class_docstr: Base class for feature selection meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
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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 iff 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 iff 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.
      """
  
      check_is_fitted(self)
  
      mask = self.support_
      return mask if not indices else np.where(mask)[0]
  
imports: import io
  import contextlib
  import warnings
  import numpy
  import scipy
  from copy import deepcopy
  from sklearn.base import clone
  from sklearn.utils.validation import check_is_fitted
  from sklearn.base import BaseEstimator, TransformerMixin
  from joblib import Parallel, delayed
  from hyperopt import fmin, tpe
  from .utils import ParameterSampler, _check_param, _check_boosting
  from .utils import _set_categorical_indexes, _get_categorical_support
  from .utils import _feature_importances, _shap_importances
label_desc: Use image node_id 3 for calling the _BoostSelector obj's underlying member method code with example usage: obj.get_support(indices) and returns: unknown