code: def get_distance_function(
      distance_function, video_width=None, video_height=None
  ):
      if distance_function == "keypoints_vote":
          return rescaled_keypoints_vote
      elif distance_function == "euclidean":
          return create_normalized_mean_euclidean_distance(
              video_width, video_height
          )
      else:
          raise ValueError(
              "'distance_function' argument should be either 'keypoints_vote' or 'euclidean'"
          )
  
file_path: norfair/demos/profiling/src/utils.py
imports: import json
  import cv2
  import numpy
  import PIL.Image
  import torch
  import torch2trt
  import torchvision.transforms
  import trt_pose.coco
  import trt_pose.models
  from torch2trt import TRTModule
  from trt_pose.parse_objects import ParseObjects
  from norfair import Detection
  from norfair.distances import create_keypoints_voting_distance, create_normalized_mean_euclidean_distance
  from norfair.filter import FilterPyKalmanFilterFactory, NoFilterFactory, OptimizedKalmanFilterFactory
label_desc: Use image node_id 3 for calling a global function with example usage: get_distance_function(distance_function, video_width, video_height) and returns: rescaled_keypoints_vote, create_normalized_mean_euclidean_distance