code: def is_time_to_save_state(self, cur_step):
      if self.save_checkpoint_step_interval is None:
          return False
      elif (
          cur_step - self.previous_step
          >= self.save_checkpoint_step_interval
      ):
          return True
      else:
          return False
  
imports: import time
  from dataclasses import asdict, dataclass, field
  from ipaddress import ip_address
  from typing import Optional
  import requests
  import torch
  import wandb
  from torch_optimizer import Lamb
  from transformers import AlbertConfig, AlbertForPreTraining, HfArgumentParser, get_linear_schedule_with_warmup
  import hivemind
  from hivemind.optim.state_averager import TrainingStateAverager
  from hivemind.utils.logging import get_logger, use_hivemind_log_handler
  from hivemind.utils.networking import log_visible_maddrs
  import utils
  from arguments import AveragerArguments, BaseTrainingArguments, OptimizerArguments
label_desc: Use image node_id 2 for calling the CheckpointHandler obj's underlying member method code with example usage: obj.is_time_to_save_state(cur_step) and returns: False, True, False