src/peft/tuners/xlora/layer.py [89:129]:
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        target_forward: Callable[..., Any],
        layer_number: int,
        config: XLoraConfig,
    ) -> None:
        super().__init__(model, target, target_forward, layer_number, config)

    def forward(self, x: Tensor, *args: Any, scalings: Optional[Tensor] = None, **kwargs: Any) -> Tensor:
        """
        This method is designed to be a drop-in-replacement for the LoRA layers' .forward method. To use it, a bound
        method must be created (bound to an instance of the XLoraLayer class).
        """

        previous_dtype = x.dtype
        if scalings is not None:
            xlora_scalings = self.get_maybe_topk_scalings(scalings)

        result = self.target.base_layer(x, *args, **kwargs)

        # Ignore if disabled. We want to make sure this is always run.
        if not self.target.merged:
            for adapter_n, active_adapter in enumerate(self.target.active_adapters):
                if active_adapter not in self.target.lora_A.keys():
                    continue
                # TODO: implement X-LoRA with Lora+Dora layers
                if self.target.use_dora[active_adapter]:
                    raise ValueError("X-LoRA currently does not support LoRA layers with DoRA")
                lora_A = self.target.lora_A[active_adapter]
                lora_B = self.target.lora_B[active_adapter]
                dropout = self.target.lora_dropout[active_adapter]
                scaling = self.target.scaling[active_adapter]
                x = x.to(lora_A.weight.dtype)  # type: ignore
                if scalings is not None:
                    x_mod = self.apply_scalings_to_x(x, xlora_scalings, adapter_n)
                    scaling_weight = self.config.global_scaling_weight
                else:
                    x_mod = x
                    scaling_weight = 1
                result += lora_B(lora_A(dropout(x_mod))) * scaling * scaling_weight

        result = result.to(previous_dtype)
        return result
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