code: def save_checkpoint(self, epoch, metric=None):
      assert epoch >= 0
      tmp_save_path = os.path.join(
          self.checkpoint_dir, "tmp" + self.extension
      )
      last_save_path = os.path.join(
          self.checkpoint_dir, "last" + self.extension
      )
      self._save(tmp_save_path, epoch, metric)
      if os.path.exists(last_save_path):
          os.unlink(last_save_path)  # required for Windows support.
      os.rename(tmp_save_path, last_save_path)
      worst_file = (
          self.checkpoint_files[-1] if self.checkpoint_files else None
      )
      if (
          len(self.checkpoint_files) < self.max_history
          or metric is None
          or self.cmp(metric, worst_file[1])
      ):
          if len(self.checkpoint_files) >= self.max_history:
              self._cleanup_checkpoints(1)
          filename = (
              "-".join([self.save_prefix, str(epoch)]) + self.extension
          )
          save_path = os.path.join(self.checkpoint_dir, filename)
          os.link(last_save_path, save_path)
          self.checkpoint_files.append((save_path, metric))
          self.checkpoint_files = sorted(
              self.checkpoint_files,
              key=lambda x: x[1],
              reverse=not self.decreasing,
          )  # sort in descending order if a lower metric is not better
  
          checkpoints_str = "Current checkpoints:\n"
          for c in self.checkpoint_files:
              checkpoints_str += " {}\n".format(c)
          _logger.info(checkpoints_str)
  
          if metric is not None and (
              self.best_metric is None
              or self.cmp(metric, self.best_metric)
          ):
              self.best_epoch = epoch
              self.best_metric = metric
              best_save_path = os.path.join(
                  self.checkpoint_dir, "model_best" + self.extension
              )
              if os.path.exists(best_save_path):
                  os.unlink(best_save_path)
              os.link(last_save_path, best_save_path)
  
      return (
          (None, None)
          if self.best_metric is None
          else (self.best_metric, self.best_epoch)
      )
  
file_path: pytorch-image-models/timm/utils/checkpoint_saver.py
imports: import glob
  import operator
  import os
  import logging
  import torch
  from .model import unwrap_model, get_state_dict
label_desc: Use image node_id 2 for calling the CheckpointSaver obj's underlying member method code with example usage: obj.save_checkpoint(epoch, metric) and returns: unknown