How to change p-value of Numpy.random.choice based on position in array?

I have a 3D array of size NxNxN. I would like to fill this array with random booleans, which I can do with:

a = np.random.choice([False,True],size=(N,N,N))

However, I would like the likelihood (or p-value) of choosing either True or False to be based on the element's position in the array. I thought maybe I could do this with the p-value parameter, but that only then works for selecting how often True/False is chosen for the entire array.

Is there any way to set specific p-values for the entire (N,N,N) array? I guess that would amount to an (N,N,N,2) array then, with the extra 2 being for the p-value for False and p-value for True (though p_True = 1 - p_False). I feel like there's a simpler way to do this that I'm not thinking of.

Edit: So say I want to create a simple array, a, of shape (1,2) (just two elements, but multidimensional on purpose). I want to fill these two elements with True/False. I have another array filled with the likelihood or p-value with which I want those elements to be False, say p_False, where p_False.shape = (1,2). Let's say I want the first element to have a 25% chance of being False, but the second element to have a 50% chance of being false, so then p_False = np.array([0.25,0.5]).

I tried something along the lines of:

a = np.random.choice([[False,True],[False,True]],p=[[.25,.75],[.5,.5]])

but I got a ValueError: a must be 1-dimensional.