code: def validate(val_data, net, criterion, ctx):
      loss = 0.0
      for data, label in val_data:
          data_list = gluon.utils.split_and_load(data, ctx)
          label_list = gluon.utils.split_and_load(label, ctx)
  
          with autograd.predict_mode():
              outpus = [net(X) for X in data_list]
              losses = [
                  criterion(X, y) for X, y in zip(outpus, label_list)
              ]
          accuracy = [
              (X.argmax(axis=1) == y.astype("float32")).mean.asscalar()
              for X, y in zip(outpus, label_list)
          ]
  
          loss_list = [l.mean().asscalar() for l in losses]
          loss += sum(loss_list) / len(loss_list)
  
      return loss / len(val_data), sum(accuracy) / len(accuracy)
  
file_path: gluon-cv/scripts/re-id/baseline/train.py
imports: from __future__ import division
  import argparse, datetime, os
  import logging
  import mxnet
  from mxnet import gluon, nd
  from mxnet.gluon.model_zoo import vision
  from mxnet.gluon.data.vision import transforms
  from mxnet import autograd
  from networks import resnet18, resnet34, resnet50
  import gluoncv
  from gluoncv.data.market1501.data_read import ImageTxtDataset
  from gluoncv.data.market1501.label_read import LabelList
  from gluoncv.data.transforms.block import RandomCrop
label_desc: Use image node_id 2 for calling a global function with example usage: validate(val_data, net, criterion, ctx) and returns: unknown,  unknown