code: def __init__(self, args):
      super().__init__()
      self.args = args
      self.length_regulator = LengthRegulator()
      self.duration_predictor = VariancePredictor(args)
      self.pitch_predictor = VariancePredictor(args)
      self.energy_predictor = VariancePredictor(args)
  
      n_bins, steps = (
          self.args.var_pred_n_bins,
          self.args.var_pred_n_bins - 1,
      )
      self.pitch_bins = torch.linspace(
          args.pitch_min, args.pitch_max, steps
      )
      self.embed_pitch = Embedding(n_bins, args.encoder_embed_dim)
      self.energy_bins = torch.linspace(
          args.energy_min, args.energy_max, steps
      )
      self.embed_energy = Embedding(n_bins, args.encoder_embed_dim)
  
imports: import logging
  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, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
  from fairseq.models.text_to_speech.tacotron2 import Postnet
  from fairseq.modules import FairseqDropout, LayerNorm, MultiheadAttention, PositionalEmbedding
label_desc: Use image node_id 1 to create a new VarianceAdaptor object from inherited base classes: nn with example: obj = VarianceAdaptor(args)