code: def evaluate(eval_model, data_source, criterion, ntokens):
      eval_model.eval()
      total_loss = 0.0
      # TODO(anj-s): Move this to the benchmark config if we want to benchmark evaluation.
      bptt = 35
  
      def get_batch(source, i, bptt):
          seq_len = min(bptt, len(source) - 1 - i)
          data = source[i : i + seq_len]
          target = source[i + 1 : i + 1 + seq_len].view(-1)
          return data, target
  
      with torch.no_grad():
          for i in range(0, data_source.size(0) - 1, bptt):
              data, targets = get_batch(data_source, i, bptt)
              output = eval_model(data)
              output = output.to(targets.device)
              output_flat = output.view(-1, ntokens)
              total_loss += (
                  len(data) * criterion(output_flat, targets).item()
              )
      return total_loss / (len(data_source) - 1)
  
imports: from collections import defaultdict
  import gc
  import logging
  import math
  import time
  import torch
  import torch.distributed
  from torch.distributed import rpc
  from torch.nn.parallel import DistributedDataParallel
  import utils
  from benchmarks.golden_configs.lm_wikitext2 import Pipe
  from fairscale.fair_dev.testing.testing import dist_init
  from fairscale.nn import Pipe
  from fairscale.nn.model_parallel import initialize_model_parallel
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(eval_model, data_source, criterion, ntokens) and returns: unknown, data,  target