def METHOD_NAME(
    trainer: BaseTrainer, stride: int, model: DetectionModel
) -> Dict[str, Any]:
    if not hasattr(trainer, "train_loader"):
        # initialize train loader (if not already initialized)
        # and set it as the trainer's attribute
        train_set_path = trainer.trainset
        train_loader, _ = create_dataloader(
            path=train_set_path,
            imgsz=trainer.args.imgsz,
            batch_size=trainer.args.batch,
            stride=stride,
        )
        trainer.train_loader = train_loader
    # convert model's arg to a namespace,
    # this is expected by the trainer's criterion
    model.args = Namespace(**model.args)
    trainer.model = model
    grad_sampler = dict(
        data_loader_builder=trainer._get_data_loader_builder(),
        loss_function=lambda preds, batch: trainer.criterion(preds, batch)[0]
        / train_loader.batch_size,
    )
    return grad_sampler