class_docstr: Based on S2T transformer encoder, with support
  to incorporate target speaker embedding.
code: def __init__(self, args):
      super().__init__(args)
  
      self.spk_emb_proj = None
      if args.target_speaker_embed:
          self.spk_emb_proj = Linear(
              args.encoder_embed_dim + args.speaker_embed_dim,
              args.encoder_embed_dim,
          )
  
imports: import logging
  from pathlib import Path
  from typing import Any, Dict, List, Optional
  import torch
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.models import FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder
  from fairseq.models.speech_to_speech.modules.stacked_embedding import StackedEmbedding
  from fairseq.models.speech_to_text import S2TTransformerEncoder
  from fairseq.models.text_to_speech import TTSTransformerDecoder
  from fairseq.models.transformer import Linear, TransformerDecoder, TransformerModelBase
label_desc: Use image node_id 1 to create a new S2STransformerEncoder object from inherited base classes: S2TTransformerEncoder with example: obj = S2STransformerEncoder(args)