code: def setup_config(
      cls: type,
      config_fpath: str,
      model_fpath: str,
      args: argparse.Namespace,
      opts: List[str],
  ):
      cfg = get_cfg()
      add_densepose_config(cfg)
      cfg.merge_from_file(config_fpath)
      cfg.merge_from_list(args.opts)
      if opts:
          cfg.merge_from_list(opts)
      cfg.MODEL.WEIGHTS = model_fpath
      cfg.freeze()
      return cfg
  
imports: import argparse
  import glob
  import logging
  import os
  import sys
  from typing import Any, ClassVar, Dict, List
  import torch
  from detectron2.config import CfgNode, get_cfg
  from detectron2.data.detection_utils import read_image
  from detectron2.engine.defaults import DefaultPredictor
  from detectron2.structures.instances import Instances
  from detectron2.utils.logger import setup_logger
  from densepose import add_densepose_config
  from densepose.structures import DensePoseChartPredictorOutput, DensePoseEmbeddingPredictorOutput
  from densepose.utils.logger import verbosity_to_level
  from densepose.vis.base import CompoundVisualizer
  from densepose.vis.bounding_box import ScoredBoundingBoxVisualizer
  from densepose.vis.densepose_outputs_vertex import DensePoseOutputsTextureVisualizer, DensePoseOutputsVertexVisualizer, get_texture_atlases
  from densepose.vis.densepose_results import DensePoseResultsContourVisualizer, DensePoseResultsFineSegmentationVisualizer, DensePoseResultsUVisualizer, DensePoseResultsVVisualizer
  from densepose.vis.densepose_results_textures import DensePoseResultsVisualizerWithTexture, get_texture_atlas
  from densepose.vis.extractor import CompoundExtractor, DensePoseOutputsExtractor, DensePoseResultExtractor, create_extractor
label_desc: Use image node_id 3 for calling the InferenceAction obj's underlying member method code with example usage: obj.setup_config(cls, config_fpath, model_fpath, args, opts) and returns: cfg