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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_batch(self, batchsize=10, returns='best', paralell=True):
 """ Run multiple optimizations using different starting coordinates. Args: batchsize (`int`):
 Number of optimizations to run. returns (`str`):
 If ``'all'``, return results of all optimizations, ordered by stress, ascending. If ``'best'`` return the projection with the lowest stress. parallel (`bool`):
 If ``True``, run optimizations in parallel. Examples: .. doctest:: 3 <class 'pymds.mds.Projection'> Returns: `list` or :py:class:`pymds.Projection`: `list`: Length batchsize, containing instances of :py:class:`pymds.Projection`. Sorted by stress, ascending. or :py:class:`pymds.Projection`: Projection with the lowest stress. """

        if returns not in ('best', 'all'):
            raise ValueError('returns must be either "best" or "all"')

        starts = [np.random.rand(self.m * 2) * 10 for i in range(batchsize)]

        if paralell:
            with Pool() as p:
                results = p.map(self.optimize, starts)
        else:
            results = map(self.optimize, starts)

        results = sorted(results, key=lambda x: x.stress)

        return results if returns == 'all' else results[0]