What is the difference between tf.gradients and tf.train.Optimizer.compute_gradient?

It seems that tf.gradients allows to compute also Jacobians, i.e. the partial derivatives of each entry of one tensor wrt. each entry of another tensor, while tf.train.Optimizer.compute_gradient only computes actual gradients, e.g. the partial derivatives of a scalar value wrt. each entry of a particular tensor or wrt. one particular scalar. Why is there a separate function if tf.gradients also implements that functionality?