class_docstr: Represents a Transformer-based decoder model.
code: def decode(
      self,
      seqs: Tensor,
      padding_mask: Optional[PaddingMask],
      *,
      state_bag: Optional[IncrementalStateBag] = None,
  ) -> Tuple[Tensor, PaddingMask]:
      seqs, padding_mask = self.decoder_frontend(
          seqs, padding_mask, state_bag=state_bag
      )
  
      decoder_output, decoder_padding_mask = self.decoder(
          seqs, padding_mask, state_bag=state_bag
      )
  
      return decoder_output, decoder_padding_mask
  
file_path: fairseq2/src/fairseq2/models/transformer/decoder_model.py
imports: from typing import Optional, Tuple, final
  from torch import Tensor
  from fairseq2.data import VocabularyInfo
  from fairseq2.models.decoder import DecoderModel
  from fairseq2.models.sequence import SequenceModelOutput
  from fairseq2.models.transformer.frontend import TransformerFrontend
  from fairseq2.nn.incremental_state import IncrementalStateBag
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.nn.projection import Projection
  from fairseq2.nn.transformer import TransformerDecoder
  from fairseq2.typing import finaloverride
label_desc: Use image node_id 2 for calling the TransformerDecoderModel obj's underlying member method code with example usage: obj.decode(seqs, padding_mask) and returns: decoder_output,  decoder_padding_mask