parameters: ()
return_statement: return args
docstring: parse arguments
docstring_summary: parse arguments
docstring_tokens: parse arguments
function: def parse_args():
      '''parse arguments'''
      description = 'Used to obtain optimal batch size for specified model file.'
      arg_parser = argparse.ArgumentParser(description = description)
      arg_parser.add_argument('--model_file', help = 'model prototxt file you specified')
      arg_parser.add_argument('--min_bs', type = int, help = 'min batch size you expect')
      arg_parser.add_argument('--max_bs', type = int, help = 'max batch size you expect')
      arg_parser.add_argument('--iterations', type = int, default = 100, help = 'how many iterations to run for caffe time')
      arg_parser.add_argument('--dummy_data_use', action = 'store_true',  help = "dummy_data_use, if set 'True', will use dummy data as input; else if set 'False', will use other data source like lmdb for neural network as input")
      args = arg_parser.parse_args()
      return args
function_tokens: def parse_args ( ) : description = 'Used to obtain optimal batch size for specified model file.' arg_parser = argparse . ArgumentParser ( description = description ) arg_parser . add_argument ( '--model_file' , help = 'model prototxt file you specified' ) arg_parser . add_argument ( '--min_bs' , type = int , help = 'min batch size you expect' ) arg_parser . add_argument ( '--max_bs' , type = int , help = 'max batch size you expect' ) arg_parser . add_argument ( '--iterations' , type = int , default = 100 , help = 'how many iterations to run for caffe time' ) arg_parser . add_argument ( '--dummy_data_use' , action = 'store_true' , help = "dummy_data_use, if set 'True', will use dummy data as input; else if set 'False', will use other data source like lmdb for neural network as input" ) args = arg_parser . parse_args ( ) return args