Performs a single optimization step.

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

def step(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('RMSprop does not support sparse gradients')
                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # ANOTHER LINE I'VE CHANGED
                    state['square_avg'] = torch.ones_like(p.data)

                    if group['momentum'] > 0:
                        state['momentum_buffer'] = torch.zeros_like(p.data)

                    if group['centered']:
                        state['grad_avg'] = torch.zeros_like(p.data)

                square_avg = state['square_avg']
                alpha = group['alpha']

                state['step'] += 1

                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

                square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)

                if group['centered']:
                    grad_avg = state['grad_avg']
                    grad_avg.mul_(alpha).add_(1 - alpha, grad)
                    # THIS LINE IS EVERYTHING THAT I CHANGED IN THIS OPTIMIZER
                    # avg = square_avg.addcmul(-1, grad_avg, grad_avg).sqrt().add_(group['eps'])
                    avg = square_avg.addcmul(-1, grad_avg, grad_avg).add(group['eps']).sqrt()
                else:
                    # THIS LINE IS EVERYTHING THAT I CHANGED IN THIS OPTIMIZER
                    # avg = square_avg.sqrt().add_(group['eps'])
                    avg = square_avg.add(group['eps']).sqrt()

                if group['momentum'] > 0:
                    buf = state['momentum_buffer']
                    buf.mul_(group['momentum']).addcdiv_(grad, avg)
                    p.data.add_(-group['lr'], buf)
                else:
                    p.data.addcdiv_(-group['lr'], grad, avg)

        return loss