code: def train_data_creator(config, batch_size):
      transform = transforms.Compose(
          [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
      )
      data_dir = config["data_dir"]
      if not is_local_path(data_dir):
          data_dir = "/tmp/dataset"
          get_remote_dir_to_local(
              remote_dir=config["data_dir"], local_dir=data_dir
          )
      trainset = torchvision.datasets.FashionMNIST(
          root=data_dir, train=True, download=False, transform=transform
      )
      trainloader = torch.utils.data.DataLoader(
          trainset, batch_size=batch_size, shuffle=True, num_workers=0
      )
      return trainloader
  
file_path: BigDL/python/orca/tutorial/pytorch/FashionMNIST/train.py
imports: import os
  import argparse
  import torch
  import torchvision
  import torch.nn
  import torchvision.transforms
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.data.file import get_remote_dir_to_local
  from bigdl.orca.learn.pytorch import Estimator
  from bigdl.orca.learn.metrics import Accuracy
  from model import model_creator, optimizer_creator
label_desc: Use image node_id 1 for calling a global function with example usage: train_data_creator(config, batch_size) and returns: trainloader