code: def __init__(self, num_classes=10):
      super(ConvNet, self).__init__()
      self.layer1 = nn.Sequential(
          nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
          nn.BatchNorm2d(16),
          nn.ReLU(),
          nn.MaxPool2d(kernel_size=2, stride=2),
      )
      self.layer2 = nn.Sequential(
          nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
          nn.BatchNorm2d(32),
          nn.ReLU(),
          nn.MaxPool2d(kernel_size=2, stride=2),
      )
      self.fc = nn.Linear(7 * 7 * 32, num_classes)
  
file_path: aim/examples/pytorch_track_images.py
imports: import logging
  import torch
  import torch.nn
  import torchvision
  import torchvision.transforms
  from aim import Run
  from aim.sdk.objects.image import convert_to_aim_image_list
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new ConvNet object from inherited base classes: nn with example: obj = ConvNet(num_classes)