code: def __init__(self) -> None:
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(1, 32, 3, 1)
      self.conv2 = nn.Conv2d(32, 64, 3, 1)
      self.dropout1 = nn.Dropout(0.25)
      self.dropout2 = nn.Dropout(0.5)
      self.fc1 = nn.Linear(9216, 128)
      self.fc2 = nn.Linear(128, 10)
  
imports: import argparse
  import sys
  import tempfile
  from argparse import Namespace
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.optim import Adadelta
  from torch.optim.lr_scheduler import StepLR
  from torch.utils.data import DataLoader
  from torcheval.metrics import MulticlassAccuracy
  from torchtnt.framework.auto_unit import AutoUnit, TrainStepResults
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import State
  from torchtnt.utils import init_from_env, seed, TLRScheduler
  from torchtnt.utils.loggers import TensorBoardLogger
  from torchvision import datasets, transforms
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net()