code: def eval_model(niter, model, valid_x, valid_y):
      with torch.no_grad():
          model.eval()
          N = len(valid_x)
          criterion = nn.CrossEntropyLoss()
          correct = 0.0
          cnt = 0
          total_loss = 0.0
          for x, y in zip(valid_x, valid_y):
              output = model(x)
              loss = criterion(output, y)
              total_loss += loss.item() * x.size(1)
              pred = output.data.max(1)[1]
              correct += pred.eq(y).sum().item()
              cnt += y.numel()
          model.train()
      return 1.0 - correct / cnt
  
imports: import os
  import sys
  import argparse
  import time
  import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.autograd import Variable
  from sru import *
  import dataloader
  import modules
label_desc: Use image node_id 1 for calling a global function with example usage: eval_model(niter, model, valid_x, valid_y) and returns: unknown