def GAN(G, D, z_dim, n_labels, resolution, n_channels):
G_train = Sequential([G, D])
G_train.cur_block = G.cur_block
shape = D.get_input_shape_at(0)[1:]
gen_input, real_input = Input(shape), Input(shape)
interpolation = Input(shape)
sub = Subtract()([D(gen_input), D(real_input)])
norm = GradNorm()([D(interpolation), interpolation])
D_train = Model([real_input, gen_input, interpolation],
[sub, norm, Reshape((1, ))(D(real_input))])
D_train.cur_block = D.cur_block
return G_train, D_train
