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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 _tighten_triplet(self, max_iterations, later_iter, max_triplets, prolong):
 """ This method finds all the triplets that are eligible and adds them iteratively in the bunch of max_triplets Parameters max_iterations: integer Maximum number of times we tighten the relaxation later_iter: integer Number of maximum iterations that we want MPLP to run. This is lesser than the initial number of iterations. max_triplets: integer Maximum number of triplets that can be added atmost in one iteration. prolong: bool It sets the continuation of tightening after all the triplets are exhausted """

        # Find all the triplets that are possible in the present model
        triangles = self.find_triangles()
        # Evaluate scores for each of the triplets found above
        triplet_scores = self._get_triplet_scores(triangles)
        # Arrange the keys on the basis of increasing order of the values of the dict. triplet_scores
        sorted_scores = sorted(triplet_scores, key=triplet_scores.get)
        for niter in range(max_iterations):
            if self._is_converged(integrality_gap_threshold=self.integrality_gap_threshold):
                break
            # add triplets that are yet not added.
            add_triplets = []
            for triplet_number in (range(len(sorted_scores))):
                # At once, we can add atmost 5 triplets
                if triplet_number >= max_triplets:
                    break
                add_triplets.append(sorted_scores.pop())
            # Break from the tighten triplets loop if there are no triplets to add if the prolong is set to False
            if not add_triplets and prolong is False:
                    break
            # Update the eligible triplets to tighten the relaxation
            self._update_triangles(add_triplets)
            # Run MPLP for a maximum of later_iter times.
            self._run_mplp(later_iter)