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 reorder_encoder_states(self, encoder_states, indices):
      enc = torch.index_select(encoder_states, 0, indices)
      return enc
  
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 3 for calling the ParlaiLlamaForCausalLM obj's underlying member method code with example usage: obj.reorder_encoder_states(encoder_states, indices) and returns: enc