src/transformers/convert_graph_to_onnx.py/OnnxConverterArgumentParser/__init__
class OnnxConverterArgumentParser:    def __init__(self):
        super().__init__("ONNX Converter")

        self.add_argument(
            "--pipeline",
            type=str,
            choices=SUPPORTED_PIPELINES,
            default="feature-extraction",
        )
        self.add_argument(
            "--model",
            type=str,
            required=True,
            help="Model's id or path (ex: bert-base-cased)",
        )
        self.add_argument("--tokenizer", type=str, help="Tokenizer's id or path (ex: bert-base-cased)")
        self.add_argument(
            "--framework",
            type=str,
            choices=["pt", "tf"],
            help="Framework for loading the model",
        )
        self.add_argument("--opset", type=int, default=11, help="ONNX opset to use")
        self.add_argument(
            "--check-loading",
            action="store_true",
            help="Check ONNX is able to load the model",
        )
        self.add_argument(
            "--use-external-format",
            action="store_true",
            help="Allow exporting model >= than 2Gb",
        )
        self.add_argument(
            "--quantize",
            action="store_true",
            help="Quantize the neural network to be run with int8",
        )
        self.add_argument("output")