code: def evaluate_performance(data_loader, discriminator, device="cpu"):
      discriminator.eval()
      test_loss = 0
      correct = 0
      with torch.no_grad():
          for input_t, target_t in data_loader:
              input_t, target_t = input_t.to(device), target_t.to(
                  device
              )
              output_t = discriminator(input_t)
              # sum up batch loss
              test_loss += nn.functional.nll_loss(
                  output_t, target_t, reduction="sum"
              ).item()
              # get the index of the max log-probability
              pred_t = output_t.argmax(dim=1, keepdim=True)
              correct += (
                  pred_t.eq(target_t.view_as(pred_t)).sum().item()
              )
  
      test_loss /= len(data_loader.dataset)
  
      print(
          "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
              test_loss,
              correct,
              len(data_loader.dataset),
              100.0 * correct / len(data_loader.dataset),
          )
      )
  
imports: import argparse
  import csv
  import json
  import math
  import time
  import numpy
  import torch
  import torch.optim
  import torch.utils.data
  from nltk.tokenize.treebank import TreebankWordDetokenizer
  from pplm_classification_head import ClassificationHead
  from torch import nn
  from torchtext import data
  from torchtext import datasets
  from tqdm import tqdm, trange
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate_performance(data_loader, discriminator, device) without return types