code: def eval(config, model):
      batch_size = config["VALID"]["batch_size"]
      num_workers = config["VALID"]["num_workers"]
      valid_loader = build_dataloader(
          "test", batch_size=batch_size, num_workers=num_workers
      )
  
      # build metric
      metric_func = create_metric
  
      outs = []
      labels = []
      for idx, data in enumerate(valid_loader):
          img_batch, label = data
          img_batch = paddle.transpose(img_batch, [0, 3, 1, 2])
          label = paddle.unsqueeze(label, -1)
          out = model(img_batch)
  
          outs.append(out)
          labels.append(label)
  
      outs = paddle.concat(outs, axis=0)
      labels = paddle.concat(labels, axis=0)
      acc = metric_func(outs, labels)
  
      strs = f"The metric are as follows: acc_topk1: {float(acc['top1'])}, acc_top5: {float(acc['top5'])}"
      logger.info(strs)
      return acc
  
imports: import paddle
  import numpy
  import os
  import paddle.nn
  import paddle.distributed
  from loss import build_loss, LossDistill, DMLLoss, KLJSLoss
  from optimizer import create_optimizer
  from data_loader import build_dataloader
  from metric import create_metric
  from mv3 import MobileNetV3_large_x0_5, distillmv3_large_x0_5, build_model
  from config import preprocess
  import time
  from paddleslim.dygraph.quant import QAT
  from slim.slim_quant import PACT, quant_config
  from slim.slim_fpgm import prune_model
  from utils import load_model
label_desc: Use image node_id 8 for calling a global function with example usage: eval(config, model) and returns: acc