class_docstr: This class maintains a pandas.dataframe of filenames for all input sections.
  The list of filenames are obtained by searching the specified folders
  or loading from an existing csv file.
  
  Users can query a subset of the dataframe by train/valid/infer partition
  label and input section names.
docstr: :return: True if the TRAIN subset of images is not empty.
docstr_tok: [":","return",":","True","if","the","TRAIN","subset","of","images","is","not","empty","."]
code: def has_training(self):
      """
  
      :return: True if the TRAIN subset of images is not empty.
      """
      return self.has_phase(TRAIN)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import random
  import shutil
  import pandas
  import tensorflow
  from niftynet.engine.signal import TRAIN, VALID, INFER, ALL
  from niftynet.utilities.decorators import singleton
  from niftynet.utilities.filename_matching import KeywordsMatching
  from niftynet.utilities.niftynet_global_config import NiftyNetGlobalConfig
  from niftynet.utilities.util_common import look_up_operations
  from niftynet.utilities.util_csv import match_and_write_filenames_to_csv
  from niftynet.utilities.util_csv import write_csv
label_desc: Use image node_id 10 for calling the ImageSetsPartitioner obj's underlying member method code with example usage: obj.has_training() and returns: self