class_docstr: BLIP2 Vicuna model.
  Supported model types:
      - vicuna7b
      - vicuna13b
  Usage:
      >>> from lavis.models import load_model
      >>> model = load_model("blip2_vicuna_xinstruct", "vicuna7b")
class_docstr_tok: ["BLIP2","Vicuna","model",".","Supported","model","types",":","-","vicuna7b","-","vicuna13b","Usage",":",">",">",">","from","lavis.models","import","load_model",">",">",">","model","=","load_model","(","``","blip2_vicuna_xinstruct","''",",","``","vicuna7b","''",")"]
code: def predict(
      self,
      samples,
      candidates=None,
      n_segments=1,
      max_length=10,
      min_length=1,
      length_penalty=-1.0,
      special_qformer_input_prompt=False,
  ):
      self.llm_tokenizer.padding_side = "left"
  
      if candidates == None:
          candidates = self.candidates
      else:
          self.candidates = candidates  # for the output targets.
  
      if self.predict_with_gen:
          output = self.generate(
              samples,
              max_length=max_length,
              min_length=min_length,
              length_penalty=length_penalty,
          )
          result = []
          for text in output:
              text = BlipCaptionProcessor().pre_caption(text)
              pred_label = ""  # default to an empty string
              for cand in candidates:
                  cand = BlipCaptionProcessor().pre_caption(cand)
                  if cand in text.split(" "):
                      pred_label = cand
                      break  # stop as soon as we find a match
              result.append(pred_label)
          return {"predictions": result, "target": samples["label"]}
  
      # If candidates is a list of lists, each sample has its candidates, then we need to iterate one by one
      if type(candidates[0]) == list:
          results = []
  
          for i in range(samples["image"].size(0)):
              this_sample = {
                  "image": samples["image"][i].unsqueeze(0),
                  "prompt": samples["prompt"],
              }
  
              if "text_input" in samples.keys():
                  this_sample["text_input"] = [samples["text_input"][i]]
  
              if "context" in samples.keys():
                  this_sample["context"] = [samples["context"][i]]
  
              if "history" in samples.keys():
                  this_sample["history"] = [samples["history"][i]]
  
              if "caption" in samples.keys():
                  this_sample["caption"] = [samples["caption"][i]]
  
              this_result = self._predict_class(
                  this_sample,
                  candidates[i],
                  n_segments,
                  special_qformer_input_prompt,
              )
              results.append(this_result)
  
          try:
              results = torch.cat(results, dim=0)
          except:
              results = [res.tolist()[0] for res in results]
  
          return results
  
      return self._predict_class(
          samples, candidates, n_segments, special_qformer_input_prompt
      )
  
imports: import logging
  import string
  from packaging import version
  import os
  from omegaconf import OmegaConf
  import torch
  from torch.cuda.amp import autocast
  import torch.nn
  from torch.nn.modules.module import _IncompatibleKeys
  from peft import get_peft_model, LoraConfig, TaskType
  import transformers
  import random
  from lavis.common.registry import registry
  from lavis.models.base_model import BaseModel
  from lavis.models.blip2_models.blip2 import Blip2Base, disabled_train, LayerNorm
  from lavis.models.ulip_models.ULIP_models import ULIP_PointBERT
  from lavis.tasks.multimodal_classification import MultimodalClassificationTask
  from lavis.common.utils import is_url
  from lavis.models.blip2_models.Qformer import BertConfig, BertLMHeadModel
  from lavis.common.dist_utils import download_cached_file
  from lavis.processors.blip_processors import BlipCaptionProcessor
label_desc: Use image node_id 11 for calling the Blip2VicunaXInstruct obj's underlying member method code with example usage: obj.predict(samples, candidates, n_segments, max_length, min_length, length_penalty, special_qformer_input_prompt) and returns: self, dict, results