code: def test_datasets_helpers_dataloader(name, shots, split):
      function = getattr(helpers, name)
      folder = os.getenv("TORCHMETA_DATA_FOLDER")
      download = bool(os.getenv("TORCHMETA_DOWNLOAD", False))
  
      dataset = function(
          folder,
          ways=5,
          shots=shots,
          test_shots=15,
          meta_split=split,
          download=download,
      )
  
      meta_dataloader = BatchMetaDataLoader(dataset, batch_size=4)
  
      batch = next(iter(meta_dataloader))
      assert isinstance(batch, dict)
      assert "train" in batch
      assert "test" in batch
  
      train_inputs, train_targets = batch["train"]
      test_inputs, test_targets = batch["test"]
  
      assert isinstance(train_inputs, torch.Tensor)
      assert isinstance(train_targets, torch.Tensor)
      assert train_inputs.ndim == 5
      assert train_inputs.shape[:2] == (4, 5 * shots)
      assert train_targets.ndim == 2
      assert train_targets.shape[:2] == (4, 5 * shots)
  
      assert isinstance(test_inputs, torch.Tensor)
      assert isinstance(test_targets, torch.Tensor)
      assert test_inputs.ndim == 5
      assert test_inputs.shape[:2] == (4, 5 * 15)  # test_shots
      assert test_targets.ndim == 2
      assert test_targets.shape[:2] == (4, 5 * 15)
  
file_path: pytorch-meta/torchmeta/tests/utils/test_dataloaders.py
imports: import pytest
  import os
  import torch
  from torch.utils.data import DataLoader
  from torchmeta.datasets import helpers
  from torchmeta.toy import Sinusoid
  from torchmeta.transforms import ClassSplitter
  from torchmeta.utils.data import Task, MetaDataLoader, BatchMetaDataLoader
label_desc: Use image node_id 5 for calling a global function with example usage: test_datasets_helpers_dataloader(name, shots, split) without return types