class_docstr: Class for black-box regressors.
docstr: Compute the loss of the regressor for samples `x`. If loss_fn not provided at init, MSE loss is computed.
  
  :param x: Input samples.
  :param y: Target values.
  :return: Loss values.
docstr_tok: ["Compute","the","loss","of","the","regressor","for","samples","`","x","`",".","If","loss_fn","not","provided","at","init",",","MSE","loss","is","computed",".",":","param","x",":","Input","samples",".",":","param","y",":","Target","values",".",":","return",":","Loss","values","."]
code: def compute_loss(
      self, x: np.ndarray, y: np.ndarray, **kwargs
  ) -> np.ndarray:
      """
      Compute the loss of the regressor for samples `x`. If loss_fn not provided at init, MSE loss is computed.
  
      :param x: Input samples.
      :param y: Target values.
      :return: Loss values.
      """
      predicted = self.predict(x)
      return self.compute_loss_from_predictions(y, predicted)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from functools import total_ordering
  import logging
  from typing import Callable, List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.estimator import BaseEstimator
  from art.estimators.regression.regressor import RegressorMixin, Regressor
  from art.estimators.classification import BlackBoxClassifier
label_desc: Use image node_id 8 for calling the BlackBoxRegressor obj's underlying member method code with example usage: obj.compute_loss(x, y) and returns: self