code: def get_args_parser(add_help=True):
      import argparse
  
      parser = argparse.ArgumentParser(
          description="PyTorch Stereo Matching Evaluation",
          add_help=add_help,
      )
      parser.add_argument(
          "--dataset",
          type=str,
          default="middlebury2014-train",
          help="dataset to use",
      )
      parser.add_argument(
          "--dataset-root",
          type=str,
          default="",
          help="root of the dataset",
      )
  
      parser.add_argument(
          "--checkpoint", type=str, default="", help="path to weights"
      )
      parser.add_argument(
          "--weights",
          type=str,
          default=None,
          help="torchvision API weight",
      )
      parser.add_argument(
          "--model",
          type=str,
          default="crestereo_base",
          help="which model to use if not speciffying a training checkpoint",
      )
      parser.add_argument("--img-folder", type=str, default="images")
  
      parser.add_argument(
          "--batch-size", type=int, default=1, help="batch size"
      )
      parser.add_argument(
          "--workers", type=int, default=0, help="number of workers"
      )
  
      parser.add_argument(
          "--eval-size",
          type=int,
          nargs="+",
          default=[384, 512],
          help="resize size",
      )
      parser.add_argument(
          "--norm-mean",
          type=float,
          nargs="+",
          default=[0.5, 0.5, 0.5],
          help="mean for image normalization",
      )
      parser.add_argument(
          "--norm-std",
          type=float,
          nargs="+",
          default=[0.5, 0.5, 0.5],
          help="std for image normalization",
      )
      parser.add_argument(
          "--use-grayscale",
          action="store_true",
          help="use grayscale images instead of RGB",
          default=False,
      )
      parser.add_argument(
          "--max-disparity",
          type=float,
          default=None,
          help="maximum disparity",
      )
      parser.add_argument(
          "--interpolation-strategy",
          type=str,
          default="bilinear",
          help="interpolation strategy",
          choices=["bilinear", "bicubic", "mixed"],
      )
  
      parser.add_argument(
          "--n_iterations",
          nargs="+",
          type=int,
          default=[10],
          help="number of recurent iterations",
      )
      parser.add_argument(
          "--n_cascades",
          nargs="+",
          type=int,
          default=[1],
          help="number of cascades",
      )
      parser.add_argument(
          "--metrics",
          type=str,
          nargs="+",
          default=["mae", "rmse", "1px", "3px", "5px", "relepe"],
          help="metrics to log",
          choices=AVAILABLE_METRICS,
      )
      parser.add_argument(
          "--mixed-precision",
          action="store_true",
          help="use mixed precision training",
      )
  
      parser.add_argument(
          "--world-size",
          type=int,
          default=1,
          help="number of distributed processes",
      )
      parser.add_argument(
          "--dist-url",
          type=str,
          default="env://",
          help="url used to set up distributed training",
      )
      parser.add_argument(
          "--device",
          type=str,
          default="cuda",
          help="device to use for training",
      )
  
      parser.add_argument(
          "--save-images",
          action="store_true",
          help="save images of the predictions",
      )
      parser.add_argument(
          "--padder-type",
          type=str,
          default="kitti",
          help="padder type",
          choices=["kitti", "sintel"],
      )
  
      return parser
  
file_path: vision/references/depth/stereo/cascade_evaluation.py
imports: import os
  import warnings
  import torch
  import torchvision
  import torchvision.prototype.models.depth.stereo
  import utils
  from torch.nn import functional
  from train import make_eval_loader
  from utils.metrics import AVAILABLE_METRICS
  from visualization import make_prediction_image_side_to_side
label_desc: Use image node_id 1 for calling a global function with example usage: get_args_parser(add_help) and returns: parser