refactor: remove unused function

import network

from lyco_helpers import factorization

from einops import rearrange

from modules import devices

class ModuleTypeOFT(network.ModuleType):

            raise ValueError("sd_module must be Linear or Conv")

        if self.is_kohya:

            #self.num_blocks = self.dim

            #self.block_size = self.out_dim // self.num_blocks

            #self.block_size = self.dim

            #self.num_blocks = self.out_dim // self.block_size

            self.constraint = self.alpha * self.out_dim

            self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)

        else:

            self.constraint = None

            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)

        if is_other_linear:

            self.lin_module = self.create_module(weights.w, "oft_diag", none_ok=True)

    def create_module(self, weights, key, none_ok=False):

        weight = weights.get(key)

        if weight is None and none_ok:

            return None

        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]

        is_conv = type(self.sd_module) in [torch.nn.Conv2d]

        if is_linear:

            weight = weight.reshape(weight.shape[0], -1)

            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)

        elif is_conv and key == "lora_down.weight" or key == "dyn_up":

            if len(weight.shape) == 2:

                weight = weight.reshape(weight.shape[0], -1, 1, 1)

            if weight.shape[2] != 1 or weight.shape[3] != 1:

                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)

            else:

                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)

        elif is_conv and key == "lora_mid.weight":

            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)

        elif is_conv and key == "lora_up.weight" or key == "dyn_down":

            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)

        else:

            raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')

        with torch.no_grad():

            if weight.shape != module.weight.shape:

                weight = weight.reshape(module.weight.shape)

            module.weight.copy_(weight)

        module.to(device=devices.cpu, dtype=devices.dtype)

        module.weight.requires_grad_(False)

        return module

    def merge_weight(self, R_weight, org_weight):

        R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)

        if org_weight.dim() == 4: