# Buggy code
def aggregate(self, func=None, *args, **kwargs):
    if self._axis != 0:
        # This is not implemented in pandas,
        # so we throw a different message
        raise NotImplementedError("axis other than 0 is not supported")

    relabeling_required = False
    if isinstance(func, dict) or func is None:

        def _reconstruct_func(func, **kwargs):
            relabeling_required, func, new_columns, order = reconstruct_func(
                func, **kwargs
            )
            # We convert to the string version of the function for simplicity.
            func = {
                k: v if not callable(v) or v.__name__ not in dir(self) else v.__name__
                for k, v in func.items()
            }
            return relabeling_required, func, new_columns, order

        relabeling_required, func_dict, new_columns, order = _reconstruct_func(
            func, **kwargs
        )

        if any(i not in self._df.columns for i in func_dict.keys()):
            from pandas.core.base import SpecificationError

            raise SpecificationError("nested renamer is not supported")
        func = func_dict
    elif is_list_like(func):
        return self._default_to_pandas(
            lambda df, *args, **kwargs: df.aggregate(func, *args, **kwargs),
            *args,
            **kwargs,
        )
    elif isinstance(func, str):
        # Using "getattr" here masks possible AttributeError which we throw
        # in __getattr__, so we should call __getattr__ directly instead.
        agg_func = self.__getattr__(func)
        if callable(agg_func):
            return agg_func(*args, **kwargs)

    result = self._apply_agg_function(
        func,
        drop=self._as_index,
        *args,
        **kwargs,
    )

    if relabeling_required:
        result = result.iloc[:, order]
        result.columns = new_columns
    return result


# Traceback (ray.exceptions.RayTaskError)
Traceback (most recent call last):
File "/localdisk/gashiman/modin/ci/benchmarks/test_benchmarks.py", line 206, in test_groupby_sum
result = benchmark.pedantic(
File "/nfs/site/home/gashiman/.local/lib/python3.8/site-packages/pytest_benchmark/fixture.py", line 139, in pedantic
return self._raw_pedantic(target, args=args, kwargs=kwargs, setup=setup, rounds=rounds,
File "/nfs/site/home/gashiman/.local/lib/python3.8/site-packages/pytest_benchmark/fixture.py", line 213, in _raw_pedantic
runner(loops_range)
File "/nfs/site/home/gashiman/.local/lib/python3.8/site-packages/pytest_benchmark/fixture.py", line 87, in runner
sys.settrace(None)
File "/localdisk/gashiman/modin/ci/benchmarks/test_benchmarks.py", line 170, in benchmark_groupby_agg_sum_function
result = gb.agg(sum)
File "/localdisk/gashiman/modin/modin/pandas/groupby.py", line 399, in aggregate
result = self._apply_agg_function(
File "/localdisk/gashiman/modin/modin/pandas/groupby.py", line 887, in _apply_agg_function
new_manager = groupby_qc.groupby_agg(
File "/localdisk/gashiman/modin/modin/backends/pandas/query_compiler.py", line 2633, in groupby_agg
new_modin_frame = self._modin_frame._apply_full_axis(
File "/localdisk/gashiman/modin/modin/engines/base/frame/data.py", line 1301, in _apply_full_axis
return self.broadcast_apply_full_axis(
File "/localdisk/gashiman/modin/modin/engines/base/frame/data.py", line 1673, in broadcast_apply_full_axis
new_axes = [
File "/localdisk/gashiman/modin/modin/engines/base/frame/data.py", line 1674, in <listcomp>
self._compute_axis_labels(i, new_partitions)
File "/localdisk/gashiman/modin/modin/engines/base/frame/data.py", line 289, in _compute_axis_labels
return self._frame_mgr_cls.get_indices(
File "/localdisk/gashiman/modin/modin/engines/ray/pandas_on_ray/frame/partition_manager.py", line 99, in get_indices
new_idx = ray.get(new_idx)
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1452, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(TypeError): ray::deploy_ray_func() (pid=4067967, ip=10.241.129.55)
File "python/ray/_raylet.pyx", line 446, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 468, in ray._raylet.execute_task
ray.exceptions.RayTaskError: ray::deploy_ray_func() (pid=4067969, ip=10.241.129.55)
File "python/ray/_raylet.pyx", line 482, in ray._raylet.execute_task
File "/localdisk/gashiman/modin/modin/engines/ray/pandas_on_ray/frame/axis_partition.py", line 105, in deploy_ray_func
result = func(*args)
File "/localdisk/gashiman/modin/modin/engines/base/frame/axis_partition.py", line 224, in deploy_axis_func
result = func(dataframe, **kwargs)
File "/localdisk/gashiman/modin/modin/engines/base/frame/data.py", line 1036, in _map_reduce_func
series_result = func(df, *args, **kwargs)
File "/localdisk/gashiman/modin/modin/backends/pandas/query_compiler.py", line 2634, in <lambda>
axis, lambda df: groupby_agg_builder(df)
File "/localdisk/gashiman/modin/modin/backends/pandas/query_compiler.py", line 2627, in groupby_agg_builder
return compute_groupby(df)
File "/localdisk/gashiman/modin/modin/backends/pandas/query_compiler.py", line 2618, in compute_groupby
result = agg_func(grouped_df, **agg_kwargs)
File "/localdisk/gashiman/modin/modin/utils.py", line 123, in wrapper
result = func(*args, **kwargs)
TypeError: unsupported operand type(s) for +: 'int' and 'tuple'

# Fixed code
def aggregate(self, func=None, *args, **kwargs):
    if self._axis != 0:
        # This is not implemented in pandas,
        # so we throw a different message
        raise NotImplementedError("axis other than 0 is not supported")

    if (
        callable(func)
        and isinstance(func, BuiltinFunctionType)
        and func.__name__ in dir(self)
    ):
        func = func.__name__

    relabeling_required = False
    if isinstance(func, dict) or func is None:

        def _reconstruct_func(func, **kwargs):
            relabeling_required, func, new_columns, order = reconstruct_func(
                func, **kwargs
            )
            # We convert to the string version of the function for simplicity.
            func = {
                k: v if not callable(v) or v.__name__ not in dir(self) else v.__name__
                for k, v in func.items()
            }
            return relabeling_required, func, new_columns, order

        relabeling_required, func_dict, new_columns, order = _reconstruct_func(
            func, **kwargs
        )

        if any(i not in self._df.columns for i in func_dict.keys()):
            from pandas.core.base import SpecificationError

            raise SpecificationError("nested renamer is not supported")
        func = func_dict
    elif is_list_like(func):
        return self._default_to_pandas(
            lambda df, *args, **kwargs: df.aggregate(func, *args, **kwargs),
            *args,
            **kwargs,
        )
    elif callable(func):
        return self._apply_agg_function(
            lambda grp, *args, **kwargs: grp.aggregate(func, *args, **kwargs),
            *args,
            **kwargs,
        )
    elif isinstance(func, str):
        # Using "getattr" here masks possible AttributeError which we throw
        # in __getattr__, so we should call __getattr__ directly instead.
        agg_func = self.__getattr__(func)
        if callable(agg_func):
            return agg_func(*args, **kwargs)

    result = self._apply_agg_function(
        func,
        *args,
        **kwargs,
    )

    if relabeling_required:
        result = result.iloc[:, order]
        result.columns = new_columns
    return result