Hey, all. I’m in the process of getting familiar with TF’s autograph — the series of posts by @pgaleone were super helpful.

If I want to compute and return gradients inside the graph definition using the `tf.function`

decorator, is using `tf.GradientTape`

a sensible approach for TF 2? This is what I have so far for the simple graph illustrated below:

```
@tf.function
def create_graph(x, y, get_grads):
with tf.GradientTape(persistent=True) as tape:
c = x + y
d = y + 1
e = c * d
if get_grads == False:
return [e, {}]
else:
de_da = tape.gradient(e, x)
de_db = tape.gradient(e, y)
de_dc = tape.gradient(e, c)
de_dd = tape.gradient(e, d)
de_de = tape.gradient(e, e)
return [e,{'d_da':de_da,
'd_db':de_db,
'd_dc':de_dc,
'd_dd':de_dd,
'd_de':de_de}]
graph = tf.function(create_graph)
```