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: Gets the weight of a hop in the Knowledge Graph.
  
  Args:
      hop: The hop of a vertex in a (predicate, object) form to get the
          weight.
  
  Returns:
      The weight of a given hop.
docstr_tok: ["Gets","the","weight","of","a","hop","in","the","Knowledge","Graph",".","Args",":","hop",":","The","hop","of","a","vertex","in","a","(","predicate",",","object",")","form","to","get","the","weight",".","Returns",":","The","weight","of","a","given","hop","."]
code: def get_weight(self, hop: Hop) -> int:
      """Gets the weight of a hop in the Knowledge Graph.
  
      Args:
          hop: The hop of a vertex in a (predicate, object) form to get the
              weight.
  
      Returns:
          The weight of a given hop.
  
      """
      return 1
  
imports: import attr
  from pyrdf2vec.graphs import KG
  from pyrdf2vec.samplers import Sampler
  from pyrdf2vec.typings import Hop
label_desc: Use image node_id 2 for calling the UniformSampler obj's underlying member method code with example usage: obj.get_weight(hop) and returns: int