code: def test_gpl_penalty_calculation():
      gpl = gradient_penalty_layer()
      # Create dummy data
      inputs = [torch.randn(4, 1)]
      conditional_inputs = [torch.randn(4, 1)]
  
      # Call forward
      _, penalty = gpl(inputs, conditional_inputs)
  
      # Since the penalty is a squared norm of the gradients minus 1, multiplied by a constant,
      # it should be non-negative
      assert penalty.item() >= 0, "Penalty should be non-negative"
  
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 15 for calling a global function with example usage: test_gpl_penalty_calculation() without return types