class_docstr: An implementation of `"NetLSD" <https://arxiv.org/abs/1805.10712>`_
  from the KDD '18 paper "NetLSD: Hearing the Shape of a Graph". The procedure
  calculate the heat kernel trace of the normalized Laplacian matrix over a
  vector of time scales. If the matrix is large it switches to an approximation
  of the eigenvalues.
  
  Args:
      scale_min (float): Time scale interval minimum. Default is -2.0.
      scale_max (float): Time scale interval maximum. Default is 2.0.
      scale_steps (int): Number of steps in time scale. Default is 250.
      scale_approximations (int): Number of eigenvalue approximations. Default is 200.
      seed (int): Random seed value. Default is 42.
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docstr: Inferring the NetLSD embeddings.
  
  Arg types:
      * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
  Return types:
      * **embedding** *(Numpy array)* - The embedding of graphs.
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code: def infer(self, graphs: List[nx.classes.graph.Graph]) -> np.array:
      """
      Inferring the NetLSD embeddings.
  
      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_netlsd(graph) for graph in graphs]
      )
      return embedding
  
file_path: karateclub/karateclub/graph_embedding/netlsd.py
imports: import numpy
  import networkx
  from typing import List
  import scipy.sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 8 for calling the NetLSD obj's underlying member method code with example usage: obj.infer(graphs) and returns: embedding