modify function note

docs/api/api_python/mindspore/mindspore.Model.rst

@@ -5,6 +5,10 @@
 
     模型训练或推理的高阶接口。 `Model` 会根据用户传入的参数封装可训练或推理的实例。
 
+    .. note::
+    如果使用混合精度功能，需要同时设置`optimizer`参数，否则混合精度功能不生效。
+    当使用混合精度时，优化器中的 `global_step` 可能与模型中的 `cur_step_num` 不同。
+
     **参数：**
 
     - **network** (Cell) – 用于训练或推理的神经网络。

mindspore/python/mindspore/common/parameter.py

@@ -210,7 +210,7 @@ class Parameter(Tensor_):
     def __deepcopy__(self, memodict):
         new_obj = Parameter(self)
         new_obj.name = self.name
-        new_obj._inited_param = self._inited_param  # pylint: disable=W0212
+        new_obj._inited_param = self._inited_param
         return new_obj
 
     @staticmethod

mindspore/python/mindspore/ops/operations/debug_ops.py

@@ -411,6 +411,10 @@ class Print(PrimitiveWithInfer):
         In pynative mode, please use python print function.
         In graph mode, the bool, int and float would be converted into Tensor to print,
         str remains unchanged.
+        This function is used for debug. When too many print data at the same time,
+        in order not to affect the main process, the framework may discard some data. At this time,
+        if you need to record the data completely, you can recommended to use the `Summary` function. Please check
+        `Summary <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html?highlight=summary#>`_.
 
     Inputs:
         - **input_x** (Union[Tensor, bool, int, float, str]) - The graph node to attach to.

mindspore/python/mindspore/train/model.py

@@ -93,6 +93,11 @@ class Model:
 
     `Model` groups layers into an object with training and inference features based on the arguments.
 
+    Note:
+        If use mixed precision functions, need to set parameter `optimizer` at the same time,
+        otherwise mixed precision functions do not take effect.
+        When uses mixed precision functions, `global_step` in optimizer may be different from `cur_step_num` in Model.
+
     Args:
         network (Cell): A training or testing network.
         loss_fn (Cell): Objective function. If `loss_fn` is None, the `network` should contain the calculation of loss