You can pass parameters to the functions in tf.cond() using **lambda** and the code is as bellow.

```
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
z = tf.placeholder(tf.float32)
def fn1(a, b):
return tf.mul(a, b)
def fn2(a, b):
return tf.add(a, b)
pred = tf.placeholder(tf.bool)
result = tf.cond(pred, lambda: fn1(x, y), lambda: fn2(y, z))
```

Then you can call it as bellowing:

```
with tf.Session() as sess:
print sess.run(result, feed_dict={x: 1, y: 2, z: 3, pred: True})
# The result is 2.0
print sess.run(result, feed_dict={x: 1, y: 2, z: 3, pred: False})
# The result is 5.0
```