class_docstr: Base class for hyper parameter search with cross-validation.
class_docstr_tok: ["Base","class","for","hyper","parameter","search","with","cross-validation","."]
docstr: Call transform on the estimator with the best found parameters.
  
  Only available if the underlying estimator supports ``transform`` and
  ``refit=True``.
  
  Parameters
  -----------
  X : indexable, length n_samples
      Must fulfill the input assumptions of the
      underlying estimator.
code: def transform(self, X):
      """Call transform on the estimator with the best found parameters.
  
      Only available if the underlying estimator supports ``transform`` and
      ``refit=True``.
  
      Parameters
      -----------
      X : indexable, length n_samples
          Must fulfill the input assumptions of the
          underlying estimator.
  
      """
      self._check_is_fitted("transform")
      return self.best_estimator_.transform(X)
  
imports: from __future__ import print_function
  from __future__ import division
  from abc import ABCMeta, abstractmethod
  from collections import Mapping, namedtuple, Sized, defaultdict, Sequence
  from functools import partial, reduce
  from itertools import product
  import operator
  import warnings
  import numpy
  from ..base import BaseEstimator, is_classifier, clone
  from ..base import MetaEstimatorMixin
  from ._split import check_cv
  from ._validation import _fit_and_score
  from ..exceptions import NotFittedError
  from ..externals.joblib import Parallel, delayed
  from ..externals import six
  from ..utils import check_random_state
  from ..utils.fixes import sp_version
  from ..utils.fixes import rankdata
  from ..utils.fixes import MaskedArray
  from ..utils.random import sample_without_replacement
  from ..utils.validation import indexable, check_is_fitted
  from ..utils.metaestimators import if_delegate_has_method
  from ..metrics.scorer import check_scoring
label_desc: Use image node_id 9 for calling the BaseSearchCV obj's underlying member method code with example usage: obj.transform(X) and returns: self