code: def __init__(
      self, channels, kernel_size, activation=nn.ReLU(), bias=True
  ):
      """Construct an ConvolutionModule object."""
      super(ConvolutionModule, self).__init__()
      # kernerl_size should be a odd number for 'SAME' padding
      assert (kernel_size - 1) % 2 == 0
  
      self.pointwise_conv1 = nn.Conv1d(
          channels,
          2 * channels,
          kernel_size=1,
          stride=1,
          padding=0,
          bias=bias,
      )
      self.depthwise_conv = nn.Conv1d(
          channels,
          channels,
          kernel_size,
          stride=1,
          padding=(kernel_size - 1) // 2,
          groups=channels,
          bias=bias,
      )
      self.norm = nn.BatchNorm1d(channels)
      self.pointwise_conv2 = nn.Conv1d(
          channels,
          channels,
          kernel_size=1,
          stride=1,
          padding=0,
          bias=bias,
      )
      self.activation = activation
  
imports: from torch import nn
label_desc: Use image node_id 1 to create a new ConvolutionModule object from inherited base classes: nn with example: obj = ConvolutionModule(channels, kernel_size, activation, bias)