class_docstr: This is the accelerated model for pytorch and onnxruntime.
  All the external API is based on Trainer, so what we have here is
  basically internal APIs and subject to change.
  
  This PytorchONNXRuntimeModel will serve for all precision models.
code: def status(self):
      status = super().status
      status.update(
          {
              "onnx_path": "onnx_saved_model.onnx",
              "metadata_path": "matadata.pkl",
              "intra_op_num_threads": self.session_options.intra_op_num_threads,
              "inter_op_num_threads": self.session_options.inter_op_num_threads,
              "output_tensors": self.output_tensors,
          }
      )
      return status
  
imports: import torch
  import os
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from ..core.onnxruntime_model import ONNXRuntimeModel
  import onnxruntime
  from bigdl.nano.pytorch.model import AcceleratedLightningModule
  from bigdl.nano.utils.pytorch import export_to_onnx, get_input_example, get_forward_args
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.pytorch.context_manager import generate_context_manager, BaseContextManager
  from bigdl.nano.utils.pytorch import patch_attrs_from_model_to_object, MetaData
  from bigdl.nano.utils.common import SafePickle
label_desc: Use image node_id 5 for calling the PytorchONNXRuntimeModel obj's underlying member method code with example usage: obj.status() and returns: status