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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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Problem description

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

mindspore.ops.Tan

class mindspore.ops.Tan[source]

Computes tangent of x element-wise.

outi=tan(xi)
Inputs:
  • x (Tensor) - The shape of tensor is (N,) where means, any number of additional dimensions. Data type must be float16 or float32.

Outputs:

Tensor, has the same shape as x.

Raises
  • TypeError – If dtype of x is neither float16 nor float32.

  • TypeError – If x is not a Tensor.

Supported Platforms:

Ascend CPU

Examples

>>> tan = ops.Tan()
>>> x = Tensor(np.array([-1.0, 0.0, 1.0]), mindspore.float32)
>>> output = tan(x)
>>> print(output)
[-1.5574081 0. 1.5574081]