code: def plot_roc(fpr, tpr, score, save_dir, class_name, tag="pixel"):
      plt.plot(
          fpr,
          tpr,
          marker="o",
          color="k",
          label=f"AUROC Score: {score:.3f}",
      )
      plt.xlabel("FPR: FP / (TN + FP)", fontsize=14)
      plt.ylabel("TPR: TP / (TP + FN)", fontsize=14)
      plt.legend(fontsize=14)
      plt.tight_layout()
      plt.savefig(f"{save_dir}/{class_name}_{tag}_roc_curve.png")
      plt.close()
  
imports: import os
  import sys
  import random
  import argparse
  import datetime
  import numpy
  import pandas
  import matplotlib.pyplot
  from tqdm import tqdm
  from sklearn.metrics import roc_curve, precision_recall_curve
  import paddle
  from paddle.io import DataLoader
  import qinspector.uad.datasets.mvtec
  from qinspector.uad.models.patchcore import get_model
  from qinspector.uad.utils.utils import *
  from qinspector.cvlib.uad_configs import ConfigParser
label_desc: Use image node_id 4 for calling a global function with example usage: plot_roc(fpr, tpr, score, save_dir, class_name, tag) without return types