docstr: Args:
      s(paddle.Tensor): batch_size x board_x x board_y
docstr_tok: ["Args",":","s","(","paddle.Tensor",")",":","batch_size","x","board_x","x","board_y"]
code: def forward(self, s):
      """
      Args:
          s(paddle.Tensor): batch_size x board_x x board_y
      """
      # batch_size x 1 x board_x x board_y
      s = paddle.reshape(s, shape=[-1, 1, self.board_x, self.board_y])
      # batch_size x num_channels 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-2) x (board_y-2)
      s = F.relu(self.bn3(self.conv3(s)))
      # batch_size x num_channels x (board_x-4) x (board_y-4)
      s = F.relu(self.bn4(self.conv4(s)))
      s = paddle.reshape(
          s,
          shape=[
              -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, axis=1), self.tanh(v)
  
file_path: PARL/examples/AlphaZero/connect4_model.py
imports: import parl
  import paddle
  import paddle.nn
  import paddle.nn.functional
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,  self