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• Function: performs the logical AND operation when the input is a tensor of the BOOL type, or performs the bitwise AND operation when the input is of the INT type.

• Formulas:

  $$out_i = self_i \& other_i$$

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• aclnnBitwiseAndTensorOut and aclnnInplaceBitwiseAndTensorOut provide the same functionality. The differences are as follows. Select the appropriate operator based on your actual scenario.
  • aclnnBitwiseAndTensorOut: You need to create an output tensor object to store the computation result.
  • aclnnInplaceBitwiseAndTensorOut: You do not need to create an output tensor object. Instead, the computation result is directly stored in the memory of the input tensor.
• Each operator is divided into two phases (../common/two-phase-api.md). You must call aclnnBitwiseAndTensorOutGetWorkspaceSize to obtain the workspace size required for computation and the executor that contains the operator execution process, and then call aclnnBitwiseAndTensorOut to perform the computation.

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• Parameter description:

  [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.

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• Parameter description:

  [object Object]

• Returns

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

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• Parameter description:

  [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]

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• Parameter description:

  [object Object]

• Returns

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

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• Deterministic computation:
  • The aclnnBitwiseAndTensorOut is implemented in a deterministic manner by default.
  • The aclnnInplaceBitwiseAndTensorOut is implemented in a deterministic manner by default.

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The following example is for reference only. For details, see .