class_docstr: This estimates the Brisque score (lower is better).
code: def __call__(self, img: np.ndarray) -> float:
      try:
          import imquality.brisque as brisque
      except ImportError:
          raise UnsupportedExperiment(
              "Brisque is not installed, please run 'pip install imquality'"
          )
      try:
          score = brisque.score(img)
      except (
          AssertionError
      ):  # oh my god, brisque can raise an assert when the data is too weird.
          score = float("inf")
      return score
  
imports: import os
  import typing
  import torch
  import numpy
  import cv2
  from nevergrad.functions.base import UnsupportedExperiment
  from nevergrad.common.decorators import Registry
label_desc: Use image node_id 1 for calling the Brisque obj's underlying member method code with example usage: obj.__call__(img) and returns: score