class_docstr: An abstract class for sampled subgraph. In the context of a
  heterogeneous graph, each field should be of `Dict` type. Otherwise,
  for homogeneous graphs, each field should correspond to its respective
  value type.
class_docstr_tok: ["An","abstract","class","for","sampled","subgraph",".","In","the","context","of","a","heterogeneous","graph",",","each","field","should","be","of","`","Dict","`","type",".","Otherwise",",","for","homogeneous","graphs",",","each","field","should","correspond","to","its","respective","value","type","."]
docstr: Returns the node pairs representing edges in csc format.
  - If `sampled_csc` is a CSCFormatBase: It should be in the csc format.
      `indptr` stores the index in the data array where each column
      starts. `indices` stores the row indices of the non-zero elements.
  - If `sampled_csc` is a dictionary: The keys should be edge type and
      the values should be corresponding node pairs. The ids inside
      is heterogeneous ids.
docstr_tok: ["Returns","the","node","pairs","representing","edges","in","csc","format",".","-","If","`","sampled_csc","`","is","a","CSCFormatBase",":","It","should","be","in","the","csc","format",".","`","indptr","`","stores","the","index","in","the","data","array","where","each","column","starts",".","`","indices","`","stores","the","row","indices","of","the","non-zero","elements",".","-","If","`","sampled_csc","`","is","a","dictionary",":","The","keys","should","be","edge","type","and","the","values","should","be","corresponding","node","pairs",".","The","ids","inside","is","heterogeneous","ids","."]
code: def sampled_csc(
      self,
  ) -> Union[CSCFormatBase, Dict[str, CSCFormatBase],]:
      """Returns the node pairs representing edges in csc format.
      - If `sampled_csc` is a CSCFormatBase: It should be in the csc format.
          `indptr` stores the index in the data array where each column
          starts. `indices` stores the row indices of the non-zero elements.
      - If `sampled_csc` is a dictionary: The keys should be edge type and
          the values should be corresponding node pairs. The ids inside
          is heterogeneous ids."""
      raise NotImplementedError
  
file_path: dgl/python/dgl/graphbolt/sampled_subgraph.py
imports: from typing import Dict, Tuple, Union
  import torch
  from dgl.utils import recursive_apply
  from .base import apply_to, CSCFormatBase, etype_str_to_tuple, isin
label_desc: Use image node_id 1 for calling the SampledSubgraph obj's underlying member method code with example usage: obj.sampled_csc() without return types