# Identity

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

```python
class tensorplay.ao.pruning.Identity
```

Prune nothing and only install the mask reparameterization.

The generated mask is a tensor of ones, which is useful to prepare a
module for later iterative pruning without removing any unit yet.

```python
classmethod apply(module: Module, name: str) → BasePruningMethod
```

Install the identity (all-ones mask) reparameterization.

Parameters:

- module – module containing the tensor to reparameterize.

- name – parameter name within module on which pruning acts.

```python
apply_mask(module: Module) → Tensor
```

Return the pruned version of the tensor held by module.

Fetches the mask and the original values from the module and returns
their elementwise product.

Parameters:

module – module holding the pruned tensor.

Returns:

The product of the mask and the original values.

```python
prune(t: Tensor, default_mask: Tensor | None = None, importance_scores: Tensor | None = None) → Tensor
```

Return a pruned copy of the input tensor t.

Applies the rule implemented by compute_mask() without any
module-side reparameterization.

Parameters:

- t – tensor to prune (same shape as default_mask).

- importance_scores – tensor of importance scores with the same shape as t; each entry ranks the corresponding element of t. When unspecified, t itself is used.

- default_mask – mask from a previous pruning iteration, if any. Pruning must respect the entries it already zeroes. When unspecified, a mask of ones is used.

Returns:

The pruned version of t.

```python
remove(module: Module) → None
```

Make the current pruning of module permanent.

The pruned values remain pruned: the product of mask and original
values is written back into the parameter name, and the auxiliary
parameter name + '_orig' and buffer name + '_mask' are
dropped.

> **Note**
>
> Pruning itself is NOT undone or reversed!