thank you linter

        elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):

            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)

        else:

            print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')

            print(f'Lora layer {key_lora} matched a layer with unsupported type: {type(sd_module).__name__}')

            continue

            raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")

            raise AssertionError(f"Lora layer {key_lora} matched a layer with unsupported type: {type(sd_module).__name__}")

        with torch.no_grad():

            module.weight.copy_(weight)

        elif lora_key == "lora_down.weight":

            lora_module.down = module

        else:

            raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")

            raise AssertionError(f"Bad Lora layer name: {key_lora} - must end in lora_up.weight, lora_down.weight or alpha")

    if keys_failed_to_match:

        print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}")