code: def test_pr_purve(self):
      random_labels = np.array(
          [
              0,
              1,
              1,
              0,
              1,
              0,
              1,
              0,
              0,
              0,
              0,
              0,
              1,
              0,
              1,
              0,
              0,
              0,
              0,
              0,
              0,
              1,
              1,
              0,
              1,
              1,
              1,
              0,
              1,
              0,
              1,
              1,
              0,
              1,
              0,
              0,
              1,
              0,
              1,
              0,
              1,
              0,
              1,
              0,
              0,
              1,
              1,
              1,
              0,
              0,
              1,
              1,
              1,
              1,
              0,
              0,
              1,
              1,
              1,
              0,
              1,
              1,
              1,
              0,
              1,
              1,
              1,
              1,
              0,
              0,
              1,
              0,
              1,
              1,
              1,
              1,
              0,
              1,
              1,
              0,
              0,
              1,
              1,
              1,
              1,
              0,
              1,
              0,
              1,
              0,
              1,
              1,
              1,
              0,
              1,
              1,
              1,
              1,
              1,
              0,
          ]
      )
      random_probs = np.array(
          [
              0.33327776,
              0.30032885,
              0.79012837,
              0.04306813,
              0.65221544,
              0.58481968,
              0.28305522,
              0.53795795,
              0.00729739,
              0.52266951,
              0.22464247,
              0.11262435,
              0.41573075,
              0.92493992,
              0.73066758,
              0.43867735,
              0.27955449,
              0.56975382,
              0.53933028,
              0.34392824,
              0.30312509,
              0.81732807,
              0.55408544,
              0.3969487,
              0.31768033,
              0.24353266,
              0.47198005,
              0.19999122,
              0.05788022,
              0.24046305,
              0.04651082,
              0.30061738,
              0.78321545,
              0.82670207,
              0.49200517,
              0.80904619,
              0.96711993,
              0.3160946,
              0.01049424,
              0.60108337,
              0.56508792,
              0.83729429,
              0.9717386,
              0.46306053,
              0.80232138,
              0.24166823,
              0.7393237,
              0.50820418,
              0.04944932,
              0.53854157,
              0.10765172,
              0.84723855,
              0.20518299,
              0.3143431,
              0.51299074,
              0.47065695,
              0.54267833,
              0.1812676,
              0.06265177,
              0.34110327,
              0.30915171,
              0.91870169,
              0.91309447,
              0.31395817,
              0.36780571,
              0.98297986,
              0.00594547,
              0.52839042,
              0.70229202,
              0.37779588,
              0.15207045,
              0.59759632,
              0.72397032,
              0.71502195,
              0.90135725,
              0.43970107,
              0.17123532,
              0.08785938,
              0.04986818,
              0.62702444,
              0.69171023,
              0.30537792,
              0.30285433,
              0.27124347,
              0.27693729,
              0.7136039,
              0.48022489,
              0.20916285,
              0.2018599,
              0.92401008,
              0.30189681,
              0.46862626,
              0.96353024,
              0.30468533,
              0.68281294,
              0.30623562,
              0.40795975,
              0.76824531,
              0.89824215,
              0.69845035,
          ],
          dtype=np.float16,
      )
      compare_proto(
          summary.pr_curve("tag", random_labels, random_probs, 1), self
      )
  
file_path: tensorboardX/tests/test_pr_curve.py
imports: import unittest
  import torch
  import numpy
  from tensorboardX import SummaryWriter
  from tensorboardX import summary
  from .expect_reader import compare_proto
label_desc: Use image node_id 2 for calling the PRCurveTest obj's underlying member method code with example usage: obj.test_pr_purve() without return types