class_docstr: Callback for NNCF compression.
  
  Assumes that the pl module contains a 'model' attribute, which is
  the PyTorch module that must be compressed.
  
  Args:
      config (dict): NNCF Configuration
      export_dir (Str): Path where the export `onnx` and the OpenVINO `xml` and `bin` IR are saved.
                        If None model will not be exported.
class_docstr_tok: ["Callback","for","NNCF","compression",".","Assumes","that","the","pl","module","contains","a","'model","'","attribute",",","which","is","the","PyTorch","module","that","must","be","compressed",".","Args",":","config","(","dict",")",":","NNCF","Configuration","export_dir","(","Str",")",":","Path","where","the","export","`","onnx","`","and","the","OpenVINO","`","xml","`","and","`","bin","`","IR","are","saved",".","If","None","model","will","not","be","exported","."]
code: def __init__(
      self, config: dict, export_dir: str | None = None
  ) -> None:
      self.export_dir = export_dir
      self.config = NNCFConfig(config)
      self.nncf_ctrl: CompressionAlgorithmController | None = None
  
file_path: anomalib/src/anomalib/utils/callbacks/nncf/callback.py
imports: from __future__ import annotations
  import os
  from pathlib import Path
  from typing import Any
  import pytorch_lightning
  from nncf import NNCFConfig
  from nncf.api.compression import CompressionAlgorithmController
  from nncf.torch import register_default_init_args
  from pytorch_lightning import Callback
  from anomalib.utils.callbacks.nncf.utils import InitLoader, wrap_nncf_model
label_desc: Use image node_id 1 to create a new NNCFCallback object from inherited base classes: Callback with example: obj = NNCFCallback(config, export_dir)