class_docstr: Gaussian LSTM Model.
  
  A model represented by a Gaussian distribution
  which is parameterized by a Long short-term memory (LSTM).
  
  Args:
      output_dim (int): Output dimension of the model.
      hidden_dim (int): Hidden dimension for LSTM cell for mean.
      name (str): Model 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.
      learn_std (bool): Is std trainable.
      init_std (float): Initial value for std.
      std_share_network (bool): Boolean for whether mean and std share
          the same network.
      layer_normalization (bool): Bool for using layer normalization or not.
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docstr: Object.__getstate__.
  
  Returns:
      dict: the state to be pickled for the instance.
docstr_tok: ["Object.__getstate__",".","Returns",":","dict",":","the","state","to","be","pickled","for","the","instance","."]
code: def __getstate__(self):
      """Object.__getstate__.
  
      Returns:
          dict: the state to be pickled for the instance.
  
      """
      new_dict = super().__getstate__()
      del new_dict["_mean_std_lstm_cell"]
      del new_dict["_mean_lstm_cell"]
      del new_dict["_mean_std_output_nonlinearity_layer"]
      del new_dict["_mean_output_nonlinearity_layer"]
      return new_dict
  
file_path: garage/src/garage/tf/models/gaussian_lstm_model.py
imports: import numpy
  import tensorflow
  import tensorflow_probability
  from garage.experiment import deterministic
  from garage.tf.models.lstm import lstm
  from garage.tf.models.model import Model
  from garage.tf.models.parameter import recurrent_parameter
label_desc: Use image node_id 6 for calling the GaussianLSTMModel obj's underlying member method code with example usage: obj.__getstate__() and returns: new_dict