code: def forward(self, x):
      x = F.relu(F.max_pool2d(self.conv1(x), 2))
      x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
      x = x.view(-1, 320)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, training=self.training)
      x = self.fc2(x)
      return F.log_softmax(x, dim=-1)
  
file_path: ignite/examples/mnist/mnist_with_tqdm_logger.py
imports: from argparse import ArgumentParser
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.optim import SGD
  from torch.utils.data import DataLoader
  from torchvision.datasets import MNIST
  from torchvision.transforms import Compose, Normalize, ToTensor
  from ignite.contrib.handlers import ProgressBar
  from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
  from ignite.metrics import Accuracy, Loss, RunningAverage
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(x) and returns: F