def METHOD_NAME(self, closure=None):
    """Performs a single optimization step.
    Arguments:
        closure (callable, optional): A closure that reevaluates the model
            and returns the loss.
    """
    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 instead.')
            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)
                # Exponential moving average of squared gradient values
                state['exp_avg_sq'] = torch.zeros_like(p)
            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_(grad, alpha=1 - beta1)
            # v_t
            exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
            # 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()
            adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps'])
            if group['weight_decay'] != 0:
                adam_step.add_(p.data, alpha=group['weight_decay'])
            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_(adam_step, alpha=-step_size * trust_ratio)
    return loss