parameters: (self, X, y=None, groups=None)
docstring: Generates indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, of length n_samples
              The target variable for supervised learning problems.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Yields
          ------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
docstring_summary: Generates indices to split data into training and test set.
docstring_tokens: Generates indices to split data into training and test set .
function: def split(self, X, y=None, groups=None):
          """Generates indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, of length n_samples
              The target variable for supervised learning problems.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Yields
          ------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          n_repeats = self.n_repeats
          rng = check_random_state(self.random_state)
  
          for idx in range(n_repeats):
              cv = self.cv(random_state=rng, shuffle=True,
                           **self.cvargs)
              for train_index, test_index in cv.split(X, y, groups):
                  yield train_index, test_index
function_tokens: def split ( self , X , y = None , groups = None ) : n_repeats = self . n_repeats rng = check_random_state ( self . random_state ) for idx in range ( n_repeats ) : cv = self . cv ( random_state = rng , shuffle = True , * * self . cvargs ) for train_index , test_index in cv . split ( X , y , groups ) : yield train_index , test_index