protected void init()

in tensorflow-framework/src/main/java/org/tensorflow/framework/metrics/AUC.java [587:643]
47 lines of code
11 McCabe index (conditional complexity)

  protected void init(Ops tf) {
    checkIsGraph(tf);
    if (shape != null && !isInitialized()) {
      setTF(tf);
      if (labelWeights != null) {
        // assert that labelWeights are non-negative.

        Op checks =
            tf.withSubScope("AUC")
                .assertThat(
                    tf.math.greaterEqual(
                        labelWeights, cast(tf, tf.constant(0), labelWeights.type())),
                    Collections.singletonList(
                        tf.constant("All values of labelWeights must be non-negative.")));

        Ops ltf =
            tf.withSubScope("updateState")
                .withControlDependencies(Collections.singletonList(checks));

        this.labelWeights = ltf.identity(this.labelWeights);
      }
      if (isMultiLabel()) {
        if (shape == null) {
          throw new IllegalArgumentException("For multiLabel, a shape must be provided");
        }
        if (shape.numDimensions() != 2)
          throw new IllegalArgumentException(
              String.format(
                  "labels must have rank=2 when multiLabel is true. Found rank %d.",
                  shape.numDimensions()));
        numLabels = (int) shape.size(1);
        variableShape = Shape.of(numThresholds, numLabels);
      } else {
        variableShape = Shape.of(numThresholds);
      }

      // Create metric variables

      Operand<T> zero = zeros.call(tf, tf.constant(variableShape), type);
      if (truePositives == null) {
        truePositives = tf.withName(getTruePositivesName()).withInitScope().variable(zero);
      }

      if (falsePositives == null) {
        falsePositives = tf.withName(getFalsePositivesName()).withInitScope().variable(zero);
      }

      if (trueNegatives == null) {
        trueNegatives = tf.withName(getTrueNegativesName()).withInitScope().variable(zero);
      }

      if (falseNegatives == null) {
        falseNegatives = tf.withName(getFalseNegativesName()).withInitScope().variable(zero);
      }
      setInitialized(true);
    }
  }