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>.
docstr: Compute output logits.
docstr_tok: ["Compute","output","logits","."]
code: def output(self, tensor):
      """
      Compute output logits.
      """
      return self.lm_head(tensor)
  
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 4 for calling the ParlaiLlamaForCausalLM obj's underlying member method code with example usage: obj.output(tensor) and returns: self