Hi,

In the above example, after applying structural pruning to some layers. How the shape of the tensor will change? weights related to one node or neuron will be completely replaced with zero after pruning? If not, is there any method to do like that?

Ex: Consider the layer “prune_low_magnitude_structural_pruning_dense”

shape before pruning : (3136, 1024)

after pruning(non zero weights shape ): (1568 , 1024) but this is contribution of all the neurons (i.e., no input or output neuron weights is **completely** zero).

Weight tensor after structural pruning:

```
[[-0.029, -0. , 0. , ..., 0. , -0. , -0.029],
[ 0.0293, -0. , -0.0372, ..., 0.0229, -0.022, 0.027],
[-0. , -0.0265 , 0.0193, ..., -0.0375, -0. , 0. ],
...,
[-0. , 0. , 0.0294, ..., -0.0153, 0. , 0. ]], dtype=float32)
```

Expected weight tensor after structural pruning:

```
[[-0., -0. , 0. , ..., 0. , -0. , -0.],
[ 0.0293, -0.026 , -0.0372 ..., 0.0229 -0.022, 0.027=],
[-0. , -0., 0., ..., - 0., -0. , 0. ],
...,
[-0.0264 , 0.0342, 0.0294, ..., -0.0153, 0.013 , 0.0213 ]], dtype=float32)
Is there any way to like this? plz suggest if any.
```

Thanks