code: def test_dimenet(Model):
      z = torch.randint(1, 10, (20,))
      pos = torch.randn(20, 3)
  
      if Model == DimeNet:
          kwargs = dict(num_bilinear=3)
      else:
          kwargs = dict(
              out_emb_channels=3, int_emb_size=5, basis_emb_size=5
          )
  
      model = Model(
          hidden_channels=5,
          out_channels=1,
          num_blocks=5,
          num_spherical=5,
          num_radial=5,
          **kwargs,
      )
      model.reset_parameters()
  
      with torch.no_grad():
          out = model(z, pos)
          assert out.size() == (1,)
  
          jit = torch.jit.export(model)
          assert torch.allclose(jit(z, pos), out)
  
      if is_full_test():
          optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
  
          min_loss = float("inf")
          for i in range(100):
              optimizer.zero_grad()
              out = model(z, pos)
              loss = F.l1_loss(out, torch.tensor([1.0]))
              loss.backward()
              optimizer.step()
              min_loss = min(float(loss), min_loss)
          assert min_loss < 2
  
imports: import pytest
  import torch
  import torch.nn.functional
  from torch_geometric.nn import DimeNet, DimeNetPlusPlus
  from torch_geometric.nn.models.dimenet import BesselBasisLayer, Envelope, ResidualLayer
  from torch_geometric.testing import is_full_test, withPackage
label_desc: Use image node_id 2 for calling a global function with example usage: test_dimenet(Model) without return types