code: def __init__(self):
      self.parser = argparse.ArgumentParser(
          description="Monodepthv2 options"
      )
  
      # PATHS
      self.parser.add_argument(
          "--data_path",
          type=str,
          help="path to the training data",
          default=os.path.join(
              os.path.expanduser("~"),
              ".mxnet/datasets/kitti",
              "kitti_data",
          ),
      )
      self.parser.add_argument(
          "--log_dir",
          type=str,
          help="log directory",
          default=os.path.join(os.path.expanduser("."), "tmp"),
      )
  
      # MODEL options
      self.parser.add_argument(
          "--model_zoo",
          type=str,
          default=None,
          help="choose depth model from model zoo model",
      )
      self.parser.add_argument(
          "--model_zoo_pose",
          type=str,
          default=None,
          help="choose pose model from model zoo model",
      )
      self.parser.add_argument(
          "--pretrained_type",
          type=str,
          help="use gluoncv pretrained model or customer model",
          choices=["gluoncv", "customer"],
          default="customer",
      )
      self.parser.add_argument(
          "--pretrained_base",
          action="store_true",
          help="whether to use pretrained resnet",
      )
      self.parser.add_argument(
          "--scales",
          nargs="+",
          type=int,
          help="scales used in the loss",
          default=[0, 1, 2, 3],
      )
      self.parser.add_argument(
          "--hybridize",
          action="store_true",
          help="whether to turn on model hybridization",
      )
  
      # DATA options
      self.parser.add_argument(
          "--split",
          type=str,
          help="which training split to use",
          choices=["eigen_zhou", "eigen_full", "odom", "benchmark"],
          default="eigen_zhou",
      )
      self.parser.add_argument(
          "--dataset",
          type=str,
          help="dataset to train on",
          default="kitti",
          choices=["kitti", "kitti_odom", "kitti_depth", "kitti_test"],
      )
      self.parser.add_argument(
          "--png",
          help="if set, trains from raw KITTI png files (instead of jpgs)",
          action="store_true",
      )
      self.parser.add_argument(
          "--height", type=int, help="input image height", default=192
      )
      self.parser.add_argument(
          "--width", type=int, help="input image width", default=640
      )
      self.parser.add_argument(
          "--disparity_smoothness",
          type=float,
          help="disparity smoothness weight",
          default=1e-3,
      )
      self.parser.add_argument(
          "--min_depth", type=float, help="minimum depth", default=0.1
      )
      self.parser.add_argument(
          "--max_depth", type=float, help="maximum depth", default=100.0
      )
      self.parser.add_argument(
          "--use_stereo",
          help="if set, uses stereo pair for training",
          action="store_true",
      )
      self.parser.add_argument(
          "--frame_ids",
          nargs="+",
          type=int,
          help="frames to load",
          default=[0, -1, 1],
      )
  
      # OPTIMIZATION options
      self.parser.add_argument(
          "--batch_size", type=int, help="batch size", default=12
      )
      self.parser.add_argument(
          "--learning_rate",
          type=float,
          help="learning rate",
          default=1e-4,
      )
      self.parser.add_argument(
          "--num_epochs", type=int, help="number of epochs", default=20
      )
      self.parser.add_argument(
          "--start_epoch",
          type=int,
          default=0,
          metavar="N",
          help="start epochs (default:0)",
      )
      self.parser.add_argument(
          "--warmup_epochs",
          type=int,
          default=0,
          help="number of warmup epochs.",
      )
      self.parser.add_argument(
          "--scheduler_step_size",
          type=int,
          help="step size of the scheduler",
          default=15,
      )
  
      # ABLATION options
      self.parser.add_argument(
          "--v1_multiscale",
          help="if set, uses monodepth v1 multiscale",
          action="store_true",
      )
      self.parser.add_argument(
          "--avg_reprojection",
          help="if set, uses average reprojection loss",
          action="store_true",
      )
      self.parser.add_argument(
          "--disable_automasking",
          help="if set, doesn't do auto-masking",
          action="store_true",
      )
      self.parser.add_argument(
          "--no_ssim",
          help="if set, disables ssim in the loss",
          action="store_true",
      )
      self.parser.add_argument(
          "--pose_model_input",
          type=str,
          help="how many images the pose network gets",
          default="pairs",
          choices=["pairs", "all"],
      )
  
      # SYSTEM options
      self.parser.add_argument(
          "--no_gpu", help="if set disables gpu", action="store_true"
      )
      self.parser.add_argument(
          "--gpu", type=int, default=0, help="GPU index"
      )
      self.parser.add_argument(
          "--num_workers",
          type=int,
          help="number of dataloader workers",
          default=12,
      )
  
      # LOADING options
      self.parser.add_argument(
          "--load_weights_folder",
          type=str,
          help="name of model to load",
      )
      self.parser.add_argument(
          "--eval_model",
          type=str,
          default=None,
          help="the name of evaluation model",
      )
      self.parser.add_argument(
          "--resume_depth",
          type=str,
          default=None,
          help="put the path to depthnet resuming file if needed",
      )
      self.parser.add_argument(
          "--resume_pose",
          type=str,
          default=None,
          help="put the path to posenet resuming file if needed",
      )
  
      # LOGGING options
      self.parser.add_argument(
          "--log_frequency",
          type=int,
          help="number of batches between each tensorboard log",
          default=250,
      )
      self.parser.add_argument(
          "--save_frequency",
          type=int,
          help="number of epochs between each save",
          default=1,
      )
  
      # EVALUATION options
      self.parser.add_argument(
          "--eval_stereo",
          help="if set evaluates in stereo mode",
          action="store_true",
      )
      self.parser.add_argument(
          "--eval_mono",
          help="if set evaluates in mono mode",
          action="store_true",
      )
      self.parser.add_argument(
          "--disable_median_scaling",
          help="if set disables median scaling in evaluation",
          action="store_true",
      )
      self.parser.add_argument(
          "--pred_depth_scale_factor",
          help="if set multiplies predictions by this number",
          type=float,
          default=1,
      )
      self.parser.add_argument(
          "--ext_disp_to_eval",
          type=str,
          help="optional path to a .npy disparities file to evaluate",
      )
      self.parser.add_argument(
          "--eval_split",
          type=str,
          default="eigen",
          choices=[
              "eigen",
              "eigen_benchmark",
              "benchmark",
              "odom_9",
              "odom_10",
          ],
          help="which split to run eval on",
      )
      self.parser.add_argument(
          "--save_pred_disps",
          help="if set saves predicted disparities",
          action="store_true",
      )
      self.parser.add_argument(
          "--no_eval",
          help="if set disables evaluation",
          action="store_true",
      )
      self.parser.add_argument(
          "--eval_eigen_to_benchmark",
          help="if set assume we are loading eigen results from npy but "
          "we want to evaluate using the new benchmark.",
          action="store_true",
      )
  
imports: import os
  import argparse
  import mxnet
label_desc: Use image node_id 1 to create a new MonodepthOptions object  with example: obj = MonodepthOptions()