Loss calculation within batch iteration

You can see there are two different loss calculation methods in following code snippet. The first approach is quite common and easy to understand, while the second one is a bit obscure to me. Can someone shed any lights on how to interpret this? Much appreciated.

for batch_index, batch_dict in enumerate(batch_generator):
    y_pred = self.model(batch_dict['train'])
    loss = self.loss_func(y_pred, target)
    ## Approach One:
    running_loss += loss.item()  
    ## Approach Two:
    running_loss += (loss.item() - running_loss) / (batch_index + 1)

This problem has been resolved.

I derived a bit and figured those two loss calculation approaches are essentially the same.

can you explain how they are the same?