[object Object]

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

• Description: Computes the bitwise AND result between each element in the input tensor and the input scalar. The input tensor and [object Object] must be INT or BOOL variables. If the input is of the BOOL type, the operator computes the result of logical AND.
• Formula:$$out_i = self_i \& other_i$$

[object Object]

• [object Object] and [object Object] implement the same function in different ways. Select a proper operator based on your requirements.

  • [object Object]: An output tensor object needs to be created to store the computation result.
  • [object Object]: No output tensor object needs to be created, and the computation result is written in place to the input tensor's memory.

• Each operator has calls. First, [object Object] is called to obtain the workspace size required for computation and the executor covering the operator computation process. Then, [object Object] is called to perform computation.

• [object Object]

• [object Object]

[object Object]

• Parameters:

  • [object Object] (aclTensor*, computation input): [object Object] in the formula. It supports . Its can be ND, and the data dimensions cannot exceed 8.
    • [object Object]Atlas training products[object Object], [object Object]Atlas inference products[object Object], [object Object]Atlas A2 training products/Atlas A2 inference products[object Object], and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be INT16, UINT16, INT32, INT64, INT8, UINT8, or BOOL. The data type must follow the type promotion rules with [object Object] (see ). The resulting data type must be within the supported range listed above.
  • [object Object] (aclScalar*, computation input): [object Object] in the formula.
    • [object Object]Atlas training products[object Object], [object Object]Atlas inference products[object Object], [object Object]Atlas A2 training products/Atlas A2 inference products[object Object], and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be INT16, UINT16, INT32, INT64, INT8, UINT8, or BOOL. The data type must follow the type promotion rules with [object Object] (see ). The resulting data type must be within the supported range listed above.
  • [object Object] (aclTensor*, computation output): [object Object] in the formula. Its data type must be one convertible from the type deduced from [object Object] and [object Object] (see ). Its shape must match that of [object Object]. Its can be ND, and the data dimensions cannot exceed 8.
    • [object Object]Atlas training products[object Object] and [object Object]Atlas inference products[object Object]: The data type can be BOOL, INT8, INT16, UINT16, INT32, INT64, UINT8, FLOAT, FLOAT16, or DOUBLE.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be BOOL, INT8, INT16, UINT16, INT32, INT64, UINT8, FLOAT, FLOAT16, DOUBLE, or BFLOAT16.
  • [object Object] (uint64_t*, output): size of the workspace to be allocated on the device.
  • [object Object] (aclOpExecutor**, output): operator executor, covering the operator computation process.

• Returns:

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

[object Object]

• Parameters:

  • [object Object] (void*, input): address of the workspace to be allocated on the device.
  • [object Object] (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling [object Object].
  • [object Object] (aclOpExecutor*, input): operator executor, covering the operator computation process.
  • [object Object] (aclrtStream, input): stream for executing the task.

• Returns:

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

[object Object]

• Deterministic computation:
  • [object Object] defaults to a deterministic implementation.

[object Object]

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