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.
code: def __call__(
      self,
      retrieved_block_ids,
      question_input_ids,
      answer_ids,
      max_length=None,
      return_tensors="pt",
  ):
      retrieved_blocks = np.take(
          self.block_records, indices=retrieved_block_ids, axis=0
      )
  
      question = self.tokenizer.decode(
          question_input_ids[0], skip_special_tokens=True
      )
  
      text = []
      text_pair = []
      for retrieved_block in retrieved_blocks:
          text.append(question)
          text_pair.append(retrieved_block.decode())
  
      concat_inputs = self.tokenizer(
          text,
          text_pair,
          padding=True,
          truncation=True,
          return_special_tokens_mask=True,
          max_length=max_length,
      )
      concat_inputs_tensors = concat_inputs.convert_to_tensors(
          return_tensors
      )
  
      if answer_ids is not None:
          return self.block_has_answer(concat_inputs, answer_ids) + (
              concat_inputs_tensors,
          )
      else:
          return (None, None, None, concat_inputs_tensors)
  
file_path: 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 ...None import AutoTokenizer
  from ...utils import logging
label_desc: Use image node_id 2 for calling the RealmRetriever obj's underlying member method code with example usage: obj.__call__(retrieved_block_ids, question_input_ids, answer_ids, max_length, return_tensors) and returns: unknown, None,  None,  None,  concat_inputs_tensors