code: def evaluate(eval_iter):
      # Turn on evaluation mode which disables dropout.
      model.eval()
  
      # If the model does not use memory at all, make the ext_len longer.
      # Otherwise, make the mem_len longer and keep the ext_len the same.
      if args.mem_len == 0:
          model.reset_length(
              args.eval_tgt_len,
              args.ext_len + args.tgt_len - args.eval_tgt_len,
              args.mem_len,
          )
      else:
          model.reset_length(
              args.eval_tgt_len,
              args.ext_len,
              args.mem_len + args.tgt_len - args.eval_tgt_len,
          )
  
      # Evaluation
      total_len, total_loss = 0, 0.0
      with torch.no_grad():
          mems = tuple()
          for i, (data, target, seq_len) in enumerate(eval_iter):
              if args.max_eval_steps > 0 and i >= args.max_eval_steps:
                  break
              ret = model(data, target, *mems)
              loss, mems = ret[0], ret[1:]
              loss = loss.mean()
              total_loss += seq_len * loss.float().item()
              total_len += seq_len
  
      # Switch back to the training mode
      model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
      model.train()
  
      return total_loss / total_len
  
file_path: thu-pacman-faster-moe/examples/transformer-xl/train.py
imports: import argparse
  import time
  import math
  import os, sys
  import itertools
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from data_utils import get_lm_corpus
  from mem_transformer import MemTransformerLM
  from utils.exp_utils import create_exp_dir
  from utils.data_parallel import BalancedDataParallel
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate(eval_iter) and returns: unknown