class_docstr: This callback monitors and reports collaborative training progress.
  In case of a catastrophic failure, it can also revert training to a backup.
code: def on_train_begin(
      self,
      args: TrainingArguments,
      state: transformers.TrainerState,
      control: transformers.TrainerControl,
      **kwargs,
  ):
      logger.info("Loading state from peers")
      self.optimizer.load_state_from_peers()
  
imports: import os
  import pickle
  import sys
  from dataclasses import asdict
  from pathlib import Path
  import torch
  import transformers
  from datasets import load_from_disk
  from torch.utils.data import DataLoader
  from torch_optimizer import Lamb
  from transformers import DataCollatorForLanguageModeling, HfArgumentParser, TrainingArguments, set_seed
  from transformers.models.albert import AlbertConfig, AlbertForPreTraining, AlbertTokenizerFast
  from transformers.optimization import get_linear_schedule_with_warmup
  from transformers.trainer import Trainer
  from transformers.trainer_utils import is_main_process
  from hivemind import DHT, Float16Compression, Optimizer, get_dht_time
  from hivemind.optim.state_averager import LRSchedulerBase
  from hivemind.utils.logging import get_logger, use_hivemind_log_handler
  from hivemind.utils.networking import log_visible_maddrs
  import utils
  from arguments import AlbertTrainingArguments, AveragerArguments, CollaborationArguments, DatasetArguments, ProgressTrackerArguments
label_desc: Use image node_id 2 for calling the CollaborativeCallback obj's underlying member method code with example usage: obj.on_train_begin(args, state, control) without return types