code: def create_base_args(
      parser: argparse.ArgumentParser, model_types=None
  ):
      """Add base arguments for Transformers based models"""
      # Required parameters
      if model_types is not None and len(model_types) > 1:
          parser.add_argument(
              "--model_type",
              default=None,
              type=str,
              choices=model_types,
              required=True,
              help="Model type selected in the list: "
              + ", ".join(model_types),
          )
      parser.add_argument(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          help="The output directory where the model predictions and checkpoints "
          "will be written.",
      )
  
      # Other parameters
      parser.add_argument(
          "--tokenizer_name",
          default="",
          type=str,
          help="Pretrained tokenizer name or path if not the same as model_name",
      )
      parser.add_argument(
          "--max_seq_length",
          default=128,
          type=int,
          help="The maximum total input sequence length after tokenization. "
          "Sequences longer than this will be truncated, sequences shorter "
          "will be padded.",
      )
      parser.add_argument(
          "--cache_dir",
          default="",
          type=str,
          help="Where do you want to store the pre-trained models downloaded "
          "from s3",
      )
      parser.add_argument(
          "--do_lower_case",
          action="store_true",
          help="Set this flag if you are using an uncased model.",
      )
      parser.add_argument(
          "--per_gpu_eval_batch_size",
          default=8,
          type=int,
          help="Batch size per GPU/CPU for evaluation.",
      )
      parser.add_argument(
          "--no_cuda",
          action="store_true",
          help="Avoid using CUDA when available",
      )
      parser.add_argument(
          "--overwrite_output_dir",
          action="store_true",
          help="Overwrite the content of the output directory",
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Overwrite the cached training and evaluation sets",
      )
  
imports: import argparse
  from nlp_architect.models.transformers.base_model import get_models
label_desc: Use image node_id 1 for calling a global function with example usage: create_base_args(parser, model_types) without return types