code: def main():
      parser = HfArgumentParser(
          (
              AlbertTrainingArguments,
              DatasetArguments,
              CollaborationArguments,
              AveragerArguments,
              ProgressTrackerArguments,
          )
      )
      (
          training_args,
          dataset_args,
          collaboration_args,
          averager_args,
          tracker_args,
      ) = parser.parse_args_into_dataclasses()
      logger.info(
          f"Found {len(collaboration_args.initial_peers)} initial peers: {collaboration_args.initial_peers}"
      )
  
      setup_transformers_logging(training_args.local_rank)
      logger.info(f"Training/evaluation parameters:\n{training_args}")
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      config = AlbertConfig.from_pretrained(
          dataset_args.config_path, cache_dir=dataset_args.cache_dir
      )
      try:
          tokenizer = AlbertTokenizerFast.from_pretrained(
              dataset_args.tokenizer_path,
              cache_dir=dataset_args.cache_dir,
          )
      except OSError:
          logger.fatal(
              f"No tokenizer data found in {dataset_args.tokenizer_path}, "
              f"please run ./tokenize_wikitext103.py before running this"
          )
          sys.exit(1)
  
      model = get_model(training_args, config, tokenizer)
      model.to(training_args.device)
  
      tokenized_datasets = load_from_disk(
          Path(dataset_args.dataset_path)
      )
      # This data collator will take care of randomly masking the tokens.
      data_collator = DataCollatorForLanguageModeling(
          tokenizer=tokenizer
      )
  
      validators, local_public_key = utils.make_validators(
          collaboration_args.run_id
      )
  
      dht = DHT(
          start=True,
          initial_peers=collaboration_args.initial_peers,
          client_mode=collaboration_args.client_mode,
          record_validators=validators,
          use_ipfs=collaboration_args.use_ipfs,
          host_maddrs=collaboration_args.host_maddrs,
          announce_maddrs=collaboration_args.announce_maddrs,
          identity_path=collaboration_args.identity_path,
      )
      log_visible_maddrs(
          dht.get_visible_maddrs(), only_p2p=collaboration_args.use_ipfs
      )
  
      total_batch_size_per_step = (
          training_args.per_device_train_batch_size
          * training_args.gradient_accumulation_steps
      )
      if torch.cuda.device_count() != 0:
          total_batch_size_per_step *= torch.cuda.device_count()
  
      adjusted_target_batch_size = (
          collaboration_args.target_batch_size
          - collaboration_args.batch_size_lead
      )
  
      # We need to make such a lambda function instead of just an optimizer instance
      # to make hivemind.Optimizer(..., offload_optimizer=True) work
      opt = lambda params: Lamb(
          params,
          lr=training_args.learning_rate,
          betas=(training_args.adam_beta1, training_args.adam_beta2),
          eps=training_args.adam_epsilon,
          weight_decay=training_args.weight_decay,
          clamp_value=training_args.clamp_value,
          debias=True,
      )
  
      no_decay = ["bias", "LayerNorm.weight"]
      params = [
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": training_args.weight_decay,
          },
          {
              "params": [
                  p
                  for n, p in model.named_parameters()
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
  
      scheduler = lambda opt: get_linear_schedule_with_warmup(
          opt,
          num_warmup_steps=training_args.warmup_steps,
          num_training_steps=training_args.total_steps,
      )
  
      optimizer = Optimizer(
          dht=dht,
          run_id=collaboration_args.run_id,
          target_batch_size=adjusted_target_batch_size,
          batch_size_per_step=total_batch_size_per_step,
          optimizer=opt,
          params=params,
          scheduler=scheduler,
          matchmaking_time=collaboration_args.matchmaking_time,
          averaging_timeout=collaboration_args.averaging_timeout,
          offload_optimizer=True,
          delay_optimizer_step=True,
          delay_grad_averaging=True,
          client_mode=collaboration_args.client_mode,
          grad_compression=Float16Compression(),
          state_averaging_compression=Float16Compression(),
          averager_opts={
              "bandwidth": collaboration_args.bandwidth,
              **asdict(averager_args),
          },
          tracker_opts=asdict(tracker_args),
          verbose=True,
      )
  
      class TrainerWithIndependentShuffling(Trainer):
          def get_train_dataloader(self) -> DataLoader:
              """Shuffle data independently for each peer to avoid duplicating batches [important for quality]"""
              torch.manual_seed(hash(local_public_key))
              return super().get_train_dataloader()
  
      trainer = TrainerWithIndependentShuffling(
          model=model,
          args=training_args,
          tokenizer=tokenizer,
          data_collator=data_collator,
          train_dataset=tokenized_datasets["train"]
          if training_args.do_train
          else None,
          eval_dataset=tokenized_datasets["validation"]
          if training_args.do_eval
          else None,
          optimizers=(optimizer, NoOpScheduler(optimizer)),
          callbacks=[
              CollaborativeCallback(
                  dht,
                  optimizer,
                  model,
                  local_public_key,
                  collaboration_args.statistics_expiration,
                  collaboration_args.backup_every_steps,
              )
          ],
      )
      trainer.remove_callback(
          transformers.trainer_callback.PrinterCallback
      )
      trainer.remove_callback(
          transformers.trainer_callback.ProgressCallback
      )
  
      # Training
      if training_args.do_train:
          latest_checkpoint_dir = max(
              Path(training_args.output_dir).glob("checkpoint*"),
              default=None,
              key=os.path.getctime,
          )
  
          trainer.train(model_path=latest_checkpoint_dir)
  
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 a global function with example usage: main() and returns: super