code: def test_layout_exact_match():
      x = (torch.randn(14336 * 3, 14336) * 10).to(torch.int8).cuda()
      for tile_size, order in ((8, 32), "col_turing"), (
          (32, 32),
          "col_ampere",
      ):
          transform = lambda x: F.transform(
              x.cuda(), from_order="row", to_order=order
          )[0].to(x.device)
          tile_indices = get_inverse_transform_indices(
              transform, tile_size
          )
          cxb = transform(x)
  
          torch.cuda.synchronize()
          restored_x = undo_layout(cxb, tile_indices)
          torch.cuda.synchronize()
          assert restored_x.is_contiguous()
          assert torch.all(torch.eq(restored_x, x))
  
file_path: bitsandbytes/tests/test_linear8bitlt.py
imports: import os
  from contextlib import nullcontext
  from itertools import product
  from tempfile import TemporaryDirectory
  import pytest
  import torch
  import bitsandbytes
  from bitsandbytes import functional
  from bitsandbytes.autograd import get_inverse_transform_indices, undo_layout
  from bitsandbytes.nn.modules import Linear8bitLt
label_desc: Use image node_id 1 for calling a global function with example usage: test_layout_exact_match() without return types