code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      kernel_size,
      n_layers,
      p_dropout,
  ):
      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.p_dropout = p_dropout
      assert n_layers > 1, "Number of layers should be larger than 0."
  
      self.conv_layers = nn.ModuleList()
      self.norm_layers = nn.ModuleList()
      self.conv_layers.append(
          nn.Conv1d(
              in_channels,
              hidden_channels,
              kernel_size,
              padding=kernel_size // 2,
          )
      )
      self.norm_layers.append(LayerNorm(hidden_channels))
      self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
      for _ in range(n_layers - 1):
          self.conv_layers.append(
              nn.Conv1d(
                  hidden_channels,
                  hidden_channels,
                  kernel_size,
                  padding=kernel_size // 2,
              )
          )
          self.norm_layers.append(LayerNorm(hidden_channels))
      self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
      self.proj.weight.data.zero_()
      self.proj.bias.data.zero_()
  
imports: import torch
  from torch import nn
  from torch.nn import Conv1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  import TTS.vc.modules.freevc.commons
  from TTS.vc.modules.freevc.commons import get_padding, init_weights
label_desc: Use image node_id 1 to create a new ConvReluNorm object from inherited base classes: nn with example: obj = ConvReluNorm(in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout)