code: def PytorchJITINT8Model(
      model,
      calib_data,
      q_config=None,
      input_sample=None,
      channels_last=False,
      thread_num=None,
      jit_strict=True,
      jit_method=None,
      enable_onednn=False,
      example_kwarg_inputs=None,
  ):
      """
      :param model: the model(nn.module) to be transform if from_load is False
             the accelerated model if from_load is True.
      :param calib_data: calibration data is required for static quantization.
      :param q_config: We support 2 types of input here:
  
             | 1. Qconfig (https://pytorch.org/docs/stable/generated/torch.quantization.
             | qconfig.QConfig.html#qconfig) is the configuration for how we insert
             | observers for a particular operator. Quantization preparation function
             | will instantiate observers multiple times for each of the layers.
             |
             | 2. QConfigMapping (https://pytorch.org/docs/stable/generated/torch.ao.
             | quantization.qconfig_mapping.QConfigMapping.html#qconfigmapping)
             | (recommended) is a collection of quantization configurations, user
             | can set the qconfig for each operator (torch op calls, functional
             | calls, module calls) in the model through qconfig_mapping.
  
      :param input_sample: torch tensor indicate the data sample to be used
             for tracing.
      :param channels_last: if set model and data to be channels-last mode.
      :param thread_num: the thread num allocated for this model.
      :param from_load: this will only be set by _load method.
      :param jit_strict: Whether recording your mutable container types.
      :param jit_method: use ``jit.trace`` or ``jit.script`` to convert a model
             to TorchScript.
      :param enable_onednn: Whether to use PyTorch JIT graph fuser based on
             oneDNN Graph API, which provides a flexible API for aggressive
             fusion. Default to ``False``.
      :param example_kwarg_inputs: keyword arguments of example inputs that will be passed
             to ``torch.jit.trace``. Default to None. Either this argument or input_sample
             should be specified when use_jit is ``True`` and torch > 2.0,
             sotherwise will be ignored.
      """
      from .jit_int8_model import PytorchJITINT8Model
  
      return PytorchJITINT8Model(
          model,
          calib_data,
          q_config=q_config,
          input_sample=input_sample,
          channels_last=channels_last,
          thread_num=thread_num,
          jit_strict=jit_strict,
          jit_method=jit_method,
          enable_onednn=enable_onednn,
          example_kwarg_inputs=example_kwarg_inputs,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: PytorchJITINT8Model(model, calib_data, q_config, input_sample, channels_last, thread_num, jit_strict, jit_method, enable_onednn, example_kwarg_inputs) and returns: PytorchJITINT8Model