code: def __init__(
      self,
      feat_dims,
      upsample_scales,
      compute_dims,
      num_res_blocks,
      res_out_dims,
      pad,
      use_aux_net,
  ):
      super().__init__()
      self.total_scale = np.cumproduct(upsample_scales)[-1]
      self.indent = pad * self.total_scale
      self.use_aux_net = use_aux_net
      if use_aux_net:
          self.resnet = MelResNet(
              num_res_blocks, feat_dims, compute_dims, res_out_dims, pad
          )
          self.resnet_stretch = Stretch2d(self.total_scale, 1)
      self.up_layers = nn.ModuleList()
      for scale in upsample_scales:
          k_size = (1, scale * 2 + 1)
          padding = (0, scale)
          stretch = Stretch2d(scale, 1)
          conv = nn.Conv2d(
              1, 1, kernel_size=k_size, padding=padding, bias=False
          )
          conv.weight.data.fill_(1.0 / k_size[1])
          self.up_layers.append(stretch)
          self.up_layers.append(conv)
  
imports: import sys
  import torch
  import torch.nn
  import numpy
  import torch.nn.functional
  import time
  from TTS.utils.audio import AudioProcessor
  from TTS.vocoder.utils.distribution import sample_from_gaussian, sample_from_discretized_mix_logistic
label_desc: Use image node_id 1 to create a new UpsampleNetwork object from inherited base classes: nn with example: obj = UpsampleNetwork(feat_dims, upsample_scales, compute_dims, num_res_blocks, res_out_dims, pad, use_aux_net)