class_docstr: Class for handling the cross-validation splits of multi-domain data.
  
  This is a wrapper to sklearn's cross-validation iterators [1]_ which
  ensures the handling of domain information with the data points. In fact,
  the data from source domain is always fully available in the training
  partition whereas the random splits are done on the data points from the
  target domain.
  
  Parameters
  ----------
  target_domain : str
      Domain considered as target.
  cv : None | BaseCrossValidator | BaseShuffleSplit, default=None
      An instance of a cross validation iterator from sklearn.
  
  References
  ----------
  .. [1] https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators
  
  Notes
  -----
  .. versionadded:: 0.4
docstr: Returns the number of splitting iterations in the cross-validator.
  
  Parameters
  ----------
  X : object
      Ignored, exists for compatibility.
  y : object
      Ignored, exists for compatibility.
  
  Returns
  -------
  n_splits : int
      Returns the number of splitting iterations in the cross-validator.
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code: def get_n_splits(self, X=None, y=None):
      """Returns the number of splitting iterations in the cross-validator.
  
      Parameters
      ----------
      X : object
          Ignored, exists for compatibility.
      y : object
          Ignored, exists for compatibility.
  
      Returns
      -------
      n_splits : int
          Returns the number of splitting iterations in the cross-validator.
      """
      return self.cv.n_splits
  
imports: import numpy
label_desc: Use image node_id 3 for calling the TLSplitter obj's underlying member method code with example usage: obj.get_n_splits(X, y) and returns: self