def step(self, closure: Optional[Callable] = None):
        """Makes optimizer step.

        Args:
            closure (callable, optional): A closure that reevaluates
                the model and returns the loss.

        Returns:
            computed loss

        Raises:
            RuntimeError: Lamb does not support sparse gradients
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError(
                        "Lamb does not support sparse gradients, "
                        "consider SparseAdam instad."
                    )

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state["step"] = 0
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                beta1, beta2 = group["betas"]

                state["step"] += 1

                # Decay the first and second moment
                # running average coefficient
                # m_t
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                # v_t
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)

                # Paper v3 does not use debiasing.
                # bias_correction1 = 1 - beta1 ** state["step"]
                # bias_correction2 = 1 - beta2 ** state["step"]
                # Apply bias to lr to avoid broadcast.
                # * math.sqrt(bias_correction2) / bias_correction1
                step_size = group["lr"]

                weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)

                adam_step = exp_avg / exp_avg_sq.sqrt().add(group["eps"])
                if group["weight_decay"] != 0:
                    adam_step.add_(group["weight_decay"], p.data)

                adam_norm = adam_step.pow(2).sum().sqrt()
                if weight_norm == 0 or adam_norm == 0:
                    trust_ratio = 1
                else:
                    trust_ratio = weight_norm / adam_norm
                state["weight_norm"] = weight_norm
                state["adam_norm"] = adam_norm
                state["trust_ratio"] = trust_ratio
                if self.adam:
                    trust_ratio = 1

                p.data.add_(-step_size * trust_ratio, adam_step)

        return loss
Makes optimizer step.

        Args:
            closure (callable, optional): A closure that reevaluates
                the model and returns the loss.

        Returns:
            computed loss

        Raises:
            RuntimeError: Lamb does not support sparse gradients
        
catalyst/contrib/optimizers/lamb.py