format python code

Author: siddvenk

src/sagemaker/djl_inference/model.py

@@ -43,6 +43,7 @@
     logging.NOTSET: "off",
 }
 
+
 class DJLEngine(Enum):
     DEEPSPEED = ("DeepSpeed", "djl_python.deepspeed")
     HUGGINGFACE_ACCELERATE = ("Python", "djl_python.huggingface")
@@ -54,6 +55,7 @@ class DJLLargeModelPredictor(Predictor):
     This is able to serialize Python lists, dictionaries, and numpy arrays to
     multidimensional tensors for DJL inference.
     """
+
     def __init__(
         self,
         endpoint_name,
@@ -140,7 +142,9 @@ def __new__(
             config_file = uncompressed_model_data + "/config.json"
 
         model_type = json.loads(s3.S3Downloader.read_file(config_file)).get("model_type")
-        cls_to_create = cls if cls is not DJLLargeModel else _determine_engine_for_model_type(model_type)
+        cls_to_create = (
+            cls if cls is not DJLLargeModel else _determine_engine_for_model_type(model_type)
+        )
         return super(DJLLargeModel, cls).__new__(cls_to_create)
 
     def __init__(
@@ -227,8 +231,10 @@ def __init__(
             model class directly.
         """
         if kwargs.get("model_data"):
-            logger.warning("DJLLargeModels do not use model_data parameter. model_data parameter will be ignored."
-                           "You only need to set uncompressed_model_data")
+            logger.warning(
+                "DJLLargeModels do not use model_data parameter. model_data parameter will be ignored."
+                "You only need to set uncompressed_model_data"
+            )
         super(DJLLargeModel, self).__init__(
             None, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs
         )
@@ -580,12 +586,15 @@ def _get_container_env(self):
             logger.warning(f"Ignoring invalid container log level: {self.container_log_level}")
             return self.env
 
-        self.env["SERVING_OPTS"] = f'"-Dai.djl.logging.level={_LOG_LEVEL_MAP[self.container_log_level]}"'
+        self.env[
+            "SERVING_OPTS"
+        ] = f'"-Dai.djl.logging.level={_LOG_LEVEL_MAP[self.container_log_level]}"'
         return self.env
 
 
 class DeepSpeedModel(DJLLargeModel):
     """A DeepSpeed SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``"""
+
     _framework_name = "djl-deepspeed"
 
     def __init__(
@@ -679,8 +688,8 @@ def generate_serving_properties(self, serving_properties={}) -> Dict[str, str]:
 
 
 class HuggingFaceAccelerateModel(DJLLargeModel):
-    """A Hugging Face SageMaker ``Model`` using HuggingFace Accelerate that can be deployed to a SageMaker ``Endpoint``.
-    """
+    """A Hugging Face SageMaker ``Model`` using HuggingFace Accelerate that can be deployed to a SageMaker ``Endpoint``."""
+
     _framework_name = "djl-deepspeed"
 
     def __init__(