fix: fix black-check, pylint errors

Author: Roja Reddy Sareddy

src/sagemaker/huggingface/model.py

@@ -297,9 +297,11 @@ def deploy(
                 would like to deploy the model and endpoint with recommended parameters.
             explainer_config (sagemaker.explainer.ExplainerConfig): Specifies online explainability
                 configuration for use with Amazon SageMaker Clarify. (default: None)
-            update_endpoint (Optional[bool]): Flag to update the model in an existing Amazon SageMaker endpoint.
-                If True, this will deploy a new EndpointConfig to an already existing endpoint and
-                delete resources corresponding to the previous EndpointConfig. Default: False
+            update_endpoint (Optional[bool]):
+                Flag to update the model in an existing Amazon SageMaker endpoint.
+                If True, this will deploy a new EndpointConfig to an already existing endpoint
+                and delete resources corresponding to the previous EndpointConfig. Default: False
+                Note: Currently this is supported for single model endpoints
         Raises:
              ValueError: If arguments combination check failed in these circumstances:
                 - If no role is specified or

src/sagemaker/model.py

@@ -1497,9 +1497,10 @@ def deploy(
             inference_ami_version (Optional [str]): Specifies an option from a collection of preconfigured
              Amazon Machine Image (AMI) images. For a full list of options, see:
              https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ProductionVariant.html
-            update_endpoint (Optional[bool]): Flag to update the model in an existing Amazon SageMaker endpoint.
-                If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
-                corresponding to the previous EndpointConfig. Default: False
+            update_endpoint (Optional[bool]):
+                Flag to update the model in an existing Amazon SageMaker endpoint.
+                If True, this will deploy a new EndpointConfig to an already existing endpoint
+                and delete resources corresponding to the previous EndpointConfig. Default: False
                 Note: Currently this is supported for single model endpoints
         Raises:
              ValueError: If arguments combination check failed in these circumstances:

src/sagemaker/serve/builder/model_builder.py

@@ -1616,9 +1616,10 @@ def deploy(
                AsyncInferenceConfig, BatchTransformInferenceConfig, ResourceRequirements]]) :
                 Additional Config for different deployment types such as
                 serverless, async, batch and multi-model/container
-            update_endpoint (Optional[bool]): Flag to update the model in an existing Amazon SageMaker endpoint.
-                If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
-                corresponding to the previous EndpointConfig. Default: False
+            update_endpoint (Optional[bool]):
+                Flag to update the model in an existing Amazon SageMaker endpoint.
+                If True, this will deploy a new EndpointConfig to an already existing endpoint
+                and delete resources corresponding to the previous EndpointConfig. Default: False
                 Note: Currently this is supported for single model endpoints
         Returns:
             Transformer for Batch Deployments

src/sagemaker/session.py

@@ -4549,7 +4549,10 @@ def create_endpoint_config(
                     {
                         "RoutingStrategy":  sagemaker.enums.RoutingStrategy.RANDOM
                     }
-            inference_ami_version (Optional [str]): Specifies an option from a collection of preconfigured
+            inference_ami_version (Optional [str]):
+             Specifies an option from a collection of preconfigured
+             Amazon Machine Image (AMI) images. For a full list of options, see:
+             https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ProductionVariant.html
 
         Example:
             >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]