code: def __init__(self):
      torch.manual_seed(24)
      super(SimpleConvNet, self).__init__()
      self.conv1 = nn.Conv2d(3, 6, 5)
      self.relu1 = nn.ReLU()
      self.pool1 = nn.MaxPool2d(2, 2)
      self.conv2 = nn.Conv2d(6, 16, 5)
      self.relu2 = nn.ReLU()
      self.pool2 = nn.MaxPool2d(2, 2)
      self.fc1 = nn.Linear(16 * 5 * 5, 120)
      self.relu3 = nn.ReLU()
      self.fc2 = nn.Linear(120, 84)
      self.relu4 = nn.ReLU()
      self.fc3 = nn.Linear(84, 10)
      self.identity = nn.Identity()
  
imports: import logging
  import os
  from typing import Any, List, Tuple, Union
  import numpy
  import pytest
  from sklearn.datasets import make_blobs
  import torch
  from torch.cuda.amp.autocast_mode import autocast
  import torch.nn
  from torch.utils.data import DataLoader
  import torchvision
  import torchvision.transforms
  from fairscale.fair_dev.common_paths import DATASET_CACHE_ROOT
  from fairscale.fair_dev.testing.testing import skip_a_test_if_in_CI
  from fairscale.optim.layerwise_gradient_scaler import LayerwiseGradientScaler
label_desc: Use image node_id 1 to create a new SimpleConvNet object from inherited base classes: nn with example: obj = SimpleConvNet()