code: def parse_args(args):
      """Parse the arguments."""
      parser = argparse.ArgumentParser(
          description="Simple training script for training a RetinaNet network."
      )
      subparsers = parser.add_subparsers(
          help="Arguments for specific dataset types.",
          dest="dataset_type",
      )
      subparsers.required = True
  
      coco_parser = subparsers.add_parser("coco")
      coco_parser.add_argument(
          "coco_path", help="Path to dataset directory (ie. /tmp/COCO)."
      )
  
      pascal_parser = subparsers.add_parser("pascal")
      pascal_parser.add_argument(
          "pascal_path",
          help="Path to dataset directory (ie. /tmp/VOCdevkit).",
      )
      pascal_parser.add_argument(
          "--image-extension",
          help="Declares the dataset images' extension.",
          default=".jpg",
      )
  
      kitti_parser = subparsers.add_parser("kitti")
      kitti_parser.add_argument(
          "kitti_path",
          help="Path to dataset directory (ie. /tmp/kitti).",
      )
  
      def csv_list(string):
          return string.split(",")
  
      oid_parser = subparsers.add_parser("oid")
      oid_parser.add_argument(
          "main_dir", help="Path to dataset directory."
      )
      oid_parser.add_argument(
          "--version",
          help="The current dataset version is v4.",
          default="v4",
      )
      oid_parser.add_argument(
          "--labels-filter",
          help="A list of labels to filter.",
          type=csv_list,
          default=None,
      )
      oid_parser.add_argument(
          "--annotation-cache-dir",
          help="Path to store annotation cache.",
          default=".",
      )
      oid_parser.add_argument(
          "--parent-label",
          help="Use the hierarchy children of this label.",
          default=None,
      )
  
      csv_parser = subparsers.add_parser("csv")
      csv_parser.add_argument(
          "annotations",
          help="Path to CSV file containing annotations for training.",
      )
      csv_parser.add_argument(
          "classes",
          help="Path to a CSV file containing class label mapping.",
      )
      csv_parser.add_argument(
          "--val-annotations",
          help="Path to CSV file containing annotations for validation (optional).",
      )
  
      group = parser.add_mutually_exclusive_group()
      group.add_argument(
          "--snapshot", help="Resume training from a snapshot."
      )
      group.add_argument(
          "--imagenet-weights",
          help="Initialize the model with pretrained imagenet weights. This is the default behaviour.",
          action="store_const",
          const=True,
          default=True,
      )
      group.add_argument(
          "--weights",
          help="Initialize the model with weights from a file.",
      )
      group.add_argument(
          "--no-weights",
          help="Don't initialize the model with any weights.",
          dest="imagenet_weights",
          action="store_const",
          const=False,
      )
      parser.add_argument(
          "--backbone",
          help="Backbone model used by retinanet.",
          default="resnet50",
          type=str,
      )
      parser.add_argument(
          "--batch-size",
          help="Size of the batches.",
          default=1,
          type=int,
      )
      parser.add_argument(
          "--gpu",
          help="Id of the GPU to use (as reported by nvidia-smi).",
      )
      parser.add_argument(
          "--multi-gpu",
          help="Number of GPUs to use for parallel processing.",
          type=int,
          default=0,
      )
      parser.add_argument(
          "--multi-gpu-force",
          help="Extra flag needed to enable (experimental) multi-gpu support.",
          action="store_true",
      )
      parser.add_argument(
          "--initial-epoch",
          help="Epoch from which to begin the train, useful if resuming from snapshot.",
          type=int,
          default=0,
      )
      parser.add_argument(
          "--epochs",
          help="Number of epochs to train.",
          type=int,
          default=50,
      )
      parser.add_argument(
          "--steps",
          help="Number of steps per epoch.",
          type=int,
          default=10000,
      )
      parser.add_argument(
          "--lr", help="Learning rate.", type=float, default=1e-5
      )
      parser.add_argument(
          "--optimizer-clipnorm",
          help="Clipnorm parameter for  optimizer.",
          type=float,
          default=0.001,
      )
      parser.add_argument(
          "--snapshot-path",
          help="Path to store snapshots of models during training (defaults to './snapshots')",
          default="./snapshots",
      )
      parser.add_argument(
          "--tensorboard-dir",
          help="Log directory for Tensorboard output",
          default="",
      )  # default='./logs') => https://github.com/tensorflow/tensorflow/pull/34870
      parser.add_argument(
          "--tensorboard-freq",
          help="Update frequency for Tensorboard output. Values 'epoch', 'batch' or int",
          default="epoch",
      )
      parser.add_argument(
          "--no-snapshots",
          help="Disable saving snapshots.",
          dest="snapshots",
          action="store_false",
      )
      parser.add_argument(
          "--no-evaluation",
          help="Disable per epoch evaluation.",
          dest="evaluation",
          action="store_false",
      )
      parser.add_argument(
          "--freeze-backbone",
          help="Freeze training of backbone layers.",
          action="store_true",
      )
      parser.add_argument(
          "--random-transform",
          help="Randomly transform image and annotations.",
          action="store_true",
      )
      parser.add_argument(
          "--image-min-side",
          help="Rescale the image so the smallest side is min_side.",
          type=int,
          default=800,
      )
      parser.add_argument(
          "--image-max-side",
          help="Rescale the image if the largest side is larger than max_side.",
          type=int,
          default=1333,
      )
      parser.add_argument(
          "--no-resize",
          help="Don" "t rescale the image.",
          action="store_true",
      )
      parser.add_argument(
          "--config",
          help="Path to a configuration parameters .ini file.",
      )
      parser.add_argument(
          "--weighted-average",
          help="Compute the mAP using the weighted average of precisions among classes.",
          action="store_true",
      )
      parser.add_argument(
          "--compute-val-loss",
          help="Compute validation loss during training",
          dest="compute_val_loss",
          action="store_true",
      )
      parser.add_argument(
          "--reduce-lr-patience",
          help="Reduce learning rate after validation loss decreases over reduce_lr_patience epochs",
          type=int,
          default=2,
      )
      parser.add_argument(
          "--reduce-lr-factor",
          help="When learning rate is reduced due to reduce_lr_patience, multiply by reduce_lr_factor",
          type=float,
          default=0.1,
      )
      parser.add_argument(
          "--group-method",
          help="Determines how images are grouped together",
          type=str,
          default="ratio",
          choices=["none", "random", "ratio"],
      )
  
      # Fit generator arguments
      parser.add_argument(
          "--multiprocessing",
          help="Use multiprocessing in fit_generator.",
          action="store_true",
      )
      parser.add_argument(
          "--workers",
          help="Number of generator workers.",
          type=int,
          default=1,
      )
      parser.add_argument(
          "--max-queue-size",
          help="Queue length for multiprocessing workers in fit_generator.",
          type=int,
          default=10,
      )
  
      return check_args(parser.parse_args(args))
  
imports: import argparse
  import os
  import sys
  import warnings
  from tensorflow import keras
  import tensorflow
  from ..None import layers
  from ..None import losses
  from ..None import models
  from ..callbacks import RedirectModel
  from ..callbacks.eval import Evaluate
  from ..models.retinanet import retinanet_bbox
  from ..preprocessing.csv_generator import CSVGenerator
  from ..preprocessing.kitti import KittiGenerator
  from ..preprocessing.open_images import OpenImagesGenerator
  from ..preprocessing.pascal_voc import PascalVocGenerator
  from ..utils.anchors import make_shapes_callback
  from ..utils.config import read_config_file, parse_anchor_parameters, parse_pyramid_levels
  from ..utils.gpu import setup_gpu
  from ..utils.image import random_visual_effect_generator
  from ..utils.model import freeze
  from ..utils.tf_version import check_tf_version
  from ..utils.transform import random_transform_generator
label_desc: Use image node_id 7 for calling a global function with example usage: parse_args(args) and returns: check_args, string