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)
  
file_path: ignite/examples/mnist/mnist_with_visdom_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.visdom_logger import global_step_from_engine, GradsScalarHandler, VisdomLogger, WeightsScalarHandler
  from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
  from ignite.handlers import ModelCheckpoint
  from ignite.metrics import Accuracy, Loss
  from ignite.utils import setup_logger
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net()