class_docstr: Wrap a llama decoder in a linear layer.
docstr: Forward pass.
  
  Apply transformer, then additional linear layer.
  return:
      bsz x num_classes
docstr_tok: ["Forward","pass",".","Apply","transformer",",","then","additional","linear","layer",".","return",":","bsz","x","num_classes"]
code: def forward(self, xs) -> torch.Tensor:
      """
      Forward pass.
  
      Apply transformer, then additional linear layer.
      return:
          bsz x num_classes
      """
      latent, _ = self.base_model(
          input=None, encoder_state=xs, sequence_classification=True
      )
      # latent: [bsz * seqlen * emb_dim]
      return self.additional_linear_layer(latent[:, -1:].squeeze(dim=1))
  
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 2 for calling the ParlaiLlamaForSequenceClassification obj's underlying member method code with example usage: obj.forward(xs) and returns: self