code: def test_to_homogeneous_padding():
      data = HeteroData()
      data["paper"].x = torch.randn(100, 128)
      data["author"].x = torch.randn(50, 64)
  
      out = data.to_homogeneous()
      assert len(out) == 2
      assert out.node_type.size() == (150,)
      assert out.node_type[:100].abs().sum() == 0
      assert out.node_type[100:].sub(1).abs().sum() == 0
      assert out.x.size() == (150, 128)
      assert torch.equal(out.x[:100], data["paper"].x)
      assert torch.equal(out.x[100:, :64], data["author"].x)
      assert out.x[100:, 64:].abs().sum() == 0
  
imports: import copy
  import pytest
  import torch
  from torch_geometric.data import HeteroData
  from torch_geometric.data.storage import EdgeStorage
  from torch_geometric.testing import get_random_edge_index, get_random_tensor_frame, withPackage
  from torch_geometric.typing import TensorFrame
label_desc: Use image node_id 11 for calling a global function with example usage: test_to_homogeneous_padding() without return types