Fix the formula display problem and some other formatting problems

mindspore/nn/cell.py

@@ -1260,7 +1260,7 @@ class GraphKernel(Cell):
         flags (dict) : Set graph flags. Default: None.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> class Relu(nn.GraphKernel):

mindspore/nn/layer/activation.py

@@ -124,7 +124,7 @@ class LogSoftmax(Cell):
         ValueError: If `axis` is not in range [-len(x), len(x)).
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)

mindspore/nn/layer/container.py

@@ -250,7 +250,7 @@ class CellList(_CellListBase, Cell):
         args (list, optional): List of subclass of Cell.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> conv = nn.Conv2d(100, 20, 3)

mindspore/nn/layer/normalization.py

@@ -309,7 +309,7 @@ class BatchNorm1d(_BatchNorm):
         Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out})`.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Raises:
         TypeError: If `num_features` is not an int.

mindspore/nn/loss/loss.py

@@ -333,7 +333,7 @@ class MAELoss(Loss):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        # Case 1: logits.shape = labels.shape = (3,)
+        >>> # Case 1: logits.shape = labels.shape = (3,)
         >>> loss = nn.MAELoss()
         >>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
         >>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32)

mindspore/nn/metrics/occlusion_sensitivity.py

@@ -47,7 +47,7 @@ class OcclusionSensitivity(Metric):
                           Default: None.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Example:
         >>> import numpy as np

mindspore/nn/optim/adam.py

@@ -287,7 +287,7 @@ class Adam(Optimizer):
         ValueError: If `weight_decay` is less than 0.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU``  ``CPU``
 
     Examples:
         >>> net = Net()
@@ -430,7 +430,7 @@ class AdamWeightDecay(Optimizer):
         ValueError: If `weight_decay` is less than 0.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> net = Net()

mindspore/nn/optim/lars.py

@@ -91,7 +91,7 @@ class LARS(Optimizer):
         Union[Tensor[bool], tuple[Parameter]], it depends on the output of `optimizer`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> net = Net()

mindspore/nn/optim/sgd.py

@@ -110,7 +110,7 @@ class SGD(Optimizer):
         ValueError: If the momentum, dampening or weight_decay value is less than 0.0.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> net = Net()

mindspore/ops/operations/math_ops.py

@@ -1165,7 +1165,7 @@ class Neg(PrimitiveWithInfer):
 
     Inputs:
         - **x** (Tensor) - The input tensor whose dtype is number.
-            :math:`(N,*)` where :math:`*` means ,any number of additional dimensions, its rank should less than 8.
+          :math:`(N,*)` where :math:`*` means ,any number of additional dimensions, its rank should less than 8.
 
     Outputs:
         Tensor, has the same shape and dtype as input.
@@ -1581,7 +1581,7 @@ class Sqrt(PrimitiveWithCheck):
 
     Inputs:
         - **x** (Tensor) - The input tensor whose dtype is number.
-         :math:`(N,*)` where :math:`*` means ,any number of additional dimensions, its rank should less than 8.
+          :math:`(N,*)` where :math:`*` means ,any number of additional dimensions, its rank should less than 8.
 
     Outputs:
         Tensor, has the same shape and data type as the `x`.
@@ -2345,7 +2345,7 @@ class FloorDiv(_MathBinaryOp):
 
     .. math::
 
-        out_{i} = \text{floor}( \frac{x_i}{y_i})
+        out_{i} = \\text{floor}( \\frac{x_i}{y_i})
 
     where the :math:`floor` indicates the operator that converts the input data into the floor data type.
 
@@ -3294,11 +3294,11 @@ class LogicalNot(PrimitiveWithInfer):
 
     .. math::
 
-        out_{i} = \neg x_{i}
+        out_{i} = \\neg x_{i}
 
     Inputs:
         - **x** (Tensor) - The input tensor whose dtype is bool.
-            :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
+          :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
 
     Outputs:
         Tensor, the shape is the same as the `x`, and the dtype is bool.
@@ -3399,7 +3399,7 @@ class LogicalOr(_LogicBinaryOp):
 
     .. math::
 
-        out_{i} = x_{i} \vee y_{i}
+        out_{i} = x_{i} \\vee y_{i}
 
     Inputs:
         - **x** (Union[Tensor, bool]) - The first input is a bool or a tensor whose data type is bool.