code: def load_data(p):
      """
      Load our saved scores and then put them into a big matrix.
      """
      global scores, keep
      scores = []
      keep = []
  
      for root, ds, _ in os.walk(p):
          for f in ds:
              if not f.startswith("experiment"):
                  continue
              if not os.path.exists(os.path.join(root, f, "scores")):
                  continue
              last_epoch = sorted(
                  os.listdir(os.path.join(root, f, "scores"))
              )
              if len(last_epoch) == 0:
                  continue
              scores.append(
                  np.load(
                      os.path.join(root, f, "scores", last_epoch[-1])
                  )
              )
              keep.append(np.load(os.path.join(root, f, "keep.npy")))
  
      scores = np.array(scores)
      keep = np.array(keep)[:, : scores.shape[1]]
  
      return scores, keep
  
file_path: privacy/research/mi_lira_2021/plot.py
imports: import os
  import scipy.stats
  import numpy
  import matplotlib.pyplot
  from sklearn.metrics import auc, roc_curve
  import functools
  import matplotlib
label_desc: Use image node_id 2 for calling a global function with example usage: load_data(p) and returns: scores,  keep