def METHOD_NAME(self, task_name):
    # Url path to register the model
    paths = [f"/{task_name}"]
    for path in paths:
        logger.info("   Transformer model request [path]={} is genereated.".format(path))
    # Unique name to create the pydantic model
    unique_name = hashlib.md5(task_name.encode()).hexdigest()
    # Create request model
    req_model = create_model(
        "RequestModel" + unique_name,
        data=(typing.Any, ...),
        __base__=RequestBase,
    )
    # Create response model
    resp_model = create_model(
        "ResponseModel" + unique_name,
        result=(typing.Any, ...),
        __base__=ResponseBase,
    )
    # Template predict endpoint function to dynamically serve different models
    def predict(request: Request, inference_request: req_model):
        result = self._app._model_manager.predict(inference_request.data, inference_request.parameters)
        return {"result": result}
    # Register the route and add to the app
    router = APIRouter()
    for path in paths:
        router.add_api_route(
            path,
            predict,
            methods=["post"],
            summary=f"{task_name.title()}",
            response_model=resp_model,
            response_model_exclude_unset=True,
            response_model_exclude_none=True,
        )
    self._app.include_router(router)