class_docstr: An implementation of `"Invariant Graph Embedding" <https://graphreason.github.io/papers/16.pdf>`_
  from the ICML 2019 Workshop on Learning and Reasoning with Graph-Structured
  Data paper "Invariant Embedding for Graph Classification". The procedure
  computes a mixture of spectral and node embedding based features. Specifically,
  it uses scattering, eigenvalues and pooled node feature embeddings to create
  graph descriptors.
  
  Args:
      feature_embedding_dimensions (list): Feature embedding dimensions. Default is [3, 5]
      spectral_embedding_dimensions (list): Spectral embedding dimensions. Default is [10, 20].
      histogram_bins (list): Number of histogram bins. Default is [10, 20].
      seed (int): Random seed value. Default is 42.
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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 self._embedding
  
file_path: karateclub/karateclub/graph_embedding/ige.py
imports: from typing import List
  import numpy
  import networkx
  import scipy.sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 9 for calling the IGE obj's underlying member method code with example usage: obj.get_embedding() and returns: self