class_docstr: Initialize the univariate feature selection.
  
  Parameters
  ----------
  score_func : callable
      Function taking two arrays X and y, and returning a pair of arrays
      (scores, pvalues) or a single array with scores.
class_docstr_tok: ["Initialize","the","univariate","feature","selection",".","Parameters","--","--","--","--","--","score_func",":","callable","Function","taking","two","arrays","X","and","y",",","and","returning","a","pair","of","arrays","(","scores",",","pvalues",")","or","a","single","array","with","scores","."]
code: def _check_params(self, X, y):
      pass
  
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 3 for calling the _BaseFilter obj's underlying member method code with example usage: obj._check_params(X, y) without return types