code: def execute(cls: type, args: argparse.Namespace):
      logger.info(f"Loading config from {args.cfg}")
      opts = []
      cfg = cls.setup_config(args.cfg, args.model, args, opts)
      logger.info(f"Loading model from {args.model}")
      predictor = DefaultPredictor(cfg)
      logger.info(f"Loading data from {args.input}")
      file_list = cls._get_input_file_list(args.input)
      if len(file_list) == 0:
          logger.warning(f"No input images for {args.input}")
          return
      context = cls.create_context(args, cfg)
      for file_name in file_list:
          img = read_image(
              file_name, format="BGR"
          )  # predictor expects BGR image.
          with torch.no_grad():
              outputs = predictor(img)["instances"]
              cls.execute_on_outputs(
                  context,
                  {"file_name": file_name, "image": img},
                  outputs,
              )
      cls.postexecute(context)
  
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 2 for calling the InferenceAction obj's underlying member method code with example usage: obj.execute(cls, args) without return types