class_docstr: Gaussian LSTM Policy.
  
  A policy represented by a Gaussian distribution
  which is parameterized by a Long short-term memory (LSTM).
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      name (str): Model name, also the variable scope.
      hidden_dim (int): Hidden dimension for LSTM cell for mean.
      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.
      learn_std (bool): Is std trainable.
      std_share_network (bool): Boolean for whether mean and std share
          the same network.
      init_std (float): Initial value for std.
      layer_normalization (bool): Bool for using layer normalization or not.
      state_include_action (bool): Whether the state includes action.
          If True, input dimension will be
          (observation dimension + action dimension).
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docstr: State info specifcation.
  
  Returns:
      List[str]: keys and shapes for the information related to the
          policy's state when taking an action.
docstr_tok: ["State","info","specifcation",".","Returns",":","List","[","str","]",":","keys","and","shapes","for","the","information","related","to","the","policy","'s","state","when","taking","an","action","."]
code: def state_info_specs(self):
      """State info specifcation.
  
      Returns:
          List[str]: keys and shapes for the information related to the
              policy's state when taking an action.
  
      """
      if self._state_include_action:
          return [
              ("prev_action", (self._action_dim,)),
          ]
  
      return []
  
imports: import akro
  import numpy
  import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models import GaussianLSTMModel
  from garage.tf.policies.policy import Policy
label_desc: Use image node_id 8 for calling the GaussianLSTMPolicy obj's underlying member method code with example usage: obj.state_info_specs() and returns: list, list