code: def forward(self, x):
      x = self.conv_layers[0](x)
      x = self.norm_layers[0](x)
      x = self.relu_drop(x)
  
      for i in range(1, self.n_layers):
          x_ = self.conv_layers[i](x)
          x_ = self.norm_layers[i](x_)
          x_ = self.relu_drop(x_)
          x = (x + x_) / 2
      x = self.proj(x)
      return x
  
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 2 for calling the ConvReluNorm obj's underlying member method code with example usage: obj.forward(x) and returns: x