class_docstr: Implements DPQuery interface for an average query with no privacy.
  
  Accumulates vectors and normalizes by the total number of accumulated vectors.
  Under some sampling schemes, such as Poisson subsampling, the number of
  records in a sample is a private quantity, so we lose all privacy guarantees
  by using the number of records directly to normalize.
  
  Also allows weighted accumulation, unlike the base class DPQuery. In a private
  implementation of weighted average, the weight would have to be itself
  privatized.
docstr: Implements `tensorflow_privacy.DPQuery.accumulate_record`.
  
  Optional `weight` argument allows weighted accumulation.
  
  Args:
    params: The parameters for the sample.
    sample_state: The current sample state.
    record: The record to accumulate.
    weight: Optional weight for the record.
  
  Returns:
    The updated sample state.
code: def accumulate_record(self, params, sample_state, record, weight=1):
      """Implements `tensorflow_privacy.DPQuery.accumulate_record`.
  
      Optional `weight` argument allows weighted accumulation.
  
      Args:
        params: The parameters for the sample.
        sample_state: The current sample state.
        record: The record to accumulate.
        weight: Optional weight for the record.
  
      Returns:
        The updated sample state.
      """
      weighted_record = tf.nest.map_structure(
          lambda t: weight * t, record
      )
      return self.accumulate_preprocessed_record(
          sample_state, (weighted_record, tf.cast(weight, tf.float32))
      )
  
file_path: privacy/tensorflow_privacy/privacy/dp_query/no_privacy_query.py
imports: import dp_accounting
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import dp_query
label_desc: Use image node_id 3 for calling the NoPrivacyAverageQuery obj's underlying member method code with example usage: obj.accumulate_record(params, sample_state, record, weight) and returns: self