code: def _prep_inputs(batch_size, normalized_shape, dtype):
      shape = (batch_size, *normalized_shape)
      fused = torch.randn(shape).cuda().requires_grad_(True)
      with torch.no_grad():
          native = fused.clone().to(dtype).requires_grad_(True)
      return native, fused
  
imports: import torch
  from apex.normalization import FusedLayerNorm
  from apex.normalization import FusedRMSNorm
  from apex.normalization import MixedFusedLayerNorm
  from apex.normalization import MixedFusedRMSNorm
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
  from itertools import product
label_desc: Use image node_id 1 for calling a global function with example usage: _prep_inputs(batch_size, normalized_shape, dtype) and returns: native,  fused