code: def __init__(self):
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
      self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
      self.conv2_drop = nn.Dropout2d()
      self.fc1 = nn.Linear(320, 50)
      self.fc2 = nn.Linear(50, 10)
  
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 1 to create a new Net object from inherited base classes: nn with example: obj = Net()