text: import sys
  sys.path.append('..')
  from utils import *
  
  import argparse
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  from torchvision import datasets, transforms
  from torch.autograd import Variable
  
  class Project5100NNet(nn.Module):
      def __init__(self, game, args):
          # game params
          self.board_x, self.board_y = game.getBoardSize()
          self.action_size = game.getActionSize()
          self.args = args
  
          super(Project5100NNet, self).__init__()
          self.conv1 = nn.Conv2d(1, args.num_channels, 3, stride=1, padding=1)
          self.conv2 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1, padding=1)
          self.conv3 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1)
          self.conv4 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1)
  
          self.bn1 = nn.BatchNorm2d(args.num_channels)
          self.bn2 = nn.BatchNorm2d(args.num_channels)
          self.bn3 = nn.BatchNorm2d(args.num_channels)
          self.bn4 = nn.BatchNorm2d(args.num_channels)
  
          print("BAY", self.board_x-4, self.board_y)
          self.fc1 = nn.Linear(args.num_channels*(self.board_x-4)*(self.board_y), 1024)
          self.fc_bn1 = nn.BatchNorm1d(1024)
  
          self.fc2 = nn.Linear(1024, 512)
          self.fc_bn2 = nn.BatchNorm1d(512)
  
          self.fc3 = nn.Linear(512, self.action_size)
  
          self.fc4 = nn.Linear(512, 1)
  
      def forward(self, s):
          #                                                           s: batch_size x board_x x board_y
          s = s.view(-1, 1, self.board_x, self.board_y)                # batch_size x 1 x board_x x board_y
          s = F.relu(self.bn1(self.conv1(s)))                          # batch_size x num_channels x board_x x board_y
          s = F.relu(self.bn2(self.conv2(s)))                          # batch_size x num_channels x board_x x board_y
          s = F.relu(self.bn3(self.conv3(s)))                          # batch_size x num_channels x (board_x-2) x (board_y-2)
          s = F.relu(self.bn4(self.conv4(s)))                          # batch_size x num_channels x (board_x-4) x (board_y-4)
          s = s.view(-1, self.args.num_channels*(self.board_x-4)*(self.board_y))
  
          s = F.dropout(F.relu(self.fc_bn1(self.fc1(s))), p=self.args.dropout, training=self.training)  # batch_size x 1024
          s = F.dropout(F.relu(self.fc_bn2(self.fc2(s))), p=self.args.dropout, training=self.training)  # batch_size x 512
  
          pi = self.fc3(s)                                                                         # batch_size x action_size
          v = self.fc4(s)                                                                          # batch_size x 1
  
          return F.log_softmax(pi, dim=1), torch.tanh(v)
  