code: def test_torch_nested_batch():
      from torch.nested import nested_tensor
  
      class MyData(Data):
          def __inc__(self, key, value, *args, **kwargs) -> int:
              return 2
  
      x1 = nested_tensor([torch.randn(3), torch.randn(4)])
      data1 = MyData(x=x1)
      assert str(data1) == "MyData(x=[2, 4])"
  
      x2 = nested_tensor(
          [torch.randn(3), torch.randn(4), torch.randn(5)]
      )
      data2 = MyData(x=x2)
      assert str(data2) == "MyData(x=[3, 5])"
  
      batch = Batch.from_data_list([data1, data2])
      assert str(batch) == "MyDataBatch(x=[5, 5], batch=[5], ptr=[3])"
  
      expected = nested_tensor(list(x1.unbind() + (x2 + 2).unbind()))
      assert torch.equal(
          batch.x.to_padded_tensor(0.0),
          expected.to_padded_tensor(0.0),
      )
  
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 14 for calling a global function with example usage: test_torch_nested_batch() and returns: int