code: def extract_all_relations(
      self, mention_x: MentionDataLight, mention_y: MentionDataLight
  ) -> Set[RelationType]:
      ret_ = set()
      ret_.add(
          self.extract_sub_relations(
              mention_x, mention_y, RelationType.WORD_EMBEDDING_MATCH
          )
      )
      return ret_
  
file_path: nlp-architect/nlp_architect/data/cdc_resources/relations/word_embedding_relation_extraction.py
imports: import logging
  import math
  from typing import List, Set
  from scipy.spatial.distance import cosine
  from nlp_architect.common.cdc.mention_data import MentionDataLight
  from nlp_architect.data.cdc_resources.embedding.embed_elmo import ElmoEmbedding, ElmoEmbeddingOffline
  from nlp_architect.data.cdc_resources.embedding.embed_glove import GloveEmbedding, GloveEmbeddingOffline
  from nlp_architect.data.cdc_resources.relations.relation_extraction import RelationExtraction
  from nlp_architect.data.cdc_resources.relations.relation_types_enums import EmbeddingMethod, RelationType
  from nlp_architect.utils.string_utils import StringUtils
label_desc: Use image node_id 2 for calling the WordEmbeddingRelationExtraction obj's underlying member method code with example usage: obj.extract_all_relations(mention_x, mention_y) and returns: ret_