code: def create_data_loader(
      dir,
      batch_size,
      num_workers,
      transform,
      subset=50,
      shuffle=True,
      sampler=False,
  ):
      train_set = CIFAR10(
          root=dir, train=True, download=True, transform=transform
      )
      # `subset` is the number of subsets. The larger the number, the smaller the training set.
      mask = list(range(0, len(train_set), subset))
      train_subset = torch.utils.data.Subset(train_set, mask)
      if sampler:
          sampler_set = SequentialSampler(train_subset)
          data_loader = DataLoader(
              train_subset,
              batch_size=batch_size,
              shuffle=shuffle,
              num_workers=num_workers,
              sampler=sampler_set,
          )
      else:
          data_loader = DataLoader(
              train_subset,
              batch_size=batch_size,
              shuffle=shuffle,
              num_workers=num_workers,
          )
      return data_loader
  
imports: import os
  import torch
  import torchmetrics
  from torch import nn
  from torchvision.datasets import CIFAR10
  from torch.utils.data import DataLoader
  import torchvision.transforms
  from torch.utils.data.sampler import SequentialSampler
  import pytorch_lightning
  from pytorch_lightning.callbacks import Callback
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from typing import Any
  from bigdl.nano.pytorch.lightning import LightningModule
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.pytorch.algorithms.selective_backprop import SelectiveBackprop
label_desc: Use image node_id 1 for calling a global function with example usage: create_data_loader(dir, batch_size, num_workers, transform, subset, shuffle, sampler) and returns: data_loader