code: def test_adaptation_linspace_noise(self, start_low, geometric):
      # `num_records` records equally spaced from 0 to 10 in 0.1 increments.
      # Test that we converge to the correct median value and bounce around it.
      num_records, vector_size, noise_multiplier = 11, 1000, 0.1
      records = [
          tf.constant(
              vector_size ** (-0.5) * x,
              shape=[vector_size],
              dtype=tf.float32,
          )
          for x in np.linspace(0.0, 10.0, num=num_records)
      ]
  
      query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
          initial_l2_norm_clip=(1.0 if start_low else 10.0),
          noise_multiplier=noise_multiplier,
          record_specs=tf.TensorSpec([vector_size]),
          target_unclipped_quantile=0.5,
          learning_rate=1.0,
          clipped_count_stddev=0.0,
          expected_num_records=num_records,
          geometric_update=geometric,
          noise_seed=1,
      )
  
      self._test_noise_multiplier(query, records, noise_multiplier)
  
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 13 for calling the QAdaClipTreeResSumQueryTest obj's underlying member method code with example usage: obj.test_adaptation_linspace_noise(start_low, geometric) without return types