Eliminate equivalent solutions / convert solutions to canonical form

[g-dspencer]

The proposal is to have the solver recognize that, say these two
calls are equivalent, so once you've tried the first you don't need to
try the second:

 tf.multiply(tf.constant(2), tf.range(5)
 tf.multiply(tf.constant(5), tf.range(2)

i.e., multiply(a,b) == multiply(b,a) and ditto for add.
Maybe a and be have to be of the same dtype however.

This is the problem I gave it (and of note is that adding constant 2 is necessary):

inputs = {
    'data': [[0, 1, 1, 0, 0],
             [0, 0, 0, 0, 0],
             [1, 1, 1, 1, 1]]
}

output = [[0, 3, 5, 6, 8],
          [0, 2, 4, 6, 8],
          [1, 3, 5, 7, 9]]

constants = [2]

And these are the solutions - of course only the first is interesting since tf-coder
is just reordering args.

Found solution: tf.add(data, tf.multiply(tf.constant(2), tf.range(5)))
Found solution: tf.add(data, tf.multiply(tf.range(5), tf.constant(2)))
Found solution: tf.add(tf.multiply(tf.constant(2), tf.range(5)), data)
Found solution: tf.add(tf.multiply(tf.range(5), tf.constant(2)), data)