code: def viusalize(image, result, output_path):
      if image["transform_info"] is None:
          image = image["image"]
      else:
          # mean = np.array([0.485 * 255, 0.456 * 255, 0.406 * 255], dtype=np.float32)
          # std = np.array([0.229 * 255, 0.224 * 255, 0.225 * 255], dtype=np.float32)
          mean = np.array(
              [0.406 * 255, 0.456 * 255, 0.485 * 255], dtype=np.float32
          )
          std = np.array([1, 1, 1], dtype=np.float32)
          p_b_w, p_b_h, padded_h, padded_w = image["transform_info"]
          image = image["image"][0]
          image = np.transpose(image, (1, 2, 0))
          image = cv2.resize(
              image,
              (padded_w, padded_h),
              interpolation=cv2.INTER_LINEAR,
          )
          image = image[
              p_b_h : padded_h - p_b_h, p_b_w : padded_w - p_b_w, :
          ]
          image = (image * std + mean).astype(np.int)
  
      # image = image[:, :, ::-1].astype(np.uint8)
      image = image.astype(np.uint8)
      bboxes = result["bboxes"]
      scores = result["scores"]
      keypoints = result["keypoints"]
      for bbox, kpt, score in zip(bboxes, keypoints, scores):
          image = cv2.rectangle(
              image,
              (int(bbox[0]), int(bbox[1])),
              (int(bbox[2]), int(bbox[3])),
              (255, 0, 0),
              2,
          )
          image = draw_skeleton(image, kpt, color=(255, 0, 0))
      cv2.imwrite(output_path, image)
      return
  
file_path: gluon-cv/scripts/pose/directpose/tvm_evaluation/evaluate_pose.py
imports: import json
  import numpy
  import os
  from pose_model import PoseEstimationInferenceModel
  import cv2
  from PIL import Image
  from tqdm import tqdm
  import matplotlib
  import matplotlib.pyplot
label_desc: Use image node_id 4 for calling a global function with example usage: viusalize(image, result, output_path) without return types