code: def laplacian_eigenmaps(self, num_dims=None, normed=True, val_thresh=1e-8):
      '''Laplacian Eigenmaps embedding.
  
      num_dims : dimension of embedded coordinates, defaults to input dimension
      normed : used for .laplacian() calculation
      val_thresh : threshold for omitting vectors with near-zero eigenvalues
      '''
      L = self.laplacian(normed=normed)
      return _null_space(L, num_dims, val_thresh, overwrite=Truef laplacian_eigenmaps(self, num_dims=None, normed=True, val_thresh=1e-8):
      '''Laplacian Eigenmaps embedding.
  
      num_dims : dimension of embedded coordinates, defaults to input dimension
      normed : used for .laplacian() calculation
      val_thresh : threshold for omitting vectors with near-zero eigenvalues
      '''
      L = self.laplacian(normed=normed)
      return _null_space(L, num_dims, val_thresh, overwrite=True)
docs: Laplacian Eigenmaps embedding.
  
      num_dims : dimension of embedded coordinates, defaults to input dimension
      normed : used for .laplacian() calculation
      val_thresh : threshold for omitting vectors with near-zero eigenvalues