class_docstr: Model optimized on CPU using DeepSparse. DeepSparse is an engine
  accelerating sparse computations on CPUs.
  
  Attributes:
      network_parameters (ModelParams): The model parameters as batch
              size, input and output sizes.
      onnx_path (str or Path): Path to the onnx model.
      input_names (List[str]): Input names used when the onnx model
          was produced.
      output_names (List[str]): Output names used when the onnx model
          was produced.
class_docstr_tok: ["Model","optimized","on","CPU","using","DeepSparse",".","DeepSparse","is","an","engine","accelerating","sparse","computations","on","CPUs",".","Attributes",":","network_parameters","(","ModelParams",")",":","The","model","parameters","as","batch","size",",","input","and","output","sizes",".","onnx_path","(","str","or","Path",")",":","Path","to","the","onnx","model",".","input_names","(","List","[","str","]",")",":","Input","names","used","when","the","onnx","model","was","produced",".","output_names","(","List","[","str","]",")",":","Output","names","used","when","the","onnx","model","was","produced","."]
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,
          input_names=self.input_names,
          output_names=self.output_names,
          **kwargs,
      )
      metadata.save(path)
  
      shutil.copy(
          self.onnx_path,
          Path(path) / ONNX_FILENAMES["model_name"],
      )
  
imports: import os
  import shutil
  from abc import ABC
  from pathlib import Path
  from typing import Union, List, Generator, Tuple, Dict, Type
  import numpy
  from loguru import logger
  from nebullvm.config import ONNX_FILENAMES
  from nebullvm.core.models import Device, ModelParams, DeepLearningFramework
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner, LearnerMetadata, PytorchBaseInferenceLearner
  from nebullvm.optional_modules.deepsparse import cpu, compile_model
  from nebullvm.optional_modules.torch import torch
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 3 for calling the DeepSparseInferenceLearner obj's underlying member method code with example usage: obj.save(path) without return types