chore: Update discovery artifacts (#2323)

## Deleted keys were detected in the following stable discovery artifacts:
aiplatform v1 https://togithub.com/googleapis/google-api-python-client/commit/5955fc9210d3a40f28bdfb1b56df9d223dbad97b
displayvideo v3 https://togithub.com/googleapis/google-api-python-client/commit/a5ce56473f5c0ca41dbf753ccc92539de958d6dd
monitoring v1 https://togithub.com/googleapis/google-api-python-client/commit/424cc24e9b240ae467aff56e8034d4d84da97b62
vmwareengine v1 https://togithub.com/googleapis/google-api-python-client/commit/de91454faf920b9dc4fd032f199558cb6200e994

## Deleted keys were detected in the following pre-stable discovery artifacts:
aiplatform v1beta1 https://togithub.com/googleapis/google-api-python-client/commit/5955fc9210d3a40f28bdfb1b56df9d223dbad97b
compute alpha https://togithub.com/googleapis/google-api-python-client/commit/e12e7c50c76d2edc3bd38d7de20412154e466420
prod_tt_sasportal v1alpha1 https://togithub.com/googleapis/google-api-python-client/commit/268437c568609174c01b54b69745d28e388ca465

## Discovery Artifact Change Summary:
feat(aiplatform): update the api https://togithub.com/googleapis/google-api-python-client/commit/5955fc9210d3a40f28bdfb1b56df9d223dbad97b
feat(analyticsadmin): update the api https://togithub.com/googleapis/google-api-python-client/commit/b863607987e5c29975f4cc7d0bc0187d78768fb1
feat(apigee): update the api https://togithub.com/googleapis/google-api-python-client/commit/358a88079e12bf85f954ae09f82b74daf94ea626
feat(apphub): update the api https://togithub.com/googleapis/google-api-python-client/commit/d48507971fdfc5f71ec67ba31c8e38e70a2a647b
feat(cloudsupport): update the api https://togithub.com/googleapis/google-api-python-client/commit/b3cdcb64f42f3e88ecc0f5ba481e23908915eda8
feat(compute): update the api https://togithub.com/googleapis/google-api-python-client/commit/e12e7c50c76d2edc3bd38d7de20412154e466420
feat(connectors): update the api https://togithub.com/googleapis/google-api-python-client/commit/a23804b142fcd94b814670d1a4618a75dc17eeb2
feat(dataflow): update the api https://togithub.com/googleapis/google-api-python-client/commit/ac90f1ee7ad4b23ab066afd9bb144abfff70ecb5
feat(dialogflow): update the api https://togithub.com/googleapis/google-api-python-client/commit/c565530e21357a144160690dd5147bac4f4c29e8
feat(discoveryengine): update the api https://togithub.com/googleapis/google-api-python-client/commit/9aa16e1fa90421c837926a566269bdb4d122504c
feat(displayvideo): update the api https://togithub.com/googleapis/google-api-python-client/commit/a5ce56473f5c0ca41dbf753ccc92539de958d6dd
feat(documentai): update the api https://togithub.com/googleapis/google-api-python-client/commit/132fea06cf026e078725383ad536e46b5132b58b
feat(gkehub): update the api https://togithub.com/googleapis/google-api-python-client/commit/3273b94fc7f18630b0df2f8486ea77b81abbd4ad
feat(logging): update the api https://togithub.com/googleapis/google-api-python-client/commit/1a1df9bd0458b6c830e730f5655282a253ca882f
feat(monitoring): update the api https://togithub.com/googleapis/google-api-python-client/commit/424cc24e9b240ae467aff56e8034d4d84da97b62
feat(notebooks): update the api https://togithub.com/googleapis/google-api-python-client/commit/980d86029db2fd3bd54bd885bb932ccdddcb1029
feat(policysimulator): update the api https://togithub.com/googleapis/google-api-python-client/commit/c916d4ac381dd1936484fe5347ec6ad990890939
feat(prod_tt_sasportal): update the api https://togithub.com/googleapis/google-api-python-client/commit/268437c568609174c01b54b69745d28e388ca465
feat(recaptchaenterprise): update the api https://togithub.com/googleapis/google-api-python-client/commit/4490a592e8084c3ff720d05b09aea42fab76abb0
feat(retail): update the api https://togithub.com/googleapis/google-api-python-client/commit/6dc53afa0157834b92c6657379ead9535d33e08a
feat(securitycenter): update the api https://togithub.com/googleapis/google-api-python-client/commit/a7471da8d3314e29359442bac1fb0603e1c26d4f
feat(tpu): update the api https://togithub.com/googleapis/google-api-python-client/commit/1c471fb99490120f9ab551e9fd805e49ed8a299a
feat(vmwareengine): update the api https://togithub.com/googleapis/google-api-python-client/commit/de91454faf920b9dc4fd032f199558cb6200e994

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

@@ -133,6 +133,9 @@ <h2>Instance Methods</h2>
 <p class="toc_element">
   <code><a href="#streamGenerateContent">streamGenerateContent(model, body=None, x__xgafv=None)</a></code></p>
 <p class="firstline">Generate content with multimodal inputs with streaming support.</p>
+<p class="toc_element">
+  <code><a href="#streamRawPredict">streamRawPredict(endpoint, body=None, x__xgafv=None)</a></code></p>
+<p class="firstline"></p>
 <p class="toc_element">
   <code><a href="#undeployModel">undeployModel(endpoint, body=None, x__xgafv=None)</a></code></p>
 <p class="firstline">Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it's using.</p>
@@ -2444,6 +2447,46 @@ <h3>Method Details</h3>
 }</pre>
 </div>
 
+<div class="method">
+    <code class="details" id="streamRawPredict">streamRawPredict(endpoint, body=None, x__xgafv=None)</code>
+  <pre>
+
+Args:
+  endpoint: string, Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required)
+  body: object, The request body.
+    The object takes the form of:
+
+{ # Request message for PredictionService.StreamRawPredict.
+  &quot;httpBody&quot;: { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can&#x27;t be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. # The prediction input. Supports HTTP headers and arbitrary data payload.
+    &quot;contentType&quot;: &quot;A String&quot;, # The HTTP Content-Type header value specifying the content type of the body.
+    &quot;data&quot;: &quot;A String&quot;, # The HTTP request/response body as raw binary.
+    &quot;extensions&quot;: [ # Application specific response metadata. Must be set in the first response for streaming APIs.
+      {
+        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
+      },
+    ],
+  },
+}
+
+  x__xgafv: string, V1 error format.
+    Allowed values
+      1 - v1 error format
+      2 - v2 error format
+
+Returns:
+  An object of the form:
+
+    { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can&#x27;t be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.
+  &quot;contentType&quot;: &quot;A String&quot;, # The HTTP Content-Type header value specifying the content type of the body.
+  &quot;data&quot;: &quot;A String&quot;, # The HTTP request/response body as raw binary.
+  &quot;extensions&quot;: [ # Application specific response metadata. Must be set in the first response for streaming APIs.
+    {
+      &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
+    },
+  ],
+}</pre>
+</div>
+
 <div class="method">
     <code class="details" id="undeployModel">undeployModel(endpoint, body=None, x__xgafv=None)</code>
   <pre>Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it&#x27;s using.

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

@@ -116,7 +116,7 @@ <h3>Method Details</h3>
   <pre>Creates a new FeatureOnlineStore in a given project and location.
 
 Args:
-  parent: string, Required. The resource name of the Location to create FeatureOnlineStores. Format: `projects/{project}/locations/{location}&#x27;` (required)
+  parent: string, Required. The resource name of the Location to create FeatureOnlineStores. Format: `projects/{project}/locations/{location}` (required)
   body: object, The request body.
     The object takes the form of:
 

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

@@ -497,7 +497,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -745,7 +745,7 @@ <h3>Method Details</h3>
       &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
       &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
       &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
         &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
         &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
       },
@@ -996,7 +996,7 @@ <h3>Method Details</h3>
       &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
       &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
       &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
         &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
         &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
       },
@@ -1277,7 +1277,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -1513,7 +1513,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -1748,7 +1748,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -2042,7 +2042,7 @@ <h3>Method Details</h3>
     &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
     &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
     &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-    &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+    &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
       &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
       &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
     },