def METHOD_NAME() -> "StateMP":
    r"""Quantum state in the computational basis.
    This function accepts no observables and instead instructs the QNode to return its state. A
    ``wires`` argument should *not* be provided since ``state()`` always returns a pure state
    describing all wires in the device.
    Note that the output shape of this measurement process depends on the
    number of wires defined for the device.
    Returns:
        StateMP: Measurement process instance
    **Example:**
    .. code-block:: python3
        dev = qml.device("default.qubit", wires=2)
        @qml.qnode(dev)
        def circuit():
            qml.Hadamard(wires=1)
            return qml.state()
    Executing this QNode:
    >>> circuit()
    array([0.70710678+0.j, 0.70710678+0.j, 0.        +0.j, 0.        +0.j])
    The returned array is in lexicographic order. Hence, we have a :math:`1/\sqrt{2}` amplitude
    in both :math:`|00\rangle` and :math:`|01\rangle`.
    .. note::
        Differentiating :func:`~pennylane.state` is currently only supported when using the
        classical backpropagation differentiation method (``diff_method="backprop"``) with a
        compatible device.
    .. details::
        :title: Usage Details
        A QNode with the ``qml.state`` output can be used in a cost function which
        is then differentiated:
        >>> dev = qml.device('default.qubit', wires=2)
        >>> qml.qnode(dev, diff_method="backprop")
        ... def test(x):
        ...     qml.RY(x, wires=[0])
        ...     return qml.state()
        >>> def cost(x):
        ...     return np.abs(test(x)[0])
        >>> cost(x)
        tensor(0.98877108, requires_grad=True)
        >>> qml.grad(cost)(x)
        -0.07471906623679961
    """
    return StateMP()