code: def test_tfr_bert_model_builder(self):
      encoder_config = encoders.EncoderConfig(
          bert=encoders.BertEncoderConfig(
              vocab_size=30522, num_layers=1
          )
      )
      encoder_network = encoders.build_encoder(encoder_config)
      preprocess_dict = {}
      scorer = tfrbert_task.TFRBertScorer(
          encoder=encoder_network, bert_output_dropout=0.1
      )
  
      example_feature_spec = {
          "input_word_ids": tf.io.FixedLenFeature(
              shape=(None,), dtype=tf.int64
          ),
          "input_mask": tf.io.FixedLenFeature(
              shape=(None,), dtype=tf.int64
          ),
          "input_type_ids": tf.io.FixedLenFeature(
              shape=(None,), dtype=tf.int64
          ),
      }
      context_feature_spec = {}
  
      model_builder = tfrbert_task.TFRBertModelBuilder(
          input_creator=tfr_model.FeatureSpecInputCreator(
              context_feature_spec, example_feature_spec
          ),
          preprocessor=tfr_model.PreprocessorWithSpec(preprocess_dict),
          scorer=scorer,
          mask_feature_name="example_list_mask",
          name="tfrbert_model",
      )
      model = model_builder.build()
  
      output = model(self._create_input_data())
      self.assertAllEqual(output.shape.as_list(), [12, 10])
  
imports: import os
  from absl import flags
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from official.nlp.configs import encoders
  from tensorflow_ranking.extension.premade import tfrbert_task
  from tensorflow_ranking.python import data
  from tensorflow_ranking.python.keras import model
  from tensorflow_serving.apis import input_pb2
label_desc: Use image node_id 2 for calling the ModelBuilderTest obj's underlying member method code with example usage: obj.test_tfr_bert_model_builder() without return types