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.
code: def __init__(
      self,
      model: Union[Module, GraphModule],
      model_quant: GraphModule,
      device: Device,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.model = model
      self.model_quant = model_quant
      self.device = device
  
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 1 to create a new NeuralCompressorInferenceLearner object from inherited base classes: BaseInferenceLearner, ABC with example: obj = NeuralCompressorInferenceLearner(model, model_quant, device)