code: def __init__(
      self,
      noise_dim,
      channel_size,
      dilations,
      strides,
      lrelu_slope,
      kpnet_conv_size,
      n_mel_channels=80,
      hop_length=256,
  ):
      super(Generator, self).__init__()
  
      self.noise_dim = noise_dim
      self.channel_size = channel_size
      self.dilations = dilations
      self.strides = strides
      self.lrelu_slope = lrelu_slope
      self.kpnet_conv_size = kpnet_conv_size
      self.mel_channel = n_mel_channels
      self.hop_length = hop_length
  
      self.res_stack = nn.ModuleList()
      hop_length_lvc = 1
      for stride in self.strides:
          hop_length_lvc = stride * hop_length_lvc
          self.res_stack.append(
              LVCBlock(
                  self.channel_size,
                  self.mel_channel,
                  stride=stride,
                  dilations=self.dilations,
                  lReLU_slope=self.lrelu_slope,
                  cond_hop_length=hop_length_lvc,
                  kpnet_conv_size=self.kpnet_conv_size,
              )
          )
  
      assert (
          hop_length_lvc == self.hop_length
      ), "multiplied value of strides {} should match n_window_stride {}".format(
          self.strides, self.hop_length
      )
  
      self.conv_pre = nn.utils.weight_norm(
          nn.Conv1d(
              self.noise_dim,
              self.channel_size,
              7,
              padding=3,
              padding_mode="reflect",
          )
      )
  
      self.conv_post = nn.Sequential(
          nn.LeakyReLU(self.lrelu_slope),
          nn.utils.weight_norm(
              nn.Conv1d(
                  self.channel_size,
                  1,
                  7,
                  padding=3,
                  padding_mode="reflect",
              )
          ),
          nn.Tanh(),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv2d
  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 1 to create a new Generator object from inherited base classes: NeuralModule with example: obj = Generator(noise_dim, channel_size, dilations, strides, lrelu_slope, kpnet_conv_size, n_mel_channels, hop_length)