class_docstr: Dialted and Depth-Separable Convolution
class_docstr_tok: ["Dialted","and","Depth-Separable","Convolution"]
code: def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
      super().__init__()
      self.channels = channels
      self.kernel_size = kernel_size
      self.n_layers = n_layers
      self.p_dropout = p_dropout
  
      self.drop = nn.Dropout(p_dropout)
      self.convs_sep = nn.ModuleList()
      self.convs_1x1 = nn.ModuleList()
      self.norms_1 = nn.ModuleList()
      self.norms_2 = nn.ModuleList()
      for i in range(n_layers):
          dilation = kernel_size**i
          padding = (kernel_size * dilation - dilation) // 2
          self.convs_sep.append(
              nn.Conv1d(
                  channels,
                  channels,
                  kernel_size,
                  groups=channels,
                  dilation=dilation,
                  padding=padding,
              )
          )
          self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
          self.norms_1.append(LayerNorm(channels))
          self.norms_2.append(LayerNorm(channels))
  
imports: import torch
  from torch import nn
  from torch.nn import Conv1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  import TTS.vc.modules.freevc.commons
  from TTS.vc.modules.freevc.commons import get_padding, init_weights
label_desc: Use image node_id 1 to create a new DDSConv object from inherited base classes: nn with example: obj = DDSConv(channels, kernel_size, n_layers, p_dropout)