code: def __init__(
      self,
      in_channels,
      out_channels,
      skip_channels,
      kernel_size,
      stride,
      padding,
      dilation,
      norm_type="gLN",
      causal=False,
  ):
      super().__init__()
      # Use `groups` option to implement depthwise convolution
      # [M, H, K] -> [M, H, K]
      depthwise_conv = nn.Conv1d(
          in_channels,
          in_channels,
          kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=in_channels,
          bias=False,
      )
      if causal:
          chomp = Chomp1d(padding)
      prelu = nn.PReLU()
      norm = choose_norm(norm_type, in_channels)
      # [M, H, K] -> [M, B, K]
      pointwise_conv = nn.Conv1d(
          in_channels, out_channels, 1, bias=False
      )
      # Put together
      if causal:
          self.net = nn.Sequential(
              depthwise_conv, chomp, prelu, norm, pointwise_conv
          )
      else:
          self.net = nn.Sequential(
              depthwise_conv, prelu, norm, pointwise_conv
          )
  
      # skip connection
      if skip_channels is not None:
          self.skip_conv = nn.Conv1d(
              in_channels, skip_channels, 1, bias=False
          )
      else:
          self.skip_conv = None
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers.adapt_layers import make_adapt_layer
label_desc: Use image node_id 1 to create a new DepthwiseSeparableConv object from inherited base classes: nn with example: obj = DepthwiseSeparableConv(in_channels, out_channels, skip_channels, kernel_size, stride, padding, dilation, norm_type, causal)