class_docstr: A combiner for utility analysis sums.
class_docstr_tok: ["A","combiner","for","utility","analysis","sums","."]
docstr: Computes metrics based on the accumulator properties.
code: def compute_metrics(self, acc: AccumulatorType) -> metrics.SumMetrics:
      """Computes metrics based on the accumulator properties."""
      (
          partition_sum,
          clipping_to_min_error,
          clipping_to_max_error,
          expected_l0_bounding_error,
          var_cross_partition_error,
      ) = acc
      return metrics.SumMetrics(
          aggregation=self._metric,
          sum=partition_sum,
          clipping_to_min_error=clipping_to_min_error,
          clipping_to_max_error=clipping_to_max_error,
          expected_l0_bounding_error=expected_l0_bounding_error,
          std_l0_bounding_error=math.sqrt(var_cross_partition_error),
          std_noise=self._get_std_noise(),
          noise_kind=self._params.noise_kind,
      )
  
imports: import abc
  import copy
  from dataclasses import dataclass
  from typing import Any, List, Optional, Tuple
  import numpy
  import math
  import pipeline_dp
  from pipeline_dp import budget_accounting
  from pipeline_dp import dp_computations
  from analysis import metrics
  from analysis import poisson_binomial
  from pipeline_dp import partition_selection
label_desc: Use image node_id 3 for calling the SumCombiner obj's underlying member method code with example usage: obj.compute_metrics(acc) and returns: metrics