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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 inverseHistogram(hist, bin_range):
 """sample data from given histogram and min, max values within range Returns: np.array: data that would create the same histogram as given """

    data = hist.astype(float) / np.min(hist[np.nonzero(hist)])
    new_data = np.empty(shape=np.sum(data, dtype=int))
    i = 0
    xvals = np.linspace(bin_range[0], bin_range[1], len(data))
    for d, x in zip(data, xvals):
        new_data[i:i + d] = x
        i += int(d)
    return new_data