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 edge ids the original graph.
  Reverse edge ids in the original graph. This is useful when edge
      features are needed.
      - If `original_edge_ids` is a tensor: It represents the
          original edge ids.
      - If `original_edge_ids` is a dictionary: The keys should be
          edge type and the values should be corresponding original
          heterogeneous edge ids.
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code: def original_edge_ids(
      self,
  ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
      """Returns corresponding reverse edge ids the original graph.
      Reverse edge ids in the original graph. This is useful when edge
          features are needed.
          - If `original_edge_ids` is a tensor: It represents the
              original edge ids.
          - If `original_edge_ids` is a dictionary: The keys should be
              edge type and the values should be corresponding original
              heterogeneous edge ids.
      """
      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 4 for calling the SampledSubgraph obj's underlying member method code with example usage: obj.original_edge_ids() and returns: None