refactor: use forward hook instead of custom forward

    # how do we revert this to unload the weights?

    def apply_to(self):

        self.org_forward = self.org_module[0].forward

        self.org_module[0].forward = self.forward

        #self.org_module[0].forward = self.forward

        self.org_module[0].register_forward_hook(self.forward_hook)

    def get_weight(self, oft_blocks, multiplier=None):

        self.constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)

        block_Q = oft_blocks - oft_blocks.transpose(1, 2)

        norm_Q = torch.norm(block_Q.flatten())

        new_norm_Q = torch.clamp(norm_Q, max=self.constraint)

        return self.finalize_updown(updown, orig_weight, output_shape)

    def forward(self, x, y=None):

        x = self.org_forward(x)

        if self.multiplier() == 0.0:

            return x

        # calculating R here is excruciatingly slow

        #R = self.get_weight().to(x.device, dtype=x.dtype)

    def forward_hook(self, module, args, output):

        #print(f'Forward hook in {self.network_key} called')

        x = output

        R = self.R.to(x.device, dtype=x.dtype)

        if x.dim() == 4:

        else:

            x = torch.matmul(x, R)

        return x

    # def forward(self, x, y=None):

    #     x = self.org_forward(x)

    #     if self.multiplier() == 0.0:

    #         return x

    #     # calculating R here is excruciatingly slow

    #     #R = self.get_weight().to(x.device, dtype=x.dtype)

    #     R = self.R.to(x.device, dtype=x.dtype)

    #     if x.dim() == 4:

    #         x = x.permute(0, 2, 3, 1)

    #         x = torch.matmul(x, R)

    #         x = x.permute(0, 3, 1, 2)

    #     else:

    #         x = torch.matmul(x, R)

    #     return x