code: def set_saved_weights_in_checkpoint_flag(config_obj: "ModelConfig"):
      """Adds a flag to all input feature encoder configs indicating that the weights are saved in the checkpoint.
  
      Next time the model is loaded we will restore pre-trained encoder weights from ludwig model (and not load from cache
      or model hub).
      """
      for input_feature in config_obj.input_features:
          encoder_obj = input_feature.encoder
          encoder_obj.saved_weights_in_checkpoint = True
  
imports: import copy
  import functools
  import os
  import random
  import subprocess
  import weakref
  from collections import OrderedDict
  from collections.abc import Mapping
  from typing import Any, Dict, TYPE_CHECKING
  import numpy
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import PROC_COLUMN
  from ludwig.globals import DESCRIPTION_FILE_NAME
  from ludwig.utils import fs_utils
  from ludwig.utils.fs_utils import find_non_existing_dir_by_adding_suffix
label_desc: Use image node_id 13 for calling a global function with example usage: set_saved_weights_in_checkpoint_flag(config_obj) without return types