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# torch.addr

torch.addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) → Tensor

  Performs the outer-product of vectors `vec1` and `vec2` and adds it to the matrix `input`.

  Optional values `beta` and `alpha` are scaling factors on the outer product between `vec1` and `vec2` and the added matrix `input` respectively.

  out=β input+α (vec1⊗vec2)\text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) out=β input+α (vec1⊗vec2)

  If `beta` is 0, then `input` will be ignored, and nan and inf in it will not be propagated.

  If `vec1` is a vector of size n and `vec2` is a vector of size m, then `input` must be broadcastable with a matrix of size (n×m)(n \times m)(n×m) and `out` will be a matrix of size (n×m)(n \times m)(n×m).

  Parameters:

    - input (Tensor) – matrix to be added

    - vec1 (Tensor) – the first vector of the outer product

    - vec2 (Tensor) – the second vector of the outer product

  Keyword Arguments:

    - beta (Number, optional) – multiplier for `input` (β\beta β)

    - alpha (Number, optional) – multiplier for vec1⊗vec2\text{vec1} \otimes \text{vec2}vec1⊗vec2 (α\alpha α)

    - out (Tensor, optional) – the output tensor.

  Example:

      >>> vec1 = torch.arange(1., 4.)
      >>> vec2 = torch.arange(1., 3.)
      >>> M = torch.zeros(3, 2)
      >>> torch.addr(M, vec1, vec2)
      tensor([[ 1.,  2.],
              [ 2.,  4.],
              [ 3.,  6.]])

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