class_docstr: The abstract base class `BaseEstimator` defines the basic requirements of an estimator in ART. The BaseEstimator is
  is the highest abstraction of a machine learning model in ART.
class_docstr_tok: ["The","abstract","base","class","`","BaseEstimator","`","defines","the","basic","requirements","of","an","estimator","in","ART",".","The","BaseEstimator","is","is","the","highest","abstraction","of","a","machine","learning","model","in","ART","."]
docstr: Compute the loss of the estimator for samples `x`.
  
  :param x: Input samples.
  :param y: Target values.
  :return: Loss values.
  :rtype: Format as expected by the `model`
code: def compute_loss(self, x: np.ndarray, y: Any, **kwargs) -> np.ndarray:
      """
      Compute the loss of the estimator for samples `x`.
  
      :param x: Input samples.
      :param y: Target values.
      :return: Loss values.
      :rtype: Format as expected by the `model`
      """
      raise NotImplementedError
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.config import ART_NUMPY_DTYPE
label_desc: Use image node_id 17 for calling the BaseEstimator obj's underlying member method code with example usage: obj.compute_loss(x, y) without return types