class_docstr: A component that locates and extract answers to a given query from Documents. It's used for performing extractive
  QA. The Reader assigns a score to every possible answer span independently of other answer spans.
  This fixes a common issue of other implementations which make comparisons across documents harder by normalizing
  each document's answers independently.
  
  Example usage:
  ```python
  p = Pipeline()
  p.add_component(instance=InMemoryBM25Retriever(document_store=InMemoryDocumentStore()), name="retriever")
  p.add_component(instance=ExtractiveReader(), name="reader")
  p.connect("retriever", "reader")
  question = "Who lives in Berlin?"
  p.run({"retriever": {"query": question}, "reader": {"query": question}})
  ```
docstr: Loads model and tokenizer
docstr_tok: ["Loads","model","and","tokenizer"]
code: def warm_up(self):
      """
      Loads model and tokenizer
      """
      if self.model is None:
          if self.device is None:
              self.device = get_device()
          self.model = AutoModelForQuestionAnswering.from_pretrained(
              self.model_name_or_path,
              token=self.token,
              **self.model_kwargs,
          ).to(self.device)
          self.tokenizer = AutoTokenizer.from_pretrained(
              self.model_name_or_path, token=self.token
          )
  
imports: from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple, Union
  import math
  import warnings
  import logging
  from haystack import component, default_to_dict, default_from_dict, ComponentError, Document, ExtractedAnswer
  from haystack.lazy_imports import LazyImport
  from haystack.utils import get_device
label_desc: Use image node_id 5 for calling the ExtractiveReader obj's underlying member method code with example usage: obj.warm_up() without return types