code: def __init__(
      self, num_layers, layer, first_layer_args, other_layer_args
  ):
      super().__init__()
      self.layers = init_stacked_lstm(
          num_layers, layer, first_layer_args, other_layer_args
      )
      # Introduces a Dropout layer on the outputs of each LSTM layer except
      # the last layer, with dropout probability = 0.4.
      self.num_layers = num_layers
  
      if num_layers == 1:
          warnings.warn(
              "dropout lstm adds dropout layers after all but last "
              "recurrent layer, it expects num_layers greater than "
              "1, but got num_layers = 1"
          )
  
      self.dropout_layer = nn.Dropout(0.4)
  
imports: import numbers
  import warnings
  from collections import namedtuple
  from typing import List, Tuple
  import torch
  import torch.jit
  import torch.nn
  from torch import Tensor
  from torch.nn import Parameter
label_desc: Use image node_id 1 to create a new StackedLSTMWithDropout object from inherited base classes: jit with example: obj = StackedLSTMWithDropout(num_layers, layer, first_layer_args, other_layer_args)