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 a 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: import torch
  import argparse
  import os
  import logging
  import time
  from torch import nn
  from contextlib import nullcontext
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  import torch.distributed
  from transformers import BertTokenizer, BertModel, AdamW
label_desc: Use image node_id 3 for calling a global function with example usage: collate_fn(batch_samples) and returns: X,  y