code: def upload_checkpoint(self, current_loss):
      logger.info("Saving optimizer")
      torch.save(
          self.state_averager.optimizer.state_dict(),
          f"{self.repo_path}/optimizer_state.pt",
      )
      self.previous_timestamp = time.time()
      logger.info("Started uploading to Model Hub")
      self.model.push_to_hub(
          repo_name=self.repo_path,
          repo_url=self.repo_url,
          commit_message=f"Step #{current_step}, loss {current_loss:.3f}",
      )
      logger.info("Finished uploading to Model Hub")
  
imports: import time
  from dataclasses import asdict, dataclass, field
  from ipaddress import ip_address
  from typing import Optional
  import requests
  import torch
  import wandb
  from torch_optimizer import Lamb
  from transformers import AlbertConfig, AlbertForPreTraining, HfArgumentParser, get_linear_schedule_with_warmup
  import hivemind
  from hivemind.optim.state_averager import TrainingStateAverager
  from hivemind.utils.logging import get_logger, use_hivemind_log_handler
  from hivemind.utils.networking import log_visible_maddrs
  import utils
  from arguments import AveragerArguments, BaseTrainingArguments, OptimizerArguments
label_desc: Use image node_id 5 for calling the CheckpointHandler obj's underlying member method code with example usage: obj.upload_checkpoint(current_loss) without return types