code: def forward(self, x):
      x = F.relu(F.max_pool2d(self.conv1(x), 2))
      x = F.relu(F.max_pool2d(self.conv2(x), 2))
      x = x.view(-1, 7 * 7 * 64)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, 0.4)
      x = self.fc2(x)
      return F.log_softmax(x, dim=1)
  
file_path: dynet/examples/mnist/basic-mnist-benchmarks/mnist_pytorch.py
imports: from __future__ import print_function
  import argparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from torch.autograd import Variable
  import time
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(x) and returns: F