code: def get_dataloader(batch_size=32, test_size=0.2, shuffle=True):
      tokenized_fables = get_tokenized_fables()
      n_test = int(len(tokenized_fables) * test_size)
      test_set = torch.utils.data.TensorDataset(
          tokenized_fables[:n_test]
      )
      train_set = torch.utils.data.TensorDataset(
          tokenized_fables[n_test:]
      )
      train_loader = torch.utils.data.DataLoader(
          train_set, batch_size=batch_size, shuffle=shuffle
      )
      test_loader = torch.utils.data.DataLoader(
          test_set, batch_size=n_test, shuffle=False
      )
      return train_loader, test_loader
  
imports: import urllib.request
  from typing import Any
  import torch
  import torch.utils.data
  from nebullvm.operations.base import Operation
  from torchvision import datasets, transforms
label_desc: Use image node_id 5 for calling a global function with example usage: get_dataloader(batch_size, test_size, shuffle) and returns: train_loader,  test_loader