Suppose you have your trained model m with a convolution layer m.conv, which contains a weight and a bias.
You want to perform some function on the weight and bias, and be able to backprop through it.
Consider the following snippet in the forward of your model:
class Model(nn.Module):
    # define init etc
    def forward(self, input):
        new_weight = self.net_weight(self.conv.weight, input)
        new_bias = self.net_bias(self.conv.bias, input)
        result = F.conv2d(input, self.conv.weight, self.conv.bias, stride)
        return result

Note that instead of using result = self.conv(input), I used the functional interface of it in its weight and bias.