code: def load_from_saved_model(saved_model_path, custom_objects=None):
      """Loads a keras Model from a SavedModel created by `export_saved_model()`.
  
      This function reinstantiates model state by:
      1) loading model topology from json (this will eventually come
         from metagraph).
      2) loading model weights from checkpoint.
  
      Example:
  
      ```python
      import tensorflow as tf
  
      # Create a tf.keras model.
      model = tf.keras.Sequential()
      model.add(tf.keras.layers.Dense(1, input_shape=[10]))
      model.summary()
  
      # Save the tf.keras model in the SavedModel format.
      path = '/tmp/simple_keras_model'
      tf.keras.experimental.export_saved_model(model, path)
  
      # Load the saved keras model back.
      new_model = tf.keras.experimental.load_from_saved_model(path)
      new_model.summary()
      ```
  
      Args:
        saved_model_path: a string specifying the path to an existing SavedModel.
        custom_objects: Optional dictionary mapping names
            (strings) to custom classes or functions to be
            considered during deserialization.
  
      Returns:
        a keras.Model instance.
      """
      warnings.warn(
          "`tf.keras.experimental.load_from_saved_model` is deprecated"
          "and will be removed in a future version. "
          "Please switch to `tf.keras.models.load_model`."
      )
      # restore model topology from json string
      model_json_filepath = os.path.join(
          compat.as_bytes(saved_model_path),
          compat.as_bytes(constants.ASSETS_DIRECTORY),
          compat.as_bytes(SAVED_MODEL_FILENAME_JSON),
      )
      with gfile.Open(model_json_filepath, "r") as f:
          model_json = f.read()
      model = model_config.model_from_json(
          model_json, custom_objects=custom_objects
      )
  
      # restore model weights
      checkpoint_prefix = os.path.join(
          compat.as_text(saved_model_path),
          compat.as_text(constants.VARIABLES_DIRECTORY),
          compat.as_text(constants.VARIABLES_FILENAME),
      )
      model.load_weights(checkpoint_prefix)
      return model
  
imports: import os
  import warnings
  from tensorflow.python.checkpoint import graph_view
  from tensorflow.python.client import session
  from tensorflow.python.framework import ops
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import optimizer_v1
  from tensorflow.python.keras.optimizer_v2 import optimizer_v2
  from tensorflow.python.keras.saving import model_config
  from tensorflow.python.keras.saving import saving_utils
  from tensorflow.python.keras.saving import utils_v1
  from tensorflow.python.keras.utils import mode_keys
  from tensorflow.python.keras.utils.generic_utils import LazyLoader
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import gfile
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.saved_model import builder
  from tensorflow.python.saved_model import constants
  from tensorflow.python.saved_model import save
  from tensorflow.python.training import saver
  from tensorflow.python.util import compat
  from tensorflow.python.util import nest
label_desc: Use image node_id 10 for calling a global function with example usage: load_from_saved_model(saved_model_path, custom_objects) and returns: model