code: def __call__(cls, *args, **kwargs):
      quant_type = kwargs.get("quant_type", None)
      use_ckpting = kwargs.get("use_ckpting", [])
      r = kwargs.get("r", 0)
      lora_alpha = kwargs.get("lora_alpha", 1)
      lora_dropout = kwargs.get("lora_dropout", 0.0)
      bias = kwargs.get("bias", False)
      merge_weights = kwargs.get("merge_weights", True)
      threshold = kwargs.get("threshold", 6.0)
      compute_dtype = kwargs.get("compute_dtype", torch.float16)
  
      if quant_type in ["bnb_8bit", "bnb_FP4", "bnb_NF4"]:
          try:
              os.environ["BITSANDBYTES_NOWELCOME"] = "1"
              import bitsandbytes as bnb
          except ImportError:
              raise ImportError(
                  "Install bitsandbytes to use 4/8bit compression"
              )
  
      if quant_type == "bnb_8bit":
          layer_class = bnb.nn.Linear8bitLt
      elif quant_type in ["bnb_FP4", "bnb_NF4"]:
          layer_class = bnb.nn.Linear4bit
      else:
          layer_class = nn.Linear
  
      class QLoraLinear_cls(layer_class, LoRALayer):
          def __init__(self, *args, **kwargs):
              if quant_type == "bnb_8bit":
                  super(QLoraLinear_cls, self).__init__(
                      *args,
                      bias=bias,
                      has_fp16_weights=False,
                      threshold=threshold,
                  )
              elif quant_type in ["bnb_FP4", "bnb_NF4"]:
                  super(QLoraLinear_cls, self).__init__(
                      *args,
                      bias=bias,
                      compute_dtype=compute_dtype,
                      quant_type=quant_type[-3:].lower(),
                  )
              else:
                  super(QLoraLinear_cls, self).__init__(
                      *args, bias=bias
                  )
              self.quant_type = quant_type
              LoRALayer.__init__(
                  self, r, lora_alpha, lora_dropout, merge_weights
              )
              # Actual trainable parameters
              if r > 0:
                  self.lora_A = nn.Parameter(
                      self.weight.new_zeros((r, args[0]))
                  )
                  self.lora_B = nn.Parameter(
                      self.weight.new_zeros((args[1], r))
                  )
                  self.scaling = self.lora_alpha / self.r
                  # Freezing the pre-trained weight matrix
                  self.weight.requires_grad = False
              self.reset_parameters()
              self.maybe_ckpt = (
                  checkpoint
                  if "lora" in use_ckpting
                  else lambda f, x: f(x)
              )
  
          def reset_parameters(self):
              # we do not super().reset_parameters() save lot of time and useless when no grad.
              if hasattr(self, "lora_A"):
                  # initialize A the same way as the default
                  # for nn.Linear and B to zero
                  nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
                  nn.init.zeros_(self.lora_B)
  
          def train(self, mode: bool = True):
              if self.quant_type is None:
                  super().train(mode)
                  if mode:
                      if self.merge_weights and self.merged:
                          # Make sure that the weights are not merged
                          if self.r > 0:
                              self.weight.data -= (
                                  self.lora_B @ self.lora_A
                              ) * self.scaling
                          self.merged = False
                  else:
                      if self.merge_weights and not self.merged:
                          # Merge the weights and mark it
                          if self.r > 0:
                              self.weight.data += (
                                  self.lora_B @ self.lora_A
                              ) * self.scaling
                          self.merged = True
              else:
                  # cannot merge/unmerge quantized weigts with unquantized lora_X
                  pass
  
          def forward(self, x: torch.Tensor):
              if self.r > 0 and not self.merged:
                  result = (
                      self.maybe_ckpt(super().forward, x)
                      + (
                          self.lora_dropout(x)
                          @ self.lora_A.transpose(0, 1)
                          @ self.lora_B.transpose(0, 1)
                      )
                      * self.scaling
                  )
              else:
                  result = self.maybe_ckpt(super().forward, x)
              return result
  
      instance = QLoraLinear_cls.__new__(
          QLoraLinear_cls
      )  # Create a new instance of QLoraLinear_cls
      instance.__init__(
          *args, **kwargs
      )  # Invoke the __init__ method of QLoraLinear_cls
  
      """
          # Check if QLoraLinear has a custom __init__ method
          if hasattr(cls, "__init__"):
              kwargs.pop("r", None)
              kwargs.pop("quant_type", None)
              # Invoke the __init__ method of QLoraLinear
              cls.__init__(instance, *args, **kwargs)
          """
      return instance
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import math
  from torch.utils.checkpoint import checkpoint
  from typing import Dict
  import os
label_desc: Use image node_id 1 for calling the QLinear obj's underlying member method code with example usage: obj.__call__(cls) and returns: instance, result