update_configured_audience_model ¶

def update_configured_audience_model(
    self,
    configured_audience_model_arn: "capo_cleanroomsml.types.configured_audience_model_arn.ConfiguredAudienceModelArn",
    *,
    config_overrides: Optional[CleanRoomsMLClientConfig] = None,
    output_config: Optional[
        "capo_cleanroomsml.types.configured_audience_model_output_config.ConfiguredAudienceModelOutputConfig"
    ] = None,
    audience_model_arn: Optional[
        "capo_cleanroomsml.types.audience_model_arn.AudienceModelArn"
    ] = None,
    shared_audience_metrics: Optional[
        "capo_cleanroomsml.types.metrics_list.MetricsList"
    ] = None,
    min_matching_seed_size: Optional[
        "capo_cleanroomsml.types.min_matching_seed_size.MinMatchingSeedSize"
    ] = None,
    audience_size_config: Optional[
        "capo_cleanroomsml.types.audience_size_config.AudienceSizeConfig"
    ] = None,
    description: Optional[
        "capo_cleanroomsml.types.resource_description.ResourceDescription"
    ] = None,
) -> "capo_cleanroomsml.types.update_configured_audience_model_response.UpdateConfiguredAudienceModelResponse":
    """<p>Provides the information necessary to update a configured audience model. Updates that impact audience generation jobs take effect when a new job starts, but do not impact currently running jobs.</p>

    Args:
        configured_audience_model_arn: <p>The Amazon Resource Name (ARN) of the configured audience model that you want to update.</p>
        output_config: <p>The new output configuration.</p>
        audience_model_arn: <p>The Amazon Resource Name (ARN) of the new audience model that you want to use.</p>
        shared_audience_metrics: <p>The new value for whether to share audience metrics.</p>
        min_matching_seed_size: <p>The minimum number of users from the seed audience that must match with users in the training data of the audience model.</p>
        audience_size_config: <p>The new audience size configuration.</p>
        description: <p>The new description of the configured audience model.</p>

    Raises:
        capo_cleanroomsml.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_cleanroomsml.errors.conflict_exception.ConflictException: <p>You can't complete this action because another resource depends on this resource.</p>
        capo_cleanroomsml.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource you are requesting does not exist.</p>
        capo_cleanroomsml.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_cleanroomsml.errors.validation_exception.ValidationException: <p>The request parameters for this request are incorrect.</p>
        capo_cleanroomsml.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_cleanroomsml.types.update_configured_audience_model_request.UpdateConfiguredAudienceModelRequest]",
    ) -> OperationResponse[
        "capo_cleanroomsml.types.update_configured_audience_model_response.UpdateConfiguredAudienceModelResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.update_configured_audience_model

        output, http_response = (
            capo_cleanroomsml._operations.aws_stark_control_service.update_configured_audience_model.update_configured_audience_model(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateConfiguredAudienceModel")
    )
    input_: capo_cleanroomsml.types.update_configured_audience_model_request.UpdateConfiguredAudienceModelRequest = {
        "configured_audience_model_arn": configured_audience_model_arn
    }
    if output_config is not None:
        input_["output_config"] = output_config
    if audience_model_arn is not None:
        input_["audience_model_arn"] = audience_model_arn
    if shared_audience_metrics is not None:
        input_["shared_audience_metrics"] = shared_audience_metrics
    if min_matching_seed_size is not None:
        input_["min_matching_seed_size"] = min_matching_seed_size
    if audience_size_config is not None:
        input_["audience_size_config"] = audience_size_config
    if description is not None:
        input_["description"] = description

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