text: import torch
  import _torch_ipex as core
  from torch.jit._recursive import wrap_cpp_module
  
  torch._C._jit_set_profiling_mode(False)
  torch._C._jit_set_profiling_executor(False)
  
  orig_script = torch.jit.script
  orig_trace = torch.jit.trace
  
  def script_(obj, optimize=None, _frames_up=0, _rcb=None):
      torch.jit.script = orig_script
      jit_m = orig_script(obj, optimize=optimize, _frames_up=_frames_up+1, _rcb=_rcb)
      torch.jit.script = script_
  
      mix_state = core.get_mix_bf16_fp32()
      # Disable mix precision in model fusion, since mixed precision cannot
      # bring any benefits for inference, but will lead to loss of accuracy
      core.disable_mix_bf16_fp32()
      if core.get_jit_opt() and hasattr(jit_m, '_c'):
          jit_m = wrap_cpp_module(torch._C._jit_pass_fold_convbn(jit_m._c))
      if mix_state:
          core.enable_mix_bf16_fp32()
      return jit_m
  
  def trace_(func, example_inputs, *args, **kwargs):
      # Disable mix precision. torch.jit.trace will check the traced output
      # against what is expected. Since mix precision will lead to
      # loss of accuracy, this will raise warning during torch.jit.trace
      mix_state = core.get_mix_bf16_fp32()
      core.disable_mix_bf16_fp32()
      jit_m = orig_trace(func, example_inputs, *args, **kwargs)
      if core.get_jit_opt() and hasattr(jit_m, '_c'):
          jit_m = wrap_cpp_module(torch._C._jit_pass_fold_convbn(jit_m._c))
      if mix_state:
          core.enable_mix_bf16_fp32()
      return jit_m
  
  
  torch.jit.script = script_
  torch.jit.trace = trace_
  