Yes, sure,
First, the tensor a your provided has size [1, 4, 6] so unsqueeze(0) will add a dimension to tensor so we have now [1, 1, 4, 6].
.unfold(dim, size, stride) will extract patches regarding the sizes. So first unfold will convert a to a tensor with size [1, 1, 2, 6, 2] and it means our unfold function extracted two 6x2 patches regarding the dimension with value 4. Then we just discard first redundant dimension created by unfold using [0]. And finally, second unfold will extract 2x2 patches regarding the dimension with value 6. So for sure it will create 3 patches out of a 6x2 tensor (3x2x2=6x2).
Now we have something like [1, 2, 3, 2, 2] and .contiguous().view(1, 6, 2, 2) will reshape our tensor to the desired one.