code: def __init__(
      self, num_res_blocks, in_dims, compute_dims, res_out_dims, pad
  ):
      super().__init__()
      k_size = pad * 2 + 1
      self.conv_in = nn.Conv1d(
          in_dims, compute_dims, kernel_size=k_size, bias=False
      )
      self.batch_norm = nn.BatchNorm1d(compute_dims)
      self.layers = nn.ModuleList()
      for _ in range(num_res_blocks):
          self.layers.append(ResBlock(compute_dims))
      self.conv_out = nn.Conv1d(
          compute_dims, res_out_dims, kernel_size=1
      )
  
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 MelResNet object from inherited base classes: nn with example: obj = MelResNet(num_res_blocks, in_dims, compute_dims, res_out_dims, pad)