update_provisioned_model_throughput ¶

def update_provisioned_model_throughput(
    self,
    provisioned_model_id: "capo_bedrock.types.provisioned_model_id.ProvisionedModelId",
    *,
    config_overrides: Optional[BedrockClientConfig] = None,
    desired_provisioned_model_name: Optional[
        "capo_bedrock.types.provisioned_model_name.ProvisionedModelName"
    ] = None,
    desired_model_id: Optional[
        "capo_bedrock.types.model_identifier.ModelIdentifier"
    ] = None,
) -> "capo_bedrock.types.update_provisioned_model_throughput_response.UpdateProvisionedModelThroughputResponse":
    """<p>Updates the name or associated model for a Provisioned Throughput. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html">Provisioned Throughput</a> in the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-service.html">Amazon Bedrock User Guide</a>.</p>

    Args:
        provisioned_model_id: <p>The Amazon Resource Name (ARN) or name of the Provisioned Throughput to update.</p>
        desired_provisioned_model_name: <p>The new name for this Provisioned Throughput.</p>
        desired_model_id: <p>The Amazon Resource Name (ARN) of the new model to associate with this Provisioned Throughput. You can't specify this field if this Provisioned Throughput is associated with a base model.</p> <p>If this Provisioned Throughput is associated with a custom model, you can specify one of the following options:</p> <ul> <li> <p>The base model from which the custom model was customized.</p> </li> <li> <p>Another custom model that was customized from the same base model as the custom model.</p> </li> </ul>

    Raises:
        capo_bedrock.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions.</p>
        capo_bedrock.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.</p>
        capo_bedrock.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_bedrock.types.update_provisioned_model_throughput_request.UpdateProvisionedModelThroughputRequest]",
    ) -> OperationResponse[
        "capo_bedrock.types.update_provisioned_model_throughput_response.UpdateProvisionedModelThroughputResponse"
    ]:
        import capo_bedrock._operations.amazon_bedrock_control_plane_service.update_provisioned_model_throughput

        output, http_response = (
            capo_bedrock._operations.amazon_bedrock_control_plane_service.update_provisioned_model_throughput.update_provisioned_model_throughput(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateProvisionedModelThroughput")
    )
    input_: capo_bedrock.types.update_provisioned_model_throughput_request.UpdateProvisionedModelThroughputRequest = {
        "provisioned_model_id": provisioned_model_id
    }
    if desired_provisioned_model_name is not None:
        input_["desired_provisioned_model_name"] = desired_provisioned_model_name
    if desired_model_id is not None:
        input_["desired_model_id"] = desired_model_id

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output