def standardize_Y(shape, Y):
	if not numpy_array(Y):
		Y = np.asarray(Y)
	if len(Y.shape) == 1:
		Y = Y.reshape(-1, 1)
	if len(Y.shape) == 2 and len(shape) == 2:
		if shape[-1] != Y.shape[-1]:
			return one_hot(Y, n=shape[-1])
		else:
			return Y
	else:
		return Y
dataset/ETHPy150Open IndicoDataSolutions/Foxhound/foxhound/preprocessing.py/standardize_Y/original