class_docstr: Computes the highest eigenvalue of the graph Laplacian given by
  :meth:`torch_geometric.utils.get_laplacian`
  (functional name: :obj:`laplacian_lambda_max`).
  
  Args:
      normalization (str, optional): The normalization scheme for the graph
          Laplacian (default: :obj:`None`):
  
          1. :obj:`None`: No normalization
          :math:`\mathbf{L} = \mathbf{D} - \mathbf{A}`
  
          2. :obj:`"sym"`: Symmetric normalization
          :math:`\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A}
          \mathbf{D}^{-1/2}`
  
          3. :obj:`"rw"`: Random-walk normalization
          :math:`\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1} \mathbf{A}`
      is_undirected (bool, optional): If set to :obj:`True`, this transform
          expects undirected graphs as input, and can hence speed up the
          computation of the largest eigenvalue. (default: :obj:`False`)
class_docstr_tok: ["Computes","the","highest","eigenvalue","of","the","graph","Laplacian","given","by",":","meth",":","`","torch_geometric.utils.get_laplacian","`","(","functional","name",":",":","obj",":","`","laplacian_lambda_max","`",")",".","Args",":","normalization","(","str",",","optional",")",":","The","normalization","scheme","for","the","graph","Laplacian","(","default",":",":","obj",":","`","None","`",")",":","1.",":","obj",":","`","None","`",":","No","normalization",":","math",":","`","\\mathbf","{","L","}","=","\\mathbf","{","D","}","-","\\mathbf","{","A","}","`","2.",":","obj",":","`","``","sym","''","`",":","Symmetric","normalization",":","math",":","`","\\mathbf","{","L","}","=","\\mathbf","{","I","}","-","\\mathbf","{","D","}","^","{","-1\/2","}","\\mathbf","{","A","}","\\mathbf","{","D","}","^","{","-1\/2","}","`","3.",":","obj",":","`","``","rw","''","`",":","Random-walk","normalization",":","math",":","`","\\mathbf","{","L","}","=","\\mathbf","{","I","}","-","\\mathbf","{","D","}","^","{","-1","}","\\mathbf","{","A","}","`","is_undirected","(","bool",",","optional",")",":","If","set","to",":","obj",":","`","True","`",",","this","transform","expects","undirected","graphs","as","input",",","and","can","hence","speed","up","the","computation","of","the","largest","eigenvalue",".","(","default",":",":","obj",":","`","False","`",")"]
code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}(normalization={self.normalization})"
  
file_path: pytorch_geometric/torch_geometric/transforms/laplacian_lambda_max.py
imports: from typing import Optional
  from scipy.sparse.linalg import eigs, eigsh
  from torch_geometric.data import Data
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
  from torch_geometric.utils import get_laplacian, to_scipy_sparse_matrix
label_desc: Use image node_id 3 for calling the LaplacianLambdaMax obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str