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 __init__(self, in_channels=1):
      super(PyNet, self).__init__()
      self.conv1 = nn.Conv2d(in_channels, 32, 3, 1)
      self.conv2 = nn.Conv2d(32, 64, 3, 1)
      self.dropout1 = nn.Dropout(0.25)
      self.dropout2 = nn.Dropout(0.5)
      self.fc1 = nn.Linear(9216, 128)
      self.fc2 = nn.Linear(128, 10)
  
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 1 to create a new PyNet object from inherited base classes: nn with example: obj = PyNet(in_channels)