code: def make_auc_roc_metric_fn(label_vocabulary=None):
      """Makes the metric function to compute AUC-ROC.
  
      Args:
        label_vocabulary: A 1-D string Tensor or string list. If `label_vocabulary`
          is None, then the labels are assumed to be castable to bool.
  
      Returns:
        A function that takes `labels` and `predictions`, and returns a dictionary
        mapping "auc_roc" to (tensor, update_op) tuples. This function computes the
        area under the Receiver Operating Characteristic (ROC) curve. Wraps around
        `tf.metrics.auc`, so that it is easier to keep track of the metric name with
        the string key. This only works in the single-task binary-classification
        setup.
  
        Args:
          labels: A 1-D Tensor castable to bool, where True means that the label for
            that instance is class 1, and False means that the label for that
            instance is class 0.
          predictions: A dictionary mapping strings to Tensors. This dictionary
            contains a `predictions` string key which maps to a tf.int64 Tensor, as
            well as `probabilities` and `log_probabilities` string keys which map to
            tf.float32 Tensors. The `probabilities` key must map to a 2-D tf.float32
            Tensor with values in range [0, 1]. The first dimension of this Tensor
            is over the examples, and the second dimension is over the classes.
  
        Returns:
          A dictionary mapping "auc_roc" to (tensor, update_op) tuples.
  
        Raises:
          NotImplementedError: if user tries to use this in a multi-task setup.
          ValueError: if predictions["probabilities"] is not a 2-D Tensor,
            or if the second dimension is not 2.
      """
  
      return _make_auc_metric_fn(
          curve="ROC", label_vocabulary=label_vocabulary
      )
  
imports: from absl import logging
  import tensorflow.compat.v2
label_desc: Use image node_id 3 for calling a global function with example usage: make_auc_roc_metric_fn(label_vocabulary) and returns: _make_auc_metric_fn