code: def __init__(self):
      super().__init__()
      self.conv1 = nn.Conv2d(1, 6, 5, 1)
      self.conv2 = nn.Conv2d(6, 16, 5, 1)
      self.fc1 = nn.Linear(16 * 4 * 4, 120)
      self.fc2 = nn.Linear(120, 84)
      self.fc3 = nn.Linear(84, 10)
      self.relu1 = nn.ReLU()
      self.relu2 = nn.ReLU()
      self.relu3 = nn.ReLU()
      self.relu4 = nn.ReLU()
      self.pool1 = nn.MaxPool2d((2, 2))
      self.pool2 = nn.MaxPool2d((2, 2))
  
file_path: nni/nni_assets/compression/mnist_model.py
imports: from pathlib import Path
  import torch
  from torch import nn
  from torch.nn import functional
  from torchvision import datasets, transforms
label_desc: Use image node_id 1 to create a new TorchModel object from inherited base classes: nn with example: obj = TorchModel()