Reorder algebra fallbacks

src/probnum/_function/_algebra_fallbacks.py

@@ -11,62 +11,6 @@
 from ._function import Function
 
 
-class ScaledFunction(Function):
-    r"""Function multiplied pointwise with a scalar.
-
-    Given a function :math:`f \colon \mathbb{R}^n \to \mathbb{R}^m` and a scalar
-    :math:`\alpha \in \mathbb{R}`, this defines a new function
-
-    .. math::
-        \alpha f \colon \mathbb{R}^n \to \mathbb{R}^m,
-        x \masto (\alpha f)(x) = \alpha f(x).
-
-    Parameters
-    ----------
-    function
-        The function :math:`f`.
-    scalar
-        The scalar :math:`\alpha`.
-    """
-
-    def __init__(self, function: Function, scalar: ScalarLike):
-        if not isinstance(function, Function):
-            raise TypeError(
-                "The function to be scaled must be an object of type `Function`"
-            )
-
-        self._function = function
-        self._scalar = utils.as_numpy_scalar(scalar)
-
-        super().__init__(
-            input_shape=self._function.input_shape,
-            output_shape=self._function.output_shape,
-        )
-
-    @property
-    def function(self) -> Function:
-        r"""The function :math:`f`."""
-        return self._function
-
-    @property
-    def scalar(self) -> ScalarType:
-        r"""The scalar :math:`\alpha`."""
-        return self._scalar
-
-    def _evaluate(self, x: np.ndarray) -> np.ndarray:
-        return self._scalar * self._function(x)
-
-    @functools.singledispatchmethod
-    def __rmul__(self, other):
-        if np.ndim(other) == 0:
-            return ScaledFunction(
-                function=self._function,
-                scalar=np.asarray(other) * self._scalar,
-            )
-
-        return super().__rmul__(other)
-
-
 class SumFunction(Function):
     r"""Pointwise sum of :class:`Function`s.
 
@@ -127,3 +71,59 @@ def __add__(self, other):
     @functools.singledispatchmethod
     def __sub__(self, other):
         return super().__sub__(other)
+
+
+class ScaledFunction(Function):
+    r"""Function multiplied pointwise with a scalar.
+
+    Given a function :math:`f \colon \mathbb{R}^n \to \mathbb{R}^m` and a scalar
+    :math:`\alpha \in \mathbb{R}`, this defines a new function
+
+    .. math::
+        \alpha f \colon \mathbb{R}^n \to \mathbb{R}^m,
+        x \masto (\alpha f)(x) = \alpha f(x).
+
+    Parameters
+    ----------
+    function
+        The function :math:`f`.
+    scalar
+        The scalar :math:`\alpha`.
+    """
+
+    def __init__(self, function: Function, scalar: ScalarLike):
+        if not isinstance(function, Function):
+            raise TypeError(
+                "The function to be scaled must be an object of type `Function`"
+            )
+
+        self._function = function
+        self._scalar = utils.as_numpy_scalar(scalar)
+
+        super().__init__(
+            input_shape=self._function.input_shape,
+            output_shape=self._function.output_shape,
+        )
+
+    @property
+    def function(self) -> Function:
+        r"""The function :math:`f`."""
+        return self._function
+
+    @property
+    def scalar(self) -> ScalarType:
+        r"""The scalar :math:`\alpha`."""
+        return self._scalar
+
+    def _evaluate(self, x: np.ndarray) -> np.ndarray:
+        return self._scalar * self._function(x)
+
+    @functools.singledispatchmethod
+    def __rmul__(self, other):
+        if np.ndim(other) == 0:
+            return ScaledFunction(
+                function=self._function,
+                scalar=np.asarray(other) * self._scalar,
+            )
+
+        return super().__rmul__(other)