Consider a version of the perceptron learning algorithm in which the learning rate can vary with each weight change step t (e.g., it might be different at different times of the day or it may be a function of the weather, or the learners mood, etc.). Prove the perceptron convergence theorem or provide a counter-example to show that the convergence theorem does not hold in the following scenario : where A and B are fixed lower and upper bounds respectively.