class_docstr: `DPQuery` for queries with a `DPQuery` numerator and fixed denominator.
  
  If the number of records per round is a public constant R, `NormalizedQuery`
  could be used with a sum query as the numerator and R as the denominator to
  implement an average. Under some sampling schemes, such as Poisson
  subsampling, the actual number of records in a sample is a private quantity,
  so we cannot use it directly. Using this class with the expected number of
  records as the denominator gives an unbiased estimate of the average.
docstr: Implements `tensorflow_privacy.DPQuery.get_noised_result`.
code: def get_noised_result(self, sample_state, global_state):
      """Implements `tensorflow_privacy.DPQuery.get_noised_result`."""
      (
          noised_sum,
          new_sum_global_state,
          event,
      ) = self._numerator.get_noised_result(
          sample_state, global_state.numerator_state
      )
  
      def normalize(v):
          return tf.truediv(
              v, tf.cast(global_state.denominator, v.dtype)
          )
  
      # The denominator is constant so the privacy cost comes from the numerator.
      return (
          tf.nest.map_structure(normalize, noised_sum),
          self._GlobalState(
              new_sum_global_state, global_state.denominator
          ),
          event,
      )
  
file_path: privacy/tensorflow_privacy/privacy/dp_query/normalized_query.py
imports: import collections
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import dp_query
label_desc: Use image node_id 6 for calling the NormalizedQuery obj's underlying member method code with example usage: obj.get_noised_result(sample_state, global_state) and returns: tf,  self,  event, tf