code: def train_loader_creator(config, batch_size):
      train_transform = A.Compose(
          [
              A.Resize(width=128, height=128, p=1.0),
              A.HorizontalFlip(p=0.5),
              A.VerticalFlip(p=0.5),
              A.RandomRotate90(p=0.5),
              A.ShiftScaleRotate(
                  shift_limit=0.01,
                  scale_limit=0.04,
                  rotate_limit=0,
                  p=0.25,
              ),
          ]
      )
  
      train_ds = BrainDataset(config["train"], train_transform)
      train_loader = torch.utils.data.DataLoader(
          train_ds, batch_size=batch_size, shuffle=True, num_workers=0
      )
      return train_loader
  
imports: from __future__ import print_function
  from sklearn.model_selection import train_test_split
  import matplotlib.pyplot
  import albumentations
  import pandas
  import argparse
  import glob
  import os
  import torch
  import torch.nn
  import torch.optim
  import torch.utils.data
  from torchvision.utils import make_grid
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.pytorch import Estimator
  from Unet import UNet
  from dataset import *
label_desc: Use image node_id 7 for calling a global function with example usage: train_loader_creator(config, batch_size) and returns: train_loader