[Docs] update formulas for math and array operators

mindspore/ops/operations/array_ops.py

@@ -3275,7 +3275,9 @@ class ScatterUpdate(_ScatterOp_Dynamic):
     Using given values to update tensor value, along with the input indices.
 
     for each `i, ..., j` in `indices.shape`:
+
     .. math::
+
         \text{input_x}[\text{indices}[i, ..., j], :] = \text{updates}[i, ..., j, :]
 
     Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
@@ -3391,7 +3393,9 @@ class ScatterMax(_ScatterOp):
     This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
 
     for each `i, ..., j` in `indices.shape`:
+
     .. math::
+
         \text{input_x}[\text{indices}[i, ..., j], :]
         = max(\text{input_x}[\text{indices}[i, ..., j], :], \text{updates}[i, ..., j, :])
 
@@ -3435,7 +3439,9 @@ class ScatterMin(_ScatterOp):
     This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
 
     for each `i, ..., j` in `indices.shape`:
+
     .. math::
+
         \text{input_x}[\text{indices}[i, ..., j], :]
         = min(\text{input_x}[\text{indices}[i, ..., j], :], \text{updates}[i, ..., j, :])
 
@@ -3479,7 +3485,9 @@ class ScatterAdd(_ScatterOp_Dynamic):
     This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
 
     for each `i, ..., j` in `indices.shape`:
+
     .. math::
+
         \text{input_x}[\text{indices}[i, ..., j], :] \mathrel{+}= \text{updates}[i, ..., j, :]
 
     Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
@@ -3529,7 +3537,9 @@ class ScatterSub(_ScatterOp):
     This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
 
     for each `i, ..., j` in `indices.shape`:
+
     .. math::
+
         \text{input_x}[\text{indices}[i, ..., j], :] \mathrel{-}= \text{updates}[i, ..., j, :]
 
     Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
@@ -3573,7 +3583,9 @@ class ScatterMul(_ScatterOp):
     This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
 
     for each `i, ..., j` in `indices.shape`:
+
     .. math::
+
         \text{input_x}[\text{indices}[i, ..., j], :] \mathrel{*}= \text{updates}[i, ..., j, :]
 
     Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
@@ -3616,7 +3628,9 @@ class ScatterDiv(_ScatterOp):
     This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
 
     for each `i, ..., j` in `indices.shape`:
+
     .. math::
+
         \text{input_x}[\text{indices}[i, ..., j], :] \mathrel{/}= \text{updates}[i, ..., j, :]
 
     Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.

mindspore/ops/operations/math_ops.py

@@ -794,6 +794,7 @@ class BatchMatMul(MatMul):
     Computes matrix multiplication between two tensors by batch.
 
     .. math::
+
         \text{output}[..., :, :] = \text{matrix}(a[..., :, :]) * \text{matrix}(b[..., :, :])
 
     The two input tensors must have the same rank and the rank must be not less than `3`.