a corresponding vector in ``x2``. The dot product is defined as:

.. math::
   \mathbf{a} \cdot \mathbf{b} = \sum_{i=0}^{n-1} \overline{a_i}b_i

over the dimension specified by ``axis`` and where :math:`\overline{a_i}`
denotes the complex conjugate if :math:`a_i` is complex and the identity
otherwise.

Parameters
----------
x1 : array_like
    First input array.
x2 : array_like
    Second input array.
axis : int, optional
    Axis over which to compute the dot product. Default: ``-1``.

Returns
-------
output : ndarray
    The vector dot product of the input.

See Also
--------
numpy.vecdot

Examples
--------
Get the projected size along a given normal for an array of vectors.

>>> v = np.array([[0., 5., 0.], [0., 0., 10.], [0., 6., 8.]])
>>> n = np.array([0., 0.6, 0.8])
>>> np.linalg.vecdot(v, n)
array([ 3.,  8., 10.])

rW