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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 kl(samples1, samples2, pdf1=False, pdf2=False, bins=30, hist_min=None, hist_max=None):
 """ Computes the Kullback-Leibler divergence for a single parameter from two distributions. Parameters samples1 : numpy.array Samples or probability density function (must also set `pdf1=True`). samples2 : numpy.array Samples or probability density function (must also set `pdf2=True`). pdf1 : bool Set to `True` if `samples1` is a probability density funtion already. pdf2 : bool Set to `True` if `samples2` is a probability density funtion already. bins : int Number of bins to use when calculating probability density function from a set of samples of the distribution. hist_min : numpy.float64 Minimum of the distributions' values to use. hist_max : numpy.float64 Maximum of the distributions' values to use. Returns ------- numpy.float64 The Kullback-Leibler divergence value. """

    hist_range = (hist_min, hist_max)
    if not pdf1:
        samples1, _ = numpy.histogram(samples1, bins=bins,
                                      range=hist_range, normed=True)
    if not pdf2:
        samples2, _ = numpy.histogram(samples2, bins=bins,
                                      range=hist_range, normed=True)
    return stats.entropy(samples1, qk=samples2)