code: def hub_model_builder(
      model_builder_func: Callable,
      pretrained: bool = False,
      progress: bool = True,
      checkpoint_path: str = "",
      default_config: Optional[Dict[Any, Any]] = None,
      **kwargs: Any,
  ) -> nn.Module:
      """
      model_builder_func (Callable): Model builder function.
      pretrained (bool): Whether to load a pretrained model or not. Default: False.
      progress (bool): Whether or not to display a progress bar to stderr. Default: True.
      checkpoint_path (str): URL of the model weight to download.
      default_config (Dict): Default model configs that is passed to the model builder.
      **kwargs: (Any): Additional model configs. Do not modify the model configuration
      via the kwargs for pretrained model.
      """
      if pretrained:
          assert (
              len(kwargs) == 0
          ), "Do not change kwargs for pretrained model."
  
      if default_config is not None:
          for argument, value in default_config.items():
              if kwargs.get(argument) is None:
                  kwargs[argument] = value
  
      model = model_builder_func(**kwargs)
      if pretrained:
          # All models are loaded onto CPU by default
          checkpoint = load_state_dict_from_url(
              checkpoint_path, progress=progress, map_location="cpu"
          )
          state_dict = checkpoint["model_state"]
          model.load_state_dict(state_dict)
      return model
  
label_desc: Use image node_id 1 for calling a global function with example usage: hub_model_builder(model_builder_func, pretrained, progress, checkpoint_path, default_config) and returns: model