code: def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer):
      """
      Return the score for a fit across one fold.
      """
      X_train, y_train = _safe_split(estimator, X, y, train)
      X_test, y_test = _safe_split(estimator, X, y, test, train)
      return rfe._fit(
          X_train,
          y_train,
          lambda estimator, features: _score(
              estimator, X_test[:, features], y_test, scorer
          ),
      ).scores_
  
imports: import numpy
  from ..utils import check_X_y, safe_sqr
  from ..utils.metaestimators import if_delegate_has_method
  from ..base import BaseEstimator
  from ..base import MetaEstimatorMixin
  from ..base import clone
  from ..base import is_classifier
  from ..externals.joblib import Parallel, delayed
  from ..model_selection import check_cv
  from ..model_selection._validation import _safe_split, _score
  from ..metrics.scorer import check_scoring
  from .base import SelectorMixin
label_desc: Use image node_id 1 for calling a global function with example usage: _rfe_single_fit(rfe, estimator, X, y, train, test, scorer) and returns: rfe