code: def write_saved_transform_from_session(
      session, inputs, outputs, export_path, as_text=False
  ):
      """Write the current session as a SavedModel."""
      predict_signature_def = tf.compat.v1.saved_model.signature_def_utils.predict_signature_def(
          inputs, outputs
      )
  
      builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(
          export_path
      )
      builder.add_meta_graph_and_variables(
          session,
          [constants.TRANSFORM_TAG],
          signature_def_map={
              constants.TRANSFORM_SIGNATURE: predict_signature_def
          },
          assets_collection=tf.compat.v1.get_collection(
              tf.compat.v1.GraphKeys.ASSET_FILEPATHS
          ),
      )
      builder.save(as_text)
  
file_path: transform/tensorflow_transform/saved/saved_transform_io.py
imports: import importlib
  import os
  import re
  import tensorflow
  from tensorflow_transform.py_func import pyfunc_helper
  from tensorflow_transform.saved import constants
  from tensorflow_transform.saved import saved_model_loader
  from tensorflow.core.protobuf import struct_pb2
  from tensorflow.python.framework import ops
  from tensorflow.python.saved_model import nested_structure_coder
  from tensorflow.python.training import saver
label_desc: Use image node_id 7 for calling a global function with example usage: write_saved_transform_from_session(session, inputs, outputs, export_path, as_text) without return types