code: def __standardise_cutoff(cutoff, type_hist="quartile"):
      """
      Standardises the cutoff values given in the configuration
  
      :param cutoff:
      :param type_hist: Type of landmark normalisation chosen (median,
      quartile, percentile)
      :return cutoff: cutoff with appropriate adapted values
      """
      cutoff = np.asarray(cutoff)
      if cutoff is None:
          return DEFAULT_CUTOFF
      if len(cutoff) > 2:
          cutoff = np.unique([np.min(cutoff), np.max(cutoff)])
      if len(cutoff) < 2:
          return DEFAULT_CUTOFF
      if cutoff[0] > cutoff[1]:
          cutoff[0], cutoff[1] = cutoff[1], cutoff[0]
      cutoff[0] = max(0.0, cutoff[0])
      cutoff[1] = min(1.0, cutoff[1])
      if type_hist == "quartile":
          cutoff[0] = np.min([cutoff[0], 0.24])
          cutoff[1] = np.max([cutoff[1], 0.76])
      else:
          cutoff[0] = np.min([cutoff[0], 0.09])
          cutoff[1] = np.max([cutoff[1], 0.91])
      return cutoff
  
file_path: NiftyNet/niftynet/utilities/histogram_standardisation.py
imports: from __future__ import absolute_import, print_function, division
  import os
  import numpy
  import numpy.ma
  import tensorflow
  from niftynet.io.misc_io import touch_folder
  from niftynet.utilities.util_common import look_up_operations, print_progress_bar
label_desc: Use image node_id 2 for calling a global function with example usage: __standardise_cutoff(cutoff, type_hist) and returns: cutoff, DEFAULT_CUTOFF, DEFAULT_CUTOFF