the sources is in the link:

edward/tests/inferences/test_bayesian_nn.py

```
def test_gan_inference(self):
with self.test_session():
N, D, W_1, W_2, W_3, b_1, b_2, X, y, X_train, y_train = self._test()
with tf.variable_scope("Gen"):
theta = tf.get_variable("theta", [1])
y = tf.cast(y, tf.float32) * theta
def discriminator(x):
w = tf.get_variable("w", [1])
return w * tf.cast(x, tf.float32)
inference = ed.GANInference(
data={y: tf.cast(y_train, tf.float32), X: X_train},
discriminator=discriminator)
inference.run(n_iter=1)
```

I didn’t get the gen and discriminator in this example.

and the implicitKLqp

```
def test_normal_run(self):
def ratio_estimator(data, local_vars, global_vars):
"""Use the optimal ratio estimator, r(z) = log p(z). We add a
TensorFlow variable as the algorithm assumes that the function
has parameters to optimize."""
w = tf.get_variable("w", [])
return z.log_prob(local_vars[z]) + w
with self.test_session() as sess:
z = Normal(loc=5.0, scale=1.0)
qz = Normal(loc=tf.Variable(tf.random_normal([])),
scale=tf.nn.softplus(tf.Variable(tf.random_normal([]))))
inference = ed.ImplicitKLqp({z: qz}, discriminator=ratio_estimator)
inference.run(n_iter=200)
```

can someone please please elaborate how to apply ImplicitKLqp in the Bayesian NN case? I wanna know what these (data, local_vars, global_vars) means in the BNN case.

Thanks very much!