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","."]
code: def get_step(self, loss, params, verbose=False):
      X = self.acquisition_func(num_acquisitions=self.num_acquisitions)
      y = loss(X)
      if verbose:
          print("Acquire at: X")
          print(X)
          print("y")
          print(y)
      self.update_posterior(X, y)
      return self.opt_differentiable(lambda x: self.gpmodel(x)[0])
  
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 6 for calling the GPBayesOptimizer obj's underlying member method code with example usage: obj.get_step(loss, params, verbose) and returns: self