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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 optimize(self, start=None, n=2):
 """Run multidimensional scaling on this distance matrix. Args: start (`None` or `array-like`):
 Starting coordinates. If `start=None`, random starting coordinates are used. If `array-like` must have shape [`m` * `n`, ]. n (`int`):
 Number of dimensions to embed samples in. Examples: .. doctest:: (3, 2) <class 'pymds.mds.Projection'> Returns: :py:class:`pymds.Projection` """

        self.n = n

        if start is None:
            start = np.random.rand(self.m * self.n) * 10

        optim = minimize(
            fun=self._error_and_gradient,
            x0=start,
            jac=True,
            method='L-BFGS-B')

        index = self.index if hasattr(self, "index") else None

        return Projection.from_optimize_result(
            result=optim, n=self.n, m=self.m, index=index)