docstr: - h,r->t: Given head and relation, predict target entities
      - hr: np.ndarray of shape (num_validation_triplets, 2)
            i-th row stores i-th (h,r)
      - t: np.ndarray of shape (num_validation_triplets,)
            i-th row stores i-th index for tail entities
docstr_tok: ["-","h",",","r-",">","t",":","Given","head","and","relation",",","predict","target","entities","-","hr",":","np.ndarray","of","shape","(","num_validation_triplets",",","2",")","i-th","row","stores","i-th","(","h",",","r",")","-","t",":","np.ndarray","of","shape","(","num_validation_triplets",",",")","i-th","row","stores","i-th","index","for","tail","entities"]
code: def valid_dict(self) -> Dict[str, Dict[str, np.ndarray]]:
      """
      - h,r->t: Given head and relation, predict target entities
          - hr: np.ndarray of shape (num_validation_triplets, 2)
                i-th row stores i-th (h,r)
          - t: np.ndarray of shape (num_validation_triplets,)
                i-th row stores i-th index for tail entities
      """
      if self._valid_dict is None:
          self._valid_dict = {}
          # h, r -> t
          self._valid_dict["h,r->t"] = {}
          self._valid_dict["h,r->t"]["hr"] = np.load(
              osp.join(self.processed_dir, "val_hr.npy")
          )
          self._valid_dict["h,r->t"]["t"] = np.load(
              osp.join(self.processed_dir, "val_t.npy")
          )
      return self._valid_dict
  
imports: from typing import Optional, Union, Dict
  import os
  import shutil
  import os.path
  import torch
  import numpy
  import pandas
  from ogb.utils.url import decide_download, download_url, extract_zip, makedirs
label_desc: Use image node_id 11 for calling the WikiKG90Mv2Dataset obj's underlying member method code with example usage: obj.valid_dict() and returns: self