code: def testRougeMultipleExamplesUnweighted(
      self,
      rouge_type,
      example_1_texts,
      example_2_texts,
      expected_precision,
      expected_recall,
      expected_fmeasure,
  ):
      example1 = {
          constants.LABELS_KEY: example_1_texts[0],
          constants.PREDICTIONS_KEY: example_1_texts[1],
      }
      example2 = {
          constants.LABELS_KEY: example_2_texts[0],
          constants.PREDICTIONS_KEY: example_2_texts[1],
      }
      rouge_key = metric_types.MetricKey(name=rouge_type)
      rouge_computation = rouge.Rouge(rouge_type).computations()[0]
      with beam.Pipeline() as pipeline:
          result = _get_result(
              pipeline=pipeline,
              examples=[example1, example2],
              combiner=rouge_computation.combiner,
          )
  
          def check_result(got):
              try:
                  got_metrics = self._check_got(got, rouge_computation)
                  self.assertAlmostEqual(
                      expected_precision,
                      got_metrics[rouge_key].precision,
                  )
                  self.assertAlmostEqual(
                      expected_recall, got_metrics[rouge_key].recall
                  )
                  self.assertAlmostEqual(
                      expected_fmeasure, got_metrics[rouge_key].fmeasure
                  )
  
              except AssertionError as err:
                  raise util.BeamAssertException(err)
  
          util.assert_that(result, check_result, label="result")
  
imports: import statistics
  from absl.testing import parameterized
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  import tensorflow_model_analysis
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.evaluators import metrics_plots_and_validations_evaluator
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import rouge
  from tensorflow_model_analysis.slicer import slicer_lib
  from google.protobuf import text_format
  from rouge_score import tokenizers
label_desc: Use image node_id 8 for calling the RogueTest obj's underlying member method code with example usage: obj.testRougeMultipleExamplesUnweighted(rouge_type, example_1_texts, example_2_texts, expected_precision, expected_recall, expected_fmeasure) without return types