class_docstr: Hugging Face Llama Agent.
  
  LlamaForCausalLM is a multi-layer decoder-only Transformer.
  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>.
code: def _set_label_vec(self, obs, add_start, add_end, truncate):
      super()._set_label_vec(obs, add_start, add_end, truncate)
      # cut off the start_token in the label_vec if any (llama_tokenizer add_bos_token = True), don't compute the loss!!
      for label_vec_type in ["labels_vec", "eval_labels_vec"]:
          if (
              label_vec_type in obs
              and obs[label_vec_type][0] == self.START_IDX
          ):
              obs.force_set(label_vec_type, obs[label_vec_type][1:])
      return obs
  
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 7 for calling the LlamaAgent obj's underlying member method code with example usage: obj._set_label_vec(obs, add_start, add_end, truncate) and returns: obs