code: def test_autocast_fused_scale_mask_softmax(self):
      for dtype in autocast_dtypes:
          msg = f"dtype: {dtype}"
          input_in_fp16 = dtype == torch.half
          input_in_bf16 = dtype == torch.bfloat16
          fused_fn, torch_fn = self._setup_fused_softmax(
              input_in_fp16,
              input_in_bf16,
              attn_mask_type=AttnMaskType.padding,
          )
  
          attention_scores_0 = (
              torch.randn((4, 12, 24, 24)).cuda().requires_grad_(True)
          )
          with torch.no_grad():
              attention_scores_1 = (
                  attention_scores_0.clone()
                  .to(dtype)
                  .requires_grad_(True)
              )
          mask = torch.randint(0, 2, (4, 1, 24, 24)).bool().cuda()
  
          expected = torch_fn(attention_scores_1, mask)
          with torch.cuda.amp.autocast(dtype=dtype):
              actual = fused_fn(attention_scores_0, mask)
              self.assertEqual(actual.dtype, dtype, msg=msg)
          self.assertEqual(actual, expected, msg=msg)
  
          g0 = torch.rand_like(actual)
          with torch.no_grad():
              g1 = g0.clone()
          expected.backward(g0)
          actual.backward(g1)
  
imports: import itertools
  import torch
  from torch.testing._internal import common_utils
  from apex.transformer import AttnMaskType
  from apex.transformer.functional import FusedScaleMaskSoftmax
label_desc: Use image node_id 4 for calling the TestFusedScaleMaskSoftmax obj's underlying member method code with example usage: obj.test_autocast_fused_scale_mask_softmax() without return types