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),
      )
  
      # Only the last (linear) layer is used for adaptation in ANIL
      self.classifier = MetaLinear(hidden_size, out_features)
  
imports: import torch.nn
  from torchmeta.modules import MetaModule, MetaLinear
label_desc: Use image node_id 1 to create a new ConvolutionalNeuralNetwork object from inherited base classes: MetaModule with example: obj = ConvolutionalNeuralNetwork(in_channels, out_features, hidden_size)