torch.sparse.sampled_addmm
torch.sparse.sampled_addmm(input, mat1, mat2, *, beta=1., alpha=1., out=None) -> Tensor
Performs a matrix multiplication of the dense matrices "mat1" and
   "mat2" at the locations specified by the sparsity pattern of
   "input". The matrix "input" is added to the final result.
Mathematically this performs the following operation:
  \text{out} = \alpha\ (\text{mat1} \mathbin{@}
  \text{mat2})*\text{spy}(\text{input}) + \beta\ \text{input}

where \text{spy}(\text{input}) is the sparsity pattern matrix of
   "input", "alpha" and "beta" are the scaling factors.
   \text{spy}(\text{input}) has value 1 at the positions where "input"
   has non-zero values, and 0 elsewhere.
Note:
 "input" must be a sparse CSR tensor. "mat1" and "mat2" must be
 dense tensors.

Parameters:
      * input (Tensor) -- a sparse CSR matrix of shape (m, n)
        to be added and used to compute the sampled matrix
        multiplication