Как использовать .any () или a.all (), используя python для НЛП?

Question

Я пытаюсь выполнить этот код, я выполняю NLP в python

 def load_data_from_fixed_indices(self, indices_file, datasets = ['train', 'valid', 'test']):
        print(' ---> loading data from fixed indices:',indices_file)

        corpusreader = PickledCorpusReader('preprocessed/corpus_normalized')
        labelreader = PickledCorpusReader('preprocessed/labellisation/Autonomous Car')
        loader = CorpusLoader(corpusreader, labelreader, level='sentence')


        x_test, y_test, i_test = loader.load(idx_file=indices_file,
                                        datasets_from_file= ['test'],
                                        include_tfidf = False)
        x_train, y_train, i_train = loader.load(idx_file=indices_file,
                                        datasets_from_file= ['train'],
                                        include_tfidf = False)

Но у меня такая ошибка:

Traceback (most recent call last):
  File "parallel.py", line 455, in <module>
    _, delta = p.load_data_from_fixed_indices('indices1575646151.0974712.pkl')
  File "parallel.py", line 56, in wrapper
    result = func(*args, **kwargs)
  File "parallel.py", line 341, in load_data_from_fixed_indices
    include_tfidf = False)
  File "C:\Users\iduboc\Documents\asd-dev\toolbox\loader.py", line 246, in load
    idx = sorted(idx)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

функция загружается в загрузчик. py вызывает ошибку и спрашивает в комментарии

def load(self,
             idx=None,
             idx_file='',
             datasets_from_file=None,
             numerical = False,
             include_tfidf = True,
             labelFilter=None,
             ignoreEmpty=True,
             return_generator = False,
             return_ndarray=True):
  if numerical and not (labelFilter is None) and isinstance(labelFilter[0], str):
            labelFilter = [self.label2number[label] for label in labelFilter]

        if not datasets_from_file is None:
            if not (idx is None):
                raise ValueError('Please specify either indices to use or 
                  file, not both.')

            # Load and Select file data
            with open(idx_file, 'rb') as f:
                self.indices = cloudpickle.load(f)
                idx = []
                for dataset in datasets_from_file:
                    idx += list(self.indices[dataset])

        if not idx is None:
            idx = sorted(idx)