Fix the Numba implementation of NegBinomialRV

aesara/link/numba/dispatch/random.py

@@ -207,7 +207,6 @@ def {sized_fn_name}({random_fn_input_names}):
 @numba_funcify.register(aer.WaldRV)
 @numba_funcify.register(aer.LaplaceRV)
 @numba_funcify.register(aer.BinomialRV)
-@numba_funcify.register(aer.NegBinomialRV)
 @numba_funcify.register(aer.MultinomialRV)
 @numba_funcify.register(aer.RandIntRV)  # only the first two arguments are supported
 @numba_funcify.register(aer.ChoiceRV)  # the `p` argument is not supported
@@ -270,6 +269,14 @@ def {np_random_fn_name}({np_input_names}):
     return make_numba_random_fn(node, np_random_fn)
 
 
+@numba_funcify.register(aer.NegBinomialRV)
+def numba_funcify_NegBinomialRV(op, node, **kwargs):
+    if not isinstance(node.inputs[0], (RandomStateType, RandomStateSharedVariable)):
+        raise TypeError("Numba does not support NumPy `Generator`s")
+
+    return make_numba_random_fn(node, np.random.negative_binomial)
+
+
 @numba_funcify.register(aer.CauchyRV)
 def numba_funcify_CauchyRV(op, node, **kwargs):
     def body_fn(loc, scale):

aesara/tensor/random/basic.py

@@ -559,6 +559,7 @@ class NegBinomialRV(ScipyRandomVariable):
 
 
 nbinom = NegBinomialRV()
+negative_binomial = NegBinomialRV()
 
 
 class BetaBinomialRV(ScipyRandomVariable):
@@ -803,4 +804,5 @@ __all__ = [
     "triangular",
     "uniform",
     "standard_normal",
+    "negative_binomial",
 ]

tests/link/test_numba.py

@@ -2946,21 +2946,20 @@ def test_shared():
             ],
             at.as_tensor([3, 2]),
         ),
-        # pytest.param(
-        #     aer.negative_binomial,
-        #     [
-        #         set_test_value(
-        #             at.lvector(),
-        #             np.array([1, 2], dtype=np.int64),
-        #         ),
-        #         set_test_value(
-        #             at.dscalar(),
-        #             np.array(0.9, dtype=np.float64),
-        #         ),
-        #     ],
-        #     at.as_tensor([3, 2]),
-        #     marks=pytest.mark.xfail(reason="Not implemented"),
-        # ),
+        (
+            aer.negative_binomial,
+            [
+                set_test_value(
+                    at.lvector(),
+                    np.array([1, 2], dtype=np.int64),
+                ),
+                set_test_value(
+                    at.dscalar(),
+                    np.array(0.9, dtype=np.float64),
+                ),
+            ],
+            at.as_tensor([3, 2]),
+        ),
         (
             aer.normal,
             [