diff --git a/imperative/python/src/tensor_utils.cpp b/imperative/python/src/tensor_utils.cpp
index 44f03c3234bcdc52fda2d558ca3d9c8829675f0d..e8f75b34ec43d3b6f9bd0823237f8e5fe058391a 100644
--- a/imperative/python/src/tensor_utils.cpp
+++ b/imperative/python/src/tensor_utils.cpp
@@ -530,11 +530,23 @@ py::object _astensor1d_cpp(
         return get_res_by_refhdl(value, dtype, device, ref);
     }
     if (lis.size() > 1) {
-        std::vector<PyObject*> c_args(lis.size() + 1);
-        for (size_t i = 0; i < lis.size(); ++i) {
-            c_args[i] = lis[i].ptr();
+        py::list flat_list;
+        for (auto item : lis) {
+            if (!PyList_Check(item.ptr())) {
+                flat_list.append(item);
+            } else {
+                py::list sub_lis =
+                        py::reinterpret_steal<py::list>(PySequence_List(item.ptr()));
+                for (auto sub_item : sub_lis) {
+                    flat_list.append(sub_item);
+                }
+            }
+        }
+        std::vector<PyObject*> c_args(flat_list.size() + 1);
+        for (size_t i = 0; i < flat_list.size(); ++i) {
+            c_args[i] = flat_list[i].ptr();
         }
-        c_args[lis.size()] = Py_None;
+        c_args[flat_list.size()] = Py_None;
         py::tuple inp_tup = py::reinterpret_steal<py::tuple>(
                 convert_inputs_cpp(NULL, c_args.data(), c_args.size()));
         if (device_obj.is_none()) {
diff --git a/imperative/python/test/unit/jit/test_tracing.py b/imperative/python/test/unit/jit/test_tracing.py
index dbd7885c06b4285075561ad2c57feaf5b7bf85db..4e4cff5d3f7fa0318e39542fb135ce8d1fec0846 100644
--- a/imperative/python/test/unit/jit/test_tracing.py
+++ b/imperative/python/test/unit/jit/test_tracing.py
@@ -161,6 +161,18 @@ def test_elemwise_fuse_in_grad(trace_mode):
         y.numpy()
 
 
+def test_repeat_in_trace():
+    @trace(symbolic=False)
+    def fun(data, repeats):
+        F.repeat(data, repeats)
+
+    data = tensor(np.random.random([1, 2, 3]).astype(np.float32))
+
+    for i in range(1, 5):
+        repeats = tensor(i)
+        fun(data, repeats)
+
+
 def test_print_in_trace():
     for symbolic in [False]:  # cannot read value in symbolic mode