class_docstr: Univariate feature selector with configurable strategy.
  
  Read more in the :ref:`User Guide <univariate_feature_selection>`.
  
  Parameters
  ----------
  score_func : callable, default=f_classif
      Function taking two arrays X and y, and returning a pair of arrays
      (scores, pvalues). For modes 'percentile' or 'kbest' it can return
      a single array scores.
  
  mode : {'percentile', 'k_best', 'fpr', 'fdr', 'fwe'}, default='percentile'
      Feature selection mode. Note that the `'percentile'` and `'kbest'`
      modes are supporting unsupervised feature selection (when `y` is `None`).
  
  param : "all", float or int, default=1e-5
      Parameter of the corresponding mode.
  
  Attributes
  ----------
  scores_ : array-like of shape (n_features,)
      Scores of features.
  
  pvalues_ : array-like of shape (n_features,)
      p-values of feature scores, None if `score_func` returned scores only.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  f_classif : ANOVA F-value between label/feature for classification tasks.
  mutual_info_classif : Mutual information for a discrete target.
  chi2 : Chi-squared stats of non-negative features for classification tasks.
  f_regression : F-value between label/feature for regression tasks.
  mutual_info_regression : Mutual information for a continuous target.
  SelectPercentile : Select features based on percentile of the highest
      scores.
  SelectKBest : Select features based on the k highest scores.
  SelectFpr : Select features based on a false positive rate test.
  SelectFdr : Select features based on an estimated false discovery rate.
  SelectFwe : Select features based on family-wise error rate.
  
  Examples
  --------
  >>> from sklearn.datasets import load_breast_cancer
  >>> from sklearn.feature_selection import GenericUnivariateSelect, chi2
  >>> X, y = load_breast_cancer(return_X_y=True)
  >>> X.shape
  (569, 30)
  >>> transformer = GenericUnivariateSelect(chi2, mode='k_best', param=20)
  >>> X_new = transformer.fit_transform(X, y)
  >>> X_new.shape
  (569, 20)
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code: def _get_support_mask(self):
      check_is_fitted(self)
  
      selector = self._make_selector()
      selector.pvalues_ = self.pvalues_
      selector.scores_ = self.scores_
      return selector._get_support_mask()
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from scipy import special, stats
  from scipy.sparse import issparse
  from ..base import BaseEstimator, _fit_context
  from ..preprocessing import LabelBinarizer
  from ..utils import as_float_array, check_array, check_X_y, safe_mask, safe_sqr
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from ..utils.extmath import row_norms, safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ._base import SelectorMixin
label_desc: Use image node_id 5 for calling the GenericUnivariateSelect obj's underlying member method code with example usage: obj._get_support_mask() and returns: selector