code: def test_mxmnet_local_message_passing():
      """Test for MXMNetLocalMessagePassing Layer."""
      seed = 123
      torch.backends.cudnn.deterministic = True
      torch.backends.cudnn.benchmark = False
      torch.manual_seed(seed)
      torch.cuda.manual_seed_all(seed)
  
      dim = 1
      h = torch.tensor(
          [[0.8343], [1.2713], [1.2713], [1.2713], [1.2713]]
      )
  
      rbf = torch.tensor(
          [
              [-0.2628],
              [-0.2628],
              [-0.2628],
              [-0.2628],
              [-0.2629],
              [-0.2629],
              [-0.2628],
              [-0.2628],
          ]
      )
  
      sbf1 = torch.tensor(
          [
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
              [-0.2767],
          ]
      )
  
      sbf2 = torch.tensor(
          [
              [-0.0301],
              [-0.0301],
              [-0.1483],
              [-0.1486],
              [-0.1484],
              [-0.0301],
              [-0.1483],
              [-0.0301],
              [-0.1485],
              [-0.1483],
              [-0.0301],
              [-0.1486],
              [-0.1485],
              [-0.0301],
              [-0.1486],
              [-0.0301],
              [-0.1484],
              [-0.1483],
              [-0.1486],
              [-0.0301],
          ]
      )
  
      idx_kj = torch.tensor([3, 5, 7, 1, 5, 7, 1, 3, 7, 1, 3, 5])
  
      idx_ji_1 = torch.tensor([0, 0, 0, 2, 2, 2, 4, 4, 4, 6, 6, 6])
  
      idx_jj = torch.tensor(
          [0, 1, 3, 5, 7, 2, 1, 3, 5, 7, 4, 1, 3, 5, 7, 6, 1, 3, 5, 7]
      )
  
      idx_ji_2 = torch.tensor(
          [0, 1, 1, 1, 1, 2, 3, 3, 3, 3, 4, 5, 5, 5, 5, 6, 7, 7, 7, 7]
      )
  
      edge_index = torch.tensor(
          [[0, 1, 0, 2, 0, 3, 0, 4], [1, 0, 2, 0, 3, 0, 4, 0]]
      )
  
      layer = dc.models.torch_models.layers.MXMNetLocalMessagePassing(
          dim, activation_fn="silu"
      )
  
      output = layer(
          h,
          rbf,
          sbf1,
          sbf2,
          idx_kj,
          idx_ji_1,
          idx_jj,
          idx_ji_2,
          edge_index,
      )
      result0 = np.array(
          [[0.7916], [1.2796], [1.2796], [1.2796], [1.2796]]
      )
      result1 = np.array(
          [[0.3439], [0.3441], [0.3441], [0.3441], [0.3441]]
      )
  
      assert np.allclose(
          result0, output[0].detach().numpy(), atol=1e-04
      )
      assert np.allclose(
          result1, output[1].detach().numpy(), atol=1e-04
      )
  
      assert output[0].shape == (5, 1)
      assert output[1].shape == (5, 1)
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 61 for calling a global function with example usage: test_mxmnet_local_message_passing() without return types