code: def should_break_training(self) -> bool:
      if compare_steps_max_steps(
          step=self.train_state.global_steps,
          max_steps=self.global_train_config.max_steps,
      ):
          return True
  
      if self.no_improvements_for_n_evals != 0:
          if (
              self.num_evals_since_improvement
              >= self.no_improvements_for_n_evals
          ):
              self.log_writer.write_entry(
                  "early_stopping",
                  {
                      "message": "early_stopped",
                      "train_state": self.train_state.to_dict(),
                  },
              )
              self.log_writer.flush()
              return True
  
      return False
  
imports: from dataclasses import dataclass
  from typing import Dict
  import torch.nn
  import jiant.proj.main.runner
  import jiant.proj.main.components.task_sampler
  from jiant.shared.runner import save_model_with_metadata, compare_steps_max_steps
  from jiant.utils.python.datastructures import ExtendedDataClassMixin
  from jiant.utils.python.functional import always_false
  from jiant.utils.torch_utils import copy_state_dict, CPU_DEVICE, get_model_for_saving
  from jiant.utils.zlog import BaseZLogger, PRINT_LOGGER
  from jiant.shared.metarunner import AbstractMetarunner
label_desc: Use image node_id 12 for calling the JiantMetarunner obj's underlying member method code with example usage: obj.should_break_training() and returns: False, True, True