[object Object]

[object Object][object Object]undefined[object Object]

• Interface function: Uses each element of [object Object] as the power of the corresponding element of [object Object] to complete the computation.

• Formula:

  $$out_i = self_i^{exponent_i}$$

[object Object]

aclnnPowTensorScalar and aclnnInplacePowTensorScalar provide the same functionality. The differences are as follows:

• aclnnPowTensorScalar: An output tensor object needs to be created to store the computation result.
• aclnnInplacePowTensorScalar: No output tensor object needs to be created, and the computation result is stored in the memory of the input tensor.

Each operator is divided into two phases (../common/two_phase.md). You must call aclnnPowTensorScalarGetWorkspaceSize or aclnnInplacePowTensorScalarGetWorkspaceSize to obtain the workspace size required for computation and the executor that contains the operator execution process, and then call aclnnPowTensorScalar or aclnnInplacePowTensorScalar to perform computation.

[object Object]

• Parameters:

  [object Object]
  • [object Object]Atlas 200I/500 A2 inference products[object Object], [object Object]Atlas inference products[object Object], and [object Object]Atlas training products[object Object]: The data type cannot be BFLOAT16.

• Returns

  [object Object]: status code. For details, see .

  The first-phase API implements input parameter validation. The following error codes may be returned.

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns

  [object Object]: status code. For details, see .

[object Object]

• Parameters:

  [object Object]

• Returns

  [object Object]: status code. For details, see .

  The first-phase API implements input parameter validation. The following error codes may be returned.

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns

  [object Object]: status code. For details, see .

[object Object]

• The INT32 integer computation will time out in the following scenarios:

  [object Object]undefined

• Deterministic computation: The default deterministic implementation is used for aclnnPowTensorScalar&aclnnInplacePowTensorScalar.

• [object Object]Atlas training products[object Object] and [object Object]Atlas inference products[object Object]: If the computation result exceeds the value range of the specified data type, the boundary value of the data type is returned as the result.

• In the exponent = 2 scenario, when the [object Object] operator is called and the input [object Object] is [object Object], the accuracy is ensured only when the result is within the range of (-2048, 1920).

[object Object]

The following examples are for reference only. For details, see .