def update(self, new_scale=None):
        """
        Updates the scale factor.
        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
        the scale is multiplied by ``growth_factor`` to increase it.
        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
        used directly, it's used to fill GradScaler's internal scale tensor. So if
        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
        affect the scale GradScaler uses internally.)
        Args:
            new_scale (float or :class:`torch.FloatTensor`, optional, default=None):  New scale factor.
        .. warning::
            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
            been invoked for all optimizers used this iteration.
        """
        if not self._enabled:
            return

        _scale, _growth_tracker = self._check_scale_growth_tracker("update")

        if new_scale is not None:
            # Accept a new user-defined scale.
            if isinstance(new_scale, float):
                self._scale.fill_(new_scale)  # type: ignore[union-attr]
            else:
                reason = "new_scale should be a float or a 1-element torch.FloatTensor with requires_grad=False."
                assert isinstance(new_scale, torch.FloatTensor), reason  # type: ignore[attr-defined]
                assert new_scale.numel() == 1, reason
                assert new_scale.requires_grad is False, reason
                self._scale.copy_(new_scale)  # type: ignore[union-attr]
        else:
            # Consume shared inf/nan data collected from optimizers to update the scale.
            # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
            found_infs = [
                found_inf.to(device=_scale.device, non_blocking=True)
                for state in self._per_optimizer_states.values()
                for found_inf in state["found_inf_per_device"].values()
            ]

            assert len(found_infs) > 0, "No inf checks were recorded prior to update."

            found_inf_combined = found_infs[0]
            if len(found_infs) > 1:
                for i in range(1, len(found_infs)):
                    found_inf_combined += found_infs[i]

            core._amp_update_scale_(
                _scale,
                _growth_tracker,
                found_inf_combined,
                self._growth_factor,
                self._backoff_factor,
                self._growth_interval,
            )

        # To prepare for next iteration, clear the data collected from optimizers this iteration.
        self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)

        Updates the scale factor.
        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
        the scale is multiplied by ``growth_factor`` to increase it.
        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
        used directly, it's used to fill GradScaler's internal scale tensor. So if
        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
        affect the scale GradScaler uses internally.)
        Args:
            new_scale (float or :class:`torch.FloatTensor`, optional, default=None):  New scale factor.
        .. warning::
            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
            been invoked for all optimizers used this iteration.
        
intel_extension_for_pytorch/cpu/autocast/_grad_scaler.py