code: def _get_tf_examples_serving_signature(model, tf_transform_output):
      """Returns a serving signature that accepts `tensorflow.Example`."""
  
      # 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(features.LABEL_KEY)
          raw_features = tf.io.parse_example(
              serialized_tf_example, raw_feature_spec
          )
          transformed_features = model.tft_layer_inference(raw_features)
          logging.info(
              "serve_transformed_features = %s", transformed_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
  
file_path: tfx/tfx/experimental/templates/taxi/models/keras_model/model.py
imports: from absl import logging
  import tensorflow
  import tensorflow_transform
  from tfx.experimental.templates.taxi.models import features
  from tfx.experimental.templates.taxi.models.keras_model import constants
  from tfx_bsl.public import tfxio
label_desc: Use image node_id 1 for calling a global function with example usage: _get_tf_examples_serving_signature(model, tf_transform_output) and returns: serve_tf_examples_fn, dict