If the "self" is strided, the number of dense dimensions could be
   specified, and a hybrid sparse tensor will be created, with
   dense_dim dense dimensions and self.dim() - 2 - dense_dim batch
   dimension.
Note:
 If the "self" layout and blocksize parameters match with the
 specified layout and blocksize, return "self". Otherwise, return
 a sparse tensor copy of "self".

Parameters:
      * layout ("torch.layout", optional) -- The desired sparse
        layout. One of "torch.sparse_coo", "torch.sparse_csr",
        "torch.sparse_csc", "torch.sparse_bsr", or "torch.sparse_bsc".
        Default: if "None", "torch.sparse_coo".
  * **blocksize** (list, tuple, "torch.Size", optional) -- Block
    size of the resulting BSR or BSC tensor. For other layouts,
    specifying the block size that is not "None" will result in a
    RuntimeError exception.  A block size must be a tuple of
    length two such that its items evenly divide the two sparse
