code: def fake_8bit_storage_with_max(w, topk=8):
      blocked_w = einops.rearrange(w.flatten(), "(h b) -> h b", b=256)
      max_val, idx = torch.sort(
          torch.abs(blocked_w), dim=1, descending=True
      )
      idx = idx[:, :topk]
      max_val = max_val[:, :topk]
  
      mask = torch.zeros_like(blocked_w)
      mask.scatter_(dim=1, index=idx, src=torch.ones_like(max_val))
      mask = mask.bool()
  
      # 1. zero out max values
      # 2. quantize + dequantize
      # 3. write back max values
      # 4. copy matrix back to weight
  
      values = blocked_w[mask]
      blocked_w[mask] = 0
  
      code = bnb.functional.create_dynamic_map()
      code = code.to(w.device)
      absmax, C = bnb.functional.quantize_blockwise(blocked_w.data)
      bnb.functional.dequantize_blockwise(absmax, C, out=blocked_w)
  
      blocked_w[mask] = values
  
      unblocked_w = blocked_w.flatten().view(w.shape)
  
      w.copy_(unblocked_w)
      return unblocked_w
  
imports: from itertools import product
  import pytest
  import torch
  from torch import nn
  import bitsandbytes
label_desc: Use image node_id 11 for calling the LinearFunction obj's underlying member method code with example usage: obj.fake_8bit_storage_with_max(w, topk) and returns: unblocked_w