code: def load_model_and_preprocess(
      name, model_type, is_eval=False, device="cpu"
  ):
      """
      Load model and its related preprocessors.
  
      List all available models and types in registry:
      >>> from lavis.models import model_zoo
      >>> print(model_zoo)
  
      Args:
          name (str): name of the model.
          model_type (str): type of the model.
          is_eval (bool): whether the model is in eval mode. Default: False.
          device (str): device to use. Default: "cpu".
  
      Returns:
          model (torch.nn.Module): model.
          vis_processors (dict): preprocessors for visual inputs.
          txt_processors (dict): preprocessors for text inputs.
      """
      model_cls = registry.get_model_class(name)
  
      # load model
      model = model_cls.from_pretrained(model_type=model_type)
  
      if is_eval:
          model.eval()
  
      # load preprocess
      cfg = OmegaConf.load(model_cls.default_config_path(model_type))
      if cfg is not None:
          preprocess_cfg = cfg.preprocess
  
          vis_processors, txt_processors = load_preprocess(
              preprocess_cfg
          )
      else:
          vis_processors, txt_processors = None, None
          logging.info(
              f"""No default preprocess for model {name} ({model_type}).
                  This can happen if the model is not finetuned on downstream datasets,
                  or it is not intended for direct use without finetuning.
              """
          )
  
      if device == "cpu" or device == torch.device("cpu"):
          model = model.float()
  
      return model.to(device), vis_processors, txt_processors
  
imports: import logging
  import torch
  from omegaconf import OmegaConf
  from lavis.common.registry import registry
  from lavis.models.base_model import BaseModel
  from lavis.models.albef_models.albef_classification import AlbefClassification
  from lavis.models.albef_models.albef_feature_extractor import AlbefFeatureExtractor
  from lavis.models.albef_models.albef_nlvr import AlbefNLVR
  from lavis.models.albef_models.albef_pretrain import AlbefPretrain
  from lavis.models.albef_models.albef_retrieval import AlbefRetrieval
  from lavis.models.albef_models.albef_vqa import AlbefVQA
  from lavis.models.alpro_models.alpro_qa import AlproQA
  from lavis.models.alpro_models.alpro_retrieval import AlproRetrieval
  from lavis.models.blip_models.blip import BlipBase
  from lavis.models.blip_models.blip_caption import BlipCaption
  from lavis.models.blip_models.blip_classification import BlipClassification
  from lavis.models.blip_models.blip_feature_extractor import BlipFeatureExtractor
  from lavis.models.blip_models.blip_image_text_matching import BlipITM
  from lavis.models.blip_models.blip_nlvr import BlipNLVR
  from lavis.models.blip_models.blip_pretrain import BlipPretrain
  from lavis.models.blip_models.blip_retrieval import BlipRetrieval
  from lavis.models.blip_models.blip_vqa import BlipVQA
  from lavis.models.blip2_models.blip2 import Blip2Base
  from lavis.models.blip2_models.blip2_opt import Blip2OPT
  from lavis.models.blip2_models.blip2_t5 import Blip2T5
  from lavis.models.blip2_models.blip2_qformer import Blip2Qformer
  from lavis.models.blip2_models.blip2_image_text_matching import Blip2ITM
  from lavis.models.blip2_models.blip2_t5_instruct import Blip2T5Instruct
  from lavis.models.blip2_models.blip2_vicuna_instruct import Blip2VicunaInstruct
  from lavis.models.blip2_models.blip2_vicuna_xinstruct import Blip2VicunaXInstruct
  from lavis.models.blip_diffusion_models.blip_diffusion import BlipDiffusion
  from lavis.models.pnp_vqa_models.pnp_vqa import PNPVQA
  from lavis.models.pnp_vqa_models.pnp_unifiedqav2_fid import PNPUnifiedQAv2FiD
  from lavis.models.img2prompt_models.img2prompt_vqa import Img2PromptVQA
  from lavis.models.med import XBertLMHeadDecoder
  from lavis.models.vit import VisionTransformerEncoder
  from lavis.models.clip_models.model import CLIP
  from lavis.models.gpt_models.gpt_dialogue import GPTDialogue
  from lavis.processors.base_processor import BaseProcessor
label_desc: Use image node_id 3 for calling a global function with example usage: load_model_and_preprocess(name, model_type, is_eval, device) and returns: model,  vis_processors,  txt_processors