define backward pass

backward.py

  def backward_grads(self, y, dy, training=True):
    dy1, dy2 = dy
    y1, y2 = y

    with tf.GradientTape() as gtape:
      gtape.watch(y1)
      gy1 = self.g(y1, training=training)
    grads_combined = gtape.gradient(
        gy1, [y1] + self.g.trainable_variables, output_gradients=dy2)
    dg = grads_combined[1:]
    dx1 = dy1 + grads_combined[0]
    x2 = y2 - gy1

    with tf.GradientTape() as ftape:
      ftape.watch(x2)
      fx2 = self.f(x2, training=training)
    grads_combined = ftape.gradient(
        fx2, [x2] + self.f.trainable_variables, output_gradients=dx1)
    df = grads_combined[1:]
    dx2 = dy2 + grads_combined[0]
    x1 = y1 - fx2

    x = x1, x2
    dx = dx1, dx2
    grads = df + dg

    return x, dx, grads