torch.foreach.lerp

torch.foreach.lerp(inputs: TensorList, end: TensorList, weight: Scalar, /) → tuple[Tensor, ...]

torch.foreach.lerp(inputs: TensorList, end: TensorList, weight: ScalarList, /) → tuple[Tensor, ...]

torch.foreach.lerp(inputs: TensorList, end: TensorList, weight: TensorList, /) → tuple[Tensor, ...]

Applies torch.lerp() to corresponding tensors in inputs and end.

This is semantically equivalent to applying torch.lerp() independently at every list position. Does not mutate its arguments and returns a tuple of result tensors.

Tensor-list arguments must be non-empty. Corresponding tensor or scalar lists must have the same length. An accelerated multi-tensor implementation is used only when supported by the inputs; otherwise the operation falls back to per-tensor execution.

weight may be one shared scalar, a scalar list or tuple, or a tensor list.

Parameters:

• inputs (list or tuple of Tensor) – starting tensors.

• end (list or tuple of Tensor) – ending tensors.

• weight (Number, list or tuple of Number, or list or tuple of Tensor) – interpolation weights.

Returns:

a tuple containing one result tensor per list position.