code: def __init__(self, args, src_dict):
      super().__init__(None)
      self.args = args
      self.n_frames_per_step = args.n_frames_per_step
      self.out_dim = args.output_frame_dim * args.n_frames_per_step
  
      self.prenet = Prenet(
          self.out_dim,
          args.prenet_layers,
          args.prenet_dim,
          args.prenet_dropout,
      )
  
      # take prev_context, prev_frame, (speaker embedding) as input
      self.attention_lstm = LSTMCellWithZoneOut(
          args.zoneout,
          args.prenet_dim + args.encoder_embed_dim,
          args.decoder_lstm_dim,
      )
  
      # take attention_lstm output, attention_state, encoder_out as input
      self.attention = LocationAttention(
          args.attention_dim,
          args.encoder_embed_dim,
          args.decoder_lstm_dim,
          (1 + int(args.attention_use_cumprob)),
          args.attention_conv_dim,
          args.attention_conv_kernel_size,
      )
  
      # take attention_lstm output, context, (gated_latent) as input
      self.lstm = nn.ModuleList(
          LSTMCellWithZoneOut(
              args.zoneout,
              args.encoder_embed_dim + args.decoder_lstm_dim,
              args.decoder_lstm_dim,
          )
          for i in range(args.decoder_lstm_layers)
      )
  
      proj_in_dim = args.encoder_embed_dim + args.decoder_lstm_dim
      self.feat_proj = nn.Linear(proj_in_dim, self.out_dim)
      self.eos_proj = nn.Linear(proj_in_dim, 1)
  
      self.postnet = Postnet(
          self.out_dim,
          args.postnet_conv_dim,
          args.postnet_conv_kernel_size,
          args.postnet_layers,
          args.postnet_dropout,
      )
  
      self.ctc_proj = None
      if getattr(args, "ctc_weight", 0.0) > 0.0:
          self.ctc_proj = nn.Linear(self.out_dim, len(src_dict))
  
      self.apply(decoder_init)
  
imports: import logging
  import torch
  from torch import nn
  from torch.nn import functional
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LSTMCellWithZoneOut, LocationAttention
label_desc: Use image node_id 1 to create a new Tacotron2Decoder object from inherited base classes: FairseqIncrementalDecoder with example: obj = Tacotron2Decoder(args, src_dict)