code: def __init__(
      self,
      channels,
      dropout,
      out_channels=None,
      use_conv=False,
      use_scale_shift_norm=False,
      up=False,
      down=False,
      kernel_size=3,
  ):
      super().__init__()
      self.channels = channels
      self.dropout = dropout
      self.out_channels = out_channels or channels
      self.use_conv = use_conv
      self.use_scale_shift_norm = use_scale_shift_norm
      padding = 1 if kernel_size == 3 else 2
  
      self.in_layers = nn.Sequential(
          normalization(channels),
          nn.SiLU(),
          nn.Conv1d(
              channels, self.out_channels, kernel_size, padding=padding
          ),
      )
  
      self.updown = up or down
  
      if up:
          self.h_upd = Upsample(channels, False)
          self.x_upd = Upsample(channels, False)
      elif down:
          self.h_upd = Downsample(channels, False)
          self.x_upd = Downsample(channels, False)
      else:
          self.h_upd = self.x_upd = nn.Identity()
  
      self.out_layers = nn.Sequential(
          normalization(self.out_channels),
          nn.SiLU(),
          nn.Dropout(p=dropout),
          zero_module(
              nn.Conv1d(
                  self.out_channels,
                  self.out_channels,
                  kernel_size,
                  padding=padding,
              )
          ),
      )
  
      if self.out_channels == channels:
          self.skip_connection = nn.Identity()
      elif use_conv:
          self.skip_connection = nn.Conv1d(
              channels, self.out_channels, kernel_size, padding=padding
          )
      else:
          self.skip_connection = nn.Conv1d(
              channels, self.out_channels, 1
          )
  
imports: import functools
  import math
  import os
  import fsspec
  import torch
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from transformers import LogitsWarper
  from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
label_desc: Use image node_id 1 to create a new ResBlock object from inherited base classes: nn with example: obj = ResBlock(channels, dropout, out_channels, use_conv, use_scale_shift_norm, up, down, kernel_size)