code: def test_aggregation_layer_values():
      from deepchem.models.torch_models.layers import (
          MolGANAggregationLayer,
      )
  
      units = 128
  
      torch.manual_seed(21)  # Setting seed for reproducibility
      layer = MolGANAggregationLayer(units=units, name="layer1")
      tf_weights = np.load(
          "deepchem/models/tests/assets/molgan_agg_layer_weights.npy",
          allow_pickle=True,
      ).item()
      with torch.no_grad():
          layer.d1.weight.data = torch.from_numpy(
              np.transpose(tf_weights["layer1/dense_27/kernel:0"])
          )
          layer.d1.bias.data = torch.from_numpy(
              tf_weights["layer1/dense_27/bias:0"]
          )
          layer.d2.weight.data = torch.from_numpy(
              np.transpose(tf_weights["layer1/dense_28/kernel:0"])
          )
          layer.d2.bias.data = torch.from_numpy(
              tf_weights["layer1/dense_28/bias:0"]
          )
  
      hidden_tensor = torch.from_numpy(
          np.load(
              "deepchem/models/tests/assets/molgan_agg_tensor.npy"
          ).astype(np.float32)
      )
      output = layer(hidden_tensor)
      output_tensor = torch.from_numpy(
          np.load(
              "deepchem/models/tests/assets/molgan_agg_layer_op.npy"
          ).astype(np.float32)
      )
  
      # Testing Values
      assert torch.allclose(output, 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 4 for calling a global function with example usage: test_aggregation_layer_values() without return types