class_docstr: Shared set-up checks and methods across selectors.
  
  Parameters
  ----------
  confirm_variables: bool, default=False
      If set to True, variables that are not present in the input dataframe will be
      removed from the indicated list of variables. See parameter variables for more
      details.
  
  Methods
  -------
  transform:
      Remove non-selected features.
  
  _confirm_variables:
      Check that the variables entered by the user exist in the df.
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docstr: Get a mask, or integer index, of the features selected.
  
  Parameters
  ----------
  indices : bool, default=False
      If True, the return value will be an array of integers, rather
      than a boolean mask.
  
  Returns
  -------
  support : array
      An index that selects the retained features from a feature vector.
      If `indices` is False, this is a boolean array of shape
      [# input features], in which an element is True if its
      corresponding feature is selected for retention. If `indices` is
      True, this is an integer array of shape [# output features] whose
      values are indices into the input feature vector.
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code: def get_support(self, indices=False):
      """
      Get a mask, or integer index, of the features selected.
  
      Parameters
      ----------
      indices : bool, default=False
          If True, the return value will be an array of integers, rather
          than a boolean mask.
  
      Returns
      -------
      support : array
          An index that selects the retained features from a feature vector.
          If `indices` is False, this is a boolean array of shape
          [# input features], in which an element is True if its
          corresponding feature is selected for retention. If `indices` is
          True, this is an integer array of shape [# output features] whose
          values are indices into the input feature vector.
      """
      mask = [
          True if f not in self.features_to_drop_ else False
          for f in self.feature_names_in_
      ]
      return mask if not indices else np.where(mask)[0]
  
imports: import numpy
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.validation import check_is_fitted
  from feature_engine._base_transformers.mixins import GetFeatureNamesOutMixin
  from feature_engine._variable_handling.variable_type_selection import _filter_out_variables_not_in_dataframe
  from feature_engine.dataframe_checks import _check_X_matches_training_df, check_X
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 6 for calling the BaseSelector obj's underlying member method code with example usage: obj.get_support(indices) and returns: unknown