class_docstr: Filter: Select the p-values corresponding to Family-wise error rate
  
  Read more in the :ref:`User Guide <univariate_feature_selection>`.
  
  Parameters
  ----------
  score_func : callable
      Function taking two arrays X and y, and returning a pair of arrays
      (scores, pvalues).
      Default is f_classif (see below "See also"). The default function only
      works with classification tasks.
  
  alpha : float, optional
      The highest uncorrected p-value for features to keep.
  
  Attributes
  ----------
  scores_ : array-like, shape=(n_features,)
      Scores of features.
  
  pvalues_ : array-like, shape=(n_features,)
      p-values of feature scores.
  
  See also
  --------
  f_classif: ANOVA F-value between label/feature for classification tasks.
  chi2: Chi-squared stats of non-negative features for classification tasks.
  f_regression: F-value between label/feature for regression tasks.
  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.
  GenericUnivariateSelect: Univariate feature selector with configurable mode.
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code: def __init__(self, score_func=f_classif, alpha=5e-2):
      super(SelectFwe, self).__init__(score_func)
      self.alpha = alpha
  
imports: import numpy
  import warnings
  from scipy import special, stats
  from scipy.sparse import issparse
  from ..base import BaseEstimator
  from ..preprocessing import LabelBinarizer
  from ..utils import as_float_array, check_array, check_X_y, safe_sqr, safe_mask
  from ..utils.extmath import norm, safe_sparse_dot, row_norms
  from ..utils.validation import check_is_fitted
  from .base import SelectorMixin
label_desc: Use image node_id 1 to create a new SelectFwe object from inherited base classes: _BaseFilter with example: obj = SelectFwe(score_func, alpha)