code: def __init__(
      self,
      channels,
      emb_channels,
      dropout,
      out_channels=None,
      dims=2,
      kernel_size=3,
      efficient_config=True,
      use_scale_shift_norm=False,
  ):
      super().__init__()
      self.channels = channels
      self.emb_channels = emb_channels
      self.dropout = dropout
      self.out_channels = out_channels or channels
      self.use_scale_shift_norm = use_scale_shift_norm
      padding = {1: 0, 3: 1, 5: 2}[kernel_size]
      eff_kernel = 1 if efficient_config else 3
      eff_padding = 0 if efficient_config else 1
  
      self.in_layers = nn.Sequential(
          normalization(channels),
          nn.SiLU(),
          nn.Conv1d(
              channels,
              self.out_channels,
              eff_kernel,
              padding=eff_padding,
          ),
      )
  
      self.emb_layers = nn.Sequential(
          nn.SiLU(),
          nn.Linear(
              emb_channels,
              2 * self.out_channels
              if use_scale_shift_norm
              else self.out_channels,
          ),
      )
      self.out_layers = nn.Sequential(
          normalization(self.out_channels),
          nn.SiLU(),
          nn.Dropout(p=dropout),
          nn.Conv1d(
              self.out_channels,
              self.out_channels,
              kernel_size,
              padding=padding,
          ),
      )
  
      if self.out_channels == channels:
          self.skip_connection = nn.Identity()
      else:
          self.skip_connection = nn.Conv1d(
              channels,
              self.out_channels,
              eff_kernel,
              padding=eff_padding,
          )
  
imports: import math
  import random
  from abc import abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import autocast
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, normalization
label_desc: Use image node_id 1 to create a new ResBlock object from inherited base classes: TimestepBlock with example: obj = ResBlock(channels, emb_channels, dropout, out_channels, dims, kernel_size, efficient_config, use_scale_shift_norm)