View source on GitHub 
Input()
is used to instantiate a Keras tensor.
tf.keras.Input(
shape=None,
batch_size=None,
name=None,
dtype=None,
sparse=None,
tensor=None,
ragged=None,
type_spec=None,
**kwargs
)
A Keras tensor is a symbolic tensorlike object, which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model.
For instance, if a
, b
and c
are Keras tensors,
it becomes possible to do:
model = Model(input=[a, b], output=c)
Args  

shape

A shape tuple (integers), not including the batch size.
For instance, shape=(32,) indicates that the expected input
will be batches of 32dimensional vectors. Elements of this tuple
can be None; 'None' elements represent dimensions where the shape is
not known.

batch_size

optional static batch size (integer). 
name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. 
dtype

The data type expected by the input, as a string
(float32 , float64 , int32 ...)

sparse

A boolean specifying whether the placeholder to be created is
sparse. Only one of 'ragged' and 'sparse' can be True. Note that,
if sparse is False, sparse tensors can still be passed into the
input  they will be densified with a default value of 0.

tensor

Optional existing tensor to wrap into the Input layer.
If set, the layer will use the tf.TypeSpec of this tensor rather
than creating a new placeholder tensor.

ragged

A boolean specifying whether the placeholder to be created is ragged. Only one of 'ragged' and 'sparse' can be True. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see this guide. 
type_spec

A tf.TypeSpec object to create the input placeholder from.
When provided, all other args except name must be None.

**kwargs

deprecated arguments support. Supports batch_shape and
batch_input_shape .

Returns  

A tensor .

Example:
# this is a logistic regression in Keras
x = Input(shape=(32,))
y = Dense(16, activation='softmax')(x)
model = Model(x, y)
Note that even if eager execution is enabled,
Input
produces a symbolic tensorlike object (i.e. a placeholder).
This symbolic tensorlike object can be used with lowerlevel
TensorFlow ops that take tensors as inputs, as such:
x = Input(shape=(32,))
y = tf.square(x) # This op will be treated like a layer
model = Model(x, y)
(This behavior does not work for higherorder TensorFlow APIs such as
control flow and being directly watched by a tf.GradientTape
).
However, the resulting model will not track any variables that were used as inputs to TensorFlow ops. All variable usages must happen within Keras layers to make sure they will be tracked by the model's weights.
The Keras Input can also create a placeholder from an arbitrary
tf.TypeSpec
, e.g:
x = Input(type_spec=tf.RaggedTensorSpec(shape=[None, None],
dtype=tf.float32, ragged_rank=1))
y = x.values
model = Model(x, y)
When passing an arbitrary tf.TypeSpec
, it must represent the signature of
an entire batch instead of just one example.