Yes, x is the input activation.
Pooling layers do use a kernel (which doesn’t contain trainable weights) and you can imagine it as a “window” or “patch” which is selecting the input locations for the pooling operation. E.g. a kernel size of 2 would select a 2x2 window (= 4 pixels) and in case of nn.MaxPool2d it would then return the max. value of these 4 inputs in the output activation.