code: def _gl_score(estimators, scoring, X, y, pb):
      """Score GeneralizingEstimator in parallel.
  
      Predict and score each slice of data.
  
      Parameters
      ----------
      estimators : list of estimators
          The fitted estimators.
      scoring : callable, string or None
          If scoring is None (default), the predictions are internally
          generated by estimator.score(). Else, we must first get the
          predictions to pass them to ad-hoc scorer.
      X : array, shape (n_samples, nd_features, n_slices)
          The target data. 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.
      pb : instance of ProgressBar
          The progress bar to update.
  
      Returns
      -------
      score : array, shape (n_estimators, n_slices)
          The score for each slice of data.
      """
      # FIXME: The level parallelization may be a bit high, and might be memory
      # consuming. Perhaps need to lower it down to the loop across X slices.
      score_shape = [len(estimators), X.shape[-1]]
      for jj in range(X.shape[-1]):
          for ii, est in enumerate(estimators):
              _score = scoring(est, X[..., jj], y)
              # Initialize array of predictions on the first score iteration
              if (ii == 0) and (jj == 0):
                  dtype = type(_score)
                  score = np.zeros(score_shape, dtype)
              score[ii, jj, ...] = _score
  
              pb.update(jj * len(estimators) + ii + 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 8 for calling a global function with example usage: _gl_score(estimators, scoring, X, y, pb) and returns: score