def _map_from_multiclass(self, eopatch, dst_shape, request_data):
        """
        `raster_value` is a dictionary specifying the intensity values for each class and the corresponding label value.

        A dictionary example for GLC30 LULC mapping is:
        raster_value = {'no_data': (0,[0,0,0,0]),
                        'cultivated land': (1,[193, 243, 249, 255]),
                        'forest': (2,[73, 119, 20, 255]),
                        'grassland': (3,[95, 208, 169, 255]),
                        'schrubland': (4,[112, 179, 62, 255]),
                        'water': (5,[154, 86, 1, 255]),
                        'wetland': (6,[244, 206, 126, 255]),
                        'thundra': (7,[50, 100, 100, 255]),
                        'artificial surface': (8,[20, 47, 147, 255]),
                        'bareland': (9,[202, 202, 202, 255]),
                        'snow and ice': (10,[251, 237, 211, 255])}
        """
        raster = np.ones(dst_shape, dtype=self.raster_dtype) * self.no_data_val

        for key in self.raster_value.keys():
            value, intensities = self.raster_value[key]
            raster[np.mean(np.abs(request_data - intensities), axis=-1) < self.mean_abs_difference] = value

        return self._reproject(eopatch, raster)
`raster_value` is a dictionary specifying the intensity values for each class and the corresponding label value.

        A dictionary example for GLC30 LULC mapping is:
        raster_value = {'no_data': (0,[0,0,0,0]),
                        'cultivated land': (1,[193, 243, 249, 255]),
                        'forest': (2,[73, 119, 20, 255]),
                        'grassland': (3,[95, 208, 169, 255]),
                        'schrubland': (4,[112, 179, 62, 255]),
                        'water': (5,[154, 86, 1, 255]),
                        'wetland': (6,[244, 206, 126, 255]),
                        'thundra': (7,[50, 100, 100, 255]),
                        'artificial surface': (8,[20, 47, 147, 255]),
                        'bareland': (9,[202, 202, 202, 255]),
                        'snow and ice': (10,[251, 237, 211, 255])}