code: def build_data_loader(
      data_source=None,
      batch_size=64,
      num_workers=8,
      shuffle=False,
      column_names=["image"],
      label_column="label",
  ):
      data_loader = torch.utils.data.DataLoader(
          Wrapper(data_source, column_names),
          batch_size=batch_size,
          num_workers=num_workers,
          shuffle=shuffle,
          drop_last=False,
          pin_memory=(torch.cuda.is_available()),
      )
      assert len(data_loader) > 0
  
      return data_loader
  
imports: import torch
  import pandas
  from tqdm import tqdm
  import torch.nn.functional
  import torch.nn
  from autogluon.multimodal import MultiModalPredictor
  from datasets import list_datasets, load_dataset
  from setfit import sample_dataset
  from imagedatasets import build_dataset
  from torch.utils.data import Dataset
label_desc: Use image node_id 9 for calling a global function with example usage: build_data_loader(data_source, batch_size, num_workers, shuffle, column_names, label_column) and returns: data_loader