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: Parse a single entry (line) of tsv file.
docstr_tok: ["Parse","a","single","entry","(","line",")","of","tsv","file","."]
code: def preprocess_line(self, line):
      """
      Parse a single entry (line) of tsv file.
      """
      if "\t" not in line:
          raise ValueError(
              f"Provided dataset does not have a form of tsv file"
          )
      id_, text = line.split("\t")
      token_ids = self.tokenizer.text_to_ids(text.strip())
      return int(id_), token_ids
  
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 5 for calling the BaseInformationRetrievalDataset obj's underlying member method code with example usage: obj.preprocess_line(line) and returns: int,  token_ids