class_docstr: PP-TinyPose Model.
class_docstr_tok: ["PP-TinyPose","Model","."]
code: def predict(
      self,
      img: Union[str, np.ndarray],
      save_path: str = "pp_tinypose_output",
      visualization: bool = False,
      use_gpu=False,
  ):
      if use_gpu:
          device = "GPU"
      else:
          device = "CPU"
      if self.detector.device != device:
          self.detector = Detector(self.det_model_dir, device=device)
          self.topdown_keypoint_detector = KeyPointDetector(
              self.keypoint_model_dir, device=device
          )
  
      self.visualization = visualization
      store_res = []
  
      # Decode image in advance in det + pose prediction
      image, _ = decode_image(img, {})
      results = self.detector.predict_image([image], visual=False)
      results = self.detector.filter_box(results, 0.5)
      if results["boxes_num"] > 0:
          keypoint_res = predict_with_given_det(
              image, results, self.topdown_keypoint_detector, 1, False
          )
          save_name = (
              img
              if isinstance(img, str)
              else os.path.join(save_path, (str(time.time()) + ".png"))
          )
          store_res.append(
              [
                  save_name,
                  keypoint_res["bbox"],
                  [
                      keypoint_res["keypoint"][0],
                      keypoint_res["keypoint"][1],
                  ],
              ]
          )
          if not os.path.exists(save_path):
              os.makedirs(save_path)
          if self.visualization:
              if isinstance(img, np.ndarray):
                  cv2.imwrite(save_name, img)
              visualize_pose(
                  save_name,
                  keypoint_res,
                  visual_thresh=0.5,
                  save_dir=save_path,
              )
      return store_res
  
imports: import argparse
  import json
  import os
  import time
  from typing import Union
  import cv2
  import numpy
  from .det_keypoint_unite_infer import predict_with_given_det
  from .infer import Detector
  from .keypoint_infer import KeyPointDetector
  from .preprocess import base64_to_cv2
  from .preprocess import decode_image
  from .visualize import visualize_pose
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 2 for calling the PP_TinyPose obj's underlying member method code with example usage: obj.predict(img, save_path, visualization, use_gpu) and returns: store_res