class_docstr: Basic CNN architecture.
class_docstr_tok: ["Basic","CNN","architecture","."]
code: def forward(self, x):
      x = F.relu(self.conv1(x))
      x = F.relu(self.conv2(x))
      x = F.relu(self.conv3(x))
      x = x.view(-1, 128 * 4 * 4)
      x = self.fc1(x)
      x = self.fc2(x)
      return x
  
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 CNN obj's underlying member method code with example usage: obj.forward(x) and returns: x