class_docstr: Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC).
  accumulating predictions and the ground-truth during an epoch and applying `compute_roc_auc`.
  
  Args:
      average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``}
          Type of averaging performed if not binary classification. Defaults to ``"macro"``.
  
          - ``"macro"``: calculate metrics for each label, and find their unweighted mean.
              This does not take label imbalance into account.
          - ``"weighted"``: calculate metrics for each label, and find their average,
              weighted by support (the number of true instances for each label).
          - ``"micro"``: calculate metrics globally by considering each element of the label
              indicator matrix as a label.
          - ``"none"``: the scores for each class are returned.
  
      output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
          construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
          lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
          `engine.state` and `output_transform` inherit from the ignite concept:
          https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
          https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
  
  Note:
      ROCAUC expects y to be comprised of 0's and 1's.
      y_pred must either be probability estimates or confidence values.
code: def __init__(
      self,
      average: Average | str = Average.MACRO,
      output_transform: Callable = lambda x: x,
  ) -> None:
      metric_fn = ROCAUCMetric(average=Average(average))
      super().__init__(
          metric_fn=metric_fn,
          output_transform=output_transform,
          save_details=False,
      )
  
file_path: MONAI/monai/handlers/roc_auc.py
imports: from __future__ import annotations
  from collections.abc import Callable
  from monai.handlers.ignite_metric import IgniteMetricHandler
  from monai.metrics import ROCAUCMetric
  from monai.utils import Average
label_desc: Use image node_id 1 to create a new ROCAUC object from inherited base classes: IgniteMetricHandler with example: obj = ROCAUC(average, output_transform)