code: def load_pretrained_weights(
      model,
      model_name=None,
      model_configs=None,
      weights_path=None,
  ):
      """
      Load pretrained weights from path or url.
      Args:
          model(nn.Module):
              Model.
          model_name(str):
              Model name
          model_configs(dict):
              Model configs
          weights_path(str, optional):
              Path to pretrained weights file on the local disk.
          verbose (bool):
              If printing is done when downloading is over.
      """
      logging.info("model_name is %s", str(model_name))
      logging.info("weights_path is %s", str(weights_path))
      assert bool(model_name), "Expected model_name"
  
      # Load or download weights
      if weights_path is None:
          logging.info(model_configs.keys())
          url = model_configs["url"]
          if url:
              logging.info(
                  "Please check hub connection in case weights can not be downloaded!"
              )
              state_dict = model_zoo.load_url(
                  url, map_location=torch.device("cpu")
              )
          else:
              raise ValueError(
                  f"Pretrained model for {model_name} is not available."
              )
      else:
          state_dict = torch.load(weights_path)
  
      # Load state dict
      if "model" in state_dict:
          ret = model.load_state_dict(state_dict["model"], strict=False)
      else:
          ret = model.load_state_dict(state_dict, strict=False)
      return ret
  
imports: from torch.utils import model_zoo
  import torch
  import logging
label_desc: Use image node_id 1 for calling a global function with example usage: load_pretrained_weights(model, model_name, model_configs, weights_path) and returns: ret