code: def save(sources, targets, masked=False):
      
      # TODO: Remove restriction
      assert len(sources) == 1 and len(targets) == 1
      array = sources[0]
      target = targets[0]
  
      # Request bitesize pieces of the source and assign them to the
      # target.
      # NB. This algorithm does not use the minimal number of chunks.
      #   e.g. If the second dimension could be sliced as 0:99, 99:100
      #       then clearly the first dimension would have to be single
      #       slices for the 0:99 case, but could be bigger slices for the
      #       99:100 case.
      # It's not yet clear if this really matters.
      all_slices = _all_slices(array)
      for index in np.ndindex(*[len(slices) for slices in all_slices]):
          keys = tuple(slices[i] for slices, i in zip(all_slices, index))
          if masked:
              target[keys] = array[keys].masked_array()
          else:
              target[keys] = array[keys].ndarray()
docs: Save the numeric results of each source into its corresponding target.
  
      Parameters
      ----------
      sources: list
          The list of source arrays for saving from; limited to length 1.
      targets: list
          The list of target arrays for saving to; limited to length 1.
      masked: boolean
          Uses a masked array from sources if True.