Hi, I’m trying to manipulate a tensor as an object’s attribute, I’m only using tf.function’s return value here. All assignments are outside the function, I don’t think I’m violating any rules here. But the result is clearly not as expected.

```
class Holder:
def __init__(self):
self.v = tf.zeros([])
@tf.function
def add(holder):
return holder.v + 1
h = Holder()
l = []
for _ in range(10):
h.v = add(h)
l.append(h.v)
print(l)
```

Result:

```
[<tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>]
```

How can I achieve this correctly without explicitly passing the tensor itself as the args of the function which would be quite a long list? I’m just using an object to pack args.

Thank you.