The intuitive motivation for the tensor product relies on the concept of tensors more generally. In particular, a tensor is an object that can be considered a special type of multilinear map, which takes in a certain number of vectors (its "order") and outputs a scalar. Such objects are useful in a number of areas of application, such as Riemannian geometry, famous for its use in Albert Einstein's general theory of relativity in modern physics, where the metric tensor is a fundamental concept. In particular, the metric tensor takes in two vectors, conceived of roughly as small arrows emanating from a specific point within a curved space, or manifold, and returns a "local" dot product of them relative to that particular point—an operation that encodes some information about the vectors' lengths as well as the angle between them. As the dot product is a scalar, the metric tensor is thus seen to deserve its name. There is one metric tensor at each point of the manifold, and variation in the metric tensor thus encodes how distance and angle concepts, and so the laws of analytic geometry, vary throughout the manifold.