def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
        """Performs a single optimization step.

        Args:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        # Exclude scaling for params with 0 weight decay.
        for group in self.param_groups:
            weight_decay = group["weight_decay"]
            momentum = group["momentum"]
            dampening = group["dampening"]
            nesterov = group["nesterov"]

            for p in group["params"]:
                if p.grad is None:
                    continue

                d_p = p.grad
                p_norm = torch.norm(p.data)
                g_norm = torch.norm(p.grad.data)

                # Apply Lars scaling and weight decay.
                if weight_decay != 0:
                    if p_norm != 0 and g_norm != 0:
                        lars_lr = p_norm / (
                            g_norm + p_norm * weight_decay + group["eps"]
                        )
                        lars_lr *= group["trust_coefficient"]

                        d_p = d_p.add(p, alpha=weight_decay)
                        d_p *= lars_lr

                # Apply momentum.
                if momentum != 0:
                    param_state = self.state[p]
                    if "momentum_buffer" not in param_state:
                        buf = param_state["momentum_buffer"] = torch.clone(d_p).detach()
                    else:
                        buf = param_state["momentum_buffer"]
                        buf.mul_(momentum).add_(d_p, alpha=1 - dampening)

                    if nesterov:
                        d_p = d_p.add(buf, alpha=momentum)
                    else:
                        d_p = buf

                p.add_(d_p, alpha=-group["lr"])

        return loss
Performs a single optimization step.

        Args:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        
lightly/utils/lars.py