code: def forward(self, x):
      """Compute convolution module.
  
      Args:
          x (torch.Tensor): Input tensor (#batch, time, channels).
  
      Returns:
          torch.Tensor: Output tensor (#batch, time, channels).
  
      """
      # exchange the temporal dimension and the feature dimension
      x = x.transpose(1, 2)
  
      # GLU mechanism
      x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
      x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
  
      # 1D Depthwise Conv
      x = self.depthwise_conv(x)
      x = self.activation(self.norm(x))
  
      x = self.pointwise_conv2(x)
  
      return x.transpose(1, 2)
  
imports: from torch import nn
label_desc: Use image node_id 2 for calling the ConvolutionModule obj's underlying member method code with example usage: obj.forward(x) and returns: x