code: def get_model_nncf_cat() -> List:
      """Test helper for getting cartesian product of models and categories.
  
      Returns:
          List: Returns a combination of models with their nncf support for each category.
      """
      model_support = [
          ("cflow", False),
          ("csflow", False),
          ("dfkde", False),
          ("dfm", False),
          ("ganomaly", False),
          # ("stfpm", True),
          ("padim", False),
          ("patchcore", False),
          ("stfpm", False),
      ]
      categories = random.sample(
          [
              "bottle",
              "cable",
              "capsule",
              "carpet",
              "grid",
              "hazelnut",
              "leather",
              "metal_nut",
              "pill",
              "screw",
              "tile",
              "toothbrush",
              "transistor",
              "wood",
              "zipper",
          ],
          k=3,
      )
  
      return [
          (model, nncf, category)
          for ((model, nncf), category) in list(
              itertools.product(*[model_support, categories])
          )
      ]
  
imports: import itertools
  import math
  import multiprocessing
  import random
  import tempfile
  from concurrent.futures import ProcessPoolExecutor
  from datetime import datetime
  from pathlib import Path
  from typing import Dict, List, Union
  import numpy
  import pandas
  import torch
  from omegaconf import DictConfig, ListConfig, OmegaConf
  from pytorch_lightning import seed_everything
  from anomalib.utils.sweep.config import flatten_sweep_params
  from tests.helpers.dataset import get_dataset_path
  from tests.helpers.model import model_load_test, setup_model_train
label_desc: Use image node_id 1 for calling a global function with example usage: get_model_nncf_cat() and returns: unknown