class_docstr: Model optimized on CPU using IntelNeuralCompressor.
  
  Attributes:
      network_parameters (ModelParams): The model parameters as batch
              size, input and output sizes.
      model (torch.fx.GraphModule): Torch fx graph model.
docstr: Save the model.
  
  Args:
      path (Path or str): Path to the directory where the model will
          be stored.
      kwargs (Dict): Dictionary of key-value pairs that will be saved in
          the model metadata file.
docstr_tok: ["Save","the","model",".","Args",":","path","(","Path","or","str",")",":","Path","to","the","directory","where","the","model","will","be","stored",".","kwargs","(","Dict",")",":","Dictionary","of","key-value","pairs","that","will","be","saved","in","the","model","metadata","file","."]
code: def save(self, path: Union[str, Path], **kwargs):
      """Save the model.
  
      Args:
          path (Path or str): Path to the directory where the model will
              be stored.
          kwargs (Dict): Dictionary of key-value pairs that will be saved in
              the model metadata file.
      """
      metadata = LearnerMetadata.from_model(self, **kwargs)
      metadata.save(path)
  
      path_orig_model = Path(path) / Path("model_orig")
      path_quant_model = Path(path) / Path("model_quant")
  
      save_with_torch_fx(self.model, path_orig_model)
      self.model_quant.save(str(path_quant_model))
  
imports: from abc import ABC
  from pathlib import Path
  from typing import Union, Tuple, Dict, Type
  from loguru import logger
  from nebullvm.core.models import Device, ModelParams, DeepLearningFramework
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner, LearnerMetadata, PytorchBaseInferenceLearner
  from nebullvm.optional_modules.neural_compressor import cfgs_to_fx_cfgs, cfg_to_qconfig
  from nebullvm.optional_modules.torch import torch, prepare_fx, convert_fx, Module, GraphModule
  from nebullvm.tools.pytorch import save_with_torch_fx, load_with_torch_fx, create_model_inputs_torch, get_torch_model_size
  from nebullvm.tools.transformations import MultiStageTransformation
  from nebullvm.tools.utils import check_module_version
label_desc: Use image node_id 3 for calling the NeuralCompressorInferenceLearner obj's underlying member method code with example usage: obj.save(path) without return types