class_docstr: An implementation of `"WaveCharacteristic" <https://arxiv.org/abs/2005.07959>`_
  from the CIKM '21 paper "Graph Embedding via Diffusion-Wavelets-Based Node Feature
  Distribution Characterization". The procedure uses characteristic functions of
  node features with wavelet function weights to describe node neighborhoods.
  These node level features are pooled by mean pooling to create graph level statistics.
  
  Args:
      order (int): Adjacency matrix powers. Default is 5.
      eval_points (int): Number of characteristic function evaluations. Default is 5.
      theta_max (float): Largest characteristic function time value. Default is 2.5.
      tau (float): Wave function heat - time diffusion. Default is 1.0.
      pooling (str): Pooling function appliead to the characteristic functions. Default is "mean".
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docstr: Getting the embedding of graphs.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of graphs.
docstr_tok: ["Getting","the","embedding","of","graphs",".","Return","types",":","*","*","*","embedding","*","*","*","(","Numpy","array",")","*","-","The","embedding","of","graphs","."]
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/waveletcharacteristic.py
imports: import math
  from typing import List, Tuple
  import numpy
  import networkx
  import scipy.sparse
  from sklearn.preprocessing import normalize
  from karateclub.estimator import Estimator
label_desc: Use image node_id 8 for calling the WaveletCharacteristic obj's underlying member method code with example usage: obj.get_embedding() and returns: np