docstr: Computes the Second order Sobol indices:
  
  Returns:
      if num_bootstrap_samples>1
          Tensor: (values,var_mc,stderr_mc)x dim(dim-1)/2
      else
          Tensor: (values)x dim(dim-1)/2
docstr_tok: ["Computes","the","Second","order","Sobol","indices",":","Returns",":","if","num_bootstrap_samples",">","1","Tensor",":","(","values",",","var_mc",",","stderr_mc",")","x","dim","(","dim-1",")","\/2","else","Tensor",":","(","values",")","x","dim","(","dim-1",")","\/2"]
code: def second_order_indices(self) -> Tensor:
      r"""Computes the Second order Sobol indices:
  
      Returns:
          if num_bootstrap_samples>1
              Tensor: (values,var_mc,stderr_mc)x dim(dim-1)/2
          else
              Tensor: (values)x dim(dim-1)/2
      """
      return self.sensitivity.second_order_indices()
  
imports: from copy import deepcopy
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import torch
  from ax.modelbridge.torch import TorchModelBridge
  from ax.models.torch.botorch import BotorchModel
  from ax.models.torch.botorch_modular.model import BoTorchModel
  from ax.utils.common.typeutils import checked_cast
  from botorch.models.model import Model, ModelList
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.sampling import draw_sobol_samples
  from botorch.utils.transforms import unnormalize
  from torch import Tensor
label_desc: Use image node_id 4 for calling the SobolSensitivityGPMean obj's underlying member method code with example usage: obj.second_order_indices() and returns: self