function inputLayer(args) {
	  return new InputLayer(args);
	}
An input layer is an entry point into a `tf.LayersModel`.

`InputLayer` is generated automatically for `tf.Sequential`` models by
specifying the `inputshape` or `batchInputShape` for the first layer.  It
should not be specified explicitly. However, it can be useful sometimes,
e.g., when constructing a sequential model from a subset of another
sequential model's layers. Like the code snippet below shows.

```js
// Define a model which simply adds two inputs.
const model1 = tf.sequential();
model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));
model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
model1.summary();
model1.predict(tf.zeros([1, 4])).print();

// Construct another model, reusing the second layer of `model1` while
// not using the first layer of `model1`. Note that you cannot add the second
// layer of `model` directly as the first layer of the new sequential model,
// because doing so will lead to an error related to the fact that the layer
// is not an input layer. Instead, you need to create an `inputLayer` and add
// it to the new sequential model before adding the reused layer.
const model2 = tf.sequential();
// Use an inputShape that matches the input shape of `model1`'s second
// layer.
model2.add(tf.layers.inputLayer({inputShape: [3]}));
model2.add(model1.layers[1]);
model2.summary();
model2.predict(tf.zeros([1, 3])).print();
```

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