code: def test_cheb_conv():
      in_channels, out_channels = (16, 32)
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      num_nodes = edge_index.max().item() + 1
      edge_weight = torch.rand(edge_index.size(1))
      x = torch.randn((num_nodes, in_channels))
  
      conv = ChebConv(in_channels, out_channels, K=3)
      assert str(conv) == "ChebConv(16, 32, K=3, normalization=sym)"
      out1 = conv(x, edge_index)
      assert out1.size() == (num_nodes, out_channels)
      out2 = conv(x, edge_index, edge_weight)
      assert out2.size() == (num_nodes, out_channels)
      out3 = conv(x, edge_index, edge_weight, lambda_max=3.0)
      assert out3.size() == (num_nodes, out_channels)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index), out1)
          assert torch.allclose(jit(x, edge_index, edge_weight), out2)
          assert torch.allclose(
              jit(
                  x,
                  edge_index,
                  edge_weight,
                  lambda_max=torch.tensor(3.0),
              ),
              out3,
          )
  
      batch = torch.tensor([0, 0, 1, 1])
      edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]])
      num_nodes = edge_index.max().item() + 1
      edge_weight = torch.rand(edge_index.size(1))
      x = torch.randn((num_nodes, in_channels))
      lambda_max = torch.tensor([2.0, 3.0])
  
      out4 = conv(x, edge_index, edge_weight, batch)
      assert out4.size() == (num_nodes, out_channels)
      out5 = conv(x, edge_index, edge_weight, batch, lambda_max)
      assert out5.size() == (num_nodes, out_channels)
  
      if is_full_test():
          assert torch.allclose(
              jit(x, edge_index, edge_weight, batch), out4
          )
          assert torch.allclose(
              jit(x, edge_index, edge_weight, batch, lambda_max), out5
          )
  
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 1 for calling a global function with example usage: test_cheb_conv() without return types