static adagrad(learningRate, initialAccumulatorValue = 0.1) {
        return new (0, _adagradOptimizer.AdagradOptimizer)(learningRate, initialAccumulatorValue);
    }
Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.
See
[http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](
http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
or
[http://ruder.io/optimizing-gradient-descent/index.html#adagrad](
http://ruder.io/optimizing-gradient-descent/index.html#adagrad)

@param learningRate The learning rate to use for the Adagrad gradient
descent algorithm.
@param initialAccumulatorValue Starting value for the accumulators, must be
positive.

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