class_docstr: The retriever of REALM outputting the retrieved evidence block and whether the block has answers as well as answer
  positions."
  
      Parameters:
          block_records (`np.ndarray`):
              A numpy array which cantains evidence texts.
          tokenizer ([`RealmTokenizer`]):
              The tokenizer to encode retrieved texts.
docstr: check if retrieved_blocks has answers.
docstr_tok: ["check","if","retrieved_blocks","has","answers","."]
code: def block_has_answer(self, concat_inputs, answer_ids):
      """check if retrieved_blocks has answers."""
      has_answers = []
      start_pos = []
      end_pos = []
      max_answers = 0
  
      for input_id in concat_inputs.input_ids:
          input_id_list = input_id.tolist()
          # Check answers between two [SEP] tokens
          first_sep_idx = input_id_list.index(
              self.tokenizer.sep_token_id
          )
          second_sep_idx = (
              first_sep_idx
              + 1
              + input_id_list[first_sep_idx + 1 :].index(
                  self.tokenizer.sep_token_id
              )
          )
  
          start_pos.append([])
          end_pos.append([])
          for answer in answer_ids:
              for idx in range(first_sep_idx + 1, second_sep_idx):
                  if answer[0] == input_id_list[idx]:
                      if (
                          input_id_list[idx : idx + len(answer)]
                          == answer
                      ):
                          start_pos[-1].append(idx)
                          end_pos[-1].append(idx + len(answer) - 1)
  
          if len(start_pos[-1]) == 0:
              has_answers.append(False)
          else:
              has_answers.append(True)
              if len(start_pos[-1]) > max_answers:
                  max_answers = len(start_pos[-1])
  
      # Pad -1 to max_answers
      for start_pos_, end_pos_ in zip(start_pos, end_pos):
          if len(start_pos_) < max_answers:
              padded = [-1] * (max_answers - len(start_pos_))
              start_pos_ += padded
              end_pos_ += padded
      return has_answers, start_pos, end_pos
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/realm/retrieval_realm.py
imports: import os
  from typing import Optional, Union
  import numpy
  from huggingface_hub import hf_hub_download
  from transformers import AutoTokenizer
  from ...utils import logging
label_desc: Use image node_id 5 for calling the RealmRetriever obj's underlying member method code with example usage: obj.block_has_answer(concat_inputs, answer_ids) and returns: has_answers,  start_pos,  end_pos