code: def forward(self, *inputs, **kwargs):
      if self.precision == "fp16":
          inputs = tuple(
              map(lambda item: apply_data_to_half(item), inputs)
          )
      inputs = tuple(map(lambda item: apply_data_to_xpu(item), inputs))
      for key, val in kwargs.items():
          new_val = apply_data_to_xpu(val)
          if self.precision == "fp16":
              new_val = apply_data_to_half(new_val)
          kwargs[key] = new_val
      return self.model(*inputs, **kwargs)
  
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 2 for calling the PytorchIPEXPUModel obj's underlying member method code with example usage: obj.forward() and returns: self