How to use the modin.error_message.ErrorMessage.catch_bugs_and_request_email function in modin

def to_numpy(self):
        """Converts Modin DataFrame to NumPy Array.

        Returns:
            NumPy Array of the QueryCompiler.
        """
        arr = self._modin_frame.to_numpy()
        ErrorMessage.catch_bugs_and_request_email(
            len(arr) != len(self.index) or len(arr[0]) != len(self.columns)
        )
        return arr

Note: These are the global indices of the object. This is mostly useful
            when you have deleted rows/columns internally, but do not know
            which ones were deleted.

        Args:
            axis: This axis to extract the labels. (0 - index, 1 - columns).
            index_func: The function to be used to extract the function.
            old_blocks: An optional previous object that this object was
                created from. This is used to compute the correct offsets.

        Returns:
            A Pandas Index object.
        """
        client = _get_global_client()
        ErrorMessage.catch_bugs_and_request_email(not callable(index_func))
        func = cls.preprocess_func(index_func)
        if axis == 0:
            # We grab the first column of blocks and extract the indices
            new_idx = (
                [idx.apply(func).future for idx in partitions.T[0]]
                if len(partitions.T)
                else []
            )
        else:
            new_idx = (
                [idx.apply(func).future for idx in partitions[0]]
                if len(partitions)
                else []
            )
        new_idx = client.gather(new_idx)
        return new_idx[0].append(new_idx[1:]) if len(new_idx) else new_idx

    @property
    def _index_grouped(self):
        if self._index_grouped_cache is None:
            if self._is_multi_by or isinstance(self._by, pandas.Grouper):
                # Because we are doing a collect (to_pandas) here and then groupby, we
                # end up using pandas implementation. Add the warning so the user is
                # aware.
                ErrorMessage.catch_bugs_and_request_email(self._axis == 1)
                ErrorMessage.default_to_pandas(
                    "Groupby with multiple columns or Grouper object"
                )
                self._index_grouped_cache = {
                    k: v.index
                    for k, v in self._df._query_compiler.to_pandas().groupby(
                        by=self._by
                    )
                }
            else:
                if self._axis == 0:
                    self._index_grouped_cache = self._index.groupby(self._by)
                else:
                    self._index_grouped_cache = self._columns.groupby(self._by)
        return self._index_grouped_cache

"""This gets the internal indices stored in the partitions.

        Note: These are the global indices of the object. This is mostly useful
            when you have deleted rows/columns internally, but do not know
            which ones were deleted.

        Args:
            axis: This axis to extract the labels. (0 - index, 1 - columns).
            index_func: The function to be used to extract the function.
            old_blocks: An optional previous object that this object was
                created from. This is used to compute the correct offsets.

        Returns:
            A Pandas Index object.
        """
        ErrorMessage.catch_bugs_and_request_email(not callable(index_func))
        func = cls.preprocess_func(index_func)
        if axis == 0:
            # We grab the first column of blocks and extract the indices
            new_idx = (
                [idx.apply(func).oid for idx in partitions.T[0]]
                if len(partitions.T)
                else []
            )
        else:
            new_idx = (
                [idx.apply(func).oid for idx in partitions[0]]
                if len(partitions)
                else []
            )
        new_idx = ray.get(new_idx)
        return new_idx[0].append(new_idx[1:]) if len(new_idx) else new_idx

Returns:
            A Pandas DataFrame
        """
        retrieved_objects = [[obj.to_pandas() for obj in part] for part in partitions]
        if all(
            isinstance(part, pandas.Series) for row in retrieved_objects for part in row
        ):
            axis = 0
        elif all(
            isinstance(part, pandas.DataFrame)
            for row in retrieved_objects
            for part in row
        ):
            axis = 1
        else:
            ErrorMessage.catch_bugs_and_request_email(True)
        df_rows = [
            pandas.concat([part for part in row], axis=axis)
            for row in retrieved_objects
            if not all(part.empty for part in row)
        ]
        if len(df_rows) == 0:
            return pandas.DataFrame()
        else:
            return pandas.concat(df_rows)

def get_indices(cls, axis, partitions, index_func=None):
        """This gets the internal indices stored in the partitions.

        Note: These are the global indices of the object. This is mostly useful
            when you have deleted rows/columns internally, but do not know
            which ones were deleted.

        Args:
            axis: This axis to extract the labels. (0 - index, 1 - columns).
            index_func: The function to be used to extract the function.

        Returns:
            A Pandas Index object.
        """
        ErrorMessage.catch_bugs_and_request_email(not callable(index_func))
        func = cls.preprocess_func(index_func)
        if axis == 0:
            new_idx = (
                [idx.apply(func).get() for idx in partitions.T[0]]
                if len(partitions.T)
                else []
            )
        else:
            new_idx = (
                [idx.apply(func).get() for idx in partitions[0]]
                if len(partitions)
                else []
            )
        # TODO FIX INFORMATION LEAK!!!!1!!1!!
        return new_idx[0].append(new_idx[1:]) if len(new_idx) else new_idx

def _index_grouped(self):
        if self._index_grouped_cache is None:
            if self._is_multi_by:
                # Because we are doing a collect (to_pandas) here and then groupby, we
                # end up using pandas implementation. Add the warning so the user is
                # aware.
                ErrorMessage.catch_bugs_and_request_email(self._axis == 1)
                ErrorMessage.default_to_pandas("Groupby with multiple columns")
                self._index_grouped_cache = {
                    k: v.index
                    for k, v in self._df._query_compiler.getitem_column_array(self._by)
                    .to_pandas()
                    .groupby(by=self._by)
                }
            else:
                if isinstance(self._by, type(self._query_compiler)):
                    by = self._by.to_pandas().squeeze()
                else:
                    by = self._by
                if self._axis == 0:
                    self._index_grouped_cache = self._index.groupby(by)
                else:
                    self._index_grouped_cache = self._columns.groupby(by)

self._partitions = np.array(
                [
                    [
                        self._partitions[i][j].add_to_apply_calls(
                            apply_idx_objs,
                            cols=self.columns[
                                slice(cum_col_widths[j], cum_col_widths[j + 1])
                            ],
                        )
                        for j in range(len(self._partitions[i]))
                    ]
                    for i in range(len(self._partitions))
                ]
            )
            ErrorMessage.catch_bugs_and_request_email(
                axis is not None and axis not in [0, 1]
            )