def reset(self):
        """Resets the accumulated gradients on the current replica."""
        if not self._gradients:
            return
        self._accum_steps.assign(0)
        for gradient in self._gradients:
            if gradient is not None:
                gradient.assign(tf.zeros_like(gradient))
Resets the accumulated gradients on the current replica.
dpkd/transformers/src/transformers/optimization_tf.py