code: def detector_to_simple_pose(
      img,
      class_ids,
      scores,
      bounding_boxs,
      output_shape=(256, 192),
      scale=1.25,
      ctx=mx.cpu(),
      thr=0.5,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
  ):
      L = class_ids.shape[1]
      upscale_bbox = []
      for i in range(L):
          if class_ids[0][i].asscalar() != 0:
              continue
          if scores[0][i].asscalar() < thr:
              continue
          bbox = bounding_boxs[0][i]
          upscale_bbox.append(
              upscale_bbox_fn(bbox.asnumpy().tolist(), img, scale=scale)
          )
      if len(upscale_bbox) > 0:
          pose_input = crop_resize_normalize(
              img, upscale_bbox, output_shape, mean=mean, std=std
          )
          pose_input = pose_input.as_in_context(ctx)
      else:
          pose_input = None
      return pose_input, upscale_bbox
  
imports: from __future__ import absolute_import
  from __future__ import division
  import math
  import random
  import numpy
  import mxnet
  from mxnet import nd, image
  from mxnet.gluon.data.vision import transforms
  from ...utils.filesystem import try_import_cv2
label_desc: Use image node_id 14 for calling a global function with example usage: detector_to_simple_pose(img, class_ids, scores, bounding_boxs, output_shape, scale, ctx, thr, mean, std) and returns: pose_input,  upscale_bbox