A pooling layer is configured entirely through hyperparameters rather than learned parameters. The key hyperparameters are the pooling window size (which can be specified as a single integer for a square window or as a tuple for a rectangular window with distinct height and width), the stride (the number of elements the window moves per step), the type of aggregation (max or average), and the amount of padding applied to the input borders. Because pooling computes a fixed, deterministic function—either the maximum or the average—there are no weight parameters to learn, and therefore no parameter initialization is needed. A widely adopted default configuration uses a $2 imes 2$ pooling window, which quarters the spatial resolution of the output (halving both height and width), providing an effective balance between downsampling and information retention.