class_docstr: Select entities according to their page rank.
  
  .. seealso::
      http://web.stanford.edu/class/cs224w/slides/04-pagerank.pdf
code: def rank(
      self, edge_index: numpy.ndarray
  ) -> numpy.ndarray:  # noqa: D102
      # sort by decreasing page rank
      return numpy.argsort(
          page_rank(
              edge_index=torch.as_tensor(edge_index), **self.kwargs
          )
          .cpu()
          .numpy()
      )[::-1]
  
imports: import logging
  from abc import ABC, abstractmethod
  from typing import Iterable, Optional, Sequence, Union
  import numpy
  import torch
  from class_resolver import ClassResolver, HintOrType, OptionalKwargs
  from torch_ppr import page_rank
  from ...triples.splitting import get_absolute_split_sizes, normalize_ratios
  from ...typing import OneOrSequence
  from ...utils import ExtraReprMixin
label_desc: Use image node_id 3 for calling the PageRankAnchorSelection obj's underlying member method code with example usage: obj.rank(edge_index) and returns: numpy