chore: Update discovery artifacts

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

@@ -127,7 +127,7 @@ <h3>Method Details</h3>
     &quot;dedicatedResources&quot;: { # A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration. # Required. The underlying DedicatedResources that the DeploymentResourcePool uses.
       &quot;autoscalingMetricSpecs&quot;: [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator&#x27;s duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator&#x27;s duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
         { # The metric specification that defines the target resource utilization (CPU utilization, accelerator&#x27;s duty cycle, and so on) for calculating the desired replica count.
-          &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`
+          &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
           &quot;target&quot;: 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
         },
       ],
@@ -244,7 +244,7 @@ <h3>Method Details</h3>
   &quot;dedicatedResources&quot;: { # A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration. # Required. The underlying DedicatedResources that the DeploymentResourcePool uses.
     &quot;autoscalingMetricSpecs&quot;: [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator&#x27;s duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator&#x27;s duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
       { # The metric specification that defines the target resource utilization (CPU utilization, accelerator&#x27;s duty cycle, and so on) for calculating the desired replica count.
-        &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`
+        &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
         &quot;target&quot;: 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
       },
     ],
@@ -300,7 +300,7 @@ <h3>Method Details</h3>
       &quot;dedicatedResources&quot;: { # A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration. # Required. The underlying DedicatedResources that the DeploymentResourcePool uses.
         &quot;autoscalingMetricSpecs&quot;: [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator&#x27;s duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator&#x27;s duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
           { # The metric specification that defines the target resource utilization (CPU utilization, accelerator&#x27;s duty cycle, and so on) for calculating the desired replica count.
-            &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`
+            &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
             &quot;target&quot;: 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
           },
         ],
@@ -364,7 +364,7 @@ <h3>Method Details</h3>
   &quot;dedicatedResources&quot;: { # A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration. # Required. The underlying DedicatedResources that the DeploymentResourcePool uses.
     &quot;autoscalingMetricSpecs&quot;: [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator&#x27;s duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator&#x27;s duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
       { # The metric specification that defines the target resource utilization (CPU utilization, accelerator&#x27;s duty cycle, and so on) for calculating the desired replica count.
-        &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`
+        &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
         &quot;target&quot;: 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
       },
     ],
@@ -461,7 +461,7 @@ <h3>Method Details</h3>
       &quot;dedicatedResources&quot;: { # A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration. # A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
         &quot;autoscalingMetricSpecs&quot;: [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator&#x27;s duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator&#x27;s duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
           { # The metric specification that defines the target resource utilization (CPU utilization, accelerator&#x27;s duty cycle, and so on) for calculating the desired replica count.
-            &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`
+            &quot;metricName&quot;: &quot;A String&quot;, # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
             &quot;target&quot;: 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
           },
         ],