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: Initializes the NormalizedQuery.
  
  Args:
    numerator_query: A SumAggregationDPQuery for the numerator.
    denominator: A value for the denominator. May be None if it will be
      supplied via the set_denominator function before get_noised_result is
      called.
code: def __init__(self, numerator_query, denominator):
      """Initializes the NormalizedQuery.
  
      Args:
        numerator_query: A SumAggregationDPQuery for the numerator.
        denominator: A value for the denominator. May be None if it will be
          supplied via the set_denominator function before get_noised_result is
          called.
      """
      self._numerator = numerator_query
      self._denominator = denominator
  
      assert isinstance(self._numerator, dp_query.SumAggregationDPQuery)
  
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 1 to create a new NormalizedQuery object from inherited base classes: dp_query with example: obj = NormalizedQuery(numerator_query, denominator)