class_docstr: Combines semantic segmentation confusion matrices.
class_docstr_tok: ["Combines","semantic","segmentation","confusion","matrices","."]
code: def merge_accumulators(
      self,
      accumulators: Iterable[Dict[int, Matrix]],
  ) -> Dict[int, Matrix]:
      accumulators = iter(accumulators)
      result = next(accumulators)
      for accumulator in accumulators:
          for class_id, confusion_matrix in accumulator.items():
              if class_id in result:
                  result[class_id] = Matrix(
                      tp=result[class_id].tp + confusion_matrix.tp,
                      tn=result[class_id].tn + confusion_matrix.tn,
                      fp=result[class_id].fp + confusion_matrix.fp,
                      fn=result[class_id].fn + confusion_matrix.fn,
                  )
              else:
                  result[class_id] = confusion_matrix
      return result
  
imports: import abc
  from typing import Any, Dict, Iterable, List, Optional, Union
  import apache_beam
  import numpy
  from tensorflow_model_analysis.contrib.aggregates import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import preprocessors
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 4 for calling the _SemanticSegmentationConfusionMatrixCombiner obj's underlying member method code with example usage: obj.merge_accumulators(accumulators) and returns: result