Use static shape info in numba DimShuffle

pytensor/link/numba/dispatch/elemwise.py

@@ -531,7 +531,7 @@ def numba_funcify_CAReduce(op, node, **kwargs):
 
 
 @numba_funcify.register(DimShuffle)
-def numba_funcify_DimShuffle(op, **kwargs):
+def numba_funcify_DimShuffle(op, node, **kwargs):
     shuffle = tuple(op.shuffle)
     transposition = tuple(op.transposition)
     augment = tuple(op.augment)
@@ -560,16 +560,26 @@ def numba_funcify_DimShuffle(op, **kwargs):
     # To avoid this compile-time error, we omit the expression altogether.
     if len(shuffle) > 0:
 
-        @numba_basic.numba_njit
-        def find_shape(array_shape):
-            shape = shape_template
-            j = 0
-            for i in range(ndim_new_shape):
-                if i not in augment:
-                    length = array_shape[j]
-                    shape = numba_basic.tuple_setitem(shape, i, length)
-                    j = j + 1
-            return shape
+        # Use the statically known shape if available
+        if all(length is not None for length in node.outputs[0].type.shape):
+            shape = node.outputs[0].type.shape
+
+            @numba_basic.numba_njit
+            def find_shape(array_shape):
+                return shape
+
+        else:
+
+            @numba_basic.numba_njit
+            def find_shape(array_shape):
+                shape = shape_template
+                j = 0
+                for i in range(ndim_new_shape):
+                    if i not in augment:
+                        length = array_shape[j]
+                        shape = numba_basic.tuple_setitem(shape, i, length)
+                        j = j + 1
+                return shape
 
     else: