class_docstr: Dummy scheduler for transformers.Trainer. The real scheduler is defined in Optimizer.scheduler
code: def state_dict(self):
      return {}
  
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 4 for calling the NoOpScheduler obj's underlying member method code with example usage: obj.state_dict() and returns: dict