code: def _parallel_fit_estimator(estimator, X, y, sample_weight):
      """Private function used to fit an estimator within a job."""
      if sample_weight is not None:
          estimator.fit(X, y, sample_weight)
      else:
          estimator.fit(X, y)
      return estimator
  
imports: import numpy
  from ..base import BaseEstimator
  from ..base import ClassifierMixin
  from ..base import TransformerMixin
  from ..base import clone
  from ..preprocessing import LabelEncoder
  from ..externals import six
  from ..externals.joblib import Parallel, delayed
  from ..utils.validation import has_fit_parameter, check_is_fitted
label_desc: Use image node_id 1 for calling a global function with example usage: _parallel_fit_estimator(estimator, X, y, sample_weight) and returns: estimator