accept_predictions ¶

def accept_predictions(
    self,
    domain_identifier: "capo_datazone.types.domain_id.DomainId",
    identifier: "capo_datazone.types.asset_identifier.AssetIdentifier",
    *,
    config_overrides: Optional[DataZoneClientConfig] = None,
    revision: Optional["capo_datazone.types.revision.Revision"] = None,
    accept_rule: Optional["capo_datazone.types.accept_rule.AcceptRule"] = None,
    accept_choices: Optional[
        "capo_datazone.types.accept_choices.AcceptChoices"
    ] = None,
    client_token: Optional["capo_datazone.types.client_token.ClientToken"] = None,
) -> "capo_datazone.types.accept_predictions_output.AcceptPredictionsOutput":
    """<p>Accepts automatically generated business-friendly metadata for your Amazon DataZone assets.</p>

    Args:
        domain_identifier: <p>The identifier of the Amazon DataZone domain.</p>
        identifier: <p>The identifier of the asset.</p>
        revision: <p>The revision that is to be made to the asset.</p>
        accept_rule: <p>Specifies the rule (or the conditions) under which a prediction can be accepted.</p>
        accept_choices: <p>Specifies the prediction (aka, the automatically generated piece of metadata) and the target (for example, a column name) that can be accepted.</p>
        client_token: <p>A unique, case-sensitive identifier to ensure idempotency of the request. This field is automatically populated if not provided.</p>

    Raises:
        capo_datazone.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_datazone.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_datazone.errors.unauthorized_exception.UnauthorizedException: <p>You do not have permission to perform this action.</p>
        capo_datazone.errors.conflict_exception.ConflictException: <p>There is a conflict while performing this action.</p>
        capo_datazone.errors.internal_server_exception.InternalServerException: <p>The request has failed because of an unknown error, exception or failure.</p>
        capo_datazone.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource cannot be found.</p>
        capo_datazone.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by the Amazon Web Services service.</p>
        capo_datazone.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_datazone.types.accept_predictions_input.AcceptPredictionsInput]",
    ) -> OperationResponse[
        "capo_datazone.types.accept_predictions_output.AcceptPredictionsOutput"
    ]:
        import capo_datazone._operations.data_zone.accept_predictions

        output, http_response = (
            capo_datazone._operations.data_zone.accept_predictions.accept_predictions(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_datazone.types.accept_predictions_input.AcceptPredictionsInput",
    ) -> dict[str, Any]:
        import capo_datazone.types.accept_predictions_input

        return capo_datazone.types.accept_predictions_input.redact_sensitive(input_)

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("AcceptPredictions", redact_input=_redact_input),
    )
    input_: capo_datazone.types.accept_predictions_input.AcceptPredictionsInput = {
        "domain_identifier": domain_identifier,
        "identifier": identifier,
    }
    if revision is not None:
        input_["revision"] = revision
    if accept_rule is not None:
        input_["accept_rule"] = accept_rule
    if accept_choices is not None:
        input_["accept_choices"] = accept_choices
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token

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