class_docstr: Tokenize entities by representing them as a bag of anchor entities.
  
  The entities are chosen by shortest path distance.
class_docstr_tok: ["Tokenize","entities","by","representing","them","as","a","bag","of","anchor","entities",".","The","entities","are","chosen","by","shortest","path","distance","."]
code: def _call(
      self,
      edge_index: torch.LongTensor,
      num_tokens: int,
      num_entities: int,
  ) -> Tuple[int, torch.LongTensor]:
      edge_index = edge_index.numpy()
      # select anchors
      logger.info(
          f"Selecting anchors according to {self.anchor_selection}"
      )
      anchors = self.anchor_selection(edge_index=edge_index)
      if len(numpy.unique(anchors)) < len(anchors):
          logger.warning(
              f"Only {len(numpy.unique(anchors))} out of {len(anchors)} anchors are unique"
          )
      # find closest anchors
      logger.info(f"Searching closest anchors with {self.searcher}")
      tokens = self.searcher(
          edge_index=edge_index,
          anchors=anchors,
          k=num_tokens,
          num_entities=num_entities,
      )
      num_empty = (tokens < 0).all(axis=1).sum()
      if num_empty > 0:
          logger.warning(
              f"{format_relative_comparison(part=num_empty, total=num_entities)} "
              f"do not have any anchor.",
          )
      # convert to torch
      return len(anchors) + 1, torch.as_tensor(tokens, dtype=torch.long)
  
imports: import logging
  import pathlib
  from abc import abstractmethod
  from collections import defaultdict
  from typing import Collection, Mapping, Optional, Tuple
  import more_itertools
  import numpy
  import torch
  from class_resolver import ClassResolver, HintOrType, OptionalKwargs
  from .anchor_search import AnchorSearcher, anchor_searcher_resolver
  from .anchor_selection import AnchorSelection, anchor_selection_resolver
  from .loader import PrecomputedTokenizerLoader, precomputed_tokenizer_loader_resolver
  from .utils import prepare_edges_for_metis, random_sample_no_replacement
  from ...constants import PYKEEN_MODULE
  from ...typing import DeviceHint, MappedTriples
  from ...utils import format_relative_comparison, get_edge_index, resolve_device
label_desc: Use image node_id 2 for calling the AnchorTokenizer obj's underlying member method code with example usage: obj._call(edge_index, num_tokens, num_entities) and returns: unknown,  torch