code: def __init__(
      self,
      in_features: int,
      out_features: int,
      bias=True,
      device=None,
      dtype=None,
      groupsize: int = 128,
      inner_k_tiles: int = 8,
      padding: bool = True,
  ) -> None:
      super().__init__()
      self.padding = padding
      if padding:
          from model import find_multiple
  
          self.origin_in_features = in_features
          in_features = find_multiple(in_features, 1024)
  
      self.in_features = in_features
      self.out_features = out_features
      assert not bias, "require bias=False"
      self.groupsize = groupsize
      self.inner_k_tiles = inner_k_tiles
  
      assert out_features % 8 == 0, "require out_features % 8 == 0"
      assert (
          in_features % (inner_k_tiles * 16) == 0
      ), "require in_features % (innerKTiles * 16) == 0"
      self.register_buffer(
          "weight",
          torch.empty(
              (
                  out_features // 8,
                  in_features // (inner_k_tiles * 16),
                  32,
                  inner_k_tiles // 2,
              ),
              dtype=torch.int32,
          ),
      )
      self.register_buffer(
          "scales_and_zeros",
          torch.empty(
              (in_features // groupsize, out_features, 2),
              dtype=torch.bfloat16,
          ),
      )
  
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 WeightOnlyInt4Linear object from inherited base classes: torch.nn with example: obj = WeightOnlyInt4Linear(in_features, out_features, bias, device, dtype, groupsize, inner_k_tiles, padding)