class_docstr: set config of preprocess, postprocess and visualize
  Args:
      model_dir (str): root path of model.yml
class_docstr_tok: ["set","config","of","preprocess",",","postprocess","and","visualize","Args",":","model_dir","(","str",")",":","root","path","of","model.yml"]
docstr: Raises:
      ValueError: loaded model not in supported model type
docstr_tok: ["Raises",":","ValueError",":","loaded","model","not","in","supported","model","type"]
code: def check_model(self, yml_conf):
      """
      Raises:
          ValueError: loaded model not in supported model type
      """
      for support_model in KEYPOINT_SUPPORT_MODELS:
          if support_model in yml_conf["arch"]:
              return True
      raise ValueError(
          "Unsupported arch: {}, expect {}".format(
              yml_conf["arch"], KEYPOINT_SUPPORT_MODELS
          )
      )
  
imports: import math
  import os
  import cv2
  import numpy
  import yaml
  from .infer import Detector
  from .keypoint_postprocess import HRNetPostProcess
  from .keypoint_preprocess import expand_crop
  from .visualize import visualize_pose
label_desc: Use image node_id 2 for calling the PredictConfig_KeyPoint obj's underlying member method code with example usage: obj.check_model(yml_conf) and returns: True