class_docstr: BLIP2 Vicuna model.
  Supported model types:
      - vicuna7b
      - vicuna13b
  Usage:
      >>> from lavis.models import load_model
      >>> model = load_model("blip2_vicuna_instruct", "vicuna7b")
class_docstr_tok: ["BLIP2","Vicuna","model",".","Supported","model","types",":","-","vicuna7b","-","vicuna13b","Usage",":",">",">",">","from","lavis.models","import","load_model",">",">",">","model","=","load_model","(","``","blip2_vicuna_instruct","''",",","``","vicuna7b","''",")"]
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 logging
  import string
  from packaging import version
  import torch
  from torch.cuda.amp import autocast
  import torch.nn
  import transformers
  from lavis.common.registry import registry
  from lavis.models.blip2_models.blip2 import Blip2Base, disabled_train
label_desc: Use image node_id 8 for calling the Blip2VicunaInstruct obj's underlying member method code with example usage: obj._lemmatize(answers) and returns: unknown, answer