You can initialize it with the same weights for each input channel. The vanilla weight updates will change the weights without the constraint to have the same values.
If you need to backpropagate, the better method would be to change the view of the input channels, so that the channels are stored in the batch dimension, and you only use a single kernel.
This should work:
x = Variable(torch.randn(batch_size, in_channels, 100))

# Gaussian kernel
kernel = Variable(torch.FloatTensor([[[0.06136, 0.24477, 0.38774, 0.24477, 0.06136]]]))
output = F.conv1d(x.view(-1, 1, 100), kernel).view(batch_size, in_channels, 96)