code: def _get_parser():
      import argparse
  
      parser = argparse.ArgumentParser(
          description="Convert IR model file formats to other format."
      )
  
      parser.add_argument(
          "-f",
          "--framework",
          type=_text_type,
          choices=["coreml"],
          required=True,
          help="Format of model at srcModelPath (default is to auto-detect).",
      )
  
      parser.add_argument(
          "-in",
          "--inputNetwork",
          type=_text_type,
          required=True,
          help="Path of the IR network architecture file.",
      )
  
      parser.add_argument(
          "-iw",
          "--inputWeight",
          type=_text_type,
          required=True,
          help="Path to the IR network weight file.",
      )
  
      parser.add_argument(
          "-o",
          "--output",
          type=_text_type,
          required=True,
          help="Path to save the destination model",
      )
  
      # Caffe
      parser.add_argument(
          "--phase",
          type=_text_type,
          choices=["train", "test"],
          default="test",
          help="[Caffe] Convert phase (train/test) for destination toolkits.",
      )
  
      # For CoreML
      parser.add_argument(
          "--inputNames",
          type=_text_type,
          nargs="*",
          help="Names of the feature (input) columns, in order (required for keras models).",
      )
      parser.add_argument(
          "--outputNames",
          type=_text_type,
          nargs="*",
          help="Names of the target (output) columns, in order (required for keras models).",
      )
      parser.add_argument(
          "--imageInputNames",
          type=_text_type,
          default=[],
          action="append",
          help="Label the named input as an image. Can be specified more than once for multiple image inputs.",
      )
      parser.add_argument(
          "--isBGR",
          action="store_true",
          default=False,
          help="True if the image data in BGR order (RGB default)",
      )
      parser.add_argument(
          "--redBias",
          type=float,
          default=0.0,
          help="Bias value to be added to the red channel (optional, default 0.0)",
      )
      parser.add_argument(
          "--blueBias",
          type=float,
          default=0.0,
          help="Bias value to be added to the blue channel (optional, default 0.0)",
      )
      parser.add_argument(
          "--greenBias",
          type=float,
          default=0.0,
          help="Bias value to be added to the green channel (optional, default 0.0)",
      )
      parser.add_argument(
          "--grayBias",
          type=float,
          default=0.0,
          help="Bias value to be added to the gray channel for Grayscale images (optional, default 0.0)",
      )
      parser.add_argument(
          "--scale",
          type=float,
          default=1.0,
          help="Value by which the image data must be scaled (optional, default 1.0)",
      )
      parser.add_argument(
          "--classInputPath",
          type=_text_type,
          default="",
          help="Path to class labels (ordered new line separated) for treating the neural network as a classifier",
      )
      parser.add_argument(
          "--predictedFeatureName",
          type=_text_type,
          default="class_output",
          help="Name of the output feature that captures the class name (for classifiers models).",
      )
      return parser
  
imports: import sys
  import google.protobuf.text_format
  from six import text_type
label_desc: Use image node_id 2 for calling a global function with example usage: _get_parser() and returns: parser