code: def compute_pro_score(
      y_true: np.ndarray,
      amaps: np.ndarray,
      steps=500,
      non_partial_AUC=False,
  ) -> float:
      fprs, pros = compute_pro(y_true, amaps, steps)
      return (
          compute_non_partial_auc(rescale(fprs), rescale(pros))
          if non_partial_AUC
          else auc(rescale(fprs), rescale(pros))
      )
  
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 15 for calling a global function with example usage: compute_pro_score(y_true, amaps, steps, non_partial_AUC) and returns: unknown