class_docstr: Combines semantic segmentation confusion matrices.
class_docstr_tok: ["Combines","semantic","segmentation","confusion","matrices","."]
docstr: Initializes the semantic segmentation confusion matrix combiner.
  
  Args:
   key: The metric key to identify the metric output.
   class_ids: The ids of classes to calculate metrics.
   ignore_ground_truth_id: The id of the ignored class. It could be used for
     the class that should not be counted (e.g. masked pixels).
docstr_tok: ["Initializes","the","semantic","segmentation","confusion","matrix","combiner",".","Args",":","key",":","The","metric","key","to","identify","the","metric","output",".","class_ids",":","The","ids","of","classes","to","calculate","metrics",".","ignore_ground_truth_id",":","The","id","of","the","ignored","class",".","It","could","be","used","for","the","class","that","should","not","be","counted","(","e.g",".","masked","pixels",")","."]
code: def __init__(
      self,
      key: metric_types.MetricKey,
      class_ids: List[int],
      ignore_ground_truth_id: Optional[int] = None,
  ):
      """Initializes the semantic segmentation confusion matrix combiner.
  
      Args:
       key: The metric key to identify the metric output.
       class_ids: The ids of classes to calculate metrics.
       ignore_ground_truth_id: The id of the ignored class. It could be used for
         the class that should not be counted (e.g. masked pixels).
      """
      self._key = key
      self._class_ids = class_ids
      self._ignore_ground_truth_id = ignore_ground_truth_id
  
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 1 to create a new _SemanticSegmentationConfusionMatrixCombiner object from inherited base classes: beam with example: obj = _SemanticSegmentationConfusionMatrixCombiner(key, class_ids, ignore_ground_truth_id)