code: def test_datasets_helpers_wrapper(name, split):
      function = getattr(helpers, name)
      folder = os.getenv("TORCHMETA_DATA_FOLDER")
      download = bool(os.getenv("TORCHMETA_DOWNLOAD", False))
  
      dataset = function(
          folder,
          ways=5,
          shots=1,
          test_shots=15,
          meta_split=split,
          download=download,
      )
  
      wrapped_dataset = NonEpisodicWrapper(dataset)
  
      assert isinstance(wrapped_dataset, Dataset)
  
      img, label = wrapped_dataset[0]
  
      dataloader = DataLoader(wrapped_dataset, batch_size=4)
      inputs, targets = next(iter(dataloader))
  
      assert isinstance(inputs, torch.Tensor)
      assert inputs.ndim == 4
      assert inputs.shape[0] == 4
  
      assert len(targets) == 2
      assert isinstance(targets[1], torch.Tensor)
  
imports: import pytest
  import os
  import torch
  from torch.utils.data import Dataset, DataLoader
  from torchmeta.datasets import helpers
  from torchmeta.utils.data import NonEpisodicWrapper
label_desc: Use image node_id 1 for calling a global function with example usage: test_datasets_helpers_wrapper(name, split) without return types