code: def resume_train_context(self, train_state, verbose=True):
      train_dataloader_dict = self.get_train_dataloader_dict()
      start_position = train_state.global_steps
      for _ in maybe_tqdm(
          range(
              start_position,
              self.jiant_task_container.global_train_config.max_steps,
          ),
          desc="Training",
          initial=start_position,
          total=self.jiant_task_container.global_train_config.max_steps,
          verbose=verbose,
      ):
          self.run_train_step(
              train_dataloader_dict=train_dataloader_dict,
              train_state=train_state,
          )
          yield train_state
  
imports: from typing import Dict
  from dataclasses import dataclass
  import torch
  import jiant.tasks.evaluate
  import jiant.utils.torch_utils
  from jiant.proj.main.components.container_setup import JiantTaskContainer
  from jiant.proj.main.modeling.primary import JiantModel, wrap_jiant_forward
  from jiant.shared.constants import PHASE
  from jiant.shared.runner import complex_backpropagate, get_train_dataloader_from_cache, get_eval_dataloader_from_cache
  from jiant.utils.display import maybe_tqdm
  from jiant.utils.python.datastructures import InfiniteYield, ExtendedDataClassMixin
label_desc: Use image node_id 4 for calling the JiantRunner obj's underlying member method code with example usage: obj.resume_train_context(train_state, verbose) without return types