class_docstr: This runner implements the LM-BFF training process in paper `Making Pre-trained Language Models Better Few-shot Learners(Gao et al. 2020) <https://arxiv.org/pdf/2012.15723.pdf>`_.
  
  Args:
      train_dataset (:obj:`List[InputExample]`): The dataset for training
      valid_dataset (:obj:`List[InputExample]`): The dataset for validation
      test_dataset (:obj:`List[InputExample]`): The dataset for test
      verbalizer (:obj:`Optional[Verbalizer]`): The manually designed verbalizer for template generation. Defaults to None.
      template (:obj:`Optional[Verbalizer]`): The manually designed template for verbalizer generation. Defaults to None.
      config (:obj:`CfgNode`): A configuration object
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code: def _train_eval(
      self, template, verbalizer, train_dataloader, valid_dataloader
  ):
      model = PromptForClassification(
          copy.deepcopy(self.model), template, verbalizer
      )
      runner = ClassificationRunner(
          model,
          config=self.config,
          train_dataloader=train_dataloader,
          valid_dataloader=valid_dataloader,
      )
      runner.clean = True
      best_score = runner.fit()
      return best_score
  
imports: from transformers.data.processors.utils import InputExample, InputFeatures
  from openprompt import PromptDataLoader, PromptForClassification
  from openprompt.pipeline_base import PromptModel
  from openprompt.prompts import ManualVerbalizer, ManualTemplate
  from typing import List, Optional, Dict, Union
  from .None import Verbalizer, PromptDataLoader
  import copy
  import warnings
  from .trainer import ClassificationRunner
  from yacs.config import CfgNode
  from openprompt.utils.logging import logger
  from openprompt.utils.cuda import model_to_device
  from openprompt.prompts import load_template_generator, load_verbalizer_generator
  from openprompt.plms import load_plm_from_config
label_desc: Use image node_id 7 for calling the LMBFFClassificationRunner obj's underlying member method code with example usage: obj._train_eval(template, verbalizer, train_dataloader, valid_dataloader) and returns: best_score