code: def prune_train(start_below, stop_below, start_above, stop_above):
      """Checks if indices above or below are empty and remove them.
  
      A utility function for checking if the train indices below the a given
      test set range are (0, 0), or if indices above the test set range is
      (n, n). In this case, these will lead to an empty array and therefore
      can safely be removed to create a single training set index range.
  
      Parameters
      ----------
      start_below : int
          index number starting below the test set. Should always be the same
          for all test sets.
  
       stop_below : int
          the index number at which the test set is starting on.
  
      start_above : int
      the index number at which the test set ends.
  
      stop_above : int
          The end of the data set (n). Should always be the same for all test
          sets.
      """
      if start_below == stop_below:
          tri = ((start_above, stop_above),)
  
      elif start_above == stop_above:
          tri = ((start_below, stop_below),)
  
      else:
          tri = ((start_below, stop_below), (start_above, stop_above))
      return tri
  
imports: from __future__ import division
  from abc import abstractmethod
  import numpy
  from ..externals.sklearn.base import BaseEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: prune_train(start_below, stop_below, start_above, stop_above) and returns: tri