code: def face_verify(
      source_image,
      target_image,
      recognition_model="recognition_04",
      detection_model="detection_03",
  ):
      """
      Runs Cognitive Service Face detection client on two input images.
  
      Parameters:
      - Source_image: Source image file path
      - Target_image: Target image file path
      - Recognition_model: Model used for facial recognition
          Supported values:
              - 'recogntion_01',
              - 'recognition_02,
              - 'recognition_03',
              - 'recognition_04'
      - Detection_model: Model used for facial detection
          Supported values: 'detection_01', 'detection_02', 'detection_03'
      """
      # Detect all faces in the source image
      source_image_stream = open(source_image, "rb")
      source_detected_faces = face_client.face.detect_with_stream(
          image=source_image_stream,
          recognition_model=recognition_model,
          detection_model=detection_model,
      )
      if len(source_detected_faces) == 0:
          print("No face detected from ", source_image)
          return 0
  
      # Detect all faces in the target image
      target_image_stream = open(target_image, "rb")
      target_detected_faces = face_client.face.detect_with_stream(
          image=target_image_stream,
          recognition_model=recognition_model,
          detection_model=detection_model,
      )
      if len(target_detected_faces) == 0:
          print("No face detected from ", target_image)
          return 0
  
      # Verify the first/largest face in the source image
      # To each face in the target image.
      maxConfidence = 0
      for i in range(len(target_detected_faces)):
          target_face_id = target_detected_faces[i].face_id
          verify_result = face_client.face.verify_face_to_face(
              source_detected_faces[0].face_id,
              target_face_id,
          )
          if verify_result.confidence > maxConfidence:
              maxConfidence = verify_result.confidence
      # Return best confidence score as the matching score.
      return maxConfidence
  
label_desc: Use image node_id 1 for calling a global function with example usage: face_verify(source_image, target_image, recognition_model, detection_model) and returns: maxConfidence, int, int