class_docstr: NaiveRNNDP-SVS module.
  
  This is an implementation of naive RNN with duration prediction
  for singing voice synthesis
  The features are processed directly over time-domain from music score and
  predict the singing voice features
class_docstr_tok: ["NaiveRNNDP-SVS","module",".","This","is","an","implementation","of","naive","RNN","with","duration","prediction","for","singing","voice","synthesis","The","features","are","processed","directly","over","time-domain","from","music","score","and","predict","the","singing","voice","features"]
code: def _reset_parameters(self, init_type):
      # initialize parameters
      if init_type != "pytorch":
          initialize(self, init_type)
  
file_path: espnet/espnet2/svs/naive_rnn/naive_rnn_dp.py
imports: from typing import Dict, Optional, Tuple
  import torch
  import torch.nn.functional
  from typeguard import check_argument_types
  from espnet2.svs.abs_svs import AbsSVS
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.torch_utils.initialize import initialize
  from espnet.nets.pytorch_backend.e2e_tts_fastspeech import FeedForwardTransformerLoss
  from espnet.nets.pytorch_backend.fastspeech.duration_predictor import DurationPredictor
  from espnet.nets.pytorch_backend.fastspeech.length_regulator import LengthRegulator
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
label_desc: Use image node_id 2 for calling the NaiveRNNDP obj's underlying member method code with example usage: obj._reset_parameters(init_type) without return types