class_docstr: Time depth separable convolution as in https://arxiv.org/pdf/1904.02619.pdf
  It shows competative results with less computation and memory footprint.
code: def forward(self, x):
      x_res = x
      x = self.time_conv(x)
      x = self.norm1(x)
      x = nn.functional.glu(x, dim=1)
      x = self.depth_conv(x)
      x = self.norm2(x)
      x = x * torch.sigmoid(x)
      x = self.time_conv2(x)
      x = self.norm3(x)
      x = x_res + x
      return x
  
file_path: TTS/TTS/tts/layers/generic/time_depth_sep_conv.py
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the TimeDepthSeparableConv obj's underlying member method code with example usage: obj.forward(x) and returns: x