class_docstr: CNN architecture. This is the same MNIST model from pytorch/examples/mnist repository
class_docstr_tok: ["CNN","architecture",".","This","is","the","same","MNIST","model","from","pytorch\/examples\/mnist","repository"]
code: def forward(self, x):
      x = self.conv1(x)
      x = F.relu(x)
      x = self.conv2(x)
      x = F.relu(x)
      x = F.max_pool2d(x, 2)
      x = self.dropout1(x)
      x = torch.flatten(x, 1)
      x = self.fc1(x)
      x = F.relu(x)
      x = self.dropout2(x)
      x = self.fc2(x)
      output = F.log_softmax(x, dim=1)
      return output
  
file_path: cleverhans/tutorials/torch/mnist_tutorial.py
imports: from absl import app, flags
  from easydict import EasyDict
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision
  from datasets import MNISTDataset
  from cleverhans.torch.attacks.fast_gradient_method import fast_gradient_method
  from cleverhans.torch.attacks.projected_gradient_descent import projected_gradient_descent
label_desc: Use image node_id 2 for calling the PyNet obj's underlying member method code with example usage: obj.forward(x) and returns: output