code: def __init__(self, channels, eps=1e-5):
      super().__init__()
      self.channels = channels
      self.eps = eps
  
      self.gamma = torch.nn.Parameter(torch.ones(channels))
      self.beta = torch.nn.Parameter(torch.zeros(channels))
  
imports: import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_svs.visinger2.ddsp import remove_above_nyquist, scale_function, upsample
  from espnet2.gan_tts.hifigan import HiFiGANMultiPeriodDiscriminator, HiFiGANMultiScaleDiscriminator, HiFiGANMultiScaleMultiPeriodDiscriminator, HiFiGANPeriodDiscriminator, HiFiGANScaleDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: torch.nn with example: obj = LayerNorm(channels, eps)