code: def __init__(self, h):
      super(Generator, self).__init__()
      self.h = h
      self.num_kernels = len(h.resblock_kernel_sizes)
      self.num_upsamples = len(h.upsample_rates)
      self.conv_pre = weight_norm(
          Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)
      )
      resblock = ResBlock1 if h.resblock == "1" else ResBlock2
  
      self.ups = nn.ModuleList()
      for i, (u, k) in enumerate(
          zip(h.upsample_rates, h.upsample_kernel_sizes)
      ):
          self.ups.append(
              weight_norm(
                  ConvTranspose1d(
                      h.upsample_initial_channel // (2**i),
                      h.upsample_initial_channel // (2 ** (i + 1)),
                      k,
                      u,
                      padding=(k - u) // 2,
                  )
              )
          )
  
      self.resblocks = nn.ModuleList()
      for i in range(len(self.ups)):
          ch = h.upsample_initial_channel // (2 ** (i + 1))
          for j, (k, d) in enumerate(
              zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)
          ):
              self.resblocks.append(resblock(h, ch, k, d))
  
      self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
      self.ups.apply(init_weights)
      self.conv_post.apply(init_weights)
  
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 1 to create a new Generator object from inherited base classes: torch.nn with example: obj = Generator(h)