code: def prc_auc_score(y: np.ndarray, y_pred: np.ndarray) -> float:
      """Compute area under precision-recall curve
  
      Parameters
      ----------
      y: np.ndarray
          A numpy array of shape `(N, n_classes)` or `(N,)` with true labels
      y_pred: np.ndarray
          Of shape `(N, n_classes)` with class probabilities.
  
      Returns
      -------
      float
          The area under the precision-recall curve. A number between 0 and 1.
      """
      precision, recall, _ = precision_recall_curve(
          y[:, 1], y_pred[:, 1]
      )
      return auc(recall, precision)
  
imports: import numpy
  import scipy.stats
  from sklearn.metrics import matthews_corrcoef
  from sklearn.metrics import recall_score
  from sklearn.metrics import cohen_kappa_score
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import precision_score
  from sklearn.metrics import precision_recall_curve
  from sklearn.metrics import auc
  from sklearn.metrics import jaccard_score
  from sklearn.metrics import f1_score
  from sklearn.metrics import roc_auc_score
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import balanced_accuracy_score
  from sklearn.metrics import top_k_accuracy_score
label_desc: Use image node_id 5 for calling a global function with example usage: prc_auc_score(y, y_pred) and returns: auc