code: def get_auto_gptq_quant_linear(gptq_quantization_config):
      """
      Get the right AutoGPTQQuantLinear class based on the quantization config file
      """
      if (
          gptq_quantization_config is not None
          and is_auto_gptq_available()
      ):
          from auto_gptq.utils.import_utils import (
              dynamically_import_QuantLinear,
          )
  
          desc_act = gptq_quantization_config.desc_act
          group_size = gptq_quantization_config.group_size
          bits = gptq_quantization_config.bits
          if hasattr(gptq_quantization_config, "use_exllama"):
              use_exllama = gptq_quantization_config.use_exllama
          else:
              use_exllama = not gptq_quantization_config.disable_exllama
          if hasattr(gptq_quantization_config, "exllama_config"):
              exllama_version = gptq_quantization_config.exllama_config[
                  "version"
              ]
          else:
              exllama_version = 1
          AutoGPTQQuantLinear = dynamically_import_QuantLinear(
              use_triton=False,
              desc_act=desc_act,
              group_size=group_size,
              bits=bits,
              disable_exllama=not (
                  use_exllama and exllama_version == 1
              ),
              disable_exllamav2=not (
                  use_exllama and exllama_version == 2
              ),
          )
          return AutoGPTQQuantLinear
      return None
  
imports: import copy
  import inspect
  import warnings
  from typing import Optional, Tuple
  import accelerate
  import torch
  from accelerate.hooks import add_hook_to_module, remove_hook_from_module
  from accelerate.utils import is_npu_available, is_xpu_available
  from safetensors.torch import storage_ptr, storage_size
  from ..import_utils import is_auto_gptq_available, is_torch_tpu_available
  from .constants import CONFIG_NAME, EMBEDDING_LAYER_NAMES, INCLUDE_LINEAR_LAYERS_SHORTHAND, SAFETENSORS_WEIGHTS_NAME, TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING, TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING, TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING, TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING, TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING, WEIGHTS_NAME, bloom_model_postprocess_past_key_value, starcoder_model_postprocess_past_key_value
label_desc: Use image node_id 15 for calling a global function with example usage: get_auto_gptq_quant_linear(gptq_quantization_config) and returns: None, AutoGPTQQuantLinear