batch is the number of non-zeros. This tensor encodes the
        block index in values and col_indices depending on where the
        given row block starts. Each successive number in the tensor
        subtracted by the number before it denotes the number of
        blocks in a given row.
  * **col_indices** (*array_like*) -- Column block co-ordinates of
    each block in values. (B+1)-dimensional tensor with the same
    length as values.

  * **values** (*array_list*) -- Initial values for the tensor.
    Can be a list, tuple, NumPy "ndarray", scalar, and other types
    that represents a (1 + 2 + K)-dimensional tensor where "K" is
    the number of dense dimensions.

  * **size** (list, tuple, "torch.Size", optional) -- Size of the
    sparse tensor: "(*batchsize, nrows * blocksize[0], ncols *
    blocksize[1], *densesize)" where "blocksize ==
    values.shape[1:3]". If not provided, the size will be inferred
