class_docstr: A combiner for aggregating error metrics across partitions
class_docstr_tok: ["A","combiner","for","aggregating","error","metrics","across","partitions"]
code: def __init__(
      self,
      dp_metrics: List[pipeline_dp.Metric],
      public_partitions: bool,
      weight_fn: Callable[
          [metrics.PerPartitionMetrics], float
      ] = equal_weight_fn,
  ):
      self._dp_metrics = dp_metrics
      self._public_partitions = public_partitions
      self._weight_fn = weight_fn
  
imports: import copy
  import pipeline_dp
  from analysis import metrics
  import dataclasses
  from typing import List, Tuple, Callable
  import math
label_desc: Use image node_id 1 to create a new CrossPartitionCombiner object from inherited base classes: pipeline_dp.combiners with example: obj = CrossPartitionCombiner(dp_metrics, public_partitions, weight_fn)