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untag_resource async

untag_resource(
    resource_arn: TaggableResourcesArn,
    tag_keys: TagKeyList,
    *,
    config_overrides: Optional[AsyncBedrockAgentRuntimeClientConfig] = None,
) -> UntagResourceResponse

Remove tags from a resource.

Parameters:

  • resource_arn (TaggableResourcesArn) –

    The Amazon Resource Name (ARN) of the resource from which to remove tags.

  • tag_keys (TagKeyList) –

    A list of keys of the tags to remove from the resource.

Raises:

  • AccessDeniedException –

    The request is denied because of missing access permissions. Check your permissions and retry your request.

  • InternalServerException –

    An internal server error occurred. Retry your request.

  • ResourceNotFoundException –

    The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.

  • ThrottlingException –

    The number of requests exceeds the limit. Resubmit your request later.

  • ValidationException –

    Input validation failed. Check your request parameters and retry the request.

  • UnknownServiceError –

    The service returned an error code this client does not model.

Source code in docs_src/capo_bedrock_agent_runtime/_services/async_bedrock_agent_runtime.py
async def untag_resource(
    self,
    resource_arn: "capo_bedrock_agent_runtime.types.taggable_resources_arn.TaggableResourcesArn",
    tag_keys: "capo_bedrock_agent_runtime.types.tag_key_list.TagKeyList",
    *,
    config_overrides: Optional[AsyncBedrockAgentRuntimeClientConfig] = None,
) -> (
    "capo_bedrock_agent_runtime.types.untag_resource_response.UntagResourceResponse"
):
    """<p>Remove tags from a resource.</p>

    Args:
        resource_arn: <p>The Amazon Resource Name (ARN) of the resource from which to remove tags.</p>
        tag_keys: <p>A list of keys of the tags to remove from the resource.</p>

    Raises:
        capo_bedrock_agent_runtime.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions. Check your permissions and retry your request.</p>
        capo_bedrock_agent_runtime.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock_agent_runtime.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_agent_runtime.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock_agent_runtime.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock_agent_runtime.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_bedrock_agent_runtime.types.untag_resource_request.UntagResourceRequest]",
    ) -> AsyncOperationResponse[
        "capo_bedrock_agent_runtime.types.untag_resource_response.UntagResourceResponse"
    ]:
        import capo_bedrock_agent_runtime._operations.amazon_bedrock_agent_run_time_service.untag_resource

        (
            output,
            http_response,
        ) = await capo_bedrock_agent_runtime._operations.amazon_bedrock_agent_run_time_service.untag_resource.async_untag_resource(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UntagResource")
    )
    input_: capo_bedrock_agent_runtime.types.untag_resource_request.UntagResourceRequest = {
        "resource_arn": resource_arn,
        "tag_keys": tag_keys,
    }

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output