docstr: Evaluate predicted probabilities for X relative to y_true.
  
  Parameters
  ----------
  method_caller : callable
      Returns predictions given an estimator, method name, and other
      arguments, potentially caching results.
  
  clf : object
      Trained classifier 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 clf.predict_proba.
  
  y : array-like
      Gold standard target values for X. These must be class labels,
      not probabilities.
  
  **kwargs : dict
      Other parameters passed to the scorer. Refer to
      :func:`set_score_request` for more details.
  
      .. versionadded:: 1.3
  
  Returns
  -------
  score : float
      Score function applied to prediction of estimator on X.
docstr_tok: ["Evaluate","predicted","probabilities","for","X","relative","to","y_true",".","Parameters","--","--","--","--","--","method_caller",":","callable","Returns","predictions","given","an","estimator",",","method","name",",","and","other","arguments",",","potentially","caching","results",".","clf",":","object","Trained","classifier","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","clf.predict_proba",".","y",":","array-like","Gold","standard","target","values","for","X",".","These","must","be","class","labels",",","not","probabilities",".","*","*","kwargs",":","dict","Other","parameters","passed","to","the","scorer",".","Refer","to",":","func",":","`","set_score_request","`","for","more","details",".","..","versionadded",":",":","1.3","Returns","--","--","--","-","score",":","float","Score","function","applied","to","prediction","of","estimator","on","X","."]
code: def _score(self, method_caller, clf, X, y, **kwargs):
      """Evaluate predicted probabilities for X relative to y_true.
  
      Parameters
      ----------
      method_caller : callable
          Returns predictions given an estimator, method name, and other
          arguments, potentially caching results.
  
      clf : object
          Trained classifier 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 clf.predict_proba.
  
      y : array-like
          Gold standard target values for X. These must be class labels,
          not probabilities.
  
      **kwargs : dict
          Other parameters passed to the scorer. Refer to
          :func:`set_score_request` for more details.
  
          .. versionadded:: 1.3
  
      Returns
      -------
      score : float
          Score function applied to prediction of estimator on X.
      """
      self._warn_overlap(
          message=(
              "There is an overlap between set kwargs of this scorer instance and"
              " passed metadata. Please pass them either as kwargs to `make_scorer`"
              " or metadata, but not both."
          ),
          kwargs=kwargs,
      )
  
      y_pred = method_caller(
          clf, "predict_proba", X, pos_label=self._get_pos_label()
      )
      scoring_kwargs = {**self._kwargs, **kwargs}
      return self._sign * self._score_func(y, y_pred, **scoring_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 1 for calling the _ProbaScorer obj's underlying member method code with example usage: obj._score(method_caller, clf, X, y) and returns: unknown