code: def _load(path, model, inplace=False):
      status = PytorchIPEXQuantizationModel._load_status(path)
      checkpoint_path = path / status["checkpoint"]
      model = torch.jit.load(checkpoint_path)
      model.eval()
      model = torch.jit.freeze(model)
      from_load = True
      thread_num = None
      if (
          status["thread_num"] is not None
          and status["thread_num"] != {}
      ):
          thread_num = int(status["thread_num"])
      return PytorchIPEXQuantizationModel(
          model,
          calib_data=None,
          channels_last=status["channels_last"],
          from_load=from_load,
          thread_num=thread_num,
          inplace=inplace,
          jit_strict=status["jit_strict"],
          enable_onednn=status.get("enable_onednn", False),
      )
  
file_path: BigDL/python/nano/src/bigdl/nano/deps/ipex/ipex_quantization_model.py
imports: from collections.abc import Sequence
  import intel_extension_for_pytorch
  from intel_extension_for_pytorch.quantization import prepare, convert
  from bigdl.nano.pytorch.model import AcceleratedLightningModule
  from bigdl.nano.pytorch.context_manager import generate_context_manager
  from bigdl.nano.utils.pytorch import patch_attrs_from_model_to_object, jit_convert
  from bigdl.nano.utils.common import compare_version
  import operator
  import torch
label_desc: Use image node_id 7 for calling the PytorchIPEXQuantizationModel obj's underlying member method code with example usage: obj._load(path, model, inplace) and returns: PytorchIPEXQuantizationModel