code: def PytorchOpenVINOModel(
      model,
      input_sample=None,
      precision="fp32",
      thread_num=None,
      device="CPU",
      dynamic_axes=True,
      logging=True,
      config=None,
      output_tensors=True,
      **kwargs,
  ):
      """
      Create a OpenVINO model from pytorch.
  
      :param model: Pytorch model to be converted to OpenVINO for inference or
                    path to Openvino saved model.
      :param input_sample: A set of inputs for trace, defaults to None if you have trace before or
                           model is a LightningModule with any dataloader attached, defaults to None.
      :param precision: Global precision of model, supported type: 'fp32', 'fp16',
                        defaults to 'fp32'.
      :param thread_num: a int represents how many threads(cores) is needed for
                         inference. default: None.
      :param device: (optional) A string represents the device of the inference. Default to 'CPU'.
                     'CPU', 'GPU' and 'VPUX' are supported for now.
      :param dynamic_axes: dict or boolean, default to True. By default the exported onnx model
                           will have the first dim of each Tensor input as a dynamic batch_size.
                           If dynamic_axes=False, the exported model will have the shapes of all
                           input and output tensors set to exactly match those given in
                           input_sample. To specify axes of tensors as dynamic (i.e. known only
                           at run-time), set dynamic_axes to a dict with schema:
  
                          | KEY (str): an input or output name. Each name must also be provided
                          | in input_names or output_names.
                          |
                          | VALUE (dict or list): If a dict, keys are axis indices and values
                          | are axis names. If a list, each element is an axis index.
  
                           If accelerator != 'openvino'/'onnxruntime', it will be ignored.
      :param logging: whether to log detailed information of model conversion. default: True.
      :param config: The config to be inputted in core.compile_model.
      :param output_tensors: boolean, default to True and output of the model will be Tensors.
                             If output_tensors=False, output of the OpenVINO model will be ndarray.
      :param **kwargs: will be passed to torch.onnx.export function or model optimizer function.
      :return: PytorchOpenVINOModel model for OpenVINO inference.
      """
      from .pytorch.model import PytorchOpenVINOModel
  
      return PytorchOpenVINOModel(
          model=model,
          input_sample=input_sample,
          precision=precision,
          thread_num=thread_num,
          device=device,
          dynamic_axes=dynamic_axes,
          logging=logging,
          config=config,
          output_tensors=output_tensors,
          **kwargs,
      )
  
imports: from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 1 for calling a global function with example usage: PytorchOpenVINOModel(model, input_sample, precision, thread_num, device, dynamic_axes, logging, config, output_tensors) and returns: PytorchOpenVINOModel