class_docstr: Base information retrieval dataset on which other datasets are built.
  
  Args:
      tokenizer: tokenizer
      max_query_length: maximum length of query in tokens
      max_passage_length: maximum length of passage in tokens
class_docstr_tok: ["Base","information","retrieval","dataset","on","which","other","datasets","are","built",".","Args",":","tokenizer",":","tokenizer","max_query_length",":","maximum","length","of","query","in","tokens","max_passage_length",":","maximum","length","of","passage","in","tokens"]
docstr: Mapping from passage id to its tokens in case of npz cache format.
docstr_tok: ["Mapping","from","passage","id","to","its","tokens","in","case","of","npz","cache","format","."]
code: def psgid2tokens_npz(self, psg_id):
      """
      Mapping from passage id to its tokens in case of npz cache format.
      """
      seq_len = self.passages[psg_id][0]
      return self.passages[psg_id][1 : seq_len + 1].tolist()
  
imports: import multiprocessing
  import os
  import pickle
  import random
  from typing import Optional
  import numpy
  from torch.utils.data import Dataset
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
label_desc: Use image node_id 10 for calling the BaseInformationRetrievalDataset obj's underlying member method code with example usage: obj.psgid2tokens_npz(psg_id) and returns: self