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.get_noised_result`.
docstr_tok: ["Implements","`","tensorflow_privacy.DPQuery.get_noised_result","`","."]
code: def get_noised_result(self, sample_state, global_state):
      """Implements `tensorflow_privacy.DPQuery.get_noised_result`."""
      (
          below_estimate_result,
          new_below_estimate_state,
          below_estimate_event,
      ) = self._below_estimate_query.get_noised_result(
          sample_state, global_state.below_estimate_state
      )
  
      # Unshift below_estimate percentile by 0.5. (See comment in
      # `_construct_below_estimate_query`.)
      below_estimate = below_estimate_result + 0.5
  
      # Protect against out-of-range estimates.
      below_estimate = tf.minimum(1.0, tf.maximum(0.0, below_estimate))
  
      loss_grad = below_estimate - global_state.target_quantile
  
      update = global_state.learning_rate * loss_grad
  
      if self._geometric_update:
          new_estimate = global_state.current_estimate * tf.math.exp(
              -update
          )
      else:
          new_estimate = global_state.current_estimate - update
  
      new_global_state = global_state._replace(
          current_estimate=new_estimate,
          below_estimate_state=new_below_estimate_state,
      )
  
      return new_estimate, new_global_state, below_estimate_event
  
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 7 for calling the QuantileEstimatorQuery obj's underlying member method code with example usage: obj.get_noised_result(sample_state, global_state) and returns: new_estimate,  new_global_state,  below_estimate_event