code: def calculate_correct_answers(
      name, model, dataloader, epoch, output_predictions
  ):
      """Calculate correct over total answers and return prediction if the
      `output_predictions` is true."""
      args = get_args()
      forward_backward_func = get_forward_backward_func()
      start_time = time.time()
      for m in model:
          m.eval()
      saved_micro_batch_size = args.micro_batch_size
      saved_global_batch_size = args.global_batch_size
  
      ds = dataloader.dataset
      if hasattr(ds, "sample_multiplier"):
          # If our dataset as a sample_multiplier attribute that means
          # each "sample" from the dataset actually has multiple samples
          # that will collapse into the batch dimension (for example in
          # the RACE dataset that has several options), we need to
          # account for that when setting the micro batch size.
          sample_multiplier = ds.sample_multiplier
      else:
          sample_multiplier = 1
      micro_batch_size_times_data_parallel = (
          args.orig_micro_batch_size * args.data_parallel_size
      )
      num_micro_batches = (
          args.orig_global_batch_size
          // micro_batch_size_times_data_parallel
      )
  
      def loss_func(output_predictions, labels, output_tensor):
          logits = output_tensor
  
          loss_dict = {}
          # Add output predictions.
          if output_predictions:
              assert False
              loss_dict["softmaxes"] = (
                  torch.nn.Softmax(dim=-1)(logits.float())
                  .data.cpu()
                  .numpy()
                  .tolist()
              )
              loss_dict["labels"] = labels.data.cpu().numpy().tolist()
              loss_dict["ids"] = batch["uid"].cpu().numpy().tolist()
          # Compute the correct answers.
          predicted = torch.argmax(logits, dim=-1)
          corrects = predicted == labels
          # Add to the counters.
          loss_dict["total"] = labels.size(0)
          loss_dict["correct"] = corrects.sum().item()
  
          return 0, loss_dict
  
      # defined inside to capture output_predictions
      def correct_answers_forward_step(batch, model):
          try:
              batch_ = next(batch)
          except BaseException:
              batch_ = batch
          tokens, types, labels, attention_mask = process_batch(batch_)
  
          # Forward model.
          args = get_args()
          output_tensor = model(
              tokens, attention_mask, tokentype_ids=types
          )
  
          return output_tensor, partial(
              loss_func, output_predictions, labels
          )
  
      with torch.no_grad():
          # For all the batches in the dataset.
          total = 0
          correct = 0
          if output_predictions:
              # This option is only possible when data parallel size is 1.
              assert mpu.get_data_parallel_world_size() == 1
              softmaxes = []
              labels = []
              ids = []
          for _, batch in enumerate(dataloader):
              # For evaluation only mode we use drop_last = False to get all the
              # samples, which means we might not have a full batch, so we
              # adjust batch_size here to actual batch size of data
              actual_batch_size = len(batch["label"])
              # ... applying sample_multiplier if necessary
              args.micro_batch_size = (
                  actual_batch_size * sample_multiplier
              )
              args.global_batch_size = (
                  actual_batch_size
                  * sample_multiplier
                  * num_micro_batches
              )
  
              loss_dicts = forward_backward_func(
                  correct_answers_forward_step,
                  batch,
                  model,
                  optimizer=None,
                  timers=None,
                  forward_only=True,
              )
  
              for loss_dict in loss_dicts:
                  if output_predictions:
                      softmaxes.extend(loss_dict["softmaxes"])
                      labels.extend(loss_dict["labels"])
                      ids.extend(loss_dict["ids"])
                  total += loss_dict["total"]
                  correct += loss_dict["correct"]
  
      for m in model:
          m.train()
      args.micro_batch_size = saved_micro_batch_size
      args.global_batch_size = saved_global_batch_size
  
      # Reduce.
      if mpu.is_pipeline_last_stage():
          unreduced = torch.tensor(
              [correct, total], dtype=torch.long, device="cuda"
          )
          torch.distributed.all_reduce(
              unreduced, group=mpu.get_data_parallel_group()
          )
  
          # Print on screen.
  
          correct_ans = unreduced[0].item()
          total_count = unreduced[1].item()
          percent = float(correct_ans) * 100.0 / float(total_count)
          elapsed_time = time.time() - start_time
          print_rank_last(
              " > |epoch: {}| metrics for {}: correct / total "
              "= {} / {} = {:.4f} %, elapsed time (sec): {:.3f}".format(
                  epoch,
                  name,
                  correct_ans,
                  total_count,
                  percent,
                  elapsed_time,
              )
          )
  
          if output_predictions:
              return correct_ans, total_count, (softmaxes, labels, ids)
          return correct_ans, total_count
      if output_predictions:
          return 0, 0, ()
      return 0, 0
  
file_path: megatron-lm/tasks/eval_utils.py
imports: import os
  import time
  from functools import partial
  import torch
  from megatron import get_args
  from megatron import print_rank_last, is_last_rank
  from megatron.core import mpu
  from megatron.schedules import get_forward_backward_func
  from tasks.finetune_utils import build_data_loader
  from tasks.finetune_utils import process_batch
label_desc: Use image node_id 2 for calling a global function with example usage: calculate_correct_answers(name, model, dataloader, epoch, output_predictions) and returns: int,  int, int,  loss_dict, output_tensor,  partial, correct_ans,  total_count, int,  int,  , correct_ans,  total_count,  softmaxes,  labels,  ids