class_docstr: LSTM Model.
  
  Args:
      output_dim (int): Dimension of the network output.
      hidden_dim (int): Hidden dimension for LSTM cell.
      name (str): Policy name, also the variable scope.
      hidden_nonlinearity (callable): Activation function for intermediate
          dense layer(s). It should return a tf.Tensor. Set it to
          None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      recurrent_nonlinearity (callable): Activation function for recurrent
          layers. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      recurrent_w_init (callable): Initializer function for the weight
          of recurrent layer(s). The function should return a
          tf.Tensor.
      output_nonlinearity (callable): Activation function for output dense
          layer. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          tf.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          tf.Tensor.
      hidden_state_init (callable): Initializer function for the
          initial hidden state. The functino should return a tf.Tensor.
      hidden_state_init_trainable (bool): Bool for whether the initial
          hidden state is trainable.
      cell_state_init (callable): Initializer function for the
          initial cell state. The functino should return a tf.Tensor.
      cell_state_init_trainable (bool): Bool for whether the initial
          cell state is trainable.
      forget_bias (bool): If True, add 1 to the bias of the forget gate at
          initialization. It's used to reduce the scale of forgetting at the
          beginning of the training.
      layer_normalization (bool): Bool for using layer normalization or not.
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docstr: Network output spec.
  
  Return:
      list[str]: List of key(str) for the network outputs.
docstr_tok: ["Network","output","spec",".","Return",":","list","[","str","]",":","List","of","key","(","str",")","for","the","network","outputs","."]
code: def network_output_spec(self):
      """Network output spec.
  
      Return:
          list[str]: List of key(str) for the network outputs.
  
      """
      return [
          "all_output",
          "step_output",
          "step_hidden",
          "step_cell",
          "init_hidden",
          "init_cell",
      ]
  
file_path: garage/src/garage/tf/models/lstm_model.py
imports: import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models.lstm import lstm
  from garage.tf.models.model import Model
label_desc: Use image node_id 4 for calling the LSTMModel obj's underlying member method code with example usage: obj.network_output_spec() and returns: list