class_docstr: Generalization Light.
  
  Fit a search-light along the last dimension and use them to apply a
  systematic cross-tasks generalization.
  
  Parameters
  ----------
  %(base_estimator)s
  %(scoring)s
  %(n_jobs)s
  %(position)s
  %(allow_2d)s
  %(verbose)s
class_docstr_tok: ["Generalization","Light",".","Fit","a","search-light","along","the","last","dimension","and","use","them","to","apply","a","systematic","cross-tasks","generalization",".","Parameters","--","--","--","--","--","%","(","base_estimator",")","s","%","(","scoring",")","s","%","(","n_jobs",")","s","%","(","position",")","s","%","(","allow_2d",")","s","%","(","verbose",")","s"]
docstr: Score each of the estimators on the tested dimensions.
  
  Parameters
  ----------
  X : array, shape (n_samples, nd_features, n_slices)
      The input samples. For each data slice, the corresponding estimator
      scores the prediction, e.g.:
      ``[estimators[ii].score(X[..., ii], y) for ii in range(n_slices)]``.
      The feature dimension can be multidimensional e.g.
      ``X.shape = (n_samples, n_features_1, n_features_2, n_estimators)``.
  y : array, shape (n_samples,) | (n_samples, n_targets)
      The target values.
  
  Returns
  -------
  score : array, shape (n_samples, n_estimators, n_slices)
      Score for each estimator / data slice couple.
code: def score(self, X, y):
      """Score each of the estimators on the tested dimensions.
  
      Parameters
      ----------
      X : array, shape (n_samples, nd_features, n_slices)
          The input samples. For each data slice, the corresponding estimator
          scores the prediction, e.g.:
          ``[estimators[ii].score(X[..., ii], y) for ii in range(n_slices)]``.
          The feature dimension can be multidimensional e.g.
          ``X.shape = (n_samples, n_features_1, n_features_2, n_estimators)``.
      y : array, shape (n_samples,) | (n_samples, n_targets)
          The target values.
  
      Returns
      -------
      score : array, shape (n_samples, n_estimators, n_slices)
          Score for each estimator / data slice couple.
      """  # noqa: E501
      check_scoring = _get_check_scoring()
      X = self._check_Xy(X, y)
      # For predictions/transforms the parallelization is across the data and
      # not across the estimators to avoid memory load.
      parallel, p_func, n_jobs = parallel_func(
          _gl_score, self.n_jobs, max_jobs=X.shape[-1], verbose=False
      )
      scoring = check_scoring(self.base_estimator, self.scoring)
      y = _fix_auc(scoring, y)
  
      context = _create_progressbar_context(self, X, "Scoring")
      with context as pb:
          score = parallel(
              p_func(self.estimators_, scoring, x, y, pb.subset(pb_idx))
              for pb_idx, x in array_split_idx(
                  X, n_jobs, axis=-1, n_per_split=len(self.estimators_)
              )
          )
  
      score = np.concatenate(score, axis=1)
      return score
  
imports: import logging
  import numpy
  from ..fixes import _get_check_scoring
  from ..parallel import parallel_func
  from ..utils import ProgressBar, _parse_verbose, array_split_idx, fill_doc, verbose
  from .base import BaseEstimator, _check_estimator
  from .mixin import TransformerMixin
label_desc: Use image node_id 7 for calling the GeneralizingEstimator obj's underlying member method code with example usage: obj.score(X, y) and returns: score