code: def compute_roc_score(
      y_true: np.ndarray,
      amaps: np.ndarray,
      steps=500,
      non_partial_AUC=False,
  ) -> float:
      # fprs, tprs = compute_roc(masks, amaps, steps)
      fprs, tprs, thresholds = roc_curve(y_true, amaps)
      return (
          compute_non_partial_auc(fprs, tprs)
          if non_partial_AUC
          else auc(fprs, tprs)
      )
  
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 16 for calling a global function with example usage: compute_roc_score(y_true, amaps, steps, non_partial_AUC) and returns: unknown