code: def main():
      parser = argparse.ArgumentParser(
          description="Model Convert Parameters"
      )
      parser.add_argument(
          "model",
          type=str,
          help=(
              "model, a path to a *directory* containing model weights"
          ),
      )
      parser.add_argument(
          "-o",
          "--outfile",
          type=str,
          required=True,
          help=("outfile,save path of output quantized model."),
      )
      parser.add_argument(
          "-x",
          "--model-family",
          type=str,
          required=True,
          help=(
              "--model-family: Which model family your input model belongs to."
              "Now only `llama`/`bloom`/`gptneox`/`chatglm` are supported."
          ),
      )
      parser.add_argument(
          "-f",
          "--model-format",
          type=str,
          required=True,
          help=(
              "The model type to be convert to a ggml compatible file."
              "Now only `pth`/`gptq` are supported."
          ),
      )
      parser.add_argument(
          "-t",
          "--outtype",
          type=str,
          default="int4",
          help="Which quantized precision will be converted.",
      )
  
      # pth specific args
      parser.add_argument(
          "-p",
          "--tmp-path",
          type=str,
          default=None,
          help="Which path to store the intermediate model during the"
          "conversion process.",
      )
  
      # gptq specific args
      parser.add_argument(
          "-k",
          "--tokenizer-path",
          type=str,
          default=None,
          help="tokenizer_path, a path of tokenizer.model",
      )
      args = parser.parse_args()
      params = vars(args)
      llm_convert(**params)
  
imports: from bigdl.llm.utils.common import invalidInputError
  import argparse
  import os
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types