code: def __init__(
      self,
      initial_channel,
      resblock,
      resblock_kernel_sizes,
      resblock_dilation_sizes,
      upsample_rates,
      upsample_initial_channel,
      upsample_kernel_sizes,
      gin_channels=0,
  ):
      super(Generator, self).__init__()
      self.num_kernels = len(resblock_kernel_sizes)
      self.num_upsamples = len(upsample_rates)
      self.conv_pre = Conv1d(
          initial_channel, upsample_initial_channel, 7, 1, padding=3
      )
      resblock = (
          modules.ResBlock1 if resblock == "1" else modules.ResBlock2
      )
  
      self.ups = nn.ModuleList()
      for i, (u, k) in enumerate(
          zip(upsample_rates, upsample_kernel_sizes)
      ):
          self.ups.append(
              weight_norm(
                  ConvTranspose1d(
                      upsample_initial_channel // (2**i),
                      upsample_initial_channel // (2 ** (i + 1)),
                      k,
                      u,
                      padding=(k - u) // 2,
                  )
              )
          )
  
      self.resblocks = nn.ModuleList()
      for i in range(len(self.ups)):
          ch = upsample_initial_channel // (2 ** (i + 1))
          for j, (k, d) in enumerate(
              zip(resblock_kernel_sizes, resblock_dilation_sizes)
          ):
              self.resblocks.append(resblock(ch, k, d))
  
      self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
      self.ups.apply(init_weights)
  
      if gin_channels != 0:
          self.cond = nn.Conv1d(
              gin_channels, upsample_initial_channel, 1
          )
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import librosa
  import numpy
  import torch
  from coqpit import Coqpit
  from torch import nn
  from torch.nn import Conv1d, Conv2d, ConvTranspose1d
  from torch.nn import functional
  from torch.nn.utils import spectral_norm
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  import TTS.vc.modules.freevc.commons
  import TTS.vc.modules.freevc.modules
  from TTS.tts.utils.speakers import SpeakerManager
  from TTS.utils.io import load_fsspec
  from TTS.vc.configs.freevc_config import FreeVCConfig
  from TTS.vc.models.base_vc import BaseVC
  from TTS.vc.modules.freevc.commons import get_padding, init_weights
  from TTS.vc.modules.freevc.mel_processing import mel_spectrogram_torch
  from TTS.vc.modules.freevc.speaker_encoder.speaker_encoder import SpeakerEncoder
  from TTS.vc.modules.freevc.wavlm import get_wavlm
label_desc: Use image node_id 1 to create a new Generator object from inherited base classes: torch.nn with example: obj = Generator(initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels)