code: def test_cross_entropy_loss(
      vocab_size,
      smoothing,
      logit_scale,
      lse_square_scale,
      inplace_backward,
      dtype,
  ):
      device = "cuda"
      rtol, atol = (
          (1e-5, 1e-6) if dtype == torch.float32 else (1e-3, 1e-4)
      )
      # set seed
      torch.random.manual_seed(0)
      batch_size = 1 if dtype == torch.float32 else 4  # Otherwise OOM
      seqlen = (
          4096
          if lse_square_scale == 0.0 and logit_scale == 1.0
          else 1024
      )  # Otherwise OOM
      x_pt = torch.randn(
          batch_size * seqlen,
          vocab_size,
          device=device,
          dtype=dtype,
          requires_grad=True,
      )
      x = x_pt.detach().clone().requires_grad_()
      y = torch.randint(
          0,
          vocab_size,
          (batch_size * seqlen,),
          dtype=torch.long,
          device=device,
      )
      if batch_size * seqlen > 10:
          y[torch.randperm(batch_size * seqlen)[:10]] = -100
      model_pt = torch.nn.CrossEntropyLoss(label_smoothing=smoothing)
      model = CrossEntropyLoss(
          label_smoothing=smoothing,
          logit_scale=logit_scale,
          lse_square_scale=lse_square_scale,
          inplace_backward=inplace_backward,
      )
      out = model(x, y)
      x_pt_scaled = (
          (x_pt.float() * logit_scale)
          if logit_scale != 1.0
          else x_pt.float()
      )
      out_pt = model_pt(x_pt_scaled, y)
      if lse_square_scale > 0.0:
          lse_pt = torch.logsumexp(x_pt_scaled, dim=-1)
          out_pt += lse_square_scale * (lse_pt[y != -100] ** 2).mean()
      assert torch.allclose(out, out_pt, rtol=1e-5, atol=1e-6)
  
      g = torch.randn_like(out)
      out_pt.backward(g)
      out.backward(g)
      assert torch.allclose(x.grad, x_pt.grad, rtol=rtol, atol=atol)
  
imports: import math
  import pytest
  import torch
  import torch.nn.functional
  from einops import rearrange
  from flash_attn.losses.cross_entropy import CrossEntropyLoss
label_desc: Use image node_id 1 for calling a global function with example usage: test_cross_entropy_loss(vocab_size, smoothing, logit_scale, lse_square_scale, inplace_backward, dtype) without return types