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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docstr: Build policy to support sampling.
  
  After build, get_action_*() methods will be available.
docstr_tok: ["Build","policy","to","support","sampling",".","After","build",",","get_action_","*","(",")","methods","will","be","available","."]
code: def _initialize(self):
      """Build policy to support sampling.
  
      After build, get_action_*() methods will be available.
  
      """
      obs_input = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None, self.obs_dim)
      )
      encoder_input = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None, self._encoder.input_dim)
      )
      latent_input = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None, self._encoder.output_dim)
      )
  
      with tf.compat.v1.variable_scope(self._encoder.name):
          encoder_dist = self._encoder.build(
              encoder_input, name="encoder"
          ).dist
  
      with tf.compat.v1.variable_scope("concat_obs_latent"):
          obs_latent_input = tf.concat([obs_input, latent_input], -1)
  
      dist, mean_var, log_std_var = (
          super()
          .build(
              obs_latent_input,
              # Must named 'default' to
              # compensate tf default worker
              name="default",
          )
          .outputs
      )
  
      embed_state_input = tf.concat(
          [
              obs_input,
              encoder_dist.sample(
                  seed=deterministic.get_tf_seed_stream()
              ),
          ],
          -1,
      )
      dist_given_task, mean_g_t, log_std_g_t = (
          super().build(embed_state_input, name="given_task").outputs
      )
  
      self._f_dist_obs_latent = (
          tf.compat.v1.get_default_session().make_callable(
              [
                  dist.sample(seed=deterministic.get_tf_seed_stream()),
                  mean_var,
                  log_std_var,
              ],
              feed_list=[obs_input, latent_input],
          )
      )
  
      self._f_dist_obs_task = (
          tf.compat.v1.get_default_session().make_callable(
              [
                  dist_given_task.sample(
                      seed=deterministic.get_tf_seed_stream()
                  ),
                  mean_g_t,
                  log_std_g_t,
              ],
              feed_list=[obs_input, encoder_input],
          )
      )
  
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 2 for calling the GaussianMLPTaskEmbeddingPolicy obj's underlying member method code with example usage: obj._initialize() without return types