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modify the documentation of the scatter_div operator

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hujiahui8 2022-08-04 14:50:47 +08:00
parent 3b3109e8e1
commit 349e41e64a
3 changed files with 5 additions and 5 deletions
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2
docs/api/api_python/ops/mindspore.ops.func_scatter_div.rst
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@ -15,7 +15,7 @@ mindspore.ops.scatter_div
参数：
- **input_x** (Parameter) - scatter_div的输入，任意维度的Parameter。
- **indices** (Tensor) - 指定相除操作的索引，数据类型必须为mindspore.int32或者mindspore.int64。
- **updates** (Tensor) - 指定与 `input_x` 取最小值操作的Tensor，数据类型与 `input_x` 相同，shape为 `indices.shape + input_x.shape[1:]` 。
- **updates** (Tensor) - 指定与 `input_x` 相除的Tensor，数据类型与 `input_x` 相同，shape为 `indices.shape + input_x.shape[1:]` 。
输出：
Tensor，更新后的 `input_x` ，shape和类型与 `input_x` 相同。

4
mindspore/python/mindspore/ops/function/array_func.py
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@ -1560,9 +1560,9 @@ def scatter_div(input_x, indices, updates):
Args:
input_x (Parameter): The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
indices (Tensor): The index to do min operation whose data type must be mindspore.int32 or
indices (Tensor): The index to do divide operation whose data type must be mindspore.int32 or
mindspore.int64.
updates (Tensor): The tensor doing the min operation with `input_x`,
updates (Tensor): The tensor doing the divide operation with `input_x`,
the data type is same as `input_x`, the shape is `indices.shape + input_x.shape[1:]`.
Outputs:

4
mindspore/python/mindspore/ops/operations/array_ops.py
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@ -4785,9 +4785,9 @@ class ScatterDiv(_ScatterOpDynamic):
Inputs:
- **input_x** (Parameter) - The target tensor, with data type of Parameter.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
- **indices** (Tensor) - The index to do min operation whose data type must be mindspore.int32 or
- **indices** (Tensor) - The index to do divide operation whose data type must be mindspore.int32 or
mindspore.int64.
- **updates** (Tensor) - The tensor doing the min operation with `input_x`,
- **updates** (Tensor) - The tensor doing the divide operation with `input_x`,
the data type is same as `input_x`, the shape is `indices.shape + input_x.shape[1:]`.
Outputs: