class_docstr: A simple pipeline for the integration testing on RankingPipeline.
code: def scoring_function(self, context_features, example_features, mode):
      with tf.compat.v1.name_scope("input_layer"):
          input_features = [
              tf.compat.v1.layers.flatten(context_features[name])
              for name in sorted(_context_feature_columns())
          ] + [
              tf.compat.v1.layers.flatten(example_features[name])
              for name in sorted(_example_feature_columns())
          ]
          input_layer = tf.concat(input_features, 1)
  
      is_training = mode == tf_estimator.ModeKeys.TRAIN
      cur_layer = tf.compat.v1.layers.batch_normalization(
          input_layer, training=is_training
      )
  
      hidden_layer_dims = [16, 8]
      for layer_width in hidden_layer_dims:
          cur_layer = tf.compat.v1.layers.dense(
              cur_layer, units=layer_width
          )
          cur_layer = tf.compat.v1.layers.batch_normalization(
              cur_layer, training=is_training
          )
          cur_layer = tf.nn.relu(cur_layer)
      return tf.compat.v1.layers.dense(cur_layer, units=1)
  
imports: import os
  from absl.testing import parameterized
  import tensorflow
  from tensorflow import estimator
  import tensorflow_ranking
  from google.protobuf import text_format
  from tensorflow_ranking.extension import pipeline
  from tensorflow_serving.apis import input_pb2
label_desc: Use image node_id 1 for calling the RankingPipelineClient obj's underlying member method code with example usage: obj.scoring_function(context_features, example_features, mode) and returns: tf