class_docstr: The base metric for semantic segmentation confusion matrix based metrics.
  
  This is the base metric for other metrics such as true postive, true negative,
  false positvie and false negative.
class_docstr_tok: ["The","base","metric","for","semantic","segmentation","confusion","matrix","based","metrics",".","This","is","the","base","metric","for","other","metrics","such","as","true","postive",",","true","negative",",","false","positvie","and","false","negative","."]
docstr: Returns the default metric name.
docstr_tok: ["Returns","the","default","metric","name","."]
code: def _default_name(self) -> str:
      """Returns the default metric name."""
      raise NotImplementedError(
          "Must have a default name for the metric."
      )
  
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 2 for calling the SemanticSegmentationConfusionMatrixMetricBase obj's underlying member method code with example usage: obj._default_name() without return types