fix(numba): Correlty report the elemwise output type

pytensor/link/numba/dispatch/elemwise.py

@@ -604,12 +604,16 @@ def _vectorized(
             builder, sig.return_type, [out._getvalue() for out in outputs]
         )
 
-    ret_type = types.Tuple(
-        [
-            types.Array(numba.from_dtype(np.dtype(dtype)), ndim, "C")
-            for dtype in output_dtypes
-        ]
-    )
+    ret_types = [
+        types.Array(numba.from_dtype(np.dtype(dtype)), ndim, "C")
+        for dtype in output_dtypes
+    ]
+
+    for output_idx, input_idx in inplace_pattern:
+        ret_types[output_idx] = input_types[input_idx]
+
+    ret_type = types.Tuple(ret_types)
+
     if len(output_dtypes) == 1:
         ret_type = ret_type.types[0]
     sig = ret_type(*arg_types)

tests/link/numba/test_elemwise.py

@@ -605,3 +605,18 @@ def test_fused_elemwise_benchmark(benchmark):
     # JIT compile first
     func()
     benchmark(func)
+
+
+def test_elemwise_out_type():
+    # Create a graph with an elemwise
+    # Ravel failes if the elemwise output type is reported incorrectly
+    x = at.matrix()
+    y = (2 * x).ravel()
+
+    # Pass in the input as mutable, to trigger the inplace rewrites
+    func = pytensor.function([pytensor.In(x, mutable=True)], y, mode="NUMBA")
+
+    # Apply it to a numpy array that is neither C or F contigous
+    x_val = np.broadcast_to(np.zeros((3,)), (6, 3))
+
+    assert func(x_val).shape == (18,)