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.
code: def __init__(self, dimensions: int = 128, seed: int = 42):
      self.dimensions = dimensions
      self.seed = seed
  
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 1 to create a new GLEE object from inherited base classes: Estimator with example: obj = GLEE(dimensions, seed)