class_docstr: Contains statistics for one or more search runs, including the number of
  internal nodes that were fetched (`visited_members`) and the number
  of times the distance metric was invoked (`computed_distances`).
  
  Other derivative metrics include the `mean_recall` and `mean_efficiency`.
  Recall is the share of queried vectors, that were successfully found.
  Efficiency describes the number of distances that had to be computed for
  each query, normalized to size of the `index`. Highest efficiency is 0.(9),
  lowest is zero. Highest is achieved, when the distance metric was computed
  just once per query. Lowest happens during exact search, when every distance
  to every present vector had to be computed.
code: def mean_recall(self) -> float:
      return self.count_matches / self.count_queries
  
imports: from __future__ import annotations
  from time import time_ns
  from typing import Tuple, Any, Callable, Union, Optional, List
  from dataclasses import dataclass, asdict
  from collections import defaultdict
  from math import ceil
  import numpy
  from usearch.io import load_matrix
  from usearch.index import Index, Matches, BatchMatches, MetricKind, MetricKindBitwise, Key
label_desc: Use image node_id 2 for calling the SearchStats obj's underlying member method code with example usage: obj.mean_recall() and returns: unknown