static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {
        return new (0, _rmspropOptimizer.RMSPropOptimizer)(learningRate, decay, momentum, epsilon, centered);
    }
Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient
descent. This implementation uses plain momentum and is not centered
version of RMSProp.

See
[http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](
http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)

@param learningRate The learning rate to use for the RMSProp gradient
descent algorithm.
@param decay The discounting factor for the history/coming gradient.
@param momentum The momentum to use for the RMSProp gradient descent
algorithm.
@param epsilon Small value to avoid zero denominator.
@param centered If true, gradients are normalized by the estimated
variance of the gradient.

@doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}