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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

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mindspore.ops.inplace_sub

mindspore.ops.inplace_sub(x, v, indices)

Subtract v in x according to the indices.

for each i, …, j in indices :

$$x[\text{indices}[i,...,j]]-=v[i,...,j]$$

Parameters

• x (Tensor) – The input tensor.

• v (Tensor) – The input tensor to subtract from x .

• indices (Union[int, tuple]) – Indices into the input x along the 0th dimension.

Returns

Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> indices = (0, 1)
>>> x = mindspore.tensor([[1, 2], [3, 4], [5, 6]], mindspore.float32)
>>> input_v = mindspore.tensor([[0.5, 1.0], [1.0, 1.5]], mindspore.float32)
>>> output = mindspore.ops.inplace_sub(x, input_v, indices)
>>> print(output)
[[0.5 1. ]
 [2.  2.5]
 [5.  6. ]]