code: def init_track_model(
      config: Union[str, Config],
      checkpoint: Optional[str] = None,
      detector: Optional[str] = None,
      reid: Optional[str] = None,
      device: str = "cuda:0",
      cfg_options: Optional[dict] = None,
  ) -> nn.Module:
      """Initialize a model from config file.
  
      Args:
          config (str or :obj:`mmengine.Config`): Config file path or the config
              object.
          checkpoint (Optional[str], optional): Checkpoint path. Defaults to
              None.
          detector (Optional[str], optional): Detector Checkpoint path, use in
              some tracking algorithms like sort.  Defaults to None.
          reid (Optional[str], optional): Reid checkpoint path. use in
              some tracking algorithms like sort. Defaults to None.
          device (str, optional): The device that the model inferences on.
              Defaults to `cuda:0`.
          cfg_options (Optional[dict], optional): Options to override some
              settings in the used config. Defaults to None.
  
      Returns:
          nn.Module: The constructed model.
      """
      if isinstance(config, str):
          config = Config.fromfile(config)
      elif not isinstance(config, Config):
          raise TypeError(
              "config must be a filename or Config object, "
              f"but got {type(config)}"
          )
      if cfg_options is not None:
          config.merge_from_dict(cfg_options)
  
      model = MODELS.build(config.model)
  
      if checkpoint is not None:
          checkpoint = load_checkpoint(
              model, checkpoint, map_location="cpu"
          )
          # Weights converted from elsewhere may not have meta fields.
          checkpoint_meta = checkpoint.get("meta", {})
          # save the dataset_meta in the model for convenience
          if "dataset_meta" in checkpoint_meta:
              if "CLASSES" in checkpoint_meta["dataset_meta"]:
                  value = checkpoint_meta["dataset_meta"].pop("CLASSES")
                  checkpoint_meta["dataset_meta"]["classes"] = value
              model.dataset_meta = checkpoint_meta["dataset_meta"]
  
      if detector is not None:
          assert not (
              checkpoint and detector
          ), "Error: checkpoint and detector checkpoint cannot both exist"
          load_checkpoint(model.detector, detector, map_location="cpu")
  
      if reid is not None:
          assert not (
              checkpoint and reid
          ), "Error: checkpoint and reid checkpoint cannot both exist"
          load_checkpoint(model.reid, reid, map_location="cpu")
  
      # Some methods don't load checkpoints or checkpoints don't contain
      # 'dataset_meta'
      # VIS need dataset_meta, MOT don't need dataset_meta
      if not hasattr(model, "dataset_meta"):
          warnings.warn(
              "dataset_meta or class names are missed, "
              "use None by default."
          )
          model.dataset_meta = {"classes": None}
  
      model.cfg = config  # save the config in the model for convenience
      model.to(device)
      model.eval()
      return model
  
imports: import copy
  import warnings
  from pathlib import Path
  from typing import Optional, Sequence, Union
  import numpy
  import torch
  import torch.nn
  from mmcv.ops import RoIPool
  from mmcv.transforms import Compose
  from mmengine.config import Config
  from mmengine.dataset import default_collate
  from mmengine.model.utils import revert_sync_batchnorm
  from mmengine.registry import init_default_scope
  from mmengine.runner import load_checkpoint
  from mmdet.registry import DATASETS
  from mmdet.utils import ConfigType
  from ..evaluation import get_classes
  from ..registry import MODELS
  from ..structures import DetDataSample, SampleList
  from ..utils import get_test_pipeline_cfg
label_desc: Use image node_id 5 for calling a global function with example usage: init_track_model(config, checkpoint, detector, reid, device, cfg_options) and returns: model