def step(self, batch_size, ignore_stale_grad=False):
        """Makes one step of parameter update. Should be called after
        `autograd.backward()` and outside of `record()` scope.

        For normal parameter updates, `step()` should be used, which internally calls
        `allreduce_grads()` and then `update()`. However, if you need to get the reduced
        gradients to perform certain transformation, such as in gradient clipping, then
        you may want to manually call `allreduce_grads()` and `update()` separately.

        Parameters
        ----------
        batch_size : int
            Batch size of data processed. Gradient will be normalized by `1/batch_size`.
            Set this to 1 if you normalized loss manually with `loss = mean(loss)`.
        ignore_stale_grad : bool, optional, default=False
            If true, ignores Parameters with stale gradient (gradient that has not
            been updated by `backward` after last step) and skip update.
        """
        rescale_grad = self._scale / batch_size
        self._check_and_rescale_grad(rescale_grad)

        if not self._kv_initialized:
            self._init_kvstore()
        if self._params_to_init:
            self._init_params()

        self._allreduce_grads()
        self._update(ignore_stale_grad)