code: def forward(self, x):
      x = self.convlayer1(x)
      x = self.convlayer2(x)
      x = x.view(-1, 64 * 6 * 6)
      x = self.fc1(x)
      x = self.drop(x)
      x = self.fc2(x)
      x = self.fc3(x)
  
      return F.log_softmax(x, dim=1)
  
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 2 for calling the CNN obj's underlying member method code with example usage: obj.forward(x) and returns: F