Repeat a 2D NumPy array stacking column wise into a larger 2D array, np.broadcast_to ? resize?

The question was answered by @QuangHoang in the comments. Using np.tile(array, (repeats, 1)) or slower: np.vstack([array]*repeats).

I have what seems to be a simple problem. I have a NumPy array I want to replicate column-wise, and keep it a 2D array, just repeated over and over again say 50x. I try with a np.repeat but the order isn't preserved - it just copies each row however many times I specify. broadcast_to I can't seem to get to work either. I'm thinking maybe I need to flatten the array with a .reshape(-1) then do something with it and reshape it back, but I can't seem to find the right commands. I know I can do it with .append and a for loop, but I'm looking for a vectorized solution. Here's an example with the initial array and then what it should look like afterwards (I'm only repeating it 3x due to space):

array = np.array([(1.00,    0.80,   0.95,   0.88,   0.97,   0.85),
(0.80,  1.00,   0.87,   0.97,   0.80,   0.92),
(0.95,  0.87,   1.00,   0.85,   0.92,   0.89),
(0.88,  0.97,   0.85,   1.00,   0.85,   0.95),
(0.97,  0.80,   0.92,   0.85,   1.00,   0.88),
(0.85,  0.92,   0.89,   0.95,   0.88,   1.00)])

Repeat 3x:

array([[1.  , 0.8 , 0.95, 0.88, 0.97, 0.85],
   [0.8 , 1.  , 0.87, 0.97, 0.8 , 0.92],
   [0.95, 0.87, 1.  , 0.85, 0.92, 0.89],
   [0.88, 0.97, 0.85, 1.  , 0.85, 0.95],
   [0.97, 0.8 , 0.92, 0.85, 1.  , 0.88],
   [0.85, 0.92, 0.89, 0.95, 0.88, 1.  ],
   [1.  , 0.8 , 0.95, 0.88, 0.97, 0.85],
   [0.8 , 1.  , 0.87, 0.97, 0.8 , 0.92],
   [0.95, 0.87, 1.  , 0.85, 0.92, 0.89],
   [0.88, 0.97, 0.85, 1.  , 0.85, 0.95],
   [0.97, 0.8 , 0.92, 0.85, 1.  , 0.88],
   [0.85, 0.92, 0.89, 0.95, 0.88, 1.  ],
   [1.  , 0.8 , 0.95, 0.88, 0.97, 0.85],
   [0.8 , 1.  , 0.87, 0.97, 0.8 , 0.92],
   [0.95, 0.87, 1.  , 0.85, 0.92, 0.89],
   [0.88, 0.97, 0.85, 1.  , 0.85, 0.95],
   [0.97, 0.8 , 0.92, 0.85, 1.  , 0.88],
   [0.85, 0.92, 0.89, 0.95, 0.88, 1.  ]])

Appreciate the help!