class_docstr: Basic LSTM model for language modeling.
  
  Arguments
  ---------
  embedding_dim : int
      The dimension of the embeddings.The input indexes are transformed into
      a latent space with this dimensionality.
  rnn_size : int
      Number of neurons to use in rnn (for each direction -> and <-).
  layers : int
      Number of RNN layers to use.
  output_dim : int
      Dimensionality of the output.
  return_hidden : bool
      If True, returns the hidden state of the RNN as well.
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code: def __init__(
      self,
      embedding_dim=128,
      rnn_size=256,
      layers=2,
      output_dim=1000,
      return_hidden=False,
  ):
      super().__init__()
      self.return_hidden = return_hidden
      self.reshape = False
  
      # Embedding model
      self.embedding = sb.nnet.embedding.Embedding(
          num_embeddings=output_dim, embedding_dim=embedding_dim
      )
  
      # LSTM
      self.rnn = torch.nn.LSTM(
          input_size=embedding_dim,
          hidden_size=rnn_size,
          bidirectional=False,
          num_layers=layers,
      )
  
      # Final output transformation + softmax
      self.out = sb.nnet.linear.Linear(
          input_size=rnn_size, n_neurons=output_dim
      )
      self.log_softmax = sb.nnet.activations.Softmax(apply_log=True)
  
imports: import torch
  import speechbrain
label_desc: Use image node_id 1 to create a new CustomModel object from inherited base classes: torch.nn with example: obj = CustomModel(embedding_dim, rnn_size, layers, output_dim, return_hidden)