code: def test_adaptation_all_equal_noise(self, start_low, geometric):
      # `num_records` equal records. Test that we converge to that record and
      # bounce around it. Unlike the linspace test, the quantile-matching
      # objective is very sharp at the optimum so a decaying learning rate is
      # necessary.
      num_records, vector_size, noise_multiplier = 10, 1000, 0.5
      records = [
          tf.constant(
              vector_size ** (-0.5) * 5.0,
              shape=[vector_size],
              dtype=tf.float32,
          )
      ] * num_records
  
      learning_rate = tf.Variable(1.0)
  
      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=learning_rate,
          clipped_count_stddev=0.0,
          expected_num_records=num_records,
          geometric_update=geometric,
          noise_seed=1,
      )
  
      self._test_noise_multiplier(
          query, records, noise_multiplier, learning_rate=learning_rate
      )
  
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 14 for calling the QAdaClipTreeResSumQueryTest obj's underlying member method code with example usage: obj.test_adaptation_all_equal_noise(start_low, geometric) without return types