docstr: Computes the first order Sobol indices:
  
  Returns:
      if num_bootstrap_samples>1
          Tensor: (values,var_mc,stderr_mc)x dim
      else
          Tensor: (values)x dim
docstr_tok: ["Computes","the","first","order","Sobol","indices",":","Returns",":","if","num_bootstrap_samples",">","1","Tensor",":","(","values",",","var_mc",",","stderr_mc",")","x","dim","else","Tensor",":","(","values",")","x","dim"]
code: def first_order_indices(self) -> Tensor:
      r"""Computes the first order Sobol indices:
  
      Returns:
          if num_bootstrap_samples>1
              Tensor: (values,var_mc,stderr_mc)x dim
          else
              Tensor: (values)x dim
      """
      return self.sensitivity.first_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 2 for calling the SobolSensitivityGPMean obj's underlying member method code with example usage: obj.first_order_indices() and returns: self