code: def save_checkpoint(model, filename, optimizer=None, meta=None):
      """Save checkpoint to file.
  
      The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
      ``optimizer``. By default ``meta`` will contain version and time info.
  
      Args:
          model (Module): Module whose params are to be saved.
          filename (str): Checkpoint filename.
          optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
          meta (dict, optional): Metadata to be saved in checkpoint.
      """
      if meta is None:
          meta = {}
      elif not isinstance(meta, dict):
          raise TypeError(
              f"meta must be a dict or None, but got {type(meta)}"
          )
      meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
  
      if is_module_wrapper(model):
          model = model.module
  
      if hasattr(model, "CLASSES") and model.CLASSES is not None:
          # save class name to the meta
          meta.update(CLASSES=model.CLASSES)
  
      checkpoint = {
          "meta": meta,
          "state_dict": weights_to_cpu(get_state_dict(model)),
      }
      # save optimizer state dict in the checkpoint
      if isinstance(optimizer, Optimizer):
          checkpoint["optimizer"] = optimizer.state_dict()
      elif isinstance(optimizer, dict):
          checkpoint["optimizer"] = {}
          for name, optim in optimizer.items():
              checkpoint["optimizer"][name] = optim.state_dict()
  
      if filename.startswith("pavi://"):
          try:
              from pavi import modelcloud
              from pavi.exception import NodeNotFoundError
          except ImportError:
              raise ImportError(
                  "Please install pavi to load checkpoint from modelcloud."
              )
          model_path = filename[7:]
          root = modelcloud.Folder()
          model_dir, model_name = osp.split(model_path)
          try:
              model = modelcloud.get(model_dir)
          except NodeNotFoundError:
              model = root.create_training_model(model_dir)
          with TemporaryDirectory() as tmp_dir:
              checkpoint_file = osp.join(tmp_dir, model_name)
              with open(checkpoint_file, "wb") as f:
                  torch.save(checkpoint, f)
                  f.flush()
              model.create_file(checkpoint_file, name=model_name)
      else:
          mmcv.mkdir_or_exist(osp.dirname(filename))
          # immediately flush buffer
          with open(filename, "wb") as f:
              torch.save(checkpoint, f)
              f.flush()
  
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 16 for calling a global function with example usage: save_checkpoint(model, filename, optimizer, meta) without return types