code: def export_serving_model(tf_transform_output, model, output_dir):
      """Exports a keras model for serving.
  
      Args:
        tf_transform_output: Wrapper around output of tf.Transform.
        model: A keras model to export for serving.
        output_dir: A directory where the model will be exported to.
      """
      # The layer has to be saved to the model for keras tracking purpases.
      model.tft_layer = tf_transform_output.transform_features_layer()
  
      signatures = {
          "serving_default": _get_tf_examples_serving_signature(
              model, tf_transform_output
          ),
          "transform_features": _get_transform_features_signature(
              model, tf_transform_output
          ),
      }
  
      model.save(output_dir, save_format="tf", signatures=signatures)
  
imports: from typing import List
  import absl
  from keras.callbacks import LambdaCallback
  import tensorflow
  import tensorflow_transform
  from tfx.components.trainer.executor import TrainerFnArgs
  from tfx.components.trainer.fn_args_utils import DataAccessor
  from tfx_bsl.tfxio import dataset_options
label_desc: Use image node_id 9 for calling a global function with example usage: export_serving_model(tf_transform_output, model, output_dir) without return types