def METHOD_NAME(self, set_to_none: bool = True):  # type: ignore[override]
    r"""Resets the gradients of all optimized :class:`torch.Tensor` s.
    Args:
        set_to_none (bool): instead of setting to zero, set the grads to None.
            This will in general have lower memory footprint, and can modestly improve performance.
            However, it changes certain behaviors. For example:
            1. When the user tries to access a gradient and perform manual ops on it,
            a None attribute or a Tensor full of 0s will behave differently.
            2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s
            are guaranteed to be None for params that did not receive a gradient.
            3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None
            (in one case it does the step with a gradient of 0 and in the other it skips
            the step altogether).
    """
    self._optim.METHOD_NAME(set_to_none)