static sgd(learningRate) {
        return new (0, _sgdOptimizer.SGDOptimizer)(learningRate);
    }
Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.

```js
// Fit a quadratic function by learning the coefficients a, b, c.
const xs = tf.tensor1d([0, 1, 2, 3]);
const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);

const a = tf.scalar(Math.random()).variable();
const b = tf.scalar(Math.random()).variable();
const c = tf.scalar(Math.random()).variable();

// y = a * x^2 + b * x + c.
const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
const loss = (pred, label) => pred.sub(label).square().mean();

const learningRate = 0.01;
const optimizer = tf.train.sgd(learningRate);

// Train the model.
for (let i = 0; i < 10; i++) {
  optimizer.minimize(() => loss(f(xs), ys));
}

// Make predictions.
console.log(
    `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);
const preds = f(xs).dataSync();
preds.forEach((pred, i) => {
  console.log(`x: ${i}, pred: ${pred}`);
});
```

@param learningRate The learning rate to use for the SGD algorithm.

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