code: def test_torch_sparse_batch(layout):
      x_dense = torch.randn(3, 4)
      x = x_dense.to_sparse(layout=layout)
      edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
      edge_attr = torch.rand(4)
      adj = to_torch_sparse_tensor(edge_index, edge_attr, layout=layout)
  
      data = Data(x=x, adj=adj)
  
      batch = Batch.from_data_list([data, data])
  
      assert batch.x.size() == (6, 4)
      assert batch.x.layout in {torch.sparse_coo, torch.sparse_csr}
      assert torch.equal(
          batch.x.to_dense(), torch.cat([x_dense, x_dense], 0)
      )
  
      assert batch.adj.size() == (6, 6)
      assert batch.adj.layout == layout
      out = to_edge_index(batch.adj.to_sparse(layout=torch.sparse_csr))
      assert torch.equal(
          out[0], torch.cat([edge_index, edge_index + 3], 1)
      )
      assert torch.equal(out[1], torch.cat([edge_attr, edge_attr], 0))
  
imports: import os.path
  import numpy
  import pytest
  import torch
  import torch_geometric
  from torch_geometric import EdgeIndex
  from torch_geometric.data import Batch, Data, HeteroData
  from torch_geometric.testing import get_random_edge_index, withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_edge_index, to_torch_sparse_tensor
label_desc: Use image node_id 13 for calling a global function with example usage: test_torch_sparse_batch(layout) without return types