fix(imperative): restrict using convert_inputs in py_apply

GitOrigin-RevId: b021aac8a6f35dfe3b87dbc98a8007fd1a5b54b2

imperative/python/src/tensor.cpp

@@ -169,7 +169,7 @@ PyObject* py_apply(
             }
             HostTensorND ht(target_cn);
             ht = npy::np2tensor(args[i], npy::Meth::copy_into(&ht), target_dtype);
-            if (PyArray_Check(args[i])) {  // non scaler
+            if (PyArray_Check(args[i]) || PyList_Check(args[i])) {  // non scaler
                 return imperative::apply(
                         CreateTensor(CreateTensor::Const, target_cn, ht.layout()),
                         HostStorage::make(ht.storage()))[0];
@@ -205,8 +205,13 @@ PyObject* py_apply(
         for (size_t i = 0; i < nargs; ++i) {
             if (TensorWrapper* tw = TensorWrapper::try_cast(args[i])) {
                 tensors[i] = tw->m_tensor->data();
-            } else {
+            } else if (
+                    DTypePromoteCfg::convert_input_enabled &&
+                    op->same_type<Elemwise>()) {
                 tensors[i] = convert_pyinput_to_tensor(i);
+            } else {
+                PyErr_SetString(PyExc_TypeError, "py_apply expects tensor as inputs");
+                return nullptr;
             }
         }
 

imperative/python/test/unit/functional/test_elemwise.py

@@ -77,6 +77,11 @@ def test_div():
         np.floor_divide(np.array([-5, -7], dtype=np.int32), 2),
     )
 
+    np.testing.assert_allclose(
+        (tensor([[5, 4, 3], [4, 2, 6]]) // [1, 2, 1]).numpy(),
+        np.floor_divide(np.array([[5, 4, 3], [4, 2, 6]], dtype=np.int32), [1, 2, 1]),
+    )
+
 
 def test_clamp():
     """Fix an issue when `lower` or `upper` is 0, it will be recognized as `False` and