code: def forward(self, x):
      x = self.conv_pre(x)
      for i in range(self.num_upsamples):
          x = F.leaky_relu(x, LRELU_SLOPE)
          x = self.ups[i](x)
          xs = None
          for j in range(self.num_kernels):
              if xs is None:
                  xs = self.resblocks[i * self.num_kernels + j](x)
              else:
                  xs += self.resblocks[i * self.num_kernels + j](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
label_desc: Use image node_id 2 for calling the Generator obj's underlying member method code with example usage: obj.forward(x) and returns: x