Trust input in test_math_scipy benchmark tests

tests/tensor/test_math_scipy.py

@@ -431,11 +431,13 @@ def test_gammaincc_ddk_performance(benchmark):
     x = vector("x")
 
     out = gammaincc(k, x)
-    grad_fn = function([k, x], grad(out.sum(), wrt=[k]), mode="FAST_RUN")
+    grad_fn = function(
+        [k, x], grad(out.sum(), wrt=[k]), mode="FAST_RUN", trust_input=True
+    )
     vals = [
         # Values that hit the second branch of the gradient
-        np.full((1000,), 3.2),
-        np.full((1000,), 0.01),
+        np.full((1000,), 3.2, dtype=k.dtype),
+        np.full((1000,), 0.01, dtype=x.dtype),
     ]
 
     verify_grad(gammaincc, vals, rng=rng)
@@ -1127,9 +1129,13 @@ class TestHyp2F1Grad:
         a1, a2, b1, z = pt.scalars("a1", "a2", "b1", "z")
         hyp2f1_out = pt.hyp2f1(a1, a2, b1, z)
         hyp2f1_grad = pt.grad(hyp2f1_out, wrt=a1 if wrt == "a" else [a1, a2, b1, z])
-        f_grad = function([a1, a2, b1, z], hyp2f1_grad)
+        f_grad = function([a1, a2, b1, z], hyp2f1_grad, trust_input=True)
 
         (test_a1, test_a2, test_b1, test_z, *expected_dds) = case
+        test_a1 = np.array(test_a1, dtype=a1.dtype)
+        test_a2 = np.array(test_a2, dtype=a2.dtype)
+        test_b1 = np.array(test_b1, dtype=b1.dtype)
+        test_z = np.array(test_z, dtype=z.dtype)
 
         result = benchmark(f_grad, test_a1, test_a2, test_b1, test_z)