code: def samples_by_indices_nomapping(self, indices):
          
          if not self._random_access:
              raise TypeError('samples_by_indices_nomapping method not '
                              'supported as one or more of the underlying '
                              'data sources does not support random access')
          if len(indices) != len(self.datasets):
              raise ValueError(
                  'length mis-match: indices has {} items, self has {} data '
                  'sources, should be equal'.format(len(indices),
                                                    len(self.datasets)))
          batch = tuple([ds.samples_by_indices_nomapping(ndx)
                         for ds, ndx in zip(self.datasets, indices)])
          return self._prepare_batch(batch)
docs: Gather a batch of samples by indices *without* applying any index
          mapping.
  
          Parameters
          ----------
          indices: list of either 1D-array of ints or slice
              A list of index arrays or slices; one for each data source
              that identify the samples to access
  
          Returns
          -------
          nested list of arrays
              A mini-batch