code: def diagonalization(
      input: Anysor, method: Optional[str] = None
  ) -> Tuple[Tensor, Tensor]:
      r"""
      Returns a (usually partial) diagonalization of a symmetric positive definite matrix (or batch of matrices).
      :math:`\mathbf A`.
      Options are either "lanczos" or "symeig". "lanczos" runs Lanczos while
      "symeig" runs LinearOperator.symeig.
  
      :param input: The matrix (or batch of matrices) :math:`\mathbf A` (... x N x N).
      :param method: Specify the method to use ("lanczos" or "symeig"). The method will be determined
          based on size if not specified.
      :return: eigenvalues and eigenvectors representing the diagonalization.
      """
      return linear_operator.diagonalization(input=input, method=method)
  
imports: from typing import Optional, Tuple, Union
  import linear_operator
  import torch
  from linear_operator import LinearOperator
  from torch import Tensor
  from .None import beta_features, distributions, kernels, lazy, likelihoods, means, metrics, mlls, models, optim, priors, settings, utils, variational
  from .functions import inv_matmul, log_normal_cdf, logdet, matmul
  from .lazy import cat, delazify, lazify
  from .mlls import ExactMarginalLogLikelihood
  from .module import Module
label_desc: Use image node_id 3 for calling a global function with example usage: diagonalization(input, method) and returns: linear_operator