code: def test_dynamic_edge_conv():
      x1 = torch.randn(8, 16)
      x2 = torch.randn(4, 16)
      batch1 = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1])
      batch2 = torch.tensor([0, 0, 1, 1])
  
      nn = Seq(Lin(32, 16), ReLU(), Lin(16, 32))
      conv = DynamicEdgeConv(nn, k=2)
      assert str(conv) == (
          "DynamicEdgeConv(nn=Sequential(\n"
          "  (0): Linear(in_features=32, out_features=16, bias=True)\n"
          "  (1): ReLU()\n"
          "  (2): Linear(in_features=16, out_features=32, bias=True)\n"
          "), k=2)"
      )
      out11 = conv(x1)
      assert out11.size() == (8, 32)
  
      out12 = conv(x1, batch1)
      assert out12.size() == (8, 32)
  
      out21 = conv((x1, x2))
      assert out21.size() == (4, 32)
  
      out22 = conv((x1, x2), (batch1, batch2))
      assert out22.size() == (4, 32)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x1), out11)
          assert torch.allclose(jit(x1, batch1), out12)
          assert torch.allclose(jit((x1, x2)), out21)
          assert torch.allclose(jit((x1, x2), (batch1, batch2)), out22)
  
file_path: pytorch_geometric/test/nn/conv/test_edge_conv.py
imports: import torch
  from torch.nn import Linear
  from torch.nn import ReLU
  from torch.nn import Sequential
  import torch_geometric.typing
  from torch_geometric.nn import DynamicEdgeConv, EdgeConv
  from torch_geometric.testing import is_full_test, withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_torch_csc_tensor
label_desc: Use image node_id 2 for calling a global function with example usage: test_dynamic_edge_conv() without return types