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_step_end(
      self,
      args: TrainingArguments,
      state: transformers.TrainerState,
      control: transformers.TrainerControl,
      **kwargs,
  ):
      control.should_log = True
      if not self.params_are_finite():
          self.restore_from_backup(self.latest_backup)
          return control
  
      local_progress = self.optimizer.local_progress
  
      if state.log_history:
          self.loss += state.log_history[-1]["loss"]
          self.steps += 1
  
          if (
              self.optimizer.local_epoch
              != self.last_reported_collaboration_step
          ):
              self.last_reported_collaboration_step = (
                  self.optimizer.local_epoch
              )
              self.total_samples_processed += self.samples
              samples_per_second = local_progress.samples_per_second
              statistics = utils.LocalMetrics(
                  step=self.optimizer.local_epoch,
                  samples_per_second=samples_per_second,
                  samples_accumulated=self.samples,
                  loss=self.loss,
                  mini_steps=self.steps,
              )
              logger.info(f"Step #{self.optimizer.local_epoch}")
              logger.info(
                  f"Your current contribution: {self.total_samples_processed} samples"
              )
              logger.info(
                  f"Performance: {samples_per_second:.3f} samples/sec"
              )
              if self.steps:
                  logger.info(
                      f"Local loss: {self.loss / self.steps:.5f}"
                  )
              if (
                  self.optimizer.local_epoch % self.backup_every_steps
                  == 0
              ):
                  self.latest_backup = self.backup_state()
  
              self.loss = 0
              self.steps = 0
              if self.optimizer.is_synchronized_with_peers():
                  self.dht.store(
                      key=self.optimizer.run_id + "_metrics",
                      subkey=self.local_public_key,
                      value=statistics.dict(),
                      expiration_time=get_dht_time()
                      + self.statistics_expiration,
                      return_future=True,
                  )
  
      self.samples = local_progress.samples_accumulated
  
      return control
  
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 3 for calling the CollaborativeCallback obj's underlying member method code with example usage: obj.on_step_end(args, state, control) and returns: control, control