code: def get_final_preds(batch_heatmaps, center, scale):
      coords, maxvals = get_max_pred(batch_heatmaps)
  
      heatmap_height = batch_heatmaps.shape[2]
      heatmap_width = batch_heatmaps.shape[3]
  
      # post-processing
      for n in range(coords.shape[0]):
          for p in range(coords.shape[1]):
              hm = batch_heatmaps[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 < heatmap_width - 1
                  and 1 < py < heatmap_height - 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)
  
      # Transform back
      for i in range(coords.shape[0]):
          preds[i] = transform_preds(
              coords[i],
              center[i],
              scale[i],
              [heatmap_width, heatmap_height],
          )
  
      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 11 for calling a global function with example usage: get_final_preds(batch_heatmaps, center, scale) and returns: preds,  maxvals