class_docstr: Performs Bayesian Optimization using a Gaussian Process as an
  emulator for the unknown function.
class_docstr_tok: ["Performs","Bayesian","Optimization","using","a","Gaussian","Process","as","an","emulator","for","the","unknown","function","."]
docstr: Given a starting point, `x_init`, takes one LBFGS step
  to optimize the differentiable function.
  
  :param function differentiable: a function amenable to torch
      autograd
  :param torch.Tensor x_init: the initial point
docstr_tok: ["Given","a","starting","point",",","`","x_init","`",",","takes","one","LBFGS","step","to","optimize","the","differentiable","function",".",":","param","function","differentiable",":","a","function","amenable","to","torch","autograd",":","param","torch.Tensor","x_init",":","the","initial","point"]
code: def find_a_candidate(self, differentiable, x_init):
      """Given a starting point, `x_init`, takes one LBFGS step
      to optimize the differentiable function.
  
      :param function differentiable: a function amenable to torch
          autograd
      :param torch.Tensor x_init: the initial point
  
      """
      # transform x to an unconstrained domain
      unconstrained_x_init = transform_to(self.constraints).inv(x_init)
      unconstrained_x = (
          unconstrained_x_init.detach().clone().requires_grad_(True)
      )
      # TODO: Use LBFGS with line search by pytorch #8824 merged
      minimizer = optim.LBFGS([unconstrained_x], max_eval=20)
  
      def closure():
          minimizer.zero_grad()
          if (torch.log(torch.abs(unconstrained_x)) > 25.0).any():
              return torch.tensor(float("inf"))
          x = transform_to(self.constraints)(unconstrained_x)
          y = differentiable(x)
          autograd.backward(
              unconstrained_x,
              autograd.grad(y, unconstrained_x, retain_graph=True),
          )
          return y
  
      minimizer.step(closure)
      # after finding a candidate in the unconstrained domain,
      # convert it back to original domain.
      x = transform_to(self.constraints)(unconstrained_x)
      opt_y = differentiable(x)
      return x.detach(), opt_y.detach()
  
file_path: pyro/examples/contrib/oed/gp_bayes_opt.py
imports: import torch
  import torch.autograd
  import torch.optim
  from torch.distributions import transform_to
  import pyro.contrib.gp
  import pyro.optim
  from pyro.infer import TraceEnum_ELBO
label_desc: Use image node_id 3 for calling the GPBayesOptimizer obj's underlying member method code with example usage: obj.find_a_candidate(differentiable, x_init) and returns: x,  opt_y, y, torch