What is the advantage of using an InputLayer (or an Input) in a Keras model with Tensorflow tensors?

A Keras model can used as a Tensorflow function on a Tensor, through the functional API, as described here.

So we can do:

from keras.layers import InputLayer

a = tf.placeholder(dtype=tf.float32, shape=(None, 784))

model = Sequential()
model.add(InputLayer(input_tensor=a, input_shape=(None, 784)))
model.add(Dense(32, activation='relu'))
model.add(Dense(10, activation='softmax'))

output = model.output

Which is a tensor:

<tf.Tensor 'dense_24/Softmax:0' shape=(?, 10) dtype=float32>

But, this also works without any InputLayer:

a = tf.placeholder(dtype=tf.float32, shape=(None, 784))

model = Sequential()
model.add(Dense(32, activation='relu', input_shape=(784,)))
model.add(Dense(10, activation='softmax'))

output = model(a)

works, and output has the same shape as before:

<tf.Tensor 'sequential_9/dense_22/Softmax:0' shape=(?, 10) dtype=float32>

I assume the first form permits:

• to explicitely attach the inputs and outputs as attributes of the model (of the same names), so we can reuse them elsewhere. For example with other TF ops.
• to transform the tensors given as inputs into Keras inputs, with additional metadata (such as _keras_history as stated in the source code).

But this is not something we cannot do with the second form, so, is there a special usage of the InputLayer (and Input a fortiori) (except for multiple inputs)?
Moreover, the InputLayer is tricky because it's using input_shape differently from other keras layers: we specify the batch size (None here), which is not usually the case...