docstr: Evaluate predicted target values for X relative to y_true.
  
  Parameters
  ----------
  estimator : object
      Trained estimator to use for scoring. Must have a predict_proba
      method; the output of that is used to compute the score.
  
  X : {array-like, sparse matrix}
      Test data that will be fed to estimator.predict.
  
  y_true : array-like
      Gold standard target values for X.
  
  sample_weight : array-like of shape (n_samples,), default=None
      Sample weights.
  
  **kwargs : dict
      Other parameters passed to the scorer. Refer to
      :func:`set_score_request` for more details.
  
      Only available if `enable_metadata_routing=True`. See the
      :ref:`User Guide <metadata_routing>`.
  
      .. versionadded:: 1.3
  
  Returns
  -------
  score : float
      Score function applied to prediction of estimator on X.
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code: def __call__(
      self, estimator, X, y_true, sample_weight=None, **kwargs
  ):
      """Evaluate predicted target values for X relative to y_true.
  
      Parameters
      ----------
      estimator : object
          Trained estimator to use for scoring. Must have a predict_proba
          method; the output of that is used to compute the score.
  
      X : {array-like, sparse matrix}
          Test data that will be fed to estimator.predict.
  
      y_true : array-like
          Gold standard target values for X.
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights.
  
      **kwargs : dict
          Other parameters passed to the scorer. Refer to
          :func:`set_score_request` for more details.
  
          Only available if `enable_metadata_routing=True`. See the
          :ref:`User Guide <metadata_routing>`.
  
          .. versionadded:: 1.3
  
      Returns
      -------
      score : float
          Score function applied to prediction of estimator on X.
      """
      if kwargs and not _routing_enabled():
          raise ValueError(
              "kwargs is only supported if enable_metadata_routing=True. See"
              " the User Guide for more information."
          )
  
      _kwargs = copy.deepcopy(kwargs)
      if sample_weight is not None:
          _kwargs["sample_weight"] = sample_weight
  
      return self._score(
          partial(_cached_call, None), estimator, X, y_true, **_kwargs
      )
  
imports: import copy
  import warnings
  from collections import Counter
  from functools import partial
  from inspect import signature
  from traceback import format_exc
  import numpy
  from ..base import is_regressor
  from ..utils import Bunch
  from ..utils._param_validation import HasMethods, StrOptions, validate_params
  from ..utils._response import _get_response_values
  from ..utils.metadata_routing import MetadataRequest, MetadataRouter, _MetadataRequester, _routing_enabled, get_routing_for_object, process_routing
  from ..utils.multiclass import type_of_target
  from .None import accuracy_score, average_precision_score, balanced_accuracy_score, brier_score_loss, class_likelihood_ratios, explained_variance_score, f1_score, jaccard_score, log_loss, matthews_corrcoef, max_error, mean_absolute_error, mean_absolute_percentage_error, mean_gamma_deviance, mean_poisson_deviance, mean_squared_error, mean_squared_log_error, median_absolute_error, precision_score, r2_score, recall_score, roc_auc_score, top_k_accuracy_score
  from .cluster import adjusted_mutual_info_score, adjusted_rand_score, completeness_score, fowlkes_mallows_score, homogeneity_score, mutual_info_score, normalized_mutual_info_score, rand_score, v_measure_score
label_desc: Use image node_id 4 for calling the _BaseScorer obj's underlying member method code with example usage: obj.__call__(estimator, X, y_true, sample_weight) and returns: self