code: def f_classif(X, y):
      """Compute the ANOVA F-value for the provided sample.
  
      Read more in the :ref:`User Guide <univariate_feature_selection>`.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} shape = [n_samples, n_features]
          The set of regressors that will be tested sequentially.
  
      y : array of shape(n_samples)
          The data matrix.
  
      Returns
      -------
      F : array, shape = [n_features,]
          The set of F values.
  
      pval : array, shape = [n_features,]
          The set of p-values.
  
      See also
      --------
      chi2: Chi-squared stats of non-negative features for classification tasks.
      f_regression: F-value between label/feature for regression tasks.
      """
      X, y = check_X_y(X, y, ["csr", "csc", "coo"])
      args = [X[safe_mask(X, y == k)] for k in np.unique(y)]
      return f_oneway(*args)
  
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 3 for calling a global function with example usage: f_classif(X, y) and returns: f_oneway