class_docstr: An implementation of `"FEATHER-N" <https://arxiv.org/abs/2005.07959>`_
  from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather,
  from Statistical Descriptors to Parametric Models". The procedure
  uses characteristic functions of node features with random walk weights to describe
  node neighborhoods.
  
  Args:
      reduction_dimensions (int): SVD reduction dimensions. Default is 64.
      svd_iterations (int): SVD iteration count. Default is 20.
      theta_max (float): Maximal evaluation point. Default is 2.5.
      eval_points (int): Number of characteristic function evaluation points. Default is 25.
      order (int): Scale - number of adjacency matrix powers. Default is 5.
      seed (int): Random seed value. Default is 42.
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 self._feature_blocks
  
file_path: karateclub/karateclub/node_embedding/attributed/feathernode.py
imports: import scipy
  import numpy
  import networkx
  from typing import Union
  from scipy.sparse import coo_matrix
  from karateclub.estimator import Estimator
  from sklearn.decomposition import TruncatedSVD
label_desc: Use image node_id 7 for calling the FeatherNode obj's underlying member method code with example usage: obj.get_embedding() and returns: self