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• Description: Performs element-wise linear interpolation on corresponding tensors in two tensor lists using the scalar weight as the interpolation coefficient.
• Formula:$$x1 = [{x1_0}, {x1_1}, ... {x1_{n-1}}], x2 = [{x2_0}, {x2_1}, ... {x2_{n-1}}]\\ y = [{y_0}, {y_1}, ... {y_{n-1}}]\\$$ $${\rm y}_i = x1_i + {\rm weight} × (x2_i - x1_i) (i=0,1,...n-1)$$

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Each operator has calls. First, aclnnForeachLerpScalarGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnForeachLerpScalar is called to perform computation.

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• Parameters

  [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]:

    • [object Object] and [object Object] must have the same shape.
    • The shape size of [object Object] must be greater than or equal to that of [object Object].

  • Ascend 950PR/Ascend 950DT:

    [object Object], [object Object] and [object Object] must have the same shape size. A tensor list can contain a maximum of 50 tensors.

• Returns

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

  The first-phase API implements input parameter verification. The following errors may be thrown.

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• Parameters

  [object Object]

• Returns

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

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• Deterministic computation:
  • aclnnForeachLerpScalar defaults to a deterministic implementation.

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