ones/zeroes_like numpy equivalents

[andrzejnovak]

Unlike this,

>>> import awkward1 as ak
>>> ar = ak.Array([[1, 2, 3], [], [4, 5]])
>>> ak.broadcast_arrays(ar, 1)
[<Array [[1, 2, 3], [], [4, 5]] type='3 * var * int64'>, <Array [[1, 1, 1], [], [1, 1]] type='3 * var * int64'>]
>>> ak.broadcast_arrays(ar, 1.0)
[<Array [[1, 2, 3], [], [4, 5]] type='3 * var * int64'>, <Array [[1, 1, 1], [], [1, 1]] type='3 * var * float64'>]

it should take its types from the data in ar. If ar is a record with x and y fields of different types, the 1's or 0's should have the same types as those fields. (I.e. it should be possible to have uint8 or whatever.)

Also, np.zeros_like for strings returns empty strings, and np.ones_like for strings returns strings with "1" in them.