Convert a tensor to compressed column storage (CSC) format. Except for strided tensors, only works with 2D tensors. If the self is strided, then the number of dense dimensions could be specified, and a hybrid CSC tensor will be created, with dense_dim dense dimensions and self.dim() - 2 - dense_dim batch dimension.
>>> dense = torch.randn(5, 5)
>>> sparse = dense.to_sparse_csc()
>>> sparse._nnz()
25
>>> dense = torch.zeros(3, 3, 1, 1)
>>> dense[0, 0] = dense[1, 2] = dense[2, 1] = 1
>>> dense.to_sparse_csc(dense_dim=2)
tensor(ccol_indices=tensor([0, 1, 2, 3]),
row_indices=tensor([0, 2, 1]),
values=tensor([[[1.]],
[[1.]],
[[1.]]]), size=(3, 3, 1, 1), nnz=3,
layout=torch.sparse_csc)
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