def METHOD_NAME(self, eval_examples_and_features, mock_logits_expected_predictions):
    eval_examples, eval_features = eval_examples_and_features
    postprocessor_class = ExtractivePipelinePostProcessor  
    scorer_type='weighted_sum_target_type_and_score_diff'
    expected_start_end_index, predictions = self._start_end_target_type_logits(eval_examples, eval_features, mock_logits_expected_predictions)
    postprocessor = postprocessor_class(k=5, n_best_size=3, max_answer_length=30,
                                        scorer_type=SupportedSpanScorers(scorer_type),
                                        single_context_multiple_passages=False)
    example_predictions = postprocessor.process(eval_examples, eval_features, predictions)
    print('example_predictions='+str(example_predictions))
    for example_id, preds in  example_predictions.items():
        predicted = preds[0]
        expected = mock_logits_expected_predictions['expected_predictions'][example_id]
        expected_start_end = expected_start_end_index[example_id]
        ptargettype = int(np.argmax(predicted['target_type_logits']))
        assert (predicted['question'] == expected['question'] )
        assert (predicted['language'] == expected['language'] )
        assert (predicted['start_index'], predicted['end_index']) == expected_start_end
        assert predicted['passage_index']  == expected['passage_index'] 
        assert ptargettype  == expected['target_type'] 