code: def forward(self, x):
      out = self.conv1(x)
      out = F.relu(out)
      out = F.max_pool2d(out, 2)
      out = out.view(-1, 16 * 12 * 12)
      out = self.fc1(out)
      out = F.relu(out)
      out = self.fc2(out)
      return out
  
file_path: CrypTen/examples/mpc_autograd_cnn/mpc_autograd_cnn.py
imports: import tempfile
  import crypten
  import crypten.communicator
  import torch
  import torch.nn
  import torch.nn.functional
  from examples.util import NoopContextManager
  from torchvision import datasets, transforms
label_desc: Use image node_id 2 for calling the CNN obj's underlying member method code with example usage: obj.forward(x) and returns: out