code: def collate_fn(batch_samples):
      batch_text = []
      batch_label = []
      for sample in batch_samples:
          batch_text.append(sample["text"])
          batch_label.append(int(sample["label"]))
      # The tokenizer will make the data to be in good format for our model to understand
      X = tokenizer(
          batch_text, padding=True, truncation=True, return_tensors="pt"
      )
      y = torch.tensor(batch_label)
      return X, y
  
imports: from torch.utils.data import DataLoader
  import torch
  import torch.optim
  from contextlib import nullcontext
  import torch.nn.functional
  import os
  import torch.nn
  import time
  from bigdl.nano.pytorch.patching import patch_encryption
  from bigdl.nano.pytorch.encryption import EncryptedDataset
  import logging
  from bigdl.ppml.kms.ehsm.client import generate_primary_key, generate_data_key, get_data_key_plaintext
  from datasets.load import load_from_disk
  import argparse
  from transformers import BertTokenizer, BertModel, AdamW
label_desc: Use image node_id 4 for calling a global function with example usage: collate_fn(batch_samples) and returns: X,  y