def auto_scale_workers(cfg, num_workers: int):
        """
        When the config is defined for certain number of workers (according to
        ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of
        workers currently in use, returns a new cfg where the total batch size
        is scaled so that the per-GPU batch size stays the same as the
        original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``.

        Other config options are also scaled accordingly:
        * training steps and warmup steps are scaled inverse proportionally.
        * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.

        For example, with the original config like the following:

        .. code-block:: yaml

            IMS_PER_BATCH: 16
            BASE_LR: 0.1
            REFERENCE_WORLD_SIZE: 8
            MAX_ITER: 5000
            STEPS: (4000,)
            CHECKPOINT_PERIOD: 1000

        When this config is used on 16 GPUs instead of the reference number 8,
        calling this method will return a new config with:

        .. code-block:: yaml

            IMS_PER_BATCH: 32
            BASE_LR: 0.2
            REFERENCE_WORLD_SIZE: 16
            MAX_ITER: 2500
            STEPS: (2000,)
            CHECKPOINT_PERIOD: 500

        Note that both the original config and this new config can be trained on 16 GPUs.
        It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``).

        Returns:
            CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.
        """
        old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE
        if old_world_size == 0 or old_world_size == num_workers:
            return cfg
        cfg = cfg.clone()
        frozen = cfg.is_frozen()
        cfg.defrost()

        assert (
            cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0
        ), "Invalid REFERENCE_WORLD_SIZE in config!"
        scale = num_workers / old_world_size
        bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale))
        lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale
        max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale))
        warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale))
        cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS)
        cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale))
        cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale))
        cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers  # maintain invariant
        logger = logging.getLogger(__name__)
        logger.info(
            f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, "
            f"max_iter={max_iter}, warmup={warmup_iter}."
        )

        if frozen:
            cfg.freeze()
        return cfg

        When the config is defined for certain number of workers (according to
        ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of
        workers currently in use, returns a new cfg where the total batch size
        is scaled so that the per-GPU batch size stays the same as the
        original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``.

        Other config options are also scaled accordingly:
        * training steps and warmup steps are scaled inverse proportionally.
        * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.

        For example, with the original config like the following:

        .. code-block:: yaml

            IMS_PER_BATCH: 16
            BASE_LR: 0.1
            REFERENCE_WORLD_SIZE: 8
            MAX_ITER: 5000
            STEPS: (4000,)
            CHECKPOINT_PERIOD: 1000

        When this config is used on 16 GPUs instead of the reference number 8,
        calling this method will return a new config with:

        .. code-block:: yaml

            IMS_PER_BATCH: 32
            BASE_LR: 0.2
            REFERENCE_WORLD_SIZE: 16
            MAX_ITER: 2500
            STEPS: (2000,)
            CHECKPOINT_PERIOD: 500

        Note that both the original config and this new config can be trained on 16 GPUs.
        It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``).

        Returns:
            CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.
        
video_chat_text/video_chat_with_MOSS/models/grit_src/third_party/CenterNet2/detectron2/engine/defaults.py