code: def _evaluate_one(self, estimator, data_preproc, scorers):
      res = []
      for X_train, X_test, y_train, y_test in data_preproc:
          X = np.vstack([X_train, X_test])
          if y_train.ndim < 2 and y_test.ndim < 2:
              y = np.hstack([y_train, y_test])
          else:
              y = np.vstack([y_train, y_test])
          train = np.arange(len(X_train))
          test = np.arange(len(X_train), len(X_test) + len(X_train))
          with warnings.catch_warnings():
              warnings.filterwarnings(
                  "ignore", category=UndefinedMetricWarning
              )
              _fit_and_score_args = (
                  {"score_params": {}}
                  if _SKLEARN_VERSION >= "1.4"
                  else {}
              )
              scores = _fit_and_score(
                  estimator,
                  X,
                  y,
                  scorer=scorers,
                  train=train,
                  test=test,
                  parameters={},
                  fit_params={},
                  verbose=self.verbose,
                  **_fit_and_score_args,
              )
          res.append(scores["test_scores"])
  
      res_mean = pd.DataFrame(res).mean(axis=0)
      try:
          # show only last step of pipeline for simplicity
          name = repr(estimator.steps[-1][1])
      except AttributeError:
          name = repr(estimator)
  
      if self.verbose:
          print("Running {}".format(name))
          print(_format_scores(res_mean))
      res_mean.name = name
      self.log_.append(res_mean)
      return res_mean
  
imports: from importlib.metadata import version
  import warnings
  import numpy
  import pandas
  import sklearn
  from sklearn.metrics import make_scorer, average_precision_score
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.multiclass import type_of_target
  from sklearn.pipeline import make_pipeline, Pipeline
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.model_selection import StratifiedKFold, KFold
  from sklearn.model_selection._validation import _fit_and_score
  from sklearn.utils.validation import check_is_fitted
  from dabl._available_if import available_if
  from sklearn.dummy import DummyClassifier
  from sklearn.experimental import enable_halving_search_cv
  from sklearn.model_selection import HalvingGridSearchCV
  from .preprocessing import EasyPreprocessor, detect_types
  from .pipelines import get_fast_classifiers, get_fast_regressors, get_any_classifiers
  from .utils import _validate_Xyt
label_desc: Use image node_id 2 for calling the _BaseSimpleEstimator obj's underlying member method code with example usage: obj._evaluate_one(estimator, data_preproc, scorers) and returns: res_mean