class_docstr: Class that finds the edges whose IDs in parent graph appeared in exclude_eids.
  
  The edge IDs can be both CPU and GPU tensors.
class_docstr_tok: ["Class","that","finds","the","edges","whose","IDs","in","parent","graph","appeared","in","exclude_eids",".","The","edge","IDs","can","be","both","CPU","and","GPU","tensors","."]
docstr: Find the set of edge indices to remove.
docstr_tok: ["Find","the","set","of","edge","indices","to","remove","."]
code: def _find_indices(self, parent_eids):
      """Find the set of edge indices to remove."""
      if self._exclude_eids is not None:
          parent_eids_np = recursive_apply(
              parent_eids, F.zerocopy_to_numpy
          )
          return _locate_eids_to_exclude(
              parent_eids_np, self._exclude_eids
          )
      else:
          assert self._filter is not None
          func = lambda x, y: x.find_included_indices(y)
          return recursive_apply_pair(self._filter, parent_eids, func)
  
file_path: dgl/python/dgl/sampling/utils.py
imports: from collections.abc import Mapping
  import numpy
  from ..None import backend, transforms, utils
  from ..base import EID
  from ..utils import recursive_apply, recursive_apply_pair
label_desc: Use image node_id 2 for calling the EidExcluder obj's underlying member method code with example usage: obj._find_indices(parent_eids) and returns: _locate_eids_to_exclude, recursive_apply_pair