code: def prepare_test_data():
      # -------------------------------------
      # Ground Truth - Binary Masks
      # -------------------------------------
      # ground truth with no tumor
      ground_truth = np.zeros((HEIGHT, WIDTH), dtype=np.uint8)
      save_as_tif("temp_ground_truth_0", ground_truth)
  
      # ground truth with one tumor
      ground_truth[around(HEIGHT // 2), around(1 * WIDTH // 7)] = 1
      save_as_tif("temp_ground_truth_1", ground_truth)
  
      # ground truth with two tumors
      ground_truth[around(HEIGHT // 2), around(2 * WIDTH // 7)] = 1
      save_as_tif("temp_ground_truth_2", ground_truth)
  
      # ground truth with three tumors
      ground_truth[around(HEIGHT // 2), around(3 * WIDTH // 7)] = 1
      save_as_tif("temp_ground_truth_3", ground_truth)
  
      # ground truth with four tumors
      ground_truth[around(HEIGHT // 2), around(4 * WIDTH // 7)] = 1
      save_as_tif("temp_ground_truth_4", ground_truth)
  
      # -------------------------------------
      # predictions - Probability Maps
      # -------------------------------------
  
      # prediction with no tumor
      prob_map = np.zeros((HEIGHT, WIDTH))
      np.save("./tests/testing_data/temp_prob_map_0_0.npy", prob_map)
  
      # prediction with one incorrect tumor
      prob_map[HEIGHT // 2, 5 * WIDTH // 7] = 0.6
      np.save("./tests/testing_data/temp_prob_map_0_1.npy", prob_map)
  
      # prediction with correct first tumors and an incorrect tumor
      prob_map[HEIGHT // 2, 1 * WIDTH // 7] = 0.8
      np.save("./tests/testing_data/temp_prob_map_1_1.npy", prob_map)
  
      # prediction with correct firt two tumors and an incorrect tumor
      prob_map[HEIGHT // 2, 2 * WIDTH // 7] = 0.8
      np.save("./tests/testing_data/temp_prob_map_2_1.npy", prob_map)
  
      # prediction with two incorrect tumors
      prob_map = np.zeros((HEIGHT, WIDTH))
      prob_map[HEIGHT // 2, 5 * WIDTH // 7] = 0.6
      prob_map[HEIGHT // 2, 6 * WIDTH // 7] = 0.4
      np.save("./tests/testing_data/temp_prob_map_0_2.npy", prob_map)
  
      # prediction with correct first tumors and two incorrect tumors
      prob_map[HEIGHT // 2, 1 * WIDTH // 7] = 0.8
      np.save("./tests/testing_data/temp_prob_map_1_2.npy", prob_map)
  
file_path: MONAI/tests/test_lesion_froc.py
imports: from __future__ import annotations
  import os
  import unittest
  from unittest import skipUnless
  import numpy
  from parameterized import parameterized
  from monai.apps.pathology.metrics import LesionFROC
  from monai.utils import optional_import
label_desc: Use image node_id 3 for calling a global function with example usage: prepare_test_data() without return types