Convolution over sequence about fold HOT 4 OPEN

That solution won't work for William, because he wants `n` to vary by input. However, the set of operations and type-shapes supported by loom (the dynamic graph implementation) is set at graph-construction time, so this won't work. To put it another way, you wouldn't have an `n` to pass in when building the fold blocks. However, there is a workaround. Let's say you wanted to do a 3x3 convolution on your n by d matrix, but this matrix is represented by a fold block that emits a sequence of d-vectors. You could take your sequence, and pass it to td.NGrams(3) in order to get all consecutive triples of rows. Then you stack the triples into 3xd matrices and run your convolution. I'm guessing it would look something like this: def _convolve_three(x, y, z): return *convolution_goes_here(*tf.stack([x, y, z], axis=2)) *sequence_block_goes_here* >> td.NGrams(3) >> td.Map(td.Function(_convolve_three)) Warning: this workaround will create extra copies of your data. This could be problematic if your implementation is memory bound.