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: Build loss function for the inference network.
  
  Args:
      i (namedtuple): Collection of variables to compute inference loss.
  
  Returns:
      tf.Tensor: Inference loss.
docstr_tok: ["Build","loss","function","for","the","inference","network",".","Args",":","i","(","namedtuple",")",":","Collection","of","variables","to","compute","inference","loss",".","Returns",":","tf.Tensor",":","Inference","loss","."]
code: def _build_inference_loss(self, i):
      """Build loss function for the inference network.
  
      Args:
          i (namedtuple): Collection of variables to compute inference loss.
  
      Returns:
          tf.Tensor: Inference loss.
  
      """
      dist = self._infer_network.dist
      old_dist = self._old_infer_network.dist
      with tf.name_scope("infer_loss"):
          traj_ll = dist.log_prob(i.latent_var, name="traj_ll_2")
  
          # Calculate loss
          traj_gammas = tf.constant(
              float(self._discount),
              dtype=tf.float32,
              shape=[self.max_episode_length],
          )
          # Pylint false alarm
          # pylint: disable=no-value-for-parameter
          traj_discounts = tf.compat.v1.cumprod(
              traj_gammas, exclusive=True, name="traj_discounts"
          )
          discount_traj_ll = traj_discounts * traj_ll
          discount_traj_ll = tf.boolean_mask(
              discount_traj_ll, i.valid_var
          )
  
          with tf.name_scope("loss"):
              infer_loss = -tf.reduce_mean(
                  discount_traj_ll, name="infer_loss"
              )
  
          with tf.name_scope("kl"):
              # Calculate predicted encoder distributions for each timestep
  
              # Calculate KL divergence
              kl = old_dist.kl_divergence(dist)
              infer_kl = tf.reduce_mean(kl, name="infer_kl")
  
          return infer_loss, infer_kl
  
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 11 for calling the TENPO obj's underlying member method code with example usage: obj._build_inference_loss(i) and returns: infer_loss,  infer_kl