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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 compute_mu_star_confidence(ee, num_trajectories, num_resamples,
                               conf_level):
    '''
    Uses bootstrapping where the elementary effects are resampled with
    replacement to produce a histogram of resampled mu_star metrics.
    This resample is used to produce a confidence interval.
    '''
    ee_resampled = np.zeros([num_trajectories])
    mu_star_resampled = np.zeros([num_resamples])

    if not 0 < conf_level < 1:
        raise ValueError("Confidence level must be between 0-1.")

    resample_index = np.random.randint(
        len(ee), size=(num_resamples, num_trajectories))
    ee_resampled = ee[resample_index]
    # Compute average of the absolute values over each of the resamples
    mu_star_resampled = np.average(np.abs(ee_resampled), axis=1)

    return norm.ppf(0.5 + conf_level / 2) * mu_star_resampled.std(ddof=1)