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 __init__(
      self,
      dht: DHT,
      optimizer: Optimizer,
      model: torch.nn.Module,
      local_public_key: bytes,
      statistics_expiration: float,
      backup_every_steps: int,
  ):
      super().__init__()
      self.model = model
      self.dht, self.optimizer = dht, optimizer
      self.local_public_key = local_public_key
      self.statistics_expiration = statistics_expiration
      self.last_reported_collaboration_step = -1
      self.samples = 0
      self.steps = 0
      self.loss = 0
      self.total_samples_processed = 0
      self.backup_every_steps = backup_every_steps
      self.latest_backup = self.backup_state()
  
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 1 to create a new CollaborativeCallback object from inherited base classes: transformers with example: obj = CollaborativeCallback(dht, optimizer, model, local_public_key, statistics_expiration, backup_every_steps)