class_docstr: A weighted mixture of different anchor selection strategies.
code: def __call__(
      self,
      edge_index: numpy.ndarray,
      known_anchors: Optional[numpy.ndarray] = None,
  ) -> numpy.ndarray:  # noqa: D102
      anchors = known_anchors or None
      for selection in self.selections:
          anchors = selection(
              edge_index=edge_index, known_anchors=anchors
          )
      return anchors
  
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 MixtureAnchorSelection obj's underlying member method code with example usage: obj.__call__(edge_index, known_anchors) and returns: anchors