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# torch.Tensor.sparse_mask

Tensor.sparse_mask(mask) → Tensor

  Returns a new sparse tensor with values from a strided tensor `self` filtered by the indices of the sparse tensor `mask`. The values of `mask` sparse tensor are ignored. `self` and `mask` tensors must have the same shape.

  Note

  The returned sparse tensor might contain duplicate values if `mask` is not coalesced. It is therefore advisable to pass `mask.coalesce()` if such behavior is not desired.

  Note

  The returned sparse tensor has the same indices as the sparse tensor `mask`, even when the corresponding values in `self` are zeros.

  Parameters:

    mask (Tensor) – a sparse tensor whose indices are used as a filter

  Example:

      >>> nse = 5
      >>> dims = (5, 5, 2, 2)
      >>> I = torch.cat([torch.randint(0, dims[0], size=(nse,)),
      ...                torch.randint(0, dims[1], size=(nse,))], 0).reshape(2, nse)
      >>> V = torch.randn(nse, dims[2], dims[3])
      >>> S = torch.sparse_coo_tensor(I, V, dims).coalesce()
      >>> D = torch.randn(dims)
      >>> D.sparse_mask(S)
      tensor(indices=tensor([[0, 0, 0, 2],
                             [0, 1, 4, 3]]),
             values=tensor([[[ 1.6550,  0.2397],
                             [-0.1611, -0.0779]],

                            [[ 0.2326, -1.0558],
                             [ 1.4711,  1.9678]],

                            [[-0.5138, -0.0411],
                             [ 1.9417,  0.5158]],

                            [[ 0.0793,  0.0036],
                             [-0.2569, -0.1055]]]),
             size=(5, 5, 2, 2), nnz=4, layout=torch.sparse_coo)

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