code: def __init__(
      self,
      base_layer,
      adapter_name,
      r: int = 0,
      lora_alpha: int = 1,
      lora_dropout: float = 0.0,
      init_lora_weights: bool = True,
      **kwargs,
  ) -> None:
      super().__init__()
      AdaLoraLayer.__init__(self, base_layer)
  
      # self.base_layer and self.quant_linear_module are the same; we need the former for consistency and the latter
      # for backwards compatibility
      self.quant_linear_module = base_layer
      self._active_adapter = adapter_name
      self.update_layer(
          adapter_name, r, lora_alpha, lora_dropout, init_lora_weights
      )
  
imports: import torch
  from .layer import AdaLoraLayer
label_desc: Use image node_id 1 to create a new SVDQuantLinear object from inherited base classes: torch.nn, AdaLoraLayer with example: obj = SVDQuantLinear(base_layer, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)