Deep learning frameworks allow the use of strided convolutions to efficiently downsample input dimensions. By configuring the stride or strides parameter in a two-dimensional convolution layer, the cross-correlation window skips intermediate locations. For example, setting the strides to $2$ for both height and width will halve the input's spatial dimensions. Frameworks also support asymmetric strides by passing a tuple; for instance, specifying a stride of $(3, 4)$ will slide the convolution window down by $3$ rows and across by $4$ columns at each step.