code: 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 - 4),
      )
  
      s = F.dropout(
          F.relu(self.fc_bn1(self.fc1(s))),
          p=self.args.dropout,
          training=self.training,
      )  # batch_size x 128
      s = F.dropout(
          F.relu(self.fc_bn2(self.fc2(s))),
          p=self.args.dropout,
          training=self.training,
      )  # batch_size x 64
  
      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)
  
imports: import os
  import math
  import time
  import numpy
  import numpy
  from collections import namedtuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import base64
  import io
label_desc: Use image node_id 2 for calling the Connect4Model obj's underlying member method code with example usage: obj.forward(s) and returns: F,  torch