3j SSKJr SSKrSSKJr SSKJrJr S\RS\\\R4S\RS\\\R4S \\\R4S \R4 S jr "S S \R5r g))UnionN)nn) KORNIA_CHECKKORNIA_CHECK_IS_TENSORsrc1alphasrc2betagammareturncH[U5 [U5 [URUR:HSURSUR35 [U[R 5(a$[URUR:HS5 O)[R "XRURS9n[U[R 5(a$[URUR:HS5 O)[R "X0RURS9n[U[R 5(a$[URUR:HS5 O)[R "X@RURS9nX-X#--U-$)aCalculate the weighted sum of two Tensors. .. image:: _static/img/add_weighted.png The function calculates the weighted sum of two Tensors as follows: .. math:: out = src1 * alpha + src2 * beta + gamma Args: src1: torch.Tensor with an arbitrary shape, equal to shape of src2. alpha: weight of the src1 elements as Union[float, torch.Tensor]. src2: torch.Tensor with an arbitrary shape, equal to shape of src1. beta: weight of the src2 elements as Union[float, torch.Tensor]. gamma: scalar added to each sum as Union[float, torch.Tensor]. Returns: Weighted torch.Tensor with shape equal to src1 and src2 shapes. Example: >>> input1 = torch.rand(1, 1, 5, 5) >>> input2 = torch.rand(1, 1, 5, 5) >>> output = add_weighted(input1, 0.5, input2, 0.5, 1.0) >>> output.shape torch.Size([1, 1, 5, 5]) Notes: torch.Tensor alpha/beta/gamma have to be with shape broadcastable to src1 and src2 shapes. z)src1 and src2 have different shapes. Got z and z%alpha has a different shape than src.)dtypedevicez$beta has a different shape than src.z%gamma has a different shape than src.) rrshape isinstancetorchTensortensorrr)rrr r r s M/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/enhance/core.py add_weightedrs"J4 4 tzz)-VW[WaWaVbbghlhrhrgs+tu%&&TZZ5;;.0WX U**T[[I$ %%TZZ4::-/UV||D 4;;G%&&TZZ5;;.0WX U**T[[I <$+ % --c^\rSrSrSrS\\\R4S\\\R4S\\\R4SS4U4Sjjr S \RS \RS\R4S jr S r U=r $) AddWeightedUaCalculate the weighted sum of two Tensors. The function calculates the weighted sum of two Tensors as follows: .. math:: out = src1 * alpha + src2 * beta + gamma Args: alpha: weight of the src1 elements as Union[float, torch.Tensor]. beta: weight of the src2 elements as Union[float, torch.Tensor]. gamma: scalar added to each sum as Union[float, torch.Tensor]. Shape: - Input1: torch.Tensor with an arbitrary shape, equal to shape of Input2. - Input2: torch.Tensor with an arbitrary shape, equal to shape of Input1. - Output: Weighted torch.Tensor with shape equal to src1 and src2 shapes. Example: >>> input1 = torch.rand(1, 1, 5, 5) >>> input2 = torch.rand(1, 1, 5, 5) >>> output = AddWeighted(0.5, 0.5, 1.0)(input1, input2) >>> output.shape torch.Size([1, 1, 5, 5]) Notes: torch.Tensor alpha/beta/gamma have to be with shape broadcastable to src1 and src2 shapes. rr r r NcF>[TU]5 XlX lX0lgN)super__init__rr r )selfrr r __class__s rrAddWeighted.__init__ss    rrr cX[XRX RUR5$r)rrr r )rrr s rforwardAddWeighted.forward{sD**dIItzzJJr)rr r ) __name__ __module__ __qualname____firstlineno____doc__rfloatrrrr#__static_attributes__ __classcell__)r s@rrrUs:5%,,./7r1s$ B8. ,,8.  $ %8. ,,8. u||# $ 8.  $ % 8.  \\ 8.v'K"))'Kr