Return a tuple from ops.shape with the Torch backend.

With Torch, `x.shape` returns a `torch.Size`, which is a subclass of `tuple` but can cause different behaviors. In particular `convert_to_tensor` does not work on `torch.Size`.

This fixes keras-team#18900

keras/src/backend/torch/core.py

@@ -234,7 +234,8 @@ def is_tensor(x):
 
 
 def shape(x):
-    return x.shape
+    # Convert from `torch.Size` to plain tuple.
+    return tuple(x.shape)
 
 
 def cast(x, dtype):
@@ -290,7 +291,8 @@ def symbolic_call(fn, args, kwargs, fill_value):
                     (args, kwargs),
                 )
                 return fn(*meta_args, **meta_kwargs)
-        except:
+        except Exception as e:
+            print("### e", e)
             with device_scope(DEFAULT_DEVICE):
                 # If the `"meta"` device placement fails, fall back to tracing
                 # eagerly with tensors on the default device. This will be

keras/src/ops/core_test.py

@@ -338,10 +338,12 @@ def test_stop_gradient_return(self):
         self.assertAllClose(x, y)
 
     def test_shape(self):
-        x = np.ones((2, 3, 7, 1))
+        x = ops.ones((2, 3, 7, 1))
+        self.assertEqual(core.shape(x).__class__, tuple)
         self.assertAllEqual(core.shape(x), (2, 3, 7, 1))
 
         x = KerasTensor((None, 3, None, 1))
+        self.assertEqual(core.shape(x).__class__, tuple)
         self.assertAllEqual(core.shape(x), (None, 3, None, 1))
 
     @pytest.mark.skipif(