# Buggy code
def _validate_dtypes_sum_prod_mean(self, axis, numeric_only, ignore_axis=False):
    """
    Raise TypeErrors for sum, prod, and mean where necessary.

    TODO: Add more details for this docstring template.

    Parameters
    ----------
    What arguments does this function have.
    [
    PARAMETER_NAME: PARAMETERS TYPES
        Description.
    ]

    Returns
    -------
    What this returns (if anything)
    """
    # We cannot add datetime types, so if we are summing a column with
    # dtype datetime64 and cannot ignore non-numeric types, we must throw a
    # TypeError.
    if (
        not axis
        and numeric_only is False
        and any(dtype == np.dtype("datetime64[ns]") for dtype in self.dtypes)
    ):
        raise TypeError("Cannot add Timestamp Types")

    # If our DataFrame has both numeric and non-numeric dtypes then
    # operations between these types do not make sense and we must raise a
    # TypeError. The exception to this rule is when there are datetime and
    # timedelta objects, in which case we proceed with the comparison
    # without ignoring any non-numeric types. We must check explicitly if
    # numeric_only is False because if it is None, it will default to True
    # if the operation fails with mixed dtypes.
    if (
        (axis or ignore_axis)
        and numeric_only is False
        and np.unique([is_numeric_dtype(dtype) for dtype in self.dtypes]).size == 2
    ):
        # check if there are columns with dtypes datetime or timedelta
        if all(
            dtype != np.dtype("datetime64[ns]") and dtype != np.dtype("timedelta64[ns]")
            for dtype in self.dtypes
        ):
            raise TypeError("Cannot operate on Numeric and Non-Numeric Types")
    # Pandas ignores `numeric_only` if `axis` is 1, but we do have to drop
    # non-numeric columns if `axis` is 0.
    if numeric_only and axis == 0:
        return self.drop(
            columns=[
                i for i in self.dtypes.index if not is_numeric_dtype(self.dtypes[i])
            ]
        )
    else:
        return self


# 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 _validate_dtypes_sum_prod_mean(self, axis, numeric_only, ignore_axis=False):
    """
    Raise TypeErrors for sum, prod, and mean where necessary.

    TODO: Add more details for this docstring template.

    Parameters
    ----------
    What arguments does this function have.
    [
    PARAMETER_NAME: PARAMETERS TYPES
        Description.
    ]

    Returns
    -------
    What this returns (if anything)
    """
    # We cannot add datetime types, so if we are summing a column with
    # dtype datetime64 and cannot ignore non-numeric types, we must throw a
    # TypeError.
    if (
        not axis
        and numeric_only is False
        and any(dtype == np.dtype("datetime64[ns]") for dtype in self.dtypes)
    ):
        raise TypeError("Cannot add Timestamp Types")

    # If our DataFrame has both numeric and non-numeric dtypes then
    # operations between these types do not make sense and we must raise a
    # TypeError. The exception to this rule is when there are datetime and
    # timedelta objects, in which case we proceed with the comparison
    # without ignoring any non-numeric types. We must check explicitly if
    # numeric_only is False because if it is None, it will default to True
    # if the operation fails with mixed dtypes.
    if (
        (axis or ignore_axis)
        and numeric_only is False
        and np.unique([is_numeric_dtype(dtype) for dtype in self.dtypes]).size == 2
    ):
        # check if there are columns with dtypes datetime or timedelta
        if all(
            dtype != np.dtype("datetime64[ns]") and dtype != np.dtype("timedelta64[ns]")
            for dtype in self.dtypes
        ):
            raise TypeError("Cannot operate on Numeric and Non-Numeric Types")

    return self._get_numeric_data(axis) if numeric_only else self