class_docstr: VC Transformer module.
  
  This is a module of the Voice Transformer Network
  (a.k.a. VTN or Transformer-VC) described in
  `Voice Transformer Network: Sequence-to-Sequence
  Voice Conversion Using Transformer with
  Text-to-Speech Pretraining`_,
  which convert the sequence of acoustic features
  into the sequence of acoustic features.
  
  .. _`Voice Transformer Network: Sequence-to-Sequence
      Voice Conversion Using Transformer with
      Text-to-Speech Pretraining`:
      https://arxiv.org/pdf/1912.06813.pdf
class_docstr_tok: ["VC","Transformer","module",".","This","is","a","module","of","the","Voice","Transformer","Network","(","a.k.a",".","VTN","or","Transformer-VC",")","described","in","`","Voice","Transformer","Network",":","Sequence-to-Sequence","Voice","Conversion","Using","Transformer","with","Text-to-Speech","Pretraining","`","_",",","which","convert","the","sequence","of","acoustic","features","into","the","sequence","of","acoustic","features",".","..","_","`","Voice","Transformer","Network",":","Sequence-to-Sequence","Voice","Conversion","Using","Transformer","with","Text-to-Speech","Pretraining","`",":","https",":","\/\/arxiv.org\/pdf\/1912.06813.pdf"]
code: def _reset_parameters(
      self, init_type, init_enc_alpha=1.0, init_dec_alpha=1.0
  ):
      # initialize parameters
      initialize(self, init_type)
  
      # initialize alpha in scaled positional encoding
      if self.use_scaled_pos_enc:
          self.encoder.embed[-1].alpha.data = torch.tensor(
              init_enc_alpha
          )
          self.decoder.embed[-1].alpha.data = torch.tensor(
              init_dec_alpha
          )
  
imports: import logging
  import torch
  import torch.nn.functional
  from espnet.nets.pytorch_backend.e2e_asr_transformer import subsequent_mask
  from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import Tacotron2Loss
  from espnet.nets.pytorch_backend.e2e_tts_transformer import TTSPlot
  from espnet.nets.pytorch_backend.e2e_tts_transformer import GuidedMultiHeadAttentionLoss
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.tacotron2.decoder import Prenet
  from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder import Decoder
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.initializer import initialize
  from espnet.nets.tts_interface import TTSInterface
  from espnet.utils.cli_utils import strtobool
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 4 for calling the Transformer obj's underlying member method code with example usage: obj._reset_parameters(init_type, init_enc_alpha, init_dec_alpha) without return types