class_docstr: Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:
  `sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
  sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.
  The input `y_pred` and `y` can be a list of `channel-first` Tensor or a `batch-first` Tensor.
  
  Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
  
  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.
code: def __init__(self, average: Average | str = Average.MACRO) -> None:
      super().__init__()
      self.average = average
  
file_path: MONAI/monai/metrics/rocauc.py
imports: from __future__ import annotations
  import warnings
  from typing import TYPE_CHECKING, cast
  import numpy
  import torch
  from monai.utils import Average, look_up_option
  from .metric import CumulativeIterationMetric
label_desc: Use image node_id 1 to create a new ROCAUCMetric object from inherited base classes: CumulativeIterationMetric with example: obj = ROCAUCMetric(average)