code: def evaluate(data_loader, model, eval_metric):
      """Evaluation."""
      args = get_args()
      config = core_transformer_config_from_args(args)
  
      # Turn on evaluation mode which disables dropout.
      model.eval()
  
      total_output = 0.0
      with torch.no_grad():
          # For all the batches in the dataset.
          for iteration, batch in enumerate(data_loader):
              if iteration % args.log_interval == 0:
                  print_rank_0(
                      "> working on iteration: {}".format(iteration)
                  )
              # Forward evaluation.
              output = forward_step(batch, model, eval_metric, config)
  
              # Reduce across processes.
              if parallel_state.is_pipeline_last_stage():
                  torch.distributed.all_reduce(
                      output,
                      group=parallel_state.get_data_parallel_group(),
                  )
  
                  total_output += output
  
      return total_output
  
file_path: megatron-lm/tasks/zeroshot_gpt/evaluate.py
imports: import math
  import torch
  from megatron import get_args
  from megatron import print_rank_0, is_last_rank
  from megatron import get_tokenizer
  from megatron.core import parallel_state, tensor_parallel
  from megatron.checkpointing import load_checkpoint
  from megatron.model import GPTModel
  from megatron.training import get_model
  from megatron.utils import get_ltor_masks_and_position_ids, unwrap_model
  from megatron.core.pipeline_parallel.p2p_communication import recv_forward, send_forward
  from megatron.arguments import core_transformer_config_from_args
  from tasks.finetune_utils import build_data_loader
  from .datasets import build_dataset
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(data_loader, model, eval_metric) and returns: total_output