diff --git a/examples/hello-world/step-by-step/higgs/sklearn-kmeans/code/sklearn_kmeans_job.py b/examples/hello-world/step-by-step/higgs/sklearn-kmeans/code/sklearn_kmeans_job.py
index 6a7d18ae1b..3a0f6a2184 100644
--- a/examples/hello-world/step-by-step/higgs/sklearn-kmeans/code/sklearn_kmeans_job.py
+++ b/examples/hello-world/step-by-step/higgs/sklearn-kmeans/code/sklearn_kmeans_job.py
@@ -12,12 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from kmeans_assembler import KMeansAssembler
+
 from nvflare import FedJob
 from nvflare.app_common.aggregators.collect_and_assemble_aggregator import CollectAndAssembleAggregator
 from nvflare.app_common.shareablegenerators import FullModelShareableGenerator
 from nvflare.app_common.workflows.scatter_and_gather import ScatterAndGather
 from nvflare.app_opt.sklearn.joblib_model_param_persistor import JoblibModelParamPersistor
-from nvflare.job_config.script_runner import ScriptRunner, FrameworkType
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 if __name__ == "__main__":
     n_clients = 3
@@ -38,16 +39,18 @@
     job.to(CollectAndAssembleAggregator(assembler_id=assembler_id), "server", id=aggregator_id)
     job.to(KMeansAssembler(), "server", id=assembler_id)
 
-    ctrl = ScatterAndGather(min_clients=n_clients,
-                            num_rounds=num_rounds,
-                            start_round=0,
-                            wait_time_after_min_received=0,
-                            aggregator_id=aggregator_id,
-                            persistor_id=persistor_id,
-                            shareable_generator_id=shareable_generator_id,
-                            train_task_name="train",
-                            train_timeout=0,
-                            allow_empty_global_weights=True)
+    ctrl = ScatterAndGather(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+        start_round=0,
+        wait_time_after_min_received=0,
+        aggregator_id=aggregator_id,
+        persistor_id=persistor_id,
+        shareable_generator_id=shareable_generator_id,
+        train_task_name="train",
+        train_timeout=0,
+        allow_empty_global_weights=True,
+    )
 
     job.to(ctrl, "server")
 
diff --git a/examples/hello-world/step-by-step/higgs/sklearn-kmeans/sklearn_kmeans_job.py b/examples/hello-world/step-by-step/higgs/sklearn-kmeans/sklearn_kmeans_job.py
index 093b4f5bd0..98a5fae371 100644
--- a/examples/hello-world/step-by-step/higgs/sklearn-kmeans/sklearn_kmeans_job.py
+++ b/examples/hello-world/step-by-step/higgs/sklearn-kmeans/sklearn_kmeans_job.py
@@ -17,7 +17,7 @@
 from nvflare.app_common.shareablegenerators import FullModelShareableGenerator
 from nvflare.app_common.workflows.scatter_and_gather import ScatterAndGather
 from nvflare.app_opt.sklearn.joblib_model_param_persistor import JoblibModelParamPersistor
-from nvflare.job_config.script_runner import ScriptRunner, FrameworkType
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 if __name__ == "__main__":
     n_clients = 3
@@ -32,28 +32,24 @@
     job = FedJob("sklearn_svm")
 
     initial_params = dict(
-        n_classes=2,
-        learning_rate="constant",
-        eta0=1e-05,
-        loss="log_loss",
-        penalty="l2",
-        fit_intercept=True,
-        max_iter=1
+        n_classes=2, learning_rate="constant", eta0=1e-05, loss="log_loss", penalty="l2", fit_intercept=True, max_iter=1
     )
     job.to(JoblibModelParamPersistor(initial_params=initial_params), "server", id=persistor_id)
     job.to(FullModelShareableGenerator(), "server", id=shareable_generator_id)
     job.to(InTimeAccumulateWeightedAggregator(expected_data_kind=DataKind.WEIGHTS), "server", id=aggregator_id)
 
-    ctrl = ScatterAndGather(min_clients=n_clients,
-                            num_rounds=num_rounds,
-                            start_round=0,
-                            wait_time_after_min_received=0,
-                            aggregator_id=aggregator_id,
-                            persistor_id=persistor_id,
-                            shareable_generator_id=shareable_generator_id,
-                            train_task_name="train",
-                            train_timeout=0,
-                            allow_empty_global_weights=True)
+    ctrl = ScatterAndGather(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+        start_round=0,
+        wait_time_after_min_received=0,
+        aggregator_id=aggregator_id,
+        persistor_id=persistor_id,
+        shareable_generator_id=shareable_generator_id,
+        train_task_name="train",
+        train_timeout=0,
+        allow_empty_global_weights=True,
+    )
 
     job.to(ctrl, "server")
 
diff --git a/examples/hello-world/step-by-step/higgs/sklearn-linear/sklearn_linear_job.py b/examples/hello-world/step-by-step/higgs/sklearn-linear/sklearn_linear_job.py
index 2389c519d4..51035f3f33 100644
--- a/examples/hello-world/step-by-step/higgs/sklearn-linear/sklearn_linear_job.py
+++ b/examples/hello-world/step-by-step/higgs/sklearn-linear/sklearn_linear_job.py
@@ -17,7 +17,7 @@
 from nvflare.app_common.shareablegenerators import FullModelShareableGenerator
 from nvflare.app_common.workflows.scatter_and_gather import ScatterAndGather
 from nvflare.app_opt.sklearn.joblib_model_param_persistor import JoblibModelParamPersistor
-from nvflare.job_config.script_runner import ScriptRunner, FrameworkType
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 if __name__ == "__main__":
     n_clients = 3
@@ -32,28 +32,24 @@
     job = FedJob("sklearn_sgd")
 
     initial_params = dict(
-        n_classes=2,
-        learning_rate="constant",
-        eta0=1e-05,
-        loss="log_loss",
-        penalty="l2",
-        fit_intercept=True,
-        max_iter=1
+        n_classes=2, learning_rate="constant", eta0=1e-05, loss="log_loss", penalty="l2", fit_intercept=True, max_iter=1
     )
     job.to(JoblibModelParamPersistor(initial_params=initial_params), "server", id=persistor_id)
     job.to(FullModelShareableGenerator(), "server", id=shareable_generator_id)
     job.to(InTimeAccumulateWeightedAggregator(expected_data_kind=DataKind.WEIGHTS), "server", id=aggregator_id)
 
-    ctrl = ScatterAndGather(min_clients=n_clients,
-                            num_rounds=num_rounds,
-                            start_round=0,
-                            wait_time_after_min_received=0,
-                            aggregator_id=aggregator_id,
-                            persistor_id=persistor_id,
-                            shareable_generator_id=shareable_generator_id,
-                            train_task_name="train",
-                            train_timeout=0,
-                            allow_empty_global_weights=True)
+    ctrl = ScatterAndGather(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+        start_round=0,
+        wait_time_after_min_received=0,
+        aggregator_id=aggregator_id,
+        persistor_id=persistor_id,
+        shareable_generator_id=shareable_generator_id,
+        train_task_name="train",
+        train_timeout=0,
+        allow_empty_global_weights=True,
+    )
 
     job.to(ctrl, "server")
 
diff --git a/examples/hello-world/step-by-step/higgs/sklearn-svm/code/sklearn_svm_job.py b/examples/hello-world/step-by-step/higgs/sklearn-svm/code/sklearn_svm_job.py
index ff5489880d..be744747cb 100644
--- a/examples/hello-world/step-by-step/higgs/sklearn-svm/code/sklearn_svm_job.py
+++ b/examples/hello-world/step-by-step/higgs/sklearn-svm/code/sklearn_svm_job.py
@@ -11,13 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from svm_assembler import SVMAssembler
+
 from nvflare import FedJob
 from nvflare.app_common.aggregators.collect_and_assemble_aggregator import CollectAndAssembleAggregator
 from nvflare.app_common.shareablegenerators import FullModelShareableGenerator
 from nvflare.app_common.workflows.scatter_and_gather import ScatterAndGather
 from nvflare.app_opt.sklearn.joblib_model_param_persistor import JoblibModelParamPersistor
-from nvflare.job_config.script_runner import ScriptRunner, FrameworkType
-from svm_assembler import SVMAssembler
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 if __name__ == "__main__":
     n_clients = 3
@@ -35,19 +36,21 @@
     initial_params = dict(kernel="rbf")
     job.to(JoblibModelParamPersistor(initial_params=initial_params), "server", id=persistor_id)
     job.to(FullModelShareableGenerator(), "server", id=shareable_generator_id)
-    job.to(CollectAndAssembleAggregator(assembler_id=assembler_id),  "server", id=aggregator_id)
-    job.to(SVMAssembler(kernel= "rbf"), "server", id = assembler_id)
-
-    ctrl = ScatterAndGather(min_clients=n_clients,
-                            num_rounds=num_rounds,
-                            start_round=0,
-                            wait_time_after_min_received=0,
-                            aggregator_id=aggregator_id,
-                            persistor_id=persistor_id,
-                            shareable_generator_id=shareable_generator_id,
-                            train_task_name="train",
-                            train_timeout=0,
-                            allow_empty_global_weights=True)
+    job.to(CollectAndAssembleAggregator(assembler_id=assembler_id), "server", id=aggregator_id)
+    job.to(SVMAssembler(kernel="rbf"), "server", id=assembler_id)
+
+    ctrl = ScatterAndGather(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+        start_round=0,
+        wait_time_after_min_received=0,
+        aggregator_id=aggregator_id,
+        persistor_id=persistor_id,
+        shareable_generator_id=shareable_generator_id,
+        train_task_name="train",
+        train_timeout=0,
+        allow_empty_global_weights=True,
+    )
 
     job.to(ctrl, "server")