function gru(args) {
	  return new GRU(args);
	}
Gated Recurrent Unit - Cho et al. 2014.

This is an `RNN` layer consisting of one `GRUCell`. However, unlike
the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates
on a sequence of inputs. The shape of the input (not including the first,
batch dimension) needs to be at least 2-D, with the first dimension being
time steps. For example:

```js
const rnn = tf.layers.gru({units: 8, returnSequences: true});

// Create an input with 10 time steps.
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 `GRUCell`'s number of units.

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