code: def test_face_distance(self):
      img_a1 = api.load_image_file(
          os.path.join(
              os.path.dirname(__file__), "test_images", "obama.jpg"
          )
      )
      img_a2 = api.load_image_file(
          os.path.join(
              os.path.dirname(__file__), "test_images", "obama2.jpg"
          )
      )
      img_a3 = api.load_image_file(
          os.path.join(
              os.path.dirname(__file__), "test_images", "obama3.jpg"
          )
      )
  
      img_b1 = api.load_image_file(
          os.path.join(
              os.path.dirname(__file__), "test_images", "biden.jpg"
          )
      )
  
      face_encoding_a1 = api.face_encodings(img_a1)[0]
      face_encoding_a2 = api.face_encodings(img_a2)[0]
      face_encoding_a3 = api.face_encodings(img_a3)[0]
      face_encoding_b1 = api.face_encodings(img_b1)[0]
  
      faces_to_compare = [
          face_encoding_a2,
          face_encoding_a3,
          face_encoding_b1,
      ]
  
      distance_results = api.face_distance(
          faces_to_compare, face_encoding_a1
      )
  
      # 0.6 is the default face distance match threshold. So we'll spot-check that the numbers returned
      # are above or below that based on if they should match (since the exact numbers could vary).
      self.assertEqual(type(distance_results), np.ndarray)
      self.assertLessEqual(distance_results[0], 0.6)
      self.assertLessEqual(distance_results[1], 0.6)
      self.assertGreater(distance_results[2], 0.6)
  
imports: import unittest
  import os
  import numpy
  from click.testing import CliRunner
  from face_recognition import api
  from face_recognition import face_recognition_cli
  from face_recognition import face_detection_cli
label_desc: Use image node_id 17 for calling the Test_face_recognition obj's underlying member method code with example usage: obj.test_face_distance() without return types