code: def test_gpl_forward():
      # Create dummy data
      gpl = gradient_penalty_layer()
      inputs = [torch.randn(4, 1)]
      conditional_inputs = [torch.randn(4, 1)]
  
      # Call forward
      output, penalty = gpl(inputs, conditional_inputs)
  
      # Asserts
      assert isinstance(
          output, torch.Tensor
      ), "Output must be a torch.Tensor"
      assert isinstance(
          penalty, torch.Tensor
      ), "Penalty must be a torch.Tensor"
      assert (
          output.shape[0] == 4
      ), "Output tensor must have the same batch size as inputs"
      assert penalty.ndim == 0 or penalty.shape == (
          4,
      ), "Penalty should be a scalar or the same size as the batch"
  
file_path: deepchem/deepchem/models/torch_models/tests/test_gan.py
imports: import deepchem
  import numpy
  import pytest
  import tempfile
  from flaky import flaky
label_desc: Use image node_id 14 for calling a global function with example usage: test_gpl_forward() without return types