class_docstr: Convolutional Neural Network.
class_docstr_tok: ["Convolutional","Neural","Network","."]
code: def forward(self, x: Tensor) -> Tensor:
      x = F.relu(self.conv1(x))
      x = F.max_pool2d(x, 3, 3)
      x = x.view(-1, 8 * 8 * 20)
      x = F.relu(self.fc1(x))
      x = self.fc2(x)
      return F.log_softmax(x, dim=-1)
  
file_path: Ax/ax/utils/tutorials/cnn_utils.py
imports: from itertools import accumulate
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchvision
  import torchvision.transforms
  from torch._tensor import Tensor
  from torch.utils.data import DataLoader, Dataset, Subset
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: F