class_docstr: GaussianMLPTaskEmbeddingPolicy.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      encoder (garage.tf.embeddings.StochasticEncoder): Embedding network.
      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.
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code: def __init__(
      self,
      env_spec,
      encoder,
      name="GaussianMLPTaskEmbeddingPolicy",
      hidden_sizes=(32, 32),
      hidden_nonlinearity=tf.nn.tanh,
      hidden_w_init=tf.initializers.glorot_uniform(
          seed=deterministic.get_tf_seed_stream()
      ),
      hidden_b_init=tf.zeros_initializer(),
      output_nonlinearity=None,
      output_w_init=tf.initializers.glorot_uniform(
          seed=deterministic.get_tf_seed_stream()
      ),
      output_b_init=tf.zeros_initializer(),
      learn_std=True,
      adaptive_std=False,
      std_share_network=False,
      init_std=1.0,
      min_std=1e-6,
      max_std=None,
      std_hidden_sizes=(32, 32),
      std_hidden_nonlinearity=tf.nn.tanh,
      std_output_nonlinearity=None,
      std_parameterization="exp",
      layer_normalization=False,
  ):
      assert isinstance(env_spec.action_space, akro.Box)
      assert not isinstance(env_spec.observation_space, akro.Dict)
      self._env_spec = env_spec
      self._name = name
      self._encoder = encoder
      self._augmented_observation_space = akro.concat(
          self._env_spec.observation_space, self.task_space
      )
      self._hidden_sizes = hidden_sizes
      self._hidden_nonlinearity = hidden_nonlinearity
      self._hidden_w_init = hidden_w_init
      self._hidden_b_init = hidden_b_init
      self._output_nonlinearity = output_nonlinearity
      self._output_w_init = output_w_init
      self._output_b_init = output_b_init
      self._learn_std = learn_std
      self._adaptive_std = adaptive_std
      self._std_share_network = std_share_network
      self._init_std = init_std
      self._min_std = min_std
      self._max_std = max_std
      self._std_hidden_sizes = std_hidden_sizes
      self._std_hidden_nonlinearity = std_hidden_nonlinearity
      self._std_output_nonlinearity = std_output_nonlinearity
      self._std_parameterization = std_parameterization
      self._layer_normalization = layer_normalization
  
      self.obs_dim = env_spec.observation_space.flat_dim
      self.action_dim = env_spec.action_space.flat_dim
  
      super().__init__(
          output_dim=self.action_dim,
          hidden_sizes=hidden_sizes,
          hidden_nonlinearity=hidden_nonlinearity,
          hidden_w_init=hidden_w_init,
          hidden_b_init=hidden_b_init,
          output_nonlinearity=output_nonlinearity,
          output_w_init=output_w_init,
          output_b_init=output_b_init,
          learn_std=learn_std,
          adaptive_std=adaptive_std,
          std_share_network=std_share_network,
          init_std=init_std,
          min_std=min_std,
          max_std=max_std,
          std_hidden_sizes=std_hidden_sizes,
          std_hidden_nonlinearity=std_hidden_nonlinearity,
          std_output_nonlinearity=std_output_nonlinearity,
          std_parameterization=std_parameterization,
          layer_normalization=layer_normalization,
          name=name,
      )
  
      self._initialize()
  
imports: import akro
  import numpy
  import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models import GaussianMLPModel
  from garage.tf.policies.task_embedding_policy import TaskEmbeddingPolicy
label_desc: Use image node_id 1 to create a new GaussianMLPTaskEmbeddingPolicy object from inherited base classes: GaussianMLPModel, TaskEmbeddingPolicy with example: obj = GaussianMLPTaskEmbeddingPolicy(env_spec, encoder, name, hidden_sizes, hidden_nonlinearity, hidden_w_init, hidden_b_init, output_nonlinearity, output_w_init, output_b_init, learn_std, adaptive_std, std_share_network, init_std, min_std, max_std, std_hidden_sizes, std_hidden_nonlinearity, std_output_nonlinearity, std_parameterization, layer_normalization)