!104 fix doc issues

Merge pull request !104 from 万万没想到/fix-doc-issues

mindspore/nn/layer/normalization.py

@@ -249,7 +249,7 @@ class LayerNorm(Cell):
             'he_uniform', etc. Default: 'zeros'.
 
     Inputs:
-        - **input_x** (Tensor) - The shape of 'input_x' is input_shape = :math:`(x_1, x_2, ..., x_R)`,
+        - **input_x** (Tensor) - The shape of 'input_x' is :math:`(x_1, x_2, ..., x_R)`,
           and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`.
 
     Outputs:

mindspore/ops/operations/array_ops.py

@@ -443,7 +443,6 @@ class Transpose(PrimitiveWithInfer):
     Examples:
         >>> input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32)
         >>> perm = (0, 2, 1)
-        >>> expect = np.array([[[1, 4], [2, 5], [3, 6]], [[7, 10], [8, 11], [9, 12]]])
         >>> transpose = Transpose()
         >>> output = transpose(input_tensor, perm)
     """
@@ -1634,7 +1633,7 @@ class Diag(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor([1, 2, 3, 4])
         >>> diag = P.Diag()
-        >>> diag(x)
+        >>> diag(input_x)
         [[1, 0, 0, 0],
          [0, 2, 0, 0],
          [0, 0, 3, 0],

mindspore/ops/operations/control_ops.py

@@ -107,8 +107,8 @@ class GeSwitch(PrimitiveWithInfer):
         >>>         ret = self.merge((add_ret, sq_ret))
         >>>         return ret[0]
         >>>
-        >>> x = Tensor(x_init, dtype=mindspore.float32)
-        >>> y = Tensor(y_init, dtype=mindspore.float32)
+        >>> x = Tensor(10.0, dtype=mindspore.float32)
+        >>> y = Tensor(5.0, dtype=mindspore.float32)
         >>> net = Net()
         >>> output = net(x, y)
     """

mindspore/ops/operations/nn_ops.py

@@ -1089,9 +1089,10 @@ class TopK(PrimitiveWithInfer):
         - **indices** (Tensor) - The indices of values within the last dimension of input.
 
     Examples:
-        >>> topk = TopK(sorted=True)
-        >>> x = Tensor(np.array([1, 2, 3, 4, 5]).astype(np.float16))
-        >>> values, indices = topk(x)
+        >>> topk = P.TopK(sorted=True)
+        >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16))
+        >>> k = 3
+        >>> values, indices = topk(input_x, k)
         >>> assert values == Tensor(np.array([5, 4, 3]))
         >>> assert indices == Tensor(np.array([4, 3, 2]))
     """