black format

Author: laurenyu

tests/unit/test_fm.py

@@ -313,7 +313,9 @@ def test_model_image(sagemaker_session):
     fm.fit(data, MINI_BATCH_SIZE)
 
     model = fm.create_model()
-    assert model.image_uri == registry(REGION, "factorization-machines") + "/factorization-machines:1"
+    assert (
+        model.image_uri == registry(REGION, "factorization-machines") + "/factorization-machines:1"
+    )
 
 
 def test_predictor_type(sagemaker_session):

tests/unit/test_session.py

@@ -1706,7 +1706,10 @@ def test_create_model_from_job_with_container_def(sagemaker_session):
     ims = sagemaker_session
     ims.sagemaker_client.describe_training_job.return_value = COMPLETED_DESCRIBE_JOB_RESULT
     ims.create_model_from_job(
-        JOB_NAME, primary_container_image_uri="some-image", model_data_url="some-data", env={"a": "b"}
+        JOB_NAME,
+        primary_container_image_uri="some-image",
+        model_data_url="some-data",
+        env={"a": "b"},
     )
     [create_model_call] = ims.sagemaker_client.create_model.call_args_list
     c_def = create_model_call[1]["PrimaryContainer"]

tests/unit/test_sparkml_serving.py

@@ -49,7 +49,9 @@ def sagemaker_session():
 
 def test_sparkml_model(sagemaker_session):
     sparkml = SparkMLModel(sagemaker_session=sagemaker_session, model_data=MODEL_DATA, role=ROLE)
-    assert sparkml.image_uri == registry(REGION, "sparkml-serving") + "/sagemaker-sparkml-serving:2.2"
+    assert (
+        sparkml.image_uri == registry(REGION, "sparkml-serving") + "/sagemaker-sparkml-serving:2.2"
+    )
 
 
 def test_predictor_type(sagemaker_session):