def random_array(shape, mean=<NUM_LIT>, std=<NUM_LIT>):
x = np.random.random(shape)<EOL>x = (x - np.mean(x)) / (np.std(x) + K.epsilon())<EOL>x = (x * std) + mean<EOL>return x<EOL>
Creates a uniformly distributed random array with the given `mean` and `std`.

    Args:
        shape: The desired shape
        mean: The desired mean (Default value = 128)
        std: The desired std (Default value = 20)

    Returns: Random numpy array of given `shape` uniformly distributed with desired `mean` and `std`.