code: def forward(
      self, src_tokens, src_lengths=None, speaker=None, **kwargs
  ):
      x = self.embed_tokens(src_tokens)
      x = x.transpose(1, 2).contiguous()  # B x T x C -> B x C x T
      for conv in self.prenet:
          x = conv(x)
      x = x.transpose(1, 2).contiguous()  # B x C x T -> B x T x C
      x = self.prenet_proj(x)
  
      padding_mask = src_tokens.eq(self.padding_idx)
      positions = self.embed_positions(padding_mask)
      x += self.pos_emb_alpha * positions
      x = self.dropout_module(x)
  
      # B x T x C -> T x B x C
      x = x.transpose(0, 1)
  
      for layer in self.transformer_layers:
          x = layer(x, padding_mask)
  
      if self.layer_norm is not None:
          x = self.layer_norm(x)
  
      if self.embed_speaker is not None:
          seq_len, bsz, _ = x.size()
          emb = self.embed_speaker(speaker).transpose(0, 1)
          emb = emb.expand(seq_len, bsz, -1)
          x = self.spk_emb_proj(torch.cat([x, emb], dim=2))
  
      return {
          "encoder_out": [x],  # T x B x C
          "encoder_padding_mask": [padding_mask]
          if padding_mask.any()
          else [],  # B x T
          "encoder_embedding": [],  # B x T x C
          "encoder_states": [],  # List[T x B x C]
          "src_tokens": [],
          "src_lengths": [],
      }
  
imports: import logging
  from typing import List, Optional
  import torch
  from torch import nn
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
  from fairseq.models.text_to_speech.tacotron2 import Postnet, Prenet
  from fairseq.modules import FairseqDropout, LayerNorm, PositionalEmbedding, TransformerDecoderLayer, TransformerEncoderLayer
label_desc: Use image node_id 2 for calling the TTSTransformerEncoder obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths, speaker) and returns: dict