chore: Update discovery artifacts

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

@@ -360,7 +360,7 @@ <h3>Method Details</h3>
         },
       ],
       &quot;healthProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes readiness probe.
-        &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+        &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
           &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
             &quot;A String&quot;,
           ],
@@ -378,7 +378,7 @@ <h3>Method Details</h3>
       &quot;predictRoute&quot;: &quot;A String&quot;, # Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container&#x27;s IP address and port. Vertex AI then returns the container&#x27;s response in the API response. For example, if you set this field to `/foo`, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the `/foo` path on the port of your container specified by the first value of this `ModelContainerSpec`&#x27;s ports field. If you don&#x27;t specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
       &quot;sharedMemorySizeMb&quot;: &quot;A String&quot;, # Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
       &quot;startupProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes startup probe.
-        &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+        &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
           &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
             &quot;A String&quot;,
           ],
@@ -630,7 +630,7 @@ <h3>Method Details</h3>
         },
       ],
       &quot;healthProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes readiness probe.
-        &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+        &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
           &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
             &quot;A String&quot;,
           ],
@@ -648,7 +648,7 @@ <h3>Method Details</h3>
       &quot;predictRoute&quot;: &quot;A String&quot;, # Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container&#x27;s IP address and port. Vertex AI then returns the container&#x27;s response in the API response. For example, if you set this field to `/foo`, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the `/foo` path on the port of your container specified by the first value of this `ModelContainerSpec`&#x27;s ports field. If you don&#x27;t specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
       &quot;sharedMemorySizeMb&quot;: &quot;A String&quot;, # Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
       &quot;startupProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes startup probe.
-        &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+        &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
           &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
             &quot;A String&quot;,
           ],
@@ -942,7 +942,7 @@ <h3>Method Details</h3>
         },
       ],
       &quot;healthProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes readiness probe.
-        &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+        &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
           &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
             &quot;A String&quot;,
           ],
@@ -960,7 +960,7 @@ <h3>Method Details</h3>
       &quot;predictRoute&quot;: &quot;A String&quot;, # Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container&#x27;s IP address and port. Vertex AI then returns the container&#x27;s response in the API response. For example, if you set this field to `/foo`, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the `/foo` path on the port of your container specified by the first value of this `ModelContainerSpec`&#x27;s ports field. If you don&#x27;t specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
       &quot;sharedMemorySizeMb&quot;: &quot;A String&quot;, # Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
       &quot;startupProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes startup probe.
-        &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+        &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
           &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
             &quot;A String&quot;,
           ],
@@ -1225,7 +1225,7 @@ <h3>Method Details</h3>
             },
           ],
           &quot;healthProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes readiness probe.
-            &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+            &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
               &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
                 &quot;A String&quot;,
               ],
@@ -1243,7 +1243,7 @@ <h3>Method Details</h3>
           &quot;predictRoute&quot;: &quot;A String&quot;, # Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container&#x27;s IP address and port. Vertex AI then returns the container&#x27;s response in the API response. For example, if you set this field to `/foo`, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the `/foo` path on the port of your container specified by the first value of this `ModelContainerSpec`&#x27;s ports field. If you don&#x27;t specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
           &quot;sharedMemorySizeMb&quot;: &quot;A String&quot;, # Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
           &quot;startupProbe&quot;: { # Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. # Immutable. Specification for Kubernetes startup probe.
-            &quot;exec&quot;: { # ExecAction specifies a command to execute. # Exec specifies the action to take.
+            &quot;exec&quot;: { # ExecAction specifies a command to execute. # ExecAction probes the health of a container by executing a command.
               &quot;command&quot;: [ # Command is the command line to execute inside the container, the working directory for the command is root (&#x27;/&#x27;) in the container&#x27;s filesystem. The command is simply exec&#x27;d, it is not run inside a shell, so traditional shell instructions (&#x27;|&#x27;, etc) won&#x27;t work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
                 &quot;A String&quot;,
               ],