class_docstr: A simple pipeline for the integration testing on RankingPipeline.
code: def build_best_exporter_pipeline(self, hparams):
      estimator = tfr.estimator.EstimatorBuilder(
          context_feature_columns=_context_feature_columns(),
          example_feature_columns=_example_feature_columns(),
          scoring_function=self.scoring_function,
          hparams=hparams,
      ).make_estimator()
      return pipeline.RankingPipeline(
          context_feature_columns=_context_feature_columns(),
          example_feature_columns=_example_feature_columns(),
          hparams=hparams,
          estimator=estimator,
          label_feature_name="utility",
          label_feature_type=tf.float32,
          best_exporter_metric="metric/ndcg_5",
      )
  
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 3 for calling the RankingPipelineClient obj's underlying member method code with example usage: obj.build_best_exporter_pipeline(hparams) and returns: pipeline