code: def forward_step(self, *inputs, **kwargs):
      flattened_inputs = []
      _flatten(inputs, flattened_inputs)
      args_length = len(inputs)
      if kwargs is not None and len(kwargs) > 0:
          # check kwargs first, to avoid user call model(t1, t2, b=t3, c=t4)
          # when the signature is def forward(a, b, c)
          for arg in kwargs:
              if arg in self.forward_args[:args_length]:
                  invalidInputError(
                      False,
                      f"You shouldn't pass arguement {arg} as it has "
                      "been passed as a positional arguement.",
                  )
          # add inputs based on original order
          for arg in self.forward_args[args_length:]:
              if arg in kwargs:
                  if isinstance(kwargs[arg], (list, tuple)):
                      flattened_inputs.extend(kwargs[arg])
                  else:
                      flattened_inputs.append(kwargs[arg])
      if len(self._forward_args) != len(flattened_inputs):
          # formatting a Tensor will cost much time,
          # so we put it in this `if` statement
          invalidInputError(
              False,
              "The length of inputs is "
              "inconsistent with the length of OpenVINO's inputs, "
              f"got model_forward_args: {self._forward_args}, "
              f"and flattened inputs's length: {len(flattened_inputs)}",
          )
      return self._infer_request.infer(list(flattened_inputs))
  
imports: from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import List, Union
  from openvino.runtime import Core
  from bigdl.nano.utils.common import invalidInputError, _flatten, invalidOperationError
  from openvino.runtime import Model
  from openvino.runtime import AsyncInferQueue
  import numpy
  from datetime import datetime
  from .utils import save, OpenVINO_LESS_2022_3
label_desc: Use image node_id 3 for calling the OpenVINOModel obj's underlying member method code with example usage: obj.forward_step() and returns: self