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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def empirical_confidence_interval(sample, interval=0.95):
 """ Compute specified symmetric confidence interval for empirical sample. Parameters sample : numpy.array The empirical samples. interval : float, optional, default=0.95 Size of desired symmetric confidence interval (0 < interval < 1) e.g. 0.68 for 68% confidence interval, 0.95 for 95% confidence interval Returns ------- low : float The lower bound of the symmetric confidence interval. high : float The upper bound of the symmetric confidence interval. Examples -------- """

    # Sort sample in increasing order.
    sample = np.sort(sample)

    # Determine sample size.
    N = len(sample)

    # Compute low and high indices.
    low_index = int(np.round((N-1) * (0.5 - interval/2))) + 1
    high_index = int(np.round((N-1) * (0.5 + interval/2))) + 1

    # Compute low and high.
    low = sample[low_index]
    high = sample[high_index]

    return [low, high]