class_docstr: A loader via torch.load.
class_docstr_tok: ["A","loader","via","torch.load","."]
code: def __call__(
      self, path: pathlib.Path
  ) -> Tuple[Mapping[int, Collection[int]], int]:  # noqa: D102
      c = torch.load(path)
      order = c["order"]
      logger.info(f"Loaded precomputed pools of shape {order.shape}.")
      num_anchors = c["anchors"].shape[0]
      # TODO: since we save a contiguous array of (num_entities, num_anchors),
      # it would be more efficient to not convert to a mapping, but directly select from the tensor
      return {
          i: anchor_ids.tolist() for i, anchor_ids in enumerate(order)
      }, num_anchors
  
file_path: pykeen/src/pykeen/nn/node_piece/loader.py
imports: import logging
  import pathlib
  import pickle
  from abc import ABC, abstractmethod
  from typing import Collection, Mapping, Tuple
  import numpy
  import torch
  from class_resolver import ClassResolver
  from tqdm.auto import tqdm
label_desc: Use image node_id 2 for calling the TorchPrecomputedTokenizerLoader obj's underlying member method code with example usage: obj.__call__(path) and returns: unknown,  num_anchors