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: 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._embedding
  
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 3 for calling the GLEE obj's underlying member method code with example usage: obj.get_embedding() and returns: self