class_docstr: Natural Policy Gradient Optimization.
  
  This is specific for RL^2
  (https://arxiv.org/pdf/1611.02779.pdf).
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (garage.tf.policies.StochasticPolicy): 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.
      pg_loss (str): A string from: 'vanilla', 'surrogate',
          'surrogate_clip'. The type of loss functions to use.
      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.
      use_softplus_entropy (bool): Whether to estimate the softmax
          distribution of the entropy to prevent the entropy from being
          negative.
      use_neg_logli_entropy (bool): Whether to estimate the entropy as the
          negative log likelihood of the action.
      stop_entropy_gradient (bool): Whether to stop the entropy gradient.
      entropy_method (str): A string from: 'max', 'regularized',
          'no_entropy'. The type of entropy method to use. 'max' adds the
          dense entropy to the reward for each time step. 'regularized' adds
          the mean entropy to the surrogate objective. See
          https://arxiv.org/abs/1805.00909 for more details.
      fit_baseline (str): Either 'before' or 'after'. See above docstring for
          a more detail explanation. Currently it only supports 'before'.
      name (str): The name of the algorithm.
docstr: Get baseline prediction.
  
  Args:
      episodes (EpisodeBatch): Batch of episodes.
  
  Returns:
      np.ndarray: Baseline prediction, with shape
          :math:`(N, max_episode_length * episode_per_task)`.
docstr_tok: ["Get","baseline","prediction",".","Args",":","episodes","(","EpisodeBatch",")",":","Batch","of","episodes",".","Returns",":","np.ndarray",":","Baseline","prediction",",","with","shape",":","math",":","`","(","N",",","max_episode_length","*","episode_per_task",")","`","."]
code: def _get_baseline_prediction(self, episodes):
      """Get baseline prediction.
  
      Args:
          episodes (EpisodeBatch): Batch of episodes.
  
      Returns:
          np.ndarray: Baseline prediction, with shape
              :math:`(N, max_episode_length * episode_per_task)`.
  
      """
      obs = [
          self._baseline.predict({"observations": obs})
          for obs in episodes.observations_list
      ]
      return pad_batch_array(
          np.concatenate(obs), episodes.lengths, self.max_episode_length
      )
  
imports: from dowel import logger, tabular
  import numpy
  from garage.np import explained_variance_1d, pad_batch_array
  from garage.tf.algos import NPO
label_desc: Use image node_id 2 for calling the RL2NPO obj's underlying member method code with example usage: obj._get_baseline_prediction(episodes) and returns: pad_batch_array