# Buggy code
def _call(self, x1, x2, out=None, where=None):
    x1, x2, out, where = self._process_inputs(x1, x2, out, where)
    # check broadcast
    x1_shape = () if np.isscalar(x1) else x1.shape
    x2_shape = () if np.isscalar(x2) else x2.shape
    shape = broadcast_shape(x1_shape, x2_shape)
    order = self._calc_order(x1, x2, out)

    inputs = filter_inputs([x1, x2, out, where])
    t = self.new_tensor(inputs, shape, order=order)

    if out is None:
        return t

    check_out_param(out, t, getattr(self, "_casting"))
    out_shape, out_dtype = out.shape, out.dtype

    # if `out` is specified, use out's dtype and shape
    if t.shape != out_shape:
        t = self.new_tensor(inputs, out_shape, order=order)
    setattr(self, "_dtype", out_dtype)

    out.data = t.data
    return out


# Traceback (TypeError)
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-9-60fd09690beb>", line 1, in <module>
df[df[0] > 0.5].execute()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 643, in execute
return run()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 639, in run
self.data.execute(session, **kw)
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 379, in execute
return run()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 374, in run
session.run(self, **kw)
File "/Users/wenjun.swj/Code/mars/mars/session.py", line 505, in run
result = self._sess.run(*tileables, **kw)
File "/Users/wenjun.swj/Code/mars/mars/session.py", line 111, in run
res = self._executor.execute_tileables(tileables, **kw)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/executor.py", line 860, in execute_tileables
chunk_graph = chunk_graph_builder.build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 347, in build
chunk_graph = super().build(
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 262, in build
self._on_tile_failure(tileable_data.op, exc_info)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 301, in inner
raise exc_info[1].with_traceback(exc_info[2]) from None
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 242, in build
tiled = self._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 337, in _tile
return super()._tile(tileable_data, tileable_graph)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 201, in _tile
tds[0]._inplace_tile()
File "/Users/wenjun.swj/Code/mars/mars/core.py", line 168, in _inplace_tile
return handler.inplace_tile(self)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 136, in inplace_tile
dispatched = self.dispatch(to_tile.op)
File "/Users/wenjun.swj/Code/mars/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/Users/wenjun.swj/Code/mars/mars/tiles.py", line 119, in dispatch
tiled = op_cls.tile(op)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/indexing/getitem.py", line 273, in tile
return cls.tile_with_mask(op)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/indexing/getitem.py", line 287, in tile_with_mask
align_dataframe_series(in_df, mask, axis='index')
File "/Users/wenjun.swj/Code/mars/mars/dataframe/align.py", line 713, in align_dataframe_series
index_splits, index_nsplits = _calc_axis_splits(left.index_value, right.index_value,
File "/Users/wenjun.swj/Code/mars/mars/dataframe/align.py", line 490, in _calc_axis_splits
right_splits = left_splits = [[c] for c in left_chunk_index_min_max]
TypeError: 'NoneType' object is not iterable

# Fixed code
def _call(self, x1, x2, out=None, where=None):
    # check tensor ufunc, if x1 or x2 is not a tensor, e.g. Mars DataFrame
    # which implements tensor ufunc, will delegate the computation
    # to it if possible
    ret = self._call_tensor_ufunc(x1, x2, out=out, where=where)
    if ret is not None:
        return ret

    x1, x2, out, where = self._process_inputs(x1, x2, out, where)
    # check broadcast
    x1_shape = () if np.isscalar(x1) else x1.shape
    x2_shape = () if np.isscalar(x2) else x2.shape
    shape = broadcast_shape(x1_shape, x2_shape)
    order = self._calc_order(x1, x2, out)

    inputs = filter_inputs([x1, x2, out, where])
    t = self.new_tensor(inputs, shape, order=order)

    if out is None:
        return t

    check_out_param(out, t, getattr(self, "_casting"))
    out_shape, out_dtype = out.shape, out.dtype

    # if `out` is specified, use out's dtype and shape
    if t.shape != out_shape:
        t = self.new_tensor(inputs, out_shape, order=order)
    setattr(self, "_dtype", out_dtype)

    out.data = t.data
    return out