code: def roc(labels, scores):
      fpr, tpr, _ = roc_curve(labels, scores)
      roc_auc = auc(fpr, tpr)
      return roc_auc
  
imports: import os
  import argparse
  import numpy
  import matplotlib
  import matplotlib.pyplot
  from sklearn.metrics import roc_curve, auc
  from tqdm import tqdm
  from skimage import measure, morphology
  from skimage.segmentation import mark_boundaries
  import paddle
label_desc: Use image node_id 20 for calling a global function with example usage: roc(labels, scores) and returns: roc_auc