code: def collate_fn(data):
      def pad_sequences(sequences):
          lengths = [len(seq) for seq in sequences]
  
          padded_sequences = torch.zeros(
              len(sequences), max(lengths)
          ).long()  # padding value = 0
  
          for i, seq in enumerate(sequences):
              end = lengths[i]
              padded_sequences[i, :end] = seq[:end]
  
          return padded_sequences, lengths
  
      item_info = {}
      for key in data[0].keys():
          item_info[key] = [d[key] for d in data]
  
      x_batch, _ = pad_sequences(item_info["X"])
      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 1 for calling a global function with example usage: collate_fn(data) and returns: x_batch,  y_batch, padded_sequences,  lengths