code: def evaluate(model, args, logger, global_step, criterion):
      evaluate_dataset_provider = NvidiaBertDatasetProvider(
          args, evaluate=True
      )
      start_shard = 0
  
      model.eval()
      timers = get_timers()
      eval_step = 0
      eval_loss = 0
      cur_loss = 0
      world_size = torch.distributed.get_world_size()
  
      with torch.no_grad():
          for shard in range(
              start_shard, len(os.listdir(args.eval_data_path_prefix))
          ):
              timers("eval_shard_time").start()
  
              (
                  dataset_iterator,
                  total_length,
              ) = evaluate_dataset_provider.get_shard(shard)
              # evaluate_dataset_provider.prefetch_shard(shard + 1)
              if torch.distributed.get_rank() == 0:
                  iterator_data = tqdm(
                      enumerate(dataset_iterator),
                      total=(
                          total_length
                          // args.eval_micro_batch_size_per_gpu
                          // world_size
                      ),
                      colour="MAGENTA",
                      smoothing=1,
                  )
              else:
                  iterator_data = enumerate(dataset_iterator)
  
              for (
                  step,
                  batch_data,
              ) in (
                  iterator_data
              ):  # tqdm(enumerate(dataset_iterator), total=(total_length // args.train_micro_batch_size_per_gpu // world_size), colour='cyan', smoothing=1):
                  # batch_data = pretrain_dataset_provider.get_batch(batch_index)
                  eval_step += 1
                  input_ids = batch_data[0].cuda()
                  attention_mask = batch_data[1].cuda()
                  token_type_ids = batch_data[2].cuda()
                  mlm_label = batch_data[3].cuda()
                  # nsp_label = batch_data[5].cuda()
  
                  output = model(
                      input_ids=input_ids,
                      token_type_ids=token_type_ids,
                      attention_mask=attention_mask,
                  )
  
                  loss = criterion(
                      output.logits, mlm_label
                  )  # prediction_scores
                  evaluate_dataset_provider.prefetch_batch()
  
                  eval_loss += loss.float().item()
  
              cur_loss = eval_loss / eval_step
              elapsed_time = timers("eval_shard_time").elapsed()
              elapsed_time_per_iteration = elapsed_time / eval_step
              ppl = math.exp(cur_loss)
  
              if args.wandb and torch.distributed.get_rank() == 0:
                  tensorboard_log = get_tensorboard_writer()
                  tensorboard_log.log_eval(
                      {
                          "loss": cur_loss,
                          "ppl": ppl,
                          "mins_batch": elapsed_time_per_iteration,
                      },
                      global_step,
                  )
  
              eval_log_str = (
                  f"evaluation shard: {shard} | step: {eval_step} | elapsed_time: {elapsed_time / 60 :.3f} minutes "
                  + f"| mins/batch: {elapsed_time_per_iteration :.3f} seconds | loss: {cur_loss:.7f} | ppl: {ppl:.7f}"
              )
  
              logger.info(eval_log_str)
              logger.info("-" * 100)
              logger.info("")
  
      evaluate_dataset_provider.release_shard()
      model.train()
      return cur_loss
  
imports: import math
  import os
  import torch
  from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider
  from tqdm import tqdm
  from utils.global_vars import get_tensorboard_writer, get_timers
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, args, logger, global_step, criterion) and returns: cur_loss