docstr: Computes the gradient absolute measure:
  
  Returns:
      if `self.num_bootstrap_samples > 1`
          Tensor: (values, var_mc, stderr_mc) x dim
      else
          Tensor: (values) x dim
docstr_tok: ["Computes","the","gradient","absolute","measure",":","Returns",":","if","`","self.num_bootstrap_samples",">","1","`","Tensor",":","(","values",",","var_mc",",","stderr_mc",")","x","dim","else","Tensor",":","(","values",")","x","dim"]
code: def gradient_absolute_measure(self) -> torch.Tensor:
      r"""Computes the gradient absolute measure:
  
      Returns:
          if `self.num_bootstrap_samples > 1`
              Tensor: (values, var_mc, stderr_mc) x dim
          else
              Tensor: (values) x dim
      """
      return self.aggregation(torch.abs)
  
imports: from copy import deepcopy
  from typing import Callable, List, Optional, Union
  import torch
  from ax.utils.common.typeutils import checked_cast, not_none
  from ax.utils.sensitivity.derivative_gp import posterior_derivative
  from botorch.models.model import Model
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.posteriors.posterior import Posterior
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.sampling import draw_sobol_samples
  from botorch.utils.transforms import unnormalize
  from gpytorch.distributions import MultivariateNormal
label_desc: Use image node_id 5 for calling the GpDGSMGpMean obj's underlying member method code with example usage: obj.gradient_absolute_measure() and returns: self