class_docstr: Class used to either find the subset of IDs that are in this
  filter, or the subset of IDs that are not in this filter
  given a second set of IDs.
  
  Examples
  --------
  >>> import torch as th
  >>> from dgl.utils import Filter
  >>> f = Filter(th.tensor([3,2,9], device=th.device('cuda')))
  >>> f.find_included_indices(th.tensor([0,2,8,9], device=th.device('cuda')))
  tensor([1,3])
  >>> f.find_excluded_indices(th.tensor([0,2,8,9], device=th.device('cuda')))
  tensor([0,2], device='cuda')
docstr: Find the index of the IDs in `test` that are not in this filter.
  
  Parameters
  ----------
  test : IdArray
      The set of IDs to to test with.
  
  Returns
  -------
  IdArray
      The index of IDs in `test` that are not in this filter.
docstr_tok: ["Find","the","index","of","the","IDs","in","`","test","`","that","are","not","in","this","filter",".","Parameters","--","--","--","--","--","test",":","IdArray","The","set","of","IDs","to","to","test","with",".","Returns","--","--","--","-","IdArray","The","index","of","IDs","in","`","test","`","that","are","not","in","this","filter","."]
code: def find_excluded_indices(self, test):
      """Find the index of the IDs in `test` that are not in this filter.
  
      Parameters
      ----------
      test : IdArray
          The set of IDs to to test with.
  
      Returns
      -------
      IdArray
          The index of IDs in `test` that are not in this filter.
      """
      return F.zerocopy_from_dgl_ndarray(
          _CAPI_DGLFilterFindExcludedIndices(
              self._filter, F.zerocopy_to_dgl_ndarray(test)
          )
      )
  
imports: from ..None import backend
  from .._ffi.function import _init_api
label_desc: Use image node_id 3 for calling the Filter obj's underlying member method code with example usage: obj.find_excluded_indices(test) and returns: F