class_docstr: Weisfeiler-Lehman walking strategy which relabels the nodes of the
  extracted random walks, providing additional information about the entity
  representations only when a maximum number of walks is not specified.
  
  Attributes:
      _inv_label_map: Stores the mapping of the inverse labels.
          Defaults to defaultdict.
      _is_support_remote: True if the walking strategy can be used with a
          remote Knowledge Graph, False Otherwise.
          Defaults to False.
      _label_map: Stores the mapping of the inverse labels.
          Defaults to defaultdict.
      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.
      sampler: The sampling strategy.
          Defaults to UniformSampler.
      wl_iterations: The Weisfeiler Lehman's iteration.
          Defaults to 4.
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docstr: Creates a label according to a vertex and its neighbors.
  
  kg: The Knowledge Graph.
  
      The graph from which the neighborhoods are extracted for the
      provided entities.
  vertex: The vertex to get its neighbors to create the suffix.
  n:  The index of the neighbor
  
  Returns:
      the label created for the vertex.
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code: def _create_label(self, kg: KG, vertex: Vertex, n: int) -> str:
      """Creates a label according to a vertex and its neighbors.
  
      kg: The Knowledge Graph.
  
          The graph from which the neighborhoods are extracted for the
          provided entities.
      vertex: The vertex to get its neighbors to create the suffix.
      n:  The index of the neighbor
  
      Returns:
          the label created for the vertex.
  
      """
      if len(self._label_map) == 0:
          self._weisfeiler_lehman(kg)
  
      suffix = "-".join(
          sorted(
              set(
                  [
                      self._label_map[neighbor][n - 1]
                      for neighbor in kg.get_neighbors(
                          vertex, is_reverse=True
                      )
                  ]
              )
          )
      )
      return f"{self._label_map[vertex][n - 1]}-{suffix}"
  
file_path: pyRDF2Vec/pyrdf2vec/walkers/weisfeiler_lehman.py
imports: from collections import defaultdict
  from hashlib import md5
  from typing import DefaultDict, Dict, List, Set, Union
  import attr
  from pyrdf2vec.graphs import KG, Vertex
  from pyrdf2vec.typings import Entities, EntityWalks, SWalk
  from pyrdf2vec.walkers import RandomWalker
label_desc: Use image node_id 1 for calling the WLWalker obj's underlying member method code with example usage: obj._create_label(kg, vertex, n) and returns: self, str, suffix