[object Object]

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

• Description: Computes the standard deviation and mean of the sample.
• Formula: If [object Object] is $i$, the dimension is computed. $N$ is the shape. $self_{i}$ is used to compute the average value $\bar{x_{i}}$ in the dimension.$$\left\{ \begin{array} {rcl} meanOut& &= \bar{x_{i}}\\ stdOut& &= \sqrt{\frac{1}{max(0, N - \delta N)}\sum_{j=0}^{N-1}(self_{ij}-\bar{x_{i}})^2} \end{array} \right.$$ If [object Object], the dimension is retained after reduction, and the dimension value in the output shape is 1. If [object Object], the dimension is not retained.

[object Object]

Each operator has calls. First, [object Object] is called to obtain the input, the workspace size required for computation, and the executor that contains 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, aclTensor on the device. are supported. The can be ND.
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT or FLOAT16.
    • [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 FLOAT, BFLOAT16, or FLOAT16.
  • [object Object] (aclIntArray*, computation input): [object Object] in the formula. It is aclIntArray on the host, indicating the dimension involved in computation. The value range is [-self.dim(), self.dim()-1], and the data must be unique. The data type can be INT64. When [object Object] is nullptr or [], all dimensions are calculated.
  • [object Object] (int64_t, calculation input): $\delta N$ value in the formula, which is an integer on the host side.
  • [object Object] (bool, computation input): [object Object] in the formula, Boolean value on the host, whether to retain the dimension in the output tensor.
  • [object Object] (aclTensor*, computation output): [object Object] in the formula, aclTensor on the device. are supported. The can be ND.
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT or FLOAT16.
    • [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 FLOAT, BFLOAT16, or FLOAT16.
  • [object Object] (aclTensor*, computation output): [object Object] in the formula, aclTensor on the device. are supported. The can be ND.
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT or FLOAT16.
    • [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 FLOAT, BFLOAT16, or FLOAT16.
  • [object Object] (uint64_t*, output): size of the workspace to be allocated on the device.
  • [object Object] (aclOpExecutor**, output): operator executor, containing 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 the first-phase API [object Object].
  • [object Object] (aclOpExecutor*, input): operator executor, containing the operator computation process.
  • [object Object] (aclrtStream, input): stream for executing the task.

• Returns:

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

[object Object]

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

[object Object]

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