class LinearWithLoRAMerged(nn.Module):
    def __init__(self, linear, rank, alpha):
        super().__init__()
        self.linear = linear
        self.lora = LoRALayer(
            linear.in_features, linear.out_features, rank, alpha
        )

    def forward(self, x):
        lora = self.lora.A @ self.lora.B # Combine LoRA matrices
        # Then combine LoRA with orig. weights
        combined_weight = self.linear.weight + self.lora.alpha*lora.T 
        return F.linear(x, combined_weight, self.linear.bias)