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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 set_perplexities(self, new_perplexities):
 """Change the perplexities of the affinity matrix. Note that we only allow lowering the perplexities or restoring them to their original maximum value. This restriction exists because setting a higher perplexity value requires recomputing all the nearest neighbors, which can take a long time. To avoid potential confusion as to why execution time is slow, this is not allowed. If you would like to increase the perplexity above the initial value, simply create a new instance. Parameters new_perplexities: List[float] The new list of perplexities. """

        if np.array_equal(self.perplexities, new_perplexities):
            return

        new_perplexities = self.check_perplexities(new_perplexities)
        max_perplexity = np.max(new_perplexities)
        k_neighbors = min(self.n_samples - 1, int(3 * max_perplexity))

        if k_neighbors > self.__neighbors.shape[1]:
            raise RuntimeError(
                "The largest perplexity `%.2f` is larger than the initial one "
                "used. This would need to recompute the nearest neighbors, "
                "which is not efficient. Please create a new `%s` instance "
                "with the increased perplexity."
                % (max_perplexity, self.__class__.__name__)
            )

        self.perplexities = new_perplexities
        self.P = self._calculate_P(
            self.__neighbors[:, :k_neighbors],
            self.__distances[:, :k_neighbors],
            self.perplexities,
            symmetrize=True,
            n_jobs=self.n_jobs,
        )