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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 mode(data):
 """Compute an intelligent value for the mode The most common value in experimental is not very useful if there are a lot of digits after the comma. This method approaches this issue by rounding to bin size that is determined by the Freedman–Diaconis rule. Parameters data: 1d ndarray The data for which the mode should be computed. Returns ------- mode: float The mode computed with the Freedman-Diaconis rule. """

    # size
    n = data.shape[0]
    # interquartile range
    iqr = np.percentile(data, 75)-np.percentile(data, 25)
    # Freedman–Diaconis
    bin_size = 2 * iqr / n**(1/3)

    if bin_size == 0:
        return np.nan

    # Add bin_size/2, because we want the center of the bin and
    # not the left corner of the bin.
    databin = np.round(data/bin_size)*bin_size + bin_size/2
    u, indices = np.unique(databin, return_inverse=True)
    mode = u[np.argmax(np.bincount(indices))]

    return mode