code: def test_sum_with_noise(self):
      record1, record2 = 2.71828, 3.14159
      stddev = 1.0
      clip = 5.0
  
      query = (
          quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
              initial_l2_norm_clip=clip,
              noise_multiplier=stddev / clip,
              target_unclipped_quantile=1.0,
              learning_rate=0.0,
              clipped_count_stddev=0.0,
              expected_num_records=2.0,
          )
      )
  
      noised_sums = []
      for _ in range(1000):
          query_result, _ = test_utils.run_query(
              query, [record1, record2]
          )
          noised_sums.append(query_result.numpy())
  
      result_stddev = np.std(noised_sums)
      self.assertNear(result_stddev, stddev, 0.1)
  
file_path: privacy/tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_sum_query_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import quantile_adaptive_clip_sum_query
  from tensorflow_privacy.privacy.dp_query import test_utils
label_desc: Use image node_id 3 for calling the QuantileAdaptiveClipSumQueryTest obj's underlying member method code with example usage: obj.test_sum_with_noise() without return types