code: def roc_auc(
      bn: BayesianNetwork, data: pd.DataFrame, node: str
  ) -> Tuple[List[Tuple[float, float]], float]:
      """
      Build a report of the micro-average Receiver-Operating Characteristics (ROC), and the Area Under the ROC curve
      Micro-average computes roc_auc over all predictions for all states of node.
  
      Args:
          bn (BayesianNetwork): model to compute roc_auc.
          data (pd.DataFrame): test data that will be used to calculate ROC.
          node (str): name of the variable to generate the report for.
  
      Returns:
          roc - auc tuple
           - roc (List[Tuple[float, float]]): list of [(fpr, tpr)] observations.
           - auc float: auc for the node predictions.
  
      Example:
      ::
          >>> from causalnex.structure import StructureModel
          >>> from causalnex.network import BayesianNetwork
          >>>
          >>> sm = StructureModel()
          >>> sm.add_edges_from([
          >>>                    ('rush_hour', 'traffic'),
          >>>                    ('weather', 'traffic')
          >>>                    ])
          >>> bn = BayesianNetwork(sm)
          >>> import pandas as pd
          >>> data = pd.DataFrame({
          >>>                      'rush_hour': [True, False, False, False, True, False, True],
          >>>                      'weather': ['Terrible', 'Good', 'Bad', 'Good', 'Bad', 'Bad', 'Good'],
          >>>                      'traffic': ['heavy', 'light', 'heavy', 'light', 'heavy', 'heavy', 'heavy']
          >>>                      }
          >>> bn = bn.fit_node_states_and_cpds(data)
          >>> test_data = pd.DataFrame({
          >>>                         'rush_hour': [False, False, True, True],
          >>>                         'weather': ['Good', 'Bad', 'Good', 'Bad'],
          >>>                         'traffic': ['light', 'heavy', 'heavy', 'light']
          >>>                         })
          >>> from causalnex.evaluation import roc_auc
          >>> roc, auc = roc_auc(bn, test_data, "traffic")
          >>> print(auc)
          0.75
      """
  
      ground_truth = _build_ground_truth(bn, data, node)
      predictions = bn.predict_probability(data, node)
  
      # update column names to match those of ground_truth
      predictions.rename(
          columns=lambda x: x.lstrip(node + "_"), inplace=True
      )
      predictions = predictions[sorted(predictions.columns)]
  
      fpr, tpr, _ = metrics.roc_curve(
          ground_truth.values.ravel(), predictions.values.ravel()
      )
      roc = list(zip(fpr, tpr))
      auc = metrics.auc(fpr, tpr)
  
      return roc, auc
  
imports: from typing import Dict, List, Tuple
  import pandas
  from sklearn import metrics
  from causalnex.network import BayesianNetwork
label_desc: Use image node_id 2 for calling a global function with example usage: roc_auc(bn, data, node) and returns: roc,  auc