Although deep learning frameworks assign default stride and padding values for pooling layers, both can be manually overridden to control the output dimensions. For instance, configuring a $3 imes 3$ max-pooling layer with a padding of $1$ and a stride of $2$ on a $4 imes 4$ input produces a $2 imes 2$ output. Padding adds extra border elements (typically zeros) to the input before pooling, allowing the pooling window to cover boundary regions. Setting the stride smaller than the window size introduces overlap between adjacent windows, whereas a larger stride increases the downsampling factor. These controls mirror the padding and stride mechanisms available in convolutional layers and are essential for achieving a desired output shape in network architectures.