code: def test_cheb_conv_batch():
      x1 = torch.randn(4, 8)
      edge_index1 = torch.tensor(
          [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]
      )
      edge_weight1 = torch.rand(edge_index1.size(1))
      data1 = Data(
          x=x1, edge_index=edge_index1, edge_weight=edge_weight1
      )
  
      x2 = torch.randn(3, 8)
      edge_index2 = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
      edge_weight2 = torch.rand(edge_index2.size(1))
      data2 = Data(
          x=x2, edge_index=edge_index2, edge_weight=edge_weight2
      )
  
      conv = ChebConv(8, 16, K=2)
  
      out1 = conv(x1, edge_index1, edge_weight1)
      out2 = conv(x2, edge_index2, edge_weight2)
  
      batch = Batch.from_data_list([data1, data2])
      out = conv(
          batch.x, batch.edge_index, batch.edge_weight, batch.batch
      )
  
      assert out.size() == (7, 16)
      assert torch.allclose(out1, out[:4], atol=1e-6)
      assert torch.allclose(out2, out[4:], atol=1e-6)
  
imports: import torch
  from torch_geometric.data import Batch, Data
  from torch_geometric.nn import ChebConv
  from torch_geometric.testing import is_full_test
label_desc: Use image node_id 2 for calling a global function with example usage: test_cheb_conv_batch() without return types