class_docstr: Quantile action value for discrete actions.
  Args:
      quantiles (torch.Tensor): (batch_size, n_taus, n_actions)
      q_values_formatter (callable):
class_docstr_tok: ["Quantile","action","value","for","discrete","actions",".","Args",":","quantiles","(","torch.Tensor",")",":","(","batch_size",",","n_taus",",","n_actions",")","q_values_formatter","(","callable",")",":"]
docstr: Return the return quantiles of given actions.
  Args:
      actions (torch.Tensor or ndarray): Array of action indices.
          Its shape must be (batch_size,).
  Returns:
      torch.Tensor: Return quantiles. Its shape will be
          (batch_size, n_taus).
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code: def evaluate_actions_as_quantiles(self, actions):
      """Return the return quantiles of given actions.
      Args:
          actions (torch.Tensor or ndarray): Array of action indices.
              Its shape must be (batch_size,).
      Returns:
          torch.Tensor: Return quantiles. Its shape will be
              (batch_size, n_taus).
      """
      return self.quantiles[
          torch.arange(self.quantiles.shape[0], dtype=torch.long),
          :,
          actions.long(),
      ]
  
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 3 for calling the QuantileDiscreteActionValue obj's underlying member method code with example usage: obj.evaluate_actions_as_quantiles(actions) and returns: self