untag_resource ¶

untag_resource(
    resource_arn: ResourceArn,
    tag_keys: TagKeyList,
    *,
    config_overrides: Optional[ElementalInferenceClientConfig] = None,
) -> None

Deletes specified tags from the specified resource in the current region.

Parameters:

resource_arn (ResourceArn) –

The ARN of the resource where you want to delete one or more tags.
tag_keys (TagKeyList) –

The keys of the tags to delete.

Raises:

AccessDeniedException –

You do not have sufficient access to perform this action.
ConflictException –

The request could not be completed due to a conflict.
InternalServerErrorException –

An internal server error occurred. This is a temporary condition and the request can be retried. If the problem persists, contact AWS Support.
ResourceNotFoundException –

The resource specified in the action doesn't exist.
TooManyRequestException –

The request was denied due to request throttling. Too many requests have been made within a given time period. Reduce the frequency of requests and use exponential backoff when retrying.
ValidationException –

The input fails to satisfy the constraints specified by the service. Check the error message for details about which parameter or field is invalid and correct the request before retrying.
UnknownServiceError –

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

Source code in docs_src/capo_elementalinference/_services/elemental_inference.py

def untag_resource(
    self,
    resource_arn: "capo_elementalinference.types.resource_arn.ResourceArn",
    tag_keys: "capo_elementalinference.types.tag_key_list.TagKeyList",
    *,
    config_overrides: Optional[ElementalInferenceClientConfig] = None,
) -> None:
    """<p>Deletes specified tags from the specified resource in the current region.</p>

    Args:
        resource_arn: <p>The ARN of the resource where you want to delete one or more tags.</p>
        tag_keys: <p>The keys of the tags to delete.</p>

    Raises:
        capo_elementalinference.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_elementalinference.errors.conflict_exception.ConflictException: <p>The request could not be completed due to a conflict.</p>
        capo_elementalinference.errors.internal_server_error_exception.InternalServerErrorException: <p>An internal server error occurred. This is a temporary condition and the request can be retried. If the problem persists, contact AWS Support. </p>
        capo_elementalinference.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource specified in the action doesn't exist.</p>
        capo_elementalinference.errors.too_many_request_exception.TooManyRequestException: <p>The request was denied due to request throttling. Too many requests have been made within a given time period. Reduce the frequency of requests and use exponential backoff when retrying. </p>
        capo_elementalinference.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by the service. Check the error message for details about which parameter or field is invalid and correct the request before retrying. </p>
        capo_elementalinference.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_elementalinference.types.untag_resource_request.UntagResourceRequest]",
    ) -> OperationResponse[None]:
        import capo_elementalinference._operations.elemental_inference.untag_resource

        output, http_response = (
            capo_elementalinference._operations.elemental_inference.untag_resource.untag_resource(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

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

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