code: def test_film_conv():
      x1 = torch.randn(4, 4)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor(
          [[0, 1, 1, 2, 2, 3], [0, 0, 1, 0, 1, 1]]
      )
      edge_type = torch.tensor([0, 1, 1, 0, 0, 1])
  
      conv = FiLMConv(4, 32)
      assert str(conv) == "FiLMConv(4, 32, num_relations=1)"
      out = conv(x1, edge_index)
      assert out.size() == (4, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x1, adj.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x1, edge_index), out, atol=1e-6)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj.t()), out, atol=1e-6)
  
      conv = FiLMConv(4, 32, num_relations=2)
      assert str(conv) == "FiLMConv(4, 32, num_relations=2)"
      out = conv(x1, edge_index, edge_type)
      assert out.size() == (4, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, edge_type, (4, 4)
          )
          assert torch.allclose(conv(x1, adj.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit(x1, edge_index, edge_type), out, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj.t()), out, atol=1e-6)
  
      # Test bipartite message passing:
      conv = FiLMConv((4, 16), 32)
      assert str(conv) == "FiLMConv((4, 16), 32, num_relations=1)"
      out = conv((x1, x2), edge_index)
      assert out.size() == (2, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 2)
          )
          assert torch.allclose(conv((x1, x2), adj.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit((x1, x2), edge_index), out, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, x2), adj.t()), out, atol=1e-6
              )
  
      conv = FiLMConv((4, 16), 32, num_relations=2)
      assert str(conv) == "FiLMConv((4, 16), 32, num_relations=2)"
      out = conv((x1, x2), edge_index, edge_type)
      assert out.size() == (2, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, edge_type, (4, 2)
          )
          assert torch.allclose(conv((x1, x2), adj.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit((x1, x2), edge_index, edge_type), out, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, x2), adj.t()), out, atol=1e-6
              )
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import FiLMConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_film_conv() without return types