code: def forward(self, x):
      x = self.conv_pre(x)
      for upsample_layer, resblock_group in zip(
          self.ups, self.resblocks
      ):
          x = F.leaky_relu(x, self.lrelu_slope)
          x = upsample_layer(x)
          xs = torch.zeros(x.shape, dtype=x.dtype, device=x.device)
          for resblock in resblock_group:
              xs += resblock(x)
          x = xs / self.num_kernels
      x = F.leaky_relu(x)
      x = self.conv_post(x)
      x = torch.tanh(x)
  
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
  from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 4 for calling the Generator obj's underlying member method code with example usage: obj.forward(x) and returns: x