code: def test_gan_container(backend_default):
      """
      Set up a GenerativeAdversarial container and make sure generator
      and discriminator layers get configured correctly.
      """
      init_norm = Gaussian(loc=0.0, scale=0.01)
      # set up container and ensure layers get wired up correctly
      generator = Sequential(
          [
              Affine(nout=10, init=init_norm),
              Affine(nout=100, init=init_norm),
          ]
      )
      discriminator = Sequential(
          [
              Affine(nout=100, init=init_norm),
              Affine(nout=1, init=init_norm),
          ]
      )
      layers = GenerativeAdversarial(generator, discriminator)
  
      assert len(layers.layers) == 4
      assert layers.layers[0].nout == 10
      assert layers.layers[1].nout == 100
      assert layers.layers[2].nout == 100
      assert layers.layers[3].nout == 1
      assert layers.generator.layers == layers.layers[0:2]
      assert layers.discriminator.layers == layers.layers[2:4]
  
file_path: neon/tests/test_gan.py
imports: from neon.initializers.initializer import Gaussian
  from neon.layers import Affine
  from neon.transforms import GANCost
  from neon.layers.container import Sequential, GenerativeAdversarial
  from utils import tensors_allclose, allclose_with_out
label_desc: Use image node_id 1 for calling a global function with example usage: test_gan_container(backend_default) without return types