class_docstr: GP-Lower Confidence Bound acquisition function with constant exploration weight.
  See:
  
  Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
  Srinivas et al., Proc. International Conference on Machine Learning (ICML), 2010
  
  :param model: GPyOpt class of model
  :param space: GPyOpt class of domain
  :param optimizer: optimizer of the acquisition. Should be a GPyOpt optimizer
  :param cost_withGradients: function
  :param jitter: positive value to make the acquisition more explorative
  
  .. Note:: does not allow to be used with cost
docstr: Computes the GP-Lower Confidence Bound and its derivative
docstr_tok: ["Computes","the","GP-Lower","Confidence","Bound","and","its","derivative"]
code: def _compute_acq_withGradients(self, x):
      """
      Computes the GP-Lower Confidence Bound and its derivative
      """
      m, s, dmdx, dsdx = self.model.predict_withGradients(x)
      f_acqu = -m + self.exploration_weight * s
      df_acqu = -dmdx + self.exploration_weight * dsdx
      return f_acqu, df_acqu
  
imports: from .base import AcquisitionBase
  from ..util.general import get_quantiles
label_desc: Use image node_id 3 for calling the AcquisitionLCB obj's underlying member method code with example usage: obj._compute_acq_withGradients(x) and returns: f_acqu,  df_acqu