class_docstr: Coefficient of discrimination metric.
  
  The coefficient of discrimination measures the differences between the average
  prediction for the positive examples and the average prediction for the
  negative examples.
  
  The formula is: AVG(pred | label = 1) - AVG(pred | label = 0)
  More details can be found in the following paper:
  https://www.tandfonline.com/doi/abs/10.1198/tast.2009.08210
docstr: Initializes coefficient of discrimination metric.
  
  Args:
    name: Metric name.
docstr_tok: ["Initializes","coefficient","of","discrimination","metric",".","Args",":","name",":","Metric","name","."]
code: def __init__(self, name: str = COEFFICIENT_OF_DISCRIMINATION_NAME):
      """Initializes coefficient of discrimination metric.
  
      Args:
        name: Metric name.
      """
      super().__init__(
          metric_util.merge_per_key_computations(
              _coefficient_of_discrimination
          ),
          name=name,
      )
  
imports: from typing import Any, Dict, Iterable, Optional
  import apache_beam
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 1 to create a new CoefficientOfDiscrimination object from inherited base classes: metric_types with example: obj = CoefficientOfDiscrimination(name)