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: Transform each data slice with all possible estimators.
  
  Parameters
  ----------
  X : array, shape (n_samples, nd_features, n_slices)
      The input samples. For estimator the corresponding data slice is
      used to make a transformation. The feature dimension can be
      multidimensional e.g.
      X.shape = (n_samples, n_features_1, n_features_2, n_estimators).
  
  Returns
  -------
  Xt : array, shape (n_samples, n_estimators, n_slices)
      The transformed values generated by each estimator.
code: def transform(self, X):
      """Transform each data slice with all possible estimators.
  
      Parameters
      ----------
      X : array, shape (n_samples, nd_features, n_slices)
          The input samples. For estimator the corresponding data slice is
          used to make a transformation. The feature dimension can be
          multidimensional e.g.
          X.shape = (n_samples, n_features_1, n_features_2, n_estimators).
  
      Returns
      -------
      Xt : array, shape (n_samples, n_estimators, n_slices)
          The transformed values generated by each estimator.
      """
      return self._transform(X, "transform")
  
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 3 for calling the GeneralizingEstimator obj's underlying member method code with example usage: obj.transform(X) and returns: self