feat(aiplatform): update the api

#### aiplatform:v1

The following keys were deleted:
- schemas.GoogleCloudAiplatformV1GroundingAttribution (Total Keys: 12)
- schemas.GoogleCloudAiplatformV1GroundingMetadata.properties.groundingAttributions (Total Keys: 2)

The following keys were added:
- resources.projects.resources.locations.resources.notebookExecutionJobs.methods.create (Total Keys: 14)
- resources.projects.resources.locations.resources.notebookExecutionJobs.methods.delete (Total Keys: 11)
- resources.projects.resources.locations.resources.notebookExecutionJobs.methods.get (Total Keys: 13)
- resources.projects.resources.locations.resources.notebookExecutionJobs.methods.list (Total Keys: 22)
- schemas.GoogleCloudAiplatformV1Candidate.properties.score (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1CountTokensRequest.properties.systemInstruction.$ref (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1CountTokensRequest.properties.tools (Total Keys: 2)
- schemas.GoogleCloudAiplatformV1CreateNotebookExecutionJobOperationMetadata (Total Keys: 4)
- schemas.GoogleCloudAiplatformV1ListNotebookExecutionJobsResponse (Total Keys: 5)
- schemas.GoogleCloudAiplatformV1NotebookExecutionJob (Total Keys: 40)
- schemas.GoogleCloudAiplatformV1RayLogsSpec (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1RaySpec.properties.rayLogsSpec.$ref (Total Keys: 1)

#### aiplatform:v1beta1

The following keys were deleted:
- schemas.GoogleCloudAiplatformV1beta1GroundingAttribution (Total Keys: 17)
- schemas.GoogleCloudAiplatformV1beta1GroundingMetadata.properties.groundingAttributions (Total Keys: 2)

The following keys were added:
- schemas.GoogleCloudAiplatformV1beta1Candidate.properties.score (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1beta1CountTokensRequest.properties.systemInstruction.$ref (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1beta1CountTokensRequest.properties.tools (Total Keys: 2)
- schemas.GoogleCloudAiplatformV1beta1RayLogsSpec (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1beta1RaySpec.properties.rayLogsSpec.$ref (Total Keys: 1)

Author: yoshi-automation

docs/dyn/aiplatform_v1.projects.locations.endpoints.html

@@ -171,6 +171,9 @@ <h3>Method Details</h3>
     &quot;raySpec&quot;: { # Configuration information for the Ray cluster. For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes. # Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
       &quot;headNodeResourcePoolId&quot;: &quot;A String&quot;, # Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn&#x27;t set.
       &quot;imageUri&quot;: &quot;A String&quot;, # Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from [Vertex prebuilt images](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers). Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
+      &quot;rayLogsSpec&quot;: { # Configuration for the Ray OSS Logs. # Optional. OSS Ray logging configurations.
+        &quot;disabled&quot;: True or False, # Optional. Flag to disable the export of Ray OSS logs to Cloud Logging.
+      },
       &quot;rayMetricSpec&quot;: { # Configuration for the Ray metrics. # Optional. Ray metrics configurations.
         &quot;disabled&quot;: True or False, # Optional. Flag to disable the Ray metrics collection.
       },
@@ -320,6 +323,9 @@ <h3>Method Details</h3>
     &quot;raySpec&quot;: { # Configuration information for the Ray cluster. For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes. # Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
       &quot;headNodeResourcePoolId&quot;: &quot;A String&quot;, # Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn&#x27;t set.
       &quot;imageUri&quot;: &quot;A String&quot;, # Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from [Vertex prebuilt images](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers). Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
+      &quot;rayLogsSpec&quot;: { # Configuration for the Ray OSS Logs. # Optional. OSS Ray logging configurations.
+        &quot;disabled&quot;: True or False, # Optional. Flag to disable the export of Ray OSS logs to Cloud Logging.
+      },
       &quot;rayMetricSpec&quot;: { # Configuration for the Ray metrics. # Optional. Ray metrics configurations.
         &quot;disabled&quot;: True or False, # Optional. Flag to disable the Ray metrics collection.
       },
@@ -410,6 +416,9 @@ <h3>Method Details</h3>
         &quot;raySpec&quot;: { # Configuration information for the Ray cluster. For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes. # Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
           &quot;headNodeResourcePoolId&quot;: &quot;A String&quot;, # Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn&#x27;t set.
           &quot;imageUri&quot;: &quot;A String&quot;, # Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from [Vertex prebuilt images](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers). Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
+          &quot;rayLogsSpec&quot;: { # Configuration for the Ray OSS Logs. # Optional. OSS Ray logging configurations.
+            &quot;disabled&quot;: True or False, # Optional. Flag to disable the export of Ray OSS logs to Cloud Logging.
+          },
           &quot;rayMetricSpec&quot;: { # Configuration for the Ray metrics. # Optional. Ray metrics configurations.
             &quot;disabled&quot;: True or False, # Optional. Flag to disable the Ray metrics collection.
           },
@@ -506,6 +515,9 @@ <h3>Method Details</h3>
     &quot;raySpec&quot;: { # Configuration information for the Ray cluster. For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes. # Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
       &quot;headNodeResourcePoolId&quot;: &quot;A String&quot;, # Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn&#x27;t set.
       &quot;imageUri&quot;: &quot;A String&quot;, # Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from [Vertex prebuilt images](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers). Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
+      &quot;rayLogsSpec&quot;: { # Configuration for the Ray OSS Logs. # Optional. OSS Ray logging configurations.
+        &quot;disabled&quot;: True or False, # Optional. Flag to disable the export of Ray OSS logs to Cloud Logging.
+      },
       &quot;rayMetricSpec&quot;: { # Configuration for the Ray metrics. # Optional. Ray metrics configurations.
         &quot;disabled&quot;: True or False, # Optional. Flag to disable the Ray metrics collection.
       },