class_docstr: An implementation of `"Graph2Vec" <https://arxiv.org/abs/1707.05005>`_
  from the MLGWorkshop '17 paper "Graph2Vec: Learning Distributed Representations of Graphs".
  The procedure creates Weisfeiler-Lehman tree features for nodes in graphs. Using
  these features a document (graph) - feature co-occurrence matrix is decomposed in order
  to generate representations for the graphs.
  
  The procedure assumes that nodes have no string feature present and the WL-hashing
  defaults to the degree centrality. However, if the parameter `use_node_attribute` is
  provided, the feature extraction happens based on the values of this key.
  
  Args:
      wl_iterations (int): Number of Weisfeiler-Lehman iterations. Default is 2.
      use_node_attribute (Optional[str]): The optional parameter from which to load node features. Default is None..
      dimensions (int): Dimensionality of embedding. Default is 128.
      workers (int): Number of cores. Default is 4.
      down_sampling (float): Down sampling frequency. Default is 0.0001.
      epochs (int): Number of epochs. Default is 10.
      learning_rate (float): HogWild! learning rate. Default is 0.025.
      min_count (int): Minimal count of graph feature occurrences. Default is 5.
      seed (int): Random seed for the model. Default is 42.
      erase_base_features (bool): Erasing the base features. Default is False.
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docstr: Getting the embedding of graphs.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of graphs.
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code: def get_embedding(self) -> np.array:
      r"""Getting the embedding of graphs.
  
      Return types:
          * **embedding** *(Numpy array)* - The embedding of graphs.
      """
      return np.array(self._embedding)
  
file_path: karateclub/karateclub/graph_embedding/graph2vec.py
imports: import numpy
  import networkx
  from typing import List, Optional
  from karateclub.estimator import Estimator
  from gensim.models.doc2vec import Doc2Vec, TaggedDocument
  from karateclub.utils.treefeatures import WeisfeilerLehmanHashing
label_desc: Use image node_id 3 for calling the Graph2Vec obj's underlying member method code with example usage: obj.get_embedding() and returns: np