class_docstr: Hugging Face LlamaForSequenceClassification.
  
  Llama is a multi-layer decoder-only Transformer. As such, the encoder
  is simply an identity layer. The decoder is initialized with pretrained
  weights from Hugging Face. Read more about this model here
  <https://huggingface.co/docs/transformers/main/model_doc/llama>.
  
  parlai em -m parlai.agents.hugging_face.llama:ParlaiLlamaClassifierAgent --classes __notok__ __ok__
docstr: Add CLI args.
docstr_tok: ["Add","CLI","args","."]
code: def add_cmdline_args(
      cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
  ) -> ParlaiParser:
      """
      Add CLI args.
      """
      super().add_cmdline_args(parser, partial_opt=partial_opt)
      group = parser.add_argument_group(
          "ParlaiLlamaClassifierAgent Arguments"
      )
      # class arguments
      group = setup_llama_args(group)
      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 the ParlaiLlamaClassifierAgent obj's underlying member method code with example usage: obj.add_cmdline_args(cls, parser, partial_opt) and returns: parser