function sequential(config) {
	  return new Sequential(config);
	}
Creates a `tf.Sequential` model.  A sequential model is any model where the
outputs of one layer are the inputs to the next layer, i.e. the model
topology is a simple 'stack' of layers, with no branching or skipping.

This means that the first layer passed to a `tf.Sequential` model should have
a defined input shape. What that means is that it should have received an
`inputShape` or `batchInputShape` argument, or for some type of layers
(recurrent, Dense...) an `inputDim` argument.

The key difference between `tf.model` and `tf.sequential` is that
`tf.sequential` is less generic, supporting only a linear stack of layers.
`tf.model` is more generic and supports an arbitrary graph (without
cycles) of layers.

Examples:

```js
const model = tf.sequential();

// First layer must have an input shape defined.
model.add(tf.layers.dense({units: 32, inputShape: [50]}));
// Afterwards, TF.js does automatic shape inference.
model.add(tf.layers.dense({units: 4}));

// Inspect the inferred shape of the model's output, which equals
// `[null, 4]`. The 1st dimension is the undetermined batch dimension; the
// 2nd is the output size of the model's last layer.
console.log(JSON.stringify(model.outputs[0].shape));
```

It is also possible to specify a batch size (with potentially undetermined
batch dimension, denoted by "null") for the first layer using the
`batchInputShape` key. The following example is equivalent to the above:

```js
const model = tf.sequential();

// First layer must have a defined input shape
model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));
// Afterwards, TF.js does automatic shape inference.
model.add(tf.layers.dense({units: 4}));

// Inspect the inferred shape of the model's output.
console.log(JSON.stringify(model.outputs[0].shape));
```

You can also use an `Array` of already-constructed `Layer`s to create
a `tf.Sequential` model:

```js
const model = tf.sequential({
  layers: [tf.layers.dense({units: 32, inputShape: [50]}),
           tf.layers.dense({units: 4})]
});
console.log(JSON.stringify(model.outputs[0].shape));
```

@doc {heading: 'Models', subheading: 'Creation'}