code: def test_adaptation_linspace(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 = 21
      records = [
          tf.constant(x)
          for x in np.linspace(
              0.0, 10.0, num=num_records, dtype=np.float32
          )
      ]
  
      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=1.0,
          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=0.25
      )
  
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 10 for calling the QAdaClipTreeResSumQueryTest obj's underlying member method code with example usage: obj.test_adaptation_linspace(start_low, geometric) without return types