code: def load_from_pretrained(self, 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"]
  
      msg = self.load_state_dict(state_dict, strict=False)
  
      # logging.info("Missing keys {}".format(msg.missing_keys))
      logging.info("load checkpoint from %s" % url_or_filename)
  
      return msg
  
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 5 for calling the Blip2Base obj's underlying member method code with example usage: obj.load_from_pretrained(url_or_filename) and returns: msg