code: def convert_gptq(module, awq=False, llm_awq=False):
      from bigdl.llm.transformers.low_bit_linear import get_block_size
  
      Q4_1 = get_block_size("asym_int4")
  
      scales = module.scales
  
      zeros = torch.bitwise_right_shift(
          torch.unsqueeze(module.qzeros, 2).expand(
              -1, -1, 32 // module.bits
          ),
          module.wf.unsqueeze(0),
      ).to(torch.int16 if module.bits == 8 else torch.int8)
      zeros = torch.bitwise_and(zeros, (2**module.bits) - 1)
  
      if not awq:
          zeros = zeros + 1
      zeros = zeros.reshape(scales.shape)
  
      if awq:
          weight = torch.bitwise_right_shift(
              torch.unsqueeze(module.qweight, 2).expand(
                  -1, -1, 32 // module.bits
              ),
              module.wf.unsqueeze(0),
          ).to(torch.int16 if module.bits == 8 else torch.int8)
          weight = torch.bitwise_and(weight, (2**module.bits) - 1)
          weight = weight.reshape(
              weight.shape[0], weight.shape[1] * weight.shape[2]
          )
          if llm_awq:
              weight = weight.t()
      else:
          weight = torch.bitwise_right_shift(
              torch.unsqueeze(module.qweight, 1).expand(
                  -1, 32 // module.bits, -1
              ),
              module.wf.unsqueeze(-1),
          ).to(torch.int8)
          weight = torch.bitwise_and(weight, (2**module.bits) - 1)
          weight = weight.reshape(
              weight.shape[0] * weight.shape[1], weight.shape[2]
          )
  
      # convert weight to ggml format
      weight = weight.reshape(
          weight.shape[0] // module.group_size,
          module.group_size,
          weight.shape[1],
      )
      weight = weight.permute(2, 0, 1).reshape(
          weight.shape[2], -1, 2, Q4_1 // 2
      )
      weight = weight.transpose(2, 3)
      weight = torch.bitwise_left_shift(
          weight,
          torch.tensor([0, 4], dtype=torch.int8).reshape(1, 1, 1, 2),
      )
      weight = torch.bitwise_or(
          weight[:, :, :, 0], weight[:, :, :, 1]
      ).contiguous()
  
      # convert zeros to ggml format
      if llm_awq:
          real_scale_num = module.in_features // module.group_size
          zeros = zeros[:, :real_scale_num]
          scales = scales[:, :real_scale_num]
          zeros = zeros.t()
          scales = scales.t()
      zeros = (
          zeros.reshape(-1, 1, zeros.shape[1])
          .permute(2, 0, 1)
          .unsqueeze(2)
          .expand(-1, -1, module.group_size // Q4_1, -1)
          .reshape(zeros.shape[1], -1, 1)
          .contiguous()
          .to(torch.float16)
      )
  
      # convert scales to ggml format
      scales = (
          scales.reshape(-1, 1, scales.shape[1])
          .permute(2, 0, 1)
          .unsqueeze(2)
          .expand(-1, -1, module.group_size // Q4_1, -1)
          .reshape(scales.shape[-1], -1, 1)
          .contiguous()
          .to(torch.float16)
      )
  
      m = -(zeros * scales)
      d = scales
  
      ggml_weight = torch.cat(
          [
              d.view(torch.uint8),
              m.view(torch.uint8),
              weight.view(torch.uint8),
          ],
          dim=-1,
      )
      ggml_weight = ggml_weight.reshape([-1])
  
      return ggml_weight
  
imports: import platform
  import torch
  import torch.nn
  from accelerate import init_empty_weights
  import warnings
  import transformers
  import importlib.util
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  from .utils import logger
  from typing import Union
  import numpy
  import os
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 5 for calling a global function with example usage: convert_gptq(module, awq, llm_awq) and returns: ggml_weight