[Dy2stat]fix doc code (#4008) (#4009)

* fix doc code

* add colon after func

Author: 0x45f

docs/guides/04_dygraph_to_static/basic_usage_cn.rst

@@ -109,7 +109,7 @@ trace是指在模型运行时记录下其运行过哪些算子。TracedLayer就
 
     paddle.enable_static()
     place = paddle.CPUPlace()
-    exe = paddle.Executor(place)
+    exe = paddle.static.Executor(place)
     program, feed_vars, fetch_vars = paddle.static.load_inference_model(save_dirname, exe)
     fetch, = exe.run(program, feed={feed_vars[0]: in_np}, fetch_list=fetch_vars)
 
@@ -120,7 +120,7 @@ trace是指在模型运行时记录下其运行过哪些算子。TracedLayer就
 
     import paddle
 
-    def func(input_var)
+    def func(input_var):
         # if判断与输入input_var的shape有关
         if input_var.shape[0] > 1:
             return paddle.cast(input_var, "float64")

docs/guides/04_dygraph_to_static/basic_usage_en.rst

@@ -104,7 +104,7 @@ Load model and run it in static graph mode:
 .. code-block:: python
 
     place = paddle.CPUPlace()
-    exe = paddle.Executor(place)
+    exe = paddle.static.Executor(place)
     program, feed_vars, fetch_vars = paddle.static.load_inference_model(save_dirname, exe)
     fetch, = exe.run(program, feed={feed_vars[0]: in_np}, fetch_list=fetch_vars)
 
@@ -114,7 +114,7 @@ However, as tracing only records operators once, if user's code contains Tensor-
 
     import paddle
 
-    def func(input_var)
+    def func(input_var):
         # if condition depends on the shape of input_var
         if input_var.shape[0] > 1:
             return paddle.cast(input_var, "float64")

docs/guides/04_dygraph_to_static/input_spec_cn.rst

@@ -226,7 +226,7 @@ InputSpec 初始化中的只有 ``shape`` 是必须参数， ``dtype`` 和 ``nam
         net.clear_gradients()
 
     # save inference model with use_act=False
-    paddle.jit.save(net, model_path='./simple_net')
+    paddle.jit.save(net, path='./simple_net')
 
 
 在上述样例中，step 为奇数时，use_act 取值为 False ； step 为偶数时， use_act 取值为 True 。动转静支持非 Tensor 参数在训练时取不同的值，且保证了取值不同的训练过程都可以更新模型的网络参数，行为与动态图一致。

docs/guides/04_dygraph_to_static/input_spec_en.rst

@@ -225,7 +225,7 @@ Currently, the ``input_spec`` from ``to_static`` decorator only receives objects
         net.clear_gradients()
 
     # save inference model with use_act=False
-    paddle.jit.save(net, model_path='./simple_net')
+    paddle.jit.save(net, path='./simple_net')
 
 
 In above example, use_act is equal to True if step is an odd number, and False if step is an even number. We support non-tensor argument applied to different values during training after conversion. Moreover, the shared parameters of the model can be updated during the training with different values. The behavior is consistent with the dynamic graph.