class_docstr: An implementation of `"Laplacian Eigenmaps" <https://papers.nips.cc/paper/1961-laplacian-eigenmaps-and-spectral-techniques-for-embedding-and-clustering>`_
  from the NIPS '01 paper "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering".
  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.
      maximum_number_of_iterations (int): Maximum number of iterations to execute with ARPACK. The value will be multiplied by the number of nodes.
      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/laplacianeigenmaps.py
imports: import numpy
  import networkx
  import scipy.sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 3 for calling the LaplacianEigenmaps obj's underlying member method code with example usage: obj.get_embedding() and returns: self