class_docstr: Creates multi-class confusion matrix at thresholds from standard inputs.
class_docstr_tok: ["Creates","multi-class","confusion","matrix","at","thresholds","from","standard","inputs","."]
code: def __init__(
      self,
      key: metric_types.MetricKey,
      eval_config: Optional[config_pb2.EvalConfig],
      example_weighted: bool,
      thresholds: List[float],
      extract_label_prediction_and_weight: Optional[Callable[..., Any]],
  ):
      self._key = key
      self._eval_config = eval_config
      self._example_weighted = example_weighted
      self._thresholds = thresholds if thresholds else [0.0]
      self._extract_label_prediction_and_weight = (
          extract_label_prediction_and_weight
      )
  
imports: from typing import Any, Callable, Dict, Iterable, Iterator, List, NamedTuple, Optional, Tuple
  import apache_beam
  import numpy
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
label_desc: Use image node_id 1 to create a new _MultiClassConfusionMatrixCombiner object from inherited base classes: beam with example: obj = _MultiClassConfusionMatrixCombiner(key, eval_config, example_weighted, thresholds, extract_label_prediction_and_weight)