code: def get_serialized(obj):
      with generic_utils.skip_failed_serialization():
          # Store the config dictionary, which may be used when reviving the object.
          # When loading, the program will attempt to revive the object from config,
          # and if that fails, the object will be revived from the SavedModel.
          return generic_utils.serialize_keras_object(obj)
  
imports: from tensorflow.python.keras.mixed_precision import policy
  from tensorflow.python.keras.saving.saved_model import base_serialization
  from tensorflow.python.keras.saving.saved_model import constants
  from tensorflow.python.keras.saving.saved_model import save_impl
  from tensorflow.python.keras.saving.saved_model import serialized_attributes
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.trackable import data_structures
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 for calling a global function with example usage: get_serialized(obj) and returns: generic_utils