code: def _reformat_and_group_data(
      sensitive_features, labels, scores, sensitive_feature_names=None
  ):
      """Reformats the data into a new pandas.DataFrame and group by sensitive feature values.
  
      The data are provided as three arguments (`sensitive_features`, `labels`, `scores`) and
      the new  DataFrame is grouped by sensitive feature values so that subsequently each group
      can be handled separately.
  
      Parameters
      ----------
      sensitive_features : list, numpy.ndarray, pandas.DataFrame, or pandas.Series
          Sensitive features to identify groups by
      labels : list, numpy.ndarray, pandas.DataFrame, or pandas.Series
          the labels of the dataset
      scores : list, numpy.ndarray, pandas.DataFrame, or pandas.Series
          the scores produced by a predictor's prediction
      sensitive_feature_names : list of strings
          list of names for the sensitive features in case they were not
          implicitly provided (e.g. if `sensitive_features` is of type
          pandas.DataFrame); default None
  
      Returns
      -------
      pandas.DataFrameGroupBy
          the training data for the mitigator, grouped by sensitive feature value
      """
      data_dict = {}
  
      # TODO: extend to multiple columns for additional group data
      # and name columns after original column names if possible
      # or store the original column names
      sensitive_feature_name = SENSITIVE_FEATURE_KEY
      if sensitive_feature_names is not None:
          if sensitive_feature_name in [SCORE_KEY, LABEL_KEY]:
              raise ValueError(
                  SENSITIVE_FEATURE_NAME_CONFLICT_DETECTED_ERROR_MESSAGE
              )
          sensitive_feature_name = sensitive_feature_names[0]
  
      _reformat_data_into_dict(
          sensitive_feature_name, data_dict, sensitive_features
      )
      _reformat_data_into_dict(SCORE_KEY, data_dict, scores)
      _reformat_data_into_dict(LABEL_KEY, data_dict, labels)
  
      return pd.DataFrame(data_dict).groupby(sensitive_feature_name)
  
imports: import logging
  from warnings import warn
  import numpy
  import pandas
  from sklearn import clone
  from sklearn.base import BaseEstimator, MetaEstimatorMixin
  from sklearn.exceptions import NotFittedError
  from sklearn.utils import Bunch
  from sklearn.utils.validation import check_is_fitted
  from ..utils._common import _get_soft_predictions
  from ..utils._input_validation import _KW_CONTROL_FEATURES, _validate_and_reformat_input
  from ._constants import BASE_ESTIMATOR_NONE_ERROR_MESSAGE, BASE_ESTIMATOR_NOT_FITTED_WARNING, LABEL_KEY, OUTPUT_SEPARATOR, SCORE_KEY, SENSITIVE_FEATURE_KEY
  from ._interpolated_thresholder import InterpolatedThresholder
  from ._tradeoff_curve_utilities import METRIC_DICT, _extend_confusion_matrix, _interpolate_curve, _tradeoff_curve
label_desc: Use image node_id 1 for calling a global function with example usage: _reformat_and_group_data(sensitive_features, labels, scores, sensitive_feature_names) and returns: pd