class_docstr: A combiner statistics generator that computes cross feature statistics.
    
class_docstr_tok: ["A","combiner","statistics","generator","that","computes","cross","feature","statistics","."]
code: def merge_accumulators(
      self,
      accumulators: Iterable[CrossFeatureStatsGeneratorAccumulator],
  ) -> CrossFeatureStatsGeneratorAccumulator:
      it = iter(accumulators)
      result = next(it)
      for accumulator in it:
          for feat_cross, cross_feat_stats in accumulator.items():
              if feat_cross not in result:
                  result[feat_cross] = cross_feat_stats
              else:
                  result[feat_cross] += cross_feat_stats
      return result
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import math
  import random
  from typing import Dict, Iterable, List, Optional, Text
  import numpy
  import pandas
  from pandas import DataFrame, Series
  import pyarrow
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import stats_generator
  from tensorflow_data_validation.utils import stats_util
  from tfx_bsl.arrow import array_util
  from tensorflow_metadata.proto.v0 import path_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 5 for calling the CrossFeatureStatsGenerator obj's underlying member method code with example usage: obj.merge_accumulators(accumulators) and returns: result