Using numba to randomly sample possible combinations of categories

I am trying to speed up a function that randomly samples a number of records with the possible combinations of a number of categories for a number of records and ensures they are unique (i.e. let's assume there's 3 records, any of them can be either 0 or 1 and I want 10 random samples of unique possible combinations of records).

If I did not use numba, I might would do something like this:

import numpy as np

def myfunc(categories, NumberOfRecords, maxsamples):
  return np.unique( np.random.choice(np.arange(categories), size=(maxsamples*10, NumberOfRecords), replace=True), axis=0 )[0:maxsamples]

Annoyingly, numba does not support axis in np.unique, so I can do something like this, but some of the records may turn out to be non-unique.

from numba import njit, int64
import numpy as np

@njit(int64[:,:](int64, int64, int64), cache=True)
def myfunc(categories, NumberOfRecords, maxsamples):
  return np.random.choice(np.arange(categories), size=(maxsamples, NumberOfRecords), replace=True) 

myfunc(categories=2, NumberOfRecords=3, maxsamples=10)

E.g. in one call (obviously there's some randomness here), I got the below (for which the indices 1 and 6, and 3 and 4, and 7 and 9 are identical rows):

array([[0, 1, 1],
       [1, 1, 0],
       [0, 1, 0],
       [1, 0, 1],
       [1, 0, 1],
       [1, 1, 1],
       [1, 1, 0],
       [1, 0, 0],
       [0, 0, 0],
       [1, 0, 0]])

My questions are:

1. Is this something where I would even expect a speed up from numba?
2. If so, how can I get a unique rows (this seems rather difficult with numba, but presumably there's a way)?
3. Perhaps there's a way to get at this more efficiently (perhaps without creating more random samples than I need in the end)?