These procedures let you repeat the values of a Tensor multiple times. This functionality exists both in numpy (`repeat`) and in Matlab (`repelem`).
A different name was chosen here to avoid confusion with nim's `repeat` function, which behaves differently (it repeats the whole input sequence, like numpy's `tile` or Matlab's `repmat` functions), and to make the name more self explanatory.

There are two versions of this procedure (with multiple overloads):

- One that repeats all values the same amount of times over a given axis.
- One that repeats each value a different amount of times, but returns a rank-1 tensor.

Note that the second one is implemented as 2 procs with different argument types (openArray[int] and Tensor[int]).

I measured the performance using the timeit library. The results show that the performance is comparable to numpy's `repeat` function. In particular, a small example which takes numpy's `repeat` ~2-3 usec per iteration, takes ~4 usec in --d:release mode, and ~1-2 usec in --d:danger mode.

This procedure lets you construct a new tensor by repeating the input tensor a number of times on one or more axes. This is similar to numpy's `tile` and Matlab's `repmat` functions.

I measured the performance using the `timeit` library. The results show that the performance is comparable to (but not as good as) numpy's `tile`. In particular, a small example which takes numpy's `tile` ~3-4 usec per iteration, takes ~8-9 usec in --d:release mode, and ~5-6 usec in --d:danger mode.

I believe that the performance could be improved further by preallocating the result Tensor before the tiling operation. The current implementation is not as efficient as it could be because it is based on calling `concat` multiple times, which requires at least as many tensor allocations (of increasing size).