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.
docstr: Implements `tensorflow_privacy.DPQuery.preprocess_record`.
docstr_tok: ["Implements","`","tensorflow_privacy.DPQuery.preprocess_record","`","."]
code: def preprocess_record(self, params, record):
      """Implements `tensorflow_privacy.DPQuery.preprocess_record`."""
      tf.debugging.assert_scalar(record)
  
      # Shift counts by 0.5 so they are centered at zero. (See comment in
      # `_construct_below_estimate_query`.)
      below = (
          tf.cast(record <= params.current_estimate, tf.float32) - 0.5
      )
      return self._below_estimate_query.preprocess_record(
          params.below_estimate_params, below
      )
  
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 6 for calling the QuantileEstimatorQuery obj's underlying member method code with example usage: obj.preprocess_record(params, record) and returns: self