How to use the openml.flows.get_flow function in openml

def test_sklearn_to_flow_list_of_lists(self):
        from sklearn.preprocessing import OrdinalEncoder
        ordinal_encoder = OrdinalEncoder(categories=[[0, 1], [0, 1]])
        extension = openml.extensions.sklearn.SklearnExtension()

        # Test serialization works
        flow = extension.model_to_flow(ordinal_encoder)

        # Test flow is accepted by server
        self._add_sentinel_to_flow_name(flow)
        flow.publish()
        TestBase._mark_entity_for_removal('flow', (flow.flow_id, flow.name))
        TestBase.logger.info("collected from {}: {}".format(__file__.split('/')[-1], flow.flow_id))
        # Test deserialization works
        server_flow = openml.flows.get_flow(flow.flow_id, reinstantiate=True)
        self.assertEqual(server_flow.parameters['categories'], '[[0, 1], [0, 1]]')
        self.assertEqual(server_flow.model.categories, flow.model.categories)

def test_run_flow_on_task_downloaded_flow(self):
        model = sklearn.ensemble.RandomForestClassifier(n_estimators=33)
        flow = self.extension.model_to_flow(model)
        flow.publish(raise_error_if_exists=False)
        TestBase._mark_entity_for_removal('flow', (flow.flow_id, flow.name))
        TestBase.logger.info("collected from test_run_functions: {}".format(flow.flow_id))

        downloaded_flow = openml.flows.get_flow(flow.flow_id)
        task = openml.tasks.get_task(119)  # diabetes
        run = openml.runs.run_flow_on_task(
            flow=downloaded_flow,
            task=task,
            avoid_duplicate_runs=False,
            upload_flow=False,
        )

        run.publish()
        TestBase._mark_entity_for_removal('run', run.run_id)
        TestBase.logger.info("collected from {}: {}".format(__file__.split('/')[-1], run.run_id))

sklearn.feature_selection.VarianceThreshold(),
            ),
            ('classifier', sklearn.tree.DecisionTreeClassifier())
        ]
        complicated = sklearn.pipeline.Pipeline(steps=steps)

        for classifier in [nb, complicated]:
            flow = self.extension.model_to_flow(classifier)
            flow, _ = self._add_sentinel_to_flow_name(flow, None)
            # publish the flow
            flow = flow.publish()
            TestBase._mark_entity_for_removal('flow', (flow.flow_id, flow.name))
            TestBase.logger.info("collected from {}: {}".format(__file__.split('/')[-1],
                                                                flow.flow_id))
            # redownload the flow
            flow = openml.flows.get_flow(flow.flow_id)

            # check if flow exists can find it
            flow = openml.flows.get_flow(flow.flow_id)
            downloaded_flow_id = openml.flows.flow_exists(
                flow.name,
                flow.external_version,
            )
            self.assertEqual(downloaded_flow_id, flow.flow_id)

def test_get_flow(self):
        # We need to use the production server here because 4024 is not the
        # test server
        openml.config.server = self.production_server

        flow = openml.flows.get_flow(4024)
        self.assertIsInstance(flow, openml.OpenMLFlow)
        self.assertEqual(flow.flow_id, 4024)
        self.assertEqual(len(flow.parameters), 24)
        self.assertEqual(len(flow.components), 1)

        subflow_1 = list(flow.components.values())[0]
        self.assertIsInstance(subflow_1, openml.OpenMLFlow)
        self.assertEqual(subflow_1.flow_id, 4025)
        self.assertEqual(len(subflow_1.parameters), 14)
        self.assertEqual(subflow_1.parameters['E'], 'CC')
        self.assertEqual(len(subflow_1.components), 1)

        subflow_2 = list(subflow_1.components.values())[0]
        self.assertIsInstance(subflow_2, openml.OpenMLFlow)
        self.assertEqual(subflow_2.flow_id, 4026)
        self.assertEqual(len(subflow_2.parameters), 13)

def test_get_flow_reinstantiate_model_no_extension(self):
        # Flow 10 is a WEKA flow
        self.assertRaisesRegex(RuntimeError,
                               "No extension could be found for flow 10: weka.SMO",
                               openml.flows.get_flow,
                               flow_id=10,
                               reinstantiate=True)

def test_get_structure(self):
        # also responsible for testing: flow.get_subflow
        # We need to use the production server here because 4024 is not the
        # test server
        openml.config.server = self.production_server

        flow = openml.flows.get_flow(4024)
        flow_structure_name = flow.get_structure('name')
        flow_structure_id = flow.get_structure('flow_id')
        # components: root (filteredclassifier), multisearch, loginboost,
        # reptree
        self.assertEqual(len(flow_structure_name), 4)
        self.assertEqual(len(flow_structure_id), 4)

        for sub_flow_name, structure in flow_structure_name.items():
            if len(structure) > 0:  # skip root element
                subflow = flow.get_subflow(structure)
                self.assertEqual(subflow.name, sub_flow_name)

        for sub_flow_id, structure in flow_structure_id.items():
            if len(structure) > 0:  # skip root element
                subflow = flow.get_subflow(structure)
                self.assertEqual(subflow.flow_id, sub_flow_id)

def test_can_handle_flow(self):
        openml.config.server = self.production_server

        R_flow = openml.flows.get_flow(6794)
        assert not self.extension.can_handle_flow(R_flow)
        old_3rd_party_flow = openml.flows.get_flow(7660)
        assert self.extension.can_handle_flow(old_3rd_party_flow)

        openml.config.server = self.test_server