code: def test_forward_pass():
      batch_size = 16
      noise_shape = (
          batch_size,
          2,
      )
      data_shape = [
          (
              batch_size,
              1,
          )
      ]
      conditional_shape = [
          (
              batch_size,
              1,
          )
      ]
  
      gan = ExampleGAN(
          noise_shape,
          data_shape,
          conditional_shape,
          create_generator(noise_shape, conditional_shape),
          create_discriminator(data_shape, conditional_shape),
      )
  
      noise = torch.rand(*gan.noise_input_shape)
      real_data = torch.rand(*gan.data_input_shape[0])
      conditional = torch.rand(*gan.conditional_input_shape[0])
      gen_loss, disc_loss = gan([noise, real_data, conditional])
  
      assert isinstance(gen_loss, torch.Tensor)
      assert gen_loss > 0
  
      assert isinstance(disc_loss, torch.Tensor)
      assert disc_loss > 0
  
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 3 for calling a global function with example usage: test_forward_pass() without return types