class_docstr: Uniform sampling strategy that assigns a uniform weight to each edge in
  a Knowledge Graph, in order to prioritizes walks with strongly connected
  entities.
  
  Attributes:
      _is_support_remote: True if the sampling strategy can be used with a
          remote Knowledge Graph, False Otherwise
          Defaults to True.
      _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.
docstr: Since the weights are uniform, this function does nothing.
  
  Args:
      kg: The Knowledge Graph.
docstr_tok: ["Since","the","weights","are","uniform",",","this","function","does","nothing",".","Args",":","kg",":","The","Knowledge","Graph","."]
code: def fit(self, kg: KG) -> None:
      """Since the weights are uniform, this function does nothing.
  
      Args:
          kg: The Knowledge Graph.
  
      """
      pass
  
imports: import attr
  from pyrdf2vec.graphs import KG
  from pyrdf2vec.samplers import Sampler
  from pyrdf2vec.typings import Hop
label_desc: Use image node_id 1 for calling the UniformSampler obj's underlying member method code with example usage: obj.fit(kg) without return types