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"]
code: def __init__(
      self,
      tokenizer: TokenizerSpec,
      max_query_length: Optional[int] = 31,
      max_passage_length: Optional[int] = 190,
  ):
      self.tokenizer = tokenizer
      self.max_query_length = max_query_length
      self.max_passage_length = max_passage_length
  
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 1 to create a new BaseInformationRetrievalDataset object from inherited base classes: Dataset with example: obj = BaseInformationRetrievalDataset(tokenizer, max_query_length, max_passage_length)