code: def forward(self, x, lora):
      if self.lora_applies(lora):
          lora_a = lora.tensors[self.key + ".lora_A.weight"]
          lora_b = lora.tensors[self.key + ".lora_B.weight"]
          out = cuda_ext.ext_q4_matmul(
              x, self.q4, self.width, lora_a, lora_b
          )
      else:
          out = cuda_ext.ext_q4_matmul(x, self.q4, self.width)
  
      # out = cuda_ext.ext_q4_matmul(x, self.q4, self.width)
      # if self.lora_applies(lora):
      #     out += self.lora_apply(lora, x)
  
      if self.bias is not None:
          out.add_(self.bias)
      return out
  
imports: import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from safetensors import safe_open
  import cuda_ext
  import json
  import math
  import gc
  from enum import Enum
label_desc: Use image node_id 5 for calling the Ex4bitLinear obj's underlying member method code with example usage: obj.forward(x, lora) and returns: out