class_docstr: Splitting walking strategy which splits each vertex (except the root
  node) present in the randomly extracted walks.
  
  Attributes:
      _is_support_remote: True if the walking strategy can be used with a
          remote Knowledge Graph, False Otherwise
          Defaults to True.
      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.
      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.
      func_split: The function to call for the splitting of vertices. In case
          of reimplementation, it is important to respect the signature
          imposed by `basic_split` function.
          Defaults to func_split.
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code: def __attrs_post_init__(self):
      if self.func_split is None:
          self.func_split = self.basic_split
  
file_path: pyRDF2Vec/pyrdf2vec/walkers/split.py
imports: import re
  from typing import Set
  import attr
  from pyrdf2vec.graphs import KG, Vertex
  from pyrdf2vec.typings import EntityWalks, List, SWalk, Walk
  from pyrdf2vec.walkers import RandomWalker
label_desc: Use image node_id 1 for calling the SplitWalker obj's underlying member method code with example usage: obj.__attrs_post_init__() without return types