code: def test_linear():
      a = torch.randint(
          -127, 127, (128, 512), dtype=torch.int8, device="cuda"
      )
      b = torch.randint(
          -127, 127, (512, 256), dtype=torch.int8, device="cuda"
      )
      c = torch.rand(256, dtype=torch.float, device="cuda")
  
      alpha = 1 / 127
      beta = 1.0
      torch_out = (
          torch.mm(a.to(torch.float) * alpha, b.to(torch.float)) + c
      )
  
      silu = torch.nn.SiLU()
      torch_out = silu(torch_out)
  
      b = b.transpose(0, 1).contiguous()
      cuda_out = smoothquant_cuda.linear_silu_a8_w8_bfp32_ofp32(
          a, b, c, alpha, beta
      )
  
      assert torch.allclose(torch_out, cuda_out, rtol=1e-02, atol=1e-02)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_linear() without return types