function simpleRNNCell(args) {
	  return new SimpleRNNCell(args);
	}
Cell class for `SimpleRNN`.

`SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its
`apply` method takes the input data of only a single time step and returns
the cell's output at the time step, while `SimpleRNN` takes the input data
over a number of time steps. For example:

```js
const cell = tf.layers.simpleRNNCell({units: 2});
const input = tf.input({shape: [10]});
const output = cell.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10]: This is the cell's output at a single time step. The 1st
// dimension is the unknown batch size.
```

Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The
most typical use of this workflow is to combine a number of cells into a
stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an
RNN. For example:

```js
const cells = [
  tf.layers.simpleRNNCell({units: 4}),
  tf.layers.simpleRNNCell({units: 8}),
];
const rnn = tf.layers.rnn({cell: cells, returnSequences: true});

// Create an input with 10 time steps and a length-20 vector at each step.
const input = tf.input({shape: [10, 20]});
const output = rnn.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
// same as the sequence length of `input`, due to `returnSequences`: `true`;
// 3rd dimension is the last `SimpleRNNCell`'s number of units.
```

To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the
`tf.layers.simpleRNN`.

@doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}