code: def make_llm_int8_linear(
      linear_module: nn.Linear,
      device: torch.device,
      threshold: float = 6.0,
  ):
      """
      ## Transform a `nn.Linear` layer to LLM.int8() linear layer
  
      :param linear_module: is the `nn.Linear` layer to transform
      :param device: is the device of the model
      :param threshold: is the threshold $\alpha$ to use for outlier detection
      """
  
      #
      assert isinstance(linear_module, nn.Linear)
  
      # Create an empty Linear8bitLt module
      int8_lin = Linear8bitLt(
          linear_module.in_features,
          linear_module.out_features,
          linear_module.bias is not None,
          has_fp16_weights=False,
          threshold=threshold,
      )
  
      # Quantize the weights
      int8_lin._parameters["weight"] = Int8Params(
          linear_module.weight.data.cpu(),
          requires_grad=False,
          has_fp16_weights=False,
      ).to(device)
  
      # Set the bias in float16 space
      if linear_module.bias is not None:
          int8_lin._parameters["bias"] = nn.Parameter(
              linear_module.bias.data, requires_grad=False
          )
  
      #
      return int8_lin
  
label_desc: Use image node_id 1 for calling a global function with example usage: make_llm_int8_linear(linear_module, device, threshold) and returns: int8_lin