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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 takeSample(self, withReplacement, num, seed=None):
 """ Return a fixed-size sampled subset of this RDD. .. note:: This method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory. 20 5 10 """

        numStDev = 10.0

        if num < 0:
            raise ValueError("Sample size cannot be negative.")
        elif num == 0:
            return []

        initialCount = self.count()
        if initialCount == 0:
            return []

        rand = random.Random(seed)

        if (not withReplacement) and num >= initialCount:
            # shuffle current RDD and return
            samples = self.collect()
            rand.shuffle(samples)
            return samples

        maxSampleSize = sys.maxsize - int(numStDev * sqrt(sys.maxsize))
        if num > maxSampleSize:
            raise ValueError(
                "Sample size cannot be greater than %d." % maxSampleSize)

        fraction = RDD._computeFractionForSampleSize(
            num, initialCount, withReplacement)
        samples = self.sample(withReplacement, fraction, seed).collect()

        # If the first sample didn't turn out large enough, keep trying to take samples;
        # this shouldn't happen often because we use a big multiplier for their initial size.
        # See: scala/spark/RDD.scala
        while len(samples) < num:
            # TODO: add log warning for when more than one iteration was run
            seed = rand.randint(0, sys.maxsize)
            samples = self.sample(withReplacement, fraction, seed).collect()

        rand.shuffle(samples)

        return samples[0:num]