code: def test_sum_with_noise(self):
      vector_size = 1000
      record1 = tf.constant(2.71828, shape=[vector_size])
      record2 = tf.constant(3.14159, shape=[vector_size])
      stddev = 1.0
      clip = 5.0
  
      query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
          initial_l2_norm_clip=clip,
          noise_multiplier=stddev / clip,
          record_specs=tf.TensorSpec([vector_size]),
          target_unclipped_quantile=1.0,
          learning_rate=0.0,
          clipped_count_stddev=0.0,
          expected_num_records=2.0,
          noise_seed=1,
      )
  
      query_result, _ = test_utils.run_query(query, [record1, record2])
  
      result_stddev = np.std(query_result.numpy())
      self.assertNear(result_stddev, stddev, 0.1)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import quantile_adaptive_clip_tree_query
  from tensorflow_privacy.privacy.dp_query import test_utils
label_desc: Use image node_id 3 for calling the QAdaClipTreeResSumQueryTest obj's underlying member method code with example usage: obj.test_sum_with_noise() without return types