code: def __init__(self):
      super(CNN, self).__init__()
  
      self.convlayer1 = nn.Sequential(
          nn.Conv2d(1, 32, 3, padding=1),
          nn.BatchNorm2d(32),
          nn.ReLU(),
          nn.MaxPool2d(kernel_size=2, stride=2),
      )
  
      self.convlayer2 = nn.Sequential(
          nn.Conv2d(32, 64, 3),
          nn.BatchNorm2d(64),
          nn.ReLU(),
          nn.MaxPool2d(2),
      )
  
      self.fc1 = nn.Linear(64 * 6 * 6, 600)
      self.drop = nn.Dropout2d(0.25)
      self.fc2 = nn.Linear(600, 120)
      self.fc3 = nn.Linear(120, 10)
  
file_path: aim/examples/pytorch_ignite_track.py
imports: import ignite
  import torch
  import torch.nn.functional
  from aim.pytorch_ignite import AimLogger
  from ignite.contrib.handlers import ProgressBar
  from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
  from ignite.handlers import EarlyStopping, global_step_from_engine
  from ignite.metrics import Accuracy, ConfusionMatrix, Loss, RunningAverage
  from torch import nn, optim
  from torch.utils.data import DataLoader
  from torchvision import datasets, transforms
label_desc: Use image node_id 1 to create a new CNN object from inherited base classes: nn with example: obj = CNN()