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.
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docstr: Returns corresponding reverse row node ids the original graph.
  Row's reverse node ids in the original graph. A graph structure
  can be treated as a coordinated row and column pair, and this is
  the mapped ids of the row.
  - If `original_row_node_ids` is a tensor: It represents the
      original node ids.
  - If `original_row_node_ids` is a dictionary: The keys should be
      node type and the values should be corresponding original
      heterogeneous node ids.
  If present, it means row IDs are compacted, and `sampled_csc`
  row IDs match these compacted ones.
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code: def original_row_node_ids(
      self,
  ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
      """Returns corresponding reverse row node ids the original graph.
      Row's reverse node ids in the original graph. A graph structure
      can be treated as a coordinated row and column pair, and this is
      the mapped ids of the row.
      - If `original_row_node_ids` is a tensor: It represents the
          original node ids.
      - If `original_row_node_ids` is a dictionary: The keys should be
          node type and the values should be corresponding original
          heterogeneous node ids.
      If present, it means row IDs are compacted, and `sampled_csc`
      row IDs match these compacted ones."""
      return None
  
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 3 for calling the SampledSubgraph obj's underlying member method code with example usage: obj.original_row_node_ids() and returns: None