fix: removing kwargs as this is breaking predictor_cls param for mode… (#4816)

* fix: removing kwargs as this is breaking predictor_cls param for model deploy

* fix: include all known fields directly in create_sagemaker_model function signature

* chore: fixing model deploy tests to incorporate new params

* chore: fix formatting

---------

Co-authored-by: shaernev <shaernev@amazon.com>

Author: ErnevSharma

src/sagemaker/model.py

@@ -1377,6 +1377,7 @@ def deploy(
         managed_instance_scaling: Optional[str] = None,
         inference_component_name=None,
         routing_config: Optional[Dict[str, Any]] = None,
+        model_reference_arn: Optional[str] = None,
         **kwargs,
     ):
         """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
@@ -1483,6 +1484,8 @@ def deploy(
                     {
                         "RoutingStrategy":  sagemaker.enums.RoutingStrategy.RANDOM
                     }
+            model_reference_arn (Optional [str]): Hub Content Arn of a Model Reference type
+                content (default: None).
         Raises:
              ValueError: If arguments combination check failed in these circumstances:
                 - If no role is specified or
@@ -1697,7 +1700,8 @@ def deploy(
                 accelerator_type=accelerator_type,
                 tags=tags,
                 serverless_inference_config=serverless_inference_config,
-                **kwargs,
+                accept_eula=accept_eula,
+                model_reference_arn=model_reference_arn,
             )
             serverless_inference_config_dict = (
                 serverless_inference_config._to_request_dict() if is_serverless else None

tests/unit/sagemaker/jumpstart/model/test_model.py

@@ -771,7 +771,7 @@ def test_jumpstart_model_kwargs_match_parent_class(self):
         and reach out to JumpStart team."""
 
         init_args_to_skip: Set[str] = set(["model_reference_arn"])
-        deploy_args_to_skip: Set[str] = set(["kwargs"])
+        deploy_args_to_skip: Set[str] = set(["kwargs", "model_reference_arn"])
 
         parent_class_init = Model.__init__
         parent_class_init_args = set(signature(parent_class_init).parameters.keys())

tests/unit/sagemaker/model/test_deploy.py

@@ -179,6 +179,8 @@ def test_deploy_accelerator_type(
         accelerator_type=ACCELERATOR_TYPE,
         tags=None,
         serverless_inference_config=None,
+        accept_eula=None,
+        model_reference_arn=None,
     )
     production_variant.assert_called_with(
         MODEL_NAME,
@@ -299,6 +301,8 @@ def test_deploy_tags(create_sagemaker_model, production_variant, name_from_base,
         accelerator_type=None,
         tags=tags,
         serverless_inference_config=None,
+        accept_eula=None,
+        model_reference_arn=None,
     )
     sagemaker_session.endpoint_from_production_variants.assert_called_with(
         name=ENDPOINT_NAME,
@@ -502,6 +506,8 @@ def test_deploy_serverless_inference(production_variant, create_sagemaker_model,
         accelerator_type=None,
         tags=None,
         serverless_inference_config=serverless_inference_config,
+        accept_eula=None,
+        model_reference_arn=None,
     )
     production_variant.assert_called_with(
         MODEL_NAME,