code: def validate(val_data, val_dataset, net, ctx, opt):
      if isinstance(ctx, mx.Context):
          ctx = [ctx]
  
      val_metric = COCOKeyPointsMetric(
          val_dataset, "coco_keypoints", in_vis_thresh=0
      )
  
      for batch in tqdm(val_data, dynamic_ncols=True):
          # data, scale, center, score, imgid = val_batch_fn(batch, ctx)
          data, scale_box, score, imgid = val_batch_fn(batch, ctx)
  
          outputs = [net(X) for X in data]
          if opt.flip_test:
              data_flip = [nd.flip(X, axis=3) for X in data]
              outputs_flip = [net(X) for X in data_flip]
              outputs_flipback = [
                  flip_heatmap(o, val_dataset.joint_pairs, shift=True)
                  for o in outputs_flip
              ]
              outputs = [
                  (o + o_flip) / 2
                  for o, o_flip in zip(outputs, outputs_flipback)
              ]
  
          if len(outputs) > 1:
              outputs_stack = nd.concat(
                  *[o.as_in_context(mx.cpu()) for o in outputs], dim=0
              )
          else:
              outputs_stack = outputs[0].as_in_context(mx.cpu())
  
          # preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
          preds, maxvals = heatmap_to_coord_alpha_pose(
              outputs_stack, scale_box
          )
          val_metric.update(preds, maxvals, score, imgid)
  
      nullwriter = NullWriter()
      oldstdout = sys.stdout
      sys.stdout = nullwriter
      try:
          res = val_metric.get()
      finally:
          sys.stdout = oldstdout
      return res
  
file_path: gluon-cv/scripts/pose/alpha_pose/validate_tools.py
imports: import sys
  from tqdm import tqdm
  import mxnet
  import gluoncv
  from gluoncv.data import mscoco
  from gluoncv.data.transforms.pose import flip_heatmap, heatmap_to_coord_alpha_pose
  from gluoncv.data.transforms.presets.alpha_pose import AlphaPoseDefaultValTransform
  from gluoncv.utils.metrics.coco_keypoints import COCOKeyPointsMetric
  from mxnet import gluon, nd
label_desc: Use image node_id 4 for calling a global function with example usage: validate(val_data, val_dataset, net, ctx, opt) and returns: res