code: def _example_serving_receiver_fn(tf_transform_output, schema):
      """Build the serving in inputs.
  
      Args:
        tf_transform_output: A TFTransformOutput.
        schema: the schema of the input data.
  
      Returns:
        Tensorflow graph which parses examples, applying tf-transform to them.
      """
      raw_feature_spec = _get_raw_feature_spec(schema)
      raw_feature_spec.pop(_LABEL_KEY)
  
      raw_input_fn = (
          tf_estimator.export.build_parsing_serving_input_receiver_fn(
              raw_feature_spec, default_batch_size=None
          )
      )
      serving_input_receiver = raw_input_fn()
  
      transformed_features = tf_transform_output.transform_raw_features(
          serving_input_receiver.features
      )
  
      return tf_estimator.export.ServingInputReceiver(
          transformed_features, serving_input_receiver.receiver_tensors
      )
  
imports: import absl
  import tensorflow
  from tensorflow import estimator
  import tensorflow_model_analysis
  import tensorflow_transform
  from tensorflow_transform.tf_metadata import schema_utils
  from tfx.components.trainer import executor
  from tfx.utils import io_utils
  from tfx.utils import path_utils
  from tfx_bsl.public.tfxio import TensorFlowDatasetOptions
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 6 for calling a global function with example usage: _example_serving_receiver_fn(tf_transform_output, schema) and returns: tf_estimator