Convert a tensor to a block sparse column (BSC) storage format of given blocksize. If the self is strided, then the number of dense dimensions could be specified, and a hybrid BSC tensor will be created, with dense_dim dense dimensions and self.dim() - 2 - dense_dim batch dimension.
>>> dense = torch.randn(10, 10)
>>> sparse = dense.to_sparse_csr()
>>> sparse_bsc = sparse.to_sparse_bsc((5, 5))
>>> sparse_bsc.row_indices()
tensor([0, 1, 0, 1])
>>> dense = torch.zeros(4, 3, 1)
>>> dense[0:2, 0] = dense[0:2, 2] = dense[2:4, 1] = 1
>>> dense.to_sparse_bsc((2, 1), 1)
tensor(ccol_indices=tensor([0, 1, 2, 3]),
row_indices=tensor([0, 1, 0]),
values=tensor([[[[1.]],
[[1.]]],
[[[1.]],
[[1.]]],
[[[1.]],
[[1.]]]]), size=(4, 3, 1), nnz=3,
layout=torch.sparse_bsc)
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