Ok - good to know this is odd 🙂
It’s created like this (I remove some bits for readability)

def _run(pipeline_id, step):
    from pipeline_broker import pipeline

    pipeline.run_step(pipeline_id=pipeline_id, step=step)


def launch(
    cfg,
    queue: str = "default",
    abort_on_failure: bool = False,
    project: str = "TrainingPipeline",
    start_locally: bool = False,
    task_regex: str = ".*",
):
    ...

    pipe = PipelineController(
        project=project,
        name=project,
        version="1.0",
        add_pipeline_tags=True,
        abort_on_failure=abort_on_failure,
        pool_frequency=0.25,
        docker=remote_exec_docker_image,
    )
    task = Task.current_task()
    task.connect(cfg.to_dict(), name="main")
    pipe.set_default_execution_queue(queue)

    pipe.add_parameter(
        name="pipeline_id",
        description="ID of the pipeline",
        default=pipeline_id,
    )

    for step in cfg.steps:
        execution_queue = queue
        if cfg[step].get("clearml_execution_queue"):
            execution_queue = cfg[step]["clearml_execution_queue"]

        logging.info(f"Adding step: {step}, execution queue: {execution_queue}")
        task = cfg[step].get("task", None)
        parents = cfg.get_parents(step)
        pipe.add_function_step(
            name=step,
            task_name=task_name,
            project_name=task if task is not None else step,
            parents=parents,
            function=_run,
            function_kwargs=dict(pipeline_id="${pipeline.pipeline_id}", step=step),
            cache_executed_step=False,
            task_type=TaskTypes.custom,
            execution_queue=execution_queue,
            docker=remote_exec_docker_image,
        )
    if start_locally:
        pipe.start_locally()
        return
    pipe.start()

So it’s a pipeline from functions contrary to what I said ealier. I’ll try to reproduce with the example provided in your documentation.