbwIs2pPHsTYTiNDFKxm3vyy8OKTz4nR8fydd/jx4RPmk+LcFW/sFzz/+lMe7R+jvKbmE94rj5441tMbcqsMV1uIhbbeUw43qGWOy5Hbh5lH+6c0OXX7jZZwfsKPgcF53LFRWiHrglbByJhfmY+vunOobXCjkvwOsqeVStpccPkY3v1g4KEtuPuM3a9kV0gUQnCE8YjlhZIL86kwHx5og0eHDV46VUOdMPkBkuK8QhNKczQV2mrECyEOEXMjDwRKFcoq3PuF0SYGJobQ0x2dBm4OjfnN0jv36pnieKarG+2uIS7hfeIwRYY9fdLwYCkiISApYX6m5ZUyLxx8wWuACvd3N0zNMbjEsL1icAnnDBcqkQI+oD4QJHJ/vGeejyQrEMeeIlgbp/kNuZ3I7Yg7GuPFU7ZX7321ReH3ThFtK9pWPKd+U4tjlEjF8AgbHEeDIJGd3/Dd8DfQujLnmbLu+XDcswkDgnFqwhATTzYXvOEN2TKtVTYkQlGC9m7E2Yp3ytVmyzjEzkiKcDoc8Zrxh8bdms+pVhmSclq7y6f4SiTxePT4XeOZv8C5iPeRFgfURUw8MjfKZkRQLh5dcrFPTJuB7eWO3ZB7ZKYYj5IRUyIMEZkEtvd88LAiG2X76BHDbmC8Csx1ppYjNd+SvZIGJY3gt1MvAAJLc9TTHa1mWlMurjbENBHDFi0z2XXrhjLfY24khInry0tGnXBVsfiASMSNAzKNjG8+5c1n99y+PfL2dEuaEtM08PzzBzi8YXO1JaQL5k8+Y3zyhN3X3ufq8cTl1QXPngrX40vMTUiYGPebHnJuYGrU7BDpUA5hxKhARtUhOiNU/LTBJ8XMmA8L3hd88IQgpI3i/AhuoK4r66nvJNbTW0LcEywy1IauCW0NLSsuXBKi4IMR/Ya1FkIwNsdXxAy5Ve7uHzjmzHXc81Hc8Zu/fcXFdsMUtrjjjs2Tid1ofDBdEn89AIX55gs4CdIiwU2I7iiWWV3h1/+n/4Cchboa5bSwu4pM+w376/eRh3usNFoR3C5SmjKvR/T4lrEkSg3MtmJ+QoLDDyuaRjR4dBjJ08Scez71n//B59zdzKyL8fj9b/H06orrxxc8vn7K/D2YF+VmLtjceifrPHWdKUgnUWjud5wJTpSHtdFUka3iZCR4iMHQGEiiRBQtCR8qzje+9+E7fPzTx1QtXDyb+Dv2H/Dy9Ws+e/2ai00kDUZZ7tgMd+wvMvGy4HjJdHFkcsbuOiEu8/bTt7z4+HOcTYQLz/X7jhcf/5j6ULAKfrNh8/wxabNj9BfMd0fyQ8PfGosupAmmfWN+9TlSIqEM+HaPa1fo6ijHhai3zHLJ7XjFZ598wbIeKeuK3F5QoscEyg9W5tMNZc3UWZilMJ9mjjcHdMnd6kQ8fvBdka4N1kzLraMAChL7w8VeKKwalG7dLtuAmzxuGHD72FMLG4graDb0BPF6BzVAdohXhAuc3zA+8d2mpy6YPuBkS7qc2L6/R4A631MOt5xeNcImEaaBcfuYaVx7ul+5wG+kL7abIw0HRBPWRmQLp7sT63EmTAVXLxE8GmeW2wN5PVHKgXYsxMt3GZ58E/7+3/nqisK7m0dsizA1RxJPlkiTiLlE0JUB2JKYNXJolUOrvHj4GXPLPLSVz+rM/XLJ1g8MNO5UsexJpVdBtUzTFXGO0UuHTLwRWBkc5HrB5TbhoqOs4FrFOWEzJXw0pot+mMVUGMqGqoL3innFB2OcYNg9J/iE94GVATWwBjob6+kIGLurS3aTMQyecRoZUiEFRwqOi20XmUgMVBrbpwn2BbcxpkePSJtE2jp8WSnlgbp6IpWQGikZcb8n0L1OgjrUG9YqCkypY8MhbFDniMBqhncZY8LrRMoDox97Rx4jrXq0+B5XmAO6CqxKisI4QErKcb7n7pWyrjNhu3B688DldseQIDoPzhNG4eLRlkbCJOCddjjQCTJ4mu9wkHOGuR51qq12eIIVsYqccyqg0+2ir515A7jqEW2YrEhZIDdYGm5ZCHEkOnDWMO2MC0FBlx6sI4LHGJJx/c6e/9H/7L9P2o2oGHf3t7x9+ZIEPNlv+eijDbv9xLTbMKRrnO+iw6thxG8McZVy/RTaDlGPaGej1KyU7LB3nrLOC3meyUskDeD9Qr35DDkekNrAHHUJlDN85PIJx0ogMDlBo0O8I0ph4Z5chXVt/OgHb7i9u+fh4Y6aBza7K54+v+bdbzxjkwLeN+4fHqhrpJpnGM9whEHFgQxAFzp57ROvmeDUMbmI0WEaFwa8QHDQUiChRKynAp0xUPOV995/yrwcuT/e8fi9J7Shca+3hJw5Pbzl7mFhdzmTwsqYMrdvFx5uV8QZzgVwlfnmSL1dOY6Vi9EzXTjKvGKLQgPbKDjB/ILGBeKKjYbuIaRK3ClxVJ5Zo66GmDE9egAEqwGXDJEHyph5KPfcnj4HqfgtpMeZIY04idTFsTl4NEe0CnV0nArcnZSyyNkDyxFHAQqoImulLo2WlbY2XOpLXT9AFkdZjLIoRZV45Qg7R5gCWqQL/1YjhnMKhgoaH3DN4bTH93r3gHMDNgjlsNDWjOUFHxJhl5D9SIjgTgs+nnAJZPK4KTDt3jD67tLgbKB5QZtQV3CuIObBEpJANoUQKmlrOLdFnAdfkM1MqoVma9/puEiL/t/prP93LgrPxg3XbuWyVkY/sspIkcTJj+zagclgIwO5bviinPil3vLp4afcW+XeGm+s8tAObCRyKY0HYMaYF+VpeoyngJ0oGJsQ2fhI9DBQ2ARhzMI07fA1sJixHRzjdmAzDIAjXu6J+w2DX8ltRAmkYMyinSq692wffUSIESeOUxmxWrFaqbNjnWdUlWG/ZxMXkjdCCMQQSckzDQPTI+u4v+/GZNPVAM1wgzI8uiSMkZgMLSOuuN6ZacP5QojKOFziWk+f8wgWRwgKEoi64jUQNGAyQOvmVq1EIBJcIKyBYRpILhDS2H3Vm1LWimaHFoFqDBfCZiOEaBxPR+5fN/KyEB6trA8zumZS6KZjfVHsmHYjFd9zG5a1U4y9J8YJJ/3id2I06xoFbMVah3GwipWMWQ9KcUBwXZ7vUKQEjIzSqbHkguWKK0aQQvCC04aaAp3mp+RuV2sOkUoIjasnW/7B//jvMF5d4KKgyy23v/g5+XSi1MI+etI2MFyObJ4/hxVYreey+AyhwjCCf9wPhnyPHhptCbRloO0mFpdZXSMPglihrYXT6zeE5YSYIS6QF0fR7tYaXYXocMEz+JHmAS+4ulLqPadcuX0o/Oh3f8Lt3R1rPvH+N77N1eP3ePej57z30WPW44llqdze32N1RHzCTxtcom+Z1RDtdgbOwKnDOG+g61k7IcIgQAo4E0ShJk8UI2L44Lo1uTZWVZ69+4jjvfD65edcf2PHlT/xeNlw//Etp8Mth3xDobDdzGhYCSwcbyrOWadRDko5VkKuLLeV3VPpTgDWs8sFQBZMDGWmtJnatDOoJiFdQJgUP1YejUpZjFYMz5Hmel7xuE+sy4FqR+4X5e72BXF0TDEwPlsZtlvGNGGyx44Riu9TwdY4usDGHEv2tNqXwWkwxHWFsF9jd5FdGutRCaMRBogTzM6Yj8bpCKvB8FhJeyONxvpS0YP1ON8EPgpugPmU8dbZh7uNI/iMSGTORj6utKVhx0qIDjc5uPSk0eCk2Nio14JtHIyOId0yNMUDBOmMrWLYbFijw1TOI9ERvOIbjDuPGx5woU/18bKA9R1gWhzL+sBxkf/fY/3/v6LwZ2/+mKWtrFo6RkdE6Z0c1pc8FaURGAhsCFRb2IpnksS33Hvc2j0mC+/4PX979w2ai/yiLjy+nti6xkYKwzAx7SfG7UBMja2rDFFIjzZstgMiUErh8kn3+XBeAIeFhIqjnO5YzqE0KTrmxcBD2gr4LVqFtjbspPgohDEhF3tSfNYN3Y4zAUOsggoDwiiOKTqGfUAkgDoiyiYlvDk0zITc6WquVQYg4rEwUMXhXOmPuaGnlbbM1FMl+gUnhpgnaMO7hgtKvBqw40p7WHFmuGjEZOw2wmYcCSkiulJuuvf8fLyj3Bw4nWZuS2FroMUoRTj88oS+CmwuRx5hTDvHMCmWV8qbt5gOqG7Q7Q7xDkM53XxKPjl8nNh/8LzfSK6BK9jSvtTco/OC6QxWkSURo/QQH6EvICxAHbqkyxzgcG4h+T4mS6yEqDgypgvYBpzDe3pH1lYsL4heIrFBbjz88AeUfcEHgWXD5tGWi+fXhM0VngXRFWcVPz5DLp6BbGD+U9Dc8YHxXVg+xg4vsNe/wA6XyPgIt/+Amx/8PvM8s6riH13jLdPmheMnb9iMmZgCPu1oDwvrsTKfKsNlxm32uGnH4AvlaMzrwpsvXvDjH33Oq5sDnx9OPPm13+TX/spv8bXvf52Ldy8oOlBK4vb1LdF7Bh95vBt5czOTlwJrQeva+3wZqCg4B85hQXHmATmzYXpRcNoo2bHkRp4LiiOkbhkxxoHkQNRYj5mn719ykSqfS+R0d+L548f85jf+Jv/b/9X/BonG9aMd0+6Gw+sT89sT8SNDDg01eKiOx78Gz74OHz2Fn/zION4ZyyRcfwvandFmWCssp0w+Fh4eVk4vDEEIGyF54fCysR4qF1fgz5YOdw8z4+7EuI1M1yNBHHdvGi8+zvz0/3ZPO3U+/8W3Pe//tcT1d0cefXBF2ghSHe00ssxKlorKgqxHwqrYCjmCj4ZzZ1bWQ8OtSqvdLsKHrrJOM5TFcA22g3VbibewvoXja7AVdgH8NcRouBmWpRdwccI2SCfHmCMXxzY0bFDavdJKt/QnOcig94reGETwJv0z+LniGnhnxC0UEcT1L7HaL+OYuj/UcjLqEdYDTJsDMggtwRAF57tljy9GcsZ29xUXhV0a2TYHGol+wPtAcD35au+M6BzRRyzuScPAOCVCbrgU8CkQ4kiugjPHoxDY7S4wL1xaYXu1YZDGaAU3jcQohAjeOzZSiAHcdkPyfYm28ZFpABciEiKUTLXOFfZ+YNj1GCXvDNkFzAxsZWmNVltP7pKOX7dVsdbIcz/MmnhaPgINCY4xBZgctonkBs4piGJjxLJBKzj6WOekQikYQKtY6/L4TsySLvTSAecC8ZEnxoYLDucTEaVv2D1uEMZNJVwVcO2MHztEHUaBOOAvP8Svr8kfz7z+2Wtu1gMmmYtBsbWwHgutGPNdpR0qLcOwX3j67jUWPC/e3LN3rRcCn2nHG9bW2Vzz3T1IJNSCvlBqVUSMEDt53FTRVllKRb9UzUaIWfFBCNNIfpjPHc2Ko3fY+J47bFiPuUL7NCFgpngpHfPNDcfQPeSdg1ER7xHxkATxK4aiCXR+oKwn5P4WVxuirSe+vb3FDxuci8jNA5LofkjhDVoKesq0tyNrqVR/S/YHvvjilkIfUNrDJ9T5AV0y7QCPLj2xKJwO3N/eoXhkO7B77zlLdhznxs9+9nPucuVhKbx9fUstgc31B/zOX3mPy+9+m/FiR9oOrDnRaqBVh4pnrbXTSNtKro1mDnzDRzpbzYFrPbZVRTAa5nqR9dUDX0aGGsMAMXjG1HUNIj29jtBVvYZiAvdrQQ2214/5/PUnbDYj0+Pn/Obf+G1evn7Fw+mW5SEy14AlT1NH84ppx9pNApXKUZVaFPcAYYDN14XpqSAZPv+iF4uYHO+863GPHKeDcnun5DcVy0qshsvgfL8kLq7Ap4r3ih4aYo7d3kjfUfRvQz5AWeDwuvH5v1x59QeVaZO5fu7YXweuvzax2ya8NWpZmUoF7e/ZrO/znBjejDZCi8JYDFVBZsOdQA6GO0A8GgwQtxA3MFzCfgBTSB44QHuAfIJ26I1QG2CZIUyCT8KuOtbFqCfgteEFXBTCnbB55AnNcEGZq5HfGs3DxZWAWfeTa7C+7JGdzYAd+L30RrHCuAUZYb0z2sEo9+CCdLPFAMEJU+6JhEv9quM4nz4l1IVoleQnUvAE74kp8cgZow9MaULSFX4cCJvEsFo/PMeADODqBrHQl2CbiATDS2HYb4jSiJaxYcC5ej4sIqNkggMZJryteOcJw4ZoGfE9vtLWhtUueiF4XIz9AJKGhBFtSpsL1hrNKs1yv7msorW7O9bWEBHctMHa2rtf77EgWASLjtK0wyHOsChY7RecQ/trRnBWcSpo025C5bt0XaxrKpwL/c+2CR8V513n7UulR/Q6kIbzlZASPhTUHNYEPZbOATfFhoRGmGvm9etbDusKrRJMOZ2UPBt5UR5OXSyzWia8Wdg9Vo6nwttXB2rsFy5JaKWw5IVcCnnJhGEgWkMeCmU1nBNicrhpAxiYUhuoGqbnHYQ1rDmc9aL4pXGgN0PC2fnLGvje8bpwpp1yjud1nXZspn3SoC8IGbTXS1zHs1vtqEoAyQVrGcsrbjWki0DR4z3e3/To1FuH20YkOczN1Jyoc2O98ZxKJbOSpXJzXCliNDHKco+tJ6QqkS1LbpRm3XohChIdbgowJI4PMy9fHfj5T1+whEQRx6wj0/6Ky3ef8vXvfYfNNz+iATln1rV3hKaGuX6t1Fa7XQNg4vpnQfcAEt/ZRLizQEq1/zngVDoE2BnheG8E5yB4Sq+7/RTzvSCoGT465lxxBvvrS168+pyWhVI93/mt76E/ctz95MhyB7aNuDiwnhRxER/PMIx5GoJKYdwY5sBlY/DCbgBJ1uGkub/O7c4zXXrCoJxyRfX8M3X9deNAohHTOTZSlXpqEIQwCGkvXH9fKAuUk2E/hPWNMt8pmcJogSF06Cyaohgbq4i0/t4DyFk1LGJ4Z9TUD9oYpOeLF2Dtlynad2RNIQhEgeGqFz7x3V9onfvl3FZwc+/prHZWk2jANc+gnvbgsYNix9p3bE7wJyFZYog9djcoXaTqle3YMKdUU1jAN0OXjry2oT8nBtJ6o+Y9FIGy9vhfCdZP9iCIM/wilEVoy7/bWf/vXBT+w//o7zO2B2I7UkujubOiGTAreByDHyAMaHW01VEsM7qRIYyETSCFoSd9eSW6C0JQwnQkuA3eVZxbEfO44JHQEJlwOJwo3o04b0jw+DjhzSHnNKw2zpTVU7JQ4sK/tSE0vAitdivroTp8NAoNy7nPYtZI5jFXwBk+BNSvoA1nEfyCiqPpQK4Nd+bPM5xA+wHoQyaeKbYuGZI9Jt16N2rCxUYIsLl6DiwohWoBtSNaG3WuSDt2wRcDyErLGS0rPt5D2iFuwOXace56R2u33PzZGz7/08/5yU9eMB8WypzJS+HVwZibsDY4NQUTYilcrDd8+jbz/rsz3/6O5/5KCWnTU9FSptSuQUFmYIfQqKnh/AYfwA2KDx7x/TBLOXR15dmaICTDeXDiiNPUbX3dAFLP9FaHuYILER8m4jgg2SFq+CkhfoN4h0TQNqB1pZWZWpTGsS+4GRA79YVn2OF3O5xE0BFaxixglkAq631GTw137Ym7LRIDKo3j6yMnO/KgM3cPfRlnboX9yMP9S44Pt8Ql8O571+x2O3zb8PDmE3JT/G7HN/7Wr6Mtc7y74dOf/IQ/+r3P+PGf31A18pv/we/w4fe+yYff/gb3p0oVYHDoOnU1bQ20pXTRCw1xDrE+AUcSbqrd7qQaxZXuoqmBkJTgQ9clOO3MMxyugWXQ1sAMqQFxvXERcYj7Et93EJWYhMv9RK0nXBQ++PYTUvs6t3c3/Mm//CF/73/wN1iPKz/945/x6S8e+O6/f8HFO5f8+N98zHvffMz10w3f/PbAFzdvup3HduDX/3bl7U3j5l4Z1Z05/kI9KXXuw9/hFuwiwAb2XwvsLhvH25XDTUbVYAKN3ealLYat4OezPf0CugryzLjaw7QTvvV3HetRaKsgVdjsAil6RudYDivOw5Mn0IqC9mldnFKKdS8jb0ivrTgvPH3iiF0OwLr2Xec8KzevlZahrnD7037oDlu4+B64S8FPgl1BPEI9CWV2tBCxsgFNqHNsSz9m130hnhq1wekkrIeRFiGMcPU4cbGvtClzb29oQ4WoTJcN2QnryTjdghvBBkF9d6U+zFAK1EO37AmT4PYwI7+Cwo6vYVmE4/oVL5rD5YhXw7eEtIlBurWF4nA9t6lf1Bg+jfhxgyMTQySESIwR8wlxjuQMlyLeCcFdUNaCWEbM0aWzCmKoDziVzkYJAScbXIqEbV84Y9IZGmWDiaLSEAl9ChCASOPLfaWnukoVo+IodJaLoFQr/QY1KK2XW7HWxSQau3tiyx2a6E+JrD34whx0drKhqlg2Si1gDTHrr0mlh7bTsNrQ2sg1U5cTtRSWWXG6gggmHieV+eHAfH/A6j1+tyFOI9MwMvvcF+CD4/XrA/cPC6Uax2zMK6wL4ANXU2SIkc20YZM88fy5XWwT+0c7pkeJzc4Rxgk/TR3Caoaa4RhImz1xGBk3A7iE80KMIGnsg4L1z8DUw3nPJE7Q1lhPR6bR8MnjosNapWajFMXrShgccRACsR9kAC32TliBbKiW7tuTXRdJqWDqsdBVuxI8TjyoodS+26iN1gq1rWjNtKI0FDtUyv0bamvUpXI6FVqtUDOWQu/2muP2zRvGac877z5jkEA5HXh7eyJMC+9997uM+w1uH1lfveDPfvgL/vD3ftw98d3I17/3PnH/iO2jPetcePXzz5iun7Lb7Uj7C26rx6z2TAWnne5rgqrgJOGdkCaPSGItjeOScTjwAiiuBtQE9UawgHmHSbd9X1tn+wQV1Pfr1mn38/ICXs7eXXRx1MNakNpQFU4l0yI8zDO/+PkLXr154PrZY/7S73yPH/3xH1MeAm0faUXIq5DXDmUcbht1bUSUI/T9W3LcvzGmbiPE5tLTJkGHvvxPEkAhouRD4/AZ3P3SiGL4QSBC1o7ZS+0dsgzn1ZSHdjTKBaxXkK6MUqE1IwSH5K4PKdo4HCtxNHZ7QXyfGppC0Z6CZx68Geqli82AT3/Zo3aHJIh3LBXmQocoS1+C6y3cfwzlAF/8l2dfouRgG4hDwA8jfhjJ7SnzsMVLxL95YJQt0Y+M4wXxmbABrjPkckEojlhhDUcCR3S953BTGa5W3Lai2xVXjWjGJgqpAfc9lbEdtU8Pre8jJPTsl3rq95E0WLLBW2GpxrF9xTYXYzASnmgDahuCVJwYej7EHA5vkaCKpAE3JoJ0y23vA9EFmvtSbVl7t91bmH/7JNJH537W27njd+e/612P8+dJws6ZB80wBVXtDzs/oB/KOFQbqo3WlNYapg1T6XOYNdQ8YmfcMWekll4wnMOa9Ye27rlyfuFCd1J10v/IzDp2aY1aDKwgVjHpXik0o64FKyutZHJW6rp2peqieHL/Rs7jpaC1YFbJS8Y7QU1J3lFJINLpn9GTdgNX1zuqryynlTwXkMQ0jkzjyMXukk1yuBCwNDElz7jrf5eGvmh3Q0Ko4HphcyLd9dIHRDxm1kVlDaRqh4UUaL3bsrMg0JpC005P1c7/tjPnW1s3ZrNmmO8PmuFCV2aKnkVxre8XrClU7c+B72wt17/OOUG8IERo/YBttaF5oRQhZ9eNAxWaKvU0M58yZa2UJVNaZzil2OEgo19Dtko3TTOlBUHCQEqJzfXE5nLCYZxevOZHP/gxH//kC24+v+Pp0wv2T3dsr5+wf/wcN1wSwoQnMMShT8f4s+2Z9IWw66rnvmvqeh+RrkdwIl0kaK5favalWLBP5kYnUXB28m1Nztcyv3IT6AXHULU+MWBnPL3/f10rowdtyvGwkjWj2pAifPLTz5i2kW9+5+vsdnuWu8ada6QxIqaUpXL7ZulsmMXIR+XeGXHf08DuD+dDN8C4AdsIbeg+ZS73zBPXhJKtm7uZw4vijc7978hkfz9Oui4pdMSxrtCO3QUDILeOu9tgFGdItC56RSlNOR26RU13GYVsSj8WjdDo+SXnBvJ41wuobnoqnuH6NTL2695XaFtYNt2LqK6e2joNXO/66wyjdkZVrLiYcaK4t5kUIikFNhsj+w3TELnwAa1XqDW0Fer5PjHJ6EPCvHY40OmZqi1EcwT1WDF0VtrcYFHI2gWTcn5P/nxGmSDVcPfGWo1V61dbFB7bCR8Ef/b4iM53MZPvB4iIh5aIBrUqOR9QV7olrcXel2sEhMqM2KYrYX3Ge4cLFQ0ZW7sHCEH7gesKiOFaQpIhTc8pSyu6Cm3xlDqfOceNyolWB1Q94hpK6z4s9UhdHK1UtGYkCyIVaDj6BW/WF8/UtdvbmodSITZcNUi1j+0+dNbM2ZhGXD4nfhnaVupimK0YM9a2ONcXSIKn1QdaXck5UNuJ1lqHVUI5W3AETJTxsgvhDq9mFMF5xaVGHD0hRcLouEjXhP3A/nFkOV10NtBSKHXCTzvCsCWNV32ia8a6GgGIQ2IcN8ShIHE4G94L0lo355IKi2K100bzXIGKCxUfrjoJBqO1AFL6jSgT1haca0wpEUOf2kwDZoVwPvw6IyIRrH/mYdjgfcDWBWsea33xTzXEGl4Ul/YdJ/UG0WGynpmaE1oLNTfWUyXP9+TZsZ4iRQ/nhsCYTzec3jbyXCk6E4cd4+RJA1gJnKsHu/aE4+cvuJ0/RfwF3/pr3+T5t97hnW9/ncOf/y4v//jH/PA/+wP+0z/+GY8urvj+Bx/wra8/ZfP++2yev8uz669hLWIWMb8h7Dc0gXU+4aUrpyV4Ih4V7XGv1ePOHZFWD6KICgMR8eeFvMUOsTSPNN9NBotDcJiBa74f/E4Ro3fGGDTpBc73/VYQRZsyHwq7Jw608faTI1nvCN7x/tUz/s0/+Vf8e3/vL/MXfvsv8Z3vfZOf/uRP+fxnt3znb18w+Mp8d+BHN5X3v+ZwzXh40fjk5433f8vxzvcc88GI1pkyYS9Mg1Aj5FSphS6VOAk5d5X/4w+NGM5HtXTVb829EfNA3J07YCfctvOZANS33Q2mKTBCaYbbKn5TGTfKOjde/qIymJ33Vd1R1M582QFBxp65Lk5o919a9BguCNMobAbp1Wky2BvrDtKH0pfvsuHu5Y7Dx5W7Pzyir0/QZrA7sJfnZteBBMQPuBAJw4bts+9wcX3J8/f3pLwS2z3RbghporQjKjNh9didw7JnsBE/CK55QkmYTD1rhoqkzDqv5CVzfFspp0bNRjYjTh7vHcE50ppZc2PN61dbFOxygrMPUPEB1Qq5UhEk5961i8ctlVwaa1FcW7u1dUgkHBKG7g/kwcXe8Zkpq/Zx1otCqkSk0/dT/dVSrcUZAaz2QA6dT9TaqKVRloVcO0umrSea5H4DqiFx6p3DWilGh3BMMRcQAo4A0f+qsy1FEAKIIbGHz0PPQdVqmHT6rbd/O8XY+SBVlFYrpWW09cU2YcXrufNoaxfLmUNjPe9RHV4c3kKnYHiHkHCjx0VwN69oJjQctcKyLsTaoCZMRlxIpO2exW1gyEitRHfJ4BPRRyRsYT7SrPWLywoWjCIrUjpsZppxTam1j+BWVtgG0IZUoSy9ffOiyL4v+2N0bC8mXBg6kWjcgGw6Syk40KWLRyUgFsEcZp0h40JEfCRKwCU5t4e9sZDztODN9xvSGTaMmO9LV221W4u3RtGVclrIa2U99ZDzXJS19TyO48OJZc6E5PD7yLAfSDlRrHvor6fub1NPlXLM1F3j+ltPefzsmzz+5gfY6RV3P/9T/pP/9f+R//zTT8jzynsm/P2/9ls8/d47PP7+e7i3QhieknhMcp7xsluFyGZPNmNeMqe3GWTsnjsukYZ+OGvryv5Wu3Cv0CcpNUdMCWeGOd/tmNXRxKN43FlDItK1C0QA61OqyHmyg6Jdi+KqYkFp1gtuoRLCFpqQDfZPnpCuDXmW+aP/07/m889nnn0P/if/83/A//l/d+SHf3yHiXLQBdOKVWX1I+nSc/G1xOlYsL2wOCG9a2hW5grHk1JPHdI5tUoUQUIg7iJXcaD6ieYcQaE0pWojrSttqWhWalZy6ZoL7w0eOSQASVDviA1C6ymLC7Cs0A5dmKZLf+4W+tncDw87kyNg8daJCgHCaLA5ky62jodEVyoftAtltXfd6oG9ZxoSw5Md4zsTF992XP7OBcfPG/l+ptzOrH+8oA/tTN9egIY2IR9uqKeXPPzS88UfO8QczhkhwtPv7pkuA3GCEu6xB7CjQYHRD/iY8NNIOD7BRY/bC9uTsNZjz6x4sXCaD9S1+05pu8LFgHMwn06sGU7lK6akzs2RmhJbpdUF1QXTRmkCeelQgXmkdraMOH/u9DvrRPrc2G9yp2eYSFAaOa8IDee6Tz6hj8fOhb4QAkS7B4ypdgy4Zmqp1NwodaU16SM5XRkJZ6Tny7na7N+a9glno70O2UoI57FViGgvFs4IPnSoAjDtzCUxwczRxCE+9ifR1rsM1Y5vm2J04YgE19kGvgu7zH1ZZe3MttEOsWg7v06HiVJPCrTuZ+QCEhxazjBYcKgVcBEpitTOUqCeiSbR4x34M/hvZ+FZCB7vQz9cTTtkcDbXEem2u+IE8YEwdOdP7wSfAmKGEyVuR0L0pBRI0xYJ2otCGs5e8do//yLnM/3sTU/XeMiZjirS37Ou9UzbsP5zcNI/Vw2937XapyntlsFladR67EWheuo8U7KSF8ilUGrHj8W6A25eF9JmQxwjIo7SMnasfU/kGiFuSFMibiamD/Zc7h1TMt7+2U/48ccf89nPPudnf/oxjwbP+OiCd97Z8+yb1+yfXJHCljQmhumSYXPBZtwybCfckGiu4dURvWczDVhzNBzqHN4ZTRqN1rHyZt2mQumwmVmH1M7XvUmn6AgdWv3yc/3yP+e+3GlBB0860wjr9FU17ew16deUd1BLxYkybhOPH41Yy5xMefr8Kaf7mZ//8GN+63e+zte/9x63x085vnpAQ8EnZdwJazbS5Ll6b+KRTNhYONVMAfJD7/ZL6AWrmlGyIdH1HUdyOOdo1iGTdtYoSOuZChTBSpfDmDsPS6EXu3aGmSz0kBkQtJ2zBirUtR+kzkG8cIT4ZVEQrGpfYhejtDOLh66/HPeCnBe4JRtlVupRWe/0DPn1qYWhv4D6ZiGost949pce3lVkAU6et+959FZoB8fNokRzUI1yNJaXM3Xpy2wUJHRm2f1DYW2BMDrYZOqhu3G0tzCGDkn5sRJXT0iJOAakJnDCmBJ1ol9jsSFNCMMe5zzmjDb3+0vbV1wUHhbYaEPbiuX7rlBVpWSP6QlRkJoQ38Oyp7HbT6ARZ54YDaVf5ebzr6yVTTJ5yUDB+YKvI12aKQSMGltnIKh0aqK4/sNva58SVu0UUwsYHh+Fs6wUfC8+tL4gDuccZnXg8Jyt6gm+w1qG70XM97Sq4GM/0MT6hNKWDoGUXvBc6MpCs0Jr1m8+ai9MYjivhBjw0eEjXZUqncLZ7VO1H4zFMC0IrS+tXaUumXJaWG9XXBL84LFUsdCx+HPEEbJkbFlxD6B17VDNWHDD+ao/L4IRI4aAnwKqSiu1Tw/meohI6MwucY4YPX4YcSF0q4hx3w25qJ0+m0KHsNIF4hs47VCNGdYKulbkLNLpi3k5d7UBXD+grFl3iGydjyjBcCRcDEjsNgfalKaVfKq0tlBbYX1o1PZAbY3WRpoutAolB3JdOxLUPEF6V9xqOwfEJMSEYkf00NBaqb7y6Om7DI8mhieJd7//Ae3hCw6f/pLf/U//3/zjP/2UT25PPI8j//FvfJ/3vnWN+/4VR+9BN+Q3gcfbR+y2V2wurthc7PH7EQtwOjwgtiG5SLwMyEmo6jDxFCcUyVQ6JLY6ozTtN632VtbalySGcL4m+uHv6DsqJ3beQSnO9WJRaHgVoGHSuhjKGmYNWsBcRWik4FiXhRgdF1db3rm+4OH0wNvDHd/59W/zySef8Ef/1R/w7//d/5hf/yvfIrvP+Rf/j3+FH43NI+HqeWI+GPtt4PFHI8+ut3z29pZPX99Sjsb9G2NZjeG6W6KbgC2d6isefKz90J8rZe33urPQocuzclezUctZgexBo/XdVIV2bnjs3Oy1c/RwJwz0S36cHJurjoyK69d1XSv20NCTko/98xN3vhU3goSemldnWO+N9VZZX3Q6tU9wYf0sMVPyQ2a8dmyvApeXkScfwC4FJu/5+cHBXSA/CH96p0yzISfl8BLe/D7Mr5T5pjsWiDdcMublyLL2+2XYe9Z7pcxGWTpl1rmOKAR304Nz0sg6btk/H7i4iOhFZJM2UAMiI956NWwoy9xJHmee91dXFLKeSLYQWGlBic4RcNgEo9sRfMCHAadCqZWl9KUqpohVyqmSFdQars6Yxr4sU8NvR7yrBMnUoSulWxMkZJLvNgghOXwxxAt4RdeVWhvF6GEddAvmIWzp0VgeRDit2rswJzR39ozBUaSHXooZRSsivb8qzvAO4Mtuzp0Pp4zLK+JdD9WYPeoURPAs3SLClFYW5tJZTdEpMSxEiah6Sj1i5YS2zNwqdVlptYfLDJIJMRFSwDXtS8fNxC5dEbcbwmZk2EUkDIQ0sNvuMT+yPsz4VxPDux4tK5oz89pZHlUhl25BoU1p1ginBtKhhOgzPiVcagQvVKkgwhAMt2onCbiuLPVe8FqoGfzsaZLJfu67GG1ddNX61/sUGUM4azAcvobuD9MqVhqK7zYNGtDa9Rk+eCzPfYHshHzK1HWhrDMP84KWDhUeHk5dkeqEFgKCpzRjKQ3HSrNuwW0FVtmgm5FlHnt4S4F6m5gpyJQYton2HNSO1M/f8m/+0X/DP/vzP+cPPv2EV6fM77z/AX/9e9/jW/+dr+OnxhsGeLgiuQum3SW760c8eXdLSlvisGX88Dnx4gkujWxb4e7FZ6zLiZIz+90eXATvWc1Yi2fNgWbd8VRb324hQi6NwyH3QVIERHHmUOmuqd58X9ZLP0TWdmbCqZLR82SgZ04guN4R9cndhIJx/5DZTIH3nu6Z74+cjgttdYzvXNI+/Yw3n9/z3/zLn/Lkya/xN35n4Kf/9Ad88fLEcYH9U8+wH5hvB958MpG2z9nuR76WIv/6v/wMTkbCKCNsrvtOoDhD1k7G0FZ4+3np3X4SvBeq8/39pIa4hh8NX+iEhHOP187LaWtGO/Eroooi+NjpocPkiJNDMGpTYoyEmPBhIIwF0orbFuJQeqNJnzq8E6hGnTvTCGf4C7h8R7oWIILbQD4Zw2B84yN4+6KRc+Gzm5kf/wn4IMRJGPaOJ+8G9h96fusdI89GWYzlg8Zv//c8JXvevmkUlPWNsbw08r1w/AzyLUy7Dr+tFY6LYQudOVcbx1LAZozeaF1/ds3F/pLRB2TsGTeJytAeIS3QWmE/HgnR0XbDV1sUON1RdMY0U3Wk99bdAllD77ajr4ROIO32DGZf/g7nvoRzHD5EVGNnWEAXnDnD0cVd4czAcLhzxCOdVvElF9LOzKHWO25Ht2x24gnB+s0nscNW89IZKtrOZKPOgkEr6rR3L+oRaX08VcVa7mpokV+N84L0yFDr3il9tD8P6lZp2plNtWZKbmc2U0MYkdDOLBfpVMm60kqjrf2wdhbwoxCjI02RGNIZJlCcVvzYu3ZUaUtBmqBjBov9kF0zMoygrbOWsny55mJwEaYvY/gCIeiZdQHeZ/BdFe7sy86Ss5CuF2yxgtQOm2Hav6+nF4kYcef1nFNFS1cki3OkYcSniB8GnHdoUTQ3GitOIiY9f6KVLmQyKrU1rCiqlfl+puSZmk+cVkXPVN5aG843QDCL/eeoHSKsrVCbkGtDGkgTPA4rtfP3zRG3ienxY9IUmCZh/vglP//sFb/45Us+/cULyumB90Lgw2895Wtff8bjdx4zDhcM04gPG0QuSBqI48iQPOP1U2IcER/J8xGGBO3E/HDPzRe3iHh2Tx+BHztziG5H4Yvig1JrRYvv7CcfEe+ppRG6zPv8NV0P02Mp+55LzzCoWSNn+xVkecYxuwBQ6ct56ZPxl8w5J53+KQGsGnfHAzkXRgJzPXJxseG9Z+/y8R/8Odd/82s8f/IBv/Yb3+Tm9sfM88L6BnZXkTobL39yxNk9X/v2no/ee8Ttbzzwxc9OHO4ymwHaKtSTsbyFKEaIhpTu4WNiaD0zp5yhItTamUOtdKuMVs8LYA8k+xIJ7mQUL7/aF5jRv6Y26nJmwGXDbysMHhlan7yadPqmdLqvObAo5IOii9GOPcPkbN7AuOkHvQ9Qi7I8GOu98drB6Z7+bw1q6wXKZ2A25peG3jVyhfpwZlqJdlQ1QEpwgdCuoATQx8LpSshHIeF4uOzWFesRFhV0BQ7w9rNKWXrErrXG/f0DeS1nk8wj4iLBlKD3OEs487y/EaI3Rvfvpl77dy4K7nRPbQtVuy11sS4Kac3RUodakq8kL2fVbuyQBNahmNCrggg9jNrSmW0jXTB1pnhKHAi+Kw69c3jfLZt7AensCs4HdY/26uOlR/CuB/OYO2+QrF8ctIa11mGLqudfV1T6jqNj3X3MbrWhljEP7hzVJ77TJt1Zo+Ck35hOz0s9U7Sci0LOtFU7FCSKutrZHx5CGihf0jaL4bQfssE5huTO+HQkDR33N6xDPz4AQjutlKWioVJHh/MRXVZ0WXsgeWlYqbiW8E7xIvjgcWnAuf7vQ7T+vZ0gllHpVgtSC9GkZ1iYYr6zhQI9slToS2LxrqvKgyONQ9+1S2ebaF77Z1uUNI34ccBvpu7Xkyu6FKooJgMmqd+spdJqodVCPTZqLtR15nB3pJQTtRxZa+oLTusex2fCMWZnDQsg4nuoUYGWFY/DtQ69SDv/PiT8fmD/7jVpFILNvPrZF/zZD37GP//Rz7j1yl94tOW7zx/x5HtfJ314QbrYY7ZlE54wTDvidk+oFTxIcoSLS1wYMIP57jU1dNjn8Pot968bw/aCx5sLfAo0M2ptvdj2Dolp6LsyzCGpx1iW0s7vq8Ny1pdAtNazek162JUamFYk65mQoWcHzf61Hne2uThDT+fndCK9Z3JKXgqnuwNOhClEDvXE1eWWLRt+/Kd/QP2LH3Lx7hO+91vf5Qd/+JLTy8r8Gty3HW1tzC9nJN/wjefXvP/4PY5/8QXL/AWncs8Yu3Ymr0Z+aMQg2NCLQfBQrf+sNEinQxvdEqIarUI9QZ0FNaM5w+/h3C3iPR01cB3GJRstGzWfaczF8KsRH4FMFTc5bOwkBqmCq3L+eoHoWG+Nejg/imEDsOleXj72rPF26LBSXXvBsr7S6MrsLxXTDXRRjgfl0IRTBrvv69TNRri/V9wG7FJIep7idkJ65FivXSe6zI7pquc+W4YHPLqA3EDWzPKglLmhs1HaQj7OZ6j2vr8gBSziSCTZ8rg+QUchDF+xzYVKI1rFtcKsAS3nGD06I8KiYHHFpxFtlbxm2jETxwk39uQiiQkfPMkJMo0430Na1tyXk166LUbwrltbBOVsDN356Zb6WJwztrrOKU+d001wmHdoE1prNG20dSXnQjkvpKt1HrrQPalMO1dbfe7WCdooCsFaZyL5QsuGHxxxSqToOkQvRltX4mbEhYiulXIuUq107H6IiWnwDPt9n3694+Jr71MOd5TlxGytO056B3HAlaVzy6HHLK696yEvVNdFR/n+SD5BDIFQG9Ml2JqhFrwacZjw04ibBrzKeQKazuI/sNHhXGd1LIdKue0MIetoP3E74ceETxM2dqGhK0oYzxObOMLF5pyK5wgp9QPGCYwe2PapbO225ngQmzucN0Z8mpBppJZCrbVPVaXSSqEuM6elkefM+nDkeHckzwtlWbCNnc3oBhg2LPcP5Dmz6B2PLp8hIVJ8Y304Aj2F7zI94bQcyXVlurrg8XfeZ7rcElrll//sj/nBD37Kv/lv/5B//fYFuTWG4PmP/vpfZ3oO8cpx2r7LODwm2h4dLykhItFjkydNO1SFrMarVzf4FDEJ5PuEnjJu8Gwvvs47v9Vp0XO7YaxbmjVKzRwON+R1oZaCtYAPgRAT035LHAOnY+Hu7S0xJlKMuJS64WRRSlbWOaPWbZ01dw6+IkgThjGcU7kCxXmsAtpoqbCJHjHPW12x48rxWDjc3LAzz24/sL3wJP+cu+mBm+EW+RPhixe3pKcjv/23/wE//IMH9Hd/xJ//+S8YPxr44IOBb/72I37/nx740bNPGJ4Y/95v/y+4v/+/cEr/ml/8/sq7X/NcXCuX78xMRWkO1gC7GV5/1njzhTFd9sJYCyyfKuoEPzkePXOkDwNNlfXQeP1FT2A06/TSEA2fhPBIcKPgUvcaK28V1l4k5tuGtkoTGIMnHxvLgzJ/roQrz3jlePw8ETeOcmqst5XDfRcDOqC+EHToJ2VpEKVDSRpAtmDJKAnyG9AZdO38ERc6d9YeOpKtKyx/buzeF8adMI7CDz43YhC2O8c7f9ETs8OtQkmOuIlsd47dO+eogCEyXI189HFjXSvrWpj/qHLzReXwVvGL76SbpfFwl1nKiWJHsr3l39x+QvAbQth/tUXhlB2pgq8N9MsEbM4ca+1Sfe3c8uB7Cls0RwipZymfVV6i0NoZAhGhmqFuAyhmFWlCPYubJEj3A5fWfYhoqCp1bZQl0+qZiRS0q12dP+sSPKpQ69p1BHJmI5wnanOc13fSC4p0rxij58IGiQQvpDQwTpEwJPzQvZSg++tIEOI44McEW48rPTJxmDxIICbPOHrG3ePeqZ2ZlwKI6yIUpPTCtCiaS1/Ghs4scGtFWu7UWRG8GG4KpBBIw8D+nUump++jtTE+eSDP3d7aO/Djrk/VJlBHtPZDpJmAFbw5kkSmi4uzGDDgE4hPZ7FaQZs/C8eUYXt5tuStEPvy3XnpPHjpxVBWd1aiOyTuMGpnPGRFS8ZqQQuUfOhe9VnJ5URejZoLZbllnUM3XJPWM3nHhNmGJkNfyGtjOcx4jHEIBPOU/ICtHtGBx9NVz91lxOLKk+fPGS/2SKosNw/c/PQzXv7oM/7wT37EZy9e8endDR9cPOfJ8z3PP7rig9/8GikGvI+k4RnbIRFjQtK2W29IopTI8ZzV0sxzd2r4GpBgWFDEebw4Fls7ZVhAmwNKz22uC6qGC4HoHcmPpDgQwkCaus1HrZVh65lSJKaBMAwo1m/6XDlSYG5Iqyy1crm7BOe7gtudYSIRKJVcFnLO6BLJo8d7YdAGsWP1Uj1jBB8adV0RKdR1ZXlYsLjy+iefElvhg7/7bb77F75B05nbm9fc/2zms0OmvReJV56chc9+svCPXv4jnn/wiH//N/67/D5/yC8/fs3tq5U0KHdLQwK4ydGKsTx0Je6w686p3jueXHuaeFxyDBeRYp61NHCZJ0PXI9TWYbUQHCE6ZBvAG00b9dj3EH5rjNdngl/QLhIzw0Ul7RR5DoTuv7TcF/KpdYizwbBxfY/p+2Ci0qcYbUIbOosnr0a+O+srz4wwWwWdwdaOYLvUoad26juQ0ox17hCeFlhv+66lnpThJ9JzmQUk9wnQFPQlsBFC8rgXC6NzxLmxOVTGa+PJrhNfsgnpFGir5+3iKa+UclLmYyNnJfstOV5/tUVhXg2tjai1U0/p3ir9v071FKWr74IQQiBJh2C863pO1c7Q0ZZ/5UeCKTINCNpplgVcUJrvzAPHgqPhfMS5M+6fjVYaWjtjQznTQl3rN1/rNM+mFT2bv3zpAdPZG9YtAlw/4J24f6tubEb0kRCEIZ0LQuxL019BVtZP9/O77stUemcgllCJ/YJNnjCO532K0dbyK056V6F2BXGtQC74cJ6SUkSagDQsdHdREyEmgxpI08Tm6oLh0QXWtHu03/SlrxcI4+asZBUsJ9pitNagdfxTTAjOM+02+CERhoQbBdSfDb7OcYnWJ4yYRiQ6sB6ILmfoxoqdK5111kww+DI3WFufwqrS2kLLSl2UvN6yLkJeu8Cx5M7Tb+VAK1M/VAfpamrnwHmkxt4NW+f3h19BIYFcuvbDVWPYBnyYkLDHtisXz66YLi64f/sFL3/+GV/89HN+/Ls/489efMJ9XSgBvv/Ocz78xhPe/84T0odPiDUR2sAwPGIYOvzWwoiYoi6gGjrbRVyfktfzrGydbeYJnRpaVsy7X1FLtWZUe5BU07538b6zvIZh6tGwMXT4znVixTB6UvLEIaBOqLERwlnkZh4v3XdquxvAB9a1UJr7tyLo2iha0bL2ydmF7tBpnKFdh/OOlDo9VWvPc+asJo8T3L982+1P/ta3+fBr77Ic7/jz3/8Rn7x8TTkZmiPXX9vTVLl7tfDZT/6Qv3PxO3z49ad84xtP+PyTW+4elPbWWBfFxZ7E1zoRje0kDAOkTQ/BGa+E5gN4TxoTh9kjawXpec2lKaUZql0r46LHksesUYp0iFP6oRx24Cq/2ntp66xAHxV30ZXKoJRjIT90tbd4iKMnxs76kda9kmo1vH7JOOwow3pv3Wiu9efq4heod+AGwY+OuJF+XmXAG6qOmul5Ka3/XS3G/edK3cM4wrYBVvuO5GCwum5LHzJcBGQx3Kz42BlPo/OsATazgyqE5rCNke+V432hrcrBJe7D9qstCm9vHtjagdFWZAiM3hGcoAQihuGp5gnF/YrH70Vo0j1HQhOydRqrzzO5dJqpM0d0265c1UxptSd/OaOJEln7ei4E4tlCvtGxn3aGW6RBtS7CitItgpuCVqX6zuEGOgUQQfCo9NfvpS+mxZ13A9Yv3BA9fjPihoC5LpF3udLXeZ33f3zxpgfzRCCEjn3mwtKM4IVN8pTViDHinGM+zpie7SukIdb58ssq3X12Evyk/RBuQK142+IvtvhpPI+hleADw+4S1wrtuGBvjkS/w3vFe8VNCec7ZcMifdLKUJeGrYAJPnnS5UDcbwi7DS66Ls4rCkvq1syup4iVVdG1dMts387LdoNzEIhD6Eoij2E0u6OVTCuVXDJlXlnnzDKvrKcDS2nk0mjrAmkgpMB2O5EeXwIOK8rt23vqvKB5RX3Eh9CnqKtH5Pt7yrKylsyj7TPEBao2bl5/xuZp4uq9x3z7N3+Th7e/4O0v/5w/+If/Bf/5H/6UP3v1lhf1wHcvHvPBk8e8/9E1T//yd9lst9Q0Ivl5bwjGALsRCwkLEcZ4pgE7qvO0KpgTLDgkd4jTGwwx9J3KWpGHQph6BsMYI+vSiedee4h7CF2Z7pxHfDgvmDNLzqzL0jU0rv88fVBSGmih+08Nw4b2eENrkE3A76gN5tPCWui7OQ83eqRloS3GbCvBhIgnSyYF9yu76tC6hxIIfhZ2Q8Ke7ZH8hJ//yS853d5y82df45vf/ogR4Uff+kN+9NNXlJcr84vK+9/d4OOJNRfmXxr/4p/+c579YuBv/q3f5u33X/GLlPnJf7Vy6QXFWIHrr3v2zx3TtfBw363dsb6ElUHwzjEWwVJgiMJ2o7y5K4gK0UH1jjB4JHqqd0ipSFWGYlTtdtw8wLi3czCQ0WalnbVMIfYIYWtQjkJ9ODsBb3umujv3gKUZbQUrEBGKQBiF/VMh3zRYDGlCSkLYORyOZbGuTwpCsr4blATyzBGSQDHyfSM9dZSDUe6MVy+VK+tI6zd+A+ZXRl6M+gGsryoLsG4q6013eNYEp8+k63yqY5MCwy4gAWZtyNOIe9LV5R8Ez32Bl8tXrGje5gODKwTpzJYgniBdVRmkL4Cltu6P0wTXHEWW/lMmoKLd1Kw7qSE2dlGIB9fqmeve4SHTipwjHy1knFNcG/oyR87Nes29I1OP6wIIEKP6Qm0R1W7E1q2xK6oLWrti1iF47W6fEgBiV4+q4WrraWjWhXJt7f7/rSno6Wxr7LoiG+mTUDIkfGlFULtHOoZJo1X9lad9Pq3dbkMz2QzvO8tHa8BSn0G7K6Z2Azu/AzvghoTzsX+fZrhmcDghy4AcV+R+JVxdnm2o+xIb+mdlOCQKDk9oHtMKvpt3SVjRXCi3J0C7aayC8xU3bJEQkWmDvn1JyytKgXCBeDnfaAPqa7cmlgHEUXLl8OqOtd5TSmPNkPOJkhs5V1o7UeuZjl8ry2FGBJa9Z5gU70Ln2zQhJYcMHvP7bnNujXx4YAqJyY0sTXh4+5q03fHow6/x4a//NeyYWT6/5T/7X/7v+fGbj/nl/QtevHjNq4cT0Tv+6tU3+LW/+jWevnvJs3cuUX3Eeig8nA5cfwiWlBpbXx5PAxYijqETBkzINZBbw/mAj5HcZqQ4vDrMn/dZrVLzCXdYiTGy2SaGGLoAszVsVTR1ZltMxiqZshbKKTMvJwxjO004D6qVMp9Y1+Xsl6S4AOM04cOA9/1zWNbe4Q+BX8F70pRohcEp+yZsN4EQHKUGnOvmfK10x2DRvpsbUm+zg9+wff+a+fM3HN/e8eJP/4R33vtrPL684Lf/+q/x8Y8+5YsvbnnIhaDKw82JnI2n748cX5345OWBf15/n+/+xp7nf/FdUsm8+vHKdgo8f2/CXwmWGs1VxlU53RilKhpBU78Gm19hDLRgqFfsviGtSyRj0jPzyDqFe+1WNDbBNDjWe+P0QtntHJudkDbC8bUy3xr11HcPww58EtyuH/RiZ9ZjM+xspqBrZzFJ65qClARLEAehPhHqBJoF/ywQQyCZ56ja9VPVqIvRsiIC45VDTg0Vw3ZKfOpgMiwYvinpkSA7+LN/obTaj7S06RoN78FmY1XpViGlTyOPHjl2I8TSjaPUhKlBWdrZClwortH01MWBX2VRiE56IXCAG3rAjvdnq4jePar2Ed+d7QnUDNXWURfVDjlI/zsJXfXsvetwylltrF3eiGiltXOAi9ezxXCXmDU1rPZA7i+DM/qQaIjvjpqqDlrtKXFWUS3UZp2+ivsVfc/OtL0vX4N8af6m1kN4zh70vShU8D160ehuikLvyiQ45EuPeOn7FjWhqeL0TKHE+oFRM0UN8z3DQXDd4kHsrDwsOO+R4NFi58ImeGuwdm2EDA4Zu3rWb6dugiW9K9FWunLYaaetyvnXZ3Uooe9EWivUXLDqujReO5UoJMW3bqfgzaG1dcEdndqL65Cd9HHmrOTuzK68Zu5v71jqHaU2lgI1L9TabTSEBTWPWf8ZNO2NgJwyJT/gXegTXBqJ0RPiSJXQw5BK66ru5HE+MOwmwi4wbLdsL/fo0rj9/DWf/slP+a//1b/m4/tXvM4HQvRsthPb7Y6vP32H6/fe4eLpns3FRD5ue5RodKQ0dsgvhr4UjwlCzyFv5ikKGc9q4CQQJLLY+v91mHB+n5V1XnGqpNghnnA5nuMkGi1DoLfppbh+3dEzgde54YMwbhLQOtVZK/X8Oal1D30563CcC3jxRN/Tw3Dn9Lvg2IweKxFnqR8WU4dFa9VuElc7k7CebeI83dv/VyHPg3BxEWFxvP30C9bjgfFx4hu/9j6/9p0rxBbmT3JnJKqSV6U1IVhBS+PlL1/z4TuRcSu898EOvwSmIXL9fEuYhLmtnPLCcFatS7Fz0FVnDZqBZqV5qL7rFvqdToedVaAq1QxWRdSIXhi2PX11qdLz1QfHMDqyU/LZ6uJMPOyOBq4nM4pKV+I3+ZVy2ut5Geg6JOeli87ajXW27/kEFbo2I3mwvTAbzFlZb422Kj6B04adfZ0UA6+9MeWsv9D+2k53hriz8O4sxqva8xLy3BXbvp2X83Rmlpmezy2hZukMqtKnksUZqylVy1dbFPy0J1FIYujwiOjPXGqJ+Nq9j5r0FyH+rEVYMrVB04osirnuBpl86Bz2GAnOs5wDR2iQqUit5wO9Ekpn6diwdnm7KutacXXtuK7rgRR2hrCkFapYP7S0UJ3D6BNDOesNGh4JQ897sS4O6rzu/hq1NWo5K69LX1A1NaQ13Gjda0Ycks76iOCx4KAp6o3mujuqU0+jdksCdYTJU3PfW1QK0gzvPSEJMXQth0qhrODTgATPeljOAzeE3AjmkTEhlwH3dNcFZhnmF2/QtaKtoXZEskfwuDShbUW10lxDfRevGYY93FPmSp4rdVZw3X8peiNuT4QQeyNwscMP/VKpqXWhgrhuOV67z1N1M9oC82Hl5etX5LpQtLBQsbnbNBvCiJ7d0s7WGpuIVCUvM/fLDQ7PEBKbdwWf9qTNjnVdKKtSH6CUxOoWwl65/N4zfu17v403z/3nr/n9/8M/5Pd+8Of8yx//jD9eH4giXITIX756wju/9g0u3n3M/v0ti15zKgH3Bsb9xO75FddjIl5fk/xA8Imw3YKLPRkNYc2VFeUkRsH13A/zrC10erIoWqRnZhdlPvVEvlS7YHN7mRCLlCKUpsTsqRawUyB63w/DArXkM+95pJ49tLQ08nGhtH4AO6eE6QEfAyEkdrunOALJl7MC2HAx0AbBaWAMAzhhGBIhOJr2DrasxmyVrJ1g4TEGq4hBM+XtfMv+eSCEgU9//2PuX3/G/ulzvv+XvsXD3/uQaVq5efPAfVtJHkIwvvjFHd/6WuBiJ/z804V/9U9+zvXzDb/919/nW08KSwkcloHrGLg9PWD1juAbaVto20YYjezPmQxH43DXKNWoZmyed8sYxFikswSlCW0RZO37AjbC9d4zFOW0gd3oGaIneGGUhvrOHoKeSuZN8KUvl+UshMvme5qsN1z6Ut6hzK7vrupbePvTSro8M9/FiC8NG5Q6VrbJkaXRTsrDHzXcCHEP49T9mLT2/rI9ATsY3MDyYBzH3ljbI0gihARyZZS3sJ7gdIT84kycCbBJxmnRHmA29wonZrjl3KSe61lzjsWE01c9KchwiSeT6HTD0YW+M9AzntOsV1VVdM1UHJrnzkPORsuVHhsolGgMBVqK2OApjN0ewrrtgVi3U2ytYpJ7ClvxlNC7ea1nDxeEYJ03j3i6B2jPrzUEC305XlXR1kdPoS+VsYJ+STzCI31+JvizNsKBq9qrufGricDRnVq1Nmrr04+Ghpk/+/HM3QrbA4Pg7yZkaMgY2D59TByFYY7E00pM4EM3CgwYzvXuvNVKmSutKLo8EDYb4mZkfLLDWyKkBFdDX4yXlbocWA8nSj7R6kLNfeoQEZxNmM2oVWpxlDZj6rrtQb7vzqQWkNTzD3x0hFFAhWadteG9Q0O31qjZ94Vpy8xHpeYDtSyUxdOskEtjmSuFswNs7uOYw3eRXnIIHtF+owqKDI6L55c0m6jVyPOK3q+c7hayvGYbn3cXV++4/Po1z7//fbZPrgkp8sN/+H/nj/7oT/knf/AjXr29IRVj4yN/9eoZm6st20db3v3OUx49ecY4bfDqcfms9I3Gdtywe7Rjd7Wlxh3OD3g/EKYt50a004PFUOdxeMZBqU3JR6W0zlRxKuRaabUzWCQEAhHvAgrcvZ2h9vvAssOHhk9GzQE/tF5nW6MVZS2VNd8RBs5Ol5E4bfE60Fql1BNt6TuiRU4E7hlSIjnBuZFmlbKuaGkE1wiDw/vY92GtZ4+P0TH5xG4IyFXsJocu4sr9/4e1P4u5NT3PM7HrHb9pDf+0x5o5iqSklmxZcjttqTtx0EGABE4Q9FGABEgHSB8ECRDkJEACJEAOAgQZTuIgCdJAB0kD3XanAxtWu63YsmTLomRRIimySFaRNe7a07//aa31De+Yg2cVlUMd1AGLAFnctfnvtb73e5/nvq+LMCamMaA5JTWOQ61c1TvK/Bxb7/Ha+d/gr/4b71HrwuXTZ8wTDPcNw2PF9EwJyrrR/MqvnfDkwwk1F15+eMOvfOs1UoJXYaEzke39FW+/ueHV/pmMFEsh6pFY5eDKOTEnTaoQayHazDxHljFiYyVrJbpjVSUSXqFJipQMqzPLN34DsFX6SXtF31q61z7vNkCcxFA47gp91FgrN/C+63BWyVLfRpYYWUJCj4k6gyqVcl0xJ+Cc3B7qUrFzxZfC5i3xO5hD4W5dae8p/An40yr7x2N6m1hJQFlXfAW9quit4mSr6Dq51c9JMdlKfAnTpTCeTAN+AHOhuPsZHJ7I6NccLWzOg2uL4LwdFKsJKXM4jpW/sEMhISWSckRD1HoUVGS5NlOqtH9LpYRMyAFikDl1kqVvQdg/tiZQQXhBxZCMkXtSlYim/MpVdhBIM7jyecMTchUWiFRrpShXlZOHDdKdqAjYqOTlWPL5XCAj/34M6KOPqZbPS1DlOPaiHPsM+lgSVaCPCkrIxFikgao0mmOjuRS5th3jTsYYSS95Kz4DJb9XYxt8a3CNkoWwcugckW2N+AukCBepKQuxxGhUYylJk2pmGWdUfEVeImF/YNplcpzIeSZGd4zBKtmVMFNLISZFXOYje8iiSkAb2VcYZ1DeopwsqygSGKgFlnkGlSl5Yo4LKQZSmNntMjnsyWkhRiujoAqxWNGe5kouGoFDm6NAiGO50WC14W7cUVRl2GxwQ4/JoKsnzQmrKo1T9JstQ2tRfcPwxhu4Grn5+EPe/+lTvveH3+GjDz/l6ulLokr0XcdmWLM+3+LXA37V065WKNuBajHKYocO5xxd52jXPcZ5ctGEqLD16L8o8nYaUmU/Z1LJZKXJRuGdO8IgI8r4nxeqUs6koihVyT4GC8qQiuL2LghkJ2ZUchinsEl2BjYqjJbPfUkCvkZl2s7gvKU5SpIKR7GOylRVPs/FEibZCQxdg6aSspCDlynhnMZZjTWaJYhcqNSKNQLG89aKw0EbrLbYdoV3EectSZ+wOLDLgdPzhrTcsBxuccZxfv4Gb779hG/+8oY/v404L3N798DKTbPRqM7x+lsdJEVJhZvLkbbtOd2eUsooHR2l2a5PWFIipoSOkve1FKxNDNYSC8wpE03E3FTqJMkcexTwuMHIzqEgb8tZ4VvNaqPZ30UhoyaF9Q7THVlkqjKT0bWQl3gsZ2q8NTK6NBprK8HIGLmg8FUTKdimsnoE7YajR1uQFFVXZGpdsW2lPVW09xXDA/E8VyruRAmu21fSrTxbjAH3SGEaKCPsbmE+OsTmCONe3NR5ks+ZbhVmo9BO0ZwghbwgOBGlwejjSL3KrWSeKktKLH9JH+dfnn2UEqFGrEoU9fljV7EkhcvI+KhUmavGRCwJlxZAELGq6p+Xx6iJgsz4UzLk1h1FPeEYeZQxT6wKV5EFipI5f1WKhMYaORCqdWAaqhIgnlWVelyAayvb+XyMUMpRI385dmKPaRnJ+NcKSUu80hyzycpI0iRTUaUQk0h08hxwq15ooikTqmCnU1QoZzHe4ltHs12jjUVrQwyQk6Eqj+86rJfWcMVQ03jMRglTvypxQQjd8ljzV5qQZKQV7kTWEw+R+SaSGnO8aUVi9djPo8A6H9valSUo4hiPS7SCNxXXaqGheo/yHqyVn4osj1DJsL/biR40zBxSIk4LcVq42gdIEzVHonLUooWwuuql4V1lVGeqEvyzsnLsWYt2DlNhv5sJKdNvTthsPMY4tFf4LtJ3ltWmwT94SH9xTnt6gj19jY/+8X/KD//wj/kP/u7v8+k845ThQTtw1lnWpxtOH5xz/vYjquupygleObek1NL2He3Zhn7oOdmuwThygsM+s5iEayyuZLQVBPwcC9cHyYwrZ9ArR2vE7rfkQtc0aFtAZdIUCaVSq8GZBmWE+RSXyt1t+Pks2GojaZUEU0o0s8LoCvrYtC+ZXAurxdN1GoYqn2/1eYsbARFWmaPPhwgZVp9Hu0Mk7CemacGsO1zbYbRiiYk8B1JN+K6CsfjGoYo6HgyK1g/UNtKuHL6/x84VbN7z+O0TwnTN/vo5lD2bzdu8/eUXxOVdfvb7VxgvC/DV6x67FHSBsVh+5VuPMVnzk3cv+fhnr3jwyPGVbz7i7vY5+2Vm3kXOT8+p6kBmwY4tS41om1m7hF57llzZzRHVehgzIc+EueL857hriw3SWZqjYqHitWLdGcZXgRrFie23Dr/qsI3DxoJKSZJjZSGVilGaxjpcsxKNOJklJVSImEXhgyJXhV9p7v2SPEhEriXGw6IgOZj2FdWBvw+bd2C4EJLD+BTcA421oCNMt0K2dSi6LyvyTSVcwuUPKznKKCkpJHJtwfZgOul0uBONWhSb1xXmbYU7CG6o1EpdMnGEdKiEm0K4qyw5sNQvuNHcjldUCosC7Wc5GCqQDaEGQPgqKRVKOb74U7DG4KzDOy9v76pirTuOeo7GqTKDSlQdUcoeV8Hg4ZjztoAR1lAp1BgILlOcwfsqbgclUsyi0xF6Jykec/ySJTL62K/QVHQ98olqRYnuS2KWUyYxUzQY2xz9BvImF1JkOV5f97c7+lWgaTxjnSjJH+kCmW41oDegvUVHhSlS+Au3E+go4u9hjS9STCupiMJTaZR2+Lah2awoRnF49hFhXpjuRu4++EykOynhO2hPT0Bpye/PM59HJqzzeK9x3uH7DbUGcsqoQ8HVIkgF5fH9gHIN2rSSQppmUsqUtKPQg7Y4qzjcLqQ4k/KBwz4RlkV+T6GiTEZbcK3GOYmHxV2g6iNCPStp7FqD0R7fHEC3KOWwfeCbv/41QHPY37F8eI3Nmt40PPhr32D91hsMj1+DeWH8zs948o/+jP/bf/wf82R/YI6JHsuvXpzTrFuaixWbtcW5FdavMWXAhBZrGranK/xqg21b2q7FrlY0bUPXN1TVEJZCyQlbLcYqVCMcnjlHDkvgcBuY04J2jiZJ9yZl8N6yWtmjea+wGydqPH4TWlGGpSQuj7QI4VQ7Qa4Xp0lHbPJSo4yOZqgktAZrLEuVZvuSFGq3HI2BCfSIUWJp0xS8jjga0Cuh8OaFON1Cyljt8I1GF41OEypO9G1H4xxGQ55n4v5AiYkaMks747sNrlmxagf0dkXDhvar5zz//hPi/CHTL/82/eN/i4ev/yrb7cSTH/0Dnr86cPdBxr1uOT8xrAfHZn3O+GqHMoYvf+MB+aahqsyTZ++zth03793x8XtXmKHw8M0tmxNPHW9ZodC+0BK5+/Ca3Zy4SZmv//o9bKMpraYshdZYurXn3jsnDEpRcuH2MDPXwt0+8tlHE/ZQGVTBWlj3XvYJVSxujW6w647+wYpnL0dqgc55Bt8Ql8A0JupVIOwicc60pmLWGnOiOPuS5u5pYLwp7C4LXkmnV+0rgQpeoVq4/44ivKgsVzA/gZ6C3SjsoLj7CEkZeWieQ9SSPrp4RxNagXaGfWE5Ti0slRwU8xM4fD/z7Ab6M0Wz1Thf6Lda+h7rhu0bctvNd5nmMrEfDTfLX+5x/5c+FHJBdH5KFhelZlnsWHDKSKEJTS0F6xzGN3iVJTZnPV5r4abXYx/hSLJSQApJPuw5glHHBrQRFoyW+XjFoEqSg6EBXQNVWWI1mARHGS2qCEsplwwxyJxXYlFUc3zLUp/z5mXBnKvcDEBEH1ZXAdh5LZv9IuCrFBVFOXRrGdC0w4BvHN6s0L47soU0rmsk8dBpgdkp0LXSGidXP6/R3sMSqDFRVcFvVuLd1ZYlLqRpJoTI4eVEWoSX5E3CD1KGc42kifISiPuF7qSTLL9R6GGQBI0Rj7FEceXaqlup8KEd1WaKMqRcCWMQneccKPEgWXHnaRvLEjNhTsxzYAoSJ63GYDonAW5ViLJxI6fCfox0TZVopJODv2iJ9BnXk21DsZ5SLSW2UCo6WLrzhm7o2Z5v2X7lyzjviK9e8P7f/2O+9+77/PinH/Gjp8+xrsF5R3vacXqxpVmvsCcnbHqNth1YWcDbpsc1HX7d4FZrbNtiXYtqOrKxLMmAM1RvMdqjqyJRWabAMhdiSqRU0L7iqIgVLZLDAih0gd3V+HOHgS6AlkN6HAW1QjkuMzsjbgkFSWVyzagkGtHPUS6pKnlZURV0xMWCNgljC+1RmILSEI+tcg3eaKw1LEXz6tWOVGAZJ8adFA33dwu17umbHm09bSeF07hbSLVgTRFtpKkUU8WHbRa00tR5JB9GCJl+u8W2ryhLYP/++3QXfx3bFrre8EtffcDWvOSz57eERmNzoo6ZQzlwmwKqNXDeEvTEOBbuXk2cnkI+WXH6jTV38468PqMMHavNBU0fQY8s8ZJ2E8lOPAm5LFycGO4Pa65PIsErVG+5GDxqWZiWQN4HukYQJFpwUCy7yn7KxDHgNuD7xLZvGUNlSeJmt75BK4NXnpI8OUMl0647msZASIQlso+ZclsZP4X9JzDvIY4Kq6TPUBZ5FDkv0+0aIB6g7kEfjhwkrxhONeod2L+q7K4r03PxNbSdQr2mSdqyLJXr20jrZV/gWoivoDSVPIDeV3yjcA0s16I0zSPsn1eatewkVClMV5V0FBx9oYdCQmMpUgs2Rt5akPalMe7YH1CUlDGNp1uv6HRBuxZlPK5mQj7yfJYstwZA3o1EmUlJVGWkB6AVrmkxxssbzTFZpKqMh3SQkz1VmTmipcapM4Qk6aE8zaIuVBV9bIrqo1c5c+wFUGU+q/6ivFbV8ZDxxxhpqsSIoB+slGaGocd2Ha7x9N7hNitc6/FtA04DGV0T+XNMNArbeNyqwbYWZSBe3ZKPnCW76qlWE2tlen7HeHdg2o3MtzMKjdEKuzKszzf41mKM3FjqHGBe6NwG22i016htjz5azPKixS9RJOKqtaEcfcC5BEqppJQYDzOH3cQ8zpRlxK0Mvi2o2rCkzBIy4z6zaIPRFuMMtumoYaHmRMChqiKVwhwSzfGhpawWHLbluKxupRmMpiRPCHITc3bN6s0L1g82nL5+TuPP2L94zqv33+Pb/49/yO88+5gfHG5YGccbq4btuqe5P7B5eCEYju6cvilgW4rr0LXi1mv8MOAagx1WWN9irCebhliVYMDNsT3diektjAvzFNilEaME4mi85NTRBU1CZzH7lQJ31zeEIgROGUdpalFMUyakglGawTe4zqJLRZcqRcssO7ilRHRJ8j0wCpuPI02VxXduDNoVNn0nSAcjhwLHj2hFYpe1ZMZXe+aUpO0fK85qxjFS8oRaO7xvsK4hzTPz3V4iMJ1m2FqUFexMXaTfk8NEOtySpklIBsMKuxog7Nn/9Annv3KFbResM3z9qw9x84I+HHjZaAiyz7gbNQcLSltU2LE/7Lm+zrx8pTlXlovzh2xfO+f26SV7s6GmnovBcv76QtU37D+7gUHTOM02KuZ55uK84fHJwO5h5OqQmZPixGumMTCPE9PLwLCymAq9M9jGEK8z41WmzBF7yHQnlvvrhtuUWEJlOYA/7fDGY0tDnJ1QhH1htVE01aNj5ObVTLwKzLvMfirsPoIYxM/ie0XKAvJzBtpj0XDZV8wMegGXgAlMUvSNZnhHo8mMLzOHF7BJis5ozCNLTRaTK7dTwDcK38qNYNlV6lpejKcA7YnC9IrDk0I0lagyh8uCaWRH4XpQt2AGjd1+wYdCbVYYHfC64PVfmKG0sXIqKoXJitSI86DGSKgRF8GaQqhZ3rZTJoeRWi0oKKbglGzIi7I46/He4Zw8eASEqLDKU5UmhcSyX2SdrCxeO0EJh0qukZIXVNaYqtDOYTt9DPuC1vKGZa1GuRZ9jBLWIjX5XArKZWpd5N6zZHDyJmadxWjZ5FSlpWnqO7SxxBxYLg+ULP7ltEApkVIXvFujtVBTa0y4QdMMLdtHj7C9RXUrzEqx3z1jOcyMd4nx+o4SFFTD9t6KfrumXQ/0F4OkoEpCx5GuzajNCbyWaB8+oOZAjhNhgpKDMHfqCdooqgFbPblOlHw85EqQmv1SOUx7xv3MfFiIyx53yBjjGL1BqfaIyYBGGfl5tJaMA2/ldlgtvgPfWN58c8XV009ZQiKNgftfOcU6T82az378PmXssaXHnqwYXjtl/fbrvPFf+i+yPnkTbTJ5ec4f/U//F/ynf/RD/t67H+Oy4rVmza+ePqJ91GLXDd2w4v7JG6w6h7UeoxU1CoPLqorrNcOmpduu8Y3H+AFtveTNiyOlwpwj5aZQ9US1Gdf2TPPENE3M11FSZQpKcRgHvmtYtYqL845xiVzdHqAu2CIteWcimAajDaV1NEX4Rn2rmXdRwgsaeiMUW5RijpU5RGKI5DtY8oQ2mqbvWHKgRo2exElONdSiSCXgrMc5QztACprGQeMMq82WpjE0nca7VtheRVGWQqqCsTC6o2kXjC7025b1vTVQSMtMmozwqkIRBLnp0Rjmwyua7RlqWjHfVOLLf4YxX8Js/i3cyciDtyPaaG4+ecp+jEwxcqf3vPVNz5ImvvMPP+HH70n81faWt3Phvfdv2R8saSy8/0dXvPpoYtN5/tv/61/g5J3K93/yMebP4fX7hl/8Vcsn39vDxUh6JPgYqjhRpifPGZfA7mXi8geJP/xw5o1vrPitv/2Qtx6/w9088mp/zXJzxfvv7bm8HrmaAnd3MM8AinurQMYRqmN7do/tRl48Qjnw6vKG3XVmaDvOv9yTc+HFqxHzGJZYyFSGC40uVdCvRrwMy6Fw87RyulGcD7AbKtopuk7ROMuyz7ikWDuIl5U5FuJdRX9SaYZEzuAuKyf3FK2DdqmUNTSdpunh5cMCAfJU2a+h30rSan9VWQ4Vk0B5RdOCMoaSv2CfgmksThe8yWhtIcniNyTROmqAYihzwniDtQqD+FlTjaiSmePn5M8gtFItVbIFBVrmq9UaitZkFDFVbM1yMFiLPi4stTU4G7HGYa0kLmI6xmJTlgTNMf8fa6KWIKMn1QgAzhnwLUplNJmSDTXno5JSUWI+YjQkz6S0QXuPt0aw3BgUmZQ0YU7UsEiDukpcNQsXDmf08XATc9r6tUeU+ZYcJm6evyKmSIqReV6Ydq9AgXaeptWoxsn1zyhSSczzRH0RIStMLXgirvXoxqFNQ9gdiONIOBwYJ2RcpAyqFRZ8QTGXQJj27Hczr17t0XWGz5NBUTyy1jv6doPvVjjX4J0nRUVKiRgd2fjjqA5isuScxU9hBfI1p8Td3UuUtfiux/VrnFlDtOQFzk8eYR6e4tYb7r2zYvvaL9JtT7DxGR/8B7/NTz56yj//9Cnvffs7fPzyhhAzr5+dy15g3WGGE9brgVW/YrU9p2uclMCURrPIvb3x2L4ha8OcMkof8Rxa9krhSBiNKZNKkD1VyhzCHSkupBzxK+naoA1ZOygZpTVLCFxf37GkwjRHQsjHQEJmngzFSPoohSJ4EYT+KWU/ecHJ5qiNpQpfXxnQleqyxGm0lu9BkttIqhk1acG6VGlCZy8eCWM0s5LEXD1GOudsaKJGmYxTFqeMjHiP3KuYEnlJaJ1Rs8bsJ2mLL7N8N3OhhkIcM9ZaSDBejsSiUNYzlcrVj56wSQ/YfusC5Qrt2YpNfoj++IbhRONdJiu4frnw/LPAd34/EkvFbzTNULj9cObly4nrW/jFXz+nNYoSCq/miWm35+vdW/zV3/wf8+3z/ycq37KvlvsPW9a9/H/ent+nqkQqgZv5iof3Bl577PjqWx0/efIKvTGYTeQuvWJfZ2a9Y64Hbq8Wrj9L2HUhG4XuoMxwe1WF2mwM4xx58QJUgsNdIIzyM9met2xWPbkqDncFfWLotcF1GtNIuVYXcF5JKmwujHUW02Gu2E3BW+k2jS8g3FbCTaUmRX+iwCqUU5hBkRcR8ygjjKU8wpQqh5eV/lyzfqSYbgXCVxfonIyktAcazeF5xTSwfqypL2AOifF2/IIPBVUFSWHUMR4qbtmQE6h81A1Xasg4c+SKKFk215pROR8Jh0mwFkZ+iFRDRr4symiwVq7gSpMkSyqxLS0Avp/DwrzBWodxDboipblUUCaJy8EYfGcp40jJFaMcaH/EPhuqccdF3ec/Bom8Gq3R1VJKplQpeyit0UetpjoKfFSRyGgJ0rStVFBaWDZOCyLcW2zTYpzBdY7h3hnzTaLcBub9gXk8sMwz+7s9y2GHaSzNuqfpBpQH7RSlZJZxYZkisRQMDqdlpOG8UE2V0aTbA2E3Mu9GphnpPFiHMdIgLsASMvPuwO56z/XLa2yNx/GEo9EOYxpc4xiahmZY4XyDtw0xKlKMxKBZEJ9BqQWlHZpIVUX+WbqSS2QOC5uzNU3X0w1rbHbkxaCD4eT8Mc39+zT3Tjn7asdgLqhL5tWP3uW7//Af8O0ffMR/+sELbqk01rAZGh6cb2lPO/yqw5kThm7Fqh/o+hWNc2QlWXZrDMo5dNvih55qnOg5i7TUVRYDX6zSM0ilCDDuaI/bLzOKglaVpmtpvMR1k3WUJZBLJcbE/u5wjGirY7L5iHMOWQqCaGoUfIFW0vKtIKEKKrlKO5kqnK6KOn7GQFUvKHL1edxC+F45VTJH30X5eTWImisxyz6MLLcRYxSz1lSVaKykpXrfHKkQIjHKQfSv1YA6EkaXZcGURf6+DDEJ44tcCYdEttLxmUvm9uMb3GbH9psV5TxufUJXBrz+GdmD6QqDg8sPJy6fJa5fwuljw3prOb3nmZ9V5uvEfA3D2nLxuGE5RK4uF66fHRifV77y1td4+Y1H7PcafZvY+pbOQdNo+uGUWkeWBHGp+Nay7hq6Byvy6Y67nJniwtObK8awcAgzcT9zd5XYvSr0u4peS7IvLjDeHWF9K82SF8KuMF1nDi9lktG2hu5MkbIjVcUYM9bJWKfbWOlrCa2HfvBYCzkVdvvCNEqE1q0rjdakCONYCa8qaS/5kH7QFKOoFuxKM06ZHGUvESdIpVImuPqkSNlwowmTos5ABG/ANwozwForyh5cpzh9YFl2hbQU8vgFs4/s7orqNdEZigkid4mZHCvBitDD4iBnDIXmSDmtFUn16CyYhpIo+uiTUjLjb5TGOI1rZSwhVFWNjkhtX4G1hsbZn8/KXeeEnGpaTIEmQTm+oivjUdrQOEWdF3IxmL7H2lYe+losVuhjJFRZtLeAfMGy7QQpcGz8ayNLQmvN8Z/pcYCNR8jeppeHorO4zuPaVsQcDVTTCFYjVy7ffY8U9pQcaLqWbiMtlhRXxGVDqkUWQkZiolobdq9uWK4Secx4lTi7OMNuW+zQooumzpl8WMi3gRIyVBl3mcYK7rtrKTWQQmTZLRxudpQQ2AxOrFxyctM7Q+MavGtoOxHyaAw6FjabcyqZZbHsdlXosyXjfI9tDcZklOtRbkHZymN/j37boaOCu0R4cgm6QQ8bhm/9Gqs3H9FfnNA18NH//v/KB7/3r/gn3/uE/3y84UVOzLXypabjrUdb3n7zDH3vhGVqKLHjzF+wPRavOtXQNA1YI0jhtmKbBtu1NH1LzJqUFZgCRVOiIqtETIIfyTUR8+ejw8DNZ1f4XtMOjrbr8I3DeUfVlkAgzJV8KOynA+2q5/R0i62JnKTEqbWg2EuRCLPO5vj50jgtTK9cA2WnKcefYSnCzrJW0fQtfiM04XlOVKulv1C1fJ6UQaFJyGLUWYtvKiUGlhCZGcmzk5tPzfjS/BzxsGnX8msYKThN+wM1BNyloV1r+RnEiG9nGt/T2BbtnRxJpWBtRzTi1V7iyKvnC/7iUx5M/xK1+iquc7R24d7q2zy/KYSbytkbJ3y6HHCbxG/+V3vu/7WW8wcn3D+7z+//zlP6B4blzsFFw9/8dx/R6sx7/+iK3/l/veCf/EdP+LP/1u/zP/r3/gf0jxYud9/l+ScHVm3H2XqAWNmPB253Vzx7MnM9jCgNIT7ho5tMDgqXNT/7fmS6lQb38GV49THMh8o0yUvsMsL1zyqHW8VwX/Hwl4WrFFLlcJeJM5w8UpxeZJydee/difEO9reweahgrUEn0qiIt5W8g+03LcNJg18ZVl+TPZvTcDYY8iFyeZl4/2eR6UZMbzkrzs41ZW0orcIpxSzRTlqEbJOj/NrTbWV1WmkLRC/vAqpCCpBvK2qRiKudKsPa8ei1NfPLCTMWli+6vHZ3EEuTNwq6AR3Fkl2UwZpGkBVtR4tk1ZOyeAB9bDRkRTXS/NUZqdppgzaKdjVgncY7hV4NR6ELgsItSQ6FtqM1knCJoZDiDClT1YwrRlgwWmHwkvuvEOYAVnDKTdvi1sdDARGL1xyhWGzncY2T1fN+QRctXQGrIEU+98eVVEUqc8Ri+0cdaChpIh+LOEZbeRCUTD4EQW6kQp4Tt0+uaQaFXzf0DzeC4KUSU6IvkbhExsPC7sk12iwYa1BTpe88dmVZDY7V/TOaocF1MkYoIZGnRC6aihNEuJU30rhk0nIgThNhSYxjYdotFFOxnaeGo3ntCKFTWm5FBnVUk2aRpR92gvgO8YgIMTjjaDcdrT/FOkfwTi551uL8mt27PyHsbslx5p1/83X6h79Ec/bLVO64+qN3ef/7H/Dd733A77z3HT6+es48Rt5YXfClrqEMFjYDTdOw0y0+PaRtW9yqoTZrcgPJO6amxbYdrnG0vadrLKZ16M7TWEvrGqoRD/RS1JF4mYiqEEsmLoXlcz5XyqhGXgzykjnsJvIYMcZSGy/ML6WwK8u22+L6Br/yKLMmFilUWq2JiMuD4xjTGU3rDLvbPTUXVDqy/ZOg35Mq6CiHiWql+X382tAkgexhNK11QmZVYIOR7gqFmg37w0zNgeZogzPKYhG2WCmVJVYuw4gqBWsUq9Oe/Si2wMYpVHJorSimgh5QTYvqGnSs1BCoFfzpGbkWYlqYdcU2X2Uqllff+zFnf/W/SeUp2j3jq7/xNwn/8k85PHnKsx1sv3zBide0fcdNuCUsF1xdv41VlXe+ecbp6RmXz655/sFzahP49f/6a3zww4mPfnTHb/9HCz/59t/jm98c+Bt/s+PkFwy5cRyK5slHV1x9vGfez6xe9/ROwgyLqXz59Uo+8so+/W4i2Yo71Xzz1y23XylMU6XZwu5Q2aXKzsBbv2LoLjTDPc28y4z7yuFGhqvJw9JKR2F/kLHO6lTRdnLLmmImjoqcFDhN1ppoPFV59rtCaYtIw2bFYSncpkrQFf9lGNxxonFqjywniFlxUiL9acQ2C4kj5rxYbl7uWebK1fNMSZX1iaVZK/bPK7eXhRwl8bS/rDR9y3n/CP2VhLNRECpf5KGQlePzc0Z9/lclNwE+n7tbiZBW9PFq/Rdv40oZtFZHDWQCLThpSRk14h+wFe2aIy9fkis6G1k0twLhq1nSMjnKDFXayBltHVrJ8roWgeulGKkgb3DmqKY04lquAerxLVlYTYLg0CahjZXj12uqKscRhFzVqz1miYzsCZRWpKrIcERwCFxMHUdmRSlpROaEbRv82tJsLNob0hxIMbOEQt9qjHZ4o7BYdNVYDKZtcY2Mdfre4o5GtLxI27nGTA3l6GeQG5aq6ugzSMQAcQqUlNHV0jSezBE7nn/+xyj/+twVUSSZRa2UKod/rdKgVcefpUzjDOrnHCNNmaUeqGxC2Z7mpMV0LZuvv0Xbn0MZ+bM/+QEf/+Gf8eQ77/Hd737IB/mWm1rQRmG9p+869KqF9QnONhjd4PUpjXM03qL6XkBh3qCcR3uPbr08pBsnO5ZGHnLKezCWpBBybJY+Sv284V6OZUaZ5NB0DU4VnJF+DFlGLUUrwZEYI+MUc+zEJPn1tJymaKWPO6hj0s1Ik9hpdezY5KPnm5+rXYUQAJ+LfouSF4XjGURVMnayWlG09PWlq5OoRfYM9eiorkf8tTzK9F80+bMQO1URXpmZolBVS5WRqJE0nFEaZSsYT9WWooTrpFzFb7bkkKlxQtcFPawIZeTyk2u2/1p3JJZGTh5/i5MHz7g+3PHZFGk7BVmzfyUyeqUKs87o2DB0LSfnPeFmz9VLzXyA/AuVb/3KPc5Waz59NvLd73zGzWWHN/f4jUcr2vNIaQzjnLjeZcYdtDQsWRJiy5LpHxpsI5/J88eJbqmoRvPaGz39dmF3SFSOhdhc2T6qvPn1ATPATOL6SWbaVeYZnIVxUZhZ0SiJvWsnYp39rsLhiNuYFKpojIFQJW2kY+XmMqP7irEwIVHlcV9YQqU9t6w3lvXWszSG+bow3RRunmXGqwIZvFOsNpZh1XF6csJnf/oxTVs4PTMsIdOsFOZoeSuyCsUaATT2TcPDe/fJNXC4mmjsF7xTaLcn9CbSm4LWlqRl5hUWMabVLKOGYo/S+KopOmOqxihAK5yWH1quGouW8YSGxhjZV+iCr/qIQgBrvbQtAWe8zGfLMUZaosirkyabBLRAJZtAjSKLKVnwDtWInKUEK41qEmnS1BJRJHxqqDmBURhzfOhpUNqQvYIo+IGiArVYebDoQlxmSoqE8ZoU3VFGl3F2hbFgXKLiMFZj157XHr2DWcmXZ/fsilcfPGO8mphuC/ffGaRt63vMyeYIyrP4foPteoHuxZFwG1nmA3W+w9hWfm2vqcmhskYXhUmCXCgpk/YFpTLeaYZ1h1+vOdyNXD65Ikcxq2mdwSkqWRDVygi7SVWxBOvPuwaJRlmyThSTyUkzxhsoETMPzE9uiYdAqpW3/va/zsXXv8WDr/91IDH92d/n+T/7P/M/+Z/953w0R+6ADsN/ef06503Dx+6aK5O5JjKkljfSmnWzpR+2DN0WZQPKFZp1KyEG6/DtgB+8qFL7FuOF8aOVk/8vScQxISykICXBkiImW6HOavDJyIPWarbrDd6JC0Nlf8R1F2IxAl4smToplnyAyaHvGpLOEmDQjmyKpOpQlOOeDWOk4xOkoKZVQSsHToFT2KwoVXo6dQ+LWmRv5hxJJQHvJQM2oSJQKjFM5Cg9n6WDrhEvQyXIiKxmco3UeHSJa7ClQWv589xdj4Q5yM9RN/hhEO6PKii9ULWRcMEY8drhXUO38dS7gFr2FB9prWO8zrz42UveXG4xbqIUjd/+dV771UvK+cz3/tkPUVeZV88W/vnvX/POv76maUdU3HNy0pOv7xjbxMm9PfcuNZcvLP/8dz/lv//f/U0enm756KfP+R/+u/+U7//ojh/9dM+9N1+j/7XA6Vd62t4Qt45b5XF5gNtAnhPLZebLDxo2mw33tyfs/u3EFArgePPxBba7Ru92THNiY6E/VZx9TfGrX32d2zHy/Z8+5+qDhbsrmGaZ1acXsF/g0SPN6lwT58InnyRunsjLpVcKtVL0K8WwVdzlzPxyoewCn7x3S+816MohLahJYsTJK177hY6LB4aLC03dwU8/CXz23sy/+u0dJULXKt553fPabw68/fojfuOv/CKHT67ZrhNvvO759Hbm5bPI1ctEroV+rWlaxel9y6vvZd55Y8Ov/JUv8cl3bnn58Y3cZL7IQ8GterxZcDqTqxeKacwcQiXnheQqKTs664T7Yg2tQq682mKVlytvzmhdKNr9vI+wmxNGZ4xKjOkOZyzWiGOg0RWjFCmLAjRlAYaRpSdRNdSiWUKixExDpCrpBpgiLWSWyDLN3N6M8pZfISqLPUpMQlXYUd6xlgoWSUdlpVA5kYuAqBodWXLGxAV/0LIgniYIIwmDNoamcfhNomkcrXaoQXDHZU68/+6fYrXQHJcKaTfL29pJg2lbTN9iho5228pLba5oHYmhUMbIcrln/3ImTws6j6xOK03foFYtdrWGKo3XUis6J0zOqLWhaS22c7jTFe1moL/Zo7unpGVHypWYQc2Zki0hKsp4AAIFQ1KK/pHD9w2+7UnJESdHvE3sn1zReXBGEXPEv5Y5fe0Nvv6b/x3s2jBd/oyf/Cf/S7797/+Af/XJh/zRyw/5yRJZEBDYQuJTb2DVc2/jWW3eoBm2nG42dP05beNovENbK8BCA7kfaLzGN5Z2tcJ3Hn0UrWjpiqGsBAYO48I0R1ROVOzRheFRrcNVhS0NSyjkqKlJYRtH6y2ts+CcMJ6SRKlV1MSYGZeZ8fZAqlI0bDqHcseFfdHEKqRNZxTBRqzRRKNZYhJlba1ykBRp0C8VdBE1rPAcJcgR6tFvUQtVRfwoacdUCjlI2khpJS6H1h294YVpzj+PlOas8FbhnaFdWaxxKCrzEkiLw2fFMjimoFGxSnEzR0FoKJhfjYJq8ZrNIRFjoVSD0mtu5pE5KObS8sGf/FO2987ptw+4u/wd5nhHrSfYW8Pv/O4rPv504uVd4v4vFdb3DWdvtUzXmSfjHR9+sGOzAk4nmj5if9rzf/k7/5L1I8s3/isn/G//w3+Pf/nb7/N3/jf/Cf+n/8Nz3nn4ii+97vjy3xoY3m746tcveOPRl5huD4yHA68evuQwBeYnN3z29Jr9VJkCLDGxn2745JMD11cBb6rcIBtNc274849vGO8yrz4otGtLf1qxHdjekhZFDordTjPXgveWt7+55e50JqdK1YqTs4amN7Sd4/56SyiVaZWI48RUFmLNNNWwjDJ+Wg6Fj78/8vR7YHPl5aeF3V0hxsov/4Y/9oQMw8qhXU8u55T+K9w+///ys+/s+Je7PUlXnJBpWBd5mned4t6pZd8p2n7N+clbXJ5/SHZXXB9efbGHQp5GostYWylYYszEmGU0oo8uAlVlPKOOKsfj1VdpjVYKi2z7OXKKtGT0qDWTcqLUgFIW5erxtiEugKKV3ItqJqdEmBZ0jVCVvJmlLIjuCqXOKCuLZlkSyAw3hEUOlnrksTct2stbVIpBWs+1EqpiyaOI0qvB1ExViqI12kbIBRUEw70cJmJYqHmmaHdUMYqnIKaKmqO0tVMhh8C0G6FE+bWVOSocNcoUQgzoYFE+4xp3xE4EUhopWXDbJRSss+KP1Ra36jDOQjVI/+lYwMuKXORqLLrPKMa1vSDO0xjQWmTxHEmx0iiXEpNrKthW5vFKE1OkzoWKk9/LmKi7gJ0qtu1otgPbx2/S3YP+dE21Nzz59vs8ef+H/PDP/jl/+p0P+fB2x1WY2BpRQKIMUXe4vqF0Dt+t6E/v0683nGxWuPYM54wgxVuPtQpUZTclwWZ4h+/E2qZ+3pYXdwcFtDMYm0VVag1Vfb7Dclhtj87dRK5yA/jcOpeILDlRpsgyz6SYWLLGeXkz99ZQ21boqSiaxqC9RRmJbnJMBllzJJHrY1xI1eNiORFSQWVBtmQM1nz+ObA4rylVMus5JxlxVSOjpipQylrLUeepsM6yjEG+bwiJ2FiNVVCKlnGXliRRSfl4c5DIdlUQo4xcKoWYArYEuaYrWGJmKZV5gWwjtQqMzxq4GyfSkijK8tG7n/JabnHDA9AZbTc03cKD+2f07TVdN3PWWqqphBJZwsy4ZPFkaM31dcAPEUomhUCcEuOrzGcf7Hjrq5ecvDbz9V9b8/LTwGfPE7eXkVdN4eyzxMWb4P7ans5VcIriKk8/WITxFRP33vAYo/BU0j7CmDFTRTtZMieVmQ6wvDgQ58r8KlNGyfd/XmJ1WeOrJhVDXgo5aerKY6sk1bAGVQ3xFsLLyFL2VCP48fEuM5dMNYVuYzDrymzEnXH9SaZMQpFexko1UoJbPQDfSOLTzCIiUknTuzWttdiqIAhyQ2t5udAR4q4yLoXLkri+LtztMuMu09aKLxHC4Ys9FJarV+jGiODEKsIkiRZVM0rJB9E7TdN6ahUDldZCMzVaYbUsaUtVVGOo5Rj3VBlTJZURa0AvVn4YpsrSWBfJfheDKYWSI2G3w/qI4DwVNSzH5XNhLjtcs5JCk1MCKyMT4sIyqqPHuNAYiY6iIMdIjZGSMzFpluXuuAdwWJ3lAeMdSytbnFq09B6qpKiKyxjtZPTkjnnxMBPDwuEG4rKQ4kKtlpAiqUixrB2sLAOr5rDfH/EcFrc1zPuZ+WZHHq+heIyydJ1hfbHCtQ7fC5qzhkwZA/kQKTVQ6sKSLCVHckksSbGYRf4sXk5oG6loEo6qQVUjHB1vsMphjGHoV7AawHm0tnz0ow+ZxwPBaZxSqMOC2c+cmjP8yQn9lx/w1t/6m7TNPZa7V3z63b/LH/3v/j7fffdTfvfpFVfA1lgeO0/rFa23WNcw28d0W4/rO2x3xv2zC4bNitW6wfsTtNEYq3Dbgba1UAvj+08xWrhOTdPineyFSMdWeilQEFFN67DOoE2mqBalHdZYIpaUMyHMhCDMmlILeY7MS2CuiXmnmcY9MSZU8awfrmj6hqFtWfe93LBSxg4G7TxKW0ouhHiEkhnheEl2vaB1IeVETgth0uSUqCVjqkP1Ct0arG0YVg05ZpZpJKdATlCzRftjYx+hcVoryti2c7x8ckVKCddrun6Nby3eWWox5BRJORIOmSUvaF05OTmhtg5KIYbCYRSZVQozSo+oY0kzlkKZA+TMwc4422J1xevE9c0dqgYao3n3T5+guoecve1Ybb5CiU/pq+ar33qTr793ybDNTLZQm8xuP5I+EUhgvx7oVy1Pn9/gZ4VWlSkdeGN7hkLz8T/fY69/B+MMv/Xv3Of7v7vngx/seff9Az/4e3tWpyMXb+y4i5lvfP2Mrq+M48xPv7Pn8DSRd5Xf/NuW/kxjWsU8ZU6KyIiyhpvrwn5fCTXDIcp3pJWFsZCRFc1QWHeG3hsGZbmbKrlqDsWQdh6tFbYzjHNmfLawf7Jwc31N1xuazsBKdnvNoDjdWNqVxupK2CluP41MO/E/33soS+7uXGFPMzpU2EP4BMojjVkSp7bhfN3AueOkLZRHnuWQiLtE3MF0KIQFXvy4cLmrnD4aef7pFasl0McFG6Yv9lAww5aSJ+KSsLZj2MgJrJqWTos9rCgpMylt0Y3D6ILywrlRRUuJo2SYM1PK0pJNC7r3spTTjmQVMSfKIXG7n1F5ASq1aXGxshxmrl7csD1pcV2LbSs6RvEy64LTjqUkpily/WLPsG3xXtqd/ZsbAaF5zyFG4mFPOIzMNdNYLbYzKg2tLD5XPd6JVyDExHS9F+Kq0gytFwmHgcEN1LYDFCpmUoooRMNJY0hLYZ4zdmXxtqHBEbUjTyNpieAqZtVSCcSyY//yFc44nHVo36GcxTorN4hqCEsm7g7kIyzQKkdWkKZEmBf2cUdJcvuJRuNclXZ4b2gaT0GJN7vIza1WxHTVWmgaoqkc9jNLGAm7wvXTisqWwVtO3jzl5Bcecvrl1xi++utUvRDGKz76nb/PJ//wZ/zg3Y/499//VzRjYoqJV7WwAK5bcXZywfq+J68G1GrFt954gKqP8WbD+WoNvbTNtbHYzRZVFDVlXsWZrjpMruyWyKDXON/jVvJ2XhWYztK3K3LNzCny2afPuLk7MC2B0+2abrvF+gYS7EMWcOMSBZGS5YVF60rjO7w1rE4gjC0pZkIC10j3pajKPM4oa1HecfvsmikXEhL7pJO/J+z33O4DacnUOXL++BRqJi0SidUKjDVghBdVDoXAgbRkYkzc3O1xFIy32JWhdw2pFBln7Su5zRKQMIqz1y9EBVszQStCqsQ5MYeIzlWKjJ3BZkteIj/62ae89uic9bqlXzUsh4nC8QZ0iISSiFW80/EotnL7CmpCl4gLew4zVJ1RdoR757z35DmX/+j3+Gu/9WuUuBBCi/a/xr/2bw/cv/yUd3/0E/74P3vJPM3Y5sBrb7cMDxb6e5ZVpxiLjL5un0em82ucMZTR8u1n0D8wXHy145f/nQd86eOFVz8Z+cM/+ZS7JzOf/HjhP/yff4Zzzzh76PmV39rw13/zIWYduak7Xt3Ak+vMeFe5+SRxmDJLrFSvSK2BHnyElx9mjNNsHlqwnrwv5LvMq12hhECN8iJ3ftay3nqaRyt8zBKkKZZz5zEXCc4Dt5c7diUylQhj4MXTzNO7zA//4EDM0FjD6crzG3/jbU7vd6zPGl6qOy7fu+b2kz2XH2ZxuURpKz9yGW72XO+esBhLajsSBqcU51uPXRWupwNnj4WounyG3P7micuPP6Fpzwl5xVi7L/ZQOH38EJ8mXApkWkyN8rCuGoqgp3WVsQ+1QEwEIoSMUlL8SUVIhjXNct0/+pjv7u5kyeyh6ddgJYpXqwhdahUNY86FOEcoiVwSOkV0FO/w51RY7RROaaGctg7nDMZUlCrUGEm1MsfIMibiNJHCgnJe5DJOo52RRrM6JkQ4cuuPpEJJhXwu9pZYUhXgknQkcsYYjp5caT9X59BdoT8/xeqMohCrZrwuhGlhniJjzTSp0GpJ4FgrfY2qrSSrjCSdyrGPUUMkBoVSkaQXsnKksJCWhRSPt5GaicemtcVCcqQsqr7pkBkGjfUW7RswEnFclpmUIiFrclTUPQy+pelatve3XPzCL9E/WGHPWu4uf8TNT55w+dNP+d63v83PfvCMD59fcbiZycZTjKezjs541sOWbr2mW22ww4amX7Hya5xf4VyP95qsvUSGnfwrhURICzFXfFcw1nD+4ILNSU8/NFgMMQdRG1YFnaBXrBaTXdd6kdR4Rc2RNIuTuCSZ4TtXcUremFWRAqVWEmIAh/NGPhNRxEelSCLEHcMIUP//vNmFwARJk2thng7UoDAVdKMJ0yL7nrSgTSuOBGcoWhHnRE6ZspMwAyDN1yKgPJUKykucVCkDrfihrbXHFrz0YOISpQV7hMG5z2/LyD4CElVBa1qcN2irSUlMhrkkisqkJRBTIeWK8xnbNBJoyI4SKioFCSekSMyJFDQ6Ffa3gTztePnkmmHtMa6hc6dcbCdytLxobtl2e5wK6N5QnRZR0ZxZDy16KqgZlPHEKAWvMstYS09gd4b900JVipO3Pf/m9g1eXSWuryKfvX/D1WcHLp9HfvjtPfPtwupepbtXGLySsdAoKUO/UVhTsb2mdE4IyIfE9DLhe8/5Wyt8e0YdC/kusOxnwj6R5koqDedbT+MMdSqkKZFSJebA3TSCStKyDgtBFRKixHTKc7pWnJ46WjfgnaXvDK3vqEExX0fqvuLvDCe24Rd/sWeaFpYpM90U2lNL2xp81vRoivXk1mF7gw8RYuAWTe+MADfPCm8FzZtnnpPBsul61t2GtT/7Yg+F7YN7tHHCx5npkAQ4d3QLFJ1RVEyVyGc9WpNimSAbapGkT81G3LDMWC0QPSjcvLxGaZGKnFWHaS3Ki/KxVpnD5kVopjnJWKqq41I1JFSOMuuuSgiqRza86p2gkFWh1EgdF7IOBFUJB0hxppSAN07m9N5g8CSCJE1iJtUsMqAotgMroU+5BRQktpmS8IiORFd7fKgZL45j1Wa8UmzPTjEmUcmEWCDMgle4mRmDRAzbDoYHG1mH5AxHl69Shko5/l5EDBIO0ndVulB1R84LOUdyEgpnJpGLWOVUBpNlhBGWwrILdG0rnYPWU5W8qYUlUeYJXTtUsugFTk5OWN0/5fSdh2y/+Q2qjYR0w9Pv/QE//cd/zod//DP++CdP+FlauCNLO9pKuap1hqZZc7Jds9qe0K3OWfenDN1Ai6FtGgG9qYSqjSQHlKaqSiKx5IUyO2oD1hkevH6PoddYp6BoliR7AbKkfJQyGKVoWoMyrfxnNslBmSIxBqjSbG9ai9ZW/qyztMXTMoncqCp8J52UqhRhHhFCe6XfNMd+SUTrglPiCykpkIMil0xcZgwNxlp8a5nnhZIjtUhx0VqFb5R4d0Iil0g8QFAZ6xz9Zk2ME7VAiYZSM1obnNGoVpbGxlqOiXAKhZAS8VDQSCyxa0WepDWUQyITQVX6psdYQ6mFeSrMcyCXdPx+RUpU1KypXqCUvnVoLPlQqTGiNZgcSUsmzwslTMRF/MdPP7rm8TvnbE4HGrthax4TW8W5v+T89BltMrDS4Au5KOIMxlhsrthkaHqF0oqcC6FGeqtoisbtDdevCs05rO9bfvUXHnKZFS9uI/5fPKF+5wW7FyOf/HTik/dHTs41r3/J8faX5H0hZRnz+A70AN1Gk01LyZqaFtoBum3D6YM1q80jUcgeFphumW8Cy1iIdcW2MxAylx/dEXYLy1QYD5mXLwOxFKqqNK3czPXxs3N20rBaOdZnAxfbezgnYqb9tBBuZ+Y4EV8WnLKs+46/8q2WV7s9+31g/6Jw6C1d72iqY60N1rcob9GDg7gnhoQuitZ4TKMpJ5G3iuKNew2n645N07HuVqzdyRd7KLx4cYtbbjFxJJUWWzJKafAW71uMd9iup0EzjTPj7oBKQjzFGFSCpGWJZqshGSvRO5M4f+cx3sPQKfz6BGdlCb3LUZZCUfC5NRSqLuAKymYSlRAjeT+xJMEW9LsI3qOUxqTIHCKGwspVNhcN3dDR9R1uoyFNkAPFtdhG3AbTmIi7iRgCoaqfM5CyUvgqHQZjRb9o+xbjFb6MFDWgjcdbi+k6rDNYp8iNwjYLcZY33mm3Jy4zu7mSpoVlrozZopeCbYy8XV+cE8aZ8W6HKopYIqUETMxMhyoL6NsbQraoKtFet/GiCi2K6PzxIAFjHE1zVG22a5QH3wgx0TSJoCxTzJTJkKqnWoNtT7Fzh1YV1Yx85b/2t+juXaCN59Wra64/eMLljz7ge3/3D/idpz/mz+6ecV0ymUqjWx7ZB7je0J9u2L72iDdfO8E3p3h3yvnpQL/qaBpPDfDq9kCaFtatBTOikuYwWXyV0IJpNI1X+MbRtB0P3j7DVNGhTvOEmhAFbEnSkzCKrKR1XXWWl4cYiUkoliUXiqvSeSmZECJxSqQpcXr/lDgFxts9c9ozbFqstwQMNy+vCCGTTcMDfcb+MHN5taPrOrpVR3d8C5yzgOTsaBhTllSQqqjWQZDy3OXuGnN3JGgZKUiqWsE4vLYoXZkJhHGSmx0LKXe4rsE2Dg8ksohhJk2wkKrMunNzpAgozWTlC26zFkFNVeScmMwBdTODqsw5Ee72GK/xK896dYZtLba1bNqWy8PI3bxgotBstRbnsRpavFthXMd4uCIxM02Bf/Yv3uXX0i/yzldWbC4CPpzQWcewLQyPPyEtNwSTuP3ojqazcOLgXs/2bM3Jo5ZsKtl74pIYX1wx7yC7hp0ZUKcXhBS5/jTwvRctnCzojeKv/Td+gV/+G+cs+5HDPnDzkx2fvb/ju79/zfd+r6K1klh8o2g6RdMqVj3c3O3FSjZKV+TkLPDaW5HTRxPxTpbO+WrHOAaWJVGNpWtacqm8vJtwCqxSeG1IVdE2nqHz3FtrqrHkqpjHmbU9o7EN05i5KoVNq7jfDZwMb6LKgkoH7rpbip0JeeK3//FTdApChnCa/sTjpxZjek6aE3qdMH2L32iusmYaM6cu4PWAVpZqEnVbUd2KZVkzF88+TFzlp1/soeDGW2xN0ri0BlvljTwDIU3EOFN3E8QEaLS2uNagtZMHk7US46sFhROBfZGazmbrMS7jbDwyZCL1eLWsaYGcKNkJdx9N4zzahOMDUVOsFNxMVlSScNQ1KBReGawxdJ2lX/X0mxX9uifrRsiTZSEl83OfgjYN0Un7LwTZC1RtQVvaZovSwhpyrh4dzRnXdlgry+3GOUy/wliNsYXcWnIfiEvg+urA/mrPYbdjd5fxXlhJ2/UGQ6QZOrQyzLuJsCwsIVCmPfOciSFL6mk8AvtClLKgszhnf74PUUZYNMrIQ1U3Dc2gpWjnOnKaiQWSRnDOOGq2hKsd6S5AUGzWPSev3RPb2eNzhjdeJ95dcfmTP+Ld3/uU9z7+jPeefMInTz/m2XSHrZVWKarydH7g7PQE4zXbzSmPzx/z+sNTrF2j9IrVYLCNEyjYfoJY8d5x/nhLzAjtdi70TcuwbVltG7SymKJx2kr2P3/u36iyL8KCAedEY5pDoU4TtpGfTdWSSlKlEomoYjBaYVRG16Pg3BR0LhKcIEOqhKmSoqROSpLkllGVnCKkgita1JNW03h5INgsMehDMeRJydgpRbqhk7hqTBAiuZRjrNSjTZF3J6dpG4s2mhLiMWmEaGBTQAfpO1ePvP4WORzmMUgPqDc0Q/fzxfs8BmJO1FJom4GmcfSDY139MeocSXeZmUTNwFLZ1wlmKX6NXcN+kQi4YcHi8FrjvaVpWnAgl7uK0Xt0PrC7nLl6fsXQN7TDCaYqvLJs+jWvPXiMfaV4/vISlQ3xAPsQuW4W+tNGIIS9ppqJGhMslbZaTFWomvHqQE2BMgXi5Qx70D2klxmdW9pkMTnQvr7Cs8FMPYccyXEmxZl90hgtb/MxI+BJc7zwx0ypimlXyLs7xkPhcJfQIdKUSoOicYrttqPve37pnRXbjcN7Qenv5yrWQWfpXCIumWWMPH16A3ux4W1XDY9PNqwGT9dqrm4uyfMEeeL8jTXnjy5wLTTLAVWy4N91JTUtfeNpisGGQlpEW1yMoS2VE+/QrafVUuD1zqCVZeNXtKrFH24Y5j3nKn2xh4IN87GFqY8oBNFx1uPbR4mZNEGcF5qmpe9X2CO0ztmGrm3IVQs4rsKiCjlKW9i1LcrE49xfSKo5Jcois3yQCKYyAsXDyOxZVRkhQEWnQk4FUwS1YI5LWN2At4rV4OhXUhDzbUvRrRApqxxQqkagYLzBuUxOAbdEiRQai7INXSfIAZTCEBn3IzkWnPG0XYPzDa3zmH5AG0AlSuMoxeGC5/rljjAF5t3Essu4bY/tHe1qjaoL2looleluJOZIDIkYZqZJPmQqJsriZBSkNL7xuEYcDrX6nzeaq29kr2E1duhoNwbtNVlb8i5QlEg3ErL+KaFQlowJClcM677j5PEZwxuPWX31bfbPb3n14Sd8+id/wp//7kf8+fMXvHv7kl2ZUEBnvXgTbEfXblmdnaM1rNZnnGwu2G5OUbolF3kAgianyniYwUjLejjpWWIlzZlEpOk8q3XLyfmA0g4VpbGqiljSZGRX8U66Ltppqlqox2ZwTQHXWVxjKVn8BapUMll0q1VhlUZZgwXcUcOqVZWfHYWSZLmYitxcvTPitFZgraZrPP7oP5b36HrsEksJ0h3HiakEDJCPLx6qSuNeK7Baoz8/wI3BCk+DEos0pY2kjqBC+bxar/i8hlRqIc0RrKZZWXzvhI23ROLNRAwzJWes8nSdExeH9RLfVhkxQgtanqTIZaYkqFkxLQshO3Giq4RXktgrSkjDuiqwFRcjNRSKSzjj2V2PXPY3PHpLfg5WW9b9ikf3HhGmwIv5hrbpSSGT58rhMqNKpoaMD5mgEjFnypKwymIMGJOBPSVHyrIw32lUNOidBgvathJ1z+A3hu25p3yl4SaMLNOOedTkPdQkoi6sohkMDo1dKfL4F2GT5Wph2ifGMdNbTd84Nt4yrFrOH5xycnLCa6cPuDjz8uLbWG73kVTlZ2MILLuFaTcTR1jmhM6VIRqGpGhSRcdA2e9J8wh5xteetfesBs/ji83Pd5k1ZQ7V0BqHS6BDgTkJfsZqXKqi+nWWRluMcnhbsbZj0/T0uqHJe1aqcOa+aPMayOldIak1TkmcUxsLi0eVRHWZGjwKyYHrRd5E0Z46O6ox0pCd7BGtUKTwsxchja4F0ysMBq01dtC42mKtxp+sJF+eK9OccFmohqoxx0WasE7sPIO3aKtxyrBaNzRG05R85OBkyt2MqLglZ+wHA1iqQrDcYU0pCT8EtPTGwXt5G7Yi1chjkWjGFOlsy2qQD5n3Dco76UfEQrKFah26ONza0tx6cmhxfeH04gK/WmOGNeHlK0IIjIcDyzQel62wTJmkFLVxNE2DWw9YDK4UurNTfN/RtB276wkVBVFeW39kIGncSU87QDWZGGb2JRAwJNexf7Fjut4RbhKvmYHXv/6Ie2/fY/uVtyjdFkxDeHnJP/1f/R95//s/4/1PbvlX00tuS2Ki0NmeVdswdA2rBxf02y26OyUNb+OXQrNeEc4fEpsN0xLZHSbcLmCcodbC5as9j7/8mPbemqVocqNRzkoBcOuovjBNB6K2rLyjc4YyL8QxUbKMh5rGYr3GeEOa5T+LJqNdoVt5+vWKEgxWL8xjkgVhDjgNDS3D/Q5vPQbLpx8/x1mLHlZgF9KUyLlijOXi4gLftrhhJQUyYP2oUvYjU6rc3e65/uyWZNSxvS9lOOc8JMPV7kAMCzmA7iyrtqNrO0zrUUH2QIdcCFMQv7hv6Y0BKzwdnzR8fkstBryhWoXNhhsdJdkXZO9WUiHOgV2cSGOUz2k7w6RZcmLRIhVe5oWr/UhaRKlqfaVOgRRl0WxzRK9PUL5BR011QjBulBO5VpbS2xISqWSqK7zxzVNefvKC8b1rvvJXCo31tJ3n/msD3VqTw8BPf3LH1781sD8Ebm9n4i4y3jTMB43XEzs1k1XEmYx+wzOcVPqTyvPxmshCJjB3hS4o7KiZoqHuLGUxzNHRd9A83NL/0tfobp5TD1vifmRzeSCMkRwz1mqai47hxPPwYUO8sailYJfA7uMrlutAvEs8ujjhtUdrzs4H3NkFZycntO2AaddstDTGIg1n3ApSJmlW2hHWhdBG7q9PcX7FMk989NMf8IPv/whVM9vW8uDROYPLGJt4+t33+ezPfoLvPN/4N76BdZGQI8+vJvxdxY0WbgN6F1BTJLtMDQqKsLPWyVJqg1UNp0ax2p5w7/SEB23L0J7z8MHM2w+/YErqbtaouKDSjF60xCiNwbgB2wScMQx2BacI8rcaVF1QNaFSIOiFEi1QKHqPUZ0wO3oFRr7YtnF0q3OJT1pDxWDq0VlrV8LFz4UwV1Qc/4KIapIkJmKmuoxCRg7WK9p+JTNpHVHVixi9t+TSgEoolUBbchGmvFYe5aXk5mtDUfMRFW5AB0iKmqHWhfX9Lc4a+rbQnp4KHbWRD2eeEmkZWQ6KYhUJSSu4zjC4Hu07cTlbR8mVTKSQ0epoSzKiPjU9tH7A+Za+abFqha4aExPGtXIr8grXtti+wVnQbqAwU3Si+iLXfIoYv3ykzIV81dDkwOZkRf/olNe+9RXOXv8Sw/k9TAPPfv8P+OjPf8Lv/os/56c/+pTntzs+mw+8KonGdtxvBs4v7uGGDtd3nGxPaPoB4weK34BZcMZhxsr+1UgqRXwXnaHtepxzrIcBPzgoiasnd1jXMqw9pw9a1ic98zhxc3lLSpX1G/cZtv3RZxxIoUqKSCnpAuSKrgVngc6y2t5j3C3c3D6n7RSNbWmGgZV3VPyR1WVJMTFNQphd7uYjqr2iE5Arqoi8pu89tnVUp2EMHO4OXF/tKTWSsxGhUSnYrsO2nmbwoKVBjAFzpwghE5dAmWAJCkKhTUCOIioqBsrx5pPLUekoI1il83EsVEkmogKQNKlEmkYYSPMyMj+TcVHJEQDTVpRXEDOH3Z5dLtzdHSifc/5DxfqK7zzDdsDUlpxlbLXeDtxGw3xkZFklN/CCEqwzos5d6TW5NdSmZ9VrXvGS+TBy/fEOe1Hp1z3r7SnOLjy6/5AvPf4SD14fuHm1p023XI+3NM2A7z3+ZIWJl0w5E8KeUirTXhPHys04EudMXgo1amJWmKjIO0UdK46G++sLmlNP1Yn45Bk1BNpiaPSafNGR5oUaAqomtqstQ9dyajtyIyU/bWDYNCS7UDeR109PGVYtrWpwtw6myJyvOdx8ymGAxrW0fkvTZrxygIc60hWPxVCHNXbTUZXntZMvEetXqCqDCSQVyWGkLIaLw4CESjR3Hz+TnguKTW0JNTL4ynbTcvFgzbDX5KpIBtJhJqWA0hmfKzZVrKm0quJqIM2vpMJTNG13+sUeCsW16DqjCELtKwWDQqv8c0OVrgIS08pitJNdQBHzE0emff3cy6BFLSi6RodyBdMUdNNhG491lopG14CqFaUaqBltwPeaumSJwyoni+xSQGcRllQrxFBlyNkKMsLU4wFyTAWUBqUjSh3BYsfESVUOY4+e3Cqyn1oMpTqUqVAk9qhUS7vZ0PQer2dMO4D3QtAKVR4WEcJSyUoeyDlqtBH4n/YDVXtyMYK9xYhD2miUkwQGVIxz4nluenTrUdVDFkJqiIKz0FkRFoVuBQHuhxUxa2pdiMhVMwOpGlRu0AEYEycPT9icPWJz8Qbnv/AWjVmRx8DLH3/Mu3/0fX703R/yh3/wPQodtzlxW6KgHdqe7fqUk/sPMUOHbTu6fkPTdmjbkmjR2gqHqGpiEBSANhbnFL6RiOswrAhpIQWR0uta8VbTDw39qqXkyKQrmio7AKcxDpIWzn+t8t8p1M9FOYqCMdB2LfurHePtHaa22JXHeY9ueqpy4iPImWk/SVFwNzNPCqUEcheDkocj4iCIQdIlYSosu5Hrq1tevbxGe4U2LVp5jBEfgj62+aEKlC6LGMZoLcGFAKHKXFgpj0E+bwopMyp1RE6qI/7i8ylqkd9PVRmSuKCXOKM68XYvIRBDlgIfmXKk5qIlnVRikZ1LLlQtLx3aGFxrcW2DaxpqqsdkXcVYjz6imcVMaqjHPWKuYDCgPE2LvDSpBmMS1jUoZi4/u6FrHLZt6azF2IZh2PLg4WtsesNyp7EqUtMOqqDHXeck4DAnuVndyG0tzYVDDOQsSHtvHRmFzooyQdxnXE242rLohnwHcxYpkXEO6x26kz8TqNQQKYeFXDWLsgzNgO8cvjX0oaEsM6TA2fpUwiPKoKMEZmpI1HEiq0ptweiO1svzplRNmoUR1jpLc7oiKE0B2qFDaUM1lWIz0QRq8tTYosZEDooQK1fz4QifROiqStN4i+8tq22H0oWYK0Er+W7HgifhvcUc4YZVKVLOLNOMieJ+UdZ8sYdCd36Bzx5TRl4FByEfnaQKdZtQVeJ5BMuw2bA96/FVkbWlaodOYqVSKmOrIpj26Fo20ChclDe06Bw2OoyyZAo2C2Gy1AmVBG/QbDtKCqRSSVWhiiNmiDFRD040nKZBp5bblwFnK5u1wm4MSnuUWkHrMCqhSIScCLMhRynbOb9CO0V1oGeJgdZSMS5Tj+YkNZ/jz9b43qDGW2K2sFhM6SAd88sxsixHUmmQhIygORJT8HDQlKJJRaPVBtUqwTcrobQqlbFUgm6ZsSxLkRvSlNFXB+YiXmSjNGnybM8HjOtwD9fkxRBmx+1yoC4JUChj8bsV7A/U6Yqv/dpvsXnnF+kefo2aD9z9i9/j8g++zT/9O/8ffvfuhg9j4KoUfvXslKZk5hJIbs329ILThw9ZfflLNN5jrKPYNdZ3UC3LbNisLcYcWQ9KUkSu1XTO06487dpx70HHy8sr6lRYtVu6oWFYd3TDmmbVoU2l9YVSHe2qI1fAC9K0ZnnwyYFQCVnwHdUUaiN9mDlMjOMdnTLkJsriuRvISlPCQpp23L644erpLa+e3rF+/RElLuQQyXTY40vElGbunuyZl8LtbJhuD+x2t9ztrjk93TCcntFtN2jvCKaQyZgpU20UxMk+4oaWgtjrxikS5oQOmVA0Q+PwTr4H1jgBpgG2HMkZJaGCtHCrrviqCCEQYuZwdWD7hhVyZ44cQhRqaoHsFFZLbyMa2WFo7zi7t5WFaymEEIVK7DWlsRzu9kzzQoyZKShq04P1GG0kdw+indQGMHjt6c414t8pXF6+pD85o1b48Q9/Rns6YDYrfF6YR/D9CW9942vsr2+IIbGfdtztFoIphMaismVajmGMJweup8z+trJ7WdCN9JBsC9vXM503WKVZIty9isz7kWW5EUlXFQW1cpZm29KetKzvi++FJaFudjwvl1jjWHcb/spv/SpnZ1suVi35XgNxhjTT+RNMTOiQiFPE6YiqCr+9YPCBpmvoNitOTk8JWTPNBT7LuFbTrjQXb53x4U/23F3NlHBgnm7QxtGtTrn/tfuszjV9Wwj7W26f7zjcBdr7j/BtJDNzOb7Cjz3takCtHf3ZmthoUinU6kmuo/Qzp6kn2JaiHKoYDlZDqDSXhWW+Yr+XwdsXeij4u8/AFpLVbIc19mTGkmmWltg4VAE7a3bjiGWk7Ap5VfBmwBuLWk8wWynZmIhaBI431T2bfIJtFdkUyjUkLQ9rdWgoQ8G2lrY5xfpACSPjBy+Z8y0lKygW1y1QGmxxmCbR9pamb9mcNYyfLeS0sNSF6fleHMrhALPHrzVuZUB3lDCRpoXx6cR8okRSY1vSsqNGRY2OeE9hfIcynqpuiWlLNYbp9gV1luVbe97Qdqdoo2jONGXfk9JEzonTB2fs91ccdnvCkwPz4Q5VNV1zxvD6gHYOhWa6eyrX6bbnRaqM407y9bsb8nViGSO340gqPRaLRzPeZB69fkbO9+GiIaaJeT6we3IFLDhgRY/vAo9/5Zs8+i/892g2iv3VM57/i/83P/6//wl//O53+dGTD3n38hW1Vow2nPcrPg4HKgrb9nzlwTtsH95n9egCs90IqyYG5nCgFMFSDNZxcb7BOrkJqbBgWo1pDXW+ZX9Z2F8qOLTc3N7gveJL33gNZxwpzLz44AOa51u61tF6zd3VS64/+ARVNY/f/Ao5BipBBE3NgNKFEEbmcU9MlVwN5XzH/ftnPHr8iBoOLGNlvh65e35DNtLd0DoQD3tqXnC+oqyWt7UEtWT8BoyHEBKffvyMFy+vefL8Gq0Kp+enPHz9IRcP1+BbitIcdjvGF1fkVGh6hXenWCOsqpPmlNY79NmaZsh0bUvXt7SrXj6TKTKOlRwWjDGs+h50kF5KkPawa61EUjuot4GwBObljvbgZanewqZtyakQl8DhbiGpTLGKYd2x3vRYY7jbLyifj+h4IzfwVDEUSbYtFSKwhr5tcW0HOhKjglywMaBDoahEsJHCqahnyehSuffwHut1w/f+8Hsc4h0LG4yGu7vnaNvx+ltv8O7VM87PGrx/yH73PnXakz7L7G7g9u6G3eHA3W1CrRXOgB0URSlyqpQ7uP0gcVeFgJiDIk2SHlLe0Zw9IM8L06uX1GVhuZE+RS0tra1YCnXMpJSpJbC/Cbz3Rz/mcG9DfbDl5OQcbxReQa8CpkaMkxBK17RoBWFaSIeRMmfGpDExkosjJ0vrCw++9oCLLz/m5Cu/Rt38EYdnTzCLx7ZWKM+14Nc93dbSnRhs903SdCDMM7dj5OblB1w9veXmu9d8/5Nb3v7aLW+91hN/+ITVdsW9tx6iHvRMNzvifqR3mmQ6sjLEkrBJY7zHbRxub2nvEiv/BR8K6/4Uow3KOPKwoXUZqypqkAYnRWaX3h8XZdbiNBjXYI3Dm0y2IiCxtYfOUmuFckLX92irULaibAskVMkUbzGuYp2lWW9wOlFTxriICV6u1FXhXAQ8FYfzhWbY4LuWZm0p1RHDQiwj0WRK0hLp6zzFa7IzR0KpRw+JoiN+AOsttm2pYaBmBVmIp9o30jDWLU2/xmhLspYapeWsBk1qBtCKoiqsLbZ02JpwTU/2DfiJrBNDnNDK0LanNCcNxonAZlPO8M2AdS3x1hE++5TbZzc8+clTxltx8/rGCOajZlFLth25aUmuYaowTZlpJ7X+k7MVm82K89MH+LVl/fo79Gdn3Hz0Hp/84Kd8+Kfv8u0/+g4/fP4xH91d8awmtsYyGINvGoLpsMbSOU9zfoFZr6nOM8UspbAYmYFapHeijGKZg3yBq4F5xmFQ1jK0jplADIGrZzccdnsar9mferpVhzGaVddSUiXuRgqJVd8xJYhT4vbFC1pfsU5hW89hd4t1hm7VEYceHQupgNEOpcTfrKylkkk5M40zWduj6GZhjgWcpzvxZOPYzSOHmwVtDLQVq2AulWQspmtYn69xXrE529JfDPihoShDLFBqFmQ3krCyDXhvaL0jhEBFYqg5V5ZpIQeB4xn7+VBDEWMhZaCR/VktilwFbJeKKFX388IyZ/nfdi2xQokSfEgswo6MVUyBSmOs4OhLyISa2d+MdCsrXRorMd+cKykmSjI4b2l6w8lmg2qaoxNdMeUi/YHrA64ahlZj24aqFeMYKNPM9eXIeqvAt/Tn59ztJq5e7Xj0uiZVS2Mb2n5FNR5lRaltc+V2NzKPM9UU7qaJOSTmGRoryP11A3djQWWFRbMZOsGgZMVUFYRJfl5oVJTQivcerxWmNZjO0lmLVxVTRYD0ebLLKodOmjJr4gHSoLFZU5RiXERQaFSlaRyNa1Fag/VUdSDnQl4UtSzEGAgB9Lwjm4XDfsfuyvLspx8z317RsrA+2+C8w1gPeEowpL1BFQ21R+EI+1fsXkQOrxLeeWrIxP3EdPmK7773At3uOb1KPHjrhGnay0GiNPpki24adM0U1dA1htXFlvXW0Ly6QqiYX+ChcHL2ZfrU4Krjdp3ZIhazXRvQ10IUjF3htG2g0+QBzKtKcpliC11wzL2sF/xkMGcaZyx9akgnGZUVegF1Jj4AlRSLTagDosI8MzJKsoq+N6R9kGKSS+hQydqQtcYngx0ajDdAwZ+eo2IkvjoQNjt0SdhSoXdU6a5hThTOCiyt/3qDUQXrNM26wST5oFWlidNMtQrlFY0ymK6TRMgugEmyxJ4DU8nkWChLRa8sDoPDQKdw5gJWFf8lK2A469BNT9otaKtwa8v63jnKecmxfzZwPe24+9mH/OEff8LLkOiHll9++yGn9ztUNZRDZXj0GHv/lLI+4a4abm4Lh6tCXBxvPHyLR195g9d+5Rdx3X1yLIx3N/zsd7/N9/7Jj/nO773PP7n5EWNdCDWTUGAc2rW4vsN1Yj7b9J784B6Tt8RcuL7biQewZJLJjGnCKFFAukstGAatYYw0i6HH8/iNc9pGcdjPfPzex8RDxGvFJ9Mdp6+fcO/RA9740le4ebHn5vKWm9tLfuU3/wZLH9lf7/n4pz/iwaP7tKdbus3Ahz/+gK5fcXrvF+Ecco0kMmUxjPOeEHf0jSVhSCpLyZHMPEX21yPVadp+xbpf8XK0XN1dcfn8QDN4si80pbLYjNluODtZ86h3OGfBV4oX5WatCp0FYdysPKBovGPzsKPvegYz8MGHzykxY6tk85fdSJgW7Grg5GxLN3SYxjPNhaILk1soIaOMxTiDVYplScwpsnt5Dd7ivOP05JSgKtOSyPvAnA9YZWlNS3PS4bXBak1Simk3EsbI9asR0pp+bejPLHXRzCEyTguEhuGsY302cG9zwi4txJJYqZZaF8KYuP7kinZo8ecb+mZDJXN9c+Dm6TXPn95y782e4dRz76tf4+Wz56RyxVe+oalug25W6KanNBvSfCAkoZZev9zz4vqWOSc5HpVgZbwztJ3hpDNM1zNaW7rO88abD/BAiYqXV5rdy0uWaSbmStndoLVh3fec9A639tiNp9WVGhIqJvxKC8lAW1rd4NfntG6gpo6YBrTS1FTZ7w0mJaxS9JsGSov3jmwUhYVUA0us7HYzhzGw38+Mzz/C/1mgV/Bg87tc54ZsNCfrwv03H7HablmdnNNrTRhhepkx5hXK98SieP7TGz5+f8cyB05f23D+6S0dld2rG/7Bd58Qd4mLxvMbX3rIYhbmGlhm2Hz1IcPpii0aM6y5/5WOt+5dcNLep/nUMIW/HDpb1Vr/UuLOXzj9Nch7dJ3Zmp5yhA0Z7YlFShFaaXQVzEGnGqYa0UoonK5mlLIUFKEsWO0ASDVitVTuc0001gNZrrQYUk3HLLyjN5rBNJz5LbMaMVSsUiSdoVoUBlTGK7G0ORfY9hdkrbgqtwQhv6GJ5INlnmVz/3Do6Dce3Wj2S6WxAafAa0cJCyqDqppQRyqaXDW3+xuomqVUPpknWiPWOe01fbsRpC2RVewFARCkcHWTZ+ZacL7nvNV0SuOSZw4LXivWzuA2sMuFm5h4vkzspjt204HPbu/wBvrece/hhmF4RC2asGRcNdQwQQicN2e0WtEZw3l3wS//2ls8/sYbvPb/Y+3PYm3b8vs87Btjjtmuufq1++b0557b1q1bLatIVlFFUhIpyYkVyBEUW1YSGYEBBzFgxEASAXEQGHASIRYM2HkwDARSFFuyWtMSRZESq0qsKrJuVd263Tn39N3u9+pnP+cYIw/zUHlkPfDhAAcbG3uvtbD2GGv8x+/3fV9/l6L2ePTRI/7Vb3yHb7//Ec+X55xkMypT40uHQCr6XsR4OCLqxPiDPoPRmCD0CEKF6GxT1SVZtubx8xOKagXCsL13k8Orh3R7Q1y3T6A8gsCn04kIVU2Z52RJSj57SRR0ieMO29shbuigHIHXQCM1ZZqSXF7w/JMpcegzGXToBmMaJ0d2HG596eeYnj+lLBNE0KFMs7YQVhvigSGIB7hen5/+4HcoZmArl/6NLk7QwfFc/I6kqTVJknBxes76xGklTYEh7r1JLdvqS9iLmc9mZFlKpSuybI5SDnFvwHpxCVqi8BlMPAaTDeLBAFdYlklBkuYsLi4xtvVzKylAB6+6CBbTtO15KQQoFy0yNJpGB9R1St1oilJg3RrXDQn8Ln5XUjWGqjEUZYqUHo7jEAaC5UVGUzdI2RD5A9xXbXqkAtsgMQTBkMBTSCnJtSDNly1eo2wTRI5ycdwAPwBpFWhJUWd0B12UJ1nOzxFNgOc4dIMWjV9XBVWREqsenUDRCVXbY3BLNCV5knH++ATlKA7v3GR04BFFPcJwTM0F97//kM++d5+j5z9idXpGtkxYC0HktHkNx4fRxoR4HBJv+Dx8fwq+g9sL2Bls4HQVQjlUl4p5ekqerCnOEp6VJdrUbVg0hmHUY9QZEA5GRNLg0pA3a8gtTWVIy4aN0Q5REBD7DsPhIRiNrkuSVU1TpwjbMIgmeD4YYUjLmrqcIgFPhnR6bSilagyXZ0dk0zXZKmNeFmzEfYbdgN1dj06nj7Cgi4LA6RFtBsQ7ESobMVvOmK8WvHh6yWfTIyqdc3sY8zhZc+3amD/7J97g//D/+B1OlwU5r8x+spWOldrivSqF6lpTApNuzNu7W7yx1+HxpeWDF5qPXvzoj1zrf+aTwuXqGGNzsCWJ8Gn+cFMQCo1uQWRIwKBwUMKhQeO++n8rHpRY2uSFoDVe1bb9njYfofFF68m1GATyX//sjlQEQuBLhe8EGFG1pSPR2qUsEmslUmgc2uicdAyNe0QtIKfglWABhKYoBFXT4qWHnkccKJQS5I3Fk7pNtQiHqmnayKMVWCo0rat1VeRIWm/BrKnwZPs7lZL03LBFhaNpGpeiMRTaIIyhsDUGS+j49N32NWsaQWE0roBQCkJfUBhDagwz3VCbhsY05NoQOBJHQ53WlI7G9RRR5EIqmKVLpstLXsqkhcI5DpE75Z45pfv0Hr1PPqbSkux0zeLuGefTc5I6xQIDf0wcegSeS+QrvHiIDCKk18Ht9ttZqGswrsRRPqEn2bSauumDgNHmJr3RkCjqggxo0oI8rTF1hQ0Nuqmx1PieR68fMBzH7F3fxI/alFp2eUGZW8qiIj1bs73RYWN3k62DXfRlQS0jROAgpSaKQ4SwLBctf0cKieuGeG57HM/yko2dGzBRCBTeyKU2irrRZMtFe2GtI4a9PQJlcHwHN/RYpQFW12jRGtKyLGG5WFE3DUW+QrkKR/pYbVFK4fkBjalZ5wmVsETKI8tryrL1aTRN+6FIuIpOLwIsum7f+9JpHSO1sdSleUW01fhBiGg0RV3huiGO52I9qLVtKalWgyNaP7OxZKlu+yjKQykP5Xuv4IntIoVpF/DSke2JxggqITBCtYIqx7bJN9uOdmUtX/3FWyyKtKhxGoFUPm7ooYRAoynzhrpqaAqN9AqMUNQ4SCOwZetZyQuN4wcYAS9m52zfvgmeYFbMuba3Qf56iZaayUWHMp1RFilJqbFa41iIrUKNuwSxT6fn09tMcaTC83yCTgc/dHAch6Ly0eWSukpJ0xU3koy8rCiLEk82dMKITtglDmOU44A1pOspxaqgyipUmjLZu0Y3DIhdRbc3aR0vdY2zrNBVitA13bCH67d+bKcyFKmLqTVSOzjddhznSomIXYrJmmKd0c9KlGm1msKTLLKKLC1ZzJdoPWe87LC97hKpjHmWscoLnJ7DpghoGnB8QbW2HE9zvvfxKZk2OK4kVoqwG6Gc1p6YpBW4Do225GnFutHU9Qoqw3oVMas8prn7M631P/Om0JgFmhqLJrVlG9WkXSzhFYTrFUfU8P+PC/pIXCFpXn3dYNFW09AW4hokPryKFhoqJObV5mExr34ONFaSi9bRXFcGZQ3qlazKF07b0MTivdp8eIW4PrENpW1/oi/ar2sEK9ugX6UpgkzQFRJPtI9RYUFAjSC3gLXtqQRLg6ARkFlweSX2tubVMxc4QjAQa1wsjjWcW8iA8tWL3W5kEDeSRSnQFlZWU7561AroZ05bMMWSWoOhRXY4siXHCm3RWU0TWoJA0e9F1CWcG5iVGSuTYq19RTtosKdt+1wBJYKh7HKoJqTN8tWG67Pd2aI7CAkihfQ1xhsgVYjre6i4i+NJsBXGEbh+SOj1CCZdjG5b5kGnSxy1XmWNIi0LyqomFwLbcXA8i+NCb9hntNFjNIkZbveJohBd5TTJCTrXUDfoRPPau7vs3LnOxo1rzD96Rm1bIXpVrAiiDkJ4XD4/o6kbvDAgGIzw3JB0vSZZrdi79Raur5BKoE3DYlmynK9ZX5zROArPDehFE8JRSRTFRGGPj++f01Q1dV1isawXS1aLJcYI6iLD8zwqr2o5TFGA340o0hn5eglZStfrUmlLXRu0lS2bSLbSdS/2MXVDU9UYR7YyKilalHZhaBpDHWo6nYiW09LgugHCdTDKoitD/QppLVo0IlZbqqzGjzuvyAES6bVFTGPb9xCibT8XEqq6bQ43Ur7q3UiEb7GNQtM+XlvaV3d7ksAPSIsMbTXDfogbK4Q15MuCJC+hauPQhamp8op1bkHplshroNAVcRBQU/N8cc4XxW3ysuD5xQU3djYZ709gKGlW1zHOisZkJPOSZVoiSxjlLlnfbQGBjs/4NYvXKHytyAJBx2k/+GRBh45pQJQkck2xTkizmkVS45YFyvNwfZ8uCq1CtBWkl6csZ2vyJCNaL9m6drOliToOYSfAYmkajbesMEWG0DVhGOAGPkiHSkvSRUyVtSM1P3RQYesL79sJTZ5g8gKR63YDqEo8r2F5mTIrNEeriqQq2TENQsNw0pA0kkop+jsdBqXANAWVbfDTmmVu+OG9S4xy6PoucRSwsTNBqpY0vZymNL5DWRscLWnyNp15kheUM0vjGErnjzmSekMq9KtJ01hIhOghhHrFRGkZQDWGWvTbpdPmhNCOVKRCqzHSFEhTIU1Fahtq4dOIIRsiJaDGQ1M5DrnoUAgf0cxQtsbB4ipF4wwoTUNSXiJN3UbBBTQyxtoax9YMraESglooShGj9RpDjWcNoZIgfbQMmJUrCtNSUGMpiVWE7yg8qek7FscRaKU4KxTWNHimpCcMOO3idGYDfBpc22CrsjVUWcHCKkrqtvwmBD2gfZUkQ0eyLS09B/xAsWo80kbjFWsWr7DjvhCM3Q6JqVmZmh6W9NWmUlrBy1oTW8GB49C1Pg4hFR0yPaPrK+4MRgQqwDUS2TSUixMWRjMSgm8pjx/amGcYHpkzMlsxdPtMwhGbtzbod3uEQYDnKQhiUArHEYSjPtYKmqLB8WM63Q5xN8aNBbKssUUbG0yTnIaCThCw2ZU4ToRyQsZjh3DQxgIHPYdyWVKuMu7+3re5+tprxL0O3b6LJzqEns946LL9+hXCyKNOj1lcniK9GON4zJ6fcuXdtwg6feL4giptHyOmZPnsnLOTGecXK8bXt7l8/pJ0sSCK+jy9f8RytkKYhtp61LrkUTXHD4ZooymrgkcvpzivMOXpck13I2LQC4gGHTaHb+H5EUL69DYtpW1YFiXzs4S6sGAcvD0YjDfwAp+qKigyQ1Nqmqzi9MEjmjzHViX9yR79rR79YYdadzhq1symc1anFavAtq91ragGDvYVm0eXDQgXqVzCgcD3OgjHoRAWRxtMU7AuU0zht6cI2RCHPdzI4niG+kxQZ+3lPyIiGHr4gUf8Kr3VGtgseBm+8gmjkFFH8dMfHXN+NiW9OmAQdHG0oUwShA4IO4re0KXJWylQlefUqaY7Ai90ULpDb+ixKkpO7r/gn/7Df8Kzh0/53nd+xH+78yW2dnbZ3B6xM1rirAqadcXprGR2kVCmKU01xRcByg9wOxF+f4gWOY3NaHKB8iWu7+MHu4RhiVIWhYvp13T6Iybja0ivxqsdvNoh7rVIFUe5GGeLajLGbhg8z0LtI7RA1CCSEOUrHM+hExdot8RojVAe8bDTGv+CiCTboshysnVG5IiWOBAGyK5E6rZU6UQlvvWhMiSLKdliRVmXlKbEM20gR3ouvU6A6IRYV5Gu5/hDHzdw8C18/viIbJFSXhQcLU7acmQ3Jhk5uOsGJ29IqeiHfYRUnOuC7KcFeW5Y+5LdgxUL3eekmPzxbgqdIMaaCommE/SxToiVDlpoHF20SkNtMSJq9XcOhLbESoWRLlLGCNFFovFt2aJ7pY+WHWRTYE2FMRWN07YCHRRN7SJMDlZTSg+hYqQxBJ5LqNP2iCegFlEboTQaz+btUdfx8FSHQMZgS5TOqR0X3ADhRoRmAFVbUDFSotwuOC6VbrC+xroONvAJ1lCXNU1ZktscPA/jeoRO0MpEmgqnTJGNwdHg15LaNkg0QhgmYYR1Q6wb4IeSoVMROpo6CPBSi8lKzELgVhmOFISeS+QOSYuEIl8xVj69wEO6itw4uMqj43tsDCOiyQjPb5NB2u/hSonf7+D0QupZQbJIeGItibV41jJtKs5IaISPLwfEfkMQ9nA6A4wIyWyAtgEdJNZpmfiO8ijrNkEWdVziwQZeFOIFPqr2aGyJcRvCHZc4NgjZ0KQZofcKf20NSV2TzFLEWjP36lYSlFdUR2esFwmDcZfDGwdk5SVVmVKWCc8fVFDW2KwiW1uEcsFRlJXl2d37OMojSTP6kwFVU3N88hJPVxi3pDuWrBdLFpcrktmK1C/w/A6DSURZLon7cXsWzYc8fvGSVZKQZRndjkMQGKS0FKlkY9JntNUjHPY42N+l1oYXZzNq6aI8j1EcUu0XLOYpaVqSL3PKagpSUicJaQamaT0j+7u72NCnWi7aO5l1hrS2vcPQDZ7vEm+0Tl7lK2Tg0RQFZdWQFw1JqV9JdhqC3KX6w0a/bnC9Hspz6XQ9qgbAtkVSLybwJZ4nKSJJtappao32WqlQYyEta0LXbeOWPYdA+RjrUGuH6byivzvEHSmy9Zr5vMB3PEa9McoH25SkyxXJum5/n5WUOmPkd4m6IWnpYF0HRcB4Y8yDjx5w9Pw5Sbri/ssPeDm/T/g0IA4qnMpga0NSaKqibqGDuiAQXnsKWnrYC+9fUwhM0+JmrJQI5wmO0yK9lVAYD5QX4AU/RsgKoQWOcVpnitPeq0gaXOHhSpeOCimbAmEaHGNwpP8qSuyA0RhdgdU4KiLyw7acp1qOl5BtEbdr/VewTKfVZ4qWUDIMJA0ejbHk9ZogAF8pOiqgCDzKqqAo15xHitD3cV2Hxi1RZ34bd3cNOhOUpUdqLJ3NWxTSMBMa57jBVwM8x8dfOwS2JSR0HMHqeknR1GRNxd7GitTETKo/5kZzHA0RpkCiiToTtOuipaSRoKoUtMbRAilcPOUSeA6eydHCRUsXhAeOgxQWXxT4Xoh1PIz0qcsKqUvQBUJ6ODgIA9QujvaQtsHKEMfxkdaifJ9OrbAYagQVAcIKpLX4Zo3nRVgVoN0QoSqELRDVikT54IU4ftSOnqocWRUUQqG9mEa4mKqCsKHd1SI82UBeUzkllfUQXgB+gO/6lFWJrkqEUjiVwWvaOwFlGpTQeFIz7A1QQdzO57uKjsza2GYYYRc1jp+jjKWTg+tIwjBAqAEBFtVUdAKPYTfE9zxWjYPr+oShT2/cwRv1UUKhKoOWPk5gUa6LM+pQVi0WeC4DStFgLCyEpLTQdXx2/CGxWyOjLjLuYfCotINpBI4CaR3AQzoRvt8lCkP63ZjBZAcVuO1zzkMqW6KdBjkIiEaaqkk4frigoo0IFtpQZBpLhRUlmcjaUV9tsGlOurpgvYrZ2NtktTynWK8plhnNhUAnOSbJcYfjFslgBVb2yJ+lSKnAjej5A2pdM52f0/PAcSRBrFjOZqwXa9JlhuunBNEOru9RNk6LUxCt6U/rirosaPKCjUmf3lDhuIKLy4rIc4l9H8/zGA87pFWJvshpjEDWFlvr1hcRutTaYDJNvsra4uJ6RdEohHAIXIcoiNpqcC5JioKlaSiShFrZV/4GQ9Dx6HYC/MjH7Xkk5zW20hTaYE39ytHcYMq2JW1F62KuihxjWg4Urm1zGg1kRY6uJZUjKaVDVWh03fqCTWkwjUDrnF4Y4HgK4bvYCsrSUBSGOsvpbHp4ccjl8Qn5qiRwQyJ3TOBq6iIlvZyySjSeVHiOJM2WlKWPX/mURUOaGRoMURBzfnTBYrrGdXwW+ZRFftEOoU2DRIAQLdpGvBonW0MoK5RRqNqlMBVKgudIpPJa3pmxVHrW+tcBRzgta02IV66JdpRtzR9a5OpXIRZDJEMCGRI7PWqbIGyJsjWKVn/r4KCkxdoK0HhOjCdDhFA0QtCJPHzfJ4wigsbF2NbSWDet9tSVgg03IBWCSli0qBlOQuIgYOh1aWKfLCtI1zmEkshxCJRE9EFql1pIlr4lsJIGTUrNeLxJZjWruiA6Ksm7stWvzhRrv3WSd1yPMvZoEMhGQ+Lh4hOb6I93Uzi88jmETRC2wHGgUR5WSlyhqWsfxwpC65Hi4jmWUBly08G1HhKXSrQlHuGAdWp8P0Q6Hlr6mCRHm5LGVCjrU1tLbQyeqSmbAKwhVB0ax0VgUKai0AEuklB4ZMLHExIXqPUaz+2A9MmkxJEFjc5JC5/ARgg3QAQBoStfOY0bxjqklB6VhbjMWj+fI3Gkjxu2l2HW0RS2h3R8HMcHZei7ICJDXqX4lQUtKIyLbzVCGoSyDHo98BX4Dv2OjxAVUlhcLyJzC5puge2OqOpl6x52fQrZx5sN2F5NiHc8gqB9re26wBfgex5+3KfvD5FGtDP5/JyiKcmFZaQjfN1nHMV8qasQtkEhiZyAJ7XBjz02N2Okk2HcAK0iNBqrc0xRkBtJXwwIjUvsu3z5q59jc2+D4aRLNIrbdItqL0uhpTiX1pBUGQ8fPOAf/uD3aOZH2BJkE7O3HzPc6jPY7OEJjRIOXqAYbk949qCmrAvOp5/x/EePWE3nJMmCYW8TPxb4AxjfmjA9mrO8SMibAaJ8JZzpDdBPZlS6JtUrBqXGEFDaiKPzz6irlki64RuOjmYkSau97Dzt4ugS8inDzoBx0MMRPqJyuba3QbwT8PzR+1w8esnlkzNMCAMkMhSIao5YOzy8/5SPf/wJm8MeGwc32NjexXQbqpWkLg216xBuj+h2Y7biAcf3T8iTS4rsksVZzdPFgjRZg/XpDCSdrs94skc60xROhZAus7MZy7xiXjWIbtIScT2Plofn4EgH00h+8i++S5YXRMMRgb9FXlQslwsujp/RVFkrX7IWYRWOExL1ruDHEqkcrAlwRIPRBl1pynxFXaU0TY5Esrn/FlF3QL66y3qxxDYQOB1GvT5K1UgnQeg+RhdondEUCWePN4mjLk0pCYYKt+PjdoaUek1/0Odg+12en59iTAYmp8zP0dYghCJ0x8RS44q2WOb7IY4TIp0Os9VzepHLsBsz2dhHCA+jLcv5kllyQalrpBPiRX18t0PkdnACga0VTaG4mNYs8xfk1QW1TqhF696oqRm7PSQVWi+x+DTCRaMwlK86KO1od2UspS1ZNHP8ysOVPp7scFlMqUxFQ93i9IWDQuHgk9gVhoZQBoQioLYNiUle3X5KpHCQwn21HtWvVlyLEK/o0LYtuxkMvvCYqIANN2RZGea6ZG0rMtOykqSQKBQRMb7w8IXLhmPBu4EO3uQ//k//6LX+Z46k/nu/8pdxWSNtTmO9VhOIxSBxRI6SEiVDlNOmgLRpF2QhVetsdjwaXBDgqhJkiBCt0SlLGrAljiixouXBW2Mpa4sUaZsocjq4jqJpDGla4DgprnLw3ABXtfAxhECaJda6tFfD7Sxe2wprlljRwXE8lOuj3RBrS7A1nuoh/YBGSC6zDCGKVyDkgKY0rTnOusigQqigbTTnGck6pSxybJXgWgclPFwvai+kPPA8jdU+RjRoGhpjiQKN7wlcL8JWJU2tyQuDUMu2mm9cSi3bC0ttEDqlqR3qSpDnGVJC4HuMhyP2RnuEoY8XSF6+WFCUSxqT0e1vgiOotebiJKFIT7G6REmfQiu0dbDCIYo0uAG4IT6gpYt0/bbuv7tPbzBg1Ouzc3WPqBMQeIpGedSVpaoMVSCwoiGvUn704BOe/vBDTh4/4uHd99nsbXF1f4cvvnOLz3/jHSab2wyGE6TISZeXZKs5y8sLXD+AsqB48YD33/+E1TqhFpbhVsyg4zOIPFwx4uXJBWfTJUkJoWxtfvMEjtMleVVjKsAUWEBbSaEFrnWIpMNbgxjP75JrwYPLKVlTU+uKpsmwjtMiik2LzPjy5ibv7u7y2p/4E4RXt/DGPYJel/mPPuDRg4d8+4MPifcndOMew+6Qdz7/Dv2tfYL+iHWV8PjJEScn57x88ITL2Zo8KShnKYvFGf0oYGvYw93wWJwlLM4SzhcJftiKm5rGkFfFv767q8qy1dbicnX/c2wdjhjv9Oh3OwRxHz8M6cUBp+crDJLOsIcWUGQVyTzl4nhGmq1Is5QXFxXLk3Osbjh86xqdoEVqSBXj0LpLslVNls9xhNeeylVMUS2w5PQHCrdR1FnJ7PyC0SRmZ3vCjev7bO4NqGtDkdUsZyui0MFzHXQtKbVFBh7x9pjjs4coX9Eb9FmlS6zUGFvx4uO7LC4SqkrT3xiy1e/jCcjTJWgPi4PGQeUVQeDQ6XhMNrfbcVqtKdKSfDVHW4OMOmjHo6oNed72h4TwsHiUucbqGmEaXCo8FSAcn8aPEWmCLRJMvsQxAm0V2iqcumnBnsKg3IjGtkXFsjSU9ZKiqFitK06SU7RpwBocNLwqKpamxBEhUrq4wqE2GY2tqE2FJwMi5dPxAhw/ZF0sScuEWrfso9j1uDPcoG4ESV1ylq0pTI0vJIFU5FrjCheFwgUqa/91yCcUIS2IpG5R+c6EWu1wkn37j1zrf+aTQhCBshJhHEzjII1u8dBG4iqJqxSe6xEoSWMllZa0GB8HHIXruFTWxwiBJwRG+AgcHONQuS10zJUGI/0WwGUt1gElDUoapBsSOIqqbMjLBt/z8HyPIIiIPLd1AguJsjVNI1s9p7atjtFaHBEgnBDHcVGOS+54YNoxSRB18cIALSWZ44B2W22odlFSIKSP9Dp0BwYcH4NLvVrQaNCNAV3iOwGBF9Lr9elvjAkiSRBYihSqMqcqUy5XCcpz8AJJEIQtUVUb3LhVgWpjqWsHVTS4VlAbqBYGU0uUlPRjD+UrojBgYzBkZ3eL7iAmGkb48ZwsnVGWS8LBBtITNEZjvCXT04SyAByXvoqoG0GWgxcCrg/KRxkDuAjl4XohfifEjyOCbpeiNtisavHOVcN6UbBeZqyVxvEqyibh7t1H3PuDj7h8+oTV8oSDjats72zz+tu3uH77Br3egDCMUcqy9hQrR1FmJd1Oh2w249PPXnB+PqeyDUHXp6oLlsuafOqQrDPyLKOua+J+F9k0WAxxL0QkAp03VKsCZTT+oENv1KVMGhwcItdjtLNNt9ejMIaZKqjPz6nzvFW01gWe5+LHAZ2dHXp+j05/xM0711GHIaofEqgRPz16yWefPeb82Yz9d25zeOUqV/evcuftO+DHVFYxfbFgtVown14yO59zfjpnNV+xPLvE7whG/YDRZpdo20NnFesLS1EmKDfCEQpXN6zynKquW71mVaGUSxhKxsMuWxtDNrYHdIIAN+zihSGDQYSKBhjpEHQCiqqiyit6UYdO3Ccrl6yzNYlZkU/nYAxXbu3jyxqJxNoIVwnMH8IbdR8lQxwnAidkuXxJoxPG2z0CrShWKZ6nCT2PXtyjHw3Z3tsE6dA0ML9IUE79KnXlkSQ1wnPp70wYbHeQLni+ojYZQkGjG5pFisucMq+ZHIy5urND4CqKdUJVCCwCK8HLwfMEXujQ7Y5aSGFZU/gVtdcmhpxOjFGKstGkZY2xJVZ4IDx00+AKF086RK4mVBHC9Sm9CJ2usUUGxRqn0tRWUFuJrECJBikMjuO2oiQrsdYnyaas05T5LCXMemDbFKYjavI0J8tzTlZTAifGEQpjGxzZ3hmFccSo36XjeUSqpfYu8jXrIqUu1iwWU1yjiXyXBJBa44g2ehwGAXEY0mQlXtOOl7aigLJprX9prREWsBptS1Kr0WaN0cHPtNb/zJvCbliiG2gahaotdd1mkYUVREISOYquGyCli3agdiByPaTnIlWrjdRO0PYJGoXAa2vqtUVIA1biSIV0/LZNYC2B1LhOhFLghR184ZI7Bc06pRN4hGFEHPfpdFyMozCyhYfpAprKtkjksqCxBkcEBG774uMIjK4RjUFZh56K8TwHIy2N57b+5spSFRYbCdwoIOqNuH7YpzEOZdnO581yjSOgMYKeGzCIuuzvbnL49g06w4ig65Iu1qwuFizOpjT5fSJPEQUe/TjCF+1FlVYW1w1otKGoNNmsZr2uWecV60wR0VIsN/bHjDcn9OKIUdzhyjs36G9P6G6OefbgCcnFBcVyjhr28X2Pumroep/x8fqMZalRQrG5McAYQZZpfF9inPaYnGUJutLYsqRK1+SzNcq6uDJgnpTEccDGKCaZNZw+P+fo2QnnqzmDocaPGqJVRmQdQi+g8Hy+8Svv8fVf+Crf+OYvoJuaLJtyOT1m3N9BWZ+uN8ZOavKjKS8/es7f/Pu/S68fs7nVZWu/B0nOs6drHj1YcTdd8/P723z5+gFf/+Vf4uTZU1CCm7/4eRaPCs6enPDgpx/ylhhy85tf4Maf+irLj0/JGoMOXXa/+TbCMxTZkqOPPuTT7/6Y1emCYlmimoqd3QnXX7vKnb/yv8Y2OU2eUNUll9MPmT+ao48G/I3/9u9zdLzg5uQ6/6v/4D9i/8oBSnpU1Zonz465/+AR//jv/A88/fgB8/M5ZSUQjo+2FdIp+dpXf5l3Pr/D574wZj1t+P38E6Yv5zhkjDoDdsYT3jjY5vgMitKgTcnFxYqoFzLZHfH2t95hvDWm04sps4KyaBEunoqQnqFuGpbLJclFCwPEgcH2gH4gGdSCo/sLTvUZrgdf/drXKbJzyrQgnWqibkDcixiOugzHIWiNrmuyIkeIG0hHoeKCYjUjXay4eNZj+bCmWTc8/eERrgoZHcR0N3ys7kJVIhGE/RGBSpBSMQqG7Fzbo0gbLo8yZKdCSYHWFffTDnFSElaabXebmxtXGMRdRKVYJyWOC2EsyOYJ0rU4HpRLnzovqPOC5XyJaXyEELimQ9D1cDtd3J0Rtc2pC0FVCIwocV0Pz1cMxoLA8ZHSwTiSwAla2ZFsEKuC0lQUr6irUoGUtqWNGo1wXYLBkHWSUaQVxbIgx0MqgXLAUHL28ISXj0/57Q/v4jk1xhas8wv2Rrc4uHqTN7/0Jd74Qg+lDLquKGZTEi0pGoFzWfOd7/wmz1885KKccpJX6LrBdRz2ogm3rh9w88YhH362YHn5DMoZX761i1nXrFPD/blhWc4pdEGlJYUpUEISyZ/tTuFnHh/9X//iXyJdrsnSEu2PONwb0R90UN0O2ckFyTJlNk8QWLywQ9jt0e8YaitojMBmBVXZMng7ccDm/iFxr0fc71AKhyrLKFdrZkmNLkt0VZHnNU2RYHUDXkToOIhXPYNBLHEjv+X5oynqhqKsadYJlZUYC0I0ZLWhKDKSxTnWiel0Iga9DkQdyjSnyHJyPHrdVqe5zBt64zGdQUxvo8vG9gg/6iC9Di/vPeH+vcc8efICJQ39oEu/E7G71UF6Abo2lKsUHSiiyGfQi8jyguXFJcvZjFpAJ2qVi2GnizIFjW5I64YqXaCNoUHglIKiENSVw6Q7ZPv6LqPdEYOtiLrRmLLGLFMmtyaMdrbZOrjKJx/eY30xJ5+vybKSLElIk5STkymnJxckaU5VNXieBuGgrYcvG8oGKi3pBzHRZh8VuWSXl8yWBcY49DsDrt25gtYls4tTkuIVd15aHONwenLC5fSCl+sTqqJgYzDm177+J3j3TszO4S57t96gNxmDdTCNhWpF84eSeKfi0w+/w/nzJ6weXWAHDucXc+59+JSb3T5XN0Ou7UWMbt1m640vMbxym7Drc/Hk25w+fcwH35mCbdg63OFzX/88cVjhqBLHtURbv05VZTRNgvIti6MZp0/O+O7v/JA/+OgnzFdtKe3OzSvsH0Ts7fvYc8WzZ0sW85r3rn6eRXnJKp9zfnHOo5dLbn7hDf7d/+Nf4c5bX0E6PmVR8S9/85/yyUcvePTolOn5EcdPTpnO5pynp68umUOG8ZB/69/+U4RYsrNL/off/yHLZYpsJL/yxje59d42W9fHbFy5QuWIV83riI9+8n2Ws5xi7bC7d0B3MiHsdamTrN04tEA5PhdlQ1ZU7SV32SZsfN+FniIvU9brNZ/+4IgXDz7CDwV/6T/897FFDgaEE+AqibGCRsP6fEZVVFRVRZFlrNKUIs+gWNLptujnuoJP3/8u6foC3SRsjm7z2q2b3Lx+DbffJ0tn5EXCKiuJRECvO2T/xk0+9/YhFYKzdcZ6fU5RCdLccO/uj0lma2g0e3ubSBOSr1YcPfyIVZYSun0m8R4yuOC1269z88ZrnF2ccHkxI1uXeIxwlaY3jLn25k2C2KWWDmscylmOdVspkchzprOSNK2IHM1ga0AYeriW9rTsQOjo1rBoW41qstKky5qyMLixR78bgSOZlhUUEqstxjYt28pViMAlcH0sBXWTc3ax5Cd/8CPmlwviYJP3fvFNDq5scPXqGFSArgWmlvR7Lp2+RxR5DHyP5XLGer3m8nLG44fnJLM5xeICf7Pg5uvXefPtN4nYRTcrjE6JPMtqfsZifsaTZx/RMT1CpYgDQ/LoiGdLj4dJl//nf/Nf/JFr/c98UiiN0zqWRcs9N8ZQ1w1VUpDmJY02BH5Ipx/geAGO52NFjmNclHSJRjHS81F+K/Po9se4nofjWBw/aotZdY2Tg3UNYHANSHyMUeAEuK581dZUWFm2Lc+iRouGsjKtFzVvECrAcRV+6KErjeMpoo4EJ26bqK6D9nwiTxEOInoqxJM+TW3Il+eMQ4UfeThKcnZyRq2hqAVnT49ZzFM8N2R3b9hKu5XEjzyMdKl1yTpPyVYa33fIVq0sPU1SyqpCS0FZgqBBOj5aVGhdU5QNdVm2REwrX5FTY7pxl43JkMGoS6cTtn5i2zop8CzJKsHYE4ok5/jRC3SRY+saTIDreXS6gh18ojAky3KSLEfXOdpCLRXDTkDdQFFbbNkgXYO2JbUpKIoVZWEokgQ/MtRNwfTyGOF2iHsRcRyQTlNm01POzk8pq5qtzV2uHx5w6/Yu2ztdBsMN/KDTsv7zinxVsLp4QWVamX0QZjCrCNcKWbucTjPKeYVTOdz+wuvcurbLjSvbTA73UOMYp1ci/R7Dg+tofMb374Go2byyzfZrt3ADQ5WdUybnaH9BWSxJFzOOPnvOg4fnPH9xwUcfPGQxXeL7DgcH2+zv7zEZu0QezI5eEuUS3+/R7QeszhuqImOZLnnz8we89ZXrHB6O+O4//21WSU2aaz77yU+ZnmdkqxLXg9FkgB9EdPP+K3uYSxx0WU9X7cx5mbIlRuzv7jIY9fniV19n7/YG/a0BwXDCIp1RlA3Li4SmBoGLcjykbZNBTVaTrzKqRrSV0UC0ClBhsdIgfQfltpjyrKrIlgnpfEXQUVy5fY246+MpC66LEBLlt4m+qjIUuabKK+raYLRDGPgslu29SDFfkS5zpCOwQlA3GnAQ0qXWNbP5kmfPj9DqJY40r8qfLnmVMltlHM/OOD/9FC0Vq0pSFZCVOVmZMp9dIBuJ7yjKZUajNUWa02DJs4qsnrG6KOkNQxx5SpI01LoiCnzGmyMGkwOEKNsOgufieq0PXheSRr8SthuB1pqsqFmvS5ImIdUNnusgqoq0rNqCqyOIwg4KhbSCVZaTrlPqum5PbeM+fhRQewGhbBvVrvCQjqBBoLVES4vyXQLfYUMp9q/uE3V6CBMReh2U8LG6lSe5jkIqRdSJ2iSbo3A9j+HYI+5P6PZ36Hb3KNYritUUEy+ZbA8JOxG9niXyd/CVS6OXhAtJNw3p73SJGbTPxS8p9vbYWCpG6z/m9FGqFcJx8XyN8gzr1ZzlfEaWaIzJ6YYd9jd3OXx9n0oY1kXB+qIktCGx2+Pwxg7DK5uEoy5uHJLOCrLlivnZMVYFGGMxxkD5CjHhSHzPQQZRy4mx4SvRdnvFvZrPqTODbiS+r6FxsI2gLHK6UUikAuKBh1OWuKHL1vZ1HBWTpjnzxYKkgt4ooj+K6A22SOc5l+cz7j17gPJqBCXL04zvf+/3OTmdcXGZ0un63Lz5Oq+99gY/97XXeHr8mPn8giRbo01JlubMludka42UhlmgGYR9kAbHtSTrNabR6MpBaonxKjQNutIgmraHXVtcZRiOY0Ybu/QHHkJAsUxYnSdoV+C4gjC0zM7mnD5+QbGac35REXcVg2HIcOcN4sEGYejj2ogim5NnCYv5itV8RdrUJNJyfW8fi6QoKz777C7T1ZI0zTCiwvNaQf0qX/Pg4QpLjTYJk+1DurFiNAw4eTRlmVyS13NG0S2++ta3uH1nl91blr0rbzIYb9LdnFAWFyxmU06Ojnn58BPSqm2gb8Xn9E97eGddHt27z92zlxgluLa1wa//b/4MWwdvE0Y3wWYkx79Jcvw+3ua3iIZvsBvdxnXGNGVONJrgj/cQxGAH1LWinP+ExdMp5/fP+Gd/6/f4ncdnPF+mNFbz1dEm71474Ff/5JcIxzutY7mpeRYpvnL7KtuHB9hbHT74UYp4smJuenzr33qPwxvXKOdz/vP/83/Kkxdz0sZnf7jFzvYOk40RYtJjd/cAz3QJmgmJvaTWGXWV8+yj5+z2FHd2Y/7y136dret7bNzYoPueg+sOEDagLuH00UOePTrl0w8usWGFH3ToRDEeAU5pMFVGcr7ASBfhebiRJPA8eAW9EzLA8xx8T7I4WpFOL0inl4y2trhy7YtMxkPiQGJcB5AtmVfXWG1wbEMncoAApTzGEwepbOtJqJbk8zW6KbFOxcZ4F8fuojBEo4haa55eHHF28pj93QM2NrYZbe7z4vSS0+MXPLz3PRq9QogQxxnTjT5H2RxR6Zd0o022BnuMewNKZ4ET9oljn8nWHZ5/dsHZ2QnHZw9xg2/w4cfP+f77v8fNq3f4pZ//Em+/c4ed926QF0uyZcbsUYrxFKZSNEsPU+VQepgMkjohT2qKdU6SnnFxdt6a97IVRy9etlgL4aLMLqHq4auYnDWNuEQ4GRvdHhvbI8abEw5ee4POUOK7Hq7wabRDkWnyTFM0CaZ2kErgeYZbt2+w3sk5PjqlPMtYVR6B7bCx5xDFHmHHw1cOTaJZLRoKrwanFTMFfp+bNwYIUWPqnFUyp8iXnD8+43T8MYeb77LZv0rVFCAtcW/C4ZVvIIWDpcSYOfG1JVHhs5vFP9Na/zOPj/6DP/9XCZwcTzZYt4OoNKbWpEXd9gCsRNqIjeGAndeucPi522xOJriewnEkJitZnM/IFmuy2Rzr9XC8gCAKSD1DvlqTnk95+XJGkeXUZYUWBlfWKEfihV185dFYw7oq6ci2dSyUpEpqyjSnKSu2xj22rl5huLXBaHdEqiyNtdhSs0pXVDnUWQsXq6s1ebLg3o/vcTw7owZuvP55mnzOcrHg2YsTJt09rty5yutfe53twzH37j7g04/ucf+Hf8Dm5AqD7pi+p6ipsGhcqVmnNRiDIwy9wYher0PU8ZmnGb5tUFiMVHhYtG5YVXkbkURihYuvXfwwwgtD/KJikTUkecl6cUFar17NRms6URfPDYiCLj//P/sS24cHjLf2GIyu4yoPAZRpyumLZ0zPLnj+6IjnT07J65LSqTl+dp/aKlABPUeQFoK8MuT5JQhBU0OygtVyius5DIY9JlGPLF2zWi1YZIZJPGRjMuT2N2+xudOn0+0ShRtsDsaga7L1jKfvP2c6TVisE0K5QIgcW5akP6747uIeR+WUpq5R1vJrv/Z1/m9//X/H5PDrZMt7LM9+gF4JqrVDuqj49O5P2kKjIzCdAf3tftvIzRP+4Dv3OT5LuFwWXNnpkK4SdF3x5q0tXn/vy2xfvcHG9ZvEnW18P8D3XUyzoiUeAvUmtrqgKS5YLs4g2mA+L/i9f/QvePb4Cf1+l/fee5doeIjsdbBdj7PpCUXW0DSC3nDCeGeXsBOjhebuJz/h8adP+OBffsL2Vo8vvHeHb/zCe7hdj9rmaGpCb5emrjCmQTqC5QyePDjmX/zz7xOPHCbbm+weXmVzsgNITGPJ1xnakaAUKgj5+N4Jq6SF6qnIw5qGOi95cP+YZHpBU6Tsv/M27753nc3tIetUUywL6qqmthpfSKTr4vg+ohFoYbBonKrm+PiCdZrgxuA5NbZpKNc14VjRj30m3QhR1VjrIJ2A/f0D1nnKcr3m2eMj1kmJrSCqFT328fsh/rZHsNnBNg2mqshZkl7OKVY51JLB1Q0644ju2ON7/+NdPv3J+/zkD/4ZjhBt3FMqvvHan8Pr1KiOoLN3yC9+/ucY9QasVnP6/ZjSkSwF7O2McQIFrsSpIStKkrTg+MWC5SyhrGoa2TB/mXB2dMaT+w94efpDGp0CNRYIvQ3Gwz3+1C//CoevHdCbjIn6E5wGJA6O47VMt7qmLEum8wuOX55xfnrO0ZNHjCa7+EGAtin7r91kb3+TWzd3cWMfJRyUlVjXoUkNtgK/42PrBoRFdlx6AWArinLNg4efcPLiBSdPn/Ps7iMmhzH7Nzf4s3/uGxwcvEU37iHEkqp8gbYCI4YEylA1DkWl2Bt9/o9c63/mk0I1O0P6rfLRVy5h6KFiyVB5OI7GaoHJFaYwZLOC6eMLgsrS6fUJOhHKcXH9EC9s0EHWRkVNTZY3HL2cUiQpVZLS1A2OsEivfaO6UuEoifJClHIQTYNbVgSRg5Sto9ePBMNBTBAFHNzYxu90kVKxvFwwrxqqWmOrCqxACZdI+EReSONYYkcg33yDa/oGjbSoQcjlk5wq9YncDpUpOD0/pv5RycPPJPPLFcnlkl5ngLA1RT5FZAaURDjQKIPVAse28nbZGExd05QWk5dUuqTWDY2WpE1NoxuyusKVGqVclGvR1lBZgaka0tWCsmnxZMORj197GGMQaAabIYPRmM2dbTb2hlgSTl9+woMPHuK6QVt0c2PW80um51Oe3XvB3ccPKHWD8hSUJV4IjmrI84LlvCTPNT3PYWd/TBhGkIcsVisqXdDYArcWdPyYyTBiOazwlUOnY/FsRuTGeNQszmY0Z6eYsqRYppw9e0mSleRVQ14sWOQLlknC4qLiRXFJrgs2pM87r29x52BAksyYf+87rE6esj59zOGd2+iyRJqcna1tlhenFHWFtoIyzSmzlIvnx2RHU4Ydxd7bI976wjepm9ZtcGVvm70rB/SHPaKBC/UabI4pgzbKbCXW1GSXz5GmwNQ1yaWhFhfM5ytWyyX7e4eMJhO6k22001BVCeXUoVgmJGuoCgeTr8guNMbCLLnk6NlzZudLHEJ+7ptf49btHbrb3daPgIcxDemivYeTUiKFz8XJc8oi5c4bNxluu8T9Ad3BBoPBsC3sITBaUxlDYyzaSLa3R3SLDlZpjJAUeUlCgu8qdBTQuAZhNMkixzGK9aygqnK0NjQ4hJ7EUR7CNdiqwRhewfFKpPHodQcMtgXZqiBfFZSrBdLUZKlhlTbEqo8XuHjKI5tmJMsF2SrFSTyGYYQ/9BnFfbr+AMfzkJ5CRq2LWmCYJw2EBiqPqqlYH2ekFynzoEHVmt3tQ+RXfp2NTkSd15gGvvxz75DlK5arFffvP+efzxb4nkNV12wMrqKVw1pUjMYjLD4Wn0n3gK2DiN7IZ29vSL8To7XBjSzqjsN6cYWTF7u8+HiDi8s102VGplcMJ5ts7Wzz+heusnu4S9TtYmWIzW3LIhMuSrXgwUbX+H6DrStoGpZnU+qiQlcVjqqxuqCpc5L1muYyQesGqzVB1Gv5Zo6k5/YRlICFQiG1oapz5qsFz5+fcXZ8yunFKfPlisVnU46OX5DMFuzu/JTJeMTBTpd4V+F2AmRUMxIpVdqQrJo/3k2B9QxMBE6MLwO6kSIKPfx+H9d3WmdsAmcv5zSrmumjU/yqoJ7UdIdjuuMIR3l4UQdb5VSppqoK8jLj6MFTqrJCCAjDLn7k4ymB8jyUY1/5nDsgDWVRUScVQdhuChhB6DkMtzcZ7W5y5e0D0lXO8nLFs3tPmS4b6rpBUTPsbuBGLnEk8aII6fpIt8+dN97EDQJKU3Lv5EOaWQedG+qJYlbNOL885vHT+xTJnF40YNAfc3h4jSS9pCyW2LzED2KkKzG2QdmoxVArhSfbjazOaup1Rl2nmLqiLiVVnaObhkZbfL9lp8jQwSjTXvyVDevFFOW5hJ2ArcMJjXbRuqApU8aHYzb3tzm8eY0g9jk7esLR44d8+vuzFloX9dgZ38CYgtVizfN7L3l2/oRGa0LVYW8YEnUknie4WGaUaYLJDZvdQ968esDGZERkuqwLy3w94/TyGemsoaNCYi/kopmzzuc0JBSX55hhhzI3HD87w1kdYfManUguFyfU1tJYSXo+5+n0gpNkxappM9Rd1+O66vCtd65xsN3l2f1HzD84IjvNqddweGOAI04Iw4Lbu3d4fj9gsVqTRApTJ6TThLPHMzrW4+rNTd74xeu896t/EZSi5edGWFOjdUKdP6Ocz6BWOAxQ4Q7WGnSVcfrZR+3MV4bMjiuS1QnT2QWzy0ve/eX32NzfJ5wMuf/gQ5bzgvXKUDYpWeJTFD4Ld0k6LVkvE56dPKZIapTy6I4nvPOFt9nc8hEipVopHLf1bU/PHuO5Hp4XEHY7nJ+dYLTgnc+9zmjXQ0gfYwPCbq/9e3AVypPkeUVR1KTriv2DDSrToFVNlkO6ylBC0Bt0UKqmrh2kMKxmGdXSkJ6tMW4JjgQVIXwBNGhTU2VrbKNAKxynpjPqEsU+w65mfVqzvmyYHa/puJbKhzqWyNEAZ+hgreHlsxckiwV1XuPIDfr9PvEoZrQzIuhobCloVoIq0YhA4CiLTips1dobqWpmz2cUaUpdLhkdbHDt4AZvvvsN3tjcIZ0XpEnO27/scXk659nDE97/wQPufvoDijrF9WN2hu9ipUOmE3AjisylLgJuHH6Zr//ydd74/AaHBx16kYsUgl4fNnb7YA15cpuz77/Lo8cJD5+vOC9fsHHQZ3O/z9U3NpkMRrheQNmA8V6Z64RHJ5AICRZNx2/vJuLARxcNlxfnFEWGg0HUBVWyZnHhkJxqyrqgtiWjwQ5+rAhihRe7CCfH2oYmkWRUJFnC8eUFTx4dM5udslheUoua9cWC7Omaz356n143YHM85AvvvMm1r79Bf39IdyvH1JckFyumJ2ve/cJf/iOX+p95fPT/+Wt/jW7cxY86LE1AdjIlXyTMVmsiLyIIAjq9LkEkGe5uMLm6hzSCRtfUVU02L5hOp6wXS+ZHp8znbR5bm4rxcEJ3GNPf6LJ5Y5sgCnGVR1obmnVJXTbkVUOTFFxezPjs/hO2BiHROCLc7DGOxrh+gPI8YsewWhSkSclqvmZrb5vhxoDNgwnxMMQ60AhDZ9AhjCJc12N2MefR3cc8e/CMH373J6RJiu+77O9tsnVtgnAdam05/eQlZ8kls2KBlZp0vsKUhu3+Jt1uhK9cPMeh190iDF3CSNBEbeEnmyd88pNPUBgcSXuctRYhBVK5BA64YYAbx/QCH4SD0bB8/hQRuvg9nys3J3i9GGFqzOUx+SLDVYKo41Kul0xnOdNFhbETCrchtxVn5yuqqqYzjHjti9f51X/zV0mXKz763vv8d//ff8EqrZHK43/6i+/w9rXbHO5fYePOTaLJADzDqjylGx0SxiPiwZiiWPHJp9/j/ff/Jb/7t99HLwpsYWikT0LNqi45ni/BaBz+kAqr0Ra0AWzDQRiwE8VMhrdYhw14JT1vyejGAVEwoiM2uLqac/PrB9z8k6/hBhtoscaKBuW/RmMkVVMxT8758Ic/5OTpKUcPp3ztT7/J9dc+z5Ub74GCsl5RFHPm53ep6iG6DtCZQEqFI1xcQtL1KWVaUqQVRjkg21PARz/+A3rdCUJ5TIsVt+9cp9+LCTyP++9PmS9zkqJivBFhHYeygeMnl8znC/I8o2lKlPEps4bpdI3bm/K5m1f5xbc+R9PfopKX5PWMB+9XnF68xAkFP/fnvsaX3vsWca9PaVKyRdF+CLAhdWLwwgA39HAcyLKGLK05f7mmf62PUYLlKmPdGKRpcEzF8+kZly+nrC6WxIOAna1tPBXw/Ok5g2FA1POJJx2adc1ilnF6smDx7BQ3CAm7MftXNok2PAyahz865kf/6tvML06xpuDNO3e4fm2XO6/tMZhM6HRDlJJ8/NMHeEbjSonbjen2tsGJyLRCLxdUmaRIfMpUkzVz0mZKkb4gCId4XohDzjItqHLQqcs7v36Ttz5/lZ/7yut03ICq0WRVzbOTBbPFnMuzKR/85k95evcFju/zxjfew6qaMq9ZXeZ89P4jjo4+4vz8Uxynx7d+/X/Ol37u63z1vQlr42CMxNOC+apAKkHcdxkOHIq6IMlzXlzOWZ7MSS4yFi8kTdEQdT2uvbfNxnhMfxAzmfToRt6/boXXuUEog/Is0bD93J0sMz776TOefPiEKq1x8VGBodOJ6PZj4mt9IuXiWMm8KJkdz5meLLj/0TNOzh+xWJ8xXb6k1CWOA77nsLnX5dbBIYfbm2yNQu7dfcnzlyfcfXiXvd0d7rx1h2/+mW/hPc746acv+O6Hj/m9T37jj1zrf+aTwqf3z1EcIzGUTUy2anPtgRvTvdKlO+6ydWWPaOBRozm7PKZeZdSppUw1i9klyap10Uau5uatQ8K4Q9CL0LLB9V2CTkB/Y4wwBtNovMbDlQ2+soQyQAcuyrPUuiBfTanTHHkmKcaSwvhQS+bJijAe0O/3uP71K6hOiBt6uD2fYDRojWxK4PkKazRFmnD++JTZyzN0UfGFr34J5Vpcv2XoWCqWyzWX0ylNvSAyFklMWaTc3L/BaNBje3uT7mD0amNyccMQKQzYkuPjU84u5yzPLtBVgesJXKUIOx2UkggpQMi2s6l8lPARZUBTl9R1wSAO8DoeXuzha03shsTdEeMbWzhBjOP7uEGA1SF5kZHla1brGaVuSIuCB5895cXDE5I05969xxR/+x9TJgXHT884iAM2X7/KwWvX+cY3f5mN/oA4DLDKEHRDpB8QqCEIia6mnL/8iKOjNccvjqjnAlHBeZKQJBld5fPOtX3GowPcuEcUbRAM+kS7Q7x+QLI8Z376gs9+63ucL9ecVymXy6dcFWO8xnJ0seLq9ZhrV65w83NvM3ZCBntDgo0+ZfpDhBohxZhiecR6kZFlDYtUUkw1tnaJBv2WpJsvWRw/4smjpwjTcrSkr1mbBZW26CKjaRykcPCVwvd62FCiAo/MqchnU9azSy4vEi7mCZW1TNclq0XBeDRgc2tCphd4A59x0CFfLVnOG1armuV0TlZUVHWDrmE9PyPLMpIkwUsLfrjOuP/kBcaLWu+9NVyeJwTSZ3t/m3iySW5TmqwmLVsRlSNcfCuRHUAYdN1Q1pqqaGjKGuMVJKlDWha8fH7SnioiSbcHdZKzPLvk/PkZ5XiLrfGYeCPmencH0bRa13xVU6c5TVPjhYrh1hCpXFTgUVMxvyxJk4wXjx5SZK/AgqN99l4/YPfqJts3NxmMN6jygnS1ohA5y/WaOq3IEofGPsdYhbEesjEUhWadNKTZZavxlYbQywjDFN/rEAUOlWx9zCrUnNw/IT1d8uC7j+gONxjsjOiMujz40UtOpk+5nJ9x9vCSyd6Iw+s7/MIv3OZysaCqDHUpObw+5vjFPscv32WRlARDj6PLB/zuv7xHXrYOcYlgeVniBx7jzR4HWwOsq6lszeXFktnFlOV0zeLIoikJMxfncUWZJiTpiLIRbE8ilBSvklwpthCAIMsrLi5XnB9d8vH3Pub4xQVSKwbRgI2dLlZrLCXpwwpTlBRpzpOX51xMT1gsF1ycT4m8LqP+Ljdv3mb3cEh3FNIdhwwnMX7g4jiCcjXjejRh+9Yd3v78l0iSiiRP+Tt/859QTVdM5x5nsz/m9NGLkzW2WGDLDN3EFFWFlC7bo6BleLgOTuTjhB7L+ZyT4xOK2YwmUTSZJEmnaOPieS7drZiDq7v0RiPicZ9pNm/biI5sS1x5TlM2UDu4Qr4qwHXQnosXuzih4sm9hCZraJYldZTjGBCNoslKhgOX0ajPtTevUFqLxmCFwTiyJRlKwXKaslpesppNObt7Trpe4TqCvZsHBD7gGCpbsji/pExzkss5ylaMuzGu6lBWObdvX2V3d5Px9oBOf4wKAoSnqGxDVaRkywUvHj9lNZtzeXaGrkvwFFI5BKGPq9rH0hgBpkA6EgcHxyqENCjPMBlMcCOF8iWOMHhWEQcR+9dGxHu7OGEH6YVYMwAKtF0znT2jKGrWq5RGNaTrlPxFwfHJjOmzU0xtaCr4hc9f540vvMnrX/4ct9/7Ag5gm5p0NQeHVvySC9bZlPXsCZdHP+LRo4xkoUnmFYEjCDqKxvPZiHu8++YVbu4fsLV3lV7/CtHWhM7NHdQ44OLkIS/vfRf9M1EAAMe4SURBVIBz9wE5NRfziqxecUsO6PsBS6fH7tY+N2/e5K2vvol0FVL6GCtIs3PcoIvjuGSrU2ZHF6yWDfOsR7ZovcJ+p4NoHOqkYG3POH36AlsFKAKiccPC1OSmpCkXVJVCSkHoS/r96+0ox4FVmbCYXjA7Oub0fEmjCiprSFKHalkxH64pcotyMsJ+nzDqMrtMWSxK5pcV+SqhqFvBjqkkRVFh0IR9RSRjsjrj9OUzKi1QqoOUHkkyZyueMKzGNA2cvHyJdBTG8eh3e3iO0ybTfDBN0xYcm4a60tSmxno1WZmyWq65PD9D2Ii6p5BCsZ4lzM8vuTw+QpeK7MYEs92hN+qTXi7JkoL5NEdXxSuchiTshiAkOJKiyMnzhtU8YTm7wPNdvH6Xrf19uuMeft9HhhIZCNJZwtnpGVmVUlQZZVGSzAXLtKFuBMIqpHQpyppVkrFYH2GMRUmHne0ujlOhHJemCWhcsEIjZMXp05pnaU22KLny5pscvLbP5uGEl59dcro4Y55eUFQN0ThiuNNj1A9odNze2yif7cOYvSs7nJ9kvDw7Z7GecnZxwovzS2wTIaWHG/jkM00YhtR1hVeC8AXaaUinJcW6pq4M0gPhgnEbLs7nNGXKYrFitqxIFwMir4UCNtmMurQ0taGWGS9fzjl7ec79D+6S54YojAlUhDaGqiqwSU6zrEkXK1bzBQ8fv2Sen1PoFC01B1tX2N874Padm9y6s008CeiMPPwgYF1krNKEc+Mw9rfYsoqeCHn64ox79+/z2fufcH55htYbWDo/01r/M4+P/uqf+d+iyjmiWrMuBRgHoTy8fpeh76Ckg0bhlJY8b0iyCm0vCIKQTqfHwcEtdu7sMdoZsrk5IM9zyqwkmxckRWtak0KDrLEtqRan6zEa9Ii7If6wA45C+S7+wOcnv/t9Lp5csnixZm9nwO7NPTavbDLe6CN8hVASqxzcwMU2hnpR8PT4gqwoSbOC3/qvf5sPHvyA5xcP+dbtX+KX/o1vsn97l+P5c158/JRsWaKtR7oSyKombCq+9qWrXP/6G+x98TbRZAMpglYBSo3RGq1ryiLl+PEJJ4+e8finn/DPf/sPeHlxwny9YG/YZzwMieMO3mCXELBakJYCVyQEoUe322UyHnLl2iH7Vw/obHRYlTNWyYKLu5foVU4/lrz1Vp/BrQmOp9DGsHh6FyeMUL0xzvALzNcLLqeX/PSHH3P/g4+Zn80pU4tTBfQHHgfXIv4X//FfZTC5jqsmzC6/ja3AER16G1/g7PkHPLr3Ib/xd/8Bz58nrOYZ6XRNUjcoJelEDt/8yogvffkdbr3+Glt3Po/b76H8HkrtIkSBtQJjHIriiKdP73Pv3sf84Le+z+IypUgaRNPjymGXW2/v8Wf/6q8Sx+/iOB2g4emnfwNbK4QZ8+L5gngQEXa6UOzw8tEZFxcrjmdLhEiQLqjA483JDQa7G/T2N4iiHe7d/z0eP/0JF8dT5qkmyzVFUuL3BwShRyd0KNIGU4AuBMklnJ0/5nL2guPFnKtvHLKzu8m13QPu//geVWGIuiO6YRelAhzlY4OMqvApc8X8bMbp2RFJukLbml/50p/ixutXuf6VbVQwh1WKuUg5msNlkDI1CU9/+5zLxQO0SdjbPmT91LK5uc3XfvUrDAZDTC+mGnTZG3TRnqCWYGeKjJLKNNSZJlWaLM2YP7/kvMiwtUEW8ODhjIvjz1hcPCXwYq7feZ2NnX36kx2OH71gdn7B6dERnahH3O/Snwzo9fvgSIy1NEmCiiKsgMX5CzYOdgi7LiosefYHz3GVZrjpMun1ePLpQ148eM6tz93i+q1rbO9us7u5R5H7ZIVltqpYJCuqsqHMDC8e1Zwc/4Tl4h5f/7lf4fU3rjAa9zg7X/P0+RHz2QWLyyesFw5p2pCXmr/21/8j4n4AokE3DVEUIpXiMsn54Pe/x+x0jkmG3Lz1FtHIxxvVjKIu3U1JOLKcPjniO795yt2fXlA0j7h6Y4ft3U2uv/YakT9AeQ7Ch6Zo+05FXqNrQ6fn0en7bOwPQTmcHs/5zb//A15+/GNWswvWyzmSfXr+gH7QwQ+PWGWXpEWCEZJBd4PI8wlVxZ3rn2fnYJe91/ZY5ivS+ZJ0umBVlSSrgjwtqZuCzcmE0eaYw9dvc/O9q4wmPfpxxOXZBWdnl5wcnXP8NKOuXaz1iDsuru/ieBIVNMRDH1NLVufwW//dj3h28Yyj9Dnp6T/+I9f6n/mkcOt6h8Dt43seMupSZCXpOufs+ZTjkxeURYltBKFyUVLiK8nrb91gfLDPYGuTXn9IU2rqec7zlxeYV+xvN/Iw5RqhapxI0xuMCHyvFb1EEZhWVDJ9MOfJo4dkaYowhoM3Drj5zh0GvzggDBWu35ZWrAP5cU6xKpgvZpTa5fz8kg8/+CE/enGPrCrakthZhaMNh91DPj6/x/O/f0IcdRiIDk2+otvpcuPWHX7pL3yRTreDkpKNjYD+7hbRcBPlRFhq6mzB4t4HnDxKmc1TTldTXj66YD49Z3rxEqkLrm4PuHFlSNARRMondAO6sYfUbVIl7krGWxMGkyEbe/sc3niTbj8iDCTp+X1UXhAWFTsDS/f6FvEgpr+7gXIUptSYosQm+6hun3A0aR2yj59y+vF97n37Y9JsReC67F3Z49qtfXavXeHaG29QJ4/49O4nXDzLGQyGdAZdpJty7/v/FY8fnrNOEm5c26WaPcAuS9ZVSj90ePeLV/jKz7/G537xTzMe7xJ3h3hRi9O2Fqr8GM9zMWlGdXzKp//oN/jeZ0/4V4+POL6ccWVnxMHOBq/dfovutQ6bV3fpRLdZlw8xuoFS8uSzmsvTMxbTBxjrMhgOcd2Q+/d+QuBFSOWD8jg/PmUxX3F+lvHb+n2U76PCAMf16PctYahpVilpVlGUDWlaMdzSdDcHXNncZnTlNlHQI/BCZpcv6Ey+STjoo1VAGAdYWzE9f0T68gRbCW6/8S7b19/Aj0P8jstoY4jWkKwyvvdPvs3pkx5GW66/cZuv/NKX2NzfoDfuUto1NCWyLrlTwbxccj4743ee/hbqRY/1wmFxUvN4+px7s4d8ePkxrnLbkpkXsNd/g273gKgzxvfPWE5breO6WVCYNjrpWMmizmmq9p8xXTq9gMlbbxEPBtSp5ujJEz78wbdpCgvSw+/18aIQiyW5nKPLlE63T9wbsHH7CuPdMX7kMr/YodN3qcuK8+eXzM5eEkc+W5Or3Lx2iF5nFEnOxpU9BqNtomiMCSRlkaEdRdjv09/o0eiKtCiIoiWv3/4iHu9x/c4W/W4P5bg4jYtONaFQ0CToPKcsFjR2Tm/g4YeGrEgoipDjkxPWWUqOJVvBalry6Ue/y4PHL9na2+LqnW3uLk+x1tI0hgcP73Fy9JLVco3nbfDm22+wvXPAzvYIV4VIKZDCkAtNiSAQhlp7hB2J7wnWZxXGarxG8Ce/+S5HBwOePXjBhz+8y9HFIy6qR6xyuO7d5s03Xmf3ypDD1w/x+zFSODSrnFFvTOD6+I5i/XRGU1QUWY1jFL0gYBD5dIb7DDdC+sMumxsRIilZrBdcJCvOj48piwbbhGz3Yy6nK9I8Qw17eIHbCs0Sxbr0wJc4G4Jf/wu3eXEy5OHLnZ9prf+ZN4XhZPCvPbCEHaSfY6WDF6zRRtBoixDgd32i0KMbB2wd7DDe36E72cBTLsk0p64aiqRGeAb1h7N9XyKVwAkkThAivdYfu1zOWc5XrBcZ5y/WHD99TpNXRI7HzrVdlOvQGfk42lKscxbrgvXyjOVxwnqeMZ3NSSo4v7jg449/wst8AcoSBC7jjTFDPyBSiufJEctkzWqxplF9el2PqB+xfXWT628e0Om3EVc/lHhRFyE9qqpmdTFjcXzEvX/1fU4fFSyWBdNizfx8RVWmNDrD9x3ivken56E6Dp5WeLh0A4UftAuY6o8Y70TEgw69cZ9OLPC9GiU0OrkguyjIs4bhSDLeHxP1hsgwwjQSYw1WBC3W2Q0xTcD82RnPP3zC448eMX85Y7TbZ7Q14fDWbQ5vTBhtb9Ltdrh8ccHli3POn61YnW8RDGIcz5Ke3+PlkwVlZRhv9cjzEsc27A5c9j93ky9+7Q2+9AvvcPjO20hpW3i/NUgnwmgw9YLZ8wumx2c8vf+Yj/7l7/Px8wteTBN6G33G/T7bO2MOb/SxvQ6mknz644ecrj+mKgts5XB095L1IiFd55RG01tqXDfkxcUFk8GYMOwilYPWmmSd8eLZCausIPJjulGPRTHjyuGQzUmH9XSBZ8FVku2Jz87BNjs7m1y7usOwt0sn7BD6HhsjzWB7TG9zTLRxyOo85ez0lM/O52xOJvQ6A9585w0m1+/gRQFu4BBKn7yp8cOE3Wt7KKEQjeDW9avsXx0RDUKa0rSx4qaGOkGUCpFXyLTBmgJXClzHRRuLMQ1JkTBbz0BA6MV0gjEXzY+JO2fEnTFBfMp6WpOnFalZU+kK+crFURqNg8VXsLk/YOvKBpPdTfxexOx4weXLimdnF4S9PqPxkBtv36ZGkS9TkosF/Z5H3AuI+yGT7QGDSQ838LDa4IVQOSVxp2ayNSGKAvqjMYPNCYe3r+EEPp3RFqukIE1O6BdDkmVOWQlqsyIOHRzXAcclDAX90YRh3GU0cXCcAGEdBkNBbcHtONQyJV8fk+QrRFZxebwkiCS1zmm0JE8LsjQns5oiq8jSjIvLY5aJoTQp0VBRri4wjaSpBdPLFvXiyNa7YTE0VU2xSMmdVy52o7FSIYUh9CXStGNbXdYURdGCOxVsTGKE3kEKKPIC9WJFXq4wpsTvhXQ3hox2NtnZ3UbEAUI62L7FczxsoVktclbzgvksYz5LsFYRxy5R7BMELe4iDF08V9KkFVVes54bFtMCKwSO6xFEISorEVpjHAftOFgkVQO6kqAljge72zGNK6jdP2b20d/6L/970vmS9WLF8XJF4KoWBlfXJGcrhDUEPcXN2/v0JgOiYQ+nahAECOMhVwYjXJASpSy2B/gC60tWVYkjLL4UIHoU85zlxYw/+Ff/Iw8ePOf8csa8XHNr8zWubB3yzu036e94qJ7BiRv8qeXiwYrjh+d878N/ykm5YK1rCjqkdomQgtgb8Ctf/XMcXN1i93oPdTCmaRY0xRK9qJk9T0imCWl+wu033+DK7au8/QufY9TrI42DraHT6SKVi0GwXJ7xwT/8iA+++yF/65/+v/HdiCgIGfb7jPow6sdsjkZMmwQ/1HQ6gtFwD1mBoy1KwMHb22zfuMb1L/0JPD8ky864vPiEe//kdxl3OmwMeqTTcz766IJVafjFf/s9tq/+Aq7bJb88Ragu0ouQQRdNhU5m5Ken/P7f+B3+xacf8Gh2yu7eDn/6L3yT2+/c4eD26yxPnjM9fc7R088wjcX1e1gn4Hvf/y5ZUaAcxbu3b/Hy/CXT2ZqT5xUfP33M7YnLn//iDv/GX/9P6Ixfw1HbGP2IMv82dfUIIW8Sdn4NYUcUi0d89//yf+c3f/Ah/68fP6bBsu8H3Bn0+V/+xV8lOBjjboX0disuP4h4evec3/id/56Hs4K01BhjeO/tA65euc727iE/uPv7eCqm0+mx98YGsfFwKkU9laiu5uXRMd/7zo8RFj535R2+cOM9fufubzKIFK6EH3z4Ke9ujnnjzia/+lff4tYX/106/T2kdJg/e0qTzbH1im58nWx9j1qfM3z3W/z+37nHD779Ef/l3/ub/PX/5H/PF3/uy+y98yal0JhGojP48J/9AWdJzspqhjsDFrMTmjRhVAoO39ykqhue3Ltg0eRUs+c0Jw/Q8w6phDUNj14eIVyN8hy6nZj5s5LlKuesmCGV5crm29za+yp/+/f+c4o6xb4ydkPrfpaig7UFrfHc0vO6XJkc8taVN/jyv/k1dg+3GG30WZucfN5w8nTB3/lvvsP+2/u8+5XX+Hf+yq/xkyfPufvhfT78wUd85fNfxCqXRgqCOGRdCrLMsDpJcSNF1FGMJgHKSoyp0aZkYydmZ6tPP4r47X/0Kf/o7/5NHty9y7X9n6c2FXmZMJ2eIYVha/MqN258gcEoZuf6gK3DPiozOEGIHwXsbfawoSXJcp48POX7//w7HD97wtnRU67v/TqTjQnjrS7bbwi6UYwUipP5BZ9++CEvnrzg4d3HNKJhuLnF4c032B6P6E66dIZdmrxh+rI9URT2Hp2OIlQ+Az0ikx6mAaey7L29we7+kO3dPlmjKRYlVa4pfY8IF0cKTFAghCYIoTeAi+OK2TJlulxxevQMax0cFAOr0NLDCyO293ehcknnGZfPL1ksUmbTcy4vT6maius3rrOzu4nLgsP9DcYbI0YH+8jCoagty6ppwxK+wQQWX0YkFWS1xVQW6QqwAptCXbiYxmJ0TdxJMSqgcWP+s//wq398m8Kv/uK/j2tKXNEQ9np0Og6uapnpq9kpwkIn6rG5P8CTrV4ijD1G/W2G/Q02dmLcOMbxXaTfUJcGXWrqtOLlecLzF8c8ePSIn372IU1VoXC4unGNTi9DeA1zLbl8WbJeTJkvHtLxe4yiMRvdTZLslDRJyNMUWyxxrUBJFy8acO3mm8QbXdwNS3djQK/nMex5hJ29Vx7dhKOnp1yenpCtUkyt6MYdwsin2ws5fjLjYj3nJLlAvDJDGSHRTY6ThqhSEOgl116/TtzvIKSlKRM6oWLQ90lmGboqMLpCOiGvvX7IlZt7XPvKe3gRNNWS2ekDlidzsD6e22dnY0Rvd5d4exfLJnleoa0mGvo0QrNeXvLs3o/RSY+syJmlF8w/uuTp42c8fvqMuy9OoLHEYcBXvnibG3d2GW+OGU62iDuGdJFx9mzKsxcfUEuBcT1MXeO5CgeBWOf84NMjji8Tkqzh//Tv/Rqf+9LnufrFrzA4OEQ3j6iKj8mrY6LOF/D8qzTmkrNHv80nP77Pf/1fPGFxfM5GEPL2wT5f/He+xXBvh+5oRE+BG/dwox5+vEO+OGE9PeL5g/e5/7jghz95wD/4jd8F10UpD1f5NEiUtPiew7Xr28jSUqQ1L8/mTIY9Dq5s8/kvvcF7119nc3OL4caYpFlzdu8Z0ycnzC+mfO5Xv8bea1cZbW+iZUaRTFmcPIJsg6jbozvsIpo1dXZGla7IpmP+wd/9e0znK775P/lLvPlzr2FVxdHJY3IxwWk6uGWHxXJF4xkaUZCdfsCHv/eUF48vOL0454uf+xy+p5guLzG2ZD07ZXbynHnlsLe7z8HOLlcOd9jb32GwMaC7NyCIWg2q8GKMbfD9PqE/4enpS44+Pebks2Mef/xjPnj0EZdpwmTvDp36goFnuDrucPXn36U/2aLX22QwGeFIibVwPjUU0yWL+ZqPH57z7pevg9Z89qPHPJynXL1zna9/84sMlOXoaMbp+Yre9ojc+pRGUOUZAg/lSsIuFGVOneSvsNceQU/iBJYnPznm+f3nWGP45b/4J4mUwRhLmmtmq4wsN2Sp5eXju0R+SDeOCUOI+2OiTkw3luQlrNc5pyenzBcXFJkmW8Nnd38LEAR+jxvX36M/DvB8xXwe4Hgruj2P69cPmRy2LWYjJf2eQz+K6YYxVlaUaUGRFMxOZ5yeZCymGfOTCxZVRZalJPM5XhQTBA6e75BnMbbJEKIm6MXEQUin02Fja5dymVIVUFSK8V6PsJvhddYk6YrTJyXnR2ueP/2AUfcqh1ev8at//hvsX99ESkm2yvnhT57z8Y8+5uP3f8J8dcrhlTvsbO3SF1N6PZcoiuj2t9FlRl4ZlgXoQLXmyKCDH3rgh6A8tK5wnRBHSITQmMppabmmpi5LElzWIuDv/Wd/8o9c63/m8dEyzXHJcEVFhaUoBVKAaSRVtkBYqKoaKUtk68xh0AvRk4ZmVCLsEBmnCFdhm5z1oiBZF8xma47m55xfTDk5uaTONG4QEHZCgkkXLwBtc+ppyTKZM1tNmc7ndJXGRBJZuhRmjaXB7SriyQYd5eJJF6TPeCemO4kJxwLVCwk9iTKCepVQZxl1sqZerTFFii4T0qVlMbsELI60XB4tmKUrpvmc0HHx/AjXDwGLKxscL2BjZ8zm3gDPU6zma6qybh2wylCWFQKD4zoE3RgcyMuC6eWMwLeYJqNIDF7Qx/N6hJ0thvubBKMYN/YRKqT2aqqs4uXRgkUyYzW95PzeMWRLkjxhuj5n+tkpz56/4PnxEStdsrU1YLIR4Y8089U5Rb1mla/Z2gpJ5jlHp1OOz6bgC9T/j7X/CpYtS/P7sN9e2+/Mnf6cPP5cf+ve8r662k37wTjMDLylCAmQqKACImUCIoOiqGCEKFEIUJBCJEIUIGgADKghAmYMMD3d09M93dNdVV2+rr/33Hu8SZ+5/d5rLT2ckl5RD/NwXjNOZkZ+39rr+3+/X82l5jjM5zl5IpkOJshZwaofsPFyn5e/8CYXntqmvmpSzU8p1ZRSKQyzizQckiJl/8lDHn98lyePdvGDnMYLG2wvLfHylYs88+I2tW4PJ2iishJhOgjLPufT11wCs8O2+xKyPWWoJGv3bjMaGcRRyjSKWFrdQBcJRVZwejDGx8QWNptLy1x6+gqXr1/k1TdeYNP38W0HV2g6WxdRcYmSJatbbbavdqk3YXH/LoPJAapa4IoFiIokn5NNXNLiCIqcIi74+L0d0jKlvdZg48YqcT6hjAt06eK4HnmeE00n5EVBVuTE+Zx7777P3bvHHB9NGcwmPHPzErVmSMu3cCyHpRWLrastCrvPyvIqK70ea8sBrbBOrVEnXF3Gb/hYtoMQHhoDw/IRTo1guYnntMGpc3yySzhdpvR8mv0WG60m622Xpzc7bL36ElYQog2HKtNUeUGRl1SFPifsArW2h2e5FEVOWmTnxTis4/se0fiUsigQwgStqJCUGCgThFZIrclSTZqXVGmBTDLSPGO6KKkomAzHOL5NLQxod+sYRY6BQa3t0+xDmpUsogxZjSkziaw08ygmziWWOcUyJVkK2af2uKKK0Vg4nsf6xva5QU44lIZiOJpQVQWzSUnYsHCsHo16yPbWCsoymSYFrUDgCgejEGhpYmHi+BZBPUDYBZWqmEzHFFqSpxFxNmKeJIBE64osBoMS0xI02+tMbYHnecxnBdF4QJbkpJmiMwypNwVhw6DdCqnXPKxtl+bSM7Qb6yyvrFLr+khTo7QCy6LTb7F5ZY28iDgbNOkt9Wm2QtpaE/gGtnPOPSuqiqwoibPz2Qh5hZlqzLmNCAqEa6OlxLYVlmXjOfrTBBlgKbRRoqWBVtVnqvWfuSk0eiGqLFFlwiSaoCclWlaoSuGZJQYKzQg58ShTRRYp1oM6i94Zg07I8MkyhRucE0fPxpyeTDmaTLk9OGZaPcKxXVq1Jb70+W/SXV+m1qtTiBmTk5zpyYzjO/sMohOKsqLhrdC0PQLXxbBLWkFIM/QJw4Ba1yPwzw1vk2mM7CZUgcQVAYHZx5YmZlEyngzIp1Py+YwqjzGzFJGnjI/OOF1MyKri3GD06UmrKTSbvk936TyXLQyXucpRLmxe7tLu1ijjnOjhhPl8QWIJ8silNCqaTZdGt87KlcsU8ZTd3X3uPjig2wxodrqsXLnB1ZduELRaOGF4vuIuJ6hyjtIZxyePOTw85sHHZ+yfjciGGdZuhW0I8jJnlsVM0lMWxRTbK9iuBVx5tUv/QhOzSDh9cooaGDSyLpXVZHK24N6jA6bTOWHHpelrqqpgdyfm6DDheL7gi51l3nj2In/lP/kWzRs/i2FNUdEfEu0ppNdGNy9Ta2yQyR1Gkx/zB7//uzz8eIyNz5//K8+zdP0a9U6bWquJrUHLGDmvMO0WKkkoqwjpxcwqiRQO/tJLhLUnrKuYFwbXefKRw+H+MacnR6xdvkQ5HpJNxoyGU1bCJhurHf7El3+Gl/7sV1naWKcVdDn48AdEpwOShcJprqMCG7HmsLHawLaPmT96l9u/9pvcP5jQXm7x2hduUjZnnJ5mnDyZMaoO8USDPLH5h79zi5//pTe48vJ1xsGY0w8/wTc7XLjwZeqtFkf5Aw4XdykXKYP5iMOzE37zv/8usyyjUAphCJYue1y9toTbXqbut+kth6xudOgtfwFhumitiOb3mZ8OkbnCC0IwPIpMkSdTDKOG4VoIv0B5JmnPZr7lcOhKjF6XZjPEXba5+PpVrl3u89JT63Ra10kzyWQy5+TxgDxRlHlJLiSZZVDYJkZgMBuWWJbFxZcv8ezaZRzXYTIdcbrzCMNp4jbaaEuTy5wE0Eqdi3MkkCqqQqPyc2x0UiYkk4R4EROPI9orPuGqTZEkDA/GIATt7WWW+10c18IwDXr9DqfHI85OzjjdXTA7PSGJUvIoQ6tzzathKMoqxfFMwnadb37tL2PYJpnO2T+7z979Xc6Oj5mM7tMY96FIGW5vc+1GD1W5zCeSeiSI0pg0mVLNFKoh0Z6kqhIm8ZzhbMj+3hNaYR0pU2Q1J80L8jwjy2Oy9BDDsLGtEIPWua0PjXh8zGR2n7ycnis871n4do9msMYX33yFC69us3lzjadv/jKO7yM1TOZzDvYHZAuFjAWtpRrPvHydSze2OTqIsKwCx6poqhLfEggDCqkYnRmUUY4gp0rn55DFTKNjCx0I8A0sZWAFBa7r0ao52HWBsAXYFnajwK8MdO58plr/ma+P/ou//X9FVVCkFYePRxSfpoDq9ZAwNECmZLMRT94Z83i0y6P5Q1xh0bRCQjvEc+rE2iBTFXExJa0SSlVSyArTaHJx5TLPXX6Wl79xkXo9xBEB8aFDkcV4nuDpF7ZYeW7t/CQlDA7uPODDP3qXH337D7HaLq2WT73mUKYZojSwhUmjV2d4NkQIRacf4HkNyjQnmcx5+GCX2XRKksS4jk+rFVKve/SaLouTiLIsUbbEqdeQsqLIU3AKlAiQ+JRK02muUQtCLLsgOo1Jo4RFPCdsB5iWRhgVJYLllRadbp3d4yHjkwmzScxoktNt1Fhb6vPyjVf54re+TGtrHX91BeGf0ykxKqLFAZ+894jd+4ec3j9kcviY46NDfnrrLiNZUqLR2sC2YLltsLZk8trne3iBjxaCwXjB5DiiTEGIOsuBRzMMaLUbDKdTsjwjTXNGowSjtHG1zbYZ8safuMyFFy6z+eaXsBwBOFAF5GlJkpwQLQ54cnwPlaQgQTTW8YM2ticwwoiWt4pZuZCYFKMRfq1OvdOltvEiqlTEiwXvf/B9OuvXQcLRR+/w1g/G7Bwe8eD0DqZZp5Qm2rD5xhdfo+F7NMLzrex0IvE9l5uvrbN/co/54YLoXsXrP/8mnudjVIrh4C1EvUlRlrz/9/8r3nvvgEdHc25NEyKpUMLAcmykMFAKqDR908ES5xTe0wJ+9su/wtrqCruT9+joBuNxxIcPnxCpAq0UQikKGYGhEELT9Wy+9q3Pc+OZK2xe3eLGs68S1EOkrkjyBFkco7JdytzCtGtYTkgYXMCy65/KpWw0FtoQGIaBKnNOHs949OGQt967w/HxiNFwwmJxQqvl0e4EXLy6ipEM6XZsnnlpg7CxQbwoGZ0uGIwihF8hfE1uhmRliioVtbzOP/tn/wKvHfJn/r1/h36nw/6TIz567zYvvHkTw/bISs3dj+9iBEsYbo2KHJmALCrKIsFVJrYNtqeI5xFpFJEtEuJRzpVnNljf6tPvXODowTFFKfGWOiwteSBM8soizXMQGqkl+w8ec3x0ymzy6fZ9vUEtCGh36rRaAa7v4vk+YcumzDLyOCGdSdKsIs8k8VwiDIkQIIQgymH/8BEff/KHUBUoJdFKIhA0ww3azWUuXetRJIrZeMH9u/e5tv1Nuj2P7mpEsOxRaUhSyd33D4jTOUWZokTFYpaBNGnXe3S7HRzLxtAm+wNNkp1SVcesLYVcf+FpLt28zPVXLzAb55S5xtEeQjsIQyAMA6kM8ionqzJs06JmCjxhYKsCYRRoFHklmE8jZlHO6SzhaHjMdFIwGxecDYZ4zTZuGGLZCY4dYlk2jqvx/QDbs7HrDi2/pKxMksLmn/yXf+3fWus/85PC0cNTtAYpIcsUBgaWbWMHHqYDMqsoc808m5GUEaUqEVojkSgkkpJKg9QaW9vU/GWEZaFNi0s3n2al3We9vUQ9XKXe9PB9l6WlNr5lUKs5bF1cor3ZwXRMqrzk3lRQ5DZuPaDess9z7IGLUVZUZXWu+Utj8iRGVjkyG2MYLlVekscx2XyKKGJ8neNaNkJloAycoIbtg+kIzMCjsdTFEAZSlpTFgko7lLjnKQazpKzm55vRZUQlJEE7oLHegjKnmM4Zj2NULomGMYtCYyiTeuCDqGNJk9E457vvvM/d2Sleu4HTaODaLXxL4AkwqzlnR3PGgwWnx2fMpweM5kMmaYQE6p5Ht9HACA18J0cbBbduzyhZcG4KzbnSFCwt1+lfWqPV6FEPezTaqxwd3OJg94z9nSFZVBEGHp1WwPNPL3Ph1efoXb6CFayhqxFVPCcd7jM4XVDJCEVEK+hhhT7CdCEIEI7EdEwcfwnf9DGkibYMXLeB7fqYXkAUDZnOUmbzGFSNxX7M9DTix7/7mPfv7TGaz4kLyXq/xcaVNS5cXWerv83Saotuv8X21Q0mswmGCc3uMgUlpjwiGj1kMn9CTYa4rotd7zGOpxwfHfAHH+5ztD9mOs9wNGw6At8yCR1Bu98kWN6k1r9IaEXYtSam36TwupRpzGx0zOGjCSfViMliwd7JAZniXI0oDJQ+R7W3OiEvfPUqr33lTa5fu0qzZ1NrGigikmyB6bSwrSVw7HNpjekhTJ/KUGhdIpRAK6ikoiwVSZoTnczYuXPKR2/v8dH92ySJpCo1ljCwvAaWH1JqiCbnsqblUU5lpmRpQZQtGM4n6KgAU5KlcybRjLKoaBpNDk8OMCYOP/7221hmQTTPmI4LnnnlKcoyZR7nyMrE0gYGYCqTSstziF5SYlChKo0uJWkaE81ikmmMlDmmpbFsi7TMUZZCa0VeZgxPM8oCkgwMC4KGi1tzqId1esslXuASpwkYPobloLSJ8H28Ro1mM8SwNFVlgjDBrrCFRPgKsyUwZElVVCRxwSIagStYu3QN1zUwlcaQkiwtsOwQ1/ZIioR0mjKbTlhkA3bPfsI86xDnPd64tEGt6SEci6C+RJEnSFkg3HPnhDAsGmGLZquObVsYhsloXhHFY5J4hJAlzc4SpRKc7I6JFpIq1wgV4QQuvu/TrNdxAgdRGZiFoMhL4ugcGulZBo59bs8rjU9FSK5FULdoFiVapVRVSpCUmLaHaQgcw8YxBZapMWRBOstJpgqEJJIxlQ7ICT9Trf/MTeHee3sIS2PaAqfexbMVli1QFhSVpEhKolnBophRqQLfcvANi8ByCSwPx3KQCBwMXDOk397A9esYtYAv/9xXqLkWRplj1fvU2xaNrk1/tU/HP5dPSF2RpBHFrGQxTfjo3X2OD6fg2Hi+wPIsDMfB0JpSnTcGNZ6TJAvKLCE+myNL+9zZoEtsDGqewPYcRM1CGtX5tY0JyinRSmPXHMJOiO04IKAc25TYVIZFkZaMozlRUuBbgsLIMFyLoNmi1q9TziEfK6bDBeNiholJuLJBI/RptAJadoP4rOR0OOPdWx8SvfP7SF2hMaixQUuYtEyDnuchLI8KwVkyJZJTcp1QaEnDclkL61xd75N3NGk2IVpkvPfuhFhqlGGw5Jm8+bk2L17q8MzXr1PrXcTyVzHtLZr3K6rS5OwgQ6gxrmPT6Phceq1N78YV6svXEG6XIp+STAdMnrzP3qM5ludR7zTZevpl/FYP4flE2YCoOsQwoOltIMwcDNB1C8dbw9A2stJMDu6wdzJlMito6x6n9wfs3Drku7/zhJ3iDgpNaLcJljs8f/0mX/ullxieClav9VjabBF6DWiVKF0gTJel5W0MWzOSDxlM7rKIfIJaAze8zNHJA27d+YS3D2NC26XW81ixYDuwWfYdNsKAK89u0Xv6dTrPfhFp7eO013Gaaxi1bX79N/4uP/nhHRZjzaI4Ja1SzMDExwOlMHSJpW26rSbbW6u8+cuv8sKLr7O5tEFeHlBUM5I8ZrQY0Gk8g++2cGt9DG+GNiw0JlUeU8kUXRRUhUueQ5KWjCcLzm6fcffWAe999Ij9yRMsN8R16zQs+/z0bgXM04ThvCBVmpW5xOpUlLokJWOUTMmTmDLLiU8NjgZDkiyj6TeZLWZkI8nv/cZ3GEV7hPUuG6s3iOcJSVUxjVJsMzgX1KAxpQlItFKovCJHUWUVUhcs0hnTwZzFKKbW0IDEMDRRFqNtCUJR6ZThWUkWV8RxRdD0MEQd2zXxAx/TFtRbdWbRhNkCVGmQpCVppfA+pRpobJRhU+GQk6FEhhYVIhSQa3QKRaGQTkpzpcnKlcv0Vmp42sAuYTydkRYZWZGSTo6RMkOWMdpK2T37AWfjVSbjV/mZb7zBynKb5rJPZ9tEVxUCjd/y0FpgWhZ+GGA6CsME09SUsiCOShaznPHJiPns/L2e7k7Jc4OyUpRljt/1aNOgVnfPWWeVhSkM4qQgmSVUUUrdc/F8E9MxkO65aMm0z+OqXW1iWSmGnSCVizQUCE1gWXiBi7BAVxWz+Yw8icnjGXI8pBJtKnvlj7cpWPUe3Z5Lu+vS7HjItETm1fnEPk+QSYbQBpe3L3Gt3MKWFcKUSGlTKotFJViyPQLPZ32lw+Ur12j02wRbIeubG1i2wDAUXiMkqFu4tiA7LnnyL3a5894j/t53/18cZvdIZUylDQwFLdejHwS02g6OaWOZ5rmMIjcoi4p5POXa6gar7Tab60us9Lept+sEvYBFXJBFA9JozHieIMsSYRg02yEtmVIVFZgCa3xGWUrypGCWjpFCoCyTEsF4WlApCLea+ElFnuWM9hfE+4eUeUaaxASVS6I0ia7wy4Tjs5S8LFjkp7imiSVtXm5dwoy7RFXCsIoY6JxUGxRSMEszYp2T6oy5WiAwaFgu12pLNDoWdtNjFCri4YTAKumHAV/5hTUuX1tiZbtN76k2zaCN57fwwg2E00eXFlVasHX1KwThVS5ce8TB3feZnqbks5y3//ktXhQxqzcv0d7+Eg9++i9JRhNkbnLx61+h2b1Ko3kJIXzydIcsPSZNLVz3KbRRMJrfwjRzTCvE8taI0tsUhU2WOhzNE4TyCTOPw98+5L/+w1/jw8PbpGWGxsQyPHQp2D3ZJzaep769xsPpI/SsYqEGlAyQ+RxDFhxpzUn5I2wnYLn7EtV0ykc//Ig/+sFHvL37hIvrfa5dXONv/5/+OheffZZmr/fpAHWOKXxcdw2qkqo8pSz3aNS+wuLRXUaffI/DfEJyO2Mpu8Hn39C88eUu7W4b21rheDrlycEjHj6+jZUo2ltL9C+v8cUXP0+gCkaPP2L06EPobWEGLrUg4HD3Y7SdYwYVmi1s28N1A1abl0jTOfPZlHvv3OVsNCAIm1y++TydGxnXlvuET5nk6Q3G04jxYMHRe6c8un0PqLi42sYySry6TdsThF5Aog0WtkM11QyOJ4zOzhidZcyynEopFkHEm9ffpKhKPti5SyUzAt9jfXWFvfsfEbZXWGr0aSy57J1mTGZTDKdAVzamrHDdiiKtqMqErJgxfjRkODojTmK2V36VTqtF2NTMzhTtjgfCIJcOoqUxTRvHdmkuecQlzFIFFWijhmGW+HaBNiV5KinSkuHBhNnJhJMAnn/1FYLQQNiKotLMJilJFJPvAF6KYWoMHFZWO8zmOQcHJ9RrHXqrLVaX29z0+sjKhArMvMKtSkihOLP4r//RP6USBlduXGP5Uod2v0WjXqOSBVkVUckCPTMoVUGuS+KBYpplVLnCSBTSLRECDOPcKugKG+HapMpgOjwiTWIMU1DvbVFVitPRIcc/nSJ1jrAVly7dYH27Q2BbLJIFsizQChx8pFPiIHBEjXbXY00ZlCXMJnPGo4gkTmg0CgKvhW3baDNjPJgSz6YsBibzfExUKWLkH29TqK/VEY4kVTnZqCCfReRJxmKRoPMUqgpRKrQy8Fwb4QcICiwRYJseNceh3ugQNhpsr/bYvLRNvRXihQFZbFBVFVZZMvp4yPHRGYcnx3wyeI/ZQcTkbMbjyX06douN1jJrlxroQJzfIRqKZDSiSAvyTOHXbVq9Oq7lsl512bi4Tn+pwZX1FqsXr+CFAZZnc7y3jzwuiWR6frpAUCQljz4+JE/mCAG1msJzPcqyJElSCnmuOzSxsISJZZyPY8pUIKWN5Th0WoJoLhCmR8P1Wb9+AV1zqRwbKR0WZ0csplPUrMRQBbqSZG6BdkuqCoLSp5VbxGVOVqWkqsC1bELDBSlp15u0Qp/+ik8U5SAcLByubVymu2yxvGpz84WLrK5t0ex08JogijniU9GQpkRWUEnQToDle/itkP72Jaz6hPloSrQ748P3D7j1YEhl73C28xDf1fRXG8wevou7e4hj3iEI2jTCFq7ToEgqTqIRyixp9ZvULJdK20SZYDhdnAvOo5Jsp2Dv1pCDh0Nu3bnDZKxZFjfw6w4LEVPpDKUSpvkej26/x7u/WedhfkYvdPEtxaNHH1FNI9Io53CakOljLGFTs+8SzzKKwRw9i/lrf+YLbF57if76BbqtIaYpKOIMo1KobI4gpbTAsBSGbYLY5vC9Wzz4+D2O9h9hra+Qlhm5XHBvd4/O3k3C0YR8fpvBrMAMHDa3LiOTgvpqh9byCsut6+SjEUUFfucyotXA9Dws12W0OMPzPZrNJqX2GE2mnJ6MeDg7ZTIYsJjGzAdgWgWZkpi7j9h/7/H5Z1ZojkdDSi0xBDz7bA+ZblALarzw5gUMY0rYsNm40MdrdkjyjFo7xPW6ZOlNyrIAHLJCkhQV40mMQLKYzbhW0zjus3RW+mxc2WKlE4DpITE5OjlkMM5ZpBW2Y6C0QCuNkRcUeU6VJ1TJ/HzYWhWUWcVHt3+PWuNp4tNljo9jOqtdhOuSlWBbBtrQVEqTZyXTeclknrMYH1KpCsMwqTXWaXRDHPf8BL1IMmRZIU9Kzk6+j+c6uI5Lrd1ESomwbWjamBoUFbmhSQYep4cjHt3/kPlhl9ONdba31llarmM5HsKwMYvqPIloCsQSbL60SZKkKDtmd39KWhj0uopMGcgSwMQwNWgTKWGRl1iWjRuC1QLLqp2TgKWiqCyUr5BK4RcOSRojVcXg9Lw5uK5LLfDIYrADG9/xmERzFsMZthK0ltsYnIt7MimR5TkGSIkCUwu0EChToB0Pu6HxXAvLLDAcF2Fb+I6Fs2JStALSTg3VNhjHFmfJZ2MfffYnhZpBnmck85Q0lRTziDLLz+/pZIbQGlObgAGeje3ZmAI8r4bnhTS7dcLuCo1Gi5VOk6B9vgiWxxX7ezN0kmFlCUcPD7j18UPuPHjIHy1+DwGYQmBbDt3GNturfW7cXKKqQaYrojxlIDNSkVJlJU7gEHbqhH6Abzg0llsEvRCv20a060jHpdIGSSWJS0VcanIEWpukZcnR8Ryo8HwHJ3SwzQCpSkqhEIbCclxsz8VDYNR8KgkWLiIQuDWH7maTk4FGJilWHnPxxW3cXh3tO+w/WSCYghEzpUKW6lNolqKySoxC45QWddNDZxIJxFVG03LwbQ9bwUqzTasd0Fq2qUhxg4Bef4kbG6v0t3yWLwZceukajXD9nAAqY+TiAJ0nqEKjdUYpK/JSYGCSJwnZIqOobKSw0a5F5WjOTuek+wMm09tMRzmNlkdJRt0RuNYEVwxptdcxl29ghh1kkhNNS7Sj6Kwtg/AoVck0izk8XrAYzUmGMcWdmA9/8oS7d/f4MLrNuvMCHXuVnhfiWWMyJmQqJ0mH7O3c5q3vwmkroVd3cFXJR+/9FDlOmc8yPjlZENgCQ4Eq7yIFbPdaPHtxnV/6uc+zdOV1nOYap7vfRxWCPCsgz1FpitAF0gRtS4TTwrB8zh5+yPHRMcfTmGa3IspmLNIR09kxw9E6aZqSzcYsEpOV5jbbF68QzxfYHZ9ao0kYrJAOYgrp4HU2ETUDTAutXKROMGhiG8ukccz4LOPgcMDkbMDkZEi6KKiqFq2mQVaUTGcp9350F5UbmHbA47M9mu0G/ZUuz39hE7tcohF2eemrF0GMEZbCdX0K5WDlGYYl8NwOlm1hOzZBEFJITZQU7DwZsXf4CAYWm6Wk2WpTXwoJNxs0rCbzKGE+m3J0csw0UecF3RTn50wFVq7Iy4QqiymiOXm5QFYlspTs7L1Lv+mQD1J2Tw7pzy/hBiGVFjRaHspQpFXB8e6M0ShlPI6Jol2EUeF4Af21Dl4Y4gU2nv/ptYtUFKXB2cN9al5Ao9GkZ1oYKKSGyNBYeYmUBTEp6YHFyd4ZJ48fMjkak40lZWSj8govrGM5LmZRkvkOdmDitCuWr3SZD2fEoxkH+0PKUiEriVnzMQ2FaWoQFUIbSAVZpWl4Jo4rcALOHe8SilJjFAptnAue0JokaiHLgtFAs5hNSYRF5gZUpcCXAYZtEscJelFiloKg2cayBBiaQulzSoDWVIbENgRYoE2NNs1z055joItz6ZKQEmEImmGArruUzQC/XdKYSayp+Ey1/jOnj776lb/AZDhiMZsjS0Wz3qTVCNleX6buC3SpSKYZg8kZQgt826HZ81ld7rO60ufi8xcwvTZa2WRnEbd+dMb+3in39h/yZHxMVAxJqlNyNUMjMbTGRLHp9On6LdprIY1rPkHboVZzKFONIW1M7dGrh5hCgyHJshlFArrSOHbJ7CxGpTl2EZMOYtKqJNGSIOgQNhzqDYdmK0BgIDQ4paDZXabZb7N2cx3P86mSlGQyoVzMQRggDFRcIJwGUjhMkpxwLaB3cZObX/oy4/kZhw/v8+j9d7mwGeLXoVIZ/91/80MWswFJFjPRJUIaGMJBuyFVotFZBWmJFHVqysUtTR5Pdwmbdfy6R1WraNkBAQ61ymH76jI333yRL/zqt8iTMW7Nxa0HoAxQU7RKQCmECkAKVJmTxAfEecEsByeecu8PbvPRd27xk8Nj/MCj1XJ59XmPq2+08OuKxeEZ77w/5PisZDg1eOUbT/P8K5/nuRe+TCN8ljIeUiQL5vMG/loLr1EjCFqczt7jeHjAw/09RveHHPw049FbMX/w4LfIZIZUIGjjWB6+ZdMLbFxH44YGQQ+i3RNOFxFHcYrifCfGFAaeZeCZ5xu9aaV4atlBlpqTYck3OyE/+9d/np//X/8FTPMSZbqgiGbMHp7it7exHA/KAUbgYDoOlusz2jlgvH+f0d4tqlqNy69/i86l67z99t/nH/zfvsPRgwVff+EbXHt9k/Xr21x+8UUct4lh22hLcLz7gMHiiFzlfO3VX+bO7bdIohGXLlyhLEtm0wX7j0+YGx6SFFnNePv7AyaLMSU5z732LL2aB7nikw9PyUczhkcD7n70gMfz25QqP49AGhZff+3n+OaX/wT/3n/6Z6jKgrLMqYqEWqMNOidd7LF3f0EclaS5pJQCp2ZieYLZyCCrcqpSoWOXO598RBzFWLbHMLqPcB38ehfHtLh/6xaPdx6yfOkqfqeP6XqkizEyFahSorIYXcYURUKaLJBlRp4I0kRylt1jpXYJ36pzGN+jF16lE66zuXyVN771NGEvxPQc7tw55ORownAQYbt1mt02jV6D/uUOnbaL51o4tkkpwTYEgWkxG2tyVZKWGXffv8/e42NOj8ccHY4xdIVSKVU5RJanaFWg0dgiYGXpClubN/jC158mbLfwXBcrSkH5YJmIGmTxmOHJGXv39phFJUvLfdbW1+lcbFNra5yagTQD8lkF0sLx21R2hpSSslCYholpOJiGg+2lmMLFFBaWo/FtjawqxsMF8yxhPo0ZHIx563u/T1XkeJ7DV771J9ne6tNq1DkbLDBECaZA23VUlaO1QGsHpyGQQlNqhShyqkJS5DmT+THFQGJUmlobttpt/MBH1Dza4oRFWjCIJf/h//I/+ONrCn/9b/zH2ICpDXJpETYCgtp5vDFA4TgmXs0lyVPSNCOJYuKDAdm0Io8VlagYZTGLPGWRzFh8GtOShYGtLRpenW69RWfDoxIGlQn+SkUnAN8x0Z7PdLogz3KKIsPVGls4WMKliOcI7xyBW7cNTMvDth1adZfhcMpsNGH85JAyN3Dcc4uZMs/TI75rc+36VfoXurRXOvQ3rlDmU6qioCgkRlIgDI1tgVfXGMIHw0ML8FpdLL+G1BVlvEM+HxCdnnBr54iz/TOGj45QEhZIJlXJ+48G6KrARNHxDSxL4zkWnXqNZ19cZ2V9heX1TQ529xkdC6anJmk5JLUtCiGoqow2DbpOwOVmyI1v3mT1+jVWn3qBxfAHuPUt/MYlqnyGVhVKV6gyZjF6jColfrBKniYMjoc8uvOEd77zQ3b3TjgbTnj5ZoutK5doLzVQ8R2enCUMJjlHpzGWUbK0YnP5mTqvfOVnWO7doB1cZfTIoQosdM3F6C7Ra3cRQjObH/P+Dz/m7HTCYDTn+9/9I86Ox0yHc2ZJDFpjGxbLbp/C0Ji2Sa9ZwwlLSp2QZGPmwymqKhEovI5JmmvyAvJC0w4c6nWHcMNHTVPKVFNlFv/pf/KzPPva17jwzFdJJkfIzKBKKua7O/jdHlbgYzgK0ymoSkW60Dx++C5ho81yfwtllWRqQVotmM6GDAYKZJ3L608zjvZx6w1Wt59HGyZn45jdwyk7P7lDNFugteblzz93/pkXOdV4wqNbTxgMxxyNh9RaWwhLgkhJtE0Q+gSBh1dqpkfHTEdjHg+nUNrkacxkdsCknKC0whY2X9/6Ot/8lc/x+W88w2rLxO6sIYImmCZpGVPkKdkiZrGokAo0gGmDlkgpmcwl6aKgzEqKKmfw5JS0zJF1OLl/TF7klKYicBXHB8ecHg8orPMEUVGVVEWBoUyENrAM6IZNHMfEtvV5YczOn3x7K+s8+9JF1jaXMdwaYbuN4/rYZoBwHJJMMpkXzCdzCg0FBmmcYdo2tmNTDwL8Rg3LNDErRYHGUBpTKqR5jnEwtCYvMx7fOeHJgwN+8sNvYzltwkaDC5eWMTT0+k0uXl9B5jlBq06j02JlqYHGOo8gRwVKgiwUxaLg8cNjbN+gv9Vgdb3LPIkYTqbs3S7oLrk0ex61lSbxIkFVAtfsYjoGhgmGaeDbHsI0zy2RKkMLE42JUWksIdH63DS5u3fG6GTI8MkBCBPX9agFNbrXuqwtt2nUahyfVNR8G8s2UZbF7GzGfJYxGsZIvcANAvxGAx+HQqdUMkXkEYaUGEpiyBLXzXAcB99v0HEXpJXJPLP4W//7v/lvrfWf+fpoeXWFpu9Qcx0K5RE0HBzPxrUcRJ7iOSZhu0YSJUwmM6pKM0wKzoZTJoOYJEqZFRmZLChFhuV5uA2PMGgQGj69WpfVZp+Vi01yDTkSq5NgmhGagrgwKBNFHlUUhaLZtPE9F9cJmFcZyrRRmOS5RJQVZQGGNtCWi+3VsHyfoNOh0azT7TXIlUQohStM+hsX6W93aK936F+4RLw4JV3EzAYpkOE4Ar/uUm+baOUhK4dKZGjHohIFRp4xOx0x2H/M/Q/e5ZPHZ8yGEdFZxGJRkSpFJjR2zcL0FJ4Ha6sOzUaXul9jqVbnxVcus3Zxi5Xty/R3Ohzsphzv55SxYFgoolJCbrHsL7Mcdri23ufCzSvUlroomWK6DqbjYQiXZD4hr0BqsCxNXkmqIieNFhwenTE8GnK4M2A6SzGbLVZWV3nutQ1WLnTwapqDj+8xHeYcH6ecziq2Nmy80MYLTE4GGePRCaIsOb6dYbRa2J0W7cs50WmELgt2j+/zzh/eZjyMiJKSO7f2mccTChnjmT080yKwHNr1OoVZIWwD37ewfEWVKZI4JSoLWh4sN036V3zyUpDngiK2aIchYbNO52Kbw8fHZFGBUVmsXu1SCx3KaUI6O0XLJqpy0Y5FYVRIo8B0LDANyrwgXkyJkilW4FPaBpYlmB2dMB4f4oRdLl1dpxZ2adQ6lPszpHKIo4w4WXA6TDk9mvPo9i6LUYxSCtMTNNsdDKk4u3uX+x88YTqLSGROY6mGsAEzR7Q8wEBlirOjM44e7zAeDznLE0wjwDI0vikxHQsFmMLlhae2uXljjc2LTbLdxwi3DoaFFJpFvCDPS6pCokwLwzofeGpAFpKqLKkqhSxLlKywfUG97SNSzUwl53HSNKUSJbX1gLAZoHWXWZrCokJIhVkLMDgPeOjKwLVNPNfE800MTCqvBK25vH2J/nKbVreG723R7Id4dQ/XqzEeJmR5RpWX1DwP33VQlk3kTD4lOZlYWqNyRYk+n1uo88U5Q0mCToAwNIaCoFVjsdQgnoTUfQe35tHrt7h+8xK2dX4NdeXmGslihuEITM+CUlHmkqpSCK2xHfN8XyASzCYxTmnSoU67v4IvI6yWxcHDM8ajmNlsTiPL8Os+ju2jhEZoE0MbGIaJISwMQ4ABShmfeq41KpeU6nyPwvIFhjRQmSSPUmrtFrbpIAybNK4oigrlS0zPQNgCYQkwDExhIgxxflU2T1kscoxhjCULSgqULgmkwrZBoDGyCtOd4XgmtVqCsksy6RBV/meq9Z+5KSxtXmSpqWjVgKqGWTv/p43SZTIsKcqSZLDg8NYuh4cjHu+dcffBh8zSKVlZ0PP6bHevsNTss766TPviBer9Gu1Nm8V0jtA5tlVQCxySKCWeRpw8jtg9PmU0mTAajEnlAtez6Xd6bN9s0+l1aLS6yOoy43nKaDLn8b2HTE5mLOYJ03LB8zeeZ6nlc+WpFS5ee52ljSWWN1voQiA+VUZ6wRIFM2SZcbxzGyUstOHiNjfwNhWOZ+F6DrZQJJOCKEkZj55w9OAjpvu7ZHf3+ORuzJNJzK1kRKRKSq2opEID2zWb622PX/36FlYzxl6SdL7S5vmrf5ZO4yqmWMYQXaAGBCxfKbg4fMTw5B5HtzJO9iOiWUHNrXH16Wv0ty6zfv05DG2RxafM9n9M+8rPICyHspzz8MPfZxDn5FisbF9ldWkJXSbc/sEn/D9//TcopWRza4M//Td+hmsvvMnWjTcwjBono9/j7PDHiB2HtZWQsOZy3a649LLLdFzy0+8v+Of/h99ikSpKrTEwaNhtOrUeL968SasyyeKCT44GPFzcIq0SAIThogHDcKg5HXpejZbvUV/2qYcGplDkiwxtci5FKisqbbC0ZvHK8x6v/NwqoesTGD7tZAUn2MT0lhG1LT648xMmswFJuuD03VtYjybI7l2mbQMrvI7lr2Ost4jKc0eAI2JC0aJUCWmyg6M9jh7e4s57/4ZuY5nZaUESw8qLIW44w3QK4vGUhn+JIrMZPx5yb+cjbLtJ3+tTLA4ZDU9ZxCmFN6XdaYNU7N16SDY3CL0611avkzUbVFZFaWQMZnOOPtljdjpmFB1TqRlSZ5zHHSas+W1e7V7GNwekUpKaNq//qseFl0LCXhczXqCMinJxwnS6xzRfQtoOTksjDJeykuR5TjzPUVWKrAriqUsezzEtwdbmdbzQZHCgOHzrhI8//gmy1LSbPV547XW2r9gYQBnnKNXDtBq0l1yULEminMHBlMHoDlKmmEJSs1pIQ4MJvSXB+299wNnpjDxuc+GpTTYvbfH8qy8TzzOSeYWcaLyWQRZLsqJCqQzLcM5psbWcMlFUVUWeTVGpjSE0lq/ZXO1QqZI8T6nmkkZNsbEVcPPKK7gN6G+2eOWNLRotH8s0QM6YxSPiE5N4YTIZDlEZCG0Qdk22NtepBT5uyyZOxzx6csxPfzpleNbic1++xFe+dA3Pu8vv/JPv8MGPP0E4Fr/yF36RtWs9St9CxhItDcAkK3IMwwQEWhfoXKBKKIsSkZS4HjSWHNa6dZjXGZoOD+5+TJ6laAWXL3+Z+ssOjSsurZ6mzCLKUmDkTVptn0Y7oLfV5dETi/ufPODO+7c4O/k+qBBhNAhMH1u4OKZLaIes93OCJrg9h1oBqQqY6T/mQfPCKbENUNJAygj7zEBnitliwemjQxajGdPhlA/vvc8snpHmCbpSbPcus7Vyiec+/xStjXW8sI5RSpIsYyHHLI7BdxsUs4L4ZM7De7cZzU6YxWOkUtRrPo1mnVe/+jSbV5aoNUOcepuNpTamLaiEJponmFlBkOT47S5Vci4a0YT0LixRq9vUrYrO+hpe4IJrIqTEQWAbJsJVWIVNGlXsPzglSUbkVUaMpl2E5EnOZDbho9tvMZwnxJnkcr1JOpszX8y4NzxmnJbkUlGh+ebNDtcu1rj2VIPei8/QWdqi3enTCmIsp4HpNrDCDXx3gdCHVMlvYgW/BNSRKudo+A7DU8XJXsWPfvcD7uxMmSxKrqz3cFaWcFcCesUWwrCRQqFqbbIi5uTx+xzv3yYuDMLGKm3lcfr2Mb/+h7/Bg8d77BwPMUv42re+xv/8P/qbxNYH3N19m3/zg3/E935rTJ4uCGzFl5/f4uaXL9FaaRJ0HdqtJVQp+dlfnPE/zAy0aGKYLcrUZTw/5PjokN/7h7f4w70PGS3GZKVEG4rArWE7PmbdR+YVVVqgxRg3PPcDm55BWmqSJOLx7j082+LaxR6/+u98jSsvrdNbadNd6lDvLiGMHGGYBPaLIBLKMmU4nqLuFMTTGce7J3zn/X0WxYdo2+M/+trz+Ct3EW0f4XY4MBeUlmbD77LznQN2nhzz3sEOL26tcfnpDldfvcQzb/xl8vkp0fSEg+GEpeYNnKDJk8E+i9mQxazgaH+G9puIehMdODQurZGGAXacs9RdRmifIi/x/DZ2YBP4AWIp4Gz/IWmSUWQl46hklgyIshGVSvBNm46/xivbz1Bf6mL5GsOd4JmarrKwRRO3vUqJZpEOEQ2TbDEiSTPmBVShRltQVCbzfEEeS/JFBZXGdXwcO0S0BablIJVmnibk2TkHp9a2cAwLr+Vz5cY2L735MpXQLOKE08enSNPF9Q1WN0PqdZ8kSjDrBfG9Jao0xVYlXuBhSI3MCn761idcufwUzz/3efo3tpGxIsslDx7tMB3OyCtFqU26ZhPlQOVI9DwmMxTCcmlpB1HXeIZFveqSm+dMWEvD0XBMGaWUcYbTbFIWikoKvK5FNjtm/+6A8VGMbzvYroPt+5hGhtes4TdDuisBeVFSlhWFLDg8OgMpiaIhL37hBrsP63z/X/+Qf/r//lu89e0v8cyVn+VP/k86vPrmRWotgz/43fv8zr/8HVYurPDmL/0JykSipQBlUnM8HM/E8y1Cr4MhDDRQpvrTuLtESUhFQuEKROghcUmyEVF8wvT9Q5486tJfWuGNr/8Mnl3H8V385QzXd1HCQkqbRsPhxgvbXLzeo5x8mQ/eeYeH9z4hKh5haIlhwMgw6JrXWBJdLvZrGN2KaW6g4z9m9lFZzUlSTZUqhrsZclJQJTmLaML4eEAWpWRxxng6pVIVtnm+pLWy2md9fZnltSVqS21Mz6NKUoqqQssKWSjOTg6JhlOmJ2fs7+6fw8dUQhAG1BoO7X6d5Qt9+lt9XN+jlCZ5cg76WsQ5SRZjWC6G6dBb7mEohaEFwvDOG4ClUY5BKc4HlJWSlIsUkeaQ5yRVxmQyZzKesfPoAJnPz/PJusJMXPI0Y7FYMD/bYZ5nJFWFNFtkaU5V5jQMTXeljtP0qa+0+cqLF7m03ePSpWVaT10gaC7j1ZpoeYwpDAzDwtAKjUJrG8NcAgSQo/Uc8jHEJXpWUI6niCrD9y3amyFu20c7kmh+gC4UUpeUOufevWP2njzkcH8Hz+8iGFBmcPjojHfeu8PJYEQmNdef2qDWsTgbHzAZnTI+OSM/G7PUtLCXt2k0mmxf32Lj4hKNjo/ng2d3EIFNd2mbbbuOsBoIMyRPYffERFspjmUTFynTPMYybWzLPn/sFZrWchNPWfiVoFd32Nq6QLu7xKJYcPRolzSPaRqaKxf7PPfCNV7/8ue5cKOHX1PYjkSINlImaK1wbInwe9gaQu1jTVKSgzG7T4bMJzEnUcmkgD/qNLDaAl03ce0WiZ2AZTCwOnzy9j77JxMeT8Z88wuvsHn9KhtXrlLv9TDkjDw2EMpDFQaVUFSZoMxzsiJHmhVBrU5W5AyGh+SywhAC27CYnS6oyoKqqCizEikkia44HRVUaUIaLZjN5ywyScP32GhdYu1Sg0atRrsW8vTKFl67jbJKEn2GY/VReYXKDDzXpcoToskpli5YzOYkSUGmHfIyRpY5VSqZzhVVDqqAwKthWjauZyMq83woWVRE05QsyymK4hxRoEEY4vz7QiOzimxeEo8SGks+Dd+jFrg4pkkOGCpDlBJTqvNkjlLookJlBSY2zW5Ib71F2PbYPzpmcDrl4GxEkWsqBNIwiaMpbjPAClyioxFxoVAYdFshtVaA6zr4pkWORisFZUWWlOiswKgkgTJQukTmBY57bqQr04LxyQgtJZbj4NZrOHZOPW9SVyU69CilQkmNbRhIo/zUPeFQb/r019tcu7nF9//oCTvxI/LZ93nm1uewXJvNi32WNkfMhseMx3NmsxSZVaAMDMPGkBqtFQKoTBC2iWEIhK2RlUZKRRQVZHGOVpKgabO+3aXZUsxnMD6dMpmMiOME//0Wru3j+jWaK2tcu75K2Apphz6qH5KXDqWso3MX9IJO02Q8aRGdjYgXc0bJiEk6xp1JvOMCp14RS5us9P54m0I1P2amK4pFwbv/8ozx2Yg0XiDLKRUZnmvTbbZo+nV8t0GzvsTqRpOlfpvuUhPHFhiJQmcVKi2wUhezcjCk5L0ff4/h4IRZ9Gkz8G2aYcja+gpLFxq0Vtu0NlYxPY8syxkfnnA8nDM5SxgcRShfstTfYml1nfWnmphWHcMUYESc7IyJioxZvSTBxq05OL5g8mhMdnREcnrMwWDE3ccH7J8O2Bvs07A9LARGpTjJFygUtin42oV1gtAhRfPgYEBSlDRtg7+83ePCcxv0bqzR//IzXLjxEn5tE9veAJ0BFVCgVQF6B9QQVeQY5jqG2cPy/hSGUCg5x1AVQR7gz4/xxyd0VIq7VsPut3nxF67RX1/HsmwGRx+RDCYYAoRn8uv/92+zexAxmUo2+ks8PBhyNJoxzXMcIWh4AZeWVvjcV65hOmP+0T/4OzRGPmtLARe2L/NL//5rdLZv4LeW0cUCGQ1R6QI1XiD1DFpLOOuXsL1lDMNGKQPcCblSpEVF4NdwbBfHcnG9Bqalz9+3yli/sMRWuMS21+GFC8tsPHed2mqXu4/u8bu/9ojyeMJWr82f+pNf4Pobr3HlC9/EIEGmd6iSW2hlUKXJuePaPsPvfwPbX6fT7uDsTJl/dMBHe2c823NQmMxGJf/47bskWlNoaBgGF22TmjBZKJe7ZUIpoOm5fOuv/UkuXXuZoHmZRfQR09EJo4NjZLbGYjLFcmNkZpAXJUpoasseYc3m9PYR7//kIbGKMTIFi4qHd85IShcMaAcxFQWFKslkzoX2BiqPmUQHSKF4pf8zfP7mF/gT/+6LtPs1vEAg1JxKCapSkccZpqOYjk84O9mjbkExG5JOUywzY3qakWZgLLWIJiVpJllMIuZnHqY4325tbjTwfAu/bkFk4dog85LhUU7pLMiSmHyaUZWKIitJZwnDg2MW05LRScTg8SH9Wpf+aoPAdEjGGfFgQXY8Ro+noAvwFIaUFHFGkZZsrW7R3ajjdEtOjo94+w/f5uDxCcPZgsbSKsJ0UcokVkOWVjdod1d5dOcJg8EJeZ7S7fZZW+3TaNYIGw5lCmVZkBcJIpJ4roEf2KhSYtiaColnarTvk2uDZJYwWYzBNPHKAEOkREWTII0YBS6WdnBMh263hlOHuhcQGstkZCxvNviFP/s5bt8pOB18wicH/5jf/o0an/vWBdav9rj5xgXuv1shlc3gLIIiRwgDy3FQeUKVBcg0QFYGtu1hmTaWIynjnDLLiaoF6WAOVUFnyeHixYsU6TrRJOHjHz/kyckTTman7P3ev0LrCsdsshS+xq/8xTd56plNLm72WG4ts0hSpnGEUc+5sPU8RC/w5OGYxz/8iN1Hj3jn4B0O82MGZ8c8Hvp0XYXl1SD4Y8ZcvP3PnzCZzZlORxwfv4OUCkMb2IaLK0xqzT7b19axgi2m4ymD41PakUnR6lJUNdKpYpiPieKck4NdzoZT8ixBVxGkJZ5n0W50sJsdGrZD3XWxek16TQdXlgw/epeHD8dEccpYpghZIpVAYrPRuUh9ySHs24ixx+l4QJIm1OycciLJ52Omo9t8kM2ZJCnDWcLO8RmyKrG0ph+GBAFsdCyurz/NdD5lEccMJ3M2bA/DNBGOzb6cU8Mj8Bu8dGOLjf4FtrdX+Nqf3qS//Sp+2MPyXKjmlNMh8fw2cTw+1zBqSWu1jtt+GdNfByshz34b5AMsu47JDcrBjxl/8mv8qf/Zh5yM83OQWCF54wbc9HyKoyEfvzfj7GjBrQ9v8+gwplIKz4bXl0Ou1X2K5YDXv/E0j3ZGPHk85MP391ha8fEaLmbDJcgV/WCLl5/+Fss3JM2eRdi28LycKn9EcrgPqcYOVrBrV7G3VjEMH0wLQxik85/y4O1P+PAHH/NPvveIWTQmK2JMCesbdW6+sMTlZ5e50GjTDJo0G32uvfkyqpKk8zknj29xaH2CkQuEmzFMhsx0youf6+Bt25TmIYN7/z1STwnaG9SXvoBlX8XTJugSXRwQf+/vce+t9/g7/3iPd4cDAg/evNjmV/7WMyyeTHn0gyP+wXcGFKUm43xOUZUSE8lYF7SskJ/72S/wn/1X/yErq9eJFhMef/hdfvSPv8/69S3Wrr/O5fUOg70h47OUuaGplE88jxicnvHx2z9kNIuZZQUNG6ZnU8aDKQeLfZQGx7CQVZ1Oq45raaoiJS2P2fRt3rx6nV/+q19g45XX6F1/Gi9oU8THlGlEtggQgXGekPIFxeyAeTLgdHTC8OyYUpcoIem0feZnEUUqMQd1jmOHybxicDxHG0v0N1a4vLaJ2yqRqWI21ByOYmzzXBT/eGcXdEw0ijn8+IxBdEpDtYhGOWc7p3j1kPXNDt/8lZfY2LxEPWwyT6Z8+NYtTk/POHhyyjSe4rgmluUxU4IoLikXOa+92md2OGXv9h7vfPyA2XCKlhbN2so5P0yVVEITBssMDk54cPsjZrNTtDJxLB+PNo5T4HkurcCnuVbHdATaBiFcPNvAsWEWCwa7p0TjOcKAy9e3aLRqNFshSVZScc4NGhyckeYZWZ6TTBImkyFFlnPqG6xsrBHYIWLm8njwAKsOnbUGS9sBpdlmcDrlOJnwwx8m1D4wWBQJrW6TotC89W++izBr2J6P36jRdhrYlo/t+LTDBl59ge8JuoZDpM+QMsNJHbzQIDRC3LLF6eGMNNUUmWDtKZeljaeJpk/x9oe3ifI9SjliHL3Dnd9XJLcvMdqeI+0GqgG0KtphTJZVpHHBeG9AZA+x+hXXzQuYssAyDVzXZKMhKIRHxB/zoPnx7mOSJCLLIrSusbayTVhv4Nol+XSG77vEaYwjz9HZhjTJs4LJeEZRCiYnA+LsU576fIhUDrbl4ddszLDCosAipUgSjosRmcxQZw6tmo1rGciiYHQypawUputge6ANE2nY2IMzCkzGowwjfsRinpLnBVAwX0xI0wnJ/AClKqTWFIAoctDVOVvLN/CCc2lQUqSYNc1Sp8WVZ6/SXwlQlSSJEnbuP8I2HEIz4OmrW1y5cZ31C6uEjQBRRagoR8UVuhLINKVa5FTawnYtbB80imzxCCM9w/LaILYwRIVSFTvf/2c8uP0B77z3kMd7c+JMYZqCC+0AxxDMphk/+O4Oi7GBISUrHZfN61u49Q615go3l/vkek7CnO5mk1QVVDpmdOLT6Dk0eyGrly9wc+05ms0efrvJ2pUetTDA810MIgxHYJQCIxCYThvhOCgjYf/+O2TzmGpRcff2x9y5u8Mnt3e482BEEAa0uiE3nt9me22Vdsun3ZFcXr9OGDTwHY+gOj1PYS0q3PUa4fIyXlADdUjbFkS6IM9jUsuk8ps47XVgDWFOSSc/Zv+9n7B3JjmdlgzTEemdH3Gys8t7+2MSDDzbRkrFR++NqRUWne4mP/+Np0iHE6LRlI/2D9FSkylNpTVr2122r6zSX1/l9scfcvfWfT55/xaHP93n0nzKdjRhdbHN/oN9okVO0FsjGSfksSSZB4wGCamS2LbLYjYiTc5BcDW7hutYeI5Jxzdp9CxsS7CsHLpejStba7xw4zI3v/QzhBs97IZmsbNDkowoy4LKDPFc0KIkLxOSaMhiNmM2jilnCwwbTN+kZgmOnwwZDiLmWAxSiyiVzKYJ0jjjaHTK6eiMbjNApiVFWjJPTRpBAFrxeP8RtmmgkoosTQmtBs16h3q7Qbw4nzfkWYHlXCIvEuS0YnhywuhgyPRsSp4n5/fngYVXs9DTClnkxEnM8d4hhpAURU7dscgsl1xDUmVIXSBsB9uv02z6oF1k7lLrrxGEIfV6nbV+h6DRwHO8c35aEOKHDn5oMRokuL4gqAmitKTIcpJZjGmfe+F9zyUMQ4KapgRyDRQlRVlRVoq8XjANpkTzmKKMmY0ipsWcdBzR6a7RbAe0lzx0mbDWd5DyAmsXr2AKA60U8zSj06uTphXj04rKFOdpNsMkKzOyvETNI2ajEyzTxcTCrQxs30YAIppDAEJLjDzneHyXqkoxdMXqapN2GNBtWRjiOvNFjzTPyTKDSRQR795lZ7iDNl3MoInf7PLqhU2G0zGj2ZR4GlEUBlrVCJsamxQDBaZAOgbKaoDZ/uNtCkejAwQZpqFotbZ5+sbXWF1Zw/XO2L9zlzieMZpOsFWAKhWmssmKEjmesZjlVFVGWkClwHIqep0LNBohnY6LIRfIYkGVTRk+TDgcjtidnZBWBYEp8A1B3RCMyhTHdVnrLJ1D8hAUVUl6eMjpSYIwTljMh1jSQUtBrEom6pBCxWhVsO741OsezaaHko1zqJol6a6E568nNaNxTKfvsrGxxisvvMHlGyFZtOBk54h8MESUDo2gzQs3L3Lllcu0V7uU8xnJ/CEmMa6VYdnbyNJFpTYEPlbo4ncsdHZKEd1Gygyv9Qx28AxKGeTzB7zzz/4e3/nJPr/xfoYQAs+1aHg2L2/38FouWWXwkz8aUCrYXqvzzTcv8tLPfo7OxlXq/Wcw6LCYP2A6vcPB0YCwd0o7EqytuARLLisbXV5/7QUu9l9F1EzyWkx37TKO1cDEI8sXaFehbTAwkVqgZEwx2uGjH/13jHePiY9Kvv37xzyczdhLI4Tr0NvucfHmRX7mV77A01tXaTgm6XiHi9ffxHM9kBGDt/4/FENNntZpfH6ZjfYVWlaL0SSiKQQDWZAsFswVpE6I3bqEUHXi8b9mtP8v+MN/dJcffpTw4W7Bw0ThBR6mbUFYZ9O26AQmlWnyk9+fcmV9necuXeIvfPUZqvs7jO4+JJ8OGCYV00rja7j41Ar9zTZJXPLD732bH37vff7o+5/QsBsMojMOTx6zfvIMu492KDLNxcs2k5MzVO5h6XXmixLDM/BrLoPpnCpPMISkF3QJQ58gMAgbCW7PJnAdWqbHRqfD868/w5vfeJNw5RVUOSafnzC+8zG5rJC2ie6V6LRE6oxFMiUanDIbz4hnOcnJDL/hUhMBQllMBglPHg3ZjRTTErJKkZUFuQTn8JB7dx5T0yZ5GZFXGbbTp9dcxrFNzmZPaAR1fOFQc23Wu1u0V5dYvrBCUSxYzGKMyZzFLCKNK2RRcfbkgKNHp0wGY8oyIWjVCeoOXs1EjRKoctIsZufODq1lD6/usNJtkEaKYpExzeaYVk5gNvFtn3Y7wDVb1GxBrdmku9Kh2a3TaRtkqaDKNCquqJSJMB3qvsfebIBtOdRDD12WFHFKukjwXB9dVqDOY6K2KzC1ga6g2QgwhI0QLsayZtQeM53OGAzOmB5FzCcTBoMdnn32Nda3l2luQDOYEjav0Flq0FyuE80l8aJiMMnp9SziuGBwahMTowwDcKjymCLOyeKCaHZMGQVUqUMaJ/Q7V/FMn2JxSqU1pUwp8hGD9PtYQhI6ASvBV2gse7RXfC6s95mNK2bznJPRjHuPHrE32mG8+xilSlxzg6Z7jQtRm93TEw5HR+dmxsDHrbl4XXAtjUJSSoPc1Cg3BP+zAfE+8/LaN/7y/xHPmlEPcq49c4Gnb16m1WoyX+R879f/Nfv3Dzjdn5IVKegS06hYWe6xsXWV1bVtVreXMcPa+ZKZJahECVoiKknLdghsE98yUOWI0ZMRp0/OePfBLaL5EXk6o0pypkmB1wrYfGYVmZTMxjmD44SzaYJUFgYWNdtmqbHC8lqP539ui1/55b/C6uo6WpdMo/fYPXzIJ/fu8Nb372OriLolWe9dQusAJ6ix8fwKTz/1OpZwODnY53f/4W+w9+SYwTDilz7/PM+8/CyXnr6K45VMn8TEw5gyPcPxFF7LoX2hRXj582A7VFXG0fwBpqFwLEHoBrjeZSyrjVQTDt/+LXbe/ojf/0fv8huPjxlkJYU2uLzR5sXnerz0wgpfev0N+hdfpLF0EeF1gfPMsmULMObE6QPG4x/z5O1Tzg4zTo8y7u8dMxrOieIUaVT81f/Vz/Piq29wZe0XmavvIYwQTzyHJRxG+S6H8wf81m//mMXjE/KzBXoiSHOXstCUWczw6IzRfMLRbMCiKBCfnsq+9MXnee0XrvLUa5e5svYFQrOJkDnJ7BG9IMRxbGzXIpcnmHYXy13HtDcY3/r73HrrX/HL/8Hb2EXF9rLLX/zlTYqL69QcWM7nPPjOGT95POeHB3OKQoE6X5zsCvjb/+f/BZ//yucpGxvE1V2KMqbMNDWu4IUt3GaNafqEd955h4/f+5D7P/gR0bDEwmFrc4XNNy9jKZfsYckf/WSHSqbYboXVrpMpSVpWjM4WFEWFVgLTaDFKDilk9umvwcMUBsI0kCrBFCaOY7O83qDWtLBsjS5TbD8kcATdmuQ/+9/9Z6ytb+N5ATL+gOn+kOlpxFw06G2t44Qmi8Vd3v+dT3h8/5TbD07wHbhwcYWbNy+gzgo2blxk9folWms3WZzuEU1njBeC/dGMcRwzXMzZeZARLzKyNMGNUpq+S1irETSv4bdtLN9AW4qV9T5hWCP0fQwLhG9h+Bb33/mAj3/6gCcPjnnh1aeI5gnJImY2PiMqFmhV4mLQanVwHRNbaLLo3KIYRQWp8mk0LRCaRzsT9sePScsEYTg8d/Wr9HurLC110b5Ju9ej02sT+BkITVWWjI5PufPxMYeHR+wdfkwhk3N4od1GyxlXty9x9eIV3F7A8HhMEmXUOkt0OwG1ekBQ79Js1olyyckkoZiP8Gs+YTNkuduh0gqpJErGmJiki4r9h1Na13x67SabrT7N9RWkSimKiMmkOl9CEya+Z2NaDrbn4HdCprOYOM2YxQnjswXTSc5omPLk0Q7H93cYH+6R5XfOB9FItJ7yaZYKAxfL0KyHa1xevszn/vQXaAR1HNMjlTazaJc4njAdLCgyQZ4YLCaas1HMcLbHcPYQzQSlJf+/Em4aDq4Z0vWvsdF1COo2XtNje9Ol0d+i1r/M/+Zv/TH6FL72+TWqMkTLDFs6yLkkKVImpzHDoynzyQKhJVsXNumvd1nd7FHzPEy7hmkFVIYmT0rIFZZdIaWJriSqTMiTksCFes04jyrWHML1Gr1pnaJ0yAoLaRo0enUszyIapeSxIosrhIZWLSAIOtRrbXrdBp3lJTrLHTZXV3n47nc4tBR11yJWBseDM0ZPZngzm95Sn5XVOtduPIehzwFUUib8wa//FienU3YGZ7AzoNdu8coXn+fVr7/MyvY2rZVVhCXI0gO0u6C+dBXL9bA8gRuCdi2UnFOVJ+iTOVkUMZ8teO+Dx9REg0rZfDiZsnP0gNPjAXt7AxaZ5EKzyYtbK7z+l77G9qULrK+vsdr18cMmjqexvDllfkIZL1gMY9KBSV7NSDSo2CMQkn6zoFiyCX2f6RwOdkbc/d338aaCq//u69TM6xicw/Xe/zd/h3fv7vPTB0Me7g5wK4ErDewEouRTBWCRkKc5SZZSlgoMg8tXtnn++Wt89evXuPLcs6xsXMCzm0STYwwl6bQvkx2/TyEUTruD3XBRYkZWnFEc/Ut+57d/wHe/fY9FnOEaoETASr3J9NDkYDjl2w93GO0mZEnFpgan6+G0AtrLbX7h57/CC1/+In53ibO9j4mLGMvyqNeW2H/rYyy3jt9ZwmuZjD85ZfDJE3qBj9eysFyb5Ss+egiHp2M+eP8hR8M5TS9gxW1Rb7fp1TyEYTJ25yjfwHQsGm7I+OACo+mE/ekTpKmpZE4lM7odG9+1cRwL7AqzNPAtk+XVOq4V4GqDQCqi+x+ziAboZpOySkmic7x1Yo2ZT3fhKOXRDx/wnR8dEscV602PG69cZPv6M1y88TLVdEK4tEzYXcJ2PSyngRMIGp7N1dVNcqmYZzEba2Nmk4jFNKLZCOg1mzTDBlXpM5svyMscXIuwE+AKizKSpHmEVXPwmgFFrqikROqStIqoNR1qjRp2zcE6s8liRZ5VnJ6eYBsGvmniOi7tdsBSv8mt2yfI3CPwPV64usGr7adxmjWCpSYtbxnfrVPz6oRLIbbjIkxBMjohjyqSqELOc1yjpO1bqNYKmczBMDFNj2bY49KlLbavrGLWPeq+TxoX4ProIiWeLVhMC9BLVFogKolpCyzLxjY9PNdDK4WUFaWRMxsvmI1jptMhxk4Ls6fwlYkIQgwUqhIgTTzPxHYshLCJFjkiVVheHRmX6FRhpzbL9QadmmZzVXNp02W03WV8eIXd/RfY27vFYn6ClCZLjVXqYYNmt0Ea7dGuBTS7IMixCHCFgWmYhL0VdLdDsVSQZjlpWhHNSybxhGTiEA/XmKcav2FiO5rB8Zzh7PicKCGnzBIHbQTYtkPgWXgeWFbxmWr9Z24KT12okaQGSeQwOtbMjmPmpJwcTJkMY4pSU2/W2Lp6gQvXt7l0cwtdKBZRQRwXpPOUIivRusSyc3R5flevipxqllA6FVWoqVSXiorCUhi2gRImFTbSdAibHhj6HMoXabQE27apNQJajWXajT795ZDaUpOgVYPU5P0Pfx8VD+jUfIzaNaKsJJmlNKjRDZr0u12WV9exEFRZxtHukLs//ik7+2cclJJngpCVbovnn7rK8sYatl8jLzW1sIOoD7GMnNaFNQyzjtaaokyYnpySJUek88cM7uak4ynR6YB3fvMn1KQkLyu+dxqzWyliDdqAiw2XFy+s8otvvMibv/otws4GrtNGywla5aByZLWgTJ+QzgbMDodEey0qQ6DrLpbZInAVZhVT9wHHxnQkZw80d390n2oBN7/4AW37GSpdMM+P+NHv/lN+8PYZP/64RNou3WaXRlDDLRVJlpJVBalR4DoeprYJSodW6PLcs9f52je+wOfeXKbdvobnr5BWOcliCKpiubPNaHACZkXdNfGdJnF2zGK+Q77/Q97/6RHvfzjjwnqIZfusrbdZXlrF3LPYm6TceyKJCuiZgo1QsHGtRX2jz8rlC/zpv/qz+P4myTxjcPiYLHPPJetti9HOHqZTozbNafYaTO4fM753yPZmDTM8Bxw2+i75g5L8eMF4dEqt7dEJPZZbTcKVZcKaj2uadERAbaNNrV2nV2swuFOwd3gEezGLfE6cnid+Ok2HwDNxXRNtC2q2Ryusce2pHi42IleIWU5+dkKkUkTcYFb6zPM5i3LKQhwTHY7IT6Y8+s4+x1NFrdXh2uVtXn79afqXbtLduk4+H2LaDUyrjiwTlLbQho9d82m2l5FCUE/muIbNtOEzrbusXt2g227TCGrMzzKMPcl8Adq3MW0DVSmKWDKbR1iZQyEhySoqAEdQyIqlfpt6YBHUErJJTCYVZVGhygwDqEwL17JpNgKanTqffLiDqRxCz+Wpa9usXX2Wxuoyta0OySRCFgZIh7XNJUolydKMQeRABKU0cYSg1bCxCHD0EoVSWI6NX/dY6a+yeXGJ1e0uJRaBF5DGOXEF80FJkWQkWUStVT9HfCgD23VwPA/H9XBsB1mUaF2hK0U0i5iMJkzGA3QkMQtFs+OSLhJ0pamKkkIIbMvAsCWVVGRpiUbhBBnpNKHMFUZhUms4ODUbx7fQay6LTpfpZkGjLSnLCmGYZKnHcucqvaUOy5ttRgOF60m8xrk/2UBhmmAq8P3GuU6gZZAV55vgiyShmVWoaQvV9ZnmTRo9CzfQ7N4f8OTkLqP5KZNojDSgVJqqBMOwqKRCpelnqvWf+frov/zb/xdsA1CC8bTO0cETRuMBZ8MRftCiu77ElZcvsXlhlbyCaZRTjlO0EhjKxCgFpmNiWgJbgBIglMKSJf12iFUVqCTmZDhgsH/I8PCEndM9kCVCaKzQp+bngCQvFNm0wK47BEt1tvtLZDOLbKZwxmckuWKRpewNnpAXGWiJLRRXLmxw+akNbrx8mWB9i6oqKLOU2SKnYwmcvGRy95j1VzdYubbNpZdfZm/3X/HkoyH3fhhj708xRUatafG1v/HnGM93yJMB/aoiW2gmc83OoeYP3r3Hk+MBj8/OOMwKpD5HDmitzy1WQNMQNAwf27AwTJO/8z+4wdNf/xqrv/hXMQyHeHyLdHIPr3kZy61jmDZVmaAyTZklzGfHpKYJlsa0JUoKdn76Cbd//AHf+dEDOhdr1Jse1UPBD+/tcjqPMYTBn1++xHGR8a/HR6DVp8gJmyC0KJVJVRlUeYXv1ak3PNYvNbhx8XWqRcLgyX3+3J96mWc//1WuvPolisUR88ET0ngG7Ssc7d8lyQaY/pijD49odJpcfuk60/tD3n7vI95+/0MutUM6UtIPHb7yl57DX/syhr9MXiQsFgOSyYjZwRE/unuP+w8OOTwc8Df/4y9w/eq36HWeIR2POHmySzyrkNUW6WJKPI6YHc554xeeJ+j6YEve/a1/zO/89nu8/+4Ttpsma6+s0Nlqs9RfwX2Y0C0ll+sGl/7Kz1EGFfN8yuHeFfZuPeLkYJdkbcQv/7n/EddvvoZlrPDB7v+DJ3ufcOfDh7z923cZnKbM5hWerxBK0PBd/uKff4ZrT91kbesGF575JYrshDw+JRo+xNaXyOYxs7Mjfv/D7zIbHpNNpjgngp8+GjNbJDzX0fz7/+0XufzSV3Br/1M0h5TZY7LFHcZHLpbdx7J7n57mbbRwcMIGlWEyGg258+EHDEcls9mM+WLEs199EUdWGElBclTDNBy0ZZD5OfWwju24WE5AlcMizxmmMccHu0yjGVGc0og6fO6Ll9hea1M+SPm7//Dvsn+0SzcM+cVvfIl23cVQOff2T+kv92mELX7tn/4mz9+4zo2nr/Han/4yjiVQGESl4GRvxmxcMpsoVpd72HUL0xfYmKgiQ6scYRYMxmNODk/46McfUg9crly7wOe++Aq97U2KvCKJMo4GBdF0wWKRcDTNKdKcSlYUJjjapawgziWr/S7NXkCz7VOXBtPJlGi+IJ1MmC3mTAZzHn5wQqlH3Hj2Mj/7S9/A69bYe3zM4/tHSLdB2PQIQhsztOg2VrBMhySPkNMSQ4tzN4ZtYnnO+V9ekJmK0pSYXsStd+9y9OSUsydjmn1NULcIAo/psEKVOSaSa8+/wnI7JAw88gKi0YSy1IigQ9uvoQ1FUiaMD6eURolyFb0t/5yQKgyszECmBlkiGZwsGO4PSeI5ST5htdMiw2deeXzv2//5v7XWf+YnhcmxwHNLLLOgKFNaoUm91mf16iWa/RC/4RM065/eQytIwXQNhLAwTRvfFhjCRGtNVSaIxEIXFVmZUFUuWhfnJqVSUlUVhSoBfa7zMxW2TkELhGniBga6UthmhRNnLB5PGAwThuOISXSCbVg4QhB6FpvLAZ4LrgsrT11mdXuJxmqDcLmFbZ6nFtzApVVfxrNqVLHGaWu8mk9Q71L3QmR2wNHuY4xFjllmmGcVt/6L/wZRpZRlwU5UklaavIQ0M7BzRa/j860v36RmP0d7Y4vW5gqtVc34eJ+j3cf863/xO0xmFeu9gL/2led49i/9ZTqXriKESzW6j5oco+MM0VOYrnsuKsnvkiU5GDWa21fwtcl8vMvZk3f46fceMjwZMR1PuXSpw9XXr9Db6jB9YYjzmwk7DwzePUv47viYREqU0gRWja3tLhcuLbFxrYOcW2QTxcHDAaXIsW2Ttgz56VtvcXFjiZ//hTd49Zs/T6vjIOe3IAspT2csTo4Y6pTD2R3iYoQWOdtbPVzT5ujWLr/2D7+HpSu2u32+/ue/TsNvEAYNOleuc3L0R0wevsfo/Qpj9Tpm3cdq+ViF4PrqGi9evspzT/0Zar6PnB/x5EfvU2u3aDfqVEZBcTjGN32Cl19g5cZLOL6gLOa0Kg+nMikqxf2Zojcp6fRNXt9co3G1Sy0MaS53qF18AW05BErT2AzobobMJm2aF26wtrFBEh/xySf/LeOjETKuuOhuMezv0dAVC0vz4o0uqxfWWblwkRe+9Mv4zhzHzJFn36YYzdG4hPUtZC4pzBhhLlhuBuQnJqOzgg8fJ3zhzTpPPfMMV9/4H7P1zBrCHrKY/28h7lFVTSrZx6r5aKNOrgWTdE6WGlRKYERz0A7aUGzduMhSlvPgzh12f7LLu7+3YPNyj/WtZdauboPK0YZEhR2kklSVJC8yCiMj1ZKsLFnoOeNJxvAw5aMHv8etOya+K9BRQTmseO7Ci/zJP/szrFxq4VhgVAW9vTnJdM58POWp1TWu3Vxh7VKdsw/3+IMf/4ThcI4jmrx040XcVkgztJnHT3CqGm5eQzQUVQWqUMhqgZFXNG2PG9ubNBpNNrbW6XQ7jHcGZElBlpeUhYvOSoRSOJZFaaYoeb5omOURRWWQV4J7d+5SC+o0wwYbqw0MJBhgOIpkljIdjhlG96jZ9f+/f7pu+HTbdbiwSmJ44EiwNKoQFNUcx/PZ7HfRKzm60MjCBK3QivOEo4wpZoo0q0jKEU5ks+S0sLqKmtBYpYnIHUJRYIU2XlARGJoqj0jIqNeWKH0bKTPmJ2csZEJVVue7KOMUw7awax6+AabpY5k2ftfDdRywoRsEyMtL51Y4x2D/zjFPDiLGp9FnqvWfuSkc7I6o1xS+rxE1Fy9wMF0fs9enueJjuefRrCrT54tNiE/lJeLcYOYKtBYopTCkxjA0ytBoLdFUSFVQlRl5mpDnKaXMMUyJaSuEqTAtAZ/ydmzbRAQmogSRlKTVAttStJcdvNUthC6hKtDzGNMp8QJBr+uxcXmJ/tYKvfUl6p0+ru3gWCb1lkPgNnFsDyGg1BHxIuLuewOePDli5//b3p/GSr7e953Y5/nvS+1Vp86+9b7d7r4rL7dLUqRISTYpU4pkyYgSW47Gscczdia2Jw4wwMRKYliDQeLJJEZsSHZsx7KthZQsaiUpcb3k5V27+/Z++vTZz6m96r+vT14cQm/DAAQSBP2p1wUUCvV/vvX8lu/3cY/RYIClfd+uNkx4tLWLLgVJCXeDnJqt49RtmisdVqvrbK7OceXyInPWizTWVqivdrDnfHbu2zh6TqdVw6hJzm4s89onPkD74hVUq0I6HJNND5FFiWZ1ELqG0AxQFBTNQOgJilpiViTpeMCsv8vW7Qfce+sBcZ4hNajWmwgJQiqYbYfljRqaKimWqmRaHRSLc4qDqzusbrRYP9eme6ZKPlKIBjkNZ5dROCZLU8xMJc+nVBp1rr18k9byIqKYEc4GJD5MRzHTUUxP9imUFEWDLC0p1IIwDZkchiT5jE53gctXLnHuuecxTBNNMSn0eaaTnNHRkOlxhOauYJsmVquGyDUaFZeV1XmatXXKbEQYDSnSELuygt1okUoNv64jjRqVC2eotOfJvCH+Xp+93RnjWUKpCjaXHTY2zrC5eYb19UvU5ubRGw20TgfUOdKoIItSCqGgVA1so8Lc2hqlUuB7I4aTPfx+SRZAkUrW11p0W3WyUOOlS02Wzi3S3dxg8cx1ivgpZdJDzvqQRqi6iWE3yZQELVQQSkYeZORxiRAa8xc2uPmhFV546QILr1wnjRTSaEQaHSJinaKskYsapaKRZIIoSRhMA6JAkucCYeioqoVuaTgNHYiJi5jhcMzhzhCnprB8dp5qp0IRnx5aZcUhCcPTvOW8IM5S4jQnSjOCIGE6ChgdTzg8fErvsEClRJYhH1h/hfNnznDtxhVyO0URJbqU6FTZzwoGx0OanRa1VhXTMfBOPPYf77G324PUoo5Dc6mDvVhHdRPyPCIOQxRFoUhV8qQkjz3SuKSIc1xDp1GvU6lVMUwLfzIiiVKyPKdApZQShIKqKSiqhqJKRKpSlNmp15amMOvPiLyUaJKhK9mpkJET+hOm0xm+71PICFWtkmcZo8EIzbGQhcStWAjNIpUxaZGRRimzSUSeRZiGjarkIKEoCxRxGiCEkCiaRIiSMs/xxyn+OCLyQ/I0QjMbOKaNXXepNcF2C9xqievWKcsM+f0XqkAqkOcJwXRE5EfMJgme52OYDk6ljm0KLFPBsnRoGGim+f0BCBBVDd02sF2T4XGAOkjJf6Ca0P8HovCVr36ZZqPG3FyLFz74AqJRQa85WJ0aqqqiCg3FsCjzFMyMUoUy0ykKIC0gFZRlgZACS9oohkRVJapiohiQewnhaMJJ7ymTyZAwmqBZAbYr0A0NhEXhh5ALTMvErVfJJhnBMCY2Z7zy6ed4+RM3eeHa53kyuMPdh7f41X/669zZ9mnGBh9dm+fK9TlWz51ncfUKmrmEJEXK6HQ8cPQeUXSIqu7j5xHvvn7E//gPv8tb44C6qXCxaXLhnEZGShJnxGnJfi6JSmgqgp8/X+eVj13itX/4U7itn0XRWoBCKTNm+QmTeJ+HO+/w3s4Oe/snrCxucOWVVa48f521z/3PKSePCR4/YfroiGJlSn3tRRpLN0jyHVBNFMWl0fos9cYBZX5AkbxO783f4e1vHPDF3x2S5AqV7unEwZ2tfR48PcZxXNYvXqd6qc3VTy/xn19bYGn5L+C4FzGti0hiijImKyIGwYhglhD6ETcGT7l/f5/DvSO2Hzzkw5c+w4uvvMjlj32KSe87pDOdZNrkYO8+s3GEn9pMFn1evPg8dUcw6W/x1ju3GI084qjgH/6j11ha+QCNuQ8ipcV0/IjJ6CnB8Cm9B3PEvkn15j3K2iF2dY66cway9xG6itIwSOIh8WxKHKVsfvIarc4NdL1OEozQW3UMa45u9yqCkp3XX+eNf/Vr/JM/eINx6tNta/za37lK8yf+AebGBzhN5MlPGzlCIw32Obp/i6233+Ek7+KLkMLJOee+jqGAis78xk/jnfwZT5884O037vIP/ttPsLZ2hUb9EopMKIsJkCNljhBdFL2NaF7HccegSrAtdLFKXpQoR9u8/c1dCi2kfXaOX/mVf4Rhn0EoOXnyO/QeHVMWLSrN/wzFhhKBLCTT0YzBKGE4Tjg+9EmiElkq2E4Nu6mCFpP1xjwd7vH4nW0evO8zGhyzeG4dTWtgNB2isUoa5qSBSh7kZElOFoM/VphGGaNwyvhJRv/JIb29p5QypmUsUFVN4nyHT3zqOleeu0RCzOAoxFB06raDWpfEusW4sBFr80TYeIFC0VS4cP4MpjB4994j/t1X/w2tapMLi+f45M++zGTWZzhKWPbmKRRJWRQU0xTfT8mSBKIZ1nyXRFFISwUfnVIXCF2nKAuKzKAUBlIq6IUAJQcBmm4hdQVpaviTE9IwZzybkG6NKJOYNA4ZDQ+QZYksJY36MtWqTRhFvP2tW8wfR9SaFapNG6WjUwYFaZgyGE3xBuPTCE5jj1rTxrJ1LNukWW3j1GycukW7WiOvRCSBJPctbt1/wPH+DgUhr370R2ivz3P1xjrNtQWarknD0vHShMmxx7jv8XRvjywtyIWK3jIJpxon05DtrW2m4T6OVaNRWaDgDNXFjKolcYompapCCUE/I/AyhJlhV3IyR6K3Dape7YcrCs9fuExnc5659XnWLi1jnPoAE6eCJA1JFQVVLShEdlrayQqkkiBLA1GoqCQouYoC6GZKXgh0IXEdQXh8wnRwQv9wlwd37hEEHnme0GhbuDUX1zKwpKQwDDRhYIsWZxcqdF9psHChS+PMa1SbVUwto/ftf8Z7X3vE1tMRz821+dSFC8x365y7PM/K6iquqZL5+0yG9ynzkjItmD3t0V6wqLbXcBb/ClUJhvIt/v7ff5d/8T+ExEjqKxnTaQFJiZBwcV3jqmrTmm/xE3/tA2xe+BSN7iZuc4E8epuy1CiLBpOpQapk5IpgLX+eRwcHlNt9VCKu3DzHxec2yZIB3s6ELFWQm2u0zyyh2zZZOmS082XUfWCYkiaP2Ht7l7u7E35zZ0rkT6gWGYtSMhCSllKwYKt84keXWDr7Mq2VS1RWN6m2VnCcKnXbJM++TTL9TySH/xFn+S+TJjqhlzB4dEg0LciSHK2uMNd0sNQu84ZGpdWBYIcv/er/jUblOdxFlcpSzObcVeR0hAzHFOKY8lbEg8cH/Pd/8Ot86OUa1154ng//6M+wOHeeQg4Ze++zu3VCsKVw+HDG/+U3/j1ZVnDl6jn+u3/6v0YxTLzxEfuP36ZwVYx6jWqlTTDMibKUUmgsdD+EEGOieJvBYILbXMGyHPLE51//4t/jm3fe5dv7W4R5xt/8L36Oz//sx2ltVMEMSSd/CrmCTCD3I+LBiK27HtvHA7ZOehxO91m+4LLSrLNYuPSfHnFyMuLR09f58IcWuHHjJ/j5X/yvWFpeQKFHkWwjYwtBCyENgp33ScdjyqzAdOtE4QzFUjAXXYpgyM47t7n3jbc4//wm525c4syVTWQ6Ic/eQkhBWVzAKBYoURFKQjwZE+c6Qe7gByWpVyIjSaWu0uq00Q0Hu2oxGWb0+wPubz1k+/07nOz3GQ9GBGmCjqChmsggJRzHBLOMPJWYdo4uFAzVRJFD0qHHZGvMyeND8kBSdRdwHZO6WrLgqrxy8Ue4cmWJRtMkPE6o1Rsoakwu++jlPIouUJyQ1AvZutdnK8mZjXNaay6rz7VZuuDwvT+7gxdEbJ885mtfUhn6M3qzCZZuUrdrp6lqdYciLZF5jkgTnNocddciXWtQrX5fJIWCDG2IE0pyjKBAKAW6JbAsC9etIzWFRBGIfI3UT8jChDj0iNIQUWTU3QqO66DrGkIryT2PIvHp9yboloOmdKjUWhRjhcQ/tQQp4xhZ5gRhyMG9J8yvrFKtV6k1qgRDH9ut4FSqzJqSOCwJ/YzJqMfC3AJ1s0qUxLi1CnZVo9bUEFnM0V6P+4MhD9/x6PUOCPwpdbeDU9HQNAVZKtRsA3N5nk6tQSqvIcuCsiwIoiliqiAp2UtCGq0WjmPjNlSUXBBFEYP+GFsMqak5K50f7Krwg+cpLHdpdNtUG3UUzUaVGkiBqioIvt9ILQSylJRZQZEWp117VSB0BVOoFKKkLHLSMCCOslOTqyxhcLBD6I2IZkOMIifntCErkxw9k1TQ6TY6GA0Fy6lQ7Sywvtqgs1Kne6aF23Q52hnw+PFT3v6zN9h6p0caC25+4ibr58/Q7NRozZmYpgtSoUhjZJpSFjlFUWJWGhiVFqrtkMoHWMZNTL2FiURRwbIEra7K4KmE4nSD9tpLi9RaZ5lbXuPGq8/jdM4h1BqhlxKHM4SioukKUjFBBuShx6N3Dhn2d5Gqz/w5F8suCb0B7996k+nDI3TdoNppUU4EJ+GEE/8YefQm3lFOOI2J06eM7vbZPY55fJzh6lBtKnRWVJZXdLrr6yysn2FjY5PO6uqpwZsjEOkIJc0orEUUtYtUVQp1TJLGxGFG6MfEQUiWlxSiRJUFSpRQjD36u8eMxxFFnBAPx7hOwOqay+aGjWnX6Y96jGYjJtkQ7yns7fcJ9kes/9TLnD//HEtLq4ThlDAYEPhDQj8mLVSEmdFd1anWznLpylVqnUV2Ht1n98lj3n/3HnGWE6c2np8wmwWkMiEXKbs7W8STXSJ/wngasrSckmcnnBzc5quvv86j3gmhLPnYp1/ig6+9ytXnX0XVBVn4faPDqCSaTPGGQ/q7u2wfaEzyErVeRynHyEIS9mK+/sfvMD0ekSc5jUadbmeTxbV1KrUzTAa7yHSKUijoQkdKKIqEZNIjnfmUeUmSSOLUJ5/llMMhtx7c4vjBE0YPj3jxpz/C+vkLLK5uEk22kVEIUkV3V8jz07jHwhOEnkYqNRKhoSo6qmGgmymWNFDNKqpmQSmYRR7TMCKNQMQqMoEkzU4br55C0tdInZgkiEmDjFJoaLlEKiWlTMmCiHji459MCMYTCk7jNyt2FS0dU8oE19Botlo4bpXj7QmWbWNVdEy3TpyVRFlO5OdEfkoZ+iR+yMHehNKap92t0u00efH6WeI0IVFKHMPBMOXpvo0CjmliWwZ2zUTmJUppYEgHs2ZQyIJZz8ObJKAK0FQSNLICigyyXJLlUAJoCkKop6XqXGBoLsLSTlPQVIGlqdCqYuslhmFTFgWeN8aPE8q8QFEKDEfFqei4rkmeKySlgiFVahUb17GxbIvBMECzLYRpUGoKfhgThiXTUYo/UZGlJM9y0jgGIRCaBkmJN50y3NfZNx0ULSLJZgTxgCj8fpJeURKnKeUsRRECpIZpGFRrDq15E9M2SNOEKAjZ2z/Gclw0zSCYTCjiDN80qLgGeWERJTlTP0C1Q2QmUDP1hysK7c15DMdCSkEwVilVBU3X0Ko2puZSyoIozygmKUUMaVpi6ipqTUGtCOrCIsh8oiBisjdkOhkTTXzCoyk7u29jqgUtx2St0SJMTIIkYhr5GJGkUbW5vHKN7pJDfblF+7kN6s0mQgjKPGH29Jt87V99jT/67e/xpeM9FnWd65tLfOSl68w9/xyqY5DOBqSljswkUGKKOTItQjELFs7dQFOr5OUe/cnfZa7+y0wGE259M2cngfmuysKqSW8nP01Zqtr8xM++yvzVz1GZv46iuaT+U8LpHuPBhLBQsWoKjbkUp10QeftM+g/41//y1zAbFq3lJpc+eZ0wPubuO7vcvnOA35vSrbW4sLwJXwv58pM7fH3/Lpelxp0yZZ+CFGgKqAmYU2DDEizNKaxe0vnk55osPPcpWhd+BsRZSh5S5E8J+u9zfP99cmnhXvgU8/M/hlZzUOwQzz8mmIb4k5hUpJRNCVqJCBOSvT6Hdx7zJ3/8LVLdQJEqWqEwCb7CB9pNlG6HSMKfHhzyveGYN+VpMMqca/Cj6y3+4l/6KyxfOEOWlzze/Y9EE5XSa2E48zgrU9x1hX/4Y5/i/PrPUausk+V9vvp7/0/eefMut271+eiPXMYwBAiJXZmjEAlRMuM73/1TTu6eEIwjMk1yZeMJezshv/ufHrATH9DVLK7PLfHL/93fort8A8VYo5QFhbBPe0VxwEnvNgeHM95/dMisWKU932J9pY46OYIDn713hvwP/+7fUtEcrp0/w6/8N3+D1eUPYFVssmTGg7f/H5hykbnqy1S6JrkckqQjxv0BslCRUoPAJ9NCvIHHwXs9fvnffwkRp5xt1fiZK+fpLK6jKB3GBw8IexlSQvOcQpQaZIkgm6gEcRtMFc3VqDQaFFpGbiYILyJWddIcgn7I7mCIF4Q4Vpvl7gbBLCctjyjImR0JTu7oGE2fZBqSBRLRrJHkCoXMiQsfvz9hdjRgcnREOBuhGhq2Y7LUqjI4GjD1A/r9Q6r1eWy3zqT/CM3W6dQWmVs9S3/vPuNZwmgvZ5qnCJkRxhHbg32KOxHp8gKLrSU+//kP0Gy4OE6FveN9egPBaKyQahmpkpMJCVJDFgW6olAzK5TNCsQa+/cGHI1nKKaJ7jrIOZChQhYLZkVBNpOUUoBdopQ5BQp+AamnnwbVGDqNSg2nqlOp6qy2TaKgZNSbcPetWyAcNEPDNm3mNzvML3fodtv4ExAxmNLEWDCptly8MCNWmjj1CoqpILWc2VZEMJwSToe4Rg27VmBYBZQlXpzghzNC/5D48ZTJkwHH3xyjmR2c+YzqcsLCRoV6d54gkAxHE2YDnzwsKAuTpfklau0G85fbLM/XSKKMySgCo4qiG4Dk5OEJo8MhWVIgcoGhdyiQxGWAWdNIhUnCD+aS+gOPpH72c79MvarRrFqsL51Fd1VUS0NxLIQWgwRZGJTllDBMmfkJUvNQ0VELFXpTxhOfyXTK0eETJtMpWRZDmXBubhGl9MniAWZcstldYnN1hec//SLtS5dwOnOomk5WHqIa4FSb5Ad/yutfepvf/pff5SvTCUlQ0tRc/sdf+EXmPtymcqZOtb2IzHPKJCSbjciMKkbFwGqY5GnObHLAdHLM8VFKWs8xXZVL1S6/+9//Lm++84SvPj3m6qKOVVXRqhofW1zl2qvPc/7FazTmqxjVMwhhE+3exs8aSLeKvtnB1dso4gF59hV+91f/iK99c8obb0VMZgGXn7NptXV2tiSDcYmp6VxYafKBD62x+3TKn/7JNv0gIc1yKEquCIc9GTMhoyo0nj+v0XIlJClPPbhxw+av/OwcN37i/4phTxByi2R0C63+Yyj2ZZJ0jyyMKUqLUl/nZPJ18kyDbImH773HuF/geSrN6w7dShMjVXjylbf43rdvsb13xKN+j8XWIktVi82ayh/f2SLKS0pFIZYlcVGQlCUp8JnNl3ntwx/kl37l76La/46xP2Zn32C+cxFDM1EQTEZ75EaG6licXfwQjw++yq23bvHP/9HrWJnC+kqXV1+9zCs/+QHQIElTLHuOh3fe4uHt2/zB77xLbxISZTlSCITUSFNJGJW8uvQJPvEXr/CZn7/C6uZFDrfv0dt/QjCaoZZrqG4D7ZLF1bPPUbGqkCvcuf0m73/rPm99+Q5fe/QdamUNo9B5u3+L/9XPfZZP/8jH+ejP/zXi9BtMjnbYe3efxRsvoZoqUkakk6egC0pRMOxtYbtraKpLFs/Y+t4Tth/u8e7b93nh2mVWzy6xdHaBneEh/jQlCwRz4iJZElMoAtFssXnpLHa1hhQGx3snCM3ArNRwWiZTL2fmJUzDEyJfw/cjdo9ug6ziTSS7j0/9t0bjAw57D4mKXS6tfYArmy9z84OLVNzTdDm/0EiDgNSLCY9Dtrd2GEym9CYzgtTDMHUsS6fayTjv6CxoKhUJP/63/xrthQWmu33+7E930VdMlj86x3L1DF/4t1/iC//uPzHMRjy3doY5t8rh7jYXzs5z9vw6r37sQ5y/tkhtbhGns0Y42ePkpMdJb0CShEyGKdNhxP7DQ6Si4jQsVi622L+vcP/BY77z5rdo6Ss0Og0a3SaNi2cwjNMbgEx1+kFAGBcUsYqCQpZJoqTEaWpUXYeKZVNkM1zDpOIarG9YqFkFfxxx//YDev0T4nhGlk9YmN9kdX2R9c1F5lcXvv+PXZIkJbFM8IuSo0hQiJI0lXh+yc69J6feUIdjsjijPb9Ie67D+rpL73hMksQYTk5wEjPo7bO98zYFKWfnXuDa2od47jMlkzDBC0ryuAlljCIFRl6jsEE3FRxLpeoKbKeC49YxjdNpxyQtyLMBe3d9drYP+fo7f0Sc9097XGiYmoOuX0A3r7N7/EMcSXU6Teo1nUbVQqubKJqgEOCFEWXunY6DlSpa7pHGGXGYkaQDUr8gmaUMjnbwAo8oDgmDCZbhYGg6umFiWAYNZ45GZYGl5WWW5ufodtu0zi1i1BzQS6RVYIga8WTIo3e/zRvf+QZ33tzi/sEJZ+fXaF9eYG1zmTMfOUf1Qg297VKqDTI/QAoF1cwpdRfFtFFMB9MwqaoVVKuLMENSI0aWGcFBxjuP+7y702caSfSGTWehTXdhhRuvvMTGpYvMbW4idA1FawAqevs8TllFWjqKoXD8+Fs82b7NO3e+y5tf2+fh44S9k5L1ZZ3OWp3uSo1q2yGLVapunSvnz3Ph0jzd+R2iYcQff/uAPINSlPTIUXSdlm7SchQoII4KNFHy6Z++xuXnzrJ24zqGW6IIkHmNPJ1H5jqKBEU7Q2EMidKMmT+hf6QwnfmMJg/o7fcpstO1+3g75cHoAG8w4857DzjaOyKJIjbnamQiJ8ojRoEgLkv8IifLwRIqhoRmzeHH/+JLvHTxE2yeWcYPv8JkawupuXRa56k4FkEwYTI+oX98wGAc4geSt0Sf0fQOUTDhUz96jmb1Iu2Ozcq6wqR/hFBMFM1EETFhEBPGKc05m0QvUcKUMMghhZpbYW1pgU9/5gNcf/UsncVVph6UZRtDzTno3yMTI/QyoSvnOL5zj8ko4vb2IXv7W+w/PGL70T4n/SEzQizFoKG5uE0Hs6OjOjm6mMd0M5w5n2m2fxrMrmqEgU84mzGbzLh9+wFVu4etW+gio2q7bF5Yob6+wNXz17ArEsUK6OibNFsqWawwfRpxNOgRBAnaIAJLx6nXUJQKR097oJuYzZR6XmXm58yChLRIiUufSISkScGkB8OBx97+bUQmyNIIXdNp1Tcx1ZLpbIsnD326bQvL0JiGBcFoRDQLmQ5C+sMhYZIh84KKnqNqAk0D8owyLUhySMKYLInRTJXWRhezcYBmqhiFwdP7Bxwd9ZilHqAx9jyyNKYf+7zQOsfi2iLrVy5g2Qky80lGu4y3d/G8mCTMSfMQSommgW3rxLk4DaovVKpthc58heX5RUTuIJUcPxoR75mY1DCkjSINZsWMqMxIc0mZZeQFpKVKbrjkWkZiRIhgynQMmqKSZDaVSp0iLZBGThiHJGkIakJvcEhZpKRhjFEzcV0LTVHxgogwK0mEhlWtURQFiAIjzzAbDvq0gjATgskTtKmGUCSVaoEf+GRpTJKmJHFKkiXkhSAuZpxMnmIJh+7ts4TSJ5Y5mtPAcm1MW8OuusRFRBanDMcxx4cz3FqD5lzC+socwlAwdIO2tYRtRDSXHDJnzM7eE2azKd7MJ4omxMUxWvFDTl5rr83RqRg0XANcgyIpSdOCURhSeB5ZlhIVOVYYUiY5WZwSjY4ZncwY9ic8OLpDVvoIUeCaNufXL9KoN7ArNRxTsrQ0x9VzGzz/ox/Fbrmoloo3GxN4U2Q0RavrmLpLf6fP977w+/zql77LNEowLYOfeekDXPzIVdZe2KSyVEXTTYRqIqghhYbQTJTK6bKPYtigVtD0Nq69hNNIacxFhHGINxiw+/BrvNeL2ApLXNOistRm/cIlbl5+hZuf+TR2pY2qV5Hy+yHiQsFaX8eQkjz3CYI97nz7N/nDL9/l33xxlwKBqqtUKhaXL7mcubbM4vkllqqrNAyHZq3L+pkXMM0K5zfus2oX7O9o7PfHTEKfSRHRsas0bItWE8JpTCpU2i2Dn/vFj7N24TV045PI7D+C1JFijaKokeUOpCGacolJFDDxPY4GR0yfOhweezw6eISSF1SrKnVXY3Y75P237/D4yVPe90e4usZ8zebKSof7gxlBmrCflGSUIE4jgeaFjm7B6kqL/+bvfI7GmdcIswHvvfHfMHnSZH79JtevXSPwtxgPnvB4630GJzMe3xqytzXj4DCgVXe5fmOdf/jLn8VtvERczhhM3+XbX3gfTVSo1bvkuSAKY1BULtxYwO3NGI9Degc+euHQaSxy8exN/sIvvEqjPQeqS69/gm1s0m5v8jCbMBV99HzCfFHjwVe/xvfevMev/sk3SL6/WAgKQkCqxMRSY92apzDB0yMKOcGwVrHqFtb8mAe9P0PVq7j2CrPjCf0nOxxt7/P1rz+m7TjULJOGY/Hpn32Ntatn6Fw+h1AWGQ+2ODl8h7WND+BW5wCdr3l/wMn7A072xxhmQmpmOPU6utbiaGsIjoXZjegWklmY4kUpqh6TuTNSLaTMDA52ck6OBxz23kbXXEzVxjGrnFnYQDAhibd58nhIMq7hGAazScS0f4zv+4y8kFiWCKGjqRYVvUTo8nQpMssJ8wxRpOSBTxpHaLqKtdCmsmqjmxZuUeFb3/0eu7sHpORUnTazOGQcREyiGdVOjaUzK6xcPU/w9F2y8ZA4jjh8exdPOoRahZQppWagGSqNuSrDSYJEIfEkjQWdM9o8unyRo0FIEI3w4yknu/tYQRc9raHqGqHpkWopiV6SxR4lCqVqUxoFkRkysxScIMQbJmRJwWRq016pYRoqUkmY+pPTFLx6zmB4hDeaMTwcU1+v0mnVsUyT3tgjSS2k6WDUNIRUUIVAM0uqc3WiQBBMoX/8Hr6vUBQ5hgV5GpJnIWk4RSApygzHblOEMybhIWE0ZuE7TZSKB5UCZ3UF03VRHQtzzUTMMvJBzMTzONjZx6lP6cQJ9YaD4bqYjkGz2WJ+LWc9bdNdafO9795jb2+fnb1tTo4mZOWQpPjBktd+4PLR3/o7/4wk9UnSGC8wUewQy1ZZbC/T7AqKOGO853Hy3pD+yTHHg33CpIefT4lyj6zM0RWNmuVyZeUMV66fY3Ghy3J3iYuXrlBpO9gtHbdaQ7U1hHkaNuLvPGD0ZJt3f/dNfvutb3E4HpBnMa8uzHP5hQ1ufPIa13/0p9EVC6UQEGaodh3FtBGWRikVECWKmpFEGWE4w/NGjJKAbHREcnzEwz/e489u97h32GPHu0eSJlxYWOavf+IzvPY3f5zu2ibN1hq2aUNZImWJUHWEUBBCADne4A73Xn+Xf/uPfoPffvxdxlGAlJK/+FqdT332RT76o8/T7TYha6LIOlZ1gdI/okg94jSgtf6zqFqTMgcvGPC93/gtvvebX+TpUYZq5SAKvIOQXjTm3I0Of/+f/AQrz/1NDLuJJEYWY4RwKEqdg8M/46g/43gUcHuvz8l7BwTDnDxvEkSHxFFCFBSsVHIYQHRc8I3RLUSZ4lBywdDonG9iNi1yS2deaSHKkjjzifYP2B0n7EwyklLy9/4X1/npn3yBudf+Br3Xf4Pp/iGT2QorH1zBaiqg+fzz/92/5KgXE+Y6l29UONyZMOnFzNlNfvxv/SUuvPg88+0XGI9/myJXUcQVsthheHCf46fvsPsgRalq6A2D2lqTh+8/pX8wwtud0ty4xNqZM7z04k2Wl86imRaoGioacRIz82e8d/stQj8hmUaET074zS/8FtvHB/QSH1M10VQLTXfQHcli3aFTsWhoJtWbC1x46Tn+3i/914jyEd/4wz/hH/0X/wRrwySXOlGk8uD+EBPJfE3jH/zcCs9//peYO3eTMi7IFZ80mxLNjvj2V+5QFiau06KqNXA7JmbDJhPLfPULv8/dt+7wcOuYy1dfodFawnbnOXp6F6tap72yzsbZeY5mGUcznyi5h5ZXCUbwva/vMfC/R5J7COlw89yPIhUPP9/m2pU5tCgjn8Xs7JwwiXyyPKWuaIg8Jy9zgjJFUWpoqoquKDiaQNE1FE0lkzFzSk5bg1XH5CN/+fPMXzmHtb7Eo70B4YFP+GjML//6vyDOdSynxtmXHVqFhZUIor7HX/7bH+fy8+dZW7lI7w9+H6SK1V6mtEumYcrYixmeDLDmuliNBk6lxruvP+Bw55Cj3adceeXjLKwvsXimw+HYY+v+Lg9vPeErf3iLWdQjyX0QGrqygKl2qZpnMBsjFNUAWSMTRzhNg2qzwkp+kbwSUloRhgjRtJIsyhluh9zf+y7NdoNr156HyOPkqEfvZMjGwjrdTp1mo0qlW6dxbpFMs3iynRInCYbtUO90aXT1035pf8qdr79LEmRQSCpNl/WVRWSecPt730FVdCqtOu2NRWbf7bM9vM2T2btYapNrCzdY7WwStxTQUiy7ytLCS6x0WmiaJC1C3nn3fWbRlKQMWJir86GPvMLFy2cZ9g/xR4KiAL0l8aYHjEYj9vdO8N99wDiGoTR5884X/t+e9T/wTeHoyWOEKBEqGJUqNWljRBbJbkZvLyAJPMa9IU+f7jLxeszCHhWh0FB0WnYLxdCZ67bpzrW5du4ci+fP0ppvs7jUobOyiqqrFErOwItIDjzC0ZQH37nDg4O7HPb2OXp8yJPegGa7wWsffIHnLrZZXF+kfW6DsDSgOPXOqNgVpGGAgCwIOTgaEMwi4lnCo1u79IZH9Mb7zLKQNJyRBjOG2wP2BiHjIKMo63z42g2uP3eRc5/9IKW1xsCrMIhSGpZE00/38ZQ8w1BUZFwyvt/n97/2+9y+8zZvbN+nSHUura9x84VlPvMXznPx0jKLnS5mGiAMgWIoaKaFVDYpS4Fe6KhqgzQaMO3d5Uu/c4eDR48Z2C61SwZpIkmTHKF6fPZHfozLN88zf/YmmjGHRKNEEOcJe3tP2d5+wrvv3sGfZfh+ys5gwvRwSBqVSBGjlTFKBkQ69496JH5I6IfM0oiztTkWXBfDGqHNd1CqNuQZwnXJophJmLH84jKVJGMtzFG0JudeuIE5v8Fw+1sUFRfn/DVq1kWqSxOQPaLZE7qtCo12BaNlc+n6VXr7R0wGYxzVoOFMEP4WiWmTjhV8L2IyeYfRsUeZRpQ51NsWRq2CWavhWmusdnW61ZTGK0tgmyRpxq1bdzi594Racx6n1uXdb3ybSRkSixJNsTna6jM8GXB49JSd4QA/z9BVg6Wzi6d++XmBqig4jopmC0rLQhUZ0fEO3/71f8FXb9/m1p2H3D8JWNRKOg3JRtPhxo8tsnL2BqtnrvDilTU6ZxYx7IjZ7AH+TMf3Uka9kEFPRTd0DNPEXuhgVB1Uw2SahNTqTTrtRe49OubR0y2Ug0OkaiCCFNty6Z30eHhfMktL/DQnzYYUOcRxztCbUmJh6DqalpKqJygiw1AFflhS03VqHZ2XOxU6cy0arQadpXlEmVDkCUkSkhYSITUUaaBmAYU4fZwymSImY/A8ipMhewfHDLOC+mDMk7sHHOz32d4+JAp8dNvFsXNcwyIYB3hBimGAbjroqk0SJQR5jq5LLDdDr2moAkQqqHZaaBUHVdfIswK3YVHzK0z6Fe69f5+trW1qb1osbl5jodFk7iNXOX5T4/2Tb3KUHYO0EHKCLGOKsofNBRAz8vIelqJjJisUM5v3ot9HNatohkXNyECUZNmpLYiGAZlk3NtHTQVKCi3dRU0Kgt6MfBKReimGaUHFpRSg6AZpKTmZDPGmKpqhoZguz33wOv44xp+EHD95yvHuMYIS2+qglAUig2gwobYEi0qTIt1gkE0JEo9xNMQQLvE0ZNQbsbW1g2vrKAqU5AwHPgIDXXORQc5ddxt/nKFpOarhoho6RVTgTQpm4xJvLFAqFVrNGm27+wOd9T+wKISDKZapY9oGppuhRxYkOaNJn2g6JAqnzGZDDsZPSYsZiJCKVccxHGzTwqy4bJ7dYHl1gcuXz1JZ2aQ612BupYbp1oijmOk45ODpMdO9I4bb+3znC1/mrf5DjuIxmmnRajZZvXiBD3/y4yxdrlKptXCdOZJco5CCEhVV18mDmDSOmIxG3H2wzag3Y3YU8+bX3+eod8DRZI+kzEmLhLRMKWSEohhoqk3NWeHmpZe59vwVGtev4U80glmOl4xpGBLNEKiqQHophigpvYTDr+/xxS9+nfv795jKGecWV3n+2jo/8ZkrfPATN6k6FqYiKMN9VNtGNR2E6oBmQ6ZRThQOJydMh/c4fvpV/tN/eINEKJh1i+U5lczTKVSFyrzDh37qE5y/fAGUOtPIpwRKITk5GnDnzj3eu/Umb77Ro4wleVIy8hLSNKAEhCZwctAzBS2BvZOAsJiQSB9TNWhVmszXa/hmQOm4lKaDLmKkZZLnGZmUuGst2lqBVubY6hJmtU3PUykm72Avn8dszeE4LqrylGR2SHC0zVzdxlmsMHe+xfrGeTo1nUnPQNUlrpFShgMyc4tkKvEGEcf7exzuHOM4dRrNORrzVUynjmY20PIW3WqG1tFZvXQDPxrz+NFjbr/7PtuBT6uzRr29zpf+w2/hawXStdlYvcyTWzucHPfYC/a+P9Nu4xgaSxsLKDIjD3y0UqdigmNpp3YMjoaYTfjOf/ot/vWX73I0DbEtFduxWezWuXymxQsvdrnw8idYufQRhJgjT5+QBDvEwRPCcQt/ojDpZwi1iaKboJtY7TaaYVNKwXjyhLIUaJqNqugEvk+ST/GTmJbeIgtSklnApJgQZRlRmpNkEVERkpUpJQW2dRXT1LGcESkDTEXBUhWKHFRHp1o3uLI6x/Wrl1jdWGXuwiaUIWUWk4U+QewjCw1KmzIck8qSFElOhnfUwzvqcXj7AYMwYrrXw/d8tt94zKPDQ26d7INQsPUS18zRihI/DsnTGLfVQNMNyBVCzyeRJUJXEE6B4qjIoERSYFYdFPN0SihLMqyqRa1Tpd6qcfC4R+jHkBYUUYsLLyywvNFhvRmxM3FQArDUKqqAQs7ws23KYpWSMWl5F6EukiYNolnClv8dDHUJS2vTsiRSaJRKSaFFVN06tqaSeFNEaqEVgoruILOMmR8gS0kcJBi2jd7OyasWaAZZmTPzIybDErfhUO9WWVnv4lRTdH3G3p2H9Gc+mqJSceuoeQx5Sjr0aCxqtGIXpkukfkpRJvjJhLZikoYJk9GYR0f3yWUKfN/NQXFxrS41Z4kiyRBim35vSrdbpbk4h1Wx0ZKC8TBmMszwJiUV26Fan6PW3PjhisIr6z+Ca6iIsuT2vcd8Zft1DqbbJHIXZIkiVFRMbMVg2V3mbG2TuUWPXOZIBZrdGleurLO4ukpncxFnroXdcHFqFrvHfR7fecit19/hy1+8y/Fom0l4QFoMEUC7Ps+Pf+Tn+fm/8xm6GzVi6WE5TSp2g4bdRFUEeZETRQEPb73JW1+5z733tvjGW99k6pckWU6aRxRlhBAKqjDQRZWSHAmoSodW7QrN5iLnLjm4z4FfG/PkrRFJPGE8Tjg8Cnm8+4TZLMKfxZw82iGItkizAZQZUqZYqsOac5W/+7/5aS5dXmJlsYE9baMUBlR19JXnUNQFhKgBAim/Q+/eN/naP/5n/OOv9jjwclIpUISKpoCmlJRFwfn5TS5e2OSzf+NjaI0pW72vsr3zde7uaBRqFd1t8Rv/+9+lVCrYjTkabR2/HOAnPl6hYbeqGKaNJioc3zoi8UZkeQ9T1qmJDhV9gYuLayTOMSfaEWqgUd7vU6/YXDzbIvIT2kLl7No8k3tT4k6J2S6JH77Pr/32N/BDyf/0r3yYtavfQMZj7v3eHZ67rhNOBVu3JJWLLgtXP8HFGz9C/OgO4ZtbzHZ7rL80T/fyT2K2usTjW0wG+0RDHzfKOXPmJqotMSslS5uXyCKFaJKz994t5s9ewLQqHD69j59G7D9+yqO3H/HN994mSFIKCciS852LdCs1/vj2HzOMR+QlNKwlLm6u0JhzcRdNWhUDEecQZMxXNFp1qLd05l68hJnqHDw+5P/8a/8CL0g5v+Tw4x9e4mf+wcfpLF2kUjmPq9XRFBUpfLLsEdPeAYkXgFzDrdYwjBjb7XHjk5/A80P6vT5atYGXjBiOj3n7i+/xvbffYdAfMV/p8qkPfAJTrbK3NyNXEmzXpVpvoDcr3H3wmLsPH3Dr4BtkMqGkBBR0fUa1XmFxpU06kVRMwVzLZn6+StPV6DQsbrxyhaW1RSoNlzIaIvOUsiiQGajSRgoBqsRouVgYSAwwFea7Z8gvxKyfW6O3l9A/GbP16AH39p+wO57glQkb1RrtqkLVSTm+/5CFdoPFswu89NEbNOoKwfCA/tEEvYjQzBqqpVLEKfFoincwQbgOVqWDblbQFI1qxUXXBa4b0zmzgTeSjI4Tfu/L/5Hl+4tsrG8QqCFCDWkYS7zU/TyK6DFO9niz/wZR+W0kOZKcUbHLqNg/fd7ISYoBaTkjSGNcc5N2Z5WbN2+yUFfRyoQimNHf8QkijyCd0RscME3GRHmMeuKysD+l3lmgdmMZrXsEWgWKRfzZEG90zPGTiN2miZLalCGnZbogQFUVKjWXpmMhU4PQS8kGMbYNq1c0OvtnmGQFqSJpGC6lkRJrBppSRZbhaclaKGiqTSkK/GJI6OscTLYo70bYhsbqwkWarQ6NeZck0cnSgjTLkfV5ErfD+Ied0fx73/pXCHH6Rc+8iH4wIitDBDZzWo2mU2Ox3UZ3dBzHpuromKaDzHVyDFTLxLMNdL1EeBG96AnqUwUj1vm9L7/JzsEhh70DdgcPCZMZJRnLjau89CMXuHzjLK9+6DWWLi5huxYV0cZQDSQlk6jPu99+iwfv3Gfr3hOe7G8xHYTEfoZemlQqOqaikCEoVfXUnpYCERYUmUVZllhug5W5JlVHZ7bvcf+rDruVIYZ7D0NPCeOA0XTK7kmfmT8h8KeE/lOKYoYiC5rmHN1Wk8WVDtdfPs+VGy/SaTZQpMo0yqlWK+huDUV1yfPHDPYf8IV//EXemfQ4Gg7oPThhb5ZQAq6hYnVUqhUDx9Tx+hHmhoJyVqHdqbFz+4h+b8ijnZiHJxFlOcLSj+icXcfzYvxgRDLOMDQTq9LCG7WIxgGiLFHijDiYYEudZeMqx+kjbNem5Vp4xRFlCqrSYmHRoX/cZzoJGR4YdFdrrG2u8Nz1y0x6jzDaDtZclezwgAuXHxMGPmc/vEHvrTd4fPeA3/5OjHE3pcygDAT/x7+8yNLmPKqxilF/xPrHP8Vi3sSqJ+T5AO/xE47vbjOKTErFQqvXMOoVFCmRccnB7TGqqqEbNuc++FFyVZKJjLw0efCd+2w92CbKx3zsI8+zvdfj/qMDqo0un/yZT/PSzef44q9+ke3DHoksmd9coNWuUatbNDomC/NdXNvAtTXWF5u4Omjk9IYnHBzvkJQef+e//Rx6e41KvcL8nMHS2gKG2UUVc8TJEUWekmcZ00nAbJBSRCW2CaZpIC0LNAfF7qIRY+QmolnBCgtcP8I7mBLMAqI0JkMgV3OWL3Z4de1jRJ6PZuqYFZdKvclrs5cYTwYcHL6K74WMpyF3Ho+4d+suZZFgmxa5kqHrCpajodcV3FqFVrtJa3H5dHkzVYiHE4rQJ89z4lISy+J0vFdR0NUcRbNOhzM8jWl/RjQLKPKC2NTJaha5a1Nr11nUwE10Kk2dQhaMZgFJIag1XFZXO1w506FhmihZgaaUNM4so7s2ojSJpn0s22HxTINCVUhjjSTMmQxGTLOcQoBpN3AXHGpLKquXBLsnN8lEyTBO0dfbnHGvszAJGI/fw60IKqrFR+Y+xb2nd0jSCA2XFoJAluSWxX/1v/wvaS5XMF2VzPNw5pZx6k3a7RYzb0gaRd9f5MuYTnxGoxk7d7fYfXyPo+NddsIhh/EDZrMxG70a5XBGmBwxnj0mCXJs3aZqV6iqBsLMUVyF+kod/6QgDwOO+w8YSpUylyRZju6JU+tsuwBdx08CQj8lfaASBBPiJKFhVqjXzlBvVJlbrtNsN1EMG6HbSFUiBMiyIJiOiL2SOM7Y3jshmE0oComimqB1sRQTXav8cEVh6+BdSnl6qTx9bARCaJjCZt6ts9hosb64iFa3KEVOToqmaGiKiZQGBeClEdKfQBAz8z0SPyMdSr7zZ6/T96YEeURWxJi2hW03uXLhKh/66Mtce/kc56+epRQKKAqaYhJP+oyGQw4ODvnmn32Te997n+27TxgEY0BDVU2azhyqriB0A80wELoFZYHIE2QWIzQTTdFoNzq0KxYaJeNxyOFkhKIFSLuHbRtkRUqYBIxnHkE0I05m5DLC0FQc3WKx1WFjZZm1M/NcublOtdJEFBZhmBKEKbgJqh+S9Y85Hn+XJw/f5I9+93d4Y5IzSk+neYQAU1cQqkQxJJopMB0FpaFjNUE4KeODIfff3uf4aMzhKOBkHJEmGSLPcFZWSZOC2PMpEoG0DJDi1FI49ZHZ6ZZymk6xlAq6sBAixdYsGqbGNJ1ilFUqis3qXBNHlShCMj/fYfXcHBvnVlm/fIZOV6LZJoZrkVg5zaZKlqc01uY4eUMlCUsK02EwyNEMaM1rdC9cp9pdRQgVRSZU2i1KY41wskMc9AknY/xhTIyJsHQU3QEEZQYyhNRPsasqhqljtzpM/B5plqIJnZ0Hu2w/3mHkT1nbXEJRFnCcCo3uWV549SbXrl3gT//9N2g6koSCZquOZZmYpknVrVBvt2h1XDpzNt26SzqYEg1DssEMRVFpLszxgVc2aXSXv9/EFpTopIlGEHok8QlZnpHlBdOpJJrkyOx0I1+zdNBMFKNCIUxypSTXdPIiJh4FePtThv0BOiWtmsPCSgenY+DOaXTXbPq7HrouqDZ05taqLIoqRTHH1YlF4IUMRz7V9h79p1tMxwFaLtFFgSpOx5MNXcV1TGrVymlJDwWZFpRRQh5GpHlKXEJCjjz1sKRQJIqeI9QMVeoEU4/Qi5CKJFYEmSwphKBac9CNgjmhorUEoZ8SeCmGNOjYBgsVk6XFJkbpIpMcI02ozbeRQiGJS4q8RNV1HLuKVBUCTvML8jQnSzJKRUG3bDRVQ9MVNKtk7ewaXhwSk4HtUpcKlqJxGI8Qjo5tmDS0LhO/SxR6kCbMl4JRlpGaDp/+1CfpblaxqgrRaIbWbKJaFooiODyxicKILE4glkxnIc2hh5KCkoRoZY7fy/HyjLRMyOII3xsz830GE5+8VHDNFrhdlHoVo6mgugruXAVbk8RThenJkCgqKEpJKSUykWiKCoaG5eqYhUpSSKajEWl26mjatJp0W/N0FtqsnenQXmijGg6oJrlaIFApC8l03GNwMmU68QllgZYGiFKiqBZS5mRZTB4FP1xRQAZoSBSgFBkKLppi0TBsrq5aLM9bdFcsjEaX8fGE46dT9IYKMkGRKbNgRrLjMdpTOBmMeXp3wMF4xr1gjC+nqIqJpdVZ616mubjMwsYSP/NXb3Lt6iW6nTnKUmXP3yaXElNv8O6f/nu++ydv8ZUvvMNx5GNqAlfXuNTt4JcKYSmZpGOIEjRpYtdsKoakSAriOIFc0qxX6TTqXG4tcNAP6U99Qm9GEG8T5QFeGaCKJRSljqrUQIspVYGq1VANlW7FYL5hcfNamzPrq8zNNZmvu4z3BuRhjj+eEavQnpbUtz3u/vP/O194e483DyYcyeLUB0oBR1cohCQXkmle4PfBG5dUzJQL51wadoYcHvPbv/Lvub07JkTSXKmR+hVG4xkHx4cot6cYCCwhWGieZRT5zOJDvOgxp/F/KgKTkhlFaRGXARYZrUKwnKlMUphzFDYqOh/ZqLH+uWs0V5eonruMVltElAEy3sOZv0beOyI92kPRQpZvfAajs0nYu8XitQ2qXYVXXguJ3hxTXxJs/FiN5iu/gqKPkMnbZAdb5IpClO7z4Dt/TPvKR9FqG9TO1Ij7PjmnExTh/pQiySjykmbtDPW5Ocx2jf2DbWbTA7SiZL2yzjvfe5NbDx8yKWJ0M+dzP/Yj/OznP4e+eIUiU+ntTzjZT1FLA1cvKAchZZmjdVVqXQfbVGi3LNaW6owfPeHbv/5ltl+/w2sfusTH/vovsnj9KkJEZN4dSj+gUKoUSHp7W+w9ekARZkjTAdNBsW2UKEMVClJqlMJC0S00S+AlIaNgRM87pHWc8eCPH3L36w95d/sWL55Z5eL5DZ7/iY+h6wb50YTX3/6PvPEn79Jx21w+e4GLP3sD1amgGhatboOqU6XVqGGqOd/9oiAJUpSjAtfM0cuUPEpwMoOmaTNXt9HzHPKEsshQLIGCPHUZzgVqmVPKHFkWSKGQpgVlmSEUDWwTVdWJkwnB8Qx/NCXzY+ZaNm5Np7MssZswHsSM+wlKaHLeUjhTFCyuryK0BkhBmflISqLxlGx8gmZYZIUgzxJsp4bSMDGs07BKM0iJ8oRUeKRHMPQC+rMBF18+j6EZyEzy3r1jjntHZNmMM50XSdU+qhJgq3u8+uJNAm/AaP89nCBD+BGRIVm66VKpVVCEAprAm6ZEgwDPD4lCnbwQlEj0UkVXLepN6G60sdyLLK23OfP2eW6PJ0xFTqnG9AaHeMGQOBsBCl7WIA9HSLlJe8Olbmso64K5myukfsx7XzcJBn1kUaJrGmRjXNekXnNpztuMxnUGw4D720/QhIKpWczX6tRWbVodnZYrcQ0VXVVQUUhQicKIPE1xTQtnpQKrOs9ZFpFMSLKCICwJju8RZCVhOPrhisIVp07XtWk4NrLlEikGpaJjmwZ1TSFXdUaTHHN2hO+FFGWKqhrExMRFStafEB0cEccp/dGEPd/DzzIy4Cev/yVUQxIwwVicY+1Sm7ULbeqdeYZjn+nYo/R8KoZg9+ETfvPXf5dbT95nOJwyDjxKJA23SbfTYPPCAmvnztKa6+I6DZ4entDv9Tl8+pjM9ymUktwt8UOJiHoEyTG3j7YZJwlZDl2liS8mTPEJZUQiAyhVhNAxCg1dMzA1A92UmKpOkWns3BkgEok3mBEenLC9/V3CwCONJ3QKmEQevWDC/ZMdJmFCLEtagKMKSiGYSklWQNVUWKjqfOBTdWzTwdRcVi/VSQ51TnYS/sO9W0RxgaoISGKm8YAgyyjKFJkMiCiJKAj6QxQJdc3mf7L5Gi986hLLz51h/sVXyMqUXu8Jj+69zjv/9pvs9ad8czLgF17d4IVPvMyZG1eZu3wFs1JDNQB9zGz/Dwn3jgge9dj4sc+j1JYwzBbFtEfQ6zPc3efeGw8xu/PITpegGDH32RnNuSbuxQtk46+i6A0UbQPtyn9GPD4i9yYsf/LzjEOPKB6RpipGZw5N6GRCp0Alj1KKNCPprBKqJnEA2ycTtr6xz/7jbe7s/p94uLtHkCcIBf7Ciy9zZfU8YaIjeu8wfdRm705Ov7eDaau4FYO6qFNtu3QWHBaWBFl0n4NbBcffynn3W3e4QMjnX21z7W+/RmVtEVEEhNtfYrIbINUq+sIZZukuMz9EGjXSMEVIC6W0sWQTraqhaho4DoHQISlQvJR3397meD+kdxDg37vHg6OHPB3tkqUJZ168wAc+coOV51cpwhiKGuqleW78yHM4bptqYwm9rjDY22ZyvIWYmjj1DlLVQQoatQZHxojd4T7Xbi5i2xqakmMQMz9f5cz5RdR4jKrrCPPUa0gVBroBlszQS52yVE+zANIUWWQUoiCKwEsNPC9n5+4TRicj/OmM8WRIPBtiioTBwwynrpIWIKXC8+eWuLyhsXrORq0ugrCReYjMjonGQ2aDCcPBmPb6RbJZQOJP0aSOVE7DijQDTEVF5gZKXCE1C7Rcp5K3GRylSBlQ5DGyTHEqNUBlGu9gixJT0TC1Rcx5D6ud4bYWWJcp+/sBfU8l8A6ZeSekKZSpQZloxFHGaDQiFSpREDEbjonTgjwpSKOcg8MeeRpQFhKuuiwPcup+xND32ay3Ke06ZbpG38vxijFhucPro3/DZnyetcM1Ls42Sdeh1DPm2hqdxgZpHBL6fXpHAaQFZqkh3Dp6nuEWgvXlZUxFRdMNlEoVXcvJ0oD+VBDnAtOtYVVqoBgMvJjxOIQyw6pVMVwVyyhQLBtNSgw3Q1OXEbOCdPpDNsTTNBPDtHAqDm67QaxbSE3D1lVEKkGqZFlBmcUUeYFuamAqkEnKJKM/mhCFIWGSMPBCRmVCKRRM3cEyVayKjmOrdC8ss3FhnuWNDnpZcLLTYzIYcrS9havr7D/d4+3vvc2RN8V2Dc6dXWDl7CoL7S6dZpvuQpPFhQXq9Rq2Y1Fvuhy4GunBU4ZZgNBU9IpFTQqsssQSUHHqrDoGeQnR3hRHN3BTAz0qGOaQyQIpMhpm/XT6SC9RbUHVLTG1jHAWMB32UJIIZaTx9MkJQeRTlgEkgv3ZjKfelMMyRn7f7E8HHEXFMlUWKgaVtsFc12Vjo8XN157D1Ctoik1zxcTvRLSqQ26c9JC4ZGHB5HDA1PfQipKaqhMVGSUlipCsagab186xvrnBxxZvcPmD83Q2mrjrBmEBzUYdU18lP7dIksdMBwEvfvJjXLjZprMq0NSCPJ6SpSlCGxCNh0QznyTKyeIM1dKQikkYqHizKd5sRhAZ2FYbtaYTpxZGvYXeXkCr3yCd7JGn8am/THORLPSJEx9pOox6A2aziFLWqFuntexSmIi6g2rniCwnFCqZH5NnCYdPT9jZ2ufJ4x1uP32EUAS2qeG4Gu25BQrd5MjzOdtusHM04smtAyZxn4ZWx8xUkjhFBB76JKd3nONHu0Rjn2gQYUY+C1c3OHv9DM3NTcpkQD4Nyf0MxaiSKw5RFDMZ94kyKKRBXICSn/6zFFoVYZpITSWRknCUEc0iZgcjvvfdh/RPAsa9GG97i8P4hGkZcX5ukc7qPNWlNqgqZREhZIFmGTRaFlZNwaxKQm9IFgzIpgMyWaew62DoSCRuxcG0DbyTEMMysUwDUQosQ8et1HAbHfLjY4RigqKRx8FpDnMpKVGRlKe/SqlRlDlFIchzQZpn5Fjkqk6u2QjTRK9YVIRNEaYUccQsTCgyFU1XsW2ThY5Da6lOZaGGkAllNCaPfOLpmCiKiZKCOIM4iilKEKpJlkFZFuRFTpakFKWCLBQQOoajIjTQTMnQz1B1A8PRMWMfX8yQZLgWlGFKFKfkZcF8ywBRngqNLjBcFVuqeNMTxqME38uxqk0MtUFZCAqRkVEQFwleHBMmOXmck0YJXhADIFQD09bQjABdUzFVE8XV0MwCLS2wNMEkFIyjnHE2YRAcoeQSe1ejkVdxGjqNRgW72iEOAwYHKYP+MVIDqUs0XUXTQTegVrFQVRWhqBSqpIh8ojQiDSOoFZQoqI5FxTGwqhpmaZLnKrmiUmSSZBZRBBmFgFSWGIpOqaiUQvxwRcFTTXT99KCv1Swcu4ai61QVSR4L8qQkCTOiNEBVdSoVB6oSZZIhQ8mDwwGTLCAqU0oJiqJiazbNSpeD+Ant6hzz7RU+8NIlVlc6NBou+/sDHn7nXW69e5c//rM/JExPoy0R0HQrXD23wic+epWf/Os/Tb2+gKnV8ccQjmakoUcmBsydW6ZFwdG3TaZxhN1w6S42qM+bVKRJTXO5fPMG7fPrRGnCb/2LXyOZdSh8l2I05V5gEpQpGSEb7WWkJcnNnMIpqLsKisw5mM2YDfYpJiaJonIy7JPIHFRJNRL0s5CeTLGFRk5JKSSpAo5psNF0ePlcm+c+0mH1hRU2P3oZq/E/QyiV02u3DAmvbxNMtrn5koUU6+w+8fjCv/syk+ARRSypKA5P4iklKq5m8vnuNT7/n/8Sz//Ux9EqLkW2SzTd5ujhbzHOI9BqLM8vUnnhPHkSIMn42H/5XyP83yftv8vs7RFeOaHQM4ymSjRxKcsO+uIcsR+hZAWFlPT2x+wdjpmFOe21y1S6DoYNTFw0R0Fx1hHWi+RCIfbHZP4xtWqLQFGZqir55ITd3SHDSYHWdCldFUszEJqN2WhhKCqilOxv9QmnI/zRhMe3dnm8fZ/D4QEZ0DIMmg2T5dUqRb3NUZGRDQ/5yGt/kW/1/g1vv/EHDLIDzNzBSKA/DskHxxz2FA56LsFgj/5owtQP+T/8wke5+pOfZeXDHwYikse/Qzo8oTBfonJhmShP6B3u0D86JldssDt4cYReVrBUG2E3KQ2TghI/GHD0KGD/QZ/3vvGA1x+9hx9PyIoAKWMUSmqWw8evv8D82hJl1WbUD8mGhyhEOK6CkkORtZBlj/7uDt6RTz7JoVEHRQdFp1QKGi0Xt24TZAm65mLoJpQltWodp9bFqCySi2NQTSQa4eCEVMQUQiCFTakmIAWy1InygiwR5IkglSnoNrprUd+EylwTmc6QWZ8duc+kFzAdFdi5xLEUujWN1Y05GhvLGItdZLJLdvA+8SRkOq6S1JuEuU0qBMOTY0ynjV6ZIw5Kci8nDVP8NCAuHXKhkZsqbq2JpmkoCnh3jqi1m9S7DbJ7Tzk52CUJhpzpLLNzMmMwGTOaTbDN51H1HC8ZMnAMIlEgqoL+8RMe3erROw5ZeG6Vzvx5LLuKVhWUMaSqIFQUYlWj0DOyIj+VS91E1VWMXJCEQ9JEULE6FHaCWcbU85AVpYE3qjLsz/Fu+IBZOWMWTzl4PGDlsMv6ygIf/ewN5s+vE8wilFxwfHKAZkuUqoqpCnQFNBWcikoiBVlRUsQBqTclz3LyUsDCGlIIzKZDo1lHq1aoZhVmScnoOMOfJExOZqRBTqkKhKPRbECY6SSF+cMVhY9cOkfVCbGsFL/08HtjkrjgOC6oaxLbNqk2arRaFaK0YBZ7uEFAPAwI+jOSIqSiCpq6hatDY65BrVWnu7TAwupl2g2bVlXhzh/9Eb+1tcP2/hHDJOXanM3ZxSr/9H/7Y/SeClTTpnt+jouvbNDoLFFtruFUXLJgTDLbJj84QaeJYdZx2s+hGCDjgucuV2mvbmLWLOrdOsketMqCjpOx8UGN6qLDuC9Q0pDIOyKNQxSr4LluFcewcIwqR8NdAjTiwsTSqyhpilpEPGfHHHpDpoWKp6hcmnOQWUQ4nbAblchCZd2o8XytxQfnSy6cbbDw1z+HsfgcerWLW21i2gWakaOaMUX5RyTTfVKvRzqwkVYNxZnn8mt/jyicIaz7nJ1/i/2ndbwyRBYZrpD81V/4ML/0iz9KY+PnsBsmquVTBL+NTFXkLEadFNT0hMnJFk933mDv/iMuXpZ8+qc6aOLrCNNB1G9C64A0TAmDKdM7A0YHHrXOPGvXn0M3DUopyMKI0e726UijUHk6fsK7v/cEbzilv1+y0jS48PwGH/qpE7rdFxFxSTgY0L/1BseTEce9E2796bdYPrdKtVon8fZ4b2+HNCmRaUFh1KnaLjXboe9DOkmJRh5P79/hpL/LNJxQyBxNM7lwYY2f/yufIj85Zn3pVc6/+qOcvHOH1x9/h9fHd/hU91WUZo6wi9NxXxwUK0d1A24+v4Ybd6lEGR//hc9RFY8o3/kGYu48enWdolxkeutPGLzt4xUKU9Ul86ZMphnD8T10s0lrxcA2axT+lNHRjPHxiIM7d/nDd7fYH46Y+mPywkXKDIWcnIQCiWLoXL/Z4Fwjp5ENOer5NN0St16nudTCLCJknlIcbVNPUhqrKuoFE8s1iKd7zIYRvd093GxCTckoZM6418Oea7HYblJTC/RkQumdIBSLbDQhjxJyzwMkUoHEyDFEilAFqDkVTGJDIdZBlSoeAtIUJY+Ii5Q0C0m8Y2bThDQqMITk8lWD1W6NxU4d1/QwqwqaWVDc/yrebY8kUxBrBXapoJgmomPgPTmCKKe0I6QHquHg1i3a1TWCUiFKcrzhlIkXoGinN+qLV5YxFAWVlOKcgc0S+48y/uAP/gClSKlZJtcXFwlmD7D1knldwvExjqpQcR2Cd7/Lra9v8WhnwgeH1/A2dlFMi3AakIk202nE8WGfzvoZalWHlm2Qt01sQ8XUVdA0Zn0VQommRJTxiCLLmaYKuuLj1BTOz5m8kKzSH+UcTRNe956wFw9QRicUD0w8HRKhoNqS7kKFaHqE//QJ37hzC0O3Tl1a5zpU84AszZj6JQcnAwZRSD8N0fZvU7nt0nQaLHYvo1YrqBWH+nwNvdrGthx0y8GPUrIyJxMZ46RklvhMiuyHKwrzczZ6mSNkTkyGrrunb1czrDLB1FR0XSHCJShTgixHhiVRdKpw9eomrY6gUYNuE6pVF91totRWqDa66HpBkYUo1TbVOYtFbY3NRcELm3XOLtc5d2WV4VUbiYZVEzjliLg/YtYXJO4qFVPD1RpYzYIkVImTgr3HPkU0wTs6JhxltOca2PNzVJeWKI2SlohoOyWV5jyoDlKB+eUNrCJESgNnzmHu6oepVutUDI2TvXeIFZvUrCPa6yjE6GVIIz3iJNAIcptIbbDUEJTjA2ZP7xK8N8bVzmDWz/ORV7pcny9YXbKpP/cySXWD0qii6hqG0kfKlDTLOQoXGR5Ome4domwdEGlTUj1CVN6nNzziZGeHuzsR06xCoc9jVZf55EWXl14+x8q5NZRGzLCXEUxnaPEYixmJH3B8MiIoNcbThOPjCKNdoXu+xdr1VdLcoox8iiAk93uoThVNr1DmLno2wWi3MJpNsiLBD3WGI5XX7w/xo4IYlajTJBrrhGODk70jxn2DfiI4KiRr6wVKHFN4Y4I0oV8k9IOCrWGVtGFgByX+YMLhJCGOJXmkkIoxlqpjaxZeYZMHGVkQMRlmhJkNokDXSlZvfIL2uXVmaRM9mSJyDTLJ737pDve3ZiTCxrB1gjIkTwWGvUinCU4lw2nF1Do1ND9CiJCk52EbAl21QUR4aYI3zekNc0aDmGkOfa0g9jJ8v8QLBPMdB76/iDgMDO5vDTnc2uHowS5H/RmTUCHOl1ANG0UGqKWCLAWCKqrZhe4GRquLXVdxZgHCriFNC6mcGgHKktMSjz2HaitoFigk5L5PNI6ZBhpWq0OzG9G2LKRQyBSbRG2BcCnTgizwyJKMcJYR+znRLEHIjFJTyFyLWNMQAhQkKgWxIoiERlRqeJlKGBUEs5xSs1CcDqZtMH8xAm+AEU9Y2jSxjBqJqBJNJXmmIqVKluSk7iKptJCaQR5m5LqONFyUuQ2Q6mmgkPBBSKQQBMIklgp5KdCEhloKECbSrCKEShZ6pJGHXlERaotci/DTElfRMGyX7nqX4zAHJccwE/yTETE5eRrzxts9Hu9NOBkHHB9OCRQHzXbICrBqKmCgCR2BRlZY3/+dgaGDbSkIxURoTQpFAWK0TEHmp9kNft4CKTDVHEX1qLo6ueIwr36AahUWWxZ2xcBSBYrpUFuqolUTZvsq4+2IoTdEKFV0vUOh6yhJDEWOoqoUapNc0cmVHJnl+GVKloeEeYDqSoxKylImqc27aBWLxHRJUchlQpFnpKJEmhqa+UO+KbRbOtlUIw0VdD3FdpqYSg1dZDjeBF0t0FSdYVYhSCKCOCaeCPwEckWn3bzI2YuSpdWSM2slpjCIZZNesYmBTRFPCIIMd+UcZxfmOa+3uPoRg4sbVToNC0XV6Mo2UZTiD47of/M3GQ0C+p7JdMnm/Jl5zq62WVxskB8P8CcB33urR3S4RTneRQwTzq8uUm2v4S5fxW4XNPQZDTPGrizjZSZ5IVk9d5lOPkbXQ9rXF1j8C3+VanuOipETPJknN6oU7gJx60U0EWEIn454ip9q+GWdvjhDTZuS7L7N+C2TXe8pce3TVDZ/jNd+aYFuO8OxSxA209QmlSAyH130kGVAlBU8mVzj6dOMw9t9qm8f0Es9RumI42DG490dvNkUGYUIo4JTuYjb/jg/9bnzPPdCjlJNKKJHHD2uc/y0wElz5qynJNmA3ZOAYbjENCgYeyovXlqmc/EMjfOXiNMO2WxMPhrAeB+1/UF0p4O0Yuz6DKtmozTbJD2P0dTl6bHGl++MmI4C0kJB39Sw1Q5Z5HA82WFUwoOTI/703T3OnB3Tsus0DJckTRnVVKaayjBbIx6oqEXGeGtGLw6JEpUktInTHuQFolDIlSZlllGWOaoKNaWBqtlYsuT8hz5Ps9Nk63iLjbRCEsF0MObf/sZ77PdjFLVNZuWMo5AkNXHNBRbbKo1OTqcTYVRt4sxjVgoGd/YwlpuYrU1k4jOZzhhMUo5GJsHYYZyl7BMxGSmkuU4pTbpaC2HUkXqFXuDy3qMhW+/vMDw4ZFZalEoHoW4irRwhB+hZCYWGUDbQrHNErfOIdh2zUeAGQ0LRIlENkkJiqQVCURCaQ+GsIHQFRcnIkyfE0ynBKGeW1LAXqrT9giXHQdENEtXFV+YoqZKnkAYeaZQxnQn8qSSYZGilhzBUpDTITRtRlqh5iqGlJJpKqKoMCp3AV4iDkmiaY7cr6NUOdvMyrXqGE+5SC/epLuh4E5fB2KGSFWSxQlmq5KVJtniRTLqU0iMY7VBaEuFU0VY3IUiQno+iJyAUcikYpSpZqkIqsYSOLlRKzQGnS+L55JMe+biPubpIUrYIlZJCddE0FafapH1mkf6RiUKMao6Yhk+ZZCmen7J1MuTIT0jKkqNego+PURWo9Tq2Xcckp1bNUXWHpHQIowqi1FHVHNMAVbqU6hyJqqGVfbTcRBQ5QhH42SIyL9GyEC0/xLI1mnaddf3DdJcLFucSKvNjKrZFXmkgal2WLJehLdCmM57OQnAWke4KqT5FKSNkIVEMBcw5lNLDUAq0KCJXNDw0xr6CkuaYEehlgNRSjELiORaKpiILkHFEbpUobgWnUv+Bzvof2BDvGc94xjOe8f//KP/f/gDPeMYznvGM/9/hmSg84xnPeMYz/pxnovCMZzzjGc/4c56JwjOe8YxnPOPPeSYKz3jGM57xjD/nmSg84xnPeMYz/pxnovCMZzzjGc/4c56JwjOe8YxnPOPPeSYKz3jGM57xjD/n/wVaBPXHF3SJqAAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from torchvision.transforms import Compose, Resize, ToTensor, Normalize\n", "from PIL import Image\n", "import torch\n", "import torchvision.transforms.functional as TF\n", "from model import TransformerNet # Make sure model definition is available\n", "import matplotlib.pyplot as plt\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "# Load trained model\n", "model = TransformerNet()\n", "model.load_state_dict(torch.load(\"final_styled_model.pth\", map_location=device))\n", "model.to(device)\n", "model.eval()\n", "\n", "# Load and preprocess input image\n", "img = Image.open(\"Testing.jpg\").convert(\"RGB\")\n", "transform = Compose([\n", " Resize(256),\n", " ToTensor(),\n", " Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", "])\n", "input_tensor = transform(img).unsqueeze(0).to(device)\n", "\n", "# Stylize\n", "with torch.no_grad():\n", " output = model(input_tensor).cpu().squeeze(0)\n", " output = torch.clamp(output * 0.5 + 0.5, 0, 1) # Denormalize\n", "\n", "# Show\n", "plt.imshow(output.permute(1, 2, 0))\n", "plt.axis('off')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 27, "id": "22df5313", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Stylized image saved as stylized_result.jpg\n" ] } ], "source": [ "import torch\n", "from torchvision.transforms import Compose, ToTensor, Normalize, Resize\n", "from PIL import Image\n", "import matplotlib.pyplot as plt\n", "from model import TransformerNet # make sure this is the same model you used for training\n", "import torchvision.transforms.functional as TF\n", "\n", "# Device setup\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "# Load trained model\n", "model = TransformerNet()\n", "model.load_state_dict(torch.load(\"final_styled_model.pth\", map_location=device))\n", "model.to(device)\n", "model.eval()\n", "\n", "# Load test image\n", "img_path = \"Testing.jpg\" # change this to your test image path\n", "image = Image.open(img_path).convert(\"RGB\")\n", "\n", "# Transform image\n", "transform = Compose([\n", " Resize(256),\n", " ToTensor(),\n", " Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", "])\n", "input_tensor = transform(image).unsqueeze(0).to(device)\n", "\n", "# Stylize\n", "with torch.no_grad():\n", " output_tensor = model(input_tensor).cpu().squeeze(0)\n", "\n", "# Denormalize\n", "output_tensor = torch.clamp(output_tensor * 0.229 + torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1), 0, 1)\n", "\n", "# Show the output\n", "plt.figure(figsize=(10, 5))\n", "plt.imshow(output_tensor.permute(1, 2, 0).numpy())\n", "plt.axis('off')\n", "plt.title(\"Stylized Output\")\n", "plt.show()\n", "\n", "# Save output\n", "output_image = TF.to_pil_image(output_tensor)\n", "output_image.save(\"stylized_result.jpg\")\n", "print(\"Stylized image saved as stylized_result.jpg\")\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "83a92670", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean pixel difference: 0.565616\n" ] } ], "source": [ "from torchvision.utils import save_image\n", "import torch\n", "from PIL import Image\n", "import torchvision.transforms as transforms\n", "from model import TransformerNet\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "model = TransformerNet().to(device)\n", "model.load_state_dict(torch.load(\"final_styled_model.pth\", map_location=device))\n", "model.eval()\n", "\n", "transform = transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(256),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406],\n", " [0.229, 0.224, 0.225])\n", "])\n", "\n", "image = Image.open(\"Testing.jpg\").convert(\"RGB\")\n", "x = transform(image).unsqueeze(0).to(device)\n", "\n", "with torch.no_grad():\n", " y = model(x)\n", "\n", "# Calculate difference\n", "diff = torch.abs(x - y).mean()\n", "print(f\"Mean pixel difference: {diff.item():.6f}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "08d2b4f8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "cudagpu", "language": "python", "name": "cuda" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 5 }