No, only one gradient step is taken. You can view this step as somewhere inbetween gradient descent and pure stochastic gradient descent: Stochastic gradient descent - Wikipedia. The reason the typical approach is to do a single step per batch rather than a single step per example is a balance of algorithmic (the ideal would be to take a single step per example) and computational efficiency (batching increases throughput on most hardware).