def updates(self, params, grads, learning_rate, eps=1E-8):
        updates = []
        for n, (param, grad) in enumerate(zip(params, grads)):
            memory = self.memory_[n]
            m_t = memory + grad ** 2
            g_t = (eps + tensor.sqrt(m_t)) / grad
            p_t = param - learning_rate * g_t
            updates.append((memory, m_t))
            updates.append((param, p_t))
        return updates
dataset/ETHPy150Open kastnerkyle/SciPy2015/graphics_code/cvae/kdl_template.py/adagrad.updates/swapped operands of `/`