code: def _lemmatize(self, answers):
      def apply(answer):
          doc = self.lemmatizer(answer)
  
          words = []
          for token in doc:
              if token.pos_ in ["NOUN", "VERB"]:
                  words.append(token.lemma_)
              else:
                  words.append(token.text)
          answer = " ".join(words)
  
          return answer
  
      return [apply(answer) for answer in answers]
  
imports: import contextlib
  import logging
  import os
  import time
  import datetime
  import torch
  import torch.nn
  import torch.distributed
  import torch.nn.functional
  import lavis.common.dist_utils
  from lavis.common.dist_utils import download_cached_file
  from lavis.common.utils import is_url
  from lavis.common.logger import MetricLogger
  from lavis.models.base_model import BaseModel
  from lavis.models.blip2_models.Qformer import BertConfig, BertLMHeadModel
  from lavis.models.eva_vit import create_eva_vit_g
  from lavis.models.clip_vit import create_clip_vit_L
  from transformers import BertTokenizer
label_desc: Use image node_id 7 for calling the Blip2Base obj's underlying member method code with example usage: obj._lemmatize(answers) and returns: unknown, answer