class_docstr: Base class of the sampling strategies.
  
  Attributes:
      _is_support_remote: True if the sampling strategy can be used with a
          remote Knowledge Graph, False Otherwise
          Defaults to False.
      _random_state: The random state to use to keep random determinism with
          the sampling strategy.
          Defaults to None.
      _vertices_deg: The degree of the vertices.
          Defaults to {}.
      _visited: Tags vertices that appear at the max depth or of which all
          their children are tagged.
          Defaults to set.
      inverse: True if the inverse algorithm must be used, False otherwise.
          Defaults to False.
      split: True if the split algorithm must be used, False otherwise.
          Defaults to False.
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docstr: Sets the value of the tagged vertices.
  
  Args:
      visited: The tagged vertices.
docstr_tok: ["Sets","the","value","of","the","tagged","vertices",".","Args",":","visited",":","The","tagged","vertices","."]
code: def visited(self, visited: Set[Tuple[Hop, int]]) -> None:
      """Sets the value of the tagged vertices.
  
      Args:
          visited: The tagged vertices.
  
      """
      self._visited = set() if visited is None else visited
  
file_path: pyRDF2Vec/pyrdf2vec/samplers/sampler.py
imports: import random
  from abc import ABC, abstractmethod
  from typing import Dict, List, Optional, Set, Tuple
  import attr
  import numpy
  from pyrdf2vec.graphs import KG
  from pyrdf2vec.typings import Hop, Walk
label_desc: Use image node_id 6 for calling the Sampler obj's underlying member method code with example usage: obj.visited(visited) without return types