code: def test_graph_convolution_layer_values():
      from deepchem.models.torch_models.layers import (
          MolGANConvolutionLayer,
      )
  
      vertices = 9
      nodes = 5
      edges = 5
      units = 128
  
      torch.manual_seed(21)  # Setting seed for reproducibility
      layer = MolGANConvolutionLayer(
          units=units, edges=edges, nodes=nodes
      )
      tf_weights = np.load(
          "deepchem/models/tests/assets/molgan_conv_layer_weights.npy",
          allow_pickle=True,
      ).item()
      with torch.no_grad():
          for idx, dense in enumerate(layer.dense1):
              # Dense1 is a list of dense layers
              weight_name = f"layer1/dense_{idx+4}/kernel:0"
              bias_name = f"layer1/dense_{idx+4}/bias:0"
              dense.weight.data = torch.from_numpy(
                  np.transpose(tf_weights[weight_name])
              )
              dense.bias.data = torch.from_numpy(tf_weights[bias_name])
  
      layer.dense2.weight.data = torch.from_numpy(
          np.transpose(tf_weights["layer1/dense_8/kernel:0"])
      )
      layer.dense2.bias.data = torch.from_numpy(
          tf_weights["layer1/dense_8/bias:0"]
      )
      adjacency_tensor = torch.randn((1, vertices, vertices, edges))
      node_tensor = torch.randn((1, vertices, nodes))
      output = layer([adjacency_tensor, node_tensor])
  
      adjacency_tensor = torch.from_numpy(
          np.load(
              "deepchem/models/tests/assets/molgan_adj_tensor.npy"
          ).astype(np.float32)
      )
      node_tensor = torch.from_numpy(
          np.load(
              "deepchem/models/tests/assets/molgan_nod_tensor.npy"
          ).astype(np.float32)
      )
      output = layer([adjacency_tensor, node_tensor])
      output_tensor = torch.from_numpy(
          np.load(
              "deepchem/models/tests/assets/molgan_conv_layer_op.npy"
          ).astype(np.float32)
      )
  
      # Testing Values
      assert torch.allclose(output[0], adjacency_tensor, atol=1e-06)
      assert torch.allclose(output[1], node_tensor, atol=1e-06)
      assert torch.allclose(output[2], output_tensor, atol=1e-04)
  
file_path: deepchem/deepchem/models/torch_models/tests/test_molgan_layers.py
imports: import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_graph_convolution_layer_values() without return types