class_docstr: Basic CNN architecture.
class_docstr_tok: ["Basic","CNN","architecture","."]
code: def __init__(self, in_channels=1):
      super(CNN, self).__init__()
      self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
      self.conv2 = nn.Conv2d(64, 128, 6, 2)
      self.conv3 = nn.Conv2d(128, 128, 5, 2)
      self.fc = nn.Linear(128 * 3 * 3, 10)
  
file_path: cleverhans/tutorials/torch/cifar10_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 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 CNN object from inherited base classes: torch.nn with example: obj = CNN(in_channels)