ó Ñ‘®iãó€•SSKJr SSKJr SSKJr SSKJr \(aSSKJr /r SS Sjjr g) é)Ú annotations)Ú TYPE_CHECKING)Ú_C_ops)Úin_dynamic_or_pir_mode)ÚTensorNcó^•[5(dS5e[R"XX#U5$)aÛ Applies matrix multiplication for `x` and `y` , `input` is added to the final result. The equation is: .. math:: out = alpha * x * y + beta * input The supported input/output Tensor layout are as follows: Note: input[SparseCsrTensor] + x[SparseCsrTensor] @ y[SparseCsrTensor] -> out[SparseCsrTensor] input[DenseTensor] + x[SparseCsrTensor] @ y[DenseTensor] -> out[DenseTensor] input[SparseCooTensor] + x[SparseCooTensor] @ y[SparseCooTensor] -> out[SparseCooTensor] input[DenseTensor] + x[SparseCooTensor] @ y[DenseTensor] -> out[DenseTensor] It supports backward propagation. Dimensions `input` , `x` , `y` must be same and >= 2D. Automatic broadcasting of Tensor is not supported. Args: input (SparseTensor|DenseTensor): The input tensor. Shape is [*, M, N]. The data type can be float32 or float64. x (SparseTensor): The input SparseTensor. Shape is [*, M, K]. The data type can be float32 or float64. y (SparseTensor|DenseTensor): The input tensor. Shape is [*, K, N]. The data type can be float32 or float64. beta (float, optional): Coefficient of `input` . Default: 1.0 alpha (float, optional): Coefficient of `x * y` . Default: 1.0 name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: SparseTensor|DenseTensor: Tensor type, date type and shape is the same with `input` . Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> # dense + csr @ dense -> dense >>> input = paddle.rand([3, 2]) >>> crows = [0, 1, 2, 3] >>> cols = [1, 2, 0] >>> values = [1., 2., 3.] >>> x = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3]) >>> y = paddle.rand([3, 2]) >>> out = paddle.sparse.addmm(input, x, y, 3.0, 2.0) >>> # dense + coo @ dense -> dense >>> input = paddle.rand([3, 2]) >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1., 2., 3.] >>> x = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3]) >>> y = paddle.rand([3, 2]) >>> out = paddle.sparse.addmm(input, x, y, 3.0, 2.0) zrsxðõ#å åÝ8æÝà €ðØØð D9Ø ðD9à ðD9ððD9ð ð D9ð ð D9ð ð D9ð öD9r