class_docstr: Cross-validation iterator over split predefined in files.
  
  This class is useful in orchestration where the train and test set is provided in
  separate files.
class_docstr_tok: ["Cross-validation","iterator","over","split","predefined","in","files",".","This","class","is","useful","in","orchestration","where","the","train","and","test","set","is","provided","in","separate","files","."]
docstr: Return the number of splits.
  
  Returns
  -------
  n_splits : int
docstr_tok: ["Return","the","number","of","splits",".","Returns","--","--","--","-","n_splits",":","int"]
code: def get_n_splits(self):
      """Return the number of splits.
  
      Returns
      -------
      n_splits : int
      """
      n_splits = 1 if self.cv is None else 1 + self.cv.get_n_splits()
      return n_splits
  
file_path: sktime/sktime/series_as_features/model_selection/_split.py
imports: import numpy
  import pandas
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 3 for calling the PresplitFilesCV obj's underlying member method code with example usage: obj.get_n_splits() and returns: n_splits