code: def extract_private_output(self, accumulator, budget):
      # Simple implementation of Laplace mechanism.
      max_abs_value = np.maximum(
          np.abs(self._min_value), np.abs(self._max_value)
      )
      l1_sensitivity = (
          self._aggregate_params.max_contributions_per_partition
          * self._aggregate_params.max_partitions_contributed
          * max_abs_value
      )
  
      # Here is an example of using noise hardened against the floating point attacks.
      # Warning: Do not use in production unsafe noise, i.e. which isn't hardened against the
      # floating point attacks.
      mechanism = dp_mechanisms.LaplaceMechanism(
          epsilon=budget.eps, sensitivity=l1_sensitivity
      )
      return mechanism.add_noise(1.0 * accumulator)
  
file_path: pipelinedp/examples/experimental/beam_combine_fn.py
imports: from absl import app
  from absl import flags
  import apache_beam
  from apache_beam.runners.portability import fn_api_runner
  import pipeline_dp
  from pipeline_dp import private_beam
  from examples.movie_view_ratings.common_utils import ParseFile
  import numpy
  from pydp.algorithms import numerical_mechanisms
label_desc: Use image node_id 5 for calling the DPSumCombineFn obj's underlying member method code with example usage: obj.extract_private_output(accumulator, budget) and returns: mechanism