code: def _load(path):
      status = PytorchJITINT8Model._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 PytorchJITINT8Model(
          model,
          calib_data=None,
          channels_last=status.get("channels_last", False),
          from_load=from_load,
          thread_num=thread_num,
          jit_strict=status.get("jit_strict", True),
          jit_method=status.get("jit_method", None),
          enable_onednn=status.get("enable_onednn", False),
      )
  
file_path: BigDL/python/nano/src/bigdl/nano/pytorch/low_precision/jit_int8_model.py
imports: from bigdl.nano.pytorch.context_manager import generate_context_manager
  from bigdl.nano.pytorch.model import AcceleratedLightningModule
  from bigdl.nano.utils.pytorch import jit_convert
  from bigdl.nano.utils.common import compare_version
  import torch
  from torch.ao.quantization import QConfig, get_default_qconfig_mapping
  from torch.quantization.quantize_fx import prepare_fx, convert_fx
  import operator
  from collections.abc import Sequence
label_desc: Use image node_id 7 for calling the PytorchJITINT8Model obj's underlying member method code with example usage: obj._load(path) and returns: PytorchJITINT8Model