code: def fit_grid_point(
      X,
      y,
      estimator,
      parameters,
      train,
      test,
      scorer,
      verbose,
      error_score="raise",
      **fit_params,
  ):
      """Run fit on one set of parameters.
  
      .. deprecated:: 0.18
          This module will be removed in 0.20.
          Use :func:`sklearn.model_selection.fit_grid_point` instead.
  
      Parameters
      ----------
      X : array-like, sparse matrix or list
          Input data.
  
      y : array-like or None
          Targets for input data.
  
      estimator : estimator object
          A object of that type is instantiated for each grid point.
          This is assumed to implement the scikit-learn estimator interface.
          Either estimator needs to provide a ``score`` function,
          or ``scoring`` must be passed.
  
      parameters : dict
          Parameters to be set on estimator for this grid point.
  
      train : ndarray, dtype int or bool
          Boolean mask or indices for training set.
  
      test : ndarray, dtype int or bool
          Boolean mask or indices for test set.
  
      scorer : callable or None.
          If provided must be a scorer callable object / function with signature
          ``scorer(estimator, X, y)``.
  
      verbose : int
          Verbosity level.
  
      **fit_params : kwargs
          Additional parameter passed to the fit function of the estimator.
  
      error_score : 'raise' (default) or numeric
          Value to assign to the score if an error occurs in estimator fitting.
          If set to 'raise', the error is raised. If a numeric value is given,
          FitFailedWarning is raised. This parameter does not affect the refit
          step, which will always raise the error.
  
      Returns
      -------
      score : float
          Score of this parameter setting on given training / test split.
  
      parameters : dict
          The parameters that have been evaluated.
  
      n_samples_test : int
          Number of test samples in this split.
      """
      score, n_samples_test, _ = _fit_and_score(
          estimator,
          X,
          y,
          scorer,
          train,
          test,
          verbose,
          parameters,
          fit_params,
          error_score,
      )
      return score, parameters, n_samples_test
  
imports: from __future__ import print_function
  from abc import ABCMeta, abstractmethod
  from collections import Mapping, namedtuple, Sized
  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 .cross_validation import check_cv
  from .cross_validation import _fit_and_score
  from .externals.joblib import Parallel, delayed
  from .externals import six
  from .utils import check_random_state
  from .utils.random import sample_without_replacement
  from .utils.validation import _num_samples, indexable
  from .utils.metaestimators import if_delegate_has_method
  from .metrics.scorer import check_scoring
  from .exceptions import ChangedBehaviorWarning
label_desc: Use image node_id 1 for calling a global function with example usage: fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score) and returns: score,  parameters,  n_samples_test