describe_tags ¶

def describe_tags(
    self,
    resource_id: "capo_machine_learning.types.entity_id.EntityId",
    resource_type: "capo_machine_learning.types.taggable_resource_type.TaggableResourceType",
    *,
    config_overrides: Optional[MachineLearningClientConfig] = None,
) -> "capo_machine_learning.types.describe_tags_output.DescribeTagsOutput":
    """<p>Describes one or more of the tags for your Amazon ML object.</p>

    Args:
        resource_id: <p>The ID of the ML object. For example, <code>exampleModelId</code>. </p>
        resource_type: <p>The type of the ML object.</p>

    Raises:
        capo_machine_learning.errors.internal_server_exception.InternalServerException: <p>An error on the server occurred when trying to process a request.</p>
        capo_machine_learning.errors.invalid_input_exception.InvalidInputException: <p>An error on the client occurred. Typically, the cause is an invalid input value.</p>
        capo_machine_learning.errors.resource_not_found_exception.ResourceNotFoundException: <p>A specified resource cannot be located.</p>
        capo_machine_learning.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_machine_learning.types.describe_tags_input.DescribeTagsInput]",
    ) -> OperationResponse[
        "capo_machine_learning.types.describe_tags_output.DescribeTagsOutput"
    ]:
        import capo_machine_learning._operations.amazon_ml_20141212.describe_tags

        output, http_response = (
            capo_machine_learning._operations.amazon_ml_20141212.describe_tags.describe_tags(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeTags")
    )
    input_: capo_machine_learning.types.describe_tags_input.DescribeTagsInput = {
        "resource_id": resource_id,
        "resource_type": resource_type,
    }

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