class_docstr: Community walking strategy which groups vertices with similar properties
  through probabilities and relations that are not explicitly modeled in a
  Knowledge Graph. Similar to the Random walking strategy, the Depth First
  Search (DFS) algorithm is used if a maximum number of walks is specified.
  Otherwise, the Breadth First Search (BFS) algorithm is chosen.
  
  Attributes:
      _is_support_remote: True if the walking strategy can be used with a
          remote Knowledge Graph, False Otherwise.
          Defaults to True.
      hop_prob: The probability to hop.
          Defaults to 0.1.
      kg: The global KG used later on for the worker process.
          Defaults to None.
      max_depth: The maximum depth of one walk.
      max_walks: The maximum number of walks per entity.
          Defaults to None.
      md5_bytes: The number of bytes to keep after hashing objects in
          MD5. Hasher allows to reduce the memory occupied by a long
          text. If md5_bytes is None, no hash is applied.
          Defaults to 8.
      random_state: The random state to use to keep random determinism with
          the walking strategy.
          Defaults to None.
      resolution: The resolution to use.
          Defaults to The resolution to use.
      sampler: The sampling strategy.
          Defaults to UniformSampler.
      with_reverse: True to extracts parents and children hops from an
          entity, creating (max_walks * max_walks) walks of 2 * depth,
          allowing also to centralize this entity in the walks. False
          otherwise.
          Defaults to False.
docstr: Extracts random walks for an entity based on a Knowledge Graph.
  
  Args:
      kg: The Knowledge Graph.
      entity: The root node to extract walks.
  
  Returns:
      A dictionary having the entity as key and a list of tuples as value
      corresponding to the extracted walks.
docstr_tok: ["Extracts","random","walks","for","an","entity","based","on","a","Knowledge","Graph",".","Args",":","kg",":","The","Knowledge","Graph",".","entity",":","The","root","node","to","extract","walks",".","Returns",":","A","dictionary","having","the","entity","as","key","and","a","list","of","tuples","as","value","corresponding","to","the","extracted","walks","."]
code: def _extract(self, kg: KG, entity: Vertex) -> EntityWalks:
      """Extracts random walks for an entity based on a Knowledge Graph.
  
      Args:
          kg: The Knowledge Graph.
          entity: The root node to extract walks.
  
      Returns:
          A dictionary having the entity as key and a list of tuples as value
          corresponding to the extracted walks.
  
      """
      canonical_walks: Set[SWalk] = set()
      for walk in self.extract_walks(kg, entity):
          canonical_walk: List[str] = [
              self._map_vertex(vertex, i)
              for i, vertex in enumerate(walk)
          ]
          canonical_walks.add(tuple(canonical_walk))
      return {entity.name: list(canonical_walks)}
  
imports: import itertools
  import math
  from collections import defaultdict
  from hashlib import md5
  from typing import List, Optional, Set
  import attr
  import community
  import networkx
  import numpy
  from pyrdf2vec.graphs import KG, Vertex
  from pyrdf2vec.typings import Entities, EntityWalks, SWalk, Walk
  from pyrdf2vec.walkers import Walker
label_desc: Use image node_id 7 for calling the CommunityWalker obj's underlying member method code with example usage: obj._extract(kg, entity) and returns: dict