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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 reduce(self, op):
 """ Reduces average value over all workers. :param op: 'sum' or 'mean', reduction operator """

        if op not in ('sum', 'mean'):
            raise NotImplementedError

        distributed = (get_world_size() > 1)
        if distributed:
            # Backward/forward compatibility around
            # https://github.com/pytorch/pytorch/commit/540ef9b1fc5506369a48491af8a285a686689b36 and
            # https://github.com/pytorch/pytorch/commit/044d00516ccd6572c0d6ab6d54587155b02a3b86
            # To accomodate change in Pytorch's distributed API
            if hasattr(dist, "get_backend"):
                _backend = dist.get_backend()
                if hasattr(dist, "DistBackend"):
                    backend_enum_holder = dist.DistBackend
                else:
                    backend_enum_holder = dist.Backend
            else:
                _backend = dist._backend
                backend_enum_holder = dist.dist_backend

            cuda = _backend == backend_enum_holder.NCCL

            if cuda:
                avg = torch.cuda.FloatTensor([self.avg])
                _sum = torch.cuda.FloatTensor([self.sum])
            else:
                avg = torch.FloatTensor([self.avg])
                _sum = torch.FloatTensor([self.sum])

            _reduce_op = dist.reduce_op if hasattr(dist, "reduce_op") else dist.ReduceOp
            dist.all_reduce(avg, op=_reduce_op.SUM)
            dist.all_reduce(_sum, op=_reduce_op.SUM)
            self.avg = avg.item()
            self.sum = _sum.item()

            if op == 'mean':
                self.avg /= get_world_size()
                self.sum /= get_world_size()