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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

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- 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.

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Problem description

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mindspore.ops.addcdiv

View Source On Gitee
mindspore.ops.addcdiv(input, tensor1, tensor2, value=1)[source]

Divide tensor1 by tensor2 element-wise, multiply the result by the scalar value , and add it to input .

y[i]=input[i]+value[i](tensor1[i]/tensor2[i])
Parameters
  • input (Tensor) – The input tensor.

  • tensor1 (Tensor) – Tensor1, the numerator.

  • tensor2 (Tensor) – Tensor2, the denominator.

  • value (Union[Tensor, number]) – The multiplier for ( tensor1 / tensor2 ). Default 1 .

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> x = mindspore.tensor([1, 1, 1, 1], mindspore.float32)
>>> x1 = mindspore.tensor([1, 2, 3, 4], mindspore.float32)
>>> x2 = mindspore.tensor([4, 4, 2, 1], mindspore.float32)
>>> y = mindspore.ops.addcdiv(x, x1, x2, 0.1)
>>> print(y)
[1.025 1.05  1.15  1.4  ]