untag_resource ¶

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

Removes tags from a bot, bot alias or bot channel.

Parameters:

resource_arn (AmazonResourceName) –

The Amazon Resource Name (ARN) of the resource to remove the tags from.
tag_keys (TagKeyList) –

A list of tag keys to remove from the resource. If a tag key does not exist on the resource, it is ignored.

Raises:

BadRequestException –

The request is not well formed. For example, a value is invalid or a required field is missing. Check the field values, and try again.
ConflictException –

There was a conflict processing the request. Try your request again.
InternalFailureException –

An internal Amazon Lex error occurred. Try your request again.
LimitExceededException –

The request exceeded a limit. Try your request again.
NotFoundException –

The resource specified in the request was not found. Check the resource and try again.
UnknownServiceError –

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

Source code in docs_src/capo_lex_model_building_service/_services/lex_model_building_service.py

def untag_resource(
    self,
    resource_arn: "capo_lex_model_building_service.types.amazon_resource_name.AmazonResourceName",
    tag_keys: "capo_lex_model_building_service.types.tag_key_list.TagKeyList",
    *,
    config_overrides: Optional[LexModelBuildingServiceClientConfig] = None,
) -> "capo_lex_model_building_service.types.untag_resource_response.UntagResourceResponse":
    """<p>Removes tags from a bot, bot alias or bot channel.</p>

    Args:
        resource_arn: <p>The Amazon Resource Name (ARN) of the resource to remove the tags from.</p>
        tag_keys: <p>A list of tag keys to remove from the resource. If a tag key does not exist on the resource, it is ignored.</p>

    Raises:
        capo_lex_model_building_service.errors.bad_request_exception.BadRequestException: <p>The request is not well formed. For example, a value is invalid or a required field is missing. Check the field values, and try again.</p>
        capo_lex_model_building_service.errors.conflict_exception.ConflictException: <p> There was a conflict processing the request. Try your request again. </p>
        capo_lex_model_building_service.errors.internal_failure_exception.InternalFailureException: <p>An internal Amazon Lex error occurred. Try your request again.</p>
        capo_lex_model_building_service.errors.limit_exceeded_exception.LimitExceededException: <p>The request exceeded a limit. Try your request again.</p>
        capo_lex_model_building_service.errors.not_found_exception.NotFoundException: <p>The resource specified in the request was not found. Check the resource and try again.</p>
        capo_lex_model_building_service.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_lex_model_building_service.types.untag_resource_request.UntagResourceRequest]",
    ) -> OperationResponse[
        "capo_lex_model_building_service.types.untag_resource_response.UntagResourceResponse"
    ]:
        import capo_lex_model_building_service._operations.aws_deep_sense_model_building_service.untag_resource

        output, http_response = (
            capo_lex_model_building_service._operations.aws_deep_sense_model_building_service.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_lex_model_building_service.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