code: def load_checkpoint(model, url_or_filename):
      if is_url(url_or_filename):
          cached_file = download_cached_file(
              url_or_filename, check_hash=False, progress=True
          )
          checkpoint = torch.load(cached_file, map_location="cpu")
      elif os.path.isfile(url_or_filename):
          checkpoint = torch.load(url_or_filename, map_location="cpu")
      else:
          raise RuntimeError("checkpoint url or path is invalid")
      state_dict = checkpoint["model"]
  
      state_dict["visual_encoder.pos_embed"] = interpolate_pos_embed(
          state_dict["visual_encoder.pos_embed"], model.visual_encoder
      )
  
      for key in list(state_dict.keys()):
          if "crossattention.self." in key:
              new_key0 = key.replace("self", "self0")
              new_key1 = key.replace("self", "self1")
              state_dict[new_key0] = state_dict[key]
              state_dict[new_key1] = state_dict[key]
          elif "crossattention.output.dense." in key:
              new_key0 = key.replace("dense", "dense0")
              new_key1 = key.replace("dense", "dense1")
              state_dict[new_key0] = state_dict[key]
              state_dict[new_key1] = state_dict[key]
  
      msg = model.load_state_dict(state_dict, strict=False)
      print("load checkpoint from %s" % url_or_filename)
      return model, msg
  
file_path: blip/models/blip_nlvr.py
imports: from models.med import BertConfig
  from models.nlvr_encoder import BertModel
  from models.vit import interpolate_pos_embed
  from models.blip import create_vit, init_tokenizer, is_url
  from timm.models.hub import download_cached_file
  import torch
  from torch import nn
  import torch.nn.functional
  from transformers import BertTokenizer
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: load_checkpoint(model, url_or_filename) and returns: model,  msg