code: def test_adaptation_all_equal(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 = 20
      records = [tf.constant(5.0)] * 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=0.0,
          record_specs=tf.TensorSpec([]),
          target_unclipped_quantile=0.5,
          learning_rate=learning_rate,
          clipped_count_stddev=0.0,
          expected_num_records=num_records,
          geometric_update=geometric,
      )
  
      self._test_estimate_clip_converge(
          query,
          records,
          expected_clip=5.0,
          tolerance=0.5,
          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 11 for calling the QAdaClipTreeResSumQueryTest obj's underlying member method code with example usage: obj.test_adaptation_all_equal(start_low, geometric) without return types