class_docstr: Ranks Documents based on the value of their specific meta field.
  The ranking can be performed in descending order or ascending order.
  
  Usage example:
  ```
  from haystack import Document
  from haystack.components.rankers import MetaFieldRanker
  
  ranker = MetaFieldRanker(meta_field="rating")
  docs = [
      Document(text="Paris", meta={"rating": 1.3}),
      Document(text="Berlin", meta={"rating": 0.7}),
      Document(text="Barcelona", meta={"rating": 2.1}),
  ]
  
  output = ranker.run(documents=docs)
  docs = output["documents"]
  assert docs[0].text == "Barcelona"
docstr: Calculates the reciprocal rank fusion. The constant K is set to 61 (60 was suggested by the original paper,
  plus 1 as python lists are 0-based and the [paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) used 1-based ranking).
code: def _calculate_rrf(rank: int, k: int = 61) -> float:
      """
      Calculates the reciprocal rank fusion. The constant K is set to 61 (60 was suggested by the original paper,
      plus 1 as python lists are 0-based and the [paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) used 1-based ranking).
      """
      return 1 / (k + rank)
  
file_path: haystack/haystack/components/rankers/meta_field.py
imports: import logging
  from collections import defaultdict
  from typing import List, Dict, Any, Optional, Literal
  from haystack import Document, component, default_to_dict
label_desc: Use image node_id 6 for calling the MetaFieldRanker obj's underlying member method code with example usage: obj._calculate_rrf(rank, k) and returns: unknown