code: def main_segmentation_evaluation(
      args: Optional[List[str]] = None, **kwargs
  ) -> None:
      opts = get_training_arguments(args=args)
  
      # device set-up
      opts = device_setup(opts)
  
      node_rank = getattr(opts, "ddp.rank", 0)
      if node_rank < 0:
          logger.error("--rank should be >=0. Got {}".format(node_rank))
  
      is_master_node = is_master(opts)
  
      # create the directory for saving results
      save_dir = getattr(opts, "common.results_loc", "results")
      run_label = getattr(opts, "common.run_label", "run_1")
      exp_dir = "{}/{}".format(save_dir, run_label)
      setattr(opts, "common.exp_loc", exp_dir)
      logger.log(
          "Results (if any) will be stored here: {}".format(exp_dir)
      )
  
      create_directories(
          dir_path=exp_dir, is_master_node=is_master_node
      )
  
      num_gpus = getattr(opts, "dev.num_gpus", 1)
      # we disable the DDP setting for evaluating segmentation tasks
      setattr(opts, "ddp.use_distributed", False)
  
      # No of data workers = no of CPUs (if not specified or -1)
      n_cpus = resources.cpu_count()
      dataset_workers = getattr(opts, "dataset.workers", -1)
  
      if dataset_workers == -1:
          setattr(opts, "dataset.workers", n_cpus)
  
      # We are not performing any operation like resizing and cropping on images
      # Because image dimensions are different, we process 1 sample at a time.
      setattr(opts, "dataset.train_batch_size0", 1)
      setattr(opts, "dataset.val_batch_size0", 1)
      setattr(opts, "dev.device_id", None)
  
      eval_mode = getattr(opts, "evaluation.segmentation.mode", None)
  
      if eval_mode == "single_image":
          num_seg_classes = getattr(
              opts, "model.segmentation.n_classes", 21
          )
          assert num_seg_classes is not None
  
          # test a single image
          img_f_name = getattr(
              opts, "evaluation.segmentation.path", None
          )
          predict_image(opts, img_f_name, **kwargs)
      elif eval_mode == "image_folder":
          num_seg_classes = getattr(
              opts, "model.segmentation.n_classes", 21
          )
          assert num_seg_classes is not None
  
          # test all images in a folder
          # This is useful for generating results for test set
          predict_images_in_folder(opts=opts, **kwargs)
      elif eval_mode == "validation_set":
          # evaluate and compute stats for labeled image dataset
          # This is useful for generating results for validation set and compute quantitative results
          predict_labeled_dataset(opts=opts, **kwargs)
      else:
          logger.error(
              "Supported modes are single_image, image_folder, and validation_set. Got: {}".format(
                  eval_mode
              )
          )
  
imports: import copy
  import glob
  import os
  from typing import List, Optional, Tuple
  import torch
  from PIL import Image
  from torch import Tensor, nn
  from torch.nn import functional
  from torchvision.transforms import functional
  from tqdm import tqdm
  from common import SUPPORTED_IMAGE_EXTNS
  from cvnets import get_model
  from data import create_test_loader
  from engine.utils import autocast_fn
  from metrics.confusion_mat import ConfusionMatrix
  from options.opts import get_training_arguments
  from utils import logger, resources
  from utils.color_map import Colormap
  from utils.common_utils import create_directories, device_setup
  from utils.ddp_utils import is_master
  from utils.download_utils import get_local_path
  from utils.tensor_utils import image_size_from_opts
  from utils.visualization_utils import convert_to_cityscape_format
label_desc: Use image node_id 8 for calling a global function with example usage: main_segmentation_evaluation(args) without return types