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: Transforming the random walks.
  
  Arg types:
      * **walks** *(list of lists)* - Random walks with string ids.
  
  Return types:
      * *(list of lists)* - The random walks as integers.
docstr_tok: ["Transforming","the","random","walks",".","Arg","types",":","*","*","*","walks","*","*","*","(","list","of","lists",")","*","-","Random","walks","with","string","ids",".","Return","types",":","*","*","(","list","of","lists",")","*","-","The","random","walks","as","integers","."]
code: def _transform_walks(self, walks: List[List[str]]) -> List[List[int]]:
      """
      Transforming the random walks.
  
      Arg types:
          * **walks** *(list of lists)* - Random walks with string ids.
  
      Return types:
          * *(list of lists)* - The random walks as integers.
      """
      return [[int(node) for node in walk] for walk in walks]
  
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 2 for calling the Role2Vec obj's underlying member method code with example usage: obj._transform_walks(walks) and returns: unknown