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)
  
imports: from argparse import ArgumentParser
  from pathlib import Path
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.optim import SGD
  from torch.optim.lr_scheduler import StepLR
  from torch.utils.data import DataLoader
  from torchvision.datasets import MNIST
  from torchvision.transforms import Compose, Normalize, ToTensor
  from tqdm import tqdm
  from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
  from ignite.handlers import Checkpoint, DiskSaver
  from ignite.metrics import Accuracy, Loss, RunningAverage
  from ignite.utils import manual_seed
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