fix some error api comments.

mindspore/dataset/engine/datasets.py

@@ -1751,12 +1751,10 @@ class MappableDataset(SourceDataset):
                 original dataset.
                 If after rounding:
 
-                    - Any size equals 0, an error will occur.
-
-                    - The sum of split sizes < K, the difference will be added to the first split.
-
-                    - The sum of split sizes > K, the difference will be removed from the first large
-                      enough split such that it will have at least 1 row after removing the difference.
+                - Any size equals 0, an error will occur.
+                - The sum of split sizes < K, the difference will be added to the first split.
+                - The sum of split sizes > K, the difference will be removed from the first large
+                  enough split such that it will have at least 1 row after removing the difference.
 
             randomize (bool, optional): Determines whether or not to split the data randomly (default=True).
                 If True, the data will be randomly split. Otherwise, each split will be created with

mindspore/nn/cell.py

@@ -1163,9 +1163,9 @@ class Cell(Cell_):
 
         Note:
             - If the computation involves something like randomization or global variable, the equivalence
-            is not guaranteed currently.
+              is not guaranteed currently.
             - If the recompute api of a primitive in this cell is also called, the recompute mode of this
-            primitive is subject to the recompute api of the primitive.
+              primitive is subject to the recompute api of the primitive.
 
         Args:
             mode (bool): Specifies whether the cell is recomputed. Default: True.

mindspore/nn/layer/normalization.py

@@ -915,7 +915,7 @@ class InstanceNorm2d(Cell):
     Supported Platforms:
         ``GPU``
 
-    Raise:
+    Raises:
         TypeError: If `num_features` is not an int.
         TypeError: If `eps` is not a float.
         TypeError: If `momentum` is not a float.

mindspore/numpy/math_ops.py

@@ -535,7 +535,7 @@ def minimum(x1, x2, dtype=None):
 
     Compares two tensors and returns a new tensor containing the element-wise minima.
 
-   Note:
+    Note:
         Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
         not supported.
         Unlike numpy, when one of the elements is a NaN, the second element is
@@ -818,11 +818,11 @@ def tensordot(a, b, axes=2):
     `a` and the first `N` dimensions of `b` are summed over.
     Three common use cases are:
 
-        ``axes = 0`` : tensor product
+    - ``axes = 0`` : tensor product
 
-        ``axes = 1`` : tensor dot product
+    - ``axes = 1`` : tensor dot product
 
-        ``axes = 2`` : (default) tensor double contraction
+    - ``axes = 2`` : (default) tensor double contraction
 
     When axes is integer_like, the sequence for evaluation will be: first the `-Nth`
     axis in `a` and 0th axis in `b`, and the -1th axis in `a` and `Nth` axis in `b` last.

mindspore/ops/operations/array_ops.py

@@ -2891,16 +2891,16 @@ class StridedSlice(PrimitiveWithInfer):
         Tensor.
         The output is explained by following example.
 
-            - In the 0th dimension, begin is 1, end is 2, and strides is 1,
-              because :math:`1+1=2\geq2`, the interval is :math:`[1,2)`.
-              Thus, return the element with :math:`index = 1` in 0th dimension, i.e., [[3, 3, 3], [4, 4, 4]].
-            - In the 1st dimension, similarly, the interval is :math:`[0,1)`.
-              Based on the return value of the 0th dimension, return the element with :math:`index = 0`,
-              i.e., [3, 3, 3].
-            - In the 2nd dimension, similarly, the interval is :math:`[0,3)`.
-              Based on the return value of the 1st dimension, return the element with :math:`index = 0,1,2`,
-              i.e., [3, 3, 3].
-            - Finally, the output is [3, 3, 3].
+        - In the 0th dimension, begin is 1, end is 2, and strides is 1,
+          because :math:`1+1=2\geq2`, the interval is :math:`[1,2)`.
+          Thus, return the element with :math:`index = 1` in 0th dimension, i.e., [[3, 3, 3], [4, 4, 4]].
+        - In the 1st dimension, similarly, the interval is :math:`[0,1)`.
+          Based on the return value of the 0th dimension, return the element with :math:`index = 0`,
+          i.e., [3, 3, 3].
+        - In the 2nd dimension, similarly, the interval is :math:`[0,3)`.
+          Based on the return value of the 1st dimension, return the element with :math:`index = 0,1,2`,
+          i.e., [3, 3, 3].
+        - Finally, the output is [3, 3, 3].
 
     Raises:
         TypeError: If `begin_mask`, `end_mask`, `ellipsis_mask`, `new_axis_mask` or `shrink_axis_mask` is not an int.

mindspore/ops/operations/nn_ops.py

@@ -521,7 +521,7 @@ class ReLUV2(PrimitiveWithInfer):
 
     Raises:
         TypeError: If `input_x`, `output` or `mask` is not a Tensor.
-        TypeError: If dtype of `output` is not same as `input_x`.
+        TypeError: If dtype of `output` is not same as `input_x` .
         TypeError: If dtype of `mask` is not unit8.
 
     Supported Platforms:

mindspore/ops/primitive.py

@@ -228,7 +228,7 @@ class Primitive(Primitive_):
 
         Note:
             - If the computation involves something like randomization or global variable, the equivalence
-            is not guaranteed currently.
+              is not guaranteed currently.
 
         Args:
             mode (bool): Specifies whether the primitive is recomputed. Default: True.