class_docstr: An implementation of `"SF" <https://arxiv.org/abs/1810.09155>`_
  from the NeurIPS Relational Representation Learning Workshop '18 paper "A Simple Baseline Algorithm for Graph Classification".
  The procedure calculates the k lowest eigenvalues of the normalized Laplacian.
  If the graph has a lower number of eigenvalues than k the representation is padded with zeros.
  
  Args:
      dimensions (int): Number of lowest eigenvalues. Default is 128.
      seed (int): Random seed value. Default is 42.
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docstr: Inferring the embedding vectors.
  
  Arg types:
      * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
  Return types:
      * **embedding** *(Numpy array)* - The embedding of graphs.
docstr_tok: ["Inferring","the","embedding","vectors",".","Arg","types",":","*","*","*","graphs","*","*","*","(","List","of","NetworkX","graphs",")","*","-","The","graphs","to","be","embedded",".","Return","types",":","*","*","*","embedding","*","*","*","(","Numpy","array",")","*","-","The","embedding","of","graphs","."]
code: def infer(self, graphs) -> np.array:
      """Inferring the embedding vectors.
  
      Arg types:
          * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
      Return types:
          * **embedding** *(Numpy array)* - The embedding of graphs.
      """
      self._set_seed()
      graphs = self._check_graphs(graphs)
      embedding = np.array(
          [self._calculate_sf(graph) for graph in graphs]
      )
      return embedding
  
file_path: karateclub/karateclub/graph_embedding/sf.py
imports: import numpy
  import networkx
  from typing import List
  from scipy.sparse.linalg import eigsh
  from karateclub.estimator import Estimator
label_desc: Use image node_id 5 for calling the SF obj's underlying member method code with example usage: obj.infer(graphs) and returns: embedding