code: def test_adaptation_target_zero(self, reset):
      record1 = tf.constant([8.5])
      record2 = tf.constant([-7.25])
  
      query = quantile_adaptive_clip_tree_query.QAdaClipTreeResSumQuery(
          initial_l2_norm_clip=10.0,
          noise_multiplier=0.0,
          record_specs=tf.TensorSpec([]),
          target_unclipped_quantile=0.0,
          learning_rate=1.0,
          clipped_count_stddev=0.0,
          expected_num_records=2.0,
          geometric_update=False,
      )
  
      global_state = query.initial_global_state()
  
      initial_clip = global_state.sum_state.clip_value
      self.assertAllClose(initial_clip, 10.0)
  
      # On the first two iterations, nothing is clipped, so the clip goes down
      # by 1.0 (the learning rate). When the clip reaches 8.0, one record is
      # clipped, so the clip goes down by only 0.5. After two more iterations,
      # both records are clipped, and the clip norm stays there (at 7.0).
  
      expected_sums = [1.25, 1.25, 0.75, 0.25, 0.0]
      expected_clips = [9.0, 8.0, 7.5, 7.0, 7.0]
      self._test_estimate_clip_expected_sum(
          query,
          global_state,
          [record1, record2],
          expected_sums,
          expected_clips,
          reset=reset,
      )
  
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 5 for calling the QAdaClipTreeResSumQueryTest obj's underlying member method code with example usage: obj.test_adaptation_target_zero(reset) without return types