code: def root_decomposition(
      input: Anysor, method: Optional[str] = None
  ) -> LinearOperator:
      r"""
      Returns a (usually low-rank) root decomposition linear operator of the
      positive definite matrix (or batch of matrices) :math:`\mathbf A`.
      This can be used for sampling from a Gaussian distribution, or for obtaining a
      low-rank version of a matrix.
  
      :param input: The matrix (or batch of matrices) :math:`\mathbf A` (... x N x N).
      :param method: Which method to use to perform the root decomposition. Choices are:
          "cholesky", "lanczos", "symeig", "pivoted_cholesky", or "svd".
      :return: A tensor :math:`\mathbf R` such that :math:`\mathbf R \mathbf R^\top \approx \mathbf A`.
      """
      return linear_operator.root_decomposition(
          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 8 for calling a global function with example usage: root_decomposition(input, method) and returns: linear_operator