class_docstr: An implementation of `"Geometric Laplacian Eigenmaps" <https://arxiv.org/abs/1905.09763>`_
  from the Journal of Complex Networks '20 paper "GLEE: Geometric Laplacian Eigenmap Embedding".
  The procedure extracts the eigenvectors corresponding to the largest eigenvalues
  of the graph Laplacian. These vectors are used as the node embedding.
  
  Args:
      dimensions (int): Dimensionality of embedding. Default is 128.
      seed (int): Random seed value. Default is 42.
docstr: Fitting a Geometric Laplacian EigenMaps model.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
code: def fit(self, graph: nx.classes.graph.Graph):
      """
      Fitting a Geometric Laplacian EigenMaps model.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      number_of_nodes = graph.number_of_nodes()
      L_tilde = nx.normalized_laplacian_matrix(
          graph, nodelist=range(number_of_nodes)
      )
      _, self._embedding = sps.linalg.eigsh(
          L_tilde,
          k=self.dimensions + 1,
          which="LM",
          return_eigenvectors=True,
      )
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/geometriclaplacianeigenmaps.py
imports: import numpy
  import networkx
  import scipy.sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 2 for calling the GLEE obj's underlying member method code with example usage: obj.fit(graph) without return types