code: def __init__(self, mod, groupsize=128, inner_k_tiles=8, padding=True):
      from model import find_multiple
  
      self.mod = mod
      self.groupsize = groupsize
      self.inner_k_tiles = inner_k_tiles
      self.padding = padding
      self.get_qparams_func = lambda w: get_group_qparams(
          w, 4, groupsize
      )
      self.quantize_func = (
          lambda w, qparams: group_quantize_tensor_from_qparams(
              w, qparams[0], qparams[1], 4, groupsize
          )
      )
      self.dequantize_func = (
          lambda q, qparams: group_dequantize_tensor_from_qparams(
              q, qparams[0], qparams[1], 4, groupsize
          ).float()
      )
      self.combine_qparams_list_func = lambda qparams_list: [
          torch.cat(x, dim=1) for x in zip(*qparams_list)
      ]
      # skip unless padding=True or its correctly sized
      self.skip_layer_func = lambda linear_weight: not (
          _check_linear_int4_k(
              linear_weight.shape[-1], groupsize, inner_k_tiles
          )
          or padding
      )
  
      # we need to do the padding here, both for q and the qparams if necessary
      def make_names_and_values_dict_func(q, qparams):
          k = q.shape[1]
          new_k = find_multiple(k, 1024)
          # how much we need to pad the weight
          delta_k = new_k - q.shape[1]
          final_q = torch.ops.aten._convert_weight_to_int4pack(
              F.pad(q, pad=(0, delta_k)), inner_k_tiles
          )
          scales_and_zeros = pack_scales_and_zeros(*qparams)
          # how many new groups we need for padded weight
          delta_groups = new_k // groupsize - scales_and_zeros.shape[0]
          final_s_and_z = F.pad(
              scales_and_zeros,
              pad=(0, 0, 0, 0, 0, delta_groups),
              value=1,
          )
          return {"weight": final_q, "scales_and_zeros": final_s_and_z}
  
      self.make_names_and_values_dict_func = (
          make_names_and_values_dict_func
      )
      super().__init__()
  
imports: import time
  from pathlib import Path
  import torch
  import torch.nn
  import torch.nn.functional
  from sentencepiece import SentencePieceProcessor
  from model import Transformer
label_desc: Use image node_id 1 to create a new WeightOnlyInt4GPTQQuantHandler object from inherited base classes: GPTQQuantHandler with example: obj = WeightOnlyInt4GPTQQuantHandler(mod, groupsize, inner_k_tiles, padding)