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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

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

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Problem description

Describe the bug so that we can quickly locate the problem.

mindspore.ops.Erfinv

class mindspore.ops.Erfinv[source]

Computes the inverse error function of input. The inverse error function is defined in the range (-1, 1) as:

erfinv(erf(x))=x
Inputs:
  • input_x (Tensor) - The input tensor to compute to, with data type float32 or float16.

Outputs:

Tensor, has the same shape and dtype as input_x.

Raises

TypeError – If dtype of input_x is neither float32 nor float16.

Supported Platforms:

Ascend

Examples

>>> x = Tensor(np.array([0, 0.5, -0.9]), mindspore.float32)
>>> erfinv = ops.Erfinv()
>>> output = erfinv(x)
>>> print(output)
[ 0.          0.47695306 -1.1630805 ]