code: def __init__(self):
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(3, 32, 3)
      self.pool = nn.MaxPool2d(2, 2)
      self.conv2 = nn.Conv2d(32, 64, 3)
      self.conv3 = nn.Conv2d(64, 128, 3)
      self.fc1 = nn.Linear(128 * 6 * 6, 120)
      self.dropout = nn.Dropout(p=0.2)
      self.fc2 = nn.Linear(120, 84)
      self.fc3 = nn.Linear(84, 10)
  
file_path: MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/train.py
imports: import torch
  import torchvision
  import torchvision.transforms
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import os
  import argparse
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net()