Deep learning frameworks allow us to programmatically apply padding to convolutional layers to manage feature map sizes. For example, applying a $3 imes 3$ kernel with $1$ pixel of padding on all sides to an $8 imes 8$ input will preserve the input's dimensionality, resulting in an $8 imes 8$ output. Similarly, if the kernel is non-square, such as $5 imes 3$, we can supply a tuple to set different padding values for the height and width (e.g., padding of $2$ for height and $1$ for width) to maintain the original spatial dimensions.