How to use the hvplot.util.process_derived_datetime_pandas function in hvplot

import xarray as xr
            if isinstance(data, xr.DataArray):
                data = data.to_dataset(name=data.name or 'value')
        if is_tabular(data):
            if self.use_index and any(c for c in self.hover_cols if
                                      c in self.indexes and
                                      c not in data.columns):
                data = data.reset_index()
            # calculate any derived time
            dimensions = []
            for dimension in [x, y, self.by, self.hover_cols]:
                if dimension is not None:
                    dimensions.extend(dimension if isinstance(dimension, list) else [dimension])

            not_found = [dim for dim in dimensions if dim not in self.variables]
            _, data = process_derived_datetime_pandas(data, not_found, self.indexes)

        return data, x, y, z

x, y = indexes
                elif kind in ('bar', 'barh'):
                    x, by = indexes

            # Rename non-string columns
            renamed = {c: str(c) for c in data.columns if not isinstance(c, basestring)}
            if renamed:
                self.data = self.data.rename(columns=renamed)
            self.variables = indexes + list(self.data.columns)

            # Reset groupby dimensions
            groupby_index = [g for g in groupby if g in indexes]
            if groupby_index:
                self.data = self.data.reset_index(groupby_index)
            not_found = [g for g in groupby if g not in list(self.data.columns)+indexes]
            not_found, self.data = process_derived_datetime_pandas(self.data, not_found, indexes)
            if groupby and not_found:
                raise ValueError('The supplied groupby dimension(s) %s '
                                 'could not be found, expected one or '
                                 'more of: %s' % (not_found, list(self.data.columns)))
        # Set data-level options
        self.x = x
        self.y = y
        self.kind = kind or 'line'
        self.datatype = datatype
        self.gridded = gridded
        self.gridded_data = gridded_data
        self.use_dask = use_dask
        self.indexes = indexes
        if isinstance(by, (np.ndarray, pd.Series)):
            self.data = self.data.assign(_by=by)
            self.by = ['_by']

data = data.sort_values(x)

        # set index to column if needed in hover_cols
        if self.use_index and any(c for c in self.hover_cols if
                                  c in self.indexes and
                                  c not in data.columns):
            data = data.reset_index()

        # calculate any derived time
        dimensions = []
        for col in [x, y, self.by, self.hover_cols]:
            if col is not None:
                dimensions.extend(col if isinstance(col, list) else [col])

        not_found = [dim for dim in dimensions if dim not in self.variables]
        _, data = process_derived_datetime_pandas(data, not_found, self.indexes)

        return data, x, y