class_docstr: DPQuery to estimate target quantile of a univariate distribution.
  
  Uses the algorithm of Andrew et al. (https://arxiv.org/abs/1905.03871). See
  the paper for details and suggested hyperparameter settings.
code: def _construct_below_estimate_query(
      self, below_estimate_stddev, expected_num_records
  ):
      # A DPQuery used to estimate the fraction of records that are less than the
      # current quantile estimate. It accumulates an indicator 0/1 of whether each
      # record is below the estimate, and normalizes by the expected number of
      # records. In practice, we accumulate counts shifted by -0.5 so they are
      # centered at zero. This makes the sensitivity of the below_estimate count
      # query 0.5 instead of 1.0, since the maximum that a single record could
      # affect the count is 0.5. Note that although the l2_norm_clip of the
      # below_estimate query is 0.5, no clipping will ever actually occur
      # because the value of each record is always +/-0.5.
      return normalized_query.NormalizedQuery(
          gaussian_query.GaussianSumQuery(
              l2_norm_clip=0.5, stddev=below_estimate_stddev
          ),
          denominator=expected_num_records,
      )
  
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 QuantileEstimatorQuery obj's underlying member method code with example usage: obj._construct_below_estimate_query(below_estimate_stddev, expected_num_records) and returns: normalized_query