code: def data_processing(num_samples, batch_size):
      dataset = load_dataset("yelp_review_full")
      tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
  
      def tokenize_function(examples):
          return tokenizer(
              examples["text"], padding="max_length", truncation=True
          )
  
      tokenized_datasets = dataset.map(tokenize_function, batched=True)
  
      tokenized_datasets = tokenized_datasets.remove_columns(["text"])
      tokenized_datasets = tokenized_datasets.rename_column(
          "label", "labels"
      )
      tokenized_datasets.set_format("torch")
  
      small_train_dataset = tokenized_datasets["train"].select(
          range(num_samples)
      )
      small_eval_dataset = tokenized_datasets["test"].select(
          range(num_samples)
      )
  
      train_dataloader = DataLoader(
          small_train_dataset, batch_size=batch_size
      )
      eval_dataloader = DataLoader(
          small_eval_dataset, batch_size=batch_size
      )
  
      return train_dataloader, eval_dataloader
  
file_path: pytorch/benchmarks/dynamo/training_loss.py
imports: import argparse
  import inspect
  import os
  import sys
  import time
  from datetime import timedelta
  import torch
  import torch._dynamo
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
label_desc: Use image node_id 1 for calling a global function with example usage: data_processing(num_samples, batch_size) and returns: train_dataloader,  eval_dataloader, tokenizer