class_docstr: An implementation of `"Role2vec" <https://arxiv.org/abs/1802.02896>`_
  from the IJCAI '18 paper "Learning Role-based Graph Embeddings".
  The procedure uses random walks to approximate the pointwise mutual information
  matrix obtained by multiplying the pooled adjacency power matrix with a
  structural feature matrix (in this case Weisfeiler-Lehman features). This way
  one gets structural node embeddings.
  
  Args:
      walk_number (int): Number of random walks. Default is 10.
      walk_length (int): Length of random walks. Default is 80.
      dimensions (int): Dimensionality of embedding. Default is 128.
      workers (int): Number of cores. Default is 4.
      window_size (int): Matrix power order. Default is 2.
      epochs (int): Number of epochs. Default is 1.
      learning_rate (float): HogWild! learning rate. Default is 0.05.
      down_sampling (float): Down sampling frequency. Default is 0.0001.
      min_count (int): Minimal count of feature occurrences. Default is 10.
      wl_iterations (int): Number of Weisfeiler-Lehman hashing iterations. Default is 2.
      seed (int): Random seed value. Default is 42.
      erase_base_features (bool): Removing the base features. Default is False.
docstr: Getting the node embedding.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of nodes.
docstr_tok: ["Getting","the","node","embedding",".","Return","types",":","*","*","*","embedding","*","*","*","(","Numpy","array",")","*","-","The","embedding","of","nodes","."]
code: def get_embedding(self) -> np.array:
      r"""Getting the node embedding.
  
      Return types:
          * **embedding** *(Numpy array)* - The embedding of nodes.
      """
      return np.array(self._embedding)
  
file_path: karateclub/karateclub/node_embedding/structural/role2vec.py
imports: from typing import Dict, List
  import numpy
  import networkx
  from gensim.models.doc2vec import Doc2Vec, TaggedDocument
  from karateclub.utils.walker import RandomWalker
  from karateclub.estimator import Estimator
  from karateclub.utils.treefeatures import WeisfeilerLehmanHashing
label_desc: Use image node_id 5 for calling the Role2Vec obj's underlying member method code with example usage: obj.get_embedding() and returns: np