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: Aux. function to make parallel predictions/transformation.
code: def _transform(self, X, method):
      """Aux. function to make parallel predictions/transformation."""
      X = self._check_Xy(X)
      method = _check_method(self.base_estimator, method)
  
      parallel, p_func, n_jobs = parallel_func(
          _gl_transform,
          self.n_jobs,
          max_jobs=X.shape[-1],
          verbose=False,
      )
  
      context = _create_progressbar_context(self, X, "Transforming")
      with context as pb:
          y_pred = parallel(
              p_func(
                  self.estimators_, x_split, method, pb.subset(pb_idx)
              )
              for pb_idx, x_split in array_split_idx(
                  X, n_jobs, axis=-1, n_per_split=len(self.estimators_)
              )
          )
  
      y_pred = np.concatenate(y_pred, axis=2)
      return y_pred
  
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 2 for calling the GeneralizingEstimator obj's underlying member method code with example usage: obj._transform(X, method) and returns: y_pred