# Buggy code
def arg_wrapper(*args, **kwargs):
    # priority: decorator > class_attribute
    # by default data is in args[1] (self needs to be taken into account)
    data = args[slice_on]
    args = list(args)

    b_size = (batch_size(data) if callable(batch_size) else batch_size) or getattr(
        args[0], "batch_size", None
    )
    # no batching if b_size is None
    if b_size is None:
        return func(*args, **kwargs)

    default_logger.debug(
        f"batching enabled for {func.__qualname__} batch_size={b_size} "
        f"num_batch={num_batch} axis={split_over_axis}"
    )

    full_data_size = _get_size(data, split_over_axis)
    total_size = _get_total_size(full_data_size, batch_size, num_batch)

    final_result = []

    data = (data, args[label_on]) if label_on else data

    yield_slice = isinstance(data, np.memmap)
    slice_idx = None

    for b in batch_iterator(
        data[:total_size], b_size, split_over_axis, yield_slice=yield_slice
    ):
        if yield_slice:
            slice_idx = b
            new_memmap = np.memmap(
                data.filename, dtype=data.dtype, mode="r", shape=data.shape
            )
            b = new_memmap[slice_idx]
            slice_idx = slice_idx[split_over_axis]
            if slice_idx.start is None or slice_idx.stop is None:
                slice_idx = None

        if not isinstance(b, tuple):
            # for now, keeping ordered_idx is only supported if no labels
            args[slice_on] = b
            if ordinal_idx_arg and slice_idx is not None:
                args[ordinal_idx_arg] = slice_idx
        else:
            args[slice_on] = b[0]
            args[label_on] = b[1]

        r = func(*args, **kwargs)

        if yield_slice:
            del new_memmap

        if r is not None:
            final_result.append(r)

    return _merge_results_after_batching(final_result, merge_over_axis)


# Traceback (TypeError)
Traceback (most recent call last):
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/peapods/pea.py", line 292, in msg_callback
self.zmqlet.send_message(self._callback(msg))
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/peapods/pea.py", line 266, in _callback
self.pre_hook(msg).handle(msg).post_hook(msg)
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/peapods/pea.py", line 160, in handle
self.executor(self.request_type)
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/executors/__init__.py", line 570, in __call__
d()
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/drivers/__init__.py", line 251, in __call__
self._traverse_apply(self.req.docs, *args, **kwargs)
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/drivers/__init__.py", line 268, in _traverse_apply
**kwargs,
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/drivers/__init__.py", line 281, in _traverse_rec
doc.chunks, doc, 'chunks', path[1:], *args, **kwargs
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/drivers/__init__.py", line 284, in _traverse_rec
self._apply_all(docs, parent_doc, parent_edge_type, *args, **kwargs)
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/drivers/search.py", line 124, in _apply_all
idx, dist = self.exec_fn(embed_vecs, top_k=int(self.top_k))
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/executors/indexers/vector.py", line 279, in query
dist = self._euclidean(_keys, self.query_handler)
File "/home/cristian/envs/lyrics/lib/python3.7/site-packages/jina/executors/decorators.py", line 241, in arg_wrapper
for b in batch_iterator(data[:total_size], b_size, split_over_axis, yield_slice=yield_slice):
TypeError: 'NoneType' object is not subscriptable

# Fixed code
def arg_wrapper(*args, **kwargs):
    # priority: decorator > class_attribute
    # by default data is in args[1] (self needs to be taken into account)
    data = args[slice_on]
    args = list(args)

    b_size = (batch_size(data) if callable(batch_size) else batch_size) or getattr(
        args[0], "batch_size", None
    )
    # no batching if b_size is None
    if b_size is None or data is None:
        return func(*args, **kwargs)

    default_logger.debug(
        f"batching enabled for {func.__qualname__} batch_size={b_size} "
        f"num_batch={num_batch} axis={split_over_axis}"
    )

    full_data_size = _get_size(data, split_over_axis)
    total_size = _get_total_size(full_data_size, batch_size, num_batch)

    final_result = []

    data = (data, args[label_on]) if label_on else data

    yield_slice = isinstance(data, np.memmap)
    slice_idx = None

    for b in batch_iterator(
        data[:total_size], b_size, split_over_axis, yield_slice=yield_slice
    ):
        if yield_slice:
            slice_idx = b
            new_memmap = np.memmap(
                data.filename, dtype=data.dtype, mode="r", shape=data.shape
            )
            b = new_memmap[slice_idx]
            slice_idx = slice_idx[split_over_axis]
            if slice_idx.start is None or slice_idx.stop is None:
                slice_idx = None

        if not isinstance(b, tuple):
            # for now, keeping ordered_idx is only supported if no labels
            args[slice_on] = b
            if ordinal_idx_arg and slice_idx is not None:
                args[ordinal_idx_arg] = slice_idx
        else:
            args[slice_on] = b[0]
            args[label_on] = b[1]

        r = func(*args, **kwargs)

        if yield_slice:
            del new_memmap

        if r is not None:
            final_result.append(r)

    return _merge_results_after_batching(final_result, merge_over_axis)