Helper function to create a svrg optimizer. SVRG optimizer encapsulates two optimizers and
        will redirect update() to the correct optimizer based on the key.

        Parameters
        ----------
        kvstore : str or KVStore
            Default `'local'`.
        optimizer: str
            Name for SVRGOptimizer
        default_opt : str or Optimizer that was passed in.
        optimizer_params : dict
           optimizer params that was passed in.

def _create_optimizer(self, optimizer, default_opt, kvstore, optimizer_params):
        """Helper function to create a svrg optimizer. SVRG optimizer encapsulates two optimizers and
        will redirect update() to the correct optimizer based on the key.

        Parameters
        ----------
        kvstore : str or KVStore
            Default `'local'`.
        optimizer: str
            Name for SVRGOptimizer
        default_opt : str or Optimizer that was passed in.
        optimizer_params : dict
           optimizer params that was passed in.
        """

        # code partially copied from mxnet module.init_optimizer() to accomodate svrg_optimizer
        batch_size = self._exec_group.batch_size

        (kv_store, update_on_kvstore) = mx.model._create_kvstore(kvstore, self._ctx_len, self._arg_params)
        if kv_store and 'dist' in kv_store.type and '_sync' in kv_store.type:
            batch_size *= kv_store.num_workers
        rescale_grad = 1.0 / batch_size

        idx2name = {}
        if update_on_kvstore:
            idx2name.update(enumerate(self._exec_group.param_names))
        else:
            for k in range(self._ctx_len):
                idx2name.update({i * self._ctx_len + k: n
                                 for i, n in enumerate(self._exec_group.param_names)})

        # update idx2name to include new keys
        for key in self._param_dict[0].keys():
            max_key = max(list(idx2name.keys())) + 1
            idx2name[max_key] = key + "_full"

        optimizer_params = dict(optimizer_params)
        if 'rescale_grad' not in optimizer_params:
            optimizer_params['rescale_grad'] = rescale_grad
        optimizer_params["default_optimizer"] = default_opt
        optimizer_params["param_idx2name"] = idx2name
        optimizer = mx.optimizer.create(optimizer, **optimizer_params)

        return optimizer