diff --git a/mindarmour/privacy/sup_privacy/mask_monitor/masker.py b/mindarmour/privacy/sup_privacy/mask_monitor/masker.py
index fd54ac2..0f67b12 100644
--- a/mindarmour/privacy/sup_privacy/mask_monitor/masker.py
+++ b/mindarmour/privacy/sup_privacy/mask_monitor/masker.py
@@ -26,10 +26,6 @@ TAG = 'suppress masker'
 class SuppressMasker(Callback):
     """
     Args:
-        args (Union[int, float, numpy.ndarray, list, str]): Parameters
-            used for creating a suppress privacy monitor.
-        kwargs (Union[int, float, numpy.ndarray, list, str]): Keyword
-            parameters used for creating a suppress privacy monitor.
         model (SuppressModel):  SuppressModel instance.
         suppress_ctrl (SuppressCtrl): SuppressCtrl instance.
 
diff --git a/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py b/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py
index b3ae7b8..2e6407b 100644
--- a/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py
+++ b/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py
@@ -107,6 +107,37 @@ class SuppressCtrl(Cell):
         lr (Union[float, int]): Learning rate.
         sparse_end (float): The sparsity to reach.
         sparse_start (Union[float, int]): The sparsity to start.
+
+    Examples:
+        >>> networks_l5 = LeNet5()
+        >>> masklayers = []
+        >>> masklayers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
+        >>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5,
+        >>>                                                 mask_layers=masklayers,
+        >>>                                                 policy="local_train",
+        >>>                                                 end_epoch=10,
+        >>>                                                 batch_num=(int)(10000/cfg.batch_size),
+        >>>                                                 start_epoch=3,
+        >>>                                                 mask_times=1000,
+        >>>                                                 lr=lr,
+        >>>                                                 sparse_end=0.90,
+        >>>                                                 sparse_start=0.0)
+        >>> net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
+        >>> net_opt = nn.Momentum(params=networks_l5.trainable_params(), learning_rate=lr, momentum=0.0)
+        >>> config_ck = CheckpointConfig(save_checkpoint_steps=(int)(samples/cfg.batch_size),
+        >>>                              keep_checkpoint_max=10)
+        >>> model_instance = SuppressModel(network=networks_l5,
+        >>>                             loss_fn=net_loss,
+        >>>                             optimizer=net_opt,
+        >>>                             metrics={"Accuracy": Accuracy()})
+        >>> model_instance.link_suppress_ctrl(suppress_ctrl_instance)
+        >>> ds_train = generate_mnist_dataset("./MNIST_unzip/train",
+        >>>                                 batch_size=cfg.batch_size, repeat_size=1, samples=samples)
+        >>> ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
+        >>>                             directory="./trained_ckpt_file/",
+        >>>                             config=config_ck)
+        >>> model_instance.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
+        >>>                 dataset_sink_mode=False)
     """
     def __init__(self, networks, mask_layers, end_epoch, batch_num, start_epoch, mask_times, lr,
                  sparse_end, sparse_start):
@@ -740,9 +771,13 @@ class MaskLayerDes:
             If False, the weights of this layer won't be clipped.
             If parameter num is greater than 100000, is_lower_clip has no effect.
         min_num (int): The number of weights left that not be suppressed.
-            If min_num is smaller than (parameter num*SupperssCtrl.sparse_end), min_num has no effect.
-        upper_bound (Union[float, int]): Max abs value of weight in this layer, default: 1.20.
-            If parameter num is greater than 100000, upper_bound has no effect.
+            If min_num is smaller than (parameter num*SupperssCtrl.sparse_end), min_num has not effect.
+        upper_bound (Union[float, int]): max abs value of weight in this layer, default: 1.20.
+            If parameter num is greater than 100000, upper_bound has not effect.
+
+    Examples:
+        >>> masklayers = []
+        >>> masklayers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
     """
     def __init__(self, layer_name, grad_idx, is_add_noise, is_lower_clip, min_num, upper_bound=1.20):
         self.layer_name = check_param_type('layer_name', layer_name, str)
diff --git a/mindarmour/privacy/sup_privacy/train/model.py b/mindarmour/privacy/sup_privacy/train/model.py
index a6513b8..b9b49bd 100644
--- a/mindarmour/privacy/sup_privacy/train/model.py
+++ b/mindarmour/privacy/sup_privacy/train/model.py
@@ -62,7 +62,6 @@ class SuppressModel(Model):
         network (Cell): The training network.
         loss_fn (Cell): Computes softmax cross entropy between logits and labels.
         optimizer (Optimizer): optimizer instance.
-        metrics (Union[dict, set]): Calculates the accuracy for classification and multilabel data.
         kwargs: Keyword parameters used for creating a suppress model.
 
     Examples: