Refactor Normal._cdf to use backend.Dispatcher

src/probnum/backend/_dispatcher.py

@@ -47,6 +47,10 @@ def torch(self, impl: Callable) -> Callable:
         return impl
 
     def __call__(self, *args, **kwargs):
+        if BACKEND not in self._impl:
+            raise NotImplementedError(
+                f"This function is not implemented for the backend `{BACKEND.name}`"
+            )
         return self._impl[BACKEND](*args, **kwargs)
 
     def __get__(self, obj, objtype=None):

src/probnum/randvars/_normal.py

@@ -501,10 +501,10 @@ def _logpdf(self, x: ArrayType) -> ArrayType:
 
         return res
 
-    def _cdf(self, x: ArrayType) -> ArrayType:
-        if backend.BACKEND is not backend.Backend.NUMPY:
-            raise NotImplementedError()
+    _cdf = backend.Dispatcher()
 
+    @_cdf.numpy
+    def _cdf_numpy(self, x: ArrayType) -> ArrayType:
         import scipy.stats  # pylint: disable=import-outside-toplevel
 
         return scipy.stats.multivariate_normal.cdf(
@@ -513,10 +513,10 @@ def _cdf(self, x: ArrayType) -> ArrayType:
             cov=self.dense_cov,
         )
 
-    def _logcdf(self, x: ArrayType) -> ArrayType:
-        if backend.BACKEND is not backend.Backend.NUMPY:
-            raise NotImplementedError()
+    _logcdf = backend.Dispatcher()
 
+    @_logcdf.numpy
+    def _logcdf_numpy(self, x: ArrayType) -> ArrayType:
         import scipy.stats  # pylint: disable=import-outside-toplevel
 
         return scipy.stats.multivariate_normal.logcdf(

src/probnum/randvars/_random_variable.py

@@ -162,7 +162,7 @@ def ndim(self) -> int:
     def size(self) -> int:
         """Size of realizations of the random variable, defined as the product over all
         components of :attr:`shape`."""
-        return functools.reduce(operator.mul, self.__shape, initial=1)
+        return functools.reduce(operator.mul, self.__shape, 1)
 
     @property
     def dtype(self) -> backend.dtype: