class_docstr: Given a large set of sentences, this evaluator performs paraphrase (duplicate) mining and
  identifies the pairs with the highest similarity. It compare the extracted paraphrase pairs
  with a set of gold labels and computes the F1 score.
code: def add_transitive_closure(graph):
      nodes_visited = set()
      for a in list(graph.keys()):
          if a not in nodes_visited:
              connected_subgraph_nodes = set()
              connected_subgraph_nodes.add(a)
  
              # Add all nodes in the connected graph
              neighbor_nodes_queue = list(graph[a])
              while len(neighbor_nodes_queue) > 0:
                  node = neighbor_nodes_queue.pop(0)
                  if node not in connected_subgraph_nodes:
                      connected_subgraph_nodes.add(node)
                      neighbor_nodes_queue.extend(graph[node])
  
              # Ensure transitivity between all nodes in the graph
              connected_subgraph_nodes = list(connected_subgraph_nodes)
              for i in range(len(connected_subgraph_nodes) - 1):
                  for j in range(i + 1, len(connected_subgraph_nodes)):
                      graph[connected_subgraph_nodes[i]][
                          connected_subgraph_nodes[j]
                      ] = True
                      graph[connected_subgraph_nodes[j]][
                          connected_subgraph_nodes[i]
                      ] = True
  
                      nodes_visited.add(connected_subgraph_nodes[i])
                      nodes_visited.add(connected_subgraph_nodes[j])
      return graph
  
file_path: sentence-transformers/sentence_transformers/evaluation/ParaphraseMiningEvaluator.py
imports: from .None import SentenceEvaluator
  import logging
  from sentence_transformers.util import paraphrase_mining
  import os
  import csv
  from typing import List, Tuple, Dict
  from collections import defaultdict
label_desc: Use image node_id 3 for calling the ParaphraseMiningEvaluator obj's underlying member method code with example usage: obj.add_transitive_closure(graph) and returns: graph