code: def get_dataloaders(batch_size):
      # Transform to normalize the input images
      transform = transforms.Compose(
          [ToTensor(), Normalize((0.5,), (0.5,))]
      )
  
      with FileLock(os.path.expanduser("~/data.lock")):
          # Download training data from open datasets
          training_data = datasets.FashionMNIST(
              root="~/data",
              train=True,
              download=True,
              transform=transform,
          )
  
          # Download test data from open datasets
          test_data = datasets.FashionMNIST(
              root="~/data",
              train=False,
              download=True,
              transform=transform,
          )
  
      # Create data loaders
      train_dataloader = DataLoader(
          training_data, batch_size=batch_size, shuffle=True
      )
      test_dataloader = DataLoader(test_data, batch_size=batch_size)
  
      return train_dataloader, test_dataloader
  
file_path: ray/python/ray/train/examples/pytorch/torch_fashion_mnist_example.py
imports: import os
  from typing import Dict
  import torch
  from filelock import FileLock
  from torch import nn
  from torch.utils.data import DataLoader
  from torchvision import datasets, transforms
  from torchvision.transforms import Normalize, ToTensor
  from tqdm import tqdm
  import ray.train
  from ray.train import ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: get_dataloaders(batch_size) and returns: train_dataloader,  test_dataloader