diff --git a/fastNLP/core/callbacks/callback.py b/fastNLP/core/callbacks/callback.py
index 982df7da..7f0c290d 100644
--- a/fastNLP/core/callbacks/callback.py
+++ b/fastNLP/core/callbacks/callback.py
@@ -126,7 +126,8 @@ class Callback:
 
         :param trainer: `fastNLP.Trainer`
         :param batch: batch 的数据，已经经过 input_mapping (如果有) 以及 移动到指定设备 。
-        :param list[int] indices: 当前的 batch 是 dataset 中的哪些数据
+        :param list[int] indices: 当前的 batch 是 dataset 中的哪些数据。仅在 DataLoader 支持得到当前 batch index 的时候有值，
+            其它时候为 None 。
         """
         pass
 
diff --git a/fastNLP/core/callbacks/load_best_model_callback.py b/fastNLP/core/callbacks/load_best_model_callback.py
index 5addd2e2..32534d2a 100644
--- a/fastNLP/core/callbacks/load_best_model_callback.py
+++ b/fastNLP/core/callbacks/load_best_model_callback.py
@@ -94,20 +94,21 @@ class LoadBestModelCallback(HasMonitorCallback):
         else:
             self.buffer.seek(0)
             trainer.load_model(folder=self.buffer, only_state_dict=self.only_state_dict)
-
-        self._delete_after_after(trainer)
-
-    def _delete_after_after(self, trainer):
-        trainer.driver.barrier()
         if self.delete_after_after:
-            if self.real_save_folder:
-                logger.info(f"Deleting {self.real_save_folder}...")
-                shutil.rmtree(self.real_save_folder, ignore_errors=True)
-                try:
-                    # 如果是 emtpy 的，就会被删除掉
-                    os.rmdir(self.save_folder)
-                except:
-                    pass
-            elif hasattr(self, 'buffer'):
-                self.buffer.close()
-                del self.buffer
\ No newline at end of file
+            trainer.driver.barrier()
+            self._delete_folder()
+            trainer.driver.barrier()
+
+    def _delete_folder(self):
+        if self.real_save_folder:
+            logger.info(f"Deleting {self.real_save_folder}...")
+            shutil.rmtree(self.real_save_folder, ignore_errors=True)
+            try:
+                # 如果是 emtpy 的，就会被删除掉
+                os.rmdir(self.save_folder)
+                logger.debug(f"Since {self.save_folder} is an empty folder, it has been removed.")
+            except:
+                pass
+        elif hasattr(self, 'buffer'):
+            self.buffer.close()
+            del self.buffer
\ No newline at end of file
diff --git a/fastNLP/core/collators/new_collator.py b/fastNLP/core/collators/new_collator.py
index 869a60a7..9123a293 100644
--- a/fastNLP/core/collators/new_collator.py
+++ b/fastNLP/core/collators/new_collator.py
@@ -6,7 +6,7 @@ from .padders.get_padder import get_padder
 import re
 
 from .utils import unpack_batch_mapping, unpack_batch_nested_mapping, pack_batch_nested_mapping, unpack_batch_sequence, \
-    pack_batch_sequence, NESTED_DICT_SEPARATOR
+    pack_batch_sequence
 
 sequence_idx_str = re.compile(r'^_\d+$')  # 形如_0, _1
 SUPPORTED_BACKENDS = ['torch', 'jittor', 'paddle', 'numpy', 'raw', None]
@@ -16,10 +16,11 @@ class Collator:
     def __init__(self, backend='torch'):
         """
         用于 pad 数据的对象。会自动将所有能够 pad （由 fastNLP 根据数据判定能否 pad ）的数据都进行 pad 操作，默认 pad 的值为 0。
-            可使用 set_pad() 函数调整。如果有些 field 不想输出，可以使用 set_ignore() 函数进行设置。
+            可使用 set_pad() 函数调整。如果有些 field 不想输出，可以使用 set_ignore() 函数进行设置。Collator 在第一次进行 pad 的
+            时候自动根据设置以及数据情况，为每个 field 获取一个 padder ，在之后的每次调用中，都将使用对应的 Padder 给对应的 field 。
 
-        :param backend: 对于可以 pad 的 field，使用哪种 tensor，支持 ['torch','jittor','paddle','numpy','raw',None]，
-            若为 None ，则不进行 padding 。
+        :param backend: 对于可以 pad 的 field，使用哪种 tensor，支持 ['torch','jittor','paddle','numpy','raw',None]。
+            若为 None ，则不进行 padding 。该参数对本身就不能进行 pad 的数据没用影响，不能 pad 的数据返回一定是 list 。
         """
         self.unpack_batch_func = None
         self.pack_batch_func = None
@@ -54,22 +55,25 @@ class Collator:
                 else:
                     self.batch_data_type = 's'
                 logger.debug(f"Since batch[0] has type:{type(batch[0])}, so the batch_data_type "
-                             f"is {self.batch_data_type}")
+                             f"is `{self.batch_data_type}`.")
             if self.batch_data_type == 's':
-                self.unpack_batch_func = lambda x:{'_single': x}  # 不需要做任何调整
-                self.pack_batch_func = lambda x:x['_single']
+                self.unpack_batch_func = lambda batch, ignore_fields: {'_single': batch}  # 不需要做任何调整
+                self.pack_batch_func = lambda x: x['_single']
             elif self.batch_data_type == 'l':
                 self.unpack_batch_func = unpack_batch_sequence
                 self.pack_batch_func = pack_batch_sequence
             elif self.batch_data_type == 'd':
-                if any([isinstance(v, Mapping) for v in batch[0].values()]):  # 可能存在 nested 的dict。{'a': {'b': xx}}->{'a@@b': value}
+                if any([isinstance(v, Mapping) for v in batch[0].values()]):  # 可能存在 nested 的dict。{'a': {'b': xx}}->{('a', 'b'): value}
                     self.unpack_batch_func = unpack_batch_nested_mapping
                     self.pack_batch_func = pack_batch_nested_mapping
                 else:
                     self.unpack_batch_func = unpack_batch_mapping
                     self.pack_batch_func = lambda x:x
 
-        unpack_batch:Dict = self.unpack_batch_func(batch)  # 将各自 field 组成 batch 形式。
+        if self.unpack_batch_func is unpack_batch_nested_mapping:  # 比较特殊，需要防止继续往下延伸
+            unpack_batch: Dict = self.unpack_batch_func(batch, self.ignore_fields, set(self.input_fields.keys()))
+        else:
+            unpack_batch:Dict = self.unpack_batch_func(batch, self.ignore_fields)  # 将各自 field 组成 batch 形式。
 
         pad_batch = {}
         if len(self.padders)==0:  # 第一次运行，准备 padder
@@ -96,13 +100,13 @@ class Collator:
 
         return self.pack_batch_func(pad_batch)  # 根据情况恢复成与输入一致的类型
 
-    def set_pad(self, field_name:str, pad_val:Union[int, float, None]=0, dtype=None, backend=None,
+    def set_pad(self, field_name:Union[str, tuple], pad_val:Union[int, float, None]=0, dtype=None, backend=None,
                 pad_fn:Callable=None) -> "Collator":
         """
         如果需要对某个 field 的内容进行特殊的调整，请使用这个函数。
 
         :param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
-            field 的 key 来表示，如果是 nested 的 dict，可以使用 @@ 来连接不同层次的 key，例如 {'a': {'b': 1}} 中的使用 a@@b;
+            field 的 key 来表示，如果是 nested 的 dict，可以使用元组表示多层次的 key，例如 {'a': {'b': 1}} 中的使用 ('a', 'b');
             如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没
             有找到，则报错；如果 __getitem__ 返回的是就是整体内容，请使用 "_single" 。
         :param pad_val: 这个 field 的默认 pad 值。如果设置为 None，则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的
@@ -126,11 +130,11 @@ class Collator:
                                                            f"index, but other field is set as dict mode."
             elif self.batch_data_type == 'l':
                 assert sequence_idx_str.match(field_name) is not None, f"Other field is set as list mode. But the new " \
-                                                                       f"field name is {field_name}"
+                                                                       f"field name is {field_name}."
 
         if field_name == '_single':
             self.batch_data_type = 's'
-        elif sequence_idx_str.match(field_name):
+        elif isinstance(field_name, str) and sequence_idx_str.match(field_name):
             self.batch_data_type = 'l'
         else:
             self.batch_data_type = 'd'
@@ -165,8 +169,8 @@ class Collator:
             collator.set_ignore('field1', 'field2')
 
         :param field_names: 需要忽略的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的，则可以直接使用对应的
-            field 的 key 来表示，如果是 nested 的 dict，可以使用 @@ 来连接不同层次的 key，例如 {'a': {'b': 1}} 中的使用 a@@b;
-            如果 __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。
+            field 的 key 来表示，如果是 nested 的 dict，可以使用元组来表示，例如 {'a': {'b': 1}} 中的使用 ('a', 'b'); 如果
+            __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。
         :return: 返回 Collator 自身
         """
         for field_name in field_names:
diff --git a/fastNLP/core/collators/padders/utils.py b/fastNLP/core/collators/padders/utils.py
index f6240219..d2d3a8e0 100644
--- a/fastNLP/core/collators/padders/utils.py
+++ b/fastNLP/core/collators/padders/utils.py
@@ -149,6 +149,7 @@ def is_number(dtype):
         if dtype in (float, int, complex, bool) and not is_numpy_generic_class(dtype) \
                 and not is_numpy_number_dtype(dtype):
             return True
+        return False
     except:
         return False
 
@@ -161,6 +162,7 @@ if __name__ == '__main__':
     # print(type(b[0]))
     # print(b)
     # import torch
+    print(is_number(type('a')))
     print(is_number_or_numpy_number(type(3)))  # True
     print(is_number_or_numpy_number(type(3.1)))  # True
     print(is_number_or_numpy_number(type('3'))) # False
diff --git a/fastNLP/core/collators/utils.py b/fastNLP/core/collators/utils.py
index 9a397c66..1a82aa23 100644
--- a/fastNLP/core/collators/utils.py
+++ b/fastNLP/core/collators/utils.py
@@ -2,54 +2,58 @@ from collections import defaultdict
 from functools import reduce
 from typing import Sequence, Mapping, Dict
 
-NESTED_DICT_SEPARATOR = '@@'
 
-
-def unpack_batch_mapping(batch:Sequence[Mapping])->Dict:
+def unpack_batch_mapping(batch:Sequence[Mapping], ignore_fields:set)->Dict:
     """
     将 Sequence[Mapping] 转为 Dict 。例如 [{'a': [1, 2], 'b': 1}, {'a': [3], 'b': 2}] -> {'a': [[1, 2], [3]], 'b': [1, 2]}
 
     :param batch:
+    :param ignore_fields:
     :return:
     """
     dict_batch = defaultdict(list)
     for sample in batch:
         for key, value in sample.items():
+            if key in ignore_fields:
+                continue
             dict_batch[key].append(value)
     return dict_batch
 
 
-def unpack_batch_nested_mapping(batch:Sequence[Mapping], _parent='')->Dict:
+def unpack_batch_nested_mapping(batch:Sequence[Mapping], ignore_fields:set, stop_deep_fields:set)->Dict:
     """
     将 nested 的 dict 中的内容展开到一个 flat dict 中
 
     :param batch:
-    :param _parent: 内部使用
+    :param ignore_fields: 需要忽略的 field 。
+    :param stop_deep_fields: 不需要继续往下衍射的
     :return:
     """
     dict_batch = defaultdict(list)
-    if _parent != '':
-        _parent += NESTED_DICT_SEPARATOR
     for sample in batch:
         for key, value in sample.items():
-            if isinstance(value, Mapping):
-                _dict_batch = _unpack_batch_nested_mapping(value, _parent=_parent + key)
+            if key in ignore_fields:
+                continue
+            if isinstance(value, Mapping) and key not in stop_deep_fields:
+                _dict_batch = _unpack_batch_nested_mapping(value, ignore_fields, stop_deep_fields, _parent=(key,))
                 for key, value in _dict_batch.items():
                     dict_batch[key].append(value)
             else:
-                dict_batch[_parent + key].append(value)
+                dict_batch[key].append(value)
     return dict_batch
 
 
-def _unpack_batch_nested_mapping(value, _parent)->Dict:
+def _unpack_batch_nested_mapping(value, ignore_fields, stop_deep_fields, _parent)->Dict:
     _dict = {}
-    _parent += NESTED_DICT_SEPARATOR
     for k, v in value.items():
-        if isinstance(v, Mapping):
-            __dict = _unpack_batch_nested_mapping(v, _parent=_parent + k)
+        _k = _parent + (k,)
+        if _k in ignore_fields:
+            continue
+        if isinstance(v, Mapping) and _k not in stop_deep_fields:
+            __dict = _unpack_batch_nested_mapping(v, ignore_fields, stop_deep_fields, _parent=_k)
             _dict.update(__dict)
         else:
-            _dict[_parent + k] = v
+            _dict[_k] = v
     return _dict
 
 
@@ -63,10 +67,11 @@ def pack_batch_nested_mapping(batch:Mapping) -> Dict:
     dicts = []
 
     for key, value in batch.items():
-        keys = key.split(NESTED_DICT_SEPARATOR)
-        d = {keys[-1]: value}
-        for key in keys[:-1:][::-1]:
-            d = {key: d}
+        if not isinstance(key, tuple):
+            key = [key]
+        d = {key[-1]: value}
+        for k in key[:-1:][::-1]:
+            d = {k: d}
         dicts.append(d)
     return reduce(_merge_dict, dicts)
 
@@ -85,17 +90,21 @@ def _merge_dict(a, b, path=None):
     return a
 
 
-def unpack_batch_sequence(batch:Sequence[Sequence])->Dict:
+def unpack_batch_sequence(batch:Sequence[Sequence], ignore_fields)->Dict:
     """
     将 Sequence[Sequence] 转为 Mapping 。例如 [[[1, 2], 2], [[3], 2]] -> {'_0': [[1, 2], [3]], '_1': [1, 2]}
 
     :param batch:
+    :param ignore_fields: 需要忽略的field
     :return:
     """
     dict_batch = defaultdict(list)
     for sample in batch:
         for i, content in enumerate(sample):
-            dict_batch[f'_{i}'].append(content)
+            field_name = f'_{i}'
+            if field_name in ignore_fields:
+                continue
+            dict_batch[field_name].append(content)
     return dict_batch
 
 
diff --git a/fastNLP/core/dataloaders/fdataloader.py b/fastNLP/core/dataloaders/fdataloader.py
deleted file mode 100644
index 742f3909..00000000
--- a/fastNLP/core/dataloaders/fdataloader.py
+++ /dev/null
@@ -1,7 +0,0 @@
-__all__ = [
-    'FDataLoader'
-]
-
-
-class FDataLoader:
-    pass
diff --git a/fastNLP/core/dataloaders/torch_dataloader/fdl.py b/fastNLP/core/dataloaders/torch_dataloader/fdl.py
index cf8e2c31..02721aaf 100644
--- a/fastNLP/core/dataloaders/torch_dataloader/fdl.py
+++ b/fastNLP/core/dataloaders/torch_dataloader/fdl.py
@@ -17,7 +17,7 @@ if _NEED_IMPORT_TORCH:
     from torch.utils.data import DataLoader, Sampler
     from torch.utils.data._utils.collate import default_collate
 else:
-    from ..fdataloader import FDataLoader as DataLoader
+    from fastNLP.core.utils.dummy_class import DummyClass as DataLoader
 
 
 class _FDataSet:
diff --git a/tests/core/collators/padders/test_numpy_padder.py b/tests/core/collators/padders/test_numpy_padder.py
index 42665857..6cc9d668 100644
--- a/tests/core/collators/padders/test_numpy_padder.py
+++ b/tests/core/collators/padders/test_numpy_padder.py
@@ -10,7 +10,7 @@ class TestNumpyNumberPadder:
     def test_run(self):
         padder = NumpyNumberPadder(ele_dtype=int, dtype=int, pad_val=-1)
         a = [1, 2, 3]
-        assert isinstance(a, np.ndarray)
+        assert isinstance(padder(a), np.ndarray)
         assert (padder(a) == np.array(a)).sum() == 3
 
 
diff --git a/tests/core/collators/test_new_collator.py b/tests/core/collators/test_new_collator.py
index 5fc82c91..7c27b3a9 100644
--- a/tests/core/collators/test_new_collator.py
+++ b/tests/core/collators/test_new_collator.py
@@ -158,7 +158,7 @@ class TestCollator:
 
         # 测试 ignore
         collator = Collator(backend='raw')
-        collator.set_ignore('str', 'int', 'lst_int', 'nested_dict@@a')
+        collator.set_ignore('str', 'int', 'lst_int', ('nested_dict', 'a'))
         raw_pad_batch = {'lst_str': [['1'], ['2', '2']], 'nest_lst_int': [[[1, 0], [0, 0]], [[1, 0], [1, 2]]], 'float': [1.1, 2.1], 'lst_float': [[1.1], [2.1]], 'bool': [True, False], 'numpy': [np.array([1.]), np.array([0.])], 'dict': {'1': ['1', '2']}, 'set': [{'1'}, {'2'}], 'nested_dict': {'b': [[1, 2], [1, 2]]}}
         findDictDiff(raw_pad_batch, collator(dict_batch))
 
@@ -171,7 +171,7 @@ class TestCollator:
         # 测试设置 pad 值
         collator = Collator(backend='raw')
         collator.set_pad('nest_lst_int', pad_val=100)
-        collator.set_ignore('str', 'int', 'lst_int', 'nested_dict@@a')
+        collator.set_ignore('str', 'int', 'lst_int', ('nested_dict','a'))
         raw_pad_batch = {'lst_str': [['1'], ['2', '2']], 'nest_lst_int': [[[1, 100], [100, 100]], [[1, 100], [1, 2]]],
                          'float': [1.1, 2.1], 'lst_float': [[1.1], [2.1]], 'bool': [True, False], 'numpy': [np.array([1.]), np.array([0.])], 'dict': {'1': ['1', '2']}, 'set': [{'1'}, {'2'}], 'nested_dict': {'b': [[1, 2], [1, 2]]}}
         findDictDiff(raw_pad_batch, collator(dict_batch))
@@ -217,6 +217,72 @@ class TestCollator:
         collator.set_pad('_single')
         findListDiff(list_batch, collator(list_batch))
 
+    def test_nest_ignore(self):
+        dict_batch = [{
+            'str': '1',
+            'lst_str': ['1'],
+            'int': 1,
+            'lst_int': [1],
+            'nest_lst_int': [[1]],
+            'float': 1.1,
+            'lst_float': [1.1],
+            'bool': True,
+            'numpy': np.ones(1),
+            'dict': {'1': '1'},
+            'set': {'1'},
+            'nested_dict': {'int': 1, 'lst_int':[1, 2], 'c': {'int': 1}}
+        },
+            {
+                'str': '2',
+                'lst_str': ['2', '2'],
+                'int': 2,
+                'lst_int': [1, 2],
+                'nest_lst_int': [[1], [1, 2]],
+                'float': 2.1,
+                'lst_float': [2.1],
+                'bool': False,
+                'numpy': np.zeros(1),
+                'dict': {'1': '2'},
+                'set': {'2'},
+                'nested_dict': {'int': 1, 'lst_int': [1, 2], 'c': {'int': 1}}
+            }
+        ]
+        # 测试 ignore
+        collator = Collator(backend='raw')
+        collator.set_ignore('str', 'int', 'lst_int', ('nested_dict', 'int'))
+        raw_pad_batch = {'lst_str': [['1'], ['2', '2']], 'nest_lst_int': [[[1, 0], [0, 0]], [[1, 0], [1, 2]]],
+                         'float': [1.1, 2.1], 'lst_float': [[1.1], [2.1]], 'bool': [True, False],
+                         'numpy': [np.array([1.]), np.array([0.])], 'dict': {'1': ['1', '2']},
+                         'set': [{'1'}, {'2'}], 'nested_dict': {'lst_int': [[1, 2], [1, 2]],
+                                                                'c': {'int':[1, 1]}}}
+        findDictDiff(raw_pad_batch, collator(dict_batch))
+
+        collator = Collator(backend='raw')
+        collator.set_pad(('nested_dict', 'c'), pad_val=None)
+        collator.set_ignore('str', 'int', 'lst_int')
+        raw_pad_batch = {'lst_str': [['1'], ['2', '2']], 'nest_lst_int': [[[1, 0], [0, 0]], [[1, 0], [1, 2]]],
+                         'float': [1.1, 2.1], 'lst_float': [[1.1], [2.1]], 'bool': [True, False],
+                         'numpy': [np.array([1.]), np.array([0.])], 'dict': {'1': ['1', '2']},
+                         'set': [{'1'}, {'2'}], 'nested_dict': {'lst_int': [[1, 2], [1, 2]],
+                                                                'c': [{'int':1}, {'int':1}]}}
+        pad_batch = collator(dict_batch)
+        findDictDiff(raw_pad_batch, pad_batch)
+
+        collator = Collator(backend='raw')
+        collator.set_pad(('nested_dict', 'c'), pad_val=1)
+        with pytest.raises(BaseException):
+            collator(dict_batch)
+
+        collator = Collator(backend='raw')
+        collator.set_ignore('str', 'int', 'lst_int')
+        collator.set_pad(('nested_dict', 'c'), pad_fn=lambda x: [d['int'] for d in x])
+        pad_batch = collator(dict_batch)
+        raw_pad_batch = {'lst_str': [['1'], ['2', '2']], 'nest_lst_int': [[[1, 0], [0, 0]], [[1, 0], [1, 2]]],
+                         'float': [1.1, 2.1], 'lst_float': [[1.1], [2.1]], 'bool': [True, False],
+                         'numpy': [np.array([1.]), np.array([0.])], 'dict': {'1': ['1', '2']},
+                         'set': [{'1'}, {'2'}], 'nested_dict': {'lst_int': [[1, 2], [1, 2]],
+                                                                'c': [1, 1]}}
+        findDictDiff(raw_pad_batch, pad_batch)
 
 
 
diff --git a/tests/core/collators/test_utils.py b/tests/core/collators/test_utils.py
index d56dacc6..74c54a36 100644
--- a/tests/core/collators/test_utils.py
+++ b/tests/core/collators/test_utils.py
@@ -4,25 +4,25 @@ from fastNLP.core.collators.utils import *
 
 def test_unpack_batch_mapping():
     batch = [{'a': [1, 2], 'b': 1}, {'a': [3], 'b': 2}]
-    assert unpack_batch_mapping(batch)=={'a': [[1, 2], [3]], 'b': [1, 2]}
+    assert unpack_batch_mapping(batch, {})=={'a': [[1, 2], [3]], 'b': [1, 2]}
 
 
 def test_unpack_batch_nested_mapping():
     batch = [{'a': [1, 2], 'b': 1, 'c': {'c': 1}}, {'a': [3], 'b': 2, 'c': {'c': 2}}]
-    assert unpack_batch_nested_mapping(batch) == {'a': [[1, 2], [3]], 'b': [1, 2], 'c@@c': [1, 2]}
+    assert unpack_batch_nested_mapping(batch, {}, {}) == {'a': [[1, 2], [3]], 'b': [1, 2], ('c','c'): [1, 2]}
 
     batch = [{'a': [1, 2], 'b': 1, 'c': {'c': {'c': 1}}}, {'a': [3], 'b': 2, 'c': {'c': {'c': 2}}}]
-    assert unpack_batch_nested_mapping(batch) == {'a': [[1, 2], [3]], 'b': [1, 2], 'c@@c@@c': [1, 2]}
+    assert unpack_batch_nested_mapping(batch, {}, {}) == {'a': [[1, 2], [3]], 'b': [1, 2], ('c', 'c', 'c'): [1, 2]}
 
     batch = [{'a': [1, 2], 'b': 1, 'c': {'c': {'c': 1, 'd':[1, 1]}, 'd': [1]}},
              {'a': [3], 'b': 2, 'c': {'c': {'c': 2, 'd': [2, 2]}, 'd': [2, 2]}}]
-    assert unpack_batch_nested_mapping(batch) == {'a': [[1, 2], [3]], 'b': [1, 2], 'c@@c@@c': [1, 2],
-                                                  'c@@c@@d':[[1, 1], [2, 2]], 'c@@d': [[1], [2, 2]]}
+    assert unpack_batch_nested_mapping(batch, {}, {}) == {'a': [[1, 2], [3]], 'b': [1, 2], ('c', 'c', 'c'): [1, 2],
+                                                          ('c','c', 'd'):[[1, 1], [2, 2]], ('c', 'd'): [[1], [2, 2]]}
 
 
 def test_pack_batch_nested_mapping():
-    batch = {'a': [[1, 2], [3]], 'b': [1, 2], 'c@@c@@c': [1, 2],
-             'c@@c@@d':[[1, 1], [2, 2]], 'c@@d': [[1], [2, 2]]}
+    batch = {'a': [[1, 2], [3]], 'b': [1, 2], ('c', 'c', 'c'): [1, 2],
+             ('c', 'c', 'd'):[[1, 1], [2, 2]], ('c', 'd'): [[1], [2, 2]]}
     new_batch = pack_batch_nested_mapping(batch)
     assert new_batch == {'a': [[1, 2], [3]], 'b': [1, 2],
                          'c': {'c':{'c': [1, 2], 'd': [[1, 1], [2, 2]]}, 'd':[[1], [2, 2]]}}
@@ -30,7 +30,7 @@ def test_pack_batch_nested_mapping():
 
 def test_unpack_batch_sequence():
     batch = [[1, 2, 3], [2, 4, 6]]
-    new_batch = unpack_batch_sequence(batch)
+    new_batch = unpack_batch_sequence(batch, {})
     assert new_batch == {'_0': [1, 2], '_1': [2, 4], '_2': [3, 6]}