code: def __init__(
      self, input_size, rnn_size=256, projection=128, layers=2
  ):
      super().__init__()
      self.layers = torch.nn.ModuleList()
  
      # Alternate RNN and projection layers.
      for i in range(layers):
          self.layers.append(
              torch.nn.LSTM(
                  input_size=input_size if i == 0 else projection,
                  hidden_size=rnn_size,
                  bidirectional=True,
              )
          )
  
          # Projection layer reduces size, except last layer, which
          # goes back to input size to create the mask
          linear_size = input_size if i == layers - 1 else projection
          self.layers.append(
              torch.nn.Linear(
                  in_features=rnn_size * 2,
                  out_features=linear_size,
              )
          )
  
      # Use ReLU to make sure outputs aren't negative (unhelpful for masking)
      self.layers.append(torch.nn.ReLU())
  
imports: import torch
label_desc: Use image node_id 1 to create a new CustomModel object from inherited base classes: torch.nn with example: obj = CustomModel(input_size, rnn_size, projection, layers)