code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      kernel_size,
      n_layers,
      dropout_rate,
  ):
      super().__init__()
      self.in_channels = in_channels
      self.hidden_channels = hidden_channels
      self.out_channels = out_channels
      self.kernel_size = kernel_size
      self.n_layers = n_layers
      self.dropout_rate = dropout_rate
      assert n_layers > 1, "Number of layers should be larger than 0."
  
      self.conv_layers = torch.nn.ModuleList()
      self.norm_layers = torch.nn.ModuleList()
      self.conv_layers.append(
          torch.nn.Conv1d(
              in_channels,
              hidden_channels,
              kernel_size,
              padding=kernel_size // 2,
          )
      )
      self.norm_layers.append(LayerNorm(hidden_channels))
      self.relu_drop = torch.nn.Sequential(
          torch.nn.ReLU(), torch.nn.Dropout(dropout_rate)
      )
      for _ in range(n_layers - 1):
          self.conv_layers.append(
              torch.nn.Conv1d(
                  hidden_channels,
                  hidden_channels,
                  kernel_size,
                  padding=kernel_size // 2,
              )
          )
          self.norm_layers.append(LayerNorm(hidden_channels))
      self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1)
      self.proj.weight.data.zero_()
      self.proj.bias.data.zero_()
  
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 ConvReluNorm object from inherited base classes: torch.nn with example: obj = ConvReluNorm(in_channels, hidden_channels, out_channels, kernel_size, n_layers, dropout_rate)