code: def fit_best(estimator, params, X, y, fit_params):
      estimator = copy_estimator(estimator).set_params(**params)
      estimator.fit(X, y, **fit_params)
      return estimator
  
imports: from __future__ import absolute_import, division, print_function
  import warnings
  from collections import defaultdict
  from threading import Lock
  from timeit import default_timer
  import numpy
  import packaging.version
  from dask.base import normalize_token
  from scipy import sparse
  from scipy.stats import rankdata
  from sklearn.exceptions import FitFailedWarning
  from sklearn.pipeline import FeatureUnion, Pipeline
  from sklearn.utils.validation import check_consistent_length
  from toolz import pluck
  from .._compat import Mapping
  from .utils import _index_param_value, _num_samples, _safe_indexing, copy_estimator
label_desc: Use image node_id 18 for calling a global function with example usage: fit_best(estimator, params, X, y, fit_params) and returns: estimator