code: def __init__(
      self,
      model: torch.nn.Module,
      thread_num=None,
      precision="fp32",
      use_ipex=False,
  ):
      super().__init__(model)
      invalidInputError(
          precision in ["fp32", "fp16"],
          f"support precision for GPU device only for fp32 and fp16, but "
          f"found {precision}",
      )
      if not use_ipex:
          warnings.warn(
              "IPEX is required to be installed for GPU device inferencing even "
              "the model is not optimized by IPEX."
          )
      self.original_state_dict = None
      if use_ipex:
          self.original_state_dict = model.state_dict()
      if precision == "fp16":
          self.model = self.model.half()
      self.model = model.to("xpu")
      if use_ipex:
          self.model = ipex.optimize(self.model)
      self.thread_num = thread_num
      self.precision = precision
      self.use_ipex = use_ipex
      self._nano_context_manager = generate_context_manager(
          accelerator=None,
          precision="fp32",
          thread_num=thread_num,
          enable_onednn=False,
          use_xpu=True,
      )
  
file_path: BigDL/python/nano/src/bigdl/nano/deps/ipex/ipex_inference_xpu_model.py
imports: from bigdl.nano.pytorch.model import AcceleratedLightningModule
  import torch
  import intel_extension_for_pytorch
  from bigdl.nano.utils.pytorch import apply_data_to_xpu, apply_data_to_half
  from bigdl.nano.pytorch.context_manager import generate_context_manager
  from bigdl.nano.utils.common import invalidInputError
  import warnings
label_desc: Use image node_id 1 to create a new PytorchIPEXPUModel object from inherited base classes: AcceleratedLightningModule with example: obj = PytorchIPEXPUModel(model, thread_num, precision, use_ipex)