code: def _load_checkpoint(filename, map_location=None):
      """Load checkpoint from somewhere (modelzoo, file, url).
  
      Args:
          filename (str): Accept local filepath, URL, ``torchvision://xxx``,
              ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
              details.
          map_location (str | None): Same as :func:`torch.load`. Default: None.
  
      Returns:
          dict | OrderedDict: The loaded checkpoint. It can be either an
              OrderedDict storing model weights or a dict containing other
              information, which depends on the checkpoint.
      """
      if filename.startswith("modelzoo://"):
          warnings.warn(
              'The URL scheme of "modelzoo://" is deprecated, please '
              'use "torchvision://" instead'
          )
          model_urls = get_torchvision_models()
          model_name = filename[11:]
          checkpoint = load_url_dist(model_urls[model_name])
      elif filename.startswith("torchvision://"):
          model_urls = get_torchvision_models()
          model_name = filename[14:]
          checkpoint = load_url_dist(model_urls[model_name])
      elif filename.startswith("open-mmlab://"):
          model_urls = get_external_models()
          model_name = filename[13:]
          deprecated_urls = get_deprecated_model_names()
          if model_name in deprecated_urls:
              warnings.warn(
                  f"open-mmlab://{model_name} is deprecated in favor "
                  f"of open-mmlab://{deprecated_urls[model_name]}"
              )
              model_name = deprecated_urls[model_name]
          model_url = model_urls[model_name]
          # check if is url
          if model_url.startswith(("http://", "https://")):
              checkpoint = load_url_dist(model_url)
          else:
              filename = osp.join(_get_mmcv_home(), model_url)
              if not osp.isfile(filename):
                  raise IOError(f"{filename} is not a checkpoint file")
              checkpoint = torch.load(
                  filename, map_location=map_location
              )
      elif filename.startswith("mmcls://"):
          model_urls = get_mmcls_models()
          model_name = filename[8:]
          checkpoint = load_url_dist(model_urls[model_name])
          checkpoint = _process_mmcls_checkpoint(checkpoint)
      elif filename.startswith(("http://", "https://")):
          checkpoint = load_url_dist(filename)
      elif filename.startswith("pavi://"):
          model_path = filename[7:]
          checkpoint = load_pavimodel_dist(
              model_path, map_location=map_location
          )
      elif filename.startswith("s3://"):
          checkpoint = load_fileclient_dist(
              filename, backend="ceph", map_location=map_location
          )
      else:
          if not osp.isfile(filename):
              raise IOError(f"{filename} is not a checkpoint file")
          checkpoint = torch.load(filename, map_location=map_location)
      return checkpoint
  
imports: import io
  import os
  import os.path
  import pkgutil
  import time
  import warnings
  from collections import OrderedDict
  from importlib import import_module
  from tempfile import TemporaryDirectory
  import torch
  import torchvision
  from torch.optim import Optimizer
  from torch.utils import model_zoo
  from torch.nn import functional
  import annotator.uniformer.mmcv
  from annotator.uniformer.mmcv.fileio import FileClient
  from annotator.uniformer.mmcv.fileio import load
  from annotator.uniformer.mmcv.parallel import is_module_wrapper
  from annotator.uniformer.mmcv.utils import mkdir_or_exist
  from annotator.uniformer.mmcv.runner import get_dist_info
label_desc: Use image node_id 11 for calling a global function with example usage: _load_checkpoint(filename, map_location) and returns: checkpoint