# Buggy code
def reduce_func(df, *args, **kwargs):
    normalize = kwargs.get("normalize", False)
    sort = kwargs.get("sort", True)
    ascending = kwargs.get("ascending", False)
    dropna = kwargs.get("dropna", True)

    try:
        result = df.squeeze(axis=1).groupby(df.index, sort=False).sum()
    # This will happen with Arrow buffer read-only errors. We don't want to copy
    # all the time, so this will try to fast-path the code first.
    except ValueError:
        result = df.copy().squeeze(axis=1).groupby(df.index, sort=False).sum()

    if not dropna and np.nan in df.index:
        result = result.append(
            pandas.Series([df.squeeze(axis=1).loc[[np.nan]].sum()], index=[np.nan])
        )
    if normalize:
        result = result / df.squeeze(axis=1).sum()

    result = result.sort_values(ascending=ascending) if sort else result

    # We want to sort both values and indices of the result object.
    # This function will sort indices for equal values.
    def sort_index_for_equal_values(result, ascending):
        """
        Sort indices for equal values of result object.

        Parameters
        ----------
        result : pandas.Series or pandas.DataFrame with one column
            The object whose indices for equal values is needed to sort.
        ascending : boolean
            Sort in ascending (if it is True) or descending (if it is False) order.

        Returns
        -------
        pandas.DataFrame
            A new DataFrame with sorted indices.
        """
        is_range = False
        is_end = False
        i = 0
        new_index = np.empty(len(result), dtype=type(result.index))
        while i < len(result):
            j = i
            if i < len(result) - 1:
                while result[result.index[i]] == result[result.index[i + 1]]:
                    i += 1
                    if is_range is False:
                        is_range = True
                    if i == len(result) - 1:
                        is_end = True
                        break
            if is_range:
                k = j
                for val in sorted(result.index[j : i + 1], reverse=not ascending):
                    new_index[k] = val
                    k += 1
                if is_end:
                    break
                is_range = False
            else:
                new_index[j] = result.index[j]
            i += 1
        return pandas.DataFrame(result, index=new_index)

    return sort_index_for_equal_values(result, ascending)


# 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 reduce_func(df, *args, **kwargs):
    normalize = kwargs.get("normalize", False)
    sort = kwargs.get("sort", True)
    ascending = kwargs.get("ascending", False)
    dropna = kwargs.get("dropna", True)

    try:
        result = df.squeeze(axis=1).groupby(df.index, sort=False).sum()
    # This will happen with Arrow buffer read-only errors. We don't want to copy
    # all the time, so this will try to fast-path the code first.
    except ValueError:
        result = df.copy().squeeze(axis=1).groupby(df.index, sort=False).sum()

    if not dropna and np.nan in df.index:
        result = result.append(
            pandas.Series([df.squeeze(axis=1).loc[[np.nan]].sum()], index=[np.nan])
        )
    if normalize:
        result = result / df.squeeze(axis=1).sum()

    result = result.sort_values(ascending=ascending) if sort else result

    # We want to sort both values and indices of the result object.
    # This function will sort indices for equal values.
    def sort_index_for_equal_values(result, ascending):
        """
        Sort indices for equal values of result object.

        Parameters
        ----------
        result : pandas.Series or pandas.DataFrame with one column
            The object whose indices for equal values is needed to sort.
        ascending : boolean
            Sort in ascending (if it is True) or descending (if it is False) order.

        Returns
        -------
        pandas.DataFrame
            A new DataFrame with sorted indices.
        """
        is_range = False
        is_end = False
        i = 0
        new_index = np.empty(len(result), dtype=type(result.index))
        while i < len(result):
            j = i
            if i < len(result) - 1:
                while result[result.index[i]] == result[result.index[i + 1]]:
                    i += 1
                    if is_range is False:
                        is_range = True
                    if i == len(result) - 1:
                        is_end = True
                        break
            if is_range:
                k = j
                for val in sorted(result.index[j : i + 1], reverse=not ascending):
                    new_index[k] = val
                    k += 1
                if is_end:
                    break
                is_range = False
            else:
                new_index[j] = result.index[j]
            i += 1
        return pandas.DataFrame(result, index=new_index, columns=["__reduced__"])

    return sort_index_for_equal_values(result, ascending)