code: def _main():
      import argparse
  
      parser = argparse.ArgumentParser(
          description="Convert other model file formats to MLKit format (.mlmodel)."
      )
      parser.add_argument(
          "--srcModelFormat",
          type=_string_types,
          choices=["auto", "caffe", "keras"],
          default="auto",
          help="Format of model at srcModelPath (default is to auto-detect).",
      )
      parser.add_argument(
          "--srcModelPath",
          type=_string_types,
          required=True,
          help="Path to the model file of the external tool (e.g caffe weights proto binary, keras h5 binary",
      )
      parser.add_argument(
          "--dstModelPath",
          type=_string_types,
          required=True,
          help="Path to save the model in format .mlmodel",
      )
      parser.add_argument(
          "--caffeProtoTxtPath",
          type=_string_types,
          default="",
          help="Path to the .prototxt file if network differs from the source file (optional)",
      )
      parser.add_argument(
          "--meanImageProtoPath",
          type=_string_types,
          default="",
          help="Path to the .binaryproto file containing the mean image if required by the network (optional). This requires a prototxt file to be specified.",
      )
      parser.add_argument(
          "--kerasJsonPath",
          type=_string_types,
          default=None,
          help="Path to the .json file for keras if the network differs from the weights file (optional)",
      )
      parser.add_argument(
          "--inputNames",
          type=_string_types,
          nargs="*",
          help="Names of the feature (input) columns, in order (required for keras models).",
      )
      parser.add_argument(
          "--outputNames",
          type=_string_types,
          nargs="*",
          help="Names of the target (output) columns, in order (required for keras models).",
      )
      parser.add_argument(
          "--imageInputNames",
          type=_string_types,
          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=_string_types,
          default="",
          help="Path to class labels (ordered new line separated) for treating the neural network as a classifier",
      )
      parser.add_argument(
          "--predictedFeatureName",
          type=_string_types,
          default="class_output",
          help="Name of the output feature that captures the class name (for classifiers models).",
      )
      parser.add_argument(
          "--respectTrainable",
          action="store_true",
          default=False,
          help="Honor Keras' 'trainable' flag.",
      )
  
      args = parser.parse_args()
      ret = _convert(args)
      _sys.exit(int(ret))
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/_scripts/converter.py
imports: import logging
  from six import string_types
  from ..models import utils
  from ..models import neural_network
  from ..models import MLModel
  from ..None import converters
  from ..None import proto
  import sys
label_desc: Use image node_id 2 for calling a global function with example usage: _main() without return types