Fix models and pre-commit

tests/torch/fx/test_models.py

@@ -56,7 +56,6 @@ def torchvision_model_builder(model_id: str, input_shape: Tuple[int,]):
     return ModelCase(model, model_id, input_shape)
 
 
-
 def ultralytics_model_builder(model_id: str, input_shape: Tuple[int,]):
     model_config = model_id + ".yaml"  # Initialize the model with random weights instead of downloading them.
     model = YOLO(model_config)
@@ -69,22 +68,15 @@ def ultralytics_model_builder(model_id: str, input_shape: Tuple[int,]):
     return ModelCase(model, model_id, input_shape)
 
 
-
 TEST_MODELS = (
     torchvision_model_builder("resnet18", (1, 3, 224, 224)),
     torchvision_model_builder("mobilenet_v3_small", (1, 3, 224, 224)),
     torchvision_model_builder("vit_b_16", (1, 3, 224, 224)),
     torchvision_model_builder("swin_v2_s", (1, 3, 224, 224)),
     ultralytics_model_builder("yolov8n", (1, 3, 224, 224)),
-    torchvision_model_builder("resnet18", (1, 3, 224, 224)),
-    torchvision_model_builder("mobilenet_v3_small", (1, 3, 224, 224)),
-    torchvision_model_builder("vit_b_16", (1, 3, 224, 224)),
-    torchvision_model_builder("swin_v2_s", (1, 3, 224, 224)),
-    ultralytics_model_builder("yolov8n", (1, 3, 224, 224)),
 )
 
 
-
 def get_dot_filename(model_name):
     return model_name + ".dot"
 
@@ -109,21 +101,17 @@ def get_ref_metatypes_from_json(
     model_name: str, model_metatypes: Dict[NNCFNodeName, Type[OperatorMetatype]]
 ) -> Dict[NNCFNodeName, Type[OperatorMetatype]]:
 
-
     model_json_name = get_json_filename(model_name)
     complete_path = get_full_path_to_json(model_json_name)
 
-
     json_parent_dir = Path(complete_path).parent
 
-
     if os.getenv("NNCF_TEST_REGEN_JSON") is not None:
         if not os.path.exists(json_parent_dir):
             os.makedirs(json_parent_dir)
         with safe_open(complete_path, "w") as file:
             json.dump(model_metatypes, file)
 
-
     with safe_open(complete_path, "r") as file:
         return json.load(file)