docstr: Save the current state. Only by the master process.
docstr_tok: ["Save","the","current","state",".","Only","by","the","master","process","."]
code: def save_checkpoint(self, checkpoint_name: str = "checkpoint.pth"):
      """
      Save the current state. Only by the master process.
      """
      if not self.is_master:
          return
      mdl_to_save = (
          self.student.module
          if hasattr(self.student, "module")
          else self.student
      )
      mdl_to_save.config.save_pretrained(self.dump_path)
      state_dict = mdl_to_save.state_dict()
      torch.save(
          state_dict, os.path.join(self.dump_path, checkpoint_name)
      )
  
imports: import math
  import os
  import time
  import psutil
  import torch
  from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
  from lm_seqs_dataset import LmSeqsDataset
  from torch import nn
  from torch.optim import AdamW
  from torch.utils.data import BatchSampler, DataLoader, RandomSampler
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm
  from transformers import get_linear_schedule_with_warmup
  from utils import logger
label_desc: Use image node_id 11 for calling the Distiller obj's underlying member method code with example usage: obj.save_checkpoint(checkpoint_name) without return types