2034          ; mapped                 ; 2032 2032 2032 #1.1  TRIPLE PRIME
2035          ; valid                  ;      ; NV8    # 1.1  REVERSED PRIME
2036          ; mapped                 ; 2035 2035     # 1.1  REVERSED DOUBLE PRIME
2037          ; mapped                 ; 2035 2035 2035 #1.1  REVERSED TRIPLE PRIME
2038..203B    ; valid                  ;      ; NV8    # 1.1  CARET..REFERENCE MARK

which is matrix inversion * tf.round now uses banker's rounding (round to even)
semantics to match NumPy. * `tf.split` now takes arguments in a reversed order
and with different keywords. In particular, we now match NumPy order as
`tf.split(value, num_or_size_splits, axis)`. * `tf.sparse_split` now takes
arguments in reversed order and with different keywords. In particular we now
match NumPy order as `tf.sparse_split(sp_input, num_split, axis)`. NOTE: we have

slice `i`, with the first `seq_lengths[i]` slices along dimension
`seq_dim` reversed.

For example:

```
# Given this:
batch_dim = 0
seq_dim = 1
input.dims = (4, 8, ...)
seq_lengths = [7, 2, 3, 5]

# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]