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"]
code: def __repr__(self):  # noqa: D105
      repr_str = super().__repr__()
      if hasattr(self, "estimators_"):
          repr_str = repr_str[:-1]
          repr_str += ", fitted with %i estimators>" % len(
              self.estimators_
          )
      return repr_str
  
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 1 for calling the GeneralizingEstimator obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str