class_docstr: A class to create and store different sparse formats needed by
  cugraph-ops. It always creates a CSC representation and can provide COO- or
  CSR-format if needed.
  
  Parameters
  ----------
  size: tuple of int
      Size of the adjacency matrix: (num_src_nodes, num_dst_nodes).
  
  src_ids: torch.Tensor
      Source indices of the edges.
  
  dst_ids: torch.Tensor, optional
      Destination indices of the edges.
  
  csrc_ids: torch.Tensor, optional
      Compressed source indices. It is a monotonically increasing array of
      size (num_src_nodes + 1,). For the k-th source node, its neighborhood
      consists of the destinations between `dst_indices[csrc_indices[k]]` and
      `dst_indices[csrc_indices[k+1]]`.
  
  cdst_ids: torch.Tensor, optional
      Compressed destination indices. It is a monotonically increasing array of
      size (num_dst_nodes + 1,). For the k-th destination node, its neighborhood
      consists of the sources between `src_indices[cdst_indices[k]]` and
      `src_indices[cdst_indices[k+1]]`.
  
  values: torch.Tensor, optional
      Values on the edges.
  
  is_sorted: bool
      Whether the COO inputs (src_ids, dst_ids, values) have been sorted by
      `dst_ids` in an ascending order. CSC layout creation is much faster
      when sorted.
  
  formats: str or tuple of str, optional
      The desired sparse formats to create for the graph. The formats tuple
      must include "csc". Default: "csc".
  
  reduce_memory: bool, optional
      When set, the tensors are not required by the desired formats will be
      set to `None`. Default: True.
  
  Notes
  -----
  For MFGs (sampled graphs), the node ids must have been renumbered.
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code: def cdst_ids(self) -> torch.Tensor:
      return self._cdst_ids
  
imports: from typing import Optional, Tuple, Union
  from cugraph.utilities.utils import import_optional
label_desc: Use image node_id 4 for calling the SparseGraph obj's underlying member method code with example usage: obj.cdst_ids() and returns: self