code: def sqrt_inv_matmul(
      input: Anysor, rhs: Tensor, lhs: Optional[Tensor] = None
  ) -> Tensor:
      r"""
      Given a positive definite matrix (or batch of matrices) :math:`\mathbf A`
      and a right hand size :math:`\mathbf R`,
      computes
  
      .. math::
         \begin{equation}
             \mathbf A^{-1/2} \mathbf R,
         \end{equation}
  
      If :attr:`lhs` is supplied, computes
  
      .. math::
         \begin{equation}
             \mathbf L \mathbf A^{-1/2} \mathbf R,
         \end{equation}
  
      where :math:`\mathbf L` is :attr:`lhs`.
      (Supplying :attr:`lhs` can reduce the number of solver calls required in the backward pass.)
  
      :param input: The matrix (or batch of matrices) :math:`\mathbf A` (... x N x N).
      :param rhs: :math:`\mathbf R` - the right hand side
      :param lhs: :math:`\mathbf L` - the left hand side
      :return: :math:`\mathbf A^{-1/2} \mathbf R` or :math:`\mathbf L \mathbf A^{-1/2} \mathbf R`.
      """
      return linear_operator.sqrt_inv_matmul(
          input=input, rhs=rhs, lhs=lhs
      )
  
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 11 for calling a global function with example usage: sqrt_inv_matmul(input, rhs, lhs) and returns: linear_operator