code: def heatmap_to_coord_alpha_pose(hms, boxes):
      hm_h = hms.shape[2]
      hm_w = hms.shape[3]
      coords, maxvals = get_max_pred(hms)
      if boxes.shape[1] == 1:
          pt1 = mx.nd.array(boxes[:, 0, (0, 1)], dtype=hms.dtype)
          pt2 = mx.nd.array(boxes[:, 0, (2, 3)], dtype=hms.dtype)
      else:
          assert boxes.shape[1] == 4
          pt1 = mx.nd.array(boxes[:, (0, 1)], dtype=hms.dtype)
          pt2 = mx.nd.array(boxes[:, (2, 3)], dtype=hms.dtype)
  
      # post-processing
      for n in range(coords.shape[0]):
          for p in range(coords.shape[1]):
              hm = hms[n][p]
              px = int(nd.floor(coords[n][p][0] + 0.5).asscalar())
              py = int(nd.floor(coords[n][p][1] + 0.5).asscalar())
              if 1 < px < hm_w - 1 and 1 < py < hm_h - 1:
                  diff = nd.concat(
                      hm[py][px + 1] - hm[py][px - 1],
                      hm[py + 1][px] - hm[py - 1][px],
                      dim=0,
                  )
                  coords[n][p] += nd.sign(diff) * 0.25
  
      preds = nd.zeros_like(coords)
      for i in range(hms.shape[0]):
          for j in range(hms.shape[1]):
              preds[i][j] = transformBoxInvert(
                  coords[i][j], pt1[i], pt2[i], hm_h, hm_w
              )
  
      return preds, maxvals
  
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 26 for calling a global function with example usage: heatmap_to_coord_alpha_pose(hms, boxes) and returns: preds,  maxvals