code: def setup_llama_args(parser):
      if parser is None:
          return parser
      parser.add_argument(
          "--llama-model-dir",
          type=str,
          default=None,
          help="dir to llama model and tokenizer",
      )
      parser.add_argument(
          "--llama-tokenizer-fast",
          type=bool,
          default=True,
          help="whether to load llama fast tokenizer, LlamaTokenizerFast is slower to load but fast in tokenization",
      )
      parser.set_defaults(
          text_truncate=1792,
          label_truncate=256,
          dict_maxexs=0,  # skip building dictionary
      )
      return parser
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from parlai.agents.hugging_face.dict import LlamaDictionaryAgent, _init_llama_path
  from parlai.core.torch_generator_agent import TorchGeneratorAgent, TorchGeneratorModel
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.utils.torch import IdentityLayer, padded_tensor
  from parlai.agents.transformer.transformer import _check_positional_embeddings
  import parlai.utils.logging
label_desc: Use image node_id 1 for calling a global function with example usage: setup_llama_args(parser) and returns: parser, parser