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# torch.nn.utils.prune.custom_from_mask

torch.nn.utils.prune.custom_from_mask(module, name, mask)

  Prunes tensor corresponding to parameter called `name` in `module` by applying the pre-computed mask in `mask`. Modifies module in place (and also return the modified module) by:

  1. adding a named buffer called `name+'_mask'` corresponding to the binary mask applied to the parameter `name` by the pruning method.

  2. replacing the parameter `name` by its pruned version, while the original (unpruned) parameter is stored in a new parameter named `name+'_orig'`.

  Parameters:

    - module (nn.Module) – module containing the tensor to prune

    - name (str) – parameter name within `module` on which pruning will act.

    - mask (Tensor) – binary mask to be applied to the parameter.

  Returns:

    modified (i.e. pruned) version of the input module

  Return type:

    module (nn.Module)

  Examples

      >>> from torch.nn.utils import prune
      >>> m = prune.custom_from_mask(
      ...     nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0])
      ... )
      >>> print(m.bias_mask)
      tensor([0., 1., 0.])

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