code: def test_compat_with_autocast(self):
      autocast_dtypes = (
          (torch.half, torch.bfloat16)
          if torch.cuda.is_bf16_supported()
          else (torch.half,)
      )
      input_shape = (512, 32, 768)
      layer_norm = FastLayerNorm(input_shape[-1]).cuda()
      input = torch.randn(input_shape).cuda()
  
      for dtype in autocast_dtypes:
          layer_norm.zero_grad(set_to_none=True)
          with self.subTest(f"autocast_dtype={dtype}"):
              with torch.cuda.amp.autocast(enabled=True, dtype=dtype):
                  out = layer_norm(input)
                  self.assertEqual(dtype, out.dtype)
              grad = torch.randn_like(out)
              out.backward(grad)
              self.assertEqual(
                  torch.float32, layer_norm.weight.grad.dtype
              )
  
file_path: apex/apex/contrib/test/layer_norm/test_fast_layer_norm.py
imports: import itertools
  import unittest
  import torch
label_desc: Use image node_id 4 for calling the TestFastLayerNorm obj's underlying member method code with example usage: obj.test_compat_with_autocast() without return types