code: def test_dmpnn_encoder_layer():
      """Test invoking DMPNNEncoderLayer."""
      torch.manual_seed(0)
  
      input_smile = "CC"
      feat = dc.feat.DMPNNFeaturizer(features_generators=["morgan"])
      graph = feat.featurize(input_smile)
  
      from deepchem.models.torch_models.dmpnn import _MapperDMPNN
  
      mapper = _MapperDMPNN(graph[0])
      (
          atom_features,
          f_ini_atoms_bonds,
          atom_to_incoming_bonds,
          mapping,
          global_features,
      ) = mapper.values
      molecules_unbatch_key = [len(atom_features)]
  
      atom_features = torch.from_numpy(atom_features).float()
      f_ini_atoms_bonds = torch.from_numpy(f_ini_atoms_bonds).float()
      atom_to_incoming_bonds = torch.from_numpy(atom_to_incoming_bonds)
      mapping = torch.from_numpy(mapping)
      global_features = torch.from_numpy(global_features).float()
  
      layer = torch_layers.DMPNNEncoderLayer(d_hidden=2)
      assert (
          layer.W_i.__repr__()
          == "Linear(in_features=147, out_features=2, bias=False)"
      )
      assert (
          layer.W_h.__repr__()
          == "Linear(in_features=2, out_features=2, bias=False)"
      )
      assert (
          layer.W_o.__repr__()
          == "Linear(in_features=135, out_features=2, bias=True)"
      )
  
      output = layer(
          atom_features,
          f_ini_atoms_bonds,
          atom_to_incoming_bonds,
          mapping,
          global_features,
          molecules_unbatch_key,
      )
      readout_output = torch.tensor([[0.1116, 0.0470]])
      assert output.shape == torch.Size([1, 2 + 2048])
      assert torch.allclose(output[0][:2], readout_output, atol=1e-4)
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 39 for calling a global function with example usage: test_dmpnn_encoder_layer() without return types