!46075 fix docs format issues

Merge pull request !46075 from luojianing/code_docs_master

mindspore/python/mindspore/ops/function/math_func.py

@@ -3610,6 +3610,7 @@ def heaviside(x, values):
 
     Supported Platforms:
         ``GPU`` ``CPU``
+
     Examples:
         >>> x = Tensor(np.array([-1.5, 0., 2.]))
         >>> values = Tensor(np.array([0.5]))
@@ -4073,8 +4074,8 @@ def addr(x, vec1, vec2, beta=1, alpha=1):
     """
     Executes the outer-product of `vec1` and `vec2` and adds it to the matrix `x`.
 
-    If `vec1` is a vector of size :vec1:`N` and `vec2` is a vector of size :vec1:`M`, then `x` must be broadcastable
-    with a matrix of size :vec1:`(N, M)` and `out` will be a matrix of size :vec1:`(N, M)`.
+    If `vec1` is a vector of size :math:`N` and `vec2` is a vector of size :math:`M`, then `x` must be broadcastable
+    with a matrix of size :math:`(N, M)` and `out` will be a matrix of size :math:`(N, M)`.
 
     The optional values `beta` and `alpha` are the scale factors on the outer product between `vec1` and `vec2`
     and the added matrix `x` respectively. If `beta` is 0, then `x` will be ignored.
@@ -4083,20 +4084,20 @@ def addr(x, vec1, vec2, beta=1, alpha=1):
         output = β x + α (vec1 ⊗ vec2)
 
     Args:
-        x (Tensor): Vector to be added. The shape of the tensor is :vec1:`(N, M)`.
-        vec1 (Tensor): The first tensor to be multiplied. The shape of the tensor is :vec1:`(N,)`.
-        vec2 (Tensor): The second tensor to be multiplied. The shape of the tensor is :vec1:`(M,)`.
+        x (Tensor): Vector to be added. The shape of the tensor is :math:`(N, M)`.
+        vec1 (Tensor): The first tensor to be multiplied. The shape of the tensor is :math:`(N,)`.
+        vec2 (Tensor): The second tensor to be multiplied. The shape of the tensor is :math:`(M,)`.
         beta (scalar[int, float, bool], optional): Multiplier for `x` (β). The `beta` must be int or
             float or bool, Default: 1.
         alpha (scalar[int, float, bool], optional): Multiplier for `vec1` ⊗ `vec2` (α). The `alpha` must
             be int or float or bool, Default: 1.
 
     Returns:
-        Tensor, the shape of the output tensor is :vec1:`(N, M)`, has the same dtype as `x`.
+        Tensor, the shape of the output tensor is :math:`(N, M)`, has the same dtype as `x`.
 
     Raises:
         TypeError: If `x`, `vec1`, `vec2` is not a Tensor.
-        TypeError: If inputs `x`, `vec1`, 'vec2' are not the same dtype.
+        TypeError: If inputs `x`, `vec1`, `vec2` are not the same dtype.
         ValueError: If `x` is not a 2-D Tensor.
             If `vec1`, `vec2` is not a 1-D Tensor.
 

mindspore/python/mindspore/ops/operations/array_ops.py

@@ -7457,7 +7457,7 @@ class FillDiagonal(Primitive):
 
     Inputs:
         - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
-        The data type must be float32, int32 or int64.
+          The data type must be float32, int32 or int64.
 
     Outputs:
         - **y** (Tensor) - Tensor, has the same shape and data type as the input `x`.

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

@@ -512,7 +512,7 @@ class Assert(PrimitiveWithInfer):
     Inputs:
         - **condition** (Union[Tensor[bool], bool]) - The condition to be identified.
         - **input_data** (Union[tuple[Tensor], list[Tensor]]) - The tensors to be printed out when the condition
-        is false.
+          is false.
 
     Raises:
         TypeError: If `summarize` is not an int.

mindspore/python/mindspore/ops/operations/image_ops.py

@@ -1115,7 +1115,7 @@ class CombinedNonMaxSuppression(Primitive):
     Raises:
         TypeError: If the dtype of `boxes`, `scores` , `iou_threshold` , `score threshold` are not float32.
         TypeError: If the dtype of `max_output_size_per_class` and `max_total_size` are not int32.
-        ValueError: If `boxes`is not 4D.
+        ValueError: If `boxes` is not 4D.
         ValueError: If `max_output_size_per_class`, `max_total_size`, `iou_threshold` and `score threshold` are not 0D.
         ValueError: If shape[0] of `boxes` is not same with shape[0] of `scores`.
         ValueError: If `scores` is not 3D.

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -6607,6 +6607,7 @@ class RaggedRange(Primitive):
           - if type of the input `starts`, input `limits` and input `deltas`
             are float32 or float64, shape of the output `rt_dense_values` is equal to
             sum(ceil(abs((limits[i] - starts[i]) / deltas[i]))).
+
     Raises:
         TypeError: If any input is not Tensor.
         TypeError: If the type of `starts` is not one of the following dtype: int32, int64, float32, float64.

mindspore/python/mindspore/ops/operations/nn_ops.py

@@ -10209,12 +10209,14 @@ class FractionalMaxPoolWithFixedKsize(Primitive):
         - **y** (Tensor) - Has the same type as the `input_x`.
           Has the shape :math:`(N, C, output\underline{~}shape{H}, output\underline{~}shape{W})`.
         - **argmax** (Tensor) -A tensor whose data type must be int64. Has the same shape as the `y`.
+
     Raises:
         TypeError: If data type of `input_x` is not one of the following: float16, float32, float64, int32, int64.
         TypeError: If data type of `random_samples` is not one of the following: float16, float32, float64.
         ValueError: If `ksize` is not a number and `ksize` is not a tuple of length 2.
         ValueError: If `output_shape` is not a number and `output_shape` is not a tuple of length 2.
-        ValueError: If the sum of `ksize`,`output_shape` and -1 is larger than the corresponding dimension of `input_x`.
+        ValueError: If the sum of `ksize`, `output_shape` and -1 is larger than the corresponding
+            dimension of `input_x`.
         ValueError: If the dimension of `random_samples` is not 3.
         ValueError: If the first dimension size of `input_x` and `random_samples` is not equal.
         ValueError: If the second dimension size of `input_x` and `random_samples` is not equal.

mindspore/python/mindspore/ops/operations/random_ops.py

@@ -283,7 +283,7 @@ class LogNormalReverse(Primitive):
 
     Inputs:
         - **input** (Tensor) - The tensor to be generated with log-normal distribution.
-        Must be one of the following types: float16, float32, float64.
+          Must be one of the following types: float16, float32, float64.
 
     Outputs:
         Tensor. A Tensor with the same type and shape of input.
@@ -295,7 +295,6 @@ class LogNormalReverse(Primitive):
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
-
     Examples:
         >>> x = Tensor(np.random.randn(3,4),mstype.float64)
         >>> mean = 2.0