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: Data that is sent to Posthog for usage analytics.
docstr_tok: ["Data","that","is","sent","to","Posthog","for","usage","analytics","."]
code: def _get_telemetry_data(self) -> Dict[str, Any]:
      """
      Data that is sent to Posthog for usage analytics.
      """
      return {"model": self.model_name_or_path}
  
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 2 for calling the ExtractiveReader obj's underlying member method code with example usage: obj._get_telemetry_data() and returns: dict