code: def __init__(
      self,
      channels: int,
      kernel: int = 3,
      norm_groups: int = 4,
      dilation: int = 1,
      activation: tp.Type[nn.Module] = nn.ReLU,
      dropout: float = 0.0,
  ):
      super().__init__()
      stride = 1
      padding = dilation * (kernel - stride) // 2
      Conv = nn.Conv1d
      Drop = nn.Dropout1d
      self.norm1 = nn.GroupNorm(norm_groups, channels)
      self.conv1 = Conv(
          channels, channels, kernel, 1, padding, dilation=dilation
      )
      self.activation1 = activation()
      self.dropout1 = Drop(dropout)
  
      self.norm2 = nn.GroupNorm(norm_groups, channels)
      self.conv2 = Conv(
          channels, channels, kernel, 1, padding, dilation=dilation
      )
      self.activation2 = activation()
      self.dropout2 = Drop(dropout)
  
imports: from dataclasses import dataclass
  import typing
  import torch
  from torch import nn
  from torch.nn import functional
  from audiocraft.modules.transformer import StreamingTransformer, create_sin_embedding
label_desc: Use image node_id 1 to create a new ResBlock object from inherited base classes: nn with example: obj = ResBlock(channels, kernel, norm_groups, dilation, activation, dropout)