Do not use deprecated variadic in jax dispatch of multiply

pytensor/scalar/basic.py

@@ -1898,7 +1898,7 @@ class Mul(ScalarOp):
     commutative = True
     associative = True
     nfunc_spec = ("multiply", 2, 1)
-    nfunc_variadic = "product"
+    nfunc_variadic = "prod"
 
     def impl(self, *inputs):
         return np.prod(inputs)

tests/link/jax/test_elemwise.py

@@ -13,7 +13,7 @@ from pytensor.tensor.math import all as at_all
 from pytensor.tensor.math import prod
 from pytensor.tensor.math import sum as at_sum
 from pytensor.tensor.special import SoftmaxGrad, log_softmax, softmax
-from pytensor.tensor.type import matrix, tensor, vector
+from pytensor.tensor.type import matrix, tensor, vector, vectors
 from tests.link.jax.test_basic import compare_jax_and_py
 from tests.tensor.test_elemwise import TestElemwise
 
@@ -129,3 +129,11 @@ def test_logsumexp_benchmark(size, axis, benchmark):
 
     exp_res = scipy.special.logsumexp(X_val, axis=axis, keepdims=True)
     np.testing.assert_array_almost_equal(res, exp_res)
+
+
+def test_multiple_input_multiply():
+    x, y, z = vectors("xyz")
+    out = at.mul(x, y, z)
+
+    fg = FunctionGraph(outputs=[out], clone=False)
+    compare_jax_and_py(fg, [[1.5], [2.5], [3.5]])