code: def parse_args(args):
      """Parse the arguments."""
      parser = argparse.ArgumentParser(
          description="Evaluation script for 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",
      )
  
      csv_parser = subparsers.add_parser("csv")
      csv_parser.add_argument(
          "annotations",
          help="Path to CSV file containing annotations for evaluation.",
      )
      csv_parser.add_argument(
          "classes",
          help="Path to a CSV file containing class label mapping.",
      )
  
      parser.add_argument("model", help="Path to RetinaNet model.")
      parser.add_argument(
          "--convert-model",
          help="Convert the model to an inference model (ie. the input is a training model).",
          action="store_true",
      )
      parser.add_argument(
          "--backbone",
          help="The backbone of the model.",
          default="resnet50",
      )
      parser.add_argument(
          "--gpu",
          help="Id of the GPU to use (as reported by nvidia-smi).",
      )
      parser.add_argument(
          "--score-threshold",
          help="Threshold on score to filter detections with (defaults to 0.05).",
          default=0.05,
          type=float,
      )
      parser.add_argument(
          "--iou-threshold",
          help="IoU Threshold to count for a positive detection (defaults to 0.5).",
          default=0.5,
          type=float,
      )
      parser.add_argument(
          "--max-detections",
          help="Max Detections per image (defaults to 100).",
          default=100,
          type=int,
      )
      parser.add_argument(
          "--save-path",
          help="Path for saving images with detections (doesn't work for COCO).",
      )
      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 (only used with --convert-model).",
      )
      parser.add_argument(
          "--group-method",
          help="Determines how images are grouped together",
          type=str,
          default="ratio",
          choices=["none", "random", "ratio"],
      )
  
      return parser.parse_args(args)
  
file_path: ImageAI/imageai_tf_deprecated/Detection/keras_retinanet/bin/evaluate.py
imports: import argparse
  import os
  import sys
  from ..None import models
  from ..preprocessing.csv_generator import CSVGenerator
  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.eval import evaluate
  from ..utils.gpu import setup_gpu
  from ..utils.tf_version import check_tf_version
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args(args) and returns: parser