# Buggy code
def broadcast_apply_full_axis(
    self,
    axis,
    func,
    other,
    new_index=None,
    new_columns=None,
    dtypes=None,
):
    """Broadcast partitions of other dataframe partitions and apply a function along full axis.

    Parameters
    ----------
        axis : 0 or 1
            The axis to apply over (0 - rows, 1 - columns).
        func : callable
            The function to apply.
        other : other Modin frame to broadcast
        new_index : list-like (optional)
            The index of the result. We may know this in advance,
            and if not provided it must be computed.
        new_columns : list-like (optional)
            The columns of the result. We may know this in
            advance, and if not provided it must be computed.
        dtypes : list-like (optional)
            The data types of the result. This is an optimization
            because there are functions that always result in a particular data
            type, and allows us to avoid (re)computing it.

    Returns
    -------
         A new Modin DataFrame
    """
    new_partitions = self._frame_mgr_cls.broadcast_axis_partitions(
        axis=axis,
        left=self._partitions,
        right=other if other is None else other._partitions,
        apply_func=self._build_mapreduce_func(axis, func),
        keep_partitioning=True,
    )
    # Index objects for new object creation. This is shorter than if..else
    if new_columns is None:
        new_columns = self._frame_mgr_cls.get_indices(
            1, new_partitions, lambda df: df.columns
        )
    if new_index is None:
        new_index = self._frame_mgr_cls.get_indices(
            0, new_partitions, lambda df: df.index
        )
    if dtypes == "copy":
        dtypes = self._dtypes
    elif dtypes is not None:
        dtypes = pandas.Series([np.dtype(dtypes)] * len(new_columns), index=new_columns)
    return self.__constructor__(
        new_partitions,
        new_index,
        new_columns,
        None,
        None,
        dtypes,
        validate_axes="all" if new_partitions.size != 0 else False,
    )


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def broadcast_apply_full_axis(
    self,
    axis,
    func,
    other,
    new_index=None,
    new_columns=None,
    dtypes=None,
):
    """Broadcast partitions of other dataframe partitions and apply a function along full axis.

    Parameters
    ----------
        axis : 0 or 1
            The axis to apply over (0 - rows, 1 - columns).
        func : callable
            The function to apply.
        other : other Modin frame to broadcast
        new_index : list-like (optional)
            The index of the result. We may know this in advance,
            and if not provided it must be computed.
        new_columns : list-like (optional)
            The columns of the result. We may know this in
            advance, and if not provided it must be computed.
        dtypes : list-like (optional)
            The data types of the result. This is an optimization
            because there are functions that always result in a particular data
            type, and allows us to avoid (re)computing it.

    Returns
    -------
         A new Modin DataFrame
    """
    new_partitions = self._frame_mgr_cls.broadcast_axis_partitions(
        axis=axis,
        left=self._partitions,
        right=other if other is None else other._partitions,
        apply_func=self._build_mapreduce_func(axis, func),
        keep_partitioning=True,
    )
    # Index objects for new object creation. This is shorter than if..else
    new_axes = [
        self._compute_axis_labels(i, new_partitions) if new_axis is None else new_axis
        for i, new_axis in enumerate([new_index, new_columns])
    ]
    if dtypes == "copy":
        dtypes = self._dtypes
    elif dtypes is not None:
        dtypes = pandas.Series([np.dtype(dtypes)] * len(new_axes[1]), index=new_axes[1])
    return self.__constructor__(
        new_partitions,
        *new_axes,
        None,
        None,
        dtypes,
        validate_axes="all" if new_partitions.size != 0 else False,
    )