class_docstr: Wrap a llama decoder in a linear layer.
code: def __init__(self, opt, dict, num_classes):
      super().__init__()
      llama_path = _init_llama_path(opt)
      logging.info(f"Loading Llama model from {llama_path}")
      llama = LlamaForCausalLM.from_pretrained(llama_path)
      self.base_model = ParlaiLlamaDecoder(
          opt, llama.get_decoder(), dict.null_idx, dict.start_idx
      )
      self.config = llama.config
      self.additional_linear_layer = torch.nn.Linear(
          self.config.hidden_size, num_classes, bias=False
      )
  
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 ParlaiLlamaForSequenceClassification object from inherited base classes: torch.nn with example: obj = ParlaiLlamaForSequenceClassification(opt, dict, num_classes)