class_docstr: Based on Transformer decoder, with support to decoding stacked units
code: def __init__(
      self,
      args,
      dictionary,
      embed_tokens,
      no_encoder_attn=False,
      output_projection=None,
  ):
      super().__init__(
          args,
          dictionary,
          embed_tokens,
          no_encoder_attn,
          output_projection,
      )
      self.n_frames_per_step = args.n_frames_per_step
  
      self.out_proj_n_frames = (
          Linear(
              self.output_embed_dim,
              self.output_embed_dim * self.n_frames_per_step,
              bias=False,
          )
          if self.n_frames_per_step > 1
          else None
      )
  
file_path: fairseq/fairseq/models/speech_to_speech/modules/transformer_decoder_aug.py
imports: from typing import Any, Dict, List, Optional
  from torch import Tensor
  from fairseq.models.transformer import Linear
  from fairseq.models.transformer.transformer_decoder_aug import AugTransformerDecoder
label_desc: Use image node_id 1 to create a new AugTransformerUnitDecoder object from inherited base classes: AugTransformerDecoder with example: obj = AugTransformerUnitDecoder(args, dictionary, embed_tokens, no_encoder_attn, output_projection)