def normalize(array, min_value=<NUM_LIT:0.>, max_value=<NUM_LIT:1.>):
arr_min = np.min(array)<EOL>arr_max = np.max(array)<EOL>normalized = (array - arr_min) / (arr_max - arr_min + K.epsilon())<EOL>return (max_value - min_value) * normalized + min_value<EOL>
Normalizes the numpy array to (min_value, max_value)

    Args:
        array: The numpy array
        min_value: The min value in normalized array (Default value = 0)
        max_value: The max value in normalized array (Default value = 1)

    Returns:
        The array normalized to range between (min_value, max_value)