class_docstr: distributional Q-function output for discrete action space.
  
  Args:
      q_dist: Probabilities of atoms. Its shape must be
          (batchsize, n_actions, n_atoms).
      z_values (ndarray): Values represented by atoms.
          Its shape must be (n_atoms,).
class_docstr_tok: ["distributional","Q-function","output","for","discrete","action","space",".","Args",":","q_dist",":","Probabilities","of","atoms",".","Its","shape","must","be","(","batchsize",",","n_actions",",","n_atoms",")",".","z_values","(","ndarray",")",":","Values","represented","by","atoms",".","Its","shape","must","be","(","n_atoms",",",")","."]
docstr: Return the return distributions of given actions.
  
  Args:
      actions (torch.Tensor): Array of action indices.
          Its shape must be (batch_size,).
  
  Returns:
      torch.Tensor: Return distributions. Its shape will be
          (batch_size, n_atoms).
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code: def evaluate_actions_as_distribution(self, actions):
      """Return the return distributions of given actions.
  
      Args:
          actions (torch.Tensor): Array of action indices.
              Its shape must be (batch_size,).
  
      Returns:
          torch.Tensor: Return distributions. Its shape will be
              (batch_size, n_atoms).
      """
      return self.q_dist[torch.arange(self.q_values.shape[0]), actions]
  
imports: import warnings
  from abc import ABCMeta, abstractmethod, abstractproperty
  import torch
  import torch.nn.functional
  from torch.distributions.utils import lazy_property
label_desc: Use image node_id 6 for calling the DistributionalDiscreteActionValue obj's underlying member method code with example usage: obj.evaluate_actions_as_distribution(actions) and returns: self