code: def evaluate(test_loader, model1, model2):
      print("Evaluating Co-Teaching Model")
      model1.eval()  # Change model to 'eval' mode.
      correct1 = 0
      total1 = 0
      for images, labels in test_loader:
          images = Variable(images).cuda()
          logits1 = model1(images)
          outputs1 = F.softmax(logits1, dim=1)
          _, pred1 = torch.max(outputs1.data, 1)
          total1 += labels.size(0)
          correct1 += (pred1.cpu() == labels).sum()
  
      model2.eval()  # Change model to 'eval' mode
      correct2 = 0
      total2 = 0
      for images, labels in test_loader:
          images = Variable(images).cuda()
          logits2 = model2(images)
          outputs2 = F.softmax(logits2, dim=1)
          _, pred2 = torch.max(outputs2.data, 1)
          total2 += labels.size(0)
          correct2 += (pred2.cpu() == labels).sum()
  
      acc1 = 100 * float(correct1) / float(total1)
      acc2 = 100 * float(correct2) / float(total2)
      return acc1, acc2
  
file_path: cleanlab/cleanlab/experimental/coteaching.py
imports: import torch
  import torch.nn.functional
  from torch.autograd import Variable
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate(test_loader, model1, model2) and returns: acc1,  acc2