code: def test_datasets_helpers_tabular(name, shots, split):
      function = getattr(helpers_tabular, name)
      folder = os.getenv("TORCHMETA_DATA_FOLDER")
      download = bool(os.getenv("TORCHMETA_DOWNLOAD", False))
      test_shots = 10
      ways = 5
  
      dataset = function(
          folder,
          ways=ways,
          shots=shots,
          test_shots=test_shots,
          meta_split=split,
          download=download,
      )
  
      assert isinstance(dataset, MetaDataset)
  
      task = dataset.sample_task()
  
      # Task is a dictionary with keys [train, test]
      assert isinstance(task, dict)
      assert set(task.keys()) == set(["train", "test"])
  
      # Train
      assert isinstance(task["train"], Task)
      assert task["train"].num_classes == ways
      assert len(task["train"]) == ways * shots
  
      # Test
      assert isinstance(task["test"], Task)
      assert task["test"].num_classes == ways
      assert len(task["test"]) == ways * test_shots
  
imports: import pytest
  import os
  import torch
  from torchmeta.utils.data import MetaDataset, Task
  from torchmeta.datasets import helpers_tabular
label_desc: Use image node_id 1 for calling a global function with example usage: test_datasets_helpers_tabular(name, shots, split) without return types