code: def __init__(
      self,
      n_mel_channels,
      n_flows,
      n_group,
      n_early_every,
      n_early_size,
      WN_config,
  ):
      super(WaveGlow, self).__init__()
  
      self.upsample = torch.nn.ConvTranspose1d(
          n_mel_channels, n_mel_channels, 1024, stride=256
      )
      assert n_group % 2 == 0
      self.n_flows = n_flows
      self.n_group = n_group
      self.n_early_every = n_early_every
      self.n_early_size = n_early_size
      self.WN = torch.nn.ModuleList()
      self.convinv = torch.nn.ModuleList()
  
      n_half = int(n_group / 2)
  
      # Set up layers with the right sizes based on how many dimensions
      # have been output already
      n_remaining_channels = n_group
      for k in range(n_flows):
          if k % self.n_early_every == 0 and k > 0:
              n_half = n_half - int(self.n_early_size / 2)
              n_remaining_channels = (
                  n_remaining_channels - self.n_early_size
              )
          self.convinv.append(Invertible1x1Conv(n_remaining_channels))
          self.WN.append(
              WN(n_half, n_mel_channels * n_group, **WN_config)
          )
      self.n_remaining_channels = n_remaining_channels
  
imports: import copy
  import torch
  from torch.autograd import Variable
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new WaveGlow object from inherited base classes: torch.nn with example: obj = WaveGlow(n_mel_channels, n_flows, n_group, n_early_every, n_early_size, WN_config)