Fix doc of ParameterAttribute

python/paddle/trainer_config_helpers/attrs.py

@@ -58,12 +58,12 @@ def is_compatible_with(x, Type):
 
 class HookAttribute(object):
     """
-    Hook Attribute object. As a member of ParameterAttribute class, the hook is an auxiliary operation that occurs 
+    Hook Attribute object. As a member of ParameterAttribute class, the hook is an auxiliary operation that occurs
     during training process of a layer with parameters, such as img_conv layer, fc layer.
 
-    :param  type: Hook type, currently supported types: 
+    :param  type: Hook type, currently supported types:
                         'pruning' :  user specify a sparsity_ratio before training started, and the
-                            network will prune the parameters based on the sparsity_ratio. 
+                            network will prune the parameters based on the sparsity_ratio.
                             eg: The definition of Hook object can be hk = HookAttribute('pruning', 0.6)
                             The specific usage can be paddle.layer.img_conv(input=img, filter_size=3,
                                                                        num_channels=3, num_filters=64,
@@ -71,10 +71,10 @@ class HookAttribute(object):
                             The pruning details can be found https://arxiv.org/pdf/1506.02626.pdf
     :type type: string
 
-    :param sparsity_ratio: Must be specified if hook type is 'pruning', 
+    :param sparsity_ratio: Must be specified if hook type is 'pruning',
                         it represents the ratio of the zero elements to be set by the Parameter.
     :type sparsity_ratio: float or None
-	
+
     """
 
     def __init__(self, type, sparsity_ratio=None):
@@ -130,10 +130,12 @@ class ParameterAttribute(object):
     :param sparse_update: Enable sparse update for this parameter. It will
                           enable both local and remote sparse update.
     :type sparse_update: bool
+    :param update_hooks: A HookAttribute object.
+    :type update_hooks: HookAttribute
     :param initializer: If not None, it should be a callable object which accepts
                         a parameter name and returns numpy array for the initial
                         value of the parameter
-    :param initializer: callable object
+    :type initializer: callable object
     """
 
     def __init__(self,