Write a function called repeatOnes that takes in an input_array (n,2) numpy array and returns a (m,2) numpy array where any row with a 1 in the second row is repeated. Specifically, the function call must look like this:

expandedData=repeatOnes(input_array)

If input_array=np.array([[5,0],[3,0],[8,1],[10,0],[4,0],[2,1]]) the function will return

np.array([[5,0],[3,0],[8,1],[8,1],[10,0],[4,0],[2,1],[2,1]]) into expandedData.

This function could be used for up-sampling data if class 1 occurred half as often as class 0.