class_docstr: Iterative process to estimate target quantile of a univariate distribution.
  
  Unlike the base class, this uses a `TreeResidualSumQuery` to estimate the
  fraction below estimate with an exact denominator. This assumes that below
  estimate value is used in a SGD-like update and we want to privatize the
  cumsum of the below estimate.
  
  See "Practical and Private (Deep) Learning without Sampling or Shuffling"
  (https://arxiv.org/abs/2103.00039) for tree aggregation and privacy
  accounting, and "Differentially Private Learning with Adaptive Clipping"
  (https://arxiv.org/abs/1905.03871) for how below estimate is used in a
  SGD-like algorithm.
code: def reset_state(self, noised_results, global_state):
      new_numerator_state = self._below_estimate_query._numerator.reset_state(  # pylint: disable=protected-access,line-too-long
          noised_results,
          global_state.below_estimate_state.numerator_state,
      )
      new_below_estimate_state = (
          global_state.below_estimate_state._replace(
              numerator_state=new_numerator_state
          )
      )
      return global_state._replace(
          below_estimate_state=new_below_estimate_state
      )
  
imports: import collections
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import dp_query
  from tensorflow_privacy.privacy.dp_query import gaussian_query
  from tensorflow_privacy.privacy.dp_query import no_privacy_query
  from tensorflow_privacy.privacy.dp_query import normalized_query
  from tensorflow_privacy.privacy.dp_query import tree_aggregation_query
label_desc: Use image node_id 2 for calling the TreeQuantileEstimatorQuery obj's underlying member method code with example usage: obj.reset_state(noised_results, global_state) and returns: global_state