def METHOD_NAME(
    shape=None,
    batch_size=None,
    dtype=None,
    sparse=None,
    batch_shape=None,
    name=None,
    tensor=None,
):
    """Used to instantiate a Keras tensor.
    A Keras tensor is a symbolic tensor-like 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 (tuple of integers or `None` objects),
            not including the batch size.
            For instance, `shape=(32,)` indicates that the expected input
            will be batches of 32-dimensional vectors. Elements of this tuple
            can be `None`; `None` elements represent dimensions where the shape
            is not known and may vary (e.g. sequence length).
        batch_size: Optional static batch size (integer).
        dtype: The data type expected by the input, as a string
            (e.g. `"float32"`, `"int32"`...)
        sparse: A boolean specifying whether the expected input will be sparse
            tensors. 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. This feature is only supported with the TensorFlow
            backend. Defaults to `False`.
        name: 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.
        tensor: Optional existing tensor to wrap into the `Input` layer.
            If set, the layer will use this tensor rather
            than creating a new placeholder tensor.
    Returns:
      A Keras tensor.
    Example:
    ```python
    # This is a logistic regression in Keras
    x = Input(shape=(32,))
    y = Dense(16, activation='softmax')(x)
    model = Model(x, y)
    ```
    """
    layer = InputLayer(
        shape=shape,
        batch_size=batch_size,
        dtype=dtype,
        sparse=sparse,
        batch_shape=batch_shape,
        name=name,
        input_tensor=tensor,
    )
    return layer.output