code: def __init__(self, in_channels, out_features, hidden_size=64):
      super(ConvolutionalNeuralNetwork, self).__init__()
      self.in_channels = in_channels
      self.out_features = out_features
      self.hidden_size = hidden_size
  
      self.features = nn.Sequential(
          conv3x3(in_channels, hidden_size),
          conv3x3(hidden_size, hidden_size),
          conv3x3(hidden_size, hidden_size),
          conv3x3(hidden_size, hidden_size),
      )
  
      self.classifier = nn.Linear(hidden_size, out_features)
  
file_path: pytorch-meta/examples/maml-higher/train.py
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  from tqdm import tqdm
  import logging
  from collections import OrderedDict
  import higher
  from torchmeta.datasets.helpers import omniglot
  from torchmeta.utils.data import BatchMetaDataLoader
label_desc: Use image node_id 1 to create a new ConvolutionalNeuralNetwork object from inherited base classes: nn with example: obj = ConvolutionalNeuralNetwork(in_channels, out_features, hidden_size)