class_docstr: Container module with an encoder, a recurrent module, and a linear.
class_docstr_tok: ["Container","module","with","an","encoder",",","a","recurrent","module",",","and","a","linear","."]
code: def __init__(
      self,
      input_dim: int,
      hidden_dim: int,
      output_dim: int,
      num_layers: int,
  ):
      super().__init__()
      self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers)
      self.linear = nn.Linear(hidden_dim, output_dim)
  
imports: from torch.ao.pruning import BaseSparsifier
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new LSTMLinearModel object from inherited base classes: nn with example: obj = LSTMLinearModel(input_dim, hidden_dim, output_dim, num_layers)