diff --git a/fastNLP/core/dataloaders/__init__.py b/fastNLP/core/dataloaders/__init__.py
index 40dd7b1c..e9dc51b4 100644
--- a/fastNLP/core/dataloaders/__init__.py
+++ b/fastNLP/core/dataloaders/__init__.py
@@ -5,10 +5,12 @@ __all__ = [
     'JittorDataLoader',
     'prepare_jittor_dataloader',
     'prepare_paddle_dataloader',
-    'prepare_torch_dataloader'
+    'prepare_torch_dataloader',
+    'indice_collate_wrapper'
 ]
 
 from .mix_dataloader import MixDataLoader
 from .jittor_dataloader import JittorDataLoader, prepare_jittor_dataloader
 from .torch_dataloader import TorchDataLoader, prepare_torch_dataloader
 from .paddle_dataloader import PaddleDataLoader, prepare_paddle_dataloader
+from .utils import indice_collate_wrapper
diff --git a/fastNLP/core/dataloaders/jittor_dataloader/fdl.py b/fastNLP/core/dataloaders/jittor_dataloader/fdl.py
index 507073a4..2345a9b9 100644
--- a/fastNLP/core/dataloaders/jittor_dataloader/fdl.py
+++ b/fastNLP/core/dataloaders/jittor_dataloader/fdl.py
@@ -12,7 +12,7 @@ if _NEED_IMPORT_JITTOR:
     from jittor.dataset import Dataset
 else:
     from fastNLP.core.dataset import DataSet as Dataset
-from fastNLP.core.utils.jittor_utils import jittor_collate_wraps
+
 from fastNLP.core.collators import Collator
 from fastNLP.core.dataloaders.utils import indice_collate_wrapper
 from fastNLP.core.dataset import DataSet as FDataSet
diff --git a/fastNLP/core/dataloaders/utils.py b/fastNLP/core/dataloaders/utils.py
index a71dc50c..2305cebe 100644
--- a/fastNLP/core/dataloaders/utils.py
+++ b/fastNLP/core/dataloaders/utils.py
@@ -1,3 +1,8 @@
+__all__ = [
+    "indice_collate_wrapper"
+]
+
+
 def indice_collate_wrapper(func):
     """
     其功能是封装一层collate_fn,将dataset取到的tuple数据分离开，将idx打包为indices。
diff --git a/tests/core/dataloaders/jittor_dataloader/test_fdl.py b/tests/core/dataloaders/jittor_dataloader/test_fdl.py
index b3124397..92b49c09 100644
--- a/tests/core/dataloaders/jittor_dataloader/test_fdl.py
+++ b/tests/core/dataloaders/jittor_dataloader/test_fdl.py
@@ -42,7 +42,6 @@ class TestJittor:
         jtl = JittorDataLoader(dataset, keep_numpy_array=True, batch_size=4)
         # jtl.set_pad_val('x', 'y')
         # jtl.set_input('x')
-        print(str(jittor.Var([0])))
         for batch in jtl:
             print(batch)
             print(jtl.get_batch_indices())