class_docstr: Extract Relation between two mentions according to computation and rule based algorithms
class_docstr_tok: ["Extract","Relation","between","two","mentions","according","to","computation","and","rule","based","algorithms"]
docstr: Return all supported relations by this class
  
  Returns:
      List[RelationType]
docstr_tok: ["Return","all","supported","relations","by","this","class","Returns",":","List","[","RelationType","]"]
code: def get_supported_relations() -> List[RelationType]:
      """
      Return all supported relations by this class
  
      Returns:
          List[RelationType]
      """
      return [
          RelationType.EXACT_STRING,
          RelationType.FUZZY_FIT,
          RelationType.FUZZY_HEAD_FIT,
          RelationType.SAME_HEAD_LEMMA,
      ]
  
file_path: nlp-architect/nlp_architect/data/cdc_resources/relations/computed_relation_extraction.py
imports: import difflib
  import logging
  import sys
  from typing import List, Set
  from nlp_architect.common.cdc.mention_data import MentionDataLight
  from nlp_architect.data.cdc_resources.relations.relation_extraction import RelationExtraction
  from nlp_architect.data.cdc_resources.relations.relation_types_enums import RelationType
  from nlp_architect.utils.string_utils import StringUtils
label_desc: Use image node_id 7 for calling the ComputedRelationExtraction obj's underlying member method code with example usage: obj.get_supported_relations() and returns: list