code: def make_ttv_yaml(corpora, path_to_ttv_file, ttv_ratio=DEFAULT_TTV_RATIO, deterministic=False):
      
      dataset = get_dataset(corpora)
      data_sets = make_ttv(dataset, ttv_ratio=ttv_ratio, deterministic=deterministic)
  
      def get_for_ttv(key):
          return (
              data_sets['test'][key],
              data_sets['train'][key],
              data_sets['validation'][key]
          )
  
      test, train, validation = get_for_ttv('paths')
  
      number_of_files_for_each_set = list(get_for_ttv('number_of_files'))
  
      number_of_subjects_for_each_set = [len(x) for x in get_for_ttv('subjects')]
  
      dict_for_yaml = {
          'split': number_of_files_for_each_set,
          'subject_split': number_of_subjects_for_each_set,
          "test": test,
          "train": train,
          "validation": validation
      }
  
      with open(path_to_ttv_file, 'w') as f:
          yaml.dump(dict_for_yaml, f, default_flow_style=False)
docs: Create a test, train, validation from the corpora given and saves it as a YAML filename.
  
          Each set will be subject independent, meaning that no one subject can have data in more than one
          set
  
      # Arguments;
          corpora: a list of the paths to corpora used (these have to be formatted accoring to notes.md)
          path_to_ttv_file: the path to where the YAML file be be saved
          ttv_ratio: a tuple (e.g. (1,4,4) of the relative sizoe of each set)
          deterministic: whether or not to shuffle the resources around when making the set.