code: def _get_transform_features_signature(model, tf_transform_output):
      """Returns a serving signature that applies tf.Transform to features."""
  
      # 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)
          return transformed_features
  
      return transform_features_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 8 for calling a global function with example usage: _get_transform_features_signature(model, tf_transform_output) and returns: transform_features_fn, transformed_features