code: def test_linear8bitlt_inference(threshold):
      l1 = (
          bnb.nn.Linear8bitLt(32, 64, threshold=threshold).cuda().half()
      )
      assert l1.weight.device.type == "cuda"
      assert l1.weight.dtype == torch.float16
  
      l1.eval()
      for i in range(100):
          b1 = torch.randn(16, 8, 32, device="cuda").half()
          o1 = l1(b1)
          if i == 1:
              assert l1.state.CxB is not None
  
imports: from itertools import product
  import pytest
  import torch
  from torch import nn
  import bitsandbytes
label_desc: Use image node_id 3 for calling a global function with example usage: test_linear8bitlt_inference(threshold) without return types