code: def init_detector(
      config: Union[str, Path, Config],
      checkpoint: Optional[str] = None,
      palette: str = "none",
      device: str = "cuda:0",
      cfg_options: Optional[dict] = None,
  ) -> nn.Module:
      """Initialize a detector from config file.
  
      Args:
          config (str, :obj:`Path`, or :obj:`mmengine.Config`): Config file path,
              :obj:`Path`, or the config object.
          checkpoint (str, optional): Checkpoint path. If left as None, the model
              will not load any weights.
          palette (str): Color palette used for visualization. If palette
              is stored in checkpoint, use checkpoint's palette first, otherwise
              use externally passed palette. Currently, supports 'coco', 'voc',
              'citys' and 'random'. Defaults to none.
          device (str): The device where the anchors will be put on.
              Defaults to cuda:0.
          cfg_options (dict, optional): Options to override some settings in
              the used config.
  
      Returns:
          nn.Module: The constructed detector.
      """
      if isinstance(config, (str, Path)):
          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)
      elif "init_cfg" in config.model.backbone:
          config.model.backbone.init_cfg = None
  
      scope = config.get("default_scope", "mmdet")
      if scope is not None:
          init_default_scope(config.get("default_scope", "mmdet"))
  
      model = MODELS.build(config.model)
      model = revert_sync_batchnorm(model)
      if checkpoint is None:
          warnings.simplefilter("once")
          warnings.warn(
              "checkpoint is None, use COCO classes by default."
          )
          model.dataset_meta = {"classes": get_classes("coco")}
      else:
          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:
              # mmdet 3.x, all keys should be lowercase
              model.dataset_meta = {
                  k.lower(): v
                  for k, v in checkpoint_meta["dataset_meta"].items()
              }
          elif "CLASSES" in checkpoint_meta:
              # < mmdet 3.x
              classes = checkpoint_meta["CLASSES"]
              model.dataset_meta = {"classes": classes}
          else:
              warnings.simplefilter("once")
              warnings.warn(
                  "dataset_meta or class names are not saved in the "
                  "checkpoint's meta data, use COCO classes by default."
              )
              model.dataset_meta = {"classes": get_classes("coco")}
  
      # Priority:  args.palette -> config -> checkpoint
      if palette != "none":
          model.dataset_meta["palette"] = palette
      else:
          test_dataset_cfg = copy.deepcopy(
              config.test_dataloader.dataset
          )
          # lazy init. We only need the metainfo.
          test_dataset_cfg["lazy_init"] = True
          metainfo = DATASETS.build(test_dataset_cfg).metainfo
          cfg_palette = metainfo.get("palette", None)
          if cfg_palette is not None:
              model.dataset_meta["palette"] = cfg_palette
          else:
              if "palette" not in model.dataset_meta:
                  warnings.warn(
                      "palette does not exist, random is used by default. "
                      "You can also set the palette to customize."
                  )
                  model.dataset_meta["palette"] = "random"
  
      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 1 for calling a global function with example usage: init_detector(config, checkpoint, palette, device, cfg_options) and returns: model