pruning will act.
     * **amount** (*int** or **float*) -- quantity of parameters
       to prune. If "float", should be between 0.0 and 1.0 and
       represent the fraction of parameters to prune. If "int", it
       represents the absolute number of parameters to prune.

     * **dim** (*int**, **optional*) -- index of the dim along
       which we define channels to prune. Default: -1.

apply_mask(module)
  Simply handles the multiplication between the parameter being
  pruned and the generated mask. Fetches the mask and the original
  tensor from the module and returns the pruned version of the
  tensor.

  Parameters:
     **module** (*nn.Module*) -- module containing the tensor to
     prune

  Returns:
     pruned version of the input tensor

  Return type:
     pruned_tensor (torch.Tensor)

compute_mask(t, default_mask)
  Computes and returns a mask for the input tensor "t". Starting
