code: def __init__(
      self,
      monitor_args: TrainingMonitorArguments,
      optimizer_args: OptimizerArguments,
      averager_args: AveragerArguments,
      dht: hivemind.DHT,
  ):
      self.save_checkpoint_step_interval = (
          monitor_args.save_checkpoint_step_interval
      )
      self.repo_path = monitor_args.repo_path
      self.repo_url = monitor_args.repo_url
      self.upload_interval = monitor_args.upload_interval
      self.previous_step = -1
  
      config = AlbertConfig.from_pretrained(
          monitor_args.model_config_path
      )
      self.model = AlbertForPreTraining(config)
  
      no_decay = ["bias", "LayerNorm.weight"]
      optimizer_grouped_parameters = [
          {
              "params": [
                  p
                  for n, p in self.model.named_parameters()
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.01,
          },
          {
              "params": [
                  p
                  for n, p in self.model.named_parameters()
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
  
      opt = Lamb(
          optimizer_grouped_parameters,
          lr=0.00176,
          weight_decay=0.01,
          clamp_value=10000.0,
          debias=True,
      )
  
      self.state_averager = TrainingStateAverager(
          dht=dht,
          optimizer=opt,
          scheduler=get_linear_schedule_with_warmup(
              opt, num_warmup_steps=5000, num_training_steps=125_000
          ),
          prefix=f"{run_id}_state_averager",
          state_compression=hivemind.Float16Compression(),
          bandwidth=optimizer_args.bandwidth,
          client_mode=optimizer_args.client_mode,
          start=True,
          **asdict(averager_args),
      )
      self.previous_timestamp = time.time()
  
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 1 to create a new CheckpointHandler object  with example: obj = CheckpointHandler(monitor_args, optimizer_args, averager_args, dht)