diff --git a/model.py b/model.py
index b71e59171f76082f4f9457080e965557c6695be2..28a6befaa8027c9272eda126cab89ac8134d6903 100644
--- a/model.py
+++ b/model.py
@@ -39,7 +39,7 @@ from .dataset_helper import DatasetHelper, connect_network_with_dataset
 from . import amp
 from ..common.api import _pynative_executor
 
-
+# test
 def _transfer_tensor_to_tuple(inputs):
     """
     If the input is a tensor, convert it to a tuple. If not, the output is unchanged.
@@ -55,6 +55,7 @@ class _StepSync(Callback):
     def step_end(run_context):
         _pynative_executor.sync()
 
+# what we can modify
 class Model:
     """
     High-Level API for training or inference.
@@ -132,7 +133,7 @@ class Model:
         >>> dataset = create_custom_dataset()
         >>> model.train(2, dataset)
     """
-
+# what we can modify
     def __init__(self, network, loss_fn=None, optimizer=None, metrics=None, eval_network=None,
                  eval_indexes=None, amp_level="O0", boost_level="O0", **kwargs):
         self._network = network
@@ -158,7 +159,7 @@ class Model:
         self._train_network = self._build_train_network()
         self._build_eval_network(metrics, self._eval_network, eval_indexes)
         self._build_predict_network()
-
+# what we can modify
     def _check_for_graph_cell(self, kwargs):
         """Check for graph cell"""
         if not isinstance(self._network, nn.GraphCell):
@@ -171,7 +172,6 @@ class Model:
                              "but got 'loss_fn': {}, 'optimizer': {}.".format(self._loss_fn, self._optimizer))
         if kwargs:
             raise ValueError("For 'Model', the '**kwargs' argument should be empty when network is a GraphCell.")
-
     def _process_amp_args(self, kwargs):
         if self._amp_level in ["O0", "O3"]:
             self._keep_bn_fp32 = False
@@ -180,7 +180,7 @@ class Model:
         if 'loss_scale_manager' in kwargs:
             self._loss_scale_manager = kwargs['loss_scale_manager']
             self._loss_scale_manager_set = True
-            
+# what we can modify
     def _check_amp_level_arg(self, optimizer, amp_level):
         if optimizer is None and amp_level != "O0":
             raise ValueError(
@@ -198,7 +198,6 @@ class Model:
             dataset.__model_hash__ = hash(self)
         if hasattr(dataset, '__model_hash__') and dataset.__model_hash__ != hash(self):
             raise RuntimeError('The Dataset cannot be bound to different models, please create a new dataset.')
-
     def _build_boost_network(self, kwargs):
         """Build the boost network."""
         boost_config_dict = ""
@@ -1081,35 +1080,28 @@ class Model:
             raise RuntimeError('Infer predict layout only supports semi auto parallel and auto parallel mode.')
         _parallel_predict_check()
         check_input_data(*predict_data, data_class=Tensor)
-
         predict_net = self._predict_network
         # Unlike the cases in build_train_network() and build_eval_network(), 'multi_subgraphs' is not set
         predict_net.set_auto_parallel()
         predict_net.set_train(False)
         predict_net.compile(*predict_data)
         return predict_net.parameter_layout_dict
-
     def _flush_from_cache(self, cb_params):
         """Flush cache data to host if tensor is cache enable."""
         params = cb_params.train_network.get_parameters()
         for param in params:
             if param.cache_enable:
                 Tensor(param).flush_from_cache()
-
     @property
     def train_network(self):
         """Get the model's train_network."""
         return self._train_network
-
     @property
     def predict_network(self):
         """Get the model's predict_network."""
         return self._predict_network
-
     @property
     def eval_network(self):
         """Get the model's eval_network."""
         return self._eval_network
-
-
 __all__ = ["Model"]
diff --git a/test.xml b/test.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e8aecca4860997a20b09780e1e97b4606295a79c
--- /dev/null
+++ b/test.xml
@@ -0,0 +1 @@
+<set> 123</set>
\ No newline at end of file