You can:

Create a convolutional kernel
conv = nn.Conv2(in, out, ksize, ...)

Copy your kernel weights to it
conv.weights.copy_(myweights)


Just be aware that you will need to use torch.no_grad if you dont want that convoluton’s weights to be updated while training.