How would you like to sum the conv layer outputs?
If you would like to sum over a specific dimension, you could simply use:
output = conv(input)
output = output.sum(dim=1)  # change the dim to your use case

On the other hand, if you would like to sum patches similar to a conv layer, you could define a convolution kernel with all ones, and apply it using the functional API:
output = torch.randn(1, 2, 8, 8) # comes from a preceding conv layer

sum_kernel = torch.ones(1, 2, 3, 3)
output_sum = F.conv2d(output, sum_kernel, stride=1, padding=1)