code: def _get_tf_examples_serving_signature(model, tf_transform_output):
      """Returns a function that parses a serialized tf.Example and applies TFT."""
  
      # We need to track the layers in the model in order to save it.
      # TODO(b/162357359): Revise once the bug is resolved.
      model.tft_layer_inference = (
          tf_transform_output.transform_features_layer()
      )
  
      @tf.function(
          input_signature=[
              tf.TensorSpec(
                  shape=[None], dtype=tf.string, name="examples"
              )
          ]
      )
      def serve_tf_examples_fn(serialized_tf_example):
          """Returns the output to be used in the serving signature."""
          raw_feature_spec = tf_transform_output.raw_feature_spec()
          # Remove label feature since these will not be present at serving time.
          raw_feature_spec.pop(_LABEL_KEY)
          raw_features = tf.io.parse_example(
              serialized_tf_example, raw_feature_spec
          )
          transformed_features = model.tft_layer_inference(raw_features)
  
          outputs = model(transformed_features)
          # TODO(b/154085620): Convert the predicted labels from the model using a
          # reverse-lookup (opposite of transform.py).
          return {"outputs": outputs}
  
      return serve_tf_examples_fn
  
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 7 for calling a global function with example usage: _get_tf_examples_serving_signature(model, tf_transform_output) and returns: serve_tf_examples_fn, dict