class_docstr: GaussianMLPEncoder with GaussianMLPModel.
  
  An embedding that contains a MLP to make prediction based on
  a gaussian distribution.
  
  Args:
      embedding_spec (garage.InOutSpec):
          Encoder specification.
      name (str): Model name, also the variable scope.
      hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the MLP for mean. For example, (32, 32) means the MLP consists
          of two hidden layers, each with 32 hidden units.
      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.
      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.
      learn_std (bool): Is std trainable.
      adaptive_std (bool): Is std a neural network. If False, it will be a
          parameter.
      std_share_network (bool): Boolean for whether mean and std share
          the same network.
      init_std (float): Initial value for std.
      std_hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the MLP for std. For example, (32, 32) means the MLP consists
          of two hidden layers, each with 32 hidden units.
      min_std (float): If not None, the std is at least the value of min_std,
          to avoid numerical issues.
      max_std (float): If not None, the std is at most the value of max_std,
          to avoid numerical issues.
      std_hidden_nonlinearity (callable): Nonlinearity for each hidden layer
          in the std network. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      std_output_nonlinearity (callable): Nonlinearity for output layer in
          the std network. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      std_parameterization (str): How the std should be parametrized. There
          are a few options:
          - exp: the logarithm of the std will be stored, and applied a
              exponential transformation
          - softplus: the std will be computed as log(1+exp(x))
      layer_normalization (bool): Bool for using layer normalization or not.
docstr: Parameters to restore from snapshot.
  
  Args:
      state (dict): Parameters to restore from.
docstr_tok: ["Parameters","to","restore","from","snapshot",".","Args",":","state","(","dict",")",":","Parameters","to","restore","from","."]
code: def __setstate__(self, state):
      """Parameters to restore from snapshot.
  
      Args:
          state (dict): Parameters to restore from.
  
      """
      super().__setstate__(state)
      self._initialize()
  
imports: import numpy
  import tensorflow
  from garage.experiment import deterministic
  from garage.tf.embeddings import StochasticEncoder
  from garage.tf.models import GaussianMLPModel, StochasticModule
label_desc: Use image node_id 16 for calling the GaussianMLPEncoder obj's underlying member method code with example usage: obj.__setstate__(state) without return types