How to use the featuretools.Relationship function in featuretools

dataframe=order_products_table,
        index="order_product_id",
        variable_types={
            "aisle_id": ft.variable_types.Categorical,
            "reordered": ft.variable_types.Boolean
        },
        time_index="order_time")

    es.entity_from_dataframe(
        entity_id="orders",
        dataframe=orders_table,
        index="order_id",
        time_index="order_time")

    es.add_relationship(
        ft.Relationship(es["orders"]["order_id"],
                        es["order_products"]["order_id"]))

    es.normalize_entity(
        base_entity_id="orders", new_entity_id="users", index="user_id")
    es.add_last_time_indexes()

    es["order_products"]["department"].interesting_values = [
        'produce', 'dairy eggs', 'snacks', 'beverages', 'frozen', 'pantry',
        'bakery', 'canned goods', 'deli', 'dry goods pasta'
    ]
    es["order_products"]["product_name"].interesting_values = [
        'Banana', 'Bag of Organic Bananas', 'Organic Baby Spinach',
        'Organic Strawberries', 'Organic Hass Avocado', 'Organic Avocado',
        'Large Lemon', 'Limes', 'Strawberries', 'Organic Whole Milk'
    ]
    return es

df.columns = [EntitySpark.recover_col_name(entity_id, col) for col in columns]

                es.entity_from_dataframe(entity_id=entity.entity_id,
                                         dataframe=df,
                                         index=EntitySpark.recover_col_name(entity_id, entity.index),
                                         variable_types=entity.variable_types,
                                         time_index=EntitySpark.recover_col_name(entity_id, entity.time_index),
                                         secondary_time_index=EntitySpark.recover_col_name(entity_id,
                                                                                           entity.secondary_time_index))

            for relationship in relationships:
                parent_entity = relationship.parent_variable.entity_id
                parent_col = EntitySpark.recover_col_name(parent_entity, relationship.parent_variable.column_name)
                child_entity = relationship.child_variable.entity_id
                child_col = EntitySpark.recover_col_name(child_entity, relationship.child_variable.column_name)
                es.add_relationship(ft.Relationship(es[parent_entity][parent_col],
                                                    es[child_entity][child_col]))

            feature_matrix, feature_dfs = ft.dfs(entityset=es,
                                                 agg_primitives=agg_primitives,
                                                 trans_primitives=trans_primitives,
                                                 target_entity=target_entity,
                                                 cutoff_time=cutoff_time,
                                                 cutoff_time_in_index=False,
                                                 n_jobs=n_jobs,
                                                 max_depth=max_depth,
                                                 training_window=training_window,
                                                 approximate=approximate,
                                                 chunk_size=chunk_size)

            feature_matrix.reset_index(inplace=True)

self.entity_col = entity_col
        self.es = ft.EntitySet(id=name)
        self.es = self.es.entity_from_dataframe(entity_id=name,
                                                dataframe=df,
                                                time_index=time_col,
                                                index="__id__",
                                                make_index=True,
                                                variable_types=variable_types
                                                )

        entity_df = pd.DataFrame([[i] for i in set(df[entity_col])], columns=[entity_col])
        self.es = self.es.entity_from_dataframe(entity_id=entity_col,
                                                dataframe=entity_df,
                                                index=entity_col
                                                )
        new_relationship = ft.Relationship(self.es[entity_col][entity_col],
                                           self.es[name][entity_col])
        self.es = self.es.add_relationship(new_relationship)

def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer):
        base_feature = dependencies[arguments['base_feature']]
        relationship = Relationship.from_dictionary(arguments['relationship'], entityset)
        child_entity = relationship.child_entity
        return cls(base_feature=base_feature,
                   child_entity=child_entity,
                   relationship=relationship,
                   name=arguments['name'])

es = es.entity_from_dataframe(entity_id="sessions",
                                      dataframe=sessions_df,
                                      index="session_id",
                                      time_index="session_start")

        es = es.entity_from_dataframe(entity_id="customers",
                                      dataframe=customers_df,
                                      index="customer_id",
                                      time_index="join_date",
                                      variable_types={"zip_code": ZIPCode})

        rels = [ft.Relationship(es["products"]["product_id"],
                                es["transactions"]["product_id"]),
                ft.Relationship(es["sessions"]["session_id"],
                                es["transactions"]["session_id"]),
                ft.Relationship(es["customers"]["customer_id"],
                                es["sessions"]["customer_id"])]
        es = es.add_relationships(rels)
        es.add_last_time_indexes()
        return es

    return {"customers": customers_df,
            "sessions": sessions_df,
            "transactions": transactions_df,
            "products": products_df}

index="product_id")

        es = es.entity_from_dataframe(entity_id="sessions",
                                      dataframe=sessions_df,
                                      index="session_id",
                                      time_index="session_start")

        es = es.entity_from_dataframe(entity_id="customers",
                                      dataframe=customers_df,
                                      index="customer_id",
                                      time_index="join_date",
                                      variable_types={"zip_code": ZIPCode})

        rels = [ft.Relationship(es["products"]["product_id"],
                                es["transactions"]["product_id"]),
                ft.Relationship(es["sessions"]["session_id"],
                                es["transactions"]["session_id"]),
                ft.Relationship(es["customers"]["customer_id"],
                                es["sessions"]["customer_id"])]
        es = es.add_relationships(rels)
        es.add_last_time_indexes()
        return es

    return {"customers": customers_df,
            "sessions": sessions_df,
            "transactions": transactions_df,
            "products": products_df}