class_docstr: Natural Policy Optimization with Task Embeddings.
  
  See https://karolhausman.github.io/pdf/hausman17nips-ws2.pdf for algorithm
  reference.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (garage.tf.policies.TaskEmbeddingPolicy): Policy.
      baseline (garage.tf.baselines.Baseline): The baseline.
      sampler (garage.sampler.Sampler): Sampler.
      scope (str): Scope for identifying the algorithm.
          Must be specified if running multiple algorithms
          simultaneously, each using different environments
          and policies.
      discount (float): Discount.
      gae_lambda (float): Lambda used for generalized advantage
          estimation.
      center_adv (bool): Whether to rescale the advantages
          so that they have mean 0 and standard deviation 1.
      positive_adv (bool): Whether to shift the advantages
          so that they are always positive. When used in
          conjunction with center_adv the advantages will be
          standardized before shifting.
      fixed_horizon (bool): Whether to fix horizon.
      lr_clip_range (float): The limit on the likelihood ratio between
          policies, as in PPO.
      max_kl_step (float): The maximum KL divergence between old and new
          policies, as in TRPO.
      optimizer (object): The optimizer of the algorithm. Should be the
          optimizers in garage.tf.optimizers.
      optimizer_args (dict): The arguments of the optimizer.
      policy_ent_coeff (float): The coefficient of the policy entropy.
          Setting it to zero would mean no entropy regularization.
      encoder_ent_coeff (float): The coefficient of the policy encoder
          entropy. Setting it to zero would mean no entropy regularization.
      use_softplus_entropy (bool): Whether to estimate the softmax
          distribution of the entropy to prevent the entropy from being
          negative.
      stop_ce_gradient (bool): Whether to stop the cross entropy gradient.
      inference (garage.tf.embeddings.StochasticEncoder): A encoder
          that infers the task embedding from state trajectory.
      inference_optimizer (object): The optimizer of the inference. Should be
          an optimizer in garage.tf.optimizers.
      inference_optimizer_args (dict): The arguments of the inference
          optimizer.
      inference_ce_coeff (float): The coefficient of the cross entropy of
          task embeddings inferred from task one-hot and state trajectory.
          This is effectively the coefficient of log-prob of inference.
      name (str): The name of the algorithm.
docstr: Joint optimization of policy and encoder networks.
  
  Args:
      policy_opt_input_values (list(np.ndarray)): Flatten policy
          optimization input values.
  
  Returns:
      float: Policy loss after optimization.
docstr_tok: ["Joint","optimization","of","policy","and","encoder","networks",".","Args",":","policy_opt_input_values","(","list","(","np.ndarray",")",")",":","Flatten","policy","optimization","input","values",".","Returns",":","float",":","Policy","loss","after","optimization","."]
code: def _train_policy_and_encoder_networks(self, policy_opt_input_values):
      """Joint optimization of policy and encoder networks.
  
      Args:
          policy_opt_input_values (list(np.ndarray)): Flatten policy
              optimization input values.
  
      Returns:
          float: Policy loss after optimization.
  
      """
      logger.log("Computing loss before")
  
      loss_before = self._optimizer.loss(policy_opt_input_values)
  
      logger.log("Computing KL before")
      policy_kl_before = self._f_policy_kl(*policy_opt_input_values)
      embed_kl_before = self._f_encoder_kl(*policy_opt_input_values)
  
      logger.log("Optimizing")
      self._optimizer.optimize(policy_opt_input_values)
  
      logger.log("Computing KL after")
      policy_kl = self._f_policy_kl(*policy_opt_input_values)
      embed_kl = self._f_encoder_kl(*policy_opt_input_values)
  
      logger.log("Computing loss after")
      loss_after = self._optimizer.loss(policy_opt_input_values)
      tabular.record(
          "{}/LossBefore".format(self.policy.name), loss_before
      )
      tabular.record(
          "{}/LossAfter".format(self.policy.name), loss_after
      )
      tabular.record(
          "{}/dLoss".format(self.policy.name), loss_before - loss_after
      )
      tabular.record(
          "{}/KLBefore".format(self.policy.name), policy_kl_before
      )
      tabular.record("{}/KL".format(self.policy.name), policy_kl)
      tabular.record(
          "{}/Encoder/KLBefore".format(self.policy.name),
          embed_kl_before,
      )
      tabular.record("{}/Encoder/KL".format(self.policy.name), embed_kl)
  
      return loss_after
  
imports: import akro
  from dowel import Histogram, logger, tabular
  import numpy
  import scipy.stats
  import tensorflow
  from garage import InOutSpec, log_performance
  from garage.experiment import deterministic
  from garage.np import discount_cumsum, explained_variance_1d, pad_batch_array, rrse, sliding_window
  from garage.np.algos import RLAlgorithm
  from garage.tf import center_advs, compile_function, compute_advantages, concat_tensor_list, discounted_returns, flatten_inputs, graph_inputs, pad_tensor_dict, positive_advs, stack_tensor_dict_list
  from garage.tf.embeddings import StochasticEncoder
  from garage.tf.optimizers import LBFGSOptimizer
  from garage.tf.policies import TaskEmbeddingPolicy
label_desc: Use image node_id 16 for calling the TENPO obj's underlying member method code with example usage: obj._train_policy_and_encoder_networks(policy_opt_input_values) and returns: loss_after