class_docstr: Train/Validation set splitter for sklearn's GridSearchCV etc.
  
  Uses user-specified train/validation set indices to split a dataset
  into train/validation sets using user-defined or random
  indices.
  
  Parameters
  ----------
  valid_indices : array-like, shape (num_examples,)
      Indices of the training examples in the training set
      to be used for validation. All other indices in the
      training set are used to for a training subset
      for model fitting.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/evaluate/PredefinedHoldoutSplit/
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docstr: Returns the number of splitting iterations in the cross-validator
  
  Parameters
  ----------
  X : object
      Always ignored, exists for compatibility.
  
  y : object
      Always ignored, exists for compatibility.
  
  groups : object
      Always ignored, exists for compatibility.
  
  Returns
  -------
  n_splits : 1
      Returns the number of splitting iterations in the cross-validator.
      Always returns 1.
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code: def get_n_splits(self, X=None, y=None, groups=None):
      """Returns the number of splitting iterations in the cross-validator
  
      Parameters
      ----------
      X : object
          Always ignored, exists for compatibility.
  
      y : object
          Always ignored, exists for compatibility.
  
      groups : object
          Always ignored, exists for compatibility.
  
      Returns
      -------
      n_splits : 1
          Returns the number of splitting iterations in the cross-validator.
          Always returns 1.
      """
      return 1
  
file_path: mlxtend/mlxtend/evaluate/holdout.py
imports: import numpy
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 3 for calling the PredefinedHoldoutSplit obj's underlying member method code with example usage: obj.get_n_splits(X, y, groups) and returns: int