code: def init_segmentor(config, checkpoint=None, device="cuda:0"):
      """Initialize a segmentor from config file.
  
      Args:
          config (str or :obj:`mmcv.Config`): Config file path or the config
              object.
          checkpoint (str, optional): Checkpoint path. If left as None, the model
              will not load any weights.
          device (str, optional) CPU/CUDA device option. Default 'cuda:0'.
              Use 'cpu' for loading model on CPU.
      Returns:
          nn.Module: The constructed segmentor.
      """
      if isinstance(config, str):
          config = mmcv.Config.fromfile(config)
      elif not isinstance(config, mmcv.Config):
          raise TypeError(
              "config must be a filename or Config object, "
              "but got {}".format(type(config))
          )
      config.model.pretrained = None
      config.model.train_cfg = None
      model = build_segmentor(
          config.model, test_cfg=config.get("test_cfg")
      )
      if checkpoint is not None:
          checkpoint = load_checkpoint(
              model, checkpoint, map_location="cpu"
          )
          model.CLASSES = checkpoint["meta"]["CLASSES"]
          model.PALETTE = checkpoint["meta"]["PALETTE"]
      model.cfg = config  # save the config in the model for convenience
      model.to(device)
      model.eval()
      return model
  
file_path: lavis/lavis/common/annotator/uniformer/mmseg/apis/inference.py
imports: import matplotlib.pyplot
  import annotator.uniformer.mmcv
  import torch
  from annotator.uniformer.mmcv.parallel import collate, scatter
  from annotator.uniformer.mmcv.runner import load_checkpoint
  from annotator.uniformer.mmseg.datasets.pipelines import Compose
  from annotator.uniformer.mmseg.models import build_segmentor
label_desc: Use image node_id 1 for calling a global function with example usage: init_segmentor(config, checkpoint, device) and returns: model