code: def cached_collate_fn(data):
      item_info = {}
      for key in data[0].keys():
          item_info[key] = [d[key] for d in data]
  
      x_batch = torch.cat(item_info["X"], 0)
      y_batch = torch.tensor(item_info["y"], dtype=torch.long)
  
      return x_batch, y_batch
  
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 2 for calling a global function with example usage: cached_collate_fn(data) and returns: x_batch,  y_batch