class_docstr: Hugging Face llama Model.
  
  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 the original Llama Model. Read more about this model here
  <https://huggingface.co/docs/transformers/main/model_doc/llama>.
code: def __init__(self, opt, dict):
      super().__init__(dict.null_idx, dict.start_idx, dict.end_idx)
      # init the model
      llama_path = _init_llama_path(opt)
      logging.info(f"Loading Llama model from {llama_path}")
      llama = LlamaForCausalLM.from_pretrained(llama_path)
      self.encoder = IdentityLayer()
      self.pad_idx = dict.null_idx
      self.decoder = ParlaiLlamaDecoder(
          opt, llama.get_decoder(), self.pad_idx, dict.start_idx
      )
      self.config = llama.config
      self.lm_head = torch.nn.Linear(
          self.config.hidden_size, self.config.vocab_size, bias=False
      )
      self._tie_weights(self.lm_head, llama.get_output_embeddings())
  
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 to create a new ParlaiLlamaForCausalLM object from inherited base classes: TorchGeneratorModel with example: obj = ParlaiLlamaForCausalLM(opt, dict)