code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      dilation,
      norm_type="gLN",
      causal=False,
  ):
      super().__init__()
      # [M, B, K] -> [M, H, K]
      conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)
      prelu = nn.PReLU()
      norm = chose_norm(norm_type, out_channels)
      # [M, H, K] -> [M, B, K]
      dsconv = DepthwiseSeparableConv(
          out_channels,
          in_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          norm_type,
          causal,
      )
      # Put together
      self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new TemporalBlock object from inherited base classes: nn with example: obj = TemporalBlock(in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type, causal)