add documentation for layers.gelu (#1643)

* add doc for gelu

* fix indent, test=document_preview

* fix hyper link, test=document_preview

* fix dtype annotations, test=document_preview

doc/fluid/api_cn/layers_cn/gelu_cn.rst

+.. _cn_api_fluid_layers_gelu:
+
+gelu
+-------------------------------
+
+.. py:function:: paddle.fluid.layers.gelu(x)
+
+逐元素计算 Gelu激活函数。更多细节请参考 `Gaussian Error Linear Units <https://arxiv.org/abs/1606.08415>`_ 。
+
+
+.. math::
+    out = 0.5 * x * (1 + erf(\frac{x}{\sqrt{2}}))
+
+参数：
+  - **x** (Variable) - Gelu Op 的输入，多维 Tensor 或 LoDTensor，数据类型为 float32 或 float64。
+
+返回：
+  - 多维 Tensor 或 LoDTensor, 数据类型为 float32 或 float64， 和输入 x 的数据类型相同，形状和输入 x 相同。
+
+返回类型：
+  - Variable
+
+**代码示例**：
+
+.. code-block:: python
+    
+    # declarative mode
+    import numpy as np
+    from paddle import fluid
+    
+    x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
+    y = fluid.layers.gelu(x)
+    
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    start = fluid.default_startup_program()
+    main = fluid.default_main_program()
+    
+    data = np.random.randn(2, 3).astype("float32")
+    exe.run(start)
+    
+    y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
+    
+    data
+    # array([[ 0.87165993, -1.0541513 , -0.37214822],
+    #         [ 0.15647964,  0.32496083,  0.33045998]], dtype=float32)
+    y_np
+    # array([[ 0.70456535, -0.15380788, -0.13207214],
+    #        [ 0.08796856,  0.20387867,  0.2080159 ]], dtype=float32)
+
+.. code-block:: python
+
+    # imperative mode
+    import numpy as np
+    from paddle import fluid
+    import paddle.fluid.dygraph as dg
+    
+    data = np.random.randn(2, 3).astype("float32")
+    place = fluid.CPUPlace()
+    with dg.guard(place) as g:
+        x = dg.to_variable(data)
+        y = fluid.layers.gelu(x)
+        y_np = y.numpy()
+    data
+    # array([[ 0.87165993, -1.0541513 , -0.37214822],
+    #        [ 0.15647964,  0.32496083,  0.33045998]], dtype=float32)
+    y_np
+    # array([[ 0.70456535, -0.15380788, -0.13207214],
+    #        [ 0.08796856,  0.20387867,  0.2080159 ]], dtype=float32)