code: def get_cached_data_loader(
      dataset, batch_size, discriminator, shuffle=False, device="cpu"
  ):
      data_loader = torch.utils.data.DataLoader(
          dataset=dataset, batch_size=batch_size, collate_fn=collate_fn
      )
  
      xs = []
      ys = []
      for batch_idx, (x, y) in enumerate(tqdm(data_loader, ascii=True)):
          with torch.no_grad():
              x = x.to(device)
              avg_rep = (
                  discriminator.avg_representation(x).cpu().detach()
              )
              avg_rep_list = torch.unbind(avg_rep.unsqueeze(1))
              xs += avg_rep_list
              ys += y.cpu().numpy().tolist()
  
      data_loader = torch.utils.data.DataLoader(
          dataset=Dataset(xs, ys),
          batch_size=batch_size,
          shuffle=shuffle,
          collate_fn=cached_collate_fn,
      )
  
      return data_loader
  
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 6 for calling a global function with example usage: get_cached_data_loader(dataset, batch_size, discriminator, shuffle, device) and returns: data_loader