Note
If the device argument is not specified the device of the given values and indices tensor(s) must match. If, however, the argument is specified the input Tensors will be converted to the given device and in turn determine the device of the constructed sparse tensor.
>>> ccol_indices = [0, 1, 2]
>>> row_indices = [0, 1]
>>> values = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
>>> torch.sparse_bsc_tensor(torch.tensor(ccol_indices, dtype=torch.int64),
... torch.tensor(row_indices, dtype=torch.int64),
... torch.tensor(values), dtype=torch.double)
tensor(ccol_indices=tensor([0, 1, 2]),
row_indices=tensor([0, 1]),
values=tensor([[[1., 2.],
[3., 4.]],
[[5., 6.],
[7., 8.]]]), size=(2, 2), nnz=2, dtype=torch.float64,
layout=torch.sparse_bsc)
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