code: def test_batch_meta_dataloader_splitter():
      dataset = Sinusoid(20, num_tasks=1000, noise_std=None)
      dataset = ClassSplitter(
          dataset, num_train_per_class=5, num_test_per_class=15
      )
      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.shape == (4, 5, 1)
      assert train_targets.shape == (4, 5, 1)
  
      assert isinstance(test_inputs, torch.Tensor)
      assert isinstance(test_targets, torch.Tensor)
      assert test_inputs.shape == (4, 15, 1)
      assert test_targets.shape == (4, 15, 1)
  
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 4 for calling a global function with example usage: test_batch_meta_dataloader_splitter() without return types