class_docstr: A simple API for GPTQ Quantization
docstr: Get the modules that should not be split across multiple devices.
  Args:
      model (`nn.Module`):
          The input model
docstr_tok: ["Get","the","modules","that","should","not","be","split","across","multiple","devices",".","Args",":","model","(","`","nn.Module","`",")",":","The","input","model"]
code: def get_no_split_module_classes(self, model):
      """
      Get the modules that should not be split across multiple devices.
      Args:
          model (`nn.Module`):
              The input model
      """
  
      block_class_name = recurse_getattr(
          model, self.block_name_to_quantize
      )[0].__class__.__name__
      no_split_module_classes = [block_class_name]
      return no_split_module_classes
  
imports: import json
  import os
  from enum import Enum
  from logging import getLogger
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer
  from transformers.pytorch_utils import Conv1D
  from transformers.utils.quantization_config import QuantizationMethod
  from ..utils import is_accelerate_available, is_auto_gptq_available
  from ..utils.modeling_utils import recurse_getattr
  from .constants import GPTQ_CONFIG
  from .data import get_dataset, prepare_dataset
  from .utils import get_block_name_with_pattern, get_device, get_layers, get_preceding_modules, get_seqlen
label_desc: Use image node_id 5 for calling the GPTQQuantizer obj's underlying member method code with example usage: obj.get_no_split_module_classes(model) and returns: no_split_module_classes