code: def __init__(self):
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
      self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
      self.fc1 = nn.Linear(7 * 7 * 64, 1024)
      self.fc2 = nn.Linear(1024, 10, bias=False)
  
file_path: dynet/examples/mnist/basic-mnist-benchmarks/mnist_pytorch.py
imports: from __future__ import print_function
  import argparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from torch.autograd import Variable
  import time
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net()