code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      result = self.quant_linear_module(x)
  
      if self.disable_adapters:
          return result
  
      for active_adapter in self.active_adapters:
          if active_adapter not in self.lora_A.keys():
              continue
          lora_A = self.lora_A[active_adapter]
          lora_B = self.lora_B[active_adapter]
          lora_E = self.lora_E[active_adapter]
          dropout = self.lora_dropout[active_adapter]
          scaling = self.scaling[active_adapter]
          ranknum = self.ranknum[active_adapter] + 1e-5
  
          requires_conversion = not torch.is_autocast_enabled()
          if requires_conversion:
              expected_dtype = result.dtype
              if x.dtype != torch.float32:
                  x = x.float()
  
          output = (
              (dropout(x) @ (lora_A * lora_E).T @ lora_B.T)
              * scaling
              / ranknum
          )
          # TODO: here, the dtype conversion is applied on the *whole expression*,
          # not the intermediate result, unlike for SVDLinear8bitLT and
          # SVDLinear4bit, is that correct?
          if requires_conversion:
              output = output.to(expected_dtype)
          result += output
      return result
  
      def __repr__(self) -> str:
          rep = super().__repr__()
          return "adalora." + rep
  
imports: import torch
  from .layer import AdaLoraLayer
label_desc: Use image node_id 2 for calling the SVDQuantLinear obj's underlying member method code with example usage: obj.forward(x) and returns: result, result, unknown