code: def gradient_penalty_layer():
      """A gradient penalty layer for testing."""
      from deepchem.models.torch_models import GradientPenaltyLayer
  
      class ExampleWGAN(dc.models.torch_models.WGANModel):
          def get_noise_input_shape(self):
              return (
                  100,
                  2,
              )
  
          def get_data_input_shapes(self):
              return [
                  (
                      100,
                      1,
                  )
              ]
  
          def get_conditional_input_shapes(self):
              return [
                  (
                      100,
                      1,
                  )
              ]
  
          def create_generator(self):
              noise_dim = self.get_noise_input_shape()
              conditional_dim = self.get_conditional_input_shapes()[0]
  
              return nn.Sequential(
                  Generator(noise_dim, conditional_dim)
              )
  
          def create_discriminator(self):
              data_input_shape = self.get_data_input_shapes()[0]
              conditional_input_shape = (
                  self.get_conditional_input_shapes()[0]
              )
  
              return nn.Sequential(
                  Discriminator(
                      data_input_shape, conditional_input_shape
                  )
              )
  
      gan = ExampleWGAN()
      discriminator = gan.discriminators[0]
      return GradientPenaltyLayer(gan, discriminator)
  
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 13 for calling a global function with example usage: gradient_penalty_layer() and returns: GradientPenaltyLayer, int,  int, list, list, nn, nn