class_docstr: Adds Laplacian positional encodings to the nodes.
  
  The first `k` eigenvectors are computed and concatenated to the node features.
  Each node will be extended with its corresponding entries in the first k
  eigenvectors.
class_docstr_tok: ["Adds","Laplacian","positional","encodings","to","the","nodes",".","The","first","`","k","`","eigenvectors","are","computed","and","concatenated","to","the","node","features",".","Each","node","will","be","extended","with","its","corresponding","entries","in","the","first","k","eigenvectors","."]
code: def __init__(self, k):
      assert k > 0, "k must be greater than 0"
      self.k = k
  
imports: import numpy
  from scipy.sparse.linalg import eigsh
  from spektral.utils import normalized_laplacian
label_desc: Use image node_id 1 to create a new LaplacianPE object  with example: obj = LaplacianPE(k)