class_docstr: Q-function output for continuous action space.
  
  See: http://arxiv.org/abs/1603.00748
  
  Define a Q(s,a) with A(s,a) in a quadratic form.
  
  Q(s,a) = V(s,a) + A(s,a)
  A(s,a) = -1/2 (u - mu(s))^T P(s) (u - mu(s))
  
  Args:
      mu (torch.Tensor): mu(s), actions that maximize A(s,a)
      mat (torch.Tensor): P(s), coefficient matrices of A(s,a).
        It must be positive definite.
      v (torch.Tensor): V(s), values of s
      min_action (ndarray): minimum action, not batched
      max_action (ndarray): maximum action, not batched
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code: def __getitem__(self, i):
      return QuadraticActionValue(
          self.mu[i],
          self.mat[i],
          self.v[i],
          min_action=self.min_action,
          max_action=self.max_action,
      )
  
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 9 for calling the QuadraticActionValue obj's underlying member method code with example usage: obj.__getitem__(i) and returns: QuadraticActionValue