class_docstr: Fairness indicators metrics.
class_docstr_tok: ["Fairness","indicators","metrics","."]
docstr: Initializes fairness indicators metrics.
  
  Args:
    thresholds: Thresholds to use for fairness metrics.
    name: Metric name.
docstr_tok: ["Initializes","fairness","indicators","metrics",".","Args",":","thresholds",":","Thresholds","to","use","for","fairness","metrics",".","name",":","Metric","name","."]
code: def __init__(
      self,
      thresholds: Sequence[float] = DEFAULT_THRESHOLDS,
      name: str = FAIRNESS_INDICATORS_METRICS_NAME,
  ):
      """Initializes fairness indicators metrics.
  
      Args:
        thresholds: Thresholds to use for fairness metrics.
        name: Metric name.
      """
      super().__init__(
          self.computations_with_logging(),
          thresholds=thresholds,
          name=name,
      )
  
file_path: model-analysis/tensorflow_model_analysis/addons/fairness/metrics/fairness_indicators.py
imports: import collections
  from typing import Any, Dict, List, Optional, Sequence
  from tensorflow_model_analysis.metrics import binary_confusion_matrices
  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_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 2 to create a new FairnessIndicators object from inherited base classes: metric_types with example: obj = FairnessIndicators(thresholds, name)