def METHOD_NAME(self):
    """Resets the accumulated gradients on the current replica."""
    if not self._gradients:
        return
    self._accum_steps.assign(0)
    for gradient in self._gradients:
        shape = (
            gradient.shape
            if gradient.shape.is_fully_defined()
            else tf.shape(gradient)
        )
        gradient.assign(tf.zeros(shape, dtype=gradient.dtype), read_value=False)