# tensorplay.ao.pruning.is_pruned

Source: https://www.tensorplay.cn/docs/generated/tensorplay.ao.pruning.is_pruned.html

```python
tensorplay.ao.pruning.is_pruned(module: Module) → bool
```

Check whether module carries an active pruning reparameterization.

Scans every submodule for forward pre-hooks that are instances of
[BasePruningMethod](/docs/generated/tensorplay.ao.pruning.BasePruningMethod.html#tensorplay.ao.pruning.BasePruningMethod).

Parameters:

module – module that is either pruned or unpruned.

Returns:

True when at least one submodule is pruned, False otherwise.

Examples

```
>>> # xdoctest: +SKIP
>>> m = nn.Linear(5, 7)
>>> print(is_pruned(m))
False
>>> random_unstructured(m, name="weight", amount=0.2)
>>> print(is_pruned(m))
True
```