In transposed convolution, strides determine the spatial arrangement of the intermediate results rather than the sliding movement of the kernel over the input. Specifically, a stride greater than $1$ increases the spacing between the broadcasted elements in the intermediate tensor, which expands both the height and width of the final output. For instance, increasing the stride from $1$ to $2$ with a $2 \times 2$ kernel places the generated values two pixels apart in the intermediate matrix. This expansion mechanism can be implemented by setting the stride parameter to stride=2 in PyTorch's nn.ConvTranspose2d or strides=2 in MXNet's nn.Conv2DTranspose.

# PyTorch
tconv = nn.ConvTranspose2d(1, 1, kernel_size=2, stride=2, bias=False)
tconv.weight.data = K
tconv(X)

# MXNet
tconv = nn.Conv2DTranspose(1, kernel_size=2, strides=2)
tconv.initialize(init.Constant(K))
tconv(X)