code: def _get_transform_features_signature(
      model, schema, tf_transform_output
  ):
      """Returns a serving signature that applies tf.Transform to features."""
  
      if (
          tf_transform_output is None
      ):  # Transform component is not used.
  
          @tf.function(
              input_signature=[
                  tf.TensorSpec(
                      shape=[None], dtype=tf.string, name="examples"
                  )
              ]
          )
          def transform_features_fn(serialized_tf_example):
              """Returns the transformed_features to be fed as input to evaluator."""
              raw_feature_spec = schema_utils.schema_as_feature_spec(
                  schema
              ).feature_spec
              raw_features = tf.io.parse_example(
                  serialized_tf_example, raw_feature_spec
              )
              logging.info("eval_features = %s", raw_features)
              return raw_features
  
      else:  # Transform component exists.
          # 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_eval = (
              tf_transform_output.transform_features_layer()
          )
  
          @tf.function(
              input_signature=[
                  tf.TensorSpec(
                      shape=[None], dtype=tf.string, name="examples"
                  )
              ]
          )
          def transform_features_fn(serialized_tf_example):
              """Returns the transformed_features to be fed as input to evaluator."""
              raw_feature_spec = tf_transform_output.raw_feature_spec()
              raw_features = tf.io.parse_example(
                  serialized_tf_example, raw_feature_spec
              )
              transformed_features = model.tft_layer_eval(raw_features)
              logging.info(
                  "eval_transformed_features = %s", transformed_features
              )
              return transformed_features
  
      return transform_features_fn
  
file_path: tfx/tfx/experimental/templates/penguin/models/model.py
imports: from typing import List
  from absl import logging
  import tensorflow
  from tensorflow import keras
  import tensorflow_transform
  from tensorflow_transform.tf_metadata import schema_utils
  from tfx import v1
  from tfx.experimental.templates.penguin.models import constants
  from tfx.experimental.templates.penguin.models import features
  from tfx_bsl.public import tfxio
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 2 for calling a global function with example usage: _get_transform_features_signature(model, schema, tf_transform_output) and returns: transform_features_fn, raw_features, transformed_features